Long-term Sea Surface Temperature Research Articles

First Analyses of the TIMELINE AVHRR SST Product: Long-Term Trends of Sea Surface Temperature at 1 km Resolution across European Coastal Zones

Coastal areas are among the most productive areas in the world, ecologically as well as economically. Sea Surface Temperature (SST) has evolved as the major essential climate variable (ECV) and ocean variable (EOV) to monitor land–ocean interactions and oceanic warming trends. SST monitoring can be achieved by means of remote sensing. The current relatively coarse spatial resolution of established SST products limits their potential in small-scale, coastal zones. This study presents the first analysis of the TIMELINE 1 km SST product from AVHRR in four key European regions: The Northern and Baltic Sea, the Adriatic Sea, the Aegean Sea, and the Balearic Sea. The analysis of monthly anomaly trends showed high positive SST trends in all study areas, exceeding the global average SST warming. Seasonal variations reveal peak warming during the spring, early summer, and early autumn, suggesting a potential seasonal shift. The spatial analysis of the monthly anomaly trends revealed significantly higher trends at near-coast areas, which were especially distinct in the Mediterranean study areas. The clearest pattern was visible in the Adriatic Sea in March and May, where the SST trends at the coast were twice as high as that observed at a 40 km distance to the coast. To validate our findings, we compared the TIMELINE monthly anomaly time series with monthly anomalies derived from the Level 4 CCI SST anomaly product. The comparison showed an overall good accordance with correlation coefficients of R > 0.82 for the Mediterranean study areas and R = 0.77 for the North and Baltic Seas. This study highlights the potential of AVHRR Local Area Coverage (LAC) data with 1 km spatial resolution for mapping long-term SST trends in areas with high spatial SST variability, such as coastal regions.

Role of Japan Sea Throughflow in the spatial variability of the long-term sea surface temperature trend

AbstractThe Japan Sea shows a much stronger warming of long-term sea surface temperature (SST) than surrounding oceans. The warming trend possesses a meridionally alternating zonal band pattern, with weak trends along the paths of the Japan Sea Throughflow and strong trends in the remaining interior region. Using idealized models of the Japan Sea Throughflow and atmospheric heating, this study examines the process behind the formation of such spatial patterns in the SST trend. We find that zonal band structures form in a flat rectangular coastline model, and heat budget analysis shows that horizontal heat transport, due to throughflow, reduces the warming effect created by the surface heat flux. A weak SST trend appears around the jet, while a strong SST trend appears elsewhere. Bathymetric effects are also examined using a model with realistic coastline settings. The location of the western boundary current stabilizes, and the coastal branch begins to disconnect from the Japanese coastline toward the north, allowing a more stable SST warming region to form in the southern interior region. Lagrangian particle tracking experiments confirm that a weak (strong) SST trend corresponds to a short (long) residence time, and eddies in the Japan Sea prolong the residence time in interior regions. The model results suggest that the accumulation time of surface heating is essential to the spatial distribution of the long-term SST warming trend.

Decoding low-frequency climate variations: A case study on ENSO and ocean surface warming

Current perspectives on lower frequency variations and secular warming have predominantly been shaped by traditional anomalies that assume an annual cycle (AC) with a time-invariant amplitude. However, this anomaly framework falls short in capturing the complexity of multiple periodic modes with intricate waveforms and time dependent amplitude—the traits, in general, shared by externally forced responses of complex dynamical systems. By allowing interannual amplitude modulation of ACs, we show that the monotonic amplitude increases of the first AC of Sea Surface Temperature (SST) are manifested as the basin-wide secular warming of the ocean surface. Notably, the first SST-AC exhibits significant interannual variances and the largest linear warming rates in the Pacific Warm Pool. While the linear warming pattern mirrors that of a long-term mean SST, it depicts an entirely different warming pattern on the surface of the tropical Pacific Ocean compared to those reported so far. Moreover, all interannual warm (El Niño) and cold (La Niña) events in tropical Pacific regions are abnormal interannual modulations in the third and fourth ACs of SST, respectively. Specifically, a strong El Niño event occurs when a positive amplitude modulation leads to the overlap of two consecutive positive phases of the third AC of SST. Conversely, the absence of such overlaps during negative amplitude modulations significantly contributes to the positive skewness of SST anomalies. No systematic decadal changes in the zonal propagation characteristics of SST in the eastern Pacific (EP) and central Pacific (CP) regions were detected. These findings underscore that the secular warming and low-frequency events in EP and CP are intrinsic to three distinct ACs.

Long term of sea surface temperature prediction for Indonesia seas using multi time-series satellite data for upwelling dynamics projection

Global warming, which impacts global temperatures, has led to an increase in sea surface temperature (SST). This rise significantly affects marine ecological systems, especially in Indonesia. As a result, long-term forecasting of SST dynamics is essential for shaping various policies. However, most studies on SST forecasting have focused on short-term predictions in local or medium-sized areas and often overlook the dynamic upwelling that influences SST. In this paper, we propose a five-year SST prediction for the Indonesian seas, incorporating multi-time-series satellite data to project upwelling dynamics. We utilized two deep learning time series models to construct a predictive model that estimates monthly SST values. This model employs extensive multiple satellite data, encompassing 14,934 points (including SST with a resolution of 0.090, wind, ENSO, heat flux, and solar radiation) from 2003 to 2021. To enrich the training data and mitigate overfitting, we applied data augmentation for time series. Experimental results reveal that all satellite datasets correlate with SST over five years. The 1D-Convolution Neural Network outperformed the Long-Short Term Memory model, exhibiting the lowest mean absolute error of 0.39 °C compared to 0.45 °C. Our model detected a consistent upwelling dynamic over a five-year pattern in the Indonesian seas. These findings suggest that our proposed model offers accurate and efficient long-term monthly SST predictions, crucial for upwelling projections.

NOAA MODIS SST Reanalysis Version 1

The first NOAA full-mission reanalysis (RAN1) of the sea surface temperature (SST) from the two Moderate Resolution Imaging Spectroradiometers (MODIS) onboard Terra (24 February 2020–present) and Aqua (4 July 2002–present) was performed. The dataset was produced using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) enterprise SST system from Collection 6.1 brightness temperatures (BTs) in three MODIS thermal emissive bands centered at 3.7, 11, and 12 µm with a spatial resolution of 1 km at nadir. In the initial stages of reprocessing, several instabilities in the MODIS SST time series were observed. In particular, Terra SSTs and corresponding BTs showed three ‘steps’: two on 30 October 2000 and 2 July 2001 (due to changes in the MODIS operating mode) and one on 25 April 2020 (due to a change in its nominal blackbody temperature, BBT, from 290 to 285 K). Additionally, spikes up to several tenths of a kelvin were observed during the quarterly warm-up/cool-down (WUCD) exercises, when the Terra MODIS BBT was varied. Systematic gradual drifts of ~0.025 K/decade were also seen in both Aqua and Terra SSTs over their full missions due to drifting BTs. These calibration instabilities were mitigated by debiasing MODIS BTs using the time series of observed minus modeled (‘O-M’) BTs. The RAN1 dataset was evaluated via comparisons with various in situ SSTs. The data meet the NOAA specifications for accuracy (±0.2 K) and precision (0.6 K), often by a wide margin, in a clear-sky ocean domain of 19–21%. The long-term SST drift is typically less than 0.01 K/decade for all MODIS SSTs, except for the daytime ‘subskin’ SST, for which the drift is ~0.02 K/decade. The MODIS RAN1 dataset is archived at NOAA CoastWatch and updated monthly in a delayed mode with a latency of two months. Additional archival with NASA JPL PO.DAAC is being discussed.

Long-term variability of the western tropical Atlantic sea surface temperature driven by greenhouse gases and AMOC

The long-term orbital-scale sea surface temperature (SST) variability in the tropics is thought to be mainly driven by greenhouse gases (GHG) forcing. However, few studies have investigated the drivers of such variability in the tropical Atlantic. Given the evidence of orbital-scale changes in Atlantic Meridional Overturning Circulation (AMOC) strength, one can hypothesize that AMOC variability also modulates the long-term tropical Atlantic SST through the bipolar seesaw mechanism. According to this mechanism, under weak [strong] AMOC conditions, the Southern Hemisphere is expected to warm up [cool down], while the Northern Hemisphere cools down [warms up]. Here, we investigate the long-term SST variability of the western tropical South Atlantic (WTSA), i.e., along the main pathway of the upper AMOC branch towards the equator, using a new 300 thousand years (kyr)-long Mg/Ca-based SST record. Our SST record shows glacial-interglacial variability superimposed by four remarkable long-term warm events during the three recorded glacial periods. These glacial warm events occurred between ca. 280–260, 160–143, 75–60, and 40–24 ka before present. Our results support the notion that atmospheric GHG plays a leading role in modulating the glacial-interglacial SST variability in the WTSA. However, it does not explain the occurrence of glacial warm events. Our study supports that the glacial warm events were caused by an orbital-scale bipolar seesaw mechanism operating in the Atlantic due to changes in the AMOC strength. These warm events may have been amplified by annual mean insolation driven by obliquity. Finally, we suggest that the long-term bipolar seesaw warmed the western tropical (South) Atlantic during the MIS 5/4 transition when the Earth's climate was cooling off.

MuSTC: A Multi-Stage Spatio–Temporal Clustering Method for Uncovering the Regionality of Global SST

Sea Surface Temperature (SST) prediction is a hot topic that has received tremendous popularity in recent years. Existing methods for SST prediction usually select one sea area of interest and conduct SST prediction by learning the spatial and temporal dependencies and patterns in historical SST data. However, global SST is a unified system of high regionality, and the SST in different sea areas shows different changing patterns due to the influence of various factors, e.g., geographic location, ocean currents and sea depth. Without a good understanding of such regionality of SST, we cannot quantitatively integrate the regionality information of SST into SST prediction models to make them adaptive to different SST patterns around the world and improve the prediction accuracy. To address this issue, we proposed the Multi-Stage Spatio–Temporal Clustering (MuSTC) method to quantitatively identify sea areas with similar SST patterns. First, MuSTC sequentially learns the representation of long-term SST with a deep temporal encoder and calculates the spatial correlation scores between grid ocean regions with self-attention. Then, MuSTC clusters grid ocean regions based on the original SST data, encoded long-term SST representation and spatial correlation scores, respectively, to obtain the sea areas with similar SST patterns from different perspectives. According to the experiments in three ocean areas, i.e., the North Pacific Ocean (NPO), the South Atlantic Ocean (SAO) and the North Atlantic Ocean (NAO), the clustering results generally match the distribution of ocean currents, which demonstrates the effectiveness of our MuSTC method. In addition, we integrate the clustering results into two representative spatio–temporal prediction models, i.e., Spatio–Temporal Graph Convolutional Networks (STGCN) and Adaptive Graph Convolutional Recurrent Network (AGCRN), to conduct SST prediction. According to the results of experiments, the integration of regionality information leads to the reduction of Root Mean Square Error (RMSE) by 1.95%, 1.39% and 1.28% in NPO, SAO and NAO, respectively, using the STGCN model, and the reduction of RMSE by 4.94%, 0.74% and 1.43% by using the AGCRN model. Such results indicate that the integration of regionality information could notably improve the prediction accuracy of SST.

Interannual variability of sea surface temperature at the polar latitudes of the Atlantic Ocean

The general regularities and regional differences in the interannual variability of the sea surface temperature in the polar latitudes of the Atlantic Ocean are revealed according to the OI SST reanalysis data for period 1982—2017. The reanalysis contains daily average sea surface temperature values at the nodes of a regular grid 0,25°×0,25°. In this work, areas where the ice concentration did not exceed
 90 % were analyzed. In both polar basins, the position of areas with a high level of sea surface temperature
 interannual variability qualitatively coincides with the position of intense currents. The maximum values of
 linear sea surface temperature trends are observed in areas of the maximum level of interannual variability,
 while the trends in long-term sea surface temperature variability in the Arctic and Antarctic are noticeably
 different. Significant positive sea surface temperature trends prevail in the Atlantic sector of the Arctic.
 Significant negative trends have been identified only off the northeastern coast of Greenland and north of the Arctic Current. The sea surface temperature decrease on a long-term scale in this area is associated with an increase of cold water transport by the East Greenland and Arctic currents, which are formed as a result
 of the intensive melting of Arctic ice observed in recent decades. In the Atlantic sector of the Antarctic, on the contrary, significant negative sea surface temperature trends prevail. In the Arctic, the maximum values of positive sea surface temperature trends are observed in August—September during the period of maximum warming of surface waters and minimum concentration of drifting ice. In the Antarctic, the maximum negative sea surface temperature trends in absolute value are observed from August to October, during the period of surface water cooling and maximum ice concentration.

Wintertime sea surface temperature variability modulated by Arctic Oscillation in the northwestern part of the East/Japan Sea and its relationship with marine heatwaves

The northwestern part of the East/Japan Sea (EJS) is a region with large sea surface temperature (SST) variability and is known as a hotspot of marine heatwaves (MHW) stress for marine environments that peaked in boreal winter (January-February-March). This could have profound impacts on the marine ecosystems over the EJS. Here, we used a set of high-resolution satellite and reanalysis products to systematically analyze the spatiotemporal SST variations and examine their linkage to a large-scale mode of climate variability, such as the Arctic Oscillation (AO). The results show that AO-related wind forcing modulates the SST variability over the EJS via the oceanic dynamic adjustment processes. In particular, the abnormally warm SSTs in the northwestern part of the EJS are driven by the anomalous anticyclonic eddy-like circulation and Ekman downwelling during a positive AO phase. This physical linkage between a positive AO and the abnormally warm SST could be conducive to MHW occurrences in the EJS as in the extremely positive AO event during the winter of 2020. These results have implications that the MHW occurrences in the EJS could be amplified by natural climate variability along with long-term SST warming.

The Cretaceous (early Albian to early Campanian) biostratigraphy and palaeotemperature reconstruction of the eastern Tethys: Calcareous nannofossil evidence from southern Tibet, China

Studies of geochemical proxies, such as carbonate δ18O and TEX86, show that the Earth's climate has changed significantly during the Cretaceous Period. However, knowledge about the sea surface temperature (SST) history of the eastern Tethys is limited. Based on the calcareous nannofossil record of the Nirang section, Tethys Himalayas, this paper attempts to establish the biostratigraphic framework and SST evolution of the Cretaceous Period in the southern Tibet. A total of 131 species were identified from the studied succession. Twelve bioevents were recognized, allowing for early Albian to the early Campanian (CC8 to CC18 biozones) in the section. Meanwhile, a disconformity within 81–71 Ma was also identified. The SST evolution was analysed by the calcareous nannofossils modified temperature index (mTI), and the results reveal that the long-term SST changes in the Tethys Himalayas of southern Tibet experienced progressive warming from the early Albian to the early Turonian and the warm interval persisted until the early Campanian. Short-term warming and cooling events, e.g., rapid warming in the early and late Albian, subsequent cooling in the latest Albian and peak warmth at the Cenomanian–Turonian transition, are also recorded in the study area. The SST history of the Albian-Cenomanian in the eastern Tethys can be well correlated to the western Tethys (central Italy). However, the compilation of TEX86 data indicates substantial global cooling during the Coniacian to Campanian. This study suggests that the northward drift of the Indian continent towards the equator is likely to be responsible for the sustained warmth in the Tethys Himalayas from the middle Turonian to the early Campanian.

Assessing the Impact of Long-Term ENSO, SST, and IOD Dynamics on Extreme Hydrological Events (EHEs) in the Kelani River Basin (KRB), Sri Lanka

Hydrological extremes are common throughout the world and can be considered a globally significant phenomenon with severe environmental and social consequences. In recent decades, especially in the second half of the 20th century, Extreme Hydrological Events (EHEs) have attracted extensive attention. Physiological and anthropogenic factors have increased the frequency and severity of hydrological extremes worldwide in the last few decades. Recently, it has become a significant environmental issue in Sri Lanka. Both floods and droughts are widespread throughout the country, and the influence of floods is becoming more common every year. Currently, the frequency and severity of EHEs in the Kelani River Basin (KRB), Sri Lanka, are very common and have increased due to climate variations. Therefore, this study focused mainly on evaluating the EHEs and the impact of long-term El Niño Southern Oscillation (ENSO), Sea Surface Temperature (SST), and Indian Ocean Dipole (IOD) dynamics on extreme events. Rainfall Anomaly Index (RAI) and Extreme Precipitation Indices (EPIs) were calculated to examine the EHEs and their spatial variability. In addition, the relationships between EHEs and ENSO were investigated using several climate indices based on SST anomalies. Both observed and satellite-derived daily precipitation from 1951 to 2019 were used to assess the EHEs in the KRB. The trend of EHEs and the change points were evaluated using the Pettitt test, and teleconnection with global indices was examined using the correlation coefficient in the R application. The result of the study revealed that the pattern of EHEs varied spatially from 1951 to 2019. The strong La Niña years showed a high degree of teleconnection with EHEs in April (r = 0.622 at 0.05 significance level) and August (r = −0.732 at 0.05 significance level). NINO3.4 and the Southern Oscillation Index (SOI) have shown a significant positive impact on EHEs in the Northeast Monsoon (NEM) period. This research on KRB will be a popular scientific measure that can provide scientific results and solutions for the comprehensive decision-making process in the future. Investigating the global physical changes that influence EHEs is critical to taking the necessary steps to reduce the severity of hydrological extremes in Sri Lanka.

South Florida estuaries are warming faster than global oceans

From extensive evaluations, it is found that, of all satellite data products of sea surface temperature (SST), MODIS SST is the most appropriate in assessing long-term trends of water temperature in the South Florida estuaries. Long-term SST data show significant warming trends in these estuaries during both daytime (0.55 °C/decade) and nighttime (0.42 °C/decade) between 2000 and 2021. The warming rates are faster during winter (0.70 °C/decade and 0.67 °C/decade for daytime and nighttime, respectively) than during summer (0.48 °C/decade and 0.28 °C/decade for daytime and nighttime, respectively). Overall, the South Florida estuaries experienced rapid warming over the past two decades, 1.7 and 1.3 times faster than the Gulf of Mexico (0.33 °C/decade and 0.32 °C/decade for daytime and nighttime), and 6.9 and 4.2 times faster than the global oceans (0.08 °C/decade and 0.10 °C/decade for daytime and nighttime).

An increase in marine heatwaves without significant changes in surface ocean temperature variability

Marine heatwaves (MHWs)—extremely warm, persistent sea surface temperature (SST) anomalies causing substantial ecological and economic consequences—have increased worldwide in recent decades. Concurrent increases in global temperatures suggest that climate change impacted MHW occurrences, beyond random changes arising from natural internal variability. Moreover, the long-term SST warming trend was not constant but instead had more rapid warming in recent decades. Here we show that this nonlinear trend can—on its own—appear to increase SST variance and hence MHW frequency. Using a Linear Inverse Model to separate climate change contributions to SST means and internal variability, both in observations and CMIP6 historical simulations, we find that most MHW increases resulted from regional mean climate trends that alone increased the probability of SSTs exceeding a MHW threshold. Our results suggest the need to carefully attribute global warming-induced changes in climate extremes, which may not always reflect underlying changes in variability.

Detection of surface temperature anomaly of the Sea of Marmara

Monitoring sea surface temperature (SST) over a long-term and detecting the anomalies highly contribute to understanding the prevailing water quality of the sea. Earth observation satellite images are the key data sources that offer the long-term SST detection in a cost and time effective way. Since the Sea of Marmara in Türkiye is surrounded by the highly populated provinces, the water quality of the sea has gained importance for scientific and public communities over the years. This article emphasizes on the significance of detecting SST trend and corresponding anomalies of the Sea of Marmara over the past 32 years. To address the SST variations of the Sea of Marmara in time, a comprehensive set of both field and satellite data regarding SSTs were obtained within the context of this study. The SST trend and its anomalies between the years 1990 and 2021 were detected by applying Seasonal-Trend decomposition procedure based on LOESS (STL) method to NOAA OISST V2 data. On the other hand, spatial SST distribution was detected with Landsat-8, Sentinel-3 and NOAA OISST V2 satellite data. SST results were verified with the in-situ data within the scope of accuracy assessment. The results showed that SST time-series data performed an increasing trend and had anomalies mostly during the spring months in the recent years.

Diagnosing Hawaii’s Recent Drought

Abstract Hawaii’s recent drought is among the most severe on record. Wet-season (November–April) rainfall deficits during 2010–19 rank second lowest among consecutive 10-yr periods since 1900. Various lines of empirical and model evidence indicate a principal natural atmospheric cause for the low rainfall, mostly unrelated to either internal oceanic variability or external forcing. Empirical analysis reveals that traditional factors have favored wetness rather than drought in recent decades, including a cold phase of the Pacific decadal oscillation in sea surface temperatures (SSTs) and a weakened Aleutian low in atmospheric circulation. But correlations of Hawaiian rainfall with patterns of Pacific sea level pressure and SSTs that explained a majority of its variability during the twentieth century collapsed in the twenty-first century. Atmospheric model simulations indicate a forced decadal signal (2010–19 vs 1981–2000) of Aleutian low weakening, consistent with recent observed North Pacific circulation. However, model ensemble means do not generate reduced Hawaiian rainfall, indicating that neither oceanic boundary forcing nor a weakened Aleutian low caused recent low Hawaiian rainfall. Additional atmospheric model experiments explored the role of anthropogenic forcing. These reveal a strong sensitivity of Hawaiian rainfall to details of long-term SST change patterns. Under an assumption that anthropogenic forcing drives zonally uniform SST warming, Hawaiian rainfall declines, with a range of 3%–9% among three models. Under an assumption that anthropogenic forcing also increases the equatorial Pacific zonal SST gradient, Hawaiian rainfall increases 2%–6%. Large spread among ensemble members indicates that no forced signals are detectable.

MIMO: A Unified Spatio-Temporal Model for Multi-Scale Sea Surface Temperature Prediction

Sea surface temperature (SST) is a crucial factor that affects global climate and marine activities. Predicting SST at different temporal scales benefits various applications, from short-term SST prediction for weather forecasting to long-term SST prediction for analyzing El Niño–Southern Oscillation (ENSO). However, existing approaches for SST prediction train separate models for different temporal scales, which is inefficient and cannot take advantage of the correlations among the temperatures of different scales to improve the prediction performance. In this work, we propose a unified spatio-temporal model termed the Multi-In and Multi-Out (MIMO) model to predict SST at different scales. MIMO is an encoder–decoder model, where the encoder learns spatio-temporal features from the SST data of multiple scales, and fuses the learned features with a Cross Scale Fusion (CSF) operation. The decoder utilizes the learned features from the encoder to adaptively predict the SST of different scales. To our best knowledge, this is the first work to predict SST at different temporal scales simultaneously with a single model. According to the experimental evaluation on the Optimum Interpolation SST (OISST) dataset, MIMO achieves the state-of-the-art prediction performance.

Annual resolution records of sea-level change since 1850 CE reconstructed from coral δ18O from the South China Sea

Compared with other ocean basins, annual resolution long-term series of sea-level records in the South China Sea (SCS) are still rare. Here we first analyzed the correlation mechanism between the oxygen stable isotopes (δ 18 O) of Porites coral and instrumental sea-level, sea surface salinity (SSS), sea surface temperature (SST) and rainfall of the SCS, and NINO 3.4 SST during 1980–2015 CE (Common Era); and we then quantitatively reconstructed sea-level at an annual resolution based on the coral δ 18 O in the SCS. The results show that the sea-level fell slightly (−0.73 ± 0.27 mm/yr) during 1850–1900 CE; then has continuously risen (1.31 ± 0.06 mm/yr) with a total increase of 152 ± 7 mm from 1900 to 2015 CE. On the inter-annual timescale, ENSO by mediating the overflow effect of seawater in the Western Pacific Warm Pool and the location of storm genesis in the western Pacific, indirectly affects sea-level, rainfall and SSS, which is finally reflected in changes in coral δ 18 O in the SCS. On the long-term timescale, especially since the 1900s, the gradual increase in SST and glaciers and ice sheets meltwater injected into the ocean due to the continuous warming mainly caused by human activities has not only promoted the rise of sea-level, but also caused a gradual negative bias of δ 18 O seawater (i.e. a gradual decrease of SSS), these processes are faithfully recorded by coral δ 18 O. Compared with SST, SSS or δ 18 O seawater may play a more important role in promoting the negative bias of coral δ 18 O in the SCS. Our study not only provides a better understanding of sea-level history and relationship mechanism around the SCS during 1850–2015 CE, but also may have the potential to provide a valuable proxy for the accurate quantitative reconstruction of sea-level at an annual resolution over a longer time scale for recent past periods. • On the interannual timescale, ENSO is the main driver of coral δ 18 O via its indirect effects on seawater δ 18 O in the SCS • On the long-term timescale, SST and injection of meltwater are the main driver of changes in coral δ 18 O and sea-level • Both SSS and SST are bridges between coral δ 18 O and sea-level • Coral δ 18 O is an ideal proxy to indicate sea-level, which has risen by 152 ± 7 mm in total from 1900 to 2015 CE in the SCS

High Precision Sea Surface Temperature Prediction of Long Period and Large Area in the Indian Ocean Based on the Temporal Convolutional Network and Internet of Things.

Impacted by global warming, the global sea surface temperature (SST) has increased, exerting profound effects on local climate and marine ecosystems. So far, investigators have focused on the short-term forecast of a small or medium-sized area of the ocean. It is still an important challenge to obtain accurate large-scale and long-term SST predictions. In this study, we used the reanalysis data sets provided by the National Centers for Environmental Prediction based on the Internet of Things technology and temporal convolutional network (TCN) to predict the monthly SSTs of the Indian Ocean from 2014 to 2018. The results yielded two points: Firstly, the TCN model can accurately predict long-term SSTs. In this paper, we used the Pearson correlation coefficient (hereafter this will be abbreviated as “correlation”) to measure TCN model performance. The correlation coefficient between the predicted and true values was 88.23%. Secondly, compared with the CFSv2 model of the American National Oceanic and Atmospheric Administration (NOAA), the TCN model had a longer prediction time and produced better results. In short, TCN can accurately predict the long-term SST and provide a basis for studying large oceanic physical phenomena.

Implications of North Atlantic warming for a possible increase of dust activity in northern East Asia

This study investigates variations of the March dust activities in northern East Asia (NEA) during 1980–2021 based on dust aerosol optical thickness (DAOT) from Moder-Era Retrospective analysis for Research and Applications version 2 dataset. In particular, the roles of the atmospheric circulation and remote sea surface temperature (SST) forcing in the long-term changes of dust activities are analyzed. Results show that the NEA dust impacts on the environment have a sign of increasing on downstream East Asia, North Pacific, and even western North America. The large-scale atmospheric circulation anomalies over Eurasia play an important role in the increased dust activities over northern East Asia. A quasi-barotropic wave train from the North Atlantic to southern Asia is identified as the key atmospheric circulation factor in the variations of dust activities over the NEA. The increased pressure gradient by the wave train in the NEA strengthens the northwesterly winds and facilitates the increase of intensity and frequency of dust activities there. Further analysis indicates that the anomalous wave train is forced by the North Atlantic sea surface temperature anomalies. Sensitivity experiments with a numerical model confirm the impact of the remote SST forcing in the North Atlantic Ocean on the formation of this anomalous wave train. A linear extrapolation of the long-term decadal North Atlantic SST anomalies provides a possibility for the near-term prediction of dust activities and it suggests that the frequency and intensity of dust activities may increase in the next decade.

Long-term variation of sea surface temperature in relation to sea level pressure and surface wind speed in southern Indian Ocean

Sea surface temperature (SST) is an essential parameter associated with fish habitat and changes in oceanic conditions. Long-term SST variation in relation to sea level pressure (SLP) and surface wind speed (SWS) was observed based on the National Oceanic and Atmospheric Administration (NOAA) data of southern Indian Ocean. Mean monthly time series showed that February and November were the warmest and coolest months, respectively. Spatial distribution showed that 0°S–30°S was warm with SST of 20°–30°C, which was higher than that at all other places throughout the year. SLP (> 1010 millibar) and SWS (> 12 m/s) were high at 20°S–40°S and 45°S–50°S, respectively. SST was strongly negatively correlated with SWS but strongly positively correlated with SLP. Long-term yearly time series of SST showed that 2016 and 1948 were the warmest and coolest years, respectively, during the study period. Decadal time series showed that 1978–1987 had the highest decadal change. SST anomalies were almost negative before 1970, positive after 1970, high after 2008, and the highest in 2016. Southwest Indian Ocean was warmer than southeast Indian Ocean in summer and vice versa in winter. SST changes during 1948–2018 were higher than those during 1948–1972 and 1973–2018 for all the 4 months.