Evaluating Operational AVHRR Sea Surface Temperature Data at the Coastline Using Benthic Temperature Loggers

The role of species thermal plasticity for alien species invasibility in a changing climate: A case study of Lophocladia trichoclados

The Mediterranean Sea provides fertile ground for understanding the complex interplay between invasive species and native habitats, particularly within the context of climate change. This thermal tolerance study reveals the remarkable ability of Lophocladia trichoclados, a red algae species that has proven highly invasive, to adapt to varying temperatures, particularly thriving in colder Mediterranean waters, where it can withstand temperatures as low as 14 °C, a trait not observed in its native habitat. This rapid acclimation, occurring in less than a century, might entail a trade-off with high temperature resistance. Additionally, all sampled populations in the Mediterranean share the same haplotype, suggesting a common origin and the possibility that we might be facing an exceptionally acclimatable and invasive strain. This high degree of acclimatability could determine the future spread capacity in a changing scenario, highlighting the importance of considering both acclimation and adaptation in understanding the expansion of invasive species' ranges.

Record-breaking marine heatwave in northern Yellow Sea during summer 2018: Characteristics, drivers and ecological impact

Globally, marine heatwaves (MHWs) are becoming more common, more intense, and longer-lasting. They could have a large ecological and societal impact when compounded by low oxygen concentrations or high acidity. Here, using a high-resolution satellite product and reanalysis datasets, we investigated the characteristics of the MHW at northern Yellow Sea (NYS) during mid-summer 2018 and the driving mechanisms of large-scale atmospheric circulations. Results showed that the MHW in mid-summer 2018 (lasting from 26 July to 18 August 2018) had been the most intense since 1982, reaching an anomaly peak of 5.15 °C. For the 2018 MHW, the onset rate was 0.49 °C/day, indicating that the reaction window was relatively short and hard to take mitigation measures, while the decline rate was 0.19 °C/day, meaning the coping window was long and easy to push an already stressed system. The synergy of the two large-scale dynamic systems, i.e., the northward-shifted western north Pacific subtropical high (WNPSH) and the northeastward-expanded South Asia high (SAH), was likely responsible for establishment and maintenance of the hot-weather conditions. These high-pressure systems could result in stronger descending motion, less cloud cover, more solar radiation, and smaller wind speeds which in combination aggravated the MHW. We further found that the unprecedented MHW was actually also impacted by terrestrial heatwave. From 14 July to 15 August 2018, Northeast China was affected by an exceptionally long and intense atmospheric heat wave (AHW). The AHW had impacted on the MHW through warm advection transportation and may significantly contribute to the record-breaking intensity of the MHW, in addition to the impact of abnormal atmospheric circulations. Finally, we showed that a mass mortality of sea cucumbers in the study region during mid-summer 2018 was highly likely caused by the MHW through severe heat stress.

Performance and validation of water surface temperature estimates from Landsat 8 of the Itaipu Reservoir, State of Paraná, Brazil

Studies on water surface temperature (WST) from thermal infrared remote sensing are still incipient in Brazil, and for many water resources, they do not exist. Many algorithms have been developed to estimate surface temperature in satellite images. There are also many difficulties in implementing these algorithms due to their complexity, especially in free software, which restricts the satisfactory processing of these data by users of the technique. Thus, this work aimed to validate an algorithm used to estimate land surface temperature (LST) when applied to the surface of inland water bodies. Water surface temperature estimates (WSTe) were generated from Itaipu State of Paraná (PR) reservoir, Brazil, calculated from Landsat 8 - TIRS satellite images (WSTs) and water surface temperature data from 37 in situ stations (WSTi). A linear regression model of the WSTe was generated in 60% of the samples and its validation with the remaining 40%, subject to prior evaluation of some statistical indicators. The model was considered significant since the coefficient of determination (r²) was 0.90 (95% of confidence), root mean square deviation (RMSD) 0.8 °C, Willmott Index (d) = 0.97, and Nash-Sutcliffe efficiency coefficient (NSE) = 0.89. The methodology used to extract WSTs from the Python QGIS plugin was relatively quick to apply, easy to understand, and had a better performance of the estimates than those presented in the literature review.

Unravelling seasonal trends in coastal marine heatwave metrics across global biogeographical realms

Marine heatwaves (MHWs) can cause dramatic changes to ecologically, culturally, and economically important coastal ecosystems. To date, MHW studies have focused on geographically isolated regions or broad-scale global oceanic analyses, without considering coastal biogeographical regions and seasons. However, to understand impacts from MHWs on diverse coastal communities, a combined biogeographical-seasonal approach is necessary, because (1) bioregions reflect community-wide temperature tolerances and (2) summer or winter heatwaves likely affect communities differently. We therefore carried out season-specific Theil–Sen robust linear regressions and Pettitt change point analyses from 1982 to 2021 on the number of events, number of MHW days, mean intensity, maximum intensity, and cumulative intensity of MHWs, for each of the world’s 12 major coastal biogeographical realms. We found that 70% of 240 trend analyses increased significantly, 5% decreased and 25% were unaffected. There were clear differences between trends in metrics within biogeographical regions, and among seasons. For the significant increases, most change points occurred between 1998 and 2006. Regression slopes were generally positive across MHW metrics, seasons, and biogeographical realms as well as being highest after change point detection. Trends were highest for the Arctic, Northern Pacific, and Northern Atlantic realms in summer, and lowest for the Southern Ocean and several equatorial realms in other seasons. Our analysis highlights that future case studies should incorporate break point changes and seasonality in MHW analysis, to increase our understanding of how future, more frequent, and stronger MHWs will affect coastal ecosystems.

Development of a Low-Power Underwater NFC-Enabled Sensor Device for Seaweed Monitoring

Aquaculture farming faces challenges to increase production while maintaining welfare of livestock, efficiently use of resources, and being environmentally sustainable. To help overcome these challenges, remote and real-time monitoring of the environmental and biological conditions of the aquaculture site is highly important. Multiple remote monitoring solutions for investigating the growth of seaweed are available, but no integrated solution that monitors different biotic and abiotic factors exists. A new integrated multi-sensing system would reduce the cost and time required to deploy the system and provide useful information on the dynamic forces affecting the plants and the associated biomass of the harvest. In this work, we present the development of a novel miniature low-power NFC-enabled data acquisition system to monitor seaweed growth parameters in an aquaculture context. It logs temperature, light intensity, depth, and motion, and these data can be transmitted or downloaded to enable informed decision making for the seaweed farmers. The device is fully customisable and designed to be attached to seaweed or associated mooring lines. The developed system was characterised in laboratory settings to validate and calibrate the embedded sensors. It performs comparably to commercial environmental sensors, enabling the use of the device to be deployed in commercial and research settings.

Spatial Variability and Trends of Marine Heat Waves in the Eastern Mediterranean Sea over 39 Years

Marine heatwaves (MHWs) can cause devastating impacts on marine life. The frequency of MHWs, gauged with respect to historical temperatures, is expected to rise significantly as the climate continues to warm. The MHWs intensity and count are pronounced with many parts of the oceans and semi enclosed seas, such as Eastern Mediterranean Sea (EMED). This paper investigates the descriptive spatial variability and trends of MHW events and their main characteristics of the EMED from 1982 to 2020 using Sea Surface Temperature (SST) data obtained from the National Oceanic and Atmospheric Administration Optimum Interpolation ([NOAA] OI SST V2.1). Over the last two decades, we find that the mean MHW frequency and duration increased by 40% and 15%, respectively. In the last decade, the shortest significant MHW mean duration is 10 days, found in the southern Aegean Sea, while it exceeds 27 days off the Israeli coast. The results demonstrate that the MHW frequency trend increased by 1.2 events per decade between 1982 and 2020, while the MHW cumulative intensity (icum) trend increased by 5.4 °C days per decade. During the study period, we discovered that the maximum significant MHW SST event was 6.35 °C above the 90th SST climatology threshold, lasted 7 days, and occurred in the year 2020. It was linked to a decrease in wind stress, an increase in air temperature, and an increase in mean sea level pressure.

Comparison of a Smartfin with an Infrared Sea Surface Temperature Radiometer in the Atlantic Ocean

The accuracy and precision of satellite sea surface temperature (SST) products in nearshore coastal waters are not well known, owing to a lack of in-situ data available for validation. It has been suggested that recreational watersports enthusiasts, who immerse themselves in nearshore coastal waters, be used as a platform to improve sampling and fill this gap. One tool that has been used worldwide by surfers is the Smartfin, which contains a temperature sensor integrated into a surfboard fin. If tools such as the Smartfin are to be considered for satellite validation work, they must be carefully evaluated against state-of-the-art techniques to quantify data quality. In this study, we developed a Simple Oceanographic floating Device (SOD), designed to float on the ocean surface, and deployed it during the 28th Atlantic Meridional Transect (AMT28) research cruise (September and October 2018). We attached a Smartfin to the underside of the SOD, which measured temperature at a depth of ∼0.1 m, in a manner consistent with how it collects data on a surfboard. Additional temperature sensors (an iButton and a TidbiT v2), shaded and positioned a depth of ∼1 m, were also attached to the SOD at some of the stations. Four laboratory comparisons of the SOD sensors (Smartfin, iButton and TidbiT v2) with an accurate temperature probe (±0.0043 K over a range of 273.15 to 323.15 K) were also conducted during the AMT28 voyage, over a temperature range of 290–309 K in a recirculating water bath. Mean differences (δ), referenced to the temperature probe, were removed from the iButton (δ=0.292 K) and a TidbiT v2 sensors (δ=0.089 K), but not from the Smartfin, as it was found to be in excellent agreement with the temperature probe (δ=0.005 K). The SOD was deployed for 20 min periods at 62 stations (predawn and noon) spanning 100 degrees latitude and a gradient in SST of 19 K. Simultaneous measurements of skin SST were collected using an Infrared Sea surface temperature Autonomous Radiometer (ISAR), a state-of-the-art instrument used for satellite validation. Additionally, we extracted simultaneous SST measurements, collected at slightly different depths, from an underway conductivity, temperature and depth (CTD) system. Over all 62 stations, the mean difference (δ) and mean absolute difference (ϵ) between Smartfin and the underway CTD were −0.01 and 0.06 K respectively (similar results obtained from comparisons between Smartfin and iButton and Smartfin and TidbiT v2), and the δ and ϵ between Smartfin and ISAR were 0.09 and 0.12 K respectively. In both comparisons, statistics varied between noon and predawn stations, with differences related to environmental variability (wind speed and sea-air temperature differences) and depth of sampling. Our results add confidence to the use of Smartfin as a citizen science tool for evaluating satellite SST data, and data collected using the SOD and ISAR were shown to be useful for quantifying near-surface temperature gradients.

Evaluation of the ABI/GOES-16 SST Product in the Tropical and Southwestern Atlantic Ocean

Sea surface temperature (SST) is an essential climate variable used for ocean and weather monitoring and forecasting. The NOAA’s next generation geostationary satellite GOES-16 was declared operational at the east position (75°W) in December 2017, carrying onboard an Advanced Baseline Imager (ABI). The hyperspectral ABI sensor now allows SST estimates every 10–15 min at both day and nighttime, with advanced options for cloud screening and water vapor correction. In the present work, we compare the first operational ABI SST product (OSI SAF, 2018) with an in situ match-up database (MDB) across the Tropical and Southwestern Atlantic Ocean, off the Brazilian coast, throughout the year of 2018. The MDB was obtained from two long-term programs, i.e., PIRATA moored buoys (FOLTZ et al., 2016) and PNBoia moored and drifting buoys (MARINHA DO BRASIL, 2017). Separate comparisons were made for each data set, analyzing the uncertainties according to the program (i.e., buoy type and region), satellite SST quality level and influence of diurnal heating. We also compare the ABI product with the OSTIA analysis L4 SST (DONLON et al., 2012) to increment our analyses on the spatio-temporal biases within the study region. The results show that the OSI SAF ABI SST L3C has a mean bias (0.1 °C) and error (RMSE, 0.5 °C) within the GHRSST standards, with an exception being coastal waters off the southeast Brazilian coast (RMSE, 0.65 °C), which are subjected to sharp thermal fronts. The highest biases are for regions/seasons subjected to persistent cloud coverage and high water-vapor content, i.e., the Intertropical and South Atlantic Convergence Zones, as well as highly dynamic frontal zones, i.e., the Brazil Malvinas Confluence Zone, the Subtropical Front and coastal waters. The ABI SST product is suitable for operational use, and applications should explore more deeply the new set of information provided.

High resolution spatiotemporal patterns of seawater temperatures across the Belize Mesoamerican Barrier Reef

Coral reefs are under increasingly severe threat from climate change and other anthropogenic stressors. Anomalously high seawater temperatures in particular are known to cause coral bleaching (loss of algal symbionts in the family Symbiodiniaceae), which frequently leads to coral mortality. Remote sensing of sea surface temperature (SST) has served as an invaluable tool for monitoring physical conditions that can lead to bleaching events over relatively large scales (e.g. few kms to 100 s of kms). But, it is also well known that seawater temperatures within a site can vary significantly across depths due to the combined influence of solar heating of surface waters, water column thermal stratification, and cooling from internal waves and upwelling. We deployed small autonomous benthic temperature sensors at depths ranging from 0-40 m in fore reef, back reef, and lagoonal reef habitats on the Belize Mesoamerican Barrier Reef System from 2000-2019. These data can be used to calculate depth-specific climatologies across reef depths and sites, and emphasize the dynamic and spatially-variable nature of coral reef physical environments.

The constraint of ignoring the subtidal water climatology in evaluating the changes of coralligenous reefs due to heating events

Predicting community-level responses to seawater warming is a pressing goal of global change ecologists. How far such predictions can be derived from a fine gradient of thermal environments needs to be explored, even if ignoring water climatology does not allow estimating subtidal marine heat waves. In this study insights about the influence of the thermal environment on the coralligenous community structure were gained by considering sites (Sardinia, Italy) at different temperature conditions. Heating events were measured (by loggers at 18 m, 23 m, 28 m, 33 m and 38 m deep) and proxies for their duration (the maximum duration of events warmer than the 90th percentile temperature), intensity (the median temperature) and variability (the number of daily ΔT larger than the mean daily ΔT, and the number of heating events larger in ΔT than the 90th percentile ΔT) were selected by GAM models. Reliable predictions of decrease in coralligenous richness of taxa/morphological groups, with relevant increment in turfs and encrusting coralline algae abundance at the expenses of bryozoans were made. Associations to the different types of heating descriptor have highlighted the aspect (intensity, duration or variability) of the heating events and the threshold for each of them responsible for the trajectories of change.

Comparison of Satellite-Based Sea Surface Temperature to In Situ Observations Surrounding Coral Reefs in La Parguera, Puerto Rico

Coral reefs are among the most biologically diverse ecosystems on Earth. In the last few decades, a combination of stressors has produced significant declines in reef expanse, with declining reef health attributed largely to thermal stresses. We investigated the correspondence between time-series satellite remote sensing-based sea surface temperature (SST) datasets and ocean temperature monitored in situ at depth in coral reefs near La Parguera, Puerto Rico. In situ temperature data were collected for Cayo Enrique and Cayo Mario, San Cristobal, and Margarita Reef. The three satellite-based SST datasets evaluated were NOAA’s Coral Reef Watch (CoralTemp), the UK Meteorological Office’s Operational SST and Sea Ice Analysis (OSTIA), and NASA’s Jet Propulsion Laboratory (G1SST). All three satellite-based SST datasets assessed displayed a strong positive correlation (>0.91) with the in situ temperature measurements. However, all SST datasets underestimated the temperature, compared with the in situ measurements. A linear regression model using the SST datasets as the predictor for the in situ measurements produced an overall offset of ~1 °C for all three SST datasets. These results support the use of all three SST datasets, after offset correction, to represent the temperature regime at the depth of the corals in La Parguera, Puerto Rico.

Inter-Comparisons of Daily Sea Surface Temperatures and In-Situ Temperatures in the Coastal Regions

In this study, seven, global, blended, sea surface temperature (SST) analyses, including Operational SST and Sea Ice Analysis (OSTIA), Canadian Meteorological Centre (CMC) analysis, Optimum Interpolation SST (OISST), Remote Sensing System (REMSS) analysis, Multi-scale Ultra-high Resolution SST (MURSST), Merged Satellite and In situ Data Global Daily SST (MGDSST), and Geo-Polar Blended SST (Blended SST) were conducted. In-situ temperature measurements were used for the years 2014–2018, from 35 narrowly-spaced buoys distributed along the Korean Peninsula coast, to investigate how well the SST analyses represent the temperatures at the coastal regions. Contrary to the overall accuracy of the SSTs in the global ocean and offshore regions, the root-mean-square errors for the analyses were relatively large over 1.27 K. Specifically, all SST analyses resulted in warm biases over 0.31 K, which became quite distinctive in the western and the southwestern coastal regions. Investigation of the errors identified relationships with the coastal zones of vigorous tidal mixing, shallow bathymetry, and absence of microwave measurements. Overall, temporal wavelet coherency between in-situ measurements and SST products revealed high coherency of greater than 0.8 in periods longer than 180 days, however, low coherency (<0.5) in the period shorter than 10 days was observed. Inter-comparisons between the SST analyses illustrated clear spatial differences in the correlations at both the coastal regions, along the southwestern coast of the Korean Peninsula and in the frontal regions, and in the marginal seas of the Northwest Pacific. Overall, the results emphasized on the importance of using real-time in-situ measurements as much as possible, to overcome the increasing SST errors in coastal regions.

An Evaluation of Autonomous In Situ Temperature Loggers in a Coastal Region of the Eastern Mediterranean Sea for Use in the Validation of Near-Shore Satellite Sea Surface Temperature Measurements

The coastal ocean is one of the most important environments on our planet, home to some of the most bio-diverse and productive ecosystems and providing key input to the livelihood of the majority of human society. It is also a highly dynamic and sensitive environment, particularly susceptible to damage from anthropogenic influences such as pollution and over-exploitation as well as the effects of climate change. These have the added potential to exacerbate other anthropogenic effects and the recent change in sea temperature can be considered as the most pervasive and severe cause of impact in coastal ecosystems worldwide. In addition to open ocean measurements, satellite observations of sea surface temperature (SST) have the potential to provide accurate synoptic coverage of this essential climate variable for the near-shore coastal ocean. However, this potential has not been fully realized, mainly because of a lack of reliable in situ validation data, and the contamination of near-shore measurements by the land. The underwater biotechnological park of Crete (UBPC) has been taking near surface temperature readings autonomously since 2014. Therefore, this study investigated the potential for this infrastructure to be used to validate SST measurements of the near-shore coastal ocean. A comparison between in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra SST data is presented for a four year (2014–2018) in situ time series recorded from the UBPC. For matchups between in situ and satellite SST data, only nighttime in situ extrapolated to the sea surface (SSTskin) data within ±1 h from the satellite’s overpass are selected and averaged. A close correlation between the in situ data and the MODIS SST was found (squared Pearson correlation coefficient-r2 > 0.9689, mean absolute error-Δ < 0.51 both for Aqua and Terra products). Moreover, close correlation was found between the satellite data and their adjacent satellite pixel’s data further from the shore (r2 > 0.9945, Δ < 0.23 for both Aqua and Terra products, daytime and nighttime satellite SST). However, there was also a consistent positive systematic difference in the satellite against satellite mean biases indicating a thermal adjacency effect from the land (e.g., mean bias between daytime Aqua satellite SST from the UBPC cell minus the respective adjacent cell’s data is δ = 0.02). Nevertheless, if improvements are made in the in situ sensors and their calibration and uncertainty evaluation, these initial results indicate that near-shore autonomous coastal underwater temperature arrays, such as the one at UBPC, could in the future provide valuable in situ data for the validation of satellite coastal SST measurements.

Editorial for the Special Issue “Remote Sensing in Coastal Zone Monitoring and Management—How Can Remote Sensing Challenge the Broad Spectrum of Temporal and Spatial Scales in Coastal Zone Dynamic?”

Coastal zones are sensitive areas responding at various scales (events to long-term trends) where the monitoring and management of physico-chemical, biological, morphological processes, and fluxes are highly challenging [...]

A Printable Device for Measuring Clarity and Colour in Lake and Nearshore Waters

Two expanding areas of science and technology are citizen science and three-dimensional (3D) printing. Citizen science has a proven capability to generate reliable data and contribute to unexpected scientific discovery. It can put science into the hands of the citizens, increasing understanding, promoting environmental stewardship, and leading to the production of large databases for use in environmental monitoring. 3D printing has the potential to create cheap, bespoke scientific instruments that have formerly required dedicated facilities to assemble. It can put instrument manufacturing into the hands of any citizen who has access to a 3D printer. In this paper, we present a simple hand-held device designed to measure the Secchi depth and water colour (Forel Ule scale) of lake, estuarine and nearshore regions. The device is manufactured with marine resistant materials (mostly biodegradable) using a 3D printer and basic workshop tools. It is inexpensive to manufacture, lightweight, easy to use, and accessible to a wide range of users. It builds on a long tradition in optical limnology and oceanography, but is modified for ease of operation in smaller water bodies, and from small watercraft and platforms. We provide detailed instructions on how to build the device and highlight examples of its use for scientific education, citizen science, satellite validation of ocean colour data, and low-cost monitoring of water clarity, colour and temperature.