global climate modelling-klauswyser - smhi
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Global Climate Modelling
Klaus Wyser
Rossby Centre, SMHI
Mal
lver
sion
1.0
200
9-09
-23
With contributions from Erik Kjellström and Colin Jo nes
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How to make good sausages
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Climate modelling is like making
sausages…
INPUTSolar radiation
Land use…
OUTPUTClimate projection
Palaeoclimate…
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A very simple climate model� The Chalmers Climate Calculator, a simple yet realistic global climate model� 1-d model (varies only with time)
� Output:
� Global mean temperature� Input:
� Reduction in emissions
� Climate model sensitivity
� Aerosol forcing
http://www.chalmers.se/ee/ccc
0.5%/year emission reduction after 2015
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More complex climate models� Demanding on computers and storage:
� A 100-year simulation may take several weeks on a supercomputer
� TBs of raw output that need to be post-processed
� Post-processed results are often available from portals, for example:
� CMIP3: http://www-pcmdi.llnl.gov/ipcc/about_ipcc.php
� PMIP2: http://pmip2.lsce.ipsl.fr/
� CMIP5: http://cmip-pcmdi.llnl.gov/cmip5/index.html?submenuheader=0� IPCC: http://www.ipcc-data.org/maps/
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Setting up a climate model experiment
Forcing• Solar radiation• GHG and aerosols• Land-use• Topography• Volcanoes
Initial conditions• Temperature• Winds• Pressure• Salinity• Currents• Sea-ice
Global climate model• EC-EARTH• ECHAM• HadGEM• CCSM4• JPSL+ many more
INPUT
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Initial conditions� Atmosphere, land and sea-ice adjust rapidly (from months to a few years)� Timescale for ocean is typically 100s (surface) to 1000s (deep ocean) of years
3 simulations with the same global model (ECHAM5)
Only difference is the initial statein 1850
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Output: what can we get from global
climate models?
� Present-day climate (hindcasts)
� Future climate� Projection (scenario)
� Prediction
� Climate of the past� Palaeoclimate
Depends on forcing
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Climate model output� Climate comprises not only mean values but also the variability
� Variance
� Extrema
� Frequencies of occurrence� PDFs
…
� In principle, models can produce the full PDF (down to the spatial and temporal resolution of the model)
� Sometimes model data are not archived at the full model resolution (for exampleonly monthly means are saved)
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Present-day climate
Data from CMIP3 experimentMulti-model multi-year mean
Figures from http://ccr.aos.wisc.edu/model/visualization/ipcc/(Center for Climatic Research (CCR) University of Wisconsin-Madison)
Temperature
Precipitation
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IPCC AR4 models (CMIP3)
Difference in monthly meantemperature to CRU data
Northern and Southern Sweden
Fig. from Lind & Kjellström
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WhyWhyWhyWhy are are are are modelsmodelsmodelsmodels ””””so badso badso badso bad”””” comparedcomparedcomparedcompared to the to the to the to the
observedobservedobservedobserved present present present present climateclimateclimateclimate????
� Different models can produce different results, especially when looking at smallerregions
� The initial state may play a role
� Models are simplifications, they don’t contain all processes, or some processesmay be wrongly described
� Models have coarse resolution and may differ from the real world
� Topography
� Land-sea mask� Land-use
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Varying land-sea masks in different AR4
climate models
MIROC3.2 hires (Japan) FGOALS-g1.0 (China)INM-CM3.0 (Russia)
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Ensembles: getting more
robust model results
Combine results from several experiments:• Different models• Different forcings• Different initial conditions
Ensembles can be constructed as plainaritmetic average, or with a sophisticatedweighting of the contributing members (see for example Christensen et al., Clim. Res., 2011)
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Future climate: projections� Projections are based on
scenarios
� A scenario is a set of assumptions about the evolution of population, development, economy, etc. The scenario describes the consequences of this evolution in terms of emissions or concentrations of GHG, aerosols, land-use, etc.
� A scenario is no prediction, it only describes a possibleevolution of the external forcingthat is later used to force the climate model
Example: IPCC scenarios
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Future climate: projection
21th century (A1B) – 20th century
Temperature Precipitation
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Future climate: predictions� Climate predictions for 10-30 years are a hot research topic� How well a climate model agrees with reality is a question of how well the initial
state is described, in particular the initial state of the ocean� Climate predictions from several state-of-the-art climate models will become
available from the CMIP5 database
Surface T
Results from DePreSys(Hadley Centre)Source: www.clivar.org
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Past climate – results from PMIP2
Temperature Precipitation
Note: different topographycompared to today
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Coupled Model Intercomparison Project –
Phase 5 (CMIP5)CMIP is a standard experimental protocol for studying the output of coupled ocean-atmosphere
general circulation models (GCMs) . It provides a community-based infrastructure in support of climate model diagnosis, validation, intercomparison, documentation and data access.
The CMIP5 (CMIP Phase 5) experiment design has been finalized with the following suites of experiments:
I Decadal Hindcasts and Predictions simulations,II "long-term" simulations, III "atmosphere-only" (prescribed SST) simulations for especially computationally-
demanding models(from http://cmip-pcmdi.llnl.gov/cmip5)
CMIP5 will maintain a data archive for the results from all participating models� Common data format CMOR2� CIM: Common Information Model (metadata, data set description, tags)
CMIP5 is done in support of IPCC AR5
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New scenarios: RCPs� A new set of scenarios has been developed, Reference Concentration Pathways (RCP), that
replace the old IPCC SRES sceanrios (A1B, A2, etc)� The new RCP scenarios have been selected by their final forcing, and the storyline that
leads to this forcing is constructed later.� Scenarios primarily for GHGs, but other forcings (land-use, aerosols) are also prescribed� Simulations with RCPs are in progress
Moss et al, Nature (2010)
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• Start from a pre-industrial spin-up run (>500 yrs)• The 20th century control run includes the observed changes in GHG,
aerosol concentrations, volcanoes, and land-use changes from 1850-2005
• 2 RCP scenarios for the 21th century
20th century control
1850 1900 1950 2000 2050 2100
RCP8.5
RCP4.5Spi
n-up
CMIP5: long-term experiments
Possibly extend with more RCP scenarios, 1% CO2 increase, …
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1960 1970 1980 1990 2000 2010 2020
10 yrs
10 yrs
10 yrs
10 yrs
10 yrs
10 yrs
10 yrs
10 yrs
10 yrs
10 yrs
10 yrs
+20 yrs
+20 yrs
• Start a 10-yr experiment every 5 years• Initialize from re-analysis (or synthesis)
of atmosphere and ocean• Extend a few runs to 30 yrs
Decadal predictions are under development:
• Many hindcast simulations to assess skills and uncertainties
• Test different initialisationtechniques
• Interpretation of results is not easy (potential predictability)
+20 yrs
CMIP5: decadal prediction experiments
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• 30-yr atmosphere-only experiment with high resolution (EC-EARTH: planned 40 km)
• Prescribed SST and sea-ice from observations• Possibly a new AMIP style experiment in the early 21th century with SST
and sea-ice from an RCP scenario experiment
1970 1980 2090 2000 2010 2020
AMIP AMIP
CMIP5: AMIP experiments
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Global climate modelling at
Rossby Centre� SMHI is a member of the EC-EARTH consortium
� Comitted to contribute withsimulations to CMIP5
� Leads the development of the nextEC-EARTH version
� Research interests� Potential predictability
� Sea-ice parameterizations
� Decadal predictions
� Funding for the work with EC-EARTH from national and EU FP7 research grants
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Summary and outlook� External forcings determine the kind of a global climate model simulation� Initial conditions matter
� Ensembles yield more robust results than a single simulation
� Global climate modelling will develop towards
� Increased resolution
� Larger ensembles� Higher complexity
� Earth System Models (=more components/modules)