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ECMWF Training course Reading, 27 April 2006

Using Seasonal Forecasts

Francisco J. [email protected]


Forecasts are relevant for users

The user needs climate information to take

action and mitigate the adverse effects of

climate


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.”


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


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.


• 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/


DEMETER Special Issue 2005

Tellus 57A, No. 3, 21 contributions


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


http://www.ecmwf.int/research/EU-projects/ENSEMBLES/news/index.html

Downscaling for s2d predictions


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).


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.


Predictions for large agricultural areas

1-month lead spring (MAM) T2m over Ukraine

3-month lead early spring (ASO) precipitation over Eastern

Australia


JRC’s CGMS

Crop Growth Indicator

Jan Feb Aug

Meteo data

Yield

Statistical model

Meteo dataSeasonal forecast

data


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


gathering cumulative evidence for early and focused response . . .

case surveillance alone = late warning

geographic/community focus

Malaria early warning systems

From M. Thomson (IRI)


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)


Malaria warning: link to climateStatistical relationship between DJF CMAP precipitation and Botswana standardised log malaria incidence for

1982-2002


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


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


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



-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



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).


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.


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


Calibrated downscaled predictionsPAGE agricultural extent

PAGE agroclimatic zones


Northern box

Forecast Correlation

BSS

Multi-model

0.57 0.12

Forecast Assimilation

0.74 0.32

Calibrated downscaled predictions

Southern box


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)


Calibrated downscaled predictions


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)


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.


Questions?