ECMWF Training course Reading, 27 April 2006
Using Seasonal Forecasts
Francisco J. [email protected]
ECMWF Training course Reading, 27 April 2006
Forecasts are relevant for users
The user needs climate information to take
action and mitigate the adverse effects of
climate
ECMWF Training course Reading, 27 April 2006
Long-range forecast objective“To utilize the ability to predict climate variability on the scale of months to a year and beyond to improve management and decision making in respect to users’ needs at local, regional, and national scales.”
ECMWF Training course Reading, 27 April 2006
Long-range forecast objective“To utilize the ability to predict climate variability on the scale of months to a year and beyond to improve management and decision making in respect to users’ needs 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
ECMWF Training course Reading, 27 April 2006
A user strategy: the end-to-end approach
• A broad range of forecast products might be offered, but user requirements need to be defined.
• End-to-end is based on collaboration and continuous feedback.
• End users develop their models taking into account climate prediction limitations.
• The level of forecast skill that provides added value is defined by the application: user-oriented verification. End users assess the final value of the predictions.
• Forecast reliability becomes a major issue.
ECMWF Training course Reading, 27 April 2006
• 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: DEMETERhttp://www.ecmwf.int/research/demeter/
ECMWF Training course Reading, 27 April 2006
DEMETER Special Issue 2005
Tellus 57A, No. 3, 21 contributions
ECMWF Training course Reading, 27 April 2006
Extremes for users: end-to-end
63………… 624321Climate forecast
………… 63624321 Downscaling
63………… 624321Application
model
0
Probability of Precipitation Probability of Crop Yield/Incidence
0
non-linear transformation
ECMWF Training course Reading, 27 April 2006
http://www.ecmwf.int/research/EU-projects/ENSEMBLES/news/index.html
Downscaling for s2d predictions
ECMWF Training course Reading, 27 April 2006
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).
ECMWF Training course Reading, 27 April 2006
Examples of applications
•Malaria incidence prediction in an epidemic region (Botswana).
•Crop yield prediction for Europe (wheat) and western India (groundnut).
•Seasonal streamflow prediction over tropical and subtropical watersheds.
ECMWF Training course Reading, 27 April 2006
Predictions for large agricultural areas
1-month lead spring (MAM) T2m over Ukraine
3-month lead early spring (ASO) precipitation over Eastern
Australia
ECMWF Training course Reading, 27 April 2006
JRC’s CGMS
Crop Growth Indicator
Jan Feb Aug
Meteo data
Yield
Statistical model
Meteo dataSeasonal forecast
data
ECMWF Training course Reading, 27 April 2006
France Germany
Denmark Greece
Wheat yield predictions for Europe
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).
From P. Cantelaube and J.-M. Terres, JRC
ECMWF Training course Reading, 27 April 2006From Challinor et al. (2005)
Correlation between de-trended observed and DEMETER ensemble-mean predicted groundnut yields for the period 1987 -1998
Groundnut yield predictions with a LAM
ECMWF Training course Reading, 27 April 2006
gathering cumulative evidence for early and focused response . . .
case surveillance alone = late warning
geographic/community focus
Malaria early warning systems
From M. Thomson (IRI)
ECMWF Training course Reading, 27 April 2006
Malaria warning: meteorological factors
The number of meteorological variables required by the users is large and changes with the region considered
Limiting variables for malaria development as obtained with the MARA rule-based model and ERA40; white areas are influenced by
all factors
From A. Jones (Univ. of Liverpool)
ECMWF Training course Reading, 27 April 2006
Malaria warning: link to climateStatistical relationship between DJF CMAP precipitation and Botswana standardised log malaria incidence for
1982-2002
ECMWF Training course Reading, 27 April 2006
Climate forecasts for malaria warningPrecipitation composites for the five years with the highest
(top row) and lowest (bottom row) standardised malaria incidence for DJF DEMETER (left) and CMAP (right)
Areas with
epidemic malaria
Quartiles define
extreme events
(epidemics) in malaria prediction
ECMWF Training course Reading, 27 April 2006
Malaria warning with statistical modelProbabilistic predictions of standardised malaria incidence
quartile categories in Botswana with five months lead time
-- high malaria years
-- low malaria years
ROC ScorePrecipitatio
nIncidence
Event DEMETER CMAP DEMETER
Very low 0.95 1.00 1.00
Very high 0.52 0.94 0.84
Very low malaria
Very high malaria
Available in March
Available in November
ECMWF Training course Reading, 27 April 2006
ERA40 raw model correct model
Daily rainfall (mm)
Cum
ulat
ive
freq
uenc
y
Dynamical malaria model: bias correctionDaily precipitation as required by the Liverpool Malaria
ModelDaily rainfall from the CERFACS experiment
(25°E, 22.5°S, November start date,
1980-2001), correction applied separately for dry and wet days, with
wet days corrected with a ratioRainfall histograms
(CERFACS, all Botswana grid points, November start date,
1980-2001)
End users require probabilistic models that
correct biases, downscale to the
appropriate grid and are able to produce daily time series with the
correct extremal properties
From A. Jones (Univ. of Liverpool)
ECMWF Training course Reading, 27 April 2006
-2.0
-1.0
0.0
1.0
2.0
1982 1984 1986 1988 1990 1992 1994 1996 1998 2000
Year
Mal
aria
An
om
aly
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
Inci
den
ce A
no
mal
y
Malaria Index LMM incidence
Malaria index for Botswana from Thomson et al. (2006) and incidence simulated by the Liverpool malaria model (LMM) using
ERA40
Malaria warning: nonlinearity
0.0
30.0
60.0
90.0
120.0
150.0
180.0
210.0
240.0
270.0
300.0
01/98 01/99 01/00 01/01
Date
Rai
nfa
ll (m
m p
er m
on
th)/
Mo
nth
ly In
cid
ence
15.0
16.5
18.0
19.5
21.0
22.5
24.0
25.5
27.0
28.5
30.0
TM
ax -
5 (d
eg C
)
rain (mm per month) incidence per month tmax-5
There is a disagreement between both models for the year 2000: is it
due to the impact of extreme temperature or precipitation?
Interaction of climate variables may affect the user predictions
From A. Jones (Univ. of Liverpool)
ECMWF Training course Reading, 27 April 2006
Interacting factors in end-user systems
•The predictions are designed to be included in an early warning system (decision making).
•Tropical disease incidence is an important factor affecting food security in tropical/semi-arid areas (socio-economic interaction).
•The previous 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 crop yields (uncertainty).
•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).
ECMWF Training course Reading, 27 April 2006
Climate change and climate variability
•The possibility of adaptation to climate change via a learning process taking place at the interannual time scale is an obvious way to achieve a high degree of integration of climate time scales.
• It implies:Involvement of both climate scientists and end-usersThat both scientists and end users/stakeholders
consider the whole range of time scales
•As an example, crop managers see the adaptation to long-term climate change as a process that takes place on a yearly basis and that benefits from predictions at various time scales.
ECMWF Training course Reading, 27 April 2006
River basin predictionsMulti-model predictions of precipitation over river basins and many other
verification diagnosticshttp://www.ecmwf.int/research/demeter/d/charts/verification/
ECMWF Training course Reading, 27 April 2006From Coelho et al. (2006)
ObservationsMulti-modelForecast
Assimilation
(mm/day)
r=0.51
r=0.28
r=0.97
r=0.82
• 3 DEMETER coupled models
• 1-month lead time DJF precipitation
• ENSO composites for 1959-2001
• 16 warm events• 13 cold events
Combined/calibrated seasonal predictions
ECMWF Training course Reading, 27 April 2006
Calibrated downscaled predictionsPAGE agricultural extent
PAGE agroclimatic zones
ECMWF Training course Reading, 27 April 2006
Northern box
Forecast Correlation
BSS
Multi-model
0.57 0.12
Forecast Assimilation
0.74 0.32
Calibrated downscaled predictions
Southern box
Forecast Correlation
BSS
Multi-model
0.62 0.16
Forecast Assimilation
0.63 0.28
From Coelho et al. (2006)
Seasonal predictions of NDJ precipitation (3-month lead time)
ECMWF Training course Reading, 27 April 2006
Calibrated downscaled predictions
Forecast Correlation
BSS
Parana 0.16 0.00
Tocantins 0.29 0.12
From Coelho et al. (2006)
Seasonal predictions of NDJ precipitation (3-month lead time)
ECMWF Training course Reading, 27 April 2006
Summary
•The multi-model ensemble 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, especially in a probabilistic framework.
•There is a clear need to link the research and development carried out about climate variability at different time scales and the users’ needs.
•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 global change.
ECMWF Training course Reading, 27 April 2006
Questions?
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