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 layersGFDL ODA:

Temperature onlyConstant background error covariance

Ensemble size: 12Retrospective forecasts from 1982

ECHAM-MOM4: (Development to start late spring)OGCM: 1° X 0.33° with 40 to 50 vertical layersNCEP GODAS ODA (kindly provided by Dave Behringer)

Temperature and salinity assimilationState dependant background error covariance

Ensemble size: 12Retrospective 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 CorrectionCCA performed regionally. Results are smoothed along overlapping areas.

Improvement for Simulations Improvement for 2-mo lead Forecasts

RPSS Relative to Original Model Ensemble2mT JJA 1957-2001

3. PDF: Flexible format of informationECHAM4.5 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 informationProbability Distribution Function (relative to climatological PDF)

Could add user-defined categoryor threshold boundaries to illustrate probability of those.

Cumulative Probability Distribution Probability of Exceedance

statistical downscaling seasonal rainfall statistics:

Indian monsoon rainfall

seasonal total rainfall 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 PracticeIRI – 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 PracticeIRI – 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

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 FoundationBuilding 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

CoverSlopes, Soils Exposed

Pop.

Typhoon Fengshen, June 08

Example: Landslides in the PhilippinesExample: Landslides in the Philippines

IRI & IFRC:Potential for Assessing Disaster Risks at Regional/National Scale