ApplicationLaboratory
InnovationsfromEarthSciences
NewFrontiersinClimatePredictions:
DevelopingClimateServicesforaSustainable
World
SwadhinBeheraandToshioYamagata
Modes of Climate VAIRATION
ElNino
Modoki
NingalooNino
JAMSTEC seasonal prediction system based on
SINTEXͲF1
SINTEX-F semi-multi-model
ensemble system
N
Non-flux
fl adjustment
dj t
t
2.2
ATMOSPHERE: ECHAM4
T106L19
T106 L19 5
OCEAN: OPA8.2 ORCAR2 Grid
20X1.50 Eq-0.5 Level 31
9-member ensemble hindcast experiments
1.Three models with different coupling physics:
(Each model has realistic ENSO & IOD simulations, Luo et al. 2005a)
m1: Ocean surface is solid to atmosphere. (|Ua| Ua for Tau & heat flux)
m2: Ocean surface current momentum is passed to atmosphere. (|Ua-Uo| (UaUo) for Tau & heat flux)
m3: Ocean surface is solid to atmosphere
atmosphere, but (|Ua
(|Ua-Uo|
Uo| (Ua-Uo)
(Ua Uo) for Tau)
2. Three initial conditions for each model:
• Model spin-up (1971-1981)
• A simple
i l coupled
l d SST
SST-nudging
d i iinitialization
iti li ti scheme
h
• 䠍day, 2 days, 3 days (weekly NCEP Reynolds data)
Forecast: 12 months from 1st day of every month.
SINTEX-F Prediction Skills
http://www.jamstec.go.jp/frcgc/research/d1/iod/index.html
Basedona9ͲmembersemiͲmultimodelensemble
ENSOPredictionSkillsin10CoupledGCMs
predictionsystem
di ti
t
Nino3.4 index
>0.9
(1982-2001)
Adapted from Jin et al. 2008, APCC CliPAS
IndianOceanDipolePredictability
EIO(1982Ͳ2004)
Both winter and spring barrier exist
Luo et al., J. Climate, 2005,.
Luoetal.,J.Climate,2007,2178Ͳ2190.
MMEpredictionskillsat1Ͳmonthleadtime
2m
Temp
Precipitation
MMEskillsof2
MME
skills of 2Ͳm
mairtemperatureandprecipitationforJune
air temperature and precipitation for JuneͲAugust
August(leftpanels)and
(left panels) and
DecemberͲFebruary(rightpanels)for1981–2003obtainedfrom14CliPASmodels’(Wang
etal.2009)
ScalesofWeatherandClimate
Weather
Climate Variations
Climate Change
El Ni /IOD
ElNino/IOD
MJO/ISO
Decades to
Centuries
Cyclone
Seasons
Tornado
Whirlwind
Weeks
Days
Hours
Minutes
NewChallengesinClimatePredictions
•CapturinglandͲsurfaceandothernonͲlinearprocesses,besidesthe
correctscaleinteractionsincoupledgeneralcirculationmodelsstill
remainsachallenge.Thisaffectsthemodelpredictabilityofsomeof
g
p
y
themajormodesofclimatevariationssuchasthemonsoons.
•PhaseandamplitudeofintraͲseasonaloscillationsandinteractions
with monsoon and other climate modes
withmonsoonandotherclimatemodes
•DecadalENSO,ENSOModokipredictionsandteleconnections
•DecadalchangeintheIndianOceananditsinfluenceonIOD
predictionsandIODteleconnection.
d
d
l
•PredictionsofsubtropicalandmidͲlatitudeclimatemodesand
regionalclimatevariationssuchasBenguelaNino,NingalooNino……
g
g
g
Temporalanomaly
correlationbetween
the observed JJAS
theobservedJJAS
rainfallandthe
ensemblemeansof
theindividual
models.Correlations
>0.2areshaded
withnegative
correlations < Ͳ0 2
correlations<Ͳ0.2
hatchedorstippled.
Kulkarnietal.
2012
SINTEXͲFSST&RainAnomaliesPredictions
SST
SST
Rai
n
Rai
n
SST
Rai
n
LowͲfrequencyvariabilityinIODaffectingitspredictability
NewtypeofIOD(AbortedIOD)
CoralrecordsofIODvariability
Nakamuraetal.2009
Predictionforthedecayphaseofnormal
IOD events
IODevents
Doietal.2013
Predictionforthedecayphaseof
aborted IOD events
abortedIODevents
TheDecadalChangesinTropicalPacificandElNinoModoki
Composite of surface temperature for El Nino Modoki and
rainfall composites for El Nino Modoki and El Nino
ENSO Modoki and decadal sea level variations
December 2004, El Nino Modoki
Weng,,H.,Ashok,K.,S.K.Behera,S.A.Rao,T.Yamagata,2007:Impactsof
RecentElNiñoModokionDroughts/FloodsinthePacificRimduringBoreal
Summer,ClimateDyn., 29,113Ͳ129
December 1997, El Nino + pIOD
Behera,S.,andT.Yamagata,2010:ImprintoftheElNiñoModokion
decadalsealevelchanges,Geophys.Res.Lett.,37,L23702,
doi:10.1029/2010GL045936.
ThePredictionSkillsofSubtropicalDipoleModesinIndianandAtlanticOceans
SouthAtlanticSubtropicalDipole
IndianOceanSubtropicalDipole
PredictabilityofNingalooNino/Nina
Oceancurrentanomaliesaveragedaboveadepth
of300m(vector;msͲ1)and479seasurfaceheight
anomalies(shaded;cm)inFebruary2011for(a)the
SINTEXͲF1 480 prediction initialized on June 1st
SINTEXͲF1480predictioninitializedonJune1st,
2010and(b)theassimilationdataofGODAS.
Doi,BeheraandYamagata,2013,
D
i B h
dY
t 2013
NatureResearchReports
Threemonthsaveragedtimeseriesalongafixedstart
timeoftheNingalooNiño
Index(NNI:SSTanomaliesaveragedin108ºͲ116ºE,28ºͲ
22ºS)
22
S)for3,6,and9monthsleadpredictionbythe
for 3, 6, and 9 months lead prediction by the
SINTEXͲF1andobservationaldataofNOAAOISSTv2(ºC).
•SINTEXͲFhasgoodskillinpredictingmostofsignificant
eventsatleastoneseasonahead
•However,theonsetstageandtheamplitudeoftheevents
arenotwellpredicted
TheLocalAirͲSeaInteractionsforNingalooNino/Nina
Kataoka,Tozuka,BeheraandYamagata2013
InfluenceofNingalooNino/NinaontherainfallvariabilityofAustraliawithandwithout
ENSO
SensitivitytoTropicalandExtraͲtropicalSSTAnomalies
Climate Services for Sustainable Societies
Observation– ProcessStudies– Modeling–
g Predictions– UserinteractionsͲ Capacity
p y
Observations
Model Predictions
Internationalinitiativeskeepingsocialscienceandstakeholdersinmind:
WMO/WCR
P/CLIVAR
Initiatives
Future
Earth
Initiatives
Belmont
Forum
Initiatives.
International
Initiatives
such as
suchas
SATREPS..
Other
regional
Initiatives:
JPI,APN,
SASSCAL…
SynergyamongInitiatives/Frameworkswouldhelpsustaininginternationaleffortsandoutcomes
SATREPS Project for a Disaster Early Warning
System in Southern Africa
JAMSTEC, U. Tokyo, ACCESS, U. Cape Town, CSIR, U. Pretoria, ARC, SAWS and SAEON
Improving CGCMs
Evaluating Predictability
Global-scale climate
prediction
di ti
Regional climate prediction
Capacity of seasonal climate prediction in South Africa is enhanced so that it can be applied to
management of environmental problems in the Southern African Region.
Improving
Early
Warning
System
MajorResearchOutcomes
9More than 40 p
published p
papers
p
((2 in Nature,, 2 in Nature Geoscience,, 2 in Nature Climate
Change and 1 in PNAS)
9First successful demonstration of mid-latitude climate prediction.
9Establishment of a multi
multi-model
model ensemble system in South Africa.
Africa
9Application of SINTEX-F Forecasts for predicting crop yield, stream flows and heat waves.
9Successful downscaling of SINTEX-F predictions for southern Africa.
Beal,L.,W.P.M.DeRuijter,A.Biastoch,R.Zahn,andSCOR/WCRP/IAPSOWorkingGroup136
(M.Cronin,J.Hermes,J.Lutjeharms,G.Quartly,T.Tozuka,S.BakerͲYeboah,T.Bornman,P.
Cipollini H Dijkstra I Hall W Park F Peeters P Penven H Ridderinkhof J Zinke) 2011 : On
Cipollini,H.Dijkstra,I.Hall,W.Park,F.Peeters,P.Penven,H.Ridderinkhof,J.Zinke),2011:On
theroleoftheAgulhassysteminoceancirculationandclimate.Nature,472,429Ͳ436.
Luo,J.ͲJ.,2011:Oceandynamicsnotrequired?Nature,477,544Ͳ546.
Richter,I.,S.K.Behera,Y.Masumoto,B.Taguchi,H.Sasaki,&T.Yamagata:Adifferentmechanism
toexplainthedevelopmentofAtlanticNinos.NatureGeosci. 6,43–47(2013)
Lübbecke2013
BjörnC.Backeberg,PierrickPenvenandMathieuRouault,2012:ImpactofintensifiedIndian
OceanwindsonmesoscalevariabilityintheAgulhassystem,NatureClimateChange2,608–
612.
Prediction
SSTA
SuccessfulSeasonalClimatePredictions
2010Ͳ11LaNina
RainfallAnom
Observation
SSTA
Summer
Fall
Floods in South Africa
FloodsinSouthAfrica
RainfallAnom
CroppredictionswiththeSINTEXͲ
F
Riverflowpredictionswiththe
SINTEXͲF
ThedominantclimaticfactorsaffectingtheyearͲtoͲyearvariationsintheyieldsofmaize,soybean,riceand
wheat.Thepiediagramsindicatethepercentagesofproductionthataresensitivetotemperature(orange)
andsoilmoisturecontent(turquoise)aswellasthoseforwhichnohindcastswereavailable(grey)in2000.
ThedarkgreyareaindicatesnonͲcropland.
g y
p
Iizumietal.2013,NatureClimateChange
TheLegacyoftheSouthAfrican
SATREPS Project
SATREPSProject
Anewprojectisunderdevelopmenttoestablisha
climateͲbasedearlywarningsystemfortheinfectious
diseasesinsouthernAfrica.
COMMONCOMMUNICABLEDISEASES,THEIRDISTRIBUTION,EPIDEMICPOTENTIALANDSENSITIVITYTOCLIMATE
61966
Including
diarrhoeal
diseases
AconceptualclimateͲbasedearlywarningsystemforMalaria
Epidemic
㸦㸯㸧Infectionforecastsofar
Hashizume,Minakawaetal.PNAS
Hashizume
Minakawa et al PNAS
(2009)1857–1862
㸮ࣨ᭶
The lead is too short particularly
for the developing country
㸦㸰㸧Now,theadditionofclimate
predictionofpossibleSouthern
Africa
2ࣨ᭶
㸦㸱㸧IfyoucanlongͲtermpredictionby
increasingtheaccuracyfurther
㸲ࣨ᭶
Well in advance, it is possible
arrangement of corresponding human
and stockpiling of medicine.
AnewSATREPSprojecttoestablishaclimateͲbasedearlywarning
systemfortheinfectiousdiseases
JAMSTEC-CSIR-SAWS
ACCESS & JAMSTEC
Weather and
climate
information
Regional climate model
(southern Africa)
JAMSTECCSIR-SAWS
Down scaling
Weather data
NEKKEN-MRC & WCU
District level
climate model
Refinement
Refinement MRC
Disease transmission
model
Disease
ill
surveillance
systems
ALL
Disease prediction model
ACCESS: Applied Center for
Climate & Earth System Science
JAMSTEC: Japan Agency for
MarineEarth Science and Technology
gy
MRC: South African Medical
Research Council
NEKKEN: Institute of Tropical
Medicine, Nagasaki University
Earlyy Warning
g
systems
New coupled GCM SINTEX-F2
AGCM
OGCM
Coupling
Sea Ice
SINTEX-F1
ECHAM4.6
T106L19
OPA8.2
2×2 L31
Every 2 hour
No flux correction
No
SINTEX-F2
ECHAM5
T106L31
OPA9
0.5×0.5 L31
Same as F1
Yes
Annual mean SST bias from
NOAA OISSTv2
Horizontal distribution of the annual mean
of TC genesis frequency from SINTEX-F2,
SINTEX-F1 and Observations
Fig 10 Horizontal distribution of the annual mean of TC genesis frequency from (a) SINTEXF2, (b) SINTEX-F1, and (c) observation.
Dynamicaldownscalingofglobalclimatepredictions
Ratna(2013)PerformanceassessmentofthreeconvectiveparameterisationschemesinWRFfor
downscaling summer rainfall over South Africa, Climate Dynamics
downscalingsummerrainfalloverSouthAfrica,ClimateDynamics
InterannualvariabilityintheWRFmodelsimulated
precipitationanomalies
Summary
‰GlobalpatternsoftemperatureandprecipitationanomaliesarepredictedwellbySINTEXͲF1atleastat3Ͳ
monthsleadtime.ThepredictabilityremainshighforseveralseasonsleadtimeinthetropicalPacificthoughit
th l d ti
Th
di t bilit
i hi h f
l
l d ti
i th t i l P ifi th
h it
deteriorateselsewhere.
‰ENSOispredictedonanextendedrangeandthepredictabilityisprettyhighupto2Ͳyrleadtime.
‰IODiswellͲpredictedatleastaseasonahead.
‰However,tropicalteleconnectionsaresometimenotwellͲrepresentedinthepredictions.
‰Decadaltrendsandsubsurfaceoceanheatcontentsarefoundtoaffectthepredictability.
‰Increaseinthemodelspatialresolution(inSINTEXͲF2)hashelpedtoovercomesomeofthemodelbiasesseen
intheresultsofSINTEXͲF1.
‰SouthernAnnularModeseemstoplayaroleinthesubtropicalclimatevariationsandperhapsindirectlythe
tropicalandotherclimatevariations
‰NewregionalmodesneedfineͲscaleclimatepredictionsandwouldbethenewfrontiersinclimatepredictions
besidesthechallengesofreachingthepotentialpredictabilityofglobalandregionalmodes.