Long-Term Variations in the Pixel-to-Pixel Variability of NOAA AVHRR SST Fields from 1982 to 2015

Sea surface temperature (SST) fields obtained from the series of space-borne five-channel Advanced Very High Resolution Radiometers (AVHRRs) provide the longest continuous time series of global SST available to date (1981–present). As a result, these data have been used for many studies and significant effort has been devoted to their careful calibration in an effort to provide a climate quality data record. However, little attention has been given to the local precision of the SST retrievals obtained from these instruments, which we refer to as the pixel-to-pixel (p2p) variability, a characteristic important in the ability to resolve structures such as ocean fronts characterized by small gradients in the SST field. In this study, the p2p variability is estimated for Level-2 SST fields obtained with the Pathfinder retrieval algorithm for AVHRRs on NOAA-07, 9, 11, 12 and 14-19. These estimates are stratified by year, season, day/night and along-scan/along-track. The overall variability ranges from 0.10 K to 0.21 K. For each satellite, the along-scan variability is between 10 and 20% smaller than the along-track variability (except for NOAA-16 nighttime for which it is approximately 30% smaller) and the summer and fall ss are between 10 and 15% smaller than the winter and spring ss. The differences between along-track and along-scan are attributed to the way in which the instrument has been calibrated. The seasonal differences result from the T4 - T5 term in the Pathfinder retrieval algorithm. This term is shown to be a major contributor to the p2p variability and it is shown that its impact could be substantially reduced without a deleterious effect on the overall p2p s of the resulting products by spatially averaging it as part of the retrieval process. The AVHRR/3s (NOAA-15 through 19) were found to be relatively stable with trends in the p2p variability of at most 0.015 K/decade

An analysis of historical Marine Heatwaves over the Australian region based on multiple satellite observation products

Marine heatwaves (MHWs) have recently been recognized as extreme events considering their devastating impacts on marine ecosystems. Both the common and differing characteristics of historical MHW events may contribute to future studies and predictions of regional MHWs globally. We firstly compare five high-resolution Sea Surface Temperature (SST) climatology data sets around the Australian coast to investigate the uncertainty and dependency introduced by the reference SSTs to current estimates of SST anomalies. The results indicate that Climate Change Initiative (CCI) and SST Atlas of the Australian Regional Seas (SSTAARS) climatologies would be suitable for use as a reference over the Australian region. We then analyze the main spatial and temporal features of historical MHW events on a pixel-wise scale over the Australian region by forming MHW metrics using CCI and Copernicus Climate Change Service (C3S) Level 4 (L4) analyses from 1981 to 2020. Relatively short-term events (<10 days) account for over half of the identified MHWs over the domain, among which nearly 90% are classified as having moderate intensity. Natural variability of the local climate system contributes to most of these short-term and less intense events, while the MHWs may emerge under the continuing warming of the ocean surface. Purposefully excluding the short-term events would benefit the long-term studies of MHWs, by highlighting the variations and impacts of the relatively longer and more intense events. Three Australian case study regions are selected: the northwest coastal region (NW region, 17°S-25°S, 110°E-119°E), southwest coastal region (SW region, 25°S-40°S, 110°E-120°E), and southeast coastal region (SETS region) containing the Tasman Sea (33°S-45°S, 147°E-167°E) and southern Tasmania region (43°S-48°S, 142°E-152°E). Multiple SST including CCI L4, IMOS Level 3 Super-Collated (L3S) products, sea level anomaly (SLA) and wind stress products are used to analyze and observe historical MHWs. SLA over the 90th percentile, as a complementary parameter is recommended to identify subsurface MHWs in regions free of strong eddy activities, which could help improve the understanding and predictability of MHWs. We also recommend using both L3S and L4 SST data to identify MHWs when analyzing small-scale spatial variability over regional seas globally if the MHW-mask from the gap-free L4 SST and MHW threshold derived from the L3S data are applied to guarantee the reliability of L3S observations

Natural variability of surface oceanographic conditions in the offshore Gulf of Mexico

AbstractThis work characterizes patterns of temporal variability in surface waters of the central Gulf of Mexico. We examine remote-sensing based observations of sea surface temperature (SST), wind speed, sea surface height anomaly (SSHA), chlorophyll-a concentration (Chl-a) and Net Primary Production (NPP), along with model predictions of mixed layer depth (MLD), to determine seasonal changes and long-term trends in the central Gulf of Mexico between the early 1980s and 2012. Specifically, we examine variability in four quadrants of the Gulf of Mexico (water depth >1000m). All variables show strong seasonality. Chl-a and NPP show positive anomalies in response to short-term increases in wind speed and to cold temperature events. The depth of the mixed layer (MLD) directly and significantly affects primary productivity throughout the region. This relationship is sufficiently robust to enable real-time estimates of MLD based on satellite-based estimates of NPP. Over the past 15–20years, SST, wind speed, and SSHA show a statistically significant, gradual increase. However, Chl-a and NPP show no significant trends over this period. There has also been no trend in the MLD in the Gulf of Mexico interior. The positive long-term trend in wind speed and SST anomalies is consistent with the warming phase of the Atlantic Multidecadal Oscillation (AMO) that started in the mid-90s. This also coincides with a negative trend in the El Niño/Southern Oscillation Multivariate ENSO Index (MEI) related to an increase in the frequency of cooler ENSO events since 1999–2000. The results suggest that over decadal scales, increasing temperature, wind speed, and mesoscale ocean activity have offsetting effects on the MLD. The lack of a trend in MLD anomalies over the past 20years explains the lack of long-term changes in chlorophyll concentration and productivity over this period in the Gulf. Understanding the background of seasonal and long-term variability in these ocean characteristics is important to interpret changes in ocean health due to episodic natural and anthropogenic events and long term climate changes or development activities. With this analysis we provide a baseline against which such changes can be measured

Satellite-based time-series of sea-surface temperature since 1981 for climate applications

A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 10^12 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km^2 and 45 km^2. The mean density of good-quality observations is 13 km^−2 yr^−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr^−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics

A global long-term (1981–2000) land surface temperature product for NOAA AVHRR

Land surface temperature (LST) plays an important role in the research of climate change and various land surface processes. Before 2000, global LST products with relatively high temporal and spatial resolutions are scarce, despite a variety of operational satellite LST products. In this study, a global 0.05∘×0.05∘ historical LST product is generated from NOAA advanced very-high-resolution radiometer (AVHRR) data (1981–2000), which includes three data layers: (1) instantaneous LST, a product generated by integrating several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST; and (3) monthly averages of ODC LST. For an assumed maximum uncertainty in emissivity and column water vapor content of 0.04 and 1.0 g cm−2, respectively, evaluated against the simulation dataset, the RF-SWA method has a mean bias error (MBE) of less than 0.10 K and a standard deviation (SD) of 1.10 K. To compensate for the influence of orbital drift on LST, the retrieved RF-SWA LST was normalized with an improved ODC method. The RF-SWA LST were validated with in situ LST from Surface Radiation Budget (SURFRAD) sites and water temperatures obtained from the National Data Buoy Center (NDBC). Against the in situ LST, the RF-SWA LST has a MBE of 0.03 K with a range of −1.59–2.71 K, and SD is 1.18 K with a range of 0.84–2.76 K. Since water temperature only changes slowly, the validation of ODC LST was limited to SURFRAD sites, for which the MBE is 0.54 K with a range of −1.05 to 3.01 K and SD is 3.57 K with a range of 2.34 to 3.69 K, indicating good product accuracy. As global historical datasets, the new AVHRR LST products are useful for filling the gaps in long-term LST data. Furthermore, the new LST products can be used as input to related land surface models and environmental applications. Furthermore, in support of the scientific research community, the datasets are freely available at https://doi.org/10.5281/zenodo.3934354 for RF-SWA LST (Ma et al., 2020a), https://doi.org/10.5281/zenodo.3936627 for ODC LST (Ma et al., 2020c), and https://doi.org/10.5281/zenodo.3936641 for monthly averaged LST (Ma et al., 2020b)

Half a century of satellite remote sensing of sea-surface temperature

Sea-surface temperature (SST) was one of the first ocean variables to be studied from earth observation satellites. Pioneering images from infrared scanning radiometers revealed the complexity of the surface temperature fields, but these were derived from radiance measurements at orbital heights and included the effects of the intervening atmosphere. Corrections for the effects of the atmosphere to make quantitative estimates of the SST became possible when radiometers with multiple infrared channels were deployed in 1979. At the same time, imaging microwave radiometers with SST capabilities were also flown. Since then, SST has been derived from infrared and microwave radiometers on polar orbiting satellites and from infrared radiometers on geostationary spacecraft. As the performances of satellite radiometers and SST retrieval algorithms improved, accurate, global, high resolution, frequently sampled SST fields became fundamental to many research and operational activities. Here we provide an overview of the physics of the derivation of SST and the history of the development of satellite instruments over half a century. As demonstrated accuracies increased, they stimulated scientific research into the oceans, the coupled ocean-atmosphere system and the climate. We provide brief overviews of the development of some applications, including the feasibility of generating Climate Data Records. We summarize the important role of the Group for High Resolution SST (GHRSST) in providing a forum for scientists and operational practitioners to discuss problems and results, and to help coordinate activities world-wide, including alignment of data formatting and protocols and research. The challenges of burgeoning data volumes, data distribution and analysis have benefited from simultaneous progress in computing power, high capacity storage, and communications over the Internet, so we summarize the development and current capabilities of data archives. We conclude with an outlook of developments anticipated in the next decade or so

Oyster Aquaculture Site Selection Using Landsat 8-Derived Sea Surface Temperature, Turbidity, and Chlorophyll a

Remote sensing data is useful for selection of aquaculture sites because it can provide water-quality products mapped over large regions at low cost to users. However, the spatial resolution of most ocean color satellites is too coarse to provide usable data within many estuaries. The Landsat 8 satellite, launched February 11, 2013, has both the spatial resolution and the necessary signal to noise ratio to provide temperature, as well as ocean color derived products along complex coastlines. The state of Maine (USA) has an abundance of estuarine indentations (∼3,500 miles of tidal shoreline within 220 miles of coast), and an expanding aquaculture industry, which makes it a prime case- study for using Landsat 8 data to provide products suitable for aquaculture site selection. We collected the Landsat 8 scenes over coastal Maine, flagged clouds, atmospherically corrected the top-of-the-atmosphere radiances, and derived time varying fields (repeat time of Landsat 8 is 16 days) of temperature (100 m resolution), turbidity (30 m resolution), and chlorophyll a (30 m resolution). We validated the remote-sensing-based products at several in situ locations along the Maine coast where monitoring buoys and programs are in place. Initial analysis of the validated fields revealed promising new areas for oyster aquaculture. The approach used is applicable to other coastal regions and the data collected to date show potential for other applications in marine coastal environments, including water quality monitoring and ecosystem management

Observational needs of sea surface temperature

Sea surface temperature (SST) is a fundamental physical variable for understanding, quantifying and predicting complex interactions between the ocean and the atmosphere. Such processes determine how heat from the sun is redistributed across the global oceans, directly impacting large- and small-scale weather and climate patterns. The provision of daily maps of global SST for operational systems, climate modeling and the broader scientific community is now a mature and sustained service coordinated by the Group for High Resolution Sea Surface Temperature (GHRSST) and the CEOS SST Virtual Constellation (CEOS SST-VC). Data streams are shared, indexed, processed, quality controlled, analyzed, and documented within a Regional/Global Task Sharing (R/GTS) framework, which is implemented internationally in a distributed manner. Products rely on a combination of low-Earth orbit infrared and microwave satellite imagery, geostationary orbit infrared satellite imagery, and in situ data from moored and drifting buoys, Argo floats, and a suite of independent, fully characterized and traceable in situ measurements for product validation (Fiducial Reference Measurements, FRM). Research and development continues to tackle problems such as instrument calibration, algorithm development, diurnal variability, derivation of high-quality skin and depth temperatures, and areas of specific interest such as the high latitudes and coastal areas. In this white paper, we review progress versus the challenges we set out 10 years ago in a previous paper, highlight remaining and new research and development challenges for the next 10 years (such as the need for sustained continuity of passive microwave SST using a 6.9 GHz channel), and conclude with needs to achieve an integrated global high-resolution SST observing system, with focus on satellite observations exploited in conjunction with in situ SSTs. The paper directly relates to the theme of Data Information Systems and also contributes to Ocean Observing Governance and Ocean Technology and Networks within the OceanObs2019 objectives. Applications of SST contribute to all the seven societal benefits, covering Discovery; Ecosystem Health & Biodiversity; Climate Variability & Change; Water, Food, & Energy Security; Pollution & Human Health; Hazards and Maritime Safety; and the Blue Economy

Recent Trends in SST, Chl-a, Productivity and Wind Stress in Upwelling and Open Ocean Areas in the Upper Eastern North Atlantic Subtropical Gyre

The global upper ocean has been warming during the last decades accompanied with a chlorophyll-a (Chl-a) and productivity decrease. Whereas subtropical gyres show similar trends, Eastern Boundary Upwelling Systems are thought to increase in productivity due to increased trade winds. This study analyzes recent trends in sea surface temperature (SST), Chl-a, net primary production (NPP) and meridional wind stress in the Eastern North Atlantic subtropical gyre (NASE) in order to examine if the global trends can be detected in open ocean and upwelling areas and how the ocean biota responds. Satellite data of such variables of the last 15–40 years were analyzed to calculate mean trends in upwelling areas in the Canary upwelling system and open ocean areas around the Azores, Madeira and the Canary Islands. Our results show significant warming in the area with a maximum of 2.7°C per century for the Azores. Moreover, a general decreasing trend for Chl-a and NPP seems to be more evident in the permanent upwelling areas, which will be responsible for a loss of 0.13% of the global NPP per century. Our results also highlight a significant expansion of the oceanic desert area of 10% with an increase in unproductive days of up to 84 days in the last 20 years. The competitive relationship of stratification and wind stress in the Canary upwelling system might be a more plausible explanation for the decrease in Chl-a and NPP in upwelling areas linked to the increase in upwelling favorable wind stress and the surface warming.En prens