In the sea ice-impacted Southern Ocean, the spring melt of sea ice modifies the upper ocean. These modified waters subduct and enter the global overturning circulation. Submesoscale processes act to modulate the stratification of the mixed layer and therefore mixed layer properties. Sparse observations mean that the role of submesoscales in exchange across the base of the mixed layer in this region is not well constrained. The goal of this study is to determine the interplay between sea ice melt, surface boundary layer forcing, and submesoscale flows in regulating the mixed layer structure in the Antarctic Marginal Ice Zone. High-resolution observations suggest that fine-scale lateral fronts, representative of submesoscale mixed layer eddies (MLEs), arise from mesoscale gradients produced by northwards advecting sea ice meltwater. The strong salinity-driven stratification at the base of the mixed layer confined the MLEs to the upper ocean, limiting submesoscale vertical fluxes across the mixed layer base. This strong stratification prevents the local subduction by submesoscale flow of these modified waters, suggesting that the subduction site that links to the global overturning circulation does not correspond with the location of sea ice melt. However, the presence of MLEs enhanced the magnitude of lateral gradients through stirring and increased the potential for Ekman-driven cross-frontal flow to modulate the stability of the mixed layer and mixed layer properties. The inclusion, particularly of submesoscale Ekman Buoyancy Flux parameterizations, in coupled-climate models, may improve the representation of mixed layer heat and freshwater transport in the ice-impacted Southern Ocean during summer.

The Greater Mekong Subregion is a transnational area bound together by the Mekong River basin and its immense hydropower resources, historically seen as the backbone of regional economic development. The basin is now punctuated by several dams, successful in attracting both international investors and fierce criticisms for their environmental and societal impacts. Surprisingly, no attention has been paid so far to the actual performance of these infrastructures: is hydropower supply robust with respect to the hydro-climatic variability characterizing Southeast Asia? When water availability is altered, what are the implications for power production costs and CO2 emissions? To answer these questions, we focus on the Laotian–Thai grid—the first international power trade infrastructure developed in the region—and use a power system model driven by a spatially-distributed hydrological-water management model. Simulation results over a 30-year period show that production costs and carbon footprint are significantly affected by droughts, which reduce hydropower availability and increase reliance on thermoelectric resources. Regional droughts across the Mekong basin are of particular concern, as they reduce the export of cheap hydropower from Laos to Thailand. To put the analysis into a broader climate-water-energy context, we show that the El Niño Southern Oscillation modulates not only the summer monsoon, but also the power system behaviour, shaping the relationship between hydro-climatological conditions, power production costs, and CO2 emissions. Overall, our results and models provide a knowledge basis for informing robust management strategies at the water-energy scale and designing more sustainable power plans in the Greater Mekong Subregion.

The Advanced Topographic Laser Altimeter System (ATLAS) on ICESat-2 offers a new remote sensing capability to measure complex sea ice surface topography. We demonstrate the retrieval of six sea ice parameters from ICESat-2/ATLAS data: surface roughness, ridge height, ridge frequency, melt pond depth, floe size distribution and lead frequency. Our results establish that these properties can be observed in high fidelity, across broad geographic regions and ice conditions. We resolve features as narrow as 7 m, and achieve a vertical height precision of 0.01 m, representing a significant advance in resolution over previous satellite altimeters. ICESat-2 employs a year-round observation strategy spanning all seasons, across both the Arctic and Southern Oceans. Because of its higher resolution, coupled with the spatial and temporal extent of data acquisition, ICESat-2 observations may be used to investigate time-varying, dynamic and thermodynamic sea ice processes.

The Advanced Topographic Laser Altimeter System (ATLAS) was launched aboard the Ice Cloud and land-Elevation Satellite-2 (ICESat-2) satellite in September 2018. ATLAS is a single wavelength (532 nm) lidar system designed to acquire high resolution measurements of the earth’s surface while also obtaining atmospheric backscatter from molecules, clouds, and aerosols. Because ATLAS is optimized for altimetry, the atmospheric data acquired is unique in many respects and requires non-standard analysis techniques. For example, the high repetition rate laser limits the vertical extent of the profiles to just 14 km and causes atmospheric scattering from above 15 km to be added to the scattering in the lower 0 -14 km profile. In addition, the limited vertical range of the acquired profiles renders it difficult to compute the magnitude of the solar background and hinders the application of standard calibration techniques. Despite these limitations, techniques have been developed to successfully produce data products that have value to the atmospheric community for cloud and aerosol research and are currently available at the National Snow and Ice Data Center (NSIDC). In this paper we describe the ICESat-2 atmospheric channel and the methods used to process the ATLAS raw photon count data to obtain calibrated backscatter and higher level products such as layer heights and type, blowing snow, column optical depth and apparent surface reflectance.

During June to July, 2020, persistent heavy precipitation in the Yangtze River Basin (YRB) is resulting in extensive flooding, with over 158 fatalities and tremendous economic losses. This year’s disastrous flooding extreme is exceptionally different from those of other years. It contains over 1000-year return period events (for 30-day cumulative precipitation) as observed in Anhui, Guizhou and Sichuan Provinces. The mean precipitation is 308 mm in July 2020, being 54 mm higher than that of July 1998, when serious floods affected the entire Basin causing tremendous socio-economic consequences. Compared with 1998, the short-term (e.g., 1 day) precipitation in YRB did not show significant increases, while the long-term (e.g., 30 days) cumulative precipitation increases significantly. The highest observed 30-day cumulative precipitation is 1221 mm (in Anhui Province) in 2020, while the highest one in 1998 was 1028 mm (in Jiangxi Province). We thus find that this persistent heavy precipitation is the main cause of flooding in 2020. At the same time, TGR may mitigate up 43% of upstream flood, although the main contributors to this year’s YRB flood are in the middle and lower reaches. Affected by COVID-19, the number of people at risk in the threatened area are increased, and their capacities to mitigate the dual impacts of COVID-19 pandemic and flooding are hindered since (a) the flooding-caused mitigations may limit people’s ability to prevent from virus spreading, and (b) the pandemic is retaining a large amount of migrant workers being within YRB and subject to flooding impacts. Overall, our main discovery is that, although the short-term precipitation in YRB did not increase significantly in 2020, the cumulative one increased significantly in 2020!

Satisfying global food demand places a significant burden on finite resources like water, energy, and land across the world. Ensuring sustainable food production requires an understanding of water, energy, and land at local scales, which is often hampered by the lack of detailed data. Taking advantage of several large datasets, this study focused on the interactions between groundwater, energy, land, and food (WELF) at multiple spatial scales in the Indian state of Andhra Pradesh. Using FAO’s CROPWAT model, MODIS evapotranspiration data, and a detailed multiscale agricultural database, we estimated the irrigation water requirements at multiple spatial scales. A detailed estimate of groundwater and energy consumption was obtained, which was used to infer the interactions between WELF. The results from our study allow us to identify local and regional hotspots of groundwater and energy consumption. Our results indicate that regional estimates of groundwater or energy consumption, while indicative of local conditions, are unable to capture the variations at finer scales. The interactions between WELF at multiple scales also indicate that the efficiency of groundwater and energy consumption for various crops varies widely across and within regions. Our study highlights the need to more effectively manage the cultivation of water-intensive crops such as paddy and sugarcane to optimize groundwater consumption. Policy-makers must focus on transitioning towards sustainable agriculture that prioritizes food and water security. Future research must take advantage of extensive datasets to better evaluate the adverse effects on water and energy resources to ensure sustainable food production.

Carbon monoxide (CO) is an ozone precursor, oxidant sink, and widely-used pollution tracer. The importance of anthropogenic versus other CO sources in the US is uncertain. Here we interpret extensive airborne measurements with an atmospheric model to constrain US fossil and non-fossil CO sources. Measurements reveal a low bias in the simulated CO background and a 30% overestimate of US fossil CO emissions in the 2016 National Emissions Inventory. After optimization we apply the model for source partitioning. During summer, regional fossil sources account for just 9-16% of the sampled boundary layer CO, and 32-38% of the North American enhancement-complicating use of CO as a fossil fuel tracer. The remainder predominantly reflects biogenic hydrocarbon oxidation plus fires. Fossil sources account for less domain-wide spatial variability at this time than non-fossil and background contributions. The regional fossil contribution rises in other seasons, and drives ambient variability downwind of urban areas.

Brazilian tropical woodlands, such as the wooded Cerrado, perform hydrological functions that need to be well understood by field data acquisition and mathematical modeling. Here, we aimed to assess the water partitioning behavior and variability of a wooded Cerrado fragment located in Southeastern Brazil by (i) measuring fluxes using eddy covariance; (ii) applying machine learning techniques to obtain a model to estimate the evapotranspiration (ET) using meteorological data as input: solar radiation (Rg), wind speed (WS), temperature (T), relative humidity (RH), and rainfall (P); and (iii) simulating a long-term water balance using stochastic climate generator inputs and the previously calibrated ET model. The average observed ET was 3.12±0.93 mm d) and P (1227±208 mm yr-1) have similar standard deviations; differently from the ET (1054±46 mm yr-1), which presented higher annual rates with a small variability throughout the simulation.

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Recently, many studies have demonstrated the use of teleseismic P wave coda autocorrelation for imaging lithosphere structures. However, the reliability of the extracted reflections remains uncertain and a means of evaluation is lacking. In this paper, we propose a velocity analysis method that conveniently solves this problem in place of a synthetic experiment. This method considers the average velocity used for the horizontal slowness correction as an unknown quantity, and then uses the continuously varying average velocity for the horizontal slowness correction. Finally, this method obtains a stacked result that varies with the average velocity and the vertical two-way travel time to produce a va−t0 diagram. This method is similar to the velocity analysis method used in exploration geophysics. In this diagram, reliable reflections correspond to focused energy clusters, while noise lacks this feature. Therefore, this method helps determine which reflections are reliable, while also finding the appropriate parameters for data processing. Synthetic data tests were performed to demonstrate the validity of this method, as well as a test of field data for station BOSA, which illustrates the successful application of the method in the case of a sharp and flat Moho discontinuity. Finally, we applied the method to the NCISP-6 dense array, and observed obvious energy clusters representing reflections from the Moho discontinuity in the results of most stations. The depth and shape of the Moho discontinuity determined by this test is consistent with receiver function results, which verifies the robustness of this method in relatively complex applications.

The long-chain diol index (LDI) is a relatively new proxy for sea surface temperature (SST) which has been rarely applied in upwelling regions. Here, we evaluated its application by comparison with other SST records obtained by commonly used proxies, i.e. the Mg/Ca ratio of the planktonic foraminifera species 37 and Mg/CaG.ruber SST records revealed an increase of 0.6-3.4°C from the Last Glacial Maximum to the late Holocene with somewhat higher amplitude in the northern part of the Arabian Sea than compared to the western part. In contrast, the LDI SSTs did not reveal major changes during the last glacial-interglacial transition which was followed by a decreasing trend during the Holocene. The LGM vs the Holocene LDI SSTs ranged between -0.2 and -2.7°C. Particularly at one record, offshore Oman, the SST decrease from during the Holocene was high in amplitude, suggesting a potential cold bias, possibly related to changes in upwelling intensity during the Holocene. This indicates that care has to be taken when applying the LDI for annual mean SST reconstruction in upwelling regions.

Aerosols acting as cloud condensation nuclei (CCN) play a fundamental role in droplet formation and cloud development in the atmosphere. Several studies have focused on the representation of aerosol-cloud interactions to improve cloud parametrizations and deep moist convection representation in numerical models. However, the effect of aerosol concentration on mixed-phase clouds is complex as it can not be directly measured and it is far from being fully understood. Then, observation of CCN distributions, variability, and exploration of possible sources can help go deeper into the effects of aerosols on cloud and precipitation processes. From October 2018 to April 2019, Cloud, Aerosol, and Complex Terrain Interactions field campaign (CACTI) took place in central Argentina. An ARM-DOE Mobility Facility was installed in Villa Yacanto, Cordoba to understand the processes affecting orographic deep convective initiation in the region. CCNs in situ observations from a Mobile Aerosol Observation System were analyzed. Amazon biomass burning is one of the known sources of aerosols in central and northern Argentina however during CACTI measurement period low rate emissions were observed from this source. Then, local aerosol source regions were investigated by backward trajectories with lagrangian dispersion model FLEXPART to determine their possible interaction with extreme precipitation events.

COVID-19’s impact on society and our daily habits has been unprecedented. With a decrease in vehicular traffic and industrial production, a decrease in local emissions was expected to occur. In order to capture any trends in ambient trace gas concentrations, approximately one thousand whole air samples were collected in intervals across the United States from April to July 2020 as part of the NASA Student Airborne Research Program (SARP). These samples were then analyzed by the UCI Rowland-Blake Lab using multi-column gas chromatography for over one hundred unique trace gases, including methane, non-methane hydrocarbons, and halocarbons, as described in Colman et al. (2001) and Barletta et al. (2002). Initial samples collected in April coincided with the peak of stay-at-home/social distancing orders in most states while samples collected later in the spring and early summer reflect the easing of these measures and initial state reopenings. Overall trends in emissions over time in select metropolitan areas will be discussed and compared to trends observed across the entire United States.

There is a growing need to address water security issues in Canada in relation to global warming effects and water management issues. Stable water isotopes (𝛿2H and 𝛿18O) have become a popular tool to investigate regional and country level public water distribution systems to identify regional water resource issues and their underlying causes. In this study, we built the first national level maps of isotopic values in tap water across Canada using 576 tap water samples that were collected during the summer season. Observed isotopic values in tap water follow similar patterns as precipitation with highest isotopic values in the coastal and Great Lakes regions and a progressive decrease as continentality increases with lowest values found in the Rocky Mountains. We classified the tap water samples based on their supply sources including groundwater, river and lake. The isotopic values of tap water sourced from groundwater and river are well-predicted by those of local annual precipitation whereas those sourced from lakes are poorly predicted. To explain this difference, we constructed a series of water balance models to predict the 𝛿2H and 𝛿18O variability of surface water across Canada. We used a digital elevation model to accumulate the isotopic composition of local precipitation weighed by precipitation (total or effective) either monthly, seasonally or annually. These water balance models strongly improved the predictability of isotopic values of tap waters sourced from rivers and lakes (particularly the annual average and monthly weighted summer models, with or without accounting for evapotranspiration). Conversely, these models did not improve predictability of the isotopic values in tap water sourced from groundwater. We argue that isotope values of tap water sourced from surface water reflect the accumulation and evapotranspiration of precipitation in catchments whereas groundwater represents an annual average of the isotopic composition of local precipitation. Regionally, we find that winter precipitation and snow melt and glaciers melt from the Rockies contribute to a large part of tap water sources around the Rockies making them vulnerable to water balance changes with ongoing climate warming. In eastern Canada water management processes appear to create some significant evaporative water losses whereas in central Canada dry continental climate causes significant evaporative water loss. Our study highlights the cycling of water from precipitation to tap water across Canada for different tap water supply with implications for water managements, contamination risks and vulnerability to climate change. Our models and databases also provide an isotopic baseline for regionally important water resources monitoring as well as for human forensic cases.

Climate and weather extremes such as heat waves, droughts, extreme precipitation events or cold surges have huge social and economic impacts that are expected to increase with climate change. Forecasting of such extreme events on the sub-seasonal time scale (from 10 days to about 3 months) is very challenging because of the poor understanding of phenomena that may increase predictability at this time scale. The Madden-Julian Oscillation (MJO) is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales and can also promote or enhance phenomena such as monsoons and hurricanes in other regions of the world. Here we develop artificial neural networks that can lead to a very competitive MJO prediction. While our average prediction skill is about 26-27 days (which competes with that obtained with computationally demanding state-of-the-art climate models), for some initial phases the methodology has a prediction skill of 60 days or longer.

Intertidal creeks (<5m width) weave through salt marshes, delivering water, nutrients, and sediments into the marsh interior and alter spatial heterogeneity in plant and animal distributions. Despite their global prevalence, creek connectivity, and the mechanisms controlling cross-marsh hydrodynamics, remain poorly resolved. In this study, we measured flow and total suspended solids (TSS) transport in three intertidal creeks within a confined drainage basin in a Georgia, USA salt marsh. We discovered that the effective drag is 3 to 12 times greater than bed drag, reaching levels similar to those observed in coral reefs. Furthermore, the drag between tidal flood and ebb phases differ, suggesting a non-symmetric drag. Analyses of along-channel momentum reveal that the friction O(10E-3 - 10E-2 ) and pressure gradient O(10E-3 - 10E-2) dominate creek momentum balance. Divergence in tidal and TSS transport between adjacent creeks reveals opposing tidal asymmetries within this confined basin. We suggest that these differences may mediate the eco-geospatial evolution of salt marshes and their response to sea level rise.

The analysis in real time of space data variability is essential for scientists and space mission controllers. Automated tools designed to extract key descriptors of variability are needed and solutions to adapt such algorithms for on-board computers are rare. This paper describes the design of an automated system for detecting directional discontinuities of a physical quantity and its implementation in Field-Programmable Gate Array (FPGA). The system is currently adapted for solar wind or terrestrial magnetosheath magnetic field directional discontinuities, i.e. sharp changes of the magnetic field directionality. Our detection algorithm uses analysis windows of adjustable width and averaging procedures in order to reduce the effects of random fluctuations. A sliding-window approach is designed for continuous monitoring and detection of magnetic directional discontinuities. A software implementation of the algorithm was tested using in-situ magnetic field measurements, and emphasised improvements of performance when using analysis windows of adjustable width. The FPGA implementation of the detection algorithm is built on DILIGENT Nexys 4 DDR featuring a comercial Xilinx Artix-7 device and is designed to be ported to space qualified infrastructure. The FPGA system was tested with synthetic and laboratory signals, and provides results in very good agreement with the software implementation. The FPGA system provides an efficient real-time monitoring solution using minimal computational and energy resources, and reducing the main on-board computer utilization.

A variety of smoke model frameworks are used to simulate smoke for research and forecast applications. Here, a comprehensive summary is provided which covers the many different smoke models that are available, while simultaneously highlighting some of the strengths and weaknesses of each model, along with the uncertainties surrounding each of these frameworks. This review also provides an in-depth discussion on coupled wildfire-atmosphere models, which is a relatively newer smoke modeling tool not previously discussed in other review papers. Key processes related to smoke transport and dispersion, such as the wildfire plume rise, are also discussed in length. This review wraps up with a discussion of future smoke modeling needs and potential new research directions for smoke transport and dispersion models.