Technical Conference on « Changing Climate and Demands for Climate Services for Sustainable...

Technical Conference on « Changing Climate and Demands for

Climate Services for Sustainable Development »

P. Bessemoulin & J.P. Céron

Météo-France

[email protected]

Research Needs for Seasonal to Inter-annual Climate Prediction

Susesi Hotel – Antalya – Turkey16 –18 February 2010

Technical ConferenceAntalya – 16 to 18 february 2010 2

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


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)

http://www.wmo.int/pages/prog/wcp/cca/documents/Doc5reviewWCPandclimateagenda.pdf


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.


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




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, … )

…


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)


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, …) ?


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


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


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


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


JJA TrendEnsemble Mean

Impact of Climate Trend on the Seasonal Forecast

Model 1 Model 2


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

…


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


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


Time Downscaling

MJO forecasts Linkage with resolution and physics :Kang – 2007 – APCC symposium


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, …



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)

…


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



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)


SWI is particularly relevant for Agriculture applications and drought monitoring



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)




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”

…


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+


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 ?


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


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

[email protected]

WORLD METEOROLOGICAL ORGANIZATION

Senousi Hotel – AntalyaTurkey

Research needs for seasonal forecastingand its applications



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


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


Downscaling Methods

Purely Dynamical and Statistical/Dynamical methods give quite comparable results

Palmer et al, 2004, BAMS (Demeter paper)



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



Use of Presao forecast by IFRCC

Implementation of climate information into the users’ decision making chain and evaluation


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


Circulation Regimes and Downscaling

Forecast Mode and use – Winter 2009 forecastsMean Anomalies

Min Temperatures / Extreme Rainfall Increased Occurrence of NAO – regimes


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


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, …)


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)


National LevelDownscaling and tailoring

Climate influence at national scale (idem)



• How to merge Informationfrom different sources • Consensus methods



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



StratosphereControl ensemble Nudged ensemble

Douville, 2009, Geophys. Res. Lett. doi :10.1029/2009GL039334

NAOEOFindex

PNAgrid cellindex



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 ?


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


Modeling issues

Resolution (horizontal) Kang – 2007 – APCC Symposium



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 »)



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).



Stratosphere influenceControl ensemble Nudged ensemble

NAOEOFindex

PNAgrid cellindex

Douville, 2009, Geophys. Res. Lett. doi :10.1029/2009GL039334



Stratosphere influenceDouville, 2009, Geophys. Res.

Lett. doi :10.1029/2009GL039334

Control ensemble Nudged ensemble

NorthernEuropeTemp.

(K)

NorthernEuropePrec.

(mm/d)


MME issues

Is Multi-model approach better than Single model forecast ? BSSs – Demeter experiment

1958-2001 – T2m – all season


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)


MME issues

Adaptations of GCM’s output over New-Caledonia

Minimum

Temperature

Maximum

Temperature

Leroy & Céron, 2007, La Météorologie


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


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)


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


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


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


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


Time Downscaling

Linkage MJO / Mid-Latitude Cassou 2008 – DOI:10.1038/nature07286


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 ?)



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 ?)





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”

…


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

Technical Conference on « Changing Climate and Demands for Climate Services for Sustainable...

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