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Coral reefs are facing severe threats and are at risk of accelerated decline due to climate change‐induced changes in their environment. Ongoing efforts to understand the mechanisms of coral response to warming rely on multiple sources of temperature data. Yet, it remains uncertain whether the Sea Surface Temperature (SST) data used for coral reef studies are consistent among different data products, despite potential implications for conservation. A better understanding of the consistency among the different SST data applied to coral reefs may facilitate the fusion of data into a standard product. This will improve monitoring and understanding of the impact of global warming on coral reefs. Four types of SST data across North‐Western and South‐Western Australia are compared to assess their differences and ability to observe high thermal stress during historical coral bleaching events. The four SST data sources included those derived from Global Circulation Models, NOAA CoralTemp SST product, ESA CCI SST product, and coral core derived SST. Coral bleaching risk indicators, Degree Heating Week (DHW), and Degree Heating Month (DHM) were calculated using these sources and compared for consistency. DHW and DHM were inconsistent among data sets and did not accurately reflect high thermal stress metrics during moderate and severe bleaching events. Some reefs did not experience bleaching in spite of high DHWs and DHMs, suggesting a mismatch in data scales, or perhaps other oceanographic factors and coral adaptation. By exploring the differences and similarities among these four data sources, this study highlights the need to compare existing indicators of thermal stress from different data sets. Plain Language Summary: Climate change and warming have resulted in global coral bleaching events, severely compromising our environment's health. Monitoring the changes in ocean temperatures around them is essential to maximizing our efforts to protect them. Different ocean temperature data products exist and are being used without understanding their differences. To highlight these differences, the present study compares historical warming from climate models and remote and in situ sensors and known bleaching events on five reefs across Western Australia. Key Points: Temperature data sources did not provide consistent risk indicators for coral bleachingAcross five reefs, coral bleaching risk indicators differed in their ability to predict the observed coral bleaching eventsTemperature data in daily and monthly temporal resolutions differed in the accuracy of coral bleaching risk indicators [ABSTRACT FROM AUTHOR]

A statistical model is derived relating the diurnal variation of sea surface temperature (SST) to the net surface heat flux and surface wind speed from a numerical weather prediction (NWP) model. The model is derived using fluxes and winds from the European Centre for Medium-Range Weather Forecasting (ECMWF) NWP model and SSTs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). In the model, diurnal warming has a linear dependence on the net surface heat flux integrated since (approximately) dawn and an inverse quadratic dependence on the maximum of the surface wind speed in the same period. The model coefficients are found by matching, for a given integrated heat flux, the frequency distributions of the maximum wind speed and the observed warming. Diurnal cooling, where it occurs, is modelled as proportional to the integrated heat flux divided by the heat capacity of the seasonal mixed layer. The model reproduces the statistics (mean, standard deviation, and 95-percentile) of the diurnal variation of SST seen by SEVIRI and reproduces the geographical pattern of mean warming seen by the Advanced Microwave Scanning Radiometer (AMSR-E). We use the functional dependencies in the statistical model to test the behaviour of two physical model of diurnal warming that display contrasting systematic errors. [ABSTRACT FROM AUTHOR]

Drifting buoy sea-surface temperature ( SST) records have been used to characterize the diurnal variability of ocean temperature at a depth of order 20 cm. We use measurements covering the period 1986-2012 from the International Comprehensive Ocean-Atmosphere Data Set ( ICOADS) version 2.5, which is a collection of marine surface observations that includes individual SST records from drifting buoys. Appropriately transformed, this dataset is well suited for estimation of the diurnal cycle, since many drifting buoys have high temporal coverage (many reports per day), and are globally distributed. For each drifter for each day, we compute the local-time daily SST variation relative to the local-time daily mean SST. Climatological estimates of subdaily SST variability are found by averaging across various strata of the data: in 10° latitudinal bands as well as globally; and stratified with respect to season, wind speed and cloud cover. A parameterization of the diurnal variability is fitted as a function of the variables used to stratify the data, and the coefficients for this fit are also provided with the data. Results are consistent with expectations based on the previous work: the diurnal temperature cycle peaks in early afternoon (circa 2 pm local time); there is an increase in amplitude and a decrease in seasonality towards the equator. Generally, the ocean at this depth cools on windy days and warms on calm days, so that a component of subdaily variability is the SST tendency on slower timescales. By not 'closing' the diurnal cycle when stratified by environmental conditions, this dataset differs from previously published diurnal-cycle parameterizations. This thorough characterization of the SST diurnal cycle will assist in interpreting SST observations made at different local times of day for climatological purposes, and in testing and constraining models of the diurnal-cycle and air-sea interaction at high temporal resolution. [ABSTRACT FROM AUTHOR]

The article presents information on the Global Ocean Data Assimilation Experiment (GODAE) high-resolution sea surface temperature (SST) pilot project. The GODAE High-Resolution SST Pilot Project (GHRSST-PP) involves the merging of SST data from heterogeneous sources that include infrared wide swatch radiometer in polar orbit, microwave radiometer in polar orbit, infrared dual-view radiometer in polar orbit, and infrared Earth disc radiometer in geostationary orbit. The project provides two types of near-real-time SST information products.

In mid-March 2022, a Siberian High brought intense cold air masses, leading to severe weather conditions across southern Europe, including the Black Sea region. This study investigates the spatial and temporal evolution of cold intermediate water (CIW) masses in the Black Sea, with a particular focus on the successive anomalously cold episodes that occurred in March 2022. The research underscores the significance of the northwestern continental slope and cyclonic gyres, especially as the only cold-water mass observations during the warm winters of 2020 and 2021 were concentrated in these areas. Following two warm winters, the cold episodes of March 2022 revealed notable convection and simultaneous cooling, particularly in the cyclonic interior and the Rim Current periphery, excluding the northeastern periphery. Subsequently, cold waters spreading isopycnally throughout the summer months were transported laterally and reached these regions. Argo float measurements provided clear evidence of widespread replenishment of the CIW, indicating that it is not confined to specific areas. The study also highlights regional variability in the characteristics of CIW formation, which is influenced by local dynamics and preconditioning temperatures. The temperatures of CIW increased from west to east, in line with the sea surface temperature gradient. Notably, thicker and colder CIW was found in the western cyclonic gyre compared to the eastern cyclonic area. Furthermore, the study confirms that the warming trend in CIW, identified in previous research, not only continues but has intensified during the recent period analyzed. These findings, observed under the extreme conditions analyzed in this research, offer valuable insights into the widespread occurrence of CIW formation in the Black Sea. Additionally, the study confirms that the warming trend in CIW, identified in previous studies, continued in the region throughout the warm winter period and after the cold spell in 2022. These insights contribute to a deeper understanding of CIW dynamics and their response to extreme weather events in the Black Sea. [ABSTRACT FROM AUTHOR]

Satellite remote sensing is the best way to derive sea surface skin temperature (SSTskin) in the Arctic. However, as surface temperature retrieval algorithms in the infrared (IR) part of the electromagnetic spectrum are designed to compensate for atmospheric effects mainly due to water vapor, MODIS SSTskin retrievals have larger uncertainties at high latitudes where the atmosphere is very dry and cold, which is an extreme in the distribution of global conditions. MODIS R2019 SSTskin fields are currently derived using latitudinally and monthly dependent algorithm coefficients, including an additional band above 60°N to better represent the effects of Arctic atmospheres. However, the R2019 processing of MODIS SSTskin still has some unrevealed error characteristics. This study uses 21 years (2002–2022) of collocated, simultaneous satellite brightness temperature (BT) data from Aqua MODIS and in situ buoy-measured subsurface temperature data from iQuam for validation. Unlike elsewhere over the oceans, the 11 μm and 12 μm BT differences are poorly related to the column water vapor at high latitudes, resulting in poor atmospheric water vapor correction. Anomalous BT difference signals are identified, caused by the temperature and humidity inversions in the lower troposphere, which are especially significant during the summer. Although the existence of negative BT differences is physically reasonable, this makes the retrieval algorithm lose its effectiveness. Moreover, the statistics of the MODIS SSTskin data when compared with the iQuam buoy temperature data show large differences (in terms of mean and standard deviation) for the matchups at the Northern Atlantic and Pacific sides of the Arctic due to the disparity of in situ measurements and distinct surface and vertical atmospheric conditions. Therefore, it is necessary to further improve the retrieval algorithms to obtain more accurate MODIS SSTskin data to study surface ocean processes and climate change in the Arctic. [ABSTRACT FROM AUTHOR]

Leveraging the Copernicus high‐resolution multi‐year Mediterranean Sea Surface Temperature (SST) dataset, 15 selected tropical‐like cyclones (TLCs) are analyzed with the objective of elucidating the anomalies of SST at the time of cyclogenesis and the connection between the change in SST during the cyclone lifetime and its characteristics. The long‐term SST increase associated with climate change is identified by comparing detrended and original anomalies. Detrending removes the effect of the intensification of SST anomaly over time, revealing that no significant anomalies generally emerge in the early stages of the TLC lifetimes. Conversely, winter events exhibit early‐stage positive SST anomalies. Also, high SST values were observed during the intensification of the most intense cyclones. A cold SST anomaly is left after the passage of the cyclones, due to the intense sea surface fluxes extracting heat from the sea. Plain Language Summary: Mediterranean tropical‐like cyclones are severe weather events able to produce large socio‐economic and environmental impact, as well as considerable damage. On average, 1.5 such events affect the Mediterranean each year. In this study we assess the Sea Surface Temperature conditions related to the events reported in the literature of the past four decades. We use a high‐resolution multi‐year SST dataset over the Mediterranean Sea, to reveal the potential relationship between the change in SST before and during the cyclone's lifetime and their features. Our results show that SST plays an important role in the intensification phases of the events, while no significant SST anomaly emerges in the early stages for most of the cyclones. Key Points: No significant SST anomalies are found at the early stage of medicanes, except for the winter cases that exhibit a positive anomalySST value is particularly high for the most intense cyclones (September cases)The passage of the cyclones over the sea induces a cooling mainly during their mature phase [ABSTRACT FROM AUTHOR]

Marine heatwaves (MHWs) represent anomalously warm temperature conditions of seawater that may affect marine life and ocean biogeochemistry. Under such conditions, phytoplankton communities may modify their structure and functions, and their resilience is not assured. This study characterizes the impact of MHWs on the phytoplankton spring bloom in the North‐Western Mediterranean Sea. Here, we synergistically combine autonomous observations from BioGeoChemical‐Argo floats, satellite‐based and marine ecosystem model data, and show that MHW events occurring during winter drastically inhibit phytoplankton carbon biomass in spring by up to 70%. Such reduction is related to the enhanced stratification of the water column under MHWs which hinders the renewal of nutrients from deep‐ocean reservoirs, thus preventing surface phytoplankton from blooming. This process negatively impacts particulate organic carbon stocks within the mixed layer, while severe events cause an earlier shift of phytoplankton phenology that provokes changes in zooplankton biomass distribution. Plain Language Summary: Marine heatwaves (MHWs) are described as an abnormal and prolonged increase of ocean temperatures. These events may occur in all the oceans, and are becoming more frequent than before. Such increase in water temperature might not be tolerated by organisms, which must need to adapt themselves to the new environmental conditions. Consequently, marine ecosystem health is endangered. Here, we analyze the effect that MHWs have on the growth of small algae called phytoplankton. Phytoplankton are vital microscopic organisms for ecosystems that use sunlight to produce organic carbon through photosynthesis. At middle and high latitudes, phytoplankton massively grows (i.e., blooms) once a year at the sea surface, and introduces a major carbon flux into the ecosystem that sustains larger animals. Through combining observations acquired by different platforms (satellites, autonomous in situ BioGeoChemical‐Argo robots, and ecosystem models), we comprehensively study how MHWs affect phytoplankton carbon production during spring blooms and trophic chains at mid‐latitudes. Results show that MHW events occurring in winter lead to a large decrease in phytoplankton carbon biomass (up to 70%) in spring. Winter MHW events, driven by local atmospheric conditions, intensify water column stratification thus hindering the deep‐ocean nutrient transport to the surface, which is essential for phytoplankton to bloom. Key Points: Marine heatwaves (MHWs) intensify water stratification leading to reduction in nutrient supply which inhibits surface phytoplankton spring bloomMHWs lead to a phytoplankton community shift toward smaller cells, while increasing the transparency of surface watersMHWs decrease carbon stocks within the mixed layer, while intense ones shift phytoplankton phenology and affect zooplankton [ABSTRACT FROM AUTHOR]

Previous studies that intercompared global Level‐4 (L4) sea surface temperature (SST) analyses were centered on the assessment of the accuracy and bias of SST by comparing them with independent near‐surface Argo profile temperature data. This type of assessment is centered on the absolute value of SST rather than on SST spatial properties (gradients), which is more relevant to the study of oceanographic features (e.g., fronts, eddies, etc.) and ocean dynamics. Here, we use, for the first time, the spectrum of singularity exponents to assess the structural and dynamical quality of different L4 gap‐filled products based on the multifractal theory of turbulence. Singularity exponents represent the geometrical projection of the turbulence cascade, and its singular spectrum can be related to the probability density function of the singularity exponents normalized by the scale. Our results reveal that the different schemes used to produce the L4 SST products generate different singularity spectra, which are then used to identify a potential loss of dynamical information or structural coherence. This new diagnostic constitutes a valuable tool to assess the structural quality of SST products and can support data satellite SST producers efforts to improve the interpolation schemes used to generate gap‐filled SST products. Plain Language Summary: Gap‐filled sea surface temperature (SST) gridded products are generated by combining satellite and, in some cases, in situ observations. Previous studies intercomparing the different SST products focused on assessing the differences between the SST products and in situ observations. However, what is relevant for ocean dynamics studies is the local spatial differences of the SST fields. In this work, we propose a new diagnostic that allows us to assess the dynamical quality of SST products. This new diagnostic is based on the multifractal theory of turbulence and enables the identification of the regions where the SST fields do not properly describe ocean dynamics. This diagnostic is a valuable tool that can help the data satellite SST producers to improve the way the different observations are blended in order to build SST fields that are more dynamically coherent. Key Points: We proposed a new diagnostic to assess the structural and dynamical properties of sea surface temperature (SST) productsThis diagnostic is based on multifractal theory of turbulence and consists computing the singularity spectrumThe different schemes used to produce gap‐filled SST products may contribute to the loss of dynamical information or structural coherence [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) is integral to our understanding of the radiative energy budget of the Earth's surface since it provides the best approximation to the thermodynamic temperature that drives the outgoing longwave flux from surface to atmosphere. Since 5 July 2017, an operational LST product has been available from the Sentinel-3A mission, with the corresponding product being available from Sentinel-3B since 17 November 2018. Here, we present the first paper describing formal products, including algorithms, for the Sea and Land Surface Temperature Radiometer (SLSTR) instruments onboard Sentinel-3A and 3B (SLSTR-A and SLSTR-B, respectively). We evaluate the quality of both the Land Surface Temperature Climate Change Initiative (LST_cci) product and the Copernicus operational LST product (SL_2_LST) for the years 2018 to 2021. The evaluation takes the form of a validation against ground-based observations of LST across eleven well-established in situ stations. For the validation, the mean absolute daytime and night-time difference against the in situ measurements for the LST_cci product is 0.77 K and 0.50 K, respectively, for SLSTR-A, and 0.91 K and 0.54 K, respectively, for SLSTR-B. These are an improvement on the corresponding statistics for the SL_2_LST product, which are 1.45 K (daytime) and 0.76 (night-time) for SLSTR-A, and 1.29 K (daytime) and 0.77 (night-time) for SLSTR-B. The key influencing factors in this improvement include an upgraded database of reference states for the generation of retrieval coefficients, higher stratification of the auxiliary data for the biome and fractional vegetation, and enhanced cloud masking. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) data from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites have been used in the Met Office's Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) since 2019 (Sentinel-3A SST data since March 2019 and Sentinel-3B data since December 2019). The impacts of using SLSTR SSTs and the SLSTR as the reference sensor for the bias correction of other satellite data have been assessed using independent Argo float data. Combining Sentinel-3A and -3B SLSTRs with two Visible Infrared Imaging Radiometer Suite (VIIRS) sensors (onboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership and National Oceanic and Atmospheric Administration-20 satellites) in the reference dataset has also been investigated. The results indicate that when using the SLSTR as the only reference satellite sensor, the OSTIA system becomes warmer overall, although there are mixed impacts in different parts of the global ocean. Using both the VIIRS and the SLSTR in the reference dataset leads to moderate but more consistent improvements globally. Numerical weather prediction (NWP) results also indicate a better performance when using both the VIIRS and the SLSTR in the reference dataset compared to only using the SLSTR at night. Combining the VIIRS and the SLSTR with latitudinal weighting shows the best validation results against Argo, but further investigation is required to refine this method. [ABSTRACT FROM AUTHOR]

Over the past few decades, the oceans have stored the majority of the excess heat in the climate system resulting from anthropogenic emissions. An accurate, long-term sea surface temperature (SST) dataset is essential for monitoring and researching the changes to the global oceans. A variety of SST datasets have been produced by various institutes over the years, and here, we present a new SST data record produced originally within the Copernicus Marine Environment Monitoring Service (which is therefore named CMEMS v2.0) and assess: (1) its accuracy compared to independent observations; (2) how it compares with the previous version (named CMEMS v1.2); and (3) its performance during two major volcanic eruptions. By comparing both versions of the CMEMS datasets using independent in situ observations, we show that both datasets are within the target accuracy of 0.1 K, but that CMEMS v2.0 is closer to the ground truth. The uncertainty fields generated by the two analyses were also compared, and CMEMS v2.0 was found to provide a more accurate estimate of its own uncertainties. Frequency and vector analysis of the SST fields determined that CMEMS v2.0 feature resolution and horizontal gradients were also superior, indicating that it resolved oceanic features with greater clarity. The behavior of the two analyses during two volcanic eruption events (Mt. Pinatubo and El Chichón) was examined. A comparison with the HadSST4 gridded in situ dataset suggested a cool bias in the CMEMS v2.0 dataset versus the v1.2 dataset following the Pinatubo eruption, although a comparison with sparser buoy-only observations yielded less clear results. No clear impact of the El Chichón eruption (which was a smaller event than Mt. Pinatubo) on CMEMS v2.0 was found. Overall, with the exception of a few specific and extreme events early in the time series, CMEMS v2.0 possesses high accuracy, resolution, and stability and is recommended to users. [ABSTRACT FROM AUTHOR]

Averaging reflected solar radiation (RSR) over the whole year/hemisphere may mask the inter-month-/region-specific signals, limiting the investigation of spatiotemporal mechanisms and hemispheric symmetry projections. This drives us to explain RSR characteristics from latitude- and month-based perspectives. The study also explores whether longer-record radiation datasets can exhibit hemispheric symmetry of RSR to understand its temporal changes. Statistics indicate that the largest trends in decreasing RSR in the Northern and Southern hemispheres (NH and SH) occur in mid-spring and are dominated by clear-sky atmospheric and cloud components and cloud components only, respectively. The interannual negative trend in the NH RSR mainly derives from 30–50° N latitude zones, attributed to the decrease in the clear-sky atmospheric component caused by reduced anthropogenic sulfate emissions and spring/summer dust frequencies and reduced cloud fraction caused by increased sea surface temperature and an unstable marine boundary layer, thus leading to a reduced cloud component. In the SH, the significant RSR decreasing trend is widespread in 0–50° S latitude zones, which is closely related to the decrease in the cloud component caused by the decrease in cloud cover over the tropical western Pacific and Southern Ocean, partially compensated by the increase in the clear-sky atmospheric component. A new data evaluation system and an uncertainty analysis reveal that only the Advanced Very High Resolution Radiometer (AVHRR) outperforms in exhibiting the Cloud and Earth Radiant Energy System (CERES) hemispheric RSR differences due to offsetting biases among different components and achieves hemispheric RSR symmetry criteria within its uncertainty, making it suitable for studying long-term RSR hemispheric symmetry changes. Furthermore, the International Satellite Cloud Climatology Project (ISCCP) agrees well with CERES regarding hemispheric cloud component asymmetry and can help in the study of the corresponding long-term changes and mechanisms. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is a crucial physical parameter in meteorology and oceanography. This study demonstrates that the influence of earth incidence angle (EIA) on the SST retrieved from the microwave radiation imager (MWRI) onboard FengYun-3 (FY-3) meteorological satellites should not be ignored. Compared with algorithms that do not consider the influence of EIA in the regression, those that integrate the EIA into the regression can enhance the accuracy of SST retrievals. Subsequently, based on the recalibrated Level 1B data from the FY-3/MWRI, a long-term SST dataset was reprocessed by employing the algorithm that integrates the EIA into the regression. The reprocessed SST data, including FY-3B/MWRI SST during 2010-2019, FY-3C/MWRI SST during 2013-2019, and FY-3D/MWRI SST during 2018-2020, were compared with the in-situ SST and the SST dataset from the Operational Sea Surface Temperature and Ice Analysis (OSTIA). The results show that the FY-3/MWRI SST data were consistent with both the in-situ SST and the OSTIA SST dataset. Compared with the Copernicus Climate Change Service V2.0 SST, the absolute deviation of the reprocessed SST, with a quality flag of 50, was less than 1.5°C. The root mean square errors of the FY-3/MWRI orbital, daily, and monthly SSTs, with a quality flag of 50, were approximately 0.82°C, 0.69°C, and 0.37°C, respectively. The primary discrepancies between the FY-3/MWRI SST and the OSTIA SST were found mainly in the regions of the western boundary current and the Antarctic Circumpolar Current. Overall, this reprocessed SST product is recommended for El Niño and La Niña events monitoring. [ABSTRACT FROM AUTHOR]

The sensitivity of the fugacity of carbon dioxide in seawater (fCO2) to temperature (denoted υ , reported in %°C-1) is critical for the accurate fCO2 measurements needed to build global carbon budgets and for understanding the drivers of air–sea CO2 flux variability across the ocean. However, understanding and computing υ have been restricted to either using empirical functions fitted to experimental data or determining it as an emergent property of a fully resolved marine carbonate system, and these two approaches are not consistent with each other. The lack of a theoretical basis and an uncertainty estimate for υ has hindered resolving this discrepancy. Here, we develop a new approach for calculating the temperature sensitivity of fCO2 based on the equations governing the marine carbonate system and the van 't Hoff equation. This shows that, to first order, ln⁡(fCO2) should be proportional to 1/tK (where tK is temperature in kelvin), rather than to temperature, as has previously been assumed. This new approach is, to first order, consistent with calculations from a fully resolved marine carbonate system, which we have incorporated into the PyCO2SYS software. Agreement with experimental data is less convincing but remains inconclusive due to the scarcity of direct measurements of υ , particularly above 25 °C. However, the new approach is consistent with field data, performing better than any other approach for adjusting fCO2 by up to 10 °C if spatiotemporal variability in its single fitted coefficient is accounted for. The uncertainty in υ arising from only measurement uncertainty in the main experimental dataset where υ has been directly measured is in the order of 0.04 %°C-1 , which corresponds to a 0.04 % uncertainty in fCO2 adjusted by +1 °C. However, spatiotemporal variability in υ is several times greater than this, so the true uncertainty due to the temperature adjustment in fCO2 adjusted by +1 °C using the most widely used constant υ value is around 0.24 %. This can be reduced to around 0.06 % using the new approach proposed here, and this could be further reduced by more measurements. The spatiotemporal variability in υ arises mostly from the equilibrium constants for CO2 solubility and carbonic acid dissociation (K1∗ and K2∗), and its magnitude varies significantly depending on which parameterisation is used for K1∗ and K2∗. Seawater fCO2 can be measured accurately enough that additional experiments should be able to detect spatiotemporal variability in υ and distinguish between different parameterisations for K1∗ and K2∗. Because the most widely used constant υ was coincidentally measured from seawater with roughly global average υ , our results are unlikely to significantly affect global air–sea CO2 flux budgets, but they may have more important implications for regional budgets and studies that adjust by larger temperature differences. [ABSTRACT FROM AUTHOR]

Satellite remote sensing of ocean colour properties allows observation of the ocean with high temporal and spatial coverage, facilitating the better assessment of changes in marine primary production. Ocean productivity is often assessed using satellite-derived chlorophyll a concentrations, a commonly used proxy for phytoplankton concentration. We used the Copernicus GlobColour remote sensing chlorophyll a surface concentration to investigate seasonal and non-seasonal variability, temporal trends, and changes in spring bloom chlorophyll a magnitude. Complementary, we analysed the chlorophyll a relationship with sea surface temperature and mixed-layer depth in the German Bight from 1998 to 2020. Empirical orthogonal functions were employed in order to investigate dominant spatial and temporal patterns (modes) related to the main processes of chlorophyll a variability. Multi covariance analysis was used to extract the dominant structures that maximize the covariance between chlorophyll a and sea surface temperature mixed-layer depth fields. High levels of chlorophyll a were found near the coast, showing a decreasing gradient towards offshore waters. A significant chlorophyll a positive trend was observed close to the Elbe estuary and adjacent area, while 55 % of the German Bight was characterized by a significant chlorophyll a negative trend. The chlorophyll a non-seasonal variability showed that the first four modes explained around 45 %, with the first and second modes related to inter- and intra-annual variability, respectively, observed in the temporal principal components spectral analyses. Monthly chlorophyll a concentration anomalies co-varied by 45 % with sea surface temperature anomalies and 23 % with mixed-layer depth anomalies. The monthly averages of chlorophyll a anomaly fields were suitable to investigate long-term trends and variability. The rising water temperature, combined with its indirect effects on other variables, can partially explain the observed trends in chlorophyll a. [ABSTRACT FROM AUTHOR]

Since 2011, the Global Energy and Water cycle Exchanges (GEWEX) Water Vapor Assessment (G-VAP) has provided performance analyses for state-of-the-art reanalysis and satellite water vapour products to the GEWEX Data and Analysis Panel (GDAP) and the user community in general. A significant component of the work undertaken by G-VAP is to characterise the quality and uncertainty of these water vapour records to (i) ensure full exploitation and (ii) avoid incorrect use or interpretation of results. This study presents results from the second phase of G-VAP, where we have extended and expanded our analysis of total column water vapour (TCWV) from phase 1, in conjunction with updating the G-VAP archive. For version 2 of the archive, we consider 28 freely available and mature satellite and reanalysis data products, remapped to a regular longitude–latitude grid of 2° × 2° and on monthly time steps between January 1979 and December 2019. We first analysed all records for a "common" short period of 5 years (2005–2009), focusing on variability (spatial and seasonal) and deviation from the ensemble mean. We observed that clear-sky daytime-only satellite products were generally drier than the ensemble mean, and seasonal variability/disparity in several regions up to 12 kg m -2 related to original spatial resolution and temporal sampling. For 11 of the 28 data records, further analysis was undertaken between 1988–2014. Within this "long period", key results show (i) trends between - 1.18 ± 0.68 to 3.82 ± 3.94 kg m -2 per decade and - 0.39 ± 0.27 to 1.24 ± 0.85 kg m -2 per decade were found over ice-free global oceans and land surfaces, respectively, and (ii) regression coefficients of TCWV against surface temperatures of 6.17 ± 0.24 to 27.02 ± 0.51 % K -1 over oceans (using sea surface temperature) and 3.00 ± 0.17 to 7.77 ± 0.16 % K -1 over land (using surface air temperature). It is important to note that trends estimated within G-VAP are used to identify issues in the data records rather than analyse climate change. Additionally, breakpoints have been identified and characterised for both land and ocean surfaces within this period. Finally, we present a spatial analysis of correlations to six climate indices within the long period, highlighting regional areas of significant positive and negative correlation and the level of agreement among records. [ABSTRACT FROM AUTHOR]

Ocean observational gridded products are vital for climate monitoring, ocean and climate research, model evaluation, and supporting climate mitigation and adaptation measures. This paper describes the 4th version of the Institute of Atmospheric Physics (IAPv4) ocean temperature and ocean heat content (OHC) objective analysis product. It accounts for recent developments in quality control (QC) procedures, climatology, bias correction, vertical and horizontal interpolation, and mapping and is available for the upper 6000 m (119 levels) since 1940 (more reliable after ∼ 1957) for monthly and 1°×1° temporal and spatial resolutions. IAPv4 is compared with the previous version, IAPv3, and with the other data products, sea surface temperatures (SSTs), and satellite observations. It has a slightly stronger long-term upper 2000 m OHC increase than IAPv3 for 1955–2023, mainly because of newly developed bias corrections. The IAPv4 0–2000 m OHC trend is also higher during 2005–2023 than IAPv3, mainly because of the QC process update. The uppermost level of IAPv4 is consistent with independent SST datasets. The month-to-month OHC variability for IAPv4 is desirably less than IAPv3 and the other OHC products investigated in this study, the trend of ocean warming rate (i.e., warming acceleration) is more consistent with the net energy imbalance at the top of the atmosphere than IAPv3, and the sea level budget can be closed within uncertainty. The gridded product is freely accessible at 10.12157/IOCAS.20240117.002 for temperature data (Cheng et al., 2024a) and at 10.12157/IOCAS.20240117.001 for ocean heat content data (Cheng et al., 2024b). [ABSTRACT FROM AUTHOR]

The atmosphere‐ocean is a highly coupled system with significant diurnal and hourly variations. However, current coupled models usually lack sub‐diurnal scale processes at the air‐sea interface due to the finite vertical resolution for ocean discretization. Previous modeling studies showed that sub‐diurnal scale air‐sea interaction processes are important for ocean mixing. Here, by designing an integrated sub‐diurnal parameterization (ISDP) scheme which combines different temperature profiling functions, we stress sub‐diurnal air‐sea interactions to better represent the local ocean mixing. This scheme has been implemented into two coupled models which contributed to the Climate Model Intercomparison Project (CMIP), referenced by the Intergovernmental Panel on Climate Change—Community Earth System Model and Coupled Model version 2. The results show that the ISDP scheme improves model simulations with better climatology and more realistic spectra, especially in the tropics and North Pacific Ocean. With the scheme, the tropical cold tongue bias is significantly relaxed by reducing the overestimation of ocean upper mixing, and the cold bias of North Pacific Ocean is reduced due to the improvement on currents and net heat fluxes. Our scheme may help better the simulation and prediction skills of coupled models when their horizontal resolution becomes fine but vertical resolution remains relatively coarse as it describes high‐frequency air‐sea interactions more realistically. Plain Language Summary: The atmosphere and ocean interact with each other. In these interactions, changes may occur over different time periods. Some changes take years or months, while others happen in days or hours. The atmosphere changes quickly throughout the day, such as air temperature, wind, and rain. These quick changes in the atmosphere can affect the ocean rapidly. Similarly, there are quick changes in the ocean. These quick changes also affect the atmosphere in return. Understanding these quick changes is important. However, it is difficult to perfectly capture the quick changes in the climate models because they do not always represent the ocean behavior accurately. In this study, we developed a new scheme (named the sub‐diurnal scale parameterization, ISDP) to better represent these quick changes. We added this new scheme into two widely‐used climate models. These models are important tools for studying climate change. Our new scheme represents better the ocean mixing and interaction based on the local weather conditions. When we use ISDP, the climate models get better at simulating climate. They're more accurate in showing ocean temperatures in the tropics. These models make the tropical oceans seem colder than they really are, but our scheme, to some extent, fixes this problem. The results also show the more accurate ocean temperatures in the North Pacific Ocean. Our new scheme has a great potential to make climate models more accurate. Key Points: An integrated sub‐diurnal scale parameterization (ISDP) scheme has a more appropriate representation for local ocean mixingThe ISDP scheme has been implemented into two models which contributed to the Climate Model Intercomparison Project, referenced by IPCC: Community Earth System Model and Coupled Model version 2The model tropical cold tongue bias with ISDP is relaxed by reducing the overestimation of ocean upper mixing [ABSTRACT FROM AUTHOR]

In this paper, we explore different prognostic methods to account for skin sea surface temperature diurnal variations in a coupled ocean–atmosphere regional model of the Mediterranean Sea. Our aim is to characterise the sensitivity of the considered methods with respect to the underlying assumption of how the solar radiation shapes the warm layer of the ocean. All existing prognostic methods truncate solar transmission coefficient at a warm-layer reference depth that is constant in space and time; instead, we implement a new scheme where this latter is estimated from a chlorophyll dataset as the e -folding depth of solar transmission, which thus allows it to vary in space and time depending on seawater's transparency conditions. Comparison against satellite data shows that our new scheme, compared to the one already implemented within the ocean model, improves the spatially averaged diurnal signal, especially during winter, and the seasonally averaged one in spring and autumn, while showing a monthly basin-wide averaged bias smaller than 0.1 K year-round. In April, when most of the drifters' measurements are available, the new scheme mitigates the bias during nighttime, keeping it positive but smaller than 0.12 K during the rest of the monthly averaged day. The new scheme implemented within the ocean model improves the old one by about 0.1 K, particularly during June. All the methods considered here showed differences with respect to objectively analysed profiles confined between 0.5 K during winter and 1 K in summer for both the eastern and the western Mediterranean regions, especially over the uppermost 60 m. The new scheme reduces the RMSE on the top 15 m in the central Mediterranean for summertime months compared to the scheme already implemented within the ocean model. Overall, the surface net total heat flux shows that the use of a skin sea surface temperature (SST) parameterisation brings the budget about 1.5 Wm-2 closer to zero on an annual basis, despite all simulations showing an annual net heat loss from the ocean to the atmosphere. Our "chlorophyll-interactive" method proved to be an effective enhancement of existing methods, its strength relying on an improved physical consistency with the solar extinction implemented in the ocean component. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is one of the essential variables of the Earth's climate system. Being at the air–sea interface, SST modulates heat fluxes in and out of the ocean, provides insight into several upper and interior ocean dynamical processes, and is a fundamental indicator of climate variability potentially impacting the health of marine ecosystems. Its accurate estimation and regular monitoring from space is therefore crucial. However, even if satellite infrared/microwave measurements provide much better coverage than what is achievable from in situ platforms, they cannot sense the sea surface under cloudy and rainy conditions. Large gaps are present even in merged multi-sensor satellite products, and different statistical strategies, mostly based on optimal interpolation (OI) algorithms, have thus been proposed to obtain gap-free (L4) images. These techniques, however, filter out the signals below the space–time decorrelation scales considered, significantly smoothing most of the small mesoscale and submesoscale features. Here, deep learning models, originally designed for single-image super resolution (SR), are applied to enhance the effective resolution of SST products and the accuracy of SST gradients. SR schemes include a set of computer vision techniques leveraging convolutional neural networks to retrieve high-resolution data from low-resolution images. A dilated convolutional multi-scale learning network, which includes an adaptive residual strategy and implements a channel attention mechanism, is used to reconstruct features in SST data at 1/100 ° spatial resolution starting from 1/16 ° data over the Mediterranean Sea. The application of this technique shows an improvement in the high-resolution reconstruction, capturing small-scale features and providing a root-mean-squared-difference improvement of 0.02 °C with respect to the L3 ground-truth data. [ABSTRACT FROM AUTHOR]

There is growing demand for seasonal forecast products for marine applications. The availability of consistent and sufficiently long observational records of ocean variables permits the assessment of the spatial distribution of the skill of ocean variables from seasonal forecasts. Here we use state-of-the-art temporal records of sea surface temperature (SST), sea surface height (SSH) and upper 300m ocean heat content (OHC) to quantify the distribution of skill, up to 2 seasons ahead, of two operational seasonal forecasting systems contributing to the seasonal multi-model of the Copernicus Climate Change Services (C3S). This study presents the spatial distribution of the skill of the seasonal forecast ensemble mean in terms of anomaly correlation and root mean square error and compares it to the persistence and climatological benchmarks. The comparative assessment of the skill among variables sheds light on sources/ limits of predictability at seasonal time scales, as well as the nature of model errors. Beyond these standard verification metrics, we also evaluate the ability of the models to represent the observed long-term trends. Results show that long-term trends contribute to the skill of seasonal forecasts. Although the forecasts capture the long-term trends in general, some regional aspects remain challenging. Part of these errors can be attributed to specific aspects of the ocean initialization, but others, such as the overestimation of the warming in the Eastern Pacific are also influenced by model error. Skill gains can be obtained by improving the trend representation in future forecasting systems. In the meantime, a forecast calibration procedure that corrects the linear trends can produce substantial skill gains. The results show that calibrated seasonal forecasts beat both the climatological and persistence benchmark almost at every location for all initial dates and lead times. Results demonstrate the value of the seasonal forecasts for marine applications and highlight the importance of representing the decadal variability and trends in ocean heat content and sea level. [ABSTRACT FROM AUTHOR]

The infrared (IR) satellite remote sensing of sea surface skin temperature (SSTskin) is challenging in the northern high-latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SSTskin retrievals is questionable. Several Saildrone uncrewed surface vehicles were deployed at the Pacific side of the Arctic in 2019, and two of them, SD-1036 and SD-1037, were equipped with a pair of IR pyrometers on the deck, whose measurements have been shown to be useful in the derivation of SSTskin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SSTskin retrievals. This study aims to assess the accuracy of MODIS-retrieved SSTskin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SSTskin data. The mean difference in SSTskin from the SD-1036 and SD-1037 measurements is ~0.4 K, largely resulting from differences in the atmospheric conditions experienced by the two Saildrones. The performance of MODIS on Aqua and Terra in retrieving SSTskin is comparable. Negative brightness temperature (BT) differences between 11 μm and 12 μm channels are identified as being physically based, but are removed from the analyses as they present anomalous conditions for which the atmospheric correction algorithm is not suited. Overall, the MODIS SSTskin retrievals show negative mean biases, −0.234 K for Aqua and −0.295 K for Terra. The variations in the retrieval inaccuracies show an association with diurnal warming events in the upper ocean from long periods of sunlight in the Arctic. Also contributing to inaccuracies in the retrieval is the surface emissivity effect in BT differences characterized by the Emissivity-introduced BT difference (EΔBT) index. This study demonstrates the characteristics of MODIS-retrieved SSTskin in the Arctic, at least at the Pacific side, and underscores that more in situ SSTskin data at high latitudes are needed for further error identification and algorithm development of IR SSTskin. [ABSTRACT FROM AUTHOR]

Coastal areas are among the most productive areas in the world, ecologically as well as economically. Sea Surface Temperature (SST) has evolved as the major essential climate variable (ECV) and ocean variable (EOV) to monitor land–ocean interactions and oceanic warming trends. SST monitoring can be achieved by means of remote sensing. The current relatively coarse spatial resolution of established SST products limits their potential in small-scale, coastal zones. This study presents the first analysis of the TIMELINE 1 km SST product from AVHRR in four key European regions: The Northern and Baltic Sea, the Adriatic Sea, the Aegean Sea, and the Balearic Sea. The analysis of monthly anomaly trends showed high positive SST trends in all study areas, exceeding the global average SST warming. Seasonal variations reveal peak warming during the spring, early summer, and early autumn, suggesting a potential seasonal shift. The spatial analysis of the monthly anomaly trends revealed significantly higher trends at near-coast areas, which were especially distinct in the Mediterranean study areas. The clearest pattern was visible in the Adriatic Sea in March and May, where the SST trends at the coast were twice as high as that observed at a 40 km distance to the coast. To validate our findings, we compared the TIMELINE monthly anomaly time series with monthly anomalies derived from the Level 4 CCI SST anomaly product. The comparison showed an overall good accordance with correlation coefficients of R > 0.82 for the Mediterranean study areas and R = 0.77 for the North and Baltic Seas. This study highlights the potential of AVHRR Local Area Coverage (LAC) data with 1 km spatial resolution for mapping long-term SST trends in areas with high spatial SST variability, such as coastal regions. [ABSTRACT FROM AUTHOR]

Trade wind clouds are ubiquitous across the subtropical oceans, including the Great Barrier Reef (GBR), playing an important role in modulating the regional energy budget. These shallow clouds, however, are by their nature sensitive to perturbations in both their thermodynamic environment and microphysical background. In this study, we employ the Weather Research and Forecasting (WRF) model with a convection-permitting configuration at 1 km resolution to examine the sensitivity of the trade wind clouds to different local forcings over the GBR. A range of local forcings including coastal topography, sea surface temperature (SST), and local aerosol loading is examined. This study shows a strong response of cloud fraction and accumulated precipitation to orographic forcing both over the mountains and upwind over the GBR. Orographic lifting, low-level convergence, and lower troposphere stability are found to be crucial in explaining the cloud and precipitation features over the coastal mountains downwind of the GBR. However, clouds over the upwind ocean are more strongly constrained by the trade wind inversion, whose properties are, in part, regulated by the coastal topography. On the scales considered in this study, the warm-cloud fraction and the ensuant precipitation over the GBR show only a small response to the local SST forcing, with this response being tied to the surface flux and lower troposphere stability. Cloud microphysical properties, including cloud droplet number concentration, liquid water path, and precipitation, are sensitive to the changes in atmospheric aerosol population over the GBR. While cloud fraction shows little responses, a slight deepening of the simulated clouds is evident over the upwind region in correspondence to the increased aerosol number concentration. A downwind effect of aerosol loading on simulated cloud and precipitation properties is further noted. [ABSTRACT FROM AUTHOR]

Slow and long-term variations of sea surface temperature anomalies have been interpreted as a red-noise response of the ocean surface mixed layer to fast and random atmospheric perturbations. How fast the atmospheric noise is damped depends on the mixed layer depth. In this work we apply this theory to determine the relevant spatial and temporal scales of surface layer thermal inertia in lakes. We fit a first order auto-regressive model to the satellite-derived Lake Surface Water Temperature (LSWT) anomalies in Lake Garda, Italy. The fit provides a time scale, from which we determine the mixed layer depth. The obtained result shows a clear spatial pattern resembling the morphological features of the lake, with larger values (7.18± 0.3 m) in the deeper northwestern basin, and smaller values (3.18 ± 0.24 m) in the southern shallower basin. Such variations are confirmed by in-situ measurements in three monitoring points in the lake and connect to the first Empirical Orthogonal Function of satellite-derived LSWT and chlorophyll-a concentration. Evidence from our case study open a new perspective for interpreting lake-atmosphere interactions and confirm that remotely sensed variables, typically associated with properties of the surface layers, also carry information on the relevant spatial and temporal scales of mixed-layer processes. [ABSTRACT FROM AUTHOR]

A 42-year climate data record of global sea surface temperature (SST) covering 1980 to 2021 has been produced from satellite observations, with a high degree of independence from in situ measurements. Observations from twenty infrared and two microwave radiometers are used, and are adjusted for their differing times of day of measurement to avoid aliasing and ensure observational stability. A total of 1.5 × 1013 locations are processed, yielding 1.4 × 1012 SST observations deemed to be suitable for climate applications. The corresponding observation density varies from less than 1 km−2 yr−1 in 1980 to over 100 km−2 yr−1 after 2007. Data are provided at their native resolution, averaged on a global 0.05° latitude-longitude grid (single-sensor with gaps), and as a daily, merged, gap-free, SST analysis at 0.05°. The data include the satellite-based SSTs, the corresponding time-and-depth standardised estimates, their standard uncertainty and quality flags. Accuracy, spatial coverage and length of record are all improved relative to a previous version, and the timeseries is routinely extended in time using consistent methods. [ABSTRACT FROM AUTHOR]

In the spring of 2021, a shipborne comparison of sea surface temperature (SST) measurements was undertaken using Thermal Infrared (TIR) and Passive Microwave (PMW) radiometers. The Danish Meteorological Institute (DMI) and the Technical University of Denmark (DTU) jointly deployed two TIR and two PMW instruments aboard the Norröna ferry, which traversed between Denmark and Iceland for a week. The primary objective was to assess the proximity-based comparison of TIR and PMW measurements, minimizing atmospheric influences and providing valuable insights into skin (TIR) and sub-skin (PMW) SSTs. A linear regression algorithm was developed using TIR SST data as a reference to derive PMW SST from brightness temperature. The data analysis primarily focused on evaluating data variability, identifying discrepancies between TIR and PMW SST, and assessing the overall uncertainty in the retrieval process. The overall root mean squared error (RMSE) of the retrieved PMW SST was 0.88 K during the ship's motion and 0.94 K under stable conditions when the ship was moored. The analysis of the retrieved SST error budget involved the consideration of observed quantities and a forward model, accounting for factors like instrument noise, wind speed, incident angles, and the RMSE of skin and sub-skin temperature. The resulting error budget indicated 0.97 K for the data acquired during motion and 0.34 K for data collected during port stay. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is one of the important parameters of global ocean and climate research, which can be retrieved by satellite infrared and passive microwave remote sensing instruments. While satellite infrared SST offers high spatial resolution, it is limited by cloud cover. On the other hand, passive microwave SST provides all-weather observation but suffers from poor spatial resolution and susceptibility to environmental factors such as rainfall, coastal effects, and high wind speeds. To achieve high-precision, comprehensive, and high-resolution SST data, it is essential to fuse infrared and microwave SST measurements. In this study, data from the Fengyun-3D (FY-3D) medium resolution spectral imager II (MERSI-II) SST and microwave imager (MWRI) SST were fused. Firstly, the accuracy of both MERSIII SST and MWRI SST was verified, and the latter was bilinearly interpolated to match the 5km resolution grid of MERSI SST. After pretreatment and quality control of MERSI SST and MWRI SST, a Piece-Wise Regression method was employed to correct biases in MWRI SST. Subsequently, SST data were selected based on spatial resolution and accuracy within a 3-day window of the analysis date. Finally, an optimal interpolation method was applied to fuse the FY-3D MERSI-II SST and MWRI SST. The results demonstrated a significant improvement in spatial coverage compared to MERSI-II SST and MWRI SST. Furthermore, the fusion SST retained true spatial distribution details and exhibited an accuracy of -0.12±0.74°C compared to OSTIA SST. This study has improved the accuracy of FY satellite fusion SST products in China. [ABSTRACT FROM AUTHOR]

The Mediterranean Sea (MS) has undergone significant surface warming, particularly pronounced during summers and associated with devastating impacts on marine life. Alongside the ongoing research on warming trends and marine heatwaves (MHWs), here we address the importance of understanding anomalously warm conditions also on the seasonal timescale. We propose the concept of extreme marine summers (EMSs) and investigate their characteristics in the MS, using sea surface temperature (SST) reanalysis data spanning 1950–2020. We define EMSs at a particular location, as the summers with a mean summer SST exceeding the 95th percentile. A marine summer may become extreme, under various SST substructures. Results show that, in most of the basin, EMSs are formed primarily due to the warmer summer days being warmer than normal. Areas where the warmest (coldest) part of the SST distribution is more variable experience EMSs primarily due to the warmest (coldest) part of the distribution being anomalously warm. MHWs occurring within EMSs are more intense, longer lasting, and more frequent than usual mainly in the northern MS regions. These enhanced MHW conditions occur mainly within the warmest part of the SST distribution. By means of temporal coverage of MHW conditions, a more pronounced occurrence of MHWs in EMSs is found for the central and eastern basin where up to 55 % of MHW days over 1950–2020 fall within EMSs. The role of air–sea heat fluxes in driving EMSs is quantified through a newly proposed metric. Results suggest that surface fluxes primarily drive EMSs in the northern half of the MS, while oceanic processes play a major role in southern regions. Upper-ocean preconditioning also contributes to the formation of EMSs. Finally, a detrended dataset was produced to examine how the SST multi-decadal variability affects the studied EMS features. Despite leading to warmer EMSs basin-wide, the multi-decadal signal does not significantly affect the dominant SST substructures during EMSs. Results also highlight the fundamental role of latent heat flux in modulating the surface heat budget during EMSs, regardless of the long-term trends. [ABSTRACT FROM AUTHOR]

Eurasian rivers provide a quarter of total fresh water to the Arctic, maintaining a persistent fresh layer that covers the surface Arctic Ocean. This freshwater export controls Arctic Ocean stratification, circulation, and basin-wide sea ice concentration. The Lena River supplies the largest volume of runoff and plays a key role in this system, as runoff outflows into the Laptev Sea as a particularly shallow plume. Previous in situ and modelling studies suggest that local wind forcing is a driver of variability in Laptev sea surface salinity (SSS) but there is no consensus on the roles of Lena River discharge and sea ice cover in contributing to this variability or on the dominant driver of variability. Until recently, satellite SSS retrievals were insufficiently accurate for use in the Arctic. However, retreating sea ice cover and continuous progress in satellite product development have significantly improved SSS retrievals, giving satellite SSS data true potential in the Arctic. In this region, satellite-based SSS is found to agree well with in situ data (r>0.8) and provides notable improvements compared to the reanalysis product used in this study (r>0.7) in capturing patterns and variability observed in in situ data. This study demonstrates a novel method of identifying the dominant drivers of interannual variability in Laptev Sea dynamics within reanalysis products and testing if these relationships appear to hold in satellite-based SSS, sea surface temperature (SST) data, and in situ observations. The satellite SSS data firmly establish what is suggested by reanalysis products and what has previously been subject to debate due to the limited years and locations analysed with in situ data; the zonal wind is the dominant driver of offshore or onshore Lena River plume transport. The eastward wind confines the plume to the southern Laptev Sea and drives alongshore transport into the East Siberian Sea, and westward wind drives offshore plume transport into the northern Laptev Sea. This finding is affirmed by the strong agreement in SSS pattern under eastward and westward wind regimes in all reanalyses and satellite products used in this study, as well as with in situ data. The pattern of SST also varies with the zonal wind component and drives spatial variability in sea ice concentration. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is one of the essential variables of the Earth climate system. Being at the interface with the atmosphere, SST modulates heat fluxes in and out of the ocean, provides insight on several upper/interior ocean dynamical processes, and it is a fundamental indicator of climate variability potentially impacting marine ecosystems' health. Its accurate estimation and regular monitoring from space is therefore crucial. However, even if satellite infrared/microwave measurements provide a much better coverage than what achievable from in situ platforms, they cannot sense the sea surface under cloudy/rainy conditions. Large gaps are present even in merged multi-sensor satellite products and different statistical strategies have thus been proposed to obtain gap-free (L4) images, mostly based on the Optimal Interpolation algorithms. This kind of techniques, however, filter out the signals below the space-time decorrelation scales considered, significantly smoothing most of the small mesoscale and submesoscale features. Here, deep learning models, originally designed for single image Super Resolution (SR), are applied to enhance the effective resolution of SST products and the accuracy of SST gradients. SR schemes include a set of computer vision techniques leveraging Convolutional Neural Networks to retrieve high-resolution data from low-resolution images. A dilated convolutional multi-scale learning network, which includes an adaptive residual strategy and implements a channel attention mechanism, is used to reconstruct features in SST data at 1/100° spatial resolution starting from 1/16° data over the Mediterranean Sea. The application of this technique shows a remarkable improvement in the high resolution reconstruction, being able to capture small scale features and providing a root-mean-squared-difference improvement of 0.02 °C with respect to the L3 ground-truth data. [ABSTRACT FROM AUTHOR]

Particulate organic carbon (POC) is an essential component of the carbon pump within marine organisms. Exploring estimation methods for POC holds substantial significance for understanding the marine carbon cycle. In this study, we investigated the spatial heterogeneity of 30 factors and POC concentrations using geodetector to account for nonlinearity, diversity, and complexity. Ultimately, 20 factors including sea surface temperature, sea surface salinity, and chlorophyll-a were selected as modeling variables. Six machine learning algorithms, RF exhibits the strongest performance, with a root mean square error of 0.11 [log(mg/m3)] and an average percentage deviation of 2.73%. Global annual average sea surface POC concentrations were estimated for 2007 and compared to NASA's POC product. The outcomes indicate that the RF model-based estimation method displays enhanced accuracy in estimating POC concentrations within intricate coastal environments, while the backpropagation neural network performed better in estimating POC concentrations in open ocean areas. Leveraging the RF model, global sea surface POC concentrations were estimated for the years 2007 through 2016, enabling a spatiotemporal analysis. The analysis unveils heightened POC concentrations in coastal regions and lower levels in open ocean areas. Furthermore, POC concentrations were greater in high-latitude regions compared to mid and low latitude counterparts. In conclusion, the global sea surface POC product in this study exhibits heightened spatial resolution and improved data completeness in contrast to other products. It enhances the accuracy of conventional POC estimation methods, particularly within coastal regions. [ABSTRACT FROM AUTHOR]

Marine heatwaves (MHWs), defined as prolonged period of extremely warm sea surface temperature (SST), have been receiving a lot of attention in the past decade as their frequency and intensity increase in a warming climate. This paper investigates the extent to which the seasonal occurrence and duration of MHWs can be predicted with the European Centre for Medium-Range Weather Forecast (ECMWF) operational seasonal forecast system. The prediction of the occurrence of MHW events, the number of MHW days per season, and their intensity and spatial extent are derived from seasonal SST forecasts and evaluated against an observation-based SST analysis using both deterministic and probabilistic metrics over the 1982–2021 period. Forecast scores show useful skill in predicting the occurrence of MHWs globally for the two seasons following the starting date. The skill is the highest in the El Niño region, the Caribbean, the wider tropics, the north-eastern extra-tropical Pacific, and southwest of the extra-tropical basins. The skill is not as good for other midlatitude eastern basins nor for the Mediterranean, with the forecast system being able to represent the low-frequency modulation of MHWs but showing poor skill in predicting the interannual variability of the MHW characteristics. Linear trend analysis shows an increase in MHW occurrence at a global scale, which the forecasts capture well. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) constitutes a pivotal physical parameter in the investigation of atmospheric, oceanic, and air–sea exchange processes. The retrieval of SST through satellite passive microwave (PMW) technology effectively mitigates the interference posed by cloud cover, addressing a longstanding challenge. Nevertheless, conventional functional representations often fall short in capturing the intricate interplay of factors influencing SST. Leveraging neural networks (NNs), known for their adeptness in tackling nonlinear and intricate problems, holds great promise in SST retrieval. Nonetheless, NNs exhibit a high sensitivity to initial weights and thresholds, rendering them susceptible to local optimization issues. In this study, we present a novel machine learning (ML) approach for SST retrieval using PMW measurements, drawing from the Sparrow Search Algorithm (SSA) and Back-Propagation neural network (BPNN) methodologies. The core premise involves the optimization of the BP neural network's initial weights and thresholds through an enhanced SSA algorithm employing various optimization strategies. This optimization aims to provide superior parameters for the training of the BP neural network. Employing AMSR2 brightness temperature data, sea surface wind speed data, and buoy SST measurements, we construct the ISSA-BP model for sea surface temperature retrieval. The validation of the ISSA-BP model against the test data is conducted and compared against the multiple linear regression (MLR) model, an unoptimized BP model, and an unimproved SSA-BP model. The results manifest an impressive R-squared (R2) value of 0.9918 and a root-mean-square error (RMSE) of 0.8268 °C for the ISSA-BP model, attesting to its superior accuracy. Furthermore, the ISSA-BP model was applied to retrieve global sea surface temperatures on 15 July 2022, yielding an R2 of 0.9926 and an RMSE of 0.7673 °C for the OISST product on the same day, underscoring its excellent concordance. The results indicate that SST can be efficiently and accurately retrieved using the model proposed in this paper, based on satellite PMW measurements. This finding underscores the potential of employing machine learning algorithms for SST retrieval and offers a valuable reference for future studies focusing on the retrieval of other sea surface parameters. [ABSTRACT FROM AUTHOR]

The long‐term absorption by the oceans of atmospheric carbon dioxide is leading to the slow decline of ocean pH, a process termed ocean acidification (OA). The Arctic is a challenging region to gather enough data to examine the changes in carbonate chemistry over sufficient scales. However, algorithms that calculate carbonate chemistry parameters from more frequently measured parameters, such as temperature and salinity, can be used to fill in data gaps. Here, these published algorithms were evaluated against in situ measurements using different data input types (data from satellites or in situ re‐analysis climatologies) across the Arctic Ocean. With the lowest uncertainties in the Atlantic influenced Seas (AiS), where re‐analysis inputs achieved total alkalinity estimates with Root Mean Squared Deviation (RMSD) of 21 μmol kg−1 and a bias of 2 μmol kg−1 (n = 162) and dissolved inorganic carbon RMSD of 24 μmol kg−1 and bias of −14 μmol kg−1 (n = 262). AiS results using satellite observation inputs show similar bias but larger RMSD, although due to the shorter time span of available satellite observations, more contemporary in situ data would provide further assessment and improvement. Synoptic‐scale observations of surface water carbonate conditions in the Arctic are now possible to monitor OA, but targeted in situ data collection is needed to enable the full exploitation of satellite observation‐based approaches. Plain Language Summary: The long‐term absorption by the oceans of atmospheric carbon dioxide is leading to the slow decline of ocean pH, a process termed ocean acidification (OA). Sea surface salinity and temperature measurements from satellites or in situ re‐analysis products can be used as input to empirical algorithms to calculate OA parameters. This paper provides a first analysis of published Arctic Ocean empirical algorithms to estimate surface water OA parameters using observation‐based data sets. Results show promise in the Atlantic influenced Seas using both in situ re‐analysis and satellite products, but satellite salinity is relatively recent, and a paucity of in situ data in the satellite salinity era precludes a robust assessment. To fully exploit satellite‐based approaches, efforts need to focus on collecting in situ data while these satellites are overhead and operating in orbit. Key Points: Observation‐based data sets enable synoptic spatio‐temporal assessments of Arctic Ocean carbonate system parametersRe‐analysis and satellite products predict total alkalinity with accuracies of ∼21 μmol kg−1Targeted in situ data collection is needed to fully exploit satellite observations in the Arctic to enable carbonate system monitoring [ABSTRACT FROM AUTHOR]

Since 2011 the Global Energy and Water cycle Exchanges (GEWEX) Water Vapor Assessment (G-VAP) has provided performance analyses for state-of-the-art reanalysis and satellite water vapour products to the GEWEX Data and Analysis Panel (GDAP) and the user community in general. A significant component of the work undertaken by G-VAP is to characterise the quality and uncertainty of these water vapour records to; i) ensure full exploitation and ii) avoid incorrect use or interpretation of results. This study presents results from the second phase of G-VAP, where we have extended and expanded our analysis of Total Column Water Vapour (TCWV) from phase 1, in conjunction with updating the G-VAP archive. For version 2 of the archive, we consider 28 freely available and mature satellite and reanalysis data products, remapped to a regular longitude-latitude grid of 2°× 2°, and on monthly time steps between January 1979 and December 2019. We first analysed all records for a 'common' short period of five years (2005–2009), focusing on variability (spatial & seasonal) and deviation from the ensemble mean. We observed that clear-sky daytime-only satellite products were generally drier than the ensemble mean, and seasonal variability/disparity in several regions up to 12 kg/m2 related to original spatial resolution and temporal sampling. For 11 of the 28 data records, further analysis was undertaken between 1988–2014. Within this 'long period', key results show i) trends between -1.18±0.68 to 3.82±3.94 kg/m2/decade and -0.39±0.27 to 1.24±0.85 kg/m2/decade were found over ice-free global oceans and land surfaces respectively, and ii) regression coefficients of TWCV against surface temperatures of 6.17±0.24 to 27.02±0.51 %/K over oceans (using sea surface temperature) and 3.00±0.17 to 7.77±0.16 %/K over land (using surface air temperature). It is important to note that trends estimated within G-VAP are used to identify issues in the data records rather than analyse climate change. Additionally, breakpoints have been identified and characterised for both land and ocean surfaces within this period. Finally, we present a spatial analysis of correlations to six climate indices within the "long period", highlighting regional areas of significant positive and negative correlation and the level of agreement among records. [ABSTRACT FROM AUTHOR]

A Passive and Active Reference Microwave to Infrared Ocean (PARMIO) physical radiative transfer model has been selected by an international team of experts to be used as a community ocean surface emissivity model. A fast version of this physical community model is developed within the framework of European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Numerical Weather Prediction Satellite Application Facilities visiting scientist programme: the SURface Fast Emissivity Model for Ocean (SURFEM‐Ocean), for implementation in the Radiative Transfer code for TOVS (RTTOV) as an alternative/replacement of the well‐known FAST microwave Emissivity Model (FASTEM). First, the configuration of the PARMIO model is adapted to microwave frequencies from 500 MHz to 700 GHz, and issues observed in the preliminary studies with cold sea surface temperatures (SST) and high ocean wind speeds (OWS) are corrected. Second, an artificial neural network method is used to reproduce the results of the PARMIO model much faster. The computation of the analytical Jacobians is also implemented. Finally, SURFEM‐Ocean results are compared with satellite observations and other ocean emissivity models. SURFEM‐Ocean extends the capability of FASTEM. It computes emissivities from 500 MHz to 700 GHz, for all incidence angles, for the fourth Stokes polarizations, for OWS from 0 to 50 m/s, SST from −2 to 30°C, sea surface salinity from 0 to 40 psu, and for all wind directions. Plain Language Summary: To use the information provided by the satellites for numerical weather prediction as well as for other applications, emissivity models are required. Here, we develop a new SURface Fast Emissivity Model in the microwave domain for the ocean. It is based on a community physical Passive and Active Reference Microwave to Infrared Ocean emissivity model, then artificial intelligence methods are used to reproduce the physical model results much faster. Our SURface Fast Emissivity Model is evaluated by comparison with satellite observations and other ocean emissivity models. It covers a large frequency range, and provides better precisions for cold sea surface temperatures, and high ocean wind speeds compared to the previous model. Key Points: An international team of experts has converged on the selection of a Passive and Active Reference Microwave to Infrared Ocean modelA fast version of the ocean microwave emissivity model has been developed, named SURface Fast Emissivity Model for Ocean (SURFEM‐Ocean)SURFEM‐Ocean is evaluated by comparisons with a large database of passive microwave satellite observations [ABSTRACT FROM AUTHOR]

By incorporating the high‐resolution satellite remote sensing sea surface temperature (SST) with low‐resolution sea level anomaly and sea surface salinity (SSS), this study explores the reconstructability of the three‐dimensional (3D) eddy structures via the "interior + surface quasigeostrophic" (isQG) method in the South Indian Ocean. We apply the incorporation of high‐resolution SST to improve the spatial resolution of the reconstruction. We also propose a correction scheme for density reconstruction within the mixed layer to offset the absence of mixed layer dynamics in the SQG framework. Comparison against the in situ observations demonstrates a satisfactory reconstructability for subsurface velocity and density anomalies. Statistically, the zonal velocity reconstructability outperforms its meridional counterpart and the corresponding velocity phase. The reconstructed shallow‐layer velocity exhibits a superior skill in eddy‐active regions, when compared with drogued drifter observations. Reconstructed subsurface velocities reproduce the spatial structures of eddy‐induced velocity anomaly along the GO‐SHIP observation transect, although present smaller magnitudes. Results demonstrate the potential applicability of the isQG method for reconstructing mesoscale eddies, particularly in the ocean at mid‐to‐high latitudes, where subsurface dynamics are strongly influenced by barotropic and the first baroclinic modes. With the upcoming high‐advanced satellite observations, the isQG framework is expected to achieve better subsurface estimations. Plain Language Summary: The South Indian Ocean (SIO) contains a significant number of mesoscale eddies that have been thoroughly studied for their horizontal structure, lifetime, and trajectory through satellite observations, particularly using the gridded sea level anomaly maps. Although the contributions of eddies to oceanic energy balance and material transports have become increasingly clear, understanding the three‐dimensional (3D) structures of smaller‐scale eddies in the SIO remains a challenge due to insufficient subsurface observations and the relatively low effective resolution of current satellite‐gridded sea surface height (SSH) products. This study investigates the potential of dynamically reconstructing the subsurface 3D structures of SIO eddies from satellite observations. We combine higher‐resolution sea surface temperature snapshots with the SSH map to enhance the effective resolution of the reconstructed subsurface density and velocity anomalies, which enables the reproduction of smaller‐scale structures associated with temperature fronts. We also propose a correction scheme to increase the reconstructability of the density anomaly within the mixed layer. Evaluations against multisource real observations suggest a satisfactory reconstructability of the interior + surface quasigeostrophic method for the eddy's 3D structures. This study would provide a new way to investigate the 3D structures of eddy's evolution. Key Points: The high‐resolution eddy's 3D structures are estimated from the satellite observations based on the interior + surface quasigeostrophic (isQG) methodIntroducing the higher‐resolution sea surface temperature observation to isQG method enhances the effective resolution of reconstructionA correction scheme in density reconstruction within the mixed layer is suggested to offset the absence of mixed layer dynamics [ABSTRACT FROM AUTHOR]

Marine heatwaves (MHWs) and Cold Spells (marine cold spells (MCSs)) are events of extreme positive and negative temperature anomalies, that last 5 days or longer. These events have been diagnosed across oceans from sea surface temperatures (SSTs) and recognized to affect ecosystems' health worldwide. Some studies have focused on the extent, evolution, and drivers of specifically intense and long‐lasting regional MHW events. Although extreme temperature anomalies could lead to coral bleaching, mortality, and ecosystems' shift from healthy to barren reefs, no studies have focused on MHWs and MCSs in the Caribbean Sea. This study characterizes them in the Caribbean Sea, explores simultaneous thermal‐stress and coral response, and reveals prevailing environmental conditions during their occurrence. We use four decades of SSTs observed daily and a recently published database of worldwide coral bleaching events. We found bleaching events occurring simultaneously with both MCSs and MHWs, often with extreme events of mild intensity and typical duration. We find a positive long‐term trend in MHWs' frequency, consistent not only with global warming, but also with the Atlantic Meridional Mode. There is a clear seasonal signal with MHWs occurring more often in summer and MCSs in autumn‐winter. Our results suggest MHWs and MCSs development is related to El Niño/La Niña conditions, respectively. Finally, we identify intense extreme temperature events that span large areas as candidates for future studies on their dynamics. Our results are relevant for the development of MHWs and MCSs forecasts applied to conservation strategies and ecosystem‐based management of marine resources, particularly in reef systems. Plain Language Summary: Events of extremely warm and cold temperatures that last 5 days or longer in the ocean are denominated Marine Heatwaves (MHWs) and marine cold spells (MCSs), respectively. Analysis of sea surface temperature observed with satellites over the past decades have shown MHWs are occurring more often and have important ecological implications. This study focuses on MHWs and MCSs in the Caribbean Sea over the last four decades to: (a) determine their typical duration, intensity, and frequency; (b) explore if their occurrence coincides with recorded coral bleaching events; (c) determine their temporal variability, as well as correlation with climate indices; and (d) identify the largest and most intense MHW and MCS events. We present evidence of Caribbean MCSs and MHWs linked to widespread coral bleaching. Additionally, we reveal MHWs/MCSs occur more often during summer/winter and El Niño/La Niña conditions and show an increase in MHWs' frequency consistent with long‐term warming, and natural climate modes of variability such as the Atlantic Meridional Mode. Our findings contribute to the improvement of tools that anticipate MHWs and MCSs for the management of reefs, which are ecosystems that host large biodiversity, support societal needs, and that are under pressure due to ongoing climate change. Key Points: Caribbean marine heatwave (MHW) and marine cold spell (MCS) are simultaneous with coral bleaching eventsMHWs/MCSs have a seasonal signal, being more frequent and intense during summer/autumn‐winter and in the SE region of the CaribbeanMHWs/MCSs are longer, stronger, and more frequent during the ±phase of Atlantic Multidecadal Oscillation, Atlantic Meridional Mode and Caribbean, and occur more often during ±El Niño Southern Oscillation conditions [ABSTRACT FROM AUTHOR]

The Sr/Ca ratio of modern coral skeletons can record local seawater temperature (T) and is an important tool for reconstructing past environments. However, site‐specific calibrations are required to ensure accurate temperature reconstructions. Here, we examine three modern coral skeletons collected at contrasting sites on the island of Oahu, Hawaii to establish the first accurate calibrations for this region and investigate site specific influences on the calibration process. Satellite T data, which is used for many calibrations, may not be able to derive an accurate thermometer. For our shallow lagoonal sites, satellite T had smaller seasonal T ranges, which resulted in significantly higher slopes of Sr/Ca‐T compared to using in situ T. The traditional age model based on aligning only min/max values can lead to errors in the Sr/Ca‐T calibration due to variable growth rates. An enhanced age model which adds midpoint alignments between the min/max peak values can account for seasonal changes in growth rate and reduce the error. On the same island, site‐ and time period specific conditions can cause notable differences in the Sr/Ca‐T calibrations. The coral from an estuarine embayment showed a high Sr/Ca offset, likely due to high Sr/Ca in ambient seawater. For corals which experienced thermal stress, lower slopes were observed probably due to elevated Sr/Ca values during the period of thermal stress. Plain Language Summary: In this study, we established the first applicable calibration of coral skeletal Sr/Ca and seawater temperature that can be applied to corals at contrasting locations (shallow lagoon vs. deeper forereef, estuary vs. non‐estuary, and windward vs. leeward) in Hawaii. Using satellite seawater temperature for Sr/Ca‐T calibrations may not be appropriate for shallow lagoons due to significant differences in actual temperature across time. Variable seasonal extension rates can also cause errors in Sr/Ca‐T calibrations. Enhancing the age model by adding date alignment points between peak values can reduce this type of error. Terrigenous influence and thermal stress also affect the relationship between Sr/Ca and seawater temperature resulting in site and time period specific calibration relationships. Key Points: Hawaiian coral Sr/Ca‐in situ Temperature calibrations were performed at shallow lagoons, deeper forereef, and estuary embaymentsSatellite temperature (T) for shallow lagoons leads to a steeper coral Sr/Ca‐T slopes, potentially resulting in an underestimated T reconstructionCoral chronology due to seasonally variable extension rates can be improved by aligning additional midpoints between the min/max values [ABSTRACT FROM AUTHOR]

Marine heatwaves (MHWs) have significant social and ecological impacts, necessitating the prediction of these extreme events to prevent and mitigate their negative consequences and provide valuable information to decision-makers about MHW-related risks. In this study, machine learning (ML) techniques are applied to predict Sea Surface Temperature (SST) time series and Marine Heatwaves (MHWs) in 16 regions of the Mediterranean Sea. ML algorithms, including Random Forest (RForest), Long short-term memory (LSTM), and Convolutional Neural Network (CNN), are used to create competitive predictive tools for SST. The ML models are designed to forecast SST and MHWs up to 7 days ahead. Alongside SST, other relevant atmospheric variables are utilized as potential predictors of MHWs. Datasets from the European Space Agency Climate Change Initiative (ESA CCI SST) v2.1 and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis from 1981 to 2021 are used to train and test the ML techniques. The results show that ML methods, particularly RForest and LSTM, performed well with minimum Root Mean Square Errors (RMSE) of about 0.1 °C at a 1-day lead time and maximum values of about 0.8 °C at a 7-day lead time. Importantly, the ML techniques outperform the dynamical Copernicus Mediterranean Forecasting System (MedFS) for both SST and MHW forecasts, especially in the early forecast days. For MHW forecasting, ML methods outperform MedFS up to 3-day lead time in most regions, while MedFS shows superior skill at 5-day lead time in 9 out of 16 regions. All methods in all regions predict the occurrence of MHWs with a confidence level greater than 50 %. Additionally, the study highlights the importance of incoming solar radiation as a significant predictor of SST variability along with SST itself. [ABSTRACT FROM AUTHOR]

The eruption of Hunga Tonga‐Hunga Ha'apai (HTHH) volcano on 15 January 2022 injected a great amount of H2O and a moderate amount of SO2 into the stratosphere, producing a pronounced and persistent sulfate aerosol layer centered around the mid‐stratosphere, mostly confined to Southern Hemisphere (SH) tropics. These aerosols affect the Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals of sea surface temperature (SST) where negative biases reached −0.3 K and an annual mean of −0.1 K north of 40°S in the SH. The spatial and temporal evolutions of MODIS SST anomalies are presented. Radiative transfer simulations demonstrate the aerosol effect on MODIS SST retrievals by causing an additional brightness temperature (BT) deficit at 11 μm and a reduction in BT differences since the characteristic of spectral attenuation between 11 and 12 μm is opposite to that of H2O. A correction for HTHH aerosol effects in the retrieval algorithm is therefore desirable. Plain Language Summary: The limit on the accuracy of sea surface temperature (SST), an Essential Climate Variable, derived from measurements of infrared radiometers on satellites is determined by the correction for the effects of the intervening atmosphere. Anomalous atmospheric conditions cause larger errors. The eruption of the submarine volcano Hunga Tonga‐Hunga Ha'apai in January 2022 injected much water vapor and other material into the atmosphere. By comparing satellite‐derived SSTs with those measured from drifting buoys in the latitude zone significantly influenced by the volcano's eruption, we quantify the negative SST errors introduced and monitor the evolution of the volcano's influence for the following year. It will be important to generate corrections to satisfy the high accuracy requirements for SST in scientific studies, for example, as input to climate models. Key Points: Stratospheric sulfate aerosols in Tonga eruption plume affect sea‐surface temperature from Moderate Resolution Imaging SpectroradiometerNegative biases of sea‐surface temperature were pronounced in Southern Hemispheric tropics initially and spread to mid‐latitude after MayA negative offset of 11–12 μm brightness temperature difference is due to more absorption by aerosol at 11 μm, opposite to H2O absorption [ABSTRACT FROM AUTHOR]

Eurasian Rivers provide a quarter of total fresh water to the Arctic, maintaining a persistent fresh layer that covers the surface Arctic Ocean. The Lena River supplies the largest volume of runoff and plays a key role in this system, as runoff outflows into the Laptev Sea as a particularly shallow plume. This freshwater export controls Arctic Ocean stratification, circulation, and basin-wide sea ice area. Previous in-situ and modelling studies suggest that local wind forcing is a primary driver of variability in Laptev sea surface salinity (SSS) with no consensus over the roles of Lena river discharge and sea ice cover in contributing to this variability. Until recently, satellite SSS retrievals were insufficiently accurate for use in the Arctic, due to the low sensitivity of the L-band signal they utilise in cold water and challenges of retrieval near sea ice. However, retreating sea ice cover and continuous progress in satellite product development have significantly improved SSS retrievals, giving satellite SSS data true potential in the Arctic. This study demonstrates a novel method of using satellite-based SSS, sea surface temperature (SST) data, in-situ observations, and reanalysis products to identify the dominant drivers of interannual variability in Laptev Sea dynamics. Satellite-based SSS is found to agree well with in-situ data in this region (r > 0.8) and provides notable improvements compared to the reanalysis product used in this study (r > 0.7) in capturing patterns and variability observed in in-situ data. The satellite SSS data firmly establishes what has previously been subject to debate due to the limited years and locations analysed with in-situ data: that the zonal wind is the dominant driver of offshore or onshore Lena river plume transport. This finding is affirmed by the strong agreement in SSS pattern in all reanalyses and satellite products used in this study under eastward and westward wind regimes. The pattern of SST also varies with the zonal wind component, and drives spatial variability in sea ice area. The strong correspondence between large scale and local zonal wind dynamics and the key role of SSS and SST variability in driving sea ice and stratification dynamics demonstrates the importance of changes in large-scale atmospheric dynamics for variability in this region as well as for future Arctic sea ice dynamics and freshwater transport. [ABSTRACT FROM AUTHOR]

Aerosol radiative forcing and cloud–climate feedbacks each have a large effect on climate, mainly through modification of solar short-wave radiative fluxes. Here we determine what causes the long-term trends in the upwelling short-wave (SW) top-of-the-atmosphere (TOA) fluxes (FSW↑) over the North Atlantic region. Coupled atmosphere–ocean simulations from the UK Earth System Model (UKESM1) and the Hadley Centre General Environment Model (HadGEM3-GC3.1) show a positive FSW↑ trend between 1850 and 1970 (increasing SW reflection) and a negative trend between 1970 and 2014. We find that the 1850–1970 positive FSW↑ trend is mainly driven by an increase in cloud droplet number concentration due to increases in aerosol, while the 1970–2014 trend is mainly driven by a decrease in cloud fraction, which we attribute mainly to cloud feedbacks caused by greenhouse gas-induced warming. In the 1850–1970 period, aerosol-induced cooling and greenhouse gas warming roughly counteract each other, so the temperature-driven cloud feedback effect on the FSW↑ trend is weak (contributing to only 23 % of the ΔFSW↑), and aerosol forcing is the dominant effect (77 % of ΔFSW↑). However, in the 1970–2014 period the warming from greenhouse gases intensifies, and the cooling from aerosol radiative forcing reduces, resulting in a large overall warming and a reduction in FSW↑ that is mainly driven by cloud feedbacks (87 % of ΔFSW↑). The results suggest that it is difficult to use satellite observations in the post-1970 period to evaluate and constrain the magnitude of the aerosol–cloud interaction forcing but that cloud feedbacks might be evaluated. Comparisons with observations between 1985 and 2014 show that the simulated reduction in FSW↑ and the increase in temperature are too strong. However, the temperature discrepancy can account for only part of the FSW↑ discrepancy given the estimated model feedback strength (λ=∂FSW∂T). The remaining discrepancy suggests a model bias in either λ or in the strength of the aerosol forcing (aerosols are reducing during this time period) to explain the too-strong decrease in FSW↑ , with a λ bias being the most likely. Both of these biases would also tend to cause too-large an increase in temperature over the 1985–2014 period, which would be consistent with the sign of the model temperature bias reported here. Either of these model biases would have important implications for future climate projections using these models. [ABSTRACT FROM AUTHOR]

Oceanic primary production (OPP) is crucial for ecosystem services and global carbon cycle. However, sensitivity to geographic and environmental characteristics limits the application of semi‐empirical OPP estimate models, such as the vertically generalized productivity model (VGPM) and its modified version, particularly in coastal regions. In addition, the difficulty in collecting necessary parameters also hampers long‐term OPP estimates. Data‐driven machine learning (ML) methods can automatically capture the relationships between the input parameters and the objective; hence, they may become new methods for global OPP estimates. In this study, the effectiveness of ML methods to estimate OPP and the key attributes influencing ML performance in different regions and seasons are discussed. First, the ML models obtain a lower root mean square error than the VGPM and Eppley‐VGPM. In addition, the random forest (RF) model achieves the best performance among the four selected ML models. The enhancement in the accuracy of OPP estimates based on the RF model is more obvious in coastal regions than in the open ocean. In the four seasons, the RF model obtains better estimates of OPP than the Eppley‐VGPM, especially for summer. Moreover, input attributes, including sea surface temperature (SST), photosynthetic active radiation (PAR), and chlorophyll‐a concentration (Chlor‐a), achieve the best performance. The suitable alternative input attributes are SST/Chlor‐a in the coastal regions, and single Chlor‐a, SST/Chlor‐a, and PAR/Chlor‐a in the open ocean. Except for the SST/PAR/Chlor‐a combination, input with Chlor‐a, that is, SST/Chlor‐a and PAR/Chlor‐a, result a relatively acceptable performance in the four seasons. Plain Language Summary: Oceanic primary production (OPP) is often estimated by traditional semi‐empirical models which are sensitive to geographic and environmental characteristics. In addition, the difficulty in collecting necessary parameters of OPP estimate algorithms also hampers long‐term OPP estimates. With the development of big data techniques, data‐driven methods, such as machine learning (ML), have been increasingly applied in oceanic feature estimates. However, the effectiveness of ML to estimate OPP and the key attributes influencing ML performance are not fully discussed. Hence, in this study, the superiority of ML methods to estimate OPP is presented, especially in coastal regions and in summer. In addition, suitable alternative input attributes for ML methods for different regions and seasons are analyzed when necessary oceanic parameters cannot be acquired simultaneously. Key Points: The effectiveness of machine learning (ML) methods for oceanic primary production estimate in different regions and seasons is analyzedThe influences of input attributes on ML methods for different regions and seasons are analyzedThe corresponding alternative input attributes are determined [ABSTRACT FROM AUTHOR]

Satellite altimetry measurements of sea surface height provide near‐global ocean state observations on sub‐monthly time scales, which are not always utilized by seasonal climate forecasting systems. As early as the mid‐1990s, attempts were made to assimilate altimetry observations to initialize climate models. These experiments demonstrated improved ocean forecasting skill, especially compared to experiments that did not assimilate subsurface ocean temperature information. Nowadays, some operational climate forecasting models utilize altimetry in their assimilation systems, whereas others do not. Here, we assess the impact of altimetry assimilation on seasonal prediction skill of ocean variables in two climate forecasting systems that are from the European Centre for Medium‐Range Weather Forecasts (SEAS5) and the Australian Bureau of Meteorology (ACCESS‐S). We show that assimilating altimetry improves the initialization of subsurface ocean temperatures, as well as seasonal forecasts of monthly variability in upper‐ocean heat content and sea level. Skill improvements are largest in the subtropics, where there are typically less subsurface ocean observations available to initialize the forecasts. In the tropics, there are no noticeable improvements in forecast skill. The positive impact of altimetry assimilation on forecast skill related to the subsurface ocean does not seem to affect predictions of sea surface temperature. Whether this is because current forecasting systems are close to the potential predictability limit for the ocean surface, or perhaps altimetry observations are not fully exploited, remains a question. In summary, we find that utilizing altimetry observations improves the overall global ocean forecasting skill, at least for upper‐ocean heat content and sea level. Plain Language Summary: Sea surface heights have been nearly continuously and globally measured by satellite‐based altimeters for almost 30 years, yet not all climate models utilize these observations during their initialization phases of seasonal forecasting. Since the local sea surface height (SSH), or sea level, is mostly determined by the ocean temperature and salinity‐controlled subsurface density structure, the altimetry measurements contain a vast amount of integrated information about the ocean climate state. Climate forecasting systems are usually most skillful when they start from the most accurate initial conditions, including the state of the ocean density structure. Unfortunately, temperature and salinity observations are sparse in many parts of the world's oceans, which degrades the initialization of models and thus possibly diminishes forecast skill. Here, we quantify the benefits of using altimetry observations to initialize two state‐of‐the‐art climate forecasting systems by verifying seasonal forecasts using observational products of upper‐ocean temperature, sea surface temperature, and SSH. In the experiments that did not use altimetry data assimilation, we find an overall decrease of seasonal forecasting skill for the subsurface temperature and SSH, which is especially pronounced in the global subtropics, although we find no widespread change in skill for sea surface temperature. Thus, for predicting the subsurface ocean, climate forecasting systems may benefit from incorporating altimetry data assimilation into their ocean initial conditions. More work is needed to assess how much of the subsurface skill improvements can potentially affect the surface ocean. Key Points: Assimilating observations of sea surface height improves initialization of subsurface ocean temperatures in climate forecasting systemsBy including altimetry assimilation, monthly forecasts are improved of upper‐ocean heat content and sea levelSea surface temperature is only minimally affected by altimetry assimilation, at least in the seasonal forecasts assessed here [ABSTRACT FROM AUTHOR]

Infrared (IR) and passive microwave (PMW) satellite sea surface temperature (SST) retrievals are valuable to assimilate into high-resolution regional ocean forecast models. Still, there are issues related to these SSTs that need to be addressed to achieve improved ocean forecasts. Firstly, satellite SST products tend to be biased. Assimilating SSTs from different providers can thus cause the ocean model to receive inconsistent information. Secondly, while PMW SSTs are valuable for constraining models during cloudy conditions, the spatial resolution of these retrievals is rather coarse. Assimilating PMW SSTs into high-resolution ocean models will spatially smooth the modeled SST and consequently remove finer SST structures. In this study, we implement a bias correction scheme that corrects satellite SSTs before assimilation. We also introduce a special observation operator, called the supermod operator, into the Regional Ocean Modeling System (ROMS) four-dimensional variational data assimilation algorithm. This supermod operator handles the resolution mismatch between the coarse observations and the finer model. We test the bias correction scheme and the supermod operator using a setup of ROMS covering the shelf seas and shelf break off Norway. The results show that the validation statistics in the modeled SST improve if we apply the bias correction scheme. We also find improvements in the validation statistics when we assimilate PMW SSTs in conjunction with the IR SSTs. However, our supermod operator must be activated to avoid smoothing the modeled SST structures on spatial scales smaller than twice the PMW SST footprint. Both the bias correction scheme and the supermod operator are easy to apply, and the supermod operator can easily be adapted for other observation variables. [ABSTRACT FROM AUTHOR]

The summer of 2022 was memorable and record-breaking, ranking as the second hottest summer in France since 1900, with a seasonal surface air temperature average of 22.7 ∘ C. In particular, France experienced multiple record-breaking heatwaves during the meteorological summer. As the main heat reservoir of the Earth system, the oceans are at the forefront of events of this magnitude which enhance oceanic disturbances such as marine heatwaves (MHWs). In this study, we investigate the sea surface temperature (SST) of French maritime basins using remotely sensed measurements to track the response of surface waters to the atmospheric heatwaves and determine the intensity of such feedback. Beyond the direct relationship between SSTs and surface air temperatures, we explore the leading atmospheric parameters affecting the upper-layer ocean heat budget. Despite some gaps in data availability, the SSTs measured during the meteorological summer of 2022 were record-breaking, the mean SST was between 1.3 and 2.6 ∘ C above the long-term average (1982–2011), and the studied areas experienced between 4 and 22 d where the basin-averaged SSTs exceeded the maximum recorded basin-averaged SSTs from 1982 to 2011. We found a significant SST response during heatwave periods with maximum temperatures measured locally at 30.8 ∘ C in the north-western Mediterranean Sea. Our results show that in August 2022 (31 July to 13 August), France experienced above-average surface solar radiation correlated with below-average total cloud cover and negative wind speed anomalies. Our attribution analysis based on a simplified mixed-layer heat budget highlights the critical role of ocean–atmosphere fluxes in initiating abnormally warm SSTs, while ocean mixing plays a crucial role in the cessation of such periods. We find that the 2 m temperatures and specific humidity that are consistently linked to the advection of warm and moist air masses are key variables across all the studied regions. Our results reveal that the influence of wind on heatwaves is variable and of secondary importance. Moreover, we observe that the incident solar radiation has a significant effect only on the Bay of Biscay (BB) and the English Channel (EC) areas. Our study findings are consistent with previous research and demonstrate the vulnerability of the Mediterranean Sea to the increasing frequency of extreme weather events resulting from climate change. Furthermore, our investigation reveals that the recurring heatwave episodes during the summer of 2022 had an undeniable impact on all the surveyed maritime areas in France. Our study therefore provides valuable insights into the complex mechanisms underlying the ocean–atmosphere interaction and demonstrates the need for an efficient and sustainable operational system combining polar-orbiting and geostationary satellites to monitor the alterations that threaten the oceans in the context of climate change. [ABSTRACT FROM AUTHOR]

Modeled global warming is often quantified using global near-surface air temperature (Tas). Meanwhile, long-term temperature datasets combine observations of Tas over land with sea surface temperature (SST) over ocean. Modeled ocean Tas warms more than SST, which can bias model--observation comparisons. Skin temperature (Ts), which is typically warmer than Tas, follows SST changes so the ocean surface temperature discontinuity δTs 5 Ts 2 Tas decreases with warming. Here I show that under CO2 forcing, decreased δTs is consistently simulated for nonpolar ocean within 6608S/N, but not for other regions. I investigate the causes of oceanic dTs decrease using a LongRunMIP climate simulation, radiative kernels, and standard methods for diagnosing forcing and feedbacks from the CMIP5 ensemble. CO2 forcing establishes longwave heating of the lower atmosphere and subsequent adjustments that result in a small Tas increase, and therefore a δTs decrease. During the subsequent warming in response to CO2 forcing, the model-mean surface evaporation feedback is 3.6 W m-2 °C-1 over oceans, which reduces Ts warming relative to Tas and further shrinks δTs. Present-day forcing and feedback contributions are of similar magnitude, and both contribute to small differences in model--observation comparisons of global warming rates when these differences are not accounted for. [ABSTRACT FROM AUTHOR]