Satellite-derived Observations Research Articles

Early Prediction of Regional Red Needle Cast Outbreaks Using Climatic Data Trends and Satellite-Derived Observations

Red needle cast (RNC), mainly caused by Phytophthora pluvialis, is a very damaging disease of the widely grown species radiata pine within New Zealand. Using a combination of satellite imagery and weather data, a novel methodology was developed to pre-visually predict the incidence of RNC on radiata pine within the Gisborne region of New Zealand over a five-year period from 2019 to 2023. Sentinel-2 satellite imagery was used to classify areas within the region as being disease-free or showing RNC expression from the difference in the red/green index (R/Gdiff) during a disease-free time of the year and the time of maximum disease expression in the upper canopy (early spring–September). Within these two classes, 1976 plots were extracted, and a classification model was used to predict disease incidence from mean monthly weather data for key variables during the 11 months prior to disease expression. The variables in the final random forest model included solar radiation, relative humidity, rainfall, and the maximum air temperature recorded during mid–late summer, which provided a pre-visual prediction of the disease 7–8 months before its peak expression. Using a hold-out test dataset, the final random forest model had an accuracy of 89% and an F1 score of 0.89. This approach can be used to mitigate the impact of RNC by focusing on early surveillance and treatment measures.

Terminus thinning drives recent acceleration of a Greenlandic lake-terminating outlet glacier

Abstract Ice-contact proglacial lakes affect ice dynamics and the transition of glacier termini from land- to lake-terminating has been shown to cause ice flow acceleration. In recent decades, the number and size of Greenlandic ice-marginal lakes has increased, highlighting the need to further understand these lake-terminating ice-margins as their influence on ice sheet mass balance increases. Here, time series of satellite-derived observations of ice velocity, surface elevation, and terminus position were generated at a lake-terminating outlet glacier, Isortuarsuup Sermia, and the nearby land-terminating Kangaasarsuup Sermia in south-west Greenland. At Isortuarsuup Sermia, annual surface velocity at the terminus increased by a factor of 2.5 to 214 ± 4 m yr−1 (2013–2021), with the magnitude of this acceleration declining with distance up-glacier. Meanwhile, near-terminus surface elevation changed at a rate of −2.3 ± 1.1 m yr−1 (2012–2021). Conversely, velocity change at Kangaasarsuup Sermia was minimal, while surface elevation change was approximately half at comparable elevations (−1.2 ± 0.3 m yr−1). We attribute these dynamic differences to thinning at Isortuarsuup Sermia and subsequent retreat from a stabilising sublacustrine moraine, and emphasise the potential of proglacial lakes to enhance future rates of mass loss from the Greenland Ice Sheet.

Relevance of near-surface soil moisture vs. terrestrial water storage for global vegetation functioning

Abstract. Soil water availability is an essential prerequisite for vegetation functioning. Vegetation takes up water from varying soil depths depending on the characteristics of its rooting system and soil moisture availability across depth. The depth of vegetation water uptake is largely unknown across large spatial scales as a consequence of sparse ground measurements. At the same time, emerging satellite-derived observations of vegetation functioning, surface soil moisture, and terrestrial water storage present an opportunity to assess the depth of vegetation water uptake globally. In this study, we characterize vegetation functioning through the near-infrared reflectance of vegetation (NIRv) and compare its relation to (i) near-surface soil moisture from the ESA's Climate Change Initiative (CCI) and (ii) total water storage from the Gravity Recovery and Climate Experiment (GRACE) mission at a monthly timescale during the growing season. The relationships are quantified through partial correlations to mitigate the influence of confounding factors such as energy- and other water-related variables. We find that vegetation functioning is generally more strongly related to near-surface soil moisture, particularly in semi-arid regions and areas with low tree cover. In contrast, in regions with high tree cover and in arid regions, the correlation with terrestrial water storage is comparable to or even higher than that of near-surface soil moisture, indicating that trees can and do make use of their deeper rooting systems to access deeper soil moisture, similar to vegetation in arid regions. At the same time, we note that this comparison is hampered by different noise levels in these satellite data streams. In line with this, an attribution analysis that examines the relative importance of soil water storage for vegetation reveals that they are controlled by (i) water availability influenced by the climate and (ii) vegetation type reflecting adaptation of the ecosystems to local water resources. Next to variations in space, the vegetation water uptake depth also varies in time. During dry periods, the relative importance of terrestrial water storage increases, highlighting the relevance of deeper water resources during rain-scarce periods. Overall, the synergistic exploitation of state-of-the-art satellite data products to disentangle the relevance of near-surface vs. terrestrial water storage for vegetation functioning can inform the representation of vegetation–water interactions in land surface models to support more accurate climate change projections.

Using a Combination of High-Frequency Coastal Radar Dataset and Satellite Imagery to Study the Patterns Involved in the Coastal Countercurrent Events in the Gulf of Cadiz

This study exploits the combination of High-Frequency Coastal Radar (HFR) information with satellite-derived observations to characterize the patterns involved in the coastal countercurrents (CCCs) events in the Gulf of Cadiz (GoC), which is situated in the SW of the Iberian Peninsula. The westward alongshore currents are observed throughout the year, but the main drivers necessary to develop this flow and its extension in both parts of the basin are not fully clear. In order to identify the main physical processes (both local and remote) that induce the development of these countercurrents and to evaluate the connection of the circulation patterns between the eastern and the western part of the GoC, we make use of several data sources available for the region. First of all, a land-based system of HFR antennas located at four different sites of the GoC provides the velocity field of the surface circulation of the basin. To achieve a significant characterization of the CCCs in the Gulf, the dataset analyzed is processed by means of a series of operations, including the Empirical Orthogonal Functions (EOFs) analysis used to identify spatial and temporal variability of the flow, a low-pass filter used to isolate the sub-inertial signal of the current and temporal interpolation to fill in the missing values. Secondly, given the known importance of the zonal component of the local winds combined with the variations in the mean pressure at sea level over the Western Mediterranean during these events, time series of meteorological data are processed and correlated with the current velocity series via a statistical analysis. Finally, sea surface temperature fields and chlorophyll-a distribution patterns are used as tracers to obtain information on the extension of the countercurrents where HFR data are missing in four cases studied during the year 2017. The conducted analysis revealed the consistent occurrence of westward coastal currents throughout the year, driven in the most intense cases by a combination of the zonal component of the local wind and atmospheric pressure fluctuations over the Western Mediterranean Sea. During those events, CCCs reached the Portuguese side of the Gulf and facilitated the advection of biological material and warmer waters.

Absence of the Great Whirl giant ocean vortex abates productivity in the Somali upwelling region

Somali upwelling is the fifth largest upwelling globally with high productivity, attracting tuna migratory species. A key control on the upwelling productivity is its interaction with one of the world’s largest oceanic eddies, the Great Whirl inducing a strong downwelling signal. Here, we use satellite-derived observations to determine the Great Whirl impact on the extent of the upwelling-driven phytoplankton bloom. We find that following decreases in upwelling intensity, productivity has declined by about 10% over the past two decades. The bloom extent has also been diminishing with an abrupt decrease around 2006–2007, coinciding with an abrupt increase in the downwelling effect. Absent or weak Great Whirl leads to the occurrence of smaller anticyclonic eddies with a resulting downwelling stronger than when the Great Whirl is present. We suggest that 2006–2007 abrupt changes in the bloom and downwelling extents’ regimes, are likely driven by Indian Ocean Dipole abrupt shift in 2006.

The link between surface and sub-surface chlorophyll-a in the centre of the Atlantic subtropical gyres: a comparison of observations and models

Satellite observations have given us a clear idea of the changes in chlorophyll in the surface ocean on both a seasonal and interannual basis, but repeated observations at depth are much rarer. The permanently-stratified subtropical gyres in the Atlantic are highly oligotrophic, with most production centred on a deep chlorophyll maximum (DCM) just above the nitracline. This study explores the variations in this feature in the core of both gyres, considering both seasonal and interannual variations, and the linkages between changes at the surface and sub-surface. The in situ observations come from the Atlantic Meridional Transect (AMT), a long-running UK monitoring programme, and also from biogeochemical Argo floats. AMT provides measurements spanning more than 25 years directed through the centres of these gyres, but samples only 2 to 4 months per year and thus cannot resolve the seasonal variations, whereas the profiling floats give coverage throughout the year, but without the rigid spatial repeatability. These observational records are contrasted with representation of the centres of the gyres in two different biogeochemical models: MEDUSA and ERSEM, thus fulfilling one of AMT’s stated aims: the assessment of biogeochemical models. Whilst the four datasets show broadly the same seasonal patterns and that the DCM shallows when surface chlorophyll increases, the depth and peak concentration of the DCM differ among datasets. For most of the datasets the column-integrated chlorophyll for both gyres is around 19 mg m-2 (with the AMT fluorescence-derived values being much lower); however the MEDUSA model has a disparity between the northern and southern gyres that is not understood. Although the seasonal increase in surface chlorophyll is tied to a commensurate decrease in concentration at depth, on an interannual basis years with enhanced surface levels of chlorophyll correspond to increases at depth. Satellite-derived observations of surface chlorophyll concentration act as a good predictor of interannual changes in DCM depth for both gyres during their autumn season, but provide less skill in spring.

A novel fusion framework embedded with zero-shot super-resolution and multivariate autoregression for precipitable water vapor across the continental Europe

Precipitable water vapor (PWV), as the most abundant greenhouse gas, significantly impacts the evapotranspiration process and thus the global climate. However, the applicability of mainstream satellite PWV products is limited by the tradeoff between spatial and temporal resolutions, as well as some external factors such as cloud contamination. In this study, we proposed a novel PWV spatio-temporal fusion framework based on the zero-shot super-resolution and the multivariate autoregression models (ZSSR-ARF) to improve the accuracy and continuity of PWV. The framework is implemented in a way that the satellite-derived observations (MOD05) are fused with the reanalysis data (ERA5) to generate accurate and seamless PWV of high spatio-temporal resolution (0.01°, daily) across the European continent from 2001 to 2021. Firstly, the ZSSR approach is used to enhance the spatial resolution of ERA5 PWV based on the internal recurrence of image information. Secondly, the optimal ERA5-MOD05 image pairs are selected based on the image similarity as inputs to improve the fusion accuracy. Thirdly, the framework develops a multivariate autoregressive fusion approach to allocate weights adaptively for the high-resolution image prediction, which primely addresses the non-stationarity and autocorrelation of PWV. The results reveal that the accuracies of fused PWV are consistent with those of the GPS retrievals (r = 0.82–0.95 and RMSE = 2.21–4.01 mm), showing an enhancement in the accuracy and continuity compared to the original MODIS PWV. The ZSSR-ARF fusion framework outperforms the other methods with R2 improved by over 24% and RMSE reduced by over 0.61 mm. Furthermore, the fused PWV exhibits similar temporal consistency (mean difference of 0.40 mm and DSTD of 3.22 mm) to the reliable ERA5 products, and substantial increasing trends (mean of 0.057 mm/year and over 0.1 mm/year near the southern and western coasts) are observed over the European continent. As the accuracy and continuity of PWV are improved, the outcome of this paper has potential for climatic analyses during the land-atmosphere cycle process.

Sea level variability and coastal inundation over the northeastern Mediterranean Sea

ABSTRACT Sea level is a key element of global-scale climatic changes with significant coastal impacts. The northeastern Mediterranean Sea (Aegean, Ionian, and Cretan Seas: AICS) consists of extended low-lying coastal zones exposed to coastal inundation due to seawater level increases. The variability of the Sea Level Anomaly (SLA) in the AICS has been evaluated with the use of satellite-derived observations from 1993 to 2021. The general SLA trend over the AICS domain was 3.6 mm/year, which is higher than that of the overall Mediterranean Sea. The coastal inundation variability along the AICS coastline, specifically in 20 characteristic study areas, was investigated based on the long-term satellite-derived sea level observations, high-resolution land elevation data, and numerical simulations, conducted with a coastal inundation (CoastFLOOD) model. The coastal zones were categorized based on the frequency of occurrence and percentage coverage of flooding incidents during the 29-year period, while the interannual trends and the seasonality of inundated littoral and inland areas also showed intense spatial variation. High flooding levels occurred at coastal areas of either extended low-lying areas and/or high sea level elevations. The coastal zones were categorized based on a heuristic Coastal Inundation Hazard Index (CIHI: 1–5 ranking) that was determined by the occurrence frequency of severe floods (temporal component) and their impact area (spatial component). Our study findings provide vital information about the coastal inundation conditions and the respective hazard level of the AICS, useful to support enhanced coastal zone management for the improvement of coastal protection and decisions about coastal hazards’ mitigation.

Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing

Abstract. Aerosol radiative forcing uncertainty affects estimates of climate sensitivity and limits model skill in terms of making climate projections. Efforts to improve the representations of physical processes in climate models, including extensive comparisons with observations, have not significantly constrained the range of possible aerosol forcing values. A far stronger constraint, in particular for the lower (most-negative) bound, can be achieved using global mean energy balance arguments based on observed changes in historical temperature. Here, we show that structural deficiencies in a climate model, revealed as inconsistencies among observationally constrained cloud properties in the model, limit the effectiveness of observational constraint of the uncertain physical processes. We sample the uncertainty in 37 model parameters related to aerosols, clouds, and radiation in a perturbed parameter ensemble of the UK Earth System Model and evaluate 1 million model variants (different parameter settings from Gaussian process emulators) against satellite-derived observations over several cloudy regions. Our analysis of a very large set of model variants exposes model internal inconsistencies that would not be apparent in a small set of model simulations, of an order that may be evaluated during model-tuning efforts. Incorporating observations associated with these inconsistencies weakens any forcing constraint because they require a wider range of parameter values to accommodate conflicting information. We show that, by neglecting variables associated with these inconsistencies, it is possible to reduce the parametric uncertainty in global mean aerosol forcing by more than 50 %, constraining it to a range (around −1.3 to −0.1 W m−2) in close agreement with energy balance constraints. Our estimated aerosol forcing range is the maximum feasible constraint using our structurally imperfect model and the chosen observations. Structural model developments targeted at the identified inconsistencies would enable a larger set of observations to be used for constraint, which would then very likely narrow the uncertainty further and possibly alter the central estimate. Such an approach provides a rigorous pathway to improved model realism and reduced uncertainty that has so far not been achieved through the normal model development approach.

A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015

Abstract. The spatio-temporal variation of surface water storage (SWS) in the Congo River basin (CRB), the second-largest watershed in the world, remains widely unknown. In this study, satellite-derived observations are combined to estimate SWS dynamics at the CRB and sub-basin scales over 1992–2015. Two methods are employed. The first one combines surface water extent (SWE) from the Global Inundation Extent from Multi-Satellite (GIEMS-2) dataset and the long-term satellite-derived surface water height from multi-mission radar altimetry. The second one, based on the hypsometric curve approach, combines SWE from GIEMS-2 with topographic data from four global digital elevation models (DEMs), namely the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing Satellite (ALOS), Multi-Error-Removed Improved Terrain (MERIT), and Forest And Buildings removed Copernicus DEM (FABDEM). The results provide SWS variations at monthly time steps from 1992 to 2015 characterized by a strong seasonal and interannual variability with an annual mean amplitude of ∼101±23 km3. The Middle Congo sub-basin shows a higher mean annual amplitude (∼71±15 km3). The comparison of SWS derived from the two methods and four DEMs shows an overall fair agreement. The SWS estimates are assessed against satellite precipitation data and in situ river discharge and, in general, a relatively fair agreement is found between the three hydrological variables at the basin and sub-basin scales (linear correlation coefficient >0.5). We further characterize the spatial distribution of the major drought that occurred across the basin at the end of 2005 and in early 2006. The SWS estimates clearly reveal the widespread spatial distribution of this severe event (∼40 % deficit as compared to their long-term average), in accordance with the large negative anomaly observed in precipitation over that period. This new SWS long-term dataset over the Congo River basin is an unprecedented new source of information for improving our comprehension of hydrological and biogeochemical cycles in the basin. As the datasets used in our study are available globally, our study opens opportunities to further develop satellite-derived SWS estimates at the global scale. The dataset of the CRB's SWS and the related Python code to run the reproducibility of the hypsometric curve approach dataset of SWS are respectively available for download at https://doi.org/10.5281/zenodo.7299823 and https://doi.org/10.5281/zenodo.8011607 (Kitambo et al., 2022b, 2023).

Importance of meteorological conditions to the prediction of surface PM2.5 using satellite-based observations in Sichuan basin, southwest China

Air pollutants are of great global concern due to their effects on peoples health. Consequently, timely predictions of surface PM2.5/PM10 can help implement preventive interventions. Machine learning algorithms are commonly used to predict or retrieve PM2.5/PM10 concentrations based on satellite-derived observations. But these algorithms often ignore the interpretability of PM2.5/PM10 predictions and generally improve their performance by adding meteorological conditions derived from numerical weather predictions (NWP) reanalysis data. This paper explores the predictive ability of different meteorological conditions, such as wind speed, wind direction, temperature, and humidity derived from NWP data at different pressure levels, and their ability to improve the PM2.5 retrieval accuracy of a random forest model in comparison with using only satellite data. Adding meteorological conditions to satellite data raised the coefficient of determination (R2) from 0.533 to 0.722 (with data at 500hPa) and 0.752 (with data at both 500hPa and 975hPa), indicating that meteorological conditions could improve PM2.5 model predictions compared with experiments using only satellite observations. In the experiment where meteorological conditions were added to satellite observations, performance of PM2.5 retrieval improved. Other experiments have shown that the performance is better when meteorological conditions are used alone compared with the case when satellite observations are used alone. In addition, feature importance of these experiments has revealed that meteorological parameters are relatively higher, and there lies a gap between satellite observations and meteorological parameters. In the experiment using both satellite observations and meteorological data, the feature with the highest importance is the temperature (0.12 for those at 975hPa and 0.12 for those at 500hPa), while those of satellite observations are less than 0.04. And the R^(2 ) using only the meteorological condition (both 500hPa and 975hPa) decreased only by 0.012 compared with the experiment using all data, while RMSE and MAE increased by 0.34, 0.207, respectively. The last experiment reduced input features to 9 parameters based on feature importance, but its R2 decreased by only 0.026. It seems evident that meteorological conditions (such as temperature, humidity and wind) are crucial to surface PM2.5 retrieval, even when satellite observations are available in RF based PM2.5 retrieval.

Impacts of droughts and floods on agricultural productivity in New Zealand as measured from space

We estimate the impact of precipitation extremes on the productivity of agricultural land parcels in New Zealand using satellite data. This type of post-disaster damage assessment aims to allow for the quantification of disaster damage when an on-the-ground assessment of damage is too costly or too difficult to conduct. It can also serve as a retroactive data collection tool for disaster loss databases where data collection did not happen at the time. We use satellite-derived observations of terrestrial vegetation (the enhanced vegetation index (EVI)) over the growing season, with data at the land parcel level identifying five land use types (annual and perennial crops, and three types of pasture), and with precipitation records, which we use to identify both excessively dry (drought) and excessively wet (flood) episodes. Using regression analyses, we examine whether these precipitation extremes had an observable impact on agricultural productivity. We find statistically significant declines in agricultural productivity that are associated with both droughts and floods. The average impact of these events is usually less than 1%, but the impacts are quite heterogeneous across years and across regions, with some parcels experiencing a much more significant decline in the EVI. We also identify several impact patterns related to the varying drought and flood vulnerability of the analysed land use types.

Synergistic combination of information from ground observations, geostationary satellite, and air quality modeling towards improved PM2.5 predictability

Concentrations of ambient particulate matter (such as PM2.5 and PM10) have come to represent a serious environmental problem worldwide, causing many deaths and economic losses. Because of the detrimental effects of PM2.5 on human health, many countries and international organizations have developed and operated regional and global short-term PM2.5 prediction systems. The short-term predictability of PM2.5 (and PM10) is determined by two main factors: the performance of the air quality model and the precision of the initial states. While specifically focusing on the latter factor, this study attempts to demonstrate how information from classical ground observation networks, a state-of-the-art geostationary (GEO) satellite sensor, and an advanced air quality modeling system can be synergistically combined to improve short-term PM2.5 predictability over South Korea. Such a synergistic combination of information can effectively overcome the major obstacle of scarcity of information, which frequently occurs in PM2.5 prediction systems using low Earth orbit (LEO) satellite-borne observations. This study first presents that the scarcity of information is mainly associated with cloud masking, sun-glint effect, and ill-location of satellite-borne data, and it then demonstrates that an advanced air quality modeling system equipped with synergistically-combined information can achieve substantially improved performances, producing enhancements of approximately 10%, 19%, 29%, and 10% in the predictability of PM2.5 over South Korea in terms of index of agreement (IOA), correlation coefficient (R), mean biases (MB), and hit rate (HR), respectively, compared to PM2.5 prediction systems using only LEO satellite-derived observations.

Impact of interannual weather variation on ammonia emissions and concentrations in Germany

Ammonia is one of the most impactful pollutants emitted from agricultural activities, harming human health and contributing to biodiversity loss. In ammonia emission inventories, the spatial distribution of annual emissions is mostly approximated by constant empirical emission fractions, which do not account for spatial variability, nor for temporal variability within a year or between years caused by weather variations. Besides, factors like manure properties, soil properties, and manure application techniques also lead to differences in the amount of ammonia emitted into the atmosphere. By not or only partly accounting for these factors, significant uncertainties are introduced into ammonia emission estimates at regional and national scales. In this study, we applied the empirical ALFAM2 model to derive spatially explicit slurry application emission fractions from cropland for use in the large-scale INTEGRATOR model, using the information on slurry properties (dry matter content and pH), manure application rate, application technique, incorporation time, air temperature, wind speed, and rainfall rate. In addition, the impact of weather on the ammonia emissions from animal housing and manure storage systems was included through a temperature-dependent scaling. We applied the method to investigate the year-to-year spatio-temporal variabilities of ammonia emissions and modeled concentrations across Germany from 2015 to 2018. Through the comparison with in situ measurements and satellite-derived observations, we studied how surface concentrations and total columns relate to local meteorology. We found that the spatio-temporal variability in emission fractions improves the ability to reproduce the interannual variability observed in ammonia concentration and total column measurements. This study shows that the developed approach to derive spatially explicit emission fractions can significantly improve ammonia emission modeling and is of great importance for studying the temporal variability between years.

Improving the predictability of the Qendresa Medicane by the assimilation of conventional and atmospheric motion vector observations. Storm-scale analysis and short-range forecast

Abstract. The coastal population in the western Mediterranean Basin is frequently affected by high-impact weather events that produce huge economic and human losses. Among the wide spectrum of maritime severe weather events, tropical-like Mediterranean cyclones (a.k.a. medicanes) draw particular attention, specially due to their poor predictability. The accurate prediction of this kind of event still remains a key challenge to the weather forecast community, mainly because of (i) errors in the initial conditions, (ii) lack of accuracy of modeling micro-scale physics processes and (iii) chaotic behavior inherent to numerical weather prediction models. The 7 November 2014 Qendresa Medicane, that took place over the Sicilian channel affecting the islands of Lampedusa, Pantelleria and Malta, was selected for this study because of its extremely low predictability behavior in terms of its track and intensity. To enhance the prediction of Qendresa, a high-resolution (4 km) ensemble-based data assimilation technique, known as ensemble Kalman filter (EnKF), is used. In this study, both in situ conventional and satellite-derived observations are assimilated with the main objective of improving Qendresa's model initial conditions and thus its subsequent forecast. The performance of the EnKF system and its impact on the Qendresa forecast are quantitatively assessed using different deterministic and probabilistic verification methods. A discussion in terms of the relevant physical mechanisms adjusted by the EnKF is also provided. Results reveal that the assimilation of both conventional and satellite-derived observations improves the short-range forecasts of the trajectory and intensity of Qendresa. In this context, the relevance of assimilating satellite-derived observations to improve the pre-convective estimation of Qendresa's upper-level dynamics is shown, which is key to obtain a realistic track and intensity forecast of this event.

Optimal Assimilation Number of Phytoplankton in the Siberian Seas: Spatiotemporal Variability, Environmental Control and Estimation Using a Region-Specific Model

The maximal value of the chlorophyll-specific carbon fixation rate in the water column or the optimal assimilation number (Pbopt) is an important parameter used to estimate water column integrated primary production (IPP) using models and satellite-derived data. The spatiotemporal variability in the Pbopt of the total and size-fractionated phytoplankton in the Siberian Seas (SSs) and its links with environmental factors were studied based on long-term (1993–2020) field and satellite-derived (MODIS-Aqua) observations. The average value of Pbopt in the SSs was equal to 1.38 ± 0.76 mgC (mg Chl a)–1 h–1. The monthly average values of Pbopt decreased during the growing season from 1.95 mgC (mg Chl a)–1 h–1 in July to 0.64 mgC (mg Chl a)–1 h–1 in October. The average value of Pbopt for small (<3 μm) phytoplankton 1.6-fold exceeded that for large (>3 μm) phytoplankton. The values of Pbopt depend mainly on incident photosynthetically available radiation (PAR). Based on the relationship between Pbopt and PAR, the empirical region-specific algorithm (E0reg) was developed. The E0reg algorithm performed better than commonly used temperature-based models. The application of E0reg for the calculation of Pbopt will make it possible to more precisely estimate IPP in the SSs.

Multimodal 4DVarNets for the Reconstruction of Sea Surface Dynamics From SST-SSH Synergies

The space-time reconstruction of sea surface dynamics from satellite observations is a challenging inverse problem due to the associated irregular sampling. Satellite altimetry provides a direct observation of the sea surface height (SSH), which relates to the divergence-free component of sea surface currents. The associated sampling pattern prevents operational schemes from retrieving fine-scale dynamics, typically below 10 days. By contrast, other satellite sensors provide higher-resolution observations of sea surface tracers such as sea surface temperature (SST). Multimodal inversion schemes then arise as appealing approaches. Though theoretical evidence supports the existence of an explicit relationship between sea surface temperature and sea surface dynamics under specific dynamical regimes, the generalization to the variety of upper ocean dynamical regimes is complex. Here, we investigate this issue from a physics-informed learning perspective. We introduce a trainable multimodal inversion scheme for the reconstruction of sea surface dynamics from multi-source satellite-derived observations, namely satellite-derived SSH and SST data. The proposed multimodal 4DVarNet schemes combine a variational formulation involving trainable observation and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> terms with a trainable gradient-based solver. An observing system simulation experiment for a Gulf Stream region supports the relevance of our approach compared with state-of-the-art schemes. We report a relative improvement greater than 60% compared with the operational altimetry product in terms of root mean square error and resolved space-time scales. We discuss further the potential and the limitations of the proposed approach for the reconstruction and forecasting of geophysical dynamics from irregularly-sampled satellite observations.

Unrevealing past and future vegetation restoration on the Loess Plateau and its impact on terrestrial water storage

Vegetation restoration attributed to China’s Grain to Green Program (GTGP) exerts noticeable impact on the ecological-hydrological nexus. However, the vegetation restoration status associated with GTGP has been insufficiently studied and its effect on water availability remains controversial. This holistic study involved investigation of long-time satellite observations, outputs of a land surface model, and future projections to quantify the historical and future variations of gross primary productivity (GPP) on the Loess Plateau (China) and the associated effect on terrestrial water storage change (TWSC). The ensemble empirical mode decomposition (EEMD) model was employed to characterize the nonlinearity of interannual variability. Satellite-derived observations revealed a trend of increase of 8.162 TgC/year in GPP during 2003–2016, with the largest proportion attributable to grassland owing to its extensive coverage. Increased GPP is related to TWSC decline, as evidenced by both Gravity Recovery and Climate Experiment satellite observations and simulations from the modified Noah land surface model. In descending order, the magnitude of such negative impact varies with coverage of grassland, cropland, and trees. Ecological restoration is manifested as a critical anthropogenic stressor altering TWSC, which is further reinforced by prominent inverse correlation (R = − 0.31) after removing climatic impacts. The bias-corrected Coupled Model Intercomparison Project phase 6 dataset illustrates that increased GPP and reduced TWS are projected in the twenty-first century when considering the climate change and human interventions. Considering that past ecological benefits associated with revegetation measures could represent short-term gains and that TWS decline is most likely to continue, the government and local stakeholders should reconsider the current hydrological condition from the perspective of balanced socioeconomic benefit and terrestrial water storage cost.

Sea Surface Temperature Trends in the Coastal Zone of Southern England

Kassem, H.; Amos, C.L., and Thompson, C.E.L., 2023. Sea surface temperature trends in the coastal zone of southern England. Journal of Coastal Research, 39(1), 18–31. Charlotte (North Carolina), ISSN 0749-0208. Sea surface temperature (SST) trends along the south coast of England (northern English Channel) were examined based on data from systematic buoy measurements deployed by the National Network of Regional Coastal Monitoring Programmes of England (NNRCMP) since 2003. These data were supplemented with: (1) long-term, coastal SST measurements by the Centre for Environment, Fisheries and Aquaculture Science (CEFAS); (2) global data sets compiled by the Hadley Centre since 1900, and (3) satellite-derived observations from Moderate Resolution Imaging Spectroradiometer (MODIS) (Aqua) since 2002. These data sets were used to evaluate de-seasoned nearshore trends in SST along the south coast of England and examine links to regional ocean-atmosphere teleconnections. The analyses of long-term, CEFAS data support the proposal that prior to the mid-1980s there were no de-seasoned trends in SST and conditions from year to year were relatively stable. Subsequently, interannual fluctuations appear to have increased, associated with a period of warming between 1985 and 2003 (0.28 °C/decade). Post 2003, interannual fluctuations in SST monitored by the NNRCMP buoys continued, and the warming trend appears to be greater (0.42 °C/decade). This trend in SST is greatest in the nearshore and decreases with distance offshore. The warming in SST also varied greatly from month to month. The greatest warming took place from December to March, whilst the least heating (and sometimes cooling) occurred between September and November. Analysis of Hadley (HadSST1.1) and MODIS data sets substantiated these trends. The greatest warming (post 2003) was found west of Portland Bill (up to 0.76 °C/decade) and decreased towards the Strait of Dover. Despite this west-to-east trend, all 12 NNRCMP stations between Penzance and Folkestone showed remarkably similar results, suggesting regional and global sources of heat rather than local sources. This is corroborated through wavelet coherence analysis linking SST anomalies to regional/global ocean-atmosphere teleconnection indices at seasonal scales.

Ambient air pollution and prostate cancer risk in a population-based Canadian case-control study.

Background:Ambient air pollution is a human carcinogen and a possible risk factor for prostate cancer.Methods:We investigated associations between ambient concentrations particulate matter 2.5 (PM2.5) and nitrogen dioxide (NO2) and incident prostate cancer in a Canadian case-control study. Between 1994 and 1997, cases were identified from provincial cancer registries, and a population-based series of controls was recruited. Among men 50 years of age or older, risk factor and residential history data (1975 to 1994) were collected from 1,420 prostate cancer cases and 1,424 controls. Three methods were used to estimate the residential mean exposure to PM2.5 and NO2 during this period: (1) satellite-derived observations; (2) satellite-derived observations scaled with historical fixed-site measurements; and (3) a national land-use regression (LUR) model. Odds ratios (ORs) and their 95% confidence intervals (CIs) in relation to interquartile range (IQR) increases in PM2.5 and NO2 were estimated using logistic regression, adjusting for personal and contextual factors.Results:We found positive associations between exposure to PM2.5 and NO2 over the previous 20 years and prostate cancer. An IQR increase in PM2.5 (3.56 µg/m3 for satellite and 4.48 µg/m3 for scaled satellite observations) yielded ORs of 1.28 (95% CI = 1.07, 1.52) and 1.20 (95% CI = 1.03, 1.40), respectively. For NO2, IQR increases (1.45 ppb for satellite, 15.18 ppb for scaled satellite-derived information, and 15.39 ppb for the national LUR) were associated with ORs of 1.09 (95% CI = 0.95, 1.24), 1.21 (95% CI = 1.02, 1.43), and 1.19 (95% CI = 1.03, 1.38), respectively.Conclusions:Our findings support the hypothesis that ambient air pollution increases the risk of prostate cancer.