Gary Grunseich

Sea surface salinity variability during the Indian Ocean Dipole and ENSO events in the tropical Indian Ocean

An ocean reanalysis that covers the period from 1871-2008 is used to analyze interannual variability of Sea Surface Salinity (SSS) in the tropical Indian Ocean. The reanalysis SSS and the SSS anomaly patterns during Indian Ocean Dipole (IOD) and El Nino – Southern Oscillation (ENSO) events are compared with patterns from Argo SSS data. The mean seasonal SSS variation is large in the northern Bay of Bengal compared to variations in the Arabian Sea and Equatorial Indian Ocean. During a positive IOD event positive SSS anomalies are found along the Sumatra coast due to the combination of wind-driven upwelling of subsurface high salinity waters, enhanced evaporation and anomalous surface circulation. The opposite is true, to a lesser extent, during negative IOD events. A Dipole Mode Index for Salinity (DMIS) based on SSS data and a new index based on the average of salinity in a region off the coast of Sumatra is introduced to monitor SSS variability during IOD and ENSO events. The impact of concomitant El Nino events on a positive IOD event is large with freshening (a negative SSS anomaly) in the equatorial Indian Ocean and salting (positive SSS anomaly) off the southern Sumatra coast. The (impact of) intense freshening reaches into the southwestern tropical Indian Ocean. The impact of concomitant La Nina with negative IOD is also large with an intense freshening in the southeastern Arabian Sea and salting off the northern Sumatra coast.

Preliminary SMOS Salinity Measurements and Validation in the Indian Ocean

Global sea surface salinity (SSS) measurements retrieved from the European Space Agencys Soil Moisture and Ocean Salinity (SMOS) mission are the first highest resolution salinity data available from space. There are many challenges to measuring salinity from space and obtaining a targeted accuracy of 0.1 psu. Comparisons of Level 2 (L2) SMOS SSS data with the 1/12° high resolution HYbrid Coordinate Ocean Model (HYCOM) simulations of SSS reveal large differences. These differences are minimized for an extent during the creation of Level 3 (L3) SMOS data through spatial and temporal averaging. Depending on the retrieval algorithm used, there are differences between ascending and descending passes with data collected during the descending pass exhibiting a bias toward lower SSS. It is challenging to process SMOS SSS data in the northern Indian Ocean due to radio frequency interference and large seasonal variability due to monsoonal circulation. Comparisons of SMOS L3 data with Argo float SSS and HYCOM SSS indicate the lowest discrepancies in SSS for these data sets occur in the southern tropical Indian Ocean and the largest differences between the compared salinity products are noticed in the Arabian Sea and Bay of Bengal with an erratic root mean square error in the latter region. Higher errors in SSS occurred in coastal areas compared to the open ocean. The accuracy of SMOS salinity measurements is increasing with the maturity of the data and new algorithms.

Detection of the Madden–Julian Oscillation in the Indian Ocean From Satellite Altimetry

The role of air-sea interaction on Madden-Julian oscillation (MJO) propagations across the tropical Indian Ocean is analyzed using integrated multimission satellite measurements of sea surface height and outgoing longwave radiation (OLR). MJO-related activity is observed in both parameters in the eastern equatorial Indian Ocean indicating a unique interaction in this region. In the eastern Indian Ocean, atmospheric conditions appear to aid in the creation of equatorial Rossby waves, while in the central and western Indian Ocean, different phases of oceanic Rossby wave propagations seem to have a strong influence on atmospheric conditions associated with the MJO. The downwelling phase of equatorial Rossby waves corresponds to a strengthening of OLR anomalies in extent and magnitude across the equatorial Indian Ocean, while the upwelling phase appears to weaken atmospheric MJO activity. This study improves climate research by identifying the MJO signal in altimetry data.

Predictability of Arctic Annual Minimum Sea Ice Patterns

AbstractPrediction of the arctic annual sea ice minimum extent and melting patterns draws interest from numerous industries and government agencies but has been an ongoing challenge for forecasters and climate scientists using statistical and dynamical models. Using the dominant independent modes of interannual sea ice concentration (SIC) variability during September–October, a new approach combining statistical analysis with physically derived links to natural climate variability sources is used to predict each mode and the total anomaly pattern. Sea ice patterns associated with each mode are predominantly shaped by the wind-driven advective convergence, forced by circulation anomalies associated with local and remote forms of naturally occurring climate variability. The impacts of the Arctic Oscillation, beginning from the preceding winter, control the leading mode of SIC variability during the annual minimum. In the three final months of the melting period, the broad impacts of the Indian and East Asia...

Arctic Sea Ice Patterns Driven by the Asian Summer Monsoon

AbstractThe fluctuation of Arctic sea ice concentration (SIC) has been associated with changes in ocean circulation, ecology, and Northern Hemisphere climate. Prediction of sea ice melting patterns is of great societal interest, but such prediction remains difficult because the factors controlling year-to-year sea ice variability remain unresolved. Distinct monsoon–Arctic teleconnections modulate summer Arctic SIC largely by changing wind-forced sea ice transport. East Asian monsoon rainfall produces a northward-propagating meridional Rossby wave train extending into the Siberian Arctic. The Indian summer monsoon excites an eastward-propagating circumglobal teleconnection along the subtropical jet, reaching the North Atlantic before bifurcating into the Arctic. The remote Asian monsoon variations induce a dominant dipole sea ice melt pattern in which the North Atlantic–European Arctic contrasts with the Siberian–North American Arctic. The monsoon-related sea ice variations are complementary and comparable...

Validation of SMOS salinity data and its applications the to Indian Ocean climatic events

Global Sea Surface Salinity (SSS) measurements retrieved from the Soil Moisture and Ocean Salinity (SMOS) mission are the highest resolution salinity data available at regular intervals at present from space. There are many challenges to measuring SSS from space and obtaining an accuracy of 0.1 psu. Comparisons of level 2 SMOS salinity data with the HYbrid Coordinate Ocean Model (HYCOM) simulations of SSS reveal large differences which should be corrected by spatial and temporal averaging during the creation of Level 3 (L3) SMOS data. SMOS Level 2 (L2) salinity data will have difficulty detecting SSS variations during climatic events in the Indian Ocean but the expected resolutions obtainable by SMOS should be able to monitor variations during intraseasonal and seasonal climatic events. The role of salinity during many oceanic processes will be better understood by measurements from SMOS.