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
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
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