ENSEMBLES Overview progress plans EMS 2007 El Escorial
ENSEMBLES Overview, progress, plans EMS 2007, El Escorial, October 2007 Chris Hewitt, Met Office Hadley Centre Project Office can be contacted on ensemblesfp 6@metoffice. gov. uk Web site is http: //www. ensembles-eu. org © Crown copyright 2007
Outline § Overview of the Ensembles project § Progress § Plans © Crown copyright 2007 Page 2
Motivation Predictions of natural climate variability on seasonal to decadal to centennial timescales, and the human impact on climate are inherently probabilistic due to uncertainties in: § initial conditions § representation of key processes within models § climatic forcing factors Reliable seasonal to decadal forecasts, and estimates of climatic risk can only be made through ensemble integrations of Earth-System Models in which these uncertainties are explicitly incorporated. The ENSEMBLES project will provide these probabilistic estimates. © Crown copyright 2007 Page 3
Motivation: Recent Status of Climate Change Prediction We can produce a small number of different predictions with little idea of how reliable they might be © Crown copyright 2007 Page 4
Probabilistic Climate Predictions required position current position Probability 100% 0% 20% 40% 60% 2080 s SE England winter rainfall © Crown copyright 2007 0% 20% 40% 60% 2080 s SE England winter rainfall Page 5
Sources of uncertainty Effects of natural variability Future emission scenarios Modelling of Earth system processes © Crown copyright 2007 Page 6
Climate Prediction Modelling From Murphy et al, Nature 2004 © Crown copyright 2007 Page 7
Motivation (continued) Seasonal forecast case study: Winter 1997 El-Niño, Precipitation DEMETER multi-model Flooding: East Africa Uruguay / Argentina Verification Rainforest fires: Amazonia, Indonesia Storm damage: California Tornados: Florida courtesy Francisco Doblas-Reyes: verification of a multi-model system ©Slide Crown copyrightof 2007 Page 8
Better performance of multi-model for seasonal fc Test of individual and multi-model probabilistic skill (area-averaged ROC) over 8 regions, 4 seasons, 4 events, 2 lead times DEMETER multi-model ECMWF 16% UKMO MPI CNRM 13% 56% 9% courtesy Francisco Doblas-Reyes ©Slide Crown copyrightof 2007 6% Page 9
Impact of ensemble size on seasonal fc Ranked probability skill score for tropical (region in blue in the inset) precipitation as a function of the ensemble size for a multi-model (red) and a single-model (blue) with the same ensemble size. The multi-model has superior performance than the single model for every ensemble size. courtesy Francisco Doblas-Reyes ©Slide Crown copyrightof 2007 Page 10
Motivation: Ensemble Climate Prediction § Run ensembles of different climate models to sample uncertainties § Measure variations in reliability between models using hindcasts § Produce probabilistic predictions of climate change § Do this for seasonal to decadal and longer timescales, and global, regional and local spatial scales, for use in a wide range of applications ENSEMBLES © Crown copyright 2007 Page 11
The ENSEMBLES Project § 5 -year Integrated Project supported by EC FP 6 funding coordinated by Met Office Hadley Centre § 67 partners: EU, Switzerland, Australia, US we welcome requests from new groups to participate on an unfunded basis – currently 19 such groups worldwide affiliated to the project § Builds upon EC FP 5 projects e. g. DEMETER, MICE, PRUDENCE, STARDEX § Integrates a wide range of research communities § Work carried out in Ten Research Themes © Crown copyright 2007 Page 12
Strategic Objectives 1. Develop an ensemble prediction system based on global and regional Earth System models, validated against observations and analyses, to produce for the first time, a probabalistic estimate of uncertainty in future climate at the seasonal, decadal and longer timescales 2. Quantify and reduce uncertainty in the representation of physical, chemical, biological and human-related feedbacks in the Earth System 3. Exploit the results by linking the outputs to a range of applications, including agriculture, health, food security, energy, water resources, insurance We are aiming to increase availability of scientific knowledge and provision of relevant information related to the impacts of climate change © Crown copyright 2007 Page 13
Research Themes (RTs) summary System Model ‘engine’: development and hindcasts, climate assembly integrations © Crown copyright 2007 Understanding, evaluation Impacts, Scenarios and policy Page 14
Progress: GCM seasonal to decadal 3 different s 2 d forecast systems to estimate model uncertainty: § Multi-model (9) system, installed at ECMWF built from EUROSIP operational activities and DEMETER experience § Perturbed parameter system, built from the decadal prediction system at the Met Office § Stochastic physics system, from the CASBS system developed for mediumrange forecasting at ECMWF Design of a set of common experiments to determine the benefits of each approach Ensemble of ocean analyses available for initialisation Improved ocean data assimilation systems for s 2 d prediction Publications demonstrating additional skill in annual-decadal projections by initialising the ocean component of CGMS © Crown copyright 2007 Page 15
Progress: GCM centennial Multi-model ACC simulations (contributed to IPCC 4 AR) § Conducted historical runs (1860 -2000) § and scenario runs (IPCC A 1 B, A 2, B 1) § including Hadley Centre perturbed parameter runs © Crown copyright 2007 Page 16
Progress: regional scenarios § Defined RCM domain § Central model data archive available at DMI § Contains 14 RCM hindcasts (1961 -2000 driven by ERA 40) at 50 km and 25 km § Matrix of driving GCMs/RCMs devised § Transient scenario runs underway § 25 km RCMs, 1950 -2050/2100 driven by GCMs, A 1 B scenario from 2001 § Regional scenario web portal launched in June 2007 DRAFT – TO BE FINALISED RCM’s GCM’s METOMPIMET CNRM HC DMI METO- 1950 HC 2100 MPIME 1950 T 2100 ETH KNMI ICTP 19502050 19502100 19502050 SMHI UCLM 19502050 C 4 I GKSS Met. No CHMI 19502050 FUB IPSL CNRM NERSC © Crown copyright 2007 0. 22º (25 km) grid mesh (courtesy of Burkhardt Rockel) 19502050 19502050 Page 17
Progress (continued) § Scientific analyses (e. g. cloud feedbacks, carbon, sea-ice, …) § Coordinated experiments (web), especially for land-sea warming contrasts § Linking impact models to probabilistic projections § Publicly available Climate Explorer http: //climexp. knmi. nl/ further developed as integrated diagnostic tool § Producing daily gridded datasets for Europe with uncertainty estimates. Particularly of use for evaluating extremes OLD (ECA daily dataset) © Crown copyright 2007 NEW Page 18
Progress (continued) New emissions scenario developed § A 1 B baseline, stabilise towards 475 ppmv CO 2 eq § provides information towards EU goal of limiting warming to less than 2°C above pre-industrial levels § Uses proposed IPCC “AR 5” design § ESMs including the carbon cycle will be driven by GHG concentrations, rather than emissions. Carbon fluxes give implied emissions § Will inform details of AR 5 design and how to exploit the runs © Crown copyright 2007 Page 19
Pre AR 4 used forward approach AR 5 likely to use reverse approach Forward approach: start with socio-economic variables Socio-economic variables Emissions Concentrations Surface temperature Reverse approach: start with stabilization scenario concentrations Socio-economic variables mitigation costs Emissions implied emissions Concentrations Surface temperature concentrations sensitivity impacts Requires interpolating and scaling © Crown copyright 2007 Page 20
Progress (continued) § West Africa to be the non-European RCM domain § Publications of ENSEMBLES results appearing in journals. ENSEMBLES Technical Reports series created § Expanding the “affiliated partners” (19 currently) FAO, Rene Gommes, RT 5&6 Univ. Zurich, Nadine Salzmann, RT 6 Nat. Univ. of Ireland, Kieran Hickey, RT 5 FRGCG, Michio Kawamiya, RT 1&2 A Nat. Inst. Earth Sciences, Seita Emori, RT 2 A University of Copenhagen, Eigil Kaas, RT 2 A Nat. Acad. Sci. Ukraine, Alexander Palonski, RT 6 OURANOS group, Daniel Caya, RT 2 B Climate Analysis Group, Philippe Gachon, RT 2 B IBIMET Institute, Massimiliano Pasqui, RT 1, RT 2 A © Crown copyright 2007 WHO, Bettine Menne, RT 5 ESSC, John Christy, RT 5 NCAR, Guy Brasseur, Jerry Meehl, Linda Mearns, RT 2 A&2 B CCSR (Uni Tokyo), Masahide Kimoto, RT 4 SINTEF Energy Research, Atle Harby, RT 6 University of Exeter, David Stephenson, RT 4 University of Newcastle, Hayley Fowler Canadian Reg. Clim. Modelling and Diag. Network (CRCMD), Colin Jones Proudman Oceanographic Laboratory, Roger Proctor, RT 6 Page 21
Plans for year 4 of the project (starts Sep 07) § Start “Stream 2” simulations: § s 2 d hindcasts 1960 onwards (previously 1991 -2001) § 1860 -2000 simulations using updated models § 21 st Century scenarios using updated models and E 1 (previously A 1 B, A 2, B 1) § RCM A 1 B Europe@25 km 1950 -2050/2100 § Plan RCM simulations for West African domain § Develop databases § s 2 d @ ECMWF building on DEMETER database § RCM @ DMI building on PRUDENCE database § GCM @ MPIMET building on IPCC WCDC activities © Crown copyright 2007 Page 22
Plans for year 4 of the project (starts Sep 07) § Develop impacts models (e. g. crops, water resources, energy) § Develop statistical downscaling tools § Improved estimates for changes in extreme events § Workshops § General Assembly, Prague, 12 -16 Nov 07 § Socio-Economic Drivers of Climatic Change, Paris, Dec 07 § Climate Change, Impacts and Adaptation in the Mediterranean, Greece, late 2008 § others to be announced © Crown copyright 2007 Page 23
Concluding remarks – innovative work § Brings together largely separate communities and integrates worldleading European research: s 2 d, anthropogenic climate change, global modellers, regional modellers (dynamical and statistical downscaling), scientific understanding, evaluation with observations, training programmes, application modellers to deliver climate impacts, emission scenario developers, § Multi-disciplinary approach allows exchange of knowledge, ideas and techniques, e. g. extensive work on extremes § Multi-model ensemble-based probability approach will quantify uncertainty, increase understanding, influence the development of the next generation of models, leading to uncertainty reduction in the future § Examples of new products: § § § § multi-model RCM system at 25 km resolution probabilistic methods for use for GCMs, RCMs, impact models probabilistic predictions from s 2 d 2 c timescales to explore impacts gridded observations for Europe with estimate of uncertainty public availability of large datasets developments to the publicly available Climate Explorer on-line tools for users to downscale Ensembles simulations © Crown copyright 2007 Page 24
Questions © Crown copyright 2007 Page 25
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