Multi Moving Average V2

Advanced Statistical Arbitrage für MetaTrader MT4 - Version 3 Statistische Arbitrage Trading Techniken (manchmal kennt sich als Konvergenz oder Paar Handel) basieren auf dem Konzept der mittleren Reversion. Das System überwacht kontinuierlich die Leistung von zwei historisch hochkorrelierten Instrumenten, die der Trader definiert. Wenn die Korrelation zwischen den beiden Instrumenten über ein vordefiniertes Niveau schwankt oder divergiert - wird V3 automatisch und simulativ das schwächste Instrument kaufen und das stärkste verkaufen. Sobald mittlere Reversion stattfindet, wird die Nettoposition, die von den beiden Trades geschaffen wird, im Allgemeinen profitieren. Diese Handelsstrategie verlangt ein gutes Verständnis von Hebel - und Risikokontrolle, die Fähigkeit, hochkorrelierte Instrumente über verschiedene Assetklassen hinweg zu analysieren und zu verstehen, wie man Spreads interpretiert. (Das Spread ist der effektive Unterschied zwischen den beiden Instrumenten, die auf potenzielle Arbitrage-Chancen überwacht werden. Das Bild unten stellt ein. Das Spreadquot, das eine Kernkomponente eines beliebigen Arbitragesystems ist. Video-Walkthrough für Spread-Grundlagen Die Screenshots oben zeigen das Potenzial für gesunde Gewinne mit statistischer Arbitrage-Umwandlung Handelstechniken. Die scharfen Augen bemerkt wird der Zeitrahmen, über die diese konzeptionellen Trades gemacht wurden von April 2009 bis September 2012 - 7 Trades in mehr als 3 Jahren definitiv qualifiziert für Niederfrequenz-Handel, obwohl die potenziellen Aufwärtsmöglichkeiten von langfristigen Arbitrage Trades Kann jedoch außergewöhnlich sein, aber die meisten Händler verlangen höhere Handelsfrequenzen, so dass ein Arbitragesystem in der Lage sein muss, auf viel niedrigeren Zeitrahmen und mit viel höheren Handelsfrequenzen zu arbeiten. Arbitrage Trading Timeframes und Perspektive Das SampP500GER40 Beispiel oben zeigte elegant die Einfachheit der mittleren Reversion Wenn jedoch hochkorrelierte Vermögenswerte auf kürzere Zeiträume analysiert werden, wird die Situation komplexer. Theoretisch ist die ideale Zeit, um Arb Trades mit konventionellen Ein - und Ausstiegslogik auszuführen, wenn der Spread als stationär bezeichnet wird. Hier ist die Ausbreitung (der Unterschied zwischen den Preisen der beiden Instrumente) ziemlich sinusförmig um den gleitenden Durchschnitt oszilliert. Idealerweise sollte der gleitende Durchschnitt so flach wie möglich sein. Der Screenshot oben für Gold und Silber zeigt, wie sich die Ausbreitung von einer direktionalen zu einer stationären Natur über einen kurzen Zeitraum ändert. Eine stationäre Ausbreitung ist ideal für den Arb-Handel, da sie Trades in beide Richtungen erlaubt - dh GoldBuying Silver verkaufen, wenn der Spread über dem oberen Trigger-Level liegt und Goldselling Silver kauft, wenn der Spread unter dem unteren Trigger-Level liegt. Die Herausforderung tritt auf, wenn sich die Spreizdynamik von stationär in direktional ändert. Eine gerichtete Ausbreitung ist, wo der gleitende Durchschnitt im Laufe der Zeit zunimmt. Mit anderen Worten, ein Paar wird kontinuierlich verstärkt, während das andere entweder unverändert oder schwächer ist. In diesem Szenario benötigen wir eine automatisierte Arbitrage-Engine, um die Richtung der Ausbreitung automatisch erkennen zu können. Im Laufe des V3-Entwicklungsprogramms haben wir mit verschiedenen Algorithmen experimentiert, um den Spread Trend zu verfolgen und zu überwachen. In der neuesten Version verwenden wir einen Multi-Timeframe-Erkennungsalgorithmus, um festzustellen, ob der Spread stationär ist (reicht) oder gerichtet (Trending). Diese werden im Detail in den modularen Übersichten beschrieben, die folgen. V3-Architektur Die ersten V3-Versionen wurden im Juni 2011 veröffentlicht und das Produkt wurde seit dem Start systematisch aktualisiert und verbessert. V3 bietet eine neue grafische Benutzeroberfläche und eine ganze Reihe weiterer Funktionen, die im Folgenden beschrieben werden. Das V3 Arbitrage System besteht aus zwei Kernkomponenten: - Der Gen Starb Fachberater (EA) Der STD Indikator In einfacher Weise überwacht der STD Indikator die Ausbreitung und liefert Eingangssignale. Der Fachberater führt Handelsabwicklungs - und Führungsfunktionen durch. Im Wesentlichen kommunizieren die beiden Anwendungen in Echtzeit mit der Tabelle MetaTrader Global Variable (GVAR). Sie sitzen beide auf einer generischen FX AlgoTrader Deployment Archtektur, die im Bild unten gezeigt wird. RISIKENBESCHREIBUNG Die Produkte auf dieser Seite sind Handelsinstrumente und sollen keine Einzelforschung oder lizenzierte Anlageberatung ersetzen. Die bisherige Wertentwicklung garantiert keine zukünftigen Ergebnisse. Handelswährungen sind mit erheblichem Risiko verbunden, und es besteht immer das Potenzial für Verlust. Es wird keine Vertretung gemacht, dass diese Produkte Gewinne garantieren oder nicht zu Verlusten aus dem Handel führen. Jede Erläuterung oder Demonstration des Produktbetriebes darf nicht als Handelsempfehlung oder als Anlageberatung ausgelegt werden. Der Kauf oder Verkauf einer Währung kann nur von einem lizenzierten BrokerDealer durchgeführt werden. Der V3-STD-Indikator Die STD-Anzeige erzeugt Echtzeit-Spread-Statistiken, die dem Generic Arbitrage-Motor über die MetaTrader Global Variable Table zur Verfügung gestellt werden. Der STD-Indikator besteht aus mehreren Komponenten, die in der folgenden Abbildung dargestellt sind. STD Multiple - Mit diesem Parameter können Händler die Triggerpegel für die Arb-Einstiegspunkte einstellen. Die STD Multiple wird durch den Zugriff auf die externen Eingangsparameter für die STD-Anzeige eingestellt. Idealerweise sollten Trader die STD Multiple so einstellen, dass Peaks in der Spreizdivergenz mit dem oberen und unteren Triggerpegel übereinstimmen. Im Screenshot unten sehen wir, dass das STD Multiple auf 0,7 auf dem Daily Chart angepasst wurde, um mit typischen Peaks in Spreizdivergenz übereinzustimmen. Datenausgänge - Die STD-Indikator berechnet den gleitenden Mittelwert (MA), die Ausbreitung und die oberen und unteren Triggerpegel (basierend auf dem STD-Vielfachen) in Echtzeit. Reversion Target - Das Reversion-Ziel zeigt den Level, wenn das System versucht, die Arb zu schließen. Standardmäßig wird der Mittelwert immer als das Arb-Exit-Ziel verwendet, aber die Händler können manuell auf das entgegengesetzte Trigger-Band wechseln, indem sie den ReversionToMA-externen Eingangsparameter in den STD-Indikatoroptionen auf FALSE ändern. Trend - Der Trendindikator basiert auf einem proprietären Multi-Timeframe-gestapelten EMA-Algorithmus. Händler können bis zu 8 Trendfilter anpassen, die den Trend auf der Basis von Multi-Timeframe-Trendanalyse berechnen. Zum Beispiel kann ein Händler es vorziehen, ihre Arb-Trades aus dem 15-Minuten-Chart auszulösen und kann die Trades in Richtung der M30-, M60- und M240-Trends sperren. In diesem Fall würde der Trader einfach die M30, M60 und M240 TFilters auf True setzen, wie im Screenshot unten gezeigt. Datenprüfung: Dies ist eine neue Funktion, die 4 Datenintegritätstests auf der Ausbreitung durchführt, wenn sie auf ein Diagramm zuerst geladen wird. Wenn die Ausbreitung die Integritätsprüfungen bestanden hat, wird das OK-Label angezeigt. Der Arbitrage-Motor kann keine Trades platzieren, wenn das Data Check-Flag nicht korrekt ist. Der V3 Expert Advisor Die generischen Arbitrage-Engines überwachen ständig die MetaTrader globale Variablentabelle für Handelseintrags - und - ausgangsdaten für die verschiedenen Bogen, die der Trader auf jedem Diagramm eingerichtet hat. Es ist wichtig zu erwähnen, dass jedes Diagramm eine separate Instanz sowohl der STD-Indikator als auch der Arbitrage-Motor haben muss. Der Screenshot unten zeigt ein komplettes Stat Arb V3, das auf einem MetaTrader-Diagramm eingerichtet ist. Screenshot der V3 EA und STD Indikator auf einem MetaTrader Diagramm. Anmerkung: Keine Daten werden als ein Wochenende belegt. Das Systemdatenmodul Das Systemdatenmodul zeigt die aktuelle Systemzeit an, welche Instrumente gehandelt werden, der Systemstatus, der Systemmodus und der E-Mail-Alarmstatus. Einzelheiten zu den Erläuterungen entnehmen Sie bitte dem Technischen Datenblatt. RISIKENBESCHREIBUNG Die Produkte auf dieser Seite sind Handelsinstrumente und sollen keine Einzelforschung oder lizenzierte Anlageberatung ersetzen. Die bisherige Wertentwicklung garantiert keine zukünftigen Ergebnisse. Handelswährungen sind mit erheblichem Risiko verbunden, und es besteht immer das Potenzial für Verlust. Es wird keine Vertretung gemacht, dass diese Produkte Gewinne garantieren oder nicht zu Verlusten aus dem Handel führen. Jede Erläuterung oder Demonstration des Produktbetriebes darf nicht als Handelsempfehlung oder als Anlageberatung ausgelegt werden. Der Kauf oder Verkauf einer Währung kann nur von einem lizenzierten BrokerDealer durchgeführt werden. Automatische und manuelle Profit-Targeting-Optionen Auf Chart-Trade-Analytics E-Mail-Alert-Einrichtung (bei Nicht-Automatik-Betrieb) Granulierte Trade-Timing-Steuerung Konfigurierbare Risikokontrolle Automatisierte Trenderkennung und Sperrfunktionalität Profit-Aggregationssystem Automatische Hedging-Funktion Globale Losgrößen - und Aggregationsziele Sprachsynthese-Alarmsystem Profit Locking Feature Variable Leg ALeg B Position Dimensionierung Multi-Instrument Unterstützung - Handel Indizes, Rohstoffe, Forex, CFDs. Genauere Reversions-, Kanal - und Spreizalgorithmen Genauere Einstiegsanzeige Logik Verzögerte Entry Control Multi-Time-Spread-Trend-Erkennungsalgorithmus Integrierte Spread-Datenüberprüfungseinrichtung Überarbeitete grafische Schnittstelle Vereinfachte Handelskontrollsysteme Die Produkte auf dieser Seite sind Handelsinstrumente und sind nicht dazu bestimmt, einzelne zu ersetzen Forschung oder lizenzierte Anlageberatung Die bisherige Wertentwicklung garantiert keine zukünftigen Ergebnisse. Handelswährungen sind mit erheblichem Risiko verbunden, und es besteht immer das Potenzial für Verlust. Es wird keine Vertretung gemacht, dass diese Produkte Gewinne garantieren oder nicht zu Verlusten aus dem Handel führen. Jede Erläuterung oder Demonstration des Produktbetriebes darf nicht als Handelsempfehlung oder als Anlageberatung ausgelegt werden. Der Kauf oder Verkauf einer Währung kann nur von einem lizenzierten BrokerDealer durchgeführt werden. V3 Expert Advisor Schnittstellenschnittstelle für V3 STD Indikator Unilaterale Arb Trading Techniken Die Produkte auf dieser Seite sind Handelsinstrumente und sollen keine Einzelforschung oder lizenzierte Anlageberatung ersetzen. Die bisherige Wertentwicklung garantiert keine zukünftigen Ergebnisse. Handelswährungen sind mit erheblichem Risiko verbunden, und es besteht immer das Potenzial für Verlust. Es wird keine Vertretung gemacht, dass diese Produkte Gewinne garantieren oder nicht zu Verlusten aus dem Handel führen. Jede Erläuterung oder Demonstration des Produktbetriebes darf nicht als Handelsempfehlung oder als Anlageberatung ausgelegt werden. Der Kauf oder Verkauf einer Währung kann nur von einem lizenzierten BrokerDealer durchgeführt werden. Stat Arb V3 ermöglicht vollautomatischen unbeaufsichtigten Arbitrage Trading aus vorkonfigurierten Charts Mit Arbitrage-Techniken erhöht sich die Wahrscheinlichkeit von profitable Trades (zeitabhängig) Stat Arb V3 bietet einen hochkörnigen Datensatz, der es den Händlern ermöglicht, die potenziellen Reversionsgewinne aus bestimmten Arb-Setups zu sehen Vor dem Eintritt in den Markt. Stat Arb V3 ist ein bewährtes, robustes Trading-Toolset, das seit 2009 iterativ entwickelt wurde. Die Produkte auf dieser Seite sind Handelsinstrumente und sollen keine Einzelforschung oder lizenzierte Anlageberatung ersetzen. Die bisherige Wertentwicklung garantiert keine zukünftigen Ergebnisse. Handelswährungen sind mit erheblichem Risiko verbunden, und es besteht immer das Potenzial für Verlust. Es wird keine Vertretung gemacht, dass diese Produkte Gewinne garantieren oder nicht zu Verlusten aus dem Handel führen. Jede Erläuterung oder Demonstration des Produktbetriebes darf nicht als Handelsempfehlung oder als Anlageberatung ausgelegt werden. Der Kauf oder Verkauf einer Währung kann nur von einem lizenzierten BrokerDealer durchgeführt werden. Leistungsdaten: Bitte geben Sie unten Ihre E-Mail-Adresse ein, um die V3-Leistungsdaten zu erhalten. Anmerkung: Die vorherige Aufführung ist einfach eine Darstellung dessen, was mit dem Werkzeugsatz erreicht werden kann. Letztlich variiert die Systemleistung erheblich, je nachdem, welche Vermögenswerte gehandelt werden, die Zeitrahmen und die verwendeten Parameter und die Fähigkeit des Händlers. Das Stat Arb V3 System ist einfach ein Werkzeugsatz, um eine automatisierte Arbitrage Trading-Strategie auf der Grundlage eines Händlers Satz von Anforderungen zu erleichtern. FX AlgoTrader NICHT per E-Mail-Adressen an Dritte weitergeben. Die Produkte auf dieser Website sind Handelsinstrumente und sind nicht dazu bestimmt, einzelne Forschung oder lizenzierte Anlageberatung zu ersetzen. Die bisherige Wertentwicklung garantiert keine zukünftigen Ergebnisse. Handelswährungen sind mit erheblichem Risiko verbunden, und es besteht immer das Potenzial für Verlust. Es wird keine Vertretung gemacht, dass diese Produkte Gewinne garantieren oder nicht zu Verlusten aus dem Handel führen. Jede Erläuterung oder Demonstration des Produktbetriebes darf nicht als Handelsempfehlung oder als Anlageberatung ausgelegt werden. Der Kauf oder Verkauf einer Währung kann nur von einem lizenzierten BrokerDealer durchgeführt werden. Datenblatt für Advanced Statistical Arbitrage V3 Bitte füllen Sie die Angaben aus und klicken Sie auf "Senden". Sie erhalten dann eine E-Mail mit einem Link zum Datenblatt FX AlgoTrader NICHT E-Mail-Adressen an Dritte weitergeben. Attachment Inhalt - New Arb Trader bezieht sich auf FX AlgoTrader Arb Werkzeuge als FxAlgo. FxAlgo wurde nach einer umfassenden Suche nach dem Internet für automatisierte Arbitrage-Softwareprodukte ausgewählt, die innerhalb der MetaTrader 4 Handelsumgebung funktionierten. FxAlgo wurde dann auf vier Demo-Broker-Konten für einen Zeitraum von zwei Wochen Handel FX Produkte nur getestet. FxAlgo stellte sowohl eine stabile automatisierte Handelsplattform als auch eine mehr als akzeptable ROCE zur Verfügung. FxAlgo wurde dann auf einem Live-Trading-Account implementiert und es hat eine Rendite von über 48 auf unsere anfängliche Aktieneinspritzung über nur eine Sechs-Tage-Handelsperiode geliefert. Die Unterstützung durch den Autor und Eigentümer von FxAlgo sowohl während der Testzeit und seit dem Umzug in Live-Betrieb wurde ausgezeichnet, die Höhe der Unterstützung, die wir erlebt haben, kann nicht fehlerhaft sein. Alle Anträge auf Unterstützung per E-Mail wurden fast umgehend beantwortet und der Besitzer hat ein großes Interesse daran geweckt, dass wir die besten Methoden zur Anwendung von FxAlgo für die Erfüllung unserer Handelsziele vollständig beurteilt haben. Die Währungspaare, die wir gehandelt haben, wurden mit dem FxAlgos Correlation Indicator ausgewählt, der sich als äußerst nützliche Ergänzung der FxAlgo V2.5 Trading Engine erwiesen hat. FxAlgo wird von uns genutzt, um Währungspaare auf den H1- und D1-Zeitrahmen zu handeln. Der H1-Zeitrahmen wurde zunächst verwendet, um eine schnellere Aufwertung der FxAlgos-Operation zu gewinnen und wie man seinen Handel kontrolliert. Seit dem Erwerb einer grundlegenden Aufwertung der FxAlgo V2.5-Trading-Engine wurde der D1-Zeitrahmen hinzugefügt und die Gewinne sind gestiegen, da Arbitrages im D1-Zeitrahmen generell höhere Gewinnspannen bieten, wenngleich sie länger dauern, um zu schließen. Die mit FxAlgo ausgelieferten Standard-Trigger-Einstellungen wurden zunächst eingesetzt, um Arbitrage-Trades auszulösen. Diese wurden vollkommen adäquat gefunden und haben einen mehr als akzeptablen ROCE produziert. Die in der mit FxAlgo gelieferten Dokumentation empfohlenen EB-Variablen funktionieren gut und haben sich als äußerst nützlich erwiesen, um FxAlgo kennenzulernen. Sie kontrollieren das grundlegende Handelsrisiko und sind eine nützliche Erweiterung des V2.5-Motors. Wir haben FxAlgo nur im Briefpapier-Rekonstruktionsmodus eingesetzt. Wir handeln derzeit FxAlgo über eine beträchtliche Anzahl von Währungspaaren und über zwei verschiedene Zeitrahmen und haben FxAlgos Global Variables von unschätzbarem Nutzen gefunden. Diese Global Variable ermöglicht es uns, Risiken und Kapitalabbau über unsere gesamte Handelsaktivität mit Konsistenz und Leichtigkeit zu verwalten. Wir handeln das individuelle Handelsrisiko durch die Manipulation der umfangreichen Parameter, die auf jedem Währungspaar individuelles Handelsblatt zur Verfügung gestellt werden. Wir haben noch keine fehlerhaften Spreads oder irgendwelche daraus resultierenden fehlerhaften Trades erlebt. Das bisher erzielte Winloss-Verhältnis liegt bei 6535. Wir haben bisher nur FxAlgo in unserem FX-Handel eingesetzt. Wir planen jedoch, unsere Nutzung von FxAlgo auf Rohstoffe und Indizes zu verlängern, nachdem wir weitere Tests gegen diese beiden Anlageklassen durchgeführt haben. Wir haben festgestellt, dass FxAlgo V2.5 und der Korrelationsindikator nicht nur exzellente und robust geschriebene Software sind, sondern auch aus geschäftlicher Sicht, um unsere bisherigen Ziele mehr als erfüllt zu haben. Wir haben vor kurzem auch das FxAlgo Zeus Risk Controller Produkt erworben, haben aber noch keine Zeit, dieses Produkt zu testen. Der im Live-Handel erzielte ROCE (bisher nur 6 Tage) hat bereits die Mehrheit der Anschaffungskosten sowohl der FxAlgo V2.5 als auch der Korrelationsindikator erreicht und wir erwarten, dass in den ersten 10 Handelstagen der Break-even-Punkt eintritt. New Arb Traders Equity Curve - Live Account Die Produkte auf dieser Website sind Trading-Tools und sind nicht dazu bestimmt, einzelne Forschung oder lizenzierte Anlageberatung zu ersetzen. Die bisherige Wertentwicklung garantiert keine zukünftigen Ergebnisse. Handelswährungen sind mit erheblichem Risiko verbunden, und es besteht immer das Potenzial für Verlust. Es wird keine Vertretung gemacht, dass diese Produkte Gewinne garantieren oder nicht zu Verlusten aus dem Handel führen. Jede Erläuterung oder Demonstration des Produktbetriebes darf nicht als Handelsempfehlung oder als Anlageberatung ausgelegt werden. Der Kauf oder Verkauf einer Währung kann nur von einem lizenzierten BrokerDealer durchgeführt werden. Wie viel kann ich mit Statistical Arbitrage EAs für MT4 machen Wie schnell können Sie laufen. Es gibt eine Menge Leute, die nach Feuer suchen und Handelssysteme vergessen, die sie auf ein Diagramm fallen lassen können, sich zurücklehnen und ihre 50 anfänglichen Eigenkapital im ersten Jahr in 10 Millionen wachsen lassen. Ja. Menschen wirklich glauben, dass Werkzeuge wie diese existieren und leider theres kein Mangel an Anbietern glücklich, ihre Produkte als Erfüllung dieser Phantasien Position FX AlgoTrader sind nicht einer dieser Anbieter. Die Stat Arb EA Werkzeuge auf dieser Seite sind Werkzeuge NICHT ROBOTER. Sie bieten eine reiche Arbitrage-Toolset, die es Händlern ermöglicht, ihre Job-Trading-Strategie zu automatisieren, was auch immer Zeitrahmen sie bevorzugen. Wenn youve nie einen Penny Trading FX oder andere Vermögenswerte die Chancen, Geld mit Arb-Tools, leider ist nicht hoch. Sie werden nicht einen verlorenen Händler in einen gewinnenden Händler verwandeln, aber sie automatisieren eine arb-Strategie und sorgen für eine solide Risikokontrolle. Wie viel Sie machen, hängt davon ab, wie gut Sie als Händler sind. Manche Leute können schneller laufen als andere - wenn du gute Ausrüstung hast, macht es die Arbeit leichter. Haben Sie Backtest-Daten für die Arbitrage-Tools Nein. Leider ist es nicht möglich, EAs in MetaTrader 4 zu testen, die mehrere Paare handeln. Ich bemerkte, dass die neue Version des Systems die Möglichkeit hat, die Positionsgrößen für jedes Bein des arb zu variieren. Wie bestimmen Sie, was die richtige Position Größe für jedes Bein sollte Mit kleinen Konten Handel Mikro-oder Mini-Lose seine nicht entscheidend, um die Beine Gleichgewicht zu machen. Wenn die Positionsgröße zunimmt, wird dies signifikanter. Zum Beispiel werden alle Paare, die USD als Zitat-Währung haben, zB Majors wie EURUSD GBPUSD den gleichen Pip-Wert haben. Also ein Standard-Los für EURUSD und GBPUSD haben beide den gleichen Pip-Wert von 10pip. Wenn die arb-Paare aus einem Kreuz wie EURJPY bestehen, wäre der Pip-Wert (basierend auf den heutigen Raten) 12.88pip. Um die Beine auszugleichen, müssten wir die Positionsgröße des EURJPY-Beins um 11.2880,78 reduzieren. Um also eine ausgewogene EURUSDEURJPY-Arb zu schaffen, müsste man 1 Los für das EURUSD-Bein und 0.78 Lose für das EURJPY-Bein verwenden. Wenn wir die Positionsgröße auf 0,1 Lose reduzieren (10.000 - ein Mini-Los), müssten die Positionsgrößen auf 0,1 und 0,078 eingestellt werden. Also, wenn du kein Mikrokonto hast, musst du zwei Mini-Lots für beide Beine laufen lassen. Sobald Sie die Positionsgröße auf Mikro-Lose reduzieren, wird der Effekt des Ausgleichs der Arb immer weniger signifikant. Der einfachste Weg, um die Pip-Wert zu berechnen ist, um eine Online-Pip-Rechner verwenden Kann ich die gleiche arb auf mehrere Zeitrahmen pro EURUSDGBPUSD auf H1 und auf M15 NO. Dont do this Die Arb-Produkte erlauben nur eine einmalige Instanz eines bestimmten arb zu laufen. Wenn Sie das gleiche Arb-Setup auf ein anderes Diagramm laden, wird es die internen Variablen verwirren, die für die Handelsverwaltung verwendet werden. Das System wird sich nicht logisch verhalten, da die beiden Arbs die internen Variablen, die ein fehlerhaftes Handelsverhalten erzeugen könnten, ständig überschreiben werden. Sie können eine beliebige Anzahl von einzigartigen Arbs auf der MT4-Plattform mit dem Tool laufen - aber sie müssen alle einzigartig sein. Z. B. eine Instanz von EURUSDGBPUSD oder AUDUSDNZDUSD etc etc Für fortgeschrittene Arb-Trader ist es möglich, das gleiche arb auf einen anderen Zeitrahmen zu erstellen, indem man die Paar-Sequenzierung umkehrt und so eine inverse arb erzeugt. ZB EURUSDGBPUSD auf H1 und GBPUSDEURUSD auf M15. Allerdings müsste der Händler die Handelsrichtung beider Arb-Setups unter Verwendung der Trendverriegelungsoptionen steuern. Dieser Ansatz kann verwendet werden, um den Abbau auf längerfristige Bogen zu hecken und zu reduzieren, aber diese Strategie ist aufgrund der Fähigkeit, die bei der Schließung der inversen Arb-Komponente erforderlich ist, komplex, wenn eine langfristige mittlere Umdrehung stattfindet. Was ist der Unterschied zwischen V2 und V3 Ist V3 für mittel - bis langfristig stat arb und V2 ist einfach für kurzfristige stat arb V2 und V3 kann in jedem Zeitraum für kurzfristige oder langfristige Arbitrage verwendet werden. V3 ist eine erweiterte Version von V2, da es Protokolle für die Spread-Analyse verwendet, die viele Vorteile wie dynamische Profit-Targeting und eine breite Palette von Trader definierte externe Eingabeparameter hat. V3 ist die logische Progression von V2 und enthält viele Trader angeforderte Erweiterungen. Muss ich in der Lage sein, die Parameter extern auf das Modell zu schätzen oder gibt das Produkt sie mir Wie würde ich über die Ermittlung der Korrelationen gefragt werden Möchten diese MT4 Indikatoren Ich möchte nur ein Gefühl für den Prozess bei der Umsetzung der Produkt. Beide Arb-Produkte haben zwei Komponenten einen Experten-Berater und einen Indikator. Der Indikator liefert die statistische Analysekomponente. V2 Arb-Produkte berechnen die Ausbreitung der Paare, indem sie eine durch die anderen teilen, dann berechnen sie den gleitenden Durchschnitt (der Ausbreitung), dann zeichnen Trader definierte Standardabweichungen auf beiden Seiten dieses gleitenden Durchschnitts. Die Handelseintrags - und Ausstiegsschwellen werden durch die STD Multiple im Indikator bestimmt (dies kann vom Händler angepasst werden) Die Handelseintragsschwellen (STDs) werden durch Augapfen der typischen Abweichung vom Mittelwert vor dem Ausbreitung der Spreads eingestellt. Offensichtlich sind Zeitrahmen und Systemparameter von entscheidender Bedeutung. 5-Minuten-Charts können zeigen, was aussieht wie eine stationäre Ausbreitung, aber das kann sich sehr schnell ändern und wird sehr richtungsweisend. Auf der anderen Seite bietet eine wöchentliche Grafik viel mehr Einblick in die mittelfristige Ausbreitungsdynamik. Kurzfristiges Arbeiten ist sehr schwierig und es ist leicht zu fangen, wenn die Paare entkoppeln. Dies wird oft gegen Ende der asiatischen Session und in der Nähe der Frankfurter Messe gesehen. Da die Liquidität in die Marktströme fließt, kann man sich über kurze Zeiträume richten. In Bezug auf die passende Pa-Paar-Auswahl können Sie den FX AlgoTrader Echtzeit-Korrelationsindikator verwenden, um in jedem Zeitrahmen hoch korrelierte Arbitrage-Paare auszuwählen. Das V3-System verwendet einen Log-Spread-Algorithmus, der es dem Trader ermöglicht, das Reversionspotential in Dollar zu sehen. Dies ermöglicht es Händlern, die Macht der längerfristigen arb im Vergleich zu kurzfristigen arb Handel zu sehen. Welches Wissen muss ich wissen, um dein Stab-Arb-Produkt zu benutzen. Du müsstest über die mittlere Reversion, die Korrelation, die Kopplungdekopplungsdivergenz usw. wissen. Du musst verstehen, dass es keine Garantie gibt, dass die Reversion stattfindet, wenn man es erwartet . Ich bemerkte, dass die Voreinstellung für die EA 5 Lose und 20 Risiko war, also habe ich beschlossen, dies zu reduzieren, nur .1 Los und wie 5, die vielleicht eine gute Idee sein kann oder auch nicht. Wenn ich die Vorlage neu geladen habe, kehrten die Einstellungen wieder auf die Standardeinstellung zurück. Ist es möglich, die Standardeinstellungen zu bekommen, um viel weniger zu sein. Also, wenn aus irgendeinem Grund ich die EA neu laden und vergessen Sie die Einstellungen, die es nicht blow das Konto Die Vorlage wird immer die Standardeinstellungen verwenden, wenn Sie sie ändern und halten Sie Ihre Änderungen nur erstellen Sie eine neue Vorlage mit dem Namen New Arb Einstellungen oder whetever Sie mögen. Dann, wenn Sie die neue Vorlage öffnen, werden Ihre geänderten Einstellungen anstelle der Standardeinstellungen verwendet. Was ist die minimale Konto Größe für arb Handel Forex Sie könnten Arbs auf einem 500 Mikro-Konto, vorausgesetzt, Sie halten die Position Dimensionierung auf ein Minimum. Es wäre nicht klug, arbs auf einem mini acocunt mit nur 500 Dollar im Eigenkapital zu führen. Sowohl V2 als auch V3 arb Produkte können auf Mikro-, Mini - und Standard-MT4-Konten laufen. Welche Zeitrahmen haben Sie gefunden, um das Beste zu sein, um Bogen zu machen Stündlich 5m Täglich Es hängt von Ihnen ab und was Sie erreichen möchten, wenn Sie kurzfristige über Nacht Arbs auf der asiatischen dünnen Liquiditätsmarkt basieren dann 5 Minuten könnte gut für Sie sein. Alternativ, wenn Sie gern anständiges Geld machen wollen, ohne den Makler viel in Spread-Kosten zu geben - Tägliche Charts würden weniger Trades mit viel größeren Gewinnen für Bogen anbieten, die auf den Mittelwert zurückgingen. In der Regel um so länger, je länger der Gewinn ist. Ein Kunde hat 1200 USD aus einem 5000 USD Konto in einer Woche gemacht. Der Typ ist ein x-kommerzieller Trader, so dass im Auge das Tool ist nur so gut wie der Trader in Bezug auf die Auswahl der richtigen Paare zu handeln und die richtigen Parameter. Also, zusammenfassend, müssen Arb-Händler experimentieren, um die besten Systemeinstellungen zu finden, die ihrem Trading-Stil, Risiko und allgemeinen Erwartungen entsprechen. Im Allgemeinen ist diese EA recht rentabel. Was ist der ungefähre ROI In Bezug auf ROI ist es schwer zu sagen, wie es hängt davon ab, welchen Zeitrahmen Sie handeln. Der potenzielle Gewinn wird von der EA unter dem Reversion Potential Datenetikett auf dem Hauptdiagramm angezeigt. Diese Zahl wird auf der Differenz zwischen der aktuellen Spread und ihrem gleitenden Durchschnitt berechnet. Wenn das Reversionsziel auf das entgegengesetzte Band gesetzt wird, wird der potenzielle Gewinn wesentlich erhöht, aber der Trader würde einen vollen Swing von einem Band zum anderen benötigen, dh 1 bis -1 STD oder Whetever Trigger Parameter, die der Trader definiert hat. In Bezug auf Zeitrahmen können Sie viel mehr Geld für längerfristige Charts im Vergleich zu kurzfristigen Hochfrequenz-Arb Trades machen. Wir produzieren keine ROI - oder Aktienkurven-Daten mehr, da die Ergebnisse sehr stark von Trader zu Trader variieren werden. Die Werkzeuge spiegeln nur die Fähigkeit des Händlers wider, die optimalen Vermögenswerte, Zeitrahmen und Parameter für den Handel auszuwählen. Es geht alles zurück, wie schnell können Sie laufen :) Die V3 scheint, einige Trades mit einem Verlust zu schließen - wie kann das passieren Es gibt eine Reihe von Gründen, die dies passieren könnte: - Die Arb Trades haben die maximalen Risikoparameter verletzt Und das System hat beide Positionen automatisch geschlossen Das System wird im Aggregationsmodus betrieben und das tägliche Profitziel wurde bereits erreicht - sobald das Profitziel getroffen wird, wird das System alle offenen Arbs ausschließen - dies könnte dazu führen, dass verlorengehende Arbs geschlossen wird Automatisch das erreichte aggregierte Ziel zu schützen. Der Trader hat die Arb-Einstiegspunkte zu nahe an den gespreizten Kostenkanal gesetzt und der potenzielle Gewinn ist so klein, dass der Schlupf das PL der Arb negativ während des arb-close-Verfahrens kippt. Dies lässt sich leicht durch den Handel auf längere Zeiträume lösen und das STD-Vielfache erhöhen, um den Handelseintrag weiter weg von dem gespreizten Kostenkanal zu bewegen. Können Sie mir helfen zu verstehen, warum die EA einen Handel nicht geschlossen hat, obwohl Reversion bereits eingetreten ist. Dies könnte aus folgenden Gründen geschehen: - V3 kann nur Arb-Trades schließen, die im Profit sind. Wenn Ihr aktuelles arb nicht in Profit ist (evtl. wie es in einem anderen Zeitrahmen geöffnet wurde), wird das System die Arb nicht abschließen. Die TradeOffTimeframe-Paramter sind für diesen Chart-Zeitrahmen nicht freigegeben Der arb-Handel wurde abgesichert Das System ist DEAKTIVIERT Was ist los Die Disable Gen Starb-Globale Variable wurde vom System gesetzt. Drücken Sie F3, um die GVAR-Tabelle anzuzeigen - es gibt ein paar Gründe, die dies passieren kann: - 1) Der Parameter CloseAllTrades ist auf true gesetzt. 2) Das aggregierte tägliche Gewinnziel wurde erreicht und die automatische Rücksetzung ist deaktiviert 3) Das Konto-Eigenkapital unterhalb der Mindestgrenze Um dieses Problem zu beheben, gehen Sie in die Globale Variablentabelle in MT4 - drücken Sie F3 - suchen Sie nach einer globalen Variablen namens Disable Gen Starb Mit einem Wert von 1. Wenn Sie die Variable löschen, wird das System wieder aktiviert. Führt das System eine dynamische Neuausrichtung durch. Im Moment gibt es keine dynamische Neugewichtung. Ich habe die Anwendung einer Skalierung im System in Erwägung gezogen, um die Arb-Position zu erhöhen, wenn eine Ausbreitung weiter entkoppelt wird, dies ähnelt einem Mittelungs-Down-Ansatz, aber die Hebelwirkung erhöht sich offensichtlich mit der Positionsgröße, wodurch das Risiko erhöht wird, wenn die Nettoposition ausfällt PL erreicht die im System eingestellten maximalen Risikoparameter. Es gibt verschiedene Denkschulen im Hinblick auf die Skalierung, Ein alternativer Ansatz ist es, die entgegengesetzte Seite des arb auf einen niedrigeren Zeitrahmen zu handeln, der eine dynamische Hecke (zu einem gewissen Grad) verursachen würde. Zusätzlicher Kommentar: Einige V3-Kunden haben mit einem alternativen Ansatz zur dynamischen Rebalancing experimentiert, in denen ein offener Arb-Handel stattfindet Entkoppelt sich von seinem MA und schafft einen Drawdown. Anstatt die Losgrößenbestimmung der bestehenden Arb zu rebalancieren, wird ein neues Arb eingerichtet, welches das genaue Gegenteil des aktuellen arb ist. Zum Beispiel, wenn Sie eine 5 Los pro Bein EURUSDGBPUSD arb, die aus einem ziemlich Diagramm ausgelöst wurde, würden Sie eine GBPUSDEURUSD arb auf einem 15-Minuten-Chart und verwenden Sie die LockLong oder LockShort-Parameter, um alle neuen Trades aus dem 15-Minuten-Chart zu erzwingen Das genaue Gegenteil von der arb auf dem längeren Zeitrahmen. Dies schafft eine perfekte Hecke und ermöglicht auch, den Drawdown zu reduzieren, da der kürzere Term arb allmählich in den Drawdown, der durch die längerfristig entkoppelte Arb entsteht, Das Prinzip basiert einfach auf dem Handel kurzfristig gespreizte Volatilität, die auf dem kürzeren Zeitrahmen gesehen wird. Dieser Ansatz ist nicht eine garantierte Get of Jail Free Card, aber es kann erheblich deaktivieren Positionen, wo erhebliche Entkopplung stattgefunden hat und im Tandem reduzieren die Größe eines potenziellen Verlustes. Ich benutze den FX AlgoTrader Korrelationsindikator und ich möchte ein System zu handeln, wenn zwei Bedingungen erfüllt sind. Sie sind: 1) Tägliche Korrelation ist mehr als 75 2) 5min Korrelation ist kleiner als -75. Diese Bedingung wird nur nur eine begrenzte Anzahl von Malen pro Tag erfüllt. Es ist sehr schwer, den ganzen Tag vor meinem PC zu warten. Meine Frage für dich ist. Welche Ihrer Produkte können negative divergencedecoupling identifizieren, wenn tägliche Korrelation ist immer noch über 75 an einem Tag Wenn ja, was ist das Produkt Die V2 oder V3 Arbitrage-Engine wird dies tun, wenn Sie sie entsprechend einstellen. Der Korrelationsindikator wurde entworfen, um für Arb-Trader verwendet zu werden, um bei ihrer Paarauswahl zu helfen. Also, wenn youre Kriterien ist tägliche Korrelation gt75 und 5 min Korrelation lt-75 Sie könnten das Arb-Produkt auf Ihrem 5-Minuten-Chart (wahrscheinlich einfacher, eine Stunde tatsächlich tatsächlich zu verwenden) und dann setzen Sie STD-Vielfache in der STD-Indikator, so dass Ihr Handel Einstiegsauslöser waren wo du sie willst. Sie könnten dies visuell tun und schauen, um nur die größten Divergenzen jeden Tag handeln. Clearaudio begann, Moving-Coil-Patronen in den 1970er Jahren zu machen, und erst später in die Moving-Magnet-Geschäft. Moving-Magnet Patronen-Designer müssen nun darauf achten, dass die meisten der heutigen Tonarms von mittlerer bis hoher Masse sind und dass daher, um kompatibel zu sein, ihre MMs von geringer bis mittlerer Compliance und höherer Masse als die der 1960er und 70er Jahre sein müssen. Um 1200 gehört der Maestro V2 zu den teuersten, wenn nicht den teuersten MM-Patronen, die man heute kaufen kann. Wie alle außer einem der vier anderen Modelle in der V2-Linie, verfügt es über einen Resonanz-optimierten Körper von Ebenholz. Clearaudios Spezifikationen für den Maestro V2 sind: Gewicht von 8.4gm, Ausgangsspannung von 3.6mV, Kanaltrennung größer als 30dB, Kanalbalance kleiner oder gleich 0,2dB und Spulenimpedanz von 660 Ohm (die letzten drei alle bei 1kHz). That coil impedance of 660 ohms is way higher than the typical MC impedance of 41508 ohms, the reason for which should by now be obvious: many more coil turns are required to achieve the higher output, with no mechanical price to pay151the coils are fixed. The Maestro V2s coil inductivity is rated at 429 millihenrys compared to the MC range of 5EcircH1505mH. The nominal loading resistance is the standard 47k ohms (though its often argued, correctly, that the nominal accepted resistive load is a standard based more on convenience than on the mathematical necessity dictated by the other numbers that are part of the circuit). Once youve calculated the actual loading for your MM cartridge, you can go into your MM preamp and, if possible, substitute the correct loading resistors for the ones supplied. Most MM owners stick with 47k ohms, though because the resistive load is what damps the systems resonant frequency, its critical. The Clearaudios loading capacitance is specified as 100 picofarads. While loading capacitance is not critical with MCs, it is critical for MMs because the high inductance can lower the systems resonant frequency to well within the audioband. But the relationship between inductance, capacitance, bandwidth, and resonant frequency is complex. (See this page of the Graham Slee website .) Because the specified capacitance includes the capacitive contribution of the tonearm cable, its a good idea to know what it is, if possible, before setting the phono preamps capacitive loading. The capacitance of most tonearm wires plus interconnect is at least 100pF (the longer the cable, the higher the capacitance) so, on average, to achieve a total loading of 100pF, the phono preamps DIP switches should be set not to 100pF but to 0pF. The Maestro V2 features a Micro HD stylus attached to a boron cantilever151a combination found only in Clearaudios most expensive MC cartridges, including the Goldfinger Statement (15,000). The lesser MMs in the V2 line151the Performer V2 (400), the Artist V2 (600), and the Virtuoso V2 (900)151have aluminum cantilevers and elliptical styli. The advantage of boron over aluminum is that boron is both far more stiff and lighter. The advantages of a Micro HD over an elliptical stylus are twofold: lower mass and improved traceability. Traceability is a styluss ability to get into the grooves tightest crevices, nooks, and crannies. ( Trackability is its ability to remain in the groove and maintain effective contact with it.) The rounder the styluss cross section, the less well it can find its way into the grooves tighter corners. The more severe ( ie . narrow and tall) the profile, the greater its ability to get all the way into those crevices to deliver all of the detail the recording contains, especially the clean reproduction of high-frequency transients. A severe stylus profile has another advantage. Think of a sinewave viewed from the side (a hill), negotiated by a round stylus (a disc). The disc moves to the top of the hill on its leading circumferential edge, but instead of immediately starting back down the hill, theres a pause as the front of the disc, which has just negotiated the uphill climb, hands the job off to the discs rear edge for the ride down the hills other side. The larger the circles diameter, the longer it takes for the circles trailing edge to begin the downward journey. The pauses of the handoffs at each crest of the groove, in both the vertical and horizontal dimensions, produce audible timing errors. Now imagine squeezing the circular disc into an ellipse, to narrow the distance between the styluss leading and trailing edges. The advantages of a stylus of such shape, which has a narrower contact patch than a spherical stylus, should be obvious. Now imagine a stylus with a severe profile that results in an even smaller contact patch. A stylus of this shape can reach deeply into crevices of the groove while producing an almost instantaneous handoff at the crest of each hill. The physical differences and distances may be microscopic, but the sonic consequences are enormous. However, to get the full benefits of a severe stylus profile, you must take greater care in setting the overhang, zenith angle (groove tangency), and, especially, the stylus rake angle (SRA). If the styluss vertical ridge inaccurately traces the grooves vertical modulations by banging against them instead of sliding smoothly up and down them, it will produce large amounts of audible intermodulation distortion (IM). Therefore, while severe stylus profiles151 eg . the Micro HD, Geiger, Replicant, and Shibata151have obvious advantages, unless all of these parameters are properly set, the disadvantages can outweigh the advantages151whether in an MC or an MM. Other ways in which the Maestro V2 resembles an MC cartridge: Its stylus cant be replaced by the owner, and it tracks at a relatively heavy 1.81502.6gm (2.2gm optimal). And like Clearaudios MCs, the Maestro V2 unprotected cantilever protrudes from the front of its ebony body, with zero margin of error for an errant finger swipe. I hear you loud and clear: Why should I buy a 1200 Maestro V2 moving-magnet cartridge when I can buy a 1200 moving - coil with many of the same advantages and disadvantages Well, if you have an MM phono stage that you really love, youre ready to go151instead of having to replace it or add a costly step-up transformer or head amp. Moving to a costly MC cartridge by adding a budget-priced step-up transformer or head amp usually produces a sideways move in sound quality. If the Maestro V2s sonic characteristics are MC-like, you can both step up the quality and save yourself some money. MC Sound from an MM I ran the Maestro V2 through the MM input of the Ypsilon VPS-100 phono preamplifier (26,000). But to keep things in the real world, I did most of my listening through the Graham Slee Era Gold Mk. V phono preamplifier (999). The Maestro V2 didnt have the speed or high-frequency extension of a good moving-coil, but it had other equally attractive151some might say more attractive151qualities, and in some ways its sound did resemble that of an MC. If you like a combination of midband richness, openness, and detail, the Maestro V2 delivered. In a single listening session, I sat through all six LPs of Oscar Petersons Exclusively for My Friends (MPS 0209478MSW) and suffered neither fatigue nor boredom as the Maestro V2 reproduced the pianos rich, woody tonality without exaggerating or greatly softening transients. Yes, some far more expensive MCs can enhance transients and better capture the woody sustain, as well as give you longer decays and thus a greater sense of space. But this set was recorded in Hans Georg Brunner-Schwers private studio151his living room, I think. There isnt much space to lose. Not as well resolved was the cymbal shimmer produced by Petersons various drummers (Louis Hayes, Ed Thigpen, Bobby Durham), which sounded somewhat closed-in compared to the far more expensive and expansive MCs I compared the Maestro V2 to151but initial transients were fast and clean, more like an MC than an MM. The Maestro V2s ability to reproduce fast transients sets it apart from most, if not all, MM cartridges Ive heard. I then played a superb reissue, on 200gm vinyl, of a 1978 recording of Mahlers Symphony 3 by Zubin Mehta and the Los Angeles Philharmonic, recorded in UCLAs Royce Hall by James Lock and Simon Eadon, and mastered from the original master tape by Willem Makkee (2 LPs, Analogue Productions LAP0117). It made a good case for why all audiophiles might consider adding a great MM cartridge to their arsenals, if swapping out is practical. The tonal balance was more middle-to-rear-of-hall, but the richness and fullness of the brass and strings were to die for: well burnished, and the Maestros ability to reproduce orchestral weight surpassed that of many MCs of any price. The horns and violins induced chills the piccolo, clarinet, and tambourine were somewhat less impressive, but overall, the Maestro V2s speed far surpassed my expectations of MM cartridges, and made up for the loss of top-end air and extension. Measured with a digital oscilloscope, the Maestro V2s channel separation of 27dB, L150R, and 28.5dB, R150L, were close to the specified 30dB it produced a wide stage. Like Shures V-15xMR, the Maestro V2 tracked and traced well everything I threw at it151but the Clearaudio had greater weight, depth and. majesty. The Maestro V2 reproduced male voices particularly well: Its overall warmth was accompanied by the speed necessary to prevent its sound from deteriorating into baritone mud. Even Capitols great series of recordings by guitarist Laurindo Almeida fared well151with these LPs, MCs usually have it all over MMs. Yes, Almeidas pluckings of his instruments strings sound faster and more precise through MCs, but the Maestro V2 reproduced the body of his guitar well151again, more of a trade-off than an outright stomping. Another great MM cartridge is Ortofons 2M Black (755), but the Maestro beat it in terms of dynamics and, especially, weight and slam151as well it should, for 445 more. However, neither MM can match the dynamic majesty of the great151and far more expensive151MCs. Summing Up In the months I spent listening to Clearaudios Maestro V2, I never felt I was missing anything. Instead, I was given an entirely different perspective on some very familiar recordings. Can a greater compliment be paid a 1200 moving-magnet cartridge Submitted by Venere 2 on May 22, 2015 - 2:03am Michael, thanks for the well written and thought out review (as usual). I do wonder if you ever feel that reviewing a 1200 cartridge in 2015 is a service Its not the dollar amount in itself that is pejorative but rather the amount compared to alternatives in the non analog realm. For the longest time I defended turntables as the best sounding front end. For the longest time, I spent my money on an analog front end, and the vinyl LPs to play on it. As recently as a few months ago, I still did. Then after much denial and lost time, I admitted to myself that digital was no longer the awful sounding crap it once was. I did not want to believe it. I certainly did not want to make the change, much less embrace it but I did. I traded my turntable for a high quality DAC. I couldnt be happier When I see people getting into vinyl buying a turntable and start building a vinyl record collection now in 2015, I feel bad for them. They remind me of the people who invest in a stock when everyone is talking about it Those people buy the stock at the wrong time (too late), and lose. Now music is so much more than dollars and cents, and the enjoyment of music has no price. But, for the guy buying a 5000 turntable NOW and building up a multi thousand dollar vinyl collection, that he will sell for pennies on the dollar in 2-3 years, I feel its a disservice to push analog right now. Mark this post if you wish, and gloat in 2 years if I am wrong. But, I have seen these kinds of trends and waves before. The vinyl bubble will burst, sooner than later. I dont wish it. But it will happen. Turntables improve, but at a slow rate. Digital improves much faster, and it keeps getting faster. Submitted by Gorm on May 27, 2015 - 4:25pm Venere: please dont feel sorry for me. I never sold my treasured albums (had a lot of duds too thought) and I own an expensive EMM Labs XDs1 for SACD and HiDef downloads. I still buy good albums and I also pay for good downloads. They can all be great, or not - depending mostly on the Musicians and engineers. The sad thing is that on HD Tracks downloads I get no information, and therefore all the players, engineers and those responsible for the final product are ignored. I can say (having invested equally in both formats) that everything being equal - the analogue gives more pleasure. But I am happy for both. So dont patronize me please and save the tears for those whose entire collection on digital could suddenly disappear one day. Submitted by geekonstereo on July 4, 2015 - 12:03pm Hello, In a comparison of various cartridges on Analog Planet, you wrote: . after youve become used to hearing this track on the other cartridges hearing it for the first time via the 2M Black is startling. Youll hear heretofore buried parts and see the singers with an ease most of the other cartridges cant come close to reproducing. Plus the Blacks rhythmic drive takes the track to another level of sonic and musical intensity. But the Blacks most salient quality is its utter transparency. It sounds less like a recording and more like live. I agreed with this after listening to the various test files on that comparison. My question is whether you think the Clearaudio Maestro beats the Black in terms of making the listener see performers and give a sense of hearing music live with better rhythmic drive. As such, would the Maestro also startle listeners I realise that its difficult to make a comparison because the set-ups may have been very different, but I wanted your opinion And while we are at it, your take on the Black or Meastro versus the Dynavector 10x5 I am planning to buy a VPI Traveler and am hunting for a suitable MM or high-output MC cartridge for it. Thank you. Several of the temperature diagrams on this site begin in the year 1979, where the fine satellite record begins. This is chosen as a general start date to make comparison between different data sources (satellite, surface, etc.) easy. On the other hand, this approach may conceal the fact that Earths climate record is much longer. It is the purpose of the present short paragraph to introduce modern climate change to this longer time perspective. Fig.1. Geological stratigraphic chart for the entire geological history of planet Earth. Modern time is indicated by the thin red line at the top of the left column. Please note that the time scale is highly compressed, and increasing so towards higher ages. The left hand column fits on top of the next column to the right, column no 2 on top of no 3, and the right hand column should be at the bottom. Planet Earth has an age of about 4600 million years. The diagram above (Subcommission for Stratigraphic Information ) shows a geological stratigraphic chart for the entire geological history, subdivided into a vast number of epochs, each consisting of a number of stages. Most (if not all) of these geological divisions are based on the recognition of environmental changes affecting the entire planet that is, past global climate changes. In other words, global climate change has been the rule for the entire history of Earth, not the exception. If each year in the time scale above (Fig.1) was represented by one millimetre, the entire stratigraphic chart would be about 4600 km (2858 miles) long. In Europe, this corresponds roughly to the distance between Madrid (Spain) to Sverdlovsk in the Ural Mountains (Russia). In North America 4600 km roughly corresponds to the distance between San Francisco (USA) and Quebec (Canada). On this scale modern humans would appear within the last 200 m, the Polar Bear within the last 150 m, and the entire global meteorological record since about 1850 would take up the last 16 cm. The period with satellite observations would fit into the final 3 cm. From time to time the planet has been affected by millions of years with relatively cold climate, each such period leading to a long succession of glacial and interglacial periods. During the last couple of millions of years, planet Earth has been in such a cold stage. The last (until now) ice age ended around 11,600 years ago, and we are for the time living in a so-called interglacial period, until the next ice age will begin some time into the future. The last four glacial periods and interglacial periods are shown in the diagram below (Fig.2), covering the last 420,000 years in Earths climatic history. Fig.2. Reconstructed global temperature over the past 420,000 years based on the Vostok ice core from the Antarctica (Petit et al. 2001 ). The record spans over four glacial periods and five interglacials, including the present. The horizontal line indicates the modern temperature. The red square to the right indicates the time interval shown in greater detail in the following figure . The diagram above (Fig.2) shows a reconstruction of global temperature based on ice core analysis from the Antarctica. The present interglacial period (the Holocene) is seen to the right (red square). The preceding four interglacials are seen at about 125,000, 280,000, 325,000 and 415,000 years before now, with much longer glacial periods in between. All four previous interglacials are seen to be warmer (1-3 o C) than the present. The typical length of a glacial period is about 100,000 years, while an interglacial period typical lasts for about 10-15,000 years. The present interglacial period has now lasted about 11,600 years. According to ice core analysis, the atmospheric CO 2 concentrations during all four prior interglacials never rose above approximately 290 ppm whereas the atmospheric CO 2 concentration today stands at nearly 390 ppm. The present interglacial is about 2 o C colder than the previous interglacial, even though the atmospheric CO 2 concentration now is about 100 ppm higher. The last 11,000 years (red square in diagram above) of this climatic development is shown in greater detail in the diagram below (Fig.3), representing the main part of the present interglacial period. Fig.3. The upper panel shows the air temperature at the summit of the Greenland Ice Sheet, reconstructed by Alley (2000) from GISP2 ice core data. The time scale shows years before modern time. The rapid temperature rise to the left indicate the final part of the even more pronounced temperature increase following the last ice age. The temperature scale at the right hand side of the upper panel suggests a very approximate comparison with the global average temperature (see comment below). The GISP2 record ends around 1854, and the two graphs therefore ends here. There has since been an temperature increase to about the same level as during the Medieval Warm Period and to about 395 ppm for CO 2 . The small reddish bar in the lower right indicate the extension of the longest global temperature record (since 1850), based on meteorological observations (HadCRUT3 ). The lower panel shows the past atmospheric CO 2 content, as found from the EPICA Dome C Ice Core in the Antarctic (Monnin et al. 2004 ). The Dome C atmospheric CO 2 record ends in the year 1777. The diagram above (Fig.3) shows the major part of the present interglacial period, the Holocene, as seen from the summit of the Greenland Ice cap. The approximate positions of some warm historical periods are shown by the green bars, with intervening cold periods. Clearly Central Greenland temperature changes are not identical to global temperature changes. However, they do tend to reflect global temperature changes with a decadal-scale delay (Box et al. 2009 ), with the notable exception of the Antarctic region and adjoining parts of the Southern Hemisphere, which is more or less in opposite phase (Chylek et al. 2010 ) for variations shorter than ice-age cycles (Alley 2003 ). This is the background for the very approximate global temperature scale at the right hand side of the upper panel. Please also note that the temperature record ends in 1854 AD, and for that reason is not showing the post Little Ice Age temperature increase. In the younger part of the GISP2 temperature reconstruction the time resolution is around 10 years. Any comparison with measured temperatures should therefore be made done using averages over periods of similar lengths. During especially the last 4000 years the Greenland record is dominated by a trend towards gradually lower temperatures, presumably indicating the early stages of the coming ice age (Fig.3). In addition to this overall temperature decline, the development has also been characterised by a number of temperature peaks, with about 950-1000 year intervals. It may even be speculated if the present warm period fits into this overall scheme of natural variations The past temperature changes show little (if any) relation to the past atmospheric CO 2 content as shown in the lower panel of figure 3. Initially, until around 7000 yr before now, temperatures generally increase, even though the amount of atmospheric CO 2 decreases. For the last 7000 years the temperature generally has been decreasing, even though the CO 2 record now display an increasing trend. Neither is any of the marked 950-1000 year periodic temperature peaks associated with a corresponding CO 2 increase. The general concentration of CO 2 is low, wherefore the theoretical temperature response to changes in CO 2 should be more pronounced than at higher concentrations, as the CO 2 forcing on temperature is decreasing logarithmic with concentration. Nevertheless, no net effect of CO 2 on temperature can be identified from the above diagram, and it is therefore obvious that significant climatic changes can occur without being controlled by atmospheric CO 2 . Other phenomena than atmospheric CO 2 must have had the main control on global temperature for the last 11,000 years. The following diagram shows the period since 1850 (indicated by the reddish bar in the diagram above), where it is possible to estimate global temperature changes from meteorological observations (Fig.4). Fig.4. Global monthly average surface air temperature since 1850 according to Hadley CRUT, a cooperative effort between the Hadley Centre for Climate Prediction and Research and the University of East Anglia s Climatic Research Unit (CRU ), UK. The blue line represents the monthly values. An introduction to the dataset has been published by Brohan et al. (2005). B ase period: 1961-1990. Last month shown: December 2010. Last diagram update: 3 January 2011. Click here to download the entire series of estimated HadCRUT3 global monthly surface air temperatures since 1850. Click here to read a description of the data file format. From figure 3 it is obvious that the global meteorological record (Fig.4) begins in the final part of the Little Ice Age, and thereby documents the following temperature increase, especially clear since about 1915. In other words, the temperature increase documented by meteorological records represents the temperature recovery following the cold Little Ice Age. The ongoing climate debate is essentially about this being mainly a natural temperature recovery, or caused by atmospheric CO 2 . especially for the time after 1975 It can, however, from figures 2, 3 and 4 be concluded that the temperature increase 1975-2000 is not unique when compared with past records, and that the net effect on temperature by atmospheric CO 2 has been small or even absent (Fig.3). From all diagrams shown above the still very short time period covered by the fine satellite observations is obvious. The period since 1979 only covers the most recent example of global warming (ca.1977-2001), but no examples of the many previous periods of warming or cooling. This should prudently be borne in mind when interpreting the temperature record since 1979 only, such as shown in several of the diagrams found on this website. As mentioned above the time since 1979 would only take up the final 3 cm of the entire 4600 km long geological climatic record, if each year is represented by one millimetre. Click here to jump back to the list of contents. Click here to view the recent (daily) global satellite temperature (AMSU-A) at various altitudes in the atmosphere. Indicate which level in the atmosphere you want to see Indicate your preference as to o C or o F Then click Draw graph You will need to have a recent version of JAVA TM installed on your computer to make use of this facility kindly made available by Dr. Roy Spencer and Dr. Danny Braswell. If you receive an error message after clicking Draw graph, try downloading the latest version (free) of Java from java. Click here to jump back to the list of contents. Land surface temperature 16 February 2017 (degrees K degrees C 273.15), at 02 and 14 hr (UTM-time), respectively. White areas are oceans or land areas without data. Map source: NOAA. Please use this and this link if you want to see the original diagrams (NOAA 18 ) or want to check for more recent updates than shown above. Click here to see the recent sea surface temperatures. Click here to jump back to the list of contents. Recent global air temperature change, an overview All temperature diagrams shown below have 1979 as starting year. This roughly marks the beginning of the recent period of global warming, after termination of the previous period of global cooling from about 1940. In addition, the year 1979 also represents the starting date for the satellite-based global temperature estimates (UAH and RSS). For the three surface air temperature estimates shown (HadCRUT, NCDC and GISS) the reference period differs. HadCRUT refers to the official normal WMO period 1961-1990, while NCDC and GISS as reference instead uses 1901-2000 and 1951-1980, respectively, which results in higher positive temperature anomalies. For all three surface air temperature records, but especially NCDC and GISS, administrative changes to anomaly values are quite often introduced, even for observations several years back in time. Some changes may be due to the delayed addition of new station data, while others probably have their origin in a change of technique to calculate average values. It is clearly impossible to evaluate the validity of such administrative changes for the outside user of these records. In addition, the three surface records represent a blend of sea surface data collected moving ships or by other means, plus data from land stations of partly unknown quality and unknown degree of representativeness for their region. Many of the land stations have also moved geographically during their existence, and their instrumentation changed. The satellite temperature records also have their problems, but these are generally of a more technical nature and therefore correctable. In addition, the temperature sampling by satellites is more regular and complete on a global basis than that represented by the surface records. It therefore is realistic to recognise that the temperature records are not of equal scientific quality. At the same time the big efforts being put into all five temperature databases should be gratefully acknowledged by all interested in climate science. On this background, the present website has decided to operate with three quality classes (1-3) for global temperature records, with 1 representing the highest quality level: The main reasons for discriminating between the three surface records are the following: 1) While both NCDC and GISS often experience quite large administrative changes. and therefore essentially must be considered unstable records, the changes introduced to HadCRUT are fewer and smaller. For obvious reasons, as the past do not change, an unstable record cannot be correct all the time. 2) A comparison with the superior Argo float sea surface temperature record shows that while HadCRUT uses a sea surface record (HadSST3) nicely in concert with the Argo record. a comparison between Argo and NCDC and GISS data shows a marked discrepancy. The differences between the individual diagrams shown below demonstrate the difficulties associated with calculating a correct average global temperature. Essex et al. (2006) have an interesting discussion of the whole concept of calculating an average global temperature. In addition, global surface air temperatures should only be considered a poor indicator of global climate heat changes, as air has relatively little mass associated with it. Ocean heat changes remain the dominant factor for global heat changes. Global air temperatures, however, continues to attract widespread interest, and many scientist assume that the air temperature at least may be considered a useful proxy for the present state of the global climate system. In the temperature diagrams below, the thick line represents the running 37 month average and the thin line the monthly temperature. Both values are the result of a number of mathematical manipulations with the original temperature data, and especially so the running average. In the text below each diagram you will find a link enabling you to download and analyze the data yourself. All diagrams below are using the same temperature scale, to enable easy visual comparison. Click here to jump back to the list of contents. Global monthly average lower troposphere temperature since 1979 according to University of Alabama at Huntsville (UAH), USA. This graph uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, interpreted by Dr. Roy Spencer and Dr. John Christy. both at Global Hydrology and Climate Center, University of Alabama at Huntsville, USA. The thick line is the simple running 37 month average, nearly corresponding to a running 3 yr average. The cooling and warming periods directly influenced by the 1991 Mt. Pinatubo volcanic eruption and the 1998 El Nio, respectively, are clearly visible. Reference period 1981-2010. Last month shown: January 2017. Last diagram update: 9 February 2017. Click here to see the latest UAH MSU global monthly lower troposphere temperature anomaly with comments. Click here to download the series of UAH MSU global monthly lower troposphere temperature anomalies since December 1978. Click here to see a maturity diagram for the MSU UAH data series. Click here to read about the latest version (6.0) of the UAH Temperature Dataset (April 28, 2015). Global monthly average lower troposphere temperature since 1979 according to Remote Sensing Systems (RSS). This graph uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, and interpreted by Dr. Carl Mears (RSS). The thick line is the simple running 37 month average, nearly corresponding to a running 3 yr average. The cooling and warming periods directly influenced by the 1991 Mt. Pinatubo volcanic eruption and the 1998 El Nio, respectively, are clearly visible. Click here for a description of RSS MSU data products. Please note that RSS January 2011 changed from Version 3.2 to Version 3.3 of their MSUAMSU lower tropospheric (TLT) temperature product. Click here to read a description of the change from version 3.2 to 3.3, and previous changes. Last month shown: January 2017. Last diagram update: 3 February 2017. Click here to download the series of RSS MSU global monthly lower troposphere temperature anomalies since January 1979. Click here to see a maturity diagram for the MSU RSS data series. Click here to read a description of the MSU products. Click here to jump back to the list of contents. Global monthly average surface air temperature since 1979 according to Hadley CRUT, a cooperative effort between the Hadley Centre for Climate Prediction and Research and the University of East Anglia s Climatic Research Unit (CRU ), UK. The thin line represents the monthly values, while the thick line is the simple running 37 month average, nearly corresponding to a running 3 yr average. An introduction to the dataset has been published by Brohan et al. (2005). Lower down the present page you will find a graph showing the entire series since 1850. Base period: 1961-1990. Last month shown: December 2016. Last diagram update: 21 January 2017. Click here to download the series of estimated HadCRUT4 global monthly surface air temperature anomalies since 1850. Click here or here to download the series of estimated HadCRUT3 global monthly surface air temperature anomalies since 1850. Click here to read a description of the data file format. Click here to see a maturity diagram for the HadCRUT data series. Click here to open a web interface to all the weather station data used by the Hadley Centre, a very useful facility developed by Clive Best, also known for his blog . Please note that the stations are split into 3 groups. 1) those going back to before 1860 2) Those going back to between 1860 and 1930 3) Those with data going back later than 1930. The last option is all stations together - but is very slow to load (gt5000 stations). Drag a rectangle to zoom in. Click on a station to see the graph of temperatures and anomalies. October 2, 2014: Please note that HadCRUT4 was released in a new version (HadCRUT.4.3.0.0). The main changes introduced by this new version is a decrease of temperatures 1850-1875 and an increase affecting observations since 2005. For further details of this version change click here. Click here to jump back to the list of contents. Global monthly average surface air temperature since 1979 according to the National Climatic Data Center (NCDC), USA. This time series is calculated using land surface data from the Global Historical Climatology Network (Version 2) and sea surface temperature anomalies from the United Kingdom MOHSST data set and the NCEP Optimum Interpolated SSTs (Version3 note version change on May 2, 2011). The thick line is the simple running 37 month average, nearly corresponding to a running 3 yr average. Base period: 1880-2016. Last month shown: January 2017. Last diagram update: 17 February 2017. Click here to download the series of the NCDC global monthly surface air temperature anomalies since 1880. Click here to see a maturity diagram for the NCDC data series. June 18, 2015: NCDC has introduced a number of rather large administrative changes to their sea surface temperature record. The overall result is to produce a record giving the impression of a continuous temperature increase, also in the 21st century. As the oceans cover about 71 of the entire surface of planet Earth, the effect of this administrative change is clearly seen in the NCDC record for global surface air temperature above. May 2, 2011: NCDC transitioned to GHCN-M version 3 as the official land component of its global temperature monitoring efforts. GHCN-M version 2 mean temperature dataset will continue to be updated through May 30, 2011, but no support for this version of the dataset will be provided. The global anomalies using GHCN-M version 2 can be accessed here: GHCN-M v2. The net effect of the change from version 2 to 3 can be seen here. Global monthly average surface air temperature since 1979 according to the Goddard Institute for Space Studies (GISS), at Columbia University, New York City, USA. GISS is a laboratory of the Earth-Sun Exploration Division of NASAs Goddard Space Flight Center and a unit of the Columbia University Earth Institute. The thick line is the simple running 37 month average, nearly corresponding to a running 3 yr average. Discussions of reasons why the GISS temperature estimate differs from other estimates can be read by clicking here. here and here. Base period: 1951-1980. Last month shown: January 2017. Last diagram update: 16 February 2017 . Click here to download the series of the GISS global monthly surface air temperature anomalies since 1880. Click here to see a maturity diagram for the GISS data series. Click here to download pre-version 3 (v.2) individual GISS station data. Click here to jump back to the list of contents. It is interesting to compare the various global air temperature estimates as to their internal degree of stability for the whole temperature record as such. Especially for surface air temperature estimates, a certain degree of change over time affecting especially the last few months is to be expected, as additional station data may be reported and incorporated in the database. But for the older part of the temperature record numerical stability over time would be expected, provided that the mathematical procedure used for estimating the global temperature is considered mature by the research team preparing the data series considered. In this context, maturity would imply that, for example, the November 1985 temperature reported by a certain database in February 2009 would be identical to the November 1985 value reported previously by the same database. Below a series of diagrams is shown to illustrate the degree of maturity, calculated for various databases by plotting the net change in their global temperature record since May 2008 (or February 2009). May 2008 (or February 2008) has been chosen as start date for this test as this represents the oldest version of the individual temperature records available to the webmaster. All diagrams below are using the same temperature scale, to enable easy visual comparison. A fine study of temporal instability can be inspected here (in Norwegian). Maturity diagram showing net change since 8 May 2008 in the global monthly lower troposphere temperature record prepared by the University of Alabama at Huntsville, USA. This temperature estimate extends back to December 1978. Click here to see a graph showing the most recent version of the UAH MSU global temperature estimate. Click here to download the entire series of UAH MSU global monthly lower troposphere temperatures since December 1978. Click here for explanation of temporal stability. Last diagram update: 9 February 2017. Click here to download the most recent version of UAH MSU global monthly lower troposphere temperature anomalies since December 1978. Click here to read about UAH version 6.0 change April 2012. Maturity diagram showing net change since 8 May 2008 in the global monthly lower troposphere temperature record prepared by the Remote Sensing Systems (RSS). This temperature estimate extends back to January 1979. Click here to see a graph showing the most recent version of the RSS MSU global temperature estimate. Please note that RSS January 2011 changed from Version 3.2 to Version 3.3 of their MSUAMSU lower tropospheric (TLT) temperature product. Click here to read a description of the change from version 3.2 to 3.3, and previous changes. Click here to download the entire series of RSS MSU global monthly lower troposphere temperatures since January 1979. Click here for explanation of temporal stability. Last diagram update: 3 February 2017. Click here to download the most recent version of RSS MSU global monthly lower troposphere temperature anomalies since January 1979. Click here to download the May 2008 version of RSS MSU global monthly lower troposphere temperature anomalies since January 1979. Maturity diagram showing net change since 25 February 2008 in the global monthly surface air temperature record prepared by the Hadley Centre for Climate Prediction and Research and the University of East Anglia s Climatic Research Unit (CRU ), UK. This temperature estimate extends back to January 1850. Click here to see a graph showing the most recent version of the HadCRUT3 and 4 global temperature estimate. Previous to the version change 3 to 4 (October 2012), the HadCRUT temperature record showed a high degree of temporal stability. The diagram below shows a comparison between the two versions of HadCRUT (stand October 2012). Another version change was made on October 2, 2014. Click here for an explanation of temporal stability. Last diagram update: 21 January 2017 . Click here to download the series of estimated HadCRUT4 global monthly surface air temperature anomalies since 1850. Click here or here to download the series of estimated HadCRUT3 global monthly surface air temperature anomalies since 1850. Click here to download the February 2008 version of estimated HadCRUT3 global monthly surface air temperature anomalies since 1850. It is interesting to note that the overall net adjustment shown by the HadCRUT surface temperature record since February 2008 (see figure above ) is that of more or less equal warming for the entire record since 1850. This is in contrast to the net adjustment of the two other surface records, NCDC and GISS. which both display a net cooling adjustment before 1950-60, and a net warming adjustment for the more recent part of the record, resulting in an overall increasing temperature increase since 1880. Diagram showing the global 37 month running average for HadCRUT3 (blue) and HadCRUT4 (red), and the difference between these averages (lower panel use scale to the right). One of the most important effects of the version change is a reduction of the post 1940 cooling. Last diagram update: 10 May 2013. Click here or here to download the series of estimated HadCRUT3 global monthly surface air temperature anomalies since 1850. Click here to download the series of estimated HadCRUT4 global monthly surface air temperature anomalies since 1850. Maturity diagram showing net change since 17 May 2008 in the global monthly surface air temperature record prepared by the National Climatic Data Center (NCDC), USA. The net result of the adjustments made are becoming substantial, and adjustments since May 2006 occasionally exceeds 0.1 o C. Before 1945 global temperatures are generally changed toward lower values, and toward higher values after 1945, resulting in a more pronounced 20th century warming (about 0.15 o C) compared to the NCDC temperature record published in May 2008. Arrows indicate two months where the adjustments over time are illustrated in the figure below. Last diagram update: 17 February 2017. Click here to download the most recent version of the NCDC global monthly surface air temperature anomalies since 1880. Click here to download the May 2008 version of the NCDC global monthly surface air temperature anomalies since 1880. Click here for a summary of the July 2009 (Smith et al. 2008 ) methodological changes in the land-ocean NCDC temperature analyses. Click here for information about the November 2011 version change (GHCN-M version 3.1.0 replaced GHCN-M version 3). Click here and here for information about the September 2012 version change (GHCN-M version 3.2.0 replaced GHCN-M version 3.1.0). Click here for an explanation of temporal stability. The two arrows in the diagram above indicate two months for which the adjustments over time is shown below . On May 2, 2011, NCDC transitioned to GHCN-M version 3 as the official land component of its global temperature monitoring efforts. GHCN-M version 2 mean temperature dataset will continue to be updated through May 30, 2011, but no further support for this version of the dataset will be provided. In November 2011, the GHCN-M version 3.1.0 replaced the GHCN-M version 3. For more information about the GHCN-M version 3.1.0. The overall net effect of the transition from GHCN-M version 2 to version 3 is to increase global temperatures before 1900, to decrease them between 1900 and 1950, and to increase temperatures after 1950. The diagram below exemplify adjustments made by NCDC since May 2008 for two months (see arrows in diagram above ) January 1915 and January 2000. Diagram showing the adjustment made since May 2008 by the National Climatic Data Center (NCDC) in the anomaly values for the two months January 1915 and January 2000. See also this diagram. Last diagram update 17 February 2017 . Maturity diagram showing net change since 17 May 2008 in the global monthly surface air temperature record prepared by the Goddard Institute for Space Studies (GISS), at Columbia University, New York City, USA. This temperature estimate extends back to January 1880. Click here to see a graph showing the most recent version of the GISS global temperature estimate. The net effects of the adjustments made since May 2008 are to generate a more smoothly increasing global temperature since 1880. Discussions on the background for the lack of temporal stability for the GISS temperature record can be read here. here and here. Arrows indicate two months where the adjustments over time are illustrated in the figure below. Last diagram update 16 February 2017 . Click here to download the most recent version of the GISS global monthly surface air temperature anomalies since 1880. Click here to download the May 2008 version of the GISS global monthly surface air temperature anomalies since 1880. Click here to see a fine study of GISS temporal instability (in Norwegian). Diagram showing the adjustment made since May 2008 by the Goddard Institute for Space Studies (GISS) in anomaly values for the months January 1910 and January 2000. See also this diagram. Last diagram update 16 February 2017 . Note added 15 (17) September 2012: Unless there is an error in the GISS temperature anomaly values downloaded on 15 September 2012 (or 15 August 2012), a major change appears to have taken place since 15 August 2012. The GISS maturity diagram below show the status per 15 August 2012, and should be compared with the diagram above from 15 September 2012. Apparently the change may reflect the September 2012 NCDC change from GHCN-M version 3.1.0 to GHCN-M version 3.2.0. Click here and here for more information on this. GISS Maturity diagram per 15 August 2012. Compare with the diagram per 15 September 2012 . Based on the above it is not possible to conclude which of the above five databases represents the best estimate on global temperature variations. The answer to this question remains elusive. All five databases are the result of much painstaking work, and they all represent admirable attempts towards establishing an estimate of recent global temperature changes. At the same time it should however be noted, that a temperature record which keeps on changing the past hardly can qualify as being correct. With this in mind, it is interesting that none of the global temperature records shown above are characterised by high temporal stability. Presumably this illustrates how difficult it is to calculate a meaningful global average temperature. A re-read of Essex et al. 2006 might be worthwhile. In addition to this, surface air temperature remains a poor indicator of global climate heat changes, as air has relatively little mass associated with it. Ocean heat changes are the dominant factor for global heat changes. Click here to jump back to the list of contents. Comparing global air temperature estimates In order to enable a visual comparison of the five different global temperature estimates shown above, the diagrams below show some or all series superimposed. As the base period differs for the different temperature estimates (see above), they are not directly comparable. All data series were therefore normalised by setting the average value of the initial 30 years from January 1979 to December 2008 equal to zero. before inclusion in the diagram below. In addition to the visual analysis below, the reader might also find it useful to inspect the maturity analysis presented above. Superimposed plot of Quality Class 1 global monthly temperature estimates. As the base period differs for the different temperature estimates, they have all been normalised by comparing to the average value of 30 years from January 1979 to December 2008. The heavy black line represents the simple running 37 month (c. 3 year) mean of the average of both temperature records. The numbers shown in the lower right corner represent the temperature anomaly relative to the above average. Values are rounded off to the nearest two decimals, even though some of the original data series come with more than two decimals. Last month shown: January 2017. Last diagram update: 15 February 2017. Superimposed plot of Quality Class 1 and Quality Class 2 global monthly temperature estimates. As the base period differs for the different temperature estimates, they have all been normalised by comparing to the average value of 30 years from January 1979 to December 2008. The heavy black line represents the simple running 37 month (c. 3 year) mean of the average of all three temperature records. The numbers shown in the lower right corner represent the temperature anomaly relative to the above average. Values are rounded off to the nearest two decimals, even though some of the original data series come with more than two decimals. Last month shown: December 2016. Last diagram update: 21 January 2017. Superimposed plot of Quality Class 1 and Quality Class 2 and Quality Class 3 global monthly temperature estimates. As the base period differs for the different temperature estimates, they have all been normalised by comparing to the average value of 30 years from January 1979 to December 2008. The heavy black line represents the simple running 37 month (c. 3 year) mean of the average of all five temperature records. The numbers shown in the lower right corner represent the temperature anomaly relative to the above average. Values are rounded off to the nearest two decimals, even though some of the original data series come with more than two decimals. Last month shown: December 2016. Last diagram update: 21 January 2017. It should be kept in mind that satellite - and surface-based temperature estimates are derived from different types of measurements, and that comparing them directly as done in the diagram above therefore in principle is problematical. For that reason, in the analysis below these two different types of global temperature estimates are compared to each other. However, as both types of estimate often are discussed together, the above diagram may nevertheless be of interest. In fact, the different types of temperature estimates appear to agree quite well as to the overall temperature variations on a 2-3 year scale, although on a short term scale there may be considerable differences. However, as shown in the paragraph below. surface temperature records seems to be drifting towards higher temperature anomalies than the satellite records. Diagram showing the average global temperature change (anomaly) during the satellite observational period (since January 1979), according to five global temperature estimates shown above. The upper panel show the average anomalies for the last 12 months, the mid panel show the average anomalies for the last 5 years, while the lower panel show the average anomalies for the last 10 years. As the base period differs for the different temperature estimates, they have all been normalised by comparing to the average value of 30 years from January 1979 to December 2008. Last month included in analysis: December 2016. Last diagram update: 21 January 2017. Usually modern surface air temperatures are compared to the so-called normal temperature, representing the so-called normal climate. This normal temperature is calculated as the average for values recorded during a 30-year period. The period 1961-1990 is the official World Meteorological Organisation (WMO) normal period. and is therefore often the time period referred to. Another 30-year period used as reference for comparisons is 1951-1980. This is partly because the total number of meteorological stations during this period reached a maximum, and since has undergone a marked reduction in number. Unfortunately, both these periods are dominated by the cold period 1945-1980, and almost any comparison with such a low average value will therefore appear as high or warm. This makes it difficult to decide if surface air temperatures at present are increasing or decreasing The only thing that will be clear is that modern temperatures are higher than back in this cold period. Click here to see a diagram showing the entire global temperature series (HadCRUT4) since 1850 with the 1945-1980 cold period and the WMO normal period indicated on the time line. Click here to jump back to the list of contents. Plot showing the average of monthly global surface air temperature estimates (HadCRUT4. GISS and NCDC ) and satellite-based temperature estimates (RSS MSU and UAH MSU ). The thin lines indicate the monthly value, while the thick lines represent the simple running 37 month average, nearly corresponding to a running 3 yr average. The lower panel shows the monthly difference between surface air temperature and satelitte temperatures. A s the base period differs for the different temperature estimates, they have all been normalised by comparing to the average value of 30 years from January 1979 to December 2008. Last month shown: December 2016. Last diagram update: 21 January 2017. As is shown by the diagram above, the average of surface based temperature estimates is not identical to that obtained by satellites. In general, however, the visual agreement is quite good on a time scale of few months. However, since about 2003, the average global surface air temperature is drifting away in positive direction from the average satellite temperature, meaning that the surface records show warming in relation to the troposphere records. The reasons for this is not clear, but is probably at least partly a result of the recurrent administrative changes of the surface records, see here. here and here. Global monthly average surface air temperature (HadCRUT4. NCDC. GISS ) minus global monthly average lower troposphere temperature (UAH MSU. RSS MSU ) since 1979. The thin blue line shows the monthly temperature difference between anomalies calculated for surface and lower troposphere observations, respectively. The thick blue line is the simple running 37 month average, nearly corresponding to a running 3 yr average. The dotted red line is the linear fit line, statistics of which is presented in the lower right corner of the diagram. As the five data series are using different reference (normal) periods, they have all been normalised by comparing to the average value of 30 years from January 1979 to December 2008. Last month shown: December 2016. Last diagram update: 21 January 2017. Please note that the linear regression is done by month, not year Click here to see the HadCRUT4 temperature anomaly diagram since 1979. Click here to see the NCDC temperature anomaly diagram since 1979. Click here to see the GISS temperature anomaly diagram since 1979. Click here to see the UAH MSU temperature anomaly diagram since 1979. Click here to see the RSS MSU temperature anomaly diagram since 1979. According to the above temperature records, the surface air temperature have been rising more rapid than the temperature in the lower troposphere since January 1979, about 0.1 o C. Click here to jump back to the list of contents. Diagram showing the latest 5, 10, 20 and 30 yr linear annual global temperature trend, calculated as the slope of the linear regression line through the data points, for two satellite-based temperature estimates (UAH MSU and RSS MSU), the Quality Class 1 series. Last month included in analysis: December 2016. Last diagram update: 21 January 2017. Diagram showing the latest 5, 10, 20, 30, 50, 70 and 100 yr linear annual global temperature trend, calculated as the slope of the linear regression line through the data points, for three surface-based temperature estimates (HadCRUT4 and GISS NCDC), the Quality Class 2 and 3 series, respectively. Last month included in analysis: December 2016. Last diagram update: 21 January 2017. The two diagrams above show the calculated linear annual global temperature trend ( calculated as the slope of the linear regression line through the data points ) for the last 5, 10, 20, 30, 50, 70 or 100 yr period. The difference between satellite - and surface-based temperatures is clear. Linear trends depend on the length of the time period considered. The shorter the period considered, the more variable the calculated trend will be as new monthly data are added to the data series. In addition, linear trends calculated for short periods often have higher numerical values than trends calculated for longer periods. When comparing linear temperature trends. is is therefore important always to use time periods of similar length. As an visual example of this effect, the diagrams below show trends calculated for the last 20, 15, 10 and 5 years, using the combined Quality Class 1 series. Linear trend analyses represent a relatively crude way of numerical analysis, and often a better approximation to the original data may be obtained by using other data models, e. g. polynomial, as shown in the diagrams below. The R 2 value may be considered an indicator of the degree of success of the data model adopted, but the number of data must also be considered. It should also be emphasized, that linear trend analyses are sensitive to values near the ends points of the data series considered, and especially for short series. Finally, any trend calculated only informs about past behaviour, and not about the future. Before attempting any linear trend (or any other) analysis of time series, a proper statistical model should be chooosen, based on statistical justification. For temperature time series t here is no a priori physical reason why the long-term trend should be linear in time. In fact, climatic time series often have trends for which a straight line is not a good approximation, as is clearly seen from several of the diagrams above and below. For a clear description of the problem encountered by many temperature time series analyses, please see Keenan, D. J. 2014: Statistical Analyses of Surface Temperatures in the IPCC Fifth Assessment Report. Last 20 years global monthly average surface air temperature according to the two satellite-based temperature estimates (UAH MSU and RSS MSU), Quality Class 1 . The thin blue line represents the monthly values. The thick black line is the linear fit, with 95 confidence intervals indicated by the two thin black lines. The thick green line represents a 5-degree polynomial fit, with 95 confidence intervals indicated by the two thin green lines. A few key statistics is given in the lower part of the diagram (note that the linear trend is the monthly trend). Last month included in analysis: January 2017. Last diagram update: 15 February 2017. Please note that the linear regression is done by month, not year Click here to download the series of UAH MSU global monthly lower troposphere temperature anomalies since December 1978. Click here to download the series of RSS MSU global monthly lower troposphere temperature anomalies since January 1979. Fits (linear as well as polynomial) only attempt to describe the past, and have little or no predictive power. Last 15 years global monthly average surface air temperature according to the two satellite-based temperature estimates (UAH MSU and RSS MSU), Quality Class 1 . The thin blue line represents the monthly values. The thick black line is the linear fit, with 95 confidence intervals indicated by the two thin black lines. The thick green line represents a 5-degree polynomial fit, with 95 confidence intervals indicated by the two thin green lines. A few key statistics is given in the lower part of the diagram (note that the linear trend is the monthly trend). Last month included in analysis: January 2017. Last diagram update: 15 February 2017. Please note that the linear regression is done by month, not year Click here to download the series of UAH MSU global monthly lower troposphere temperature anomalies since December 1978. Click here to download the series of RSS MSU global monthly lower troposphere temperature anomalies since January 1979. Fits (linear as well as polynomial) only attempt to describe the past, and have little or no predictive power. Last 10 years global monthly average surface air temperature according to the two satellite-based temperature estimates (UAH MSU and RSS MSU), Quality Class 1 . The thin blue line represents the monthly values. The thick black line is the linear fit, with 95 confidence intervals indicated by the two thin black lines. The thick green line represents a 5-degree polynomial fit, with 95 confidence intervals indicated by the two thin green lines. A few key statistics is given in the lower part of the diagram (note that the linear trend is the monthly trend). Last month included in analysis: January 2017. Last diagram update: 15 February 2017. Please note that the linear regression is done by month, not year Click here to download the series of UAH MSU global monthly lower troposphere temperature anomalies since December 1978. Click here to download the series of RSS MSU global monthly lower troposphere temperature anomalies since January 1979. Fits (linear as well as polynomial) only attempt to describe the past, and have little or no predictive power. Last 5 years global monthly average surface air temperature according to the two satellite-based temperature estimates (UAH MSU and RSS MSU), Quality Class 1 . The thin blue line represents the monthly values. The thick black line is the linear fit, with 95 confidence intervals indicated by the two thin black lines. The thick green line represents a 5-degree polynomial fit, with 95 confidence intervals indicated by the two thin green lines. A few key statistics is given in the lower part of the diagram (note that the linear trend is the monthly trend). Last month included in analysis: January 2017. Last diagram update: 15 February 2017. Please note that the linear regression is done by month, not year Click here to download the series of UAH MSU global monthly lower troposphere temperature anomalies since December 1978. Click here to download the series of RSS MSU global monthly lower troposphere temperature anomalies since January 1979. Fits (linear as well as polynomial) only attempt to describe the past, and have little or no predictive power. Click here to jump back to the list of contents. Running 50 year linear annual temperature trend calculated from monthly global average surface air temperature anomaly provided by to Hadley CRUT, a cooperative effort between the Hadley Centre for Climate Prediction and Research and the University of East Anglia s Climatic Research Unit (CRU ), UK. The blue line represents the 50x12 month linear trend, plotted at the last month included in the analysis. As the HadCRUT3 record begins in January 1850, the first 50 yr plot is for December 1899. Last month included in analysis: December 2016. Last diagram update: 21 January 2017. Click here to download the series of estimated HadCRUT4 global monthly surface air temperature anomalies since 1850. The variation shown by the moving 50 yr linear global temperature trend suggests the existence of an 60-65 yr long periodic natural temperature variation. The diagram was kindly suggested by a visitor to this web site. Click here and here to see two other examples of apparent cyclic natural climate variations, relating to sea surface temperature and atmospheric pressure, respectively. Click here to jump back to the list of contents. The Central England surface air temperature series is the longest existing meteorological record. Thin lines annual values. Thick lines running 11 yr average. The above graphs for annual, summer and winter temperatures have been prepared using the composite monthly meteorological series originally painstakingly homogenized and published by the late professor Gordon Manley (1974). The data series is now updated by the Hadley Centre. Last diagram update: 2 December 2015. Click here to see a larger version of the above diagram. Click here for the Met Office UK year to date plot. Click here to download the entire Central England data series since 1659 (shown above) Click here to jump back to the list of contents. Global monthly average lower troposphere temperature since 1979 for the tropics and the northern and southern extratropics, according to University of Alabama at Huntsville, USA. This graph uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, interpreted by Dr. Roy Spencer and Dr. John Christy. both at Global Hydrology and Climate Center, University of Alabama at Huntsville, USA. Thick lines are the simple running 37 month average, nearly corresponding to a running 3 yr average. The cooling and warming periods directly influenced by the 1991 Mt. Pinatubo volcanic eruption and the 1998 El Nio, respectively, are clearly visible, especially in the tropics and the northern extratropics. Reference period 1981-2010. Last month shown: January 2017. Last diagram update: 9 February 2017. Click here to download the entire series of UAH MSU global monthly lower troposphere temperatures since December 1978. Global monthly average lower troposphere temperature since 1979 for the tropics and the northern and southern extratropics, according to Remote Sensing Systems (RSS). These graphs uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, and interpreted by Dr. Carl Mears (RSS). Thick lines are the simple running 37 month average, nearly corresponding to a running 3 yr average. Click here for a description of RSS MSU data products. Please note that RSS January 2011 changed from Version 3.2 to Version 3.3 of their MSUAMSU lower tropospheric (TLT) temperature product. Click here to read a description of the change from version 3.2 to 3.3, and previous changes. Last month shown: January 2017. Last diagram update: 3 February 2017. Click here to download the entire series of RSS MSU global monthly lower troposphere temperatures since January 1979. Global monthly average lower troposphere temperature since 1979 for the North Pole and South Pole regions, according to University of Alabama at Huntsville, USA. This graph uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, interpreted by Dr. Roy Spencer and Dr. John Christy. both at Global Hydrology and Climate Center, University of Alabama at Huntsville, USA. Thick lines are the simple running 37 month average, nearly corresponding to a running 3 yr average. Reference period 1981-2010. Last month shown: January 2017. Last diagram update: 9 February 2017. Click here to download the entire series of UAH MSU global monthly lower troposphere temperatures since December 1978. Global monthly average lower troposphere temperature since 1979 for the northern (60-82.5N) and southern (60-70S) polar regions, according to Remote Sensing Systems (RSS). These graphs uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, and interpreted by Dr. Carl Mears (RSS). Thick lines are the simple running 37 month average, nearly corresponding to a running 3 yr average. Click here for a description of RSS MSU data products. Please note that RSS January 2011 changed from Version 3.2 to Version 3.3 of their MSUAMSU lower tropospheric (TLT) temperature product. Click here to read a description of the change from version 3.2 to 3.3, and previous changes. Last month shown: January 2017. Last diagram update: 3 February 2017. Click here to download the entire series of RSS MSU global monthly lower troposphere temperatures since January 1979. Click here to jump back to list of contents. Global monthly average lower troposphere temperature since 1979 measured over land and oceans, respectively, according to University of Alabama at Huntsville, USA. This diagram uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, interpreted by Dr. Roy Spencer and Dr. John Christy. both at Global Hydrology and Climate Center, University of Alabama at Huntsville, USA. Thick lines are the simple running 37 month average, nearly corresponding to a running 3 yr average. Reference period 1981-2010. Last month shown: January 2017. Last diagram update: 9 February 2017. Click here to download the entire series of UAH MSU global monthly lower troposphere temperatures since December 1978. Northern hemisphere monthly average lower troposphere temperature since 1979 measured over land and oceans, respectively, according to University of Alabama at Huntsville, USA. This diagram uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, interpreted by Dr. Roy Spencer and Dr. John Christy. both at Global Hydrology and Climate Center, University of Alabama at Huntsville, USA. Thick lines are the simple running 37 month average, nearly corresponding to a running 3 yr average. Reference period 1981-2010. Last month shown: January 2017. Last diagram update: 9 February 2017. Click here to download the entire series of UAH MSU global monthly lower troposphere temperatures since December 1978. Southern hemisphere monthly average lower troposphere temperature since 1979 measured over land and oceans, respectively, according to University of Alabama at Huntsville, USA. This diagram uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, interpreted by Dr. Roy Spencer and Dr. John Christy. both at Global Hydrology and Climate Center, University of Alabama at Huntsville, USA. Thick lines are the simple running 37 month average, nearly corresponding to a running 3 yr average. Reference period 1981-2010. Last month shown: January 2017. Last diagram update: 9 February 2017. Click here to download the entire series of UAH MSU global monthly lower troposphere temperatures since December 1978. Click here to jump back to list of contents. Tropics-Polar monthly anomaly difference from average lower troposphere temperatures since 1979, according to University of Alabama at Huntsville, USA. This diagram uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, interpreted by Dr. Roy Spencer and Dr. John Christy. both at Global Hydrology and Climate Center, University of Alabama at Huntsville, USA. Thick lines are the simple running 37 month average, nearly corresponding to a running 3 yr average. Reference period 1981-2010. Last month shown: January 2017. Last diagram update: 9 February 2017. Click here to download the entire series of UAH MSU global monthly lower troposphere temperatures since December 1978. Click here to jump back to list of content The diagram table below contains clickable monthly spatial temperature diagrams since 2005, to illustrate the changeable geographical pattern of surface air temperature variations, integrated by graphs like that above. These diagrams are geographical asymmetrical to cover most of the planets land areas, ranging from 72 o N to 60 o S. Spatial distribution of monthly surface air temperature deviation between 72 o N and 60 o S in relation to the average for the period 1998-2006. Warm colours indicates areas with higher temperature than the 1998-2006 average, while blue colours indicate lower than average temperatures. Starting from 2015, the past 10 years are used as reference level. In the individual diagrams the month is indicated by a number: 1 January, 2 February, etc. Click on the individual small diagrams to open full-size diagrams. Please also read the notes below before interpreting the diagrams. Similar spatial temperature diagrams showing the polar regions can be seen by clicking here. Data source: NASA Goddard Institute for Space Studies (GISS). Last diagram update 16 February 2017. It is important to note that the map projection used above is of the type Mercator. This is a useful cylindrical map projection that preserves angles at all locations, but scale varies from place to place, distorting the size of land areas. In particular, areas closer to the poles are more affected, making land areas of similar size looking increasingly oversized towards the poles. To exemplify this effect, the areas of Mexico (1,972,550 km 2 ) and Greenland (2,166,086 km 2 ) are comparable in size. Greenland, however, in the map looks very much bigger than Mexico, even though only the southern half of Greenland is shown. The visual effect of this popular map type is to overstate the importance of temperature variations near the poles, compared to equatorial regions. To avoid the worst effects of this cartographic distortion of areas, the two Polar Regions are therefore shown in separate, polar projections. Click here to go to the polar spatial temperature diagrams. To monitor the present global temperature trend, up or down, it is not efficient to compare with some past period like, e. g. 1961-1990. even though this is what is frequently done. This will not inform about the current temperature trend. It seems to make more sense to compare with a more recent period. This is why the diagrams in the table above all use 1998-2006 as reference period. In addition, by using this recent reference period, is will gradually be possible to visualize if 1998-2006 represents a peak period for the global average temperature, or if modern temperatures are increasing to a even higher level. It should therefore be carried in mind that such a visual comparison does not represent a statistical test, but only a way of obtaining an visual overview of temperature patterns within the month considered. Positive or negative temperature deviations represent the result of monthly weather variations, and any clear pattern of overall climatic warming or cooling will take several years to be identified in a statistical sense. All the diagrams in the table above were prepared using gridded data downloaded from the public domain NASA Goddard Institute for Space Studies (GISS) web page. For surface interpolation of the gridded data a kriging algorithm was used, plotting all data in a polar projection map. The kriging procedure attempts to express trends and is widely considered one of the more flexible interpolation methods, producing a smooth map with few bull eyes. It is usually recommended for gridding almost any type of data set, especially data sets with a heterogeneous point distribution, such as characterising the present data set. It should be noted that the observation network within the two regions considered is not of equal density or quality all over the geographical regions covered by the diagrams. Click here to jump back to the list of contents. Anomalies of global annual surface air temperature (MAAT) since 1850 according to Hadley CRUT, a cooperative effort between the Hadley Centre for Climate Prediction and Research and the University of East Anglia s Climatic Research Unit (CRU ), UK. The average for 1979-2008 (30 yrs) has been set to zero, to make comparison with other temperature data series (above and below) easy. Last year shown: 201 6. Last figure update: 21 January 2017. Click here to download the entire series of estimated HadCRUT4 global monthly surface air temperatures since 1850. Click here to open a web interface to all the weather station data used by the Hadley Centre, a very useful facility developed by Clive Best, also known for his blog . Please note that the stations are split into 3 groups. 1) those going back to before 1860 2) Those going back to between 1860 and 1930 3) Those with data going back later than 1930. The last option is all stations together - but is very slow to load (gt5000 stations). Drag a rectangle to zoom in. Click on a station to see the graph of temperatures and anomalies. Anomalies of global annual surface air temperature (MAAT) since 1880 according to the National Climatic Data Center (NCDC), USA. This time series is calculated using land surface data from the Global Historical Climatology Network (Version 2) and sea surface temperature anomalies from the United Kingdom MOHSST data set and the NCEP Optimum Interpolated SSTs (Version2). The average for 1979-2008 (30 yrs) has been set to zero, to make comparison with other temperature data series (above and below) easy. Last year shown: 201 6. Last figure update: 21 January 2017. Click here to download the entire series of the NCDC global annual surface air temperatures since 1850 Anomalies of global annual surface air temperature (MAAT) since 1880 according to the Goddard Institute for Space Studies (GISS), at Columbia University, New York City, USA. GISS is a laboratory of the Earth-Sun Exploration Division of NASAs Goddard Space Flight Center and a unit of the Columbia University Earth Institute. The average for 1979-2008 (30 yrs) has been set to zero, to make comparison with other temperature data series (above and below) easy. Last year shown: 201 6. Last figure update: 19 January 2017 . Click here to download the entire series of the GISS global monthly surface air temperatures since 1880. Anomalies of global annual surface air temperature (MAAT) since 1979 according to the University of Alabama at Huntsville, USA. This graph uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, interpreted by Dr. Roy Spencer and Dr. John Christy. both at Global Hydrology and Climate Center, University of Alabama at Huntsville, USA. The average for 1979-2008 (30 yrs) has been set to zero, to make comparison with other temperature data series (above and below) easy. Last year shown: 201 6. Last figure update: 4 January 2017. Click here to download the entire series of UAH MSU global monthly lower troposphere temperatures since December 1978. Anomalies of global annual surface air temperature (MAAT) since 1979 according to the Remote Sensing Systems (RSS). This graph uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, and interpreted by Dr. Carl Mears (RSS). The average for 1979-2008 (30 yrs) has been set to zero, to make comparison with other temperature data series (above and below) easy. Last year shown: 201 6. Last figure update: 6 January 2017 . Click here to download the entire series of RSS MSU global monthly lower troposphere temperatures since January 1979. Click here to jump back to the list of contents. Anomalies of Northern Hemisphere annual surface air temperature (MAAT) since 1850 according to Hadley CRUT, a cooperative effort between the Hadley Centre for Climate Prediction and Research and the University of East Anglia s Climatic Research Unit (CRU ), UK. The average for 1979-2008 (30 yrs) has been set to zero, to make comparison with other temperature data series (above and below) easy. Last year shown: 201 6. Last figure update: 21 January 2017. Click here to download the entire series of estimated HadCRUT3 global monthly surface air temperatures since 1850. Click here to open a web interface to all the weather station data used by the Hadley Centre, a very useful facility developed by Clive Best, also known for his blog . Please note that the stations are split into 3 groups. 1) those going back to before 1860 2) Those going back to between 1860 and 1930 3) Those with data going back later than 1930. The last option is all stations together - but is very slow to load (gt5000 stations). Drag a rectangle to zoom in. Click on a station to see the graph of temperatures and anomalies. Anomalies of Northern Hemisphere annual surface air temperature (MAAT) since 1880 according to the National Climatic Data Center (NCDC), USA. This time series is calculated using land surface data from the Global Historical Climatology Network (Version 2) and sea surface temperature anomalies from the United Kingdom MOHSST data set and the NCEP Optimum Interpolated SSTs (Version2). The average for 1979-2008 (30 yrs) has been set to zero, to make comparison with other temperature data series (above and below) easy. Last year shown: 201 6. Last figure update: 21 January 2017. Click here to download the entire series of the NCDC global annual surface air temperatures since 1850 Please note that the early part of the NCDC record has now been changed so much towards lower values than just a few years ago, that the graph now extents below the x-axis. Click here for further details on temporal instability of the NCDC temperature record. Anomalies of Northern Hemisphere annual surface air temperature (MAAT) since 1880 according to the Goddard Institute for Space Studies (GISS), at Columbia University, New York City, USA. GISS is a laboratory of the Earth-Sun Exploration Division of NASAs Goddard Space Flight Center and a unit of the Columbia University Earth Institute. The average for 1979-2008 (30 yrs) has been set to zero, to make comparison with other temperature data series (above and below) easy. Last year shown: 201 6. Last figure update: 19 January 2017 . Click here to download the entire series of the GISS global monthly surface air temperatures since 1880. Anomalies of Northern Hemisphere annual surface air temperature (MAAT) since 1979 according to the University of Alabama at Huntsville, USA. This graph uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, interpreted by Dr. Roy Spencer and Dr. John Christy. both at Global Hydrology and Climate Center, University of Alabama at Huntsville, USA. The average for 1979-2008 (30 yrs) has been set to zero, to make comparison with other temperature data series (above and below) easy. Last year shown: 201 6. Last figure update: 4 January 2017. Click here to download the entire series of UAH MSU global monthly lower troposphere temperatures since December 1978. Anomalies of Northern Hemisphere annual surface air temperature (MAAT) since 1979 according to the Remote Sensing Systems (RSS). This graph uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, and interpreted by Dr. Carl Mears (RSS). The average for 1979-2008 (30 yrs) has been set to zero, to make comparison with other temperature data series (above and below) easy. Last year shown: 201 6. Last figure update: 6 January 2017 . Click here to download the entire series of RSS MSU global monthly lower troposphere temperatures since January 1979. Click here to jump back to the list of contents. Anomalies of Southern Hemisphere annual surface air temperature (MAAT) since 1850 according to Hadley CRUT, a cooperative effort between the Hadley Centre for Climate Prediction and Research and the University of East Anglia s Climatic Research Unit (CRU ), UK. The average for 1979-2008 (30 yrs) has been set to zero, to make comparison with other temperature data series (above and below) easy. Last year shown: 201 6. Last figure update: 21 January 2017. Click here to download the entire series of estimated HadCRUT3 global monthly surface air temperatures since 1850. Click here to open a web interface to all the weather station data used by the Hadley Centre, a very useful facility developed by Clive Best, also known for his blog . Please note that the stations are split into 3 groups. 1) those going back to before 1860 2) Those going back to between 1860 and 1930 3) Those with data going back later than 1930. The last option is all stations together - but is very slow to load (gt5000 stations). Drag a rectangle to zoom in. Click on a station to see the graph of temperatures and anomalies. Anomalies of Southern Hemisphere annual surface air temperature (MAAT) since 1880 according to the National Climatic Data Center (NCDC), USA. This time series is calculated using land surface data from the Global Historical Climatology Network (Version 2) and sea surface temperature anomalies from the United Kingdom MOHSST data set and the NCEP Optimum Interpolated SSTs (Version2). The average for 1979-2008 (30 yrs) has been set to zero, to make comparison with other temperature data series (above and below) easy. Last year shown: 201 6. Last figure update: 21 January 2017. Click here to download the entire series of the NCDC global annual surface air temperatures since 1850 Anomalies of Southern Hemisphere annual surface air temperature (MAAT) since 1880 according to the Goddard Institute for Space Studies (GISS), at Columbia University, New York City, USA. GISS is a laboratory of the Earth-Sun Exploration Division of NASAs Goddard Space Flight Center and a unit of the Columbia University Earth Institute. The average for 1979-2008 (30 yrs) has been set to zero, to make comparison with other temperature data series (above and below) easy. Last year shown: 201 6. Last figure update: 19 January 2017 . Click here to download the entire series of the GISS global monthly surface air temperatures since 1880. Anomalies of Southern Hemisphere annual surface air temperature (MAAT) since 1979 according to the University of Alabama at Huntsville, USA. This graph uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, interpreted by Dr. Roy Spencer and Dr. John Christy. both at Global Hydrology and Climate Center, University of Alabama at Huntsville, USA. The average for 1979-2008 (30 yrs) has been set to zero, to make comparison with other temperature data series (above and below) easy. Last year shown: 201 6. Last figure update: 4 January 2017. Click here to download the entire series of UAH MSU global monthly lower troposphere temperatures since December 1978. Anomalies of Southern Hemisphere annual surface air temperature (MAAT) since 1979 according to the Remote Sensing Systems (RSS). This graph uses data obtained by the National Oceanographic and Atmospheric Administration (NOAA) TIROS-N satellite, and interpreted by Dr. Carl Mears (RSS). The average for 1979-2008 (30 yrs) has been set to zero, to make comparison with other temperature data series (above and below) easy. Last year shown: 201 6. Last figure update: 6 January 2017 . Click here to download the entire series of RSS MSU global monthly lower troposphere temperatures since January 1979. Click here to jump back to the list of contents. Global monthly average temperature in different altitudes according to University of Alabama at Huntsville (UAH). The thin lines represent the monthly average, and the thick line the simple running 37 month average, nearly corresponding to a running 3 yr average. Last month shown: December 2016. Last diagram update: 6 January 2017. Click here. here. here and here to download the series of UAH MSU global monthly atmosphere temperature anomalies since December 1978. Global monthly average temperature in different altitudes according to Remote Sensing Systems (RSS). The thin lines represent the monthly average, and the thick line the simple running 37 month average, nearly corresponding to a running 3 yr average. Last month shown: January 2017. Last diagram update: 3 February 2017. Click here to download the series of RSS MSU global monthly atmosphere temperature anomalies since January 1979. Click here to read a description of the MSU products. Click here to jump back to the list of contents. Modelled zonally averaged, equilibrated temperature change with altitude associated with doubling atmospheric CO 2 (Lee et al. 2007 ). Units for modelled temperature change are given in degrees Celcius. The horizontal axis begins at 90 o N to the left, and ends at 90 o S to the right. The vertical axis begins at the planet surface and extends to 10 hPA (ca. 16 km height). For the 200, 300 and 1000 hPa levels (ca. 12, 9 and 0 km altitude, respectively) the observed temperature change since 1979 is shown in the diagrams below . Lindzen (1999 and 2007) argued that the surface temperature anomalies are not the best way of identifying the effect of an atmospheric CO 2 increase. He stressed that the radiation in the energy flux balance relations can be thought of as coming mainly from the atmospheric layer where the infrared optical depth is near 1. This characteristic emission layer is high above the surface and is typically located at an altitude somewhat below the tropopause. The height of the tropopause varies with latitude. In the tropics, the tropopause height is about 16-17 km, near 30 latitude about 12 km, and near the poles the tropopause height is around 8 km above the surface. The diagrams above shows how temperature changes when CO 2 is doubled in 4 different General Circulation Models (Lee et al. 2007 ). These model runs differ from those that were run for the IPCC in that the models were simplified to isolate the effects of CO 2 forcing and climate feedbacks (Lindzen 2007 ). Also the models were run until equilibrium was established rather than run in a transient mode in order to simulate the past. Thus, they tend to isolate greenhouse warming from other things that might be going on. The model runs shown in the above diagrams all suggest warming due to CO 2 doubling to peak not at the surface in the tropics, but in the troposphere near the 200-300 hPa level, roughly corresponding to 12-9 km altitude. The main reason for the inter-model variation is that the amount of water vapour differs among the models. The expected warming above the tropics is 2-3 times larger than near the surface, regardless of the sensitivity of the particular model. This is, in fact, the very signature of greenhouse warming (cf. Lindzen 2007 ). In the diagrams below the temperature change at and above Equator is shown, using the Hadley Centres radiosonde temperature product HadAT (200 and 300 hPa), and HadCRUT3 meteorological surface data. HadAT consists of temperature anomaly time series on 9 standard reporting pressure levels (850hPa to 30hPa), and is derived from 676 individual radiosonde stations with long-term records. Data uncertainties and limitations are described here. The latitudinal band used in the diagrams below is from 20 o N to 20 o S. To enable easy comparison with the global temperature changes shown higher up this page, 1979 has been chosen as start year. The full HadAT data series, however, goes back to 1958. Please note that the temperature scale in these diagrams are different from the scale used above, to accommodate the larger temperature variations at height. All data series were normalised by setting their starting value in January 1979 0, before inclusion in the diagrams below. Temperature change at 200hPa (c. 12 km height) between 20 o N and 20 o S since 1979, according to HadAT. The thin blue line shows the monthly values, while the thick blue line represents the simple running 37 month average, nearly corresponding to a running 3 yr average. The stippled red line shows the linear fit for the period shown, with basic statistics shown in the upper left corner of the diagram. The data were normalised by setting the average of their initial 120 months (10 years) from January 1979 to December 198 8 0. Last month shown: December 2012. Last diagram update: 4 May 2013. Please note that the linear regression is done by month, not year Click here to download the entire HadAT series since 1958. Temperature change at 300hPa (c. 9 km height) between 20 o N and 20 o S since 1979, according to HadAT. The thin blue line shows the monthly values, while the thick blue line represents the simple running 37 month average, nearly corresponding to a running 3 yr average. The stippled red line shows the linear fit for the period shown, with basic statistics shown in the upper left corner of the diagram. The data were normalised by setting the average of their initial 120 months (10 years) from January 1979 to December 198 8 0. Last month shown: December 2012. Last diagram update: 4 May 2013. Please note that the linear regression is done by month, not year Click here to download the entire HadAT series since 1958. Temperature change at surface between 20 o N and 20 o S since 1979, according to HadCRUT4 . The thin blue line shows the monthly values, while the thick blue line represents the simple running 37 month average, nearly corresponding to a running 3 yr average. The stippled red line shows the linear fit for the period shown, with basic statistics shown in the upper left corner of the diagram. The data were normalised by setting the average of the initial 120 months (10 years) from January 1979 to December 198 8 0. Last month shown: April 2013. Last diagram update: 8 June 2013. Please note that the linear regression is done by month, not year Click here to download the entire HadCRUT3 series since 1850. The initial versions of satellite and radiosonde datasets suggested that the tropical surface had warmed more than the troposphere, while climate models consistently showed tropospheric amplification of surface warming in response to human-caused increases in well-mixed greenhouse gases, as shown by the diagrams above. This observation gave rise to deep concern, and resulted in a number of studies (e. g. NRC 2000 ) where strong attempts were made to find warming in the troposphere. As new data sets have been made available and new corrections introduced, the scientific literature have witnessed a number of attempts of reconciling the modelled and the observed atmospheric warming pattern. Conflicting conclusions have, however, been reached. Some scientists conclude that a discrepancy between modelled and observed trends in tropical lapse rates still exists, while other argue that there is no longer a serious discrepancy. A few key references on this debate are represented by Lindzen 1999 and 2007. NRC 2000. Douglass et al 2007. and Santer et al 2008. Ongoing web-based discussions can be followed here and here. This debate reflects the importance of the point raised by Lindzen (1999) on monitoring temperature changes at the height in the troposphere corresponding to an infrared optical depth near 1. Diagram showing observed linear decadal temperature change at surface, 300 hPa and 200 hPa, between 20 o N and 20 o S, since January 1979. Data source: HadAT and HadCRUT4. Click here to compare with modelled altitudinal temperature change pattern for doubling atmospheric CO 2 . Last month included in analysis: December 2012. Last diagram update: 4 May 2013. The three diagrams above (using data from HadAT and HadCRUT4 ) show the linear trend of the temperature change since 1979 between 20 o N and 20 o S to be ca. 0.00089 o Cmonth at the surface, 0.00095 o Cmonth at 300 hPa, and -0.00009 o Cmonth at 200 hPa, corresponding to 0.10698, 0.11414 and -0.01022 o Cdecade, respectively (see bar chart above). Thus, these radiosonde and surface meteorological data from the Equatorial region do not at the moment display the signature of enhanced greenhouse warming. With the observed warming rate of about 0.10698 o Cdecade at the surface, a warming rate of about 0.21-0.31 o Cdecade would have been expected at the 200 and 300 hPa levels to comply with the prognosis on this derived from the CO 2 hypothesis. Click here to jump back to the list of contents. Weekly a bsolute (above) and anomaly (below) outgoing long wave radiation (OLR) at the top of the atmosphere 10-16 February 2017. Base period January 1979 - December 1995. Source: National Oceanographic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL). Last diagram update: 18 February 2017 . Click here to see the original ESRL diagram showing OLR absolute values, or to check for a more recent diagram. Click here to see the original ESRL diagram showing OLR anomaly values, or to check for a more recent diagram. First of all, it should be noted that the above maps are Mercator projection maps, whereby the polar regions are visually highly exaggerated as to their apparent surface area. In reality, it is the regions near Equator which are important as to the real surface area. The general long wave (infrared) pattern is characterised by a gradient towards relatively low values at high latitudes, and higher values near the Equator (upper panel). This zone of relative high long wave radiation follows the sun throughout the seasons, being displaced north during the Northern Hemisphere summer, and vice versa during the Northern Hemisphere winter. The strongest contrast within latitudinal belts exist in the low latitudes, where the high outgoing radiation of the subtropical anticyclones (high pressure zones) and other dry zones contrast sharply with the low outgoing radiation of the major cloudy regions of the tropics. Also at middle latitudes there may be substantial longitudinal variations, particular in the Northern Hemisphere. Such variations are often caused by massive penetration of cold air from the polar regions to middle latitudes, associated with strong blocking patterns in higher latitudes (Gruber and Winston 1978 ), and are most frequently observed in the Northern Hemisphere during Northern Hemisphere winter. Average total (left) and anomaly (right) outgoing long wave radiation (OLR) between 10 o N and 10 o S at the top of the atmosphere, since 18 February 2016 (top of diagram). Base period January 1979 - December 1995. Source: National Oceanographic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL). Last day shown: 16 February 2017 (bottom of diagram). Last diagram update: 18 February 2017 . Click here to see the original ESRL diagram showing OLR absolute values, or to check for a more recent diagram. Click here to see the original ESRL diagram showing OLR anomaly values, or to check for a more recent diagram. The region near the Equator is of high importance because of the huge surface areas involved. Variations are seen to be especially large within the region 60 o E-120 o W, covering the Indian Ocean, Indonesia and most of the Pacific Ocean between 10 o N and 10 o S. The red area of the Suns spectrum (upper panel in figure above) is absorbed by the atmosphere and the Earths surface. The warmed surface emits infrared radiation as indicated by the white areas on the individual molecules spectrum (lower panels). The grey bits are the parts of the spectra that are absorbed by the atmosphere. The blue area on the Earths emission spectrum (upper panel) is known as the infrared window through which most of the Earths radiation passes to space unhindered by being absorbed by any of the greenhouse gases. This short text is from Barett Bellamy Climate. where a more thorough description of the greenhouse effect is provided. The diagrams below all show infrared radiation within this window. Outgoing longwave radiation (OLR) at the top of the atmosphere between 180 o W and 179 o E (0 o E and 359.5 o E) and 90 o N and 90 o S since June 1974 according to the National Oceanographic and Atmospheric Administration (NOAA). The thin blue line represents the monthly value, while the thick red line is the simple running 37 month average, nearly corresponding to the running 3 yr average. The infrared wavelength covered is 10.5-12.5 m ( Gruber and Winston 1978 ) and covers the main part of the atmospheric infrared window. Last month shown: October 2010. Last diagram update: 13 February 2011. Click here to download the entire series of NOAA monthly OLR-values since June 1974. Choose first OLR then Select field. Scatter plot showing outgoing longwave radiation (OLR) at the top of the atmosphere between 180 o W and 179 o E (0 o E and 359.5 o E) and 90 o N and 90 o S since June 1974, as function of atmospheric CO 2 . OLR data from the National Oceanographic and Atmospheric Administration (NOAA). CO2 data measured at the Mauna Loa Observatory. Hawaii, reported as a dry mole fraction defined as the number of molecules of carbon dioxide divided by the number of molecules of dry air (water vapour removed), multiplied by one million (ppm). The red line represent a two-degree polynomial fit, specified in the lower left corner of the diagram. As the amount of atmospheric CO2 has been increasing over the entire period (ignoring annual variations), the x-axis can be seen as as rough timeline from 1974 (left) to 2010 (right). The infrared wavelength covered is 10.5-12.5 m ( Gruber and Winston 1978 ) and covers the main part of the atmospheric infrared window. Last month shown: October 2010. Last diagram update: 13 February 2011 . Click here to download the entire series of NOAA monthly OLR-values since June 1974. Choose first OLR then Select field. Click here to download the entire series of monthly CO 2 values since March 1958. Diagram showing outgoing longwave radiation (OLR) at the top of the atmosphere between 180oW and 179oE (0oE and 359.5oE) and 90oN and 90oS since December 1978 ( red line National Oceanographic and Atmospheric Administration (NOAA), and the g lobal monthly average lower troposphere temperature (blue line University of Alabama at Huntsville, USA). The thin lines represent the monthly values, while the thick lines are the simple running 37 month averages, nearly corresponding to running 3 yr averages. The infrared wavelength covered is 10.5-12.5 m (Gruber and Winston 1978 ) and covers the main part of the atmospheric infrared window. Last month shown: October 2010 (OLR) and January 2011 (UAH). Last diagram update: 13 February 2011 . Click here to download the entire series of NOAA monthly OLR-values since June 1974. Choose first OLR then Select field. Click here to download the entire series of UAH MSU global monthly lower troposphere temperatures since December 1978. Click here to jump back to the list of contents. Outgoing longwave radiation (OLR) at the top of the atmosphere between 180oW and 179oE (0oE and 359.5oE) and 20oN and 20oS since June 1974 according to the National Oceanographic and Atmospheric Administration (NOAA). The thin blue line represents the monthly value, while the thick red line is the simple running 37 month average, nearly corresponding to the running 3 yr average. The infrared wavelength covered is 10.5-12.5 m (Gruber and Winston 1978 ) and covers the main part of the atmospheric infrared window. Last month shown: October 2010. Last diagram update: 13 February 2011. Click here to download the entire series of NOAA monthly OLR-values since June 1974. Choose first OLR then Select field. Scatter plot showing outgoing longwave radiation (OLR) at the top of the atmosphere between 180oW and 179oE (0oE and 359.5oE) and 20oN and 20oS since June 1974, as function of atmospheric CO2. OLR data from the National Oceanographic and Atmospheric Administration (NOAA). CO2 data measured at the Mauna Loa Observatory. Hawaii, reported as a dry mole fraction defined as the number of molecules of carbon dioxide divided by the number of molecules of dry air (water vapour removed), multiplied by one million (ppm). The red line represent a two-degree polynomial fit, specified in the lower left corner of the diagram. As the amount of atmospheric CO2 has been increasing over the entire period (ignoring annual variations), the x-axis can be seen as as rough timeline from 1974 (left) to 2010 (right). The infrared wavelength covered is 10.5-12.5 m (Gruber and Winston 1978 ) and covers the main part of the atmospheric infrared window. Last month shown: October 2010. Last diagram update: 13 February 2011 . Click here to download the entire series of NOAA monthly OLR-values since June 1974. Choose first OLR then Select field. Click here to download the entire series of monthly CO 2 values since March 1958. Diagram showing outgoing longwave radiation (OLR) at the top of the atmosphere between 180oW and 179oE (0oE and 359.5oE) and 20oN and 20oS since December 1978 ( red line National Oceanographic and Atmospheric Administration (NOAA), and the g lobal monthly average lower troposphere temperature (blue line University of Alabama at Huntsville, USA). The thin lines represent the monthly values, while the thick lines are the simple running 37 month averages, nearly corresponding to running 3 yr averages. The infrared wavelength covered is 10.5-12.5 m (Gruber and Winston 1978 ) and covers the main part of the atmospheric infrared window. Last month shown: October 2010 (OLR) and January 2011 (UAH). Last diagram update: 13 February 2011 . Click here to download the entire series of NOAA monthly OLR-values since June 1974. Choose first OLR then Select field. Click here to download the entire series of UAH MSU global monthly lower troposphere temperatures since December 1978. Outgoing longwave radiation (OLR red graph) anomaly at the top of the atmosphere above Equator between 160oE and 160oW since 1979 according to the National Oceanographic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC). Base period: 1979-1995. Surface air t emperature change (blue graph) between 20oN and 20oS since 1979, according to HadCRUT3. The thin lines represent the monthly values, while the thick lines is simple running 37 month averages, nearly corresponding to running 3 yr averages. Within the time period 1996-2009, light blue areas indicate periods of surface cooling, and light red areas indicate surface warming. The entire OLR data series goes back to June 1974, but is here shown from January 1979 to enable easy comparison with the temperature diagrams shown above. The infrared wavelength covered is 10.5-12.5 m (Gruber and Winston 1978 ) and covers the main part of the atmospheric infrared window. Last month shown: January 2011 (OLR) and December 2010 (HadCRUT3). Last diagram update: 12 February 2011 . Click here to download the entire series of NOAA monthly OLR-values since June 1974. Click here to download the entire HadCRUT3 series since 1850. For the equatorial region, the diagram above suggests a certain chain of events, indicating the existence of a mechanism regulating the surface temperature: Periods of surface warming appears initially to be associated with decreasing outgoing longwave radiation (OLR). After some surface warming, OLR then stops decreasing and instead begins to increase, and after a while, surface air temperature then begins to decrease, etc. This chain of events is clearly illustrated by, e. g. the time period around the 1998 El Nio event (diagram above). Part of the explanation of the above succession of events might be that tropical surface warming leads to enhanced atmospheric convectional transport of heat to high levels of the atmosphere above the Equator, resulting in enhanced longwave radiation at the top of the atmosphere. This, in turn, eventually leads to surface cooling, which results in reduced atmospheric convection, etc. Also the potential connection to variations in tropical sea surface temperatures and the tropical cloud cover is interesting, and should be considered in a more detailed analysis. Click here to jump back to the list of contents. Outgoing longwave radiation (OLR) at the top of the atmosphere between 180oW and 179oE (0oE and 359.5oE) and 70oN and 90oN since June 1974 according to the National Oceanographic and Atmospheric Administration (NOAA). The thin blue line represents the monthly value, while the thick red line is the simple running 37 month average, nearly corresponding to the running 3 yr average. The infrared wavelength covered is 10.5-12.5 m (Gruber and Winston 1978 ) and covers the main part of the atmospheric infrared window. Last month shown: October 2010. Last diagram update: 13 February 2011 . Click here to download the entire series of NOAA monthly OLR-values since June 1974. Choose first OLR then Select field. Scatter plot showing outgoing longwave radiation (OLR) at the top of the atmosphere between 180oW and 179oE (0oE and 359.5oE) and 70oN and 90oN since June 1974, as function of atmospheric CO2. OLR data from the National Oceanographic and Atmospheric Administration (NOAA). CO2 data measured at the Mauna Loa Observatory. Hawaii, reported as a dry mole fraction defined as the number of molecules of carbon dioxide divided by the number of molecules of dry air (water vapour removed), multiplied by one million (ppm). The red line represent a two-degree polynomial fit, specified in the lower left corner of the diagram. As the amount of atmospheric CO2 has been increasing over the entire period (ignoring annual variations), the x-axis can be seen as as rough timeline from 1974 (left) to 2010 (right). The infrared wavelength covered is 10.5-12.5 m (Gruber and Winston 1978 ) and covers the main part of the atmospheric infrared window. Last month shown: October 2010. Last diagram update: 13 February 2011. Click here to download the entire series of NOAA monthly OLR-values since June 1974. Choose first OLR then Select field. Click here to download the entire series of monthly CO 2 values since March 1958. Diagram showing outgoing longwave radiation (OLR) at the top of the atmosphere between 180oW and 179oE (0oE and 359.5oE) and 70oN and 90oN since December 1978 ( red line National Oceanographic and Atmospheric Administration (NOAA), and the g lobal monthly average lower troposphere temperature (blue line University of Alabama at Huntsville, USA). The thin lines represent the monthly values, while the thick lines are the simple running 37 month averages, nearly corresponding to running 3 yr averages. The infrared wavelength covered is 10.5-12.5 m (Gruber and Winston 1978 ) and covers the main part of the atmospheric infrared window. Last month shown: October 2010 (OLR) and January 2011 (UAH). Last diagram update: 13 February 2011 . Click here to download the entire series of NOAA monthly OLR-values since June 1974. Choose first OLR then Select field. Click here to download the entire series of UAH MSU global monthly lower troposphere temperatures since December 1978. Click here to jump back to the list of contents. Outgoing longwave radiation (OLR) at the top of the atmosphere between 180oW and 179oE (0oE and 359.5oE) and 70oS and 90oS since June 1974 according to the National Oceanographic and Atmospheric Administration (NOAA). The thin blue line represents the monthly value, while the thick red line is the simple running 37 month average, nearly corresponding to the running 3 yr average. The infrared wavelength covered is 10.5-12.5 m (Gruber and Winston 1978 ) and covers the main part of the atmospheric infrared window. Last month shown: October 2010. Last diagram update: 13 February 2010. Click here to download the entire series of NOAA monthly OLR-values since June 1974. Choose first OLR then Select field. Scatter plot showing outgoing longwave radiation (OLR) at the top of the atmosphere between 180oW and 179oE (0oE and 359.5oE) and 70oS and 90oS since June 1974, as function of atmospheric CO2. OLR data from the National Oceanographic and Atmospheric Administration (NOAA). CO2 data measured at the Mauna Loa Observatory. Hawaii, reported as a dry mole fraction defined as the number of molecules of carbon dioxide divided by the number of molecules of dry air (water vapour removed), multiplied by one million (ppm). The red line represent a two-degree polynomial fit, specified in the lower left corner of the diagram. As the amount of atmospheric CO2 has been increasing over the entire period (ignoring annual variations), the x-axis can be seen as as rough timeline from 1974 (left) to 2010 (right). The infrared wavelength covered is 10.5-12.5 m (Gruber and Winston 1978 ) and covers the main part of the atmospheric infrared window. Last month shown: October 2010. Last diagram update: 13 February 2011 . Click here to download the entire series of NOAA monthly OLR-values since June 1974. Choose first OLR then Select field. Click here to download the entire series of monthly CO 2 values since March 1958. Diagram showing outgoing longwave radiation (OLR) at the top of the atmosphere between 180oW and 179oE (0oE and 359.5oE) and 70oS and 90oS since December 1978 ( red line National Oceanographic and Atmospheric Administration (NOAA), and the g lobal monthly average lower troposphere temperature (blue line University of Alabama at Huntsville, USA). The thin lines represent the monthly values, while the thick lines are the simple running 37 month averages, nearly corresponding to running 3 yr averages. The infrared wavelength covered is 10.5-12.5 m (Gruber and Winston 1978 ) and covers the main part of the atmospheric infrared window. Last month shown: October 2010 (OLR) and January 2011 (UAH). Last diagram update: 13 February 2011 . Click here to download the entire series of NOAA monthly OLR-values since June 1974. Choose first OLR then Select field. Click here to download the entire series of UAH MSU global monthly lower troposphere temperatures since December 1978. Click here to jump back to the list of contents. Diagram showing the HadCRUT4 estimate for the global annual surface temperature anomaly since 1885, and the average annual excess of duration of the day, defined as the difference between the astronomically determined duration of the day and 86400 seconds, and also called the length of day (LOD). The thin lines are showing the annual values, and the thick lines are the running 11 yr average. Temperature data source: the Hadley Centre for Climate Prediction and Research and the University of East Anglia s Climatic Research Unit (CRU ), UK. LOD data source: International Earth Rotation and Reference Systems Service (IERS). Last year shown: 2014. Last diagram update: 13 June 2015. Click here to download the entire series of HadCRUT4 global monthly surface air temperature anomaly data since 1850. Click here to read more about how to measure the irregularities of planet Earths rotation. Diagram showing the monthly HadCRUT4 estimate for the global surface temperature anomaly since January 1962 (upper panel), the average angular velocity of Earth (mid panel), and the average monthly excess of duration of the day (lower panel). The excess duration of the day (LOD) is defined as the difference between the astronomically determined duration of the day and 86400 seconds. The thin lines are representing the monthly values, and the thick lines are the running 37 month average (about 3 yr). Temperature data source: the Hadley Centre for Climate Prediction and Research and the University of East Anglia s Climatic Research Unit (CRU ), UK. Angular velocity and LOD data source: International Earth Rotation and Reference Systems Service (IERS). Last month shown: April 2014. Last diagram update: 13 June 2015. Click here to download the entire series of HadCRUT4 global monthly surface air temperature anomaly data since 1850. Click here to read more about how to measure the irregularities of planet Earths rotation. The length of day (LOD) as shown above are subject to variations due to variations in oceanic tides (smaller than 0.03 ms in absolute value), variations in the atmospheric circulation, and to internal effects and to transfer of angular momentum to the Moon orbital motion. Also t he dynamical influence of the liquid core of the earth may account for slow variations, but then generally expressed as overall long-term trends (Akoi et al. 1982 ). Zatman and Bloxham (1997) found that torsional oscillations have their sources at the outer-inner core boundary of earth. Duhau and de Jager (2012) found semi-secular (40-60 yr) oscillations in LOD to be linearly related to cycles in solar orbital parameters, and that the semi-secular LOD oscillations presumably are exited by planetary orbital motions, especially Jupiter and Saturn. The above diagrams show that periods with relatively high planetary rotation velocity (and low LOD) tend to be associated with relatively warm periods, and vice versa. Good examples are the peak of LOD in the early 20th century, concurrent with the last cold spell of The Little Ice Age and the loss of Titanic. Also the cold period 1965-1977 was associated with long day length (high LOD) and low planetary angular velocity. The generally increasing rotation velocity of Earth (and decreasing LOD) since then has taken place along with the period of late 20th century warming. Variations in LOD has also been associated with the Atmospheric Circulation Index (ACI) and variations in commercial catches of different fish species. Some of these associations are thoroughly described and discussed by Klyashtorin and Lyubushin (2007). Click here to jump back to the list of contents.