High-resolution Coupled Model Research Articles

The influence of Urmia Lake desiccation on an extreme snowfall event: A case study using the WRF-Lake model

Urmia Lake in the northwest Iran has undergone significant desiccation in recent decades, shrinking to a much smaller size than its original. This study implements the WRF-Lake coupled modeling system to examine the impacts of the observed physical changes over the Lake during a heavy snowfall event occurred February 1–2, 2017. Modifications are introduced to represent the shrunken, salt-encrusted lake surface and surrounding arid environment through updating land use, soil characteristics, lake size and depth, and albedo data. Experiments incorporating lake-atmosphere coupling and land surface refinements (Lake_CTL, Lake_LULC, Lake_AL) outperform simulations without a lake (WRF_noLake) in reproducing observed precipitation patterns at stations surrounding the Urmia Lake. All simulations underestimate the extreme 290 mm snowfall recorded at the Urmia station; however, the Lake_LULC and Lake_AL improve snowfall representation at other stations around the lake. Regarding snow cover validation, Lake_LULC and Lake_AL exhibit a higher correlation with satellite observations and show a significant improvement in snow detection near Urmia Lake. They have a Probability of Detection (POD) of 0.94 and a Critical Success Index (CSI) of 0.91, compared to a POD of 0.79–0.80 and a CSIs of ∼0.77 for the default lake model (Lake_CTL) and the WRF_noLake experiments, respectively. Additionally, adjusting the initial lake surface temperature to match observations substantially reduces cold biases in near-surface air temperatures over Urmia Lake. The lack of lake temperature updating in WRF-Lake poses ongoing challenges in the model, though. In summary, this study demonstrates that refining the representations of desiccated lakes and their surroundings in high-resolution coupled models would improve simulations of meteorological processes influenced by lake-atmosphere interactions.

The link between intertropical convergence zone stagnation and bias in local shortwave cloud radiative forcing over tropical Africa in climate models

The northward migration of the intertropical convergence zone (ITCZ) is a significant feature of the West African (WA) monsoon. An accurate simulation of ITCZ migration is essential for the realistic representation of WA precipitation in global coupled models. In this study, we employ the energetics and dynamics framework with a subset of CMIP6 models to investigate the bias in the simulated WA precipitation. Models were found to simulate more local positive (negative) shortwave cloud radiative forcing (SWCRF) in the Southeastern Atlantic Ocean (over the African continent). The effect of the excess local SWCRF is linked to the stagnation of the ITCZ latitudinal migration and the associated biases in the asymmetry index of precipitation. In the models, there is more (less) moist static energy in the lower (mid and upper) troposphere than in the reanalysis. The worst models have a stronger bias, especially over land. The vertical transport of moisture is confined to the boundary layer in the worst model ensemble. In most cases, the high-resolution coupled models show substantial northward migration of the ITCZ compared to the low-resolution models. Furthermore, the best-performing models capture local circulation and energetic processes more accurately than the worst-performing models.

Effective generation mechanisms of tropical instability waves as represented by high-resolution coupled atmosphere–ocean prediction experiments

Cusp-shaped fluctuations of the sea surface temperature (SST) front in the tropical Pacific, now known as tropical instability waves (TIWs), were discovered by remote sensing in the 1970s. Their discovery was followed by both theoretical and analytical studies, which, along with in situ observations, identified several possible generation mechanisms. Although modeling studies have shown that TIWs strongly influence the heat budget, their influence on local variations of realistically initialized predictions is not yet understood. We here evaluate a series of medium-range (up to ~ 10 days) coupled atmosphere–ocean predictions by a coupled model with different horizontal resolutions. Observational SST, surface wind stress, heat flux, and pressure data showed that representation of temporally and spatially local variations was improved by resolving fine-scale SST variations around the initialized coarse-scale SST front fluctuations of TIWs. Our study thus demonstrates the advantage of using high-resolution coupled models for medium-range predictions. In addition, analysis of TIW energetics showed two dominant sources of energy to anticyclonic eddies: barotropic instability between equatorial zonal currents and baroclinic instability due to intense density fronts. In turn, the eddy circulation strengthened both instabilities in the resolved simulations. This revealed feedback process refines our understanding of the generation mechanisms of TIWs.

Evaluation and Correction of Climate Simulations for the Tibetan Plateau Using the CMIP6 Models

This study evaluates the abilities of fifteen High-resolution Coupled Model Intercomparison Project phase 6 (CMIP6) models to simulate temperature and precipitation over the Tibetan Plateau (TP) for the years 1980–2014. The impacts of terrain correction and Empirical Orthogonal Function (EOF) correction on simulations of temperature and precipitation are examined. The results show that equal-weighted ensemble averaging of the CMIP6 high-resolution model provides a good representation of the spatial distribution of temperature over the TP, although simulations underestimate observations by 1.87 °C. The simulated spatial range of temperature cooling significantly exceeds the observed range, particularly in the central and southwestern TP. The performances of the simulations for precipitation are far poorer than those for temperature, and although the CMIP6 model represents the distribution of annual mean precipitation, simulations of precipitation show significant deviations from observations. Furthermore, model simulations of precipitation are 1.57 mm lower than observed, and 30% lower than observed in the southeastern TP. However, the CMIP6 model overestimated the intensity of precipitation in most regions, especially in the southeastern part of the TP. Meanwhile, the EOF analysis indicates that the effects of the correction of temperature exceed that of precipitation. Therefore, a range of methods should be considered for correcting temperature and precipitation over a complex terrain.

The East Asian Summer Monsoon Response to Global Warming in a High Resolution Coupled Model: Mean and Extremes

Current climate models still have considerable biases in the simulation of the East Asian summer monsoon (EASM), which in turn reduces their reliability of monsoon projections under global warming. We hypothesize that a higher-resolution coupled climate model with atmospheric and oceanic components at horizontal resolutions of 0.25° and 0.1°, respectively, will better capture regional details and extremes of the EASM. Present-day (PD), 2 × CO2 and 4 × CO2 simulations are conducted with the Community Earth System Model (CESM1.2.2) to evaluate PD simulation performance and quantify future changes. Indeed, our PD simulation well reproduces the climatological seasonal mean and intra-seasonal northward advancement of the monsoon rainband, as well as climate extremes. Compared with the PD simulation, the perturbed CO2 experiments show an intensified EASM response to CO2-induced warming. We find that the precipitation increases of the Meiyu-Baiu-Changma band are caused by comparable contributions from the dynamical and thermodynamical components in 2 × CO2, while they are more driven by the thermodynamical component in 4 × CO2 due to stronger upper atmospheric stability. The regional changes in the probability distribution of the temperature show that extreme temperatures warm faster than the most often temperatures, increasing the skewness. Fitting extreme precipitation values with a generalized Pareto distribution model reveals that they increase significantly in 4 × CO2. Changes of temperature extremes scale with the CO2 concentrations over the monsoon domain but not for precipitation extreme changes. The 99th percentile of precipitation over the monsoon region increases at a super Clausius-Clapeyron rate, ~ 8% K–1, which is mainly caused by increased moisture transport through anomalous southerly winds.

Oceanic Advection Controls Mesoscale Mixed Layer Heat Budget and Air–Sea Heat Exchange in the Southern Ocean

Abstract We analyze the role of mesoscale heat advection in a mixed layer (ML) heat budget, using a regional high-resolution coupled model with realistic atmospheric forcing and an idealized ocean component. The model represents two regions in the Southern Ocean, one with strong ocean currents and the other with weak ocean currents. We conclude that heat advection by oceanic currents creates mesoscale anomalies in sea surface temperature (SST), while the atmospheric turbulent heat fluxes dampen these SST anomalies. This relationship depends on the spatial scale, the strength of the currents, and the mixed layer depth (MLD). At the oceanic mesoscale, there is a positive correlation between the advection and SST anomalies, especially when the currents are strong overall. For large-scale zonal anomalies, the ML-integrated advection determines the heating/cooling of the ML, while the SST anomalies tend to be larger in size than the advection and the spatial correlation between these two fields is weak. The effects of atmospheric forcing on the ocean are modulated by the MLD variability. The significance of Ekman advection and diabatic heating is secondary to geostrophic advection except in summer when the MLD is shallow. This study links heat advection, SST anomalies, and air–sea heat fluxes at ocean mesoscales, and emphasizes the overall dominance of intrinsic oceanic variability in mesoscale air–sea heat exchange in the Southern Ocean.

Prediction of Arctic Temperature and Sea Ice Using a High-Resolution Coupled Model

AbstractUnder global warming, surface air temperature has risen rapidly and sea ice has decreased markedly in the Arctic. These drastic climate changes have brought about various severe impacts on the vulnerable environment and ecosystem there. Thus, accurate prediction of Arctic climate becomes more important than before. Here we examine the seasonal to interannual predictive skills of 2-m air temperature (2-m T) and sea ice cover (SIC) over the Arctic region (70°–90°N) during 1980–2014 with a high-resolution global coupled model called the Met Office Decadal Prediction System, version 3 (DePreSys3). The model captures well both the climatology and interannual variability of the Arctic 2-m T and SIC. Moreover, the anomaly correlation coefficient of Arctic-averaged 2-m T and SIC shows statistically significant skills at lead times up to 16 months. This is mainly due to the contribution of strong decadal trends. In addition, it is found that the peak warming trend of Arctic 2-m T lags the maximum decrease trend of SIC by 1 month, in association with the heat flux forcing from the ocean surface to lower atmosphere. While the predictive skill is generally much lower for the detrended variations, we find a close relationship between the tropical Pacific El Niño–Southern Oscillation and the Arctic detrended 2-m T anomalies. This indicates potential seasonal to interannual predictability of the Arctic natural variations.

Effects of Wave-Induced Sea Ice Break-Up and Mixing in a High-Resolution Coupled Ice-Ocean Model

Arctic sea ice plays a vital role in modulating the global climate. In the most recent decades, the rapid decline of the Arctic summer sea ice cover has exposed increasing areas of ice-free ocean, with sufficient fetch for waves to develop. This has highlighted the complex and not well-understood nature of wave-ice interactions, requiring modeling effort. Here, we introduce two independent parameterizations in a high-resolution coupled ice-ocean model to investigate the effects of wave-induced sea ice break-up (through albedo change) and mixing on the Arctic sea ice simulation. Our results show that wave-induced sea ice break-up leads to increases in sea ice concentration and thickness in the Bering Sea, the Baffin Sea and the Barents Sea during the ice growth season, but accelerates the sea ice melt in the Chukchi Sea and the East Siberian Sea in summer. Further, wave-induced mixing can decelerate the sea ice formation in winter and the sea ice melt in summer by exchanging the heat fluxes between the surface and subsurface layer. As our baseline model underestimates sea ice cover in winter and produces more sea ice in summer, wave-induced sea ice break-up plays a positive role in improving the sea ice simulation. This study provides two independent parameterizations to directly include the wave effects into the sea ice models, with important implications for the future sea ice model development.

Erratum to: An Examination of the Predictability of Tropical Cyclone Genesis in High-Resolution Coupled Models with Dynamically Downscaled Coupled Data Assimilation Initialization

Predicting tropical cyclone (TC) genesis is of great societal importance but scientifically challenging. It requires fine-resolution coupled models that properly represent air-sea interactions in the atmospheric responses to local warm sea surface temperatures and feedbacks, with aid from coherent coupled initialization. This study uses three sets of high-resolution regional coupled models (RCMs) covering the Asia-Pacific (AP) region initialized with local observations and dynamically downscaled coupled data assimilation to evaluate the predictability of TC genesis in the West Pacific. The AP-RCMs consist of three sets of high-resolution configurations of the Weather Research and Forecasting-Regional Ocean Model System (WRF-ROMS): 27-km WRF with 9-km ROMS, and 9-km WRF with 3-km ROMS. In this study, a 9-km WRF with 9-km ROMS coupled model system is also used in a case test for the predictability of TC genesis. Since the local sea surface temperatures and wind shear conditions that favor TC formation are better resolved, the enhanced-resolution coupled model tends to improve the predictability of TC genesis, which could be further improved by improving planetary boundary layer physics, thus resolving better air-sea and air-land interactions.

An Examination of the Predictability of Tropical Cyclone Genesis in High-Resolution Coupled Models with Dynamically Downscaled Coupled Data Assimilation Initialization

Predicting tropical cyclone (TC) genesis is of great societal importance but scientifically challenging. It requires fine-resolution coupled models that properly represent air-sea interactions in the atmospheric responses to local warm sea surface temperatures and feedbacks, with aid from coherent coupled initialization. This study uses three sets of high-resolution regional coupled models (RCMs) covering the Asia-Pacific (AP) region initialized with local observations and dynamically downscaled coupled data assimilation to evaluate the predictability of TC genesis in the West Pacific. The AP-RCMs consist of three sets of high-resolution configurations of the Weather Research and Forecasting-Regional Ocean Model System (WRF-ROMS): 27-km WRF with 9-km ROMS, and 9-km WRF with 3-km ROMS. In this study, a 9-km WRF with 9-km ROMS coupled model system is also used in a case test for the predictability of TC genesis. Since the local sea surface temperatures and wind shear conditions that favor TC formation are better resolved, the enhanced-resolution coupled model tends to improve the predictability of TC genesis, which could be further improved by improving planetary boundary layer physics, thus resolving better air-sea and air-land interactions.

A high-resolution Asia-Pacific regional coupled prediction system with dynamically downscaling coupled data assimilation

A regional coupled prediction system for the Asia-Pacific (AP-RCP) (38°E–180°, 20°S–60°N) area has been established. The AP-RCP system consists of WRF-ROMS (Weather Research and Forecast, and Regional Ocean Model System) coupled models combined with local observational information through dynamically downscaling coupled data assimilation (CDA). The system generates 18-day forecasts for the atmosphere and ocean environment on a daily quasi-operational schedule at Pilot National Laboratory for Marine Science and Technology (Qingdao) (QNLM), consisting of 2 different-resolution coupled models: 27 km WRF coupled with 9 km ROMS, 9 km WRF coupled with 3 km ROMS, while a version of 3 km WRF coupled with 3 km ROMS is in a test mode. This study is a first step to evaluate the impact of high-resolution coupled model with dynamically downscaling CDA on the extended-range predictions, focusing on forecasts of typhoon onset, improved precipitation and typhoon intensity forecasts as well as simulation of the Kuroshio current variability associated with mesoscale oceanic activities. The results show that for realizing the extended-range predictability of atmospheric and oceanic environment characterized by statistics of mesoscale activities, a fine resolution coupled model resolving local mesoscale phenomena with balanced and coherent coupled initialization is a necessary first step. The next challenges include improving the planetary boundary physics and the representation of air-sea and air-land interactions to enable the model to resolve kilometer or sub-kilometer processes.

Estimating the Annual Exceedance Probability of Water Levels and Wave Heights from High Resolution Coupled Wave-Circulation Models in Long Island Sound

Accurately estimating the probability of storm surge occurrences is necessary for flood risk assessments. This research models Long Island Sound using a coupled coastal circulation and wave model (FVCOM-SWAVE) to hindcast the 44 highest storms between 1950–2018 and fitted Poisson-GPD distributions to modelled water levels and wave heights. Floodwater elevations and significant wave heights for 10% (1/10), 3% (1/30), 2% (1/50), and 1% (1/100) annual exceedance probabilities are provided for all Connecticut coastal towns. The results show that both water levels and their corresponding return intervals are higher along the western coast of Connecticut than the eastern coast, whereas significant wave heights increase eastward. Comparing our model results with those from the North Atlantic Coast Comprehensive Study (NACCS) shows that the mean NACCS results are higher for water levels and lower for significant wave heights for longer return periods. Likewise, the Federal Emergency Management Agency (FEMA) results in large errors compared to our results in both eastern and western coastal Connecticut regions. In addition to evaluating historical risks, we also added a sea-level height offset of 0.5 m for 2050 estimates in order to examine the effect of rising sea-levels on the analysis. We find that sea-level rise reduces the return period of a 10-year storm to two years. We advise periodically updating this work as improved sea-level rise projections become available.

FluxSat: Measuring the Ocean–Atmosphere Turbulent Exchange of Heat and Moisture from Space

Recent results using wind and sea surface temperature data from satellites and high-resolution coupled models suggest that mesoscale ocean–atmosphere interactions affect the locations and evolution of storms and seasonal precipitation over continental regions such as the western US and Europe. The processes responsible for this coupling are difficult to verify due to the paucity of accurate air–sea turbulent heat and moisture flux data. These fluxes are currently derived by combining satellite measurements that are not coincident and have differing and relatively low spatial resolutions, introducing sampling errors that are largest in regions with high spatial and temporal variability. Observational errors related to sensor design also contribute to increased uncertainty. Leveraging recent advances in sensor technology, we here describe a satellite mission concept, FluxSat, that aims to simultaneously measure all variables necessary for accurate estimation of ocean–atmosphere turbulent heat and moisture fluxes and capture the effect of oceanic mesoscale forcing. Sensor design is expected to reduce observational errors of the latent and sensible heat fluxes by almost 50%. FluxSat will improve the accuracy of the fluxes at spatial scales critical to understanding the coupled ocean–atmosphere boundary layer system, providing measurements needed to improve weather forecasts and climate model simulations.

Coupled data assimilation and parameter estimation in coupled ocean–atmosphere models: a review

Recent studies have started to explore coupled data assimilation (CDA) in coupled ocean–atmosphere models because of the great potential of CDA to improve climate analysis and seamless weather–climate prediction on weekly-to-decadal time scales in advanced high-resolution coupled models. In this review article, we briefly introduce the concept of CDA before outlining its potential for producing balanced and coherent weather–climate reanalysis and minimizing initial coupling shocks. We then describe approaches to the implementation of CDA and review progress in the development of various CDA methods, notably weakly and strongly coupled data assimilation. We introduce the method of coupled model parameter estimation (PE) within the CDA framework and summarize recent progress. After summarizing the current status of the research and applications of CDA-PE, we discuss the challenges and opportunities in high-resolution CDA-PE and nonlinear CDA-PE methods. Finally, potential solutions are laid out.

Drought Trend Analysis Based on the Standardized Precipitation–Evapotranspiration Index Using NASA’s Earth Exchange Global Daily Downscaled Projections, High Spatial Resolution Coupled Model Intercomparison Project Phase 5 Projections, and Assessment of Potential Impacts on China’s Crop Yield in the 21st Century

Drought is among the costliest natural disasters on both ecosystems and agroeconomics in China. However, most previous studies have used coarse resolution data or simply stopped short of investigating drought projection and its impact on crop yield. Motivated by the newly released higher-resolution climate projection dataset and the crucial need to assess the impact of climate change on agricultural production, the overarching goal of this study was to systematically and comprehensively predict future droughts at unprecedented resolutions over China as a whole. rather than region-specific projections, and then to further investigate its impact on crop yield by innovatively using a soil water deficit drought index. Methodologically, the drought projections were quantified from very high resolution climate data and further predicted impacts on crop yield over China using the standardized precipitation–evapotranspiration index (SPEI) at a relatively high (25 km) spatial resolution from NASA’s Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). The results showed that (1) overall, China is projected to experience a significant decrease in SPEI (−0.15/decade under RCP (representative concentration pathway) 4.5; −0.14/decade under RCP8.5). Seasonally, the decreasing rate of SPEI is projected to be largest in winter (−0.2/decade and −0.31/decade) and the least in summer (−0.08/decade and −0.10/decade) under respective RCPs. (2) Regionally, winter/spring will get drier, especially at high latitudes/altitudes (North China and Tibetan plateau), and summer/autumn will get wetter in southern China. (3) Both the frequency and duration for medium and severe drought are projected to decrease, while extreme drought, particularly in high latitudes/altitudes, is projected to increase. (4) The percentage of the potential crop production affected by drought would increase to 36% (47%) by 2100 under RCP4.5 (RCP8.5). Especially, the ratio impacted by extreme drought is projected to increase over time and with much worse magnitude under RCP8.5; thus, adaptive crop policies are expected to address such a risk.

A comparison of CCSM4 high-resolution and low-resolution predictions for south Florida and southeast United States drought

It is important to have confidence in seasonal climate predictions of precipitation, particularly related to drought, as implications can be far-reaching and costly—this is particularly true for Florida. Precipitation can vary on fine spatial resolutions, and high-resolution coupled models may be needed to correctly represent precipitation variability. We study south Florida and southeast United States drought in Community Climate System version 4 low resolution (1° atmosphere/ocean) and high-resolution (0.5°atmosphere/0.1°ocean) predictions for time means ranging from 3 to 36 months. The very high-resolution in the ocean is of interest here given the potential importance of Gulf Stream on south Florida rainfall. Skill of shorter time-mean South Florida predictions (i.e. 3- and 12-months) are not impacted by increased resolution, but skill of 36-month mean south Florida precipitation is somewhat increased in the high resolution predictions. Notably, over the broader southeast United States the high-resolution model has higher skill for the 36-month mean rainfall predictions, associated with an improved relationship with tropical Pacific and Gulf Stream SSTA. Why this improvement in the broader southeast United States does not extend to Florida is an open question, but does suggest that even further resolution refinements may be needed.

Challenges and opportunities for improved understanding of regional climate dynamics

Dynamical processes in the atmosphere and ocean are central to determining the large-scale drivers of regional climate change, yet their predictive understanding is poor. Here, we identify three frontline challenges in climate dynamics where significant progress can be made to inform adaptation: response of storms, blocks and jet streams to external forcing; basin-to-basin and tropical–extratropical teleconnections; and the development of non-linear predictive theory. We highlight opportunities and techniques for making immediate progress in these areas, which critically involve the development of high-resolution coupled model simulations, partial coupling or pacemaker experiments, as well as the development and use of dynamical metrics and exploitation of hierarchies of models.

The dependence on atmospheric resolution of ENSO and related East Asian-western North Pacific summer climate variability in a coupled model

The authors present results for El Nino-Southern Oscillation (ENSO) and East Asian-western North Pacific climate variability simulated in a new version high-resolution coupled model (ICM.V2) developed at the Center for Monsoon System Research of the Institute of Atmospheric Physics (CMSR, IAP), Chinese Academy of Sciences. The analyses are based on the last 100-year output of a 1000-year simulation. Results are compared to an earlier version of the same coupled model (ICM.V1), reanalysis, and observations. The two versions of ICM have similar physics but different atmospheric resolution. The simulated climatological mean states show marked improvement over many regions, especially the tropics in ICM.V2 compared to those in ICM.V1. The common bias in the cold tongue has reduced, and the warm biases along the ocean boundaries have improved as well. With improved simulation of ENSO, including its period and strength, the ENSO-related western North Pacific summer climate variability becomes more realistic compared to the observations. The simulated East Asian summer monsoon anomalies in the El Nino decaying summer are substantially more realistic in ICM.V2, which might be related to a better simulation of the Indo-Pacific Ocean capacitor (IPOC) effect and Pacific decadal oscillation (PDO).

Challenges and Prospects for Reducing Coupled Climate Model SST Biases in the Eastern Tropical Atlantic and Pacific Oceans: The U.S. CLIVAR Eastern Tropical Oceans Synthesis Working Group

Abstract Well-known problems trouble coupled general circulation models of the eastern Atlantic and Pacific Ocean basins. Model climates are significantly more symmetric about the equator than is observed. Model sea surface temperatures are biased warm south and southeast of the equator, and the atmosphere is too rainy within a band south of the equator. Near-coastal eastern equatorial SSTs are too warm, producing a zonal SST gradient in the Atlantic opposite in sign to that observed. The U.S. Climate Variability and Predictability Program (CLIVAR) Eastern Tropical Ocean Synthesis Working Group (WG) has pursued an updated assessment of coupled model SST biases, focusing on the surface energy balance components, on regional error sources from clouds, deep convection, winds, and ocean eddies; on the sensitivity to model resolution; and on remote impacts. Motivated by the assessment, the WG makes the following recommendations: 1) encourage identification of the specific parameterizations contributing to the biases in individual models, as these can be model dependent; 2) restrict multimodel intercomparisons to specific processes; 3) encourage development of high-resolution coupled models with a concurrent emphasis on parameterization development of finer-scale ocean and atmosphere features, including low clouds; 4) encourage further availability of all surface flux components from buoys, for longer continuous time periods, in persistently cloudy regions; and 5) focus on the eastern basin coastal oceanic upwelling regions, where further opportunities for observational–modeling synergism exist.

Modeling Salinity Exchanges Between the Equatorial Indian Ocean and the Bay of Bengal

Abstract : With a focus on the Bay of Bengal, models ranging from a 1/12.5 global ocean model to a 1/4 regional Indian Ocean model to a 2 km local high-resolution coupled model are used to simulate salinity exchanges in the Indian Ocean. Global Hybrid Coordinate Ocean Model simulations show a surprisingly large persistent flow of high-salinity water from the equatorial Indian Ocean into the Bay of Bengal during the northeast monsoon, although it is weaker than during the southwest monsoon. On average, salt is transported into the Bay of Bengal between 83E and 95E, and low-salinity water flows southward near the east coast of Sri Lanka and east of 95E. The Regional Ocean Modeling System shows that knowledge of river input of freshwater is essential for modeling surface salinities correctly in the Bay of Bengal. High-resolution coupled model simulations are in agreement with recent observations and show that a strong subsurface current with a speed of about 1 m s -1 intrudes into the Bay of Bengal beneath southward-flowing low-salinity water during the northeast monsoon. The subsurface high-salinity water, which originates in the northern Arabian Sea, spreads northward into the Bay of Bengal and downward along constant density surfaces. North of 10N, the model simulation implies that mixing takes place on density surfaces at depths of 100 -150 m after advection of cold, low-salinity water from the north, and subsequent stirring of the two water masses. Vertical diffusion plays an insignificant role in this mixing.