An evaluation of satellite derived air-sea fluxes through use in ocean general circulation model

Vijay K Agarwal, Rashmi Sharma, Neeraj Agarwal Meteorology and Oceanography Group

Space Applications CentreAhmedabad

Objective

To underline the opportunity offered by spaceborne ocean sensorsto improve the ocean circulation model simulations and their understanding

BackgroundTo improve upon OGCM simulations -

To understand the air-sea interaction Sea surface temperature

Surface salinity Near-surface specific humidity Air temperature Wind stress components (zonal and meridional) Cloud cover Net solar radiation at the ocean surface

Use of these parameters in Ocean Model is essential as boundary/ initial conditions:

-To generate complete ocean state analysis-To understand 3-D variability of oceanic variables at different temporal scales

Requirement:A more realistic and less constraining surface boundary conditions for obtaining novel OGCM simulation

Sources:Re-analysis products, estimates from direct bulk-formula usage and calculations from atmospheric state estimate residuals

Problem Area:A lot of uncertainty remains in estimates of these parameters

Approach: To assess the uncertainties of the above estimates using OGCM

Model description

• OGCM for this study is based on GFDL MOM ver. 3.0

• Domain : 80S-80N; 180W-180E

• Horizontal Grid: Variable; 0.5° in the Indian Ocean and 2° elsewhere.

• Vertical Grid: 38 levels; 21 levels in the top 180m.

• Bathymetry is ETOPO5

• Philander Pacanowski Vertical mixing scheme: Richardson dependent values of mixing coefficients

HORIZONTAL GRID STRUCTURE

Set of Experiments:

Experiment ( #1) with winds (NCEP/NCAR and QuikSCAT scatterometer)

Experiment (#2) with short wave radiation (NCEP and satellite derived)

Experiment (#3) with fresh water flux (GPCP, NCEP)

ERRORS IN TWO WIND PRODUCTS (V-comp) at DIFFERENT BUOY LOCATIONS

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0n110w

0n156e

2n125w

2n156e

2s125w

2s170w

5n140w

5n170w

5s155w

8n110w

8n165e

8s155w

BUOY LOCATION

RM

SE

(m/s

)

QSCAT

NCEP

ERRORS IN TWO WIND PRODUCTS (U-comp) at DIFFERENT BUOY LOCATIONS

0

0.5

1

1.5

2

2.5

3

3.5

4

BUOY LOCATION

RM

SE

(m/s

)

QSCAT

NCEP

Experiment 1: Impact of surface wind on OGCM Simulations

Errors in two wind products

Simulation of SLA variabililty (cms)

Relative performance of the model using the two wind products

From 1997 till 1999 The model is forcedwith NCEP and then till 2004 with scatterometer winds

Buoy

Model

SST

D20 Isotherm

Equatorial Pacific Ocean

Equatorial Indian Ocean

BuoyNCEP

Scat

Summary (Exp #1)

•Winds from scatterometer improve SLA, currents and deeper layer temperature simulation.

•However the RMSE for SST is higher in the QSCAT runs which may be possibly due to thermodynamic imbalance in the air-sea interaction parameters

Errors in SW Fluxes: NCEP (61 watts/m2 ),

LY (37 watts/m2 ) and OLR-based (45 watts/m2)

Wa

tts/

m2

LY

OLR

NCEP

Time Variation of SW Radiation in central Bay of Bengal

Experiment 2: Impact of Shortwave Radiation on model simulations

Summary (Exp#2)

The short wave flux is responsible for generation of ISO in the Bay of Bengal

8-16 day ISO signal can be seen in buoy and model simulations.

ISO signal is relatively weak in NCEP wind driven solution

Buoy LY

NCEP

Experiment 3: Impact of Fresh Water Flux

Exp (#3.1) Model forced with GPCP precipitation, model evaporation and climatological river discharge was used.

Salient results:Sea level rise was abnormally large

Reason: Physical inconsistency in the fresh water flux equation due to global fresh water flux imbalance

Exp (#3.2) Next, NCEP daily climatological E-P was used to force the model.

Salient Results:Still there was 10-11 cm rise per year in SL

Reason: Imbalance in fresh water flux caused by polar ice and river discharge.

Exp (#3.3) Corrected climatological E-P (by subtracting global integral) data was used in the model

Results: SL variation became normal. No bias in the simulation.

Sea

Lev

el (

cm) Before correction

After correction

• MT is likely to provide wind speed, E-P and SW flux at top of atmosphere

•Vector attachment?•Sensible heat?•Surface insolation in cloudy and clear sky?

•This is a major portion of information required for OGCM.

•However, this information may still require blending with AGCM data to solve the problem of balancing.

•The science activity under MT will concentrate on these issues to understand the physical processes and will address the problem of physical inconsistencies for the global tropical oceans.

How Megha Tropiques (MT) can help?

Thank You