Low marine clouds are a major source of uncertainty in cloud feedbacks across climate models and in forcing by aerosol-cloud interactions. The evolution of these clouds and their response to aerosol are sensitive to the ambient environmental conditions, so it is important to be able to determine different responses over a representative set of conditions. Here, we propose a novel approach to encompassing the broad range of conditions present in low marine cloud regions, by building a library of observed environmental conditions. This approach can be used, for example, to more systematically test the fidelity of Large Eddy Simulations (LES) in representing these clouds. ERA5 reanalysis and various satellite observations are used to extract and derive macrophysical and microphysical cloud-controlling variables (CCVs) such as SST, estimated inversion strength (EIS), subsidence, and cloud droplet number concentrations. A few locations in the stratocumulus (Sc) deck region of the Northeast Pacific during summer are selected to fill out a phase space of CCVs. Thereafter, Principal Component Analysis (PCA) is applied to reduce the dimensionality and to select a reduced set of components that explain most of the variability among CCVs in order to efficiently select cases for LES simulations that encompass the observed CCV phase space. From this phase space, 75-100 cases with distinct environmental conditions will be selected and used to initialize 2-day LES modeling to provide a spectrum of aerosol-cloud interactions and Sc-to-Cumulus transition under observed ambient conditions. Such a large number of simulations will help create statistics to assess how well the LES can simulate the cloud lifecycle when constrained by the ‘best estimate’ of the environmental conditions, and how sensitive the modeled clouds are to changes in these driving fields.

We examine how zooplankton influence phytoplankton bloom phenology from the top-down, then use inverse modelling to infer the distribution and drivers of mean community zooplankton grazing dynamics based on the skill with which different simulated grazing formulations are able to recreate the observed seasonal cycle in phytoplankton biomass. We find that oligotrophic (eutrophic) biomes require more (less) efficient grazing dynamics, characteristic of micro- (meso-) zooplankton, leading to a strong relationship between the observed mean annual phytoplankton concentration in a region and the optimal grazing parameterization required to simulate it’s observed phenology. Across the globe, we found that a type III functional response consistently exhibits more skill than a type II response, suggesting the mean dynamics of a coarse model grid-cell should offer stability and prey refuge at low biomass concentrations. These new observationally-based global distributions will be invaluable to help constrain, validate and develop next generation of biogeochemical models.

Stream confluences are ubiquitous interfaces in freshwater networks and serve as junctions of previously independent landscapes. However, few studies have investigated how confluences influence transport, mixing, and fate of organic matter and inorganic nutrients at the scale of river networks. To understand how network biogeochemical fluxes may be altered by confluences, we conducted two sampling campaigns at five confluences in summer and fall 2021 spanning the extent of a mixed land use stream network. We sampled the confluence mainstem and tributary reaches as well as throughout the mixing zone downstream. We predicted that biologically reactive solutes would mix non-conservatively downstream of confluences and that alterations to downstream biogeochemistry would be driven by differences in chemistry and size of the tributary and upstream reaches. In our study, confluences were geomorphically distinct downstream compared to reaches upstream of the confluence. Dissolved organic matter and nutrients mixed non-conservatively downstream of the five confluences. Biogeochemical patterns downstream of confluences were only partially explained by contributing reach chemistry and drainage area. We found that the relationship between geomorphic variability, water residence time, and microbial respiration differed between reaches upstream and downstream of confluences. The lack of explanatory power from network-scale drivers suggests that non-conservative mixing downstream of confluences may be driven by biogeochemical processes within the confluence mixing zone. The unique geomorphology, non-conservative biogeochemistry, and ubiquity of confluences highlights a need to account for the distinct functional role of confluences in water resource management in freshwater networks.

Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.

Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.

Biomass burning has shaped many of the ecosystems of the planet and for millennia humans have used it as a tool to manage the environment. When widespread fires occur, the health and daily lives of millions of people can be affected by the smoke, often at unhealthy to hazardous levels leading to a range of short-term and long-term health consequences such as respiratory issues, cardiovascular issues, and mortality. It is critical to adequately represent and include smoke and its consequences in atmospheric modeling systems to meet needs such as addressing the global climate carbon budget and informing and protecting the public during smoke episodes. Many scientific and technical challenges are associated with modeling the complex phenomenon of smoke. Variability in fire emissions estimates has an order of magnitude level of uncertainty, depending upon vegetation type, natural fuel heterogeneity, and fuel combustion processes. Quantifying fire emissions also vary from ground/vegetation-based methods to those based on remotely sensed fire radiative power data. These emission estimates are input into dispersion and air quality modeling systems, where their vertical allocation associated with plume rise, and temporal release parameterizations influence transport patterns, and, in turn affect chemical transformation and interaction with other sources. These processes lend another order of magnitude of variability to the downwind estimates of trace gases and aerosol concentrations. This chapter profiles many of the global and regional smoke prediction systems currently operational or quasi-operational in real time or near-real time. It is not an exhaustive list of systems, but rather is a profile of many of the systems in use to give examples of the creativity and complexity needed to simulate the phenomenon of smoke. This chapter, and the systems described, reflect the needs of different agencies and regions, where the various systems are tailored to the best available science to address challenges of a region. Smoke forecasting requirements range from warning and informing the public about potential smoke impacts to planning burn activities for hazard reduction or resource benefit. Different agencies also have different mandates, and the lines blur between the missions of quasi-operational organizations (e.g. research institutions) and agencies with operational mandates. The global smoke prediction systems are advanced, and many are self-organizing into a powerful ensemble, as discussed in section 2. Regional and national systems are being developed independently and are discussed in sections 3-5 for Europe (11 systems), North America (7 systems), and Australia (3 systems). Finally, the World Meteorological Organization (WMO) effort (section 6) is bringing together global and regional systems and building the Vegetation Fire and Smoke Pollution Advisory and Assessment Systems (VFSP-WAS) to support countries with smoke issues and who lack resources.

The northern Antarctic Peninsula (NAP) is a key region of the Southern Ocean due to its complex ocean dynamics, distinct water mass sources, and the climate-driven changes taking place in the region. Despite the importance of macronutrients in fuelling primary production and driving the strong carbon uptake and storage, little is known about their spatiotemporal variability along the NAP. Hence, we explored a 24-year time series in this region, primarily sampled by the Brazilian High Latitude Group, to understand the processes involved in the spatial and interannual variability of macronutrients. We found high macronutrients concentrations, even in surface waters and under strong phytoplankton blooms. Minimum concentrations of dissolved inorganic nitrogen (16 μmol/ kg), phosphate (0.7 μmol/kg), and silicic acid (40 μmol/kg) along the NAP are higher than those recorded in surrounding regions. The main source of macronutrients is the intrusions of modified Circumpolar Deep Water (mCDW), and this is enhanced by local sources, such as organic matter remineralisation, water mass mixing, and mesoscale structures. However, we identified a depletion in silicic acid due to influence of Dense Shelf Water (DSW) from the Weddell Sea. Macronutrient concentrations shows substantial interannual variability driven by the balance between the intrusions of mCDW and advection of DSW, which is largely modulated by the Southern Annular Mode and to some extent by El Niño-Southern Oscillation. These findings are critical to improving our understanding of the natural variability of this Southern Ocean ecosystem and how it is responding to climate changes. Associate Editor

Sustainable river management can be supported by models predicting long-term morphological developments. Even for one-dimensional morphological models, run times can be up to several days for simulations over multiple decades. Alternatively, analytical tools yield metrics that allow estimation of migration celerity and damping of bed waves, which have potential for being used as rapid assessment tools to explore future morphological developments. We evaluate the use of analytical relations based on linear stability analyses of the St. Venant-Exner equations, which apply to bed waves with spatial scales much larger than the water depth. With a one-dimensional numerical morphological model, we assess the validity range of the analytical approach. The comparison shows that the propagation of small bed perturbations is well-described by the analytical approach. For Froude numbers over 0.3, diffusion becomes important and bed perturbation celerities reduce in time. A spatial-mode linear stability analysis predicts an upper limit for the bed perturbation celerity. For longer and higher bed perturbations, the dimensions relative to the water depth and the backwater curve length determine whether the analytical approach yields realistic results. For higher bed wave amplitudes, non-linearity becomes important. For Froude numbers ≤0.3, the celerity of bed waves is increasingly underestimated by the analytical approach. The degree of underestimation is proportional to the ratio of bed wave amplitude to water depth and the Froude number. For Froude numbers exceeding 0.3, the net impact on the celerity depends on the balance between the decrease due to damping and the increase due to non-linear interaction.

A study was conducted in none tilled coffee agroforestry fields of Eastern Uganda to understand the effects of application of inorganic fertilizers on soil nutrient loss in form of gas for mitigation of unsustainable agricultural practices. This study specifically i) assessed the effect of application of inorganic fertilizers on greenhouse gas emissions, ii) determined their effect on microbial carbon, nitrogen and phosphorus and iii) determined their effect on leaf litter decomposition under Albizzia-coffee growing systems of the Mount Elgon. Soil gas emissions were measured with the static chamber method for twelve months in a field experiment with five different fertilizer treatments. The effect of treatments was separated using ANOVA in Genstat discovery version 13. Microbial carbon, nitrogen and phosphorus was separated using Mann-Whitney U test. Results showed that annual emissions ranged from 19.6 to 26.1 (t C/ha/yr), 3.5 to 9 (Kg N/ha/yr) and 6.9 to 9.2 (Kg C/ha/yr) for carbon dioxide, nitrous oxide and methane respectively. Significant effects on soil emissions only occurred for nitrous oxide (P=0.017), microbial carbon (p=0.001) and microbial phosphorus (p<0.001) for the study period. The mixture of NPK fertilizers presented the lowest carbon dioxide loss and application of TSP presented the lowest nitrous oxide emission from soil. This study underscores the need for establishment of long-term experiments across several agro-ecological zones to confirm farmers’ perceptions of their soil fertility levels and ascertain the contribution of farm practices towards the retention of nutrients in the soil with minimal emission, to inform decisions of small holder farmers, policy and development partners for sustainable production.

Flood events are expected to increase in their frequency and severity, which results in higher flood risk without additional adaptation measures. The information gained from flood risk models is essential in effective disaster risk management. However, vulnerability estimations are often a large driver of uncertainty and flood damage is rarely estimated due to a lack of empirical damage data from flood events. This study uses a unique dataset with experienced damages and the implementation of flood damage mitigation (FDM) measures on the household level, collected after the flood event in the Netherlands in 2021. Flood damage models that control for several hazard, exposure and vulnerability indicators are estimated and allow for an additional input in flood risk models. Previous estimates of the effectiveness of FDM measures are prone to a selection bias, as households that do, and do not implement FDM measures systematically differ in their risk profiles. By using an Instrumental Variable (IV)-estimation, this study overcomes this selection bias and finds significant reductions in flood damage due to FDM measures. These reductions can be incorporated in multivariate flood vulnerability estimations, which indicate that FDM measures significantly reduce flood damage. Highlighting the relevance of information provision on both of these FDM categories and early warning systems for effective flood risk management.

Freshwater ecosystems provide vital services, yet are facing increasing risks from global change. In particular, lake thermal dynamics have been altered around the world as a result of climate change, necessitating a predictive understanding of how climate will continue to alter lakes in the future as well as the associated uncertainty in these predictions. Numerous sources of uncertainty affect projections of future lake conditions but few are quantified, limiting the use of lake modeling projections as management tools. To quantify and evaluate the effects of two potentially important sources of uncertainty, lake model selection uncertainty and climate model selection uncertainty, we developed ensemble projections of lake thermal dynamics for a dimictic lake in New Hampshire, USA (Lake Sunapee). Our ensemble projections used four different climate models as inputs to five vertical one-dimensional (1-D) hydrodynamic lake models under three different climate change scenarios to simulate thermal metrics from 2006 to 2099. We found that almost all the lake thermal metrics modeled (surface water temperature, bottom water temperature, Schmidt stability, stratification duration, and ice cover, but not thermocline depth) are projected to change over the next century. Importantly, we found that the dominant source of uncertainty varied among the thermal metrics, as thermal metrics associated with the surface waters (surface water temperature, total ice duration) were driven primarily by climate model selection uncertainty, while metrics associated with deeper depths (bottom water temperature, stratification duration) were dominated by lake model selection uncertainty. Consequently, our results indicate that researchers generating projections of lake bottom water metrics should prioritize including multiple lake models for best capturing projection uncertainty, while those focusing on lake surface metrics should prioritize including multiple climate models. Overall, our ensemble modeling study reveals important information on how climate change will affect lake thermal properties, and also provides some of the first analyses on how climate model selection uncertainty and lake model selection uncertainty interact to affect projections of future lake dynamics.

Avalanches and other hazardous mass movements pose a danger to the population and critical infrastructure in alpine areas. Hence, understanding and continuously monitoring mass movements is crucial to mitigate their risk. We propose to use Distributed Acoustic Sensing (DAS) to measure strain rate along a fiber-optic cable to characterize ground deformation induced by avalanches. We recorded 12 snow avalanches of various dimensions at the Vallée de la Sionne test site in Switzerland, utilizing existing fiber-optic infrastructure and a DAS interrogation unit during the winter 2020/2021. By training a Bayesian Gaussian Mixture Model, we automatically characterize and classify avalanche-induced ground deformations using physical properties extracted from the frequency-wavenumber and frequency-velocity domain of the DAS recordings. The resulting model can estimate the probability of avalanches in the DAS data and is able to differentiate between the avalanche-generated seismic near-field, the seismo-acoustic far-field and the mass movement propagating on top of the fiber. By analyzing the mass-movement propagation signals, we are able to identify group velocity packages within an avalanche that propagate faster than the phase velocity of the avalanche front, indicating complex internal structures. Importantly, we show that the seismo-acoustic far-field can be detected before the avalanche reaches the fiber-optic array, highlighting DAS as a potential research and early warning tool for hazardous mass movements.

Pulsing seepages of native hydrogen (H2) have been observed at the surface on several emitting structures. It is still unclear whether this H2 pulsed flux is controlled by deep migration processes, atmosphere/near-surface interactions or by bacterial fermentation. Here, we investigate mechanisms that may trigger pulsating fluid migration at depth and the resulting periodicity. We set up a numerical model to simulate the migration of a deep constant fluid flow. To verify the model’s formulation to solve complex fluid flows, we first simulate the morphology and amplitude of 2D thermal anomalies induced by buoyancy-driven water flow within a fault zone. Then, we simulate the H2 gas flow along a 1-km draining fault, crosscut by a lower permeable rock layer to investigate the conditions for which a pulsing system is generated from a deep control. For a constant incoming flow of H2 at depth, persistent bursts at the surface only appear in the model if: (I) a permeability with an effective-stress dependency is used, (II) a strong contrast of permeability exists between the different zones, (III) a sufficiently high value of the initial effective stress state at the base of the low permeable layer exists, and (IV) the incoming and continuous fluid flow of H2 at depth remains low enough so that the overpressure does not “open” instantly the low permeability layer. The typical periodicity expected for this type of valve-fault control of H2 pulses at the surface is at a time scale of the order of 100 to 300 days.

Detection and monitoring of tropical forest degradation is crucial to climate change mitigation and biodiversity conservation efforts. Several algorithms have been recently developed to monitor forest degradation and disturbance using remote sensing. However, these algorithms differ in local predictions due to the variation in the biogeophysical parameters used as degradation proxies. It is crucial to assess their relative performance and shortcomings in order to develop a clear understanding of the conditions under which each algorithm will detect a disturbance. In this study, we used GEDI lidar data on forest structure to examine the sensitivity of widely used forest disturbance and degradation products in a frontier tropical forest landscape in the Peruvian Amazon. We compared a leading spectral-based degradation algorithm (Continuous Degradation Detection (CODED)) with a radar-based algorithm (ALOS-2 PalSAR-2 based Radar Forest degradation Index (RFDI)). Given the sensitivity of radar to canopy cover and volume, we hypothesized that a single radar observation may detect degradation better than a long spectral time series. We first identified stable forests for reference structure in two ways: using disturbance stratification data from CODED, and using Peruvian protected areas. Our analysis showed that CODED performed below expectations in detecting forest degradation, often including patches that were regrowing after clear-felling in its “degraded” class. As CODED classified spectral changes over time rather than capturing structural variability, it classified 82% of palm plantations area as “degraded.” CODED also failed to detect degradation in forest areas that were likely partially disturbed (i.e., with low height and high cover). By contrast, the PalSAR-2 RFDI showed a significant relationship with forest height (detecting low height in degraded forests), although its predictive ability was limited due to high variability and signal saturation. Our study supports the conclusion that radar-based observation can detect forest degradation that the time series observation failed to detect. Given the limited correspondence between radar and spectral algorithms, we suggest that integrations of spectral and radar data may be beneficial for mapping forest degradation.

The usefulness of extended records of streamflow from tree-ring based hydrologic reconstructions seems obvious- a longer record provides a broader range of the variability of extremes and allows recent and/or ongoing events to be evaluated in a long-term context. The information from these centuries-long records may have clear implications for water resource management, but it is often unclear exactly how this information can be applied to management. In this presentation, I will discuss some of the challenges I have observed that are involved in using streamflow reconstructions in management decisions. These range from issues related to an agency’s capacity to use new types of data to mismatches between what is needed (e.g., daily resolution, a network of gage inputs) and what reconstruction data provide. The skillfulness of a streamflow reconstruction also has a bearing on its perceived credibility in terms of useable data. In spite of these challenges, there is a variety of ways that these data have been used by water resource managers in the western US. The uses are often not immediately evident, but can take the form of, for example, sensitively assessment, awareness raising, and shifts in prior assumptions. Relationship building between researchers and resource managers can yield mutual respect and understanding that lead to both interesting research questions and relevant and valuable information, even if the application to management is not tangible or immediate.

The biogeochemical cycles of iron (Fe) and manganese (Mn) in lakes and reservoirs have predictable seasonal trends, largely governed by stratification dynamics and redox conditions in the hypolimnion. However, short-term (i.e., sub-weekly) trends in Fe and Mn cycling are less well-understood, as most monitoring efforts focus on longer-term (i.e., monthly to yearly) time scales. The potential for elevated Fe and Mn to degrade water quality and impact ecosystem functioning, coupled with increasing evidence for high spatiotemporal variability in other biogeochemical cycles, necessitates a closer evaluation of the short-term Fe and Mn cycling dynamics in lakes and reservoirs. We adapted a UV-visible spectrophotometer coupled with a multiplexor pumping system and PLSR modeling to generate high spatiotemporal resolution predictions of Fe and Mn concentrations in a drinking water reservoir (Falling Creek Reservoir, Vinton, VA, USA) equipped with a hypolimnetic oxygenation (HOx) system. We quantified hourly Fe and Mn concentrations during two distinct transitional periods: reservoir turnover (Fall 2020) and initiation of the HOx system (Summer 2021). Our sensor system was able to successfully predict mean Fe and Mn concentrations as well as capture sub-weekly variability, ground-truthed by traditional grab sampling and laboratory analysis. During fall turnover, hypolimnetic Fe and Mn concentrations began to decrease more than two weeks before complete mixing of the reservoir occurred, with rapid equalization of epilimnetic and hypolimnetic Fe and Mn concentrations in less than 48 hours after full water column mixing. During the initiation of hypolimnetic oxygenation in Summer 2021, we observed that Fe and Mn were similarly affected by physical mixing in the hypolimnion, but displayed distinctly different responses to oxygenation, as indicated by the rapid oxidation of soluble Fe but not soluble Mn. This study demonstrates that Fe and Mn concentrations are highly sensitive to shifting DO and stratification and that their dynamics can substantially change on hourly to daily time scales in response to these transitions.

There is no doubt anymore that Earth Observation (EO) is contributing toward meeting the Sustainable Development Goals and addressing environmental challenges. Digital Earth Africa’s objective is to make freely available an EO data cube for all of Africa that democratizes the capacity to process and analyse satellite data. It allows to track changes across Africa in unprecedented detail and will provide data on a vast number of issues, including soil and coastal erosion, agriculture, forest and desert development, water quality, and changes to human settlements. To realise full benefits of an advanced Platform like Digital Earth Africa, Digital Earth Africa has co-designed and co-developed with five institutions namely the Regional Centre For Mapping Of Resources For Development (RCMRD, Kenya), Centre de Suivi Écologique (Senegal), l’observatoire du Sahara et du Sahel (Tunisia), AFRIGIST (Nigeria) and AGRHYMET (Niger). This was meant to ensure it meets end-users needs, this program has been developed by the future deliverers of the program. From the trainers’ perspective, the program is built to consider the recent changes in teaching approaches and methodologies including pedagogy that emerged from a Covid-19, and post Covid-19, pandemic world. On the end-user side, the curriculum covered a wide spectrum of topics, from understanding satellite images, python scripting in the JupyterLab environment to identifying solutions to SDGs challenges through use cases, available in English and French. Digital Earth Africa’s Gender Equity, Diversity and Social Inclusion principles strategy (GEDSI) is imprinted as a watermark across the whole program. It prioritises gender equality, diversity, and social inclusion so that women, people with disabilities and marginalised individuals and communities have the same opportunities to benefit from EO data. In addition, Digital Earth Africa started live virtual sessions, to stay connected with end users, who have developed impactive stories in their communities. Digital Earth Africa seeks to support the capacity development of individuals, academic and governmental institutions, and private sector organisations to empower present and next generation of decision makers to drive toward a sustainable future, leaving on one and place behind.

Estimating the three geophysical variables significant wave height (SWH), sea surface height, and wind speed from satellite altimetry continues to be challenging in the coastal zone because the received radar echoes exhibit significant interference from strongly reflective targets such as mud banks, sheltered bays, ships etc. Fully focused SAR (FF-SAR) processing exhibits a theoretical along-track resolution of up to less than half a metre. This suggests that the application of FF-SAR altimetry might give potential gains over unfocused SAR (UF-SAR) altimetry to resolve and mitigate small-scale interferers in the along-track direction to improve the accuracy and precision of the geophysical estimates. The objective of this study is to assess the applicability of FF-SAR-processed Sentinel-6 Michael Freilich (S6-MF) coastal altimetry data to obtain SWH estimates as close as possible to the coast. We have developed a multi-mission FF-SAR processor and applied the coastal retracking algorithm CORALv2 to estimate SWH. We assess different FF-SAR and UF-SAR processing configurations, as well as the baseline Level-2 product from EUMETSAT, by comparison with the coastal, high-resolution SWAN-Kuststrook wave model from the Deltares RWsOS North Sea operational forecasting system. This includes the evaluation of the correlation, the median offset, and the percentage of cycles with high correlation as a function of distance to the nearest coastline. Moreover, we analyse the number of valid records and the L2 noise of the records. The case study comprises five coastal crossings of S6-MF that are located along the Dutch coast and the German coast along the East Frisian Islands in the North Sea. We find that the FF-SAR-processed dataset with a Level-1b posting rate of 140 Hz shows the greatest similarity with the wave model. We achieve a correlation of ~0.8 at 80% of valid records and a gain in precision of up to 29% of FF-SAR vs UF-SAR for 1-3 km from the coast. FF-SAR shows, for all cycles, a high correlation of greater than or equal to 0.8 for 1-3 km from the coast. We estimate the decay of SWH from offshore at 30 km to up to 1 km from the coast to amount to 26.4% +- 3.1%.

A document by Michael Lockwood. Click on the document to view its contents.

Vegetation turnover time (τ) is a central ecosystem property to quantify the global vegetation carbon dynamics. However, our understanding of vegetation dynamics is hampered by the lack of long-term observations of the changes in vegetation biomass. Here we challenge the steady state assumption of τ by using annual changes in vegetation biomass that derived from remote-sensing observations. We evaluate the changes in magnitude, spatial patterns, and uncertainties in vegetation carbon turnover times from 1992 to 2016. We found that the forest ecosystem is close to a steady state at global scale, contrasting with the larger differences between τ under steady state and τ under non-steady state at the grid cell level. The observation that terrestrial ecosystems are not in a steady state locally is deemed crucial when studying vegetation dynamics and the potential response of biomass to disturbance and climatic changes.

The seismic waves emitted during granular flows are generated by different sources: high frequencies by inter-particle collisions and low frequencies by global motion and large scale deformation. To unravel these different mechanisms, an experimental study has been performed on the seismic waves emitted by dry, dense, quasi-steady granular flows. The emitted seismic waves were recorded using shock accelerometers and the flow dynamics were captured with a fast camera. The mechanical characteristics of the particle collisions were analyzed, along with the intervals between collisions and the correlations in particles’ motion. The high-frequency seismic waves (1-50 kHz) were found to originate from particle collisions and waves trapped in the flowing layer. The low-frequency waves (20-60 Hz) were generated by particles’ oscillations along their trajectories, i.e. from cycles of dilation/compression during coherent shear. The profiles of granular temperature (i.e. the mean squared value of particle velocity fluctuations) and average velocity were measured and related to each other, then used in a simple steady granular flow model, in which the seismic signal consists of the variously attenuated contributions of shear-induced Hertzian collisions throughout the flow, to predict the rate at which seismic energy was emitted. Agreement with the measured seismic power was reasonable, and scaling laws relating the seismic power, the shear strain rate and the inertial number were derived. In particular, the emitted seismic power was observed to be approximately proportional to the root mean square velocity fluctuation to the power $3.1 \pm 0.9$, with the latter related to the mean flow velocity.

Changes in atmospheric iron (Fe) deposition to the open ocean affect net primary productivity, nitrogen fixation, and carbon uptake rates. We investigate the changes in soluble Fe (SFe) deposition from the pre-industrial period to the late 21st century using the EC-Earth3-Iron Earth System model, which stands out for its comprehensive representation of the atmospheric oxalate, sulfate, and Fe cycles. We show how anthropogenic activity has modified the magnitude and spatial distribution of SFe deposition by increasing combustion Fe emissions along with atmospheric acidity and oxalate levels. We find that SFe deposition has doubled since the early Industrial Era using the Coupled Model Intercomparison Project Phase 6 (CMIP6) emission inventory, with acidity being the main solubilization pathway for dust Fe, and ligand-promoted (oxalate) processing dominating the solubilization of combustion Fe. We project a global SFe deposition increase of 40% by the late 21st century relative to present day under Shared Socioeconomic Pathway (SSP) 3-7.0, which assumes weak climate change mitigation policies. In contrast, sustainable and business-as-usual SSPs (1-2.6 and 2-4.5) result in 35% and 10% global decreases, respectively. Despite these differences, SFe deposition consistently increases and decreases across SSPs over the (high nutrient low chlorophyl) equatorial Pacific and Southern Ocean (SO), respectively. Future changes in dust and wildfires with climate remains a key challenge for constraining SFe projections. We show that the equatorial Pacific and the SO would be sensitive not only to changes in Australian or South American dust emissions, but also to those in North Africa.

Graphene or graphene-based nanomaterials have emerged as novel scaffolds for developing robust bio-catalytic systems and a fast-developing promising contender for bioremediation. The interaction of bacteria and graphene is such an elusive issue that its implication in environmental biotechnology is unclear. The complexity and recalcitrant nature of the dyes make the conventional techniques inadequate and remain a challenge for industrial effluent treatment. Many scientists have developed hybrid processes and hybrid materials to enhance the treatment processes to satisfy increasingly stringent laws and criteria related to effluent discharge. The current study explicitly focuses on immobilization and growth of dye-degrading marine bacterial isolates on graphene oxide and their application in methylene blue dye degradation. The synergistic effects of adsorption and biodegradation achieved a unique clean-up performance that the counterpart-free bacteria could not fulfill. Further, toxicity analysis of intermediates also confirmed the non-toxic nature of the intermediates formed after synergistic treatment. This work has the potential to lead to zero effluent treatment processes.

The causes of the variations in CO2 of the past million years remain poorly understood. Imbalances between the input of elements from rock weathering and their removal from the atmosphere-ocean-biosphere system to the lithosphere likely contributed to reconstructed changes. We employ the Bern3D Earth system model of intermediate complexity to investigate carbon-climate responses to step-changes in the weathering input of phosphorus, alkalinity, carbon, and carbon isotope ratio (δ13C) in simulations extending up to 600,000 years. CO2 and climate approach a new equilibrium within a few ten thousand years, whereas the equilibration lasts several hundred thousand years for δ13C. These timescales represent a challenge for the initialization of sediment-enabled models and unintended drifts may be larger than forced signals in simulations of the last glacial-interglacial cycle. Changes in dissolved CO2 change isotopic fractionation during marine photosynthesis and δ13C of organic matter. This mechanism and changes in the organic matter export cause distinct spatio-temporal perturbations in δ13C of dissolved inorganic carbon. A cost-efficient emulator is built with the Bern3D responses and applied in contrasting literature-based weathering histories for the past 800,000 years. Differences between scenarios for carbonate rock weathering reach around a third of the glacial-interglacial CO2 amplitude, 0.05 ‰ for δ13C, and exceed reconstructed variations in marine carbonate ion. Plausible input from the decomposition of organic matter on shelves causes variations of up to 10 ppm in CO2 , 4 mmol m−3 in CO2−3, and 0.09‰ in δ13C. Our results demonstrate that weathering-burial imbalances are important for past climate variations.