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A joint effort between the Copernicus Climate Change Service (C3S) and the Group for High Resolution Sea Surface Temperature (GHRSST) has been dedicated to an intercomparison study of eight global gap-free sea surface temperature (SST) products to assess their accurate representation of the SST relevant to climate analysis. In general, all SST products show consistent spatial patterns and temporal variability during the overlapping time period (2003–18). The main differences between each product are located in the western boundary current and Antarctic Circumpolar Current regions. Linear trends display consistent SST spatial patterns among all products and exhibit a strong warming trend from 2012 to 2018 with the Pacific Ocean basin as the main contributor. The SST discrepancy between all SST products is very small compared to the significant warming trend. Spatial power spectral density shows that the interpolation into 1° spatial resolution has negligible impacts on our results. The global mean SST time series reveals larger differences among all SST products during the early period of the satellite era (1982–2002) when there were fewer observations, indicating that the observation frequency is the main constraint of the SST climatology. The maturity matrix scores, which present the maturity of each product in terms of documentation, storage, and dissemination but not the scientific quality, demonstrate that ESA-CCI and OSTIA SST are well documented for users' convenience. Improvements could be made for MGDSST and BoM SST. Finally, we have recommended that these SST products can be used for fundamental climate applications and climate studies (e.g., El Niño). [ABSTRACT FROM AUTHOR]

Abstract: Different methodologies to estimate the amplitude of the sea surface temperature diurnal variation (DV) and remove it from remotely sensed SST images have been proposed in the last years. Among these, the parameterization proposed by Stuart-Menteth et al. (2004a,b) [Stuart-Menteth, A.C., Robinson, I.S., & Weller, R.A. (2004a). Sensitivity of the diurnal warm layer to meteorological fluctuations Part 1: observations, submitted to Journal of Atmospheric and Ocean Science; Stuart-Menteth, A.C., Robinson, I. S., & Donlon, C.J. (2004b). Sensitivity of the diurnal warm layer to meteorological fluctuations Part 2: a new parameterisation for diurnal warming, submitted to Journal of Atmospheric and Ocean Science] and adopted by the GHRSST-PP (Donlon, 2004) [Donlon, C.J., ad the GHRSST-PP Science Team, 2004: The GHRSST-PP data processing specification v1.0 (GDS v1.0, revision 1.5), GHRSST-PP Report N. 17, Published by the International GHRSST-PP Project Office, pp. 241] appeared as the most promising. In fact, it takes into account wind and insolation variations during the day, that effectively drive the SST diurnal cycle. This parameterization has been tested on 6 months of NOAA-16 AVHRR images acquired and processed at CNR with Pathfinder algorithm. The tests evidenced some limits for a correct estimation of the DV in low-wind regimes for any insolation condition, and in high insolation regimes (>600 W/m2) when the wind intensity increases or decreases of more than 2 m/s during the morning. The limits of applicability of the DV correction to NOAA-16 AVHRR data (at least for the Mediterranean area) were thus identified, and data outside these limits were flagged. However, some anomalous heating were not corrected even with these constraints, due to the lack of accuracy in the wind field used for the correction. As a result, a strategy to flag residual outliers in the corrected daily images has been developed, based on the comparison to an optimally interpolated night SST field of the previous day. [Copyright &y& Elsevier]

Remote sensing data provide a huge number of sea surface observations, but cannot give direct information on deeper ocean layers, which can only be provided by sparse in situ data. The combination of measurements collected by satellite and in situ sensors represents one of the most effective strategies to improve our knowledge of the interior structure of the ocean ecosystems. In this work, we describe a Multi-Layer-Perceptron (MLP) network designed to reconstruct the 3D fields of ocean temperature and chlorophyll-a concentration, two variables of primary importance for many upper-ocean bio-physical processes. Artificial neural networks can efficiently model eventual non-linear relationships among input variables, and the choice of the predictors is thus crucial to build an accurate model. Here, concurrent temperature and chlorophyll-a in situ profiles and several different combinations of satellite-derived surface predictors are used to identify the optimal model configuration, focusing on the Mediterranean Sea. The lowest errors are obtained when taking in input surface chlorophyll-a, temperature, and altimeter-derived absolute dynamic topography and surface geostrophic velocity components. Network training and test validations give comparable results, significantly improving with respect to Mediterranean climatological data (MEDATLAS). 3D fields are then also reconstructed from full basin 2D satellite monthly climatologies (1998–2015) and resulting 3D seasonal patterns are analyzed. The method accurately infers the vertical shape of temperature and chlorophyll-a profiles and their spatial and temporal variability. It thus represents an effective tool to overcome the in-situ data sparseness and the limits of satellite observations, also potentially suitable for the initialization and validation of bio-geophysical models. [ABSTRACT FROM AUTHOR]

Estimating long-term modifications of the sea surface temperature (SST) is crucial for evaluating the current state of the oceans and to correctly assess the impact of climate change at regional scales. In this work, we analyze SST variations within the Mediterranean Sea and the adjacent Northeastern Atlantic box (west of the Strait of Gibraltar) over the last 37 years, by using a satellite-based dataset from the Copernicus Marine Environment Monitoring Service (CMEMS). We found a mean warming trend of 0.041 ± 0.006 ∘ C/year over the whole Mediterranean Sea from 1982 to 2018. The trend has an uneven spatial pattern, with values increasing from 0.036 ± 0.006 ∘ C/year in the western basin to 0.048 ± 0.006 ∘ C/year in the Levantine–Aegean basin. The Northeastern Atlantic box and the Mediterranean show a similar trend until the late 1990s. Afterwards, the Mediterranean SST continues to increase, whereas the Northeastern Atlantic box shows no significant trend, until ~2015. The observed change in the Mediterranean Sea affects not only the mean trend but also the amplitude of the Mediterranean seasonal signal, with consistent relative increase and decrease of summer and winter mean values, respectively, over the period considered. The analysis of SST changes occurred during the "satellite era" is further complemented by reconstructions also based on direct in situ SST measurements, i.e., the Extended Reconstructed SST (ERSST) and the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST), which go back to the 19th century. The analysis of these longer time series, covering the last 165 years, indicates that the increasing Mediterranean trend, observed during the CMEMS operational period, is consistent with the Atlantic Multidecadal Oscillation (AMO), as it closely follows the last increasing period of AMO. This coincidence occurs at least until 2007, when the apparent onset of the decreasing phase of AMO is not seen in the Mediterranean SST evolution. [ABSTRACT FROM AUTHOR]