Historical Sea Surface Temperature Research Articles

Effects of Background Synoptic Environment in Controlling South China Sea Tropical Cyclone Intensity and Size Changes in Pseudo–Global Warming Experiments

Abstract Assessing how global warming affects tropical cyclones (TC) is immensely important for climate change adaptation and hazard mitigation. However, projected intensity and size change can vary greatly among different individual storms, even under the same forcing in pseudo–global warming (PGW) experiments. Here we hypothesize that these variations are related to the historical environment in which each TC was embedded. Twenty-five TCs in the South China Sea (SCS) region were simulated using the Weather Research and Forecasting (WRF) Model. Their changes in the near (2036–65) and far future (2075–99) following the representative concentration pathways 8.5 (RCP8.5) and 4.5 (RCP4.5) under phase 5 of the Coupled Model Intercomparison Project (CMIP5) were investigated by the PGW technique. The mean changes in TC intensity and gale-force wind radius (R17) in the SCS were +6.4% and +1.5% for a 2°C warming, respectively. Multiple linear regression and stepwise regression analysis revealed that storm intensity variations were positively correlated with historical sea surface temperature and negatively with outer (i.e., outside the TC’s R17) atmospheric instability, while the R17 variations correlated positively with outer midtropospheric relative humidity (RH) and surface outer wind speed (OWS). Ertel potential vorticity (EPV) diagnostics further showed a moister SCS background can cause stronger diabatic heating and EPV production at the spiral rainbands under PGW, which increases R17. Additionally, stronger background absolute angular momentum (AAM) promoted stronger AAM influx, leading to larger R17. Implications were drawn to explain the uncertainties in projected TC intensity and size due to natural variability. Significance Statement Changes in the intensity and size of individual tropical cyclones in future climates are highly variable. This study aims to understand the environmental factors influencing these variations. We selected 25 storms that entered the South China Sea region and modeled them in both present and future climates. The mean intensity and size were projected to increase by 6.4% and 1.5% for a 2°C warming, respectively. We found that tropical cyclones embedded in historically warmer oceans and more stable atmospheres intensified more than the others, while historically wetter and windier environments promoted larger size growth. Our results suggest that certain tropical cyclones can exhibit a greater increase in intensity and size in a warmer climate under favorable environmental conditions.

Trends in Atmospheric Heat Transport Since 1980

Abstract We investigate the linear trends in meridional atmospheric heat transport (AHT) since 1980 in atmospheric reanalysis datasets, coupled climate models, and atmosphere-only climate models forced with historical sea surface temperatures. Trends in AHT are decomposed into contributions from three components of circulation: (i) transient eddies, (ii) stationary eddies, and (iii) the mean meridional circulation. All reanalyses and models agree on the pattern of AHT trends in the Southern Ocean, providing confidence in the trends in this region. There are robust increases in transient-eddy AHT magnitude in the Southern Ocean in the reanalyses, which are well replicated by the atmosphere-only models, while coupled models show smaller magnitude trends. This suggests that the pattern of sea surface temperature trends contributes to the transient-eddy AHT trends in this region. In the tropics, we find large differences between mean-meridional circulation AHT trends in models and the reanalyses, which we connect to discrepancies in tropical precipitation trends. In the Northern Hemisphere, we find less evidence of large-scale trends and more uncertainty, but note several regions with mismatches between models and the reanalyses that have dynamical explanations. Throughout this work we find strong compensation between the different components of AHT, most notably in the Southern Ocean where transient-eddy AHT trends are well compensated by trends in the mean-meridional circulation AHT, resulting in relatively small total AHT trends. This highlights the importance of considering AHT changes holistically, rather than each AHT component individually.

Ocean Temperatures Do Not Account for a Record-Setting Winter in the U.S. West

The record-setting winter of 2022–2023 came as an answer to both figurative and literal prayers for political leaders, policy makers, and water managers reliant on snowpacks in the Upper Colorado River Basin, a vital source of water for tens of millions of people across the Western United States. But this “drought-busting” winter was not well-predicted, in part because while interannual patterns of tropical ocean temperatures have a well-known relationship to precipitation patterns across much of the American West, the Upper Colorado is part of a liminal region where these connections tend to be comparatively weak. Using historical sea surface temperature and snowpack records, and leveraging a long-term cross-basin relationship to extend the timeline for evaluation, this analysis demonstrates that the 2022–2023 winter did not present in accordance with other high-snowpack winters in this region, and that the associative pattern of surface temperatures in the tropical Pacific, and snow water equivalent in the regions that stored and supplied most of the water to the Colorado River during the 2022–2023 winter, was not substantially different from a historically incoherent arrangement of long-term correlation. These findings suggest that stochastic variability plays an outsized role in influencing water availability in this region, even in extreme years, reinforcing the importance of other trends to inform water policy and management.

Future Projections of the El Niño—Southern Oscillation and Tropical Pacific Mean State in CMIP6

AbstractThe El Niño—Southern Oscillation (ENSO) is an important mode of tropical Pacific atmosphere‐ocean variability that drives teleconnections with weather and climate globally. However, prior studies using state‐of‐the‐art climate models lack consensus regarding future ENSO projections and are often impacted by tropical Pacific sea‐surface temperature (SST) biases. We used 173 simulations from 29 climate models participating in the Coupled Model Intercomparison Project, version 6 (CMIP6) to analyze model biases and future ENSO projections. We analyzed two ENSO indices, namely the ENSO Longitude Index (ELI), which measures zonal shifts in tropical Pacific deep convection and accounts for changes in background SST, and the Niño 3.4 index, which measures SST anomalies in the central‐eastern equatorial Pacific. We found that the warm eastern tropical‐subtropical Pacific SST bias typical of previous generations of climate models persists into many of the CMIP6 models. Future projections of ENSO shift toward more El Niño‐like conditions based on ELI in 48% of simulations and 55% of models, in association with a future weakening of the zonal equatorial Pacific SST gradient. On the other hand, none of the models project a significant shift toward La Niña‐like conditions. The standard deviation of the Niño 3.4 index indicates a lack of consensus on whether an increase or decrease in ENSO variability is expected in the future. Finally, we found a possible relationship between historical SST and low‐level cloud cover biases in the ENSO region and future changes in ELI; however, this result may be impacted by limitations in data availability.

Northeast Pacific marine heatwaves linked to Kuroshio Extension variability

Marine heatwave events in the Northeast Pacific Ocean from 2013-2015 and 2019-2020 have had significant impacts on ocean life and livelihoods in the region. Numerous studies have linked these marine heatwaves to known modes of climate variability. Here we show that the observed evolution of the 2013-2015 Northeast Pacific marine heatwave best correlates with the evolution of historical sea surface temperatures in response to variations in the Kuroshio Extension. By using ocean and atmospheric reanalysis data from 1981-2020 and ocean nutrient data from 1993-2020 from an ocean biogeochemistry model, we further report the physical and biogeochemical changes during this heat event and their relation to these same Kuroshio variations. Using these results, we propose an atmospheric teleconnection between Kuroshio Extension variations and Marine Heatwaves in the Northeast Pacific. This teleconnection’s influence further extends to the marine biogeochemistry and productivity in the Northeast Pacific region via Kuroshio-influenced modifications to mixed layer thickness.

Understanding the Forcing Mechanisms of the 1931 Summer Flood along the Yangtze River, the World’s Deadliest Flood on Record

Abstract The 1931 Yangtze River flood in eastern China, which had an associated death toll of over 2 million, is regarded as one of the world’s deadliest natural disasters on record. However, due to the lack of meteorological data before the 1950s, the causes of this event are rarely investigated. Here, we combine multiple lines of evidence from recently available historical observations, reanalysis datasets, and atmospheric general circulation model simulations driven by historical sea surface temperatures (SSTs) that cover the early twentieth century to reveal the physical mechanisms underlying this flood. We find that the flooding in 1931 along the Yangtze River valley was dominated by July rainfall. Although the rainfall totals in July 1931 were not the largest in history (ranking second over the past century), the totals exceeded those for many other pluvial years between 1951 and 2010 in terms of its persistence, which was associated with a steady western Pacific subtropical high (WPSH). The flooding resulted from the combined effects of tropical El Niño–related SST forcing and extratropical wave activities over the Eurasian continent. On the one hand, warm SST anomalies in the tropical Indian Ocean following an El Niño event led to the southwestward extension of the WPSH. On the other hand, the southward shift of the westerly jet due to extratropical wave activities prevented the normal northward movement of the WPSH typical in July. Additionally, the impact that the preceding springtime precipitation and soil moisture had on the summer flood of 1931 is also discussed.

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.

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.

Time-variations of the climate feedback parameter λ are associated with the Pacific Decadal Oscillation

Climate models suggest that the climate feedback parameter λ, which denotes the magnitude of the Earth radiative response to a change in global surface temperature, varies with time. This is because λ depends on the pattern of sea-surface temperature. However, the time-variability of λ and its relation to the sea-surface temperature pattern has not been evaluated in multi-decadal observations. Here, using up-to-date observations, we evaluate the global energy budget over successive 25-year windows and derive a time-series of λ over 1970–2005. We find λ varied within the range [−3.2, −1.0]W ⋅ m−2 ⋅ K−1 since 1970. These variations are linked to the sea-surface temperature pattern changes associated with the Pacific Decadal Oscillation. Climate model simulations forced with observations of historical sea-surface temperature show a 1970–2005 mean λ that is consistent with observations. However, they fail in reproducing observed λ time-variations since 1970 which are associated to the Pacific Decadal Oscillation, meaning that climate models underestimate the pattern effect at decadal time scales.

Global and Regional Discrepancies between Early-Twentieth-Century Coastal Air and Sea Surface Temperature Detected by a Coupled Energy-Balance Analysis

Abstract A major uncertainty in reconstructing historical sea surface temperature (SST) before the 1990s involves correcting for systematic offsets associated with bucket and engine-room intake temperature measurements. A recent study used a linear scaling of coastal station-based air temperatures (SATs) to infer nearby SSTs, but the physics in the coupling between SATs and SSTs generally gives rise to more complex regional air–sea temperature differences. In this study, an energy-balance model (EBM) of air–sea thermal coupling is adapted for predicting near-coast SSTs from coastal SATs. The model is shown to be more skillful than linear-scaling approaches through cross-validation analyses using instrumental records after the 1960s and CMIP6 simulations between 1880 and 2020. Improved skill primarily comes from capturing features reflecting air–sea heat fluxes dominating temperature variability at high latitudes, including damping high-frequency wintertime SAT variability and reproducing the phase lag between SSTs and SATs. Inferred near-coast SSTs allow for intercalibrating coastal SAT and SST measurements at a variety of spatial scales. The 1900–40 mean offset between the latest SST estimates available from the Met Office (HadSST4) and SAT-inferred SSTs range between −1.6°C (95% confidence interval: [−1.7°, −1.4°C]) and 1.2°C ([0.8°, 1.6°C]) across 10° × 10° grids. When further averaged along the global coastline, HadSST4 is significantly colder than SAT-inferred SSTs by 0.20°C ([0.07°, 0.35°C]) over 1900–40. These results indicate that historical SATs and SSTs involve substantial inconsistencies at both regional and global scales. Major outstanding questions involve the distribution of errors between our intercalibration model and instrumental records of SAT and SST as well as the degree to which coastal intercalibrations are informative of global trends. Significance Statement To evaluate the consistency of instrumental surface temperature estimates before the 1990s, we develop a coupled energy-balance model to intercalibrate measurements of sea surface temperature (SST) and station-based air temperature (SAT) near global coasts. Our model captures geographically varying physical regimes of air–sea coupling and outperforms existing methods in inferring regional SSTs from SAT measurements. When applied to historical temperature records, the model indicates significant discrepancies between inferred and observed SSTs at both global and regional scales before the 1960s. Our findings suggest remaining data issues in historical temperature archives and opportunities for further improvements.

Using a Green’s Function Approach to Diagnose the Pattern Effect in GFDL AM4 and CM4

Abstract Global radiative feedbacks exhibit large dependence on the spatial structure of sea surface temperature (SST) changes, which is referred to as the “pattern effect.” A Green’s function (GF) approach has been demonstrated to be useful in identifying and understanding contributions of regional SST changes to global radiative feedbacks. Here, we explore the ability of the GF approach in quantifying the pattern effect in an atmospheric model (AM4) and a coupled model (CM4) recently developed at NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL), including the impact of SST changes on global-mean and local responses of key variables important to climate. Given historical SST patterns, the GF derived from idealized experiments with SST warming patches can largely reproduce AM4 simulated global-mean and regional responses. When AM4 is forced by SST patterns retrieved from the CM4 abrupt quadrupling of carbon dioxide experiment, the same GF captures interannual variations of AM4 simulated global-mean responses but falls short of reproducing the magnitude of the responses. A decomposition of such SST patterns into global-mean values plus remaining anomalies helps reduce biases. Additional idealized experiments are conducted to examine the sensitivity of the GF to the amplitude and sign of SST perturbations and to the integration time and the confidence level of the significance test. Impacts of these factors on the performance of the GF are discussed.

Cloudiness delays projected impact of climate change on coral reefs

The increasing frequency of mass coral bleaching and associated coral mortality threaten the future of warmwater coral reefs. Although thermal stress is widely recognized as the main driver of coral bleaching, exposure to light also plays a central role. Future projections of the impacts of climate change on coral reefs have to date focused on temperature change and not considered the role of clouds in attenuating the bleaching response of corals. In this study, we develop temperature- and light-based bleaching prediction algorithms using historical sea surface temperature, cloud cover fraction and downwelling shortwave radiation data together with a global-scale observational bleaching dataset observations. The model is applied to CMIP6 output from the GFDL-ESM4 Earth System Model under four different future scenarios to estimate the effect of incorporating cloudiness on future bleaching frequency, with and without thermal adaptation or acclimation by corals. The results show that in the low emission scenario SSP1-2.6 incorporating clouds into the model delays the bleaching frequency conditions by multiple decades in some regions, yet the majority (>70%) of coral reef cells still experience dangerously frequent bleaching conditions by the end of the century. In the moderate scenario SSP2-4.5, however, the increase in thermal stress is sufficient to overwhelm the mitigating effect of clouds by mid-century. Thermal adaptation or acclimation by corals could further shift the bleaching projections by up to 40 years, yet coral reefs would still experience dangerously frequent bleaching conditions by the end of century in SPP2-4.5. The findings show that multivariate models incorporating factors like light may improve the near-term outlook for coral reefs and help identify future climate refugia. Nonetheless, the long-term future of coral reefs remains questionable if the world stays on a moderate or higher emissions path.

Global Spatiotemporal Graph Attention Network for Sea Surface Temperature Prediction

Accurately predicting sea surface temperature (SST) plays an important role in the study of marine ecosystems and global climate. The SST prediction problem is usually formulated as a time series regression problem, i.e., the future SST is predicted based on the historical SST. However, existing methods are typically devoted to modeling the highly nonlinear temporal correlations in SST data. They often ignore the dynamic spatial correlations that exist. This can limit the performance of these models, making accurately predicting SST challenging. To address this challenge, by combining graph neural networks that have a clear advantage in modeling spatial correlations, we propose a global spatio-temporal graph attention network (GSTGAT). Specifically, we capture the global dynamic spatial correlations of nodes through a global graph attention network module that fuses the static adjacency matrix learned adaptively by the graph learning module with the dynamic attention coefficients. A gated temporal convolutional network module is used to capture the nonlinear temporal correlations. Then, the above modules are integrated into a unified neural network to predict SST. We conduct experiments on multiple time-scale datasets in the Bohai Sea and the South China Sea. The experimental results show that GSTGAT achieves the best performance and consistently outperforms other methods for different prediction horizons in the two sea areas.

Understanding CMIP6 biases in the representation of the Greater Horn of Africa long and short rains

The societies of the Greater Horn of Africa (GHA) are vulnerable to variability in two distinct rainy seasons, the March–May ‘long’ rains and the October–December ‘short’ rains. Recent trends in both rainy seasons, possibly related to patterns of low-frequency variability, have increased interest in future climate projections from General Circulation Models (GCMs). However, previous generations of GCMs historically have poorly simulated the regional hydroclimate. This study conducts a process-based evaluation of simulations of the long and short rains in CMIP6, the latest generation of GCMs. Key biases in CMIP5 remain or are worsened, including long rains that are too short and weak and short rains that are too long and strong. Model biases are driven by a complex set of related oceanic and atmospheric factors, including simulations of the Walker Circulation. Biased wet short rains in models are connected with Indian Ocean zonal sea surface temperature (SST) gradients that are too warm in the west and convection that is too deep. Models connect equatorial African winds with the strength of the short rains, though in observations a robust connection is primarily found in the long rains. Model mean state biases in the timing of the western Indian Ocean SST seasonal cycle are associated with certain rainfall timing biases, though both biases may be due to a common source. Simulations driven by historical SSTs (AMIP runs) often have larger biases than fully coupled runs. A path towards using biases to better understand uncertainty in projections of GHA rainfall is suggested.

Changes of the seasonal predictability of historical sea surface temperature related to Atlantic hurricanes

Changes of the seasonal predictability of historical sea surface temperature related to Atlantic hurricanes

On the Effect of Historical SST Patterns on Radiative Feedback

AbstractWe investigate the dependence of radiative feedback on the pattern of sea‐surface temperature (SST) change in 14 Atmospheric General Circulation Models (AGCMs) forced with observed variations in SST and sea‐ice over the historical record from 1871 to near‐present. We find that over 1871–1980, the Earth warmed with feedbacks largely consistent and strongly correlated with long‐term climate sensitivity feedbacks (diagnosed from corresponding atmosphere‐ocean GCM abrupt‐4xCO2 simulations). Post 1980, however, the Earth warmed with unusual trends in tropical Pacific SSTs (enhanced warming in the west, cooling in the east) and cooling in the Southern Ocean that drove climate feedback to be uncorrelated with—and indicating much lower climate sensitivity than—that expected for long‐term CO2 increase. We show that these conclusions are not strongly dependent on the Atmospheric Model Intercomparison Project (AMIP) II SST data set used to force the AGCMs, though the magnitude of feedback post 1980 is generally smaller in nine AGCMs forced with alternative HadISST1 SST boundary conditions. We quantify a “pattern effect” (defined as the difference between historical and long‐term CO2 feedback) equal to 0.48 ± 0.47 [5%–95%] W m−2 K−1 for the time‐period 1871–2010 when the AGCMs are forced with HadISST1 SSTs, or 0.70 ± 0.47 [5%–95%] W m−2 K−1 when forced with AMIP II SSTs. Assessed changes in the Earth's historical energy budget agree with the AGCM feedback estimates. Furthermore satellite observations of changes in top‐of‐atmosphere radiative fluxes since 1985 suggest that the pattern effect was particularly strong over recent decades but may be waning post 2014.

Examining the Relationship between Climate Change and Vibriosis in the United States: Projected Health and Economic Impacts for the 21st Century.

Background:This paper represents, to our knowledge, the first national-level (United States) estimate of the economic impacts of vibriosis cases as exacerbated by climate change. Vibriosis is an illness contracted through food- and waterborne exposures to various Vibrio species (e.g., nonV. cholerae O1 and O139 serotypes) found in estuarine and marine environments, including within aquatic life, such as shellfish and finfish.Objectives:The objective of this study was to project climate-induced changes in vibriosis and associated economic impacts in the United States related to changes in sea surface temperatures (SSTs).Methods:For our analysis to identify climate links to vibriosis incidence, we constructed three logistic regression models by Vibrio species, using vibriosis data sourced from the Cholera and Other Vibrio Illness Surveillance system and historical SSTs. We relied on previous estimates of the cost-per-case of vibriosis to estimate future total annual medical costs, lost income from productivity loss, and mortality-related indirect costs throughout the United States. We separately reported results for V. parahaemolyticus, V. vulnificus, V. alginolyticus, and “V. spp.,” given the different associated health burden of each.Results:By 2090, increases in SST are estimated to result in a 51% increase in cases annually relative to the baseline era (centered on 1995) under Representative Concentration Pathway (RCP) 4.5, and a 108% increase under RCP8.5. The cost of these illnesses is projected to reach annually under RCP4.5, and annually under RCP8.5, relative to in the baseline (2018 U.S. dollars), equivalent to 140% and 234% increases respectively.Discussion:Vibriosis incidence is likely to increase in the United States under moderate and unmitigated climate change scenarios through increases in SST, resulting in a substantial burden of morbidity and mortality, and costing billions of dollars. These costs are mostly attributable to deaths, primarily from exposure to V. vulnificus. Evidence suggests that other factors, including sea surface salinity, may contribute to further increases in vibriosis cases in some regions of the United States and should also be investigated. https://doi.org/10.1289/EHP9999a

MUST: A Multi-source Spatio-Temporal data fusion Model for short-term sea surface temperature prediction

Sea surface temperature (SST) plays an important role in various oceanic applications, including climate prediction, ocean environment monitoring and marine disaster warning. Although many approaches have been developed for predicting SST, most of them conduct the prediction only based on historical SST data. However, SST is essentially affected by many external factors, e.g., the short-wave radiation from the sun and the long-wave radiation from the atmosphere and ground, which are ignored by existing approaches. In this work, we proposed a Multi-source Spatio-Temporal data fusion model (MUST) to fuse multi-source data, including SST data and external factors, to improve the accuracy of short-term SST prediction. Concretely, MUST first introduces Bicubic Convolutional Interpolation (BCI) to address the issue of inconsistent spatial resolutions of multi-source data, then employs the Spatio-Temporal Dilated ConvLSTM (ST-DC) to learn the spatio-temporal features of SST and external factors, and finally fuses the learned features from multi-source data to predict SST with a Cross Data Fusion (CDF) component. As validated on two real datasets, MUST achieves much better performance than existing SST prediction approaches.

The recent normalization of historical marine heat extremes

Climate change exposes marine ecosystems to extreme conditions with increasing frequency. Capitalizing on the global reconstruction of sea surface temperature (SST) records from 1870-present, we present a centennial-scale index of extreme marine heat within a coherent and comparable statistical framework. A spatially (1° × 1°) and temporally (monthly) resolved index of the normalized historical extreme marine heat events was expressed as a fraction of a year that exceeds a locally determined, monthly varying 98th percentile of SST gradients derived from the first 50 years of climatological records (1870–1919). For the year 2019, our index reports that 57% of the global ocean surface recorded extreme heat, which was comparatively rare (approximately 2%) during the period of the second industrial revolution. Significant increases in the extent of extreme marine events over the past century resulted in many local climates to have shifted out of their historical SST bounds across many economically and ecologically important marine regions. For the global ocean, 2014 was the first year to exceed the 50% threshold of extreme heat thereby becoming “normal”, with the South Atlantic (1998) and Indian (2007) basins crossing this barrier earlier. By focusing on heat extremes, we provide an alternative framework that may help better contextualize the dramatic changes currently occurring in marine systems.

Multiple Granularity Spatiotemporal Network for Sea Surface Temperature Prediction

Sea surface temperature (SST) prediction has an important practical value in marine disaster prevention and mitigation. Most current methods only use the temporal correlation of SST during prediction, but the spatial correlation is not considered, resulting in low prediction accuracies. In addition, the changing trend of SST as reflected by the single granularity feature is unreliable, and the degrees of dependence between historical SST and future SST tend to vary. In order to overcome these issues, the multiple granularity spatiotemporal network (MGSN) is proposed for SST prediction. The proposed method consists of three parts. First, a multibranch network structure is constructed to extract different temporal features of different granularities. Second, a temporal dependence representation module is developed to represent the different degrees of dependence between historical SST and predicted SST in the temporal dimension. Third, the spatiotemporal fusion prediction module is used to achieve a spatiotemporal prediction of the SST and fuse the prediction results of different granular features. Comparative experiments have been conducted. The experimental results show that the root-mean-square error (RMSE) of the proposed method is reduced by 0.1360, 0.1608, and 0.1448 compared with the RMSE of convolutional LSTM (ConvLSTM), when predicting SST for the next one day, three days, and seven days, respectively. Our method has strong spatiotemporal feature modeling capabilities and is suitable for regional SST prediction.