Impact of stratospheric aerosol geoengineering on sea surface temperature in the angolan upwelling system

Late Pliocene to early Pleistocene astronomically forced sea surface productivity and temperature variations in the Mediterranean

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing.

For many years, satellite observations of sea surface height (SSH) and sea surface temperature (SST) have provided invaluable information on the dynamics of the upper ocean at many scales. SSH and SST variables are dynamically linked, and are very often used together for many scientific studies (e.g. estimating heat transport in the upper layer by SSH-derived geostrophic currents). As observations are unevenly distributed in space and time (SSH is measured along one-dimensional trajectories and SST measurements are affected by clouds), many scientific and operational applications rely on gridded SSH and SST products. However, these products suffer from two main limitations. Firstly, conventional mapping techniques rely on static optimal interpolation schemes, which limits the estimation of nonlinear dynamics at scales poorly sampled by altimetry or, for SST, in regions densely affected by clouds (e.g. near western boundary currents). Secondly, SSH and SST reconstructions are performed separately, without relying on synergies between the two variables, which has an impact on the consistency of the two reconstructed fields. We introduce an original dynamical mapping algorithm to simultaneously reconstruct SSH and SST from multi-sensor satellite observations. This innovative method combines a weakly constrained, reduced-order, 4-dimensional variational scheme with simple physical models – quasi-geostrophic for SSH and advection-diffusion for SST. The weak constraint of the models on the inversion procedure ensures that the reconstructed SSH and SST fields closely match the observations while preserving the space-time continuity of the dynamical structures. The work focuses on the North Atlantic Ocean over the year 2023 and considers the available along-track altimetric SSH, microwave and infrared SST data. The performances of the method are evaluated through Observing System Experiments, utilizing independent altimetric (from conventional and SWOT satellites) and drifter data. Results show a significant improvement of the reconstruction of short energetic structures, both in terms of SSH and SST, compared to operational products. The benefit of using SST observations for reconstructing SSH fields increases as the number of altimeters is reduced. This opens new opportunity to use the method for sea-level related climate applications that rely on a stable two altimeters constellation.

As the climate warms, sea surface temperature (SST) is projected to increase, along with atmospheric variables which may have an impact on tropical cyclone (TC) properties. Climate models have well-known errors in simulating current climate SSTs that will likely affect future TC projections. Therefore, a better understanding of the impact of SST changes will help us identify the largest uncertainty in projecting TC changes. This study employs three different and independent methodologies to investigate the impact of sea surface and atmospheric temperature changes and tropical cyclone (TC) characteristics, focusing on three historically damaging TCs in the Philippines: Typhoons Haiyan, Bopha, and Mangkhut. These methodologies include initially simulations with uniform SST anomalies between −4 to +4°C, then experiments using delta from CMIP6 CESM2 for SST and atmospheric temperature in the far future, and, finally, simulations imposing Radiative-Convective Equilibrium (RCE) conditions. The experiments reveal significant insights into TC dynamics under varying environmental conditions. Changes in SSTs resulted in changes in TC track, intensity, and rainfall. In the positive SST simulations, TCs tended to move northwards and resulted in substantial increases in maximum wind speeds reaching a difference of up to 10, 13, 23 ms−1 for Typhoons Haiyan, Bopha, and Mangkhut, respectively. Analysis of the accumulated rainfall also showed that increased SST results in increased rainfall. Inclusion of atmospheric warming offsets the intensification due to SST change. Moreover, warmer SSTs resulted in slower-moving TCs and increased TC size. Further analyses incorporating atmospheric temperature adjustments derived from CESM2 and RCE simulations offer better insights on TC response. Under near-RCE conditions, TCs exhibit reduced sensitivity to SST changes, with smaller intensity and size modifications simulated when stable relative humidity is imposed. The smaller changes in TC intensity and size observed in these experiments suggest that maintaining atmospheric stability through pre-storm atmospheric adjustments dampens the response of TCs to SST warming.

The Adriatic Sea and its coastal region have experienced significant environmental changes in recent decades, aggravated by climate change. The most prominent effects of climate change (namely, an increase in sea surface and air temperature together with changes in the precipitation regime) could have an adverse effect on social and environmental processes. In this study, we analyzed the time series of sea surface temperature and air temperature measured at three meteorological stations in the Croatian part of the Adriatic Sea. To assess the trends and variations in the time series of sea surface and air temperature, different statistical methods were employed, i.e., linear and quadratic regressions, Mann–Kendall test, Rescaled Adjusted Partial Sums method, and autocorrelation. The results evidenced increasing trends in the mean annual sea surface temperature and air temperature; furthermore, sudden variations in values were observed in 1998 and 1992, respectively. Increasing trends in the mean monthly sea surface temperature and air temperature occurred in the warmer parts of the year (from March to August). The results of this study could provide a foundation for stakeholders, decision–makers, and other scientists for developing effective measures to mitigate the negative effects of climate change in the scattered environment of the Adriatic islands and coastal region.

Sea surface temperature (SST) is an important indicator for climate change. Nowadays many sensors in orbit have the capacity of SST observations. Long-term climate data record of global SST requires satellite SST products with high accuracy and stability. In order to evaluate the quality of satellite SST products, proper collection of in situ sea surface skin temperature is needed. The infrared SST autonomous radiometer (ISAR) has been deployed on the research vessel Dong Fang Hong II of Ocean University of China since 2009. The ISAR data collected during five cruises are used to validate the Level-2 WST products from Sea and Land Surface Temperature Radiometer (SLSTR). The comparison results show negative bias of 0.085K and standard deviation of 0.356K. Due to the influence of the weather conditions and short temporal extent of the SLSTR L2 WST archived, only 148 match-ups are produced. Further studies will be carried out with more available data from in situ and satellites.

The Sentinel-3 satellites are equipped with dual-band Microwave Radiometers (MWR) to derive the wet tropospheric correction (WTC) for satellite altimetry. The deployed MWR lack the 18 GHz channel, which mainly provides information on the surface emissivity. Currently, this information is considered using additional parameters, one of which is the sea surface temperature (SST) extracted from static seasonal tables. Recent studies show that the use of a dynamic SST extracted from Numerical Weather Models (ERA5) improves the WTC retrieval. Given that Sentinel-3 carries on board the Sea and Land Surface Temperature Radiometer (SLSTR), from which SST observations are derived simultaneously with those of the Synthetic Aperture Radar Altimeter and MWR sensors, this study aims to develop a synergistic approach between these sensors for the WTC retrieval over open ocean. Firstly, the SLSTR-derived SSTs are evaluated against the ERA5 model; secondly, their impact on the WTC retrieval is assessed. The results show that using the SST input from SLSTR, instead of ERA5, has no impact on the WTC retrieval, both globally and regionally. Thus, for the WTC retrieval, there seems to be no advantage in having collocated SST and radiometer observations. Additionally, this study reinforces the fact that the use of dynamic SST leads to a significant improvement over the current Sentinel-3 WTC operational algorithms.

Sea surface salinity (SSS) and sea surface temperature (SST) in the western Pacific warm pool (130–180°E; 10°N–10°S) are analyzed for the period 1992–2000 taking advantage of complementary data from the ship of opportunity program and the Tropical Atmosphere‐Ocean (TAO)‐Triangle Trans‐Ocean Buoy Network (TRITON) array of moored buoys. Covariability of these variables with surface wind stress, surface zonal currents, evaporation, precipitation, and barrier layer thickness is also examined. These fields all go through large oscillations related to the El Niño Southern Oscillation (ENSO) cycle, most notably during the record breaking 1997–1998 El Niño and subsequent strong 1998–2000 La Niña. East of about 160°E, during El Niño, precipitation minus evaporation increases in the equatorial band, in conjunction with anomalous increases in westerly winds, eastward surface currents, SST, and decreases in SSS. Opposite tendencies are evident during La Niña. Peak to peak 2°N–2°S averaged variations reached as much as 1.2 m s−1 for zonal currents and 1.5 practical salinity units (psu) for SSS. West of about 160°E, SST cools during El Niño and warms during La Niña, opposite to what occurs further east. To understand these SST tendencies west of 160°E, a proxy indicator for barrier layer formation is developed in terms of changes in the zonal gradient of SSS (∂S/∂x). Zonal SSS gradients have been shown in modeling studies to be related to barrier layer formation via subduction driven by converging zonal currents in the vicinity of the salinity front at the eastern edge of the warm pool. Correlation between changes in ∂S/∂x and changes in SST a few degrees longitude to the west is significantly nonzero, consistent with the idea that increased barrier layer thickness is related to warmer SSTs during periods of westward surface flow associated with La Niña, and vice versa during El Niño. Direct evidence of barrier layer thickness variations in support of this hypothesis is also presented.

North Atlantic sea surface temperature (SST) distributions derived from observations and a coupled model from NOAA's Geophysical Fluid Dynamics Laboratory, CM2.1, are compared to evaluate the model's ability to simulate recent (1900 to the present) oceanic surface characteristics. The North Atlantic focus will limit our analyses to spatial scales less than gyre, scales usually not addressed in previous model‐observation comparisons. Identifying model differences from observations at these scales will assist modelers in identifying problems to be considered and remedies to be applied. The properties compared are the mean annual SST, standard deviation, amplitude of the annual and semiannual harmonic, decadal meridional movements of the axis of the Gulf Stream, propagation of SST anomalies along the axis of the Gulf Stream, and 100‐year trends in SST records. Because of the dependence of SST on surface currents, observed flow from surface drifters and simulated flow from 15 m fields are also compared. The model simulates the large‐scale properties of all the variables compared. However, there are areas of differences in some variables that can be related to inadequacies in the simulated current fields. For example, the model Gulf Stream (GS) axis after separation from the western boundary is located some 100 km north of the observed axis, which contributes to an area of warmer simulated SSTs. The absence of a slope current in the same region that advects colder water from the Labrador Sea in the observations also contributes to this area of higher model SSTs. The model North Atlantic Current (NAC) is located to the east of the observed NAC contributing to a large area of SST discrepancy. The patterns of the amplitude of the annual harmonic are similar with maximum amplitude off the east coast of northern North America. The semiannual harmonic exhibits relatively large amplitudes (>1°C) north of about 55°N, a signal not found in the observations. In both the model and observations, a region of increased standard deviations encompasses the GS and NAC. The model simulates north‐south migrations of the GS core but at a longer period (20 years) than observed. The model does not simulate the SST anomalies that propagate along the observed GS and NAC. The model captures both the spatial and temporal characteristics of the Atlantic Multidecadal Oscillation. Both model and observations exhibit a dipole in trends, with positive trends in the subtropical Atlantic and negative trends in the subpolar gyre. The modeled region of negative trends is limited to the western subpolar Atlantic. The observed trends extend farther to the east.

Estuarine temperature variability: Integrating four decades of remote sensing observations and in-situ sea surface measurements

Among techniques proposed to limit global warming, there is Stratospheric Aerosol Geoengineering (SAG) which is aiming to increase Earth-atmosphere albedo by injecting sulfur dioxide into the stratosphere in order to reduce the solar radiation that reaches the earth. This study aims to assess the potential impact of SAG on Sea Surface Temperature (SST) in the Northern Gulf of Guinea and its causes using GLENS (Geoengineering Large Ensemble) simulations performed under a high anthropogenic emission scenario (RCP8.5). Here, we focus on two dynamically different regions: Sassandra Upwelling in Côte d’Ivoire (SUC, located east of Cape Palmas) and Takoradi Upwelling in Ghana (TUG, located east of Cape Three Points). Results show that in the SUC region, under climate change, there is an increase in SST (referred to as the current climate) all year long (by 1.52 °C on average) mainly due to an increase in net heat flux (lead by the decrease in longwave radiation) and also in weak vertical mixing (caused by strong stratification which dominates the vertical shear). Under SAG, SST decreases all the seasonal cycle with its maximum in December (−0.4 °C) due to a reduction in the net heat flux (caused by a diminution of solar radiation) and an increase in vertical advection (due to an increase in vertical temperature gradient and vertical velocity). In the TUG region, under climate change, SST warming is a little more intense than in the SUC region and SST changes are driven by an increase in the net heat flux and strong stratification. The cooling of the SST in TUG is similar to the SUC region, but contrary to this region, the cooling under SAG is not only explained by a decrease in the net heat flux but also by the remote forcing of wind changes at the western equatorial Atlantic.

We identify and analyze tropical instability waves (TIWs) in the equatorial Atlantic Ocean during 2010–2018 using satellite derived observations of sea surface salinity (SSS), sea surface temperature (SST), sea level anomaly, and Argo profiles. In particular, the weekly 50‐km resolution SSS time series from the climate change initiative project provides an unprecedented opportunity to observe the salinity structure at a scale closer to the SST scale. We examine the relative contributions of SSS and SST to the horizontal surface density gradient on seasonal and interannual time scales and how they contribute to the TIW properties and energetics. For the central Atlantic TIWs, the maximum of the SST contribution to the density anomaly lags the SSS one by approximately one month. Argo vertical profiles indicate that temperature and salinity both significantly contribute to TIW‐related density anomalies. In May–June, salinity contributes to 50% of the perturbation potential energy in the top 60 m, and between 30% and 45% from July to September. While variations in SST appear to be related to dynamic processes, the interannual variability of SSS is also influenced by precipitations. However, the two leading modes of variability in the region (Atlantic Meridional and Zonal modes) do not well explain at 1°N these interannual variations.

Variability at large to meso-scale in sea surface salinity (SSS) and sea surface temperature (SST) is investigated in the subtropical North Atlantic Ocean during the Subtropical Atlantic Surface Salinity Experiment Strasse/SPURS in August 2012—August 2013. The products of the Soil Moisture and Ocean Salinity (SMOS) mission corrected from large scale systematic errors are tested and used to retrieve meso-scale salinity features, while OSTIA products, resolving meso-scale temperature features are used for SST. The comparison of corrected SMOS SSS data with drifter's in situ measurements from SPURS experiment shows a reasonable agreement, especially during winter time with RMS differences on the order of 0.15 pss (for 10 days, 75 km resolution SMOS product). The analysis of SSS (SST) variability reveals that the meso-scale eddies contribute to a substantial freshening (cooling) in the central high salinity region of the subtropical gyre, albeit smaller than Ekman and atmospheric freshwater (heat) seasonal flux, which are the leading terms in SSS (SST) budget. An error is estimated along with SSS and SST budgets; as well as sensitivity to the different products in use and residuals are discussed. The residuals in the SSS budget are large and can arise from errors in the advection fields and freshwater flux, from neglected small scale or unresolved local processes (salt fingering, vertical mixing, and small scale subduction, etc.). However, their magnitude is similar to what is often parameterized as eddy horizontal diffusion to close large scale budgets.\n

Sea surface temperature (SST) is one of the most appropriate and important climate parameters: a widespread increase is an indicator of global warming and modifications of the geographical distribution of SST are an extremely sensitive indicator of climate change. There is high demand for accurate, reliable, high-spatial-and-temporal-resolution SST measurements for the parameterization of ocean-atmosphere heat, momentum, and gas (SST is therefore critical to understanding the processes controlling the global carbon dioxide budget) fluxes, for detailed diagnostic and process-orientated studies to better understand the behavior of the climate system, as model boundary conditions, for assimilation into climate models, and for the rigorous validation of climate model output. In order to achieve an overall net flux uncertainty < 10 W/m2 (Bradley and Fairall, 2006), the sea surface (skin) temperature (SSST) must be measured to an error < 0.1 C and a precision of 0.05 C. Anyone experienced in shipboard meteorological measurements will recognize this is a tough specification. These demands require complete confidence in the content, interpretation, accuracy, reliability, and continuity of observational SST data—criteria that can only be fulfilled by the successful implementation of an ongoing data product validation strategy.

Uncharacteristically strong storm activity in the UK during recent years has focused attention once again upon climatic impacts. Rapid climate change, which in turn affects sea surface temperatures (SST), ocean circulation, and wind speed patterns, is most likely contributing to these unprecedented and prolonged storm events. Previous studies focused on individual factors rather than cumulative effects of climate change; however, a knowledge gap exists. Consequently, this quantitative study analysed the current influence of climate change on UK sea surface and mean air temperatures, together with mean wind speeds. It also assessed whether climate change scenarios will create favourable conditions for the genesis of catastrophic hurricanes in the UK using a 2-path analysis (SST, wind speed, temperatures). Accordingly, in situ SST, mean wind speed, and mean temperatures for the period 2000–2014 are analysed. Primary results revealed that increasing trends in regional SST (0.1–0.7 °C), mean temperature (0.4–0.8 °C), and wind speed (0.5–2 m/s) patterns. It is expected that significant changes in climate will cause sea surface temperature increases between 1 and 4 °C, which will accelerate temperature and wind speed further. These adverse changes would negatively affect future UK weather patterns and create positive conditions for the formation of super storms and high winds in the near future, although not necessarily hurricanes.