Sylwia Trzaska

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Climate Risk Management Seasonal Climate
Prediction

• Sylwia Trzaska
• IRI Steve Zebiak, Lisa Goddard, Simon Mason,
  Tony Barnston, Madeleine Thomson, Neil Ward,
  Ousmane NDiaye and many others
• ECMWF Magdalena Balmaseda
• Meteo-France J.-P. Céron

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Climate Risk Management Seasonal 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

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Linking Science to Society
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• The IRIs mission
• To 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.
• Motivation
• Research 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.
• Activities
• With 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

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Relationship of overall GDP, agricultural GDP and
rainfall in Ethiopia (Grey and Sadoff, 2005)
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Figure 1
Figure 2

• 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 gtripple
  through the economy

Figure 3
Figure 4
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Climate Prediction
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ExampleTime Scales of Variability
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Weather Climate Prediction
Initial ProjectedAtmospheric Composition
Initial ProjectedState of Ocean
Initial ProjectedState of Atmosphere
CurrentObservedState
Uncertainty
Time Scale, Spatial Scale
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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.

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Influence of SST on tropical atmosphere
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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

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Epidemic Malaria Interannual variability gt
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)

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Malaria and Rainfall
The disease is highly seasonal and follows the
rainy season with a lag of about 2 months
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Biological Mechanism for the Relationship of
Malaria Incidence to Rainfall

• Increases in rainfall gt increase breeding site
  availability gt increase in malaria vector
  populations
• Increases in rainfall increases in humidity gt
  higher adult vector survivorship gt 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

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Incidence-based decisions
Purchase of drugs interventions
Report national level
Threshold in malaria cases
Drugs/interventions available at district
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Rainfall-based decisions
Threshold in Rainfall amounts
Drugs/interventions available at districts
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Forecast-based decisions
Drugs/interventions available at national level
Purchase of drugs interventions
Report national level
malaria monitoring
Predicted rainfall
Rainfall monitoring
Drugs/interventions available at districts

• Match between scale/accuracy/confidence/lead
• of the information and decision/interventions
• More effective use of limited resources
• Interactions with end-users are crucial

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Exemple 2 Senegal River Basin
Manantali Dam, Senegal River

• Multi-user dam
• Hydropower,
• flow regulation flood control, irrigation,
• water for flood recession agriculture,
• minimum ecological impact

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Manantali Dam, Senegal River
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 gt Forecast water stock in the
reservoir at the end of the monsoon season
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Seasonal Forecasts
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Methods of Seasonal Forecats
Statistical Methods identify statistical
relationships in the past
Ex. 3 SST indices used in stat forecast of
seasonal rainfall in JAS in the Sahel
Ex. Rainfall in East Africa vs Nino3.4 SST

• Pbs.
• Spurious relationship (SST correlated by chance)
• Instability of relationships (e.g. Sahel-ENSO)

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Methods of Seasonal Forecats
Dynamical Methods General Circulation Models
Constrains on computing time constrains on
resolution Typical grid size 250x250km Time
step 15min

• Sources of error
• Scale of numerous processes ltlt resolved scale
• Models of different sub-systems developped
  separately pb when coupling

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Weather Climate Prediction
Initial ProjectedAtmospheric Composition
Initial ProjectedState of Ocean
Initial ProjectedState of Atmosphere
CurrentObservedState
Uncertainty
Time Scale, Spatial Scale
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What probabilistic forecasts represent
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Probabilistic forecasts
Near-Normal
BelowNormal
AboveNormal
Historical distribution
FREQUENCY
Forecast distribution
NORMALIZED RAINFALL
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.
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Example of seasonal rainfall forecast

• Regional
• 3-month average
• Probabilistic

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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
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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

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Use of Ocean Data
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IRI DYNAMICAL CLIMATE FORECAST SYSTEM
2-tier OCEAN
ATMOSPHERE
GLOBAL ATMOSPHERIC MODELS ECPC(Scripps)
ECHAM4.5(MPI) CCM3.6(NCAR)
NCEP(MRF9) NSIPP(NASA)
COLA2 GFDL
PERSISTED GLOBAL SST ANOMALY
Persisted SST Ensembles 3 Mo. lead
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POST PROCESSING MULTIMODEL ENSEMBLING
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FORECAST SST TROP. PACIFIC
(multi-models, dynamical and
statistical) TROP. ATL, INDIAN
(statistical) EXTRATROPICAL (damped
persistence)
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Forecast SST Ensembles 3/6 Mo. lead
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M.A. Balmaseda ( ECMWF)
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Most common practice for initialization of
coupled forecastsUncoupled initialization of
ocean and atmosphere

• Atmosphere Initialization (from NWP or AMIP)
• atmos model (atmos obsassimilation
  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)
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Real Time Ocean Observations
M.A. Balmaseda ( ECMWF)
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Data coverage for Nov 2005
Ocean Observing System
Data coverage for June 1982
Changing observing system is a challenge for
consistent reanalysis
Todays Observations will be used in years to come
?Moorings SubsurfaceTemperature ? ARGO floats
Subsurface Temperature and Salinity XBT
Subsurface Temperature
M.A. Balmaseda ( ECMWF)
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Main Objective to provide ocean Initial
conditions for coupled forecasts
Coupled Hindcasts, needed to estimate
climatological PDF, require a historical ocean
reanalysis
M.A. Balmaseda ( ECMWF)
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Importance of ARGO Data
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Atlantic Anomalies 2005 versus 2006
T _at_30W Aug 2005
T _at_30W Aug 2006

• The temperature anomaly in the North
  Southtropical Atlantic is much weaker in 2006.

M.A. Balmaseda ( ECMWF)
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Ocean Observing System Experiments (OSES)
Effect of Argo
All NoArgo 2001-2005 mean
Surface Salinity (CI0.1psu)
M.A. Balmaseda ( ECMWF)
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Impact on Forecast Skill
No Data/ Data assim
Ocean Data Assimilation improves forecast skill
in the Equatorial Pacific, especially in the
Western Part
M.A. Balmaseda ( ECMWF)
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Misc. TOGA-TAO failure in E Pacif June-Oct 2006
Long x depth cross sections in the Pacific 2S-2N
Nov 2006
June 2006
July 2006
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Research!
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Loss of skill in AGCM due to imperfect
predictions of SST
(Goddard Mason ,Climate Dynamics, 2002)
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Climate Variability in the Atlantic Sector
CLIVAR TAV
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Interannual Climate Variability in the South
Atlantic Linking Tropics and Subtropics

• Coupled air-sea variability in S. Atlantic

• Similar spatial patterns and temporal scales
  despite absence of ocean dynamics in the model
• 5yr and QB component on red noise

Surface Temperature composites of 4 phases of
QB component (model)
Leading mode of SST- SLP covariability

• 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

Trzaska S., A.W. Robertson, J.D. Farrara and C.R.
Mechoso, J. Climate, 2006 sub judice
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CONCLUSION

• 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

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Climate Information
http//iri.columbia.edu

• Data Library numerous data incl. seasonal
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• Tutorials and Manuals
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