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

2. 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

3. 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

4. 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 …11)

5. 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 ….

6. 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

8. 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)

9. Organized by the DCPP Panel

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

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

12. 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

14. Met Office website Five-year average forecasts

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

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

17. 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?

18. 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)

19. 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?

20. 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)

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

22. 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

23. Hiatus+: role of tropical Pacific
Observed trend
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

24. Atlantic driving the Pacific?
Observed trends
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

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

26. 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

28. 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

29. Volcanic effects in simulations
Haywood et al., 2014

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

32. The DCPP and CMIP6

33. The new CMIP approach

35. 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.

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

37. Early Participation Survey
Now officially “CMIP6-endorsed”

38. (Hindcasts)
(Forecasts)

39. end of presentation