Description: Freshwater input from Greenland ice sheet melt has been increasing in the past decades from warming temperatures. To identify the impacts from enhanced meltwater input into the subpolar North Atlantic from 1997 to 2021, we use output from two nearly identical simulations in the eddy-rich model VIKING20X (1/20°) only differing in the freshwater input from Greenland: one with realistic interannually varying runoff increasing in the early 2000s and the other with climatologically (1961–2000) continued runoff. The majority of the additional freshwater remains within the boundary current enhancing the density gradient toward the warm and salty interior waters yielding increased current velocities. The accelerated boundary current shows a tendency to enhanced, upstream shifted eddy shedding into the Labrador Sea interior. Further, the experiments allow to attribute higher stratification and shallower mixed layers southwest of Greenland and deeper mixed layers in the Irminger Sea, particularly in 2015–2018, to the runoff increase in the early 2000s. Key Points The West Greenland Current (WGC) freshens and cools with the observed recent increase in meltwater runoff from Greenland The density gradient across the boundary current intensifies, strengthening the WGC and increasing local eddy formation Enhanced meltwater runoff contributed to an eastward shift in deep convection towards the Irminger Sea (2015–2018) Plain Language Summary Global warming has accelerated the melting of the Greenland ice sheet over the past few decades resulting in enhanced freshwater input into the North Atlantic. The additional freshwater can potentially inhibit deep water formation and have future implications on ocean circulation. To determine the influence from Greenland melt, we compare two high-resolution model experiments all with the same forcing but differing input of Greenland freshwater fluxes from 1997 to 2021. We find that in the experiment with realistically increasing Greenland meltwater, the water becomes fresher and cooler along the continental shelf and boundary of the subpolar gyre. The density difference between the shelf and interior increases with more freshwater, resulting in faster West Greenland Current speeds and enhanced eddy formation. Deeper mixed layers are found in the eastern Irminger Sea, particularly in 2015–2018. From 2009 to 2013, there were shallower mixed layers in the Labrador Sea where less Greenland meltwater was mixed downwards and spread eastward, causing mixed layers to deepen in the Irminger Sea.

Description: Submarine groundwater discharge (SGD) is a globally important process supplying nutrients and trace elements to the coastal environment, thus playing a pivotal role in sustaining marine primary productivity. Along with nutrients, groundwater also contains allochthonous microbes that are discharged from the terrestrial subsurface into the sea. Currently, little is known about the interactions between groundwater‐borne and coastal seawater microbial populations, and groundwater microbes' role upon introduction to coastal seawater populations. Here, we investigated seawater microbial abundance, activity and diversity in a site strongly influenced by SGD. In addition, through laboratory‐controlled bottle incubations, we mimicked different mixing scenarios between groundwater and seawater. Our results demonstrate that the addition of 0.1 μm filtered groundwater stimulated heterotrophic activity and increased microbial abundance compared to control coastal seawater, whereas 0.22 μm filtration treatments induced primary productivity and Synechococcus growth. 16S rRNA gene sequencing showed a strong shift from a SAR11‐rich community in the control samples to Rhodobacteraceae dominance in the 〈0.1 μm treatment, in agreement with Rhodobacteraceae enrichment in the SGD field site. These results suggest that microbes delivered by SGD may affect the abundance, activity and diversity of intrinsic microbes in coastal seawater, highlighting the cryptic interplay between groundwater and seawater microbes in coastal environments, which has important implications for carbon cycling. Plain Language Summary Submarine groundwater discharge (SGD) is an important process where groundwater flows into the ocean along the coast. When the groundwater mixes with seawater, the microbes from both sources interact with each other, which can impact the diversity, activity, and amount of microbes in the coastal environment. Currently, little is known about how groundwater‐borne microbes affect marine microbial populations. Our research shows that when groundwater microbes are removed before mixing groundwater with seawater, the abundance and activity of certain microbes that consume organic matter significantly increase. Additionally, we noticed a significant difference in the types of microbes present between the sites where SGD occurs versus background (uninfluenced) coastal water, especially in terms of the microbes that consume organic matter. Overall, this study suggests that there is a connection between groundwater and seawater microbes, which can influence the delicate balance between organisms that produce carbon and those that consume it. This has important implications for how carbon cycles globally. Key Points Groundwater discharge into the coastal zone delivers both nutrients and allochthonous microbes Groundwater microbes interact with seawater populations, by which affecting the delicate autotroph‐heterotroph balance Subterranean microbial processes are key drivers of food webs, potentially affecting biogenic carbon fluxes in the ocean

Description: To reach their net-zero targets, countries will have to compensate hard-to-abate CO2 emissions through carbon dioxide removal (CDR). Yet, current assessments rarely include socio-cultural or institutional aspects or fail to contextualize CDR options for implementation. Here we present a context-specific feasibility assessment of CDR options for the example of Germany. We assess 14 CDR options, including three chemical carbon capture options, six options for bioenergy combined with carbon capture and storage (BECCS), and five options that aim to increase ecosystem carbon uptake. The assessment addresses technological, economic, environmental, institutional, social-cultural and systemic considerations using a traffic-light system to evaluate implementation opportunities and hurdles. We find that in Germany CDR options like cover crops or seagrass restoration currently face comparably low implementation hurdles in terms of technological, economic, or environmental feasibility and low institutional or social opposition but show comparably small CO2 removal potentials. In contrast, some BECCS options that show high CDR potentials face significant techno-economic, societal and institutional hurdles when it comes to the geological storage of CO2. While a combination of CDR options is likely required to meet the net-zero target in Germany, the current climate protection law includes a limited set of options. Our analysis aims to provide comprehensive information on CDR hurdles and possibilities for Germany for use in further research on CDR options, climate, and energy scenario development, as well as an effective decision support basis for various actors. Key Points: - More context-specific assessments of carbon dioxide removal (CDR) options are needed to guide national net-zero decision making - Ecosystem-based CDR options with comparably low implementation hurdles in Germany show relatively small CO2 removal potentials - High CDR potential options in Germany face high institutional, technological and societal hurdles linked in many ways to geological storage

Description: The study of offshore freshened groundwater (OFG) is gaining importance due to population growth and environmental pressure on coastal water resources. Marine controlled source electromagnetic (CSEM) methods can effectively map the spatial extent of OFG systems using electrical resistivity as a proxy. Integrating these resistivity models with sub-surface properties, such as host-rock porosity, allows for estimates of pore-water salinity. However, evaluating the uncertainty in pore-water salinity using resistivity models obtained from deterministic inversion approaches presents challenges, as they provide only one best-fit model, with no associated estimate of uncertainty. To address this limitation, we employ trans-dimensional Markov-Chain Monte-Carlo inversion on marine time-domain CSEM data, acquired in the Canterbury Bight, New Zealand. We integrate resistivity posterior probability distributions with borehole and seismic reflection data to estimate pore-water salinity with corresponding uncertainty estimates. The results highlight a low-salinity groundwater body in the center of the survey area, hosted by consecutive silty- and fine-sand layers approximately 20–60 km from the coast. The posterior probability distribution of resistivity models indicates freshening of the OFG body toward the shoreline within a permeable, coarse-sand layer 40–150 m beneath the seafloor, suggesting an active connection between the OFG body and the terrestrial groundwater system. The approach demonstrates how Bayesian inversion constrains the uncertainties in resistivity models and subsequently in pore-water salinity estimates. Our findings highlight the potential of Bayesian inversion to enhance our understanding of OFG systems and provide uncertainty constraints for hydrogeological modeling, thereby contributing to sustainable water resource development. Key Points A Bayesian workflow is employed to evaluate uncertainty in pore-water salinity estimates Offshore groundwater in Canterbury Bight stores freshened pore-water in fine-grained sediments, likely extending from the onshore aquifer Correlation between pore-water salinities and seismic-derived stratigraphy provides boundary conditions for hydrogeological modeling Plain Language Summary Geophysical methods that measure the electromagnetic properties of the Earth are effective in investigating freshwater sources beneath the seafloor. By combining the geophysical and geological information, we can better assess the quality of this groundwater. In this study, we develop a workflow that uses statistical methods to integrate electromagnetic observations with borehole and acoustic measurements along the eastern coast of the South Island of New Zealand. We aim to improve our understanding of the groundwater quality beneath the seafloor. Our research confirms the presence of freshened groundwater within the sandy seafloor up to 60 km from the coastline. Importantly, our observations indicate that the groundwater quality increases toward the coast. These findings are significant as they enhance the hydrogeological modeling of the groundwater system and suggest its potential as a source of freshwater.

Description: Although marine controlled source electromagnetic (CSEM) methods are effective for investigating offshore freshened groundwater (OFG) systems, interpreting the spatial extent and salinity of OFG remains challenging. Integrating CSEM resistivity models with information on sub-surface properties, such as host-rock porosity, allows for estimates of pore-water salinity. However, deterministic inversion approaches pose challenges in quantitatively analyzing these estimates as they provide only one best-fit model with no associated estimate of model parameter uncertainty. To address this limitation, we employ a trans-dimensional Markov-Chain Monte-Carlo inversion on marine CSEM data, under the assumption of horizontal stratification, collected from the Canterbury Bight, New Zealand. We integrate the resulting posterior distributions of electrical resistivity with borehole and seismic reflection data to quantify pore-water salinity with uncertainty estimates. The results reveal a low-salinity groundwater body in the center of the survey area at varying depths, hosted by consecutive silty- and fine-sand layers approximately 20 to 60 km from the coast. These observations support the previous study’s results obtained through deterministic 2-D inversion and suggest freshening of the OFG body closer to the shore within a permeable, coarse-sand layer 40 to 150 m beneath the seafloor. This implies a potential active connection between the OFG body and the terrestrial groundwater system. We demonstrate how the Bayesian approach constrains the uncertainties in resistivity models and subsequently in pore-water salinity estimates. Our findings highlight the potential of Bayesian inversions in enhancing our understanding of OFG systems, providing crucial boundary conditions for hydrogeological modeling and sustainable water resource development.

Description: Mineral dust is one of the most abundant atmospheric aerosol species and has various far-reaching effects on the climate system and adverse impacts on air quality. Satellite observations can provide spatio-temporal information on dust emission and transport pathways. However, satellite observations of dust plumes are frequently obscured by clouds. We use a method based on established, machine-learning-based image in-painting techniques to restore the spatial extent of dust plumes for the first time. We train an artificial neural net (ANN) on modern reanalysis data paired with satellite-derived cloud masks. The trained ANN is applied to cloud-masked, gray-scaled images, which were derived from false color images indicating elevated dust plumes in bright magenta. The images were obtained from the Spinning Enhanced Visible and Infrared Imager instrument onboard the Meteosat Second Generation satellite. We find up to 15% of summertime observations in West Africa and 10% of summertime observations in Nubia by satellite images miss dust plumes due to cloud cover. We use the new dust-plume data to demonstrate a novel approach for validating spatial patterns of the operational forecasts provided by the World Meteorological Organization Dust Regional Center in Barcelona. The comparison elucidates often similar dust plume patterns in the forecasts and the satellite-based reconstruction, but once trained, the reconstruction is computationally inexpensive. Our proposed reconstruction provides a new opportunity for validating dust aerosol transport in numerical weather models and Earth system models. It can be adapted to other aerosol species and trace gases. Key Points: - We present the first fast reconstruction of cloud-obscured Saharan dust plumes through novel machine learning applied to satellite images - The reconstruction algorithm utilizes partial convolutions to restore cloud-induced gaps in gray-scaled Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager Dust RGB images - World Meteorological Organization dust forecasts for North Africa mostly agree with the satellite-based reconstruction of the dust plume extent