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Minutes of the ENSEMBLES RT6 Meeting: Assessments of Impacts of Climate Change United Kingdom Meteorological Office, Exeter 6-8 June 2005 Alexandre Gagnon (University of Liverpool) and Liam Clarke (London School of Economics) Partners present (28): QUEST/University of Bristol (Colin Prentice and Sarah Cornell), Hadley Centre (Richard Betts and Jean Palutikof), Potsdam Institute for Climate Impact Research (PIK) (Wolfgang Cramer and Dieter Gerten), University of Lund (Thomas Hickler), Finnish Environment Institute (Tim Carter and Stefan Fronzek), UK Met Office (Tom Holt and Bruce Ingleby), University of Reading (Andrew Challinor and Tom Osborne), University of Firenze (Marco Bindi), Frei Universität Berlin (Gregor Leckebusch), Polish Academy of Sciences (Maciej Radziejewski), Finnish Meteorological Institute (Ari Venäläinen), National Observatory of Athens (Christos Giannakopoulos), Danish Institute of Agricultural Sciences (Tove Heidmann), Swedish Meteorological and Hydrological Institute (Phil Graham), University of Liverpool (Andy Morse, Alexandre Gagnon, and Anne Jones), ARPA EmiliaRomagna (Vittorio Marletto), Electricité de France (Clarisse Fil and Marta Benito), London School of Economics (Liam Clarke), and Climatic Research Unit/University of East Anglia (Clare Goodess). Partners absent, but sent presentations (2): MeteoSwiss and Joint Research Centre (JRC) Monday June 6 Andy Morse: Welcome Objectives of meeting: to establish progress, to plan for the next 18 months, to share expertise and experiences, to identify gaps and seek solutions, and to promote communication among partners Online discussion forum: http://rt6-ensembles.blogspot.com Sources of data: ERA-40 reanalysis and DEMETER Ensemble Prediction System (EPS) data in GRIB or NetCDF format from ECMWF http://data.ecmwf.int/data or in ASCII format from KNMI Climate Explorer http://climexp.knmi.nl (monthly data only) Draft list of RT3 common Regional Climate Model (RCM) outputs http://ensemblesrt3.dmi.dk/main.html A list of atmospheric and oceanic common variables for seasonal-to-decadal experiments can be found at: http://www.ecmwf.int/research/EU_projects/ENSEMBLES/news/common_variables.html Some issues across Research Themes (RT) activities include: bias correction, choice of scenario, data availability and user access, and long climate data runs There will be a problem with the availability of ENSEMBLES data and the timing of the project, as some of the data, such as the RCM data will not be available prior to month 48. Strong collaboration among group partners for obtaining and formatting data is recommended, as some application groups might have problems handling the raw data There is a need among application groups for an easy-to-use downscaling software and weather generator to get daily climatic time series at the spatial scale necessary to run their models. Chris Hewitt: ENSEMBLES overview ENSEMBLES website: http://www.ensembles-eu.org/ Five-year project funded by the European Commission involving 66 partners that work in 10 different RT groups. The project builds on the work of DEMETER. An overview article on the ENSEMBLES project was published by the EOS newsletter of the American Geophysical Union (AGU) and it can be downloaded from the ENSEMBLES website. EOS, Vol. 85, No. 52, 28 December 2004 RT0 is the management team 1 Objectives of project are (1) to develop an EPS to produce an objective probabilistic estimate of uncertainty in future climate at seasonal, decadal, and longer time-scales, and (2) to maximize the application of the results by linking them to impact applications A list of ENSEMBLES-related meetings is maintained at: http://ensembleseu.metoffice.com/meetings.html Forthcoming meetings o ENSEMBLES General Assembly, 5-9 September 2005, Athens o Wengen Workshop on Climate, Climate Change and Human Health, 12-14 September 2005 An annual progress report is due in September, guidance on writing this report can be obtained at: http://ensembles-eu.metoffice.com/project_reporting.html Overview of the three Work Packages (WP’s) Colin Prentice: WP 6.1 “Global changes in biophysical and biogeochemical processes - integrated analysis of impacts and feedbacks” Tim Carter: WP 6.2 “Linking impact models to probabilistic scenarios of climate change” Our challenge is to link probabilistic climate change to probabilistic impacts Publication on risk-based inverse approach to impact studies Andy Morse: WP 6.3 “Impact modelling at seasonal-to-decadal time scales” The objective is to maximize skill in the impact models driven at seasonal-to-decadal timescales and at regional scales End-to-end approach; involvement of forecasters, developers, users, and stakeholders This WP is closely tied to WP 5.5 (evaluation of seasonal-to-decadal scale impact models forced with downscaled ERA-40 hindcasts and gridded observational datasets) Morse et al. (2005) Tellus A, Vol. 57, No. 33 Christos Giannakopoulos: RT8: Dissemination, Education, and Training Internet dissemination is encouraged, ENSEMBLES publications will be listed on website, electronic newsletter Workshops and short courses are organised by this RT group PhD training activities: staff and student exchange programs within ENSEMBLES Bruce Ingleby: Producing seasonal-to-decadal predictions for ENSEMBLES Report on RT2a activities GloSea HadCM3 for seasonal forecasts and DePreSys HadCM3 for decadal forecasts He provided details on the models and model runs. For seasonal forecats, for example, a lot of effort is put into ocean data assimilation because the oceans provide good forecasting skills. The multi-model approach accounts for uncertainties in the model formulation while the ensemble approach accounts for uncertainties in the initial conditions The ENSEMBLES system consists of 7 coupled General Circulation Models (GCMs) running at ECMWF and 9-member ensembles Andy Morse: Report on the ENSEMBLES workshop: Impact studies and climate model outputs: Synergies and challenges. Evora, Portugal, May 9-11, 2005. There is a gap between climate model outputs and the users’ scales of interest 2 Expectations from the impacts community for climate data versus limitations; there is poor communication on limitations Check RT8 website for details Clare Goodess: Report on RT2 activities Ensemble climate scenarios: what will be available from RT1-RT3 for use by RT6, and when the data will be delivered to RT6 Time scales: (1) anthropogenic climate change and (2) seasonal-to-decadal forecasts Spatial scales: (1) global climate models, (2) regional climate models, and (3) statistical downscaling Forcing: (1) Emission scenarios (Special Report on Emission Scenarios (SRES)), (2) reanalysis, and (3) perturbed/stochastic physics RT2a - Global simulations, stream 1 - month 18 deliverable (RT2a will produce seasonal-to-decadal hindcasts and climate change scenarios) o Multi-decadal simulations o 7 modeling groups, SRES scenarios: B1, A1B, and A2 o Outputs from RT2a stream 1: http://cera-www.dkrz.de/CERA Currently monthly data for 4 ENSEMBLES GCMs at: http://www-pcmdi.llnl.gov, same data will be available from the Intergovernmental Panel on Climate Change - Data Distribution Centre (IPCCDDC) RT1 (i.e., development of ENSEMBLE Prediction System) and RT2A, stream 2 – perturbed physics HadCM3 runs - month 24, there is interest in storing the probability density functions only as opposed to the raw data, this decision will be taken at a RT meeting in Toulouse RT2A, stream 2 o Based on RT1 EPS and RT7 scenarios (RT7 is concerned with scenarios and policy) o Years 3 and 4 o Even larger ensembles RT2A – Seasonal-to-decadal forecasts o multi-model, built from ECMWF, UK Met Office, and Météo-France operational activities and DEMETER experience o based on RT1 o due by month 48: seasonal, annual and multi-annual integrations o new set of ocean initial conditions from ENACT and/or RT1 o 1960-1991 Data dissemination: MARS, http://www.ecmwf.int/services/archive, maybe KNMI Climate Explorer and OPenDAP (DODS) See RT1 website for details on simulations, proposed variables to be stored, and downscaling document An agreement has been reached on the grid domain, documentation is provided on RT3 website RT3 (i.e., this RT works on the formulation of very high resolution RCM Ensembles for Europe) and RT2B RCM simulations: first at spatial scales of 50 km at a European-wide scale and later also at as high a resolution as 20 km for specified sub-regions (diffenrent scales but same forcing) RT2B: Production of regional climate scenarios for impact assessments o Same model versions as RT3 o 1950-2050 or 1950-2100 o Boundary conditions from RT2A – stream 1 o Deliverered on month 36 (i.e., August 2007) but on month 51 for non-European regions Grid observations will be available from RT5 at 20 km resolution by month 36 and from Joint Research Institute (JRC) at 50 km resolution. ECMWF will also archive INM seasonal-to-decadal dynamical downscaling outputs. 3 Statistical downscaling projects that might have data include: MICE (http://www.cru.uea.ac.uk/projects/mice/), PRUDENCE (http://prudence.dmi.dk/), and STARDEX (http://www.cru.uea.ac.uk/cru/projects/stardex/) Tuesday June 7 Members of each WP were meeting separately to establish progress so far, planning for the next 18 months, and to improve communication and make contacts, especially for research assistants/associates who will be working on the project on a daily basis. These minutes refer to WP 6.3 only as both co-authors of these minutes are part of this group. Details on this Work Package: Co-leaders: University of Liverpool (Morse) and University of Bristol (Prentice) Participants: UREADMM (Slingo), ARPA-SIM (Marletto), JRC-IPSC (Genovese), METEOSWISS (Appenzeller), LSE (Smith), IRI (Thomson), EDF (Dubus), DWD (Becker) The primary objective of this WP is to integrate application models within a probabilistic ESM and within RCM systems. This integration links the human dimension to ESM and allows subsequent evaluation of the ENSEMBLES EPS. The two tasks of this WP are: Consultation on seasonal-to-decadal application models requirements for downscaling and RCM integrations with partners in RT2B and WP3.6 Integration of seasonal-to-decadal application models within a probabilistic ESM based on DEMETER hindcasts The main difference between WP6.2 and WP6.3 is the time-scale; WP 6.2 looks at long-term climate change whereas WP 6.3 looks at seasonal-to-decadal time-scales. Key issues arose during meeting: Data access (DEMETER, ERA and others) Running full ensembles and not the ensemble mean only; data processing: encouraging the use of R language Integration of seasonal-to-decadal application models within an EPS (DEMETER) – running the DEMETER dataset and incorporating it in an application model. This involves downscaling and bias correction; it is not clear who will do the downscaling; help might be available for Europe but that will be more difficult for Africa Skill scores and linkages to WP 5.5, i.e., model validation Setting up a mailing list for this the RT and WP RCM data will only be available at month 48 Secure site: ID and password available upon request from Andy Morse (University of Liverpool) Outputs from the integrated EPS/application model runs will be validated in WP5.5 against ERA-40 and where available grid station data. Presentations by partners of WP6.3 Vittorio Marletto Hindcasting wheat yields from downscaled DEMETER outputs Developed an integrated crop growth/soil water balance model, as water stress is an important variable that affects transpiration He ran the model with DEMETER hindcasts and found that there is some skill 1-2 months before harvest Marletto et al. 2005, Tellus A, Vol. 57,No. 3, 488-497 Sub-sampling is done in order to avoid running the crop model for the 72 ensembles runs (used the 5, 10, 25, 50, 75, 90, and 95 th percentiles) 4 Help needed for downscaling and for generating daily data with weather generators; the help he used to get from the Danish group is no longer available Clarisse Fil Part of a 7-member team at Electricité de France (EDF) that works on climate change and weather/climate prediction Climate forecasting is important for EDF o Producing forecasts of electricity production capacities o River temperature forecasts for nuclear power plants (need water for cooling). 2003 was the first year with river temperature too warm. o Wind power forecasts Meteorological variables of interest are: temperature, precipitation, and wind They evaluate seasonal-to-decadal forecasts on electricity demand using o ERA40 data o High-resolution reanalysis o DEMETER forecasts o ENSEMBLES forecasts They need downscaled seasonal-to-decadal temperature forecasts, temporal downscaling at daily time-scales, and they do spatial downscaling of local precipitation in France using ERA40 largescale patterns as predictors They evaluated seasonal forecasts from DEMETER and ECMWF, monthly and seasonal mean temperature over France (internal report) Fil et al. (2005) Tellus A, Vol. 57, No. 3. (Winter climate regimes over the North Atlantic and European region in ERA40 reanalysis and Demeter seasonal hindcasts) Liam Clarke Work on forecast validation at the Centre for the Analysis of Time Series (CATS) at the London School of Economics (LSE) Evaluation of probability forecast and not just the ensemble mean Publications available from CATS website: http://cats.lse.ac.uk/, e.g. Do multi-model ensemble forecasts yield added value? And evaluating probabilistic forecasts using information theory. Main aim of this group is evaluating the value of forecasts Downscaling software for matlab – emtools written by Jochen Broecker Andrew Challinor Agricultural impacts, focus on India Works in WP 5.5 and 6.1 as well He referred to the World Meteorological Organization (WMO) website for forecast verification, information is also available from the DEMETER report Evaluation of ERA40 over India during the 1966-1989 period (reanalysis versus observed): general overestimation of light rains and underestimation of heavy rains, which result in lower runoff; no monsoon onset in some areas, i.e., the reanalysis does not capture well the seasonal cycle over some areas of India. He used DEMETER hindcasts with the General Large Area Model (GLAM) crop model to create an ensemble of crop yields (groundnut) See paper in special issue of Tellus Anne Jones Weather-driven malaria modeling. Main component of research is running a malaria model with the DEMETER dataset (daily temperature and rainfall) Model includes two parts: the mosquito and the infection parts; the larval stage is rainfall dependent while the biting/laying stage and the sporogonic cycle are temperature dependent Model outputs include mosquito population size and malaria prevalence in the human population Malaria model is written in C language with a Visual Basics – Excel version for model analysis of single grid points ERA-40 and DEMETER data were obtained from MARS Analysis consists of: o sensitivity testing of malaria model to parameters and input data o comparison of malaria transmission maps obtained from ERA-40 data with MARA malaria map 5 o o o o o comparison of model output time series for selected grid points with outputs from MARA model Comparison of malaria anomalies simulated by the model with recorded epidemics Interannual variability of malaria Position of epidemic fringe There is a need to evaluate ERA-40 dataset for this study region MeteoSwiss, (absent but Andy Morse reported on their activities) Seasonal forecasts and weather-based financial products (e.g., re-insurance companies) Joint Research Centre (JRC) (http://www.jrc.cec.eu.int/), (absent but Andy Morse reported on their activities) They are integrating a crop yield model with fully downscaled and bias corrected probabilistic ensemble hindcasts at seasonal-to-decadal scales Clare Goodess She did not receive much response about the joint downscaling discussion paper. There is a Spanish group committed to downscaling seasonal-to-decadal forecasts, but they will produce outputs for Spain only in the first 18 months. The following table presents the downscaled data needed by each partner of WP6.3 Institution Variables ARPA T2m, Tmax Tmin, Precip JRC T2m, Tmax, Tmin, Precip RH, 10mU 10mV, GlobalRad EDF T2m, Tmax Tmin, Precip (10mU,10mV) LIV T2m, Tmax Reading Tmin, Precip T2m, Tmax Tmin, Precip GlobalRad Area Northern Italy Time Step Spatial Res Target Data daily 25KM y EU-25 daily 50KM y EU-25 daily 50KM Africa: West and daily Southern Africa 50KM y but not public N Africa India 50KM Y daily Some short-term actions to be taken by some group members: Circulation of an internal report by EDF that consists of comparing the ERA-40 and DEMETER monthly and seasonal means Andy Challinor to list references/ educational resources on skill scores LSE will provide further details on downscaling tools Outline for the next 18 months: Moving towards further integration and automation of DEMETER datasets Downscaling and bias correction methodologies Different weighting methodologies Preliminary access to decadal data and pre-production runs Wednesday June 8 Morning: Report of previous day activities from each working group and outline for the 18 month plan Discussion on cross-cutting activities DEMETER dataset covers the last 40 years, but not all the 7 models do 6 Afternoon: Meeting of WP leaders End of RT6 2005 meeting 7