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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PRECIS workshop, Exeter, UK, May 2014
Demonstrate how different GCM ensembles can be used with PRECIS to generate an ensemble of downscaled projections
Demonstrate sub-selection of ensemble membersAims of this session
Table of Contents
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:
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
Top 5%, summer
Top 5%, winter
Top 1%, winter
Top 1%, summer
-10 -5 -2 -1 0 1 2 5 10Initial conditions Ensembles
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.
(IPCC CMIP3 multi-model ensemble)
(QUMP perturbed physics ensemble)
Quantifying Uncertainty in Model Projections
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?
Choose members that:
Criteria for 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)
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
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
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.
Can lose important information
Ensemble spread (minimum, maximum)
Ensemble distribution - estimate probabilities of outcomes
MIROC-ESM, MIROC-ESM-CHEM, FGOALS-g2, BNU-ESM and bcc-cms1-1