Transcript IRI – Examples of Climate Risk Management Research and Practice

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.
RPSS Relative to Original Model Ensemble
2mT JJA 1957-2001
Improvement for Simulations
Improvement for 2-mo lead Forecasts
3. PDF: Flexible format of information
ECHAM4.5 2m Temperature: JFM 1983 – El Nino
X
Forecasts for the full
PDF allows users to
produce probabilistic
forecasts for any
category or threshold
of interest.
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 Distribution
Probability of Exceedance
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
SST
CFS
ECHAMCA
ECHAMMOM
International Research Institute for Climate and Society
Research in support of climate risk management
Leaders in the
development and
assessment of
forecast products.
Experts in the use of remotely sensed
data to establish regional climate
patterns where direct observations are
missing
10%
graph courtesy of U. Redding
Innovators in
the sectoral
analysis of
climate
impacts (e.g.,
malaria early
warning tool)
Basic research to unravel and
understand climate mechanisms
IRI – Examples of Climate Risk Management Research and Practice
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
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
IRI – Examples of Climate Risk Management Research 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
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
Climate Research for Greater Social Utility
• Development and testing of forecasts and
other products tailored to the needs of
users
IRI and Google.org Foundation
IRI and Google.org/Moore Foundation
Draws on IRI’s
Some partners
Climate Program
ICPAC
Environmental Monitoring Program
WHO
Data Library/Map Rooms
Reading University
Health specialists
National met agencies
Economists
Educations and Trainers
Project Management
IRI and Google.org/Moore Foundation
Building communities of practice
Ethiopia CHWG Sep 08
Ethiopia CHWG/MERIT Dec 08
Madagascar CHWG Oct 08
Kenya CHWG Dec 08
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
IRI & IFRC:Potential for Assessing Disaster Risks at
Regional/National Scale
Example: Landslides in the Philippines
Recent
Rainfall
Typhoon Fengshen, June 08
Land Cover
Land
Cover
Slopes, Soils
Slopes,
Soils
Exposed Pop.
Exposed
Pop.