Decadal Prediction and Predictability Presenter: Edwin K. Schneider COLA SAC Meeting September 2011

Research Areas 1)Decadal prediction and predictability using the NCEP CFS (version 2) CGCM. Extension of NWP, seasonal, decadal forecasting to decadal time scales. 2)Understanding the mechanisms and predictability of Atlantic decadal variability, currently in a CGCM (CCSM/CESM). Long-standing project (Schneider and Kinter 1994; discovered the AO and AAO, but didn’t comment on AO). 3)Issues in climate change, including role of ocean dynamics, aerosol effects, and the expansion of the tropics.

Major Results 1)Decadal prediction We have completed CMIP5 core decadal forecasts with CFSv2/NEMOVAR, which are now being evaluated. We are wrestling with the model bias issue and making some progress. We are pushing beyond CMIP5, in collaboration with other groups (e.g. NCEP parallel runs, IRI, GSFC). 2)Mechanisms of decadal variability We are developing a better understanding of mechanisms of interannual to decadal SST variability and the interactions between internal and externally forced variability in “perfect model” simulations. We are settling a controversy: Do AMIP simulation have a fundamental flaw in simulating the SST forced response of the atmosphere? Answer: no. 3)Issues in climate change (please see Jian Lu’s poster): Role of Wind-Evaporation-SST (WES) feedback: Wind stress “overriding” experiments with CCSM3. WES feedback is of major importance to explain the response to doubled CO 2 SST and precipitation patterns in the tropics. WES is important for the Southern Annular mode internal variability.

Research Area 1 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 (Thanks to NCEP for providing the model, data sets and some technical assistance)

Project Plan 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.

Technical Description Model –CFS version 2 provided by NCEP EMC (identical to model used by NCEP for operational S-I prediction and CMIP5) 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 NAS)

CFS v2 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: Biases Bias, defined as forecast minus verification as a function of lead time. –Average of all lead times –Evolution high latitudes –Bias development year 1 Verification data is NCEP/NCAR reanalysis 2m air temperature (NB: too cold in high latitudes vs. CFSRR).

Mean Bias T2m Averaged Years 0-10 Small bias in tropics Large bias in high latitudes

Bias Evolution T2m 65°N to 75°N 65°S to 75°S

Sea Ice/AMOC/Salinity Biases: Bohua Huang’s talk

Results: Ensemble Mean Hindcast Anomalies NINO3.4 SST index North Atlantic AMV SST index Ensemble mean for each hindcast Annual cycle removed

NINO3.4 Decadal Hindcasts

NINO3.4 SSTA Hindcasts Individual Cases Model Obs

NINO 3.4 SSTA Forecast Scores Rebound of skill in year 2: Tim DelSole will return to this issue. CorrelationRMS Error

North Atlantic AMV Index (SST Averaged 0N-60N, 300E-360E) HindcastsObservations

North Atlantic AMV Index (SST Averaged 0N-60N, 300E-360E) Bias Corrected HindcastsObservations

AMV SSTA Forecast Scores CorrelationRMS Error

Possible Issue Initial state choice –CMIP5 protocol: Limited number of initial years may lead to biased evaluations. –Suggest that a better experimental design for the same cost might be to sample the initial states more thoroughly with fewer ensemble members.

Summary Decadal Prediction Multiyear predictability of ENSO has been found. –Is this real or does it reflect inadequate sampling of initial conditions? Have noticed some problems and have had some success in addressing biases. –Inadequate AMOC strength and variability is a serious model bias. Decadal predictability in the North Atlantic has been identified, associated with the long term warming trend. –What is the source of this trend? –What is the source of the skill? (probably not AMOC)

Research Area 2: Mechanisms of Decadal Variability Understanding the mechanisms and predictability of Atlantic decadal variability in a CGCM (CCSM/CESM). –Research group COLA/GMU: Schneider, Huang, Chen, Colfescu Collaboration: RSMAS – Kirtman group

Motivation Diagnose and understand the mechanisms of simulated low frequency Atlantic SST variability. In particular, what were the roles of weather noise forcing, coupled feedbacks, and ocean dynamics? What are the implications for “decadal” predictability?

Approach Perfect model/perfect data framework Isolate the weather noise in a CGCM simulation. Force an Interactive Ensemble CGCM (IE- CGCM) with the weather noise in controlled experiments.

Data and Models NCAR CCSM –CCSM3 (T85 x 1 and T42 x 1) 300 year control simulation (T42 x 1) 20C3M simulation (T85 x 1) –CAM3 (AGCM component of CCSM3) + CLM3 (LSM component of CCSM3) –Interactive ensemble CCSM3 –Ensemble mean of 6 copies of CAM3 coupled to ocean, land, sea ice –T42 x 1

NCAR CCSM3 Is an appropriate model for this study. It produces reasonable simulations of decadal/multidecadal modes of SST variability (i.e., you might guess that this was a model of Earth). –NAO, AMV, TAV

Fig.5 Upper panel: regression of SST anomalies against its normalized AMV index. Lower panel: AMV index. The red curves are the annual mean and the black curves indicate the 11-year running mean with high frequency removed. Data are detrended. Atlantic Multidecadal Variability “Obs” (130 years)“Ctrl” (300 years)

Weather Noise Forced Interactive Ensemble AGCM 1 … 2N SST Response 1Response 2Response N … Ensemble Mean Surface Fluxes OGCM Weather Noise Surface Fluxes

Response to Observed External Forcing CCSM3 and IE-CCSM (20C3M) Colors = ensemble members Black= ensemble mean Red = CCSM3 ensemble mean Blue = CCSM3 IE CCSM3 CMIP4 Global Means Ts Precip Z

ExperimentDescriptionPurpose 1. Control (“Ctrl”) 300-yr CGCM runControl run 2. AGCM ensemble (“signal”) 6 member AGCM ensemble, forced by the same SST from “Ctrl” To remove the atmospheric noise and get the forced response to the “Ctrl” SST Weather Noise Total (from “Ctrl”) “Signal” Experimental Design

Controversy! Important for Our Method! No SST forced AGCM produces a realistic response to observed SST. Is this due to fundamental flaw in uncoupled vs. coupled simulations? –This is the result described in publications: Wu and Kirtman (2004), Wang et al. (2005). Is this due to model bias? Test in a perfect model framework to eliminate model bias (see Hua Chen’s poster-includes comparison with results from above papers).

Answer! Important for Our Method! No SST forced AGCM produces a realistic response to observed SST. Is this due to fundamental flaw in uncoupled vs. coupled simulations? No Is this due to model bias?

Answer! Important for Our Method! No SST forced AGCM produces a realistic response to observed SST. Is this due to fundamental flaw in uncoupled vs. coupled simulations? No Is this due to model bias? Yes: there is no other possibility.

Linear Trends in the Tropics 4. Copsey, D., R. Sutton, and J. R. Knight, 2006: Recent trends in sea level pressure in the Indian Ocean region. GRL, 33.

Trends Copsey et al. CCSM3 20C3M SST SLP Obs CGCM SLP AGCM

On the Limitations of Prescribing Sea Surface Temperatures in Atmospheric General Circulation Model Experiments James W. Hurrell 1*, Simon Brown 2, Adam Phillips 1, and Christophe Cassou 1 1 National Center for Atmospheric Research, Boulder, CO, USA. 2 United Kingdom Meteorological Office, Bracknell, U.K. * Corresponding author Climate Dynamics April 2004 (unpublished, figures lost)

Hurrell et al’s Comparison Examined ratio of standard deviations CGCM:AGCM. Their results appear to agree with predictions of Barsugli and Battisti (1998) model. Our results appear to agree with Hurrell et al’s., and additionally provide support to Barsugli and Battisti’s assumptions. Our determination of weather noise is in fact based on Hurrell et al’s “limitations.”

Point Correlation of SST IE-CCSM3 with Global Noise Forcing vs. CCSM3 Control (“Truth”) Monthly AnomaliesAnnual Anomalies => Most of the SST variability is weather noise forced.

ExperimentDescriptionPurpose 3. IE_G_allIE-CGCM forced by 3 types of weather noise surface fluxes globally (100 years) To isolate the role of weather noise in the low frequency SST variability from other mechanisms ExperimentDescriptionPurpose 4. IE_ATL_allNoise surface fluxes restricted to the whole Atlantic only; zero elsewhere (100 years) To isolate the role of local weather noise from other mechanisms IE Experiments Global weather noise Weather noise in Whole Atlantic only …

Summary Decadal Prediction Our decadal prediction project is well under way, using CFS_v2, and is generating questions: –ENSO skill rebound? –Sampling of initial states? –Biases North Atlantic and polar regions? –Source of skill in hindcasting AMV?

Summary - Decadal Mechanisms Contrary to some findings in the literature, we find (in CCSM3) that AMIP simulations are able to capture the SST-forced response of the atmosphere. Weather noise and external forcing are the major sources of SST variability in CCSM3. –Weather noise is the cause of variability about the ensemble mean for the CCSM3 20C3M ensemble. –Using our method, we will arrive at a reasonably complete understanding of the mechanisms of decadal variability in CGCMs.