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