In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.

The Indian summer monsoon intraseasonal oscillations (MISOs) induce pronounced intraseasonal sea surface temperature (SST) variability in the Bay of Bengal (BoB), which has important feedbacks to atmospheric convection. An ocean general circulation model (OGCM) is employed to investigate the upper‐ocean processes affecting intraseasonal SST variability and its feedback to the MISO convection. In the BoB, the MISO induces intraseasonal SST variability predominantly through surface heat flux forcing with comparable contributions from shortwave radiation and turbulent heat flux, and to a much smaller extent through wind‐driven ocean mixed layer entrainment. The ocean salinity stratification, represented by mixed layer depth (MLD) and barrier layer thickness (BLT), has a strong control on SST but weak impact on convection of the MISO. The MLD is critical for the amplitude of SST response to various forcing processes, while the BLT mainly affects entrainment by determining the temperature difference between the mixed layer and the water below. From May to mid‐June, the shallow MLD and thin barrier layer greatly enhance intraseasonal SST anomalies, which can amplify convection fluctuations of the MISO through air‐sea interaction and leads to intense but short‐duration postconvection break spells. When either the MLD or the BLT is large, intraseasonal SSTs tend to be weak. Further investigation reveals that freshwater flux of the monsoon gives rise to the shallow MLD and thick barrier layer, and its overall effect on intraseasonal SSTs is a 20% enhancement. These results provide implications for improving the simulation and forecast of the MISO in climate models.

Atlantic Water (AW) transported from the Nordic Seas is the major source of oceanic heat to the Arctic Ocean. Based on results from the TOPAZ reanalysis, a regional coupled ice‐ocean data assimilation system, we show that interannual variability of AW temperature in the Fram Strait (FS) is associated with the strength of the Greenland Sea gyre (GSG) circulation. The response of the GSG to the anomalous wind stress curl over the Nordic Seas modifies the AW inflow and thus influences the variability of AW temperature in the FS. A stronger (weaker) GSG circulation increases (decreases) the AW flow speed toward FS, leading to increased (decreased) oceanic heat content and higher (lower) AW temperature therein. This implies that the Nordic Seas circulation is not only a passive conduit of AW, but its response to overlying atmospheric variability can also largely influence the AW temperature in the FS by modifying the transport of AW.

Cyclone Phailin, which developed over the Bay of Bengal in October 2013, was one of the strongest tropical cyclones to make landfall in India. We study the response of the salinity-stratified north Bay of Bengal to Cyclone Phailin with the help of hourly observations from three open-ocean moorings 200 km from the cyclone track, a mooring close to the cyclone track, daily sea surface salinity (SSS) from Aquarius, and a one-dimensional model. Before the arrival of Phailin, moored observations showed a shallow layer of low-salinity water lying above a deep, warm “barrier” layer. As the winds strengthened, upper-ocean mixing due to enhanced vertical shear of storm-generated currents led to a rapid increase of near-surface salinity. Sea surface temperature (SST) cooled very little, however, because the prestorm subsurface ocean was warm. Aquarius SSS increased by 1.5–3 psu over an area of nearly one million square kilometers in the north Bay of Bengal. A one-dimensional model, with initial conditions and surface forcing based on moored observations, shows that cyclone winds rapidly eroded the shallow, salinity-dominated density stratification and mixed the upper ocean to 40–50-m depth, consistent with observations. Model sensitivity experiments indicate that changes in ocean mixed layer temperature in response to Cyclone Phailin are small. A nearly isothermal, salinity-stratified barrier layer in the prestorm upper ocean has two effects. First, near-surface density stratification reduces the depth of vertical mixing. Second, mixing is confined to the nearly isothermal layer, resulting in little or no SST cooling.

Sustained Indian Ocean Observing System and societal needs, and a framework for more regional and coastal monitoring. This paper reviews the societal and scientific motivations, current status, and future directions of IndOOS, while also discussing the need for enhanced coastal, shelf, and regional observations. The challenges of sustainability and implementation are also addressed, including capacity building, best practices, and integration of resources. The utility of IndOOS ultimately depends on the identification of, and engagement with, end-users and decision-makers and on the practical accessibility and transparency of data for a range of products and for decision-making processes. Therefore we highlight current progress, issues and challenges related to end user engagement with IndOOS, as well as the needs of the data assimilation and modeling communities. Knowledge of the status of the Indian Ocean climate and ecosystems and predictability of its future, depends on a wide range of socioeconomic and environmental data, a significant part of which is provided by IndOOS.