Chong Jia

The oceanic cool skin effect is characterized by a temperature depression between the ocean's submillimeter thermal skin layer (SST _skin ) and the underlying water (SST _depth ). This study evaluates the ability of the widely used Fairall et al. (1996), https://doi.org/10.1029/95jc03190 model (F96) to simulate the cool skin effect using surface forcing data from the Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2), validated against high‐accuracy infrared radiometric measurements from the Marine‐Atmospheric Emitted Radiance Interferometer (M‐AERI) and the Infrared Sea Surface Temperature Autonomous Radiometer (ISAR) collected during research cruises spanning diverse oceanic regions. Results show that F96 model simulations driven by MERRA‐2 inputs generally match the observed skin effects, with mean biases of −0.03 to −0.04 K and standard deviations ∼0.1 K, attributed primarily to uncertainties in humidity, longwave radiative flux, and wind speed. A 24‐year global analysis (2000–2023) revealed a mean cool skin effect of approximately −0.2 K, with pronounced spatial and seasonal variability. Strongest cooling (−0.3 K or more) occurs in regions such as western boundary currents during colder months, while the Southern Ocean shows much weaker skin effects (−0.1 K) due to persistent high wind speeds and reduced net heat loss. These results suggest that the commonly applied constant correction of −0.17 K in satellite SST _skin retrievals does not accurately represent the true magnitude and variability of the cool skin effect. A more accurate correction scheme incorporating latitude and seasonal dependence is recommended to improve the accuracy of satellite‐derived SST _skin fields, and better support climate model applications.

Ocean surface turbulent heat fluxes, sensible heat flux (SHF) and latent heat flux (LHF), are crucial for regulating the Earth's energy and water budgets. However, standard bulk flux algorithms neglect the enhanced surface area caused by the ocean wave slopes. This study introduces a geometric correction to SHF and LHF based on the mean square slope (MSS) of ocean waves, derived analytically and applied globally using ERA5 reanalysis data from 1940 to 2024. After correcting MSS to account for unresolved high‐frequency waves, we estimate that the effective air‐sea interface area is enhanced by ∼2% on average, leading to mean corrections of 0.29 W/m ² for SHF and 2.34 W/m ² for LHF. Climatological trends in MSS and LHF corrections reveal statistically significant increases over the past several decades. These corrections are spatially and seasonally variable, and may have cumulative impacts on climate simulations, ocean heat content assessments, and flux‐dependent satellite retrievals.

Ocean surface turbulent heat fluxes, sensible heat flux (SHF) and latent heat flux (LHF), are crucial for regulating the Earth’s energy and water budgets. However, standard bulk flux algorithms neglect the enhanced surface area caused by the ocean wave slopes. This study introduces a geometric correction to SHF and LHF based on the mean square slope (MSS) of ocean waves, derived analytically and applied globally using ERA5 reanalysis data from 1940–2024. After correcting MSS to account for unresolved high-frequency wave components, we estimate that the effective air-sea interface area is enhanced by ~2% on average, leading to mean corrections of 0.29 W/m2 for SHF and 2.34 W/m2 for LHF. Climatological trends in MSS and LHF corrections reveal statistically significant increases over the past several decades. These corrections are spatially and seasonally variable, and may have cumulative impacts on climate simulations, ocean heat content assessments, and flux-dependent satellite retrievals.

Satellite-retrieved sea-surface skin temperature (SST_skin) is essential for many Near-Real-Time studies. This study aimed to assess the potential to improve the accuracy of satellite-based SST_skin retrieval in the Caribbean region by using atmospheric correction algorithms based on four readily available machine learning (ML) approaches: eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Random Forest (RF), and the Artificial Neural Network (ANN). The ML models were trained on an extensive dataset comprising in situ SST measurements and atmospheric state parameters obtained from satellite products, reanalyzed datasets, research cruises, surface moorings, and drifting buoys. The benefits and shortcomings of various ML methods were assessed through comparisons with withheld in situ measurements. The results demonstrate that the ML-based algorithms achieve promising accuracy, with mean biases within 0.07 K when compared with the buoy data and ranging from −0.107 K to 0.179 K relative to the ship-derived SST_skin data. Notably, both XGBoost and RF stand out for their superior correlation and efficacy in the statistical results of validation. The improved SST_skin derived using the ML-based algorithms could enhance our understanding of vital oceanic and atmospheric characteristics and have the potential to reduce uncertainty in oceanographic, meteorological, and climate research.

Satellite remote sensing is the best way to derive sea surface skin temperature (SST_skin) 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 SST_skin 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 SST_skin 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 SST_skin 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 SST_skin 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 SST_skin data to study surface ocean processes and climate change in the Arctic.

The infrared (IR) satellite remote sensing of sea surface skin temperature (SST_skin) is challenging in the northern high-latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SST_skin 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 SST_skin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SST_skin retrievals. This study aims to assess the accuracy of MODIS-retrieved SST_skin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SST_skin data. The mean difference in SST_skin 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 SST_skin 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 SST_skin 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 SST_skin in the Arctic, at least at the Pacific side, and underscores that more in situ SST_skin data at high latitudes are needed for further error identification and algorithm development of IR SST_skin.

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 differences in SSTskin from the SD-1036 and SD-1037 measurements are different by ~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.

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.

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 40ºS 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 μm and 12 μm is opposite to that of H2O. A correction for HTHH aerosol effects in the retrieval algorithm is therefore desirable.

The existence of a cool sea surface skin layer in the global ocean during both day and night is generally recognized. However, a warm skin should be present if the total surface net heat flux () were to be from the atmosphere into ocean. Saildrone, an advanced uncrewed surface vehicle, has been shown to be able to provide sufficiently accurate sea skin temperature (SST _skin ) and subsurface temperature (SST _depth ) data at high latitudes. Using those SST data along with meteorological parameters from a Saildrone deployed in the Arctic in the summer of 2019, some warm skin layers were identified due to the gain resulting from the combined effect of positive air‐sea temperature difference, humid surface air and cloudy skies. Furthermore, most warm skins here were found during and shortly after rainfall events. It is essential to incorporate the ability to simulate warm skin layers in the present cool skin models.