Cool Skin Effect Research Articles

Cool Skin Effect as Seen from a New Generation Geostationary Satellite Himawari-8

The cool skin effect refers to the phenomenon where the surface skin temperature of the ocean is always slightly cooler than the temperature of the water directly underneath due to the ubiquitous cooling processes at the ocean surface, especially in the absence of solar radiation. The cool skin effect plays a critical role in the estimation of heat, momentum, and gas exchange between the air and the sea. However, the scarcity of observational data greatly hinders the accurate assessment of the cool skin effect. Here, the matchup data from the new generation geostationary satellite Himawari-8 and in situ sea surface temperature (SST) observations are used to evaluate the performance and dependence on the cool skin effect in the low/mid-latitude oceans. Results show that the intensity of the cool skin effect as revealed by Himawari-8 (−0.16 K) is found to be relatively weaker than previously published cool skin models based on in situ concurrent observations. A considerable amount of warm skin signals has been detected in the high-latitude oceans (e.g., Southern Ocean) under the circumstances of positive air–sea temperature difference and high wind, which may be the main cause of discrepancies with previous thoughts on the cool skin effect.

Regional Study on the Oceanic Cool Skin and Diurnal Warming Effects: Observing and Modeling

The cool skin and diurnal warming effects are important factors affecting the vertical temperature gradient in the upper ocean. Accurately understanding skin effects is of great significance for studying ocean–atmosphere modeling and climate change. The skin models need to be validated for their applicability under different oceanic conditions to improve their accuracy. Shipboard measurements from August 2015 to October 2018 in the Northwest Pacific Ocean are used to validate some of the current models. The results show that the Tropical Ocean-Global Atmosphere (TOGA) Coupled Ocean–Atmosphere Response Experiment (COARE) cool skin model obtains a mean cool skin value of −0.25 K, which is close to the averaged observed value of −0.23 K. A significant positive correlation between the sea–air temperature difference and the amplitude of the cool skin effect is observed in this study. Three diurnal warming models are discussed and compared. The profiles of ocean surface heating (POSH) model performed the best and was the closest one to the observation. The mean temperature differences bewteen the COARE and POSH models are close to 0 K, while the other model shows the overestimation with a mean temperature difference of 0.21 K. The measurements and validations of the thermal skin effects in this study can be useful for regional research on the air–sea interaction and upper ocean gradients.

Cool skin effect and warm skin phenomenon observed by shipboard radiometer in the Northwest Pacific

Sea surface temperature (SST) is an important variable in the study of ocean boundary layers and heat exchange. The accurate simulation and measurement of skin effects are vital to air–sea model processing and satellite SST retrieval. Shipboard measurements from eleven cruises in the Northwest Pacific between August 2015 and October 2018 were used to estimate the cool skin effect and compare model results. The temperature difference ΔT between the sea surface skin temperature (SSTskin), as measured by an infrared radiometer, and the sea surface depth temperature (SSTdepth) at around 4 meters showed a mean difference and a standard deviation of the same 0.2 K, with a total of 5-min 39909 measurements. Both daytime and nighttime ΔT values were compared to physical model simulations and were found to have relatively larger mean values. A set of new coefficients for an exponential parameterization of the cool skin effect was derived in the research area, which performed well in comparison to previous empirical models. In nighttime observations from two summer cruises, the reverse process of heat flux transfer from the air to the sea in the form of a warm skin was distinguished. There were 667 positive ΔT values out of the 1917 nighttime observations, with magnitudes ranging from around 0 to 0.3 K. A high proportion of the cases of the warm skin phenomenon occurred when the air was very humid and much warmer than the sea surface.

Significant Diurnal Warming Events Observed by Saildrone at High Latitudes

AbstractThe sea surface temperature (SST) is one of the essential parameters needed to understand the climate change in the Arctic. Saildrone, an advanced autonomous surface vehicle, has proven to be a useful tool for providing accurate SST data at high latitudes. Here, data from two Saildrones, deployed in the Arctic in the summer of 2019, are used to investigate the diurnal variability of upper ocean thermal structure. An empirical cool skin effect model with dependence on the wind speed with new coefficients was generated. Several local large diurnal warming events were observed, the amplitudes of warming in the skin layer >5 K, rarely reported in previous studies. Furthermore, the warming signals could persist beyond 1 day. For those cases, it was found surface warm air suppressed the surface turbulent heat loss to maintain the persistence of diurnal warming under low wind conditions. Salinity also plays an important role in the formation of upper ocean density stratification during diurnal warming at high latitudes. A less salty and hence less dense surface layer was likely created by precipitation or melting sea ice, providing favorable conditions for the formation of upper ocean stratification. Comparisons with two prognostic diurnal warming models showed the simulations match reasonably well with Saildrone measurements for moderate wind speeds but exhibit large differences at low winds. Both schemes show significant negative biases in the early morning and late afternoon. It is necessary to improve the model schemes when applied at high latitudes.

Evaluation of Wind and Solar Insolation Influence on Ocean Near-Surface Temperature from In Situ Observations and the Geostationary Himawari-8 Satellite

The skin sea surface temperature (SST) observed by the geostationary Himawari-8 satellite and bulk SST, including four in situ observations from ships, drifters, Argo, and buoys constitute more than 90,000 SST pairs used to analyze near-surface temperature variations. From July 2015 to May 2022, an average SST bias of 0.10 °C and root mean square error of 0.99 °C were observed in the waters adjacent to Taiwan. This study effectively observed that the skin effect generated by ocean wind and solar shortwave radiation caused the occurrence of a cool skin layer and diurnal warm layer (DWL), and 90% of the SST bias was in a range of −1.55~1.71 °C. In the daytime, the skin layer received solar shortwave radiation, thus increasing temperature and causing a DWL. With the increase in insolation, the SST bias in the DWL became more obvious. During winter, strong wind, or low shortwave radiation, the DWL may disappear and turn into a cool skin layer. At night, the near-surface SST was dominated by the cool skin effect, but the DWL generated in the daytime would remain if the wind speed was weak. However, the different hydrological characteristics of the observation position and its distance from the coast could affect the results of the skin effect. Whether there is a rapid change in ocean stratification in a spatial grid of nearly four square kilometers needs to be explored in the future.

Update on the Temperature Corrections of Global Air‐Sea CO2 Flux Estimates

AbstractThe oceans are a major carbon sink. Sea surface temperature (SST) is a crucial variable in the calculation of the air‐sea carbon dioxide (CO2) flux from surface observations. Any bias in the SST or any upper ocean vertical temperature gradient (e.g., the cool skin effect) potentially generates a bias in the CO2 flux estimates. A recent study suggested a substantial increase (∼50% or ∼0.9 Pg C yr−1) in the global ocean CO2 uptake due to this temperature effect. Here, we use a gold standard buoy SST data set as the reference to assess the accuracy of insitu SST used for flux calculation. A physical model is then used to estimate the cool skin effect, which varies with latitude. The bias‐corrected SST (assessed by buoy SST) coupled with the physics‐based cool skin correction increases the average ocean CO2 uptake by ∼35% (0.6 Pg C yr−1) from 1982 to 2020, which is substantially smaller than the previous correction. After these temperature considerations, we estimate an average net ocean CO2 uptake of 2.2 ± 0.4 Pg C yr−1 from 1994 to 2007 based on an ensemble of surface observation‐based flux estimates, in line with the independent interior ocean carbon storage estimate corrected for the river induced natural outgassing flux (2.1 ± 0.4 Pg C yr−1).

Use of hyperspectral sounders to retrieve daytime sea-surface temperature from mid-infrared radiances: Application to IASI

We propose a physically-based methodology for sea-surface skin-temperature (SST) retrievals from daytime spectra recorded by the hyperspectral Infrared Atmospheric Sounding Interferometer (IASI). The approach, which takes sun light into account, minimizes the differences between measured and computed radiances at numerous points within two windows centered near 3.7 and 4.0 μm. We demonstrate that, even when the solar contribution is large, with brightness temperatures increased by several tens of kelvin at the shortest wavelengths, very consistent SSTs are retrieved in both windows. Furthermore, the values obtained are, after correction for the cool-skin effect, in excellent agreement (<0.05 K on average) with the in-situ measurements of the depth temperature provided by nearby drifters. This opens renewed perspectives for daytime SST determinations from space, since the IASI mid-infrared windows used here provide SSTs that are, with respect to those retrieved around 9 and 11 μm, much less sensitive to errors in the computed radiative contribution of water vapor.

Global Synthesis of Air-Sea CO2 Transfer Velocity Estimates From Ship-Based Eddy Covariance Measurements

The air-sea gas transfer velocity (K660) is typically assessed as a function of the 10-m neutral wind speed (U10n), but there remains substantial uncertainty in this relationship. HereK660of CO2derived with the eddy covariance (EC) technique from eight datasets (11 research cruises) are reevaluated with consistent consideration of solubility and Schmidt number and inclusion of the ocean cool skin effect.K660shows an approximately linear dependence with the friction velocity (u*) in moderate winds, with an overall relative standard deviation (relative standard error) of about 20% (7%). The largest relative uncertainty inK660occurs at low wind speeds, while the largest absolute uncertainty inK660occurs at high wind speeds. There is an apparent regional variation in the steepness of theK660-u*relationships: North Atlantic ≥ Southern Ocean &amp;gt; other regions (Arctic, Tropics). Accounting for sea state helps to collapse some of this regional variability inK660using the wave Reynolds number in very large seas and the mean squared slope of the waves in small to moderate seas. The grand average of EC-derivedK660(−1.47 + 76.67u*+ 20.48u*2 or 0.36 + 1.203U10n+ 0.167U10n2)is similar at moderate to high winds to widely used dual tracer-basedK660parametrization, but consistently exceeds the dual tracer estimate in low winds, possibly in part due to the chemical enhancement in air-sea CO2exchange. Combining the grand average of EC-derivedK660with the global distribution of wind speed yields a global average transfer velocity that is comparable with the global radiocarbon (14C) disequilibrium, but is ~20% higher than what is implied by dual tracer parametrizations. This analysis suggests that CO2fluxes computed using aU10n2dependence with zero intercept (e.g., dual tracer) are likely underestimated at relatively low wind speeds.

Measurement and Scaling of Lake Surface Skin Temperatures

AbstractWater temperature microprofiles at the air‐water interface of a stratified lake were measured concurrently with moderate wind speeds of 0.8–8.2 ms−1 above the lake surface. The day‐time measurements revealed the cool skin effect of water surface temperatures that triggers the cooling of the lake surface and the occurrence of natural convection. Natural convection velocity scale mediates the thickness of the diffusive thermal sublayer and the corresponding net heat flux at the interface. The diffusive thermal sublayer thickness ranged from 0.7 to 3.6 mm. The temperature microprofiles and proposed parameterization of skin‐to‐bulk temperatures enable similarity scaling of temperature microprofiles over the diffusive thermal sublayer thickness. The proposed parameterizations allow for the estimation of diffusive thermal sublayer thickness and skin‐to‐bulk temperature difference in the presence of natural convection. Our results have the potential to facilitate the validation and integration of satellite‐derived lake surface temperatures.

Skin Temperature Correction for Calculations of Air‐Sea Oxygen Flux and Annual Net Community Production

AbstractWe evaluated the influence of skin temperature correction on the calculations of air‐sea O2 flux and annual net community production (ANCP). The skin temperature correction term (ΔT) was derived from the fifth generation European Center for Medium‐Range Weather Forecasts Reanalysis, which has large spatial and temporal variations that are consistent with independent measurements on three ocean moorings from areas with very different air‐sea heat flux and ΔT. The result revealed that ΔT is dominated by the cool skin effect (which leads to the increase in air‐to‐sea O2 flux and decrease in ANCP), except for the equatorial region or summertime when the warm layer effect is significant. Using data from three Argo profiling floats in the subarctic, subtropical, and tropical Pacific as examples, the calculations indicated that the correction could lead to −1%–+36% difference in annual air‐to‐sea O2 flux and −33%–+5% difference in ANCP.

Retrieval of Sea Surface Temperature From HY-1B COCTS

The Chinese Ocean Color and Temperature Scanner (COCTS) on board HY-1 series satellites has two thermal infrared channels with the spectrum range of 10.30-11.40 &#x03BC;m and 11.40-12.50 &#x03BC;m for sea surface temperature (SST) observations. To reprocess the Haiyang-1B (HY-1B) COCTS SST, the Bayesian cloud detection and optimal estimation (OE) SST retrieval were applied to COCTS data in this study. The Bayesian cloud detection algorithm that has been developed is based on the Bayes&#x2019; theorem and uses simulation of COCTS observations. The MODerate resolution atmospheric TRANsmission (MODTRAN) model was used for simulation of COCTS brightness temperatures. SSTs were retrieved from COCTS by OE from 2009 to 2011 in the northwest Pacific. Comparison of COCTS OE SST with in situ SST showed that the COCTS SSTs are cooler than buoy measurements by &#x2013;0.23 &#x00B0;C on average, and the standard deviation (SD) of differences was 0.51 &#x00B0;C. A large component of the mean difference is attributable to the cool skin effect at the ocean surface (typically &#x2013;0.15 to &#x2013;0.2 &#x00B0;C), the remainder being attributable to simulation and calibration biases. The mean difference of COCTS OE SST with matched skin temperatures from the Advanced Along Track Scanning Radiometer (AATSR) is closer to zero, being &#x2013;0.09 &#x00B0;C, with a SD of 0.49 &#x00B0;C. These validation results of COCTS OE SST demonstrate that Bayesian cloud detection and OE SST retrieval algorithm work well for improving COCTS SST accuracy, and show the potential of these methods to help develop SST products for operational HY-1 satellites, HY-1C and HY-1D.

Cool Skin Effect and its Impact on the Computation of the Latent Heat Flux in the South China Sea

AbstractThe cool skin effect, known as the temperature difference (ΔT) across the skin layer of sea surface, is of vital importance for the accurate computation of the latent heat flux (LHF). The observed features of ΔT in the South China Sea are analyzed using in situ data from a buoy platform over an ∼6‐week period. Only nighttime data are used to exclude the possible warm layer effect. The positive values of ΔT falling into the range of 0–1 K comprise 95% of the data, and the most frequently observed values occur in the range of 0.4–0.6 K (38%). The cool skin model in the Coupled Ocean‐Atmosphere Response Experiment 3.0 algorithm is then validated against those observations. The cool skin model has an efficient but insufficient ability to reduce the overestimation of the LHF. The overestimation of the LHF is reduced to 9.5% from 18.0%, leaving nearly half of the biases in the LHF unresolved. The Saunders constant (λ) in the cool skin model is markedly underestimated, leading to a much weaker prediction of ΔT. A strong linear relationship exists between the mean values of λ and the LHF with a slope of −0.9 W m−2. With an approximately doubled λ, the biases in ΔT and in the LHF could be eliminated. Considering the possible uncertainties in sensors, the value of λ is estimated as 11.6 ± 6.7 in the current study.

Nighttime Cool Skin Effect Observed from Infrared SST Autonomous Radiometer (ISAR) and Depth Temperatures

AbstractThe nighttime ocean cool skin signal ΔT [defined as skin sea surface temperature (SSTskin) minus depth SST (SSTdepth)] is investigated using 103 days of matchups between shipborne Infrared SST Autonomous Radiometer (ISAR) SSTskin and water intake SSTdepth at ~7.1–9.9-m depths, in oceans around Australia. Before data analysis, strict quality control of ISAR SSTskin data is conducted and possible diurnal warming contamination is carefully minimized. The statistical distribution of ΔT, and its dependencies on wind speed, heat flux, etc., are consistent with previous findings. The overall average ΔT value is −0.23 K. It is observed that the magnitude of the cool skin signal increases after midnight and a coolest skin offset (with an average value of −0.36 K) is found at around dawn. The dependency of ΔT on SST conditions is observed. Direct warm skin events are discovered when the net heat flux direction is from the atmosphere to the ocean, which is more likely to occur at high latitudes when the air is very humid and warmer than the SST. In addition, several cool skin models are validated: one widely used physical model performs best and can capture most skin-effect trends and details; the empirical models only reflect the basic features of the observed ΔT values. If the user cannot apply the physical model (due to, e.g., the algorithm complexity or missing inputs), then the empirical parameterization in the form proposed in a 2002 study can be used. However, we recommend using a new set of parameters, calculated in this study, based on much more representative dataset, and with more rigorous quality control.

Comparison of Methods to Estimate Lake-Surface-Water Temperature Using Landsat 7 ETM+ and MODIS Imagery: Case Study of a Large Shallow Subtropical Lake in Southern Brazil

Water temperature regulates many processes in lakes; therefore, evaluating it is essential to understand its ecological status and functioning, and to comprehend the impact of climate change. Although few studies assessed the accuracy of individual sensors in estimating lake-surface-water temperature (LSWT), comparative analysis considering different sensors is still needed. This study evaluated the performance of two thermal sensors, MODIS and Landsat 7 ETM+, and used Landsat methods to estimate the SWT of a large subtropical lake. MODIS products MOD11 LST and MOD28 SST were used for comparison. For the Landsat images, the radiative transfer equation (RTE), using NASA’s Atmospheric Correction Parameter Calculator (AtmCorr) parameters, was compared with the single-channel algorithm in different approaches. Our results showed that MOD11 obtained the highest accuracy (RMSE of 1.05 ° C), and is the recommended product for LSWT studies. For Landsat-derived SWT, AtmCorr obtained the highest accuracy (RMSE of 1.07 ° C) and is the recommended method for small lakes. Sensitivity analysis showed that Landsat-derived LSWT using the RTE is very sensitive to atmospheric parameters and emissivity. A discussion of the main error sources was conducted. We recommend that similar tests be applied for Landsat imagery on different lakes, further studies on algorithms to correct the cool-skin effect in inland waters, and tests of different emissivity values to verify if it can compensate for this effect, in an effort to improve the accuracy of these estimates.

LakeSST: Lake Skin Surface Temperature in French inland water bodies for 1999–2016 from Landsat archives

Abstract. The spatial and temporal coverage of the Landsat satellite imagery make it an ideal resource for the monitoring of water temperature over large territories at a moderate spatial and temporal scale at a low cost. We used Landsat 5 and Landsat 7 archive images to create the Lake Skin Surface Temperature (LakeSST) data set, which contains skin water surface temperature data for 442 French water bodies (natural lakes, reservoirs, ponds, gravel pit lakes and quarry lakes) for the period 1999–2016. We assessed the quality of the satellite temperature measurements by comparing them to in situ measurements and taking into account the cool skin and warm layer effects. To estimate these effects and to investigate the theoretical differences between the freshwater and seawater cases, we adapted the COARE 3.0 algorithm to the freshwater environment. We also estimated the warm layer effect using in situ data. At the reservoir of Bimont, the estimated cool skin effect was about −0.3 and −0.6 °C most of time, while the warm layer effect at 0.55 m was negligible on average, but could occasionally attain several degrees, and a cool layer was often observed in the night. The overall RMSE of the satellite-derived temperature measurements was about 1.2 °C, similar to other applications of satellite images to estimate freshwater surface temperatures. The LakeSST data can be used for studies on the temporal evolution of lake water temperature and for geographical studies of temperature patterns. The LakeSST data are available at https://doi.org/10.5281/zenodo.1193745.

Air–lake boundary layer and performance of a simple lake parameterization scheme over the Tibetan highlands

The Tibetan Plateau (TP) is covered by thousands of lakes which affect the regional and global heat and mass budget with important implications for the current and future climate change. However, the heat and mass budget of TP lakes and the performance of contemporary lake models over TP have not been quantified to date. We utilise 3-yr observations from Ngoring Lake, the largest lake in the Yellow River source region of TP, to investigate the typical properties of the lake–air boundary layer and to evaluate the performance of a simplified lake scheme from the Community Land Model version 4.5 (SLCLM) as one of the most popular lake parameterization schemes in atmospheric models. The strong boundary layer instability during the entire open-water period is a distinguishing feature of the air–lake exchange, similar to the situation over tropical and subtropical lakes, while contrasting to the generally stable atmospheric conditions commonly observed over ice-free temperate and boreal lakes from spring to summer. The rather simple algorithm of SLCLM demonstrated good performance in these conditions. A series of sensitivity simulations with SLCLM revealed strong shortwave solar radiation and cold air temperatures because of high altitude as the primary factors causing the boundary layer instability. The outcomes of the study (1) demonstrate the role of TP lakes as accumulators of shortwave solar radiation releasing the heat into the atmosphere during the entire open-water period; (2) justify the use of simple lake models for the Tibetan highlands, while revealing remarkable uncertainties in the estimations of the latent heat flux; (3) qualify the strong cool-skin effect on the lake surface which results from permanent negative air–lake temperature difference, and should be taken into account when interpreting remote sensing data from highland areas.

An operational analysis of Lake Surface Water Temperature

Operational analyses of Lake Surface Water Temperature (LSWT) have many potential uses including improvement of numerical weather prediction (NWP) models on regional scales. In November 2011, LSWT was included in the Met Office Operational Sea Surface Temperature and Ice Analysis (OSTIA) product, for 248 lakes globally. The OSTIA analysis procedure, which has been optimised for oceans, has also been used for the lakes in this first version of the product. Infra-red satellite observations of lakes and in situ measurements are assimilated. The satellite observations are based on retrievals optimised for Sea Surface Temperature (SST) which, although they may introduce inaccuracies into the LSWT data, are currently the only near-real-time information available. The LSWT analysis has a global root mean square difference of 1.31 K and a mean difference of 0.65 K (including a cool skin effect of 0.2 K) compared to independent data from the ESA ARC-Lake project for a 3-month period (June to August 2009). It is demonstrated that the OSTIA LSWT is an improvement over the use of climatology to capture the day-to-day variation in global lake surface temperatures.

Modelagem unidimensional da camada limite oceânica durante a passagem de uma frente fria na região da confluência Brasil-Malvinas

Um modelo unidimensional com fechamento de segunda ordem é usado para investigar a resposta da superfície do oceano à passagem de uma frente fria na região da Confluência Brasil-Malvinas. Dados da boia ARGOS-32056 foram utilizados para forçar e validar o modelo para o período entre 12 e 20 de Janeiro de 2002, correspondente à ação de uma frente fria. O modelo foi capaz de reproduzir tanto o ciclo diurno, quanto o resfriamento da temperatura da superfície do mar observado com a passagem da frente fria. Os resultados da simulação numérica mostraram um aumento de 0,2 ºC no efeito do resfriamento de pele e um aprofundamento de aproximadamente 16 metros na camada de mistura oceânica com a passagem da frente fria. Essas variações foram consistentes com uma mudança no balanço de energia em superfície, devido ao aumento abrupto dos fluxos turbulentos em resposta ao aumento da velocidade do vento e da diferença de temperatura entre o ar e o mar. Após a passagem da frente fria, os fluxos de calor diminuem lentamente mostrando que, se não houver a entrada de uma nova frente fria ou advecção de massa de água, a superfície do mar e o ar adjacente tendem ao equilíbrio.

Ocean haline skin layer and turbulent surface convections

The ocean haline skin layer is of great interest to oceanographic applications, while its attribute is still subject to considerable uncertainty due to observational difficulties. By introducing Batchelor micro‐scale, a turbulent surface convection model is developed to determine the depths of various ocean skin layers with same model parameters. These parameters are derived from matching cool skin layer observations. Global distributions of salinity difference across ocean haline layers are then simulated, using surface forcing data mainly from OAFlux project and ISCCP. It is found that, even though both thickness of the haline layer and salinity increment across are greater than the early global simulations, the microwave remote sensing error caused by the haline microlayer effect is still smaller than that from other geophysical error sources. It is shown that forced convections due to sea surface wind stress are dominant over free convections driven by surface cooling in most regions of oceans. The free convection instability is largely controlled by cool skin effect for the thermal microlayer is much thicker and becomes unstable much earlier than the haline microlayer. The similarity of the global distributions of temperature difference and salinity difference across cool and haline skin layers is investigated by comparing their forcing fields of heat fluxes. The turbulent convection model is also found applicable to formulating gas transfer velocity at low wind.

Validation of the AATSR Meteo Product Sea Surface Temperature

AbstractThe Advanced Along Track Scanning Radiometer (AATSR) Sea Surface Temperature (SST) Meteo product, a fast-delivery level-2 product at 10 arc min spatial resolution, has been available from the European Space Agency (ESA) since 19 August 2002. Validation has been performed on these data at the Met Office on a daily basis, with a 2-day lag from data receipt. Meteo product skin SSTs have been compared with point measurements of buoy SST, a 1° climate SST analysis field compiled from in situ measurements and Advanced Very High Resolution Radiometer (AVHRR) SSTs, and a 5° latitude–longitude 5-day averaged in situ dataset. Comparisons of the AATSR Meteo product against Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) SSTs are also presented. These validation results have confirmed the AATSR Meteo product skin SST to be within ±0.3 K of in situ data.Comparisons of the AATSR skin SSTs against buoy SSTs, from 19 August 2002 to 20 August 2003, give a mean difference (AATSR – buoy) of 0.04 K (standard deviation = 0.28 K) during nighttime, and a mean difference of 0.02 K (standard deviation = 0.39 K) during the day. Analyses of the buoy matchups have shown that there is no cool skin effect observed in the nighttime observations, implying that the three-channel AATSR product skin SST may be 0.1–0.2 K too warm. Comparisons with TMI SSTs confirm that the lower-latitude SSTs are not significantly affected by residual cloud contamination.