Climate Risk Management: Enhancing Society's Capability to Manage Seasonal Climate Fluctuations

2. Climate Risk Management: Seasonal Climate Prediction • 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

3. 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

4. Linking Science to Society

5. The IRI’s 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

6. Relationship of overall GDP, agricultural GDP and rainfall in Ethiopia (Grey and Sadoff, 2005)

7. 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 =>ripple through the economy Figure 3 Figure 4

8. Climate Prediction

9. Example:Time Scales of Variability

10. ClimateChange Decadal Initial & ProjectedAtmospheric Composition Weather & Climate Prediction Initial & ProjectedState of Ocean Initial & ProjectedState of Atmosphere CurrentObservedState Uncertainty Time Scale, Spatial Scale

11. Basis of Seasonal Climate Prediction: Changes in boundary conditions, such as SST and land surface characteristics, can influence the characteristicsof weather(e.g. strength or persistence/absence), and thus influence the seasonal climate.

12. Influence of SST on tropical atmosphere

13. 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

14. Example 1: Malaria Early Warning System Epidemic Malaria = Interannual variability => Climate control 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)

15. Malaria and Rainfall The disease is highly seasonal and follows the rainy season with a lag of about 2 months

16. Biological Mechanism for the Relationship of Malaria Incidence to 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

17. Incidence-based decisions Purchase of drugs interventions Report national level Threshold in malaria cases Drugs/interventions available at district

18. Purchase of drugs interventions Report national level Rainfall-based decisions Threshold in Rainfall amounts Drugs/interventions available at districts

19. 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

20. 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

21. 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 => Forecast water stock in the reservoir at the end of the monsoon season

22. Seasonal Forecasts

23. 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)

24. 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 << resolved scale • Models of different sub-systems developped separately – pb when coupling

25. ClimateChange Decadal Initial & ProjectedAtmospheric Composition Weather & Climate Prediction Initial & ProjectedState of Ocean Initial & ProjectedState of Atmosphere CurrentObservedState Uncertainty Time Scale, Spatial Scale

26. What probabilistic forecasts represent Climatological Average “SIGNAL” Forecast Mean “UNCERTAINTY” 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.

27. 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.

28. Example of seasonal rainfall forecast • Regional • 3-month average • Probabilistic

29. PRESANOR PRES-AO (9) GHACOF (18) PRES-AC (3) SARCOF (10) 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

30. 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

31. Use of Ocean Data

32. 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 10 POST PROCESSING MULTIMODEL ENSEMBLING 24 24 10 FORECAST SST TROP. PACIFIC (multi-models, dynamical and statistical) TROP. ATL, INDIAN (statistical) EXTRATROPICAL (damped persistence) 12 Forecast SST Ensembles 3/6 Mo. lead 24 24 30 12 30 30

33. ECMWF: Weather and Climate Dynamical Forecasts 10-Day Medium-Range Forecasts Seasonal Forecasts Monthly Forecasts Atmospheric model Atmospheric model Wave model Wave model Ocean model Real Time Ocean Analysis ~8 hours New Delayed Ocean Analysis ~11 days M.A. Balmaseda ( ECMWF)

34. 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)

35. ARGO floats XBT (eXpandable BathiThermograph) Moorings Satellite SST Sea Level Real Time Ocean Observations M.A. Balmaseda ( ECMWF)

36. Ocean Observing System Data coverage for Nov2005 Data coverage for June 1982 Changing observing system is a challenge for consistent reanalysis Today’s Observations will be used in years to come • ▲Moorings: SubsurfaceTemperature • ◊ ARGO floats: Subsurface Temperature and Salinity • + XBT : Subsurface Temperature M.A. Balmaseda ( ECMWF)

37. Real time Probabilistic Coupled Forecast time Ocean reanalysis Consistency between historical and real-time initial initial conditions is required Quality of reanalysis affects the climatological PDF 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)

38. Importance of ARGO Data

39. No Data assimilation: T @30W: Aug 2005 • With subsurface data (mainly ARGO) the anomaly is stronger. Atlantic Anomalies: 2005 versus 2006 T @30W: Aug 2005 • The temperature anomaly in the North Southtropical Atlantic is much weaker in 2006. T @30W: Aug 2006 M.A. Balmaseda ( ECMWF)

40. Ocean Observing System Experiments (OSES): Effect of Argo All – NoArgo: 2001-2005 mean Surface Salinity (CI=0.1psu) M.A. Balmaseda ( ECMWF)

41. 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)

42. 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 ….

43. Research!

44. Hindcast (persisted SSTa) Simulation Dominant pattern of precipitation error associated with dominant pattern of SST prediction error Loss of skill in AGCM due to imperfect predictions of SST (Goddard & Mason ,Climate Dynamics, 2002)

45. Climate Variability in the Atlantic Sector CLIVAR TAV

46. Model: UCLA AGCM coupled to uniform depth mixed-layer ocean in the Atlantic, 34 yr run SST MTM spectrum model LF 5yr QB obs LF 5yr QB transition mature mature transition 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 maintainedby 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

47. 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

48. Climate Information http://iri.columbia.edu • Data Library: numerous data incl. seasonal forecast, mapping &analysis tools • Tutorials and Manuals • Climate Prediction Tool