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The fundamentals of Seasonal Forecasting J.P. Cron Mto-France

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Some Vocabulary Long Range Forecasts and Climate Forecasts Forecast Range, Forecast period and Lead time. Forecast Range Lead TimeForecast Period Forecast issue time

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Coupled Forecast : Range of 6 months LT - 1 month Forecast issue time May June July Aug Sept Octo Nov Seasonal Forecast 1

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Coupled Forecast : Range of 6 months Forecast issue time May June July Aug Sept Octo Nov LT - 2 monthSeasonal Forecast 2

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Coupled Forecast : Range of 6 months Seasonal Forecast 3 Forecast issue time May June July Aug Sept Octo Nov LT - 3 month

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The Scientific bases 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.

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Limitation of numerical forecast : Daily forecast Daily Scores over Northern Hemisphere

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Limitation of numerical forecast : Daily forecast Daily Scores over Northern Hemisphere + Persistence Scores

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Limitation of numerical forecast : Daily forecast Daily Scores over Northern Hemisphere + Perfect model Scores

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Limitation of numerical forecast : Monthly forecast Daily Scores over Northern Hemisphere + Monthly running mean Scores

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Limitation of numerical forecast : Seasonal forecast Daily Scores over Northern Hemisphere + seasonal running mean Scores

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Limitation of numerical forecast : Seasonal forecast Daily Scores over Northern Hemisphere + Ensemble forecast, seasonal running mean and SST forecast

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The Predictability a Thunderstorm will be observed next Sunday over the Toulouse Mtopole 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

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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)

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Predictability Space Scales Local 10-100 km Regional 100-1000 km Synoptic 1000-5000 km Supra-synoptic > 5000- km seasonal Forecasting - supra-synoptic scales

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Predictability Actors and Associated Scales

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Predictability The different views of the Predictability Through the observations Through the models

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The evolution of external forcing conditions of Sea Surface temperature (SST) 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

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The ENSO The planetary influence of El Nio (left) and La Nia (right)

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The ENSO Through the observations in Winter

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The ENSO Through the observations in Summer

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The ENSO Through the observations in Winter

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The ENSO Through the observations in Summer

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The fundamentals of seasonal Forecasting The climatic variability The forecasting models Statistical models SST forced Atmospheric General Circulation Models Ocean/Atmosphere Coupled General Circulation Models The verifications Verification of the forecasts Verification of the usefulness of the forecats The chaos Link with the climatic variability Link with the ensemble forecast

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The fundamentals of Seasonal Forecasting The climatic variability : slow variation in the Atmosphere 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, 1083- 1126) 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)

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The North Atlantic Oscillation

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Rainfall in Winter Temperature in Winter

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The Pacific Decadal Oscillation

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The climatic variability

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The fundamentals of Seasonal Forecasting The climate variability

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The climatic variability The fundamentals of Seasonal Forecasting

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The fundamentals of seasonal Forecasting The climatic variability

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The fundamentals of seasonal Forecasting The climatic variability Atlantic El Nino Pirata buoy network

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The fundamentals of seasonal Forecasting The climatic variability JAS Observed Sahel Rainfall Vs JAS Observed THC index r = 0.45

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The fundamentals of seasonal Forecasting Forecasting models Statistical models SST forced Atmospheric Global Circulation Models Ocan/Atmosphre Coupled General Circulation Models

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The fundamentals of seasonal Forecasting The Statistical models East African Rainfall vs Nino3 Index Thanks to Simon Mason

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The fundamentals of seasonal Forecasting The Statistical models East African Rainfall vs Nino3 Index

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The fundamentals of seasonal Forecasting The Statistical models East African Rainfall vs Nino3 Index

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The fundamentals of seasonal Forecasting The Statistical models

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The fundamentals of seasonal Forecasting The Statistical models

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The fundamentals of seasonal Forecasting The Statistical models

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The fundamentals of seasonal Forecasting The Statistical models

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The fundamentals of seasonal Forecasting The Statistical models

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The fundamentals of seasonal Forecasting Numerical models

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The fundamentals of seasonal Forecasting The numerical models

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The fundamentals of seasonal Forecasting Coupled vs Forced models

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The fundamentals of seasonal Forecasting 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 users 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. Forecast Verifications

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Score/Skill and Value 2 complementary point of view : The scientific point of view : Quality of the forecast = Scores Interest of the use of the forecast = Skills 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)

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Score/Skill and Value Score point of view :

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Cost/Lost approach 2 categories : e.g. dryer / wetter and ratio Cost/Lost e.g. = 0.5 C1=Averaged cost using climatological forecast C2 =Averaged cost using perfect model forecast C3= Averaged cost using real model Score/Skill and Value Event obsnon obs forecast cc non forcast L0 Event obsnon obs forecast n 11 n 12 non forecast n 21 n 22

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Score/Skill and Value Value point of view :

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The fundamentals of seasonal Forecasting Verifications WMO Normes (parameters, scores, zones)

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Verification in Hindcast mode retrospective forecasts European research Projects PROVOST (CEPMMT, UKMO, EDF, LODYC, MPI, IMGA, DMI, U. Alcala) : Perfect Ocan forecast, 4 different models, 15 years x 4 seasons x 9 membres Rsults : clear in Tropics, some skill on North hemisphere in winter, but local uncertainties. Interest of the Multi-model approach. ELMASIFA (SNM Maroc, Algrie Tunisie) POTENTIALS (DMI, CINECA, LMD, MPI) DEMETER (CEPMMT, UKMO, LODYC, CERFACS, MPI, ADGB, IMGA, DMI, JRC, U. Liverpool, INM) : Forecasted Ocan using coupled models, 6 different models, 40 years x 4 seasons x 6 month x 9 membres Rsults : Provost revisited, improvment in Tropical regions and degradation in mid-latitude, extension of the range of the forecast.

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The fundamentals of seasonal Forecasting Chaos and ensemble forecast 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 Uncertainty Sources :

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The fundamentals of seasonal Forecasting Chaos and ensemble forecast Model errors (Ocanic and/or atmospheric)

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Butterfly effect (JFM 2003)

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The fundamentals of seasonal Forecasting Chaos and ensemble forecast To sample the initial state uncertainty analysis disturbances Corresponding to the most unstable modes Compatible with analysis errors Methods : Singular vectors, breeding To sample the modelisation uncertainty model disturbances Using several models Using stochastic physics Modifying some physical parameters To sample all the possible solutions for the Ocean Atmosphere system

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The fundamentals of seasonal Forecasting Chaos and climatic variability COLD & WETHOT & DRY

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The fundamentals of seasonal Forecasting Chaos and Climate variability

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The fundamentals of seasonal Forecasting The forecast sytem The description of the initial state of the Ocean/Atmosphere system Atmospheric Data Oceanic Data Data from the continental surface Assimilation data scheme Elaboration of products Direct Methods (Deterministic vs Probablilistic products) Indirect Methods (notably PPM or MOS) Adaptation of the products (notably downscaling) Interpretation of the forecast Transformation of the forecast to the benefit of the user Following-up of the process Update of the forecast Users Evaluation of the forecast (value, use and impact)

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The fundamentals of seasonal Forecasting The forecasting suite

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The fundamentals of seasonal Forecasting Description of the initial state of the Ocean/Atmosphere system

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The fundamentals of seasonal Forecasting Description of the initial state of the Ocean/Atmosphere system

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The fundamentals of seasonal Forecasting Description of the initial state of the Ocean/Atmosphere system

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The fundamentals of seasonal Forecasting The Oceanic data assimilation

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The fundamentals of seasonal forecasting Assimilation of the surface wind

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The fundamentals of seasonal Forecasting Description of the initial state of the Ocean/Atmosphere system

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The fundamentals of seasonal forecasting Elaboration of products Direct Methods (deterministic vs Probabilistic products) Indirect Methods (Statistical adaptations notably) Adaptation of the products (downscaling)

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The fundamentals of seasonal forecasting 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, userss needs) Elaboration of products

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Elaboration of numerical products Direct Methods (deterministic and probabilistic products) formulation as Indices or Anomalies Model Forecast : raw information not usefull !

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Elaboration of numerical products Direct Methods (deterministic and probabilistic products) formulation as Indices or Anomalies Debiased model forecast : better formulation

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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

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Elaboration of numerical products Direct Methods (deterministic and probabilistic products) formulation as Indices or Anomalies Indices : Model forecats compared to its own climatology Anomalies : Adaptation to local observation properties

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Downscaling Problem Seasonal predictability and associated scales adaptation to the user G l o b a l L o c a l statistical methods : Observations, Downscaling models numerical methods : Numerical models using GCM simulations as boundary conditions (single column, LAM, )

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Dowscaling Problem Seasonal predictability and associated scales adaptation to the user SeasonalSeasonal intra seasonal statistical methods : Observations, Downscaling models numerical methods : Numerical models using GCM simulations as boundary conditions (single column, LAM, )

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The fundamentals of seasonal forecasting Interpretation of the forecast transformation to the benefit of the user Translation in terms of actions, risks, scenario, and associated probabilities Following_up of the process Update of the forecast continuous process Evaluation on a user point of view Processs Experience Feedbacks

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Highlights of seasonal forecasting 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,