global climate modelling-klauswyser - smhi

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)

global climate modelling-klauswyser - smhi

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