Ocean Surface Temperature Research Articles

Sea surface temperature error under tropical cyclone in the western North Pacific.

Tropical cyclone forecasting relies on accurate sea surface temperature, yet the errors in available sea surface temperature products remain unclear. Here, I evaluate the sea surface temperature errors under tropical cyclones in the western North Pacific based on surface drifters across eight widely-used datasets, including satellite, atmospheric and oceanic analysis-or-reanalysis products, spanning from 2014 to 2023. The ensemble mean bias and root-mean-squared-error are firstly quantified. Both satellite and oceanic analysis sea surface temperatures exhibit a cold bias, while atmospheric analysis-or-reanalysis sea surface temperature shows a warm bias. Specifically, atmospheric analysis-or-reanalysis products consistently show a warm bias in the right-rear quadrant of the tropical cyclone coordinate system. Regarding root-mean-squared-error, for most products, the right side consistently exhibits higher errors than the left side. SST bias significantly impacts tropical cyclone intensity change as indicated by empirical relationship. These results provide direct insights for operational tropical cyclone forecasting and research.

Temperature and Ozone Response to Different Forcing in the Lower Troposphere and Stratosphere

To evaluate the contributions of different forcings to the temperature and atmospheric composition changes between 1980 and 2020, we exploited the chemistry-climate model (CCM) SOCOLv3. The study examined ozone content and atmospheric temperature response to (1) ozone-depleting substances; (2) greenhouse gas concentrations, ocean surface temperature, and sea ice coverage; (3) solar irradiance; and (4) stratospheric aerosol loading and, separately, (5) greenhouse gas concentrations, (6) ocean surface temperature and sea ice coverage, and (7) NOx surface emissions. To evaluate the impacts of specific factors, we performed model runs driven by each factor (1–7) variability as well as a reference experiment that accounted for the influence of all factors simultaneously. We identified the relative contribution of different factors to the evolution of the temperature and ozone content of the lower troposphere and stratosphere from 1980 to 2020. The model results were in good agreement with the reanalyses (MERRA2 and ERA5). We showed that stratospheric ozone depletion before the Montreal Protocol introduction and partial recovery after that were chiefly driven by ODS. Stratospheric aerosol from major volcanic eruptions caused only short-term (up to 5 years) ozone decline. Increased greenhouse gas emissions dominate the ongoing long-term stratospheric cooling as well as tropospheric and surface warming. Solar irradiance contributed to short-term fluctuations but had a minimal long-term impact. Furthermore, our analysis of the solar signal in the tropical stratosphere underscores the complex interplay of solar radiation with volcanic, oceanic, and atmospheric factors, revealing significant altitudinal distributions of temperature and ozone responses to solar activity. Our findings advocate further innovative methodologies to take into account the nonlinearity of the atmospheric processes.

On the Possible Climatic Consequences of the Large Oil Spills in Oceans

In the North Atlantic and the Northern Ocean, from the second half of 2010 to 2014, satellite imagery data showed increased surface water temperatures (in the Icelandic Depression area in September–October 2010, it was 1.3 °C higher than in 2009). The peak of the annual sum of mean monthly ocean surface temperatures near the Icelandic Depression in 2010 (109.3 °C), as well as the negative values of the monthly averaged North Atlantic Oscillation (NAO) indices, estimated in the second half of 2010 and until March 2011, can be explained by the appearance of an additional film of oil origin on the water surface, formed after an oil spill accident at the Deepwater Horizon drilling rig in the Gulf of Mexico. Insufficient evaporative cooling of surface waters near the Icelandic Depression related to the formation of an additive film due to the influence of pollution of the North Sea by oil can explain the earlier peak in the annual sum of mean monthly ocean surface temperatures near the Icelandic Depression in 2003 (107.2 °C). Although global warming is usually ascribed to increased greenhouse gases in the atmosphere, ocean surface water pollution could increase the heat content of the ocean and explain the steady temperature stratification and desalination of these waters due to the melting of Greenland’s glaciers. Thus, when analyzing the concept of global warming, it is necessary to take into account the aspects of pollution of the ocean surface waters to assess the changes in their capacity to accumulate solar radiation, as well as the changes in the heat content of the ocean mixing zone (~200 m).

Cloud detection for HY-1C/COCTS over the ocean based on spectral-and-textural-information-guided deep neural network

AbstractGiven that clouds can absorb and scatter radiation signals in the visible and infrared bands, cloud detection is a key preprocessing step for ocean color and sea surface temperature retrievals. In this research, a Spectral-and-Textural-Information-Guided deep neural Network (STIGNet) is designed for cloud detection in global ocean data from the Haiyang-1C (HY-1C)/Chinese Ocean Color and Temperature Scanner (COCTS). Considering the spectral and textural properties of clouds, the model incorporates HY-1C/COCTS spectral data, differences in brightness temperature (BT), local statistical characteristics of BT, and geographical location information–all of which are closely related to cloud features. Notably, an edge learning module is implemented to emphasize edge features during the training process. We construct a HY-1C/COCTS cloud detection dataset to train and test the cloud detection model. In the dataset, labels are generated by combining the Bayesian cloud detection method with a manual mask. Analysis of the resulting cloud detection images indicates that STIGNet exhibits accurate performance across various types of clouds while showing minimal overestimated errors in areas such as ocean fronts or sun glints, where they tend to occur frequently. The ablation experiments performed on physical-based input features and edge learning modules show enhancements in cloud detection accuracy. Evaluation results demonstrate an overall accuracy of 96.64%, with a cloud overestimated error of 1.61% and a cloud missed error of 1.76%. These findings highlight the effectiveness of STIGNet in generating precise cloud masks for HY-1C/COCTS data.

NOAAGlobalTemp version 6: An AI-based global surface temperature dataset

Abstract The National Oceanic and Atmospheric Administration (NOAA) Global Surface Temperature (NOAAGlobalTemp) dataset is widely used for scientific research, operational monitoring, and climate assessment activities. Aligning with NOAA’s mission values, NOAAGlobalTemp has been updated to version 6 (i.e., NGTv6), which includes two enhancements over its predecessor (NGTv5). The first enhancement is the expansion of the spatial coverage to encompass the entire globe and the extension of temporal coverage back to 1850 (an interim version of NOAAGlobalTemp with these features was released in February 2023). The expansion of spatial coverage is accomplished by utilizing surface air temperatures over the Arctic Ocean and by eliminating the data reconstruction mask used in NGTv5 that had suppressed interpolation in data-sparse regions. This change has important implications for global temperature trends since the Arctic region has been warming at a much faster pace, more than four times the global average, in the twenty-first century to date. The second enhancement is the implementation of a methodology based on artificial intelligence (AI) for reconstructing surface air temperature over the global land surface and the Arctic Ocean. The AI model employs an artificial neural network to fill data gaps and is demonstrated to be more robust, stable, and accurate than the previous gap-filling method, particularly in observation-sparse areas such as the polar regions. The model outperforms the previous approach across all evaluated statistical metrics, and the output reaches a stable state more quickly as observations are received, which facilitates climate monitoring.

Coati optimization algorithm based Deep Convolutional Forest method for prediction of atmospheric and oceanic parameters

Droughts and floods are examples of extreme weather events that can result from changes in ocean temperature. Ocean temperature is a key component of the global open sea system. Currently, real-time sea surface temperature (SST) forecasts are generated by numerical models based on physics principles and influenced by boundary and initial conditions. These models generally perform better over large areas than at specific locations. To address this and improve prediction accuracy, particularly in high-precision areas, the Coati Optimization Algorithm-based Deep Convolutional Forest (COA-DCF) method is proposed. This optimization approach is utilized to train the Deep Convolutional Forest (DCF) classifier, which then applies the prediction strategy. The COA-DCF method forecasts ocean surface temperature anomalies by considering key variables such as SST, Sea Surface Height (SSH), soil moisture, and wind speed, using historical data ranging from 1 to 10 days across six different locations. The proposed method achieves improved accuracy with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, and a high Pearson’s correlation coefficient (r) of 0.493, 0.487, and 0.4733, respectively, thereby enhancing the overall performance of the deep learning model.

Near-Term Mediterranean Summer Temperature Climate Projections: A Comparison of Constraining Methods

Abstract There are several methods to constrain multimodel projections of future climate. This study assesses the quality of four constraining methods in representing the near-term summer temperature projections of the Mediterranean region. Three are based on phasing in ocean surface temperature variations based on observations or decadal predictions, and the method is based on the measuring performance and independence of the individual simulations. The comparison has been carried out with a new framework inspired by the forecast quality assessment of decadal predictions. The framework led to quality estimates of the constrained projection approaches obtained by producing 20-yr temperature estimates every year from 1970 to 2000 and computing quality metrics against observational references. The evaluation results show some differences between constraining approaches. The improvement or deterioration against quality measures of the full, unconstrained, phase 6 of the Coupled Model Intercomparison Project (CMIP6) ensemble shows strong spatial heterogeneity. From the analysis of the selection approaches it is found that the constraints based on sea surface temperature (SST) fields are affected not only by the variability but also by the warming trend. The weighting method generally shows small quality differences with respect to the full CMIP6 ensemble. Despite caveats of the different methods, there is potential to improve the near-term climate projections as some significant quality enhancements were found in some approaches according to the evaluation metrics used. This study suggests a good understanding of the constraining methods, and their forecast quality is required before using them to take informed decisions. Our study opens the door to optimizing these methods for the Mediterranean climate and highlights the need for evaluating the constraints through retrospective assessments against observational references.

Effects of climate warming on energetics and habitat of the world's largest marine ectotherm

Responses of organisms to climate warming are variable and complex. Effects on species distributions are already evident and mean global surface ocean temperatures are likely to warm by up to 4.1 °C by 2100, substantially impacting the physiology and distributions of ectotherms. The largest marine ectotherm, the whale shark Rhincodon typus, broadly prefers sea surface temperatures (SST) ranging from 23 to 30 °C. Whole-species distribution models have projected a poleward range shift under future scenarios of climate change, but these models do not consider intraspecific variation or phenotypic plasticity in thermal limits when modelling species responses, and the impact of climate warming on the energetic requirements of whale sharks is unknown. Using a dataset of 111 whale shark movement tracks from aggregation sites in five countries across the Indian Ocean and the latest Earth-system modelling produced from Coupled Model Intercomparison Project Phase 6 for the Intergovernmental Panel on Climate Change, we examined how SST and total zooplankton biomass, their main food source, may change in the future, and what this means for the energetic balance and extent of suitable habitat for whale sharks. Earth System Models, under three Shared Socioeconomic Pathways (SSPs; SSP1–2.6, SSP3–7.0 and SSP5–8.5), project that by 2100 mean SST in four regions where whale shark aggregations are found will increase by up to 4.9 °C relative to the present, while zooplankton biomass will decrease. This reduction in zooplankton is projected to be accompanied by an increase in the energetic requirements of whale sharks because warmer water temperatures will increase their metabolic rate. We found marked differences in projected changes in the extent of suitable habitat when comparing a whole-species distribution model to one including regional variation. This suggests that the conventional approach of combining data from different regions within a species' distribution could underestimate the amount of local adaptation in populations, although parameterising local models could also suffer from having insufficient data and lead to model mis-specification or highly uncertain estimates. Our study highlights the need for further research into whale shark thermal tolerances and energetics, the complexities involved in projecting species responses to climate change, and the potential importance of considering intraspecific variation when building species distribution models.

West African Monsoon System’s Responses to Global Ocean–Regional Atmosphere Coupling

Abstract This study explores the added value (AV) of a regional Earth system model (ESM) compared to an atmosphere-only regional climate model (RCM) in simulating West African monsoon (WAM) rainfall. The primary goals are to foster discussions on the suitability of coupled RCMs for WAM projections and deepen our understanding of ocean–atmosphere coupling’s influence on the WAM system. The study employs results from dynamical downscaling of the ERA-Interim reanalysis and Max Plank Institute ESM, low resolution (MPI-ESM-LR), by two RCMs, atmosphere only (REMO) and REMO coupled with Max Planck Institute Ocean Model (MPIOM) (ROM), at ∼25-km horizontal resolution. Results show that in regions distant from coupling domain boundaries such as West Africa (WA), constraint conditions from ERA-Interim are more beneficial than coupling effects. REMO, reliant on oceanic sea surface temperatures (SSTs) from observations and influenced by ERA-Interim, is biased under coupling conditions, although coupling offers potential advantages in representing heat and mass fluxes. Contrastingly, as intended, coupling improves SSTs and monsoon fluxes’ relationships under ESM-forced conditions. In this latter case, the coupling features a dipole-like spatial structure of AV, improving precipitation over the Guinea Coast but degrading precipitation over half of the Sahel. Our extensive examination of physical processes and mechanisms underpinning the WAM system supports the plausibility of AV. Additionally, we found that the monsoonal dynamics over the ocean respond to convective activity, with the Sahara–Sahel surface temperature gradient serving as the maintenance mechanism. While further efforts are needed to enhance the coupled RCM, we advocate for its use in the context of WAM rainfall forecasts and projections.

Modelling Mediterranean ocean biogeochemistry of the Last Glacial Maximum

Abstract. We present results of simulations performed with a physical–biogeochemical ocean model of the Mediterranean Sea for the Last Glacial Maximum (LGM) and analyse the differences in physical and biochemical states between the historical period and the LGM. Long-term simulations with an Earth system model based on ice sheet reconstructions provide the necessary atmospheric forcing data, oceanic boundary conditions at the entrance to the Mediterranean Sea, and river discharge to the entire basin. Our regional model accounts for changes in bathymetry due to ice sheet volume changes, reduction in atmospheric CO2 concentration, and an adjusted aeolian dust and iron deposition. The physical ocean state of the Mediterranean during the LGM shows a reduced baroclinic water exchange at the Strait of Gibraltar, a more sluggish zonal overturning circulation, and the relocation of intermediate and deep-water-formation areas – all in line with estimates from palaeo-sediment records or previous modelling efforts. Most striking features of the biogeochemical realm are a reduction in the net primary production, an accumulation of nutrients below the euphotic zone, and an increase in the organic matter deposition at the seafloor. This seeming contradiction of increased organic matter deposition and decreased net primary production challenges our view of possible changes in surface biological processes during the LGM. We attribute the origin of a reduced net primary production to the interplay of increased stability of the upper water column, changed zonal water transport at intermediate depths, and lower water temperatures, which slow down all biological processes during the LGM. Cold water temperatures also affect the remineralisation rates of organic material, which explains the simulated increase in the organic matter deposition, which is in good agreement with sediment proxy records. In addition, we discuss changes in an artificial tracer which captures the surface ocean temperature signal during organic matter production. A shifted seasonality of the biological production in the LGM leads to a difference in the recording of the climate signal by this artificial tracer of up to 1 K. This could be of relevance for the interpretation of proxy records like, e.g., alkenones. Our study not only provides the first consistent insights into the biogeochemistry of the glacial Mediterranean Sea but will also serve as the starting point for transient simulations of the last deglaciation.

Climate Change Threatens Barringtonia racemosa: Conservation Insights from a MaxEnt Model

Barringtonia racemosa (L.) Spreng. (Lecythidaceae), a crucial species in mangrove ecosystems, is facing endangerment primarily due to habitat loss. To address this issue, research is imperative to identify suitable conservation habitats for the endangered B. racemosa within mangrove ecosystems. The utilization of the optimized Maximum Entropy (MaxEnt) model has been instrumental in predicting potential suitable regions based on global distribution points and environmental variables under current and future climates conditions. The study revealed that the potential distribution area of B. racemosa closely aligns with its existing range with an Area Under the Curve (AUC) greater than 0.95. The Jackknife, AUC, percent contribution (PC), and permutation importance (PI) tests were employed alongside the optimized MaxEnt model to examine the influence of environmental variables on the distribution of B. racemosa. The primary factors identified as significant predictors of B. racemosa distribution included the average temperature of the ocean surface (Temperature), average salinity of the ocean surface (Salinity), precipitation of the warmest quarter (Bio18), precipitation of the driest month (Bio14), seasonal variation coefficient of temperature (Bio4), and isothermality (Bio3). Currently, the habitat range of B. racemosa is predominantly found in tropical and subtropical coastal regions near the equator. The total suitable habitat area measures 246.03 km2, with high, medium, low, and unsuitable areas covering 3.90 km2, 8.57 km2, 16.94 km2, and 216.63 km2, respectively. These areas represent 1.58%, 3.48%, 6.88%, and 88.05% of the total habitat area, respectively. The potential distribution area of B. racemosa demonstrated significant variations under three climate scenarios (SSP126, SSP245, and SSP585), particularly in Asia, Africa, and Oceania. Both low and high suitable areas experienced a slight increase in distribution. In summary, the research suggests that B. racemosa primarily flourishes in coastal regions of tropical and subtropical areas near the equator, with temperature and precipitation playing a significant role in determining its natural range. This study offers important implications for the preservation and control of B. racemosa amidst habitat degradation and climate change threats. Through a comprehensive understanding of the specific habitat needs of B. racemosa and the implementation of focused conservation measures, efforts can be made to stabilize and rejuvenate its populations in their natural environment.

Toward an ecologically realistic experimental system to investigate the multigenerational effects of ocean warming and acidification on benthic invertebrates

AbstractHuman activities over the past 150 yr have led to significant carbon dioxide (CO2) emissions, causing global warming and ocean acidification. Surface ocean temperature has risen by 0.93°C since 1850, with projections of an additional +1.42°C to 3.47°C by 2080–2099. Ocean acidification, driven by CO2 absorption, has already lowered seawater pH by 0.1 units, affecting calcifying organisms, including shelled mollusks. Long‐term multigenerational studies on mollusk responses to both ocean acidification and warming, under realistic environmental conditions, are scarce. To address this knowledge gap, two mobile experimental units that can be deployed at the vicinity of shellfish farming areas were developed within the framework of the CocoriCO2 project. The experimental systems were designed to manipulate temperature and pH as offsets from ambient conditions. The experimental units have shown their effectiveness in terms of controlling and maintaining pH and temperature to assess the multigenerational effects of ocean warming and acidification on benthic invertebrates. Finally, the developed experimental systems can be modified easily to provide an educated assessment of the impact of other relevant environmental changes such as deoxygenation and changes in salinity.

The Evolution of Meteorological Satellite Cloud-Detection Methodologies for Atmospheric Parameter Retrievals

The accurate detection of clouds is an important first step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology, creating a new era in space observation exploration. Additionally, the initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, but more recently, cloud screening in satellite-acquired data is playing an increasingly critical role in the investigation of cloud-free data for the retrieval of soil moisture, vegetation cover, ocean color concentration and sea surface temperatures, as well as the environmental monitoring of a host of products, e.g., atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this review, the evolution of cloud-detection methodologies is highlighted with advancement in sensor hardware technology and machine learning algorithmic advances. The review takes into consideration the meteorological sensors usually used for atmospheric parameters estimation (thermodynamic profiles, aerosols, cloud microphysical parameters). Moreover, a discussion is presented on methods for obtaining the cloud-truth data needed to determine the accuracy of these cloud-detection approaches.

10 keV electron irradiation of methane ices at ocean world surface temperatures

The surface environmental conditions of many of the icy ocean worlds in the Solar System are outside of the thermal stability field of methane ice. However, mechanisms may exist that entrain or trap methane within water ice that arrives at the surface via plume eruptions or resurfacing events. At the surface, the ice grains may experience charged particle irradiation that could lead to the production of new complex hydrocarbons. We investigated the irradiation products of 12CH4 versus 13CH4, the temperature dependence of CH4 radiation processing and product stability, and the temperature stability of CH4 when mixed with H2O in a cryogenic vacuum environment. Significantly, methane ice experimentally encased in water ice was irradiated at 100 K in an ultra-high vacuum environment and produced similar radiation-formed secondary phases as pure methane ice irradiated at 20 K. The methane radiation products produced outside of the methane ice thermal stability field, while encased in a water ice film, were most notably 13CO2 and a candidate hydrocarbon feature. Evidence for complex hydrocarbons, such as C2H6, was observed during warming and sublimation of the sample.

A Numerical Study of Tropical Cyclone and Ocean Responses to Air‐Sea Momentum Flux at High Winds

AbstractThe relationship between minimum sea level pressure (MSLP) and maximum wind speed, is a commonly employed metric for assessing tropical cyclone (TC) forecast skill. However, accurately reproducing this relationship in TC forecasts is challenging. By introducing a new air‐sea momentum flux scheme considering both wave state and saturation (decrease) effect at high winds into a fully coupled atmosphere‐ocean‐wave model, our numerical results reveal that the maximum wind speed and MSLP respond oppositely to the air‐sea momentum flux change. The simulated wind‐pressure relationship aligns well with observations when the new flux scheme is used. This study highlights the large sensitivity of wind‐pressure relationship to the air‐sea momentum flux parameterization at high winds. Furthermore, the air‐sea momentum flux has a significant effect on ocean wave characteristics and sea surface temperature (SST) cooling in TC simulations.

Extreme Marine Heatwaves in the Southern Java during 2016

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

Beyond the First Tipping Points of Southern Hemisphere Climate

Analysis of observations, reanalysis, and model simulations, including those using machine learning methods specifically designed for regime identification, has revealed changes in aspects of the Southern Hemisphere (SH) circulation and Australian climate and extremes over the last half-century that indicate transitions to new states. In particular, our analysis shows a dramatic shift in the metastability of the SH climate that occurred in the late 1970s, associated with a large-scale regime transition in the SH atmospheric circulation, with systematic changes in the subtropical jet, blocking, zonal winds, and storm tracks. Analysis via nonstationary clustering reveals a regime shift coincident with a sharp transition to warmer oceanic sea surface temperatures and increased baroclinicity in the large scales of the Antarctic Circumpolar Circulation (ACC), extending across the whole hemisphere. At the same time, the background state of the tropical Pacific thermocline shoaled, leading to an increased likelihood of El Niño events. The SH climate shift in the late 1970s is the first hemispheric regime shift that can be directly attributed to anthropogenic climate change. These changes in dynamics are associated with additional regional tipping points, including reductions in mean and extreme rainfall in south-west Western Australia (SWWA) and streamflow into Perth dams, and also with increases in mean and extreme rainfall over northern Australia since the late 1970s. The drying of south-eastern Australia (SEA) occurred against a background of accelerating increases in average and extreme temperatures across the whole continent since the 1990s, implying further inflection points may have occurred. Analysis of climate model simulations capturing the essence of these observed shifts indicates that these systematic changes will continue into the late 21st century under high greenhouse gas emission scenarios. Here, we review two decades of work, revealing for the first time that tipping points characteristic of regime transitions are inferred to have already occurred in the SH climate system.

Impact of Satellite Wind on Improving Simulation of the Upper Ocean Response to Tropical Cyclones

Accurate modeling of the ocean response to tropical cyclones (TCs) requires high-quality wind fields to force ocean models. In this study, blended wind fields are generated using multi-source satellite data and the Climate Forecast System Reanalysis (CFSR) wind data. We utilize the hybrid wind fields to drive the Regional Ocean Modeling System (ROMS) for simulating oceanic dynamic and thermodynamic parameters. The model’s simulated ocean surface and sub-surface temperatures, as well as current speeds, are generally consistent with satellite and in situ observations collected during TC Winston and Freddy. The results are significantly better than those simulated by ROMS using wind forcing from CFSR alone. These results suggest that incorporating satellite wind data into the atmospheric forcing has the potential to enhance vertical mixing and improve simulations of the upper ocean response to TCs.

Global ocean surface and subsurface temperature forecast skill over subseasonal to seasonal timescales

Global ocean surface and subsurface temperature forecast skill over subseasonal to seasonal timescales

Polar Vortex Disruptions by High Latitude Ocean Warming

AbstractMid‐latitude extreme cold outbreaks are associated with disruptions of the polar vortex, which often happen abruptly in connection to a sudden stratospheric warming. Understanding global warming (particularly Arctic amplification) impacts on forecasting such events is challenging for the scientific community. Here we apply clustering analysis on the Northern Annular Mode to identify surface precursors and the governing mechanisms causing polar vortex disruption events. Two clusters of vortex breakdown emerge; 65% of the events, mainly displacements, are associated with high‐latitude Ocean warming in the North Pacific and in Barents‐Kara Sea. Such warming may cause large scale modifications of the tropospheric flow that favors a slowdown of the stratospheric vortex. The persistence of Ocean surface temperature patterns favors polar vortex disruptions, potentially improving prediction skills at the sub‐seasonal to seasonal time scales.