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Tropical cyclones (TCs), with an intensive wind pump impact, induce sea surface temperature cooling (SSTC) on the upper ocean. SSTC is a pronounced indicator to reveal TC evolution and oceanic conditions. However, there are few effective methods for accurately approximating the amplitude of the spatial structure of TC-induced SSTC. This study proposes a novel explainable machine learning framework to model and interpret the amplitude of the spatial structure of SSTC over the northwest Pacific (NWP). In particular, 12 predictors related to TC characteristics and pre-storm ocean states are considered as inputs. A composite analysis technique is used to characterize the amplitude of the spatial structure of SSTC across the TC track. Extreme gradient boosting (XGBoost) is utilized to predict the amplitude of SSTC from the 12 predictors. To better interpret the ocean–atmosphere interaction, a SHapely Additive explanations (SHAP) method is further employed to identify the contributions of predictors in determining the amplitude of the TC-induced SSTC, bringing the attribute-oriented explainability to the proposed method. The results showed that the proposed method could accurately predict the amplitude of the spatial structure of SSTC for different TC intensity groups and outperforms a numerical model. The proposed method also serves as an effective tool for reconstructing composite maps of both interannual and seasonal evolutions of SSTC spatial structure. The study offers insight into applying machine learning to model and interpret the responses of oceanic conditions triggered by extreme weather conditions (e.g., TCs).

This study proposes to construct a model using random forest method, an efficient machine learning-based method, to predict the spatial structure and temporal evolution of the sea surface temperature (SST) cooling induced by northwest Pacific tropical cyclones (TCs), a process of the so-called wind pump. The predictors in use include 12 predictors related to TC characteristics and pre-storm ocean conditions. The model is shown to skillfully predict the spatiotemporal evolutions of the cold wake generated by TCs of different intensity groups, and capture the cross-case variance in the observed SST response. Another model is further built based on the same method to assess the relative importance of the 12 predictors in determining the magnitude of the maximum cooling. Computations of feature scores of those predictors show that TC intensity, translation speed and size, and pre-storm mixed layer depth and SST dominate, depending on the area where the cooling is considered.

Bivalve shell fossils, cemented by authigenic carbonates, are widely spread in the Haima cold seep, Qiongdongnan Basin of the South China Sea (SCS). In this study, we examined an element profile of Gigantidas platifrons in the Haima cold seeps at a water depth of 1 381 m. Based on the scanning electron microscope (SEM) analyses, the prismatic layer and nacreous layer were identified, which are characterized by prismatic structure and stratified structure, respectively. In addition, the profile can be subdivided into two parts: altered and unaltered zones. Laser inductively coupled plasma mass spectrometry (LA-ICP-MS) mapping shows that the element concentrations of the altered zones were influenced by the authigenic carbonate rocks, whereas the element concentrations of unaltered zones remain stable. In-situ X-ray diffraction (XRD) analyses show that the mineral constituent of the nacreous layer is mainly composed of aragonite. Along with the growth profile, Mg/Ca ratios of unaltered zones have minor variations, ranging 0.72–0.97 mmol/mol (mean=0.87 mmol/mol), with estimated temperatures of 3.8–4.1 °C, indicating that the temperature of the surrounding seawater remains constant and agree with the measured data of 3.9 °C which was conducted by a conductivity-temperature-depth system (CTD). The minor variations of Ba/Ca ratios (0.01–0.06 mmol/mol; mean=0.04 mmol/mol) indicate a relatively stabilized salinity of the surrounding seawater. S/Ca ratios show large variations of 0.04–4.15 mmol/mol (mean=1.37 mmol/mol). S/Ca ratios have regular variations which generally correspond to the variations of the Mg/Ca ratios, highlighting that the S/Ca ratios of bivalve shells show the potential to reflect the growth rate of the Gigantides. However, further studies should be carried out on the understanding of the links between the S/Ca ratios and seepage intensity of cold-seep fluids.

Sea surface height anomaly (SSHA) induced by tropical cyclones (TCs) is closely associated with oscillations and is a crucial proxy for thermocline structure and ocean heat content in the upper ocean. The prediction of TC-induced SSHA, however, has been rarely investigated. This study presents a new composite analysis-based random forest (RF) approach to predict daily TC wind pump induced SSHA. The proposed method utilizes TC's characteristics and prestorm upper oceanic parameters as input features to predict TC-induced SSHA up to 30 days after TC passage. Simulation results suggest that the proposed method is skillful at inferring both the amplitude and temporal evolution of SSHA induced by TCs of different intensity groups. Using a TC-centered 5° × 5° box, the proposed method achieves highly accurate prediction of TC-induced SSHA over the Western North Pacific with root mean square error of 0.024 m, outperforming alternative machine learning methods and the numerical model. Moreover, the proposed method also demonstrated good prediction performance in different geographical regions, i.e., the South China Sea and the Western North Pacific subtropical ocean. The study provides insight into the application of machine learning in improving the prediction of SSHA influenced by extreme weather conditions. Accurate prediction of TC-induced SSHA allows for better preparedness and response, reducing the impact of extreme events (e.g., storm surge) on people and property.

Sea surface height anomaly (SSHA) induced by tropical cyclones (TCs) is closely associated with oscillations and is a crucial proxy for thermocline structure and ocean heat content in the upper ocean. The prediction of TC-induced SSHA, however, has been rarely investigated. This study presents a new composite analysis-based random forest (RF) approach to predict daily TC-induced SSHA. The proposed method utilizes TC’s characteristics and pre-storm upper oceanic parameters as input features to predict TC-induced SSHA up to 30 days after TC passage. Simulation results suggest that the proposed method is skillful at inferring both the amplitude and temporal evolution of SSHA induced by TCs of different intensity groups. Using a TC-centered 5°×5° box, the proposed method achieves highly accurate prediction of TC-induced SSHA over the Western North Pacific with root mean square error of 0.024m, outperforming alternative machine learning methods and the numerical model. Moreover, the proposed method also demonstrated good prediction performance in different geographical regions, i.e., the South China Sea and the Western North Pacific subtropical ocean. The study provides insight into the application of machine learning in improving the prediction of SSHA influenced by extreme weather conditions. Accurate prediction of TC-induced SSHA allows for better preparedness and response, reducing the impact of extreme events (e.g., storm surge) on people and property.

In this chapter we review the history of coastal altimetry. We illustrate the challenges associated with data processing, improvement and exploitation, including (1) what altimeter data are available today and what are the issues in coastal zones; (2) what efforts are underway to fill the gaps in coastal altimetry and what still needs to be done; (3) how coastal altimetry can be used in support of coastal oceanography. After nearly two decades of data collection near coasts, the planned reprocessing of multi-mission global record now appears to be necessary for the European research efforts, in particular the main outcomes of the COASTALT project, by showcasing improved corrections (with special emphasis on the wet tropospheric effect), waveform analysis and novel retracking techniques, as well as the structure of the new processor for Envisant RA-2 coastal records. This is of interest to a broad range of data integrators who will be able to use the improved altimeter data in their operational products or services.

In this chapter we review the history of coastal altimetry. We illustrate the challenges associated with data processing, improvement and exploitation, including (1) what altimeter data are available today and what are the issues in coastal zones; (2) what efforts are underway to fill the gaps in coastal altimetry and what still needs to be done; (3) how coastal altimetry can be used in support of coastal oceanography. After nearly two decades of data collection near coasts, the planned reprocessing of multi-mission global record now appears to be necessary for the European research efforts, in particular the main outcomes of the COASTALT project, by showcasing improved corrections (with special emphasis on the wet tropospheric effect), waveform analysis and novel retracking techniques, as well as the structure of the new processor for Envisant RA-2 coastal records. This is of interest to a broad range of data integrators who will be able to use the improved altimeter data in their operational products or services.

Analysis of satellite remote sensing data has revealed changes in distribution of chlorophyll-a (Chl-a) and sea surface temperature (SST) in the Indian Ocean during the South Asian tsunami in December 2004. Chl-a data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-ofview Sensor (SeaWiFS) images were examined for the period from 1998 to 2005. Around the epicentre of the Sumatra earthquake, the Chl-a concentrationwas found to increase prior to the main event on 26 December 2004 and then decrease during the tsunami event, while a high SST (~30-31°C) was observed in and around the epicentral region. Chl-a concentrations in the coastal waters of the Southeast Asian countries were remarkably low during and after the tsunami. Similar but relatively small variations inChl-a and SST were observed during the second earthquake on 28 March 2005. Analysis of Chl-a, SST, wind and upwelling water has provided information for understanding the changes in Chl-a concentration during the tsunami. A very large offshore phytoplankton bloom (~300 km2) appeared to the southeast of Sri Lanka about 3 weeks after the tsunami; this might have been caused by a tropical storm that could be responsible for the enhancement of nutrients. © 2009 Taylor & Francis., SCOPUS: ar.j, info:eu-repo/semantics/published

Analysis of satellite remote sensing data has revealed changes in distribution of chlorophyll-a (Chl-a) and sea surface temperature (SST) in the Indian Ocean during the South Asian tsunami in December 2004. Chl-a data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-ofview Sensor (SeaWiFS) images were examined for the period from 1998 to 2005. Around the epicentre of the Sumatra earthquake, the Chl-a concentrationwas found to increase prior to the main event on 26 December 2004 and then decrease during the tsunami event, while a high SST (~30-31°C) was observed in and around the epicentral region. Chl-a concentrations in the coastal waters of the Southeast Asian countries were remarkably low during and after the tsunami. Similar but relatively small variations inChl-a and SST were observed during the second earthquake on 28 March 2005. Analysis of Chl-a, SST, wind and upwelling water has provided information for understanding the changes in Chl-a concentration during the tsunami. A very large offshore phytoplankton bloom (~300 km2) appeared to the southeast of Sri Lanka about 3 weeks after the tsunami; this might have been caused by a tropical storm that could be responsible for the enhancement of nutrients. © 2009 Taylor & Francis., SCOPUS: ar.j, info:eu-repo/semantics/published

The Luzon Strait is a channel between the Philippine Sea and the South China Sea. This area is traditionally classified as an oligotrophic zone with low primary productivity. Even so, high concentrations of pigment were detected 100 km southwest of the Strait through analysis of historical Coastal Zone Color Scanner (CZCS) data that the Nimbus-7 satellite collected during the winters of 1979 to 1986. These blooms were observed in December 1979, February 1983, February 1985, and January 1986, when sea surface temperatures measured with the National Oceanic and Atmospheric Administration's (NOAAs) Advanced Very High Resolution Radiometer (AVHRR) were 23 to 25°C, the lowest in the year. High pigment concentrations (1 to 3 mg m-3) persisted for at least 1 to 3 wk and extended over areas of about 100 x 200 km2. Results of oceanographic survey published in 1995 also showed that a high chlorophyll a concentration (~2.0 mg m-3) occurred in the winter of 1980 in this region. The winter phytoplankton blooms in this area appeared to be related to upwelling, which brings nutrients to the surface waters and lowers the water temperature.