Seasonal Climate Prediction Seasonal Climate Prediction

Youmin TangYoumin Tang

Environmental Science and Environmental Science and Engineering, University of Northern Engineering, University of Northern

British Columbia British Columbia

IntroductionIntroduction Basic theory and methods for the Basic theory and methods for the

dynamical climate prediction systemdynamical climate prediction system International activities on the International activities on the

seasonal climate predictionseasonal climate prediction Seasonal prediction in CanadaSeasonal prediction in Canada Climate prediction in Climate prediction in UNBCUNBC Future ChallengesFuture Challenges

Contents

Weather PredictionWeather Prediction Prediction of long-term Prediction of long-term

climate changeclimate change Seasonal PredictionSeasonal Prediction

Three types of predictions

dt

dSdt

dT p

dt

d

0

v

1

pST ,,

0z

w

v

gz

p

u, v, T, S

w, p,

Prognostic & diagnostic Eq.

NWP:NWP: time evolution of the exact time evolution of the exact state of the atmospherestate of the atmosphere

Long-term prediction:Long-term prediction: gross gross features of a changed climate features of a changed climate averaged over many yearsaveraged over many years

Seasonal Predictions:Seasonal Predictions: describe describe statistic aspects of atmospheric statistic aspects of atmospheric anomalies over 1-3 monthsanomalies over 1-3 months

Differences

Goal: to forecast the exact state of Goal: to forecast the exact state of the atmosphere from initial the atmosphere from initial conditions, at high time resolution conditions, at high time resolution over several days.over several days.

Combination of statistical technique, Combination of statistical technique, experience, and intuition, in the final experience, and intuition, in the final stage of most forecast.stage of most forecast.

Mid-latitude, the limit predictability is Mid-latitude, the limit predictability is usually considered to be 10-14 days usually considered to be 10-14 days (Lorenz, 1982).(Lorenz, 1982).

Weather prediction

Predictability arises solely from Predictability arises solely from internal atmospheric dynamicsinternal atmospheric dynamics

– Accurate atmospheric initial conditionsAccurate atmospheric initial conditions– Smaller errors in the initial state can Smaller errors in the initial state can

grow rapidly and lead to a poor forecast grow rapidly and lead to a poor forecast even with a perfect modeleven with a perfect model

– Slightly different initial conditions are Slightly different initial conditions are used for ensemble forecastused for ensemble forecast

Slowly evolving lower boundary Slowly evolving lower boundary conditions are often assumed to be conditions are often assumed to be constantconstant

Weather prediction

Goal: to characterize changes in the Goal: to characterize changes in the long-term mean atmospheric and long-term mean atmospheric and oceanic circulation and especially to oceanic circulation and especially to characterize mean changes at the characterize mean changes at the earth’s surfaceearth’s surface

Tools: a coupled atmosphere-ocean-Tools: a coupled atmosphere-ocean-land-ice model --- Climate system land-ice model --- Climate system modelmodel

Concern with gross features of a Concern with gross features of a changed climate averaged over many changed climate averaged over many yearsyears

Prediction of long-term climate change

Focus fairly qualitatively on a few key Focus fairly qualitatively on a few key climate variablesclimate variables

– Surface temperatureSurface temperature– PrecipitationPrecipitation

Distinct from both NWP and Climate Distinct from both NWP and Climate Simulation in three aspects:Simulation in three aspects:

– In PurposeIn Purpose– In ApproachesIn Approaches– In timescaleIn timescale

Seasonal Prediction

The New Challenge: Linking Climate to Weather

Climate is traditionally viewed as the integration of discrete weather events and variables over time and space

agriculture

Why Seasonal PredictionWhy Seasonal Prediction• Growing demand for reliable seasonal forecasts

tourismenergy

Venezuela Germany

India

Use of better Drought forecasts to Improved Dam and hydropower management for hydroelectric power generation has enabled the domestic and manufacturing industries in Kenya and Tanzania to run at optimum capacity

Economic Loss caused by 1982/1983 El Nino Event

Theoretical limit

Actual predictability

Internal chaotic Process

Method (model)

Quality of IC & BC

In climate prediction, Potential predictability is usually regarded as the predictability with full information of future boundary condition (e.g., SST). Thus, predictability is varied with similarity between the response of real atmosphere and prediction method to the same BC.

Potential predictability

Predictability of seasonal predictionPredictability of seasonal prediction

Obs.

From Prof. In-Sik kangFrom Prof. In-Sik kang

The limitation of predictability arises The limitation of predictability arises fromfrom

– The imperfections of the forecast modelThe imperfections of the forecast model– The nonlinearity of the climate systemThe nonlinearity of the climate system

The predictive skill depends on:The predictive skill depends on:– The field consideredThe field considered– The model used for forecastThe model used for forecast– The initial state of the systemThe initial state of the system

Why --- limit of Predictability

Any statement about the predictive Any statement about the predictive skill should include the word “for this skill should include the word “for this model”model”

Predictive skill should be calculated Predictive skill should be calculated for a large number of situations in for a large number of situations in order to make the most general order to make the most general statement possiblestatement possible

Limitation of Predictability

Statistical MethodStatistical Method

Dynamical MethodDynamical Method

Hybrid MethodHybrid Method

Methods for seasonal prediction

Since the beginning of the twentieth century Since the beginning of the twentieth century (e.g., Quayle, 1929) (e.g., Quayle, 1929)

Based on historical data and employ a mathBased on historical data and employ a mathematical relationship between predicted anematical relationship between predicted and predictor variables. d predictor variables.

SST anomalies (especially over the tropical SST anomalies (especially over the tropical Pacific Ocean) are the sole predictor for the Pacific Ocean) are the sole predictor for the statistical forecasts of seasonal climate anostatistical forecasts of seasonal climate anomalies (e.g., Folland et al., 1991; Ward and malies (e.g., Folland et al., 1991; Ward and Folland, 1991; Barnston, 1994) Folland, 1991; Barnston, 1994)

Statistical Methods for seasonal prediction

– Regression approachesRegression approaches e.g., Knaff and Landsea, 1997e.g., Knaff and Landsea, 1997

– Canonical correlation analysis (CCA)Canonical correlation analysis (CCA) e.g., Barnston and Ropelewski, 1992 e.g., Barnston and Ropelewski, 1992

– Neural network models Neural network models e.g., Sahai et al., 2000; Tanggang et al, 199e.g., Sahai et al., 2000; Tanggang et al, 199

8, Tang et al. 2001; 2002; 2003.8, Tang et al. 2001; 2002; 2003.

…………

Statistical Methods for seasonal prediction

LimitationsLimitations ：：– require a long and accurate data of the eartrequire a long and accurate data of the eart

h’s climateh’s climate– require an understanding of the physically brequire an understanding of the physically b

ased relationships between predicted and pased relationships between predicted and predictor variablesredictor variables Unstable relationship (e.g. Krishna Kumar, 1999Unstable relationship (e.g. Krishna Kumar, 1999

) )

– No physicsNo physics

Statistical Methods for seasonal prediction

Since late 80’s last century Since late 80’s last century

Based on mathematical representation of Based on mathematical representation of physical laws governing the behavior of the physical laws governing the behavior of the atmosphere or the coupled atmosphere-atmosphere or the coupled atmosphere-ocean system. ocean system.

Dynamical Methods for seasonal prediction

Be able to estimate uncertainty of prediction Be able to estimate uncertainty of prediction

through Ensemble prediction. through Ensemble prediction. High potential to improve skill in the future High potential to improve skill in the future

The Basis for all predictions at timescales The Basis for all predictions at timescales longer than a month is the hypothesis that, longer than a month is the hypothesis that, on these timescales, the atmospheric on these timescales, the atmospheric statistics are in equilibrium with the statistics are in equilibrium with the surface boundary conditionssurface boundary conditions

A prediction of Surface Boundary A prediction of Surface Boundary conditions will lead to some statistical conditions will lead to some statistical knowledge of the atmosphereknowledge of the atmosphere

Seasonal-to-interannual prediction

Slowly varying boundary conditions, imposSlowly varying boundary conditions, impose a slow variation of atmospheric statisticse a slow variation of atmospheric statistics

– Sea surface temperatureSea surface temperature– Soil moistureSoil moisture– Sea ice extentSea ice extent– Surface albedoSurface albedo– …………

Seasonal-to-interannual prediction

Strong interact with the atmosphere:Strong interact with the atmosphere:– Soil moisture --- rainfall & evaporationSoil moisture --- rainfall & evaporation– Albedo --- Snow and ice extentAlbedo --- Snow and ice extent– SST --- fluxes of heat and momentum frSST --- fluxes of heat and momentum fr

om atmosphereom atmosphere Evolve with their own dynamicsEvolve with their own dynamics

A climate system model is A climate system model is neededneeded

Boundary conditions

Operational predictionOperational prediction －－ Two tiersTwo tiers

STEP-I

PredictSSTA

ENSO prediction

Observed SSTA

互联网

合成海温异常

STEP-II

AGCM

Initial condition

SST

Model SSTA Cli

mato-logy

EENNSSEEMMbbLLEE

EnsembleSSTA

ECMWF

Ensemble PredictionEnsemble Prediction

The Ensemble prediction simulates The Ensemble prediction simulates possible initial uncertainties by adding, to possible initial uncertainties by adding, to the original analysis, small perturbations the original analysis, small perturbations within the limits of uncertainty of the within the limits of uncertainty of the analysis. From these alternative analyses, a analysis. From these alternative analyses, a number of alternative forecasts are number of alternative forecasts are produced produced

Ensemble runsEnsemble runs How to How to optimally perturb system?optimally perturb system?

The model dimensionality is large, typically The model dimensionality is large, typically 10^6.10^6.

We must perturb the system wisely such that We must perturb the system wisely such that

we can use affordable perturbation members we can use affordable perturbation members for ensemble predictions.for ensemble predictions.

We need to find the optimal perturbation We need to find the optimal perturbation patternspatterns singular vectors or breeding singular vectors or breeding vectors of the linearized operator of the vectors of the linearized operator of the original system.original system.

( )

( )

)

Lt

L

t

A

The growth (forecast error) of The growth (forecast error) of perturbation (or initial error) in the time perturbation (or initial error) in the time interval interval can be expressed in the can be expressed in the formform::

*

A A

A A

The vector of the small perturbation The vector of the small perturbation

that that maximizes maximizes is the is the

first first eigenvectoreigenvector of ,i.e, the of ,i.e, the singular vector of singular vector of A. where A* is the A. where A* is the ad jointad joint operator of A. operator of A.

*A A

International Research International Research Activities on the dynamical Activities on the dynamical

Seasonal predictionSeasonal prediction

Prediction and predictability

CLIVARCLIVAR (Study of (Study of CLICLImate mate VARVARiability and Piability and Predictability)redictability)

SMIPSMIP ( ( SSeasonal Prediction easonal Prediction MModel odel IIntercomparntercomparison ison PProject --- Phase I & Phase II )roject --- Phase I & Phase II )

NSIPPNSIPP ( (NNASA ASA SSeasonal to easonal to IInterannual nterannual PPredictirediction on PProject)roject)

PROVOSTPROVOST ( (PRPRediction ediction OOf Climate f Climate VVariation ariation OOn n SSeasonal to interannual easonal to interannual TTime scale)ime scale)

DEMETERDEMETER （（ DDevelopment of a evelopment of a EEuropean uropean MMulultimodel timodel EEnsemble system for Seasonal to interansemble system for Seasonal to interannual prediction) nnual prediction)

APCN Multi-model Ensemble Project APCN Multi-model Ensemble Project …………

International Projects

Development of a

European Multi-Model Ensemble System

forSeasonal to Interannual Prediction

（ http://www.ecmwf.int/research/demeter/general/index.html)

Multi-Model Ensemble SystemMulti-Model Ensemble System

Partner Atmosphere Ocean

ECMWF IFS HOPE

LODYC IFS OPA

CNRM ARPEGE OPA

INGV ECHAM OPA

MPI ECHAM HOPE

UKMO UM UM

•• DEMETER system: 6 coupled global circulation modelsDEMETER system: 6 coupled global circulation models

•• Hindcast production for: 1987-1998 (1958-2001)Hindcast production for: 1987-1998 (1958-2001)

9 member ensembles9 member ensembles

ERA-40 initial conditionsERA-40 initial conditions

SST and wind perturbationsSST and wind perturbations

4 start dates per year4 start dates per year

6 months hindcasts6 months hindcasts

APCN Multi-Model Ensemble System

Participating ModelsMember Member

EconomiesEconomiesAcronymAcronym OrganizationOrganization

Model Model ResolutionResolution

AustraliaAustralia POAMAPOAMA Bureau of Meteorology Research Centre  Bureau of Meteorology Research Centre  T47L17T47L17

Canada Canada MSCMSC Meteorological Service of CanadaMeteorological Service of Canada 1.875 1.875 1.875 1.875 L50L50

ChinaChinaNCCNCC National Climate Center/CMANational Climate Center/CMA T63L16T63L16

IAPIAP Institute of Atmospheric PhysicsInstitute of Atmospheric Physics 4 4 5 5 L2 L2

Chinese TaipeiChinese Taipei CWBCWB Central Weather BureauCentral Weather Bureau T42L18T42L18

JapanJapan JMAJMA Japan Meteorological AgencyJapan Meteorological Agency T63L40T63L40

KoreaKorea

GDAPS/KMAGDAPS/KMA Korea Meteorological AdministrationKorea Meteorological Administration T106L21T106L21

GCPS/KMAGCPS/KMA Korea Meteorological AdministrationKorea Meteorological Administration T63L21T63L21

METRI/KMAMETRI/KMA Meteorological Research InstituteMeteorological Research Institute 4 4 5 5 L17 L17

RussiaRussiaMGOMGO Main Geophysical ObservatoryMain Geophysical Observatory T42L14T42L14

HMCHMC Hydrometeorological Centre of RussiaHydrometeorological Centre of Russia 1.125 1.125 1.40625 1.40625 L28L28

USAUSA

COLACOLA Center for Ocean-Land-Atmosphere StudiesCenter for Ocean-Land-Atmosphere Studies T63L18T63L18

IRIIRI International Research InstituteInternational Research Institutefor Climate Predictionfor Climate Prediction T42L18T42L18

NCEPNCEP NCEP Coupled Forecast SystemNCEP Coupled Forecast System T62L64T62L64

NSIPP/NASANSIPP/NASA National Aeronautics and Space AdministrationNational Aeronautics and Space Administration 2 2 2.5 2.5 L34 L34

International Research Institute for Climate Prediction (IRInternational Research Institute for Climate Prediction (IRI) I) (Initiated in 1994)(Initiated in 1994)

Climate Prediction Center(CPC/NCEP)Climate Prediction Center(CPC/NCEP) ECMWF ECMWF (Initiated in 1995)(Initiated in 1995) UK Met office UK Met office (Initiated in 1987, Ward and Folland, 199(Initiated in 1987, Ward and Folland, 199

1))1)) CCCma CCCma (Canada)(Canada) RPN (Canada)RPN (Canada) BMRC (Australia)BMRC (Australia) Experimental climate prediction center(ECPC), Scripps InExperimental climate prediction center(ECPC), Scripps In

stitute of Oceanographystitute of Oceanography Korea Meteorology Administration (KMA)Korea Meteorology Administration (KMA) Japan Meteorology Administration (JMA)Japan Meteorology Administration (JMA)

…… ……

Research Institutions

Seasonal Prediction in CanadaSeasonal Prediction in Canada

Since September 1995, the Since September 1995, the Canadian Meteorological CentreCanadian Meteorological Centre has been producing 0-3 has been producing 0-3 month outlooks for Canadamonth outlooks for Canada. .

The seasonal forecast results from an ensemble of The seasonal forecast results from an ensemble of 12 model runs: 6 runs from a Global 12 model runs: 6 runs from a Global Environmental Multiscale model (Environmental Multiscale model (GEMGEM) of RPN, ) of RPN, that has a horizontal resolution of 1.875 degrees that has a horizontal resolution of 1.875 degrees with 50 vertical levels, and 6 runs from a Climate with 50 vertical levels, and 6 runs from a Climate model (model (GCM2GCM2) of the CCCma. ) of the CCCma.

Surface Air Temperature Forecast Surface Air Temperature Forecast an average of the daily temperature as an average of the daily temperature as

predicted by the models. predicted by the models.

The climatologies of the models are then The climatologies of the models are then subtracted from the mean forecast seasonal subtracted from the mean forecast seasonal temperatures to derived the forecast temperatures to derived the forecast anomalies of each model. The anomalies of the anomalies of each model. The anomalies of the two models are then normalized and combined two models are then normalized and combined using an arithmetic average.using an arithmetic average.

The anomalies are divided in three categories (The anomalies are divided in three categories (above, near and below the normalabove, near and below the normal).).

Precipitation Forecast Precipitation Forecast The forecasts are made using the total The forecasts are made using the total

accumulated water precipitation over the accumulated water precipitation over the season. The precipitation predicted is the total season. The precipitation predicted is the total liquid and includes all types: snow, rain, ice liquid and includes all types: snow, rain, ice pellets, etc. The climatology of the models is pellets, etc. The climatology of the models is subtracted from the total precipitation forecast subtracted from the total precipitation forecast to derive the anomalies. The anomalies of the to derive the anomalies. The anomalies of the two models are then combined using a simple two models are then combined using a simple normalized average. Finally the precipitation normalized average. Finally the precipitation anomalies are divided in three categories (anomalies are divided in three categories (above, near and below the normalabove, near and below the normal) as is done for ) as is done for the temperature anomaly forecast. the temperature anomaly forecast.

Skill of Summer TSkill of Summer T

Skill of Winter TSkill of Winter T

Skill of Summer PSkill of Summer P

Skill of Winter PSkill of Winter P

The prediction is issued The prediction is issued operationallyoperationally

http://meteo.ec.gc.ca/saisons/http://meteo.ec.gc.ca/saisons/index_e.html#climatologyindex_e.html#climatology

Prediction and Predictability of Prediction and Predictability of the Global Atmosphere-Ocean the Global Atmosphere-Ocean

SystemSystemfrom Days to Decadesfrom Days to Decades

A Five-Year Network in Canada A Five-Year Network in Canada funded by Canadian foundation of funded by Canadian foundation of Climate and Atmospheric Climate and Atmospheric Sciences. Sciences.

Climate Prediction and Climate Prediction and Predictability in UNBCPredictability in UNBC

Dr. Youmin Tang (group leader) Dr. Youmin Tang (group leader) Dr. Ziwang Deng Dr. Ziwang Deng Dr. Xiaobing ZhouDr. Xiaobing Zhou Peter MillsPeter Mills Jasion Ambadan Jasion Ambadan YangJie ChengYangJie Cheng Zhiyu WangZhiyu Wang

Ocean modelOcean model

OPA8.1 OGCMOPA8.1 OGCM

25 layers25 layers in the vertical direction with 17 concentrated in in the vertical direction with 17 concentrated in the top 250m of the ocean.the top 250m of the ocean.

Model domainModel domain: 30N - 30S and 120E - 75W.: 30N - 30S and 120E - 75W.

ResolutionResolution: 1 degree in the zonal direction; in the : 1 degree in the zonal direction; in the meridional direction, the resolution is 0.5 degree within 5 meridional direction, the resolution is 0.5 degree within 5 degree of the equator, smoothly increasing up to 2.0 degree of the equator, smoothly increasing up to 2.0 degree at 30N and 30S.degree at 30N and 30S.

Time stepTime step is 1.5 hour. is 1.5 hour.

Atmospheric modelsAtmospheric models

Model1: Statistical model. Model1: Statistical model. HCM1HCM1

Model2: Intermediate complexity dynamicalModel2: Intermediate complexity dynamical model model HCM2HCM2

Tang et al. 2004, J. Geophy. ResTang et al. 2004, J. Geophy. Res (ocean), 109, C05014)(ocean), 109, C05014) Tang et al. 2004, Geophy. Res. Letters, Vol. 30, No. 13,Tang et al. 2004, Geophy. Res. Letters, Vol. 30, No. 13, 1694 1694 Tang et al. 2004, J Phys. Oceangr. Vol 34, No. 3, 623-642.Tang et al. 2004, J Phys. Oceangr. Vol 34, No. 3, 623-642.

Extending the geographical range of Extending the geographical range of SST predictionsSST predictions

Model initializationModel initialization– Land data assimilation system Land data assimilation system – Oceanic data assimilation systemOceanic data assimilation system

Ensemble runsEnsemble runs– Multi-model ensemble techniqueMulti-model ensemble technique

Model developmentModel development– Progress in dynamical seasonal prediction in Progress in dynamical seasonal prediction in

the future depends critically on improvement the future depends critically on improvement of coupled ocean-atmosphere-land modelsof coupled ocean-atmosphere-land models

Future Challenges

Krishnamurti et al.Krishnamurti et al. （（ 20002000 ））

ACC for Niño3 SST for Multimodel, ECMWF, MetFr, MetOf, and LODYC

Multi-model hindcastsMulti-model hindcasts