Optimum Interpolation Sea Surface Temperature Research Articles

Influence of mesoscale sea‐surface temperature structures on the Mediterranean cyclone Ianos in convection‐permitting simulations: Contributions of surface warming and cold wakes

AbstractThis study examined the influence of mesoscale sea‐surface temperature (SST) structures and warming on the tropical‐like cyclone Ianos. The controlling mechanisms were investigated using a set of sensitivity experiments with the non‐hydrostatic weather research and forecasting model. A simulation forced by a high‐resolution SST field from the mesoscale eddy‐resolving HYCOM reanalysis (CTRLH) performed better than a simulation forced by a coarse‐resolution SST field from Optimum Interpolation Sea Surface Temperature (OISST) in terms of precipitation, wind intensity, and landfall location. The suppression of mesoscale SST variability affected the cyclone's intensity, its path, and associated processes. This effect depends on the nature of the anomalies (cold/warm). The suppression of mesoscale SST forcing intensified wind and precipitation in the presence of cold anomalies, leading to a northwestward shift of the storm's path during its mature phase. The magnitude of these changes varied with the scale of smoothing (mesoscale SST). Compared to the CTRLH simulation, a simulation in which a 60‐km smoothing filter was applied to the SST field resulted in a cyclone centered slightly to the west (or delayed by 3–6 hr), which was further shifted to the west (or delayed) when the smoothing filter was increased to 150 km. The intensification of wind and precipitation may be associated with the reduction in the storm's cold wake, which would lead to enhanced turbulent fluxes and increased atmospheric water vapor. These results highlight the role of small‐scale air–sea interaction processes that could have a strong impact on Mediterranean cyclones' path and intensity.

Sensitivity of Australian Rainfall to Driving SST Data Sets in a Variable‐Resolution Global Atmospheric Model

AbstractIn this study, we employ the Conformal Cubic Atmospheric Model (CCAM), a variable‐resolution global atmospheric model, driven by two distinct sea surface temperature (SST) data sets: the 0.25° Optimum Interpolation Sea Surface Temperature (CCAM_OISST) version 2.1 and the 2° Extended Reconstruction SSTs Version 5 (CCAM_ERSST5). Model performance is assessed using a benchmarking framework, revealing good agreement between both simulations and the climatological rainfall spatial pattern, seasonality, and annual trends obtained from the Australian Gridded Climate Data (AGCD). Notably, wet biases are identified in both simulations, with CCAM_OISST displaying a more pronounced bias. Furthermore, we have examined CCAM’s ability to capture El Niño‐Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) correlations with rainfall during Austral spring (SON) utilizing a novel hit rate metric. Results indicate that only CCAM_OISST successfully replicates observed SON ENSO‐ and IOD‐rainfall correlations, achieving hit rates of 86.6% and 87.5%, respectively, compared to 52.7% and 41.8% for CCAM_ERSST5. Large SST differences are found surrounding the Australian coastline between OISST and ERSST5 (termed the “Coastal Effect”). Differences can be induced by the spatial interpolation error due to the discrepancy between model and driving SST. An additional CCAM experiment, employing OISST with SST masked by ERSST5 in 5° proximity to the Australian continent, underscores the “Coastal Effect” has a significant impact on IOD‐Australian rainfall simulations. In contrast, its influence on ENSO‐Australian rainfall is limited. Therefore, simulations of IOD‐Australian rainfall teleconnection are sensitive to local SST representation along coastlines, probably dependent on the spatial resolution of driving SST.

Extreme Marine Heatwaves in the Southern Java during 2016

Abstract In this manuscript, we investigated the characteristics of marine heatwaves (MHWs) in southern Java and the effect of local (air-sea heat flux) and remote forcing (El Niño–Southern Oscillation/ENSO and Indian Ocean Dipole/IOD) on the formation of MHWs. We utilized remote sensing data from the National Oceanic and Atmospheric Administration Optimum Interpolation Sea Surface Temperature V2.1 dataset and European Centre for Medium-Range Weather Forecasts Interim reanalysis, covering the period 01 January 1982 to 31 December 2021. The datasets were processed by statistical analysis using MATLAB. We found that the most prolonged durations and the highest cumulative intensities of MHWs in southern Java were recorded during 2016, spanning approximately 255 days and 419.28 °C, respectively. During 2016, the combination of El Niño decay and negative IOD led to optimal warm conditions for long-lasting MHWs and hit southern Java throughout the year. Furthermore, this research highlighted a stronger correlation between ENSO and MHWs in southern Java compared to the IOD, with a 5–8 months lag time. We suggest that long-lasting MHW events may have serious implications for social and ecological systems. Therefore, an in-depth and comprehensive understanding of MHWs due to ongoing climate change becomes an urgent matter to predict such extreme events.

Variations of surface marine heatwaves in the Northwest Pacific during 1993–2019

Parameters of surface marine heatwaves (MHWs) in the Northwest Pacific during 1993–2019 are derived from two sea surface temperature (SST) products: the Optimum Interpolation SST based on satellite remote sensing (OISST V2.1) and the Global Ocean Physics Reanalysis based on data-assimilative global ocean model (GLORYS12V1). Similarities and differences between the MHW parameters derived from the two datasets are identified. The spatial distributions of the mean annual MHW total days, frequency, duration, mean intensity and cumulative intensity, and interannual variations of these parameters are generally similar, while the MHW total days and duration from GLORYS12V1 are usually higher than that from OISST V2.1. Based on seasonal-mean values from GLORYS12V1, longer MHW total days (>7) have the largest spatial coverage in both the shelf and deep waters in summer, while the smallest coverage in spring. In selected representative regions, interannual variations of the MHW total days are positively correlated with the SST anomalies. In summer, the MHW total days have positive correlations with the Western Pacific Subtropical High intensity, and negative correlations with the East Asia Monsoon intensity, over nearly the whole South China Sea (SCS) and the low-latitude Pacific. In winter, positive correlations with both the Subtropical High and Monsoon intensities present over the western part of SCS. Strong El Niño is followed by longer MHW total days over the western half of SCS in winter, and over the whole SCS and low-latitude Pacific in summer of the next year. These correlation relationships are valuable for developing forecasts of MHWs in the region.

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.

Characterization of marine heat waves in the eastern tropical Atlantic Ocean

Marine heat waves (MHWs) can potentially alter ocean ecosystems with far-reaching ecological and socio-economic consequences. In this study, we characterize the MHWs in the eastern tropical Atlantic Ocean with a focus on the Gulf of Guinea (GG). The Optimum Interpolation Sea Surface Temperature (OISST) data from January 1991 to December 2020 and PIRATA network temperature data, from October 2019 to March 2020, have been used for this purpose. The results show that the eastern tropical Atlantic has experienced an annual average of 2 MHWs events in recent decades. Based on the spatial distribution of the different characteristics of these MHWs, we subdivided the eastern tropical Atlantic Ocean into three zones: the northern coast of the GG, the equatorial zone and the Congo-Gabon coastal region. The trend associated with MHWs events showed an increase in the number of MHWs since 2015 in the different zones. This increase was greater at the northern coast of the GG than at the equator and the Congo-Gabon coast. Long-duration MHWs are more frequent in the equatorial zone. High intensity MHWs were observed in different areas with sea surface temperature anomalies greater than 2°C. These anomalies are more intense at the Congo-Gabon coast. The results also revealed that the MHWs event at the equator from mid-October 2019 to March 2020 initially occurred in the subsurface before it appeared at the surface. These results also suggested that ocean temperatures are a potential predictor of MHW events.

Sea surface temperature trends for Tampa Bay, West Florida Shelf and the deep Gulf of Mexico

Sea surface temperatures for Tampa Bay, the West Florida Continental Shelf (WFS) and the adjacent deep Gulf of Mexico are examined for trends. Data sets are from stations maintained by the Hillsborough County Environmental Protection Commission, buoys maintained by the University of South Florida Coastal Ocean Monitoring and Prediction System and the National Oceanic and Atmosphere Administration (NOAA) National Data Buoy Center, the Optimum Interpolation Sea Surface Temperature analyses by the NOAA National Centers for Environmental Information, and the Hadley Centre Sea Surface Temperature. These various data sets, each with different record lengths, require the consideration of trends both on the basis of record length and start time. Tampa Bay shows a warming trend, but with considerable inter-annual variability and start time bias resulting in a lack of statistical significance in more recent years. The WFS is also generally warming, and its inter-annual variability is largely controlled by the upwelling of cooler, deeper Gulf of Mexico water across the shelf break. The deep GOM shows statistically significant warming in most of the data except for the “gappy” records from buoys, both along the continental shelf and in the deep water. Trends in the Gulf of Mexico are mostly between 0.1 and 0.5 °C/decade, somewhat larger than the secular rise found globally, although within the range of the observed decadal variability.

Marine Heatwave Characteristics in the Makassar Strait and Its Surrounding Waters

This study aimed to investigate the characteristics of marine heatwaves (MHWs) in the Makassar Strait and adjacent waters (MSAW) using the Optimum Interpolation Sea Surface Temperature (OISSTv2) dataset from 1982 to 2021. The analysis revealed a significant positive trend in the frequency and duration of MHWs, while the mean and maximum intensities displayed a negative trend over the 40-year period. By examining two different phases of the Pacific Decadal Oscillation (PDO), it was observed that the cold phase (more frequent La Niña events than El Niño) had higher MHW frequency and duration, whereas the warm phase (more frequent El Niño events than La Niña) exhibited higher mean and maximum intensities. Moreover, the occurrence of high-intensity MHWs was associated with weakened winds, the decaying phase of El Niño and positive Indian Ocean Dipole (IOD), and high net surface heat flux (NSHF). An intriguing finding emerged, indicating a notable lag of 7–9 months (10 months) between El Niño (positive IOD) events and the emergence of high-intensity MHWs. This lag suggests that MHWs are more likely to manifest during the transition towards La Niña and negative IOD conditions. Additionally, the study highlighted a stronger correlation between the El Niño–Southern Oscillation and MHW intensity in the MSAW compared to the IOD. Furthermore, the different intensities of MHWs were found to be linked to NSHF, which exhibited higher values during the warm phase of the PDO compared to the cold phase.

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 Long-term variability of Global Typical Oceanic Fronts

Using high-resolution NOAA Optimum Interpolation sea surface temperature (OISST) data, the temporal and spatial characteristics of typical oceanic fronts in the past 39 years, including Oyashio front, Gulf Stream front, Peru current front and southern Indian Ocean polar front, are analyzed. The results show that the intensities of the four typical fronts have all strengthened, among which the Peru current is the most significant and the South Indian Ocean polar front is the weakest. The movement of the fronts are quite different: the Oyashio front has a significant poleward movement, and the Gulf Stream front extending into the open sea part has a marginal poleward movement; while the south Indian Ocean polar front and Peru current front have no obvious movements.

Comparing eight remotely sensed sea surface temperature products and Bayesian maximum entropy-based data fusion products

Sea surface temperature is an important oceanographic physical attribute. In this work, 5 kinds of infrared SST data sets (including FY3C-VIRR, MetopB-AVHRR3, MODIS-Aqua, MODIS-Terra, SNPP-VIIRS) and 3 kinds of microwave SST data sets (including AMSR-2, GMI, WindSat) were considered for fusion purposes. The area with geographical coordinates ranging from 100° W to 180° W and from 10° N to 10° S was chosen as the study area. Using the Bayesian maximum entropy (BME) method, four SST fusion products were generated from January 1 to January 31, 2020, with temporal resolution of 1 day, spatial resolution of 0.25 decimal degrees, and coverage rate of 100%. The Holdout cross-validation method was employed to evaluate the performance of BME for SST interpolation purposes by using two data sets, i.e., WinSat SST and MODIS-Terra SST data; and the validation results showed that the mean absolute error (MAE) values of the WindSat SST data and the MODIS-Terra SST data were 0.117 mg/m3 and 0.191 mg/m3, respectively, and the root mean square error (RMSE) values were 0.158 mg/m3 and 0.321 mg/m3, respectively. In addition, four SST interpolation products were compared with the Optimum Interpolation Sea Surface Temperature (OISST) and the Group for High-Resolution Sea Surface Temperature (GHRSST) products by combining Argo data with Buoy data treated as in-situ measured data sets. Three (MW-WindSat SST product, ALL-Terra SST product and ALL-WindSat SST product) of the four interpolation products performed better than the OISST and GHRSST data sets. Among them, the ALL-WindSat SST product considering WindSat SST data and other 7 SST data as hard and soft data exhibited the best performance in terms of all three indicators: the coefficient of determination value (0.744), the MAE value (0.82 mg/m3) and the RMSE value (1.082 mg/m3). In sum, the present work made full use of the inclusiveness and integration abilities of the BME method for various types of data, and demonstrated its high accuracy in a number of real world data, thus demonstrating its considerable potential in the context of marine data interpolation and fusion.

Marine Heatwaves in the Indonesian Fisheries Management Areas

Ocean temperatures increased during the 20th century and are predicted to continue to rise during the 21st century. Simultaneously, the extreme phenomena of shorter time ocean warming, known as Marine Heatwaves (MHWs), are also taking place. The present study used the Daily Optimum Interpolation Sea Surface Temperature (DOISST) v2.1 with a spatial resolution of 0.25˚. The time period of the DOISST data used in this study was from January 1, 1982 to December 31, 2020, and the region was 90° E–150° E and 16° S–16° N, which is divided into 11 Fishing Management Areas (FMAs). MHWs have a set of metrics derived from the SST data to describe the statistical characteristics of each event. To examine and quantify the influence of the Pacific Ocean and the Indian Ocean, we used the Niño 3.4 SST index and the Dipole Mode Index (DMI), respectively. Based on the data analysis, there has been an increase in the duration and frequency of the occurrence of MHWs in the study area, with the highest increase occurring in FMA 573, FMA 716, and FMA 711. Based on the severity, MHWs in Indonesia are dominated by category I, which is dominantly located in FMA 716, category II in FMA 573, category III with the center of events in FMA 771, and category IV with irregular spatial patterns.

Modelling and forecasting the effects of increasing sea surface temperature on coral bleaching in the Indo-Pacific region

ABSTRACT The Coral Triangle (CT) and the South China Sea (SCS) are the world’s great tropical seas, located in the Indo-Pacific (IP) region. It is home to the richest marine ecosystem on Earth, with a total of 76% reef-building coral species as well as 37% coral reef fish species. Unfortunately, this sensitive area is now vulnerable to Sea Surface Temperature (SST) warming. This research explored the possible consequences of SST warming on the rich ecosystems of the IP region, specifically on bleaching of its coral reefs. Reefbase provided coral bleaching records together with the daily NOAA AVHRR Optimum Interpolation (OI) SST V2 dataset (OISSTv2) were used to explore the relationship between coral bleaching and SST in the IP region. Three different categories of monthly mean SST were tested as potential covariates: minimum SST, mean SST and maximum SST, obtained from the OISSTv2. The fitted logistic regression (LR) model revealed a significant and large correlation between coral bleaching and annual maximum monthly mean SST in the study area using the bleaching data from an online database and the time-series of AVHRR images. Predicted maps of coral bleaching based on the LR model were highly consistent with NOAA Coral Reef Watch (CRW) Degree heating Weeks (DHW) maps. However, some important discrepancies resulted from the more specific local fitting used in the LR model. The maximum SST was forecasted from 2020 to 2100 based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) dataset under the Representative Concentration Pathways (RCP2.6) scenario. The fitted logistic regression model was employed to transform the forecasted maximum SST values into maps of the probability of coral bleaching from 2020 to 2100. The results provide considerable cause for concern, including the likelihood of widespread coral bleaching in many places in the IP region over the next 30 years.

Estimation of Albacore Tuna Potential Fishing Grounds in the Southeastern Indian Ocean

To maintain sustainable fisheries, albacore tuna is one of the commercial fish species whose management is regulated globally by the Regional Fisheries Management Organizations. Generally, sea surface temperature (SST) and chlorophyll-a (SSC) are used to determine fish suitability based on the catch data. In this research, we attempt to identify optimal habitats for albacore tuna in the southeastern Indian Ocean by analyzing in situ data based on onboard observers on tuna long line vessels from 2006–2019 and optimum interpolation sea surface temperature from NOAA-Advanced Very High-Resolution Radiometer and sea surface chlorophyll-a from Aqua MODIS satellites. The albacore tuna’s habitat suitability index was examined using the generalized additive model. The results suggested that albacore tuna inhabited waters with an SST range of 15–26 °C and SSC concentration of 0.075–0.125 mg/m3. In the Indian Ocean, most albacore tunas were captured at higher latitudes of 20°-30° S.

Sensitivity of Typhoon Forecast to Prescribed Sea Surface Temperature Data

This study investigates the impact of the sea surface temperature (SST) on the forecast of two typhoons, which consecutively hit South Korea in 2020. SST data were obtained from the Daily Optimum Interpolation Sea Surface Temperature (OISST) version 2 and HYbrid Coordinate Ocean Model/Navy Coupled Ocean Data Assimilation (HYCOM/NCODA; GLBy0.08/expt_93.0). When verified using in situ observational data, the OISST data did not accurately estimate the changes in SST during each typhoon’s landfall period compared to the HYCOM data since it has a relatively low temporal resolution. To investigate the impact of these two SST data on typhoon forecasts, we conducted sensitivity experiments using the Weather Research and Forecasting (WRF) model. The results showed that simulated typhoon intensities were significantly improved in the simulations with HYCOM data (HY runs), while typhoon track forecast performances were similar in both runs. In addition, the forecast performances of the maximum wind speed at 10 m during the typhoon landfall period were improved in the HY runs. Therefore, this study showed that the overall typhoon intensity and forecast performances during the landfall period could be improved when the higher temporal-resolution SST data were prescribed in the model boundary conditions for a better representation of typhoon-induced SST changes.

Marine Heatwaves, Upwelling, and Atmospheric Conditions during the Monsoon Period at the Northern Coast of the Gulf of Guinea

Ocean conditions influence the economies and climate of West Africa. Based on the 30-year daily Optimum Interpolation Sea Surface Temperature (OISST) dataset during May–October, upwelling surface variability and marine heatwaves (MHWs) at the northern coast of the Gulf of Guinea are investigated. The cooling surface decreases more rapidly around Cape Palmas than around Cape Three Points and extends eastward. MHWs variability exhibits a frequent occurrence of such events since 2015 that is consistent with the observed oceanic warming and the decrease in upwelling surface. The empirical orthogonal functions performed on the annual cumulated intensity of MHWs show four variability modes that include the whole northern coast, an east–west dipole between the two capes, a contrast between the northern coast at the two capes and the meridional section east of 5° E, and a north–south opposition. These patterns show 3-year, 6-year, and 8-year trends, and are related to coastal upwelling at the northern coast of the Gulf of Guinea. Similarly, surface ocean and atmospheric conditions are modified according to MHW periods. These changes take place before, during, and after MHW events. These results could be used to understand how this change influences the marine ecosystem, the local fisheries resources, and the extreme rainfall episodes in West Africa.

Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation

AbstractThere is a growing interest in developing data‐driven reduced‐order models for atmospheric and oceanic flows that are trained on data obtained either from high‐resolution simulations or satellite observations. The data‐driven models are non‐intrusive in nature and offer significant computational savings compared to large‐scale numerical models. These low‐dimensional models can be utilized to reduce the computational burden of generating forecasts and estimating model uncertainty without losing the key information needed for data assimilation (DA) to produce accurate state estimates. This paper aims at exploring an equation‐free surrogate modeling approach at the intersection of machine learning and DA in Earth system modeling. With this objective, we introduce an end‐to‐end non‐intrusive reduced‐order modeling (NIROM) framework equipped with contributions in modal decomposition, time series prediction, optimal sensor placement, and sequential DA. Specifically, we use proper orthogonal decomposition (POD) to identify the dominant structures of the flow, and a long short‐term memory network to model the dynamics of the POD modes. The NIROM is integrated within the deterministic ensemble Kalman filter (DEnKF) to incorporate sparse and noisy observations at optimal sensor locations obtained through QR pivoting. The feasibility and the benefit of the proposed framework are demonstrated for the NOAA Optimum Interpolation Sea Surface Temperature (SST) V2 data set. Our results indicate that the NIROM is stable for long‐term forecasting and can model dynamics of SST with a reasonable level of accuracy. Furthermore, the prediction accuracy of the NIROM gets improved by almost one order of magnitude by the DEnKF algorithm.

Characteristics of Marine Heatwaves (2008–2021) in the Savu Sea, East Nusa Tenggara

In this paper, we investigate the characteristics and the mechanism underlying the formation of marine heatwaves (MHWs) in the Savu Sea, known as the richest marine biodiversity area in the world. The focus is on identifying the frequency, intensity, and duration of occurrence MHWs generated by local forcing in the form of net surface heat flux. The study utilizes the remote sensing data from NOAA Optimum Interpolation (OI) Sea Surface Temperature (SST) for SST and The European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA-5) for net surface heat flux components (shortwave radiation, longwave radiation, sensible heat flux, and latent heat flux). All data covers the period for the last 14 years (2008–2021) during the cold phase of the Pacific Decadal Oscillation (PDO) and then processed by statistical analysis methods. The results showed that the mean frequency, intensity, and duration of occurrence MHWs in the Savu Sea were around two times a year, 1.4–1.6 °C, and 10–12 days, respectively. The highest frequency and intensity MHWs occurred at around 122–124 °E and 8.30–9.30 °S (northern Savu Sea) generated by the most significant net surface heat flux in these areas. This study reveals the most dominant contribution of net surface heat flux in the northern Savu Sea comes from high shortwave radiation (heat gain) and low latent heat flux through the evaporation process (heat loss), thus keeping the ocean warm.

Interannual, Seasonal, and Monthly Variability of Sea Surface Temperature Fronts in Offshore China from 1982–2021

The offshore China (OC) region is a significant sea area in the Western Pacific and many researchers have been interested in the distribution of sea surface temperature (SST) fronts in this area. In this study, the Cayula and Cornillon single image edge detection algorithm was used to detect SST fronts using the Daily Optimum Interpolation Sea Surface Temperature data from 1982 to 2021. The results revealed that there are eighteen SST fronts in OC— three in the Bohai Sea, seven in the Yellow Sea, two in the East China Sea, five in the South China Sea, one in the Pacific Ocean east of Taiwan province, China—and among them a new front was detected in the Yellow Sea and named the Yellow Sea Ring Front. The frequency of most fronts showed a tendency of initially increasing and then decreasing from January to September, followed by a trend of growing steadily from October to December. The frequency of a few fronts showed a decreasing tendency from January to September and an increasing tendency from October to December. The frequency of most fronts is highest in winter and lowest in summer. In spring and autumn, the frequency of most fronts is lower than that in winter and higher than that in summer. The annual average frontal probability of five-ninths of the fronts showed an upward trend, and the annual average frontal probability value of one-third of the fronts showed a downward trend. The rest of the fronts showed a stable trend. The results of this paper also showed that the Liaodong Bay Front and the Bohai-Laizhou Bay Front did not form a complete front, as previously reported. In addition, the frontal probability of the Bohai Front to the north of 39° N was in the tendency of decreasing.

Global Trends in Marine Heatwaves and Cold Spells: The Impacts of Fixed Versus Changing Baselines

AbstractMarine heatwaves (MHW) and cold spells (MCS) are considered to be anomalously warm or cool events in sea surface temperature that are commonly defined relative to a long‐term fixed baseline. As a result, because of climate warming, it is often concluded that MHW will become stronger, longer, and more frequent in the future, whereas MCS will become weaker, shorter, and less frequent. Here, it is argued that in order to understand the physics of MHW/MCS, it is important to remove the climate signal when detecting events. The 1982–2021 National Oceanic and Atmospheric Administration optimum interpolation sea surface temperature data set (NOAA OISST V2) is used to show that using a fixed baseline leads on average to overestimating MHW and underestimating MCS strengths between 1982 and 2021 compared to using a linear trend to remove the climate signal. If a linear trend is used to remove the climate signal, global trends over the 40‐year record indicate a shift to weaker MHW and MCS in the tropics, especially in the eastern Pacific Ocean, and a shift to stronger MHW and MCS in the northern Pacific and Atlantic Oceans. The trends in the tropics are likely related to El Niño conditions predominating during the first half of the record and La Niña conditions predominating during the second half of the record. The trends at higher latitudes are consistent with changes in the North Atlantic and Atlantic Multidecadal Oscillations.