Working with climate model ensemblesPRECIS workshop

Tanzania Meteorological Agency, 29th June – 3rd July 2015

Aims of this session

• Explain how different types of climate models ensembles are used to explore different types of uncertainty (link to earlier uncertainties talk)

• Demonstrate how the ‘QUMP’ or CMIP5 ensembles can be used with PRECIS to generate an ensemble of downscaled projections

• Demonstrate sub-selection of ensemble members to design manageable experiments

Contents

• Why use ensembles?

• The different types of ensemble that can be downscaled with PRECIS

• GCM selection strategies for designing downscaling experiments with PRECIS

Why use model ensembles?

• For our regional projections to be useful/policy-relevant, we need to be able to say something about our confidence in them.

Why use an ‘ensemble’?

What is an ensemble?

Ensemble = “all the parts of a thing taken together, so that each part is considered only in relation to the whole.”

Or, (in climate-modeling context)…

“ the results from several models, so that each single model is considered only in context of the results of all of the models”

What do ensembles tell us?• What is the range and distribution of different

future climates that the models available indicate

• Could be considered a ‘snap shot’ of current model projections suggest, based on a set of conditions (e.g. GHG scenario)

• Or, a ‘Non-discountable climate change envelope’ (a multi-variable form of a lower bound on the maximum range of uncertainty)

• What DON’T ensembles tell us (on their own)...– What is a more/most likely future climate outcome– The finite range of plausible outcomes

Which kinds of uncertainty can we explore using ensembles?

Uncertainty Source Commonly represented in Climate Scenarios ?

Ways to incorprate in climate scenarios

Socio-economics (i.e. Emissions level)

Yes Directly via SRES scenarios in CMIP3, via SSPs linked to RCPs for CMIP5.

Atmospheric emissions/ concentrations of GHGs

In CMIP3, No/YesIn CMIP5, Yes/Yes

4 RCP scenarios - 2.6, 4.5, 6.0, 8.5

Global and large-scale regional climate response to forcing

Yes Use multiple members of GCM ensemble

Natural variability Often Use multiple realisations to increase sample size

Adding high resolution detail (statistical or dynamical downscaling)

Sometimes Use multiple approaches

Applying projection information

Sometimes Explore multiple approaches

Types of ensemble that can be downscaled with PRECIS

What types of ensemble can we downscale with PRECIS?

• Multi-model Ensemble (CMIP5)– PRECIS 2.1 due for release December 2015 will feature ability

to downscale multiple models from CMIP5 (initially GFDL-CM3 and CNRM-CM5). Other models to follow.

• Perturbed-Physics ensemble (‘QUMP’)– 17-member ensemble of HadCM3 (HadCM3 Q0-16)now available, CMIP3-generation, SRES A1B.– We can select a sub-set of the 17 models in order to run a

computationally ‘affordable’ experiment.• Initial conditions ensemble

– E.g. Currently a 3 - member HadAM3P ensemble, later ensemble members from CMIP5.

1.) Multi-model ensembles

• Models from different modelling centres around the world use different structural choices in model formulation → different future climate projections:– ‘Structural uncertainties’

• CMIP3 (~22), CMIP5 (~40): modelling centres submit results from equivalent simulations to allow inter-model comparisons > analysis for IPCC reports

• ‘Disagreements’ between models can be large at the regional scale

– i.e. between an overall increase or decrease in rainfall in a region

• ‘Ensemble of opportunity’– Members not entirely independent, not all uncertainties in modelling

process are ‘sampled’

Structural and parameter uncertainties

Structural Uncertainty

(e.g. CMIP3/5 multi-model ensemble)

1.) Multi-model ensembles

Parameter Uncertainty

(e.g. QUMP perturbed physics

ensemble)

Projections from CMIP5 models

McSweeney and Jones, In Press

3.) Perturbed-Physics Ensembles

• An alternative route to exploring GCM uncertainty

• Perturb a single model to give range of future outcomes.

• Many processes in GCMs are ‘parameterised’• Parameterisations represent

sub-gridscale processes• Values of parameters are

unobservable and uncertain• Explore model uncertainty

by varying the values of the parameters in one model

The QUMP 17-member perturbed physics ensemble

• 17 members (HadCM3 Q0..Q16)

• 17 models sample ‘parameter space’ systematically

– Wider range of climate sensitivity– Not as wide a range of regional

variations (no structural uncertainty)

• PPE approach can also be used to generate very large ensembles, which sample known parameter uncertainty, as a basis for probabilistic projections (e.g. UKCP09)

Quantifying Uncertainty in Model Projections

3.) Initial Conditions ensembles• Useful for understanding variability, changes in variability or

improving detectability of climate change signals.

Years of Simulation

3.) Initial Conditions ensembles

• Internal or natural variability

– 1 model run more than once gives slightly different responses

– Which changes are ‘signal’, and which changes are ‘noise’..?

– I.e. which changes are reliable?

– Particularly use for studies of (a) Spatial or temporal details (e.g. Extremes and (b) Variables/regions with strong multi-decadal variability

Run 1, winter

Run 2, summer

Run 3, winter

Run 2, winter

Run 1, summer

Run 3, summer

-80 -40 -20 -10 -5 5 10 20 40 80Change (%)

Kendon, E. J., D. P. Rowell, R. G. Jones, and E. Buonomo, 2008: Robustness of future changes in local precipitation extremes. J. Climate, doi: 10.1175/2008JCLI2082.1.

Initial conditions Ensembles

• Coloured areas where signal is discernable from noise, and changes are ‘reliable’.

• White areas where signal is not clear, and changes are ‘unreliable’.

• Can discern significant changes over much of Europe in winter and parts of Europe in summer, but signal is still unclear in many areas, particularly in extremes.

Mean, winter

Top 5%, summer

Top 1%, winter

Top 5%, winter

Mean, summer

Top 1%, summer

-10 -5 -2 -1 0 1 2 5 10

Kendon, E. J., D. P. Rowell, R. G. Jones, and E. Buonomo, 2008: Robustness of future changes in local precipitation extremes. J. Climate, doi: 10.1175/2008JCLI2082.1.

GCM selection strategies for designing downscaling experiments with PRECIS

Regional projections for impact studies: Issues and approaches

• To provide the best possible information currently available we can:– Strategically sample the simulated climate changes from

the global climate models– Downscale these to provide information relevant to

applications which accounts for this range of possible future climate changes

– Also provides a set of internally-consistent inputs for impacts models

Uncertainty information (ensemble)

High resolution projections

Large resource

implications!

What is a good ‘sampling’ strategy?

Maybe I will choose ensemble members to span the range of global sensitivities?

I can only downscale one GCM. Shall I

choose a mid-range projection?

I will choose the ‘best’ 5 models according to their validation. I could use the

models with the lowest RMSE for temperature

and precipitation.

I might choose 4 members that span the

widest possible range of mean rainfall change for

the GCM gridbox that Exeter lies in.

Selecting a subset of the ‘QUMP’ or ‘CMIP5’ ensembles

Recommended approach:(a) Evaluation: Eliminate any models which are so poor

that we have good reason to not trust the projections(b) Future projections: sample the range of future

outcomes

– Both can be analysed using the GCM monthly mean fields available from BADC

Example of Model sub-selection: Vietnam

Criteria for selection

• Validation– Selected models should represent Asian summer monsoon

(position, timing, magnitude), and associated rainfall realistically, as this is key process

• Future – Magnitude of response: greatest/least regional/local

warming, greatest/least magnitude of change in precipitation– Characteristics of response

• Direction of change in wet-season precipitation (increases and decreases)

• Spatial patterns of precipitation response over south-east Asia

• Response of the monsoon circulation

Validation:Monsoon behaviour

• Monsoon flow has some systematic error – a little too high, but timing (and position) of features is very good.

• All do a reasonably good job at simulating rainfall in the region

• Those that best represent the characteristics of the monsoonal flow don’t necessarily also best represent the local rainfall…

• No reason to eliminate any models on grounds of validation

Range of Future changes

Spatial patterns of future changes (precip)

←Typical

Atypical →

Selected QUMP members for this region

– HadCM3Q0 – The standard model

– HadCM3Q3 – A model with low sensitivity (smaller temperature changes)

– HadCM3Q13 – A model with high sensitivity (larger temperature changes)

– HadCM3Q10 – A model that gives the driest projections – HadCM3Q11 – A model that gives the wettest projections

– Including Q10 and Q13 means that we also cover models which characterise the different spatial patterns of rainfall change, and different monsoon responses.

Examples of Excluding models: JJAS Monsoon Circulation in CMIP5

Applying sub-selection to CMIP5Select from remaining models to span range (8/16)

Summary• There’s much more to uncertainty than

emissions scenarios!• Using an ensemble of models gives us an

indication of the range of plausible future climate outcomes – at the regional scale, GCM uncertainty is significant and should be accounted for in projections

• With PRECIS, we can use ensemble or sub-sets of ensembles from QUMP PPE or (soon) CMIP5

• Sub-selection strategies are an important means of capturing uncertainty whilst running feasible experiments

Any Questions?

QUMP vs CMIP3 responses

Process for requesting ‘QUMP’ boundary data from the Hadley

Centre

1.) Email us to let us know that you are interested in using the QUMP/CMIP5 ensemble with PRECIS, and let us know what you are planning to use the data for.

2.) Download the mean GCM fields from BADChttp://badc.nerc.ac.uk/browse/badc/hadcm3/data/PRECIS/

Use these fields to choose a model subset which validates well, and spans a wide range of future outcomes

3.) Email us a 1-2 page summary of your analysis of the GCM fields, and your selected ensemble members

4.) If we agree that your selection is based on good criteria, we’ll then send your boundary data, and you can begin your runs