Using atmospheric observations to quantify annual biogenic carbon dioxide fluxes on the Alaska North Slope

The continued warming of the Arctic could release vast stores of carbon into the atmosphere from high-latitude ecosystems, especially from thawing permafrost. Increasing uptake of carbon dioxide (CO2) by vegetation during longer growing seasons may partially offset such release of carbon. However, evidence of significant net annual release of carbon from site-level observations and model simulations across tundra ecosystems has been inconclusive. To address this knowledge gap, we combined top-down observations of atmospheric CO2 concentration enhancements from aircraft and a tall tower, which integrate ecosystem exchange over large regions, with bottom-up observed CO2 fluxes from tundra environments and found that the Alaska North Slope is not a consistent net source nor net sink of CO2 to the atmosphere (ranging from −6 to +6 Tg C yr−1 for 2012–2017). Our analysis suggests that significant biogenic CO2 fluxes from unfrozen terrestrial soils, and likely inland waters, during the early cold season (September–December) are major factors in determining the net annual carbon balance of the North Slope, implying strong sensitivity to the rapidly warming freeze-up period. At the regional level, we find no evidence of the previously reported large late-cold-season (January–April) CO2 emissions to the atmosphere during the study period. Despite the importance of the cold-season CO2 emissions to the annual total, the interannual variability in the net CO2 flux is driven by the variability in growing season fluxes. During the growing season, the regional net CO2 flux is also highly sensitive to the distribution of tundra vegetation types throughout the North Slope. This study shows that quantification and characterization of year-round CO2 fluxes from the heterogeneous terrestrial and aquatic ecosystems in the Arctic using both site-level and atmospheric observations are important to accurately project the Earth system response to future warming.</p

Impact of stratospheric air and surface emissions on tropospheric nitrous oxide during ATom

We measured the global distribution of tropospheric N2O mixing ratios during the NASA airborne Atmospheric Tomography (ATom) mission. ATom measured concentrations of ∼ 300 gas species and aerosol properties in 647 vertical profiles spanning the Pacific, Atlantic, Arctic, and much of the Southern Ocean basins, nearly from pole to pole, over four seasons (2016–2018). We measured N2O concentrations at 1 Hz using a quantum cascade laser spectrometer (QCLS). We introduced a new spectral retrieval method to account for the pressure and temperature sensitivity of the instrument when deployed on aircraft. This retrieval strategy improved the precision of our ATom QCLS N2O measurements by a factor of three (based on the standard deviation of calibration measurements). Our measurements show that most of the variance of N2O mixing ratios in the troposphere is driven by the influence of N2O-depleted stratospheric air, especially at mid- and high latitudes. We observe the downward propagation of lower N2O mixing ratios (compared to surface stations) that tracks the influence of stratosphere–troposphere exchange through the tropospheric column down to the surface. The highest N2O mixing ratios occur close to the Equator, extending through the boundary layer and free troposphere. We observed influences from a complex and diverse mixture of N2O sources, with emission source types identified using the rich suite of chemical species measured on ATom and the geographical origin calculated using an atmospheric transport model. Although ATom flights were mostly over the oceans, the most prominent N2O enhancements were associated with anthropogenic emissions, including from industry (e.g., oil and gas), urban sources, and biomass burning, especially in the tropical Atlantic outflow from Africa. Enhanced N2O mixing ratios are mostly associated with pollution-related tracers arriving from the coastal area of Nigeria. Peaks of N2O are often associated with indicators of photochemical processing, suggesting possible unexpected source processes. In most cases, the results show how difficult it is to separate the mixture of different sources in the atmosphere, which may contribute to uncertainties in the N2O global budget. The extensive data set from ATom will help improve the understanding of N2O emission processes and their representation in global models.This research has been supported by the National Aeronautics and Space Administration (grant nos. NNX15AJ23G, NNX17AF54G, NNX15AG58A, NNX15AH33A, and 80NSSC19K0124) and the National Science Foundation (grant nos. 1852977, AGS-1547626, and AGS-1623745)

Investigation of ammonia and inorganic particulate matter in California during the CalNex campaign, An

2012 Fall.Includes bibliographical references.Over the last century, the rise of industrial agriculture has greatly increased the emission of ammonia (NH3) from livestock waste and synthetic crop fertilizers to the atmosphere. Ammonium (NH4+) aerosol, which can be formed through the neutralization of atmospheric acids by NH3, is a key component of particulate matter (PM) in the atmosphere. PM causes negative human health effects and reduces visibility, and transport and deposition of excess NH3 can cause environmental degradation. Airborne observations of gas precursors and inorganic aerosol taken during the CalNex campaign in May and June 2010 are used in this study to investigate the role of NH3 in PM formation in California and test the representation of the key processes relevant to this chemical system in the GEOS-Chem chemical transport model. Evaluation of the 0.5° x 0.667° horizontal resolution nested model with observations shows a large underestimation (5.4 ppb median bias in the lowest 1 km) of NH3 in the Central Valley. This NH3 underestimation is lower in the area surrounding Los Angeles (LA), only 1.4 ppb. Sulfur dioxide (SO2) is also underestimated in both regions, while nitric acid (HNO3) shows little bias. Near-surface simulated inorganic PM is under-predicted by 1.28 µg sm-3 in the LA region and over-predicted in the Valley by 0.27 µg sm-3. Investigation of model sensitivity to the processes of gas-particle partitioning, wet deposition, dry deposition and emissions reveal that emissions have the largest potential for correction of model deficiencies. Increases to anthropogenic livestock NH3 emissions by a factor of 5 and anthropogenic SO2 emissions in the Valley by factors from 3 - 10 eliminates the bias in the simulation of gases in both regions and PM near LA, where under-prediction of nitrate (NO3-) is reduced from 0.64 µg sm-3 to 0.12 µg sm-3 in the lowest 1 km. An increase in NH3 emissions in the LA region is critical to capturing ammonium nitrate (NH4NO3) formation down-wind of the city core. Using this modified emissions simulation, seasonal PM differences in the two regions and the export of excess NH3 out of the Valley are explored. Mean June simulated PM concentration in the lowest 1 km is 3.48 µg sm-3 near LA (38% NO3- and 39% SO42-, by mass) and 1.98 µg sm-3 in the Valley (44% NO3- and 32% SO42-, by mass). These simulated PM concentrations are 2 times greater in the Valley in December than in June, when NH4NO3 formation is favored by colder temperatures. However, LA simulated PM concentration falls by 53% in December compared to June, likely due to lower winter NH3 emissions. Both the model and IASI satellite observations indicate that large amounts of excess NH3 are transported from the Valley to southeastern California in the summertime which may negatively affect ecosystems in this area

Exploring interactions between agriculture and air quality on regional to global scales

Thesis: Ph. D. in Environmental Chemistry, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 123-135).As concern grows over increasing human population and the effects of industrialization on the environment, agriculture and air quality have become important areas of research. Both are vital to human prosperity, determining what we eat and what we breathe. The interactions between agriculture and air quality (defined by ozone and particulate matter (PM) concentrations) are many and often poorly understood. This thesis examines their interactions in two parts. First, we investigate the influence and characterize the importance of the variability in agricultural ammonia emissions on surface inorganic fine PM (PM₂.₅). In a case study, airborne observations indicate that summertime concentrations of ammonia throughout California and PM₂.₅ in Los Angeles are underestimated in a global chemistry model (GEOS-Chem) used to understand air quality issues. We find that increasing ammonia emissions from livestock and fertilizer allows the model to better represent the observations, thereby improving the model's prediction of PM₂.₅ conditions in wintertime, when concentrations and impacts on human health are greater. We also use new observations (surface, aircraft, and satellite) to find that the model underrepresents the summertime ammonia concentration near large source regions throughout the United States. Meteorology dominates the underestimated year-to-year variability in the model over reductions in acid-precursors. Introduction of varying ammonia emissions does not improve the model comparison and has little impact on PM₂.₅. Second, we quantify the impact of air quality on global crop production under current and future emissions scenarios. Using a relativistic approach, we find that the maximum positive impact (highly uncertain) from total PM light scattering can outweigh the negative impact from ozone damage in certain crops and regions. Future scenarios indicate that reductions in air pollution may have a net negative effect on crop production in areas dominated by the PM effect. We then employ a crop model (pDSSAT) to more realistically predict the lessened impact of PM under stress from resource restrictions. We also assess the effect of nitrogen deposition on crops compared to PM. Overall, we highlight the need for better observations of both ammonia concentrations and the impacts of PM on crop growth to reduce uncertainty in these interactions.by Luke D. Schiferl.Ph. D. in Environmental Chemistr

Evaluation of Enhanced High Resolution MODIS/AMSR-E SSTs and the Impact on Regional Weather Forecast

Over the last few years, the NASA Short-term Prediction Research and Transition (SPoRT) Center has been generating a 1-km sea surface temperature (SST) composite derived from retrievals of the Moderate Resolution Imaging Spectroradiometer (MODIS) for use in operational diagnostics and regional model initialization. With the assumption that the day-to-day variation in the SST is nominal, individual MODIS passes aboard the Earth Observing System (EOS) Aqua and Terra satellites are used to create and update four composite SST products each day at 0400, 0700, 1600, and 1900 UTC, valid over the western Atlantic and Caribbean waters. A six month study from February to August 2007 over the marine areas surrounding southern Florida was conducted to compare the use of the MODIS SST composite versus the Real-Time Global SST analysis to initialize the Weather Research and Forecasting (WRF) model. Substantial changes in the forecast heat fluxes were seen at times in the marine boundary layer, but relatively little overall improvement was measured in the sensible weather elements. The limited improvement in the WRF model forecasts could be attributed to the diurnal changes in SST seen in the MODIS SST composites but not accounted for by the model. Furthermore, cloud contamination caused extended periods when individual passes of MODIS were unable to update the SSTs, leading to substantial SST latency and a cool bias during the early summer months. In order to alleviate the latency problems, the SPoRT Center recently enhanced its MODIS SST composite by incorporating information from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) instruments as well as the Operational Sea Surface Temperature and Sea Ice Analysis. These enhancements substantially decreased the latency due to cloud cover and improved the bias and correlation of the composites at available marine point observations. While these enhancements improved upon the modeled cold bias using the original MODIS SSTs, the discernable impacts on the WRF model were still somewhat limited. This paper explores several factors that may have contributed to this result. First, the original methodology to initialize the model used the most recent SST composite available in a hypothetical real ]time configuration, often matching the forecast initial time with an SST field that was 5-8 hours offset. To minimize the differences that result from the diurnal variations in SST, the previous day fs SST composite is incorporated at a time closest to the model initialization hour (e.g. 1600 UTC composite at 1500 UTC model initialization). Second, the diurnal change seen in the MODIS SST composites was not represented by the WRF model in previous simulations, since the SSTs were held constant throughout the model integration. To address this issue, we explore the use of a water skin-temperature diurnal cycle prediction capability within v3.1 of the WRF model to better represent fluctuations in marine surface forcing. Finally, the verification of the WRF model is limited to very few over-water sites, many of which are located near the coastlines. In order to measure the open ocean improvements from the AMSR-E, we could use an independent 2-dimensional, satellite-derived data set to validate the forecast model by applying an object-based verification method. Such a validation technique could aid in better understanding the benefits of the mesoscale SST spatial structure to regional models applications

Scaling waterbody carbon dioxide and methane fluxes in the arctic using an integrated terrestrial-aquatic approach

In the Arctic waterbodies are abundant and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. We demonstrate how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO _2 and CH _4 fluxes across the YK Delta of south-west AK. We found that waterbody size and contour were strong predictors for aquatic CO _2 emissions, attributing greater than two-thirds of the influence to the scaling model. Small ponds (<0.001 km ^2 ) were hotspots of emissions, contributing fluxes several times their relative area, but were less than 5% of the total carbon budget. Small to medium lakes (0.001–0.1 km ^2 ) contributed the majority of carbon emissions from waterbodies. Waterbody CH _4 emissions were predicted by a combination of wetland landcover and related drivers, as well as watershed hydrology, and waterbody surface reflectance related to chromophoric dissolved organic matter. When compared to our machine learning approach, traditional scaling methods that did not account for relevant landscape characteristics overestimated waterbody CO _2 and CH _4 emissions by 26%–79% and 8%–53% respectively. This study demonstrates the importance of an integrated terrestrial-aquatic approach to improving estimates and uncertainty when scaling C emissions in the arctic

Interannual variability of ammonia concentrations over the United States: sources and implications

International audienceThe variability of atmospheric ammonia (NH3), emitted largely from agricultural sources, is an important factor when considering how inorganic fine particulate matter (PM2.5) concentrations and nitrogen cycling are changing over the United States. This study combines new observations of ammonia concentration from the surface, aboard aircraft , and retrieved by satellite to both evaluate the simulation of ammonia in a chemical transport model (GEOS-Chem) and identify which processes control the variability of these concentrations over a 5-year period (2008–2012). We find that the model generally underrepresents the ammonia concentration near large source regions (by 26 % at surface sites) and fails to reproduce the extent of interannual variability observed at the surface during the summer (JJA). Variability in the base simulation surface ammonia concentration is dominated by meteorology (64 %) as compared to reductions in SO2 and NOx emissions imposed by regulation (32 %) over this period. Introduction of year-to-year varying ammonia emissions based on animal population, fertilizer application, and meteorologically driven volatilization does not substantially improve the model comparison with observed ammonia concentrations, and these ammonia emissions changes have little effect on the simulated ammonia concentration variability compared to those caused by the variability of meteorology and acid-precursor emissions. There is also little effect on the PM2.5 concentration due to ammonia emissions variability in the summer when gas-phase changes are favored , but variability in wintertime emissions, as well as in early spring and late fall, will have a larger impact on PM2.5 formation. This work highlights the need for continued improvement in both satellite-based and in situ ammonia measurements to better constrain the magnitude and impacts of spatial and temporal variability in ammonia concentrations

Soil respiration strongly offsets carbon uptake in Alaska and Northwest Canada

Soil respiration (i.e. from soils and roots) provides one of the largest global fluxes of carbon dioxide (CO _2 ) to the atmosphere and is likely to increase with warming, yet the magnitude of soil respiration from rapidly thawing Arctic-boreal regions is not well understood. To address this knowledge gap, we first compiled a new CO _2 flux database for permafrost-affected tundra and boreal ecosystems in Alaska and Northwest Canada. We then used the CO _2 database, multi-sensor satellite imagery, and random forest models to assess the regional magnitude of soil respiration. The flux database includes a new Soil Respiration Station network of chamber-based fluxes, and fluxes from eddy covariance towers. Our site-level data, spanning September 2016 to August 2017, revealed that the largest soil respiration emissions occurred during the summer (June–August) and that summer fluxes were higher in boreal sites (1.87 ± 0.67 g CO _2 –C m ^−2 d ^−1 ) relative to tundra (0.94 ± 0.4 g CO _2 –C m ^−2 d ^−1 ). We also observed considerable emissions (boreal: 0.24 ± 0.2 g CO _2 –C m ^−2 d ^−1 ; tundra: 0.18 ± 0.16 g CO _2 –C m ^−2 d ^−1 ) from soils during the winter (November–March) despite frozen surface conditions. Our model estimates indicated an annual region-wide loss from soil respiration of 591 ± 120 Tg CO _2 –C during the 2016–2017 period. Summer months contributed to 58% of the regional soil respiration, winter months contributed to 15%, and the shoulder months contributed to 27%. In total, soil respiration offset 54% of annual gross primary productivity (GPP) across the study domain. We also found that in tundra environments, transitional tundra/boreal ecotones, and in landscapes recently affected by fire, soil respiration often exceeded GPP, resulting in a net annual source of CO _2 to the atmosphere. As this region continues to warm, soil respiration may increasingly offset GPP, further amplifying global climate change