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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

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ocean prediction and predictability with focus on atlantic
Ocean Prediction and Predictabilitywith Focus on Atlantic

Goal: Understanding ocean’s role in climate predictability from ISItodecadal 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
Outline

Enhancing tropical simulation and initializationfor 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

slide3

CFS Bias in Tropical Atlantic

CFSv1

CFSv2

Does the lack of low cloud cause warm bias?

realistic low cloud reduces bias substantially

Hu et al. 2011, ClimDyn

Realistic Low Cloud Reduces Bias Substantially

SIM ERR

OBS

SIM

LCLD

LCLD ERR

LCLD: Prescribed low cloud water content based on observations

slide5

Under more realistic mean state, equatorial seasonal transition is improvedSeasonal cycle along Equatorial Atlantic: SST and surface wind stress

OBS

LCLD

SIM

slide6

Diurnal mixed layer (DML) enhances SST Variability

TAO Mooring

CFSv1 with DML

CFSv1 without DML

Total

Total

Total

SST, Equator, 140oE

Diurnal

Diurnal

Diurnal

Li 2011, PhD diss.

slide7

Mean Diurnal SST Magnitude (Daily Maximum-Minimum)

CFSv1 with DML parameterization

Observation-based estimates (Clayson et al. 2007)

Reconstructed DSST(Clayson et. al. 2007)

heat content hc uncertainty in ocean analyses what happens to tropical atlantic variability

Zhu et al. 2011, ClimDyn, accepted

Heat Content (HC) Uncertainty in Ocean AnalysesWhat happens to tropical Atlantic variability?

1979-2007

SNR

Data Sources

ECMWF: ORA-S3, COMBINE-NV

NCEP: GODAS, CFSR

UM/TAMU: SODA

GFDL : CEDA

HC=300m mean temperature

slide9

CFSR

ECMWF

How should we deal with high uncertainty in tropical Atlantic?

Leading EOF modes from different ocean analyses show quite different features

slide10

Implication:

Ensemble hindcasts from multiple ocean initial states may be the key for prediction in tropical Atlantic

Ensemble mean improves the signal-to-noise ratio

slide11

Prediction Skill of Hurricane Main Development Region (MDR) Index (10-20N, 20W-80W)

Ensembleaveraging of multiple ocean hindcasts has higher prediction skill

“Rebound” occurs in SST prediction skill during hurricane season (SON)

SST

A M J J A S O N D J F M

slide12

Prediction Skill of MDR Index (10-20N, 20W-80W)

“Rebound” of SST skill is due to re-emergence of sub-surface memory

HC

A M J J A S O N D J F M

A M J J A S O N D J F M

atlantic meridional overturning circulation amoc
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 multidecadaloscillations?

What determines the model dependence?

slide14

Multidecadal AMOC Oscillation

Anything in common among models?

slide15

GFDL CM2.1

NCAR CCSM3.0

Phase1

GFDL CM2.1 and NCAR CCSM3 show similar AMOC evolution within a cycle

Phase 2

Phase 3

Phase 4

slide17

Phase1

Phase 2

CM2.1

CCSM3.0

Phase 3

Phase 4

North Atlantic SST is increased following stronger AMOC

slide18

Phase1

Phase 2

CM2.1

CCSM3.0

Phase 3

Phase 4

North Atlantic HCA forces SSTA. Both are induced by AMOC heat transport

slide19

Phase1

Phase 2

CM2.1

CCSM3.0

Phase 3

Phase 4

Surface latent heat flux anomalies damp SSTA

slide20

Phase1

Phase 2

CM2.1

CCSM3.0

Phase 3

Phase 4

Strong AMOC induces sea ice melting

slide21

Phase1

Phase 2

CM2.1

CCSM3.0

Phase 3

Phase 4

The AMOC oscillation is associated with NAO

slide22

Phase1

Phase 2

CM2.1

CCSM3.0

Phase 3

Phase 4

Air temperature is warm after a strong AMOC

slide23

Observed multidecadal variability in North Atlantic sea level height

Frankcombe and Dijkstra. GRL, 2009

East

There is observational basis for 20-30yr variability in North Atlantic, faster than AMO

West

slide24

Why do different CGCMs project different amounts of AMOC decline during 21st century?

Much of variability in AMOC decline

explained by variability in northern

N Atl surface density decline.

Most of the variability in

N Atlsurface density decline is due to variability in salinity decline.

density decrease

fractional overturning decrease

normalized surf. dens decrease

S contribution

T contribution

solid symbols: total

SRES A1B vs. 20C3M

Klinger 2011, J Clim in review

slide30

CFSR IC

OBS

ECMWF IC

Model surface water is too fresh in North Atlantic

slide31

CFSR IC

OBS

ECMWF IC

Considerable freshening occurs in upper 200 meters

slide32

Slid lines: CFSR IC

Dashed lines: ECMWF IC

Where does the excessive freshwater come from?

slide33

Sea Ice, ECMWF IC

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

slide34

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

1.0oC (Control 10.0oC)

Based on suggestions from Dr. Xingren Wu

(EMC/NCEP)

slide35

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

1.0oC (Control 10.0oC)

Based on suggestions from Dr. Xingren Wu

(EMC/NCEP)

slide37

ICE Run

Sea Surface Salinity

November

Ice Run

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

slide38

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

slide39

SummaryDeficit 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 predictionSome climate models produce a 20-30 yr oscillation in North Atlantic, which has observational basisand may be a source of decadal predictabilityExcessive freshening weakens AMOC in CFSv2; Arctic climate is improved by adjusting sea ice albedo and marginal sea outflowWork is ongoing in collaboration with NCEP scientists to improve CFSv2 simulation and to investigate predictability with these improvements