Ocean Prediction and Predictability with Focus on Atlantic Goal: Understanding ocean’s role in climate predictability from ISI to decadal scales using complex climate models Approach: Examining mechanisms of coupled oceanic-atmospheric processes and contributing to the improvement of national models Bohua Huang, Jieshun Zhu, Zeng-Zhen Hu (COLA, CPC/NCEP), Jian Li, Barry Klinger, Xingren Wu (EMC/NCEP), Shaoqing Zhang (GFDL)

Outline  Enhancing tropical simulation and initialization for ISI predictability using NCEP Climate forecast System (CFS) Low cloud effect on CFS Bias Simulating diurnal SST variability Uncertainty of tropical Atlantic Heat Content (HC) variability Understanding oceanic processes in North Atlantic for decadal predictability Mechanisms of multidecadal AMOC variability Improving Arctic climate in CFSv2

CFS Bias in Tropical Atlantic CFSv1 CFSv2 Does the lack of low cloud cause warm bias?

Realistic Low Cloud Reduces Bias Substantially LCLD: Prescribed low cloud water content based on observations Hu et al. 2011, Clim Dyn OBS SIM LCLD SIM ERR LCLD ERR

Under more realistic mean state, equatorial seasonal transition is improved Seasonal cycle along Equatorial Atlantic: SST and surface wind stress LCLD SIM OBS

TAO Mooring CFSv1 with DML CFSv1 without DML SST, Equator, 140 o E Total Diurnal Diurnal mixed layer (DML) enhances SST Variability Li 2011, PhD diss.

Reconstructed DSST (Clayson et. al. 2007) Mean Diurnal SST Magnitude (Daily Maximum-Minimum) CFSv1 with DML parameterization Observation-based estimates (Clayson et al. 2007)

Heat Content (HC) Uncertainty in Ocean Analyses What happens to tropical Atlantic variability? ECMWF: ORA-S3, COMBINE-NV NCEP: GODAS, CFSR UM/TAMU:SODA GFDL :CEDA Data Sources SNR Zhu et al. 2011, Clim Dyn, accepted HC=300m mean temperature

Leading EOF modes from different ocean analyses show quite different features How should we deal with high uncertainty in tropical Atlantic? CFSRECMWF

Ensemble mean improves the signal-to-noise ratio Implication: Ensemble hindcasts from multiple ocean initial states may be the key for prediction in tropical Atlantic

Prediction Skill of Hurricane Main Development Region (MDR) Index (10-20N, 20W-80W) Ensemble averaging of multiple ocean hindcasts has higher prediction skill “Rebound” occurs in SST prediction skill during hurricane season (SON) A M J J A S O N D J F M SST

A M J J A S O N D J F M HC Prediction Skill of MDR Index (10- 20N, 20W-80W) “Rebound” of SST skill is due to re- emergence of sub- surface memory

Atlantic Meridional Overturning Circulation (AMOC) Potential source of predictability due to multi-year timescales associated with dynamics. Multi-decadal oscillations in AMOC due to internal air-sea dynamics Forced decrease in AMOC associated with global warming Confidence in potential to predict decadal variability in AMOC reduced by model- dependence of both these features. What causes the multidecadal oscillations? What determines the model dependence?

Multidecadal AMOC Oscillation Anything in common among models?

Phase1 Phase 2 Phase 3 Phase 4 GFDL CM2.1 and NCAR CCSM3 show similar AMOC evolution within a cycle GFDL CM2.1 NCAR CCSM3.0

Orientation of several subsequent figures

Phase1 Phase 2 Phase 3 Phase 4 CM2.1 CCSM3.0 North Atlantic SST is increased following stronger AMOC

Phase1 Phase 2 Phase 3 Phase 4 CM2.1 CCSM3.0 North Atlantic HCA forces SSTA. Both are induced by AMOC heat transport

Phase1 Phase 2 Phase 3 Phase 4 CM2.1 CCSM3.0 Surface latent heat flux anomalies damp SSTA

Phase1 Phase 2 Phase 3 Phase 4 CM2.1 CCSM3.0 Strong AMOC induces sea ice melting

Phase1 Phase 2 Phase 3 Phase 4 CM2.1 CCSM3.0 The AMOC oscillation is associated with NAO

Phase1 Phase 2 Phase 3 Phase 4 CM2.1 CCSM3.0 Air temperature is warm after a strong AMOC

East Observed multidecadal variability in North Atlantic sea level height Frankcombe and Dijkstra. GRL, 2009 West There is observational basis for 20-30yr variability in North Atlantic, faster than AMO

Why do different CGCMs project different amounts of AMOC decline during 21 st century? Much of variability in AMOC decline explained by variability in northern N Atl surface density decline. Most of the variability in N Atl surface density decline is due to variability in salinity decline. normalized surf. dens decrease fractional overturning decrease density decrease S contribution T contribution solid symbols: total SRES A1B vs. 20C3M Klinger 2011, J Clim in review

Why is AMOC weak in CFSv2?

Initial month Final month Weak AMOC in CFSR initial condition (IC)?

Initial month Final month Strong initial AMOC in ECMWF IC is also weakened

Model surface water is too fresh in North Atlantic OBS CFSR IC ECMWF IC

OBS CFSR IC ECMWF IC Considerable freshening occurs in upper 200 meters

Where does the excessive freshwater come from? Slid lines: CFSR IC Dashed lines: ECMWF IC

Artificial Sea Ice melting in Arctic Ocean could be a major source of freshwater flux into North Atlantic Sea Ice, ECMWF IC

Sensitivity Experiment Albedo (ICE) Run (10-yr) Sea Ice Albedo 0.8 (Control 0.6) Temperature range of albedo change from dry to wet ice 1.0 o C (Control 10.0 o C) Based on suggestions from Dr. Xingren Wu (EMC/NCEP)

Sensitivity Experiment Albedo (ICE) Run (10-yr) Sea Ice Albedo 0.8 (Control 0.6) Temperature range of albedo change from dry to wet ice 1.0 o C (Control 10.0 o C) Based on suggestions from Dr. Xingren Wu (EMC/NCEP)

Improved Sea Ice increases AMOC (but not enough)

Brackish water from Baltic Sea may be another source of North Atlantic freshening It causes a serious freshening of the northeastern part of the North Atlantic Ocean ICE Run Sea Surface Salinity November Ice Run

Sensitivity Experiment TOPO Run (10-yr): Sill depth between Baltic Sea and North Atlantic is raised from 100m (Control) to 30m The freshening in the eastern part of North Atlantic is reduced Sea Surface Salinity Difference TOPO-ICE

Summary Deficit of low cloud is a major source of tropical Atlantic warm bias in CFSv1 and v2 Diurnal mixed layer parameterization generates realistic diurnal SST variability in CFSv1 and should also be helpful in v2 Current ocean analyses show high uncertainty in tropical Atlantic heat content variability; ensemble ocean initialization is necessary for tropical Atlantic prediction Some climate models produce a yr oscillation in North Atlantic, which has observational basis and may be a source of decadal predictability Excessive freshening weakens AMOC in CFSv2; Arctic climate is improved by adjusting sea ice albedo and marginal sea outflow Work is ongoing in collaboration with NCEP scientists to improve CFSv2 simulation and to investigate predictability with these improvements