Health and environmental hazards related to high pollutant concentrations have become a serious issue from the perspectives of public policy and human health. The objective of this research is to improve the estimation of grid-wise PM2.5, a criteria pollutant, by reducing systematic bias in estimating PM2.5 empirically from speciation provided by MERRA-2 using a ML approach. We present a unique application of machine learning (ML) for estimating hourly PM2.5 concentrations at grid points of Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). The model was trained using various meteorological parameters and aerosol species simulated by MERRA-2 and ground measurements from Environmental Protection Agency (EPA) air quality system (AQS) stations. monitors. The ML approach significantly improved performance and reduced mean bias in the 0-10 µg m-3 range. We also used the Random Forest ML model for each EPA region using one year of collocated datasets. The resulting ML models for each EPA region were validated and the aggregate data set has a Pearson correlation of 0.88 (RMSE = 4.8 µg m-3) and 0.82 (RMSE = 5.8 µg m-3) for training and testing, respectively. The correlation (and RMSE) increased to 0.89 (4.0), 0.95 (1.6), 0.94 (1.1) for daily, monthly, and yearly average comparisons. The results from initial implementation of the ML model for global region are encouraging but require more research and development to overcome challenges associated with data gaps in many parts of the world.

We quantify the shape of hooked hairs which is a newly observed phenotype of epidermal cell extensions [1] in the common bean genotype L88-57 (Phaseolus vulgaris). The hooked hairs emerge below-ground before the root hairs and have a distinct ‘hooking’ morphology. We generated a dataset capturing their full distribution under the microscope within 3-5 days of germination. We quantify their shape by a novel computational pipeline that can automatically phenotype morphology. Our phenotyping pipeline quantifies traits like length, curvature, perimeter, area, and ‘hooking.’ Our objective is to quantify their response to nutrient stress to determine the function of hooked hairs in common bean during early development. We used the pipeline for analyzing our dataset of hydroponically grown beans and observed statistically significant responses compared to the control for length, curvature, perimeter, and area to nitrogen (p<0.001**) and phosphorus (p<0.001**) stress treatments. The calculation of ‘hooking’ for our dataset is still ongoing. We are simultaneously developing a landmark-free method for the two-dimensional shape analysis of our dataset and believe that our phenotyping efforts will enable the high-throughput analysis of morphological root hair traits for any plant species.

Important progress has been made in recent years in characterizing surface soil moisture (SSM) at regional scales, through remote sensing estimates and the implementation of new in situ networks. Each of these sources of information has intrinsic features, such as the dynamic range of the SSM and the temporal frequency of acquisition. Another relevant factor is the period of data availability. Improving the knowledge of the limitations and biases of these features is crucial to increase the potential and the consistency of data sources validations. As a case of study we considered an agricultural area in the Argentinean Pampas, characterized by a sub-humid climate with a marked seasonal dynamic. It also holds a synchronized cropping rhythm and is subject to flooding and waterlogging that can last from days to months. The features mentioned above and considering that the region is almost devoid of irrigation, offer a natural laboratory that is distinguished by a wide dynamic range of SSM conditions. In this context, we analyze and expose different sources of SSM data gaps over long periods of time, using information from in situ stations and from the SMOS and SMAP satellite systems, during 2015-2019. We found SMAP data gaps resulting from the filtering of high SSM signals that are not spurious but typical for this flood-prone region. Reports from national institutions and comparison with other data sources allowed us to identify that high soil water content in the same period in which the data gaps occurred. In a different way, the SMOS register has a low-frequency range of data due to radio frequency interference over the study area. This data gap occurs during a long-anomalously wet period and it is relevant to take it into account when analyzing SMOS data for the full period. Our study shows the importance of using multiple sources of information and the relevance of examining the availability of data.

Harmful algal blooms (HABs) caused by the dinoflagellate Karenia brevis on the West Florida Shelf have become a nearly annual occurrence causing widespread ecological and economic harm. Effects range from minor respiratory irritation and localized fish kills to large-scale and long-term events causing massive mortalities to marine organisms. Reports of hypoxia on the shelf have been infrequent; however, there have been some indications that some HABs have been associated with localized hypoxia. We examined oceanographic data from 2004 to 2019 across the West Florida Shelf to determine the frequency of hypoxia and to assess its association with known HABs. Hypoxia was present in 5 of the 16 years examined and was always found shoreward of the 50-meter bathymetry line. There were 2 clusters of recurrent hypoxia: midshelf off the Big Bend coast and near the southwest Florida coast. We identified 3 hypoxic events that were characterized by multiple conductivity, temperature, and depth (CTD) casts and occurred concurrently with extreme HABs in 2005, 2014, and 2018. These HAB-hypoxia events occurred when K. brevis blooms initiated in early summer months and persisted into the fall likely driven by increased biological oxygen demand from decaying algal biomass and reduced water column ventilation due to stratification. There were also four years, 2011, 2013, 2015, and 2017, with low dissolved oxygen located near the shelf break that were likely associated with upwelling of deeper Gulf of Mexico water onto the shelf. We had difficulty in assessing the spatiotemporal extent of these events due to limited data availability and potentially unobserved hypoxia due to the inconsistent difference between the bottom of the CTD cast and the seafloor. While we cannot unequivocally explain the association between extreme HABs and hypoxia on the West Florida Shelf, there is sufficient evidence to suggest a causal linkage between them.

Barrier islands are especially vulnerable to hurricanes and other large storms, owing to their mobile composition, low elevations, and detachment from the mainland. Conceptual models of barrier-island evolution emphasize ocean-side processes that drive landward migration through overwash, inlet migration, and aeolian transport. In contrast, we found that the impact of Hurricane Dorian (2019) on North Core Banks, a 36-km barrier island on the Outer Banks of North Carolina, was primarily driven by inundation of the island from Pamlico Sound, as evidenced by storm-surge model results and observations of high-water marks and wrack lines. Analysis of photogrammetry products from aerial imagery collected before and after the storm indicate the loss of about 18% of the subaerial volume of the island through the formation of over 80 erosional washout channels extending from the marsh and washover platform, through gaps in the foredunes, to the shoreline. The washout channels were largely co-located with washover fans deposited by earlier events. Net seaward export of sediment resulted in the formation of deltaic bars offshore of the channels, which became part of the post-storm berm recovery by onshore bar migration and partial filling of the washouts with washover deposits within two months. The partially filled features have created new ponds and lowland habitats that will likely persist for years. We conclude that this event represents a setback in the overwash/rollover behavior required for barrier transgression.

Climate models project a distinct seasonality to future changes in daily extreme precipitation. In particular, models project that over land in the extratropical Northern Hemisphere the summer response is substantially weaker than the winter response in percentage terms. Here we decompose the projected response into thermodynamic and dynamic contributions and show that the seasonal contrast arises due to a negative dynamic contribution in northern summer, and a positive dynamic contribution and an anomalously strong thermodynamic contribution in northern winter. The negative dynamic contribution in northern summer is due to weakened ascent and is strongly correlated with decreases in mean near-surface relative humidity which tend to inhibit convection. Finally, we show that the summer-winter contrast is also evident in observed trends of daily precipitation extremes in northern midlatitudes, which provides support for the contrast found in climate-model simulations.

Near-Earth asteroids and meteoroids constitute various levels of impact danger to our planet. On the one end, billions of events associated with small-sized meteoroids have resulted in trivial effects. On the other end, the occurrences of large-sized asteroidal collisions that can cause mass extinctions and may wipe out the modern human civilization are extremely rare. In addition, large near-Earth asteroids are being monitored constantly for accurate and precise predictions of potential hazardous visits to our planet. However, small asteroids and large meteoroids can still often go under the radar and cause bolide explosions with potential of significant damage to communities on the ground. To facilitate management of bolide hazard, a number of scholarly works have been dedicated to estimation of frequencies of bolide events from a global perspective for planetary defense and mitigation. Nevertheless, few of the existing bolide frequency models were developed for local hazard management. In this presentation, the author introduces two recently developed frequency models for local management of bolide hazard. The first one, called the Dome model, computes the expected frequency of bolide explosions within a dome-shaped volume around a location. The second one, called the Coffee Cup model, is for a column-shaped volume above an area. Both models are based on empirical calibrations with historical data on energy, latitude, altitude, and frequency of bolide events. The modeling results indicate a linearly decreasing trend of frequency of bolide events from south to north latitudinally around the globe. The presented models can be applied to any location or area on Earth, including the entire surface of the planet.

The fate of organic carbon (C) in permafrost soils is important to the climate system due to the large global stocks of permafrost C. Thawing permafrost can be subject to dynamic hydrology, making redox processes an important factor controlling soil organic matter (SOM) decomposition rates and greenhouse gas production. In iron (Fe)-rich permafrost soils, Fe(III) can serve as a terminal electron acceptor, suppressing methane (CH4) production and increasing carbon dioxide (CO2) production. Current large-scale models of Arctic C cycling do not include Fe cycling or pH interactions. Here, we coupled Fe redox reactions and C cycling in a geochemical reaction model to simulate the interactions of SOM decomposition, Fe(III) reduction, pH dynamics, and greenhouse gas production in permafrost soils subject to dynamic hydrology. We evaluated the model using measured CO2 and CH4 fluxes as well as changes in pH, Fe(II), and dissolved organic C concentrations from oxic and anoxic incubations of permafrost soils from polygonal permafrost sites in northern Alaska, United States. In simulations of higher frequency oxic-anoxic cycles, rapid oxidation of Fe(II) to Fe(III) during oxic periods and gradual Fe(III) reduction during anoxic periods reduced cumulative CH4 fluxes and increased cumulative CO2 fluxes. Lower pH suppressed CH4 fluxes through its direct impact on methanogenesis and by increasing Fe(III) bioavailability. Our results suggest that models that do not include Fe-redox reactions and its pH dependence could overestimate CH4 production and underestimate CO2 emissions and SOM decomposition rates in Fe-rich, frequently waterlogged Arctic soils.

We report the characterisation of anthropogenic magnetic particulate matter (MPM) collected on leaves from roadside Callistemon trees from Lahore, Pakistan, and on known sources of traffic-related particulates to assess the potential of first-order reversal curve (FORC) diagrams to discriminate between different sources of anthropogenic magnetic particles. Magnetic measurements on leaves indicate the presence of surface-oxidised magnetite spanning the superparamagnetic (< 30 nm) to single-domain (~30-70 nm) to vortex size range (~70-700 nm). Fe-bearing particles are present both as discrete particles on the surface of larger mineral dust or carbonaceous particles and embedded within them, such that their aerodynamic sizes may be decoupled from their magnetic grain sizes. FORC diagrams of brake-pad residue specimens show a distinct combination of narrow central ridge, extending from 0-200 mT, and a low-coercivity, vertically spread signal, attributed to vortex and multi-vortex behaviour of metallic Fe. This is in agreement with scanning electron microscopy results that show the presence of metallic as well as oxidised Fe. Exhaust-pipe residue samples display a more conventional ‘magnetite-like’ signal comprising a lower coercivity central ridge (0-80 mT) and a tri-lobate signal attributed to vortex state and/or magnetostatic interactions. The FORC signatures of leaf samples combine aspects of both exhaust residue and brake-pad endmembers, suggesting that FORC fingerprints have the potential to identify and quantify the relative contributions from exhaust and non-exhaust (brake-wear) emissions. Such measurements may provide a cost-effective way to monitor the changing balance of future particulate emissions as the vehicle fleet is electrified over the coming years.

We aim to identify the relative importance of vapour pressure deficit (VPD), soil water content (SWC) and photosynthetic photon flux density (PPFD) as drivers of tree canopy conductance, which is a key source of uncertainty for modelling vegetation responses under climate change. We use sap flow time series of 1858 trees in 122 sites from the SAPFLUXNET global database to obtain whole-tree canopy conductance (G). The coupling, defined as the percentage of variance (R2) of G explained by the three main hydrometeorological drivers (VPD, SWC and PPFD), was evaluated using linear mixed models. For each hydrometeorological driver we assess differences in coupling among biomes, and use multiple linear regression to explain R2 by climate, soil and vegetation structure. We found that in most areas tree canopy conductance is better explained by VPD than by SWC or PPFD. We also found that sites in drylands are less coupled to all three hydrometeorological drivers than those in other biomes. Climate, soil and vegetation structure were common controls of all three hydrometeorological couplings with G, with wetter climates, fine textured soils and tall vegetation being associated to tighter coupling. Differences across sites in the hydrometeorological coupling of tree canopy conductance may affect predictions of ecosystem dynamics under future climates, and should be accounted for explicitly in models.

It is predicted by both theory and models that high-altitude clouds will occur higher in the atmosphere as a result of climate warming. This produces a positive longwave feedback and has a substantial impact on the Earth’s response to warming. This effect is well established by theory, but is poorly constrained by observations, and there is large spread in the feedback strength between climate models. We use the NASA Multi-angle Imaging SpectroRadiometer (MISR) to examine changes in Cloud-Top-Height (CTH). MISR uses a stereo-imaging technique to determine CTH. This approach is geometric in nature and insensitive to instrument calibration and therefore is well suited for trend analysis and studies of variability on long time scales. In this article we show that the current MISR record does have an increase in CTH for high-altitude cloud over Southern Hemisphere (SH) oceans but not over Tropical or the Northern Hemisphere (NH) oceans. We use climate model simulations to estimate when MISR might be expected to detect trends in CTH, that include the NH. The analysis suggests that according to the models used in this study MISR should detect changes over the SH ocean earlier than the NH, and if the model predictions are correct should be capable of detecting a trend over the Tropics and NH very soon (3 to 10 years). This result highlights the potential value of a follow-on mission to MISR, which no longer maintains a fixed equator crossing time and is unlikely to be making observations for another 10 years.

Most marine plastic pollution originates on land. However, once plastic is at sea, it is difficult to determine its origin. Here we present a Bayesian inference framework to compute the probability that a piece of plastic found at sea came from a particular source. This framework combines information about plastic emitted by rivers with a Lagrangian simulation, and yields maps indicating the probability that a particle sampled somewhere in the ocean originates from a particular source. We applied the framework to the South Atlantic Ocean, focusing on floating river-sourced plastic. We computed the probability as a function of the particle age, at three locations, showing how probabilities vary according to the location and age. We computed the source probability of beached particles, showing that plastic found at a given latitude is most likely to come from the closest source. This framework lays the basis for source attribution of marine plastic.

Covid- 19 dominantly impacted the Indian agricultural sector. During the period of COVID-19 the southwest monsoon covered a major part of the country, thus resulting in an increase of 9 percent coverage in rainfall than the usual average period. Due to the good amount of rainfall the area under cultivation during the kharif season stood above 4.8% than the previous year. During, the initial lockdown period the agriculture has not been much affected and an increase in migration resulted an increase in people employed in agriculture. Through regression analysis the relationship between the yield and rainfall has been determined. The R2 values have been calculated and the spatial relationship between them has been established. Regions with higher R2 values have been found to be more dominantly affected by Covid-19, though in certain areas strong R2 has shown a weaker spatial relationship owing to certain other factors and policies taken by the Government. Therefore, regression analysis can be used as a suitable method to study the relationship of rainfall and agricultural yield during Covid-19. Keywords: Agriculture, Regression Analysis, Spatial relationship, Rainfall, Covid-19.

The interface between rivers and groundwater is a key driver for the turnover of reactive nitrogen compounds, that cause eutrophication of rivers and endanger drinking-water production from groundwater. Molecular-biological data and omics tools have been used to characterize microorganisms responsible for the turnover of nitrogen compounds. While transcripts of functional genes and enzymes are used as measures of microbial activity it is not yet clear how they quantitatively relate to actual turnover rates under variable environmental conditions. We developed a reactive-transport model for denitrification that simultaneously predicts the distributions of functional-gene transcripts, enzymes and reaction rates. Applying the model, we evaluate the response of transcripts and enzymes at the river–groundwater interface to stable and dynamic hydrogeochemical regimes. While functional-gene transcripts respond to short-term (diurnal) fluctuations of substrate availability and oxygen concentrations, enzyme concentrations are stable over such time scales. The presence of functional-gene transcripts and enzymes globally coincides with the zones of active denitrification. However, transcript and enzyme concentrations do not directly translate into denitrification rates in a quantitative way because of non-linear effects and hysteresis caused by variable substrate availability and oxygen inhibition. Based on our simulations, we suggest that molecular-biological data should be combined with aqueous chemical data, which can typically be obtained at higher spatial and temporal resolution, to parameterize and calibrate reactive-transport models.

We investigate the Chukchi and the Beaufort seas, where salty and warm Pacific Water flows in from the Bering Strait and interacts with the sea ice, contributing to its summer melt. For the first time, thanks to in-situ measurements recorded by two saildrones deployed during summer 2019 and to refined sea ice filtering in satellite L-Band radiometric data, we demonstrate the ability of satellite Sea Surface Salinity (SSS) observed by SMOS and SMAP to capture SSS freshening induced by sea ice melt, referred to as meltwater lenses (MWL). The largest MWL observed by the saildrones during this period occupied a large part of the Chukchi shelf, with a SSS freshening reaching -5 pss. it persisted for up to one month, to this MWL, induced low SSS pattern which restricted the transfer of air-sea momentum to the upper, as illustrated by measured wind speed and vertical profiles of currents. Combined with satellite-based Sea Surface Temperature, satellite SSS provides a monitoring of the different water masses encountered in the region during summer 2019. Using sea ice concentration and estimated Ekman transport, we analyse the spatial variability of sea surface properties after the sea ice edge retreat over the Chukchi and the Beaufort seas. The two MWL captured by both, the saildrones and the satellite measurements, result from different dynamics. Over the Beaufort Sea, the MWL evolution follows the meridional sea ice retreat, whereas in the Chukchi Sea, a large persisting MWL is generated by advection of a sea ice filament.

Due to the mixed distribution of buildings and vegetation, wildland-urban interface (WUI) areas are characterized by complex fuel distributions and geographical environments. The behavior of wildfires occurring in the WUI often leads to severe hazards and significant damage to man-made structures. Therefore, WUI areas warrant more attention during the wildfire season. Due to the ever-changing dynamic nature of California’s population and housing, the update frequency and resolution of WUI maps that are currently used can no longer meet the needs and challenges of wildfire management and resource allocation for suppression and mitigation efforts. Recent developments in remote sensing technology and data analysis algorithms pose new opportunities for improving WUI mapping methods. WUI areas in California were directly mapped using building footprints extracted from remote sensing data by Microsoft along with the fuel vegetation cover from the LANDFIRE dataset in this study. To accommodate the new type of datasets, we developed a threshold criteria for mapping WUI based on statistical analysis, as opposed to using more ad-hoc criteria as used in previous mapping approaches. This method removes the reliance on census data in WUI mapping, and does not require the calculation of housing density. Moreover, this approach designates the adjacent areas of each building with large and dense parcels of vegetation as WUI, which can not only refine the scope and resolution of the WUI areas to individual buildings, but also avoids zoning issues and uncertainties in housing density calculation. Besides, the new method has the capability of updating the WUI map in real-time according to the operational needs. Therefore, this method is suitable for local governments to map local WUI areas, as well as formulating detailed wildfire emergency plans, evacuation routes, and management measures.

This study focuses on the projections and time of emergence (TOE) for temperature extremes over Australian regions in the phase 6 of Coupled Model Intercomparison Project (CMIP6) models. The model outputs are based on the Shared Socioeconomic Pathways (SSPs) from the Tier 1 experiments (i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) in the Scenario Model Intercomparison Project (ScenarioMIP), which is compared with the Representative Concentration Pathways (RCPs) in CMIP5 (i.e., RCP2.6, RCP4.5 and RCP8.5). Furthermore, two large ensembles (LEs) in CMIP6 are used to investigate the effects of internal variability on the projected changes and TOE. As shown in the temporal evolution and spatial distribution, the strongest warming levels are projected under the highest future scenario and the changes for some extremes follow a “warm-get-warmer” pattern over Australia. Over subregions, tropical Australia usually shows the highest warming. Compared to the RCPs in CMIP5, the multi-model medians in SSPs are higher for some indices and commonly exhibit wider spreads, likely related to the different forcings and higher climate sensitivity in a subset of the CMIP6 models. Based on a signal-to-noise framework, we confirm that the emergence patterns differ greatly for different extreme indices and the large uncertainty in TOE can result from the inter-model ranges of both signal and noise, for which internal variability contributes to the determination of the signal. We further demonstrate that the internally-generated variations influence the noise. Our findings can provide useful information for mitigation strategies and adaptation planning over Australia.

This study deals with the riverbed of the Columbia river in the vicinity of the Hanford 300 Area study site in eastern Washington, where fluctuations in river stage take place both naturally (i.e. seasonally) and in conjunction with hydroelectric power dam operations. These fluctuations create conditions conducive to the influx and transport of fine-grained POM (a biological colloid originating from the river water and/or in situ periphyton production), within near-surface riverbed sediments. Although a great deal is known about dissolved organic matter (DOM) transport and metabolism in hyporheic zone sediments, there is a paucity of quantitative information on POM dynamics and its influence on hyporheic zone biogeochemistry (e.g. dissolved oxygen dynamics). We have developed a hydrobiogeochemical model capable of simulating the transport and metabolism of POM and its impact on dissolved oxygen (DO) distribution within the riverbed as influenced by periodic changes in river stage and fluid flow rate and direction. The model was employed as a tool to interpret the results of in situ measurements of POM intrusion into the riverbed made using “POM traps” emplaced within the upper 20 cm of the riverbed, as well as real-time in situ dissolved oxygen concentrations determined with a novel optical sensor buried directly in the riverbed at 20 cm depth. The simulations reproduced the accumulation of fresh POM within the upper few 5 cm of the riverbed observed in field POM trap deployments. Once sufficient surface POM accumulation takes place, an underlying zone of DO depletion develops as a consequence of variation in the rate of fluid exchange and POM/DOM degradation. The model predicted cyclic, hydrologically-driven variations in near-surface DO that are consistent with the results of the in situ DO probe deployments together with parallel measurements of fluid conductivity and hydrologic pressure. Our results suggest a complex interplay between fluid flow rate/direction and DO distribution that has important implication for riverbed biogeochemical dynamics at a variety of scales, as influenced by hydrological variability as well as the relative intensity of POM input and the availability of oxygen and other electron acceptors for microbial metabolism.

The impacts of global change especially the recent climate-related extremes such as floods and droughts reveal significant vulnerability and exposure of freshwater ecosystems and related human systems to current climate variability. However, the effects of the extreme drought in the Okavango Delta system are not well understood and documented. Therefore, the objective of this use case was to apply the products from Digital Earth Africa namely: the Water Observation from Space (WOfS) derived from Landsat, vegetation cover baseline derived from Sentinel 2 data; and data from the meteorological agencies such as rainfall and measured river discharge data to evaluate the effects of drought in the Okavango Delta wetland system in relation to its upstream areas in Angola. In particular, we used the 2019 drought as a case study to assess inundation extent and vegetation cover dynamics with an emphasis on floodplain and dryland vegetation. Our preliminary results reveal that the Okavango Delta permanent marshes are resilient to drought, whereas seasonal floodplains are susceptible to drought. Further, we discovered that the geospatial location of floodplains has a direct effect on the timing of desiccation, with the western tributaries that flow into Lake Ngami and Thamalakane River being the last to dry out due to drought. In addition, we found that the drought phenomenon in the Cubango-Okavango River Basin region started earlier than 2019 spanning over a period of 5 years; with 2018 as the year when the wetland system reached a minimum threshold for a tipping point triggered by the 2019 drought. In addition, the results contribute to the development of large-scale drought risk information and products for the Cubango- Okavango River Basin with a major focus in the Okavango Delta. Further, this use case provides recent baseline information on the effects of drought on vegetation cover and river flows in the Okavango Delta system at a landscape approach, which are essential elements for making informed science-based decisions on climate risks management and Sustainable Development Goals (SDGs) by relevant authorities in the Okavango Delta and the whole of Cubango-Okavango River Basin. In conclusion, this use case will be upscaled to other transboundary river basins in the Southern Africa Development Community.

Terrestrial primary productivity plays a pivotal role as a forcing factor of atmospheric CO2 and drives biospheric carbon dynamics. India is one of the largest GHGs emitters, yet less is understood in carbon cycling in terrestrial ecosystems. Here we explored the trend and magnitude of gross and net productivities of India for the last two decades (2000 – 2019) by integrating satellite observation from MODIS, remote sensing-based CASA model and twenty DGVMs from the TRENDY ensemble. Preliminary results exhibited a unimodal response across the data products with an overall positive trend and a declining decadal trend for 2010 – 2019. Alongside, the SPEI drought severity index across various ecological zones indicated India was more positively sensitive to wet span than the dry. We found that the ecosystems were drastically shifting their nature to C source with a positive trend in the productivities and were mediated by the changing climate. The analysis also revealed the increasing decadal amplitude of GPP by 0.0884 Pg C/Year, NBP by 0.0096 Pg C/Year, NEP by 0.0195 Pg C/Year, NPP by 0.0448 Pg C/Year and NEE by 0.0161 Pg C/Year. CASA underestimated the magnitudes but with the temporal synchronisation of the ensemble. Seasonal variability across the agro-ecological zones was more sensitive and was an offset for the declining productivities in the primaeval forests of India. The monsoon season contributed to the interannual variability of India. Higher uncertainty in productivities was observed in the high greening areas, whereas it contradicted NBP by reflecting a stable trend. Our results underscore the nature of C variability in the terrestrial ecosystems of India; and, they indicate that C release has reacted stronger than the C uptake, which was substantially inferred from NEE across the ecological zones.

Radon is a natural radioactive gas accounting for approximately one in ten lung cancer deaths, with substantially higher death rates in sub-Arctic communities. Radon transport is significantly reduced in permafrost, but permafrost is now thawing due to climate change. The effect of permafrost thawing on domestic radon exposure is unknown. Here we present results from radon transport modeling through soil, permafrost and model buildings either with basements or built on piles. We find that permafrost acts as an effective radon barrier, reducing radiation exposure to a tenth of the background level, while producing a ten-fold increase in the radon activity behind the barrier. When we model thawing of the permafrost barrier, we find no increase in radon to the background level for buildings on piles. However, for buildings with basements the radon increases to over one hundred times its initial value and can remain above the 200 Bq/m3 threshold for up to seven years depending on the depth of the permafrost and the speed of thawing. When thawing speed is taken into account, radiations remains higher than the threshold for all scenarios where 40% thawing occurs within 15 years. This new information suggests that a significant sub-Arctic population could be exposed to radon levels dangerous to health as a result of climate change thawing of permafrost, with implications for health provision, building codes and ventilation advice.

Minnesota is the U.S. state with the strongest winter warming in the contiguous United States. We performed regional climate projections at 10 km horizontal resolution using the WRF model forced by an ensemble of eight CMIP5 GCMs. The selected GCMs have previously been found to be in relatively good agreement with observations compared to other members of the CMIP5 model ensemble. Our projections suggest ongoing warming in all seasons, especially in winter, as well as shallower snow cover and fewer days with snow cover. On the other hand, we expect significant increases in spring and early summer heavy precipitation events. Our comparisons between different time slices and two different emission scenarios indicate a climate for the state of Minnesota at the end of the 21st century that is significantly different from what has been observed by the end of the 20th century. Winters and summers are expected to be up to 6oC and 4oC warmer, respectively, over northern and central Minnesota and spring precipitation may increase by more than 1 mm d-1 over northern Minnesota. Especially over the central part of the state, winter snow height is suggested to decrease by more than 0.5 meters and the number of days per year with snow height of more than 0.0254 meters (one inch) is expected to decrease by up to 60.

Terrestrial ecosystems of Canada store a large amount of organic carbon (C) in soils, peats and plant materials, yet little is known about the C stock size and distributions, both spatially and in various C pools. As temperature rises, C is becoming available for disturbance, decomposition and eventual release into the atmosphere, which makes the quantification of C stocks in terrestrial ecosystems of Canada of high interest for the assessment of climate change impacts and conservation efforts. We used a large number of field measurements, multisource satellite, climate and topographic data and a machine learning algorithm to produce the first wall-to-wall estimates of C stocks and uncertainties in plants and soils of Canada at 250 m spatial resolution. Our findings show that above and belowground live biomass and detritus store a total of 21.1 Pg C. Whereas the Canadian soils store 384 (±214, 90% confidence interval) Pg organic C in the top 1 m, 92 Pg C of which are stored in peatlands, confirming that the soil organic C dominates terrestrial carbon stocks in Canada. We also find previously under-reported large soil organic C stocks in forested peatlands on the boreal shields of Canada. Given that Canada is warming twice the global average rate and Canadian soils store approximately 25% of world soil C stocks in top 1 m, initiatives to understand their vulnerabilities to climate change and disturbance are indispensable not only for Canada but also for the global C budget and cycle.

Several bills moving through Congress are likely to provide significant funding for expanding research and results in climate change solutions (CCS). This is also a priority of the Biden-Harris Administration. The National Science Foundation (NSF) will be expected to distribute and manage much of this funding through its grant processes. Effective solutions require both a continuation and expansion of research on climate change–to understand and thus plan for potential impacts locally to globally and to continually assess solutions against a changing climate–and rapid adoption and implementation of this science with society at all levels. NSF asked AGU to convene its community to help provide guidance and recommendations for enabling significant and impactful CCS outcomes by 1 June. AGU was asked in particular to address the following: 1. Identify the biggest, more important interdisciplinary/convergent challenges in climate change that can be addressed in the next 2 to 3 years 2. Create 2-year and 3-year roadmaps to address the identified challenges. Indicate partnerships required to deliver on the promise. 3. Provide ideas on the creation of an aggressive outreach/communications plan to inform the public and decision makers on the critical importance of geoscience. 4. Identify information, training, and other resources needed to embed a culture of innovation, entrepreneurialism, and translational research in the geosciences. Given the short time frame for this report, AGU reached out to key leaders, including Council members, members of several committees, journal editors, early career scientists, and also included additional stakeholders from sectors relevant to CCS, including community leaders, planners and architects, business leaders, NGO representatives, and others. Participants were provided a form to submit ideas, and also invited to two workshops. The first was aimed at ideation around broad efforts and activities needed for impactful CCS; the second was aimed at in depth development of several broad efforts at scale. Overall, about 125 people participated; 78 responded to the survey, 82 attended the first workshop, and 28 attended the more-focused second workshop (see contributor list). This report provides a high-level summary of these inputs and recommendations, focusing on guiding principles and several ideas that received broader support at the workshops and post-workshop review. These guiding principles and ideas cover a range of activities and were viewed as having high importance for realizing impactful CCS at the scale of funding anticipated. These cover the major areas of the charge, including research and solutions, education, communication, and training. The participants and full list of ideas and suggestions are provided as an appendix. Many contributed directly to this report; the listed authors are the steering committee.