Model Sea Surface Temperature Research Articles

Mitigating Masked Pixels in a Climate-Critical Ocean Dataset

Clouds and other data artefacts frequently limit the retrieval of key variables from remotely sensed Earth observations. We train a natural language processing (NLP)-inspired algorithm with high-fidelity ocean simulations to accurately reconstruct masked or missing data in sea surface temperature (SST) fields—one of 54 essential climate variables identified by the Global Climate Observing System. We demonstrate that the resulting model, referred to as Enki, repeatedly outperforms previously adopted inpainting techniques by up to an order of magnitude in reconstruction error, while displaying exceptional performance even in circumstances where the majority of pixels are masked. Furthermore, experiments on real infrared sensor data with masked percentages of at least 40% show reconstruction errors of less than the known uncertainty of this sensor (root mean square error (RMSE) ≲0.1 K). We attribute Enki’s success to the attentive nature of NLP combined with realistic SST model outputs—an approach that could be extended to other remotely sensed variables. This study demonstrates that systems built upon Enki—or other advanced systems like it—may therefore yield the optimal solution to mitigating masked pixels in in climate-critical ocean datasets sampling a rapidly changing Earth.

Contribution of Atmospheric Factors in Predicting Sea Surface Temperature in the East China Sea Using the Random Forest and SA-ConvLSTM Model

Atmospheric forcings are significant physical factors that influence the variation of sea surface temperature (SST) and are often used as essential input variables for ocean numerical models. However, their contribution to the prediction of SST based on machine-learning methods still needs to be tested. This study presents a prediction model for SST in the East China Sea (ECS) using two machine-learning methods: Random Forest and SA-ConvLSTM algorithms. According to the Random Forest feature importance scores and correlation coefficients R, 2 m air temperature and longwave radiation were selected as the two most important key atmospheric factors that can affect the SST prediction performance of machine-learning methods. Four datasets were constructed as input to SA-ConvLSTM: SST-only, SST-T2m, SST-LWR, and SST-T2m-LWR. Using the SST-T2m and SST-LWR, the prediction skill of the model can be improved by about 9.9% and 9.43% for the RMSE and by about 8.97% and 8.21% for the MAE, respectively. Using the SST-T2m-LWR dataset, the model’s prediction skill can be improved by 10.75% for RMSE and 9.06% for MAE. The SA-ConvLSTM can represent the SST in ECS well, but with the highest RMSE and AE in summer. The findings of the presented study requires much more exploration in future studies.

Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks: Climatology, Interannual Variability, and Extremes

Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks: Climatology, Interannual Variability, and Extremes

North Atlantic Temperature Change Across the Eocene‐Oligocene Transition From Clumped Isotopes

AbstractThe Eocene‐Oligocene transition (EOT) (∼34 Ma) is marked by the rapid development of a semi‐permanent Antarctic ice‐sheet, as indicated by ice‐rafted debris and a 1–1.5‰ increase in deep sea δ18O. Proxy reconstructions indicate a drop in atmospheric CO2 and global cooling. How these changes affected surface ocean temperatures in the North Atlantic and ocean water stratification remains poorly constrained. In this study, we apply clumped‐isotope thermometry to well‐preserved planktonic foraminifera, that are associated with lower mixed‐layer to subthermocline dwelling depths from the drift sediments at international ocean discovery program Site 1411, Newfoundland, across four intervals bracketing the EOT. The thermocline/lower mixed‐layer dwelling foraminifera record a cooling of 1.9 ± 3.5 K (mean ± 95% CI) across the EOT. While the cooling amplitude is similar to previous sea surface temperature (SST) reconstructions, absolute temperatures (Eocene 20.0 ± 2.9°C, Oligocene 18.0 ± 2.2°C) appear colder than previous organic proxy reconstructions for the northernmost Atlantic extrapolated to this location. We discuss seasonal bias, recording depth, and appropriate consideration of paleolatitudes, all of which complicate the comparison between SST reconstructions and model output. Our subthermocline dwelling foraminifera record a larger cooling across the EOT (Eocene 19.0 ± 3.5°C, Oligocene 13.0 ± 3.2°C, cooling of 5.5 ± 4.6 K) than foraminifera from the thermocline/lower mixed‐layer, consistent with global cooling and an increase in ocean stratification which may be related to the onset or intensification of the Atlantic meridional overturning circulation.

Diurnal Variability of the Upper Ocean Simulated by a Climate Model

AbstractWe use a version of the NOAA Climate Forecast System with enhanced (up to 1‐m) ocean model vertical resolution to investigate the mean diurnal cycles of upper ocean temperature and currents. The model sea surface temperature diurnal cycle agrees well with a global observational analysis. The simulated time‐depth profiles of temperature and current also match closely observations from densely instrumented moorings in the tropical Pacific. Our analyses provide new insights into subsurface ocean diurnal cycles. Significant temperature diurnal range occurs, with seasonal modulation, at depths greater than 10 m across broad areas of the subtropical and midlatitude oceans. Significant current diurnal cycles are evident below 30 m across parts of the tropics, including in areas where deep‐cycle turbulence has been observed.

Seasonal predictability of the extreme Pakistani rainfall of 2022 possible contributions from the northern coastal Arabian Sea temperature

During the summer of 2022, Pakistan and northwestern India were hit by an unprecedented spell of heavy rainfall. Despite capturing major tropical climate anomalies, almost all seasonal prediction systems failed to predict the extreme event, far surpassing the 2010 event. While trying to find possible causes of such a failure through an analysis of inter-member anomalies and numerical experiments, we have discovered that the positive sea surface temperature anomalies in the northern coastal Arabian Sea were one of the key factors behind the event. Although the ensemble mean prediction by the original system captured only 7% of the observed precipitation anomalies over Pakistan, the sensitivity experiments, in which the model sea surface temperature in the northern Arabian Sea was nudged to observation, captured about 25% of the observed anomalies. Improving our ability to predict the conditions of the northern Arabian Sea will contribute to better forecasting of extreme rainfall events such as the one experienced in Pakistan in 2022. Further analysis and possible coordinated sensitivity experiments with multi-models are necessary for a better understanding and improving the prediction.

An evaluation of the LLC4320 global-ocean simulation based on the submesoscale structure of modeled sea surface temperature fields

Abstract. We have assembled 2 851 702 nearly cloud-free cutout images (sized 144 km × 144 km) of sea surface temperature (SST) data from the entire 2012–2020 Level-2 Visible Infrared Imaging Radiometer Suite (VIIRS) dataset to perform a quantitative comparison to the ocean model output from the MIT General Circulation Model (MITgcm). Specifically, we evaluate outputs from the LLC4320 (LLC, latitude–longitude–polar cap) 148∘ global-ocean simulation for a 1-year period starting on 17 November 2011 but otherwise matched in geography and the day of the year to the VIIRS observations. In lieu of simple (e.g., mean, standard deviation) or complex (e.g., power spectrum) statistics, we analyze the cutouts of SST anomalies with an unsupervised probabilistic autoencoder (PAE) trained to learn the distribution of structures in SST anomaly (SSTa) on ∼ 10–80 km scales (i.e., submesoscale to mesoscale). A principal finding is that the LLC4320 simulation reproduces, over a large fraction of the ocean, the observed distribution of SSTa patterns well, both globally and regionally. Globally, the medians of the structure distributions match to within 2σ for 65 % of the ocean, despite a modest, latitude-dependent offset. Regionally, the model outputs reproduce mesoscale variations in SSTa patterns revealed by the PAE in the VIIRS data, including subtle features imprinted by variations in bathymetry. We also identify significant differences in the distribution of SSTa patterns in several regions: (1) in an equatorial band equatorward of 15∘; (2) in the Antarctic Circumpolar Current (ACC), especially in the eastern half of the Indian Ocean; and (3) in the vicinity of the point at which western boundary currents separate from the continental margin. It is clear that region 3 is a result of premature separation in the simulated western boundary currents. The model output in region 2, the southern Indian Ocean, tends to predict more structure than observed, perhaps arising from a misrepresentation of the mixed layer or of energy dissipation and stirring in the simulation. The differences in region 1, the equatorial band, are also likely due to model errors, perhaps arising from the shortness of the simulation or from the lack of high-frequency and/or wavenumber atmospheric forcing. Although we do not yet know the exact causes for these model–data SSTa differences, we expect that this type of comparison will help guide future developments of high-resolution global-ocean simulations.

A climate modeling study over Northern Africa and the Mediterranean basin with multi-physics ensemble and coupling to a regional ocean modeling system

We have developed a physics ensemble of Weather Research and Forecasting (WRF) model simulations for the Middle East, Mediterranean and North Africa (MEMNA) regions. These simulations use different configurations for the cumulus, microphysics, surface layer, planetary boundary layer, and land surface schemes and are forced by the Community Earth System Model (CESM) General Circulation Model for the historical period 1979–1993. We have also created a complementary ensemble in which the WRF model is fully-coupled to the Regional Ocean Modelling System (ROMS) that simulates the dynamics of the entire Mediterranean Sea. Analysis of our ensembles reveals that the simulated precipitation and near surface temperature (T2) fields in WRF are largely influenced by the cumulus and the land surface schemes during the summer and winter seasons, respectively. The coupling of Weather Research and Forecasting to Regional Ocean Modelling System yields Mediterranean sea surface temperatures that are directly correlated with T2 and have higher spatial resolution than the global model. Meanwhile no significant difference is found between the atmospheric fields from the coupled and uncoupled runs because the Community Earth System Model sea surface temperatures over the Mediterranean, that are used for surface forcing in the uncoupled runs, are already in close agreement with both Regional Ocean Modelling System and observations. We conclude that our high-resolution coupled atmosphere-ocean modelling system is capable of producing climate data of good quality, and we identify those combinations of physics schemes that result in an acceptable level of bias that facilitates their use in future studies.

Determining the long-term impact area of coastal thermal discharge based on a harmonic model of sea surface temperature

AbstractCoastal nuclear power plants discharge large amounts of warm cooling water, which may have environmental impacts. This study proposes a method for determining the long-term impact area based on the average distribution of sea surface temperate (SST) increases. Taking the Daya Bay Nuclear Power Plant as a case study, 101 TM/ETM+ images acquired from 2000 to 2013 were used to obtain SST products. Cross-validation with NR_2P products showed that the accuracy of the SST products, in terms of the systematic error, root-mean-square error, and mean absolute error of 1,000 randomly selected verification points, was all <0.3°C, while Willmott’s index of agreement values was all >0.7. An annual SST cycle harmonic model was established. The mean difference between the modeled and observed SSTs was −2.1 to 2.5°C with a standard deviation range of 0–1°C. The long-term impact area was extracted by the harmonic analysis method and multi-year average method for comparison. The following conclusions can be drawn: 1) with sufficient SST samples, the temperature distributions of the two methods are similar, with the multi-year average method giving less noise and clearer boundaries. 2) When SST data are lacking for some months, the mean and standard deviation of the percentage of pixels belonging to areas of different temperature rise were calculated. The standard deviations of the two methods were both <0.04 in the temperature-rise classes of 1–2, 2–3, 3–4, and 4–5°C, while in the 0–1°C class, the standard deviation of the multi-year average method was 0.461, which is much higher than that of the multi-year average method (0.098). Performing statistical analysis on all pixels of >0°C, the multi-year average method had a standard deviation of 0.506, while the harmonic analysis method had a value of 0.128. Overall, the harmonic analysis method makes it possible to obtain and evaluate the long-term stability impact area of the thermal discharge over a period of time comprehensively and quantitatively. Even though it introduces a small amount of noise, it has less dependence on the input SST products and could improve the stability and reliability of thermal discharge monitoring, providing technical support for precise pollution control.

Coupling ecological concepts with an ocean-colour model: Phytoplankton size structure

Phytoplankton play a central role in the planetary cycling of important elements and compounds. Understanding how phytoplankton are responding to climate change is consequently a major question in Earth Sciences. Monitoring phytoplankton is key to answering this question. Satellite remote sensing of ocean colour is our only means of monitoring phytoplankton in the entire surface ocean at high temporal and large spatial scales, and the continuous ocean-colour data record is now approaching a length suitable for addressing questions around climate change, at least in some regions. Yet, developing ocean-colour algorithms for climate change studies requires addressing issues of ambiguity in the ocean-colour signal. For example, for the same chlorophyll-a concentration (Chl-a) of phytoplankton, the colour of the ocean can be different depending on the type of phytoplankton present. One route to tackle the issue of ambiguity is by enriching the ocean-colour data with information on sea surface temperature (SST), a good proxy of changes in three phytoplankton size classes (PSCs) independent of changes in total Chl-a, a measure of phytoplankton biomass. Using a global surface in-situ dataset of HPLC (high performance liquid chromatography) pigments, size-fractionated filtration data, and concurrent satellite SST spanning from 1991 to 2021, we re-tuned, validated and advanced an SST-dependent three-component model that quantifies the relationship between total Chl-a and Chl-a associated with the three PSCs (pico-, nano- and microplankton). Similar to previous studies, striking dependencies between model parameters and SST were captured, which were found to improve model performance significantly. These relationships were applied to 40 years of monthly composites of satellite SST, and significant trends in model parameters were observed globally, in response to climate warming. Changes in these parameters highlight issues in estimating long-term trends in phytoplankton biomass (Chl-a) from ocean colour using standard empirical algorithms, which implicitly assume a fixed relationship between total Chl-a and Chl-a of the three size classes. The proposed ecological model will be at the centre of a new ocean-colour modelling framework, designed for investigating the response of phytoplankton to climate change, described in subsequent parts of this series of papers.

Warmer western tropical South Atlantic during the Last Interglacial relative to the current interglacial period

The Last Interglacial (LIG, 129–116 thousand years ago) is an excellent case study for global warming scenarios and a target for proxy-model comparisons. The LIG global average sea surface temperature (SST) was ~0.5 °C higher than pre-industrial (PI). Contrary to the global average, tropical SST proxy compilations and model simulations show a negative anomaly in LIG SST relative to PI. Here, we present a LIG SST record from marine sediment core GL-1180 retrieved from the western tropical South Atlantic (WTSA). The SST record is based on Mg/Ca ratios of planktonic foraminifera Globigerinoides ruber (white). Our results indicate a warmer LIG in the WTSA relative to PI and Holocene conditions. We show that a positive LIG SST anomaly in tropical regions can be explained by polar sea ice loss during the LIG, which warms the ocean surface all the way into the tropics. The disagreement between proxy results and model simulations from the fourth phase of the Paleoclimate Modelling Intercomparison Project could result from uncertainties in our proxy for SST and/or limitations of numeric models in capturing transient forcings and feedbacks in Earth's climate system and imprecise boundary conditions. Additional studies are warranted to better constrain the LIG SST evolution in tropical regions.

Prediction of Sea Surface Temperature in the East China Sea Based on LSTM Neural Network

Sea surface temperature (SST) is an important physical factor in the interaction between the ocean and the atmosphere. Accurate monitoring and prediction of the temporal and spatial distribution of SST are of great significance in dealing with climate change, disaster prevention, disaster reduction, and marine ecological protection. This study establishes a prediction model of sea surface temperature for the next five days in the East China Sea using long-term and short-term memory neural networks (LSTM). It investigates the influence of different parameters on prediction accuracy. The sensitivity experiment results show that, based on the same training data, the length of the input data of the LSTM model can improve the model’s prediction performance to a certain extent. However, no obvious positive correlation is observed between the increase in the input data length and the improvement of the model’s prediction accuracy. On the contrary, the LSTM model’s performance decreases with the prediction length increase. Furthermore, the single-point prediction results of the LSTM model for the estuary of the Yangtze River, Kuroshio, and the Pacific Ocean are accurate. In particular, the prediction results of the point in the Pacific Ocean are the most accurate at the selected four points, with an RMSE of 0.0698 °C and an R2 of 99.95%. At the same time, the model in the Pacific region is migrated to the East China Sea. The model was found to have good mobility and can well represent the long-term and seasonal trends of SST in the East China Sea.

Seasonal extrema of sea surface temperature in CMIP6 models

Abstract. CMIP6 model sea surface temperature (SST) seasonal extrema averaged over 1981–2010 are assessed against the World Ocean Atlas (WOA18) observational climatology. We propose a mask to identify and exclude regions of large differences between three commonly used climatologies (WOA18, WOCE-Argo Global Hydrographic climatology (WAGHC) and the Hadley Centre Sea Ice and Sea Surface Temperature data set (HadISST)). The biases in SST seasonal extrema are largely consistent with the annual mean SST biases. However, the amplitude and spatial pattern of SST bias vary seasonally in the 20 CMIP6 models assessed. Large seasonal variations in the SST bias occur in eastern boundary upwelling regions, polar regions, the North Pacific and the eastern equatorial Atlantic. These results demonstrate the importance of evaluating model performance not simply against annual mean properties. Models with greater vertical resolution in their ocean component typically demonstrate better representation of SST extrema, particularly seasonal maximum SST. No significant relationship of SST seasonal extrema with horizontal ocean model resolution is found.

Past the Precipice? Projected Coral Habitability Under Global Heating.

Coral reefs are rapidly declining due to local environmental degradation and global climate change. In particular, corals are vulnerable to ocean heating. Anomalously hot sea surface temperatures (SSTs) create conditions for severe bleaching or direct thermal death. We use SST observations and CMIP6 model SST to project thermal conditions at reef locations at a resolution of 1 km, a 16‐fold improvement over prior studies, under four climate emissions scenarios. We use a novel statistical downscaling method which is significantly more skillful than the standard method, especially at near‐coastal pixels where many reefs are found. For each location we present projections of thermal departure (TD, the date after which a location with steadily increasing heat exceeds a given thermal metric) for severe bleaching recurs every 5 years (TD5Y) and every 10 years (TD10Y), accounting for a range of post‐bleaching reef recovery/degradation. As of 2021, we find that over 91% and 79% of 1 km2 reefs have exceeded TD10Y and TD5Y, respectively, suggesting that widespread long‐term coral degradation is no longer avoidable. We project 99% of 1 km2 reefs to exceed TD5Y by 2034, 2036, and 2040 under SSP5‐8.5, SSP3‐7.0, and SSP2‐4.5 respectively. We project that 2%–5% of reef locations remain below TD5Y at 1.5°C of mean global heating, but 0% remain at 2.0°C. These results demonstrate the importance of further improving ecological projection capacity for climate‐vulnerable marine and terrestrial species and ecosystems, including identifying refugia and guiding conservation efforts. Ultimately, saving coral reefs will require rapidly reducing and eliminating greenhouse gas emissions.

Exploring AMOC Regime Change over the Past Four Decades through Ocean Reanalyses

We examine North Atlantic climate variability using an ensemble of ocean reanalysis datasets to study the Atlantic Meridional Overturning Circulation (AMOC) from 1979 to 2018. The dataset intercomparison shows good agreement for the latest period (1995–2018) for AMOC dynamics, characterized by a weaker overturning circulation after 1995 and a more intense one during 1979–1995, with varying intensity across the various datasets. The correlation between leading empirical orthogonal functions suggests that the AMOC weakening has connections with cooler (warmer) sea surface temperature (SST) and lower (higher) ocean heat content in the subpolar (subtropical) gyre in the North Atlantic. Barotropic stream function and Gulf Stream index reveal a shrinking subpolar gyre and an expanding subtropical gyre during the strong-AMOC period and vice versa, consistently with Labrador Sea deep convection reduction. We also observed an east–west salt redistribution between the two periods. Additional analyses show that the AMOC variability is related to the North Atlantic Oscillation phase change around 1995. One of the datasets included in the comparison shows an overestimation of AMOC variability, notwithstanding the model SST bias reduction via ERA-Interim flux adjustments: further studies with a set of numerical experiments will help explain this behavior.

Feedback of Tropical Cyclones Over the Western North Pacific on La Niña Flavor

AbstractThe relationship between La Niña and tropical cyclone (TC) activity has not received as much attention as that between El Niño and TCs; in particular, the important role of TCs in affecting La Niña flavor has not been reported. This study reveals a clear feedback of TCs over the western North Pacific (WNP) on La Niña flavor by modulating the Walker circulation and thermocline. When strong WNP accumulated cyclone energy (ACE) occurs 3 months earlier, the center of sea surface temperature (SST) anomalies shifts to the equatorial eastern Pacific, favoring the development of eastern‐Pacific (EP) La Niña. In contrast, weak WNP ACE is associated with central‐Pacific (CP) La Niña, and the influence of WNP TCs on the intensity of CP La Niña may be larger than for EP La Niña. More evidence is provided in the predictions of EP and CP La Niña events using the ACE + SST model.

Surface temperature and salinity in the northern Bay of Bengal: in situ measurements compared with satellite observations and model output

The northern Bay of Bengal (BoB) has been traditionally understudied and undersampled. Satellite and modeling products could compensate for the scarcity of in situ measurements, but this requires evaluating the accuracy of satellite and modeling products first. We present a comparison of sea surface temperature (SST) and sea surface salinity (SSS) products (satellite and model output) with 46 in situ observations in the northern BoB. We used satellite and modeled SST (daily) and SSS (weekly and daily) in this comparison. The results are as follows. (1) Both model and satellite-derived SSTs agreed well with in situ observations and with each other, with small biases (<1 ° C) and large correlation coefficients (r > 0.77). (2) Neither model nor satellite SSSs agreed well with in situ observations (biases > 0.5 PSU, r < 0.54). (3) Calculations of the d-index support the argument that model and satellite SSTs agreed well with in situ observations (d-index values of 0.68 and 0.65, respectively), while the model and satellite SSSs did not agree well with observations (d-index values of 0.31 and 0.40, respectively). The results suggest that additional work is needed to improve both model prediction and satellite retrieval algorithms for SSS in the northern BoB.

Correction of the model dynamics for the Northern seas using observational altimetry data

The earlier derived data assimilation method called Generalized Kalman filter (GKF) is applied in conjunction with the Nucleus for European Modelling of the Ocean (NEMO) circulation model to the calculation of the dynamics in the North Seas of Russia. By assimilating the satellite altimetry data from archive AVISO (Archiving, validating and interpolating of satellite observations) this method corrects the direct model calculations and improves the ocean state. The model fields, in particular, sea level and sea surface temperature with and without assimilation are constructed and compared with each other. The brief analysis of the results is also performed.

Potential influence of sea surface temperature representation in climate model simulations over CORDEX‐SEA domain

AbstractRegional climate simulations from the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment – Southeast Asia (SEA) indicated model biases in temperature and rainfall over SEA. Given the influence of sea surface temperature (SST) variability on SEA climate, this study examines SST representation in climate models to investigate its potential contribution to the resulting model biases over the Philippines. Observed SST over SEA is first characterized by its spatial patterns and temporal variability. An analysis of the SST representation over SEA and its potential influence on modelled climate over the Philippines in Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) is then conducted, followed by an assessment of the potential influence of SST representation in CMIP5 GCMs on downscaled regional climate output. Our results show that GCMs with well represented SSTs (i.e., low bias, well captured variability, and pattern) can produce climate simulations well over the Philippines. Whether or not the GCMs with poor SST representation can perform well is inconclusive. During boreal winter (summer), climate variables with high (low) spatial correlation with model SST get poor (better) spatial correlation with observed climate. Over west of the Philippines, where model SST seasonal variability is captured well, models also adequately simulate climate variables. Results suggest that the negative temperature biases, and positive precipitation and wind speed biases, in both GCMs and downscaled simulations, are associated with negative model SST biases. These findings give a better understanding on how SST potentially influences modelled climatology over the Philippines.

Higher order numerical schemes for the solution of fractional delay differential equations

Caputo and Riemann–Liouville (R–L) are the most commonly used fractional derivative operators in the field of fractional calculus. We present here two higher-order schemes, based on R–L and Caputo definitions for the solution of fractional delay differential equations (FDDEs). For the R–L fractional derivative, interpolation-based approximation and the finite difference based approximation for Caputo fractional (C-F) derivative are employed to obtain the higher-order schemes i.e.,Oh3−α,α∈(0,1),the fractional order for FDDEs. For the proposed schemes the stability and error estimates are presented. The noted distinct feature is that the finite difference based approximation takes almost 50% less computation time than the interpolation-based approximation. Varied examples have been worked out to verify the efficacy of the schemes, including non-linear problems such as, the chaotic behavior occurring in the fractional order Ikeda equation, the fractional order version of the population dynamics of Lemmings and the dynamical model for sea surface temperature.