Representation of Bay of Bengal upper-ocean salinity in general circulation models

The Bay of Bengal (BoB) upper-ocean salinity is examined in the National Centers for Environmental Prediction-Climate Forecasting System version 2 (CFSv2) coupled model, Modular Ocean Model version 5 (MOM5), and Indian National Centre for Ocean Information Services Global Ocean Data Assimilation System (INC-GODAS). CFSv2 displays a large positive salinity bias with respect to World Ocean Atlas 2013 in the upper 40 m of the water column. The prescribed annual mean river discharge and excess evaporation are the main contributors to the positive bias in surface salinity. Overestimation of salinity advection also contributes to the high surface salinity in the model during summer. The surface salinity bias in MOM5 is smaller than in CFSv2 due to prescribed local freshwater flux and seasonally varying river discharge. However, the bias is higher around 70 m in summer and 40 m in fall. This bias is attributed to excessive vertical mixing in the upper ocean. Despite the fact that representation of salinity in INC-GODAS is more realistic due to data assimilation, the vertical mixing scheme still imposes systematic errors. The small-scale processes that control oceanographic turbulence are not adequately resolved in any of these models. Better parameterizations based on dedicated observational programs may help improve freshwater representation in regional and global models

Interannual variability of upper ocean stratification in Bay of Bengal: observational and modeling aspects

The annual cycle and interannual variability of stratification in Bay of Bengal (BoB) are studied using both observations and Global Ocean Data Assimilation System (GODAS) analysis during 2003–2012. Annual cycle of stratification and sea surface temperature (SST) evolve coherently, highlighting its role on modulating air-sea interaction over this climatologically important region. Spatial distribution of stratification shows strong seasonality in ARGO observations, whereas it is highly underestimated in GODAS with highest discrepancies during fall and spring. The annual cycle of sea surface salinity (SSS) in GODAS is out of phase with observations implying potential feedbacks. During La Niña years, SSS drop in fall and winter and are lesser than those reported during El Niño years. All these features are misrepresented in GODAS. As stratification modulates air-sea interaction over BoB especially during El Niño and La Niña years, such misrepresentation of ocean stratification may lead to unrealistic thermocline-SST coupling in the models. The mean stratification and its interannual variability in GODAS are weaker than observed even though interannual variability in freshwater flux (P-E) is higher in GODAS. Detailed analysis of GODAS with in situ observations reveals that upper ocean current shear (vertical) is overestimated in GODAS, leading to unrealistically strong mixing which is primarily responsible for the deeper penetration of surface warm and freshwater resulting weaker stratification. As GODAS is used to initialize the ocean component of the coupled forecasting system for seasonal prediction of Asian monsoon, proper representation of stratification is essential. This study advocates the need of accurate representation of upper ocean salinity in GODAS for improved stratification. We speculate that improved stratification and mixing in the BoB improve summer monsoon forecas

Tropical Indian Ocean surface salinity bias in Climate Forecasting System coupled models and the role of upper ocean processes

In the present study sea surface salinity (SSS) biases and seasonal tendency over the Tropical Indian Ocean (TIO) in the coupled models [Climate Forecasting System version 1 (CFSv1) and version 2 (CFSv2)] are examined with respect to observations. Both CFSv1 and CFSv2 overestimate SSS over the TIO throughout the year. CFSv1 displays improper SSS seasonal cycle over the Bay of Bengal (BoB), which is due to weaker model precipitation and improper river runoff especially during summer and fall. Over the southeastern Arabian Sea (AS) weak horizontal advection associated with East Indian coastal current during winter limits the formation of spring fresh water pool. On the other hand, weaker Somali jet during summer results for reduced positive salt tendency in the central and eastern AS. Strong positive precipitation bias in CFSv1 over the region off Somalia during winter, weaker vertical mixing and absence of horizontal salt advection lead to unrealistic barrier layer during winter and spring. The weaker stratification and improper spatial distribution of barrier layer thickness (BLT) in CFSv1 indicate that not only horizontal flux distribution but also vertical salt distribution displays large discrepancies. Absence of fall Wyrtki jet and winter equatorial currents in this model limit the advection of horizontal salt flux to the eastern equatorial Indian Ocean. The associated weaker stratification in eastern equatorial Indian Ocean can lead to deeper mixed layer and negative Sea Surface Temperature (SST) bias, which in turn favor positive Indian Ocean Dipole bias in CFSv1. It is important to note that improper spatial distribution of barrier layer and stratification can alter the air–sea interaction and precipitation in the models. On the other hand CFSv2 could produce the seasonal evolution and spatial distribution of SSS, BLT and stratification better than CFSv1. However CFSv2 displays positive bias in evaporation over the whole domain and negative bias in precipitation over the BoB and equatorial Indian Ocean, resulting net reduction in the fresh water availability. This net reduction in fresh water forcing and the associated weaker stratification lead to deeper (than observed) mixed layer depth and is primarily responsible for the cold SST bias in CFSv2. However overall improvement of mean salinity distribution in CFSv2 is about 30 % and the mean error has reduced by more than 1 psu over the BoB. This improvement is mainly due to better fresh water forcing and model physics. Realistic run off information, better ocean model and high resolution in CFSv2 contributed for the improvement. Further improvement can be achieved by reducing biases in the moisture flux and precipitation

Processes associated with the Tropical Indian Ocean subsurface temperature bias in a Coupled Model

Subsurface temperature biases in coupled models can seriously impair their capability in generating skillful seasonal forecasts. The National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), coupled model, which is used for seasonal forecast in several countries including India, displays warm (cold) subsurface (surface) temperature bias in the tropical Indian Ocean (TIO), with deeper than observed mixed layer and thermocline. In the model, the maximum warm bias is reported between 150- and 200-m depth. Detailed analysis reveals that the enhanced vertical mixing by strong vertical shear of horizontal currents is primarily responsible for TIO subsurface warming. Weak upper-ocean stability corroborated by surface cold and subsurface warm bias further strengthens the subsurface warm bias in the model. Excess inflow of warm subsurface water from Indonesian Throughflow to the TIO region is partially contributing to the warm bias mainly over the southern TIO region. Over the north Indian Ocean, Ekman convergence and downwelling due to wind stress bias deepen the thermocline, which do favor subsurface warming. Further, upper-ocean meridional and zonal cells are deeper in CFSv2 compared to the Ocean Reanalysis System data manifesting the deeper mixing. This study outlines the need for accurate representation of vertical structure in horizontal currents and associated vertical gradients to simulate subsurface temperatures for skillful seasonal forecasts