IRI Seasonal Forecasting Update

Models Run at IRI: 2-Tier ECHAM4.5 T42L19 GHG Forcing will be added New SST scenario strategy ECHAM5 T42L19 GHG Forcing will be added CCM3 T42L19 CAM3/4? T42L19: GHG Forcing will be added

Models Run at IRI: 1-Tier ECHAM-MOM3: (Real-Time in next few months) OGCM: 1.5° X 0.5° with 25 vertical layers GFDL ODA: Temperature only Constant background error covariance Ensemble size: 12 Retrospective forecasts from 1982 ECHAM-MOM4: (Development to start late spring) OGCM: 1° X 0.33° with 40 to 50 vertical layers NCEP GODAS ODA (kindly provided by Dave Behringer) Temperature and salinity assimilation State dependant background error covariance Ensemble size: 12 Retrospective forecasts from 1982

IRI 1-Tier Multi-Model Ensemble Initially the current IRI 2-Tier MME will not include 1-Tier models A separate 1-Tier MME will be made: Length of retrospective forecasts is shorter than 2-tier: (1982 start versus 1957 start) Possible that 2-Tier and 1-Tier MME will merge into a single product in future

MULTI-MODEL PROBABILISTIC FORECASTS Current Method: - Performance-based weighting of models, including “climatology” as a model - Historical performance from AMIP-type runs - Produces 3-Category forecasts (i.e. Terciles) New Method: - Models recalibrated individually before combination  Spatial bias correction  Local bias correction - Historical performance from HINDCASTS (AGCMs forced with predicted SSTs - Produces full probability distribution

1. Model Calibration: Spatial Bias Correction CCA performed regionally. Results are smoothed along overlapping areas. Improvement for Simulations Improvement for 2-mo lead Forecasts RPSS Relative to Original Model Ensemble 2mT JJA

3. PDF: Flexible format of information ECHAM4.5 2m Temperature: JFM 1983 – El Nino Forecasts for the full PDF allows users to produce probabilistic forecasts for any category or threshold of interest. X

3. PDF: Flexible format of information Probability Distribution Function (relative to climatological PDF) Could add user-defined category or threshold boundaries to illustrate probability of those. Cumulative Probability DistributionProbability of Exceedance

statistical downscaling seasonal rainfall statistics: Indian monsoon rainfall seasonal totalrainfall frequency JJAS rainfall correlation skill ECHAM4-CA: made from June 1

prediction skill of SW monsoon onset over Philippines ECHAM- CA CFS ECHAM- MOM SST

Basic research to unravel and understand climate mechanisms International Research Institute for Climate and Society Research in support of climate risk management Experts in the use of remotely sensed data to establish regional climate patterns where direct observations are missing Innovators in the sectoral analysis of climate impacts (e.g., malaria early warning tool) Leaders in the development and assessment of forecast products. 10% graph courtesy of U. Redding

IRI – Examples of Climate Risk Management Research and Practice Weather indexed insurance for farmers in Malawi, Tanzania, Ethiopia Improved use of agroclimatological information to design insurance contracts Advances in use of remote sensed data climatology to fill data voids Work with local farmer’s collectives, financial institutions, World Bank, Oxfam, Swiss Re Desert Locust Early Warning Systems Training of national control authorities Product Integration in UN Food and Agriculture Organization’s early response system Climate variability and agriculture in Southeast South America Improved understanding and predictability of climate impacts on the sector Collaboration with national agriculture research institutes in the southern cone

IRI – Examples of Climate Risk Management Research and Practice Climate Research for Greater Social Utility Development and testing of forecasts and other products tailored to the needs of users Training of Sectoral and Climate Specialists On-going collaboration with WHO, WMO, Red Cross, national ministries, NGO’s and research partners to bridge gaps between climate knowledge and practice Reservoir Management Tools Improvements in hydroelectric capacity with tailored climate information Innovative financial instruments to off-set impacts of water shortages Collaboration with reservoir managers in the Philippines and Chile

IRI and Google.org Foundation Three-year invited project from Google/Moore (March 08) Geographical Focus: Greater Horn of Africa Focal country case study: Ethiopia Climate side: increase capacity to supply useful information products to help guide health interventions Health side: increase capacity to demand and use climate products for more effective interventions

Draws on IRI’s Climate Program Environmental Monitoring Program Data Library/Map Rooms Health specialists Economists Educations and Trainers Project Management Some partners ICPAC WHO Reading University National met agencies IRI and Google.org/Moore Foundation

Ethiopia CHWG Sep 08 Madagascar CHWG Oct 08 Ethiopia CHWG/MERIT Dec 08 Kenya CHWG Dec 08 IRI and Google.org/Moore Foundation Building communities of practice

IRI and International Federation of Red Cross/Red Crescent Goal is to use advanced climate information to improve disaster preparedness and response Provide a global six-day forecast tool for IFRC Form Partnerships with RC/RC national societies

IRI and International Federation of Red Cross/Red Crescent

Land Cover Slopes, Soils Exposed Pop. Recent Rainfall Land Cover Slopes, Soils Exposed Pop. Typhoon Fengshen, June 08 Example: Landslides in the Philippines IRI & IFRC:Potential for Assessing Disaster Risks at Regional/National Scale