Daily Sea Surface Temperature Research Articles

Global Increase of the Intensity of Tropical Cyclones under Global Warming Based on their Maximum Potential Intensity and CMIP6 Models

Future changes in the intensity of tropical cyclones (TCs) under global warming are uncertain, although several studies have projected an upward trend in TC intensity. In this study, we examined the changes in the strength of TCs in the twenty-first century based on the Hurricane Maximum Potential Intensity (HuMPI) model forced with the sea surface temperature (SST) from the bias-corrected CMIP6 dataset. We first investigated the relationship between the mean lifetime maximum intensity (LMI) of major hurricanes (MHs) and the maximum potential intensity (MPI) using the SST from the Daily Optimum Interpolation SST database. The LMI of MHs and the MPI in the last two decades was, on average, 2–3% higher than mean values in the sub-period 1982–2000, suggesting a relationship between changes in MPI and LMI. From our findings, the projected changes in TC intensity in the near-future period (2016–2040) will be almost similar for SSP2-4.5 and SSP5-8.5 climate scenarios. However, TCs will be 9.5% and 17% more intense by the end (2071–2100) of the twenty-first century under both climate scenarios, respectively, compared with the mean intensity over the historical period (1985–2014). In addition, the MPI response to a warmed sea surface temperature per degree of warming is a 5–7% increase in maximum potential wind speed. These results should be interpreted as a projection of changes in TC intensity under global warming since the HuMPI formulation does not include environmental factors (i.e., vertical wind shear, mid-level moisture content and environmental stratification) that influence TC long-term intensity variations.HighlightsThe maximum potential intensity (MPI) of tropical cyclones is a predictor of their climatological intensities.Tropical cyclones will be 17% more intense than today by the end of the 21st Century.The maximum potential wind speed will increase by 5–7%/ºC under global warming.

The rapid rise of severe marine heat wave systems

We introduce a new methodology to study marine heat waves (MHWs), extreme events in the sea surface temperature (SST) of the global ocean. Motivated by previously large and impactful MHWs and by theoretical expectation that the dominant heating processes coherently affect large regions of the ocean, we introduce a methodology from computer vision to construct marine heat wave systems (MWHSs) – the collation of SST extrema in dimensions of area and time. We identify 649 475 MHWSs in the 37 year period (1983–2019) of daily SST records and find that the duration (days), maximum area (km2), and total ‘volume’ (days km2) for the majority of MHWSs are well-described by power-law distributions: and . These characteristics confirm SST extrema exhibit strong spatial coherence that define the formation and evolution of MHWs. Furthermore, the most severe MHWSs deviate from these power-laws and are the dominant manifestation of MHWs: extrema in ocean heating are driven by the systems with largest area and duration. We further demonstrate that the previously purported rise in the incidence of MHW events over the past decade is only significant in these severe systems. A change point analysis reveals a rapid increase in days under a severe MHW in most regions of the global ocean over the period of 2000–2005. Understanding the origin and impacts of MHWs in the current and future ocean, therefore, should focus on the production and evolution of the largest-scale and longest-duration heating phenomena.

An inversion method of subsurface thermohaline field based on deep learning and remote sensing data

ABSTRACT Seawater temperature and salinity are basic marine environmental parameters, which can be used to calculate other marine environmental parameters. However, most of the on-site observation data have the problems of uneven spatial distribution and time discontinuity, and it is difficult for remote sensing observation methods to obtain subsurface information. In this study, we proposed a deep learning model with combining remote sensing temperature and salinity as well as in-situ measured data by Argo profiles, and the nonlinear relationship was revealed. An effective and direct inversion method was realized for underwater three-dimensional thermohaline structure based on remote sensing temperature and salinity at accurate points on global scale. The SST data were obtained from the FY3C-VIRR daily sea surface temperature product, the SSS data were acquired from the SMAP Level 3 8-day running averages sea surface salinity product, and the Argo scatter data were got from the ‘Global Ocean Argo Scatter Data Set’. Based on the temporal and spatial location information of the data, this paper matched the remote sensing data from 2016 to 2019 with the Argo data, 64751 valid pairing points were obtained. The deep learning model was constructed as a multilayer perceptron model with 5 hidden layers. The RMSE of temperature had a maximum value of 2.106°C in 130 m depth and a minimum value of 0.367°C in 1000 m with an average of 1.174°C for validation dataset. And the RMSE of salinity had a maximum value of 0.356 psu in 0 m and a minimum value of 0.045 psu in 1000 m with an average of 0.202 psu. Compared with other methods based on fixed mesh products, this study realized a direct inversion method at any location in the global ocean and improved the inversion accuracy, which provides a reliable data support for refined marine monitoring.

Co-Occurrence of Atmospheric and Oceanic Heatwaves in the Eastern Mediterranean over the Last Four Decades

Heatwaves are now considered one of the main stressors of global warming. As a result of anthropogenic warming, atmospheric and oceanic heatwaves have increased in frequency, intensity and duration in recent decades. These extreme events have recently become a major concern in climate research due to their economic and environmental impacts on ecosystems. In this study, we investigated the co-occurrence and relationship between atmospheric and marine heatwaves (AHW/MHW) in the Eastern Mediterranean (EMED) over the last four decades (1982–2021). Furthermore, the spatio-temporal variability and trends of sea surface temperature (SST), near-surface air temperature (SAT), AHW and MHW characteristics (frequency and duration) were examined. For these objectives, we used daily gridded high-resolution satellite SST data (0.05° × 0.05°) and the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF-ERA5) atmospheric reanalysis SAT and wind components (0.25° × 0.25°). The results showed an average warming trend of about 0.38 ± 0.08 °C/decade and 0.43 ± 0.05 °C/decade for SAT and SST, respectively. A high statistically significant (p < 0.05) correlation (R = 0.90) was found between AHW and MHW frequency. Our results showed that more than half of the MHWs in the EMED co-occurred with AHWs throughout the study period. The most intense summer MHW in 2021, which co-occurred with AHW, was associated with higher positive anomalies of SAT and SST, and a decrease in the wind speed anomaly.

Empirical evidence of temperature variability as a concurrent limiting factor for abalone distribution

Understanding how the performance, fitness, and distribution of species are impacted by changes in temperature is crucial for forecasting the ecological effects of climate change. Increasing mean sea surface temperature may alter the distribution of abalone species in the northeastern Pacific, although no apparent pattern has yet emerged. Ecophysiological observations (energy budget and the temperature performance curve based on scope for growth), satellite-derived daily sea surface temperature data, and observations of the population density distribution of wild abalones were integrated. The highest temperature-dependent scope for growth (physiological optimum) was discovered between 20.5 and 28.5 °C, peaking at 24 °C, with the upper thermal limit about 30 °C. Off the west coast of Baja California, the distribution of abalone appears to be dictated by the tradeoff between a mean temperature close to the physiological optimum and exposure to a highly variable environment (i.e., more stress and the risk of reaching suboptimal and lethal warm temperatures at the daily scale). The findings suggest that future models of the distribution of marine species in relation to thermal habitat should contain time-domain variability in addition to spatial-domain variability.

Predicting Subseasonal Tropical Cyclone Activity Using NOAA and ECMWF Reforecasts

Abstract Tropical cyclones are extreme events with enormous and devastating consequences to life, property, and our economies. As a result, large-scale efforts have been devoted to improving tropical cyclone forecasts with lead times ranging from a few days to months. More recently, subseasonal forecasts (e.g., 2–6-week lead time) of tropical cyclones have received greater attention. Here, we study whether bias-corrected, subseasonal tropical cyclone reforecasts of the GEFS and ECMWF models are skillful in the Atlantic basin. We focus on the peak hurricane season, July–November, using the reforecast years 2000–19. Model reforecasts of accumulated cyclone energy (ACE) are produced, and validated, for lead times of 1–2 and 3–4 weeks. Week-1–2 forecasts are substantially more skillful than a 31-day moving-window climatology, while week-3–4 forecasts still exhibit positive skill throughout much of the hurricane season. Furthermore, the skill of the combination of the two models is found to be an improvement with respect to either individual model. In addition to the GEFS and ECMWF model reforecasts, we develop a statistical modeling framework that solely relies on daily sea surface temperatures. The reforecasts of ACE from this statistical model are capable of producing better skill than the GEFS or ECMWF model, individually, and it can be leveraged to further enhance the model combination reforecast skill for the 3–4-week lead time.

Oceanic productivity and high-frequency temperature variability—not human habitation—supports calcifier abundance on central Pacific coral reefs

Past research has demonstrated how local-scale human impacts—including reduced water quality, overfishing, and eutrophication—adversely affect coral reefs. More recently, global-scale shifts in ocean conditions arising from climate change have been shown to impact coral reefs. Here, we surveyed benthic reef communities at 34 U.S.-affiliated Pacific islands spanning a gradient of oceanic productivity, temperature, and human habitation. We re-evaluated patterns reported for these islands from the early 2000s in which uninhabited reefs were dominated by calcifiers (coral and crustose coralline algae) and thought to be more resilient to global change. Using contemporary data collected nearly two decades later, our analyses indicate this projection was not realized. Calcifiers are no longer the dominant benthic group at uninhabited islands. Calcifier coverage now averages 26.9% ± 3.9 SE on uninhabited islands (compared to 45.18% in the early 2000s). We then asked whether oceanic productivity, past sea surface temperatures (SST), or acute heat stress supersede the impacts of human habitation on benthic cover. Indeed, we found variation in benthic cover was best explained not by human population densities, but by remotely sensed metrics of chlorophyll-a, SST, and island-scale estimates of herbivorous fish biomass. Specifically, higher coral and CCA cover was observed in more productive waters with greater biomass of herbivores, while turf cover increased with daily SST variability and reduced herbivore biomass. Interestingly, coral cover was positively correlated with daily variation in SST but negatively correlated with monthly variation. Surprisingly, metrics of acute heat stress were not correlated with benthic cover. Our results reveal that human habitation is no longer a primary correlate of calcifier cover on central Pacific island reefs, and highlight the addition of oceanic productivity and high-frequency SST variability to the list of factors supporting reef builder abundance.

Seven-day sea surface temperature prediction using a 3DConv-LSTM model

Due to the application demand, users have higher expectations for the accuracy and resolution of sea surface temperature (SST) products. Recent advances in deep learning show great advantages in exploiting massive ocean datasets, and provides opportunities for investigating regional SST predictions in an efficiency approach. However, for deep learning-based SST prediction to be adopted by users, the output must be accurate. This paper investigates the 7-day SST prediction over the China seas and their adjacent waters at a 0.05° spatial resolution. To improve the prediction’s accuracy, we designed a deep learning model combining the three-dimensional convolution and long short-term memory under multi-input multi-output strategy. The Operational SST and Sea Ice Analysis (OSTIA) SST anomaly was used as training data. To test the model prediction ability, we verified the predicted results with the Sub-seasonal to Seasonal (S2S) prediction data from 2015 to 2019. Validation of the predicted SSTs using the OSTIA test datasets show that the root-mean-square error increases from 0.27°C to 0.53°C during the 1- to 7-day lead time, with predictability decreases from southeast to northwest in the study area. Furthermore, the comparison of predicted SST and S2S data with Argo shows that our model is slightly more accurate, which can achieve -0.08°C bias, with a standard deviation of 0.35°C for a 1-day lead time and -0.07°C bias, with a standard deviation of 0.59°C for a 7-day lead time. The results indicate that the proposed deep learning model is accurate and can be applied in regional daily SST prediction.

Spatial Gap-Filling of GK2A Daily Sea Surface Temperature (SST) around the Korean Peninsula Using Meteorological Data and Regression Residual Kriging (RRK)

Satellite remote sensing can measure large ocean surface areas, but the infrared-based sea surface temperature (SST) might not be correctly calculated for the pixels under clouds, resulting in missing values in satellite images. Early studies for the gap-free raster maps of satellite SST were based on spatial interpolation using in situ measurements. In this paper, however, an alternative spatial gap-filling method using regression residual kriging (RRK) for the Geostationary Korea Multi-Purpose Satellite-2A (GK2A) daily SST was examined for the seas around the Korean Peninsula. Extreme outliers were first removed from the in situ measurements and the GK2A daily SST images using multi-step statistical procedures. For the pixels on the in situ measurements after the quality control, a multiple linear regression (MLR) model was built using the selected meteorological variables such as daily SST climatology value, specific humidity, and maximum wind speed. The irregular point residuals from the MLR model were transformed into a residual grid by optimized kriging for the residual compensation for the MLR estimation of the null pixels. The RRK residual compensation method improved accuracy considerably compared with the in situ measurements. The gap-filled 18,876 pixels showed the mean bias error (MBE) of −0.001 °C, the mean absolute error (MAE) of 0.315 °C, the root mean square error (RMSE) of 0.550 °C, and the correlation coefficient (CC) of 0.994. The case studies made sure that the gap-filled SST with RRK had very similar values to the in situ measurements to those of the MLR-only method. This was more apparent in the typhoon case: our RRK result was also stable under the influence of typhoons because it can cope with the abrupt changes in marine meteorology.

Small‐Scale Spatial Variations of Air‐Sea Heat, Moisture, and Buoyancy Fluxes in the Tropical Trade Winds

AbstractObservations from two autonomous Wave Gliders and six Lagrangian Surface Wave Instrument Float with Tracking drifters in the northwestern tropical Atlantic during the January–February 2020 NOAA Atlantic Tradewind Ocean‐atmosphere Mesoscale Interaction Campaign (ATOMIC) are used to evaluate the spatial variability of bulk air‐sea heat, moisture, and buoyancy fluxes. Sea surface temperature (SST) gradients up to 0.7°C across 10–100 km frequently persisted for several days. SST gradients were a leading cause of systematic spatial air‐sea sensible heat flux gradients, as variations over 5 Wm−2 across under 20 km were observed. Wind speed gradients played no significant role and air temperature adjustments to SST gradients sometimes acted to reduce spatial flux gradients. Wind speed, air temperature, and air humidity caused high‐frequency spatial and temporal flux variations on both sides of SST gradients. A synthesis of observations demonstrated that fluxes were usually enhanced on the warm SST side of gradients compared to the cold SST side, with variations up to 10 Wm−2 in sensible heat and upward buoyancy fluxes and 50 Wm−2 in latent heat flux. Persistent SST gradients and high‐frequency air temperature variations each contributed up to 5 Wm−2 variability in sensible heat flux. Latent heat flux was instead mostly driven by air humidity variability. Atmospheric gradients may result from convective structures or high‐frequency turbulent fluctuations. Comparisons with 0.05°‐resolution daily satellite SST observations demonstrate that remote sensing observations or lower‐resolution models may not capture the small‐scale spatial ocean variability present in the Atlantic trade wind region.

Temperature variations in the northern Gulf of Alaska across synoptic to century-long time scales

Surface and subsurface moored buoy, ship-based, remotely sensed, and reanalysis datasets are used to investigate thermal variability of northern Gulf of Alaska (NGA) nearshore, coastal, and offshore waters over synoptic to century-long time scales. NGA sea surface temperature (SST) showed a larger positive trend of 0.22 ± 0.10 °C per decade over 1970–2021 compared to 0.10 ± 0.03 °C per decade over 1900–2021. Over synoptic time scales, SST covariance between two stations is small (<10%) when separation exceeds 100 km, while stations separated by 500 km retain 50% of their co-variability for seasonal and longer fluctuations. Relative to in situ sensor data, remotely sensed SST data has limited accuracy in some NGA settings, capturing 60–70% of the daily SST anomaly in coastal and offshore waters, but often <25% nearshore. North Pacific and NGA leading modes of SST variability leave 25–50% of monthly variance unresolved. Analysis of the 2014–2016 Pacific marine heatwave shows that NGA coastal surface temperatures warmed contemporaneously with offshore waters through 2013, but deep inner shelf waters (200–250 m) exhibited delayed warming. Offshore surface waters cooled from 2014 to 2016, while shelf waters continued to warm from the combined effects of local air-sea and advective heat fluxes. We find that annually averaged Sitka air temperature is a leading predictor (r2 = 0.37, p < 0.05) for following-year NGA coastal water column temperature. Our results can inform future environmental monitoring designs, assist forward-looking projections of marine conditions, and show the importance of in situ measurements for nearshore studies that require knowledge of thermal conditions over time scales of days and weeks.

The role of air–sea coupling on November–April intraseasonal rainfall variability over the South Pacific

We investigate the impact of resolving air-sea interaction on the simulation of the intraseasonal rainfall variability over the South Pacific using the ECHAM5 atmospheric general circulation model coupled with the Snow-Ice-Thermocline (SIT) ocean model. We compare the fully coupled simulation with two uncoupled ECHAM5 simulations, one forced with sea surface temperature (SST) climatology and one forced with daily SST from the coupled model. The intraseasonal rainfall variability over the South Pacific is reduced by 17% in the uncoupled model forced with SST climatology and increased by 8% in the uncoupled simulation forced with daily SST, suggesting the role of air–sea coupling and SST variability. The coupled model best simulates the key characteristics of the two dominant patterns (modes) of intraseasonal rainfall variability over the South Pacific with reasonable propagation and correct periodicity. The spatial structure of the two rainfall modes in all three simulations is very similar, suggesting the dynamics of the atmosphere primarily generate these modes. The southeastward propagation of rainfall anomalies associated with two leading rainfall modes in the South Pacific depends upon the eastward propagating Madden–Julian Oscillation (MJO) signals from the Indian Ocean and western Pacific. Air-sea interaction improves such propagation as both eastward and southeastward propagations are substantially reduced in the uncoupled model forced with SST climatology. The simulation of both eastward and southeastward propagations considerably improved in the uncoupled model forced with daily SST; however, the periodicity differs from the coupled model. Such discrepancy in the periodicity is attributed to the changes in the SST-rainfall relationship with weaker correlations and the nearly in-phase relationship, attributed to enhanced positive latent heat flux feedbacks.

Challenges of sperm cryopreservation in transferring heat adaptation of corals across ocean basins.

Reef-building corals live very close to their upper thermal limits and their persistence is imperiled by a rapidly warming climate. Human interventions may be used to increase the thermal limits of sensitive corals by cross-breeding with heat-adapted populations. However, the scope of breeding interventions is constrained by regional variation in the annual reproductive cycle of corals. Here we use cryopreservation technology to overcome this barrier and cross-breed conspecific coral populations across ocean basins for the first time. During regional spawning events, sperm samples were cryopreserved from populations of the widespread Indo-Pacific coral, Platygyra daedalea, from the southern Persian Gulf (maximum daily sea surface temperature of 36 °C), the Oman Sea (33 °C), and the central Great Barrier Reef (30 °C). These sperm samples were thawed during a later spawning event to test their ability to fertilize freshly spawned eggs of P. daedalea colonies from the central Great Barrier Reef. Average fertilization success for the Persian Gulf (9%) and Oman Sea (6%) sperm were 1.4–2.5 times lower than those for the native cryopreserved sperm from Great Barrier Reef (13–15%), potentially due to lower sperm quality of the Middle Eastern sperm and/or reproductive incompatibility between these distant populations. Overall, fertilization success with cryopreserved sperm was low compared with fresh sperm (>80%), likely due to the low motility of thawed sperm (≤5%, reduced from 50% to >90% in fresh sperm). To evaluate whether cross-bred offspring had enhanced thermal tolerance, the survival of larvae sired by Persian Gulf cryopreserved sperm, Great Barrier Reef cryopreserved sperm, and Great Barrier Reef fresh sperm was monitored for six days at ambient (27 °C) and elevated (33 °C) temperature. Against expectations of thermal tolerance enhancement, survival of larvae sired by Persian Gulf cryopreserved sperm was 2.6 times lower than larvae sired by Great Barrier Reef fresh sperm at 33 °C (27% versus 71%), but did not differ at 27 °C (77% versus 84%). This lack of enhanced thermal tolerance was unlikely due to outbreeding depression as survival was equally poor in larvae sired by Great Barrier Reef cryopreserved sperm. Rather, follow-up tests showed that cryoprotectant exposure during fertilization (0.1% DMSO) has a negative effect on the survival of P. daedalea larvae which is exacerbated at elevated temperature. Collectively, our findings highlight challenges of breeding corals for enhanced thermal tolerance using cryopreserved sperm, which may be overcome by methodological advances in the collection and preservation of high-quality motile sperm and minimizing the exposure time of eggs to cryoprotectants.

Sea Surface Temperature Variability and Marine Heatwaves in the Black Sea

Marine heatwaves (MHWs) have recently been at the forefront of climate research due to their devastating impacts on the marine environment. In this study, we have evaluated the spatiotemporal variability and trends of sea surface temperature (SST) and MHWs in the Black Sea. Furthermore, we investigated the relationship between the El Niño–Southern Oscillation (ENSO) and MHW frequency. This is the first attempt to investigate MHWs and their characteristics in the Black Sea using high-resolution remote-sensing daily satellite SST data (0.05° × 0.05°) from 1982 to 2020. The results showed that the spatial average of the SST warming rate over the entire basin was about 0.65 ± 0.07 °C/decade. Empirical orthogonal function (EOF) analysis revealed that SST in the Black Sea exhibited inter-annual spatiotemporal coherent variability. The maximum spatial SST variability was discovered in the central Black Sea, whereas the lowest variability was in the Batumi and Caucasus anti-cyclonic eddies in the eastern Black Sea. The highest SST temporal variability was found in 1994. More than two-thirds of all MHW events were recorded in the last decade (2010–2020). The highest annual MHW durations were reported in 1994 and 2020. The highest MHW frequency was detected in 2018 (7 waves). Over the whole study period (1982–2020), a statistically significant increase in annual MHW frequency and duration was detected, with trends of 1.4 ± 0.3 waves/decade and 2.8 ± 1.3 days/decade, respectively. A high number of MHW events coincided with El Niño (e.g., 1996, 1999, 2007, 2010, 2018, and 2020). A strong correlation (R = 0.90) was observed between the annual mean SST and the annual MHW frequency, indicating that more MHWs can be expected in the Black Sea, with serious consequences for the marine ecosystem.

The Trend and Interannual Variability of Marine Heatwaves over the Bay of Bengal

Marine heatwaves (MHWs) are long-lasting extreme oceanic warming events that can cause devastating effects on warm-water corals and associated ecosystems. The linear trend and interannual variability of MHWs over the Bay of Bengal (BOB) during 1982–2020 are investigated by a high-resolution daily sea surface temperature (SST) dataset. In regions where warm-water coral reefs are concentrated, annual MHW days and frequency significantly increase during 1982–2020, at rates exceeding that of the global mean. The coldest boreal winter season witnesses significant and steady increase trends in MHW days and frequency. In contrast, the trend is insignificant in the climatological warmest season (March to June) south of 15° N in the BOB, mainly due to large interannual variability. El Niño and Southern Oscillation (ENSO) dominates the interannual variability of BOB MHWs, which are highly consistent with the evolution of the mean SST. The negative phase of North Atlantic Oscillation (NAO) also modulates the occurrences of MHWs, especially over the northeastern BOB. The two climate modes synergistically explain about 50~70% of the interannual variances in the BOB’s MHWs. Correlation analysis reveals that south of 15° N in the BOB, the effect of El Niño on MHWs is evident from the boreal autumn of its developing phase to the boreal summer of its decaying phase, along with limited influence from NAO. However, in the northeast of the BOB, the effect of El Niño merely emerges from April to August of its decaying stage. In comparison, boreal winter-to-spring NAO exerts a strong control over March-to-June MHWs in the northeastern BOB. The results suggest that various climate modes may jointly or separately influence MHWs at certain seasons and locations, which is important for the seasonal prediction of MHWs. Indeed, when combining the Niño3.4 mature winter index and boreal winter-to-spring NAO index to build a regression model, it is more effective in reproducing the BOB’s MHW frequency compared to the Niño3.4 index alone.

Improvements of Sea Fog Forecasting Based on CMA-TYM

Based on the operational version of the China Meteorological Administration Typhoon Model (CMA-TYM, formerly known as GRAPES_TYM), a series of numerical tests are conducted by optimizing the boundary layer parameterization scheme, vertical resolution, and boundary conditions. Instead of the sea surface temperature (SST) from the Global Forecast System (GFS) model, more accurate daily SST data reflecting the daily SST variation are used as the boundary condition. The new SST dataset is capable of representing the key points in the area, including the low coastal SST related to upwelling, the intrusion of the Yellow Sea (YS) Warm Current, and the ocean front between the YS and the East China Sea. An analysis of the performances of two boundary layer parameterization schemes (the Yonsei University scheme and the Medium-Range Forecast scheme) in characterizing turbulent heat exchange reveals that the former can more accurately reflect offshore turbulence and forecast the fog area. By increasing the number of vertical layers of the model to 68 and reducing the height of the bottom layer to approximately 10 m, the model presents a better performance in simulating the rapid formation and dissipation of sea fog. With the above improvements, the equitable threat score (ETS) for the hindcasting of eleven sea fog cases in the spring of 2018 increases by 61%, mainly due to the increase in the correctly forecasted fog area.

High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension

Sea SurfaceTemperature (SST) is a critical parameter for monitoring the marine environment and understanding various ocean phenomena. While SST can be regularly retrieved from satellite data, it often suffers from missing data due to various reasons including cloud contamination. In this study, we proposed a novel two-step data fusion framework for generating high-resolution seamless daily SST from multi-satellite data sources. The proposed approach consists of (1) SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using the SSTs derived from two satellite sensors (i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer 2(AMSR2)), and (2) SST improvement through data fusion using random forest for consistency with in situ measurements with two schemes (i.e., scheme 1 using the reconstructed MODIS SST variables and scheme 2 using both MODIS and AMSR2 SST variables). The proposed approach was evaluated over the Kuroshio Extension in the Northwest Pacific, where a highly dynamic SST pattern can be found, from 2015 to 2019. The results showed that the reconstructed MODIS and AMSR2 SSTs through DINCAE yielded very good performance with Root Mean Square Errors (RMSEs) of 0.85 and 0.60 °C and Mean Absolute Errors (MAEs) of 0.59 and 0.45 °C, respectively. The results from the second step showed that scheme 2 and scheme 1 produced RMSEs of 0.75 and 0.98 °C and MAEs of 0.53 and 0.68 °C, respectively, compared to the in situ measurements, which proved the superiority of scheme 2 using multi-satellite data sources. Scheme 2 also showed comparable or even better performance than two operational SST products with similar spatial resolution. In particular, scheme 2 was good at simulating features with fine resolution (~50 km). The proposed approach yielded promising results over the study area, producing seamless daily SST products with high quality and high feature resolution.

北西太平洋の台風通過に伴って生じる気象庁準リアルタイム版全球海面水温解析のバイアス

The near-real-time merged satellite and in-situ data global daily sea surface temperature (SST) of the Japan Meteorological Agency (hereinafter abbreviated as R-MGD) is subjected to filtering out short-time-scale fluctuations from observations prior to the analysis time. Therefore, the rapid SST change due to the passage of tropical cyclones (TCs) is thought to cause biases. Here, the biases in the R-MGD with respect to in-situ observations were quantified along the passage of TCs in the western North Pacific. First, we examined a case study on the approach of three successive TCs in August–September 2020. The R-MGD had positive biases of > 2°C just after the passage of three TCs, and negative biases were observed after one week of the last TC's passage. The comparison of the R-MGD with a moored buoy indicates that the biases can be explained by short-term fluctuations filtered out and the SST prior to the analysis time in R-MGD analysis. Second, the composite analysis from May 2015–October 2020 indicates that the statistically significant biases at the observation points ranged between −1 days and +4 days for positive biases and between +7 days and +14 days for negative biases relative to the time of the closest approach of a TC within 500 km. The positive SST bias is largely associated with cold subsurface water and intense TCs, being pronounced in the mid-latitude, except around the Kuroshio and Kuroshio extension regions. The assimilation of in-situ observations recorded within 72 h prior to the R-MGD analysis time through additional optimal interpolation alleviates these biases because this process redeems short-time-scale fluctuations. The impact on TC forecasts and the validity of the optimal interpolation experiment against the independent observations were also investigated.

Characteristics of Spectra of Daily Satellite Sea Surface Temperature Composites in the Seas around the Korean Peninsula

Characteristics of Spectra of Daily Satellite Sea Surface Temperature Composites in the Seas around the Korean Peninsula

Retrieval of Daily Mean VIIRS SST Products in China Seas

Sea surface temperature (SST) is one of the most important factors in regulating air-sea heat flux and, thus, climate change. Most of current global daily SST products are derived from one or two transient measurements of polar-orbiting satellites, which are not the same to daily mean SST values. In this study, high-temporal-resolution SST measurements (32–40 snapshots per day) from a geostationary satellite, FengYun-4A (FY–4A), are used to analyze the diurnal variation of SST in China seas. The results present a sinusoidal pattern of the diurnal variability in SST, with the maximum value at 13:00–15:00 CST and the minimum at 06:00–08:00 CST. Based on the diurnal variation of SST, a retrieval method for daily mean SST products from polar-orbiting satellites is established and applied to 7716 visible infrared imaging radiometer (VIIRS) data in China seas. The results suggest that it is feasible and practical for the retrieval of daily mean SST with an average RMSE of 0.133 °C. This retrieval method can also be utilized to other polar-orbiting satellites and obtain more daily mean satellite SST products, which will contribute to more accurate estimation and prediction between atmosphere and ocean in the future.