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

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

Linking Science to Society

The IRI’s mission Motivation Activities 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

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

Semi-arid areas in Africa prone to negative, anti-development outcomes 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

Climate Prediction

Example: Time Scales of Variability

Weather & Climate Prediction Initial & Projected Atmospheric Composition Initial & Projected State of Ocean Climate Change Initial & Projected State of Atmosphere Current Observed State Decadal Uncertainty Time Scale, Spatial Scale

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. Will emphasize SST, as this is overall the dominant factor in regional predictability. So, want to predict changes in SST field, and use those to predict changes in the seasonal climate

Influence of SST on tropical atmosphere - SST gradients/pressure gradients drive low-level winds - Winds want to move towards warmest waters (lower pressures)  convergence  atmos. Heating & upper level divergence, influencing remote response

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 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)

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

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

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

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

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

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

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

Seasonal Forecasts

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)

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

Weather & Climate Prediction Initial & Projected Atmospheric Composition Initial & Projected State of Ocean Climate Change Initial & Projected State of Atmosphere Current Observed State Decadal Uncertainty Time Scale, Spatial Scale

What probabilistic forecasts represent Climatological Average “SIGNAL” Forecast Mean The SIGNAL represents the ‘most likely’ outcome, but quantifying the UNCERTAINTY 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” Same figure, slightly different description. Means, shift, uncertainty.

Probabilistic forecasts Near-Normal Below Normal Above Normal Historical distribution FREQUENCY Forecast distribution Breakpoints of categories are determined by historical observations. The probabilities of this distribution are the climatological probabilities. Forecast distribution (say of the ensemble members at a point, or over a region) represent a shift in the range of possibilities. Now categorical probabilities are not equal – they differ from climatology. 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.

Example of seasonal rainfall forecast Regional 3-month average Probabilistic 3-category forecasts : climatological probabilities, by construction, are equal chances for any category This map shows the likelihood/probability for seasonal climate to fall within each of the categories (must sum to 100%). Far from the detailed “answer” most users are looking for Regional scale, in part, determined by resolution of the global models – shown by the size of the squares on the map.

Regional Outlook Forum PRES-AO (9) GHACOF (18) PRES-AC (3) SARCOF (10) PRESANOR 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

Use of Ocean Data

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 Only ECHAM4.5 and CCM3.6 are run at IRI. The other models are run at by their developing institution – with the exception of the NCEP-MRF9 model, which is run by QCCA (Queensland Centre for Climate Applications) in Australia. 24 24 30 12 30 30

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

Data Assimilation struggles to correct the systematic error 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 Observations Moorings ARGO floats XBT (eXpandable BathiThermograph) Satellite SST Sea Level M.A. Balmaseda ( ECMWF)

Ocean Observing System Data coverage for June 1982 Changing observing system is a challenge for consistent reanalysis Data coverage for Nov 2005 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)

Main Objective: to provide ocean Initial conditions for coupled forecasts Ocean reanalysis Real time Probabilistic Coupled Forecast time Coupled Hindcasts, needed to estimate climatological PDF, require a historical ocean reanalysis Consistency between historical and real-time initial initial conditions is required Quality of reanalysis affects the climatological PDF M.A. Balmaseda ( ECMWF)

Importance of ARGO Data

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

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

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)

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 ….

Research!

Loss of skill in AGCM due to imperfect predictions of SST Simulation Hindcast (persisted SSTa) Loss of skill in AGCM due to imperfect predictions of SST (Goddard & Mason ,Climate Dynamics, 2002) Dominant pattern of precipitation error associated with dominant pattern of SST prediction error Again, we have the potential to predict West Africa variability, if we can get the Atlantic SSTs right. If not, we lose that potential. Figure (upper) shows correlation skill between model simulated precipitation and observations. On left the SSTs are as observed – good pcp skill. On right, the SSTs are persisted (simplest prediction, but better than most evolving SST forecasts) – most skill is lost. Figure (lower) shows canonical correlation (dominant patterns of co-variability) between SST differences, from forcings used in experiments shown above, and differences in resulting precipitation fields. Message is that errors in the equatorial Atlantic are most to blame for error in predicted rainfall. So, for example, if we don’t predict the equatorial Atlantic to warm during the forecast season, we will fail to predict the wet conditions that are likely to develop.

Climate Variability in the Atlantic Sector CLIVAR TAV … so how accurate are the observations?

Surface Temperature composites of 4 phases of QB component (model) Interannual Climate Variability in the South Atlantic: Linking Tropics and Subtropics Coupled air-sea variability in S. Atlantic Model: UCLA AGCM coupled to uniform depth mixed-layer ocean in the Atlantic, 34 yr run LF 5yr QB obs SST MTM spectrum model 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 mature transition 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

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 right and wrong if the observations used for verification are accurate with a good spatial and temporal coverage

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