The UK’s TechDemoSat-1 (TDS-1), launched 2014, has demonstrated the use of global positioning system (GPS) signals for monitoring ocean winds and sea ice. Here it is shown, for the first time, that Galileo and BeiDou signals detected by TDS-1 show similar promise. TDS-1 made seven raw data collections, recovering returns from Galileo and BeiDou, between November 2015 and March 2019. The retrieved open ocean delay Doppler maps (DDMs) are similar to those from GPS. Over sea ice, the Galileo DDMs show a distinctive triple peak. Analysis, adapted from that for GPS DDMs, gives Galileo’s signal-to-noise ratio (SNR), which is found to be inversely sensitive to wind speed, as for GPS. A Galileo track transiting from open ocean to sea ice shows a strong instantaneous SNR response. These results demonstrate the potential of future spaceborne constellations of GNSS-R (global navigation satellite system–reflectometry) instruments for exploiting signals from multiple systems: GPS, Galileo, and BeiDou.
Abstract Ocean color sensors are crucial for understanding global phytoplankton dynamics. However, the limited life spans of sensors make multisensor data sets necessary for estimating long‐term trends. Discontinuities may be introduced when merging data between sensors, potentially affecting trend estimates and their uncertainties. We use a Bayesian spatiotemporal model to investigate the presence of discontinuities and their impacts on estimated chlorophyll trends. The discontinuities considered are the introduction of Medium Resolution Imaging Spectrometer, Moderate Resolution Imaging Spectroradiometer‐Aqua, and Visible Infrared Imaging Radiometer Suite and the termination of Sea‐Viewing Wide Field‐of‐View Sensor. Discontinuities are detected in ~70% of regions, affecting trend estimates (~60% of regions have statistically different trends) and potentially even biasing trend estimates (opposite sign in ~13% of regions). Considering a single discontinuity increases trend uncertainty by an average of 0.20%/year (0.59%/year for two discontinuities). This difference in trend magnitude and uncertainty highlights the importance of minimizing discontinuities in multisensor records and taking into account discontinuities when analyzing trends.
The Sentinel-3 tandem project represents the first time that two ocean colour satellites have been flown in the same orbit with minimal temporal separation (~30 s), thus allowing them to have virtually identical views of the ocean. This offers an opportunity for understanding how differences in individual sensor uncertainty can affect conclusions drawn from the data. Here, we specifically focus on trend estimation. Observational chlorophyll-a uncertainty is assessed from the Sentinel-3A Ocean and Land Colour Imager (OLCI-A) and Sentinel-3B OLCI (OLCI-B) sensors using a bootstrapping approach. Realistic trends are then imposed on a synthetic chlorophyll-a time series to understand how sensor uncertainty could affect potential long-term trends in Sentinel-3 OLCI data. We find that OLCI-A and OLCI-B both show very similar trends, with the OLCI-B trend estimates tending to have a slightly wider distribution, although not statistically different from the OLCI-A distribution. The spatial pattern of trend estimates is also assessed, showing that the probability distributions of trend estimates in OLCI-A and OLCI-B are most similar in open ocean regions, and least similar in coastal regions and at high northern latitudes. This analysis shows that the two sensors should provide consistent trends between the two satellites, provided future ageing is well quantified and mitigated. The Sentinel-3 programme offers a strong baseline for estimating long-term chlorophyll-a trends by offering a series of satellites (starting with Sentinel-3A and Sentinel-3B) that use the same sensor design, reducing potential issues with cross-calibration between sensors. This analysis contributes an important understanding of the reliability of the two current Sentinel-3 OLCI sensors for future studies of climate change driven chlorophyll-a trends.
Climate change is predicted to affect oceanic phytoplankton abundance with impacts on fisheries and feedbacks on climate. The presence, magnitude, and even direction of long-term trends in phytoplankton abundance over the past few decades is still debated in the literature. The challenges affecting these studies include the low signal-to-noise ratio, the large degree of natural variability, and the shortness of the satellite ocean colour record, which is itself a composite of multiple shorter records. Previous work, however, has typically focused on using linear temporal models to determine the presence of trends in chlorophyll, where each grid cell is considered independently. To improve the assessment of trends a statistical model that explicitly models the relationship between neighbouring grid cells is used. A hierarchical Bayesian spatio-temporal model is fitted to global ocean colour data (1997 – 2013). This results in a notable improvement in accuracy in model fit, an order of magnitude smaller global trend, and larger uncertainty when compared to a model without spatial correlation. To help separate long-term trends from natural variability, trends from coupled physical-biogeochemical models are incorporated in to the model as Bayesian priors. The introduction of priors tends to reduce the magnitude and uncertainty of trend estimates, although the amount is deemed to be not statistically different from zero in any of the regions considered. Finally, the model is used to analyse the effect of taking into account discontinuities on estimated chlorophyll trends. The discontinuities considered are those relating to the launch and termination of individual ocean colour sensors. Considering discontinuities leads to statistically different trends in most regions, which can have a reversed sign as well as increased uncertainty. The improvement in trend estimate accuracy, and the more realistic representation of their uncertainty, emphasizes the solution that spatio-temporal modelling offers for studying global long-term change.
Changes in marine primary productivity are key to determine how climate change might impact marine ecosystems and fisheries. Satellite ocean color sensors provide coverage of global ocean chlorophyll with a combined record length of ~ 20 years. Coupled physical-biogeochemical models can inform on expected changes and are used here to constrain observational trend estimates and their uncertainty. We produce estimates of ocean surface chlorophyll trends, by using Coupled Model Intercomparison Project (CMIP5) models to form priors as a "first guess", which are then updated using satellite observations in a Bayesian spatio-temporal model. Regional chlorophyll trends are found to be significantly different from zero in 18/23 regions, in the range ± 1.8% year-1. A global average of these regional trends shows a net positive trend of 0.08 ± 0.35% year-1, highlighting the importance of considering chlorophyll changes at a regional level. We compare these results with estimates obtained with the commonly used "vague" prior, representing no independent knowledge; coupled model priors are shown to slightly reduce trend magnitude and uncertainties in most regions. The statistical model used here provides a robust framework for making best use of all available information and can be applied to improve understanding of global change.
Abstract Shipboard sampling of ocean biogeochemical properties is necessarily limited by logistical and practical constraints. As a result, the majority of observations are obtained for the spring/summer period and in regions relatively accessible from a major port. This limitation may bias the conceptual understanding we have of the spatial and seasonal variability in important components of the Earth system. Here we examine the influence of sampling bias on global estimates of carbon export flux by sub-sampling a biogeochemical model to simulate real, realistic and random sampling. We find that both the sparseness and the ‘clumpy’ character of shipboard flux observations generate errors in estimates of globally extrapolated export flux of up to ~ ± 20%. The use of autonomous technologies, such as the Biogeochemical-Argo network, will reduce the uncertainty in global flux estimates to ~ ± 3% by both increasing the sample size and reducing clumpiness in the spatial distribution of observations. Nevertheless, determining the climate change-driven trend in global export flux may be hampered due to the uncertainty introduced by interannual variability in sampling patterns.
Abstract. The estimation of the regional Ocean Heat Content (OHC) is essential for climate analysis and future climate predictions. In this study, we propose a method to estimate and propagate uncertainties in regional OHC changes. The OHC is estimated with space geodetic steric data corrected from salinity variations estimated with in situ measurements. A variance-covariance matrix method is used to propagate uncertainties from space geodetic data to the OHC change. The integrated OHC change over the Atlantic basin is 0.17 W m-2 which represents 21 % of the global OHC trend, with significant trends observed in 52 % of the Atlantic basin. Uncertainties in OHC trends are mainly attributed to manometric sea level change uncertainties. We validate our space geodetic OHC estimates at two test sites, representing the subtropical and subpolar regions of the North Atlantic, highlighting their importance in understanding climate dynamics. Our results show good agreement between space geodetic estimates and in situ measurements in the North Atlantic region. The space geodetic OHC trends reveal a warming pattern in the southern and western parts of the North Atlantic, particularly in the Gulf Stream region, while the northeastern part exhibits cooling trends. Overall, our study provides valuable insights and a new framework to estimate regional OHC change and its uncertainties, contributing to a better understanding of the Earth's climate system and its future projections. The space geodetic OHC change product (version 1.0) is freely available at https://doi.org/10.24400/527896/a01-2022.012 (Magellium/LEGOS, 2022)
Global Navigation Satellite System Reflectometry (GNSS-R) is a rapidly developing Earth observation technology that makes use of signals of opportunity from Global Navigation Satellite Systems that have been reflected off the Earth’s surface. The Cyclone Global Navigation Satellite System (CyGNSS) is a constellation of eight small satellites launched by NASA in 2016, carrying dedicated GNSS-R payloads to measure ocean surface wind speed at low latitudes (±35° North/South). The ESA ECOLOGY project evaluated CyGNSS v3.0 products, which were recently released following various calibration updates. This paper examines the performance of the new calibration by evaluating CyGNSS v3.0 Level-1 Normalised Bistatic Radar Cross Section (NBRCS) and Leading Edge Slope (LES) data from individual CyGNSS units and different GPS transmitters under constant ocean wind conditions. Results indicate that L1 NBRCS from individual CyGNSS units are well inter-calibrated and remarkably stable over time, a significant improvement over previous versions of the products. However, prominent geographical biases reaching over 3 dB are found in NBRCS, linked to factors including the choice of GPS transmitter and the bistatic geometry. L1 LES shows similar anomalies as well as a secondary geographical pattern of biases. These findings provide a basis for further improvement of CyGNSS Level-2 wind products and have wider applicability to improving the calibration of GNSS-R sensors for the remote sensing of non-ocean Earth surfaces.
Abstract. The estimation of the regional Ocean Heat Content (OHC) is essential for climate analysis and future climate predictions. In this study, we propose a method to estimate and propagate uncertainties in regional OHC changes. The OHC is estimated with space geodetic steric data corrected from salinity variations estimated with in situ measurements. A variance-covariance matrix method is used to propagate uncertainties from space geodetic data to the OHC change. The integrated OHC change over the Atlantic basin is 0.17 W m-2 which represents 21 % of the global OHC trend, with significant trends observed in 52 % of the Atlantic basin. Uncertainties in OHC trends are mainly attributed to manometric sea level change uncertainties. We validate our space geodetic OHC estimates at two test sites, representing the subtropical and subpolar regions of the North Atlantic, highlighting their importance in understanding climate dynamics. Our results show good agreement between space geodetic estimates and in situ measurements in the North Atlantic region. The space geodetic OHC trends reveal a warming pattern in the southern and western parts of the North Atlantic, particularly in the Gulf Stream region, while the northeastern part exhibits cooling trends. Overall, our study provides valuable insights and a new framework to estimate regional OHC change and its uncertainties, contributing to a better understanding of the Earth's climate system and its future projections. The space geodetic OHC change product (version 1.0) is freely available at https://doi.org/10.24400/527896/a01-2022.012 (Magellium/LEGOS, 2022)
<p>Assessing ongoing changes in marine primary productivity is essential to determine the impacts of climate change on marine ecosystems and fisheries. Satellite ocean color sensors provide detailed coverage of ocean chlorophyll in space and time, now with a combined record length of just over 20 years. Detecting climate change impacts is hindered by the shortness of the record and the long timescale of memory within the ocean such that even the sign of change in ocean chlorophyll is still inconclusive from time-series analysis of satellite data. Here we use a Bayesian hierarchical space-time model to estimate long-term trends in ocean chlorophyll. The main advantage of this approach comes from the principle of &#8221;borrowing strength&#8221; from neighboring grid cells in a given region to improve overall detection. We use coupled model simulations from the CMIP5 experiment to form priors to provide a &#8220;first guess&#8221; on observational trend estimates and their uncertainty that we then update using satellite observations. We compare the results with estimates obtained with the commonly used vague prior, reflecting the case where no independent knowledge is available.&#160; A global average net positive chlorophyll trend is found, with stronger regional trends that are typically positive in high and mid latitudes, and negative at low latitudes outside the Atlantic. The Bayesian hierarchical model used here provides a framework for integrating different sources of data for detecting trends and estimating their uncertainty in studies of global change.</p>
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