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The Decadal Climate Prediction Project (DCPP)
G.J. BoerCANSISE WESTVictoria, May 9, 2014
Where does a decadal prediction fit?
WGSIP WGCM
volcano occurrence
External forcing includes:• GHGs• anthropogenic aerosols • volcanic aerosols• solar •…
WCRP Grand Challenge #1
WGCM Paris (2008): CMIP5 decadal prediction component adopted formation of a “Joint WGCM-WGSIP Contact Group on
Decadal Predictability/Prediction” Evolved into the Decadal Climate Prediction Panel
Antecedent CMIP5 decadal component
Hindcasts for bias correction, calibration, combination, historical skill ….
Bias correction/adjustment
(Kharin et al. 2012)
forecasts initialized from observations “drift” toward the model climate
bias adjustment is a post processing step which attempts to remove this bias
to advise on CMIP5 practicalities recommended updates to CMIP5 protocol
produce forecasts initialized every year over the period
reduce the priority of “high frequency” multi-level decadal prediction data (3 and 6-hourly) in the archive
add the historical climate simulations made with the same model as used for decadal predictions (to compare simulations with predictions)
produced document on drift/bias adjustment organize and support Workshops and Meetings
Decadal Climate Prediction Panel
CMIP5 decadal prediction component Has had a positive affect on research
and offers promise for applications: many investigations and publications based
on results input to Chapter 11 IPCC AR5 expanded interest and activity in decadal
prediction predictability studies assessment of local, global and modal skill quasi-operational decadal prediction
Evolution of CMIP and of DCPP
WGCM meeting in Victoria, October 2013 new distributed CMIP
approach Panel interests broaden
propose a Decadal Climate Prediction Project
new viewof CMIP
(http://dcpp.pacificclimate.org/)
Proposed and organized by the DCPP Panel
A
B
C
D
Component A: CMIP-decadalA decadal hindcast experiment
Initialization and ensemble generation including the “deep” ocean
Extensive hindcast production (1960 to the present) and analysis as basis for drift correction calibration and post processing of forecasts multi-model combination of forecasts skill assessment understanding mechanisms and
predictability (possible applications)
Data aspects
Earth System Grid (ESG) data approach as general for CMIP6
coordination via DCPP Panel members who are also on CMIP panel and WGCM Infrastructure Panel (WIP)
Component B: Experimental decadal forecasts
decadal forecasts (not hindcasts) currently being made by a number of groups
propose decadal prediction protocol collection, calibration and combination of
forecasts forecasts and data made available in
support of research and applications to evolve as CMIP-decadal results become
available
2012-13
2014-15 CCCma decadal forecast system
Met Office 5-year average forecast
Component C: Predictability and Mechanisms
Predictability: a feature of the climate system reflecting its “ability to be predicted”
Skill: the “ability to predict” aspects of the system
What are the mechanisms determining decadal predictability and permitting (or making difficult) decadal prediction skill?
internal
forced
total
global and local “predictability” and “skill”
mechanisms determining skill
importance of initialization vs external forcing
deep ocean processes etc.
predictability and skill as a function of forecast range - does difference between and r offer:
guidance on mechanisms
hope for improvement
Boer et al. (2013)
Predictability and skill for annual mean T
what predictability results and mechanisms explain loss of actual skill in
southern ocean compared to predictability
comparative lack of skill of initialized internal component over land
other variables of interest e.g. precipitation, sea-ice, snow, etc etc
Possible coordinated multi-model case studies include:• the hiatus• the behaviour of AMV, PDV, …• climate “shifts”• AMOC behaviour • etc.
DCPP Component D: Case studies
Decadal Climate Prediction Project
Four components A. CMIP-decadal hindcasts B. Experimental multi-model forecasting C. Predictability and mechanisms D. Case studies
Currently Components A,B “broadly” in hand Components C,D in development Data treatment common to all components
Next step is input from the community via a DCPP Survey
end of presentation
Current DCPP Panel members
George Boer (Chair) Canada Christophe Cassou France Francisco Doblas-Reyes Spain Gokhan Danabasoglu USA Ben Kirtman USA Yochanan Kushnir USA Kimoto Masahide Japan Jerry Meehl USA Rym Msadek USA Wolfgang Mueller Germany Doug Smith UK Karl Taylor USA Francis Zwiers Canada
Aspen 2013
Panel members provided inputs directed toward a decadalprediction component of CMIP6