sylwia trzaska iri: steve zebiak, lisa goddard, simon mason, tony barnston, madeleine thomson, neil...
Post on 26-Dec-2015
216 Views
Preview:
TRANSCRIPT
Sylwia Trzaska
•IRI: Steve Zebiak, Lisa Goddard, Simon Mason, Tony Barnston, Madeleine Thomson, Neil Ward, Ousmane N’Diaye and many others•ECMWF: Magdalena Balmaseda•Meteo-France: J.-P. Céron
Climate Risk Management: Climate Risk Management: Seasonal Climate PredictionSeasonal Climate Prediction
•IRI: Linking Climate and Society•Climate Prediction•Seasonal Climate Forecast•Use of Ocean Data•Importance of ARGO data
Climate Information & Climate Prediction Tool
Climate Risk Management: Climate Risk Management: Seasonal Climate PredictionSeasonal Climate Prediction
• The IRI’s missionTo enhance society's capability to understand, anticipate and manage the impacts of seasonal climate fluctuations, in order to improve human welfare and the environment, especially in developing countries.
• MotivationResearch and practical experience already gained with many collaborators has convinced us that achievement of global (sustainable) development goals is strongly dependent on recognition of the role of climate, and effective use of climate information in policy and in practice.
•ActivitiesWith many partners, developing the capacity to manage climate-related risks in key climate-sensitive sectors: agriculture, food security, water resources management, public health, disasters
Climate knowledge/information as a resource
! Uptake of climate information is NOT trivial
Semi-arid areas in Africa prone to negative, anti-development outcomes •hunger (figure 1), •disasters (figure 2), •epidemic disease outbreaks (figures 3-4).
climate impacts across many sectors =>ripple through the economy
Figure 1 Figure 2
Figure 3 Figure 4
Weather & Climate Prediction
Climate Change
Unce
rtain
ty
Time Scale, Spatial Scale
CurrentObserved
State
Initial & ProjectedState of Atmosphere
Initial & Projected
Atmospheric Composition
Decadal
Initial & Projected
State of Ocean
Basis of Seasonal Climate Prediction:
Changes in boundary conditions, such as SST and land surface characteristics, can influence the characteristics of weather (e.g. strength or persistence/absence), and thus influence the seasonal climate.
What we can foresee now
Effective management of climate related risks (opportunities) for improved:
• Agricultural production– Stocking, cropping calendar, crop selection, irrigation, insurance,
livestock/trade
• Water resource management– Dynamic reservoir operation, power generation, pricing/insurance
• Food security– Local, provincial, regional scales
• Public health– Warning, vaccine supply/distribution, surveillance measures,…
• Natural resource management– Forests/fire, fisheries, water/air quality
• Infrastructure development
Epidemic Malaria = Interannual variability => Climate control
Example 1: Malaria Early Warning System
Temperature: “highland malaria”Precipitation:
“desert-fringe malaria”
•Awareness, use of prevention measures (bednets)•(timely) Availability & access to health care/diagnostic/treatment•Lags in intervention implementation (esp. if remote resources)
Month
JULMAYMARJANNOVSEP
200
100
0
Rainfall (mm)
Malaria incidence
The disease is highly seasonal and follows the rainy season with a lag of about 2 months
Malaria and Rainfall
• Increases in rainfall => increase breeding site availability => increase in malaria vector populations
• Increases in rainfall ~ increases in humidity => higher adult vector survivorship => greater probability of transmission.
Precise numerical models of host/vector/parasite cycle and/or population/epidemics exist but require very fine environmental data (breeding sites, rainfall, temperature, humidity…)
– Scale/info mismatch between environmental conditions forecast/monitoring and such models
Frequent lack of evidence of links btwn large scale epidemics and climate for public health services
– Many other factors: accuracy of the data, access to drugs/health services, intervention policies, population migration
Biological Mechanism for the Relationship of Malaria Incidence to Rainfall
Incidence-based decisions
Month
JULMAYMARJANNOVSEP
200
100
0
Rainfall (mm)
Malaria incidence
Threshold in malaria cases
Report national level
Purchase of drugsinterventions
Drugs/interventions available at district
Rainfall-based decisions
Month
JULMAYMARJANNOVSEP
200
100
0
Rainfall (mm)
Malaria incidence
Threshold in Rainfall amounts
Drugs/interventions available at districts
Report national level
Purchase of drugsinterventions
Forecast-based decisions
Month
JULMAYMARJANNOVSEP
200
100
0
Rainfall (mm)
Malaria incidence
Drugs/interventions available at districts
Report national level
Purchase of drugsinterventions
Predicted rainfall
malaria monitoring
Rainfall monitoring
Drugs/interventions available at national level
•Match between scale/accuracy/confidence/lead of the information and decision/interventions•More effective use of limited resources•Interactions with end-users are crucial
Manantali Dam, Senegal River
MANANTALI
KAEDI, october 1999
BAKEL
0 50 100 150 200 250 km
Multi-user dam• Hydropower, • flow regulation: flood control, irrigation,water for flood recession agriculture,minimum ecological impact
Exemple 2: Senegal River Basin
Manantali Dam, Senegal River
0
500
1000
1500
2000
2500
3000
1975 1980 1985 1990 1995 2000 2005
forecasted
observed
year
natu
ral dis
charg
e (
m3/s
)
R2 = 0.6507
0
500
1000
1500
2000
2500
3000
0 500 1000 1500 2000 2500
1979-2000 (calibration)2000-2005 (validation)80% interval90% interval
Qd = f (V1,…V5) : forecasted natural discharge (m3/s)
observ
ed n
atu
ral dis
charg
e (
m3/s
)
August 20 – reservoir management decision for water release for traditional agriculture Sept-Oct, given electricity and irrigation demands Sept-July
Management strategy using Aug-Oct seasonal forecast made at Meteo-France end of July
=> Forecast water stock in the reservoir at the end of the monsoon season
Methods of Seasonal Forecats
Statistical Methods: identify statistical relationships in the past
Ex. Rainfall in East Africa vs Nino3.4 SST Ex. 3 SST indices used in stat forecast of seasonal rainfall in JAS in the Sahel
Pbs. • Spurious relationship (SST correlated by chance)• Instability of relationships (e.g. Sahel-ENSO)
Sources of error :•Scale of numerous processes << resolved scale•Models of different sub-systems developped separately – pb when coupling
Constrains on computing time= constrains on resolution
Typical grid size ~ 250x250kmTime step 15min
Dynamical Methods: General Circulation Models
Methods of Seasonal Forecats
Weather & Climate Prediction
Climate Change
Unce
rtain
ty
Time Scale, Spatial Scale
CurrentObserved
State
Initial & ProjectedState of Atmosphere
Initial & Projected
Atmospheric Composition
Decadal
Initial & Projected
State of Ocean
What probabilistic forecasts represent
Climatological Average
Forecast Mean “SIGNAL”
The SIGNAL represents the ‘most likely’ outcome, but quantifying theUNCERTAINTY is an important part of the forecast. The UNCERTAINTY represents the internal atmospheric chaos, uncertainties in the boundary conditions, and random errors in the models.
“UNCERTAINTY”
BelowNormal
AboveNormal
Historically, the probabilities of above and below are 0.33. Shifting the mean by half a standard-deviation and reducing the variance by 20% changes the probability of below to 0.15 and of above to 0.53.
Historical distribution
Forecast distribution
Probabilistic forecasts Near-Normal
NORMALIZED RAINFALL
FR
EQ
UE
NC
Y
PRES-AO (9)
GHACOF (18)
PRES-AC (3)
SARCOF (10)
PRESANOR
Regional Outlook Forum
Operational Seasonal Climate Forecasts for main rainy seasons:- Country level- Consensus regional forecasts released- Blend of statistical and dynamical methods
E.g. PRESAO
Optimizing probabilistic information
• Reliably estimate the good uncertainty
-- Minimize the random errors
e.g. multi-model approach (for both response & forcing)
• Eliminate the bad uncertainty
-- Reduce systematic errors
e.g. MOS correction, calibration
30
12
30
24
12
24
24
24
10
FORECAST SST
TROP. PACIFIC (multi-models, dynamical and statistical)
TROP. ATL, INDIAN (statistical)
EXTRATROPICAL (damped persistence)
GLOBAL ATMOSPHERIC
MODELS
ECPC(Scripps)
ECHAM4.5(MPI)
CCM3.6(NCAR)
NCEP(MRF9)
NSIPP(NASA)
COLA2
GFDL
ForecastSST
Ensembles3/6 Mo. lead
PersistedSST
Ensembles3 Mo. lead
IRI DYNAMICAL CLIMATE FORECAST SYSTEM
POSTPROCESSING
MULTIMODELENSEMBLING
PERSISTED
GLOBAL
SST
ANOMALY
2-tier OCEAN ATMOSPHERE
30
10
Delayed Ocean Analysis ~11 days
Real Time Ocean Analysis ~8 hours
New
ECMWF:
Weather and Climate Dynamical Forecasts
ECMWF:
Weather and Climate Dynamical Forecasts
10-Day Medium-Range
Forecasts
10-Day Medium-Range
Forecasts
Seasonal Forecasts
Seasonal Forecasts
Monthly Forecasts
Monthly Forecasts
Atmospheric model
Wave model
Ocean model
Atmospheric model
Wave model
M.A. Balmaseda ( ECMWF)
Most common practice for initialization of coupled forecasts:Uncoupled initialization of ocean and atmosphere
Atmosphere Initialization (from NWP or AMIP):
atmos model +(atmos obs+assimilation system)+prescribed SST
Ocean Initialization:
ocean model + ocean obs +assimilation system+ prescribed surface fluxes
• So far mainly subsurface Temperature, and altimeter.
• Salinity from ARGO is used in the new ECMWF system.
•Atmospheric Fluxes are a large source of systematic error in the ocean state.
•Data Assimilation struggles to correct the systematic error
M.A. Balmaseda ( ECMWF)
Real Time Ocean ObservationsARGO floats
XBT (eXpandable BathiThermograph)
Moorings
Satellite
SST
Sea Level
M.A. Balmaseda ( ECMWF)
60°S 60°S
30°S30°S
0° 0°
30°N30°N
60°N 60°N
60°E
60°E
120°E
120°E
180°
180°
120°W
120°W
60°W
60°W
0°
0°
X B T p r o b e s : 9 3 7 6 p r o f i l e sOBSERVATION MONITORING Changing observing
system is a challenge for consistent reanalysis
Today’s Observations will be used in years
to come
60°S 60°S
30°S30°S
0° 0°
30°N30°N
60°N 60°N
60°E
60°E
120°E
120°E
180°
180°
120°W
120°W
60°W
60°W
0°
0°▲Moorings: SubsurfaceTemperature
◊ ARGO floats: Subsurface Temperature and Salinity
+ XBT : Subsurface Temperature
Data coverage for June 1982
Ocean Observing System
M.A. Balmaseda ( ECMWF)
Data coverage for Nov 2005
Coupled Hindcasts, needed to estimate climatological PDF, require a historical ocean reanalysis
Real time Probabilistic Coupled
Forecasttime
Ocean reanalysis
Quality of reanalysis affects the climatological
Consistency between historical and real-time initial initial conditions is required
Main Objective: to provide ocean Initial conditions for coupled forecasts
M.A. Balmaseda ( ECMWF)
Atlantic Anomalies: 2005 versus 2006
• The temperature anomaly in the North Southtropical Atlantic is much weaker in 2006.
60OS 40OS 20OS 0O 20ON 40ON 60ON
Latitude
300
250
200
150
100
50
0
Depth
(m
etr
es)
300
250
200
150
100
50
0Plot resolution is 1 in x and 10 in y
Meridional section at 30.2 deg WPotential temperature contoured every 0.5 deg CASSIM: E0 Anomaly (1981-2005 clim)
Interpolated in x and y
20050816 ( 31 days mean)
-9.5-8.5-7.5-6.5-5.5-4.5-3.5-2.5-1.5-0.50.51.52.53.54.55.56.57.58.59.5
MAGICS 6.10 bee14 - emos Tue Aug 1 11:20:49 2006
60OS 40OS 20OS 0O 20ON 40ON 60ON
Latitude
300
250
200
150
100
50
0
Depth
(m
etr
es)
300
250
200
150
100
50
0Plot resolution is 1 in x and 10 in y
Meridional section at 30.2 deg WPotential temperature contoured every 0.5 deg CS3 ASSIM (E0): Anomaly (1981-2005 clim)
Interpolated in x and y
20060816 ( 31 days mean)
-9.5-8.5-7.5-6.5-5.5-4.5-3.5-2.5-1.5-0.50.51.52.53.54.55.56.57.58.59.5
MAGICS 6.10 bee07 - emos Mon Sep 11 12:52:49 2006
T @30W: Aug 2005
T @30W: Aug 2006
60OS 40OS 20OS 0O 20ON 40ON 60ON
Latitude
300
250
200
150
100
50
0
Depth
(m
etr
es)
300
250
200
150
100
50
0Plot resolution is 1 in x and 10 in yMeridional section at 30.2 deg WPotential temperature contoured every 0.5 deg CCONTROL: E0 Anomaly (1981-2005 clim)
Interpolated in x and y
20050816 ( 31 days mean)
-9.5-8.5-7.5-6.5-5.5-4.5-3.5-2.5-1.5-0.50.51.52.53.54.55.56.57.58.59.5
MAGICS 6.10 bee10 - emos Tue Aug 1 11:17:45 2006
No Data assimilation:
T @30W: Aug 2005
•With subsurface data (mainly ARGO) the anomaly is stronger.
M.A. Balmaseda ( ECMWF)
Ocean Observing System Experiments (OSES): Effect of Argo
50OE 100OE 150OE 160OW 110OW 60OW 10OW
Longitude
80OS
60OS
40OS
20OS
0O
20ON
40ON
60ON
80ON
Latit
ude
80OS
60OS
40OS
20OS
0O
20ON
40ON
60ON
80ON
Plot resolution is 1.4063 in x and 1 in yHorizontal section at 5.0 metres depthSalinity contoured every 0.1 psu: erfk - esrz
Interpolated in y 0 ( 5 year mean)
difference from20060101 ( 5 year mean)
-0.1-0.1
-0.1
0.1
0.1
0.1
0.10.1
0.1
0.1
0.1
0.1
0.1
0.1
0.2
0.2 0.20.2
0.2
0.2
0.2
0.2
0.2
0.3
0.3
0.3
0.30.30.4
0.40.5
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0.1
0.3
0.5
0.7
0.9
1.1
MAGICS 6.9.1 hyrokkin - neh Tue Aug 8 17:04:33 2006
Surface Salinity (CI=0.1psu)All – NoArgo:
2001-2005 mean
M.A. Balmaseda ( ECMWF)
Impact on Forecast Skill
0 1 2 3 4 5 6 7Forecast time (months)
0.4
0.5
0.6
0.7
0.8
0.9
1
An
om
aly
co
rre
latio
n
wrt NCEP adjusted OIv2 1971-2000 climatology
NINO4 SST anomaly correlation
0 1 2 3 4 5 6 7Forecast time (months)
0
0.2
0.4
0.6
0.8
Rm
s e
rro
r (d
eg
C)
Ensemble sizes are 3 (esj6) and 3 (esj6) 76 start dates from 19870101 to 20051001
NINO4 SST rms errors
Fc esj6/m1 Fc esj6/m0 Persistence Ensemble sd
MAGICS 6.10 hyrokkin - neh Thu Sep 7 19:11:46 2006
Ocean Data Assimilation improves forecast skill in the Equatorial Pacific, especially in the Western Part
No Data/ Data assim
M.A. Balmaseda ( ECMWF)
Misc. TOGA-TAO failure in E Pacif June-Oct 2006
June 2006
Long x depth cross sections in the Pacific 2S-2N
July 2006 Nov 2006
….
Loss of skill in AGCMdue to imperfect predictions of SST
Dominant pattern ofprecipitation errorassociated withdominant pattern ofSST prediction error
(Goddard & Mason ,Climate Dynamics, 2002)
Simulation Hindcast (persisted SSTa)
Interannual Climate Variability in the South Atlantic: Linking Tropics and Subtropics
Trzaska S., A.W. Robertson, J.D. Farrara and C.R. Mechoso, J. Climate, 2006: sub judice
maturemature
transitiontransition
transitiontransition
maturemature
Model: UCLA AGCM coupled to uniform depth mixed-layer ocean in the Atlantic, 34 yr run
Similar spatial patterns and temporal scales despite absence of ocean dynamics in the model
5yr and QB component on red noise
Quasi-biennial component Anomaly propagation from extratropics to tropics (also seen
in obs), strongly tied to the seasonal cycle of convection
SST forcing on atmosphere in the tropics, atmospheric forcing of the SST in the subtropics via atmospheric bridge
Reversed surface flux feedback in the east vs west and ITCZ
East - dominated by shallow clouds - SST anomalies generated and maintained by SST- cloud/radiation feedback, damped by SST- wind/evaporation
West and ITCZ - deep convection - SST anomalies generated and maintained by SST- wind/evaporation, damped by SST- cloud/radiation feedback
Surface Temperature composites of 4 phases of QB component (model)
LF 5yr QB
ob
s
SST MTM spectrum
LF 5yr QB
mo
de
l
Coupled air-sea variability in S. Atlantic
Leading mode of SST- SLP covariability
CONCLUSIONCONCLUSION
Skillful climate prediction requires skillful SST prediction in the tropics.
Skillful SST prediction requires accurate GCMs• GCMs can be used for prediction and process
studies if they do the right thing.
We can really only assess what they do rightand wrong if the observations used for verification are accurate with a good spatial and temporal coverage
top related