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 Centre Ahmedabad

Objective To underline the opportunity offered by spaceborne ocean sensors to improve the ocean circulation model simulations and their understanding Background To 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)

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 forced with NCEP and then till 2004 with scatterometer winds Buoy Model SST D20 Isotherm Equatorial Pacific Ocean

Equatorial Indian Ocean Buoy NCEP 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/m 2 ), LY (37 watts/m 2 ) and OLR-based (45 watts/m 2 ) Watts/m 2 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 BuoyLY 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 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 Level (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