Decadal Prediction and Predictability and Mechanisms of Climate Variability Edwin K. Schneider COLA SAC Meeting April 2012

A Brief History of COLA’s Decadal Pillar Decadal prediction component was added to COLA Omnibus research in Prior to that COLA has been solely focused on shorter seasonal-to- interannual time scales. Is there evidence for decadal predictability? Wide open field.

Outline of Research Areas 1.Analysis of observational and model data a.Scientific basis of decadal predictability b.Detection and attribution c.Empirical prediction d.Isolate modes of decadal variability e.Re-examination of the assumption of a static annual cycle 2.Statistical tools: development and application a.Relative entropy b.Ensemble Empirical Mode Decomposition (EEMD) 3.Modeling a.Decadal predictability CFSv2 CMIP5+ runs Multimodel evaluation b.Mechanisms and predictability of decadal climate variability Interactive Ensemble CGCM Role of weather noise c.Climate sensitivity Isolate the causes of differences between models

Selected Results: Analysis of Observational and Model Data

Decadal SST Influences on Indian Monsoon Krishnamurthy and Krishnamurthy

Regression Analysis: Effect of ENSO and PDO on Great Plains Precipitation

In-phase of ENSO and PDO favors Great Plains drought/flood Out-of-phase of ENSO and PDO favors Great Plains neutral –NINO3.4 –PDO Hu and Huang

Consequences of Definition of Annual Cycle: Traditional vs. EEMD Wu, Schneider, Kirtman, Sarachik, Huang, and Tucker ENSO Phase Locking to Annual Cycle

9 QBO QBO in a 2D EEMD Analysis Hu and Huang

Selected Results: Decadal Modeling Research

CFS-based Decadal Prediction Decadal prediction and predictability using the NCEP CFS (version 2) CGCM. –COLA Team: Cash, DelSole, Huang, Kinter, Klinger, Krishnamurthy, Lu, Marx, Schneider, Stan, Zhu –Collaborations: NCEP EMC and CPC; IRI; GSFC; IITM (Thanks to NCEP for providing the model, data sets and technical assistance and NASA for computing resources)

CFS-based Decadal Prediction 1.COLA is participating in the CMIP5 decadal prediction enterprise, including running the prescribed protocol and providing the output data to the CMIP5 archives for public consumption. 2.The work is being done collaboratively with NCEP, including experimental design, sharing results and possible joint papers. 3.The preliminary results were similar to and consistent with what other groups are finding, esp. the problem of insufficient sample size, which motivated a more exhaustive set of hindcasts. 4.Our goal is to use the results to establish the scientific basis for decadal prediction.

Seamless Prediction: Feedback of Decadal Predictions on Shorter Time Scale Predictions By using the same model that is used for operational seasonal prediction, our results can have an impact on the way operational climate prediction is done, including identifying and quantifying erroneous and/or pathological behavior of the prediction model and dependency on the ocean initialization method.

CFS-based Decadal Prediction 1)Complete and analyze the CMIP5 “core” hindcast/forecast cases as a baseline. 1)Produce additional runs to address problems with the experimental design. 1)Conduct additional experiments to address issues of model bias, improve the hindcasts.

COLA-NCEP Collaboration: CMIP5 Decadal Predictions Identical model being used by both groups – enables direct comparison of results COLA uses ECMWF (NEMOVAR) ocean initial conditions 1960-present in CFSv2. NCEP uses CFSR ocean initial conditions, available 1980-present in CFSv2.

Technical Description Model –CFS version 2 provided by NCEP EMC –Identical to model used by NCEP for operational S-I prediction and CMIP5 (to be documented in Saha et al. 2012) Initial data –Atmosphere, land, sea ice: CFSR reanalysis (1980-present) –Ocean: NEMOVAR (ECMWF) interpolated to CFS (1960-present) 4-member ensembles –10 year predictions from Nov. 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005 –Extend to 30 years for 1960, 1980, 2005 cases Computer resources –NASA Pleiades (Thanks to NASA)

CFS v2 (Saha et al.) 1.An atmosphere of T126L64 2.An interactive ocean with 40 levels in the vertical, to a depth of 4737 m, and horizontal resolution of 0.25 degree at the tropics, tapering to a global resolution of 0.5 degree northwards and southwards of 10N and 10S respectively 3.An interactive 3 layer sea-ice model 4.An interactive land model with 4 soil levels

Results: Predictability of Atlantic SST Variability Evaluate predictability of three Atlantic SST indices in the CMIP5 decadal prediction ensemble means. –Atlantic Multidecadal Variability (TAV) –North Atlantic “Tripole” (associated with NAO) –Tropical meridional mode (TAV for “tropical Atlantic variability”). Compare CFSv2 predictions with other CMIP5 models.

COLA CMIP5 Decadal Prediction Database Model CCCma CNRM COLA-CFS HadCM3 IPSL MIROC4h MIROC5 MPI MRI NASA-GMAO NOAA-NCEP NOAA-GFDL

Atlantic Multidecadal Variability SST Index Observed Index region 80°W-0°E,0°N-59°N Color key to line plots Smoothed model forecast Smoothed persistence forecast Model bias Smoothed bias ECMWF ICs

Atlantic Multidecadal Variability SST Index ECMWF ICs NCEP ICs

Tropical Atlantic Meridional Mode SST Index Observed Index region 80°W-30°E,5°S-20°N minus 60°W-30°E, 20°S-5°S ECMWF ICs Color key to line plots Smoothed model forecast Smoothed persistence forecast Model bias Smoothed bias

North Atlantic Tripole SST Index Observed Index region 60°W-40°W,40°N-55°N minus 80°W-60°W, 25°N-35°N ECMWF ICs Color key to line plots Smoothed model forecast Smoothed persistence forecast Model bias Smoothed bias

Extended NINO3.4 Predictability Sample of 10 decadal predictions is too small to make robust inferences about interannual or longer time scale predictability. So … Fill out the cases to include at least 2 member ensembles out to 3 years lead time for all years (no volcanoes).

Multiyear NINO3.4 Index Index region 170°W-120°W,5°S-5°N Observed Color key to line plots Model forecast Persistence forecast Model bias Smoothed bias

Multiannual Predictability Using All Years AMVTripoleTAV

Sea Ice/AMOC/Salinity Biases in CFS

How Serious a Problem is CFSv2 AMOC bias? Consider AMOC in CFSv1 Huang, Hu, Schneider, Wu, Xue, and Klinger 2012

CFSv2 AMOC in 30-year runs CFSR Ocean Initial Conditions NEMOVAR Ocean Initial Conditions Huang and Zhu

Are AMOC Biases Crucially Important for Decadal Prediction? The scientific basis for decadal prediction of internal AMV variability has been supposed to be due to AMOC heat flux variability. On the other hand, the AMV index seems to have decadal predictability for CFSv2 despite AMOC bias. Why? AMOC heat flux potentially provides a substantial positive feedback to externally forced climate change (prediction of externally forced variability). Several CMIP5 models have AMOC biases similar to CFSv2 (private communications).

Mechanisms of Climate Variability

Attribution and Predictability of Global Mean Temperature Traditional view: acceleration of global warming in recent decades is externally forced. Another view: recent acceleration is due to an internal mode. Internal variability: is it the response to weather noise?

Global Mean Surface Temperature 5-yr Running Mean CCSM3 20C3M Externally Forced Component (5 year running mean) Observed (GISS analysis)

Influence of the Most Predictable Mode DelSole and Shukla

CCSM3 Response to Observed External Forcing (20C3M) Colors = ensemble members Black= ensemble mean Red = CCSM3 ensemble mean Blue = CCSM3 IE CCSM3 CMIP3 Interactive Ensemble vs. Ensemble Mean Ensemble Members vs. Ensemble Mean

Attribution Results Using the Interactive Ensemble (Model World) The interactive ensemble simulation captures the externally forced global mean temperature variability as given by the CCSM3 20C3M ensemble mean. Since the interactive ensemble filters out the weather noise forcing of the ocean, land, and sea ice, the variability of the CCSM3 20C3M ensemble members relative to the ensemble mean can be attributed to weather noise forcing.

Understanding the Differences Between Coupled and Uncoupled Simulations New results since the last SAC meeting: Question: In a perfect model world, is the weather noise statistically the same in 1) a coupled model and 2) an atmospheric model forced by the SST from the coupled model? –Assuming that the SST forced response is the same in coupled and uncoupled simulations.

Diagnosis of the Weather Noise For any field: Weather Noise = CGCM – (AMIP ensemble mean) AMIP ensemble is forced by CGCM SST A long CGCM simulation provides “observations”

Hurrell et al BAMS

Ratio of Surface Heat Flux Anomaly Standard Deviations CGCM:AGCM TotalNoise <1=1

Explanation Signal is identical in coupled and uncoupled by construction. Noise variance is the same in coupled and uncoupled. Covariance is different –Uncoupled: covariance(signal, noise) = 0 –Coupled: covariance(signal, noise) ≠ 0 because the noise forces the signal. Variance (total) = variance (signal) + variance (noise) + covariance (signal, noise)

So What? Who cares about surface heat flux variability? You can’t even measure it.

Ratio of Precipitation Anomaly Standard Deviations CGCM:AGCM TotalNoise <1=1

A Real World Consequences: Tier 2 Prediction “Samescaling” CFSv2 seasonal forecasts using GFS –use the AGCM component (GFS) of the CGCM, forced by the SST predicted by the CGCM (CFSv2). Shukla and Zhu

Precipitation Standard Deviation Seasonal Forecasts CGCM CMAP AGCM

Summary Observations: applications of new techniques Information theory: new techniques –Relative Entropy –EEMD Modeling –Decadal prediction: industrial strength effort –Mechanisms for internal variability: novel methodology, beautiful results

Questions for Omnibus Proposal How can we build on the CMIP5 prediction experiments to better define the scientific basis for decadal predictability? –Initialization? –Model bias abatement? –Idealized experiments? –Auxiliary models (like interactive ensemble)? –Multimodel strategy? –Seamless strategy?