Sea Surface Temperature Measurements Research Articles

Global ensemble of temperatures over 1850–2018: quantification of uncertainties in observations, coverage, and spatial modeling (GETQUOCS)

Abstract. Instrumental global temperature records are derived from the network of in situ measurements of land and sea surface temperatures. This observational evidence is seen as being fundamental to climate science. Therefore, the accuracy of these measurements is of prime importance for the analysis of temperature variability. There are spatial gaps in the distribution of instrumental temperature measurements across the globe. This lack of spatial coverage introduces coverage error. An approximate Bayesian computation based multi-resolution lattice kriging is developed and used to quantify the coverage errors through the variance of the spatial process at multiple spatial scales. It critically accounts for the uncertainties in the parameters of this advanced spatial statistics model itself, thereby providing, for the first time, a full description of both the spatial coverage uncertainties along with the uncertainties in the modeling of these spatial gaps. These coverage errors are combined with the existing estimates of uncertainties due to observational issues at each station location. It results in an ensemble of 100 000 monthly temperatures fields over the entire globe that samples the combination of coverage, parametric and observational uncertainties from 1850 to 2018 over a 5∘×5∘ grid.

Meanders of the West Greenland Current near Cape Farewell

The cyclonic boundary current that circumnavigates the Labrador Sea plays an important role in modulating the strength of wintertime convection in the interior basin, as well as restratifying the newly ventilated water in spring. Modeling studies indicate that meso and sub-mesoscale processes in the boundary current flux a significant amount of heat and freshwater offshore, although observations of this small-scale variability are lacking. Using four years of data from a mooring array west of Cape Farewell, Greenland, together with satellite altimetry and sea surface temperature measurements, we present the first observations of a meandering West Greenland Current. We describe the statistics, structure, characteristics, and formation mechanism of these features. The meanders occur roughly 30% of the time and are more prevalent in winter and early spring, with an increasing trend over the four-year record. It is shown that baroclinic instability of the boundary current is the cause of the meanders, triggered by seasonal steepening of the isopycnals between the interior basin and the boundary due to offshore convection. We argue that the meandering leads to the formation of small-scale eddies, and estimate the resulting seaward heat flux. Finally, possible connections between the meanders and the production of Irminger Rings are explored.

Interannual Variability of the Lena River Plume Propagation in 1993–2020 during the Ice-Free Period on the Base of Satellite Salinity, Temperature, and Altimetry Measurements

The Lena River plume significantly affects the thermohaline, optical and chemical properties of the eastern Arctic seas. We use sea surface salinity (SSS), temperature (SST), and altimetry measurements to study features of the Lena plume propagation during 1993–2020. A comparison of Soil Moisture Active Passive (SMAP) SSS measurements with in situ data obtained using the flow-through system in oceanographic surveys in 2018–2019 demonstrates good coincidence with correlation ~ 0.96 and RMSD ~ 1 psu. The SMAP data were used to reconstruct the plume evolution in 2015–2020 and to identify three main types of Lena plume propagation, which are mainly related to the variability of dominant zonal wind direction: «northern»—the plume moves to the north from the delta up to 78° N; «eastern»—the plume moves eastward along the Siberian coast up to 180° E; «mixed» between two main types. Brackish plume waters were characterized by increased temperature and sea level, which provides the opportunity for studying the Lena plume dynamics using satellite altimetry and infrared measurements. These data were analyzed to study the interannual variability of plume propagation during the ice-free period of 1993–2020. The obtained results show that the «northern» type is observed twice more often than the «eastern» one, but the «eastern» type has intensified since 2010.

Extensive Marine Heatwaves at the Sea Surface in the Northwestern Pacific Ocean in Summer 2021

In July–August 2021, intense marine heatwaves (MHWs) occurred at the sea surface over extensive areas of the northwestern Pacific Ocean, including the entire Sea of Japan and part of the Sea of Okhotsk. In extent and intensity, these MHWs were the largest since 1982, when satellite measurements of global sea surface temperatures started. The MHWs in summer 2021 were observed at the sea surface and occurred concomitantly with a stable shallow oceanic surface boundary layer. The distribution of the MHWs was strongly related to heat fluxes at the sea surface, indicating that the MHWs were generated mainly by atmospheric forcing. The MHWs started to develop after around 10 July, concurrent with an extreme northward shift of the atmospheric westerly jet. The MHWs developed rapidly under an atmospheric high-pressure system near the sea surface, associated with a northwestward expansion of the North Pacific Subtropical High. The MHWs exhibited peaks around 30 July to 1 August. Subsequently, following the southward displacement of the westerly jet, the MHWs weakened and then shrank abruptly, synchronously with rapid deepening of the oceanic surface boundary layer. By 18 August, the MHWs had disappeared.

Oceanographic conditions in HMAS perth maritime conservation area, Serang Regency, Banten Province

The efforts to protect underwater sites such as shipwreck sites by assigning them as Maritime Conservation Areas are needed because they are limited, non-renewable and fragile cultural heritage. The location of the battleship HMAS Perth is one of the underwater shipwreck sites in Banten Bay waters. Several sites of other sunken warships and merchant ships are currently in Banten Bay waters. HMAS Perth designated as a Maritime Conservation Area on February 28, 2018, through the Ministry of Marine Affairs and Fisheries Decree No. 21/KEPMEN-KP/2018. The Central Government and Regional Governments carry out the management. One of the efforts to map the environmental conditions in the area is to carry out biophysical monitoring activities, including oceanographic conditions. Observations of oceanographic parameters were focused on the HMAS Perth ship site area and were covering nine stations. The parameters observed were depth, sea surface temperature, pH, DO, TDS, conductivity, and salinity. Observations show that the depth HMAS Perth Maritime Conservation Area ranges from 31.60-44.65 meters, the measurement of sea surface temperature showed a value of 30.4-31.2 °C. The pH concentration indicates that the waters are in acidic conditions, while the DO concentration was in the range of 5.95-6.94 mg/L. The TDS value at nine stations has an average value at six stations was 24 mg/L, while two stations showed an average of 5 mg/L. The salinity concentration found around 25-35 ppt, and the conductivity values have been ranges 38.6-40.1 mS/cm. Overall, the oceanographic conditions HMAS Perth Maritime Conservation Areas are suitable and meet the marine quality standards following the Minister of Environment Decree No. 51/2004 to survive the marine organism.

Evaluation of AIRS and CrIS SST Measurements Relative to Three Globally Gridded SST Products Between 2013 and 2019

Globally gridded sea surface temperatures (SSTs) provide key data for long-term monitoring of the stability of satellite data. Despite apparent limitations, accurate hyperspectral data can provide useful independent information to critique the stability of global SST products on the annual-to-decadal time scale. We compared data from atmospheric infrared sounder (AIRS) on EOS Aqua and Crosstrack Interferometer Sounder (CrIS) on SNPP with the SST products from NOAA/NESDIS [real time global (RTG)], the Canadian Meteorological Centre (CMC), and the U.K. Met Office [Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA)]. For the 2013–2019 period, the overall standard deviation of the difference between AIRS and the RTG was 0.55 K, with an increasing trend over time. In contrast, the standard deviation of the difference between the AIRS, the CMC, and the OSTIA dropped steadily to below 0.4 K, a level previously seen only in SST products relative to independent buoy data. Unexplained biases between the observed and the gridded SSTs at the 100-mK level are consistent with the already existing estimates of the AIRS and CrIS absolute calibration accuracy. However, the AIRS and CrIS observations both show artifacts in all three SST products, increasing with distance from the equator, with the CMC artifacts being the smallest. Even with the CMC, a trend of 4 mK per year relative to AIRS and CrIS was observed between 2013 and 2019 for the 30S–30N oceans. Investigation of the underlying causes of the observed discrepancies requires further work.

High-Resolution Ocean Currents from Sea Surface Temperature Observations: The Catalan Sea (Western Mediterranean)

Current observations of ocean currents are mainly based on altimetric measurements of Sea Surface Heights (SSH), however the characteristics of the present-day constellation of altimeters are only capable to retrieve surface currents at scales larger than 50–70 km. By contrast, infrared and visible radiometers reach spatial resolutions thirty times higher than altimeters under cloud-free conditions. During the last years, it has been shown how the Surface Quasi-Geostrophic (SQG) approximation is able to reconstruct surface currents from measured Sea Surface Temperature (SST), but it has not been yet used to retrieve velocities at scales shorter than those provided by altimeters. In this study, the velocity field of ocean structures with characteristic lengths between 10 and 20 km has been derived from infrared SST using the SQG approach and compared to the velocities derived from the trajectories of Lagrangian drifters. Results show that the SQG approach is able to reconstruct the direction of the velocity field with observed RMS errors between 8 and 15 degrees and linear correlations between 0.85 and 0.99. The reconstruction of the modulus of the velocity is more problematic due to two limitations of the SQG approach: the need to calibrate the level of energy and the ageostrophic contributions. If drifter trajectories are used to calibrate velocities and the analysis is restricted to small Rossby numbers, the RMS error in the range of 10 to 16 cm/s and linear correlations can be as high as 0.97.

Novel Method for the Estimation of Vertical Temperature Profiles Using a Coastal Acoustic Tomography System

Systems based on remote sensing technology, which use reciprocal acoustic signals to continuously monitor changes in the coastal oceanic environment, are referred to as coastal acoustic tomography (CAT) systems. These systems have been applied in regions in which heavy ship traffics, fishing and marine aquaculture activities make it difficult to establish in situ oceanic sensor moorings. Conventionally, CAT measurements were used to successfully produce horizontal maps of the depth-averaged current velocity and temperature in these coastal regions without attempting to produce a vertical temperature profile. This prompted us to propose a new method for vertical temperature profile estimation (VTPE) from CAT data using the available sea surface temperature (SST), near-bottom temperature (NBT), and water depth. The VTPE method was validated using data-assimilated and tide-included high-resolution ocean model outputs, including tide data, by comparing the estimated and simulated temperatures. Measurements were performed in the southern coastal region of Korea, where two CAT stations were moored to establish a continuous coastal ocean monitoring system. The validation results revealed that the algorithm performed well across all seasons. Sensitivity tests of the VTPE method with reasonable realistic random errors in the SST, NBT, and acoustic travel time measurements demonstrate that the method is applicable to CAT observation data because the monthly mean root-mean-squared difference (RMSD) for the vertical profiles for February, May, August, and November were 0.23, 0.30, 0.50, and 0.24°C, respectively. The VTPE method was applied to the CAT observation datasets acquired in February and August. The transceivers at the CAT stations were at depths 11 and 6 m on average. The RMSD between the estimated and observed temperatures in the middle layer (∼3 m depth) between two stations in February and August were 0.08 and 0.60°C, respectively, the accuracy of which is sufficient in largely time-varying coastal environments. We provide a novel method for continuous coastal subsurface environmental monitoring without interrupting maritime traffic, fishing, and marine aquaculture activities.

Persistence of marine heat waves for coral bleaching and their spectral characteristics around Andaman coral reef.

Coral reefs are fragile and endangered ecosystems in the tropical marine and coastal environment. Thermal stress due to marine heat waves (MHW) could cause significantly negative impacts on the health conditions, i.e., bleaching of the coral ecosystem. The current study is an attempt to quantify the intensity of coral bleaching in the Andaman region in recent decades using the intensity of marine heat wave (IMHW) estimated from satellite measured sea surface temperature (SST). A linear regression model was developed between IMHW and in situ observations of percent coral bleaching (PCB) which has the slope 7.767 (of IMHW unit) and intercept (- 141.7). Further, an attempt was also made to establish the relationship between PCB and the ratio between the remote sensing reflectance (Rrs) at 443 and 531nm to upscale the percentage of coral bleaching at synoptic scales. A significant positive correlation between the PCB and band ratio index was found (R2 = 0.72). This approach can be used for the operational monitoring of coral reef beaching in this region.

Correcting Observational Biases in Sea Surface Temperature Observations Removes Anomalous Warmth during World War II

AbstractMost historical sea surface temperature (SST) estimates indicate warmer World War II SSTs than expected from forcing and internal climate variability. If real, this World War II warm anomaly (WW2WA) has important implications for decadal variability, but the WW2WA may also arise from incomplete corrections of biases associated with bucket and engine room intake (ERI) measurements. To better assess the origins of the WW2WA, we develop five different historical SST estimates (reconstructions R1–R5). Using uncorrected SST measurements from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) version 3.0 (R1) gives a WW2WA of 0.41°C. In contrast, using only buckets (R2) or ERI observations (R3) gives WW2WAs of 0.18° and 0.08°C, respectively, implying that uncorrected biases are the primary source of the WW2WA. We then use an extended linear-mixed-effect method to quantify systematic differences between subsets of SSTs and develop groupwise SST adjustments based on differences between pairs of nearby SST measurements. Using all measurements after applying groupwise adjustments (R4) gives a WW2WA of 0.13°C [95% confidence interval (c.i.): 0.01°–0.26°C] and indicates that U.S. and U.K. naval observations are the primary cause of the WW2WA. Finally, nighttime bucket SSTs are found to be warmer than their daytime counterparts during WW2, prompting a daytime-only reconstruction using groupwise adjustments (R5) that has a WW2WA of 0.09°C (95% c.i.: −0.01° to 0.18°C). R5 is consistent with the range of internal variability found in either the CMIP5 (95% c.i.: −0.10° to 0.10°C) or CMIP6 ensembles (95% c.i.: −0.11° to 0.10°C). These results support the hypothesis that the WW2WA is an artifact of observational biases, although further data and metadata analyses will be important for confirmation.

Evaluation of the in situ Sea Surface Temperature Quality Control in the NOAA in situ SST Quality Monitor (iQuam) System

AbstractIn situ sea surface temperature (SST) measurements play a critical role in the calibration/validation (Cal/Val) of satellite SST retrievals and ocean data assimilation. However, their quality is not always optimal, and proper quality control (QC) is required before they can be used with confidence. The in situ SST Quality Monitor (iQuam) system was established at the National Oceanic and Atmospheric Administration (NOAA) in 2009, initially to support the Cal/Val of NOAA satellite SST products. It collects in situ SST data from multiple sources, performs uniform QC, monitors the QC’ed data online, and distributes it to users. In this study, the iQuam QC is compared with other QC methods available in some of the in situ data ingested in iQuam. Overall, the iQuam QC performs well on daily-to-monthly time scales over most global oceans and under a wide variety of environmental conditions. However, it may be less accurate in the daytime a when pronounced diurnal cycle is present, and in dynamic regions, due to the strong reliance on the “reference SST check”, which employs daily low-resolution level 4 (L4) analyses with no diurnal cycle resolved. The iQuam “performance history check”, applied to all in situ platforms, is an effective alternative to the customary “black/gray” lists, available only for some platforms (e.g., drifters and Argo floats). In the future, iQuam QC will be upgraded (e.g., using improved reference field(s), with enhanced temporal and spatial resolutions). More comparisons with external QC methods will be performed to learn and employ the best QC practices.

Assessment of a Fusion Sea Surface Temperature Product for Numerical Weather Predictions in China: A Case Study

A common approach used for multi-source observation data blending is the fusion method. This study assesses the applicability of the first-generation fusion sea surface temperature (SST) product of the China Meteorological Administration (CMA) in the Yellow–Bohai Sea region for numerical weather predictions. First, daily and 6 h fusion SST measurements are compared with data derived from 21 buoy sites for 2019 to 2020. The error analysis results show that the root-mean-square error (RMSE) of the daily SST ranges from 0.64 to 1.36 °C (overall RMSE of 0.996 °C). The RMSE of the 6 h SST varies from 0.64 to 1.73 °C (overall RMSE of 1.06 °C). According to the simulation result, the SST difference could affect the value and location distribution of liquid water content in the fog area. A lower SST is favorable for increasing the liquid water content, which fits the mechanisms of advection fog formation by warm air flowing over colder water.

Impacts of the Assimilation of Satellite Sea Surface Temperature Data on Volume and Heat Budget Estimates for the North Sea

AbstractMechanisms controlling the heat budget of the North Sea are investigated based on a combination of satellite sea surface temperature measurements and numerical model simulations. Lateral heat fluxes across the shelf edge and into the Baltic Sea as well as vertical ocean‐atmosphere heat exchange are considered. A 3‐D variational (3DVAR) data assimilation (DA) scheme is applied, which contains assumed model error correlations that depend on the mixed layer depth derived from a coupled circulation/ocean wave model. The analysis balances pressure gradients introduced by temperature modifications. Significant hydrodynamic model response to DA was found, which should be considered in the heat budget estimations. The observed change of the current velocity field decreases the lateral advective volume/heat exchanges between the North Sea and the Atlantic, yielding an increased heat flux from the Atlantic into the North Sea and more heat flux from the sea to the atmosphere. The largest DA impact on volume/heat transport is in the Norwegian Channel, where the dominant process is Eulerian transport, followed by tidal pumping and wind pumping. Further analysis reveals an acceleration of the along‐shelf current at the northern edge of the North Sea, a decrease in the horizontal pressure gradient from the Atlantic to the North Sea, and a reduction of the Eulerian transport of volume/heat outward the North Sea. Furthermore, the coupling between the circulation model and the wave model has significant impacts on lateral heat advection in the DA run, which is due to the wave impact on the mixed layer depth.

More than Skin Deep: Sea Surface Temperature as a Means of Inferring Atlantic Water Variability on the Southeast Greenland Continental Shelf Near Helheim Glacier

AbstractEnhanced outlet glacier discharge accounts for almost half of the Greenland Ice Sheet's mass loss since 1990. Warming subsurface Atlantic Water (AW) has been implicated in much of that loss, particularly along Greenland's southeastern coast. However, oceanographic observations are sparse prior to the last decade, making it difficult to diagnose changes in AW properties reaching the glaciers. Here, we investigate the use of sea surface temperature (SST) measurements to quantify ocean temperature variability on the continental shelf near Sermilik Fjord and Helheim Glacier. We find that after removing the short‐term, atmospheric‐driven variability in non‐winter months, regional SSTs provide a reliable upper ocean (surface mixed layer) temperature record. In the trough region near Sermilik Fjord, the adjusted SSTs correlate well with moored ocean measurements of the water entering the fjord at depth and driving glacier melting. Using this relationship, we reconstruct the AW variability on the shelf dating back to 2000, 8 years before the first mooring deployments. Across the 19‐year record, the AW temperatures in the trough do not always track properties in the source waters of the Irminger Current, which flows along the continental break. Instead, the properties of the waters found at the fjord mouth depend on variations in the source AW and, also, in the Polar Water that flows into the region from the Arctic Ocean. Satellite‐derived SSTs, when combined with local oceanography considerations, have the potential to improve understanding around previously unanswered glacier‐ocean questions in areas surrounding Greenland.

Combining Statistical, Physical, and Historical Evidence to Improve Historical Sea-Surface Temperature Records

Reconstructing past sea-surface temperatures (SSTs) from historical measurements containing more than 100 million ship-based observations taken by over 500,000 ships from more than 150 countries using a variety of methodologies creates a wide range of historical, scientific, and statistical challenges. The reconstruction of historical SSTs for studying climate change is particularly challenging because SST measurements are uncertain and contain systematic biases of order 0.1^\circC to 1^\circC—these systematic biases are in the range of the historical global warming signal of approximately 1^\circC. The biases are complicated and have generally been addressed using simplified corrections. In this review, I introduce a history of SST observations, review a statistical method developed for quantifying SST biases, and illustrate scientific insights obtained from adjusted SSTs. This article also documents the scientific journey of my Ph.D. work. As a result, I report personal stories on both successes, difficulties, and setbacks along the way. The statistical method for correcting SSTs (i.e., a linear-mixed-effect intercomparison framework) depends on identifying systematic offsets between intercomparable groups of SST observations. Combining estimated offsets with physical and historical evidence has allowed for correcting discrepancies associated with SSTs, including the North Atlantic warming twice as fast as the North Pacific in the early 20th century and anomalously warm SSTs during World War II. Corrections also permit better hindcasting of Atlantic hurricanes. I conclude with some discussion on how the SST records might be further improved. Given the importance of SSTs for understanding historical changes in climate, I hope that this review can help others appreciate challenges that are present and spark some interest and ideas for further improvement.

An Updated Assessment of Near‐Surface Temperature Change From 1850: The HadCRUT5 Data Set

AbstractWe present a new version of the Met Office Hadley Centre/Climatic Research Unit global surface temperature data set, HadCRUT5. HadCRUT5 presents monthly average near‐surface temperature anomalies, relative to the 1961–1990 period, on a regular 5° latitude by 5° longitude grid from 1850 to 2018. HadCRUT5 is a combination of sea‐surface temperature (SST) measurements over the ocean from ships and buoys and near‐surface air temperature measurements from weather stations over the land surface. These data have been sourced from updated compilations and the adjustments applied to mitigate the impact of changes in SST measurement methods have been revised. Two variants of HadCRUT5 have been produced for use in different applications. The first represents temperature anomaly data on a grid for locations where measurement data are available. The second, more spatially complete, variant uses a Gaussian process based statistical method to make better use of the available observations, extending temperature anomaly estimates into regions for which the underlying measurements are informative. Each is provided as a 200‐member ensemble accompanied by additional uncertainty information. The combination of revised input data sets and statistical analysis results in greater warming of the global average over the course of the whole record. In recent years, increased warming results from an improved representation of Arctic warming and a better understanding of evolving biases in SST measurements from ships. These updates result in greater consistency with other independent global surface temperature data sets, despite their different approaches to data set construction, and further increase confidence in our understanding of changes seen.

Efficient downscaling of satellite oceanographic data with convolutional neural networks

Space-borne satellite radiometers measure Sea Surface Temperature (SST), which is pivotal to studies of air-sea interactions and ocean features. Under clear sky conditions, high resolution measurements are obtainable. But under cloudy conditions, data analysis is constrained to the available low resolution measurements. We assess the efficiency of Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNN) to downscale oceanographic data from low spatial resolution (SR) to high SR. With a focus on SST Fields of Bay of Bengal, this study proves that Very Deep Super Resolution CNN can successfully reconstruct SST observations from 15 km SR to 5km SR, and 5km SR to 1km SR. This outcome calls attention to the significance of DL models explicitly trained for the reconstruction of high SR SST fields by using low SR data. Inference on DL models can act as a substitute to the existing computationally expensive downscaling technique: Dynamical Downsampling. The complete code is available on this Github Repository.

A machine learning approach to estimating the error in satellite sea surface temperature retrievals

Global, repeated, and accurate measurements of Sea Surface Temperature (SST) are critical for weather and climate projections. While thermometers on buoys measure SST relatively accurately, only sensors aboard satellites give global and repeated SST measurements necessary for many applications, including climate modeling. For satellite-based thermal infrared sensors, an atmospheric correction converts calibrated brightness temperatures measured at orbital height into an SST estimate, but imperfect assumptions in the correction algorithm coupled with variability in atmospheric conditions and viewing geometries can lead to a wide range of errors and uncertainties in the satellite-derived SST retrievals. Estimates of the resulting errors are imperative for satellite-derived SST assimilation into climate models. This paper evaluates the use of machine learning Decision Tree algorithms to predict the central tendency and dispersion of errors in satellite-derived SST retrievals. First, using records from the NASA R2014.1 MODIS Aqua SST Matchup Database, which includes matched-up satellite and in situ SST measurements, a set of seven variables was derived that addresses the assumptions and known issues in the satellite SST retrieval process. Then, both Random Forest and Cubist Decision Trees were used to predict the SST residual (satellite SST minus skin-corrected buoy SST) for each matchup. While both Decision Tree methods performed similarly well, the Cubist model is more easily interpreted and yields better predictions of errors for relatively infrequent conditions. Various characteristics of the groups of matchups identified by Cubist were explored, and uncertainty values for the error estimates were derived for each group. Overall, the Cubist model predicted the skin SST residual with a root-mean-squared-error of 0.380 °C across nighttime cloud-filtered domains, demonstrating that a Cubist model is viable for quantitatively and accurately predicting Single Sensor Error Statistics per pixel as required by the Group for High Resolution SST. In this paper, we present the training and testing of both Decision Tree models. Because of its interpretability, we explore in detail the characteristics of the Cubist-derived groups to gain new geophysical insight into the satellite-derived SST retrieval error across different measurement conditions.

Historical Estimates of Surface Marine Temperatures.

Surface temperature documents our changing climate, and the marine record represents one of the longest widely distributed, observation-based estimates. Measurements of near-surface marine air temperature and sea-surface temperature have been recorded on platforms ranging from sailing ships to autonomous drifting buoys. The raw observations show an imprint of differing measurement methods and are sparse in certain periods and regions. This review describes how the real signal of global climate change can be determined from these sparse and noisy observations, including the quantification of measurement method-dependent biases and the reduction of spurious signals. Recent progress has come from analysis of the observations at increasing levels of granularity and from accounting for artifacts in the data that depend on platform types, measurement methods, and environmental conditions. Cutting across these effects are others caused by how the data were recorded, transcribed, and archived. These insights will be integrated into the next generation of global products quantified with validated estimates of uncertainty and the dependencies of its correlation structure. Further analysis of these records using improved data, metadata, and methods will certainly uncover more idiosyncrasies and new ways to improve the record.

Impacts of Tropical Cyclone Seroja on the Phytoplankton Chlorophyll-a and Sea Surface Temperature in the Savu Sea, Indonesia

The tropical cyclone (TC) Seroja is one of the rare climatic events in the Indonesian Seas, particularly in the Savu Sea. This unprecedented event, which matured on April 4, 2021, caused fatalities and severe damage to the region’s infrastructure and economy. High spatio-temporal resolution satellite measurements of surface winds, chlorophyll-a, and sea surface temperature (SST) are used to disentangle the impact of extreme wind speed (> 10 m∙s-1) on chlorophyll-a and SST. High wind speed associated with TC Seroja induced strong upwelling and vertical mixing in the Savu Sea, which led to phytoplankton blooms and SST depression. An abrupt change of daily variability and positive anomaly in phytoplankton chlorophyll-a concentrations reaches 13 mg∙m-3 and 0.3 mg∙m-3, respectively. At the same time, the SST shows significant cooling up to 3°C. Our results provide novel insights on the exceptional occurrence of a TC within the Indonesian Seas and highlight its impact on the surface ocean.