Sea Surface Temperature Datasets Research Articles

Prediction of Sea Surface Temperature Using U-Net Based Model

Sea surface temperature (SST) is a key parameter in ocean hydrology. Currently, existing SST prediction methods fail to fully utilize the potential spatial correlation between variables. To address this challenge, we propose a spatiotenporal UNet (ST-UNet) model based on the UNet model. In particular, in the encoding phase of ST-UNet, we use parallel convolution with different kernel sizes to efficiently extract spatial features, and use ConvLSTM to capture temporal features based on the utilization of spatial features. Atrous Spatial Pyramid Pooling (ASPP) module is placed at the bottleneck of the network to further incorporate the multi-scale features, allowing the spatial features to be fully utilized. The final prediction is then generated in the decoding stage using parallel convolution with different kernel sizes similar to the encoding stage. We conducted a series of experiments on the Bohai Sea and Yellow Sea SST data set, as well as the South China Sea SST data set, using SST data from the past 35 days to predict SST data for 1, 3, and 7 days in the future. The model was trained using data spanning from 2010 to 2021, with data from 2022 being utilized to assess the model’s predictive performance. The experimental results show that the model proposed in this research paper achieves excellent results at different prediction scales in both sea areas, and the model consistently outperforms other methods. Specifically, in the Bohai Sea and Yellow Sea sea areas, when the prediction scales are 1, 3, and 7 days, the MAE of ST-UNet outperforms the best results of the other three compared models by 17%, 12%, and 2%, and the MSE by 16%, 18%, and 9%, respectively. In the South China Sea, when the prediction ranges are 1, 3, and 7 days, the MAE of ST-UNet is 27%, 18%, and 3% higher than the best of the other three compared models, and the MSE is 46%, 39%, and 16% higher, respectively. Our results highlight the effectiveness of the ST-UNet model in capturing spatial correlations and accurately predicting SST. The proposed model is expected to improve marine hydrographic studies.

Multimodal fusion-based spatiotemporal incremental learning for ocean environment perception under sparse observation

Accurate ocean environment perception is crucial for weather and climate prediction. Environmental limitations and deployment costs constrain satellite and buoy real-time observation, leading to sparse data availability. This paper proposes a novel approach, multimodal fusion-based spatiotemporal incremental learning, enhancing the ocean environment perception under sparse observations. This method uses sparse real-time observations to comprehend, reconstruct, and predict the full environment. First, spatiotemporal disentanglement decouples intrinsic features by integrating physical principles and data learning. Subsequently, incremental extension captures the dynamic environment through stable representation updating and dynamic behavior learning. Then, multimodal information fusion synergizes multisource intrinsic features, enabling the full perception of the ocean environment. Finally, the methodology is supported by convergence analysis and error boundary evaluation. Validation with global sea surface temperature and western Pacific Ocean high-dimensional temperature datasets demonstrates its potential for advancing ocean research and applications using sparse real-time observation.

A Relative Sea Surface Temperature Index for Classifying ENSO Events in a Changing Climate

Abstract El Niño–Southern Oscillation (ENSO) is often characterized through the use of sea surface temperature (SST) departures from their climatological values, as in the Niño-3.4 index. However, this approach is problematic in a changing climate when the climatology itself is varying. To address this issue, van Oldenborgh et al. proposed a relative Niño-3.4 SST index, which subtracts the tropical mean SST anomaly from the Niño-3.4 index and multiplies by a scaling factor. We extend their work by providing a simplified calculation procedure for the scaling factor, and confirm that the relative index demonstrates reduced sensitivity to climate change and multidecadal variability. In particular, we show in three observational SST datasets that the relative index provides a more consistent classification of historical El Niño and La Niña oceanic conditions that is more robust across climatological periods compared to the nonrelative index. Forecast skill of the relative Niño-3.4 index in the North American Multimodel Ensemble (NMME) and ACCESS-S2 is slightly reduced for targets during the first half of the year because subtracting the tropical mean removes a source of additional skill. For targets in the second half of the year, the relative and nonrelative indices are equally skillful. Observed ENSO teleconnections in 200-hPa geopotential height and precipitation during key seasons are sharper and explain more variability over Australia and the contiguous United States when computed with the relative index. Overall, the relative Niño-3.4 index provides a more robust option for real-time monitoring and forecasting ENSO in a changing climate. Significance Statement The goal of this study is to further explore a relative sea surface temperature index for monitoring and prediction of El Niño–Southern Oscillation. Sea surface temperature indices are typically computed as a difference from a 30-yr climatological average, and El Niño and La Niña events occur when values exceed a certain threshold. This method is suitable when the climate is stationary. However, because of climate change and other lower-frequency variations, historical El Niño and La Niña events are reclassified depending on which climatological period is selected. A relative index is investigated to ameliorate this problem.

The 1991–2020 sea surface temperature normals

AbstractThe 1991–2020 climate normals for sea surface temperature (SST) are computed based on the NOAA Daily Optimum Interpolation SST dataset. This is the first time that high‐resolution SST normals with global coverage can be achieved in the satellite SST era. Normals are one of the fundamental parameters in describing and understanding weather and climate and provide decision‐making information to industry, public, and scientific communities. This product suite includes SST mean, standard deviation, count and extreme parameters at daily, monthly, seasonal and annual time scales on 0.25° spatial grids. The main feature of the SST mean state revealed by the normals is that in the Tropics, the Indo‐Pacific Ocean is dominated by the warm pool (SST ≥ 28°C) while the eastern Pacific is characterized by the cold tongue (SST ≤ 24°C); in the midlatitudes, SSTs are in zonal patterns with high meridional gradients. Daily SST standard deviations are generally small (<1.0°C) except in frontal zones (>1.5°C) mostly associated with ocean currents such as the Gulf Stream, Kuroshio and Equatorial Currents. Compared to the 1982–2011 climatology, the 1991–2020 mean SSTs increased over most global areas but obvious cooling is seen in the Southern Ocean, eastern tropical South Pacific Ocean and North Atlantic warming hole. The Indo‐Pacific warm pool (IPWP) is found to have strengthened in both intensity and coverage since 1982–2011. By a count parameter criterion of ≥300 days annually with SST ≥ 28°C, the IPWP coverage increased 33% from 1982–2011 to 1991–2020. The global mean SST of 1991–2020 is warmer than that of 1982–2011, and the warming rate over 1991–2020 doubles that over 1901–2020.

Comparative analysis of spatiotemporal trends in sea surface temperature in the major marine protected areas of the Eastern Tropical Pacific

The Eastern Tropical Pacific (ETP) is characterized by complex oceanographic dynamics that affect the regional sea surface temperature (SST), a key parameter driving marine ecosystems. To date no comparative studies have been conducted on the spatial and temporal variations of SST among the several marine protected areas in the ETP. The present study used remote sensing to evaluate the monthly, annual, and decadal variations in SST from 1982 to 2019 in Coiba National Park (Coiba), Cocos Island National Park (Cocos), Malpelo Fauna and Flora Sanctuary (Malpelo), Gorgona Island National Park (Gorgona), Galápagos Marine Reserve (Galápagos) and La Plata Island (La Plata). An overall increasing trend in SST was observed across all study sites, except for West Galápagos. A decrease in SST anomaly was observed from February through April at Malpelo, Coiba, Gorgona and La Plata. These observations are likely in response to the Humboldt Current and upwelling events in the Colombian Pacific Basin. Using a longer SST dataset is recommended to determine if the annual, monthly and decadal trends observed here are driven by local, regional or global processes. Consistent monitoring of SST trends is a strategic practice in understanding and planning for current and projected changes in the marine environment.

Unbiased Proxy Calibration

AbstractThe linear calibration model is a powerful statistical tool that can be utilized to predict an unknown response variable, Y, through observations of a proxy or predictor variable, X. Since calibration involves estimation of regression model parameters on the basis of a limited amount of noisy data, an unbiased calibration slope estimation is of utmost importance. This can be achieved by means of state-of-the-art, data-driven statistical techniques. The present paper shows that weighted least-squares for both variables estimation (WLSXY) is able to deliver unbiased slope estimations under heteroscedasticity. In the case of homoscedasticity, besides WLSXY, ordinary least-squares (OLS) estimation with bias correction (OLSBC) also performs well. For achieving unbiasedness, it is further necessary to take the correct regression direction (i.e., of Y on X) into account. The present paper introduces a pairwise moving block bootstrap resampling approach for obtaining accurate estimation confidence intervals (CIs) under real-world climate conditions (i.e., non-Gaussian distributional shapes and autocorrelations in the noise components). A Monte Carlo simulation experiment confirms the feasibility and validity of this approach. The parameter estimates and bootstrap replications serve to predict the response with CIs. The methodological approach to unbiased calibration is illustrated for a paired time series dataset of sea-surface temperature and coral oxygen isotopic composition. Fortran software with implementation of OLSBC and WLSXY accompanies this paper.

Uncertainties in critical slowing down indicators of observation-based fingerprints of the Atlantic Overturning Circulation

Observations are increasingly used to detect critical slowing down (CSD) to measure stability changes in key Earth system components. However, most datasets have non-stationary missing-data distributions, biases and uncertainties. Here we show that, together with the pre-processing steps used to deal with them, these can bias the CSD analysis. We present an uncertainty quantification method to address such issues. We show how to propagate uncertainties provided with the datasets to the CSD analysis and develop conservative, surrogate-based significance tests on the CSD indicators. We apply our method to three observational sea-surface temperature and salinity datasets and to fingerprints of the Atlantic Meridional Overturning Circulation derived from them. We find that the properties of these datasets and especially the specific gap filling procedures can in some cases indeed cause false indication of CSD. However, CSD indicators in the North Atlantic are still present and significant when accounting for dataset uncertainties and non-stationary observational coverage.

Impact of microwave observations on the estimation of Arctic sea surface temperatures

The frequent and persistent cloud cover in the Arctic limits the extent to which sea surface temperature (SST) can be retrieved from thermal infrared (IR) satellite sensors. Passive microwave (PMW) observations provide highly complementary information to IR, enabling measurements through non-precipitating clouds, although at a coarser spatial resolution. The differences in coverage, accuracy, footprint size, spatial resolution and error characteristics between IR and PMW SSTs require a systematic assessment of how to best combine IR and PMW SST retrievals. This is provided in this study on the basis of the ESA-CCI PMW SST climate data record (CDR) and an existing IR-based gap-free SST and sea ice surface temperature CDR covering the Arctic (>58°N), where cloud cover is a serious limitation to IR sensors. An important step towards a combined IR and PMW SST CDR is to correct for systematic biases in the PMW and IR SST datasets relative to each other. The PMW SSTs show reduced biases against in situ SSTs compared to the IR SSTs, but for consistency with time periods when no Arctic PMW SSTs were available, the PMW SSTs have been adjusted to the IR SSTs in this study. This is done using a dynamic bias correction to generate a consistent combined IR and PMW Arctic SST CDR for the period 2002–2017. Including PMW SSTs reduces the standard deviations from 0.54 °C, 0.55 °C and 0.47 °C to 0.47 °C, 0.54 °C and 0.41 °C against drifters, moorings and Argo floats, respectively. The improved performance is seen in almost all regions (including those already covered by IR observations), with the largest improvement in IR data sparse regions. The average theoretical uncertainty reduces by 0.08 °C, which is in good agreement with the observed improvement in the standard deviation against drifters. The results are very promising and expected to improve even further in the future with the launch of the Copernicus Imaging Microwave Radiometer (CIMR), which will enable PMW SST retrievals with lower uncertainties and much closer to coasts and sea ice (where the largest uncertainties arise) than what is possible with previous and current PMW radiometers.

Analysis of Diurnal Sea Surface Temperature Variability in the Tropical Indian Ocean

Based on the 30-year global hourly sea surface temperature (SST) dataset (MLSST) produced by the National Marine Environmental Forecasting Center, Ministry of Natural Resources of China, we analyzed the variability of diurnal sea surface temperature amplitude (DSST) of the tropical Indian Ocean at multiple time scales, as well as its influencing factors. The results show that the DSST in the Arabian Sea, Bay of Bengal, and equatorial Indian Ocean exhibits a bimodal seasonal variation with a semi-annual cycle, while the DSST in the southern Indian Ocean shows an annual cycle. The seasonal variation of DSST is mainly influenced by factors such as sea surface wind speed, shortwave solar radiation, and precipitation. The DSST in the equatorial Indian Ocean is generally higher during El Niño years compared to La Niña years. At the intraseasonal scale, the large standard deviation of DSST in boreal winter is mainly distributed in the southern hemisphere, while the large standard deviation of DSST in boreal summer shifts northward. The intraseasonal variation amplitude of DSST in boreal winter of the tropical Indian Ocean is greater than that in boreal summer. The DSST in the tropical Indian Ocean exhibits significant variation characteristics at multi-time scales. This study provides reference for numerical simulation of air-sea interaction patterns in the tropical Indian Ocean, as well as improvement of short-term climate prediction.

Seasonal forecast of tropical cyclones in the Southwest Pacific Ocean

AbstractPredictions of tropical cyclone (TC) activity have been a topic of recurrent interest and research in the past. Here we utilize reanalysis datasets of sea surface temperature (SST) and mixed layer depth (MLD) to build a statistical seasonal forecasting model that produces outlooks of expected TC counts in the region of the Southwest Pacific (SWP). Nevertheless, the model applicability can be extended to other regions and basins. A novel TC predictor index is developed at the daily scale and used to obtain an objective classification of synoptic weather patterns. This classification has been performed by clustering the daily index predictor fields, previously transformed into principal components, using a K‐mean algorithm. As a result, 49 daily weather types (DWTs) are presented which inform about the mean representative features and spatial patterns of both predictor and predictand variables. Thus, statistical relationships between TC activity and nonlinear combinations of predictor variables are found to assign daily rates of expected TCs. The cluster‐based model is calibrated from 1982 to 2019 and validated by recent TC season observations, demonstrating the operational application using ensembles of long‐term predictions in the Southwest Pacific. Results have shown which synoptic types of SST and MLD are favourable to cyclogenesis and activity, with additional information related to concurrent sea level pressure and precipitation synoptic patterns, as well as seasonal and interannual climate variability.

Multivariate Sea Surface Prediction in the Bohai Sea Using a Data-Driven Model

Data-driven predictions of marine environmental variables are typically focused on single variables. However, in real marine environments, there are correlations among different oceanic variables. Additionally, sea–air interactions play a significant role in influencing the evolution of the marine environment. Both internal dynamics and external drivers contribute to these changes. In this study, a data-driven model is proposed using sea surface height anomaly (SSHA), sea surface temperature (SST), and sea surface wind (SSW) in the Bohai Sea. This model combines multivariate empirical orthogonal functions (MEOFs) with long and short-term memory (LSTM). MEOF analysis is used on the multivariate dataset of SSHA and SST, considering the correlation among sea surface variables. SSW is introduced as a predictor to enhance the predictability of the multivariate sea surface model. In the case of the Bohai Sea, the comparative tests of the model without wind field effect, the fully coupled model, and the proposed prediction model were carried out. MEOF analysis is employed in comparative experiments for oceanic variables, atmospheric variables, and combined atmospheric and oceanic variables. The results demonstrate that using wind field as a predictor can improve the forecast accuracy of SSHA and SST in the Bohai Sea. The root mean square errors (RMSE) for SSHA and SST in a 7-day forecast are 0.016 m and 0.3200 °C, respectively.

Observed and Projected Changes in North Atlantic Seasonal Temperature Reduction and Their Drivers

AbstractThe autumn‐winter seasonal temperature reduction (STR) of the surface North Atlantic Ocean is investigated with control and climate change simulations of a coupled model and an observation‐based sea surface temperature (SST) data set. In the climate change simulation, an increase in the magnitude of the STR is found over much of the North Atlantic, and this change is particularly marked in sea‐ice affected regions and the subpolar gyre. Similar results for the mid‐high latitude North Atlantic are obtained in the observational analysis. In particular, both the observation and climate model based results show that the STR has increased in magnitude by up to 0.3°C per decade in the subpolar gyre over the period 1951–2020. Drivers for the stronger STR are explored with a focus on potential contributions from increases in either ocean heat loss or the sensitivity of SST to heat loss. Over a large part of the mid‐high latitude North Atlantic surface heat loss is found to have weakened in recent decades and is therefore not responsible for the stronger STR (exceptions to this are the near‐coastal areas where sea‐ice loss is important). In contrast, analysis of daily sensible and latent heat flux data reveals that the sensitivity of SST to heat loss has increased indicating that this term has played a major role in the stronger STR. Areas of greater SST sensitivity (and greater STR) are associated with increased surface stratification brought about predominantly by warming of the northern ocean regions.

Temperature amplification and marine heatwave alteration in shallow coastal bays

Shallow coastal ecosystems are threatened by marine heatwaves, but few long-term records exist to quantify these heatwaves. Here, 40-year records of measured water temperature were constructed for a site in a system of shallow bays with documented heatwave impacts and a nearby ocean site; available gridded sea-surface temperature datasets in the region were also examined. Water temperatures at both sites increased significantly though bay temperatures were consistently 3-4°C hotter in summer and colder in winter and were more variable overall, differences not captured in high-resolution gridded sea-surface temperature datasets. There was considerable overlap in heatwave events at the coastal bay and ocean sites. Annual heatwave exposure was similar and significantly increased at both sites while annual heatwave intensity was significantly higher at the bay site owing to the high variance of the daily temperature anomaly there. Event frequency at both sites increased at a rate of about 1 event/decade. Future simulations indicate all heatwave metrics increase, as do days above 28°C, a heat stress threshold for seagrass. Ocean temperatures on the U.S. mid-Atlantic margin have rarely exceeded this threshold, while summer bay temperatures commonly do, allowing ocean exchange with coastal bays to provide thermal relief to bay ecosystems. This will have changed by 2100, creating a thermal environment that threatens seagrass communities in these systems. Documenting such change requires development of long-term water temperature records in more shallow coastal systems.

Better-constrained climate sensitivity when accounting for dataset dependency on pattern effect estimates

Abstract. The best estimate of equilibrium climate sensitivity (ECS) constrained based on the instrumental record of historical warming becomes coherent with other lines of evidence when the dependence of radiative feedback on the pattern of surface temperature change (pattern effect) is incorporated. Pattern effect strength is usually estimated with atmosphere-only model simulations forced with observed historical sea-surface temperature (SST) and sea-ice change and constant pre-industrial forcing. However, recent studies indicate that pattern effect estimates depend on the choice of SST boundary condition dataset, due to differences in the measurement sources and the techniques used to merge and construct them. Here, we systematically explore this dataset dependency by applying seven different observed SST datasets to the MPI-ESM1.2-LR model covering 1871–2017. We find that the pattern effect ranges from -0.01±0.09 to 0.42±0.10 W m−2 K−1 (standard error), whereby the commonly used Atmospheric Model Intercomparison Project II (AMIPII) dataset produces by far the largest estimate. When accounting for the generally weaker pattern effect in MPI-ESM1.2-LR compared to other models, as well as dataset dependency and intermodel spread, we obtain a combined pattern effect estimate of 0.37 W m−2 K−1 [−0.14 to 0.88 W m−2 K−1] (5th–95th percentiles) and a resulting instrumental record ECS estimate of 3.2 K [1.8 to 11.0 K], which as a result of the weaker pattern effect is slightly lower and better constrained than in previous studies.

Sea level pressure response to the specification of eddy-resolving sea surface temperature in simulations of Australian east coast lows

Four east coast lows (ECLs) were simulated with the Weather Research and Forecast model to investigate the influence of the sea surface temperature (SST) distribution on the sea level pressure (SLP). Each ECL was simulated with two different SST datasets: the Bluelink SST field and NCEP skin temperature field. The former resolved eddies in the East Australian Current while the latter did not. The simulated SLP fields in the eddy-resolving SST runs were compared with those in the non-eddy-resolving SST runs. On time-scales of about 48 hours, higher SSTs were asso-ciated with lower SLPs. The spatial scale of the SLP response was similar to that of the ocean eddies, indicative of the rapidity and robustness of the response given the rapidly evolving conditions within the storms. On shorter time-scales, the SLP response to SST change can become substantially larger. The largest reductions in SLP in the eddy-resolving SST runs were associated with regions of deep atmospheric convection that warm the tropospheric column. These areas were shown to be related to the SST distribution with the greatest SLP reductions associated with convection over strong SST gradient regions. The landfall of a damaging convective mesoscale low pressure system on 8 June 2007 was also investigated. It was found that a region of strong SST gradients on the southern flank of a large warm ocean eddy was associated with lower pressures at the time of formation of this meso-low. In addition, the only case that simulated the low pressure at the correct time (albeit at not quite the correct location) was the eddy-resolved SST run. It was hy-pothesized that the development of this meso-low that impacted the coast around Newcastle, was enhanced because of the eddy-scale SST distribution at the time.

Spatiotemporal networks for ENSO forecasting with LICOM3 and remote sensing data

El Niño/Southern Oscillation (ENSO) is a complex coupled ocean-atmosphere event, usually manifested as an abnormal increase or decrease in Sea Surface Temperature (SST) in the equatorial Pacific Ocean. ENSO is one of the critical factors causing global climate extremes and ecosystem turbulence. Therefore, long-term robust ENSO predictions are essential for people’s productive life and social development. The statistical and dynamic models still face challenges in making long-lead-time ENSO forecasts, and the accuracy can be improved. Here, we have created three innovative efforts for ENSO forecasting: (1) To compensate for the lack of Remote Sensing (RSS) dataset, we apply transfer learning with the large SST dataset simulated from the LASG/IAP Climate Ocean Model Version 3 (LICOM3). (2) We propose a novel encoder-decoder structure with multiple Spatiotemporal prediction units (ST-Pred) named ED-PredRNN. The ST-Pred changes the original transfer direction by adding spatiotemporal memory cells, allowing the model to extract more information. (3) To reduce the memory bandwidth load, we use FP32 and FP16 mixed-precision computing methods to improve model training and inference speed.The result shows that during 2011–2020 verification period, the correlation skills of the Niño 3.4 index predicted by ED-PredRNN are over 0.77/0.64/0.53 within the lead time of 6/12/18 months respectively, which is superior to existing statistical models. Also, the precise predictions of the El Niño event (such as 2015/16) and the La Niña event (such as 2010/11), as well as a reasonable forecast for neutral ENSO conditions (2014), demonstrate the effectiveness and superiority of our proposed model.

Tropical coral reefs in Sri Lanka are threatened due to the fluctuation of seasonal and interannual sea surface temperature.

The thermal stress of oceans causes coral bleaching, which induces loss of life in coral reefs and makes them exposed to other threats which directly and indirectly affect millions of other species that inhabit the reef. However, studies focusing on how those thermal stresses affect Sri Lankan fringing reef ecosystems are scarce. Hence, the patterns of long-term and short-term fluctuations of sea surface temperature (SST) over the shallow reefs around the country were studied by separating them into different zones as the eastern coast (Passikudha, Kayankerni, Adukkuparu, Parrot Rock, and Pigeon Island), the southern coast (Beruwala Barbarian, Hikkaduwa, Unawatuna, Ahangama, Mirissa, Madiha, Polhena, and Devundara), and northern-northwestern coasts (Valiththoondal, Palk Bay, Mannar, Kalpitiya, Thalwila, and Uswatakeiyawa). The 1km Multiscale Ultrahigh Resolution (MUR) Level 4 SST dataset was used to analyze seasonal and interannual SST variability from 2005 to 2021. The data were correlated with the Indian Ocean Dipole (IOD), Ekman velocity, and wind stress curl. The annual, seasonal, and monthly variability of SST on different coasts is significantly different. Higher increasing trends of SST from 0.0324 to 0.0411 ℃/year are observed on different coasts, and most of the time higher positive anomalies are recorded after 2014. The First Inter Monsoon (IM-1) and the month of April are more critical since they reach the maximum SST, and the minimum is in the North West Monsoon (NWM) and January. Significant positive relationships are recorded between the Indian Ocean Dipole (IOD) index and the monthly average SST on different coasts, which was robust on the southern coast. Therefore, tropical coral reefs in Sri Lanka are severely threatened due elevation of SST by global warming and climate variability.

Evaluation of SST Data Products from Multi-Source Satellite Infrared Sensors in the Bohai-Yellow-East China Sea

The measurement of sea surface temperature (SST) is of utmost importance in the realm of oceanography. The increasing utilization of satellite data in SST research has highlighted the crucial need to compare and evaluate various satellite data sources. Using iQuam2 in situ SST data, this study aims to assess the accuracy of SST datasets obtained from three polar-orbiting satellites (AVHRR, Modis-Aqua, and Modis-Terra) and one geostationary satellite (Himawari-8) in the Bohai-Yellow-East China Sea (BYECS) throughout 2019. The results showed a strong correlation between satellite and in situ data, with R correlation coefficients exceeding 0.99. However, the accuracy of the satellite datasets exhibited some variability, with Himawari-8 showing the highest deviation error and MODIS-Aqua showing the least. Subsequently, the Modis-Aqua data were used as a benchmark to evaluate the SST data of the other three satellites over the previous six years (July 2015–June 2021). The results indicate that, in addition to intricate temporal variations, the deviations of the three satellites from Modis-Aqua also show significant spatial disparities due to the effect of seawater temperature. Compared to Modis-Aqua, the deviation of Himawari-8 generally displayed a negative trend in BYECS and showed pronounced seasonal variation. The deviation of AVHRR showed a negative trend across all regions except for a substantial positive value in the coastal region, with the time variation exhibiting intricate features. The SST values obtained from MODIS-Terra exhibited only marginal disparities from MODIS-Aqua, with positive values during the day and negative values at night. All three satellites showed significantly abnormal bias values after December 2020, indicating that the MODIS-Aqua-derived SST reference dataset may contain outliers beyond this period. In conclusion, the accuracy of the four satellite datasets varies across different regions and time periods. However, they could be effectively utilized and integrated with relevant fusion algorithms to synthesize high-precision datasets in the future.

Cloud-Resolving Modeling and Evaluation of Microphysical Schemes for Flash Flood–Producing Convection over the Black Sea

Abstract In the present study, the convective event over the Black Sea area in September 2018 is analyzed using the Weather Research and Forecasting (WRF) Model configured with a fully convective-resolving setup. We test the WRF sensitivity to the choice of sea surface temperature (SST) dataset and microphysics scheme. The simulation is verified using weather radar measurements and ground observations. Both the choice of the microphysical scheme and SST dataset have a significant impact on the dynamic properties of the maritime convective system and associated rainfall. The best results are achieved with the WDM6 microphysical scheme and a more detailed and slightly warmer (compared to the default OSTIA SST) G1SST dataset. The optimally configured WRF simulations add value to coarser driving operational analysis, with more accurate amount and pattern of rainfall and the earlier arrival of the convective system, which is in better agreement with radar and weather station measurements. The vertical structure of the reflectivity profiles in the WDM6 scheme that simulates 15%–20% larger rainwater loading compared to other schemes agrees best with the observed data. Other schemes reproduce excessive reflectivity above the freezing level. Enhanced rainfall estimates and faster convective system propagation in the G1SST WDM6 simulations are linked to stronger cold pools caused by enhanced evaporation due to the higher rainwater content and droplet number concentrations. Stronger cold pools result in the 15%–20% enhancement of latent and sensible heat fluxes, reflecting the strong sensitivity of ocean–atmosphere heat and moisture exchange to the choice of microphysics scheme and SST dataset.

Changes in the West Africa Monsoon Precipitation Extremes during ENSO developing Phases

ABSTRACT A 37-year record of rainfall gridded data covering West Africa and a Global Sea Surface Temperature (SST) dataset are used to investigate the remote influence of SST anomalies in the Equatorial Pacific on the interannual variability of West Africa’s extreme rainfall indices over the period 1981–2018. The top five (5) years with the strongest and weakest peak of Niño3.4 SST monthly anomalies are selected, and May-to-September (MJJAS) composite anomalies of the total and extreme rainfall indices are performed. Results reveal that Equatorial Pacific SST’s impacts on daily rainfall intensity are generally more robust than that on their frequency. The significant changes in the mean zonal atmospheric circulation associated with the SST lead to significant dynamic and thermodynamic changes that affect the West African monsoon system locally. During El Niño (La Niña) years, (i) a weakening (strengthening) of the Tropical Easterly Jet (TEJ), (ii) a strengthening (weakening), and southward (northward) shift position of the African Easterly Jet (AEJ), and (iii) a decrease (increase) of the monsoon flow are noted. These changes in the atmospheric circulation prevent (encourage) a supply of moisture, resulting in a reduction (increase) in extreme precipitation observed across West Africa. Equatorial eastern Pacific warming (cooling) is also shown to lead to stable (unstable) atmospheric conditions over West Africa that block (generate) the development of convective systems.