1. Near-Term Climate Change IRI-PRED Priority Area
Lisa Goddard
Arthur Greene
Alessandra Giannini
Brad Lyon
Joshua Qian
Andy Robertson
NTCC Group Mtg – 1 July 2008

2. Overview of Aspen Mtg on Decadal Pred
Agenda
NTCC WG Objectives
Overview of Aspen Mtg on Decadal Pred
Report on visitors from Univ. of Uruguay
IRI Work/Progress
Future seminars?
NTCC Group Mtg – 1 July 2008

3. Overview of Aspen Mtg on Decadal Pred
Agenda
NTCC WG Objectives
Overview of Aspen Mtg on Decadal Pred
Report on visitors from Univ. of Uruguay
IRI Work/Progress
Future seminars?
NTCC Group Mtg – 1 July 2008

4. Improved regional CC projections
NTCC WG Objectives
CRKs for next years
Improved regional CC projections
Information on decadal-scale variability (characterization, prediction?)
Uncertainties appropriate to timescale of information
Diagnostic understanding of relevant processes contributing to regional CC and DCV, as well as relative role(s) of potential predictors
NTCC Group Mtg – 1 July 2008

5. Overview of Aspen Mtg on Decadal Pred
Agenda
NTCC WG Objectives
Overview of Aspen Mtg on Decadal Pred
Report on visitors from Univ. of Uruguay
IRI Work/Progress
Future seminars?
Photo credit: Arthur Greene
NTCC Group Mtg – 1 July 2008

6. AGCI Workshop – Climate Prediction to 2030: Is it possible, what are the scientific issues, and how would those predictions be used?
Meeting Goals:
1) Experimental design originally discussed in 2006 for AR5 that explicitly included short-term climate predictions to be performed for assessment by the international climate modeling community. The 2008 AGCI session carried this concept to the next level by tackling the formidable science issues involved with designing and running short term climate projections (now more commonly referred to as "decadal prediction”).
2) Address the important issues of the utility and applications of this information for decision support and impacts research.
NTCC Group Mtg – 1 July 2008

7. Global Climate Change Projections
2030
Source: IPCC 4th Assessment Report, Working Group 1: The Physical Science Basis for Climate Change
25 June 2008
AGCI: Climate Prediction to 2030

8. Coordinated Decadal Prediction for AR5
Basic model runs:
1.1) 10 year integrations with initial dates towards the end of 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995 and 2000 and 2005 (see below).
- Ensemble size of 3, optionally increased to O(10)
- Ocean initial conditions should be in some way representative of the observed anomalies or full fields for the start date.
- Land, sea-ice and atmosphere initial conditions left to the discretion of each group.
1.2) Extend integrations with initial dates near the end of 1960, 1980 and 2005 to 30 yrs.
- Each start date to use a 3 member ensemble, optionally increased to O(10)
- Ocean initial conditions represent the observed anomalies or full fields.
NTCC Group Mtg – 1 July 2008

9. Coordinated Decadal Prediction for AR5
Additional model runs:
1.3) 10 year integrations each year in Argo era from near end of 2001, 2002, 2003, 2004, 2006 (2007, ..)
1.4) For models w/ 20th century runs, run additional ensemble members that extend to These runs form a “control” against which the value of initializing short-term climate and decadal forecasts can be measured.
1.5) For models which do not have 20th century and other standard runs, suggest making a 100 year control integration, and a 70 year run with a 1% per year increase in CO2. These integrations will allow an evaluation of model drift, climate sensitivity and ocean heat uptake, and give some idea of the natural modes of variability of the model.
2) Further studies which would be of interest
Comparison of initialization strategies
Repeat of the forecast with a high and/or low anthropogenic aerosol scenario
Repeat of the forecast with an imposed “Pinatubo” eruption in 2010
Impact of Interactive Ozone chemistry
Air quality
NTCC Group Mtg – 1 July 2008

10. Coordinated Decadal Prediction for AR5
Participating (/represented) Modeling Groups:
BoM - Australia
CCCMA - Canada
COLA - USA
GFDL - USA
UKMO/Hadley Centre – UK
IMF-GEOMAR – Germany
MPI - Germany
NCAR - USA
RSMAS - USA
JMA & U. Tokyo - Japan
NTCC Group Mtg – 1 July 2008

11. Coordinated Decadal Prediction for AR5
Main Scientific Issues:
Initialization
- Assimilation issues/products; initialize ocean models with anomalies v full values v forced by atmosphere; etc.
Ensemble Generation Strategy
- perturb ocean and/or atmosphere; perturb model physics
Ensemble Size
External Forcing, particularly volcanoes
Verification
- Modes of variability (e.g. ocean - AMO, PDO), regional surface climate, probabilistic v deterministic, ‘trend’ v ‘natural variability’
NTCC Group Mtg – 1 July 2008

12. Scientific Basis for Decadal Prediction
Existence of decadal predictability needs to be proven
Null hypothesis: decadal fluctuations in SST associated
with the MOC or PDO arise from low-pass filtering of
unpredictable atmospheric noise by the slow
components of the climate system such as the oceans
But there is some tantalizing evidence from models:
PREDICATE  60% of decadal variance in Europe/
North Atlantic climate potentially predictable
GFDL workshop  potential predictability of MOC
(Courtesy: Joe Tribbia, NCAR)

13. Warm North Atlantic linked to …
North Atlantic Temperature
Atlantic Meridional
Overturning Circulation
(AMOC)
Warm North Atlantic
linked to …
Drought
More strong hurricanes
More rain over Sahel
and western India
Two important aspects:
Decadal-multidecadal fluctuations
Long-term trend
(Courtesy: Joe Tribbia, NCAR)

14. MOC in 20th Century Ensemble Integrations
PI CONTROL
(Courtesy: Joe Tribbia, NCAR)

15. Decadal Prediction But there are challenges … Initialization
Many different global reanalysis products, but
significant differences exist
Ocean observing net not global or comprehensive
Tropical Upper Ocean T Anomalies (Upper 300 m)
Pacific 
Eq Pac: Uncertainty decreases with time.
Relatively robust interannual variability.
Increased uncertainty after Why?
Eq Indian: Uncertainty remains large throughout the record.
Signal to noise <1. Outliers
This is also the case for the Eq Atlantic
Indian 
(Courtesy: Joe Tribbia, NCAR)

16. But there are challenges … Atlantic Salinity Anomalies (upper 300 m)
Decadal Prediction
But there are challenges …
Initialization
Many different global reanalysis products, but
significant differences exist
Large inherent uncertainty in driving of AMO
Atlantic Salinity Anomalies (upper 300 m)
Tropics 
Mid-Lat 
(Courtesy: Joe Tribbia, NCAR)

17. Predictions? / Projections?
DOI: /science
, 796 (2007); 317 Science
NTCC Group Mtg – 1 July 2008

18. Smith et al (2007)
PROs
View of increasing uncertainty at longer time horizons, as well as over long-time average
Improved projections relative to original system
CONs
Global average
Little to no evidence of [predictable] LF climate variability at long lead
Only 4 ensemble members
Figure 2
NTCC Group Mtg – 1 July 2008

19. Smith et al (2007)
Added PRO: View of change in uncertainty with time scale 1) Uncertainty in decadal-average 2) Uncertainty through a decade due to interannual variability
3) Realization of natural variability through decade
Figure 4
Figure 2
NTCC Group Mtg – 1 July 2008

20. Smith et al (2007) Regionality?
Ts projections improved over many regions
Climate variability?
Ts projection worse over N.Atlantic
Much improvement in regional T is associated with improvement in regional H, which bears striking resemblance to regions where T is dominated by externally-forced signal.
Figure 5
Ratio of Externally-forced to Internally-Forced Variance
(Courtesy: M. Ting et al, J.Climate, submitted)
NTCC Group Mtg – 1 July 2008

21. Keenlyside et al (2008) PRO:
Focus on mode(s) of natural climate variability
CONs:
Statements/conclusions seem at odds with evidence (ie. fcst evolution)
Uncertainty given by spread of 3 ensemble members
Demonstration of natural climate variability (AMOC) not obvious
Figure 4
“… the initialized prediction indicates a slight cooling relative to levels, while the anthropogenic-forcing-only simulation suggests a near 0.3 K rise.”
NTCC Group Mtg – 1 July 2008

22. Keenlyside et al (2008) Regionality?
Difference in RMSE (deg. K)
Regionality?
New method seems to have greater errors in most places, especially the N. Atlantic
What does improved performance in eastern Pacific suggest for ENSO variability?
Climate variability?
Lack of verifying observations, so don’t really know truth
But – according to available truth, hindcast has no skill
Supp. Figure 2c
Figure 3a
Maximum MOC Strength
NTCC Group Mtg – 1 July 2008

23. Predictions? / Projections?
Yes – seems possible to provide better estimates of near-term anthropogenic climate change (at least T), due largely to correcting biases in ICs
Predictions?
Not yet – Some evidence of potential predictability (perfect model/ICs) and slight evidence of real experimental predictability, but very little available at regional scales (and nothing yet demonstrated for precipitation).
NTCC Group Mtg – 1 July 2008

24. Potential Users(/uses)
Global temperature
Public, media, policy: attribution for any deviation from monotonic linear upward trend
Regional temperature
Long-term agricultural planning (viticulture, pine plantations)
Regional precipitation
Guidelines for reservoir operations and management policies; inter-state compacts (regional water managers, hydropower plants)
Infrastructure design and economic analysis (reservoirs, well fields, water & wastewater treatment plants, dikes, etc.)
Regional extremes (e.g. hurricanes)
Re-insurance
Regional ocean climate
Fisheries, ecosystem management
 Major issue: Tolerance for uncertainty??
NTCC Group Mtg – 1 July 2008

25. Overview of Aspen Mtg on Decadal Pred
Agenda
NTCC WG Objectives
Overview of Aspen Mtg on Decadal Pred
Report on visitors from Univ. of Uruguay
IRI Work/Progress
Future seminars?
NTCC Group Mtg – 1 July 2008

26. Example: Hydropower in SE S.America
1) Improving year-to-year management:
How do decadal fluctuations modulate ENSO impacts in the region?
2) Long-term planning of water & energy contracts (typically 5-10 years):
Can mean conditions for next 5-10 years be predicted within ‘some level of confidence’?
NTCC Group Mtg – 1 July 2008

27. Streamflow & Hydropower for SE S.America
Different methods for time series analysis find dominant mode at about 9-year frequency. That mode, determined by different methodologies, is most robust in phasing and amplitude when ‘externally-forced’ variability and ENSO are first removed.
NTCC Group Mtg – 1 July 2008

28. Streamflow & Hydropower for SE S.America
Have information on 8-9 year ‘period’ component, plus anthropogenic signal, and a strong relationship with ENSO
How to characterize uncertainty over next year? Next 3-4 years?
NTCC Group Mtg – 1 July 2008

29. Outstanding Issues & Future Work
1) Estimate uncertainty in decadal variability, and examine reliability of providing that information. How to layer uncertainties relative to other timescales?
2) Use information in forced signal + decadal variability, combined with ENSO variability (assumed unchanged characteristics) to estimate frequency (or changes in frequency) of events such as precipitation deficits.
3) Refine ‘trend’ information, especially for precipitation
4) Decadal Variability/Predictability
Characterization baseline
Statistical v dynamical predictability
Climate Diagnostics of possible mechanism(s)
5) Co-variability of temperature and precipitation (could be relevant for energy demand in SESA – winter season). Do they care in Indonesia?
NTCC Group Mtg – 1 July 2008

30. Overview of Aspen Mtg on Decadal Pred
Agenda
NTCC WG Objectives
Overview of Aspen Mtg on Decadal Pred
Report on visitors from Univ. of Uruguay
IRI Work/Progress
Future seminars?
NTCC Group Mtg – 1 July 2008

31. Overview of Aspen Mtg on Decadal Pred
Agenda
NTCC WG Objectives
Overview of Aspen Mtg on Decadal Pred
Report on visitors from Univ. of Uruguay
IRI Work/Progress
Future seminars?
NTCC Group Mtg – 1 July 2008

32. Tropical Drought & ENSO in a Warming World
Research Seminars?
Tropical Drought & ENSO in a Warming World
Invited speakers from GFDL on dynamical decadal prediction?
Others?
NTCC Group Mtg – 1 July 2008