Seasonality in the relationship between equatorial-mean heat content and interannual eastern equatorial Atlantic sea surface temperature variability

Interannual sea surface temperature (SST) variations in the tropical Atlantic Ocean lead to anomalous atmospheric circulation and precipitation patterns with important ecological and socioeconomic consequences for the semiarid regions of sub-Saharan Africa and northeast Brazil. This interannual SST variability is characterized by three modes: an Atlantic meridional mode featuring an anomalous cross-equatorial SST gradient that peaks in boreal spring; an Atlantic zonal mode (Atlantic Niño mode) with SST anomalies in the eastern equatorial Atlantic cold tongue region that peaks in boreal summer; and a second zonal mode of variability with eastern equatorial SST anomalies peaking in boreal winter. Here we investigate the extent to which there is any seasonality in the relationship between equatorial warm water recharge and the development of eastern equatorial Atlantic SST anomalies. Seasonally stratified cross-correlation analysis between eastern equatorial Atlantic SST anomalies and equatorial heat content anomalies (evaluated using warm water volume and sea surface height) indicate that while equatorial heat content changes do occasionally play a role in the development of boreal summer Atlantic zonal mode events, they contribute more consistently to Atlantic Niño II, boreal winter events. Event and composite analysis of ocean adjustment with a shallow water model suggest that the warm water volume anomalies originate mainly from the off-equatorial northwestern Atlantic, in agreement with previous studies linking them to anomalous wind stress curl associated with the Atlantic meridional mode.

Application of Non-stationary Extreme Value Analysis to Satellite-Observed Sea Surface Temperature Data for Past Decades

Extreme value analysis (EVA) has been extensively used to understand and predict long-term return extreme values. This study provides the first approach to EVA using satellite-observed sea surface temperature (SST) data over the past decades. Representative EVA methods were compared to select an appropriate method to derive SST extremes of the East/Japan Sea (EJS). As a result, the peaks-over-threshold (POT) method showed better performance than the other methods. The Optimum Interpolation Sea Surface Temperature (OISST) database was used to calculate the 100-year-return SST values in the EJS. The calculated SST extremes were 1.60–3.44°C higher than the average value of the upper 5th-percentile satellite-observed SSTs over the past decades (1982–2018). The monthly distribution of the SST extremes was similar to the known seasonal variation of SSTs in the EJS, but enhanced extreme SSTs exceeding 2°C appeared in early summer and late autumn. The calculated 100-year-return SSTs were compared with the simulation results of the Coupled Model Intercomparison Project 5 (CMIP5) climate model. As a result, the extreme SSTs were slightly smaller than the maximum SSTs of the model data with a negative bias of –0.36°C. This study suggests that the POT method can improve our understanding of future oceanic warming based on statistical approaches using SSTs observed by satellites over the past decades.

First SMOS Sea Surface Salinity dedicated products over the Baltic Sea

This paper presents the first Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) dedicated products over the Baltic Sea. The SSS retrieval from L-band brightess temperature (TB) measurements over this basin is really challenging due to important technical issues, such as the land-sea and ice-sea contamination, the high contamination by Radio-Frequency Interferences (RFI) sources, the low sensitivity of L-band TB at SSS changes in cold waters and the poor characterization of dielectric constant models for the low SSS and SST ranges in the basin. For these reasons, exploratory research in the algorithms used from the level 0 up to level 4 has been required to develop these dedicated products. This work has been performed in the framework of the European Space Agency regional initiative Baltic+ Salinity Dynamics. Two Baltic+ SSS products have been generated for the period 2011–2019 and are freely distributed: the Level 3 (L3) product (daily generated 9-day maps in a 0.25° grid, https://doi.org/10.20350/digitalCSIC/13859) (González-Gambau et al., 2021a) and the Level 4 (L4) product (daily maps in a 0.05° grid, https://doi.org/10.20350/digitalCSIC/13860) (González-Gambau et al., 2021b)), that are computed by applying multifractal fusion to L3 SSS with Sea Surface Temperature (SST) maps. The accuracy of L3 SSS products is typically around 0.7–0.8 psu. The L4 product has an improved spatio-temporal resolution with respect to the L3 and the accuracy is typically around 0.4 psu. Regions with the highest errors and limited coverage are located in Arkona and Bornholm basins and Gulfs of Finland and Riga. The impact assessment of Baltic+ SSS products has shown that they can help in the understanding of salinity dynamics in the basin. They complement the temporally and spatially very sparse in situ measurements, covering data gaps in the region and they can also be useful for the validation of numerical models, particularly in areas where in situ data are very sparse.

Predictive relationships between Indian Ocean sea surface temperatures and Indian summer monsoon rainfall

Monthly sea surface temperature (SST) data of 49 years (1950-98) have been analysed to examine the relationship of SST anomalies in the Indian Ocean with Indian summer monsoon rainfall (ISMR) and to derive useful predictors for long-range forecasts of ISMR. There is significant positive relationship between ISMR and SST anomalies over the Arabian Sea during November to January and also in May. SST anomalies over southeast Indian Ocean during February to March and over North Pacific during May are also positively correlated with ISMR. The composite analysis revealed that in Non-ENSO drought years (1966, 1968, 1974 and 1979) negative SST anomalies are observed over south Indian Ocean from February which slowly spread towards equator during the subsequent months. These negative SST anomalies which persist during the monsoon season may be playing an important role in modulating ISMR especially in non-ENSO years. We have derived two indices, ARBSST (SST anomalies in Arabian Sea averaged over 15o - 25o N, 50o -70o E and November-December-January) and SIOSST (SST anomalies over south Indian Ocean averaged over 15o -30o S, 70o -110o E and February and March) as useful predictors for the long-range forecasts of ISMR. The correlation coefficient (for the period 1950-98) of ARBSST and SIOSST with ISMR is 0.45 and 0.46 respectively which is statistically significant at 99.9 % level. SIOSST index has shown consistently stable relationship with ISMR. However the ARBSST index showed significant correlation with ISMR only after 1976.

DMSP-SSM/I retrieval of proper surface winds during monsoon depression

Recently developed various global microwave algorithms for DMSP-SSM/I satellite data are used for the estimation of surface winds over the Indian ocean. Sea surface wind speeds from these algorithms are compared with sea surface wind speeds reported by coincidental Minicoy island (lowest height 2 m a.s.l.) station over the Arabian sea. A statistical comparison of these algorithms is made in terms of rms error, correlation coefficient, bias and standard deviation. Algorithm of Petty showed best results in the comparison. On the basis of this algorithm a notable characteristic feature such as acquiring of large area of strong surface winds (12-15 ms-1) to the south of dipping of monsoon trough in head Bay and then encircling of these winds during further development of low and depression (22-27 July 1992) is observed. This complete life cycle monitoring assessment of monsoon depression in respect of surface winds based on DMSP-SSM/I satellite data encourages to utilise our IRS-P4 (Oceansat-1) satellite data at different frequencies to emerge more details of various weather systems over the Indian region.

Widespread natural methane and oil leakage from sub-marine Arctic reservoirs

Parceling the anthropogenic and natural (geological) sources of fossil methane in the atmosphere remains problematic due to a lack of distinctive chemical markers for their discrimination. In this light, understanding the distribution and contribution of potential geological methane sources is important. We present empirical observations of hitherto undocumented, widespread and extensive methane and oil release from geological reservoirs to the Arctic Ocean. Methane fluxes from >7,000 seeps significantly deplete in seawater, but nevertheless reach the sea surface and may transfer to the air. Oil slick emission spots and gas ebullition are persistent across multi-year observations and correlate to formerly glaciated geological structures, which have experienced km-scale glacial erosion that has left hydrocarbon reservoirs partially uncapped since the last deglaciation ~15,000 years ago. Such persistent, geologically controlled, natural hydrocarbon release may be characteristic of formerly glaciated hydrocarbon-bearing basins which are common across polar continental shelves, and could represent an underestimated source of natural fossil methane within the global carbon cycle.