Sea Surface Research Articles

In situ airborne measurements of atmospheric parameters and airborne sea surface properties related to offshore wind parks in the German Bight during the project X-Wakes

Abstract. Between 14 March 2020 and 11 September 2021, meteorological measurement flights were conducted above the German Bight in the framework of the project X-Wakes. The scope of the measurements was to study the transition of the wind field and atmospheric stability from the coast to the sea, to study the interaction of wind park wakes, and to study the large-scale modification of the marine atmospheric boundary layer by the presence of wind parks. In total, 49 measurement flights were performed with the research aircraft Dornier 128 of the Technische Universität (TU) Braunschweig during different seasons and different stability conditions. Seven of the flights in the time period from 24 to 30 July 2021 were organised using a second research aircraft, the Cessna F406 of TU Braunschweig. The instrumentation of both aircraft consisted of a nose boom with sensors for measuring the wind vector, temperature and humidity and, additionally, a surface temperature sensor. The Dornier 128 was further equipped with a laser scanner for deriving sea state properties and two downward-looking cameras in the visible and infrared wavelength range. The Cessna F406 was additionally equipped with shortwave and longwave broadband radiation sensors for measuring upward and downward solar and terrestrial radiation. A detailed overview of the aircraft, sensors, data post-processing and flight patterns is provided here. Further, averaged profiles of atmospheric parameters illustrate the range of conditions. The potential use of the dataset has been already shown by the first few publications. The data of both aircraft are publicly available on the world data centre PANGAEA at https://doi.org/10.1594/PANGAEA.955382 (Rausch et al., 2023a).

Impact of sea surface waves and bubbles on water-leaving reflectance using Monte Carlo simulations

Impact of sea surface waves and bubbles on water-leaving reflectance using Monte Carlo simulations

Intensified upwelling: normalized sea surface temperature trends expose climate change in coastal areas

Abstract. Eastern boundary upwelling systems (EBUSs) provide valuable natural resources due to their high primary production. However, there is significant uncertainty in how climate change may affect the mechanisms that sustain these ecosystems in the future. Therefore, assessing the effects of climate change on EBUSs under the current global warming scenario is crucial for efficient ecosystem management. In 1990, Andrew Bakun suggested an increase in the upwelling intensity due to the rise of the ocean–land pressure gradient. Since there is a significant link between thermal gradients and offshore Ekman transport, we use sea level pressure (SLP) and deseasonalized sea surface temperature (SST) data from remote sensing to elucidate this hypothesis and validate it using in situ observations. SST is an indicator of coastal upwelling, and our long-term analysis of monthly and deseasonalized SST records shows that the seasonal and synoptic processes have minimal influence on the SST–upwelling intensity relationship. Upwelling within the same EBUS is not usually evenly distributed along coastlines, leading to upwelling in specific areas, referred to as upwelling centers. We compare the SST trends in the main upwelling centers of the four EBUSs with those in open ocean waters through a new index, αUI, designed to characterize upwelling changes in the EBUS. An a-dimensional number allows us to normalize the trends independently of the upwelling system and compare all of them. Furthermore, we have complemented the SST index with sea level pressure gradient data. This new index (supported by SLP gradient trends) indicates intensification in all the EBUSs, revealing a coherent pattern within EBUSs in the same ocean (i.e., Canarian and Benguela or Californian and Humboldt upwelling systems).

Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India

This research aims to forecast maximum temperatures and the frequency of heatwave days across four different temperature zones (Zone 1, 2, 3 and 4) in India. These four zones are categorized based on the 30-year average maximum temperatures (T30AMT) during the summer months of April, May, and June (AMJ). Two Machine Learning (ML) algorithms eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR) are employed to achieve this goal. The study utilizes nine key atmospheric variables namely air temperature, geopotential height, relative humidity, U-wind, V-wind, soil moisture, solar radiation, sea surface temperature, and mean sea level pressure at a daily scale spanning from 1991 to 2020 for the months of March, April, May, and June as predictors. The India Meteorological Department daily maximum temperature data spanning from 1991 to 2020 for the months of AMJ serves as the predictands. ML models are developed using spatially averaged atmospheric variables and daily maximum temperature across the grids falling within each temperature zone. Results indicate that for a 7-day lead time, SVR outperforms XGBoost in Zone-1 (T30AMT > 38 °C) and Zone-2 (T30AMT: 35.01 °C–38 °C) by more accurately capturing peak temperatures during training and testing. Conversely, for a 15-day lead time in Zone-1, XGBoost better predicts temperature peaks in both phases. In Zone-3 (T30AMT: 30 °C–35 °C) and Zone-4 (T30AMT < 30 °C) for both lead times, the performance of both models decline, indicating models and input variables are more effective in predicting higher temperatures typical of Zone-1 and 2 but less so in Zone-3 and 4. In a nutshell, the study attempts to highlight the capability of advanced ML techniques combined with spatial climate data to enhance the prediction of extreme heatwave events. These insights can aid in heatwave preparedness, climate management, and adaptation strategies for different Indian temperature zones.

Photoautotrophic picoplankton of the Kara Sea in the middle of summer: Effect of first-year ice retreat on carbon and chlorophyll biomass and primary production

The Arctic warming leads to a decline in sea-ice extent and thickness, rapid warming and freshening of the sea surface which impact the distribution of phytoplankton size composition. Picophytoplankton is an ecologically important component of Arctic pelagic marine ecosystems, and its role may be altered by global warming. In this study, the abundance and biomass, the chlorophyll a (Chl-a) and primary production (PP) of picophytoplankton, and its spatial and temporal distribution were investigated in the Kara Sea during the ice-melt season in July 2019. Picophytoplankton played a major role in the surface PP in the southern and western areas of the Kara Sea. In the surface layer, the contribution of picophytoplankton to total Chl-a increased insignificantly, and the contribution of picophytoplankton to total PP decreased significantly with the time of sea ice retreat. In the euphotic zone, the Chl-a concentration of picophytoplankton and its contribution to total Chl-a decreased with the time of sea ice retreat. The average picophytoplankton biomass determined in the present study (2.72 ± 5.10 mg C m−3) corresponded to the biomass estimates in the Arctic. The picophytoplankton community was strongly dominated by eukaryotes, cyanobacteria were only detected at 3 out of 11 stations, with maximum abundances (0.07 × 109 cells m−3) observed at depths below 15 m. The obtained results contribute significantly to the study of the picophytoplankton dynamics during the ice-melting season in the hard-to-reach Kara Sea.

Influence of CyGNSS L2 wind data on tropical cyclone analysis and forecasts in the coupled HAFS/HYCOM system

This study examines the influence of NASA Cyclone Global Navigation Satellite System (CyGNSS) Level 2-derived 10 m (near-surface) wind speed over the ocean on analyses and forecasts within the NOAA operational Hurricane Analysis and Forecast System (HAFS). HAFS is coupled with a regional configuration of the HYCOM ocean model. The primary advantages of data from the CyGNSS constellation of satellites include higher revisit frequency compared to polar-orbiting satellites, and the availability of reliable wind observations over the ocean surface during convective precipitation. CyGNSS data are available early in the life cycle of tropical cyclones (TCs) when aerial reconnaissance observations are not available. We focus on TCs whose forecasts were initialized when the TC was a depression or tropical storm. In the present study, we find first, that assimilation of CyGNSS near-surface winds improves storm track, intensity, and structure statistics in the analysis and early in the forecast, for many cases. Second, we find that assimilation of CyGNSS observations provides additional insights into the evolution of air-sea interaction in intensifying TCs: In effect, the ocean responds in the coupled model to modifications in the initial 10 m wind field, thereby impacting forecasts of intensity, storm structure, and sea surface height, as demonstrated by two case studies. We also discuss some forecasts where assimilating CYGNSS appears to degrade performance for either intensity or structure.

Concurrent Inter‐Model Spread of Boreal Winter Westerly Jet Meridional Positions Between the Northern and Southern Hemispheres in CMIP6 Models

ABSTRACTThis study investigates the inter‐model spread of climatological extratropical westerly jets in boreal winter, using the historical simulation of 52 Coupled Model Intercomparison Project phase 6 (CMIP6) models from 1851 to 2014. The results show that there is a substantial spread in the latitude of the upper‐tropospheric westerly jet across models, characterised by large inter‐model standard deviations to both the poleward and equatorward sides of the jet axis, although the multi‐model ensemble mean (MME) performs well in simulating meridional position of westerly jets. Furthermore, we detect the consistency of inter‐model jet position spread between the Northern and Southern Hemispheres, based on the inter‐model empirical orthogonal function (EOF) decomposition and correlation of regional‐averaged zonal winds. Specifically, the models that simulate the westerly jets poleward/equatorward relative to the MME position in one hemisphere also tend to simulate the jets poleward/equatorward in the other hemisphere. Accordingly, we define a global jet spread index to depict the concurrence of jet shift in the two hemispheres. The results of inter‐model regression analyses based on this index indicate that the models positioning the jets poleward than the MME tend to simulate a wider Hadley Cell, a poleward‐shifted Ferrel Cell in the Southern Hemisphere, enhanced precipitation in the subtropics and suppressed precipitation in the tropics, and warmer sea surface temperatures in the subtropics and mid‐latitudes. The present results suggest that improving the simulation of jet positions in climate models requires a comprehensive consideration of thermal states in the tropics and subtropics/mid latitudes.

Correction: A spatio-temporal predictive learning model for efficient sea surface temperature forecasting

Correction: A spatio-temporal predictive learning model for efficient sea surface temperature forecasting

Environmental Conditions Associated with Four Index Cases of Pacific Oyster Mortality Syndrome (POMS) in Crassostrea gigas in Australia Between 2010 and 2024: Emergence or Introduction of Ostreid herpesvirus-1?

Warm water temperature is a risk factor for recurrent mass mortality in farmed Pacific oysters Crassostrea gigas caused by Ostreid herpesvirus-1, but there is little information on environmental conditions when the disease first appears in a region—the index case. Environmental conditions between four index cases in Australia (2010, 2013, 2016 and 2024) were compared to provide insight into possible origins of the virus. Each index case was preceded by unusually low rainfall and higher rates of temperature change that could increase oyster susceptibility through thermal flux stress. Water temperature alone did not explain the index cases, there being no consistency in sea surface, estuary or air temperatures between them. Tidal cycles and chlorophyll-a levels were unremarkable, harmful algae were present in all index cases and anthropogenic environmental contamination was unlikely. The lack of an interpretable change in the estuarine environment suggests the recent introduction of OsHV-1; however, viral emergence from a local reservoir cannot be excluded. Future events will be difficult to predict. Temperature flux and rainfall are likely important, but they are proxies for a range of undetermined factors and to identify these, it will be necessary to develop comprehensive protocols for data acquisition during future index cases.

Tropical Indian Ocean drives Hadley circulation change in a warming climate

Abstract The weakening and poleward expansion of the Hadley circulation (HC) are considered robust responses of atmospheric meridional circulation to anthropogenic warming. Climate impacts arising from these changes enhance drought conditions and reduce food production in the affected regions. Therefore, understanding the mechanisms of HC changes is critical to anticipating the resultant climate risks. First, we demonstrate that robust future HC changes in boreal winter, and the uncertainty in their future projections, are both largely related to sea surface temperature (SST) warming. Next, we investigate the impact of anthropogenic regional ocean warming on the future HC. Accordingly, we conduct a large ensemble of individual ocean basin perturbation experiments at 1.5 °C, 2 °C, and 3 °C warming thresholds (as in the Paris Agreement). These experiments highlight (i) the leading role of tropical Indian Ocean warming in HC changes and (ii) inter-model differences in tropical Pacific warming as a source of uncertainty in HC projections.

An RD-Domain Virtual Aperture Extension Method for Shipborne HFSWR

High-frequency surface wave radar (HFSWR) is widely used for detecting sea surface or low-altitude targets due to its all-weather operation and over-the-horizon detection capability. To further enhance the maneuverability and detection range of HFSWR, shipborne HFSWR has been developed. However, compared to shore-based platforms, shipborne platforms face challenges such as a small array aperture and reduced Direction of Arrival (DOA) estimation performance due to their limited size. The traditional time–domain virtual aperture extension method, based on the principle of space-time equivalence, aims to improve the array aperture but has limitations when used for HFSWR background or multiple targets with different speeds. To address these issues, this paper proposes a range-Doppler domain (RD-domain) virtual aperture extension method for the uniform linear array, based on the uniform motion model. The contributions of this work include (1) a continuous motion model for shipborne HFSWR, (2) a virtual aperture processing flowchart for shipborne HFSWR, and (3) an RD-domain aperture extension method suitable for HFSWR background or multiple targets with varying speeds. Through simulation and experimental data, we validate the proposed method and analyze its performance.

Unveiling the Relationship Between Mediterranean Tropical‐Like Cyclones and Rising Sea Surface Temperature

AbstractLeveraging the Copernicus high‐resolution multi‐year Mediterranean Sea Surface Temperature (SST) dataset, 15 selected tropical‐like cyclones (TLCs) are analyzed with the objective of elucidating the anomalies of SST at the time of cyclogenesis and the connection between the change in SST during the cyclone lifetime and its characteristics. The long‐term SST increase associated with climate change is identified by comparing detrended and original anomalies. Detrending removes the effect of the intensification of SST anomaly over time, revealing that no significant anomalies generally emerge in the early stages of the TLC lifetimes. Conversely, winter events exhibit early‐stage positive SST anomalies. Also, high SST values were observed during the intensification of the most intense cyclones. A cold SST anomaly is left after the passage of the cyclones, due to the intense sea surface fluxes extracting heat from the sea.

Synchronous decadal climate variability in the tropical Central Pacific and tropical South Atlantic

Pantropical climate interactions across ocean basins operate on a wide range of timescales and can improve the accuracy of climate predictions. Here, we show in observations that Central Pacific (CP) El Niño-like sea surface temperature (SST) anomalies have coevolved with tropical South Atlantic SST anomalies on a quasi-decadal (~10-year) timescale over the past seven decades. During the austral autumn–winter season, decadal warm SSTs in the tropical CP effectively induce tropical SST cooling in the South Atlantic, mainly by strengthening the South Atlantic subtropical anticyclone via an extratropical atmospheric wave teleconnection in the southern hemisphere. Partially coupled pacemaker simulations corroborate the observational findings, indicating that tropical CP decadal SSTs play a primary pacing role, while Atlantic feedback is of secondary importance throughout the study period. Our results suggest that the tropical CP could be an important source of decadal predictability for tropical South Atlantic SST and the surrounding climate.

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.

Prediction on Yellowfin Tuna (Thunnus albacares) Fishing Ground in Waters Near the Marshall Islands Based on SMOTETomek‐RF

ABSTRACTThis study monitored 37 longliners fishing in waters near the Marshall Islands from 2020 to 2022 by Liancheng Overseas Fishery (Shenzhen) Co., Ltd.'s operation management system. This study developed nine predictive models on the relationship between catch per unit effort (CPUE) data for yellowfin tuna (Thunnus albacares) and the environmental data. The environmental data integrate 48 variables, including eddy kinetic energy, chlorophyll a concentration, sea surface height, and additional measures of vertical oceanic conditions, alongside spatiotemporal parameters (year, month, day, longitude, and latitude). This study employed four spatial resolutions (0.25° × 0.25°, 0.5° × 0.5°, 1° × 1°, and 2° × 2°) to develop nine predictive models: KNN, RF, GBDT, CART, LightGBM, XGBoost, CatBoost, AdaBoost, and Stacking (RF, KNN, GBDT, and LR). These models, with a daily time resolution, were trained using 75% of the data and tested with the remaining 25%. The optimal spatial resolution and model were determined through a comprehensive comparison of model evaluation metrics across these spatial resolutions. The SMOTETomek algorithm was then applied to resample 75% of the data at the optimal spatial resolution, forming a new training dataset. This dataset was used to refine the model, subsequently tested with the remaining 25% of the data. Results indicated that (1) the optimal spatial resolution is 0.25° × 0.25° and the optimal model is RF; (2) the SMOTETomek algorithm enhances the model's predictive performance; and (3) the developed SMK‐RF model, exhibiting Acc and AUC values of 76.73% and 82.47%, respectively, accurately predicts the central fishing grounds for yellowfin tuna, consisting closely with actual fishing activity.

Single-Use Aircraft Launch System with Small Helium Balloon for 1 kPa Flight Testing

A single-use aircraft launch system using a small helium balloon concept was proposed to elevate the likelihood of successful high-altitude flight tests above 30 km. The system, capable of carrying a payload of 0.5 kg, has been developed and reported. Completing the flight within a range of 92 km, which represents the maximum communication distance between the airborne flight system and the ground station for operational phase control, is imperative. Therefore, a straightforward method for simulating flight trajectories was outlined to determine launch windows and suitable locations to ensure flight completion within uninhabited areas. Simulation studies revealed that the shortest landing distance was obtained during summer at the selected launch site. Additionally, a parameter study was conducted to assess the influence of varying ascent velocities on the targeted flight test altitude, which depends on the payload weight and helium gas volume. Consequently, the parameter study results established the constraints for flight model design. A demonstration flight was conducted in 2023 to validate the feasibility of the system for high-altitude flight experiments. This flight successfully released the flight model at 31 km and recovered flight data before landing on the sea surface. The proposed system concept offers an expedited approach to aircraft design for operating in thin air. It enables component function tests and scaled model flights as an alternative to employing large high-altitude balloons for high-altitude experiments.

Melting glaciers, emerging pandemics?

The world contains over 198,000 to 200,000 glaciers, covering in total over 726,000 square kilometers of Earth’s surface (Davies; “Glacier Quick Facts”). The repetitive cycle of snowfall forming layers and compressing into ice sheets over the course of centuries has allowed the formation of these unique, massive masses of ice. Apart from being huge reservoirs of water, glaciers are also hosts of many interconnected microbiomes, fostering active and dormant bacteria, fungi, viruses and other extremophiles. During the Last Glacial Maximum (LGM), or the peak of the ice age around 23,000-19,000 years ago, the sea surface temperature was at its lowest accompanied by low atmospheric Carbon Dioxide (CO2) concentration (“How Does Present Glacier Extent”; Quamar et al.). This was when glaciers covered over 32% of Earth’s total surface. However, today, glaciers account for just around 10% of land (“Glacier Quick Facts”). The rise of human choices, especially our indulgence in activities like industrialisation, consumerism and wastage have contributed to the emission of greenhouse gasses, which has in turn led to global warming. A consequence of this phenomenon is the melting of glaciers. Melting glaciers not only results in the release of water and rising sea levels, but also the unveiling of previously trapped microbiomes. This raised the hypothesis that melting glaciers, and other sections of the cryosphere can expose previously frozen, potentially pandemic-causing pathogens to the world. This descriptive research paper will serve the purpose of encapsulating existing research regarding whether the disintegration of the cryosphere can cause pandemics.

Response of Upwelling Parameter Before, During, and After Tropical Cyclone (Case Study: Tropical Cyclone Marcus)

Tropical cyclones (TC) can cause damage when they are on the land surface, but on the other hand TC contribute to ocean productivity through upwelling or downwelling when they pass through the ocean. Information of the location and timing of upwelling and downwelling is important for fishing - activities estimation. High chlorophyll-a concentrations and low sea surface temperatures are proxies for upwelling events in the ocean. This study aims to investigate the effects of the TC Marcus on the chlorophyll-a distribution in the Timor Sea using a combination of remotely sensed data from Aqua MODIS chlorophyll-a and ocean-atmosphere reanalysis outputs. The results showed that wind speed, sea surface temperature, salinity, and ocean currents were increased during the TC Marcus event. Spatial analysis reveals that the concentration of chlorophyll-a is high in the waters of the Timor Sea, at coordinates around 12°–13° S and 125°–129° E. High chlorophyll-a concentrations occurred before and after TC Marcus event, according to temporal analysis. The distribution of chlorophyll-a concentrations decreased on March 17–24, 2018, during the occurrence of the TC Marcus in the Timor Sea.

A Novel Method for the Estimation of Sea Surface Wind Speed from SAR Imagery

Wind is one of the important environmental factors influencing marine target detection as it is the source of sea clutter and also affects target motion and drift. The accurate estimation of wind speed is crucial for developing an efficient machine learning (ML) model for target detection. For example, high wind speeds make it more likely to mistakenly detect clutter as a marine target. This paper presents a novel approach for the estimation of sea surface wind speed (SSWS) and direction utilizing satellite imagery through innovative ML algorithms. Unlike existing methods, our proposed technique does not require wind direction information and normalized radar cross-section (NRCS) values and therefore can be used for a wide range of satellite images when the initial calibrated data are not available. In the proposed method, we extract features from co-polarized (HH) and cross-polarized (HV) satellite images and then fuse advanced regression techniques with SSWS estimation. The comparison between the proposed model and three well-known C-band models (CMODs)—CMOD-IFR2, CMOD5N, and CMOD7—further indicates the superior performance of the proposed model. The proposed model achieved the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), with values of 0.97 m/s and 0.62 m/s for calibrated images, and 1.37 and 0.97 for uncalibrated images, respectively, on the RCM dataset.

Influence of Sea Surface Temperature Changes on the Development of Sprat (Sprattus sprattus phalericus) (Pisces: Clupeidae) Living on the South-Western Shelf of Crimea

A study of changes in sea surface temperature (SST) on the South-Western shelf of the Crimea in 2000–2020 was conducted. The equations of trends in the change of SST are found. It is shown that according to the nature of changes in the average annual sea temperature, three periods can be divided: 2000–2007, 2008–2013 and 2014–2020. The influence of changes in the average annual SST on the development of pelagic fish of the cold-waters complex (on the example of the Black Sea sprat) is considered. Changes in the main parameters of populations and their relationship with the change in the average annual SST were found. It was found that the observed increase in sea temperature negatively affected the development of the sprat population, the average age of the population decreased by 1.3 times, in general, the population was rejuvenated, the average size and weight of individuals decreased.