IPCC AR5: RCPs and CMIP5 data: how to choose scenarios and GCMs for climate modelling studies

1. IPCC AR5: RCPs and CMIP5 data: how to choose scenarios and GCMs for climate modelling studies Carol McSweeney, Met Office Hadley Centre

2. Aims of this session • Understand how to select driving GCM data for RCM simulations to capture/prioritise different sources of uncertainty • Understand the difference between the new ‘RCPs’ and the ‘old’ SRES emissions scenarios • Understand how we can sample the uncertainty from a large ensemble using a smaller subset of models.

3. GCMs available for downscaling with PRECIS RCP 4.5 RCP 2.6 RCP 8.5 RCP 6.0 ECHAM4 HadAM3P 20+ CMIP5 GCMs SRES A2 SRES B2 SRES A1B HadCM3Q11 HadCM3Q0 HadCM3Q13 HadCM3Q1 HadCM3Q6 HadCM3Q9 HadCM3Q10 HadCM3Q15 HadCM3Q2 HadCM3Q3 HadCM3Q7 HadCM3Q16 HadCM3Q4 HadCM3Q14 HadCM3Q8 HadCM3Q5 HadCM3Q12

4. Constraints on RCM simulations • Boundary data availability • Computing resources • Some large regional collaborations can draw on resources from several large institutions to generate large • For most individual institutions, it is not possible to downscale all the GCMs that are currently (or soon to be) available, and for all emissions scenarios, so how can we make the best choices about which ones to use?

5. Table of Contents • Sources of uncertainty in projections of future climate • Emissions scenarios: SRES vs RCPs • Ensembles: Perturbed Physics and multi-model • Ensemble Sub-selection for Downscaling

6. Main Sources of Uncertainty Socio- Economic Uncertainty Uncertainty in the model representation of physical processes Natural annual-decadal variability (‘Internal variability’)

7. Sources of Uncertainty in Climate Projections uncertaint*

8. Q: Which are the most important sources of uncertainty? Natural variability most important on timescales 0-20 years, small by 100 years Emissions scenario important on timescales 40 years + Model uncertainty important at all timescales A: That depends on the timescale that we are looking at…

9. Natural Variability: ‘Noise’ mainly relevant for short lead times (2-20 yrs) • Model uncertainty: • Variations in global climate sensitivity from model-to-model • More significantly, at regional level, determines characteristic of changes or the Signal (e.g. will my region get wetter or drier in the future?) • Very significant at all lead times, particularly for precipitation • Emissions scenario: determines rate at which the changes indicated by any one model will occur

10. Prioritising more models or more emissions scenarios? 1 model, 3 different emissions scenarios => 1 pattern of change, 3 different magnitudes 4 different models, 1 emissions scenario => 4 patterns of change, 1 magnitude

11. Emissions Scenarios

12. SRES Emissions Scenarios 1. Socio-economic scenarios 3. Atmospheric concentrations 2. Emissions scenarios

13. Socio-economic scenarios Emissions scenarios Atmospheric concentrations Climate scenarios Impacts Sequential approach to developing climate scenarios • Climate modellers await results from socio-economic modellers • Emissions scenarios chosen early on are restrictive.. E.g. no exploration of deliberate mitigation strategies, difficult to explore uncertainties in carbon cycle feedbacks.

14. Representative Concentration Pathways (RCPs)

15. Atmospheric concentrations (‘Representative Concentration Pathway’, RCPs) • Emissions scenarios Climate scenarios Integrated assessment modellers and climate modellers worksimultaneously and collaboratively Socio-economics Policy Intervention (mitigation or adaptation) Carbon cycle and atmospheric chemistry Impacts Parallel approach to generating climate scenarios

16. Choosing scenarios • Remember that near term (10-40 years) projections are ‘emissions independent’ • For longer lead times, Emissions scenario is important for determining rate of change, but does not determine pattern of change • RCPs useful for exploring ‘avoidable’ climate impacts • SRES useful where we want to compare with previous projections

17. Model Uncertainty

18. Climate Model Uncertainties Structural Uncertainty (IPCC CMIP3/CMIP5 multi-model ensembles) Parameter Uncertainty (QUMP perturbed physics ensemble)

19. Rainfall change: IPCC AR4 • Combination of pattern and some sign differences lead to lack of consensus

20. Perturbed-Physics Ensembles • An alternative route to exploring GCM uncertainty • 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

21. The QUMP 17-member perturbed physics ensemble • 17 members (HadCM3 Q0..Q16) • ‘parameter space’ = range/combinations of plausible values of parameters • Range of plausible values of parameters determined according to expert opinion • 17 models sample ‘parameter space’ systematically Quantifying Uncertainty in Model Projections

22. Rainfall change: Hadley QUMP • Again significant range of different projected changes • Similar range and behaviour to IPCC models?

23. Multi-model (MME) Vs Perturbed Physics Ensembles (PPE) PPE Strengths: • Can control the experimental design – Systematic sampling of modelling uncertainties • Wider range of physically plausible climate outcomes • Potentially include 1000s of members • Can design ensemble so that results can be interpreted probabilistically (e.g. UKCP09) • Logistically, More straightforward logistics for distributing boundary data due to consistent formats and MOHC ownership (i.e. feasible to provide boundary conditions from QUMP to PRECIS users!) PPE Weaknesses: • Doesn’t sample structural uncertainty (i.e. between models from different centres)

24. Approaching Model Sub-Selection

25. Why sub-select? • How do we provide ‘scientifically defendable range of climate outcomes’? • If we can represent the range of outcomes from the full ensemble with a subset of 4-6… • Save on computing resources required to run RCMs • Save on boundary data required • More feasible to apply outputs from fewer RCMs to impacts models • Why take the time to sub-select carefully? • Because it is a waste of time to spend months running an experiment that’s not carefully planned • So, which ones do we use?

26. Case Study #1 • Jack is considering the impacts of climate change on international terrorism. • He decides to use the PRECIS regional modelling system and some members of the QUMP ensemble to explore model uncertainty. Maybe I will choose members Q1, Q7 and Q16 to span the range of global sensitivities?

27. Case Study #2 • Beeker is exploring climate impacts on frog and pig populations. He will apply outputs from regional models to his species models. • Beeker wants to use a range of projections to help him understand the uncertainty in the future projections climate variables that are predictors in his model. I will choose the ‘best’ 5 models according to their validation. I could use the models with the lowest RMSE for temperature and precipitation.

28. Case study #3 • Inspector Lewis is considering the impact of climate change on flood frequency at some popular riverside crime scenes in Oxford. • Lewis will use the PRECIS outputs to drive a catchment model of the Cherwell and estimate peak flows. I might choose 4 members that span the widest possible range of mean precipitation change for the GCM gridbox that Oxford lies in.

29. Case study #4 • Uncle Bulgaria is interested in the impacts of climate change on burrow habitats on Wimbledon Common. • He needs regional climate model data to explore the possible changes in soil moisture and temperature in the future. • Uncle Bulgaria has very limited resources and can only run one simulation. Shall I just use Q7 to give me a mid-range projection?

30. Can we select the models that validate the best in our region, or eliminate/downweight those that don’t?

31. Why is selecting/eliminating models on grounds of validation a can of worms? • ‘Horses for courses’ • Infinite number of potential metrics • Circular reasoning – models calibrated with the same data that they are evaluated against (i.e. tuning leads to convergence) • ‘right for the wrong reasons’

32. Some advice from Reto about combining information from multiple model projections 1.Metrics and criteria for evaluation must be demonstrated to relate to projection 2.It may be less controversial to downweight or eliminate specific projections that are clearly unable to mimic important processes than to agree on the best model. 3. Process understanding must complement ‘broad brush metrics’. For more see Knutti, 2010, Climatic Change 102.

33. Basis for selection: 1.) Exclude models which we have good reason to think give unrealistic projections of the future • Avoid cherry picking best models (might be right for wrong reasons) • But we should reject models that really don’t represent the key large-scale processes 2.) Span the range of future outcomes in the region • Span range of magnitudes of change (i.e. global sensitivity, and regional sensitivity) • Span multiple variables/characteristics of change • E.g models that are wetter or drier in different regions • Different spatial patterns of change • Change in key large-scale processes

34. Sub-selecting ensemble members for downscaling with PRECIS – an example from Vietnam

35. Example of Model sub-selection: Vietnam Criteria for selection • Validation • Selected models should represent Asian summer monsoon (position, timing, magnitude), and associated rainfall well, 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

36. Validation:Monsoon Onset • 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

37. Range of Future changes

38. Spatial patterns of future changes (precip) ←Typical Atypical →

39. Recommended 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.

40. Summary

41. Summary • Increasing numbers of available GCMs, as well as new emissions scenarios, make decisions about which GCMs runs to downscale more complicated • Need to balance requirement to sample a wide range of uncertainty with minimising the number of necessary RCM runs • Experimental design (or choice of models/scenarios) is important – ‘ad-hoc’ selections may leave us with results that are very difficult to interpret • GCM uncertainty is the most important source of uncertainty at the regional level due to differences between models is patterns of rainfall change • We can generate downscaled projections that represent the range of uncertainty in a large ensemble by carefully sub-selecting models for downscaling

42. Questions and answers

43. Ensemble regional prediction: Issues and approaches • The major uncertainties in the simulated broad-scale climate changes come from GCMs • Global climate models provide information which is often too coarse for applications thus downscaling is required • To provide the best possible information currently available we can: • Consider a range of 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