Kimawirkungen, eine systematische Übersicht

Transkript

1 Kimawirkungen, eine systematische Übersicht Klimadefinitionen und Klassifikationen

2 Klima ist die Synthese des Wetters über einen Zeitraum, der lang genug ist, um dessen statistische Eigenschaften bestimmen zu können (nach WMO 1979, siehe Hupfer 1996)

3 Climate in a narrow sense is usually defined as the average weather or more rigorously, as the statistical description in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. The classical period for averaging these variables is 30 years, as defined by the World Meteorological Organization. The relevant quantities are most often surface variables such as temperature, precipitation and wind. Climate in a wider sense is the state, including a statistical description, of the climate system IPCC, WG I, 2007

4 Climate in a narrow sense is usually defined as the average weather or more rigorously, as the statistical description in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. The classical period for averaging these variables is 30 years, as defined by the World Meteorological Organization. The relevant quantities are most often surface variables such as temperature, precipitation and wind. Climate in a wider sense is the state, including a statistical description, of the climate system The climate system is the highly complex system consisting of five major components: the atmosphere, the hydrosphere, the cryosphere, the land surface and the biosphere, and the interactions between them. The climate system evolves in time under the influence of its own internal dynamics and because of external forcings such as volcanic eruptions, solar variations and anthropogenic forcings such as the changing composition of the atmosphere and land use change IPCC, WG I, 2007

5 Climate in a narrow sense is usually defined as the average weather or more rigorously, as the statistical description in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. The classical period for averaging these variables is 30 years, as defined by the World Meteorological Organization. The relevant quantities are most often surface variables such as temperature, precipitation and wind. Climate in a wider sense is the state, including a statistical description, of the climate system Climate change refers to a change in the state of the climate that can be identified (e.g., by using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer. IPCC, WG I, 2007

6 Wettervariablen: Messung, Übergang zum Klima: Eigenschaften der Verteilungsfunktionen, Bsp.: Säkularstation Potsdam Trendbestimmung: Temeratur. Bsp. - Säkularstation Potsdam, Bootstrapping zur Signifikanzbestimmung.

7 Flächendeckende Globale Wetter- und Klimadatensätze: (interpolierte Stationsdaten) Cramer/Leemanns: Walther-Diagramme im 0.5x0.5 Raster (720x360 Gitterpunkte) T, Pr, I: klim. Monatsmittel Climate Research Unit (Norwich, UK) T, Pr, I: Monatsmittel über 100 Jahre

8 Klimaklassifikationen (a) Effektive Klimaklassifikationen (Köppen u.a.) (b) Genetische Klimaklassifikationen (Hendl u.a.) (c) Statistische Klimaklassifikationen (Kropp u.a.)

9 The Köppen climate classification is one of the most widely used climate classification systems. It was developed by Wladimir Köppen, a German climatologist, around 1900 (with several further modifications by Köppen himself, notably in 1918 and 1936). It is based on the concept that native vegetation is the best expression of climate; thus, climate zone boundaries have been selected with vegetation distribution in mind. It combines average annual and monthly temperatures and precipitation, and the seasonality of precipitation.

10 * Tropical rain climates (A) Equatorial (Af) Monsoon(Am) Savanna (Aw,As) * Arid climates (B) Desert (Bwh) Semi-Arid (BS..)

11 * Temperate rain climates (C) Humid/subtropical (Cfa, Cwa) Oceanic (Cfb, Cwb, Cfc) Mediterranean (Csa, Csb) * Boreal forest and snow climates (D) Humid continental (Df/w,a/b) Subarctic (Df/w,c,d) Mediterranean continental (Dsa/b). Other cont. (Dsc/d) * Cold snow climates (E) alpine (ETH) polar (EF) + ET(undra)

12 * Tropical rain climates (A) where the mean temperature of the coldest month exceeds C. * Arid climates (B) are defined as follows on the basis of the average annual precipitation sum R (cm) and the annual mean temperature T ( C): R < 2T + 28 (where summer rain is dominating) R < 2T +14 (where no pronounced annual cycle is observed) R < 2T (where winter rain is dominating) * Temperate rain climates (C) where the mean temperature of the coldest month is between 3.0 C and C. * Boreal forest and snow climates (D) are characterized by a mean temperature of the warmest month exceeding 10.0 C and a mean temperature of the coldest month below 3.0 C. * Cold snow climates (E) are defined by a mean temperature of the warmest month below 10.0 C.

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14 * Tropical rain climates (A) Equatorial (Af) Monsoon(Am) Savanna (Aw,As) Af: All twelve months have average precipitation of at least 60 mm Am: driest month less than 60 mm, but more than (100 [total annual precipitation in mm/25]) Aw: driest month less than 60 mm, but less than (100 [total annual precipitation in mm/25])

15 % Landfläche, die vom entsprechenden Köppen-typ Bedeckt ist (ohne Antarktis & Grönland), Beck et al., 2006

16 Distribution of Global Climate Classes (36d) Klima-Clusterung Kohonen Karte J. Kropp, 1999

17 SOMTOP vs BIOME Biome model: Tropical dryforest SOMTOP: node17 Green: Biome-Model Yellow: SOMTOP Red: Coincidence Kropp (1999)

18 SOMTOP vs BIOME Kropp & Schellnhuber (2007)

19 Genetische Klimaklassifikation nach Hendl, 1963/91

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21 Vorhersage von Klimawandel-Wirkungen: Welche Eigenschaften des zukünftigen Klimawandels können mit welcher Sicherheit vorhergesagt werden? Treiber: zukünftige anthropogene Treibhausgasemissionen (CO2, CH4, N2O etc.). - Hängen von politischen/wirtschaftlichen/sozialen Entwicklungen ab. Schwer vorherzusagen (Reflexivität: Vorhersagen über zukünftige Entwicklungen beeinflussen diese möglicherweise!) -> plausible Szenarien (z.b. die SRES Szenarien) Globale Strahlungsbilanz-Rechnungen -> T global (hohe Konvergenz seit Arrhenius) AOGCM-Rechnungen: ständige Annäherung an die Notwendigkeiten von Impact-Rechnungen

22 SRES, IPCC

23 Sind Emissionen und CO2-Concentration trivial vermittelt?

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25 Vorhersage von Klimawandel-Wirkungen: Welche Eigenschaften des zukünftigen Klimawandels können mit welcher Sicherheit vorhergesagt werden? Treiber: zukünftige anthropogene Treibhausgasemissionen (CO2, CH4, N2O etc.). - Hängen von politischen/wirtschaftlichen/sozialen Entwicklungen ab. Schwer vorherzusagen (Reflexivität: Vorhersagen über zukünftige Entwicklungen beeinflussen diese möglicherweise!) -> plausible Szenarien (z.b. die SRES Szenarien) Globale Strahlungsbilanz-Rechnungen -> T global (hohe Konvergenz seit Arrhenius) AOGCM-Rechnungen: ständige Annäherung an die Notwendigkeiten von Impact-Rechnungen

26 Stefan-Boltzmann: Langwellige Abstrahlung (mit T4), [CO2] modifiziert = Kurzwellige (Sonnen-) Einstrahlung, Albedo-modifiziert

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28 AR4, WG1 (2007): the global average surface warming following a doubling of carbon dioxide concentrations. It is likely to be in the range 2 C to 4.5 C with a best estimate of about 3 C, and is very unlikely to be less than 1.5 C. Values substantially higher than 4.5 C cannot be excluded, but agreement of models with observations is not as good for those values

29 Vorhersage von Klimawandel-Wirkungen: Welche Eigenschaften des zukünftigen Klimawandels können mit welcher Sicherheit vorhergesagt werden? Treiber: zukünftige anthropogene Treibhausgasemissionen (CO2, CH4, N2O etc.). - Hängen von politischen/wirtschaftlichen/sozialen Entwicklungen ab. Schwer vorherzusagen (Reflexivität: Vorhersagen über zukünftige Entwicklungen beeinflussen diese möglicherweise!) -> plausible Szenarien (z.b. die SRES Szenarien) Globale Strahlungsbilanz-Rechnungen -> T global (hohe Konvergenz seit Arrhenius) AOGCM-Rechnungen: ständige Annäherung an die Notwendigkeiten von Impact-Rechnungen

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