The Decadal Climate Prediction Project (DCPP) G.J. Boer Canadian Centre for Climate Modelling and Analysis WGSIP September 2015

Where does a decadal prediction fit? WGSIP WGCM CLIVAR? Decadal prediction: - annual, multi-annual, up to a decade - initialized forecasts of both forced and internally generated components of variability

Where does a decadal prediction fit? WGSIP WGCM WGNE WGDP Decadal prediction: - annual, multi-annual, up to a decade - initialized forecasts of both forced and internally generated components of variability

Antecedent CMIP5 decadal component WGCM Paris (2008): CMIP5 decadal prediction component adopted formation of a “Joint WGCM-WGSIP Contact Group on Decadal Predictability/Prediction” (Taylor et al. 2009…11)

Decadal Climate Prediction “Panel” Origin a child of WGSIP and WGCM and the decadal prediction component of CMIP5 Focus the development and support of both the science and practice of decadal prediction the provision of an archive of decadal prediction information for research and applications to provide advice on CMIP5 practicalities drift adjustment every year initial conditions data priorities ….

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 foster interest and activity in decadal prediction

Prediction and skill of annual mean T 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 - difference between r and r may offer: guidance on mechanisms hope for improvement Boer et al. (2013)

Organized by the DCPP Panel

now replaced by the Grand Challenge of “Near Term Climate Prediction” A B C

Component A: Hindcasts (output is archive of results) if at all possible otherwise every second year (historical simulations)

Component B: Experimental decadal forecasts decadal forecasts (not hindcasts) currently being made by a number of groups propose decadal prediction protocol aim of the collection, calibration and combination of forecasts forecasts and data made available in support of research and applications to evolve as Component A results become available

Met Office website Five-year average forecasts

CanSIPS “operational” temperature forecasts Probability forecast Annual mean T for 2014 Observed percentiles

Producing centres provide guidance for Regional Climate Centres, Regional Climate Outlook Forums

Component C: Predictability, Mechanisms and Case Studies What are the mechanisms determining decadal predictability and permitting (or making difficult) decadal prediction Skill? Can we explain/predict particular cases of climate variation? What investigations will benefit from Coordinated Multi-Model Experiments as part of the DCPP?

Rationale for DCPP-Component C in CMIP6 Component A: An extension of CMIP5 first attempt, based on what we have learnt. Component B: An extension of CMIP5 to link to societal demand (WCRP grand challenge#1, climate services etc. ) (now Decadal Prediction) Component C: A new endeavour for CMIP6 … that’s why it is complicated! Component A: Need for long sequence of historical forecasts initialized every year for (i) statistical stability of results, (ii) to provide historical skill assessment (ii) to allow correct drift adjustment Component B: evaluate the range of climate outcome for the next 10 years in a quasi real time framework (inclusion of an artificial volcano eruption) WCRP Grand Challenge #1 regional climate information: Can we provide skilful regional climate predictions at seasonal to decadal time scales and reliable and actionable long term regional climate change projections? Rationale for the existence of Component C: Need for a better understanding of the mechanisms and processes determining decadal predictability and permitting (or making difficult) decadal prediction skill Need for improved characterization, simulation and understanding (including attribution) of past decadal climate variabilities i.e. the modulation of the anthropogenic warming trends (C. Cassou)

The La Hague list Predictability and predictive skill over land: why so weak model deficiencies in simulating the teleconnections too large variability that masks a predictable signal Regional differences in predictability and skill: why weaker in the Pacific than in the N. Atlantic is it due to the intrinsic nature of the climate system What is the relationship between Atlantic Multidecadal Variability (AMV) and Interdecadal Pacific Variability (IPV) what does it imply in terms of predictability and predictive skill What is the respective role of the oceanic basins for predictability at global scale? What is the role of the atmospheric forcing in setting decadal variability? what is the role of strong interannual events (e.g. El Nino 98) vs long-lasting forcing (e.g. NAO+ in the 1990’s) Role of ocean initial conditions in climate prediction to what extent must the ocean be initialized (role of deep ocean? tropics vs extratropics? are there regions in which the correct initialization is particularly important what are the roles of initial salinity anomalies vs. temperature anomalies Role of volcanic forcing to what extent do volcanic eruptions affect skill can strong volcanic eruptions produce NAO-type atmospheric responses that temporarily offset the decadal modes or trigger specific phases of the modes?

The La Hague choice Meeting resulted in a draft Predictability and predictive skill over land: why so weak model deficiencies in simulating the teleconnections too large variability that masks a predictable signal Regional differences in predictability and skill: why weaker in the Pacific than in the N. Atlantic is it due to the intrinsic nature of the climate system What is the relationship between Atlantic Multidecadal Variability (AMV) and Interdecadal Pacific Variability (IPV) what does it imply in terms of predictability and predictive skill What is the respective role of the oceanic basins for predictability at global scale? What is the role of the atmospheric forcing in setting decadal variability? what is the role of strong interannual events (e.g. El Nino 98) vs long-lasting forcing (e.g. NAO+ in the 1990’s) Role of ocean initial conditions in climate prediction to what extent must the ocean be initialized (role of deep ocean? tropics vs extratropics? are there regions in which the correct initialization is particularly important what are the roles of initial salinity anomalies vs. temperature anomalies Role of volcanic forcing to what extent do volcanic eruptions affect skill can strong volcanic eruptions produce NAO-type atmospheric responses that temporarily offset the decadal modes or trigger specific phases of the modes? Hiatus+ (i.e. of both signs) what is its origin? can it be well predicted? Meeting resulted in a draft specification of Component C (C. Cassou)

AGCI Aspen Workshop, June 2015 Decadal climate prediction:  Improving our understanding of processes and mechanisms to make better predictions

Hiatus+ questions What are the mechanisms involved in long timescale variations (of both signs) in global mean temperature (and other variables)? particular “modes” (e.g. the IPV, AMV) or other processes variations in forcing (solar, volcanoes….) internal mechanisms What is the predictability of accelerated/retarded warmings in models

Hiatus+: role of tropical Pacific Observed trend 2002-2012 Model forced by SST in purple box Global temperature Model forced by SST in eastern tropical Pacific (purple box) Reproduces observed global mean temperatures And many aspects of spatial trends Key role of tropical Pacific Kosaka and Xie 2013

Atlantic driving the Pacific? Observed trends 1991-2011 Model forced by Atlantic SST trend Increase in Pacific trade wind is simulated by model driven by observed Atlantic SST trend Potential key role of North Atlantic warming McGregor et al 2014

Component C1: accelerated and retarded temperature change “Pacemaker Experiments”

Atlantic SPG and decadal predictions JJA rainfall change associated with 1995 SPG warming Impact of initialization on skill yrs 2-5 Doblas-Reyes et al 2013 Robson et al 2013 Perfect model skill of Atlantic tropical storms and AMOC : no skill when SPG initialized with climatology (blue curves) North Atlantic SPG is the region showing most improved skill from initialization Potentially influences rainfall over Sahel, USA, Europe, Amazon Influences Atlantic tropical storms and AMOC in perfect model experiments Motivates further experiments to understand processes Repeat hindcasts but initialize SPG with climatology Dunstone et al 2011

Volcano questions What is the effect of realistic volcanoes on multi-model forecast skill? Pinatubo, El Chichon, Agung What is potential effect of a large volcano on the skill of future forecasts if present/absent in initial conditions

Volcanic effects in simulations Haywood et al., 2014

Volcanic effects on skill over Indian Ocean Guemas et al., 2013

The DCPP and CMIP6

The new CMIP approach

P P P Forcing data ESGF etc. P * P P P P P P * DCPP Components A, B, C separately tiered. Groups are invited to participate in any or all.

Data aspects Earth System Grid (ESG) data approach is general for CMIP6 CMIP panel and WGCM Infrastructure Panel (WIP)

Early Participation Survey Now officially “CMIP6-endorsed”

(Hindcasts) (Forecasts)

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