Climate modelling. Dr. Heike Huebener Hessian Agency for Environment and Geology Hessian Centre on Climate Change

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1 Hessisches Landesamt für Umwelt und Geologie Climate modelling Dr. Heike Huebener Hessian Agency for Environment and Geology Hessian Centre on Climate Change

2 Climate: Definition Weather: momentary state of the atmosphere at a given location Climate: Mean state over a long time period (WMO: 30 years) Variability (hot/cold or wet/dry years, ENSO, ) Extremes (extreme rainfall, extreme dry periods, ) Climate change: change in mean values, variability, extremes over a long time 2

3 The climate system 3 Source: IPCC AR4, 2007

4 The climate system Very complex system with complex interactions Different temporal and spatial scales The climate system is partly chaotic a there are several equally possible realizations of the state of the system Thus, climate PROJECTIONS are NOT deterministic, but probabilistic Climate models can by definition (in the best case) only reproduce the mean, variability and extremes. 4

5 SCENARIOS 5

6 IPCC SRES-scenarios Economic Global Regional Ecologic Population Agriculture Hamburger Bildungsserver, altered Economy Technology Forcings Energy 6

7 Scenario of energy demand Scenario of CO 2 - Emissions How to produce a climate projection Carbon cycle model Change in CO 2 - concentration Climate model Impact model Change in climate variables Socio-economic impacts 7

8 Observed CO 2 -Emissions Observed Emissions (till 2008) exceed all scenarios! Quelle: Le Quéré et al., 2009, Nature Geosciences

9 GLOBAL CLIMATE MODELS 9

10 Physical equations The atmosphere follows the laws of physics Can be expressed as mathematical equations Can be programmed in computer language o Models only depict the processes that are incorporated / programmed o Only large scale processes o Only known processes / interactions 10

11 3-dimensional grids Physical equations solved for each grid-box of the model a transport to neighbouring boxes Combination of all boxes a simulated state of the climate system for a time step 11

12 Empirical equations Parametrization Processes on scales that are not resolved by the model Examples: clouds, convective rain, turbulence, Which processes are parametrized depends on the resolution of the model! 12

13 REGIONAL CLIMATE 13

14 Downscaling In principle: two methods of downscaling Dynamical downscaling How: Use of a regional climate model Pro: Physical coupling to driving global model Con: Compute expensive Statistical downscaling How: Use of statistical correlations Pro: Fast to apply Con: Depends on long data series Con: Prone to misunderstandings 14

15 Dynamical downscaling 15 IPCC AR4

16 Statistical downscaling GCM: large scale variable (e.g. pressure field) Transfer-function Local variable (e.g. rain / snow) Usefull, if local variable is better represented by statistical downscaling than by the GCM Transfer-function should be robust (no temporal variability) and physically sensible! 16

17 UNCERTAINTY / CONFIDENCE 17

18 (Un)Certainties in climate projections Sources of uncertainty: Scenario Model uncertainty Internal (natural) climate variability Degree of confidence depends on the variable (e.g. T high, P low), spatial and temporal resolution (the higher the required teporal or spatial detail, the lower the confidence) geographic location (in mid-latitudes high internal variability)

19 Confidence in climate projections There is no best model g use ensembles of models g avoid overconfidence from using too few models In most cases, qualitative assessment is possible, even though quantitative values may be unsure For variables with high internal variability (e.g. rain) use long time horizons to separate the climate change signal from internal noise

20 Hessisches Landesamt für Umwelt und Geologie Thank you for your attention

21 ADDITIONAL INFORMATION 21

22 Causes of climate change: Natural or anthropogenic causes? Temperature rise since 1850: Model and observation IPCC AR4 22

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