Ocean Thermal Structure Research Articles

Persistently active El Niño–Southern Oscillation since the Mesozoic

The El Niño-Southern Oscillation (ENSO), originating in the central and eastern equatorial Pacific, is a defining mode of interannual climate variability with profound impact on global climate and ecosystems. However, an understanding of how the ENSO might have evolved over geological timescales is still lacking, despite a well-accepted recognition that such an understanding has direct implications for constraining human-induced future ENSO changes. Here, using climate simulations, we show that ENSO has been a leading mode of tropical sea surface temperature (SST) variability in the past 250 My but with substantial variations in amplitude across geological periods. We show this result by performing and analyzing a series of coupled time-slice climate simulations forced by paleogeography, atmospheric CO2 concentrations, and solar radiation for the past 250 My, in 10-My intervals. The variations in ENSO amplitude across geological periods are little related to mean equatorial zonal SST gradient or global mean surface temperature of the respective periods but are primarily determined by interperiod difference in the background thermocline depth, according to a linear stability analysis. In addition, variations in atmospheric noise serve as an independent contributing factor to ENSO variations across intergeological periods. The two factors together explain about 76% of the interperiod variations in ENSO amplitude over the past 250 My. Our findings support the importance of changing ocean vertical thermal structure and atmospheric noise in influencing projected future ENSO change and its uncertainty.

Improving the Statistical Representation of Tropical Cyclone In-Storm Sea Surface Temperature Cooling

Abstract This study uses fixed buoy time series to create an algorithm for sea surface temperature (SST) cooling underneath a tropical cyclone (TC) inner core. To build predictive equations, SST cooling is first related to single variable predictors such as the SST before storm arrival, ocean heat content (OHC), mixed layer depth, sea surface salinity and stratification, storm intensity, storm translation speed, and latitude. Of all the single variable predictors, initial SST before storm arrival explains the greatest amount of variance for the change in SST during storm passage. Using a combination of predictors, we created nonlinear predictive equations for SST cooling. In general, the best predictive equations have four predictors and are built with knowledge about the prestorm ocean structure (e.g., OHC), storm intensity (e.g., minimum sea level pressure), initial SST values before storm arrival, and latitude. The best-performing SST cooling equations are broken up into two ocean regimes: when the ocean heat content is less than 60 kJ cm−2 (greater spread in SST cooling values) and when the ocean heat content is greater than 60 kJ cm−2 (SST cooling is always less than 2°C), which demonstrates the importance of the prestorm oceanic thermal structure on the in-storm SST value. The new equations are compared to what is currently used in a statistical–dynamical model. Overall, since the ocean providing the latent heat and sensible heat fluxes necessary for TC intensification, the results highlight the importance for consistently obtaining accurate in-storm upper-oceanic thermal structure for accurate TC intensity forecasts. Significance Statement The ocean provides the heat and moisture necessary for tropical cyclone (TC) intensification. Since the heat and moisture transfer depend on the sea surface temperature (SST), we create statistical equations for the prediction of SST underneath the storm. The variables we use combine the initial SST before the storm arrives, the upper-ocean thermal structure, and the strength and translation speed of the storm. The predictive equations for SST are evaluated for how well they improve TC intensity forecasts. The best-performing equations can be used for prediction in operational statistical models, which can aid intensity forecasts.

Competition between ocean thermal structure and tropical cyclone characteristics modulates ocean environmental responses in the Yellow and Bohai Seas

To study the environmental responses of tropical cyclones (TCs) in continental shelf regions, TCs passing over the Yellow Sea and Bohai Sea (YBS) during 2002–2020 were investigated, with a special focus on how competition between ocean thermal structure and TC characteristics modulates ocean surface changes. The spatial distributions of the climatic mixed layer depth (MLD), accumulated wind forcing power index (WPi), accumulated sea surface temperature (SST) changes and accumulated chlorophyll (Chl-a) changes in the YBS were calculated. The linear regressions indicate that both the TC-induced SST cooling and TC-induced Chl-a increase are correlated with the TC wind speed rather than the translation speed, especially when the TC forcing depth (Zmixing) is greater than the MLD. Otherwise, both the changes in SST and Chl-a are correlated with the TC translation speed when Zmixing is shallower than the MLD. Further study has shown that whether TCs can break the MLD is also a key condition for oceanic responses. In the southern YBS, which has a deep-sea basin and MLD, the TC wind speed is the major factor affecting SST cooling and Chl-a increase, as TCs need more strength to reach the MLD. However, in the northern YBS, which has the shallowest sea basin and MLD, even weak TCs can easily break the MLD and reach the seabed; thus, ocean surface changes are associated mainly with the TC translation speed. The composite results reveal that both the maximum SST cooling center (1.64 °C) and the maximum Chl-a increasing center (0.14 log10(mg/m3)) are located on the right and behind the TC center, respectively. In addition, TC-induced SST cooling and Chl-a increase were initiated two days prior to TC passage and then reached their maximum values after 1 day. It takes approximately 7–8 days for the Chl-a concentration to recover, but it takes a much longer time (>15 days) for the SST to recover.

Advancing ocean subsurface thermal structure estimation in the Pacific Ocean: A multi-model ensemble machine learning approach

Estimation of the ocean subsurface thermal structure (OSTS) is important for understanding thermodynamic processes and climate variability. In the present study, a novel multi-model ensemble machine learning (Ensemble-ML) model is developed to retrieve subsurface thermal structure in the Pacific Ocean by integrating sea surface data with Argo observations. The Ensemble-ML model integrates four individual machine learning models to enhance estimation accuracy and reliability. Our results exhibit good agreement between the satellite sea surface temperature (SST) and sea surface salinity (SSS) data and Argo observations, providing validation for the utilization of these datasets in the Ensemble-ML model. The Ensemble-ML model exhibits better performance compared to individual machine learning models, with an average root mean square error (RMSE) of 0.3273 °C and an average coefficient of determination (R²) of 0.9905. Notably, incorporating geographical information as input variables enhance model performance, emphasizing the importance of considering spatial context in OSTS estimation. The Ensemble-ML model accurately captures the spatial distribution of OSTS across depths and seasons in the Pacific Ocean, effectively reproducing critical temperature features while maintaining strong agreement with Argo observations. Nevertheless, its performance shows relative weakness within the thermocline layer and the equatorial Pacific region (spanning from 10°S to 10°N latitude), which are characterized by complex circulation systems. Despite these challenges, the Ensemble-ML model effectively reproduces the spatial distribution of OSTS of the Pacific Ocean. This indicates the potential of machine learning models, particularly ensemble models, for enhancing OSTS estimation in the Pacific Ocean and other regions, offering valuable insights for future research and applications in physical oceanography.

Intraseasonal oscillations of the Andaman Sea thermocline

Variations in the upper ocean thermal structure have significant implications for air-sea interaction and upper-ocean ecosystem processes. Vertical profiles of temperature measured by a moored buoy located at 10.5°N, 94°E in the Andaman Sea and simulation by an Indian Ocean model are used in this study to characterise the intraseasonal variations (ISV) in the Andaman Sea (AndS) thermal structure and identify their sources. The seasonal variations in the upper ocean thermal structure show a strong semi-annual cycle driven by monsoons. The sub-surface temperature shows significant intraseasonal oscillations within a band of 40–110 days, which are de-coupled from that in the mixed layer, which has dominant periodicity in the band of 90–120 days. Thermocline ISV are seasonally modulated with a primary peak during August–September and a secondary peak during February–March, with significant year-to-year variations. A cross-wavelet analysis shows that ISV in the 40–60 days period is in phase with that at the eastern boundary and they are locally forced by the passage of eddies. The 60–110 day band is out of phase with the eastern boundary and is forced by Rossby waves radiated from the equatorially generated intraseasonal coastal Kelvin waves.

Estimating thermohaline structures in the tropical Indian Ocean from surface parameters using an improved CNN model

Accurately estimating the ocean’s subsurface thermohaline structure is essential for advancing our understanding of regional and global ocean dynamics. In this study, we propose a novel neural network model based on Convolutional Block Attention Module-Convolutional Neural Network (CBAM-CNN) to simultaneously estimate the ocean subsurface thermal structure (OSTS) and ocean subsurface salinity structure (OSSS) in the tropical Indian Ocean using satellite observations. The input variables include sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly (SSHA), eastward component of sea surface wind (ESSW), northward component of sea surface wind (NSSW), longitude (LON), and latitude (LAT). We train and validate the model using Argo data, and compare its accuracy with that of the original Convolutional Neural Network (CNN) model using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R²). Our results show that the CBAM-CNN model outperforms the CNN model, exhibiting superior performance in estimating thermohaline structures in the tropical Indian Ocean. Furthermore, we evaluate the model’s accuracy by comparing its estimated OSTS and OSSS at different depths with Argo-derived data, demonstrating that the model effectively captures most observed features using sea surface data. Additionally, the CBAM-CNN model demonstrates good seasonal applicability for OSTS and OSSS estimation. Our study highlights the benefits of using CBAM-CNN for estimating thermohaline structure and offers an efficient and effective method for estimating thermohaline structure in the tropical Indian Ocean.

Significant Diurnal Warming Events Observed by Saildrone at High Latitudes

AbstractThe sea surface temperature (SST) is one of the essential parameters needed to understand the climate change in the Arctic. Saildrone, an advanced autonomous surface vehicle, has proven to be a useful tool for providing accurate SST data at high latitudes. Here, data from two Saildrones, deployed in the Arctic in the summer of 2019, are used to investigate the diurnal variability of upper ocean thermal structure. An empirical cool skin effect model with dependence on the wind speed with new coefficients was generated. Several local large diurnal warming events were observed, the amplitudes of warming in the skin layer >5 K, rarely reported in previous studies. Furthermore, the warming signals could persist beyond 1 day. For those cases, it was found surface warm air suppressed the surface turbulent heat loss to maintain the persistence of diurnal warming under low wind conditions. Salinity also plays an important role in the formation of upper ocean density stratification during diurnal warming at high latitudes. A less salty and hence less dense surface layer was likely created by precipitation or melting sea ice, providing favorable conditions for the formation of upper ocean stratification. Comparisons with two prognostic diurnal warming models showed the simulations match reasonably well with Saildrone measurements for moderate wind speeds but exhibit large differences at low winds. Both schemes show significant negative biases in the early morning and late afternoon. It is necessary to improve the model schemes when applied at high latitudes.

An offshore subsurface thermal structure inversion method by coupling ensemble learning and tide model for the South Yellow Sea

The South Yellow Sea Cold Water Mass (SYSCWM), which occurs in the South Yellow Sea (SYS) during summer, significantly impacts the hydrological characteristics and marine ecosystems but lacks fine interior data. With satellite observations, significant achievements have been made in reconstructing high-resolution ocean subsurface thermohaline structure based on machine learning. However, the accuracy of offshore subsurface parameter estimation will be affected due to the macro-tidal environment and fewer in situ observations. In this paper, we coupled the TPXO tide model and Light Gradient Boosting Machine algorithm to develop an inversion model of offshore subsurface thermal structure for the SYS using sea surface data and in situ observations. After light modelling, the subsurface temperature structure in the SYS is retrieved from sea surface parameters with a spatial resolution of 0.25° at depths of 0-55 m. Observation-based dataset (ARMOR3D) and in situ observations are used for model evaluation. According to the validation of the mooring buoy observations, the overall coefficient of determination (R2), which determines the percentage of variance in the dependent variable that can be explained by the independent variable, is more than 0.95. Furthermore, the R2 is improved by 12% due to coupling tide model below the thermocline during the maturity stage of SYSCWM, which is helpful for a better reconstruction of SYSCWM. Comparing with the cruise data, the average R2 of the proposed model is 0.927 which is slightly better than the accuracy of the observation-based ARMOR3D dataset. Since the R2 exceeds 0.8 in the most area of 121°E~123.5°E, 33°N~36°N, the reconstruction is reliable in this area. The method provides a new explorable direction for reconstructing the ocean thermal structure in offshore areas.

Hurricane Sally (2020) Shifts the Ocean Thermal Structure across the Inner Core during Rapid Intensification over the Shelf

Abstract Prediction of rapid intensification in tropical cyclones prior to landfall is a major societal issue. While air–sea interactions are clearly linked to storm intensity, the connections between the underlying thermal conditions over continental shelves and rapid intensification are limited. Here, an exceptional set of in situ and satellite data are used to identify spatial heterogeneity in sea surface temperatures across the inner core of Hurricane Sally (2020), a storm that rapidly intensified over the shelf. A leftward shift in the region of maximum cooling was observed as the hurricane transited from the open gulf to the shelf. This shift was generated, in part, by the surface heat flux in conjunction with the along- and across-shelf transport of heat from storm-generated coastal circulation. The spatial differences in the sea surface temperatures were large enough to potentially influence rapid intensification processes suggesting that coastal thermal features need to be accounted for to improve storm forecasting as well as to better understand how climate change will modify interactions between tropical cyclones and the coastal ocean. Significance Statement The connections between the underlying thermal energy in the ocean that powers tropical cyclones and rapid intensification of storms over continental shelves are limited. An exceptional set of data collected in the field as well as from space with satellites was used to identify spatial variations in sea surface temperatures across the inner core of Hurricane Sally (2020), a storm that rapidly intensified over the shelf. The spatial differences were due to the heat loss from the surface of the ocean as well as heat transport by shelf currents. The spatial differences were large enough to potentially influence how quickly storms can intensify, suggesting that coastal thermal features need to be accounted for to improve storm forecasting.

An Ensemble-Based Machine Learning Model for Estimation of Subsurface Thermal Structure in the South China Sea

Reconstructing the vertical structures of the ocean from sea surface information is of great importance for ocean and climate studies. In this study, an ensemble machine learning (Ens-ML) model is proposed to retrieve ocean subsurface thermal structure (OSTS) by using satellite-derived sea surface data and Argo data in the South China Sea (SCS). The input data include sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS), sea surface wind (SSW), and geographic information (including longitude and latitude). We select three stable machine learning models, namely, extreme gradient boosting (XGBoost), RandomForest and light gradient boosting machine (LightGBM) as our benchmark models, and then use an artificial neural network (ANN) technique to combine outputs from the three individual models. The proposed Ens-ML model using sea surface data only by SSH, SST, SSS, and SSW performs less satisfactorily than that considering the contribution of geographical information, indicating that the geographical information is essential to estimate the OSTS accurately. The estimated OSTS from the Ens-ML model are compared with Argo data. The results show that the proposed Ens-ML model can accurately estimate the OSTS (upper 1000 m) in the SCS, which is relatively more accurate and precise than the individual models. The performance of the Ens-ML model also varies with season, and better estimation is obtained in winter, which is probably due to stronger mixing and weaker stratification. This study shows the great potential and advantage of the multi-model ensemble of machine learning algorithm for the ocean’s interior information retrieving, showing great potential in expanding the scope of ocean observations.

Upper-Ocean Temperature Variability in the Gulf of Mexico with Implications for Hurricane Intensity

AbstractStrong winds in tropical cyclones (TCs) mix the ocean, causing cooler water from below the thermocline to be drawn upward, reducing sea surface temperature (SST). This decreases the air–sea temperature difference, limits available heat energy, and impacts TC intensity. Part of TC forecast accuracy therefore depends upon the ability to predict sea surface cooling; however, it is not well understood how underlying ocean conditions contribute to this cooling. Here, ~4400 Argo profiles in the Gulf of Mexico were used in a principal component analysis to identify the modes of variability in upper-ocean temperature, and a 1D mixed layer model was used to determine how the modes respond to surface forcing. It was found that the first two modes explain 75% of the variance in the data, with high mode-1 scores being broadly characterized as having warm SST and deep mixed layer and mode-2 scores being characterized as having high SST and a shallow mixed layer. Both modes have distinct seasonal and spatial variability. When subjected to the same model forcing, mode-1- and mode-2-characteristic waters with equal tropical cyclone heat potential (TCHP) respond very differently. Mode-2 SST cools faster than mode 1, with the difference being most pronounced at lower wind speeds and when comparing early-season storms with late-season storms. The results show that using TCHP as a marker for SST response during TC forcing is insufficient because it does not fully capture subsurface ocean thermal structure. This result underscores the need for continual subsurface monitoring so as to accurately initialize the upper ocean in coupled TC models.

Time‐Varying Upper Ocean Circulation and Control of Coral Bleaching in the Western Tropical Pacific

AbstractThe western tropical Pacific Ocean (WTPO) features complicated ocean circulation systems and has the warmest world open‐ocean waters. Small upper ocean temperature change there can exert significant impact on the regional coral reef ecosystems. In the past three decades, moderate to severe coral bleaching events have been observed in the WTPO surrounding Palau in 1998, 2010, 2016, 2017, and 2020. Reflecting the diversity of El Niño‐Southern Oscillation (ENSO) variability, the observed coral bleaching severity does not correspond simply to the amplitude of an ENSO index, such as Niño‐3.4. By conducting an upper ocean temperature budget, we found the time‐varying upper ocean circulation advection acted to damp the anomalous surface heat flux forcing and played critical roles in controlling the surface ocean thermal conditions around Palau. This happened either directly via the advective temperature flux convergence, or indirectly through the pre‐conditioning of upper ocean thermal structures.

Effects of Ocean Optical Properties and Solar Attenuation on the Northwestern Atlantic Ocean Heat Content and Hurricane Intensity

AbstractThis study investigated how ocean optical properties and solar attenuation may affect the upper ocean temperature structure and ocean heat content (OHC). We employed a realistic three‐dimensional ocean circulation model for the northwestern Atlantic to simulate ocean states during the active Atlantic hurricane season of 2017. Sensitivity experiments were performed by coupling the ocean circulation prediction with either a conventional water type‐based solar attenuation model or an inherent optical properties (IOP)‐based model. Validations against in‐situ ocean temperature observations and remote sensing‐derived OHC showed that ocean simulations using the IOP‐based model outperformed simulations using the conventional water type‐based model in predicting sea surface temperature, upper ocean thermal structure, and OHC. An OHC‐hurricane intensity relationship derived for five major hurricanes in 2017 suggests that the ocean optical properties and the application of an appropriate solar attenuation model are important for the forecast of hurricane intensity.

Decision-Tree-Based Classification of Lifetime Maximum Intensity of Tropical Cyclones in the Tropical Western North Pacific

The National Typhoon Center of the Korea Meteorological Administration developed a statistical–dynamical typhoon intensity prediction model for the western North Pacific, the CSTIPS-DAT, using a track-pattern clustering technique. The model led to significant improvements in the prediction of the intensity of tropical cyclones (TCs). However, relatively large errors have been found in a cluster located in the tropical western North Pacific (TWNP), mainly because of the large predictand variance. In this study, a decision-tree algorithm was employed to reduce the predictand variance for TCs in the TWNP. The tree predicts the likelihood of a TC reaching a maximum lifetime intensity greater than 70 knots at its genesis. The developed four rules suggest that the pre-existing ocean thermal structures along the track and the latitude of a TC’s position play significant roles in the determination of its intensity. The developed decision-tree classification exhibited 90.0% and 80.5% accuracy in the training and test periods, respectively. These results suggest that intensity prediction with the CSTIPS-DAT can be further improved by developing independent statistical models for TC groups classified by the present algorithm.

Subsurface Temperature Estimation from Sea Surface Data Using Neural Network Models in the Western Pacific Ocean

Estimating the ocean subsurface thermal structure (OSTS) based on multisource sea surface data in the western Pacific Ocean is of great significance for studying ocean dynamics and El Niño phenomenon, but it is challenging to accurately estimate the OSTS from sea surface parameters in the area. This paper proposed an improved neural network model to estimate the OSTS from 0–2000 m from multisource sea surface data including sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and sea surface wind (SSW). In the model experiment, the rasterized monthly average data from 2005–2015 and 2016 were selected as the training and testing set, respectively. The results showed that the sea surface parameters selected in the paper had a positive effect on the estimation process, and the average RMSE value of the ocean subsurface temperature (OST) estimated by the proposed model was 0.55 °C. Moreover, there were pronounced seasonal variation signals in the upper layers (the upper 200 m), however, this signal gradually diminished with increasing depth. Compared with known estimation models such as the random forest (RF), the multiple linear regression (MLR), and the extreme gradient boosting (XGBoost), the proposed model outperformed these models under the data conditions of the paper. This research can provide an advanced artificial intelligence technique for estimating subsurface thermohaline structure in major sea areas.

Upper ocean hydrographic changes in response to the evolution of the East Asian monsoon in the northern South China Sea during the middle to late Miocene

The evolution of the East Asian monsoon (EAM) in the geologic time is of great importance to our understanding of the global climate system. However, variations in the monsoon rainfall and the upper ocean thermal gradient are not well understood in the South China Sea (SCS) during the middle to late Miocene. Here, we present δ 18 O and Mg/Ca-derived temperature records of the surface and subsurface waters from International Ocean Discovery Program (IODP) Site U1501, and use them to reconstruct the upper ocean thermal structure, which reflects changes in the East Asian summer monsoon (EASM) in the SCS over 13.6–7.3 Ma. The differences between the surface and thermocline records (Δδ 18 O surface-thermocline and ΔT surface-thermocline ) indicate a shallower thermocline and increased thermal gradient between the surface and subsurface waters, implying that upper water mixing was weaker during 9.4–7.3 Ma, which may relate to an increased EASM rainfall. Changes in the local seawater δ 18 O seawater (δ 18 O SW ) of U1501 also suggest a decrease in the intensity of the EASM rainfall during 13.6–10.2 Ma and an increase during 10.2–7.3 Ma. From the perspectives of moisture supply and general circulation, the initial formation of the western Pacific warm pool (WPWP) and uplift of Tibetan Plateau essentially influenced the variations in the EASM during the middle to late Miocene. • We present the first upper ocean thermal structure and surface hydrography records in the South China Sea over 13.6-7.3 Ma. • The local δ18OSW of Site U1501 suggested an increase in the East Asian summer monsoon rainfall intensity during 9.3-7.3 Ma. • Variations in the EASM rainfall are likely linked to the initial formation of the western Pacific warm pool during 9.3-7.3 Ma.

Atmosphere-ocean dynamics of persistent cold states of the tropical Pacific Ocean

AbstractPersistent multiyear cold states of the tropical Pacific Ocean drive hydroclimate anomalies worldwide, including persistent droughts in the extratropical Americas. Here, the atmosphere and ocean dynamics and thermodynamics of multiyear cold states of the tropical Pacific Ocean are investigated using European Centre for Medium-Range Weather Forecasts reanalyses and simplified models of the ocean and atmosphere. The cold states are maintained by anomalous ocean heat flux divergence and damped by increased surface heat flux from the atmosphere to ocean. The anomalous ocean heat flux divergence is contributed to by both changes in the ocean circulation and thermal structure. The keys are an anomalously shallow thermocline that enhances cooling by upwelling and anomalous westward equatorial currents that enhance cold advection. The thermocline depth anomalies are shown to be a response to equatorial wind stress anomalies. The wind stress anomalies are shown to be a simple dynamical response to equatorial SST anomalies as mediated by precipitation anomalies. The cold states are fundamentally maintained by atmosphere-ocean coupling in the equatorial Pacific. The physical processes that maintain the cold states are well approximated by linear dynamics for ocean and atmosphere and simple thermodynamics.

Influence of multi-mission chlorophyll-a data on the simulation of upper ocean thermal structure in the eastern Pacific Ocean

ABSTRACT The importance of providing multi satellite-based chlorophyll-a in an ocean model for accurate representation of penetrative shortwave radiation is demonstrated in this study using Modular Ocean Model. It questions the traditional usage of chlorophyll-a climatology in models. The model sensitivity experiments Chl_IA (with interannually varying multi satellite-based chlorophyll-a) and Chl_Clim (with chlorophyll-a climatology) during 1998–2016 reveals the importance of using the interannual chlorophyll. The maximum difference between the two chlorophyll-a products is found during the peak phase of strong El Niño (January-March 1998 and 2016) in the Niño1 + 2 region, allowing 5–11 W m−2 more shortwave radiation to penetrate below 25 m in Chl_IA, effectively warming the subsurface temperature and cooling the sea surface temperature (SST) by ~0.4°C in the Niño1 + 2 region. The reduction in SST bias (warm) by 0.5°C, deepening of mixed layer and isothermal layer, enhancement of upwelling, reduction of upper ocean stability with realistic distribution of upper ocean heat content are some of the major improvements in Chl_IA. The interannual chlorophyll-a variability in fact rectifies the warm (cold) bias in surface (subsurface) temperature commonly found in ocean models especially in the eastern Pacific region during the peak phase of strong El Niño.

Altimetry-derived ocean thermal structure reconstruction for the Bay of Bengal cyclone season

The ocean thermal structure (OTS) is a significant element that affects tropical cyclone (TC) intensities, as energy drawn from the ocean fuels TCs. Satellite altimetry can be used to derive the OTS for TC research. The cooling of sea surface temperature (SST) caused by TCs is closely related to the OTS and differs according to differences in the OTS in different regions. However, the accuracy of existing methods in spatially describing the OTS is not sufficient. To overcome the limited resolution and accuracy of current OTS-derived methods, a ridge regression model is proposed for the Bay of Bengal (RRBOB) in this paper to derive isotherms for 47 layers (D5–D28) from satellite altimetry data and from in situ Argo thermal profiles. RRBOB improves the derivation accuracy by allowing small deviations to achieve higher precision than unbiased estimators. Compared with in situ observations, most errors are limited to within 20%. The accuracy of the derivations is within a reasonable range, and no significant biases are observed. Although the ridge regression derivation method has limitations in predicting the temperature inversion, which occurs in the northern of the BOB during the periods of post monsoon and winter, the derivation can characterize the subsurface OTS in detail in most regions. The ability to monitor the subsurface ocean will improve seasonal predictions of TCs.

Oceanic thermal structure mediates dive sequences in a foraging seabird.

Changes in marine ecosystems are easier to detect in upper‐level predators, like seabirds, which integrate trophic interactions throughout the food web.Here, we examined whether diving parameters and complexity in the temporal organization of diving behavior of little penguins (Eudyptula minor) are influenced by sea surface temperature (SST), water stratification, and wind speed—three oceanographic features influencing prey abundance and distribution in the water column.Using fractal time series analysis, we found that foraging complexity, expressed as the degree of long‐range correlations or memory in the dive series, was associated with SST and water stratification throughout the breeding season, but not with wind speed. Little penguins foraging in warmer/more‐stratified waters exhibited greater determinism (memory) in foraging sequences, likely as a response to prey aggregations near the thermocline. They also showed higher foraging efficiency, performed more dives and dove to shallower depths than those foraging in colder/less‐stratified waters.Reductions in the long‐term memory of dive sequences, or in other words increases in behavioral stochasticity, may suggest different strategies concerning the exploration–exploitation trade‐off under contrasting environmental conditions.