ENSEMBLES Final Symposium, 17-19 November 2009, Met Office Exeter Page 1

ENSEMBLES

ENSEMBLES Final Symposium, 17-19 November 2009, Met Office Exeter Page 2

ENSEMBLES

Clare GoodessUniversity of East Anglia

Setting the scene for the third project aim: Maximising the results by linking the ensemble prediction system to a range of applications

ENSEMBLES Final Symposium, 17-19 November 2009, Met Office Exeter Page 3

Two methods of impact assessment using outputs from the EPS

Tim Carter, SYKE & RT6

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What are preferred output formats?

Time series most popularAlso PDFs & threshold exceedencesSome interest in maps and joint probabilitiesClimate response surfacesSome challenges & contradictions

Tailoring of ENSEMBLES regional climate scenario outputs to user needs: a questionnaire for users

e.g., extremes

Impact response surfaces

But users don’t just want data – also guidance and tools

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ENSEMBLES regional scenarios portal

www.cru.uea.ac.uk/projects/ensembles/ScenariosPortal

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ENSEMBLES data archives

www.cru.uea.ac.uk/projects/ensembles/ScenariosPortal

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A.S. Cofiño, D. San-Martín, C. Sordo, J. Fernández, D. Frías, M.A. Rodríguez, S.

Herrera, R. Ancell, M. Pons, B. Orfila, E. Díez

José Manuel Gutiérrez

Santander Met Group & AEMET

The ENSEMBLES Downscaling Portal

A user-friendly Web tool for GCM post-processing and downscaling

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The ENSEMBLES Downscaling Portal

Connecting end-users with data providers (CERA, DMI, ECMWF) in a Web-based transparent way to undertakeregional impact studies (statistical downscaling).

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GCM Data Availability. Seasonal & ACC

• DEMETER. Multi-model seasonal prediction experiment including seven models ran for six months four times a year using 9 different perturbed initial conditions (9 members).

• ENSEMBLES Stream 2 multi-model experiment.

Daily worldwide datasets obtained from different sources:

• CERA (ENSEMBLES repository)• PCMDI• Local Providers.

• CERA_MPI-ECHAM5, including 20th century data (1961-2000) and scenarios A1B, B1, and A2 (2001-2100).

• CNRM-CM3 (local provider), including 20th century (1961-2000) and scenarios A1B, B1, and A2 (2001-2100).

Some examples:

Seasonal Predictions:

Climate Change Projections:

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Portal activities within ENSEMBLES

The downscaling portal has been in operations for the last three years:

•The portal has been used in different Impact studies done by RT6 end-users (see e.g. Marta Nogaj EDF presentation at 11). But it has alsocontributed to other activities:

• Assessing the robustness of statistical downscalig methods.

• Assessing the skill of multi-model seasonal predictions in Europeconditioned to ENSO events.

• Producing ensembles of local scenarios with different SD methods.

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Future ...

The ENSEMBLES downscaling portal will continue in operation after the end of the ENSEMLBES project partially supported by other national and European initiatives:

•QWeCI Quantifying Weather and Climate Impacts on Health in Developing Countries•FUME (Forest fires under climate, social and economic changes in Europe, the Mediterranean and other fire affected areas of the world).•METAFOR (Common Metadata for Climate Modelling Digital Repositories)•Spanish program for regional scenarios.

• Full integration with GRID technologies to provide more resources for new users. Collaboration with EGEE, EGI, SSC-ES, EELA and others initiatives.

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Facilitating the use of downscaled outputs – some examples

Assessments of robustness of informationRCM changesStationarity of statistical downscalingValidation and evaluation (process-based)

Weightingschemes for dynamical & statistical downscalingsensitivity analyses

Construction of PDFs & other probabilistic outputs

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Robustness of temperature change projections for Europe

Climate-change signal (2021–2050 minus 1961–1990) for annual temperature (°C) for the multi-model mean of 16 ENSEMBLES RCMs. A1B scenario. (Philip Lorenz, MPI)

Inter-model standard deviationAnnual temperature change

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Robustness of precipitation change projections for Europe

Climate-change signal (2021–2050 minus 1961–1990) for annual precipitation total (%) for the multi-model mean of 16 ENSEMBLES RCMs. A1B scenario. (Philip Lorenz, MPI)

Annual precipitation change Number of models showing increase

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Robustness of direction of change in seasonal precipitation

Location of model grid points with a significant increase (light grey) or decrease (dark grey) in total rainfall at 2021-2050 (compared with 1961-1990) for winter (left) and summer (right). A weather regime approach is used to complete the ENSEMBLES GCM/RCM matrix. A1B emissions scenario.

Michel DéquéMétéoFrance

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Regional weighting metricsFor SDS:

SDS model performanceReproduction of trends/climate statesPerformance of driving model – predictorsStability of relationshipsCorrection for multiplicity of SDS models

For RCMsf1: large scale circulation based on a weather regime classificationf2: meso-scale signal based on seasonal temperature and precipitation analysisf3: probability density distribution match of daily and monthly temperature and precipitation analysisf4: extremes in terms of re-occurrence periods for temperature and precipitationf5: trend analysis for temperaturef6: representation of the annual cycle in temperature and precipitation

How sensitive are projections & impacts to weighting?Are the weights relevant to users? Do they reflect physical processes? What about GCM weighting?

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Sensitivity to weighting

0 2 4 6 8NO WEIGHT

0 2 4 6 8WEIGHT #1 (performance)

0 2 4 6 8WEIGHT #1 (performance) * #2 (reproduction of trend)

PDFs of daily Tmax from 39 SDS models, winter, Strážnice, CZ. Black bars: weights of individual SDS models. Red line: estimated PDF. Bottom: without weighting; centre: weighted by criterium (i); top: weighted by the product of criteria (i) and (ii). (Radan Huth, CAS)

PDFs of Tmean change for Heathrow (2021-2050 minus 1961-1990, A1B scenario) from CRU WG perturbed by RCM change factors. Red curve: unweighted. Black curve: weighted using RT3 product-based scheme. Coloured vertical bars: changes calculated directly from the 17 RCMs. (David Lister & Clare Goodess, UEA)

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Some examples of PDFs

All these PDFs are conditional (A1B, assumptions….)And subjective – do not sample ‘full’ uncertainty range

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Some of the ENSEMBLES case studies

www.cru.uea.ac.uk/projects/ensembles/ScenariosPortal

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RT6 regional impacts studies

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A new impact feedback?!

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Applications outside ENSEMBLES

CIRCE – RCM outputs provide wider uncertainty framework for new coupled A-O RCM simulations for MediterraneanClimateCost – main source of European RCM

scenario data for bottom-up economic assessments. E1 runs will also be used.ClarisLPB – drawing on methods, e.g.,

gridding observed data, model metrics……………………………………..…………………………………..

So ENSEMBLES provides a major knowledge and data legacy for future applications users!