technical conference on « changing climate and demands for climate services for sustainable...
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Technical Conference on « Changing Climate and Demands for
Climate Services for Sustainable Development »
P. Bessemoulin & J.P. Céron
Météo-France
Research Needs for Seasonal to Inter-annual Climate Prediction
Susesi Hotel – Antalya – Turkey16 –18 February 2010
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Outline
LRF landscapeCapabilities and needs
The LRF and Application framework
How to improve Climate Products and Services ?Improvement of Global Climate Models
Large Scale information
Regional and National level
Climate impacts on sectorial activities and decision making
Conclusion
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Capabilities and Needs
- WCRP Seasonal Prediction Workshop, Barcelona Spain, 4-7 June 2007 (WCRP Position Paper on Seasonal Prediction, 2008: WCRP Informal Report No. 3/2008; Ben Kirtman and Anna Pirani, 2009: The State of the Art of Seasonal Prediction: Outcomes and Recommendations from the First WCRP Workshop on Seasonal Prediction. BAMS, Vol. 90, issue 4, pp 455–458).
-World Modelling Summit for Climate Prediction, Reading, UK, May 6-9, 2008 (Workshop Report WCRP No. 131; and Palmer and Shukla (2008): Advances in Modelling and Seamless Prediction (WMO/TD-No. 1458). )
- Review of the World Climate Research Programme (WCRP) (Feb. 2009) : Report from an ICSU-WMO-IOC-IGFA Review Panel
- Review of the World Climate Programme (WCP) and Climate Agenda (Feb. 2009): http://www.wmo.int/pages/prog/wcp/cca/documents/Doc5reviewWCPandclimateagenda.pdf
- WMO EC-RTT Report on the Challenges and Opportunities in Research on Climate, Weather, Water and Environment (WMO/TD-No. 1496) (June 2009).
-WCC-3 white papers/presentations at Sessions on « Advancing climate prediction science » and « Seasonal-to-interannual climate variability » (Sep. 2009)
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How to improve Climate products and services ?
Improvements of Global Climate modelsResolution (Horizontal and vertical)
Physics / Parameterizations
Additional sources of predictability Soil moisture : impacts notably on intraseasonal signal and Summer season Snow : Potential impact on Indian Summer monsoon, Winter AO/NAO, and
seasonal predictability Stratosphere : Polar stratosphere => Influence of ENSO, QBO, blocking
events over Northern Atlantic and Northern Pacific. Equatorial stratosphere (QBO) => impact on tropical and extratropical climate. Tropical Stratosphere/ Troposphere interaction => impact on extratropical climate.
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Modeling issuesResolution (both vertical and horizontal)
Current resolution for operational models (GPCs)GPCs Horizontal Vertical
Beijing T63 L16 Coupled
ECMWF T159 L62 Coupled
Exeter 1°25 x 1°875 L38 Coupled
Melbourne T47 L17 Coupled
Montreal T32, T63, T95 Tier-2
Moscou 1°125 x 1°406 L28 Tier-2
Seoul T106 L21 Tier-2
Tokyo T95 L40 Tier-2
Toulouse T63 L91 Coupled
Washington T62 L64 Coupled
CPTEC T62 L28 Tier-2
* Pretoria information not yet available
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How to improve Climate products and services ?
Large Scale information
MME issues
Circulation regimes / Modes of variability
Other parameters to be investigated (TC, Number of days, SPI, Extreme Events, Psi and Khi parameters, … )
Climate trend and Seasonal forecast
Prediction of the predictability
Intraseasonal information (including MJO, monthly desaggregation of LRF, … )
…
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MME issues
MME intiatives :Operational MME : Euro-Sip, IRI, APCN
Lead-Centre for MME (joint initiative from KMA and NCEP under the umbrella of WMO)
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MME issues
Is Multi-model approach better than Single model forecast ?
Weigel et ali, , 2008, QJMRS
RPSS maps – DEMETER experiment
1960-2001 – T2m – JJA season
a) model S2 b) model GS
c) MME equal weights d) MME optimal weight (IGN method)
Still some work to do on the best combination (e.g. coupled, 2 tiered, all, optimal combinations) and the evaluation of MME products
Which products (Large Scale, global outlook, …) ?
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MME issues
Is Multi-model approach better than Single model ?
Weigel et ali, 2008, QJMRS
MME not necessarily the best for local applications (improvements versus operationnal constraints)
Regions where MME (IGN method) outperforms single model forecasts
comparison of grid points RPSS : Multi-Model (IGN) against Single Models
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Summer Regime (JJA -1st of May Initialisation – Z500)
NCEP
Model 1
Model 2
NAO- Atl.Low/NAO+ S-Blocking Atl.Ridge
Atmospheric Circulation RegimesChabot et al. – EMS-ECAC conference - 2008
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Model 1 Model 2
NAO-
NAO+
AtlanticRidge
Blocking
NINANINO
NATL
NewfoundlandPattern
Horse Shoesshape +Horse shoes
shape -
NINO
NINO
Favoured when :Not favoured
when :
Oceanic Precursors of winter regimes
NINA
Consistent with Greatbatch et al., 2004
Consistent with Sutton, 2001
Consistent with Rodwell, 2002
Model 1 shows a better sensitivity to ocean than Model 2 despite this is not reflected in the scores
Consistent with Kushnir et al.,2002
Atmospheric Circulation Regimes
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Other Large Scale Parameters
In regions where the predictability is quite low, it becomes crucial for the use of climate information to give information on the predictability of individual years or seasons (especially in mid-latitudes) and expected teleconnections
New Model Diagnosis and associated evaluations (e.g. Stream function and Velocity Potential)
JFM 2010 forecasts
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JJA TrendEnsemble Mean
Impact of Climate Trend on the Seasonal Forecast
Model 1 Model 2
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How to improve products and services ?
Regional and National LevelsImproving Regional Climate Modelling
Downscaling and tailoring (statistical vs dynamical)
Spatial and temporal
Best practices (software, guidance, …)
Large Scale influence at regional and national scale
…
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Downscaling Methods
GCM : Global Climate Model
RCM :Regional Climate Model
NH models User’s
models
Useful Forecast at smaller scales
State of the Climate system (ocean-atmosphere+ Cryosphere-Biosphere)
Statistical models
(PP or MOS)
Statistical models
Large Scale Information
User’s models
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Downscaling Methods
Downscaling challenge for applications :
DEMETER Precipitation
Downscaling on ISBA mesh - 8km
First step : Intermediate information on
SYMPOSIUM zones
Cumulated rain in mm over March-April-May 1998
SAFRANReference
from 2°5 to 8 km
Evaluation and propagation of uncertainty
Second step : downscaling
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Time Downscaling
MJO forecasts Linkage with resolution and physics :Kang – 2007 – APCC symposium
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Downscaling methods
Best Practices Softwares (developed on best practices considerations, needs of specifications and qualification)
Guidances (joint effort of WCRP and WCP) to the benefit of RCOFs, RCCs, NMHS, NCCs, …
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How to improve products and services ?
Climate influence at regional and national scalesImpact of external forcings
Circulation regimes / modes of variability
Teleconnection patterns (and their representation in GCMs)
Other parameters to predict ( Number of rain or dry days, length of the rainy season, SPI, … )
Predicting other components of the Climate system (Hydrology notably)
…
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How to improve climate products and services ?
Hydrological seasonal forecats : ensemble riverflow forecast
9
9 runs
1 state 1 state
Céron et al. – ASL 2010 – DOI: 10.1002/asl.256
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How to improve products and services ?
Hydrological seasonal forecats : ensemble riverflow forecast
Correlations : Soil Wetness Index
Ensemble mean compared to the SIM reference on the training file(period 1979-2001) – Spring period (MAM)
Céron et al. – ASL 2010 – DOI: 10.1002/asl.256
SWI is particularly relevant for Agriculture applications and drought monitoring
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How to improve products and services ?
River flow forecasts : Probabilistic / Deterministic forecast quite interesting
Scores for 4 river catchments and spring period (MAM)
Hydrological components can have a higher predictability than the atmospheric parameters (partly related to slowly varying forcings like snow cover)
Céron et al. – ASL 2010 – DOI: 10.1002/asl.256
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How to improve products and services ?
Climate impact on sectorial activities and decision making
User oriented evaluations (e.g. use and impact of use of LRF, socio-economic benefits, …)
Use of seasonal forecasting information (decision making, evaluation, …)
Seamless use of Climate information
Communication to the users including uncertainty and vulnerability assessments
Assessment of climate impacts
Climate “awareness”
…
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User oriented evaluations
User oriented evaluation
(values) : based on Hits and Misses and users cost functions
RhgcbhbfidaN
VSPS 1
2 1 9
2 RVCli
3
2 MaxV
Profit
Loss R
CliMAX
CliSPSSPS VV
VVVSS
The basic information is included within the LRF-SVS level 3 (contingency tables to be adapted ?)
Close collaboration with the user sectors (economical models, …)
Forecst / Obs
T- T0 T+
T- a b c O-T0 d e f O0
T+ g h i O+ F- F0 F+
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Use of Seasonal Forecasts
Evaluation of the users Decision Making chainJulie & Céron Elements for Life - 2007
35000
40000
45000
50000
55000
60000
65000
70000
75 80 85 90 95
theoritical management, with Qd exactly known
management without knowledge of Qdmanagement with Qd forecasted with ARPEGE results
interannual average of electric power production (Mw)
interannual average of recessing
crops surface
(ha)
Evaluation over the training period (1979-2000) :
energy production optimisation up to 35-40%, artificial flood, allowing a surface of 50 000 ha for recession culture, guaranteed 4 years out of 5 compared to 1 out of 5 in natural regime water resource saving around 10%
But in the real life ?
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Highlights
Modelling issues Improvement of Global Climate models
Additional sources of predictability
Large Scale Information MME issues
Circulation regimes and modes
Other LS parameters
Impact of climate trend on Seasonal forecasting
Regional and National levels Space and Time downscaling
Other component of the climate system
Climate impact on sectorial activities and Decision Making User-oriented evaluation
Use of Climate information
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Conclusion
Research needs on LRF
Still a lot of work to achieve on both WCRP and WCP sides !
Needs of better and sustainable linkage between Research, Operations and Users (which mechanism ?)
Climate Services perspectives (GFCS)
Priorities for the next intersession period (e.g. Downscaling effort, MME issues, GCMs improvements, … ?)
…
A number of cross-cutting themes between WCRP and WCP
WCRP / WCP coordination and joint efforts (especially between WGSIP and OPACE 3)
Inter-Commission Task Team on Seasonal to Inter-Annual forecasts (WGSIP / OPACE 3 / CBS ET-ELRF) ?
…
Technical Conference
P. Bessemoulin & J.P. Céron
Météo-France / Direction of Climatology
WORLD METEOROLOGICAL ORGANIZATION
Senousi Hotel – AntalyaTurkey
Research needs for seasonal forecastingand its applications
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Use of Seasonal Forecasts
Seamless use of Climate information
Continuum of information on both space and time scales :
Best compromise between users’ needs and science and related feasible products Consistency between use of information and information Needs of information on the use of the products (actions/decisions, decisional calendar, critical time scales)
Range extension
Provision of Climate Information and Services should be an Action Driven Process
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The LRF and Application framework
National Level(NMHS, NCC,
NCOF, ...)
Global LevelMonitoring & Forecast
(GPCs, LCs, GDC, GMC, …)
Regional Level(RCC , RCOF, RCW, …)
Users(National)
Water ressourcesAgriculture
EnergyHealth
…
NationalPartners
RegionalPartners
InternationalPartners
National Organisation& NGO
MWG
MEDIA
RegionalUsers
MEDIA
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Downscaling Methods
Purely Dynamical and Statistical/Dynamical methods give quite comparable results
Palmer et al, 2004, BAMS (Demeter paper)
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Use of Seasonal Forecasts
Choice of the best strategy taking into account the concurent uses of the water.
Dispatching of the forecast to Dakar desagregated by month and for the region of interest at the beginning of August
SON Forecasts issued in Toulouse by the end of July.
Merging informations from the water management and the seasonal forecast
Implementation of the climate information into the user decision making chain and evaluation
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Use of Seasonal Forecasts
Use of Presao forecast by IFRCC
Implementation of climate information into the users’ decision making chain and evaluation
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Atmospheric Circulation RegimesScores - projection
on model regimes
Model 1 Model 2
NAO- 11
NAO+ 10
Atlantic Ridge 7
Blocking 10 10
8
10
6
9
3
8
7
8
6
6
6
Hits
False Alarme
Euro-Sip models
Chabot et al. – EMS-ECAC conference - 2008
Scores - projection
on reanalysis
Model 1 Model 2
NAO- 12
NAO+ 10
Atlantic Ridge 16
Blocking 7 16
8
15
6
7
4
7
6
8
8
5
5
Technical ConferenceAntalya – 16 to 18 february 2010 35
Atmospheric Circulation RegimesScores - projection
on model regimes
Model 1 Model 2
NAO- 11
NAO+ 10
Atlantic Ridge 7
Blocking 10 10
8
10
6
9
3
8
7
8
6
6
6
Hits
False Alarme
Euro-Sip models
Chabot et al. – EMS-ECAC conference - 2008
Scores - projection
on reanalysis
Model 1 Model 2
NAO- 12
NAO+ 10
Atlantic Ridge 16
Blocking 7 16
8
15
6
7
4
7
6
8
8
5
5
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Circulation Regimes and Downscaling
Forecast Mode and use – Winter 2009 forecastsMean Anomalies
Min Temperatures / Extreme Rainfall Increased Occurrence of NAO – regimes
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Trend JJAForecast Z500 JJA 2008
Provision of Climate forecasts at seasonal scales :
(Seasonal + Trend) or Seasonal and Trend separately ?
Impact of Climate Trend on the Seasonal Forecast
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LRF Context
Operationnal LRF at global scalesGPCs or GPCs-like centresProvision of MME products (Euro-Sip, IRI, APCC) WMO Lead-Centres (LRF-MME and SVS-LRF)
Needs of LRF productsReduction of vulnerability to the current Climate VariabilityAssessment of the value and success stories demonstrating the usefulness of LRF products.
LRF and CC issuesCoping with the current Climate Variability is the first step to CC adaptationLessons learnt from LRF useful for CC issues (tools, methods, user liaison, …)
WCC3 : from products to Climate ServicesNeeds to move from products to Climate Services tailored to users’needsLRF framework consistent with GFCSPrioritary domains (water resources, food security, health, …)
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The LRF and Applications components
Global levelGlobal Climate Models
Description of the climate system (notably initial state and different components)
influence of slowly varying forcings on Climate at global scale (including teleconnections) Climate impacts on sectorial activities and decision making
Regional LevelRegional Climate Models
Downscaling and tailoring
Climate influence at regional scale (idem)
Climate impact on sectorial activities and decision making
National LevelDownscaling and tailoring
Climate influence at national scale (idem)
Climate impact on sectorial activities and decision making
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• How to merge Informationfrom different sources • Consensus methods
Use of Seasonal Forecasts
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Modeling issues
Resolution (horizontal)Kang – 2007 – APCC symposium
For Seasonal Forecasting Some improvements from 300 to 100 km (mean climate, scores) Benefits less clear from 100 to 20 km
Forecasting extremes
MJO
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Additional sources of predictability
StratosphereControl ensemble Nudged ensemble
Douville, 2009, Geophys. Res. Lett. doi :10.1029/2009GL039334
NAOEOFindex
PNAgrid cellindex
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Additional sources of predictability
StratosphereA.C. Mayock et al., 2009, Climate Dynamics,
doi :10.10007/s00382-009-0665-x
Poor representation of stratospheric circulationPresently no discernable predictability in the extratopical stratosphere Robust large scale response (weakened Polar Vortex, NAM -)
Z500 Composites for the 10-30 days following the 7 largest peak amplitude « warming » events
Impact of resolution Impact of physics (GWD, Convection, ..) Modelling challenge ?
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Modeling issuesPhysics :
No independance between the horizontal and vertical resolution and the physicsNo independance between the phenomenum of interest, the resolution and the physicsGenerally, the finer the resolution, the more refined the physical packageFor operations, balance between the horizontal resolution, vertical resolution, physical package and computing resources
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Modeling issues
Resolution (horizontal) Kang – 2007 – APCC Symposium
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Additional sources of predictability
Surface conditionsSoil moisture (impacts notably on intraseasonal signal and Summer season)
Douville 2009, Koster et al. 2010
Snow Potential impact on Indian Summer monsoon : Robock et al. 2003, Fasullo 2004
(Obs.), Ferranti and Molteni 1999 (GCMs), Peings and Douville 2009 (CMIP3) Potential impact on Winter AO/NAO : Qian and Saunders 2003, Cohen 2007 (Obs)
Gong et al. 2003, Fletcher et al. 2007, Fletcher et al. 2009 (GCM s), Hardiman et al. 2008 (CMIP3)
Potential impact on seasonal predictability : e.g. Cohen and Fletcher 2007 (Statistical hindcasts), Orsolini and Kvamsto 2009, Douville 2009 (Dynamical « hindcasts »)
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Additional sources of predictability
Stratosphere Polar stratosphere => Influence of ENSO (e.g. Ineson and Scaife
2009), QBO (e.g. Hamilton 1998, Thompson et al. 2002, Marshall and Scaife 2009), blocking events over Northern Atlantic and Northern Pacific (e.g. Martius et al. 2009).
Equatorial stratosphere (QBO) => impact on tropical (e.g. Giorgetta et al. 1999) and extratropical climate (e.g. Boer and Hamilton 2008).
Tropical troposphere => impact on extratropical climate (e.g. Jung et al. 2008).
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Additional sources of predictability
Stratosphere influenceControl ensemble Nudged ensemble
NAOEOFindex
PNAgrid cellindex
Douville, 2009, Geophys. Res. Lett. doi :10.1029/2009GL039334
Technical ConferenceAntalya – 16 to 18 february 2010 49
Additional sources of predictability
Stratosphere influenceDouville, 2009, Geophys. Res.
Lett. doi :10.1029/2009GL039334
Control ensemble Nudged ensemble
NorthernEuropeTemp.
(K)
NorthernEuropePrec.
(mm/d)
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MME issues
Is Multi-model approach better than Single model forecast ? BSSs – Demeter experiment
1958-2001 – T2m – all season
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MME issues
Is Multi-model approach better than Single model forecast ?
Weigel et ali, 2008, QJMRS
comparison of grid points RPSS : Multi-Model (IGN) against Single Models
Regions where MME (IGN method) outperforms single model forecasts
MME not necessarily the best for local applications (improvements versus operationnal constraints)
Technical ConferenceAntalya – 16 to 18 february 2010 52
MME issues
Adaptations of GCM’s output over New-Caledonia
Minimum
Temperature
Maximum
Temperature
Leroy & Céron, 2007, La Météorologie
Technical ConferenceAntalya – 16 to 18 february 2010 53
NAO- NAO+
Ridge Blocking
SST Composites of November (standardized anomalies) vs winter regime occurrences in DJF (Model 1 model)
Lower Tercile
Upper Tercile
Upper Tercile
Lower Tercile
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Atmospheric Circulation Regimes
Relationship regimes / variability modes : Decomposition in terms of variability modes (December - Model 1 - ensemble mean)
EOF analysis and varimax rotation (Linear method)
MODE 1 - Model 1
Explained Variance : 21,9 %
MODE 6 - Model 1
Explained Variance : 6,9 %
Correlation with NAO- : - 0,75
No correlation with NAO+ No correlation with NAO-
Correlation with NAO+ : - 0,85
Mode 1 is the morecorrelated to ENSO
Mode 1 is related to NAO- occurrences
the only regime not related to Mode 1is NAO+ (stronglyrelated to Mode 6)
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Composite Analysis (DCLIM)
Teleconnections
(MedUp project – SON season)
200
EN
S
200 ERA40200 ERA40
200
M
ED
2
00
N
PB
R
200
N
PH
R
200
EN
S2
00
M
ED
200
N
PB
R2
00
N
PH
ROther Large Scale Parameters
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The Downscaling problem
Relevant ScalesMesh of the GCM ~ 200 km,
3 month averaged information
(or month by month)
Scales of applications ~ 1m to 10 km,
Day, 10 days, month,
Climate parameters (RR, Tn, Tx,
Number of days …),
Parameters from the application
domain (Agriculture, Water resources,
Health, … ),
Substantial benefits (scores, …) in
correcting GCMs output
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Downscaling Methods
Dynamical downscalingAvailable RCM models
• MM 5• PRECIS,• ALADIN • HIRLAM,• …. ,
Hight Resolution Global Circulation Models • Full HR GCM • Stretched Grid
Available models (HR GCM)• Earth Simulator (~10 km - Research),• ECMWF (T159 L62 / N80 for the physics - operational - ~125 km),• Arpège (T63 L31 C3.5 - ~50km - Research),
Needs for clear specifications and guidances in the use of dynamical downscaling
Coupled vs uncoupled RCM
Forecasting extremes
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Time Downscaling
New-Caledonia :Probabilities of rainfall above the upper quintile (strong rainfall) in JFM vs MJO phases (Real time MJO Multivariate Index – Wheeler & Hendon – MWR - 2004)
Intraseasonal variabilityIntraseasonal evolutions
Intraseasonal modulations
Significant intraseasonal
forcing of the MJO on Tn,
Tx and RR
Technical ConferenceAntalya – 16 to 18 february 2010 59
Time Downscaling
Linkage MJO / Mid-Latitude Cassou 2008 – DOI:10.1038/nature07286
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User oriented evaluations
Use of Seasonal Forecast in the Insurance domain – case of a « free » market
Forecast / Obs
E- E0 E+
E- a b c O-
E0 d e f O0
E+ g h i O+
Pr- Pr0 Pr+
RhgcbhbfidaN
VSPS 1
2 1 9
2 RVCli
3
2 MaxV
Profit
Loss R
CliMAX
CliSPSSPS VV
VVVSS
The basic information is included within the LRF-SVS level 3 (contingency tables to be adapted ?)
Close collaboration with the user sectors (economical models, …)
Technical ConferenceAntalya – 16 to 18 february 2010 61
User oriented evaluations Infering Indices for evaluating the value for an « Insurance Company »
RhgcbhbfidaN
VSPS 1
RgcbgdaN
VSPS 1
RchgcfiN
VSPS 1
RVV CliCli 19
1 3
1 MaxMax VV
2 1 9
2 RVCli
3
2 MaxV
Pr
ofit
LossR
CliMAX
CliSPSSPS VV
VVVSS
The basic information is included within the LRF-SVS level 3 (contengency tables to be adapted ?)
Close collaboration with the user sectors (economical models, …)
Technical ConferenceAntalya – 16 to 18 february 2010 62
How to improve Climate products and services ?
Large Scale information
MME issues
Circulation regimes
Climate trend and Seasonal forecast
Intraseasonal information (including MJO, monthly desegregation of LRF, … )
Other parameters to be investigated (TC, extreme events, Psi and Khi parameters, … )
Prediction of the predictability
Climate “awareness”
…
Technical ConferenceAntalya – 16 to 18 february 2010 63
How to convey uncertainty ?
Ensemble forecasts : SST forecast
Expertised/supervisedcombination ?
Evaluation of uncertainty and its propagation along the whole chain
Uncertainty must be disseminated with the products