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Solar-induced fluorescence (SIF) is a proxy of ecosystem photosynthesis that often scales linearly with gross primary productivity (GPP) at the canopy scale. However, the mechanistic relationship between GPP and SIF is still uncertain, especially at smaller temporal and spatial scales. We deployed a ultra-hyperspectral imager over two grassland sites in California throughout a soil moisture dry down. The imager has high spatial resolution that limits mixed pixels, enabling differentiation between plants and leaves within one scene. We find that imager SIF correlates well with diurnal changes in leaf-level physiology and gross primary productivity under well-watered conditions. These relationships deteriorate throughout the dry down event. Our results demonstrate an advancement in SIF imaging with new possibilities in remotely sensing plant canopies from the leaf to the ecosystem. These data can be used to resolve outstanding questions regarding SIF's meaning and usefulness in terrestrial ecosystem monitoring.

Juno has allowed clear, high‐resolution imaging of Io's polar volcanoes using the Jovian Infrared Auroral Mapper (JIRAM) instrument. We have used data from JIRAM's M‐band (4.78 μm) imager from 11 Juno orbits to construct a global map of volcanic flux. This map provides short‐term insight into the spatial distribution of volcanoes and the ways in which high‐ and low‐latitude volcanoes differ. Using spherical harmonic analysis, we quantitatively compare our volcanic flux map to the surface heat flow distribution expected from models of Io's tidal heat deposition (summarized in de Kleer, Park, et al. (2019, https://doi.org/10.26206/d4wc‐6v82). Our observations confirm previously detected systems of bright volcanoes at high latitudes. Our study finds that both poles are comparably active and that the observed flux distribution is inconsistent with an asthenospheric heating model, although the south pole is viewed too infrequently to establish reliable trends. Plain Language Summary: Our study uses data from an infrared camera on Juno called the Jovian Infrared Auroral Mapper (JIRAM) to image Io, the innermost Galilean moon of Jupiter. Io is the most volcanically active body in the Solar System. Io's volcanoes are powered by both the extreme tides from Jupiter and the gravitational interactions between it and Jupiter's other moons. These tides generate friction inside Io. Simulations of Io's interior suggest that, depending on how deep that friction is being generated, the surface heat flow will be higher in certain areas (de Kleer, Park, et al., 2019, https://doi.org/10.26206/d4wc‐6v82). Using JIRAM, we have mapped where volcanoes are producing the most power and compared that to where we expect higher heat flow from the interior models. Our map doesn't agree with any of these models very well. JIRAM observed more volcanic activity at the poles than we expected to see based on previous observations. However, since the south pole was only observed twice, it's possible that these observations don't represent the average volcanic activity of the south pole. Very bright volcanoes that may have been continuously active for decades were also imaged during these Juno fly‐bys, some of which are nearer the poles than the equator. Key Points: We have produced a global volcanic flux map of Io using data from 11 Juno fly‐bysOur flux map doesn't agree well with tidal heat flow models reported in de Kleer, Park, et al. (2019, https://doi.org/10.26206/d4wc‐6v82)The M‐band flux is anti‐correlated with the asthenospheric heating model and has only very weak agreement with the global magma ocean model [ABSTRACT FROM AUTHOR]

The lack of high vertical resolution atmospheric thermodynamic structure observations inside or near major weather events impedes our understanding of physical processes and their predictability in numerical weather prediction (NWP) models. Airborne Global Navigation Satellite System (GNSS) radio occultation (airborne radio occultation [ARO]) has proven to be a viable remote sensing option to offer dense soundings near flight tracks. The global fleet of commercial aircraft already equipped with GNSS receivers could be leveraged to produce an unprecedented number of ARO soundings along global flight paths. Eleven cases of atmospheric bending angle and refractivity profiles were successfully retrieved and compared with the colocated European Center for Medium‐Range Weather Forecasting global reanalysis data. Good quality measurements are obtained with median refractivity differences less than 1% in the middle and upper troposphere, between 5.5 and 11.5 km. Given the use of aircraft data (e.g., Aircraft Meteorological DAta Relay) for data assimilation, incorporating ARO profiles would be a valuable addition, further enhancing the accuracy of aviation and weather forecasts. Plain Language Summary: Global Navigation Satellite System radio occultation (RO) can be used to obtain vertical profiles of atmospheric conditions. This study shows how position data from commercial aircraft with existing hardware can be used to retrieve vertical profiles of atmospheric thermodynamic conditions (such as temperature, pressure, and humidity). Preliminary results show that high‐quality RO profiles can be extracted from unaltered aircraft information with low biases in the middle‐ and upper‐troposphere. The large number of commercial aircraft fleets could offer dense thermodynamic profiles in addition to the in‐situ observations to be operationally assimilated into global numerical weather prediction models, and greatly enhance regional model forecasts. Key Points: For the first time, Global Navigation Satellite System (GNSS) radio occultation (RO) profiles from commercial aircraft with no purpose‐built science payload were extractedGood quality RO profiles are retrievable from commercial closed‐loop GNSS receivers onboard aircraft between flight level and 4 km above mean‐sea‐levelCommercial aircraft could offer thermodynamic profiles along with observations to be assimilated into numerical weather prediction models, and enhance forecasts [ABSTRACT FROM AUTHOR]

We investigate whether geomorphic signatures of permafrost are embedded in planforms of river meanders, and we inquire as to how physical factors unique to permafrost environments are able to affect their dynamics. By exploiting satellite imagery, a data set of 19 freely‐meandering Arctic rivers is compared against an independent data set of 23 freely‐meandering streams flowing through temperate and tropical regions. Suitable dimensionless metrics are defined to characterize morphometric properties of meanders in terms of the spatio‐temporal distribution of curvature and channel width. Results show the absence of marked contrasts in the amplitude of bend‐curvature between the two data set. Differently, we find a permafrost signature in the channel width response, which manifests itself through larger values of the average bend‐width and by peaks of width fluctuations. Field data suggest that permafrost meanders tend to widen for increasing bend sinuosity, likely promoting a shift of their morphodynamic regime as final cutoff is approached. Plain Language Summary: One of the most striking impacts of climate warming in the Arctic region is permafrost thaw. Arctic rivers typically flow through perennially‐frozen floodplains, thus they are particularly susceptible to ground thawing. In order to understand the response of Arctic rivers to climate variability, basic knowledge about key differences with respect to non‐permafrost streams is needed. Despite recent studies which have emphasized the slower yearly movement rates distinguishing Arctic streams, we still do not understand whether permafrost‐affected rivers show distinctive features in their morphology due to specific physical mechanisms. By exploiting satellite imagery, we show that permafrost leaves a signature in the shape of meandering Arctic rivers. Specifically, their average bend‐width increases as sinuosity develops, while the amplitude of width oscillations is larger than that displayed by their non‐permafrost kin. Key Points: Permafrost is found to leave a morphological signature in the spatio‐temporal signal of channel width at the bend scalePermafrost meander bends show larger amplitude of width oscillations and widen as sinuosity increasesBend curvature as a standalone indicator does not provide evidence of any significant permafrost‐fingerprint [ABSTRACT FROM AUTHOR]

Severe storms produce ocean waves with periods of 18–26 s, corresponding to wavelengths 500–1,055 m. These waves radiate globally as swell, generating microseisms and affecting coastal areas. Despite their significance, long waves often elude detection by existing remote sensing systems when their height is below 0.2 m. The new Surface Water Ocean Topography (SWOT) satellite offers a breakthrough by resolving these waves in global sea level measurements. Here we show that SWOT can detect 25‐s waves with heights as low as 3 cm, and resolves period and direction better than in situ buoys. SWOT provides detailed maps of wave height, wavelength, and direction across ocean basins. These measurements unveil intricate spatial patterns, shedding light on wave generation in storms, currents that influence propagation, and refraction, diffraction and reflection in shallow regions. Notably, the magnitude of reflections exceeds previous expectations, illustrating SWOT's transformative impact. Plain Language Summary: Wind storms at sea make waves that increase in size with wind speed, and with the distance over which the high winds have been able to amplify the waves. Once generated these waves propagate as swell around the world ocean: in that stage the wave period remains constant while the wave height decay away from the source. Waves with periods longer than 18 s are relatively infrequent, but they are an important source of seismic waves and coastal impacts. However, current remote sensing techniques miss long waves under 0.2 m high. The Surface Water Ocean Topography (SWOT) satellite mission changes this, spotting 25‐s waves with heights as low as 3 cm. SWOT maps wave height, wavelength, and direction worldwide, revealing the influence of winds, currents and water depth. For example, We found stronger than expected coastal reflection, which will help revise wave forecasting models and their application in seismology. Key Points: Surface Water Ocean Topography (SWOT) data provide the first open ocean spatial measurements of phase‐resolved swells with wavelength 500–1,050 mSwells with heights as low as 3 cm are well detected by SWOT, allowing tracking across oceansSwell reflection off the coast can be separated from incident waves [ABSTRACT FROM AUTHOR]

Sedimentary landforms on Earth and other planetary bodies are built through scour, transport, and deposition of sediment. Sediment connectivity refers to the hypothesis that pathways of sediment transport do not occur in isolation, but rather are mechanistically linked. In dryland river systems, one such example of sediment connectivity is the transport of fluvially deposited sediment by wind. However, predictive tools that can forecast fluvial‐aeolian sediment connectivity at meaningful scales are rare. Here we develop a suite of models for quantifying the availability of river‐sourced sediment for aeolian transport as a function of river flow, wind regime, and land cover across 168 km of the Colorado River in Grand Canyon, USA. We compare and validate these models using topographic changes observed over 10 years in a coupled river sandbar‐aeolian dunefield setting. The models provide a path forward for directly linking fluvial hydrology with the management and understanding of aeolian landscapes. Plain Language Summary: Landscapes on Earth are built from sediment, which is deposited by a variety of processes, especially water and wind. In rivers located in dry regions, sediment is deposited by flowing water during river floods. Once this sediment dries, it is often then moved by wind across the landscape. Although we frequently observe this sediment connectivity between water and wind, we are not currently able to predict when and where windblown transport of river‐deposited sediment may occur. We used maps of sand in Grand Canyon along 168 km of the Colorado River, along with records of river flow, wind speed, and vegetation cover, to develop four models that predict how much sediment is available for wind to move. We compared the results of these models to actual windblown sediment transport at a study site in Grand Canyon, finding that when models predicted more sand was available at the site, we also observed more landscape changes there which were due to wind. Our research makes it possible to understand how changes in river flows affect the amount of sediment moved by wind. Key Points: We developed four methods for modeling the extent of exposed, bare sand available for windblown transport in river valleysOur models employ data on river flow, land cover, wind velocity, and vegetation to predict exposed, bare sand extent at corridor scalesWhere models predict increased areal extent of bare sand, we also observe a greater volume of topographic changes driven by wind [ABSTRACT FROM AUTHOR]

Satellite imagery provides a global perspective for studying river hydrology and water quality, but clouds remain a fundamental limitation of optical sensors. Explicit studies of this problem were limited to specific locations or regions. In this study, we characterize the global severity of this limitation by analyzing 22 years of daily satellite cloud cover data and modeled river discharge for a global sample 21,642 river reaches of diverse sizes and climates. Our results show that the bias in observed river discharge is highly organized in space, particularly affecting Tropical and Arctic rivers. Given the fundamental nature of this cloud limitation, optical satellites will always provide a biased representation of river conditions. We discuss several strategies to mitigate bias, including modeling, data fusion, and temporal averaging, yet these methods introduce their own challenges and uncertainties. Plain Language Summary: This study examines how optical satellite imagery, which is vital for understanding rivers worldwide, is hindered by clouds specifically when observing rivers. We analyzed 22 years of daily data covering 21,642 sections of rivers of various sizes and climates. Our findings reveal that cloud cover significantly biases the distribution of river discharges we observe, especially for Tropical and Arctic rivers. This means that satellite images do not represent river conditions accurately. Our research provides the first comprehensive analysis of its extent and impact. Ultimately, while satellite technology continues to improve, clouds remain a challenge in obtaining precise river data, highlighting the need for innovative solutions. Key Points: We evaluated the impact of cloud cover on satellite optical remote sensing across a global sample of river reachesDirect optical satellite observations can present a highly biased, and geographically variable, view of river conditionsSeasonal relationships of clouds and discharge predict the ability of optical satellites to observe the distribution of river discharge [ABSTRACT FROM AUTHOR]

Despite in‐situ observations of perennial firn aquifers (PFAs) at specific locations of the Antarctic ice sheet, a comprehensive continent‐wide mapping of PFA distribution is currently lacking. We present an estimate of their distribution across Antarctica in the form of a probability assessment using a Monte Carlo technique. Our approach involves a novel methodology that combines observations from Sentinel‐1 and Advanced SCATterometer (ASCAT) with output from a regional climate model. To evaluate our method, we conduct an extensive comparison with Operation Ice Bridge observations from the Greenland Ice Sheet. Application to Antarctica reveals high PFA probabilities in the Antarctic Peninsula (AP), particularly along its northern, northwestern, and western coastlines, as well as on the Wilkins, Müller, and George VI ice shelves. Outside the AP, PFA probability is low, except for some locations with marginally higher probabilities, such as on the Abbot, Totten, and Shackleton ice shelves. Plain Language Summary: We explore the presence of subsurface water storage within the firn layer in Antarctica, known as perennial firn aquifers (PFA), using a new method that combines satellite data and climate models. These PFAs, previously identified in Greenland, store and transmit meltwater, and could influence ice‐sheet behavior. Our study maps the likelihood of PFAs across Antarctica, finding high probabilities along the Antarctic Peninsula's northern and western coasts, as well as on specific ice shelves. Outside this region, PFAs are unlikely, except for a few spots with somewhat enhanced probability on Abbot, Totten, and Shackleton ice shelves. This inventory enhances our understanding of Antarctic hydrology and has broader implications for understanding ice‐sheet dynamics. Key Points: An innovative aquifer detection method, combining satellite and regional climate model data, is applied to AntarcticaThe evaluation of the methodology in Greenland, showing 91% correspondence, suggests good adaptability for AntarcticaThe Antarctic Peninsula stands out as the only region with high aquifer probability; in the rest of Antarctica, the likelihood is low [ABSTRACT FROM AUTHOR]

Terminus change is a complex outcome of ice‐ocean boundary processes and poses challenges for ice sheet models due to inadequate calving laws, creating uncertainty in sea level change projections. To address this, we quantify glacier termini sinuosity and convexity, testing the hypothesis that terminus morphology reflects dominant calving processes. Using 10 glaciers with diverse calving styles in Greenland over the period from 1985 to 2021, we establish a supervised classification of calving style by comparing morphology and literature‐derived calving observations. Validation with four of these glaciers and flotation conditions and subglacial discharge routing observations confirms concave, smooth termini indicate buoyant flexure dominated‐calving, while convex, sinuous termini suggest serac failure dominated‐calving. We also identify a mixed style where both calving types may occur. We use these classes to label calving style from 1985 to 2021 for all 10 glaciers and explore how this changes over time as glaciers retreat. Plain Language Summary: The ice‐ocean boundary (terminus) of the Greenland ice sheet changes over space and time due to the melt and calving of ice. There is a range of calving behaviors due to many environmental and geometric factors, including changing ocean temperatures, glacier melting, and the geometry of the glacier. This makes representing how these boundary changes in ice sheet models are difficult, leading to uncertainties in predicting sea level rise from retreating glaciers. The study focuses on the shape (morphology) of 10 glaciers from 1985 to 2021, finding that smooth, concave termini indicate a type of calving related to the flotation of the glacier, while sinuous, convex termini indicate melt‐dominated calving. The research helps classify calving styles and understand how they change over time as glaciers retreat. Key Points: Two calving styles are linked to distinct terminus morphologies but both can exist for a single glacier (over time or space)Mixed styles of calving are more difficult to classifyClassifying terminus morphology over time can aid in identifying how terminus calving mechanisms change over time [ABSTRACT FROM AUTHOR]

Phytoplankton sustains marine ecosystems and influences the global carbon cycle. This study analyzes trends in surface chlorophyll‐a concentration (Schl), a proxy for phytoplankton biomass, using six of the most widely used merged satellite products. Significant regional variations are observed, with contrasting trends observed among different products. To assess if these trends can be related to changes in the environment or to bias in radiometric products, a convolutional neural network is used to examine the relationship between physical ocean variables versus Schl. The results suggest that the merging algorithm of Globcolour‐GSM is not reliable for trend detection and that observed changes in Schl after 2012/2016 are not supported by changes of the physical ocean. These results emphasize the need for careful interpretation of satellite‐derived Schl trends and highlight the potential of machine learning in understanding the complex interactions in marine ecosystems. Plain Language Summary: This study investigates trends in surface concentration of chlorophyll‐a (Schl), a proxy of phytoplankton biomass in the ocean. Our study reveals different trends according to the considered satellite products, challenging previous assumptions relating some Schl negative trends to the anthropogenic signal. To investigate the reliability of these trends, a machine learning approach is applied, showing that the observed changes in Schl trends may not always be related to changes in some physical ocean properties but sometimes to sensor drift. These findings underscore the importance of considering multiple factors when interpreting Schl trends and highlight the potential of advanced statistical tools in understanding marine ecosystems. Key Points: An analysis of remotely sensed surface chlorophyll‐a concentration reveals contrasted trends between available merged productsA convolutional neural network can effectively reconstruct surface chlorophyll‐a concentration from the properties of the ocean surfaceRecent trends in surface chlorophyll‐a concentration merged products are not all supported by changes in properties of the ocean surface [ABSTRACT FROM AUTHOR]

Grid‐ or pixel‐based models, used across various scientific disciplines from microscopic to planetary scales, contain an unquantified error that bias our interpretation of the data. The error is produced by projecting 3D data onto a 2D grid. For Digital Terrain Models (DTMs) the projection error affects all slope‐dependent topographic metrics, like surface area or slope angle. Due to the proportionality of the error to the cosine of the slope, we can correct for it. We quantify the error and test the correction using synthetic landscapes for which we have analytical solutions of their metrics. Application to real‐world landscapes in California, reveal the systematic underestimation of surface area by up to a third, and mean slope angles by up to 10° in steep topography in current DTMs. Correcting projection errors allow for true estimates of surface areas and slope distributions enabling physics‐based models of surface processes at any spatial scale. Plain Language Summary: Hiking up a steep mountain slope feels longer than the horizontal distance measured on a map. The slope angle is calculated by taking the height over the horizontal distance. The length of a (mountain) slope is always greater than the horizontal plane. This mistake is also very common in Digital Terrain Models (DTMs). Here landscapes images captured in a bird's eye view are projected onto horizontal gridded surfaces. In the model, each grid cell has the same length and contains the height of the landscape. Due to the view, the slopes are only represented by the horizontal distance, which is shorter. We call this the projection error. Because the error depends on the slope, we use it to fix it. We test the correction on different landscapes with steep and gentle topography. We find that uncorrected models underestimate surface area and length by up to a third. Fixing the projection error shows that mountain slopes are much steeper and longer than usually reported. Knowing the true length of a mountain slope, we can use the topographic data to better understand and maybe predict processes and volumes, like landslides or fluid transport. Key Points: The projection error in gridded models biases our view of surface topography at all scalesCorrecting for the projection error allows exploring physics‐based erosion and transport lawsThe correction enables better sourcing of the topographic data we have access to [ABSTRACT FROM AUTHOR]

The distribution of tropical cyclone (TC) eye cloud heights is documented for the first time using compact Raman lidar (CRL) measurements with high spatial resolution. These cloud heights act as tracers for low‐level vertical mixing in the eye region. Cloud height distributions using all available data from nine Atlantic TCs in 2021 and 2022 show significant vertical variance, dispelling the notion of a flat stratiform eye cloud deck. Eye cloud widths are multiscale, with shallow convective clouds dominating CRL returns. Data from Hurricane Sam (2021) highlight the evolution of shallow convective clouds in the TC eye and their associated temperature inversions. The frequent appearance of convective eye clouds, along with observed vertical wind fluctuations, suggests that vertical mixing from the boundary layer frequently occurs in the TC eye, even beneath strong inversions. This strong vertical mixing should be accurately portrayed by TC simulations and forecasts. Plain Language Summary: While the temperature inversion and associated clouds within the tropical cyclone (TC) eye region are essential elements of storm structure, observing eye cloud heights remains a challenge, and the vertical distribution of these clouds is poorly constrained. This study documents TC eye cloud characteristics using a new aircraft‐based remote sensing technique for the first time, with a focus on cloud height distributions. Both case study and statistical approaches confirm that most of these clouds are convective in nature, not stratiform. The formation of shallow convective clouds within TC eyes is driven by shallow upward mixing as documented by aircraft flight level vertical wind measurements. Vertical mixing across the eye inversion layer changes inner core thermodynamics, highlights a potential pathway for intensity change, and emphasizes why this process must be captured in high resolution TC forecast models. Key Points: New, aircraft‐based compact Raman lidar cloud, temperature, and water vapor measurements detail tropical cyclone inner core structureCase studies and statistics demonstrate how shallow convective clouds with high spatial variability are the most common eye cloud typeVertical velocity and cloud height analyses suggest that low level vertical mixing drives shallow convective eye cloud formation [ABSTRACT FROM AUTHOR]

This study assesses a 40‐day 3.25‐km global simulation of the Simple Cloud‐Resolving E3SM Model (SCREAMv0) using high‐resolution ground‐based observations from the Atmospheric Radiation Measurement (ARM) Green Ocean Amazon (GoAmazon) field campaign. SCREAMv0 reasonably captures the diurnal timing of boundary layer clouds yet underestimates the boundary layer cloud fraction and mid‐level congestus. SCREAMv0 well replicates the precipitation diurnal cycle, however it exhibits biases in the precipitation cluster size distribution compared to scanning radar observations. Specifically, SCREAMv0 overproduces clusters smaller than 128 km, and does not form enough large clusters. Such biases suggest an inhibition of convective upscale growth, preventing isolated deep convective clusters from evolving into larger mesoscale systems. This model bias is partially attributed to the misrepresentation of land‐atmosphere coupling. This study highlights the potential use of high‐resolution ground‐based observations to diagnose convective processes in global storm resolving model simulations, identify key model deficiencies, and guide future process‐oriented model sensitivity tests and detailed analyses. Plain Language Summary: This research examines how well a kilometer grid scale global atmospheric model—the Simple Cloud‐Resolving Energy Exascale Earth System Model (SCREAMv0)—performs in simulating clouds and rainfall over the Amazon rainforest region. The model was assessed by comparing to high‐resolution ground‐based observations from the Green Ocean Amazon field campaign supported by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program. The model struggles to produce enough middle‐level clouds. When comparing the simulated rainfall to radar observations, SCREAMv0 showed good performance on the diurnal pattern of rain rate, but tends to form too many small rain clusters while failing to create large ones. A possible contributor to these errors could be the inaccurate depiction of how the earth's surface and the atmosphere interact within the model. Overall, this study shows that using detailed DOE ARM data can help improve our understanding of clouds and rainfall in global storm resolving kilometer grid scale models. Key Points: Convective processes in a global storm resolving model (SCREAMv0) are evaluated using ground‐based observations over a tropical rainforestSCREAMv0 captures the morning development of shallow convection and the early afternoon precipitation peak but lacks mid‐level congestusSCREAMv0 struggles to form large precipitation clusters greater than 128 km and produces smaller ones more often than observed [ABSTRACT FROM AUTHOR]

A THz radar, with its wide bandwidth, is capable of high‐resolution imaging down to the centimeter scale. In this study, a THz radar is applied to detect hydrometeors generated in a spray chamber. The observed backscattering signals show fluctuations at centimeter scales, indicating various hydrometeor distribution patterns along the radar beam. A co‐located High‐Speed Imaging (HSI) sensor is used to measure the Drop Size Distributions (DSD) in the spray chamber. The radar sampling beam is well aligned with the HSI probes, allowing an objective comparison between the remote sensing and in situ observations. In this study, the observed radar power is compared with the power estimated from the HSI measurements. Results show great consistency, with power difference smaller than 0.5 dB. This study demonstrates the feasibility and great potential of using a THz radar for ultra‐high‐resolution observations of clouds in a laboratory facility, and in the real atmosphere. Plain Language Summary: An improved understanding of how precipitation forms in clouds calls for new observational instruments and novel measurement strategies. Radars operating at ultra‐high frequency (a.k.a. THz radars) are known for their unprecedented resolution down to centimeter scale. Here, for the first time, we applied a THz radar to detect hydrometeors generated in a spray chamber. An additional in situ probe is utilized within the chamber to validate the THz radar observations. The radar beam is directed through the same volume that is sampled by the in situ probe, thus enabling an objective comparison between the in situ and remote sensing measurements. Different hydrometeor distributions are generated in the chamber, each being jointly observed by the radar and the in situ probes. Results show that the THz radar measurements agree well with the radar power calculated from the in situ observations. This unique experiment indicates promising scenarios of applying THz radar to cloud and precipitation studies in a laboratory facility and in the real atmosphere. Key Points: A novel experiment is conducted by applying a THz radar to detect hydrometeors generated in a spray chamberGood agreement between in situ and radar observations demonstrates the unique capability of THz radar for cloud and precipitation studiesImplications of applying THz radar to design novel observational strategies for solving fundamental gaps in cloud physics are demonstrated [ABSTRACT FROM AUTHOR]

Source‐to‐sink transfer of sediment and organic carbon (OC) is regulated by river mobility. Quantifying trends in river mobility is, however, challenging due to diverse planform morphologies (e.g., meandering, braided) and measurement methods. Here, we utilize a remote‐sensing method applicable to all planform morphologies to quantify the mobility timescales of 80 rivers worldwide. Results show that, across the continuum from meandering to braided rivers, there is a systematic reduction in the timescales of channel mobility and—to a lesser extent—floodplain reworking. This leads to a decrease in the efficiency with which braided rivers rework old floodplain material compared to their meandering counterparts. Reduced floodplain reworking efficiency of braided rivers leads to smaller channel‐belt areas relative to their size. Results suggest that river‐mobility timescales derived from remote sensing can aid in the characterization of sediment and OC storage and transit times at a global scale. Plain Language Summary: Rivers transport sediment and organic carbon (OC) from the mountains to the sea, and river movement affects sediment and OC transit times in landscapes. Understanding the controls on timescales of river movement is critical for assessing how fluvial processes influence the terrestrial carbon cycle. While previous work has quantified different river planform shapes on the Earth's surface (e.g., meandering, wandering, and braided), little is known about how the pace and nature of river movement is different between river planform shapes. This is because we lack methods to measure and compare river movement across different river planform shapes. Here, we leverage a tool to quantify river movement from time series of satellite imagery that is equally applicable to all river planform shapes. We apply this method to calculate the timescales of river movement of 80 rivers worldwide for the last 37 years. We show that (a) braided rivers migrate faster than meandering rivers, and (b) braided rivers move over a smaller area relative to their size. Comparing results with geochemical observations from the Río Bermejo—a well‐studied river for sediment‐OC cycling—we show that our mobility framework may provide a new way to assess sediment and OC storage timescales in floodplains. Key Points: We quantify timescales of floodplain reworking and channel‐overlap decay from satellite imagery of 80 rivers worldwideFrom meandering to braided planform morphology, there is a reduction in the overlap timescale and the reworking timescaleMobility timescales provide a means for remotely quantifying terrestrial‐sediment and organic‐carbon storage and transit [ABSTRACT FROM AUTHOR]

Global warming has intensified the intensity of compound drought‐hot extremes (CDHEs), posing more severe impacts on human societies and ecosystems than individual extremes. The vulnerability of global terrestrial ecosystems under CDHEs, along with its key influencing factors, remains poorly understood. Based on multiple remote sensing data, we construct a Vine Copula model to appraise vegetation vulnerability under CDHEs, and attribute it to climatic and biotic factors for five different vegetation types. High vulnerability is detected in central and southern regions of North America, eastern and southern regions of South America, Southern Africa, northern and western Europe, and northern and eastern Australia. The drier the climate, the higher will be the vulnerability. Furthermore, biodiversity and biomass are key biotic factors influencing the vulnerability of various vegetation types, such that ecosystems with richer biodiversity and higher biomass have lower vulnerability to CDHEs. The findings deepen understanding of terrestrial ecosystem response to CDHEs. Plain Language Summary: Drought and hot‐related extremes often coincide or follow one another, known as compound drought‐hot extremes (CDHEs), adversely affecting various vegetation processes. Thus, investigating the response relationship between vegetation dynamics and CDHEs is critical for maintaining the sustainable development of ecosystems under the background of climate warming. High ecosystem vulnerability is detected in central and southern regions of North America, eastern and southern regions of South America, Southern Africa, northern and western Europe, and northern and eastern Australia. Climatic factors (precipitation, arid index, temperature, and radiation) dominate the vulnerability of all vegetation types. The drier the climate, the higher will be the vulnerability of vegetation to CDHEs. Furthermore, biodiversity and biomass are key biotic factors influencing the vulnerability. The results provide us with essential insights for managing and adapting terrestrial ecosystems against climate change and are of interest to a wide range of audiences, including but not limited to ecologists, hydrologists, and climatologists. Key Points: A framework is developed to estimate the vegetation vulnerability in response to compound drought‐hot extremes and attribute it to various factorsVegetation vulnerability is higher for grasses and shrubs than for evergreen and deciduous forestsVegetation vulnerability is both affected by climate and biotic factors, but climate factors are more dominant [ABSTRACT FROM AUTHOR]

Anthropogenic activity drives extensive tropical deforestation, particularly in Southeast Asia where 16% of total forest cover was lost between 2000 and 2020. While land surface changes significantly affect the atmosphere, their net impact on convective clouds is not well‐constrained. Here, we use satellite data to demonstrate long‐term deforestation in Southeast Asia robustly alters cloud properties and provide the first observational evidence that the magnitude of this response depends on the atmospheric environment. Deforestation drives a shift toward more widespread, shallower clouds during the daytime, with amplified effects in dry inland areas compared with moist coastal regions. Aerosols only weakly modulate the cloud fraction response, but offset the cloud top height response to deforestation, suggesting the influence of aerosol indirect effects. We conclude that the local signature of forest loss is not uniform, and regional differences in climatology must be considered when assessing deforestation impacts on clouds and the climate system. Plain Language Summary: Humans are driving widespread deforestation in the tropics. Changes to the land surface following forest loss are generally known to affect the atmosphere, but it is hard to tell how deforestation will impact clouds in a given area. Here, we focus on Southeast Asia, a region of the world facing dramatic large‐scale deforestation. We use two decades of satellite data to estimate how the loss of tropical forests impacts cloud properties. On average, we find that deforestation leads to more widespread and shallower clouds. We then look further into how this cloud response to deforestation depends on other environmental factors like moisture and aerosols. This gives us a better idea of which regions are most sensitive to changes in forest cover. Overall, our results show there is an observable cloud response to deforestation, but this response may be stronger in some regions than in others depending on underlying moisture and aerosol conditions. As forest loss continues in Southeast Asia and across the world, it is important to further study these region‐dependent interactions between the atmosphere and the land surface so we can better understand the impacts of human‐driven deforestation on weather and climate. Key Points: Deforestation in Southeast Asia drives a robust shift toward more widespread and shallower clouds on an annual timescaleThis effect has been debated in modeling studies, but we demonstrate this observationally using two decades of satellite dataSome regions are especially vulnerable to deforestation‐driven changes in clouds, depending on atmospheric moisture and aerosol loading [ABSTRACT FROM AUTHOR]

We identify and analyze a large shortening structure (surface expression of a thrust fault) in western Arabia Terra, Mars, exhibiting recent, repeated, and long‐lasting tectonic activity. Where the fault system deforms Marsabit crater rim, four landslides with differing degradation states extend onto the crater floor. We propose these were triggered by episodic re‐activation of the thrust system. Using a morphological map and crater size frequency statistics we show that the fault system experienced at least four landslide‐inducing events during the Middle to Late Amazonian. We note that 1.4 km total displacement on the fault plane must have required many events to accumulate if motion was by brittle failure rather than continuous creep. The current understanding of tectonic activity and stress‐sources since 3.6 Ga, cannot account for these repeated and large Amazonian marsquakes—suggesting revaluation of sources of stress to account for a more active and complex Amazonian tectonic history. Plain Language Summary: We identify a large fault system, called a shortening structure, that has been repeatedly active during the last 2 billion years. This is suggested by a series of landslides with a variety of degradation states. These originate from where a large‐scale shortening structure deforms the wall of Marsabit crater. We propose these were triggered by at least four large tectonic events. The shortening structure has a scarp face height of about 700 m, implying that about 1.4 km of movement has occurred along the fault; requiring tens to thousands of marsquakes to accumulate. Our evidence that multiple marsquakes occurred "recently" (in the last 2 billion years) is important because Mars is thought to have been barely tectonically active during this time. This raises questions about the regional/global processes responsible for recent tectonic activity of this magnitude which would have likely re‐activated other local tectonic structures. Our study shows that repeated and long‐lasting tectonic activity occurred in the last 2 billion years, and finding more young tectonic structures would help to better understand the spatial and temporal extent of Mars' recent tectonic history. Key Points: A large shortening structure in western Arabia Terra is associated with four landslides of various degradation states inside a single craterWe propose that the landslides were tectonically‐triggered and formed at different times during the Middle to Late Amazonian epochThe shortening structure shows evidence of repeated, long‐lasting, and recent compressional tectonic activity in western Arabia Terra [ABSTRACT FROM AUTHOR]

Diagnosing land‐atmosphere fluxes of carbon‐dioxide (CO2) and methane (CH4) is essential for evaluating carbon‐climate feedbacks. Greenhouse gas satellite missions aim to fill data gaps in regions like the humid tropics but obtain very few valid measurements due to cloud contamination. We examined data yields from the Orbiting Carbon Observatory alongside Sentinel‐2 cloud statistics. We find that the main contribution to low data yields are frequent shallow cumulus clouds. In the Amazon, the success rate in obtaining valid measurements vary from 0.1% to 1.0%. By far the lowest yields occur in the wet season, consistent with Sentinel‐2 cloud patterns. We find that increasing the spatial resolution of observations to ∼200 m would increase yields by 2–3 orders of magnitude and allow regular measurements in the wet season. Thus, the key to effective tropical greenhouse gas observations lies in regularly acquiring high‐spatial resolution data. Plain Language Summary: Our research looks at how well satellites are able to observe greenhouse gases such as carbon dioxide and methane in tropical areas, which is important for understanding climate change. We find that these satellites often cannot make good measurements in places like the Amazon rainforest due to clouds. By using space‐based instruments that can peek in between clouds (requiring about ∼200 m spatial resolution), we would get much more frequent information, even during the rainy season. Our study shows that high‐spatial resolution is needed to regularly observe greenhouse gases in the tropics. Key Points: Data yields of current remotely sensed greenhouse gas (GHG) missions in the humid tropics are often below 1%Shallow cumulus clouds cause most of the low data yields, esp. in the wet seasonFiner spatial resolution (∼200 m) can overcome the data sparsity in the tropics [ABSTRACT FROM AUTHOR]

Present understanding of Greenland's subglacial geology is derived mostly from interpolation of geologic mapping of its ice‐free margins and unconstrained by geophysical data. Here we refine the extent of its geologic provinces by synthesizing geophysical constraints on subglacial geology from seismic, gravity, magnetic and topographic data. North of 72°N, no province clearly extends across the whole island, leaving three distinct subglacial regions yet to be reconciled with margin geology. Geophysically coherent anomalies and apparent province boundaries are adjacent to the onset of faster ice flow at both Petermann Glacier and the Northeast Greenland Ice Stream. Separately, based on their subaerial expression, dozens of unusually long, straight and sub‐parallel subglacial valleys cross Greenland's interior and are not yet resolved by current syntheses of its subglacial topography. Plain Language Summary: The Greenland Ice Sheet obscures the rocks beneath 79% of Greenland. By necessity, scientists have relied mostly on studying the rocks exposed along Greenland's edge to understand the island's interior geology. We examine geophysical data from seismometers on the ground, satellites that measure Earth's gravity and magnetic fields and surface topography, and aircraft that measure those same properties and ice thickness. We draw a new map of Greenland's geology beneath the ice sheet by examining where those data show similar signals regarding the nature of the underlying rock, and where they could be related to mapped rock exposures. We also find evidence of some areas with geophysical expressions that are distinct from the rocks found at the island's edges. Some geologic structures, which are entirely covered by ice, may affect how ice flows from Greenland's vast interior toward its coast. Finally, we identify many valleys beneath the ice that are very long and often aligned with each other, but which are not yet fully captured in present maps of the topography beneath the ice. Key Points: We produced a new synthesis of subglacial boundaries for Greenland's geologic provinces from seismic, gravity, magnetic and topography dataThree subglacial regions in central and northern Greenland cannot yet be reconciled with surface‐exposed geologic provincesWe find evidence for a large subglacial valley network that is not fully resolved by subglacial topography syntheses for Greenland [ABSTRACT FROM AUTHOR]

While remote sensing has provided extensive insights into the global terrestrial water, carbon, and energy cycles, space‐based retrievals remain limited in observing the belowground influence of the full soil moisture (SM) profile on ecosystem function. We show that this gap can be addressed when coupling 70 m resolution ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station retrievals of land surface temperature (LST) with in‐situ SM profile measurements. These data sets together reveal that ecosystem water use decreases with depth with 93% of sites showing significant LST coupling with SM shallower than 20 cm while 34% of sites have interactions with SM deeper than 50 cm. Furthermore, the median depth of peak ecosystem water use is estimated to be 10 cm, though forests have more common peak interactions with deeper soil layers (50–100 cm) in 37% of cases. High spatial resolution remote sensing coupled with field‐level data can thus elucidate the role of belowground processes on land surface behavior. Plain Language Summary: Belowground processes, like how roots use soil water for transpiration and how soil water is used for soil evaporation, remain as large uncertainties in the Earth system. This is because these belowground processes are difficult to widely observe and thus tend to only be studied at sparsely located field sites. We address these limitations here by simultaneously using high resolution (70 m scale) remote sensing measurements of land surface temperature (LST), which integrates ecosystem soil and vegetation behavior and water use, and ground networks of soil moisture (SM) measurements between 5 and 100 cm. We find that the relationship between LST and SM at different soil depths shows how ecosystems use moisture across the soil profile. Across vegetation types, our analysis suggests most water use originates from upper soil layers where soil evaporation occurs and where roots presumably preferentially draw water under nominal climatic conditions. Grassland sites tend to have a greater preference for use of moisture in upper soil layers than for forested sites, which show an increase in deeper water use below 50 cm. We therefore demonstrate that such methods can reveal how ecosystems respond to SM across the rootzone and across a range of globally available sites. Key Points: High resolution satellite retrievals of land surface temperature can reveal ecosystem water use when coupled with soil moisture (SM) networksAcross vegetation types, evaporation tends to mainly use water from the upper soil layers with a decrease of SM use with depthGrassland sites tend to have more frequent ecosystem water use of upper layer SM than forested sites [ABSTRACT FROM AUTHOR]

In recent decades, the Greenland ice sheet has been losing mass through glacier retreat and ice flow acceleration. This mass loss is linked with variations in submarine melt, yet existing ocean models are either coarse global simulations focused on decadal‐scale variability or fine‐scale simulations for process‐based investigations. Here, we unite these scales with a framework to downscale from a global state estimate (15 km) into a regional model (3 km) that resolves circulation on the continental shelf. We further downscale into a fjord‐scale model (500 m) that resolves circulation inside fjords and quantifies melt. We demonstrate this approach in Scoresby Sund, East Greenland, and find that interannual variations in submarine melt at Daugaard‐Jensen glacier induced by ocean temperature variability are consistent with the decadal changes in glacier ice dynamics. This study provides a framework by which coarse‐resolution models can be refined to quantify glacier submarine melt for future ice sheet projections. Plain Language Summary: Over the past several decades, the Greenland ice sheet has been losing ice and contributing to sea‐level rise. About half of this ice loss is induced by melt that occurs where glaciers meet the ocean. Using coarse‐scale ocean models that simulate circulation around the globe, previous studies have noted a strong link between ocean temperature and enhanced glacier ice loss. However, due to the small scale of Greenland's fjords, coarse models are unable to directly quantify circulation in these fjords and melt on submerged glaciers. In this study, we develop a new framework to "zoom in" on a fjord, using high‐resolution models driven by larger coarse‐resolution models. In this approach, we simulate melt on one of Greenland's biggest glaciers and find that periods of higher melt coincide with more ice loss as observed from satellites. Since this framework is adaptable to other regions, it could also be used to simulate melt on other glaciers and support estimates of future sea‐level rise. Key Points: Subsurface temperature variability is simulated in a narrow fjord network using regional models downscaled from a global state estimateModeled increases in ocean melt at Daugaard‐Jensen glacier coincide with the onset of acceleration in 2005 and retreat and thinning in 2011Model variations in shelf‐to‐fjord ocean properties match with observations, providing a basis to estimate ocean forcing in ice projections [ABSTRACT FROM AUTHOR]

In this work the electric field of narrow bipolar events (NBEs) measured at a remote location is used to extract the current waveform of the source discharge. All calculations correspond to a vertical linear current source above a perfectly conducting ground plane. The current study uses the well established formulation of electromagnetic fields in the frequency domain, and develops a deconvolution based technique to obtain exact reconstruction of the source current, improving upon previous modeling of NBEs, which often require tuning several inter‐dependent parameters to determine the current that best reproduces the observed electric field. Our proposed solution, although readily available in standard electromagnetic textbooks, has never been employed in the context of lightning related discharges, and offers a simple and efficient alternative to previous conventional time domain calculations. Plain Language Summary: Narrow bipolar events (NBEs) are associated with highly impulsive sources of electromagnetic radiation in the Earth's natural environment that occur in association with initiation of lightning discharges or in isolation. The research community has accumulated significant amount of data on radio emissions associated with these sources. However, the interpretation of these events is currently conducted using source current fitting models that employ large numbers of fitting parameters that do not have physical grounding or clear relevance to these isolated sources. In this work we use standard frequency domain analysis of radiation from these sources that does not require any fitting parameters and allows direct reconstruction of source currents. The proposed approach assumes that the observed radiation is emitted due to polarization of a conducting region in externally applied electric field. The approach is grounded on previous first principles modeling of the polarization process, indicating that the polarization current should be maximum in the middle of conducting region and take zero values at the ends. This first order physics based interpretation of NBE sources does not require specification of the ionization mechanism creating the conducting region, but allows exact reconstruction of the current that radiates the observed wideband NBE waveforms. Key Points: The frequency domain analysis is applied for the first time to analysis of radiation from NBE source currentsThe proposed new approach is validated by detailed comparisons with previously published NBE studiesThe methodology is developed that allows exact reconstruction of source currents that radiate the observed wideband NBE waveforms [ABSTRACT FROM AUTHOR]

Abstract Juno has allowed clear, high‐resolution imaging of Io's polar volcanoes using the Jovian Infrared Auroral Mapper (JIRAM) instrument. We have used data from JIRAM's M‐band (4.78 μm) imager from 11 Juno orbits to construct a global map of volcanic flux. This map provides short‐term insight into the spatial distribution of volcanoes and the ways in which high‐ and low‐latitude volcanoes differ. Using spherical harmonic analysis, we quantitatively compare our volcanic flux map to the surface heat flow distribution expected from models of Io's tidal heat deposition (summarized in de Kleer, Park, et al. (2019, https://doi.org/10.26206/d4wc‐6v82). Our observations confirm previously detected systems of bright volcanoes at high latitudes. Our study finds that both poles are comparably active and that the observed flux distribution is inconsistent with an asthenospheric heating model, although the south pole is viewed too infrequently to establish reliable trends.

Abstract We investigate whether geomorphic signatures of permafrost are embedded in planforms of river meanders, and we inquire as to how physical factors unique to permafrost environments are able to affect their dynamics. By exploiting satellite imagery, a data set of 19 freely‐meandering Arctic rivers is compared against an independent data set of 23 freely‐meandering streams flowing through temperate and tropical regions. Suitable dimensionless metrics are defined to characterize morphometric properties of meanders in terms of the spatio‐temporal distribution of curvature and channel width. Results show the absence of marked contrasts in the amplitude of bend‐curvature between the two data set. Differently, we find a permafrost signature in the channel width response, which manifests itself through larger values of the average bend‐width and by peaks of width fluctuations. Field data suggest that permafrost meanders tend to widen for increasing bend sinuosity, likely promoting a shift of their morphodynamic regime as final cutoff is approached.

Abstract Satellite imagery provides a global perspective for studying river hydrology and water quality, but clouds remain a fundamental limitation of optical sensors. Explicit studies of this problem were limited to specific locations or regions. In this study, we characterize the global severity of this limitation by analyzing 22 years of daily satellite cloud cover data and modeled river discharge for a global sample 21,642 river reaches of diverse sizes and climates. Our results show that the bias in observed river discharge is highly organized in space, particularly affecting Tropical and Arctic rivers. Given the fundamental nature of this cloud limitation, optical satellites will always provide a biased representation of river conditions. We discuss several strategies to mitigate bias, including modeling, data fusion, and temporal averaging, yet these methods introduce their own challenges and uncertainties.

Abstract Sedimentary landforms on Earth and other planetary bodies are built through scour, transport, and deposition of sediment. Sediment connectivity refers to the hypothesis that pathways of sediment transport do not occur in isolation, but rather are mechanistically linked. In dryland river systems, one such example of sediment connectivity is the transport of fluvially deposited sediment by wind. However, predictive tools that can forecast fluvial‐aeolian sediment connectivity at meaningful scales are rare. Here we develop a suite of models for quantifying the availability of river‐sourced sediment for aeolian transport as a function of river flow, wind regime, and land cover across 168 km of the Colorado River in Grand Canyon, USA. We compare and validate these models using topographic changes observed over 10 years in a coupled river sandbar‐aeolian dunefield setting. The models provide a path forward for directly linking fluvial hydrology with the management and understanding of aeolian landscapes.

Abstract Terminus change is a complex outcome of ice‐ocean boundary processes and poses challenges for ice sheet models due to inadequate calving laws, creating uncertainty in sea level change projections. To address this, we quantify glacier termini sinuosity and convexity, testing the hypothesis that terminus morphology reflects dominant calving processes. Using 10 glaciers with diverse calving styles in Greenland over the period from 1985 to 2021, we establish a supervised classification of calving style by comparing morphology and literature‐derived calving observations. Validation with four of these glaciers and flotation conditions and subglacial discharge routing observations confirms concave, smooth termini indicate buoyant flexure dominated‐calving, while convex, sinuous termini suggest serac failure dominated‐calving. We also identify a mixed style where both calving types may occur. We use these classes to label calving style from 1985 to 2021 for all 10 glaciers and explore how this changes over time as glaciers retreat.

Abstract Despite in‐situ observations of perennial firn aquifers (PFAs) at specific locations of the Antarctic ice sheet, a comprehensive continent‐wide mapping of PFA distribution is currently lacking. We present an estimate of their distribution across Antarctica in the form of a probability assessment using a Monte Carlo technique. Our approach involves a novel methodology that combines observations from Sentinel‐1 and Advanced SCATterometer (ASCAT) with output from a regional climate model. To evaluate our method, we conduct an extensive comparison with Operation Ice Bridge observations from the Greenland Ice Sheet. Application to Antarctica reveals high PFA probabilities in the Antarctic Peninsula (AP), particularly along its northern, northwestern, and western coastlines, as well as on the Wilkins, Müller, and George VI ice shelves. Outside the AP, PFA probability is low, except for some locations with marginally higher probabilities, such as on the Abbot, Totten, and Shackleton ice shelves.

Abstract Grid‐ or pixel‐based models, used across various scientific disciplines from microscopic to planetary scales, contain an unquantified error that bias our interpretation of the data. The error is produced by projecting 3D data onto a 2D grid. For Digital Terrain Models (DTMs) the projection error affects all slope‐dependent topographic metrics, like surface area or slope angle. Due to the proportionality of the error to the cosine of the slope, we can correct for it. We quantify the error and test the correction using synthetic landscapes for which we have analytical solutions of their metrics. Application to real‐world landscapes in California, reveal the systematic underestimation of surface area by up to a third, and mean slope angles by up to 10° in steep topography in current DTMs. Correcting projection errors allow for true estimates of surface areas and slope distributions enabling physics‐based models of surface processes at any spatial scale.

Abstract The distribution of tropical cyclone (TC) eye cloud heights is documented for the first time using compact Raman lidar (CRL) measurements with high spatial resolution. These cloud heights act as tracers for low‐level vertical mixing in the eye region. Cloud height distributions using all available data from nine Atlantic TCs in 2021 and 2022 show significant vertical variance, dispelling the notion of a flat stratiform eye cloud deck. Eye cloud widths are multiscale, with shallow convective clouds dominating CRL returns. Data from Hurricane Sam (2021) highlight the evolution of shallow convective clouds in the TC eye and their associated temperature inversions. The frequent appearance of convective eye clouds, along with observed vertical wind fluctuations, suggests that vertical mixing from the boundary layer frequently occurs in the TC eye, even beneath strong inversions. This strong vertical mixing should be accurately portrayed by TC simulations and forecasts.

Abstract Phytoplankton sustains marine ecosystems and influences the global carbon cycle. This study analyzes trends in surface chlorophyll‐a concentration (Schl), a proxy for phytoplankton biomass, using six of the most widely used merged satellite products. Significant regional variations are observed, with contrasting trends observed among different products. To assess if these trends can be related to changes in the environment or to bias in radiometric products, a convolutional neural network is used to examine the relationship between physical ocean variables versus Schl. The results suggest that the merging algorithm of Globcolour‐GSM is not reliable for trend detection and that observed changes in Schl after 2012/2016 are not supported by changes of the physical ocean. These results emphasize the need for careful interpretation of satellite‐derived Schl trends and highlight the potential of machine learning in understanding the complex interactions in marine ecosystems.

Abstract Solar‐induced fluorescence (SIF) is a proxy of ecosystem photosynthesis that often scales linearly with gross primary productivity (GPP) at the canopy scale. However, the mechanistic relationship between GPP and SIF is still uncertain, especially at smaller temporal and spatial scales. We deployed a ultra‐hyperspectral imager over two grassland sites in California throughout a soil moisture dry down. The imager has high spatial resolution that limits mixed pixels, enabling differentiation between plants and leaves within one scene. We find that imager SIF correlates well with diurnal changes in leaf‐level physiology and gross primary productivity under well‐watered conditions. These relationships deteriorate throughout the dry down event. Our results demonstrate an advancement in SIF imaging with new possibilities in remotely sensing plant canopies from the leaf to the ecosystem. These data can be used to resolve outstanding questions regarding SIF's meaning and usefulness in terrestrial ecosystem monitoring.

Abstract This study assesses a 40‐day 3.25‐km global simulation of the Simple Cloud‐Resolving E3SM Model (SCREAMv0) using high‐resolution ground‐based observations from the Atmospheric Radiation Measurement (ARM) Green Ocean Amazon (GoAmazon) field campaign. SCREAMv0 reasonably captures the diurnal timing of boundary layer clouds yet underestimates the boundary layer cloud fraction and mid‐level congestus. SCREAMv0 well replicates the precipitation diurnal cycle, however it exhibits biases in the precipitation cluster size distribution compared to scanning radar observations. Specifically, SCREAMv0 overproduces clusters smaller than 128 km, and does not form enough large clusters. Such biases suggest an inhibition of convective upscale growth, preventing isolated deep convective clusters from evolving into larger mesoscale systems. This model bias is partially attributed to the misrepresentation of land‐atmosphere coupling. This study highlights the potential use of high‐resolution ground‐based observations to diagnose convective processes in global storm resolving model simulations, identify key model deficiencies, and guide future process‐oriented model sensitivity tests and detailed analyses.

Characterizing and understanding the changes in the flow regimes of rivers have been challenging. Existing global river network data sets are not updated and can only identify rivers wider than 30 m. We propose a novel automated method to map river networks on a monthly basin scale for the first time at 10‐m resolution using Sentinel‐1 Synthetic Aperture Radar, Sentinel‐2 multispectral images, and the AW3D30 Digital Surface Model. This method achieved an overall accuracy of 95.8%. The total length of the Yellow River network produced is 40,280 km, approximately 3.2 times that of the Global River Widths from Landsat (GRWL) database, more effectively covering small and medium rivers. The monthly river geometry revealed a positive correlation between river network area and precipitation. This study is expected to provide a cost‐effective alternative to accurately mapping global river networks and advance our understanding of the changes and drivers of river systems. Plain Language Summary: Understanding the impacts of climate change and human activities on water resources across different regions greatly depends on the knowledge of river networks with high spatial and temporal resolution. Small tributaries are important components in river network evolution and water transmission. To date, several studies have mapped interannual variations in rivers with widths >30 m; however, the distribution and variations in small rivers remain unclear. By integrating multispectral and radar satellite remote sensing images as well as topographic data, we created continuous monthly river network maps at the basin scale, allowing us to capture the details of dynamic changes in river networks with higher spatiotemporal resolution. As a result, the method used in this study provides detailed information on small and medium rivers, with the length of the connected rivers being thrice that of the existing data sets. We demonstrate the possibility of mapping global river networks monthly at a resolution of 10 m, providing valuable information for global surface water resource planning and management and improving our understanding of spatial links between land and water interfaces. Key Points: We proposed a new method for mapping 10 m resolution continuous river networks on a monthly basin scale using satellite images and DEMsThis method provided detailed information on small‐ and medium‐sized rivers with an overall accuracy of 95.8%There is a strong positive correlation between monthly river network area and precipitation [ABSTRACT FROM AUTHOR]

The effect of topography on shortwave downward radiation (SWDR) is interest in the geoscience. However, such effects are rarely quantiatively and systematically evalulated, especially over the Tibetan Plateau region. With the geostationaly satellite measurements and topographic radiation model, this study reveals a heightened significance of topography on SWDR with increasing slope. Particularly in abrupt terrain (slopes >15°) the impact becomes pronounced, wherein the topographic radiative forcing (TRF) contributes 9.5% of the annual‐average SWDR. And the ratio of TRF to SWDR reaches a peak during winter, exceeding 150%. In annual‐average scales, the SWDR is 169 ± 38.4 W/m2 and the corresponding TRF is 16.2 ± 22.6 W/m2. Seasonal variations manifest on northern and southern slopes, with the sourthern slopes significant in summer, while the northern ones significant in winter. Notably, topographic effects persist across spatial scales and remain evident at 5 km resolution, emphasizing the necessity of considering topography in SWDR product utilization. Plain Language Summary: Shortwave downward radiation is the main source of surface energy. In mountainous areas, the terrain significantly alters the amount of received SWDR. This study comrephensively examines the topographic influence on SWDR across the Tibetan Plateau for the first time. We investigate the influence of diverse topographic factors on the distribution of surface shortwave radiation in mountainous terrains. With the slope increasing, the impact of topography on SWDR becomes more and more significant. The topographic impact of northern and sourthern slopes behaves obvious seasonal variations, with the sourthern slopes significant in summer, while the northern ones significant in winter. With the increasing of spatial scale, the topographic effect gradually decreases and tends to be stable, but it can never disappear. As remote sensing data resolution coarsens, topographic radiative forcing diminishes, but even at 5 km resolution, terrain significantly affects SWDR distribution. Key Points: In abrupt slopes, the annual‐average proportion of topographic radiative forcing to shortwave downward radiation (SWDR) can reach up to 9.5%The proportion of topographic radiative forcing to SWDR exceeding 150% in winter on the northern slopesDespite decreasing influence with coarser spatial resolutions, topographic effects persist even at 5 km [ABSTRACT FROM AUTHOR]

Submesoscale vertical velocities, w, are important for the oceanic transport of heat and biogeochemical properties, but observing w is challenging. New remote sensing technologies of horizontal surface velocity at O(1) km resolution can resolve surface submesoscale dynamics and offer promise for diagnosing w subsurface. Using machine learning models, we examine relationships between the three‐dimensional w field and remotely observable surface variables such as horizontal velocity, density, and their horizontal gradients. We evaluate the machine learning models' sensitivities to different inputs, spatial resolution of surface fields, the addition of noise, and information about the subsurface density. We find that surface data is sufficient for reconstructing w, and having high resolution horizontal velocities with minimal errors is crucial for accurate w predictions. This highlights the importance of finer scale surface velocity measurements and suggests that data‐driven methods may be effective tools for linking surface observations with vertical velocity and transport subsurface. Plain Language Summary: Vertical velocities, w, are associated with ocean currents that move toward or away from the ocean surface and are important for connecting the surface and deep ocean. It is extremely difficult to measure w directly, but measurements of other variables that are related to w, such as horizontal currents that move along the surface in the north‐south or east‐west directions, can be exploited to predict w. Here, we investigate the feasibility of inferring w from other more easily measurable data. We compare three machine learning methods to see which is best at finding relationships between more easily measurable variables (the input data) and w at different depths. We test how using different input variables, adding noise to the input data, or changing the spatial resolution of the input data, impact the w predictions. Our results show that machine learning models are successful at reconstructing the 3D w field using high‐resolution (∼1 km) surface data, and in particular, surface horizontal velocities are the most important to include. This study shows that data‐driven methods are promising for relating remotely sensed surface measurements of the ocean to vertical velocities below the surface, which can help provide us with a better understanding of the 3D ocean. Key Points: Machine learning models diagnose the oceanic 3D submesoscale vertical velocity field within and below the mixed layer from surface dataHigh resolution horizontal surface velocity data is crucial for obtaining accurate vertical velocities subsurfaceConvolutional Neural Networks are relatively robust to noisy data and coarser data resolution [ABSTRACT FROM AUTHOR]

Retrieving the physical properties and water content of marine aerosols requires understanding the links between the particles' optical and microphysical properties. By using a morphologically realistic model with varying salt mass fractions fm, describing the transition from irregularly shaped, dry salt crystals to brine‐coated geometries, optical properties relevant to polarimetric remote sensing are computed at wavelengths of 532 and 1,064 nm. The extinction cross section and its color ratio depend on particle size, but are insensitive to changes in fm; thus, measured extinction coefficients at two wavelengths contain information on both particle number and size. The lidar ratio's dependence on both size and wavelength has implications for inverting the lidar equation. The results suggest that active observations of the backscattering cross section's color ratio and the depolarization ratio, as well as, passive observations of the degree of linear polarization offer avenues to obtain the water content of marine aerosols. Plain Language Summary: Salt aerosols ejected into air from the ocean surface are the most abundant type of particles in the atmosphere. Clouds form by condensation onto these particles, which, in turn, reflect, absorb, and emit light and thermal radiation, thereby influencing the climate system. To better describe these processes in models, one needs to monitor salt aerosols from satellites. The main difficulty is to obtain information on the particles' concentration, size, and water content from satellite measurements that can only observe light scattered by the aerosols. In this study a model is being employed to study how the shape, size, and composition of marine aerosols impact their light‐scattering properties. The results suggest that the extinction of light measured at two wavelengths provides a robust method for obtaining the concentration and size of the particles. The water content can be obtained from observing the intensity of backscattered laser light at two wavelengths, or the change in polarization of the backscattered light at a single wavelength. One can also observe the polarization of scattered near‐infrared sunlight close to the backscattering direction to obtain information on the water content of the particles. Key Points: Modeled extinction coefficient of marine aerosol depends on particle radius and wavelength, but not on water contentThe depolarization ratio and the color ratio of the backscattering cross section generally decrease with growing aerosol water contentThe linear polarization peak near backscattering angles at NIR wavelength could be used in passive polarimetry to retrieve water content [ABSTRACT FROM AUTHOR]

Super‐coarse dust particles (diameters >10 μm) are evidenced to be more abundant in the atmosphere than model estimates and contribute significantly to the dust climate impacts. Since super‐coarse dust accounts for less dust extinction in the visible‐to‐near‐infrared (VIS‐NIR) than in the thermal infrared (TIR) spectral regime, they are suspected to be underestimated by remote sensing instruments operates only in VIS‐NIR, including Aerosol Robotic Networks (AERONET), a widely used data set for dust model validation. In this study, we perform a radiative closure assessment using the AERONET‐retrieved size distribution in comparison with the collocated Atmospheric Infrared Sounder (AIRS) TIR observations with comprehensive uncertainty analysis. The consistently warm bias in the comparisons suggests a potential underestimation of super‐coarse dust in the AERONET retrievals due to the limited VIS‐NIR sensitivity. An extra super‐coarse mode included in the AERONET‐retrieved size distribution helps improve the TIR closure without deteriorating the retrieval accuracy in the VIS‐NIR. Plain Language Summary: Dust particles suspended in the atmosphere span a wide size range (0.001–100 μm). Notably, super‐coarse dust particles (diameter >10 μm) have been observed to be more abundant than what climate models suggest. Theoretically, these super‐coarse particles present little radiative signatures in visible to near‐infrared (VIS‐NIR) but significantly affect the thermal infrared (TIR) radiation. This study addresses the question of whether remote sensing techniques operating in the VIS‐NIR can capture these dust particles. We use side‐by‐side observations associated with a dust plume in both VIS‐NIR and TIR to assess whether the dust properties, including the size distribution, inferred by VIS‐NIR observations can generate well‐matched radiative signatures with TIR observations. We found that the simulated outgoing radiation at the top of the atmosphere in TIR using the VIS‐NIR‐inferred dust properties is greater than the observations because of not enough dust extinction, potentially led by the absence of super‐coarse dust. By introducing an extra super‐coarse mode in the size distribution, we found a better match with the TIR observation, while the dust optical properties retrieved in VIS‐NIR can be mostly conserved. Our result demonstrates the importance of combining VIS‐NIR and TIR observations to retrieve the dust size distribution. Key Points: Aerosol Robotic Networks (AERONET) dust retrievals are assessed by the thermal infrared (TIR) radiative closure study compared with the collocated AIRS observationsThe warm bias in the TIR radiative closure suggests the possibility of AERONET underestimating the super‐coarse dustAdding super‐coarse dust to the AERONET size distribution improves the TIR closure without deteriorating its inherent retrieval accuracy [ABSTRACT FROM AUTHOR]

Sea ice topography information can be obtained from altimetry but these data are spatially and temporally limited compared to recent synthetic aperture radar (SAR) missions such as the RADARSAT Constellation Mission (RCM). We analyze the relationship between sea ice roughness and height obtained from three Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 tracks on two dates in March 2022, with RCM backscatter from 17 images in the McClintock Channel, Canadian Arctic. We analyze how this relationship varies with ice type, polarization, and incidence angle. We find particularly notable relationships between sea ice roughness and horizontal‐transmit/vertical‐receive backscatter for first‐year ice, and sea ice height and backscatter for multi‐year ice. We develop a preliminary model for winter sea ice roughness retrieval using RCM. In comparison with independent ICESat‐2 data in our study region, we find the model performs effectively at estimating a roughness distribution and key roughness statistics, and characterizes spatial variations in roughness at a sub‐kilometer scale. Plain Language Summary: Sea ice topography estimates are important for improving ocean‐climate models, understanding sea ice evolution, and planning ice travel. Estimates can be obtained from altimetry missions, but these data are spatially and/or temporally limited compared to recent synthetic aperture radar missions such as the C‐band RADARSAT Constellation Mission (RCM). By comparing RCM backscatter with measurements of height and roughness obtained using Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 in the Canadian Arctic, we demonstrate the potential for using RCM to estimate sea ice topography. We show there is a notable relationship between sea ice roughness and backscatter on first‐year ice in late winter, particularly in the horizontal‐transmit/vertical‐receive channel, and a notable relationship between sea ice height and backscatter on multi‐year ice. We also observe that the relationship between the RCM signal and roughness varies with incidence angle. Based on this analysis we develop a preliminary model for winter sea ice roughness retrieval using RCM. In comparison with ICESat‐2 data, we find the model performs effectively at estimating key roughness statistics, and characterizes spatial variations in roughness at a sub‐kilometer scale. These findings will facilitate the use of RCM to validate geophysical models, map rough areas of ice, assess ice evolution and develop new topographic models. Key Points: Sea ice roughness measured by Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 and horizontal‐transmit/vertical‐receive RADARSAT Constellation Mission (RCM) backscatter exhibit a notable relationship on late‐winter first‐year iceSea ice height, as measured by ICESat‐2, and RCM backscatter exhibit a notable relationship on late‐winter multi‐year iceWe develop/validate a model, based on comparison with ICESat‐2 data, for estimating sea ice roughness using RCM backscatter in the Arctic [ABSTRACT FROM AUTHOR]

Using data from the NASA Soil Moisture Active/Passive mission, Koster et al. (2023, https://doi.org/10.1038/s41467‐023‐39318‐3) conclude that, for medium‐scale basins in the contiguous United States, a quarter of interannual variability in springtime streamflow is explained by interannual anomalies in late‐fall soil moisture. This lagged relationship can be leveraged for seasonal hydrologic forecasting, but only if effectively captured by existing prediction models. Here, we extend the analysis in Koster et al. (2023, https://doi.org/10.1038/s41467‐023‐39318‐3) to diagnose systematic errors present in the United States National Water Model (NWM). Results demonstrate that the NWM tends to underestimate both the trans‐winter temporal memory of 0–1 m soil moisture as well as the correlation between 0 and 1 m soil moisture and streamflow—thereby reducing the NWM's ability to leverage vertically averaged soil moisture as a source of hydrologic predictability. Plain Language Summary: Hydrologic forecasting systems attempt to predict future streamflow at lead times ranging from hours to months. Recent work has underscored the importance of antecedent soil moisture conditions for such forecasts. That is, knowledge of current soil moisture conditions can guide our expectations regarding future streamflow amounts. However, it is currently unclear whether existing hydrologic forecasting models accurately capture the usefulness of soil moisture for this purpose. Here we use new data provided by the NASA Soil Moisture Active/Passive satellite mission to assess the degree to which the United States National Water Model—currently being developed for operational streamflow forecasting in the United States—accurately captures the degree of correlation between current soil moisture conditions and future streamflow. Results suggest that changes to the National Water Model could improve its ability to provide accurate long‐term streamflow forecasts. Key Points: Understanding soil moisture processes is critical for long‐term stream flow forecastingStream flow forecasting skill depends on a model's ability to accurately capture soil moisture auto‐correlation and coupling with runoffComparisons with new remotely sensing products suggests that the National Water Model is underestimating both of these quantities [ABSTRACT FROM AUTHOR]

A powerful earthquake, particularly in mountainous regions, has the ability to trigger an immense number of landslides, resulting in severe damages. However, effectively assessing their recoveries in the years following the mainshocks has not yet been attempted due to the lack of continuously updated landslide inventories. Therefore, we proposed a novel detection framework utilizing time‐series Landsat images from 2000 to 2021, which enabled us to achieve a long‐term landslide inventory in the seismic zone for the first time. The response patterns of landslide development under different conditions were explored in detail. The restoration models were quantitatively established, revealing that the recoveries of post‐earthquake landslide activity and vital environmental indicators require a range from ∼5.29 to ∼25.00 years. This pioneering research is highly beneficial in comprehending the impact of earthquake‐induced hazards on short‐term land surface damage and long‐term ecological equilibrium. Plain Language Summary: The Wenchuan earthquake triggered tens of thousands of landslides and caused severe surface damages, leading to adverse impacts on the local environment in the following period. Therefore, we implemented an approach that combines change detection techniques with long‐term remote sensing images to track the landslide activities from 2000 to 2021. Consequently, continuous landslide maps have been produced, and the post‐earthquake landslide activities were analyzed under different conditions considering various factors. We also explored the restorations for the undisturbed landslides after the earthquake using important ecological indicators. The results suggest that the recovery of landslide activity and surface evapotranspiration was rapid. However, the regrowth and succession of damaged plants required a longer time. Moreover, it would take decades to fully restore the vegetation ecological function, which shatters our original impression on the persistence of post‐earthquake landslide impacts. Key Points: The inventories with over 21 years of continuous landslide activities prior to and following the Wenchuan earthquake were compiledApart from acknowledged attenuation, diverse environment exposed various evolutionary patterns of post‐earthquake landslide activityDamaged plant succession and ecological function restoration underwent lengthier durations than post‐seismic landslide stabilization [ABSTRACT FROM AUTHOR]

In this study, we assimilated microwave (MW) all‐sky radiances from low‐Earth‐orbiting satellites and examined their impact on the analyses and forecasts of weather hazards associated with the 10 August 2020 Midwest derecho. Compared with the baseline that assimilated conventional surface and upper‐air observations and infrared (IR) all‐sky radiances from geostationary satellites, the addition of MW all‐sky radiances improved the analyzed and forecasted convection‐stratiform structures of the derecho. Results show that MW all‐sky radiances provided additional information, compared with IR radiances, on hydrometeors within the storm, leading to improved forecasts out to 2 hr with quantitatively more accurate surface gusts. This is the first study to assimilate MW all‐sky radiances for a severe weather event using a convection‐permitting numerical weather prediction model (our model resembles NOAA's High‐Resolution Rapid Refresh), and the results suggest promising avenues for improving severe weather forecasts worldwide in the future. Plain Language Summary: Satellite observations are the backbone of modern weather forecast operations, especially for severe weather monitoring and prediction. However, they are also severely underutilized by computer weather models. Many satellite observations impacted by clouds and precipitation are not used in these models, despite their ability to characterize important features of ongoing severe weather events. This study focuses on satellite observations at microwave (MW) frequencies that are impacted by clouds and precipitation. We explore their potential benefits by incorporating them into a computer weather model using data assimilation and examining their impact on severe weather forecasts. We utilize the 10 August 2020 Midwest derecho that produced extensive wind damage as a case study. Because these MW observations contain contributions from precipitating particles within the derecho, assimilating these observations yields better depictions of the derecho structure in the computer model. Consequently, more accurate forecasts of surface gusts are produced. The results of this study suggest a promising avenue for improving severe weather forecasts worldwide in the future, especially for regions that lack the resources and infrastructure to support high‐spatiotemporal‐resolution weather observations. Key Points: Microwave (MW) all‐sky radiances are impacted by hydrometeors within clouds, complementing cloud‐top dominated infrared all‐sky radiancesAssimilating MW all‐sky radiances leads to improved storm structure in the analyses of the 10 August 2020 Midwest derechoWith improved depiction of the derecho's structure, accurate gust forecasts are obtained 2 hr earlier [ABSTRACT FROM AUTHOR]

Abstract Source‐to‐sink transfer of sediment and organic carbon (OC) is regulated by river mobility. Quantifying trends in river mobility is, however, challenging due to diverse planform morphologies (e.g., meandering, braided) and measurement methods. Here, we utilize a remote‐sensing method applicable to all planform morphologies to quantify the mobility timescales of 80 rivers worldwide. Results show that, across the continuum from meandering to braided rivers, there is a systematic reduction in the timescales of channel mobility and—to a lesser extent—floodplain reworking. This leads to a decrease in the efficiency with which braided rivers rework old floodplain material compared to their meandering counterparts. Reduced floodplain reworking efficiency of braided rivers leads to smaller channel‐belt areas relative to their size. Results suggest that river‐mobility timescales derived from remote sensing can aid in the characterization of sediment and OC storage and transit times at a global scale.

Abstract Anthropogenic activity drives extensive tropical deforestation, particularly in Southeast Asia where 16% of total forest cover was lost between 2000 and 2020. While land surface changes significantly affect the atmosphere, their net impact on convective clouds is not well‐constrained. Here, we use satellite data to demonstrate long‐term deforestation in Southeast Asia robustly alters cloud properties and provide the first observational evidence that the magnitude of this response depends on the atmospheric environment. Deforestation drives a shift toward more widespread, shallower clouds during the daytime, with amplified effects in dry inland areas compared with moist coastal regions. Aerosols only weakly modulate the cloud fraction response, but offset the cloud top height response to deforestation, suggesting the influence of aerosol indirect effects. We conclude that the local signature of forest loss is not uniform, and regional differences in climatology must be considered when assessing deforestation impacts on clouds and the climate system.

Abstract We identify and analyze a large shortening structure (surface expression of a thrust fault) in western Arabia Terra, Mars, exhibiting recent, repeated, and long‐lasting tectonic activity. Where the fault system deforms Marsabit crater rim, four landslides with differing degradation states extend onto the crater floor. We propose these were triggered by episodic re‐activation of the thrust system. Using a morphological map and crater size frequency statistics we show that the fault system experienced at least four landslide‐inducing events during the Middle to Late Amazonian. We note that 1.4 km total displacement on the fault plane must have required many events to accumulate if motion was by brittle failure rather than continuous creep. The current understanding of tectonic activity and stress‐sources since 3.6 Ga, cannot account for these repeated and large Amazonian marsquakes—suggesting revaluation of sources of stress to account for a more active and complex Amazonian tectonic history.

Abstract Present understanding of Greenland's subglacial geology is derived mostly from interpolation of geologic mapping of its ice‐free margins and unconstrained by geophysical data. Here we refine the extent of its geologic provinces by synthesizing geophysical constraints on subglacial geology from seismic, gravity, magnetic and topographic data. North of 72°N, no province clearly extends across the whole island, leaving three distinct subglacial regions yet to be reconciled with margin geology. Geophysically coherent anomalies and apparent province boundaries are adjacent to the onset of faster ice flow at both Petermann Glacier and the Northeast Greenland Ice Stream. Separately, based on their subaerial expression, dozens of unusually long, straight and sub‐parallel subglacial valleys cross Greenland's interior and are not yet resolved by current syntheses of its subglacial topography.

Abstract While remote sensing has provided extensive insights into the global terrestrial water, carbon, and energy cycles, space‐based retrievals remain limited in observing the belowground influence of the full soil moisture (SM) profile on ecosystem function. We show that this gap can be addressed when coupling 70 m resolution ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station retrievals of land surface temperature (LST) with in‐situ SM profile measurements. These data sets together reveal that ecosystem water use decreases with depth with 93% of sites showing significant LST coupling with SM shallower than 20 cm while 34% of sites have interactions with SM deeper than 50 cm. Furthermore, the median depth of peak ecosystem water use is estimated to be 10 cm, though forests have more common peak interactions with deeper soil layers (50–100 cm) in 37% of cases. High spatial resolution remote sensing coupled with field‐level data can thus elucidate the role of belowground processes on land surface behavior.

Abstract In recent decades, the Greenland ice sheet has been losing mass through glacier retreat and ice flow acceleration. This mass loss is linked with variations in submarine melt, yet existing ocean models are either coarse global simulations focused on decadal‐scale variability or fine‐scale simulations for process‐based investigations. Here, we unite these scales with a framework to downscale from a global state estimate (15 km) into a regional model (3 km) that resolves circulation on the continental shelf. We further downscale into a fjord‐scale model (500 m) that resolves circulation inside fjords and quantifies melt. We demonstrate this approach in Scoresby Sund, East Greenland, and find that interannual variations in submarine melt at Daugaard‐Jensen glacier induced by ocean temperature variability are consistent with the decadal changes in glacier ice dynamics. This study provides a framework by which coarse‐resolution models can be refined to quantify glacier submarine melt for future ice sheet projections.

Slow-moving landslides are hydrologically driven. Yet, landslide sensitivity to precipitation, and in particular, precipitation extremes, is difficult to constrain because landslides occur under diverse hydroclimatological conditions. Here we use standardized open-access satellite radar interferometry data to quantify the sensitivity of 38 landslides to both a record drought and extreme rainfall that occurred in California between 2015 and 2020. These landslides are hosted in similar rock types, but span more than ∼2 m/yr in mean annual rainfall. Despite the large differences in hydroclimate, we found these landslides exhibited surprisingly similar behaviors and hydrologic sensitivity, which was characterized by faster (slower) than average velocities during wetter (drier) than average years, once the impact of the drought diminished. Our findings may be representative of future landslide behaviors in California where precipitation extremes are predicted to become more frequent with climate change.