CLIMAG Meeting Geneva, 11 May 2005
Recent Developments in Dynamical Climate Seasonal
Forecasting
Francisco J. Doblas-Reyes, Renate Hagedorn, Tim N. [email protected]
European Centre for Medium-Range Weather Forecasts
CLIMAG Meeting Geneva, 11 May 2005
CLIMAG objective
“To utilize the ability to predict climate variability on the scale of months to a year to improve management and decision making in respect to crop production at local, regional, and national scales.”
CLIMAG Meeting Geneva, 11 May 2005
CLIMAG objective
“To utilize the ability to predict climate variability on the scale of months to a year to improve management and decision making in respect to crop production at local, regional, and national scales.”
Requirements by the end user:
• predict climate variability: skilfully deal with uncertainties in climate prediction
• seasonal-to-interannual time scales: coupled ocean-atmosphere general circulation models
• variable spatial scale: downscaling
CLIMAG Meeting Geneva, 11 May 2005
• Research project funded by the Vth FP of the EC, with 11 partners.
• Integrated multi-model ensemble prediction system for seasonal time scales.
• More than a multi-model exercise: seasonal hindcasts used to assess the skill, reliability and value of end-user predictions.
• Applications in crop yield and tropical infectious disease forecasting.
• Officially finished in September 2003, but with an operational follow up.
End-to-end: DEMETER
http://www.ecmwf.int/research/demeter/
CLIMAG Meeting Geneva, 11 May 2005
Multi-model ensemble approach
Uncertainty
initial conditionsmodel formulation
Estimation
ensemblemulti-model
multi-model ensemble forecast multi-model ensemble forecast systemsystem
N models x M ensemble membersN models x M ensemble members
CLIMAG Meeting Geneva, 11 May 2005
Multi-model ensemble system
• DEMETER system: 7 coupled global circulation models
• Hindcast production for: 1980-2001 (1958-2001)
9 member ensembles
ERA-40 initial conditions
SST and wind perturbations
4 start dates per year
6 months hindcasts
Partner Atmosphere Ocean
ECMWF IFS HOPE
LODYC IFS OPA
CNRM ARPEGE OPA
CERFACS ARPEGE OPA
INGV ECHAM-4 OPA
MPI ECHAM-5 MPI-OM1
UKMO HadCM3 HadCM3
CLIMAG Meeting Geneva, 11 May 2005
Multi-model ensemble system
Feb 87 May 87 Aug 87 Nov 87 Feb 88 ...
7 models x 9 ensemble members
63 member multi-model ensemble
DEMETER system: 7 coupled global circulation models
CNRM (FR) ECMWF (INT) INGV (IT) LODYC (FR) MPI (DE) UKMO (UK) CERFACS (FR)
CLIMAG Meeting Geneva, 11 May 2005
Multi-model ensemble system
Feb 87 May 87 Aug 87 Nov 87 Feb 88 ...
DEMETER system: 7 coupled global circulation models
CNRM (FR) ECMWF (INT) INGV (IT) LODYC (FR) MPI (DE) UKMO (UK) CERFACS (FR)
CLIMAG Meeting Geneva, 11 May 2005
Multi-model ensemble system
Feb 87 May 87 Aug 87 Nov 87 Feb 88 ...
DEMETER system: 7 coupled global circulation models
CNRM (FR) ECMWF (INT) INGV (IT) LODYC (FR) MPI (DE) UKMO (UK) CERFACS (FR)
CLIMAG Meeting Geneva, 11 May 2005
Multi-model ensemble system
Feb 87 May 87 Aug 87 Nov 87 Feb 88 ...
63 member multi-model ensemble
= 1 hindcast
DEMETER system: 7 coupled global circulation models
CNRM (FR) ECMWF (INT) INGV (IT) LODYC (FR) MPI (DE) UKMO (UK) CERFACS (FR)
CLIMAG Meeting Geneva, 11 May 2005
Forecast quality assessment
Forecast quality assessment is a basic component of the prediction process
Information about the quality and the uncertainty of the predictions is as important as the prediction
itself
CLIMAG Meeting Geneva, 11 May 2005
ENSO predictionsMulti-model seasonal (MAM) predictions for Niño3.4 SSTs
CLIMAG Meeting Geneva, 11 May 2005
River basin predictionsMulti-model predictions of precipitation over river basins and many other
verification diagnosticshttp://www.ecmwf.int/research/demeter/d/charts/verification/
CLIMAG Meeting Geneva, 11 May 2005
DEMETER end-to-end methodolgy
63………… 624321Seasonal forecast
………… 63624321 Downscaling
63………… 624321Application
model
0
Probability of Precipitation Probability of Future Crop Yield
0
non-linear transformation
CLIMAG Meeting Geneva, 11 May 2005
Downscaling for s2d predictions
• Use dynamical and empirical/statistical methods.
• Correct systematic errors of global models and obtain reliable (statistical properties similar to the observed data) probabilistic predictions (with only relatively short, i.e., 15-30 years, training samples).
• Deal with full ensembles, not a deterministic prediction or the ensemble mean, maximising the benefit of limited simulations with regional models.
• Consider model and initial condition uncertainty.
• Generate high-resolution (e.g., daily) time series of surface variables (using, e.g., weather generators with statistical methods).
CLIMAG Meeting Geneva, 11 May 2005
http://www.ecmwf.int/research/EU-projects/ENSEMBLES/news/index.html
Downscaling for s2d predictions
CLIMAG Meeting Geneva, 11 May 2005
France Germany
Denmark Greece
Wheat yield predictions for Europe
From P. Cantelaube and J.-M. Terres, JRC
SIMULATION WEIGHTED YIELD ERROR (%)
± STANDARD ERROR
JRC February 7.1 ± 0.9
JRC April 7.7 ± 0.5
JRC June 7.0 ± 0.6
JRC August 5.4 ± 0.5
DEMETER (Feb. start)
6.0 ± 0.4
DEMETER multi-model predictions (7 models, 63 members, Feb starts) of average wheat yield for four European countries (box-
and-whiskers) compared to Eurostat official yields (black horizontal lines) and crop results from a simulation forced with downscaled
ERA40 data (red dots).
CLIMAG Meeting Geneva, 11 May 2005
DEMETER Special Issue 2005
Tellus 57A, No. 3, 21 contributions
CLIMAG Meeting Geneva, 11 May 2005
ECMWF public data server
A service that gives researchers immediate and free access to datasets hosted at ECMWF
• DEMETER• ERA-40• ERA-15• ENACT- Monthly and daily data- Select area- GRIB or NetCDF- Plotting facility
http://data.ecmwf.int/data/
CLIMAG Meeting Geneva, 11 May 2005
Future developments
• Integration of weather and climate predictions at different time scales.
• Interaction between different climate-related end-user systems. User-oriented verification.
• Optimisation of the a-posteriori multi-model information through single-model weighting depending on past performance.
• Anthropogenic impact on seasonal climate predictions.
• The ENSEMBLES project: probabilistic climate prediction at seasonal, interannual and longer time scales.
CLIMAG Meeting Geneva, 11 May 2005
1) Prediction of different time scales
Probabilistic seamless forecast system at ECMWF:
1-10 days: medium range EPS (TL399L60)
10 days-1 month: monthly forecast system (TL255L60)
1 month-12 months: seasonal forecast system (TL159L40)
10d
1mth
12mth
01/01 01/02 01/0315/01 29/01 12/02 26/02
CLIMAG Meeting Geneva, 11 May 2005
2) Interacting factors: tropical malaria• Tropical disease incidence is a major factor
affecting food security in tropical/semi-arid areas (socio-economic interaction).
• The following example deals with uncertainty in malaria prediction using a probabilistic approach to reduce forecast error and can easily be extended to prediction of climate-related yields (uncertainty).
• The predictions are designed to be included in an early warning system (decision making).
• Seasonal prediction allows users to become familiar with the use of climate information and understand methods to mitigate the impact of and adapt to future global change (climate change).
CLIMAG Meeting Geneva, 11 May 2005
2) Malaria warning: seasonal predictionRelationship between DJF CMAP precipitation and
Botswana standardised log malaria incidence for 1982-2002
CLIMAG Meeting Geneva, 11 May 2005
-- high malaria years
-- low malaria years
2) Malaria warning: seasonal predictionProbabilistic predictions of standardised malaria incidence
in Botswana five months in advance of the epidemic
Very low malaria
Very high malaria
0.840.940.52Very high
1.001.000.95Very low
DEMETERCMAPDEMETEREvent
IncidencePrecipitationROC Score
0.840.940.52Very high
1.001.000.95Very low
DEMETERCMAPDEMETEREvent
IncidencePrecipitationROC Score
Available in November
Available in March
CLIMAG Meeting Geneva, 11 May 2005From Coelho et al. (2005)
3) Calibrated downscaled predictionsPAGE agricultural extent
PAGE agroclimatic zones
CLIMAG Meeting Geneva, 11 May 2005
Northern box
Forecast Correlation
BSS
Multi-model
0.57 0.12
Forecast Assimilation
0.74 0.32
3) Calibrated downscaled predictions
From Coelho et al. (2005)
Southern box
Forecast Correlation
BSS
Multi-model
0.62 0.16
Forecast Assimilation
0.63 0.28
CLIMAG Meeting Geneva, 11 May 2005
Constant GHGCorrelation = 0.52
4) Anthropogenic effect: T2m predictions
Variable GHGCorrelation = 0.77
1-month lead, summer (JJA) predictions of global T2m
CLIMAG Meeting Geneva, 11 May 2005
5) The future: ENSEMBLES project
• Integrated Project funded by the EC within the VIth FP, 69 partners.
• Start date: 1 September 2004, Duration: 5 years
• Integrated probabilistic prediction system for time scales from seasons to decades, and beyond.
• Seasonal-to-decadal hindcasts will be used to assess the reliability of forecast systems used for scenario runs.
• Comparison of the benefits of the multi-model, perturbed parameters and stochastic physics approaches to assess forecast uncertainty.
• Great diversity of applications: health, crop yield, energy production, river streamflow, etc.
CLIMAG Meeting Geneva, 11 May 2005
Summary
• The multi-model has proven to be an effective approach to reduce forecast error by tackling both initial condition and model uncertainty.
• The end-to-end approach has shown promising results in seasonal forecasting.
• There is a clear need to link the research and development carried out about climate variability at different time scales.
• Seasonal-to-interannual forecasting can evolve into a field where end-users learn to use (and verify) climate information before developing adaptation/mitigation strategies for environmental global change.
CLIMAG Meeting Geneva, 11 May 2005
Questions?
CLIMAG Meeting Geneva, 11 May 2005
Generalized ensemble approach
Uncertainty
initial conditionsmodel formulation
Estimation
ensemble perturbed parameters
perturbed parameters ensembleperturbed parameters ensemble
N versions x M ensemble membersN versions x M ensemble members
CLIMAG Meeting Geneva, 11 May 2005
Generalized ensemble approach
Uncertainty
initial conditionsmodel formulation
Estimation
ensemble with stochastic physics
Ensemble with stochastic physicsEnsemble with stochastic physics
M ensemble membersM ensemble members
CLIMAG Meeting Geneva, 11 May 2005
Multi-model benefits: Reliability
0.0390.8990.141
BSSRel-ScRes-Sc
0.0390.8990.140
0.0950.9260.169
-0.001 0.877 0.123
0.0650.9180.147
-0.064 0.838 0.099
0.0470.8930.153
0.2040.9900.213
Reliability for T2m>0, 1-month lead, May start, 1980-2001
CLIMAG Meeting Geneva, 11 May 2005
River basin predictionsMulti-model predictions of precipitation over the Nile basin
CLIMAG Meeting Geneva, 11 May 2005
JRC’s CGMS in DEMETER
Crop Growth Indicator
Jan Feb Aug
Meteo data
Yield
Statistical model
Meteo data ERA / DEMETER data
CLIMAG Meeting Geneva, 11 May 2005
gathering cumulative evidence for early and focused response . . .
case surveillance alone = late warning
geographic/community focus
Malaria early warning systems
CLIMAG Meeting Geneva, 11 May 2005
Malaria warning: seasonal predictionPrecipitation composites for the five years with the highest
(top row) and lowest (bottom row) standardised malaria incidence for NDJ DEMETER (left) and DJF CMAP (right)
Areas with
epidemic malaria
CLIMAG Meeting Geneva, 11 May 2005
Bayesian procedure:
• Climate model ensembles give
• But we are interested in , not !!!
• Bayes’ theorem updates and obtain
Forecast assimilation
Bayes’ theorem
tXtY : Obs
: Forecasts
Bayes’ theorem
tXtY : Obs
: Forecasts
)X(p t
)X|Y(p tt)X(p t
)Y(p t)X|Y(p tt
CLIMAG Meeting Geneva, 11 May 2005
ObservationsMulti-modelForecast
Assimilation
(mm/day)
r=0.51
r=0.28
r=0.97
r=0.82
Calibrated South American Precipitation
From Coelho (2005)
• 3 DEMETER coupled models
• 1-month lead time DJF precipitation
• ENSO composites for 1959-2001
• 16 warm events• 13 cold events
CLIMAG Meeting Geneva, 11 May 2005
ENSEMBLES: General information• Integrated Project funded by the VI FP of the EC
• Integrated probabilistic prediction system for time scales from seasons to decades and beyond
• 69 partners
• Seasonal-to-decadal hindcasts will be used to assess the reliability of model systems used for climate change experiments
• Great diversity of climate applications
• 2 consultants @ ECMWF
• Start date: 1 September 2004, Duration: 5 years
• http://ensembles-eu.metoffice.com
CLIMAG Meeting Geneva, 11 May 2005
OrganizationThe project is organized in ten Research
Themes (RT), ECMWF involvement in red:• RT0: Management
• RT1: Development of the EPS
• RT2A: Global model engine
• RT2B: Production of regional climate scenarios
• RT3: High resolution regional ensembles
• RT4: Analysis of processes
• RT5: Evaluation
• RT6: Assessment of impacts
• RT7: Scenarios and policy implications
• RT8: Dissemination and training