Physische Geographie - Klimatologie · 2021. 1. 25. · Christoph Gerbig, Sönke Zaehle Max-Planck-Institut für Biogeochemie Hans-Knöll Str. 10, PF 100164, 07701 Jena Tel.: (03641)

Christoph Gerbig, Sönke ZaehleMax-Planck-Institut für Biogeochemie

Hans-Knöll Str. 10, PF 100164, 07701 JenaTel.: (03641) 57-6373 (Gerbig) -6300 (Zaehle)

Vorlesungswebsite:

https://www.bgc-jena.mpg.de/bsi/index.php/Services/LectureFSUClimatology

Email: [email protected]@bgc-jena.mpg.de

http://www.bgc-jena.mpg.de/~christoph.gerbighttp://www.bgc-jena.mpg.de/~soenke.zaehle

Physische Geographie - Klimatologie

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!!! ONLINE OPEN-BOOK KLAUSUR !!!

!!! 11.2. 10 Uhr !!!

!!! Details am Donnerstag !!!

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Räumliche Änderungen der Oberflächentemperatur

Twelfth Session of Working Group I Approved Summary for Policymakers

IPCC WGI AR5 SPM-27 27 September 2013

Figure SPM.1 [FIGURE SUBJECT TO FINAL COPYEDIT]

• Die Erwärmung ist global verteilt, aber stärker über Land IPCC AR5 (2014): Summary for Policymakers,

Figure SPM1b 4

Final Draft (7 June 2013) Chapter 2 IPCC WGI Fifth Assessment Report

Do Not Cite, Quote or Distribute 2-145 Total pages: 163

FAQ 2.1, Figure 2: Multiple independent indicators of a changing global climate. Each line represents an independently-derived estimate of change in the climate element. In each panel all datasets have been normalized to a common period of record. A full detailing of which source datasets go into which panel is given in the Supplementary Material 2.SM.5.

Land surface air temperature: 4 datasets

Mas

s ba

lanc

e (1

015G

T)

1.0

0.5

0.0

-0.5

-1.0

0.4

0.2

0.0

-0.2

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12

10

8

6

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10

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-5

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20

10

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-10

0.60.40.20.0

-0.2-0.4-0.6-0.8

10050

0-50

-100-150-200

Tropospheric temperature:7 datasets

Ocean heat content(0-700m):5 datasets

Specific humidity:4 datasets

Glacier mass balance:3 datasets

Sea-surface temperature: 5 datasets

Marine air temperature: 2 datasets

Sea level: 6 datasets

1850 1900 1950 2000 1940 1960 1980 2000

Summer arctic sea-ice extent: 6 datasets

Sea

leve

lan

omal

y (m

m)

Tem

pera

ture

anom

aly

(ºC

)Te

mpe

ratu

rean

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y (º

C)

Tem

pera

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aly

(ºC

)

Tem

pera

ture

anom

aly

(ºC

)O

cean

hea

t con

tent

anom

aly

(1022

J)

Spec

ific

hum

idity

anom

aly

(g/k

g)Ex

tent

ano

mal

y (1

06 km

2 )6420

-2-4-6

Exte

nt (1

06 km

2 )

Year Year

Northern hemisphere (March-April) snow cover: 2 datasets

IPCC AR5 (2014): Chapter 2,

FAQ 2.1, Figure 1

Beobachtete Änderungen im Erdsystem - Zusammenfassung

5

Beiträge der wichtigsten Treiberfaktoren zur Änderung des Strahlungsanstriebs seit 1750Final Draft (7 June 2013) Chapter 8 IPCC WGI Fifth Assessment Report


Figure 8.15: Bar chart for RF (hatched) and ERF (solid) for the period 1750–2011, where the total ERF is derived from Figure 8.16. Uncertainties (5–95% confidence range) are given for RF (dotted lines) and ERF (solid lines).

Natu

ral

Anth

ropo

geni

c

Well MixedGreenhouse Gases

Forcing agentRadiative forcing of climate between 1750 and 2011

-1 0 1 2 3Radiative Forcing [W m-2]

CO2

Other WMGHG CH4 N2O Halocarbons

TroposphericStratospheric Ozone

Stratospheric watervapour from CH4

Land Use Black carbon on snow Surface Albedo

Contrail induced cirrus Contrails

Aerosol-Radiation Interac.

Aerosol-Cloud Interac.

Total anthropogenic

Solar irradiance

IPCC AR5 Ch. 8 Fig 8.15 6

Fragen

Welche Klimaprozesse werden durch eine Zunahme der folgenden Treiberfaktoren direkt beeinflusst? In welcher Richtung?

Treibhausgase

Landwirtschaftsflächen

Aerosole

Wärmestrahlungsbilanz (“Treibhauseffekt”)Zunahme -> Erwärmung

AlbedoZunahme der Landwirtschaftsflächen -> Zunahme Albedo -> Abkühlung

Albedo der Stratosphäre Zunahme -> Abkühlung

Strahlungsbilanz Zunahme -> Erwärmung

Albedo, Wolkenalbedo, Treibhauseffekt Zunahme -> i.a. Abkühlung

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Mass der Klimaänderung als Reaktion auf eine Änderung im Strahlungsantrieb

ΔT = λ F [K (W m-2)-1]

Klimasensitivität

•berücksichtigt schnelle Änderungen im Klimasystem (z.B. Wasserkreislauf)

• vernachlässigt langfristige Rückkopplungen/Prozesse (z.B. durch biogeochemische Zyklen, Eisschilder)

Spezialfall: Gleichgewichts-Temperaturzunahme auf eine Verdopplung der atmosphärischen CO2 Konzentration

8

Gleichgewichts-Klimasensitivität in

AR5 Änderung der Temperatur bei einer

Verdoppelung der atmosphärischen CO2 Konzentration

IPCC, 2014, AR5, WG I, Ch 10, Fig. 10.20

Final Draft (7 June 2013) Technical Summary IPCC WGI Fifth Assessment Report

Do Not Cite, Quote or Distribute TS-113 Total pages: 127

TFE.6, Figure 1: Probability density functions, distributions and ranges for equilibrium climate sensitivity, based on Figure 10.20b plus climatological constraints shown in IPCC AR4 ( Box AR4 10.2 Figure 1), and results from CMIP5 (see Table 9.5). The grey shaded range marks the likely 1.5°C to 4.5°C range, grey solid line the extremely unlikely less than 1°C, the grey dashed line the very unlikely greater than 6°C. Adapted from Box 12.2, Figure 1. See Figure 10.20b and Chapter 10 Supplementary Material for full caption and details. Labels refer to studies since AR4. {Box 12.2, Figure 1}

9

Attribution des KLimawandels

ARTICLES NATURE CLIMATE CHANGE

(ref. 16), Goddard Insitute for Space Studies Surface Temperature (GISTEMP)17 and National Oceanographic and Atmospheric Administration Merged Land Ocean Global Surface Temperature Analysis (NOAAGlobalTemp)18 datasets yield estimates of observed GSAT warming in 2010–2019 of 1.18 °C, 1.18 °C and 1.12 °C, respectively (Supplementary Table 1). For the remainder of the study, we primarily report results based on the non-infilled HadCRUT4 dataset, and to ensure a like-for-like comparison, we use masked and blended model output when comparing with HadCRUT4 observations, including in all regressions. However, we report attributable warming based on simulated glob-ally complete GSAT.

Attribution of global mean temperature changesTo quantify the contributions of individual forcings to observed trends, we used the CMIP6 (ref. 12) Detection and Attribution Model Intercomparison Project (DAMIP)19 simulations from the 13 CMIP6 models for which the necessary simulations were avail-able (Fig. 1b, Extended Data Fig. 1 and Supplementary Table 2): the Australian Community Climate and Earth System Simulator (ACCESS-ESM1-5)20, the Beijing Climate Center Climate System

Model (BCC-CSM2-MR)21, the Canadian Earth System Model ver-sion 5 (CanESM5)22, the Community Earth System Model version 2 (CESM2)23, the sixth generation Centre National de Recherches Météorologique Coupled Model (CNRM-CM6-1)24, the Flexible Global Ocean-Atmosphere-Land System Model grid-point version 3 (FGOALS-g3)25, the Geophysical Fluid Dynamics Laboratory Earth System Model version 4 (GFDL-ESM4)26, the Goddard Insitute for Space Studies climate model (GISS-E2-1-G)27, the Hadley Centre Global Environment Model version 3 (HadGEM3-GC31-LL)28, the Institut Pierre-Simon Laplace Climate Model (IPSL-CM6A-LR)29, the Model for Interdisciplinary Research on Climate version 6 (MIROC6)30, the Meteorological Research Institute Earth System Model (MRI-ESM2-0)31 and the Norwegian Earth System Model (NorESM2-LM)32. We primarily used output from four experi-ments: historical-ssp245 (driven with changes in all anthropogenic and natural forcings), hist-aer (driven with changes in anthropo-genic aerosol emissions and burdens only), hist-nat (driven with changes in natural forcings only) and hist-GHG (driven with changes in well-mixed greenhouse gas concentrations only). These CMIP6 historical-ssp245 simulations show very little net anthropo-genic warming before the 1960s (Fig. 1b). This contrasts with the CMIP5 historical simulations, which showed on average approxi-mately 0.2 °C warming by the mid-twentieth century8. This could be due in part to a stronger aerosol forcing or response in these CMIP6 models. If these CMIP6 simulations are correct, this would imply that there was very little net anthropogenic contribution to the early twentieth century warming, and that almost all anthropo-genic warming has occurred since the 1960s. We use global mean temperature in our main attribution analysis, as previous work7,33 has shown that including more spatial detail may not result in more robust results, perhaps because model uncertainty in spatial patterns of response is larger. We use five-year means rather than decadal means33,34, in an attempt to better constrain the natural forcing response, which includes the short-timescale response to volcanic eruptions. Internal variability was estimated from intra-ensemble anomalies (Methods).

Regression coefficients of observed temperature changes against the simulated responses of individual models to natural (NAT) and anthropogenic (ANT) forcings are shown in Fig. 2a (Methods). The anthropogenic response is detected using 12 of 13 models (the uncertainty ranges on the ANT regression coefficients are above zero). The only exception is ACCESS-ESM1-5, which exhibits apparently unrealistic GMST evolution in its historical simulations, with almost no warming before 1980 (ref. 20) (Fig. 1a). By contrast, the natural forcing response is only detected using CanESM5, CESM2, CNRM-CM6-1, FGOALS-g3 and IPSL-CM6A-LR, and its regression coefficient is significantly less than unity using 7 of the 13 models, meaning that the simulated NAT response in these models is significantly stronger than observed. The natural forcing response appears to be somewhat less detectable and consistent based on these CMIP6 simulations than using CMIP5 simulations8,33–35. On the basis of this regression, the combined anthropogenic response is of realistic magnitude in ACCESS-ESM1-5, BCC-CSM2-MR, CESM2, CNRM-CM6-1, FGOALS-g3, GISS-E2-1-G, HadGEM3-GC31-LL, IPSL-CM6A-LR and NorESM2-LM, significantly overestimated by CanESM5 (ref. 22) (also apparent from Fig. 1a), and significantly underestimated by GFDL-ESM4, MIROC6 and MRI-ESM2-0. Note that it is expected that significant differences between the simulated climate response in models and observations can increasingly be identified as the observational record lengthens.

The realism of the scaled simulated responses to each set of forc-ings can be assessed by comparing residual observed variability, after subtraction of these responses, with simulated internal vari-ability. The results of a residual consistency test33,36 (Fig. 2c) indicate that residuals are significantly larger than expected based on pooled simulated internal variability for ACCESS-ESM1-5, CanESM5,

a 2.0ACCESS-ESM1-5 HadGEM3-GC31-LL

IPSL-CM6A-LRMIROC6MRI-ESM2-0NorESM2-LMHadCRUT4Model mean GMSTModel mean GSAT

BCC-CSM2-MRCanESM5CESM2CNRM-CM6-1FGOALS-g3GFDL-ESM4GISS-E2-1-G

Anthropogenic and natural forcingsGreenhouse gasesAerosolsNatural forcingsHadCRUT4

1.5

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1860 1880 1900 1920 1940 1960 1980 2000 2020Year

1860 1880 1900 1920 1940 1960 1980 2000 2020Year

GM

ST

anom

aly

(°C

)G

MS

Tan

omal

y (°

C)

b

Fig. 1 | Comparison of 1850–2019 global mean temperature evolution in observations and CMIP6 simulations. a, Coloured lines show HadCRUT4-masked and blended GMST5 anomalies relative to the 1850–1900 base period in one historical-ssp245 simulation from each model. The thick brown line shows the multimodel mean, using all ensemble members with equal weights given to each model. The thick red line shows the corresponding multimodel mean of globally complete GSAT. The thick black line shows HadCRUT4 (ref. 11). b, HadCRUT4 GMST compared with: GMST from CMIP6 historical-ssp245 simulations with anthropogenic and natural forcings, natural forcing simulations, well-mixed greenhouse gas-only simulations and aerosol-only simulations. The multimodel means (lines) and 5–95% ensemble ranges (shaded regions) are shown, both calculated with equal weight given to each model.

NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange

Gillet et al. 2021, Nature Climate Change

10

Simulierte und beobachtete Temperaturentwicklung

seit 1900

Auf Grundlage von Thermometermessungen

und Schiffsbeobachtungen

Simuliert von Klimamodellen

ALL: Alle Treiberkräfte NAT: Nur “natürliche”

Treiberkräfte

IPCC, 2007, AR4, WG I 11



Figure 10.7: Global, land, ocean and continental annual mean temperatures for CMIP3 and CMIP5 historical (red) and historicalNat (blue) simulations (multi-model means shown as thick lines, and 5–95% ranges shown as thin light lines) and for HadCRUT4 (black). Mean temperatures are shown for Antarctica and six continental regions formed by combining the sub-continental scale regions defined by Seneviratne et al. (2012). Temperatures are shown with respect to 1880–1919 for all regions apart from Antarctica where temperatures are shown with respect to 1950–2010. Adapted from Jones et al. (2013 ).

Tem

pera

ture

ano

mal

y, o C

-1.0-0.5

0.0

0.5

1.0

1.52.0

1880 1920 1960 2000

Global

1880 1920 1960 2000

Global Land

-1.0-0.5

0.0

0.5

1.0

1.52.0

1880 1920 1960 2000

Global ocean

-1.0-0.5

0.0

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1.52.0

North America

South America

-1.0-0.5

0.0

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Europe

-1.0-0.5

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Africa

1880 1920 1960 2000

Asia

-1.0-0.5

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1880 1920 1960 2000

Australasia

-1.0-0.5

0.0

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1.0

1.52.0

1880 1920 1960 2000

Antarctica historical 5-95%historicalNat 5-95%HadCRUT4

IPCC AR5 (2014), Fig 10.2 12

Änderungen der Ozeantemperatur in den letzten 40 Jahren

Natürliche Variabilität (blau) Änderungen in Solar- und Vulkan-aerosol Treibern (grau)

Anthropogener Treiber (grün) Beobachtete Änderungen (rot)

IPCC, 2007, AR4, WG I 13

Zusammenfassung (detection & attribution)

• Es ist extrem wahrscheinlich, dass mehr als 50% der zwischen 1951 und 2010 beobachteten globalen Temperaturänderungen auf den Menschen zurückzuführen ist

• Treibhausgase haben dazu wahrscheinlich 0.5-1.3 °C beigetragen• Andere anthropogene Antriebskräfte (z.B) Aerosole hatten wahrscheinlich

einen Effekt von -0.6°C bis 0.1 °C• Interne Variabilität und natürliche Klimaantriebe hatten jeweils einen

Effekt zwischen -0.1 und 0.1 °C• Zusammengenommen sind diese Trends konsistent mit der Erwärmung von

0.6-0.7°C in dieser Periode – Dies gilt für alle Kontinente (bis auf die Antarktis, wegen unzureichender

Messungen)

IPCC (2014), AR5 Summary for Policymakers 14

Zusammenfassung (detection & attribution)

• Der anthropogene Antrieb ist sehr wahrscheinlich

• für die beobachtete Erwärmung der Troposphäre und der Abkühlung der unteren Stratosphäre verantwortlich

• ein wesentlicher Grund für die Zunahme des Ozeanischen Wärmegehalt seit 1970

• Der anthropogene Antrieb ist wahrscheinlich für die Intensivierung des globalen Wasserkreislaufes verantwortlich, insbesondere

• der Zunahme des atmosphärischen Feuchte (medium confidence)

• globale Änderungen des Niederschlagsmusters (medium confidence)

• die Intensivierung von Starkregenereignissen über land (medium confidence)


• Der anthropogene Klimaantrieb hat • sehr wahrscheinlich zur Reduzierung des arktischen

Seeeises • wahrscheinlich zum Rückzug der Gletscher seit 1960, sowie

zum Abbau des Grönland Eisschildes seit 1993 beigetragen. Aussagen zu der Periode vorher, sowie zum Antarktischen Eisschildes sind aufgrund mangelnder Daten nicht robust möglich

• zur Reduzierung der Frühlingsschneebedeckung der nördlichen Hemisphäre

• zum Meeresspiegelanstieg seit 1970• beigetragen.• Änderungen der Sonneneinstrahlung haben nicht zur

Temperaturänderung seit 1970 beigetragen (high confidence)

Zusammenfassung


Klimawandel Simulationen

17

Elemente einer Klima-Projektion

Keine “Prognosen” sondern “Wenn - dann - Aussagen”

Final Draft Chapter 10 IPCC WG1 Fourth Assessment Report

1

2

3

4

5 6 7 8 9

10 11

Figure 10.1. Several steps from emissions to climate response contribute to the overall uncertainty of a

climate model projection. These uncertainties can be quantified through a combined effort of observation,

process understanding, a hierarchy of climate models, and ensemble simulations. In a comprehensive climate

model, physical and chemical representations of processes permit a consistent quantification of uncertainty.

Note that the uncertainty associated with the future emission path is of an entirely different nature and not

part of Chapter 10. Bottom row adapted from Figure 10.25, A1B scenario, for illustration only.

Do Not Cite or Quote 10-108 Total pages: 44

18

Emissions-Szenarien

Final Draft (7 June 2013) Technical Summary IPCC WGI Fifth Assessment Report


Figure TS.19: Compatible fossil fuel emissions simulated by the CMIP5 models for the four RCP scenarios. Top:

timeseries of annual emission (PgC yr–1). Dashed lines represent the historical estimates and RCP emissions calculated

by the integrated assessment models (IAM) used to define the RCP scenarios, solid lines and plumes show results from

CMIP5 ESMs (model mean, with 1 standard deviation shaded). Bottom: cumulative emissions for the historical period

(1860–2005) and 21st century (defined in CMIP5 as 2006–2100) for historical estimates and RCP scenarios. Left bars

are cumulative emissions from the IAMs, right bars are the CMIP5 ESMs multi-model mean estimate, and dots denote

individual ESM results. From the CMIP5 ESMs results, total carbon in the land-atmosphere-ocean system can be

tracked and changes in this total must equal fossil fuel emissions to the system. Hence the compatible emissions are

given by cumulative Emissions = ǻCA + ǻCL + ǻCO , while emission rate = d/dt [CA +CL +CO], where CA, CL, CO are

carbon stored in atmosphere, land and ocean respectively. Other sources and sinks of CO2 such as from volcanism,

sedimentation or rock weathering, which are very small on centennial timescales are not considered here. {Box 6.4;

Figure 6.25}

IPCC, Assessment Report 5, 2013

“RCPs”:

“Representative Concentration Pathways” - Repräsentative Emissionsszenarien kategorisiert nach Strahlungsantrieb im Jahre 2100 (in W/m2)

19




Zunehmende Emissionen führen zu einer weiteren

Erwärmung.

Das exakte Ausmass hängt von dem gewählten

Szenarium der Emissionen ab

IPCC, Assessment Report 5, 2013 20

Berechnete Änderungen der Temperatur und des Niederschlages bis zum Ende dieses Jahrhunderts





Änderungen in der Saisonalität

WinterWinter

SommerSommer

IPCC, 2007, AR4, WG I

22

Zunahme des MeeresspiegelsTwelfth Session of Working Group I Approved Summary for Policymakers




Entwicklung des Meereises in der nördlichen HemisphäreFinal Draft (7 June 2013) Technical Summary IPCC WGI Fifth Assessment Report


Figure TS.17: Northern Hemisphere sea-ice extent in September over the late 20th century and the whole 21st century

for the scenarios RCP2.6, RCP4.5, RCP6.0 and RCP8.5 in the CMIP5 models, and corresponding maps of multi-model

results in 2081–2100 of Northern Hemisphere September sea ice extent. In the time series, the number of CMIP5

models to calculate the multi-model mean is indicated (subset in brackets). Time series are given as 5 year running

means. The projected mean sea ice extent of a subset of models that most closely reproduce the climatological mean

state and 19792012 trend of the Arctic sea ice is given (solid lines), with the min-max range of the subset indicated

with shading. Black (grey shading) is the modelled historical evolution using historical reconstructed forcings. The

CMIP5 multi-model mean is indicated with dashed lines. In the maps, the CMIP5 multi-model mean is given in white

color, the results for the subset in grey colour. Filled areas mark the averages over the 2081–2100 period, lines mark the

sea ice extent averaged over the 1986–2005 period. The observed sea ice extent is given in pink as a time series and

averaged over 1986–2005 as a pink line in the map. {Figures 12.18, 12.29, 12.31}

IPCC, 2014, AR5, WG I, TS.17

(Eis-albedo Feedback -> stärke des Polaren Wirbels -> Stärke/Phase der NAO)

24

Änderung in Extremereignissen

IPCC, 2011, Special Report on Climate Extremes

25

Änderungen in Extremereignissen



Figure 12.13: CMIP5 multi model mean geographical changes (relative to a 1981–2000 reference period in common

with CMIP3) under RCP8.5 and 20-year smoothed time series for RCP2.6, RCP4.5 and RCP8.5 in the (a,b) annual

minimum of minimum daily temperature, (c,d) annual maximum of maximum daily temperature, (e,f) frost days

(number of days below 0°C) and (g,h) tropical nights (number of days above 20°C). White areas over land indicate

regions where the index is not valid. Shading in the time series represents the interquartile ensemble spread (25th and

75th quantiles). The box-and-whisker plots show the interquartile ensemble spread (box) and outliers (whiskers) for 11

CMIP3 model simulations of the SRES scenarios A2 (orange), A1B (cyan), and B1 (purple) globally averaged over the

respective future time periods (2046–2065 and 2081–2100) as anomalies from the 1981–2000 reference period.

Stippling indicates grid points with changes that are significant at the 5% level using a Wilcoxon signed-ranked test.

Updated from Sillmann et al. (2013), excluding the FGOALS-s2 model.

IPCC, 2014, AR5, WG I, Ch 12. Fig 13 26

Trägheit des Klimasystems:Stabilisierung Treiberfaktoren != Stabilisierung Klimasystem

IPCC, 2007, AR4, WG I 27

Zusammenfassung(Klimawandelprojektionen)

• Die relativen Muster des Klimawandels gleichen sich zwischen dem Anfang und Ende des 21sten Jahrhunderts.

• Interannuale Variabilität wird einen starken Einfluss auf das Klima behalten, besonders in nächsten Dekaden und auf regionaler Skala.

• Erst ab Mitte des 21sten Jahrhunderts unterscheiden sich Szenarien deutlicher

• Die Reduzierung der Unsicherheiten in Temperaturprognosen ist hauptsächlich durch den neuen Ansatz (RCP Szenarios) bedingt, da die Kohlenstoff-Klimainteraktionen die Projektionen nicht beeinflussen, und nicht durch verbessertes Verständnis

• Die erhöhten Abschätzungen des Meeresspiegelanstiegs ist durch eine Verbesserung der Modellierungen von dynamischen Land-Eis Prozessen (Gletscher)

28

Zusammenfassung• Globale Oberflächentemperatur in 2016-2035: 0.3°C - 0.7°C

(relativ zu 1986-2005; medium confidence)

• Globale Oberflächentemperatur in 2081-2100: RCP 2.6: 0.3-1.7°CRCP 4.5: 1.1-2.6°C RCP 6.0: 1.4-3.1°CRCP 8.5: 2.6-4.8°Czusätzlich zu den 0.6-0.7°C von 1850 bis 1985-2005

• Erwärmung in der Arktis > globale Erwärmung (very high confidence)Erwärmung über Land > Erwärmung über Ozeanen (very high confidence)

• Szenarion RCP 6.0 und 8.5: Erwärmung wahrscheinlich >2°C. Szenarien 2.6 und 4.5: Die Überschreitung der 2°C Marke ist wahrscheinlicher als die nicht-Überschreitung

• Zunahme von heissen und Abnahme von kalten Temperaturextremen gilt als beinahe sicher

29

Biogeocheminsche Spurenstoffkreisläufe im Erdsystem

Kohlenstoffkreislauf

�30

A simple view of the “fast” and “slow” global carbon cycle in the Earth System - “natural” preindustrial state

CO2Atmosphere

Land BiosphereOcean

PhysicalClimateSystem

OtherClimate Forcings

Geological carbon reservoirs (Carbonate rocks, organic sediments, fossil organic carbon

Volcanism

Weathering

Sedimentation

Carbon burial

31

Erster Nachweis von Jahresgang und Zunahme des CO2 in der Atmosphäre

C. D. Keeling, Tellus, v12, 200, 1960

Charles David Keeling

(1928-2005)

�32

�33

Atmospheric Carbon Cycle

Main constituent CO2, (410 ppm, 2019) = 869 PgC (1015 gC, “GtC”)1 ppm in global atmosphere = 2.12 PgC

Other carbon containing compounds

CH4 (1.866 ppm, 2019)CO (< 0.2 ppm)Volatile organic compounds (VOC, < 10-3 ppm)Halocarbons (<10-3 ppm)

CO2 increase Current (2010-2019): 2.4 ppm a-1 = ~ 0.5% a-1

Since preindustrial: ~131 ppm = ~ 47%

34

Atmosphärische Messungen von CH4

35

Dome C (Concordia) Drilling Site (EPICA Project)

�36

History of the three major greenhouse gases inferred from ice core measurements

IPCC AR5, Chapter 6, 2013

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1800 1900 2000 2020

Year

Ice Cores

Photo: margin of Quelcaya Ice Cap (L. Thompson)

Photo: GISP-2 drilling site (M. Morrison)

Ice accumulation records from ice sheets and glaciers in

polar, mid and tropical latitudes can provide records of cli-

mate variability that are resolvable at an annual, sometimes

seasonal level. Isotopic signatures in the ice itself yield quan-

titative information on past temperature and hydrological

regimes. This can be set alongside records of changing

atmospheric trace gases and dust/aerosol loading from the

same core

LB/PAG1/99-1

Luftblasen

konserviert in

einem Eisstück

aus einem

antarktischen

Bohrkern

ca. 1 cm

[curtesy E. Wolff!

ca. 1 cm

CO2

CH4

N2O

37

Biogeochemie: Grundlegende KonzepteReservoir (box, compartment, component) Inhalt eines Reservoirs: “burden”, Einheit: Masse oder Mol (M)

Fluss (exchange flux) nomalerweise als Massefluss (Masse / Zeit od. Mol / Zeit); oft auch als Masse/Zeit/Fläche (= flux density) (F) (= “Fluss, Flussdichte”)

Quellen/Senken (source/sink) (Symbol S od. Q) (Senke oft als negative Quellen)

Budget (Bilanz von Quellen, Senken und Austauschflüssen eines Reservoirs) (= “Bilanz”)

τ: Umsatzzeit = M/F (or M/S) (= “turnover time”)

Cycle, biogeochemical cycle (= “Biogeochemische Kreisläufe”)�38

A simple view of the “fast” and “slow” global carbon cycle in the Earth System - “natural” preindustrial state

CO2Atmosphere

Land BiosphereOcean




Volcanism

Weathering

Sedimentation

Carbon burial

39

Kenngrössen des globalen CO2-Kreislaufs

Brutto-Fluss (F)(Pg C / Jahr)

CO2-Masse der Atmosphäre (M)

(Pg C)Umsatzzeit (τ=M/F)

(Jahr)

Land-Atmosphäre 123 829 7.3

Ozean-Atmosphäre 80 829 11.1

Land+Ozean-Atmosphäre 203 829 4.1

Land (heute): Brutto-Fluss: 123 Pg C / Jahr C-Masse: ~1950-3050 Pg C —> Umsatzzeit: 15-24 Jahre

Ozean (heute): Brutto-Fluss: 80 Pg C / Jahr C-Masse: ~40000 Pg C —> Umsatzzeit: 500 Jahre

CO2Atmosphere

Land BiosphereOcean




Volcanism

Weathering

Sedimentation

Carbon burial

�40

A simple view of the “fast” and “slow” global carbon cycle in the Earth System - anthropogenic perturbation

Emissions from burning of fossil fuels and cement production

Changes in land use and

land management

CO2Atmosphere

Land BiosphereOcean




Volcanism

Weathering

Sedimentation

Carbon burial

41

Globale Emissionen des CO2 aus der Verbrennung von Kohle, Erdöl und Erdgas, der Zementherstellung und aus Änderungen

der Landnutzung (u.a. Waldrodungen)Globale Emissionen (ohne Landnutzung)

2019: 36.4 GtCO2 a-1

42

43

Exkurs: CO2 Emissionen für 2020 CO2 “Vorhersage” für 2020

The global atmospheric CO2 concentration is forecast to average 412 ppm in 2020, increasing 2.5 ppm in 2020Lower emissions in 2020 due to the COVID-19 pandemic have had little effect on the atmospheric CO2 concentration

44

Nur ungefähr die Hälfte der anthropogenen Emissionen akkumulieren in der Atmosphäre

1960 1970 1980 1990 2000

320

340

360

380

400

420

440

ppm

Kumulative fossile CO2 Emissionenseit 1959 konvertiert in ppm

1960 1970 1980 1990 2000

320

340

360

380

400

420

440

ppm

45

“Airborne Fraction” = ∆Natm/(Qfoss+Qlanduse)

46

Mittelwert 1960-2019: 44%Mittelwert 2010-2019: 46%

Source: NOAA-ESRL; Global Carbon Budget 2020

Zunahme des atmosphärischen CO2 und Abnahme des atmosphärischen O2

O2

CO2

Northern hemisphere: Mauna Loa, Alert Southern hemisphere: South Pole, Cape Grim

Data: R. Keeling et al., http://scrippsco2.ucsd.edu, http://scrippso2.ucsd.edu

1960 1970 1980 1990 2000 2010

250

300

350

400

-100

-80

-60

-40

-20

0

CO2@ppmD

DO2@ppmD

47

Atmosphärische Bilanzgleichungen

dNCO2 /dt = Qfoss - Sbio - Soc

dNO2/dt = ff Qfoss - fb Sbio

DIC

fb ≈ -1.1

ff ≈ -1.4

fr ≈ -1.4

Gasaustausch

Photosynthese

Respiration

Verbrennung von Kohle, Öl, Gas

Kopplung der Kreisläufe von CO2 und O2

Photosynthese & Respiration: CO2 + H2O <-> CH2O + O2

�48

�49

CO2 und O2 Beobachtungen in Mauna Loa

50

360 380 400 420 440 460

−150

−100

−50

CO2 (ppm)

ΔO2 (

ppm

)

●●●

●●

●●

●●●

●●

●

●●●

●

●●

●

●

●

●

●●

●

●

●●

●

● ObservedFossilLandOcean

Source: NOAA

Konzentrationsdifferenz zwischen Mauna Loa und Südpol

dargestellt als Funktion der Differenz der Emissionen in Nord- und Südhemisphäre

90°N

Cs

Qn Qs

Cn - Cs = (Qn - Qs) τ/2

90°S

Cn

0°

τ

Ê

ÊÊ

Ê

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Ê

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ÊÊÊ

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2 3 4 5 6 7 8 9

0

1

2

3

4

Qfoss,N-Qfoss,S HPgC yr-1L

CMLO-CSPOHppmL

�51

Fragen zum atmosphärischen CO21. Wie gross ist die Turnoverzeit des atmosphärischen CO2 gegenüber dem

Ozean und der Landbiosphäre (einzeln und gemeinsam); heute und vor der industriellen Revolution?

2. Wie gross ist die Turnoverzeit des Kohlenstoffs in der Landbiosphäre (Vegetation + Boden)?

3. Wie gross ist die Turnoverzeit des “schnellen” Kohlenstoffkreislauf bezüglich des “langsamen” Kohlenstoffkreislaufs?

4. Die N-S Konzentrationsdifferenz Cn - Cs ist proportional Qn - Qs. Berechne die atmosphärische Austauschzeit zwischen Nord- und Südhemisphäre

5. Extrapoliere Qn - Qs zurück zum vorindustriellen Zustand - Was lässt sich über Cn - Cs sagen? Wie lässt sich dies interpretieren?

Ozean, Land, Ozean+Landheute: 11.1 yr, 7.3 yr, 4.1yrvorindustriell: 9.8yr, 5.5 yr, 3.5yr

15-24 yr

~ 100’000 yr (~ 43000 PgC/0.4 PgCyr-1)

0.85 yr

Cn-Cs negativ! -> natürliche Senke in N-Hemisphäre und gleich grosse Quelle in S-Hemisphäre

�52

Prozesse - Ozean

53

Ozeanischer Kohlenstoffkreislauf3 Transportmechanismen:

• Advektion und Mischung durch Meeresströmungen (“Löslichkeitspumpe”)

Marine Biologische “Pumpen”:• Organischer Kohlenstoff• Karbonate

DIC =

H2CO3 + HCO3- + CO3=

DIC Gleichungen:Fas = kex (pCO2,atm- pCO2,oc)pCO2,oc = α [H2CO3]

H2CO3 H+ + HCO3

-

HCO3- H+ + CO3

=

Bufferfactor:∆pCO2/pCO2 ≈ 10 ∆DIC/DIC 54

Kohlenstoff Umsatzraten im Ozean

EinheitenFluss: Pg C / JahrReservoir: Pg C

�55

Zunahme des atmosphärischen CO2 und Abnahme des atmosphärischen O2

O2

CO2

Northern hemisphere: Mauna Loa, Alert Southern hemisphere: South Pole, Cape Grim

Data: R. Keeling et al., http://scrippsco2.ucsd.edu, http://scrippso2.ucsd.edu

1960 1970 1980 1990 2000 2010

250

300

350

400

-100

-80

-60

-40

-20

0

CO2@ppmD

DO2@ppmD

Ê ÊÊ Ê Ê

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Ê

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Ê Ê

Ê

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Ê

Jan MarMay Jul Sep Nov Jan MarMay-10

-5

0

5

10

15

CO2,DO2@ppmD

Seasonal cycle of CO2 and O2 at Alert HALTL

CO2 O2

56

Inventar des anthropogenen Kohlenstoffs im Ozean

The Oceanic Sink forAnthropogenic CO2

Christopher L. Sabine,1* Richard A. Feely,1 Nicolas Gruber,2

Robert M. Key,3 Kitack Lee,4 John L. Bullister,1 Rik Wanninkhof,5

C. S. Wong,6 Douglas W. R. Wallace,7 Bronte Tilbrook,8

Frank J. Millero,9 Tsung-Hung Peng,5 Alexander Kozyr,10

Tsueno Ono,11 Aida F. Rios12

Using inorganic carbon measurements from an international survey effort in the1 9 9 0 s and a tracer-based separation technique, we estimate a global oceanicanthropogenic carbondioxide (CO2 ) sink for theperiod from 1 8 0 0 to 1 9 9 4 of 1 1 8 !1 9 petagramsof carbon. Theoceanic sink accounts for"4 8 %of the total fossil-fueland cement-manufacturing emissions, implying that the terrestrial biosphere wasa net source of CO2 to the atmosphere of about 3 9 ! 2 8 petagrams of carbon forthis period. The current fraction of total anthropogenic CO2 emissions stored in theocean appears to be about one-third of the long-term potential.

Since the beginning of the industrial period inthe late 18th century, i.e., over the anthropo-cene (1), humankind has emitted large quan-tities of CO2 into the atmosphere, mainly as aresult of fossil-fuel burning, but also becauseof land-use practices, e.g., deforestation (2).Measurements and reconstructions of the at-mospheric CO2 history reveal, however, thatless than half of these emissions remain in theatmosphere (3). The anthropogenic CO2 thatdid not accumulate in the atmosphere musthave been taken up by the ocean, by the landbiosphere, or by a combination of both. The

relative roles of the ocean and land bio-sphere as sinks for anthropogenic CO2 overthe anthropocene are currently not known.Although the anthropogenic CO2 budgetfor the past two decades, i.e., the 1980s and1990s, has been investigated in detail (3),the estimates of the ocean sink have notbeen based on direct measurements ofchanges in the oceanic inventory of dis-solved inorganic carbon (DIC).

Recognizing the need to constrain the oce-anic uptake, transport, and storage of anthro-pogenic CO2 for the anthropocene and toprovide a baseline for future estimates ofoceanic CO2 uptake, two international oceanresearch programs, the World Ocean Circu-lation Experiment (WOCE) and the JointGlobal Ocean Flux Study (JGOFS), jointlyconducted a comprehensive survey of inor-ganic carbon distributions in the global oceanin the 1990s (4). After completion of the U.S.field program in 1998, a 5-year effort wasbegun to compile and rigorously quality-con-trol the U.S. and international data sets, in-

cluding a few pre-WOCE data sets in regionsthat were data limited (5). The final data setconsists of 9618 hydrographic stations col-lected on 95 cruises, which represents themost accurate and comprehensive view of theglobal ocean inorganic carbon distributionavailable (6). As individual basins were com-pleted, the ocean tracer–based #C*method(7) was used to separate the anthropogenicCO2 component from the measured DIC con-centrations (8–10). Here we synthesize theindividual ocean estimates to provide anocean data-constrained global estimate of thecumulative oceanic sink for anthropogenicCO2 for the period from "1800 to 1994 (11).Distribution and inventories of an-

thropogenic CO2 in the ocean. The objec-tively gridded individual sample estimateswere vertically integrated to produce thecolumn inventory map shown in Fig. 1.Because the global survey had limited datacoverage in the marginal basins and theArctic Ocean (north of 65°N), these areaswere excluded from the mapped regions.The cumulative oceanic anthropogenic CO2

sink in 1994, for the ocean region shown inFig. 1, is 106 ! 17 Pg C. Accounting forthe excluded regions, we estimate a globalanthropogenic CO2 sink of 118 ! 19 Pg C.The uncertainty in the total inventory isbased on uncertainties in the anthropogenicCO2 estimates and mapping errors (11).

Figure 1 shows that this anthropogenicCO2 is not evenly distributed throughout theoceans. The highest vertically integrated con-centrations are found in the North Atlantic.As a result, this ocean basin stores 23% of theglobal oceanic anthropogenic CO2, despitecovering only 15% of the global ocean area(table S1). By contrast, the Southern Oceansouth of 50°S has very low vertically inte-grated anthropogenic CO2 concentrations,containing only 9% of the global inventory.More than 40% of the global inventory isfound in the region between 50°S and 14°S

1National Oceanic and Atmospheric Administration(NOAA) Pacific Marine Environmental Laboratory, 7 6 00Sand Point Way NE, Seattle, WA 9 8 115, USA. 2Univer-sity of California–Los Angeles, Institute of Geophysicsand Planetary Physics and Department of Atmosphericand Oceanic Sciences, Los Angeles, CA 90095, USA.3Princeton University, Program in Atmospheric and Oce-anic Science, Forrestal Campus/Sayre Hall, Princeton, NJ08 54 4 , USA. 4Pohang University of Science and Tech-nology, San 31, Nam-gu, Hyoja-dong, Pohang 7 9 0-7 8 4 ,South Korea. 5NOAA Atlantic Oceanographic and Me-teorological Laboratory, 4 301 Rickenbacker Causeway,Miami, FL 3314 9 , USA. 6 Institute of Ocean Sciences,Climate Chemistry Laboratory, Post Office Box 6 000,Sidney, BC V8 L 4 B2, Canada. 7Forschungsbereich MarineBiogeochemie, Leibniz Institut fur Meereswissenschafte,an der Universitat Kiel, (IFM-GEOMAR), DusternbrookerWeg 20, D-24 105 Kiel, Germany. 8Commonwealth Sci-entific and Industrial Research Organisation (CSIRO)Marine Research and Antarctic Climate and EcosystemCooperative Research Center, Hobart, Tasmania 7 001,Australia. 9University of Miami, Rosenstiel School ofMarine and Atmospheric Science, Division of Marine andAtmospheric Sciences, 4 6 00 Rickenbacker Causeway,Miami, FL 3314 9 , USA. 10Carbon Dioxide InformationAnalysis Center, Oak Ridge National Laboratory, U.S.Department of Energy, Mail Stop 6 335, Oak Ridge, TN37 8 31–6 335, USA. 11Frontier Research System forGlobal Change/Institute for Global Change Research,Sumitomo Hamamatsu-cho, Building 4 F, 1-18 -16Hamamatsutyo, Minato-ku, 105-0013, Japan. 12Institutode Investigaciones Marinas, Consejo Superior deInvestigationes Cientificas, c/Eduardo Cabello, 6 , 36 208Vigo, Spain.

*To whom correspondence should be addressed. E-mail: [email protected]

Fig. 1. Column inventory of anthropogenic CO2 in the ocean (mol m$ 2). High inventories are

associated with deep water formation in the North Atlantic and intermediate and mode waterformation between 30° and 50°S. Total inventory of shaded regions is 106 ! 17 Pg C.

R E S E A R C H A R T I C L E S

www.sciencemag.org SCIENCE VOL 305 16 JULY 2004 367

Sabine et al. 2004�57

Schema der thermohalinen Zirkulation (vereinfacht)

Rote Linien: Oberflächenströmungen, Blau: Salzgehalt < 34‰Blaue/Lila Lininen: Tiefenströmungen, Grün: Salzgehalt > 36‰

Gelb: Absinkgebiete �58

Anthropogener Kohlenstoff im Ozean:

Vertikalschnitte A: Atlantik B: Pazifik

C: Indischer Ozean

Sabine et al. 2004

because of the substantially higher verticallyintegrated concentrations and the large oceanarea in these latitude bands (Fig. 1, table S1).About 60% of the total oceanic anthropogen-ic CO2 inventory is stored in the SouthernHemisphere oceans, roughly in proportion tothe larger ocean area of this hemisphere.

Figure 2 shows the anthropogenic CO2

distributions along representative meridi-onal sections in the Atlantic, Pacific, andIndian oceans for the mid-19 9 0s. Becauseanthropogenic CO2 invades the ocean bygas exchange across the air-sea interface,the highest concentrations of anthropogenicCO2 are found in near-surface waters.Away from deep water formation regions,the time scales for mixing of near-surfacewaters downward into the deep ocean canbe centuries, and as of the mid-19 9 0s, the

anthropogenic CO2 concentration in mostof the deep ocean remained below the de-tection limit for the !C*technique.

Variations in surface concentrations are re-lated to the length of time that the waters havebeen exposed to the atmosphere and to thebuffer capacity, or Revelle factor, for seawater(12, 13). This factor describes how the partialpressure of CO2 in seawater (PCO2) changes fora given change in DIC. Its value is proportionalto the ratio between DIC and alkalinity, wherethe latter term describes the oceanic chargebalance. Low Revelle factors are generallyfound in the warm tropical and subtropical wa-ters, and high Revelle factors are found in thecold high latitude waters (Fig. 3). The capacityfor ocean waters to take up anthropogenic CO2

from the atmosphere is inversely proportionalto the value of the Revelle factor; hence, the

lower the Revelle factor, the higher the oceanicequilibrium concentration of anthropogenicCO2 for a given atmospheric CO2 perturbation.The highest anthropogenic CO2 concentrations(" 60 #mol kg$ 1) are found in the subtropicalAtlantic surface waters because of the low Rev-elle factors in that region. By contrast, the near-surface waters of the North Pacific have a high-er Revelle factor at comparable latitudes andconsequently lower anthropogenic CO2 con-centrations primarily because North Pacific al-kalinity values are as much as 100 #mol kg$ 1

lower than those in the North Atlantic (Fig. 3).About 30% of the anthropogenic CO2 is

found at depths shallower than 200 m andnearly 50% at depths above 400 m. Theglobal average depth of the 5 #mol kg$ 1

contour is " 1000 m. The majority of theanthropogenic CO2 in the ocean is, therefore,confined to the thermocline, i.e., the region ofthe upper ocean where temperature changesrapidly with depth. Variations in the penetra-tion depth of anthropogenic CO2 are deter-mined by how rapidly the anthropogenic CO2

that has accumulated in the near-surface wa-ters is transported into the ocean interior. Thistransport occurs primarily along surfaces ofconstant density called isopycnal surfaces.

The deepest penetrations are associatedwith convergence zones at temperate lati-tudes where water that has recently been incontact with the atmosphere can be transport-ed into the ocean interior. The isopycnal sur-faces in these regions tend to be thick andinclined, providing a pathway for the move-ment of anthropogenic CO2-laden waters intothe ocean interior. Low vertical penetration isgenerally observed in regions of upwelling,such as the Equatorial Pacific, where inter-mediate-depth waters, low in anthropogenicCO2, are transported toward the surface. Theisopycnal layers in the tropical thermoclinetend to be shallow and thin, minimizing themovement of anthropogenic CO2-laden wa-ters into the ocean interior.

Figure 4A shows the distribution of an-thropogenic CO2 on a relatively shallowisopycnal surface (see depths in Fig. 2) witha potential density (%&) of 26.0. About 20%of the anthropogenic CO2 is stored in waterswith potential densities equal to or less thanthat of this surface. The highest concentra-tions are generally found closest to where thisdensity intersects the surface, an area referredto as the outcrop. Concentrations decreaseaway from these outcrops in the Indian andPacific oceans, primarily reflecting the aging ofthese waters, i.e., these waters were exposed tolower atmospheric CO2 concentrations whenthey were last in contact with the atmosphere.The Atlantic waters do not show this trendbecause the 26.0 %& surface is much shallowerand therefore relatively well connected to theventilated surface waters throughout most ofthe Atlantic (Figs. 2A and 4A).

Fig. 2 . Representative sections of anthropogenic CO2 (#mol kg$1) from (A) the Atlantic, (B) Pacific, and

Indian (C) oceans. Gray hatched regions and numbers indicate distribution of intermediate water masses(and North Atlantic DeepWater) on the given section and the total inventory of anthropogenic CO2 (PgC) within these water masses. The southern water masses in each ocean represent Antarctic Interme-diate Water. The northern water masses represent the North Atlantic Deep Water (A), North PacificIntermediate Water (B), and Red Sea/Persian Gulf Intermediate Water (C). The two bold lines in eachpanel give the potential density [%& ' (density – 1) ( 1000] contours for the surfaces shown in Fig. 4 .Insets show maps of the cruise tracks used. Note that the depth scale for (A) is twice that of the otherfigures, reflecting the deeper penetration in the North Atlantic.

R E S E A R C H A R T I C L E S

16 JULY 2004 VOL 305 SCIENCE www.sciencemag.org368

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CO2 Aufnahme durch den Ozean

Begrenzende Prozesse:

Karbonatchemie (Pufferung) -> Langfristige (>1000 a) Ozeanaufnahme: ~90%

Physikalischer Transport durch Thermokline (Mischung und Tiefenwasserbildung)

Eindringtiefe des anthropogenen Kohlenstoffs ist bestimmt durch die Zeitskala der atmosphärischen Störung (2000: ~ 400-500m)

Marine Biosphäre ist vorwiegend begrenzt durch Nährstoffangebot (N, P, a.o.), Licht oder andere Faktoren (z.B. Fraß), jedoch nicht durch DIC -> kein signifikanter Beitrag zur heutigen Ozeanaufnahme

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Schema der wichtigsten Kohlenstoffflüsse in der Landbiosphäre

GPP: Gross Primary Production NPP: Net Primary ProductionNEP: Net Ecosystem Production NBP: Net Biome Production �61

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