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Fresh submarine groundwater discharge (FSGD) can deliver significant fluxes of water and solutes from land to sea. In the Arctic, which accounts for ∼34% of coastlines globally, direct observations and knowledge of FSGD are scarce. Through integration of observations and process‐based models, we found that regardless of ice‐bonded permafrost depth at the shore, summer SGD flow dynamics along portions of the Beaufort Sea coast of Alaska are similar to those in lower latitudes. Calculated summer FSGD fluxes in the Arctic are generally higher relative to low latitudes. The FSGD organic carbon and nitrogen fluxes are likely larger than summer riverine input. The FSGD also has very high CO2 making it a potentially significant source of inorganic carbon. Thus, the biogeochemistry of Arctic coastal waters is potentially influenced by groundwater inputs during summer. These water and solute fluxes will likely increase as coastal permafrost across the Arctic thaws. Plain Language Summary Groundwater flows from land to sea, transporting freshwater, organic matter, nutrients, and other solutes that impact coastal ecosystems. However, along coasts of the rapidly‐warming Arctic, there is limited knowledge regarding how much fresh groundwater enters the ocean. Using field observations and numerical models, we show that groundwater flowing from tundra in northern coastal Alaska carries large amounts of freshwater, organic matter, and carbon dioxide to the Arctic lagoons during summer. These inputs are likely significant to coastal biogeochemical cycling and marine food webs. Groundwater discharge and the associated transport of dissolved materials are expected to increase due to longer periods of above‐zero temperatures that thaw frozen soils below the tundra. Key Points Summer fresh submarine groundwater discharge (FSGD) to the Alaskan Beaufort Sea is only 3%–7% of rivers but carries as much organic matter Summer FSGD delivers a median of 116 (interquartile range: 35–405) and 6 (2–21) kg/d per km dissolved organic carbon and nitrogen Fresh groundwater at the beach of Simpson Lagoon (SL) has a median PCO2 of ∼33,000 μatm implying substantial CO2 flux

Understanding the complex interrelationships between wildfire and its environmental and anthropogenic controls is crucial for wildfire modeling and management. Although machine learning (ML) models have yielded significant improvements in wildfire predictions, their limited interpretability has been an obstacle for their use in advancing understanding of wildfires. This study builds an ML model incorporating predictors of local meteorology, land‐surface characteristics, and socioeconomic variables to predict monthly burned area at grid cells of 0.25° × 0.25° resolution over the contiguous United States. Besides these predictors, we construct and include predictors representing the large‐scale circulation patterns conducive to wildfires, which largely improves the temporal correlations in several regions by 14%–44%. The Shapley additive explanation is introduced to quantify the contributions of the predictors to burned area. Results show a key role of longitude and latitude in delineating fire regimes with different temporal patterns of burned area. The model captures the physical relationship between burned area and vapor pressure deficit, relative humidity (RH), and energy release component (ERC), in agreement with the prior findings. Aggregating the contribution of predictor variables of all the grids by region, analyses show that ERC is the major contributor accounting for 14%–27% to large burned areas in the western US. In contrast, there is no leading factor contributing to large burned areas in the eastern US, although large‐scale circulation patterns featuring less active upper‐level ridge‐trough and low RH two months earlier in winter contribute relatively more to large burned areas in spring in the southeastern US. Plain Language Summary Wildfire burned areas have increased by 10 times since the 1980s in the United States, posing more threats to properties and human life, and degrading air quality. There is a demand for wildfire controls by accurate predictions and a better understanding of wildfires. Machine learning (ML) is an effective tool for resolving the nonlinear relationships between wildfire and its predictors. This study builds an ML model and incorporates a game‐theory‐based interpretation to predict wildfires and explain the relationships between burned area and their key controlling factors. We show that including predictors representing large‐scale meteorological patterns favorable for wildfires significantly improves burned area predictions. Using the novel interpretation method, we identify the roles of the coordinate variables in distinguishing fire regimes with different temporal patterns of burned area as well as the physical relationships between local meteorology and burned area. Additionally, we show that fuel dryness is the most important predictor of large burned areas in the western US while the large‐scale meteorological patterns featuring dry winters contribute more in the southeastern US. Our study provides a better elucidation of the complex processes contributing to wildfires using ML tools and the game theory interpretation. Key Points US fire burned areas are well predicted by a machine learning model and SHAP improves interpretation of the contributing factors Including large‐scale circulation patterns conducive to wildfires as predictors improve prediction of burned areas in several regions Fire‐season fuel dryness and dry winters are important contributors to large burned areas in western and southeastern US, respectively

Mortality due to extreme temperatures is one of the most worrying impacts of climate change. In this analysis, we use historic mortality and temperature data from 106 cities in the United States to develop a model that predicts deaths attributable to temperature. With this model and projections of future temperature from climate models, we estimate temperature‐related deaths in the United States due to climate change, changing demographics, and adaptation. We find that temperature‐related deaths increase rapidly as the climate warms, but this is mainly due to an expanding and aging population. For global average warming below 3°C above pre‐industrial levels, we find that climate change slightly reduces temperature‐related mortality in the U.S. because the reduction of cold‐related mortality exceeds the increase in heat‐related deaths. Above 3°C warming, whether the increase in heat‐related deaths exceeds the decrease in cold‐related deaths depends on the level of adaptation. Southern U.S. cities are already well adapted to hot temperatures and the reduction of cold‐related mortality drives overall lower mortality. Cities in the Northern U.S. are not well adapted to high temperatures, so the increase in heat‐related mortality exceeds the reduction in cold‐related mortality. Thus, while the total number of climate‐related mortality may not change much, climate change will shift mortality in the U.S. to higher latitudes. Plain Language Summary Deaths due to extreme temperatures is one of the most important impacts of climate change. Here we estimate future temperature related deaths in the US using climate projection, population projection, and adaptation assumptions. We find that in 3°C of global warming level, temperature‐related deaths will increase by a factor of five, mostly due to aging and increasing population. Below 3°C warming, impact of climate change slightly decreases temperature‐related deaths, because the decrease in cold‐related deaths exceeds the increase in heat‐related deaths. Above 3°C warming, impact of climate change depends on the level of adaptation. Furthermore, we find that temperature‐related mortality shifts Northward. This is due to increase of heat‐related deaths in Northern cities, which are currently poorly adapted to heat. Key Points Temperature‐related deaths in the U.S. will increase by a factor of 5 with 3°C of warming, mostly due to aging and increasing population Impact of climate change is not significant until 3°C of global warming, and it depends on the level of adaptation beyond that point Temperature‐related deaths will shift Northward, due to increasing heat‐related deaths in poorly heat‐adapted Northern cities

Throughout Indonesia ecological degradation, agricultural expansion, and the digging of drainage canals has compromised the integrity and functioning of peatland forests. Fragmented landscapes of scrubland, cultivation, degraded forest, and newly established plantations are then susceptible to extensive fires that recur each year. However, a comprehensive understanding of all the drivers of fire distribution and the conditions of initiation is still absent. Here we show the first analysis in the region that encompasses a wide range of driving factors within a single model that captures the inter‐annual variation, as well as the spatial distribution of peatland fires. We developed a fire susceptibility model using machine learning (XGBoost random forest) that characterizes the relationships between key predictor variables and the distribution of historic fire locations. We then determined the relative importance of each predictor variable in controlling the initiation and spread of fires. The model included land‐cover classifications, a forest clearance index, vegetation indices, drought indices, distances to infrastructure, topography, and peat depth, as well as the Oceanic Niño Index (ONI). The model performance consistently scores highly in both accuracy and precision across all years (>75% and >67.5% respectively), though recall metrics are much lower (>25%). Our results confirm the anthropogenic dependence of extreme fires in the region, with distance to settlements and distance to canals consistently weighted the most important driving factors within the model structure. Our results may help target the root causes of fire initiation and propagation to better construct regulation and rehabilitation efforts to mitigate future fires. Key Points The primary driving factors affecting fires in the ex‐Mega Rice Project area are anthropogenic The predictive capability of fire models is limited by the influence of human agency on the location of fire ignition More comprehensive understanding of the driving social factors affecting fire initiation are required

The NASA Psyche mission will explore the structure, composition, and other properties of asteroid (16) Psyche to test hypotheses about its formation. Variations in radar reflectivity, density, thermal inertia, and visible to near‐infrared (VNIR) reflectance spectra of Psyche suggest a highly metallic composition with mafic silicate minerals (e.g., pyroxene) heterogeneously distributed on the surface in low abundance (<10 vol.%). The Psyche spacecraft's Multispectral Imager is designed to map ≥80% of the surface at high spatial resolution (≤20 m/pixel) through a panchromatic filter and provide compositional information for about ≥80% of the surface using seven narrowband filters at VNIR wavelengths (∼400–1,100 nm) and at spatial scales of ≤500 m/pixel. We analyzed 359 reflectance spectra from samples consistent with current uncertainties in Psyche's composition and compared them to published reflectance spectra of the asteroid using a chi‐square test for goodness of fit. The best matches for Psyche include iron meteorite powder, powders from the sulfide minerals troilite and pentlandite, and powder from the CH/CBb chondrite Isheyevo. Comparison of absorption features support the interpretation that Psyche's surface is a metal‐silicate mixture, although the exact abundance and chemistry of the silicate component remains poorly constrained. We convolve our spectra to the Imager's spectral throughput to demonstrate preliminary strategies for mapping the surface composition of the asteroid using filter ratios and reconstructed band parameters. Our results provide predictions of the kinds of surface compositional information that the Psyche mission could reveal on the solar system's largest M‐type asteroid. Plain Language Summary Current observations of the asteroid (16) Psyche suggest it to be metal‐rich, but not entirely made of metal. We compared reflected light from a wide variety of Psyche‐relevant materials to measurements of reflected light from the asteroid. This analysis confirms that Psyche's composition could be less metal‐rich than previously thought. Other materials with reflectance properties similar to Psyche are metal‐rich carbonaceous chondrites and sulfide minerals. We show how an instrument on the Psyche spacecraft, which will study the asteroid in detail, can resolve some uncertainties about the surface composition of the asteroid. Key Points Visible to near‐infrared spectra of (16) Psyche are consistent with meteorites (irons and metal‐rich chondrites) and sulfides The Psyche mission's Multispectral Imager can identify and potentially discriminate such materials if present on the surface of Psyche Imager‐convolved data indicate that the instrument can accurately recover absorption band parameters in certain metal‐silicate mixtures

Interferometric synthetic aperture radar (InSAR) has been used to quantify a range of surface and near surface physical properties in permafrost landscapes. Most previous InSAR studies have utilized spaceborne InSAR platforms, but InSAR datasets over permafrost landscapes collected from airborne platforms have been steadily growing in recent years. Most existing algorithms dedicated toward retrieval of permafrost physical properties were originally developed for spaceborne InSAR platforms. In this study, which is the first in a two part series, we introduce a series of calibration techniques developed to apply a novel joint retrieval algorithm for permafrost active layer thickness retrieval to an airborne InSAR dataset acquired in 2017 by NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar over Alaska and Western Canada. We demonstrate how InSAR measurement uncertainties are mitigated by these calibration methods and quantify remaining measurement uncertainties with a novel method of modeling interferometric phase uncertainty using a Gaussian mixture model. Finally, we discuss the impact of native SAR resolution on InSAR measurements, the limitation of using few interferograms per retrieval, and the implications of our findings for cross‐comparison of airborne and spaceborne InSAR datasets acquired over Arctic regions underlain by permafrost. Key Points We develop and present several calibration and postprocessing methods for seasonal subsidence estimation from interferometric synthetic aperture radar deformation Novel methods for phase referencing and uncertainty quantification due to nonergodicity within the multilook window are proposed Residual sources of uncertainty in active layer thickness estimation are discussed and quantified

Modeling of global soil organic carbon (SOC) is accompanied by large uncertainties. The heavy computational requirement limits our flexibility in disentangling uncertainty sources especially in high latitudes. We build a structured sensitivity analyzing framework through reorganizing the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE)‐aMeliorated Interactions between Carbon and Temperature (MICT) model with vertically discretized SOC into one matrix equation, which brings flexibility in comprehensive sensitivity assessment. Through Sobol's method enabled by the matrix, we systematically rank 34 relevant parameters according to variance explained by each parameter and find a strong control of carbon input and turnover time on long‐term SOC storages. From further analyses for each soil layer and regional assessment, we find that the active layer depth plays a critical role in the vertical distribution of SOC and SOC equilibrium stocks in northern high latitudes (>50°N). However, the impact of active layer depth on SOC is highly interactive and nonlinear, varying across soil layers and grid cells. The stronger impact of active layer depth on SOC comes from regions with shallow active layer depth (e.g., the northernmost part of America, Asia, and some Greenland regions). The model is sensitive to the parameter that controls vertical mixing (cryoturbation rate) but only when the vertical carbon input from vegetation is limited since the effect of vertical mixing is relatively small. And the current model structure may still lack mechanisms that effectively bury nonrecalcitrant SOC. We envision a future with more comprehensive model intercomparisons and assessments with an ensemble of land carbon models adopting the matrix‐based sensitivity framework. Key Points One matrix equation reproduces spatial‐temporal dynamics of SOC from the vertically discretized ORCHIDEE‐MICT model The matrix representation enables comprehensive sensitivity analyses (e.g., Sobol's method) for complex models Active layer depth is critical for modeling SOC distribution in high latitudes

We use Ice, Cloud, and land Elevation Satellite 2 (ICESat‐2) laser altimetry crossovers and repeat tracks collected over the North Slope of Alaska to estimate ground surface‐height change due to the seasonal freezing and thawing of the active layer. We compare these measurements to a time series of surface deformation from Sentinel‐1 interferometric synthetic aperture radar (InSAR) and demonstrate agreement between these independent observations of surface deformation at broad spatial scales. We observe a relationship between ICESat‐2‐derived surface subsidence/uplift and changes in normalized accumulated degree days, which is consistent with the thermodynamically driven seasonal freezing and thawing of the active layer. Integrating ICESat‐2 crossover estimates of surface‐height change yields an annual time series of surface‐height change that is sensitive to changes in snow cover during spring and thawing of the active layer throughout spring and summer. Furthermore, this time series exhibits temporal correlation with independent reanalysis datasets of temperature and snow cover, as well as an InSAR‐derived time series. ICESat‐2‐derived surface‐height change estimates can be significantly affected by short length‐scale topographic gradients and changes in snow cover and snow depth. We discuss optimal strategies of post‐processing ICESat‐2 data for permafrost applications, as well as the future potential of joint ICESat‐2 and InSAR investigations of permafrost surface‐dynamics. Plain Language Summary NASA's Ice, Cloud, and Land Elevation Satellite 2 (ICESat‐2) was designed to accurately measure surface heights in order to study changes to Earth's ice sheets, sea ice, and biomass. In this paper, we analyze changes in estimated surface‐height from ICESat‐2 data collected over an area in the Alaskan Arctic, where seasonal freezing and thawing of the ground causes the Earth's surface to deform with time. We compare these estimates of surface‐height change with independent estimates of surface deformation acquired by the European Space Agency's Sentinel‐1 spacecraft, which was specifically designed to precisely measure surface deformation. By comparing changes in estimated surface height from the ICESat‐2 mission to surface deformation measurements from Sentinel‐1, we demonstrate agreement of the estimated spatial patterns of surface deformation, suggesting that ICESat‐2 data can be used to quantify surface dynamics in tundras. Further, the different strengths of ICESat‐2 laser altimetry and Sentinel‐1 interferometric synthetic aperture radar (InSAR) could be jointly leveraged to provide novel insights into periglacial surface processes. We discuss several phenomena that can complicate ICESat‐2 surface‐height change estimation and introduce errors, as well as future methods that might be employed to mitigate such errors. Key Points ICESat‐2 altimetry can resolve surface subsidence that is related to changes in snow‐cover depth and seasonal thawing of the active layer ICESat‐2 measurements of surface‐height change are affected by along‐track topographic gradients and complex surface roughness Complementary ICESat‐2 and InSAR datasets can be jointly leveraged for future studies in periglacial environments

In addition to spaceborne Interferometric Synthetic Aperture Radar (InSAR), airborne data such as those obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) have also been utilized to measure surface subsidence in permafrost areas in recent years. Motivated by the integration of multiplatform InSAR data, we generated two UAVSAR interferograms and one Advanced Land Observing Satellite (ALOS)‐2 L‐band interferogram over a permafrost area near Yellowknife, Canada, then compared the surface subsidence in the thaw seasons of 2017. The correlation coefficient and the root mean square error (RMSE) of subsidence difference are calculated to compare the airborne and spaceborne InSAR measurements. The results demonstrate that the two UAVSAR measurements are self‐consistent, with the correlation coefficient between independent airborne measurements ∼0.7. While the RMSE of the difference between surface subsidence measured by UAVSAR and ALOS2 is ∼2.0 cm, and the correlation coefficients are less than 0.41, that is, a noticeable deviation exists between the UAVSAR and ALOS2 results possibly due to different spatial resolution and the calibration processing of airborne and spaceborne InSAR data. In addition, both UAVSAR and ALOS2 interferograms show larger surface subsidence within taiga needleleaf forest regions than in regions of other biome types (including needleleaf forest, shrubland, and grassland). The results demonstrate that a scheme for the elimination of systematic differences needs to be developed before merging multisource InSAR results. This intercomparison will provide valuable insights for narrowing the gap between radar‐based measurements and planning the integration of airborne and satellite InSAR measurements in permafrost environments. Key Points We compare the seasonal subsidence derived from airborne and satellite InSAR measurements Two UAVSAR interferograms are self‐consistent while a significant deviation exists between the UAVSAR and ALOS2 results We discuss the difference and the potential combination use of spaceborne and airborne InSAR to improve subsidence measurements over permafrost regions