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At least 199 records · Page 11

On the Feasibility of Monitoring Carbon Monoxide in the Lower Troposphere from a Constellation of Northern Hemisphere Geostationary Satellites (PART 1)

By the end of the current decade, there are plans to deploy several geostationary Earth orbit (GEO) satellite missions for atmospheric composition over North America, East Asia and Europe with additional missions proposed. Together, these present the possibility of a constellation of geostationary platforms to achieve continuous time-resolved high-density observations over continental domains for mapping pollutant sources and variability at diurnal and local scales. In this paper, we use a novel approach to sample a very high global resolution model (GEOS-5 at 7 km horizontal resolution) to produce a dataset of synthetic carbon monoxide pollution observations representative of those potentially obtainable from a GEO satellite constellation with predicted measurement sensitivities based on current remote sensing capabilities. Part 1 of this study focuses on the production of simulated synthetic measurements for air quality OSSEs (Observing System Simulation Experiments). We simulate carbon monoxide nadir retrievals using a technique that provides realistic measurements with very low computational cost. We discuss the sampling methodology: the projection of footprints and areas of regard for geostationary geometries over each of the North America, East Asia and Europe regions; the regression method to simulate measurement sensitivity; and the measurement error simulation. A detailed analysis of the simulated observation sensitivity is performed, and limitations of the method are discussed. We also describe impacts from clouds, showing that the efficiency of an instrument making atmospheric composition measurements on a geostationary platform is dependent on the dominant weather regime over a given region and the pixel size resolution. These results demonstrate the viability of the "instrument simulator" step for an OSSE to assess the performance of a constellation of geostationary satellites for air quality measurements.

GEOS-5↗

Analysis of Summer-Time Ozone and Precursor Species in the Southeast United States

Ozone (O3) is a greenhouse gas and toxic pollutant which plays a major role in air quality and atmospheric chemistry. The understanding and ability to model the horizontal and vertical structure of O3 mixing ratios is difficult due to the complex formation/destruction processes and transport pathways that cause large variability of O3. The Environmental Protection Agency has National Ambient Air Quality Standards for O3 set at 75 ppb with future standards proposed to be as low as 65 ppb. These lower values emphasize the need to better understand/simulate the transport processes, emission sources, and chemical processes controlling precursor species (e.g., NOx, VOCs, and CO) which influence O3 mixing ratios. The uncertainty of these controlling variables is particularly large in the southeast United States (US) which is a region impacted by multiple different emission sources of precursor species (anthropogenic and biogenic) and transport processes resulting in complex spatio-temporal O3 patterns. During this work we will evaluate O3 and precursor species in the southeast US applying models, ground-based and airborne in situ data, and lidar observations. In the summer of 2013, the UAH O3 Differential Absorption Lidar (DIAL) (part of the Tropospheric Ozone Lidar Network (TOLNet)) measured vertical O3 profiles from the surface up to approximately 12 km. During this period, the lidar observed numerous periods of dynamic temporal and vertical O3 structures. In order to determine the sources/processes impacting these O3 mixing ratios we will apply the CTM GEOS-Chem (v9-02) at a 0.25 deg x 0.3125 deg resolution. Using in situ ground-based (e.g., SEARCH Network, CASTNET), airborne (e.g., NOAA WP-3D - SENEX 2013, DC-8 - SEAC4RS), and TOLNet lidar data we will first evaluate the model to determine the capability of GEOS-Chem to simulate the spatio-temporal variability of O3 in the southeast US. Secondly, we will perform model sensitivity studies in order to quantify which emission sources (e.g., anthropogenic, biogenic, lighting, wildfire) and transport processes (e.g., stratospheric, long-range, local scale) are contributing to these TOLNet-observed dynamic O3 patterns. Results from the evaluation of the model and the study of sources/processes impacting observed O3 mixing ratios will be presented.

Optical Radar↗

Considering Combined or Separated Roughness and Vegetation Effects in Soil Moisture Retrievals

For more than six years, the Soil Moisture and Ocean Salinity (SMOS) mission has provided multi angular and full-polarization brightness temperature (TB) measurements at L-band. Geophysical products such as soil moisture (SM) and vegetation optical depth at nadir (tau(sub nad)) are retrieved by an operational algorithm using TB observations at different angles of incidence and polarizations. However, the quality of the retrievals depends on several surface effects, such as vegetation, soil roughness and texture, etc. In the microwave forward emission model used in the retrievals (L-band Microwave Emission Model, L-MEB),soil roughness is modeled with a semi-empirical equation using four main parameters (Q(sub r), H(sub r), N(sub rp), with p = H or V polarizations). At present, these parameters are calibrated with data provided by airborne studies and in situ measurements made at a local scale that is not necessarily representative of the large SMOS footprints (43 km on average) at global scale. In this study, we evaluate the impact of the calibrated values of N(sub rp) and H(sub r) on the SM and tau(sub nad) retrievals based on SMOS TB measurements (SMOS Level 3 product) over the Soil Climate Analysis Network (SCAN) network located in North America over five years (2011-2015). In this study, Qr was set equal to zero and we assumed that N(sub rH)= N(sub rV). The retrievals were performed by varying N(sub rp) from −1 to 2 by steps of 1 and H(sub r) from 0 to 0.6 by steps of 0.1. At satellite scale, the results show that combining vegetation and roughness effects in a single parameter provides the best results in terms of soil moisture retrievals, as evaluated against the in situ SM data. Even though our retrieval approach was very simplified, as we did not account for pixel heterogeneity, the accuracy we obtained in the SM retrievals was almost systematically better than those of the Level 3 product. Improved results were also obtained in terms of optical depth retrievals. These new results may have key consequences in terms of calibration of roughness effects within the algorithms of the SMOS (ESA) and the SMAP (NASA) space missions.

full-polarization↗

An Overview of NASA SPoRT GOES-R JPSS Proving Ground Testbed Activities

The Short-term Prediction Research and Transition (SPoRT) Center is funded by NASA's Earth Science Division and NOAA's JPSS and GOES-R Proving Grounds to transition satellite products and capabilities to the NWS to improve short term (0-48 hr) forecasts on a regional and local scale. SPoRT currently collaborates with 30+ NWS WFOs (at least one in each NWS region) and 5 National Centers/Testbeds. SPoRT matches user-identified forecast challenges to specific products, providing access to these data in AWIPS through new plug-in development, and generating applications-based training to use the products for their needs (R20). Upon transition, SPoRT collaborates with the user to assess the product impact in a real-world environment for feedback to product developers (O2R) and to benefit their peers.

GOES↗

The Global Methane Budget 2000-2012

The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase, making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (approximately biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modeling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations).For the 2003-2012 decade, global methane emissions are estimated by top-down inversions at 558 TgCH4 yr(exp -1), range 540-568. About 60 of global emissions are anthropogenic (range 50-65%). Since 2010, the bottom-up global emission inventories have been closer to methane emissions in the most carbon-intensive Representative Concentrations Pathway (RCP8.5) and higher than all other RCP scenarios. Bottom-up approaches suggest larger global emissions (736 TgCH4 yr(exp -1), range 596-884) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the top-down budget, it is likely that some of the individual emissions reported by the bottom-up approaches are overestimated, leading to too large global emissions. Latitudinal data from top-down emissions indicate a predominance of tropical emissions (approximately 64% of the global budget, less than 30deg N) as compared to mid (approximately 32%, 30-60deg N) and high northern latitudes (approximately 4%, 60-90deg N). Top-down inversions consistently infer lower emissions in China (approximately 58 TgCH4 yr(exp -1), range 51-72, minus14% ) and higher emissions in Africa (86 TgCH4 yr(exp -1), range 73-108, plus 19% ) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40% on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (http://doi.org/10.3334/CDIAC/GLOBAL_ METHANE_BUDGET_2016_V1.1) and the Global Carbon Project.

Saunois, Marielle↗

Full-Coverage High-Resolution Daily PM(sub 2.5) Estimation using MAIAC AOD in the Yangtze River Delta of China

Satellite aerosol optical depth (AOD) has been used to assess population exposure to fine particulate matter (PM (sub 2.5)). The emerging high-resolution satellite aerosol product, Multi-Angle Implementation of Atmospheric Correction(MAIAC), provides a valuable opportunity to characterize local-scale PM(sub 2.5) at 1-km resolution. However, non-random missing AOD due to cloud snow cover or high surface reflectance makes this task challenging. Previous studies filled the data gap by spatially interpolating neighboring PM(sub 2.5) measurements or predictions. This strategy ignored the effect of cloud cover on aerosol loadings and has been shown to exhibit poor performance when monitoring stations are sparse or when there is seasonal large-scale missngness. Using the Yangtze River Delta of China as an example, we present a Multiple Imputation (MI) method that combines the MAIAC high-resolution satellite retrievals with chemical transport model (CTM) simulations to fill missing AOD. A two-stage statistical model driven by gap-filled AOD, meteorology and land use information was then fitted to estimate daily ground PM(sub 2.5) concentrations in 2013 and 2014 at 1 km resolution with complete coverage in space and time. The daily MI models have an average R(exp 2) of 0.77, with an inter-quartile range of 0.71 to 0.82 across days. The overall Ml model 10-fold cross-validation R(exp 2) (root mean square error) were 0.81 (25 gm(exp 3)) and 0.73 (18 gm(exp 3)) for year 2013 and 2014, respectively. Predictions with only observational AOD or only imputed AOD showed similar accuracy.Comparing with previous gap-filling methods, our MI method presented in this study performed bette rwith higher coverage, higher accuracy, and the ability to fill missing PM(sub 2.5) predictions without ground PM(sub 2.5) measurements. This method can provide reliable PM(sub 2.5)predictions with complete coverage that can reduce biasin exposure assessment in air pollution and health studies.

PM2.5↗

Regional Impacts of Urbanization in the United States

We simulate the impact of impervious surface areas (ISA) on the U.S. local and regional climate. At a local scale, we find the urban area warmer than the surrounding vegetation in most cities, except in arid climate cities where urban temperature is cooler for much of the daytime. For all 9 regions studied, simulated results show that the growing season maximum surface temperature difference between urban and the dominant vegetation occurs around mid-day and is strongest in the northern regions. Regional temperature differences of 3.0 C, 3.4 C, and 3.9 C were simulated in the Northeast, Midwest, and Northwest, respectively. In these regions evaporative cooling, during the growing season, creates a stronger urban heat island (UHI). The UHI is less pronounced during winter when vegetation is dormant. Our results suggest that the ISA temperature is set by building material's characteristics and its departure from that of the surrounding vegetation is essentially driven by evaporative cooling. Except when rainfall is small, the highest surface runoff to precipitation ratios are simulated in most cities, especially when precipitation events occur as heavy downpours. In terms of photosynthesis, we provide a detailed distribution of maximum production in the U.S., a needed product for policy and urban planners.

Bounoua, Lahouari↗

Considerations for Using Hybrid Ensemble-Variational Data Assimilation in NASA-GMAO's Next Reanalysis System

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) is the latest reanalysis produced by GMAO, and provides global data spanning the period 1980-present. The atmospheric data assimilation component of MERRA-2 used a 3D-Var scheme, which was operational at the time of its design. Since then, a Hybrid 3D-Var, then a Hybrid 4D-EnVar were implemented, adding an ensemble component to the data assimilation scheme. In this work, we will be examining the benefits of using hybrid ensemble flow-dependent covariances to represent errors and uncertainties in historic periods. Specifically, periods of pre- and post-satellites, as well as periods of active tropical cyclone seasons. Finally, we will also be exploring the use of adaptive localization scales.

MERRA-↗

Comparison of Satellite Rainfall Estimates and Rain Gauge Measurements in Italy, and Impact on Landslide Modeling

Landslides can be triggered by intense or prolonged rainfall. Rain gauge measurements are commonly used to predict landslides even if satellite rainfall estimates are available. Recent research focuses on the comparison of satellite estimates and gauge measurements. The rain gauge data from the Italian network (collected in the system database Verifica Rischio Frana" (VRF)) are compared with the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) products. For the purpose, we couple point gauge and satellite rainfall estimates at individual grid cells, evaluating the correlation between gauge and satellite data in different morpho-climatological conditions. We then analyze the statistical distributions of both rainfall data types and the rainfall events derived from them. Results show that satellite data underestimates ground data, with the largest differences in mountainous areas. Power-law models, are more appropriate to correlate gauge and satellite data. The gauge and satellite-based products exhibit different statistical distributions and the rainfall events derived from them differ. In conclusion, satellite rainfall cannot be directly compared with ground data, requiring local investigation to account for specific morpho-climatological settings. Results suggest that satellite data can be used for forecasting landslides, only performing a local scaling between satellite and ground data.

landslide; satellite rainfall; TRMM; rain gauges; ↗

Reconstruction of Snow on Arctic Sea Ice

Snow on Arctic sea ice is a poorly observed variable that plays an important role in the Arctic climate system and impacts the remote sensing systems that monitor Arctic sea ice. We present and validate a reconstruction of Arctic snow depth based on observed sea ice motion and snowfall derived from reanalysis data. Overall, the reconstruction is in good agreement with direct measurements of snow depth from Operation IceBridge, slightly underestimating mean IceBridge snow depth. At the local scale (10 km), the reconstruction is more skilled than a weighted climatology over first year ice, but underestimates deeper snow over multiyear ice. Reconstructions of single buoy snow depths are unskilled, but the reconstruction shows skill in simulating the mean snow depth across all buoys. Spring snow depths show a lowtohigh crossArctic gradient and tend to be greatest in the Atlantic sector of the eastern Arctic. The relationship between ice type (multiyear or firstyear ice) and snow depth previously documented in the western Arctic is not evident in the eastern Arctic. Using ice type to weight snow depths for satellite ice thickness retrievals may not be justifiable in the eastern Arctic. Reconstructed snow depth across the Arctic shows significant interannual variability, suggesting that use of a fixed snow depth climatology may lead to biases in retrieved ice thickness and its variability. However, interannual variability in panArctic mean snow depth is comparable or smaller than the uncertainty in both the reconstruction and IceBridge, highlighting the need for high accuracy snow depth products and reconstructions.

sea ice↗

Aqueous Alteration on Mars

The occurrence of extensive valley networks and layered deposits of phyllosilicates and sulfates during the late Noachian/Hesperian periods (approx. 3-4 Gyrs) indicates a past martian climate that was capable of maintaining liquid water at the surface. The planet’s climate drastically changed after these early “episodes” of water to a drier and colder environment during the Amazonian period (past 3.0 Gyrs). The objective of this paper is to describe aqueous alteration/weathering scenarios on Mars based on observations returned by rover and lander missions. The chemistry of most outcrops, rocks, and “soils” that have interacted with water has not been extensively changed from average Mars crustal basaltic composition. Little chemical variation suggests closed hydrologic systems were prominent on early Mars and/or the water/rock ratios were low. Open hydrologic systems occur at local scales, e.g., high Si and Ti rocks and “soil” deposits around a volcanic feature in Gusev crater. Geochemical and mineralogical indicators for aqueous alteration include jarosite and other Fe-sulfates at several locations suggesting acid-sulfate alteration conditions. High Si and Ti rocks, sediments, and “soil” deposits are consistent with basaltic residues extenively leached by extremely acidic fluids. Variations in the Fe/Mn ratio of fracture veins infilled with sulfate-rich materials suggest changes in redox and/or pH conditions of the migrating fluids. The increase of nanophase iron oxides and salts with depth in several “soil” pits escavated by the Spirit rover’s wheel in Gusev crater suggests the translocation/mobolization of these phases by liquid water. This “pedogenic” process is the result of limited water movement through the surface sediments during the Amazonian period; however, it is likely that paleosols exist on Mars that formed during the early “wetter” history of the planet. Soil scientists have the opportunity to continue to (and should) be involved in the exploration of the Red planet.

Ming, Douglas W.↗

Redeployable Sensor Probe for In-Situ Lunar Resource Mapping from Small Landers

The use of in-situ resources in lunar regolith for production of propellant, life support, and construction (e.g. polar water ice, hydrogen, helium-3, and regolith minerals) will enable sustainable robotic and human space exploration and pave the way for commercialization of lunar exploration. Currently, the search for and characterization of resources on the Moon uses orbital datasets and local geological and geophysical surveys to map and characterize potential deposits. To develop efficient ISRU systems, it is essential to find, characterize, and map lunar resources in-situ, at local scales, using deployable, analytical payloads. We have developed a 3 kg, TRL4 scientific payload, MoonSHOT (Moon Subsurface Hydrogen Optical Tool), to characterize and map lunar resources from a small lander or rover.

Sobron, P.↗

Sensitivity of Maize Yield in Smallholder Systems to Climate Scenarios in Semi-Arid Regions of West Africa: Accounting for Variability in Farm Management Practices

Climate change is estimated to exacerbate existing challenges faced by smallholder farmers in Sub-Sahara Africa. However, limited studies quantify the extent of variation in climate change impact under these systems at the local scale. The Decision Support System for Agro-technological Transfer (DSSAT) was used to quantify variation in climate change impacts on maize yield under current agricultural practices in semi-arid regions of Senegal (Nioro du Rip) and Ghana (Navrongo and Tamale). Multi-benchmark climate models (Mid-Century, 2040–2069 for two Representative Concentration Pathways, RCP4.5 and RCP8.5), and multiple soil and management information from agronomic surveys were used as input for DSSAT. The average impact of climate scenarios on grain yield among farms ranged between −9% and −39% across sites. Substantial variation in climate response exists across farms in the same farming zone with relative standard deviations from 8% to 117% at Nioro du Rip, 13% to 64% in Navrongo and 9% to 37% in Tamale across climate models. Variations in fertilizer application, planting dates and soil types explained the variation in the impact among farms. This study provides insight into the complexities of the impact of climate scenarios on maize yield and the need for better representation of heterogeneous farming systems for optimized outcomes in adaptation and resilience planning in smallholder systems.

Freduah, Bright S.↗

Surface melting over the Greenland ice sheet from enhanced resolution passive microwave brightness temperatures (1979–2019)

Surface melting is a major component of the Greenland ice sheet (GrIS) surface mass balance, affecting sea level rise through direct runoff and the modulation on ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting in view of their long temporal coverage (1979–to date) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, being historically of the order of tens of kilometres. Here, we use a newly released passive microwave dataset (37 GHz, horizontal polarization) made available through the NASA MeASUREs program to study the spatiotemporal evolution of surface melting over the GrIS at an enhanced spatial resolution of 3.125 Km. We assess the outputs of different detection algorithms through data collected by Automatic Weather Stations (AWS) and the outputs of the MAR regional climate model. We found that surface melting is well captured using a dynamic algorithm based on the outputs of MEMLS model, capable to detect sporadic and persistent melting. Our results indicate that, during the reference period 1979–2019 (1988–2019), surface melting over the GrIS increased in terms of both duration, up to ~4.5 (2.9) days per decade, and extension, up to 6.9 % (3.6 %) of the GrIS surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season has started up to ~4 (2.5) days earlier and ended ~7 (3.9) days later per decade. We also explored the information content of the enhanced resolution dataset with respect to the one at 25 km and MAR outputs through a semi-variogram approach. We found that the enhanced product is more sensitive to local scale processes, hence confirming the potential interest of this new enhanced product for studying surface melting over Greenland at a higher spatial resolution than the historical products and monitor its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modelled melt extent at high resolution and potentially to assimilate this data in climate models.

Surface melting↗

Landslide Hazard and Exposure Modelling in Data‐Poor Regions: The Example of the Rohingya Refugee Camps in Bangladesh

Landslide hazards significantly affect economies and populations around the world, but locations where the greatest proportional losses occur are in data‐poor regions where capacity to estimate and prepare for these hazards is most limited. Earth observation (EO) data can fill key knowledge gaps, and can be rapidly used in settings with lower analytical capacity. In this study, we describe a novel series of methods designed to analyze landslide susceptibility, hazard and exposure in the region in and around the Rohingya refugee camps in Bangladesh, where limited data is juxtaposed with a major humanitarian crisis. We demonstrate that a high degree of accuracy is possible even when estimating susceptibility of relatively small landslides. In the context of this example, we also explore how estimates of landslide hazard and exposure are most beneficial to decisions made by humanitarian stakeholders relevant to natural hazards and risk. The unique opportunity to work alongside humanitarian end‐users has allowed us to produce focused products that can be tested while in development. In particular, we stress the importance of communicating the difference between a landslide “early warning system”—for which satellite data may be unsuitable at local scales—and a model that provides relative hazard estimates, where EO may be valuable. The toolbox of methods presented here could be used to generate landslide hazard and exposure maps in other data‐poor regions around the globe.

R A Emberson↗

The Global Methane Budget 2000–2017

Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4/yr (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4/yr or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4/yr or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is 29 Tg CH4/yr larger than our estimate for the previous decade (2000–2009), and 24 Tg CH4/yr larger than the one reported in the previous budget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4/yr, range 594–881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (∼ 65 % of the global budget, < 30° N) compared to mid-latitudes (∼ 30 %, 30–60° N) and high northern latitudes (∼ 4 %, 60–90° N). The most important source of uncertainty in the methane budget is attributable to natural emissions, especially those from wetlands and other inland waters. Some of our global source estimates are smaller than those in previously published budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4/yr lower due to improved partition wetlands and other inland waters. Emissions from geological sources and wild animals are also found to be smaller by 7 Tg CH4/yr by 8 Tg CH4/yr, respectively. However, the overall discrepancy between bottom-up and top-down estimates has been reduced by only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane budget include (i) a global, high-resolution map of water-saturated soils and inundated areas emitting methane based on a robust classification of different types of emitting habitats; (ii) further development of process-based models for inland-water emissions; (iii) intensification of methane observations at local scales (e.g., FLUXNET-CH4 measurements) and urban-scale monitoring to constrain bottom-up land surface models, and at regional scales (surface networks and satellites) to constrain atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/or co-emitted species such as ethane to improve source partitioning.

methane budget↗

Improving SMAP freeze-thaw retrievals for pavements using effective soil temperature from GEOS-5: Evaluation against in situ road temperature data over the U.S

Seasonal freeze-thaw (FT) affects over half the northern hemisphere and impacts many key processes of the Earth System such as energy exchange, hydrology and vegetation. Nearly all past studies using spaceborne FT retrievals have focused on characterizing FT specifically for natural environments. FT in the built environment is also routinely studied and a topic of great interest, especially with regards to transportation infrastructure. Whereas natural FT process are frequently investigated using spaceborne observations, FT studies of roads are often limited to local scales, using in situ or nearby weather station data only. Comparisons between FT retrievals obtained from NASA's Soil Moisture Active Passive (SMAP) satellite and roads in Alaska (AK) and the Contiguous United States (CONUS) showed that spaceborne FT retrievals had good agreement with road data. But those results also indicated that NASA FT retrievals in CONUS were relatively too warm compared to road data. If SMAP FT retrievals were to be used for identifying FT transition timing for applications by the transportation community, it is also important for frozen conditions to be identified more accurately. This work is primarily concerned with improving frozen retrievals made in CONUS by calculating new Normalized Polarization Ratio (NPR) thresholds as compared to those currently used in SMAP FT. We found that focusing on a temporal subset of October through May for comparisons greatly improved the correlation between NPR and effective soil temperature (Teff, one of SMAP's ancillary datasets), often from about zero to 0.6. We then applied linear regression between NPR and Teff to obtain new NPR thresholds resulting in the FT-Roads (FT-R) product. NASA FT and FT-R were evaluated against road data at about 1000 locations in CONUS and a battery of different tests indicated that FT-R performed better under nearly all conditions compared to NASA FT. Overall, NASA FT accuracies were 69% and 80% for 6 am and 6 pm SMAP retrievals, while FT-R achieved accuracies of 79% and 82%. We also investigated the potential for using Teff for road FT (6 am, only) and found that those comparisons were even more accurate (84%). We've also quantified inter- and intraregional differences of SMAP FT performance and found that accuracy metrics vary over twice as much between geographic subdivisions (9%) as compared to between the states within a subdivision (4%). Most importantly, the main goal of improving the detection of in situ frozen conditions in CONUS was realized, with FT-R accurately detecting frozen conditions >50% more frequently than NASA FT.

Passive microwave↗

Optical design of the Mapping Imaging Spectrometer for Europa (MISE)

e Mapping Imaging Spectrometer for Europa (MISE) is a high-throughput pushbroom imaging spectrometer designed for NASA’s planned flyby mission to Jupiter’s moon Europa. The MISE design utilizes heritage from previously demonstrated instruments on airborne platforms, while advancing the state of the art to operate within Europa’s challenging environment. The instrument operates at F/1.4 and covers a spectral range from 0.8 to 5 microns with 10 nm spectral sampling. Through high resolution mapping, MISE is designed to identify distributions of organics, salts, acid hydrates, water ice phases, altered silicates, radiolytic compounds, and warm thermal anomalies at global, regional, and local scales. Such distribution maps will help study surface and subsurface geologic processes, and assess the habitability of Europa’s ocean. We discuss the optical specifications and baseline performance of the MISE optical design.

Van Gorp, Byron E.↗