The Decadal Climate Prediction Project (DCPP) G.J. Boer CANSISE WEST Victoria, May 9, 2014

Where does a decadal prediction fit? WGSIPWGCM

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 view of CMIP

( Proposed and organized by the DCPP Panel

A B C D

Component A: CMIP-decadal A 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

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 KushnirUSA  Kimoto Masahide Japan  Jerry Meehl USA  Rym Msadek USA  Wolfgang Mueller Germany  Doug Smith UK  Karl Taylor USA  Francis ZwiersCanada

Aspen 2013 Panel members provided inputs directed toward a decadal prediction component of CMIP6