The fundamentals of Seasonal
Forecasting
J.P. Céron – Météo-France
Some Vocabulary
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Long Range Forecasts and Climate Forecasts
Forecast Range, Forecast period and Lead time.
Lead Time
Forecast
issue
time
Forecast Period
Forecast Range
LT - 1 month
May
Forecast
issue
time
June
July
Seasonal Forecast 1
Aug
Sept
Octo
Coupled Forecast : Range of 6 months
Nov
LT - 2 month
May
Forecast
issue
time
June
July
Seasonal Forecast 2
Aug
Sept
Octo
Coupled Forecast : Range of 6 months
Nov
LT - 3 month
May
Forecast
issue
time
June
July
Seasonal Forecast 3
Aug
Sept
Octo
Coupled Forecast : Range of 6 months
Nov
The Scientific bases
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The evolution of the atmosphere is partly driven by the
evolution of external forcing conditions (SST and
continental surfaces).
The evolution of external forcings is often slow and
predictable. It gives a slow memory to the atmosphere ; the
evolution of the latter becoming partly predictable.
The successive instantaneous states of the atmosphere have
a limited predictability while the mean states of the
atmosphere have a greater predictability.
The mean circulation in tropical regions is strongly
inflenced by the large scale organised convection.
Limitation of numerical forecast :
Daily forecast
Daily Scores
over Northern
Hemisphere
Limitation of numerical forecast :
Daily forecast
Daily Scores
over Northern
Hemisphere
+
Persistence
Scores
Limitation of numerical forecast :
Daily forecast
Daily Scores
over Northern
Hemisphere
+
Perfect model
Scores
Limitation of numerical forecast :
Monthly forecast
Daily Scores
over Northern
Hemisphere
+
Monthly
running mean
Scores
Limitation of numerical forecast :
Seasonal forecast
Daily Scores
over Northern
Hemisphere
+
seasonal
running mean
Scores
Limitation of numerical forecast :
Seasonal forecast
Daily Scores
over Northern
Hemisphere
+
Ensemble
forecast,
seasonal
running mean
and SST
forecast
The Predictability
« a Thunderstorm will be observed next Sunday
over the Toulouse « Météopole » between
15h and 16h »
 Irrealistic, the confidence that one can
have in this forecast is very low
« a rainy system will cross the Toulouse region
Sunday afternoon »
 realistic, one can be quite confident in
this forecast
The Predictability
The predictability depends on :
 The scale of the forecasted phenomenum
(Thunderstorm, Easterly Wave, Blocking
situation, ENSO, …)
 The Range of the forecast (NowCasting,
Short , Medium , Seasonnal , Climatic)
Predictability

Space Scales
Local  10-100 km
 Regional  100-1000 km
 Synoptic  1000-5000 km
 Supra-synoptic > 5000- km

seasonal Forecasting - supra-synoptic scales
Predictability

Actors and
Associated
Scales
Predictability
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The different views of the Predictability
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Through the observations
Through the models
The evolution of external forcing
conditions
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Evolution of Sea Surface temperature (SST)
 Interannual variability (like ENSO)
 Decadal variability (like PDO)
Evolution of continental surface conditions
 Influence of continental surface conditions (snow,
albedo, ..),
 Intraseasonal variability (notably soil moisture),
Mutual influences
 Decadal/ENSO
 ENSO/Intraseasonal
 Intraseasonal/Synoptic
The ENSO

The planetary influence of El Niño (left) and La Niña (right)
The ENSO
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Through the observations in
Winter
The ENSO
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Through the observations in Summer
The ENSO
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Through the observations in
Winter
The ENSO
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Through the observations in Summer
The fundamentals of seasonal Forecasting
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The climatic variability
The forecasting models
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The verifications
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Statistical models
SST forced Atmospheric General Circulation Models
Ocean/Atmosphere Coupled General Circulation Models
Verification of the forecasts
Verification of the usefulness of the forecats
The chaos
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Link with the climatic variability
Link with the ensemble forecast
The fundamentals of Seasonal Forecasting
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The climatic variability : slow variation in the
Atmosphere
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NAO
PNA mode
PDO
QBO or TBO
http://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.html
and Barnston and Livezey 1987, Mon. Wea. Rev., 115, 10831126)
East Atlantic (EA) , East Atlantic Jet (EA-Jet) , East
Atlantic/Western Russia , Scandinavia (SCAND) , Polar/Eurasia
Asian Summer , West Pacific (WP) , East Pacific (EP) , North
Pacific (NP) , Tropical/Northern Hemisphere (TNH) ,
Pacific Transition (PT)
The « North Atlantic Oscillation»
The « North Atlantic Oscillation»
Temperature in Winter
Rainfall in Winter
The Pacific Decadal Oscillation
The Pacific Decadal Oscillation
The climatic variability
The climatic variability
The fundamentals of Seasonal Forecasting
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The climate variability
The fundamentals of Seasonal Forecasting
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The climatic variability
The
fundamentals
of seasonal
Forecasting

The climatic
variability
The fundamentals of seasonal Forecasting

The climatic
variability
Atlantic “El Nino” – Pirata buoy network
The fundamentals of seasonal Forecasting
The climatic variability
120
6
100
5
80
4
3
60
2
40
1
20
0
0
Sahel Rain
THC
-1
1990
1983
1976
1969
1962
1955
1948
1941
-4
1934
-60
1927
-3
1920
-40
1913
-2
1906
-20
Year

JAS Observed Sahel Rainfall Vs JAS Observed THC index r = 0.45
The fundamentals of seasonal Forecasting
Forecasting models
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Statistical models
SST forced Atmospheric Global Circulation Models
Océan/Atmosphère Coupled General Circulation Models
The fundamentals of seasonal Forecasting
The Statistical models
East African Rainfall vs Nino3 Index
Thank’s to Simon Mason
The fundamentals of seasonal Forecasting
The Statistical models
East African Rainfall vs Nino3 Index
The fundamentals of seasonal Forecasting
The Statistical models
East African Rainfall vs Nino3 Index
The fundamentals of seasonal Forecasting
The Statistical models
The fundamentals of seasonal Forecasting
The Statistical models
The fundamentals of seasonal Forecasting
The Statistical models
The fundamentals of seasonal Forecasting
The Statistical models
The fundamentals of seasonal Forecasting
The Statistical models
The fundamentals of seasonal Forecasting
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Numerical models
The fundamentals
of seasonal
Forecasting
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The numerical models
The fundamentals of seasonal Forecasting
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Coupled vs Forced models
Coupled vs Forced models
The fundamentals of seasonal Forecasting
Forecast Verifications
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Verification in « real time » (following up of the bias,
pointing out and monitoring of the errors, …),
Verification in hindcast forecast mode ,
Verification of the predictability of forecasting events,
Verification of the forecast value in a user’s point of view,
Verification of the use and impact of the forecast,
« Deterministic » vs « Probabilistic » Verifications
Comparison with climatology and persistence (often use
as references by users), …
Problem of relevant and reliable dataset for verification
purpose.
Score/Skill and Value
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2 complementary point of view :
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The scientific point of view :
Quality of the forecast = Scores
Interest of the use of the forecast = Skills
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The user point of view :
Usefulness of the forecast = Skills (using current
forecast strategy of the users – e.g. Climatology)
Value of the forecast = Economonical evaluation
of the use of the forecast (Cost/Lost approach)
Score/Skill and Value
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Score point of view :
Score/Skill and Value
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Cost/Lost approach
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2 categories : e.g. dryer / wetter and ratio Cost/Lost e.g. = 0.5
Event
obs
non
obs
Event
obs
non obs
forecast
c
c
forecast
n11
n12
non
forcast
L
0
non
forecast
n21 n22
C1=Averaged cost using climatological forecast
C2 =Averaged cost using perfect model forecast
C3= Averaged cost using real model
V100 C1C3
C1C2
Score/Skill and Value
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Value point of view :
The fundamentals of seasonal Forecasting
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Verifications
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WMO Normes (parameters, scores, zones)
Verification in Hindcast mode
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« retrospective » forecasts
European research Projects
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PROVOST (CEPMMT, UKMO, EDF, LODYC, MPI, IMGA,
DMI, U. Alcala) : Perfect Océan forecast, 4 different models, 15
years x 4 seasons x 9 membres
Résults : clear in Tropics, some skill on North hemisphere in winter, but
local uncertainties. Interest of the Multi-model approach.
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ELMASIFA (SNM Maroc, Algérie Tunisie)
POTENTIALS (DMI, CINECA, LMD, MPI)
DEMETER (CEPMMT, UKMO, LODYC, CERFACS, MPI,
ADGB, IMGA, DMI, JRC, U. Liverpool, INM) : Forecasted
Océan using coupled models, 6 different models,
40 years x 4 seasons x 6 month x 9 membres
Résults : Provost revisited, improvment in Tropical regions and
degradation in mid-latitude, extension of the range of the forecast.
The fundamentals of seasonal Forecasting
Chaos and ensemble forecast
Uncertainty Sources :
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Differences between analysis and real initial state
 Assimilation system Imperfection
 Lack of observations
Model Errors (both Oceanic and/or atmospheric)
Natural variability of the climate system
Interpretation of the forecast
The fundamentals of seasonal Forecasting
Chaos and ensemble forecast
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Model errors (Océanic and/or atmospheric)
Butterfly effect (JFM 2003)
Butterfly effect (JFM 2003)
The fundamentals of seasonal Forecasting
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Chaos and ensemble forecast
To sample the initial state uncertainty  analysis
disturbances
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Corresponding to the most unstable modes
Compatible with analysis errors
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Methods : Singular vectors, breeding …
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To sample the modelisation uncertainty  model
disturbances
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Using several models
Using stochastic physics
Modifying some physical parameters
To sample all the possible solutions for the Ocean
Atmosphere system
The fundamentals of seasonal Forecasting
Chaos and climatic variability
HOT & DRY
COLD & WET
The fundamentals of seasonal Forecasting
Chaos and Climate variability
The fundamentals of seasonal Forecasting
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The forecast sytem
The description of the initial state of the Ocean/Atmosphere system
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Elaboration of products
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Direct Methods (Deterministic vs Probablilistic products)
Indirect Methods (notably PPM or MOS)
Adaptation of the products (notably downscaling)
Interpretation of the forecast
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Atmospheric Data
Oceanic Data
Data from the continental surface
Assimilation data scheme
Transformation of the forecast to the benefit of the user
Following-up of the process
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Update of the forecast
User’s Evaluation of the forecast (value, use and impact)
The fundamentals of seasonal Forecasting
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The forecasting suite
The fundamentals of seasonal Forecasting
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Description of the initial state of the Ocean/Atmosphere
system
The fundamentals of seasonal Forecasting

Description of the initial state of the Ocean/Atmosphere
system
The fundamentals of seasonal Forecasting

Description of the initial state of the Ocean/Atmosphere
system
The fundamentals of seasonal
Forecasting
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The Oceanic data assimilation
The
fundamentals of
seasonal
forecasting
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Assimilation of
the surface wind
The fundamentals of seasonal Forecasting

Description of the initial state of the Ocean/Atmosphere
system
The fundamentals of seasonal forecasting
Elaboration of products
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Direct Methods (deterministic vs Probabilistic products)
Indirect Methods (Statistical adaptations notably)
Adaptation of the products (downscaling)
The fundamentals of seasonal forecasting
Elaboration of products
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Deterministic vs Probabilistic product
Is the product usefull?
Is the product well adapted to the user?
If no, can we do something?
Can the quality, the value and the use of the product
be checked and verified?
Require a Forecaster/User discussion through
Multidisciplinary groups (Experts in forecasting,
communication, users’s needs)
Elaboration of numerical products
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Direct Methods (deterministic and probabilistic products)
formulation as Indices or Anomalies
Model Forecast : raw information
not usefull !
A
F



C, mm/D, m/s,...
Elaboration of numerical products

Direct Methods (deterministic and probabilistic products)
formulation as Indices or Anomalies
Debiased model forecast : better
formulation
A
F F



C, mm/D, m/s,...
Elaboration of numerical products

Direct Methods (deterministic and probabilistic products)
formulation as Indices or Anomalies
Normalized model forecast : Model
forecats compared to its own climatology
A  F F
F
%



Elaboration of numerical products

Direct Methods (deterministic and probabilistic products)
formulation as Indices or Anomalies
Indices : Model forecats compared
to its own climatology
A
Anomalies :
Adaptation to « local »
observation properties
F F O
F
%
C, mm/D, m/s,...


Downscaling Problem
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Seasonal predictability and associated
scales  adaptation to the user
Global
Local
 statistical methods : Observations, Downscaling models
 numerical methods : Numerical models using GCM
simulations as boundary conditions (single column, LAM, …)
Dowscaling Problem
Seasonal predictability and associated scales
 adaptation to the user
Seasonal
intra seasonal
 statistical methods : Observations, Downscaling models
 numerical methods : Numerical models using GCM
simulations as boundary conditions (single column, LAM, …)
The fundamentals of seasonal forecasting
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Interpretation of the forecast
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transformation to the benefit of the user
Translation in terms of actions, risks, scenario, … and associated
probabilities
Following_up of the process
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Update of the forecast – continuous process
Evaluation on a user point of view – Processs Experience
Feedbacks
Highlights of seasonal forecasting
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Basically Probabilistic forecast,
Forecast of the Mean State and not of the Weather,
Not usefull elsewhere neither for everything,
Confidence in the forecast depending of the year and the
parameter,
Evaluation of both aspects quality (scientific) and
usefulness (economical value, use),
Usefull in a decision making context and in meteo
sensitive activities (in an economical sense),
Since a few years better knowledge of the limits of the
seaonnal predictability and its potential uses,
Operational forecast systems aiming to provide targeted
products,
Improvements : ERA40, coupled models, donwscalling,
intraseasonal forecasts, Observation system, …