Download ENSEMBLES Project Plan WP 6

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
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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
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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
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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
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ENSEMBLES General Assembly, 5-9 September 2005, Athens
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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”
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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
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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
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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.
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There is a gap between climate model outputs and the users’ scales of interest
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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
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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)
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Multi-decadal simulations
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7 modeling groups, SRES scenarios: B1, A1B, and A2
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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
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Based on RT1 EPS and RT7 scenarios (RT7 is concerned with scenarios and policy)
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Years 3 and 4
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Even larger ensembles
RT2A – Seasonal-to-decadal forecasts
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multi-model, built from ECMWF, UK Met Office, and Météo-France operational
activities and DEMETER experience
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based on RT1
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due by month 48: seasonal, annual and multi-annual integrations
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new set of ocean initial conditions from ENACT and/or RT1
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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
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Same model versions as RT3
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1950-2050 or 1950-2100
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Boundary conditions from RT2A – stream 1
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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.
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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)
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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
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Producing forecasts of electricity production capacities
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River temperature forecasts for nuclear power plants (need water for cooling). 2003
was the first year with river temperature too warm.
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Wind power forecasts
 Meteorological variables of interest are: temperature, precipitation, and wind
 They evaluate seasonal-to-decadal forecasts on electricity demand using
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ERA40 data
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High-resolution reanalysis
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DEMETER forecasts
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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
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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
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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.
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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
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Afternoon: Meeting of WP leaders
End of RT6 2005 meeting
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