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Downscaling GCM ensembles

Downscaling GCM ensembles. PRECIS workshop, Exeter, UK, May 2014 Carol McSweeney. Explain how different types of climate models ensembles are used to explore different types of uncertainty

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Downscaling GCM ensembles

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  1. Downscaling GCM ensembles PRECIS workshop, Exeter, UK, May 2014 Carol McSweeney

  2. Explain how different types of climate models ensembles are used to explore different types of uncertainty Demonstrate how different GCM ensembles can be used with PRECIS to generate an ensemble of downscaled projections Demonstrate sub-selection of ensemble members Aims of this session

  3. Working with climate model ensembles Table of Contents • What is an ‘ensemble’? • 3 types of ensemble • Generating ensembles of high-resolution simulations with PRECIS • Sub-selecting ensemble members for downscaling with PRECIS – an example from Vietnam

  4. 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” 3 main types: • Initial conditions ensemble • Multi-model ensemble • Perturbed-physics ensemble

  5. Revisiting the sources of uncertainties

  6. GCM ensembles that can be downscaled with PRECIS • Initial conditions ensemble • 3 - member HadAM3P ensemble (projections under SRESA2) • Useful for capturing climate change signal in regions/variables with high variability on inter-annual or decadal timescales e.g. precipitation extremes. • Perturbed-Physics ensemble (‘QUMP’) • 17-member ensemble of HadCM3 (HadCM3 Q0-16) projections under SRES A1B • Multi-model ensemble (CMIP5) • New for PRECIS2.0 release! • Currently able to downscale HadGEM2-ES projections under RCP4.5/8.5 and ERA-Interim. • Capability to downscale additional CMIP5 GCMs coming soon.

  7. 3 types of ensemble

  8. Run 1, winter Run 1, summer Run 2, summer Run 2, winter Run 3, winter Run 3, summer -80 -40 -20 -10 -5 5 10 20 40 80 Change (%) 1.) Initial Conditions ensembles • Internal or natural variability • Increase sample size • Which changes are ‘signal’, and which changes are ‘noise’..? • I.e. which changes are reliable? • Internal variability greatest issue when we are looking for: • (a) Spatial or temporal details (e.g. extremes) • (b) Variables with strong multi-decadal variability

  9. Mean, winter Mean, summer Top 5%, summer Top 5%, winter Top 1%, winter Top 1%, summer -10 -5 -2 -1 0 1 2 5 10 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. 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.

  10. 2.) Multi-model ensembles • CMIP3/5 ensembles: modelling centres submitted results from equivalent simulations to allow inter-model comparisons (results summarised in IPCC AR4/5 reports) • Models from different modelling centres around the world use different structural choices in model formulation → different future climate projections: • ‘Structural uncertainties’ • ‘Disagreements’ between models can be large at the regional scale due to differences in spatial patterns of change e.g. regional mean precip change…

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

  12. 3.) Perturbed-Physics Ensembles • An alternative route to exploring GCM uncertainty, method underpins UKCP09 methodology • 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

  13. Climate Model Uncertainties Structural Uncertainty (IPCC CMIP3 multi-model ensemble) Parameter Uncertainty (QUMP perturbed physics ensemble)

  14. 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

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

  16. 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) PPE Weaknesses: • Doesn’t sample structural uncertainty (i.e. between models from different centres)

  17. Generating ensembles of high-resolution simulations with PRECIS

  18. Downscaling from large GCM ensembles • Significant uncertainties in the simulated broad-scale climate changes come from GCMs • Downscaling all members of a GCM ensemble (17 members of QUMP or 38 members of CMIP5) is not feasible! • How do we choose which to use??

  19. I can only downscale one GCM. Shall I choose a mid-range projection? I might choose 4 members that span the widest possible range of mean precipitation change for the GCM grid-box that my city lies in. I will choose the ‘best’ 5 models according to their validation. I could use the models with the lowest RMSE for temperature and precipitation. Maybe I will choose ensemble members to span the range of global sensitivities?

  20. Generating ‘useful’ projections with PRECIS • Our recommended approach is to sample from the available GCMs • Capture a representative range of future climate outcomes by looking at the GCM projections • Set up a feasible/manageable downscaling experiment • Provides a set of future projections which broadly represent the uncertainties in future climate projections, consistent with IPCC AR5 global projections • Strategic sampling is key! • An ‘ad-hoc’ sample GCMs tells us little about uncertainty!

  21. Selecting a subset of the ‘QUMP’ or CMIP5 ensembles Choose members that: • sample the range of future outcomes well • perform reasonably well when compared with observational data • Both can be analysed using the GCM monthly mean fields available from BADC

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

  23. 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

  24. 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

  25. Range of Future changes

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

  27. 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.

  28. But… • At the regional scale, it can be easier to identify models that are clearly worse than others • We have to sub-select (downscaling all models not an option) • Including a ‘worst’ model in a sub-set gives it a higher relative weight (i.e. is 1/5 rather than 1/30) Should information about model performance be used in sub-selection? Can we link aspects of poor performance directly to a model’s ability to simulate plausible future climates?? (See guidance by Knutti et al, 2010) • ‘Horses for courses’ – all models have something wrong! • Infinite number of potential metrics/thresholds • Circular reasoning – models calibrated with the same data that they are evaluated against (i.e. tuning leads to convergence) • Encourages ‘climate model beauty contests’

  29. Examples from CMIP: JJAS Monsoon Circulation

  30. 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 ensemble with PRECIS 2.) Download the mean GCM fields from BADC http://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

  31. Summary • Using an ensemble of models gives us an indication of how large model uncertainties are • Different kinds of ensemble tell us about different sources of uncertainty • We can use ensemble(s), or sub-sets of ensembles, to try to capture as wide a range of plausible outcomes as we can for use in regional climate change assessments or impacts studies • Strategic sampling approaches allow us to design manageable downscaling experiments and provide meaningful indications of uncertainty

  32. Questions and answers Key References: McSweeney et al (Submitted to Climate Dynamics) Sub-selecting CMIP5 GCMs for downscaling over multiple regions.McSweeney et al (2012) Selecting ensemble members to provide regional climate change information, Journal of Climate 25(20) pp. 7100-7121

  33. PPE vs MME: Example: Changes in Sahelian Rainfall(from Ben Booth, MOHC) Kerry, Nature, 2008 In some cases, a PPE might not capture the full spread of the CMIP3 ensemble so we would need to consider other models too. In other regions, CMIP3 may not capture the full spread of the PPE.

  34. Interpreting Ensemble results Ensemble mean • Gives the signal that is common to most models…’best guess’ • Is this really the most likely/accurate…? • Averages out signal that is not common to all models Can lose important information • Averages out variability Ensemble spread (minimum, maximum) • But, to what extent does the degree of model spread reflect certainty? Be wary of model consensus….! • Conversely, Very large model spread can imply no information And sometimes… Ensemble distribution - estimate probabilities of outcomes • But only if we have a very large ensemble, and some advanced statistical techniques!

  35. Europe • Storm track density 5-25˚N MIROC-ESM, MIROC-ESM-CHEM, FGOALS-g2, BNU-ESM and bcc-cms1-1

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