nasa-gesdisc/es-publications-researchareas huggingface dataset:
Weakened aerosol‐PBL interaction during COVID‐19 lockdown in northern China (2021)

Anthropogenic emissions were greatly constrained during COVID 19 lockdown in China. Nevertheless, observations still showed high loadings of fine particles (PM<SUB>2.5</SUB>) over northern China with secondary aerosols increasing by 15 μg/m<SUP>3</SUP> yet a ∼10% drop in light absorbing black carbon (BC). Such a chemical transition in aerosol composition tended to make the atmosphere more scattering, indicated by satellite retrieved aerosol absorption optical depth falling by 60%. Comparison between weather forecast and radiosonde observations illustrated that, without upper level heating induced by BC, the stabilized stratification diminished, which was conducive for planetary boundary layer (PBL) mixing and thus near surface pollution dispersion. Furthermore, coupled dynamic chemistry simulations estimated that emission reduction during the lockdown weakened aerosol PBL interaction and thus a reduction of 25 μg/m<SUP>3</SUP> (∼50%) in PM<SUB>2.5</SUB> enhancement. Based on the unique natural experiment, this work observationally confirmed and numerically quantified the importance of BC induced meteorological feedback, further highlighting the priority of BC control in haze mitigation.

Research areas: Air Quality
Measurements of Tropospheric Bromine Monoxide Over Four Halogen Activation Seasons in the Canadian High Arctic (2020)

Bromine explosions and corresponding ozone depletion events are common in the Arctic spring. The snowpack on sea ice and sea salt aerosols (SSA) are both thought to release bromine, but the relative contribution of each source is not yet known. Furthermore, the role of atmospheric conditions is not fully understood. Long-term measurements of bromine monoxide (BrO) provide useful insight into the underlying processes of bromine activation. Here we present a 4-year data set (2016-2019) of springtime BrO partial columns retrieved from Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements in Eureka, Canada (80.1°N, 86.4°W, 610 m asl). Due to the elevation of the measurement site, the instrument often measures BrO in the free troposphere, except during strong wind episodes and storms that generate a deep boundary layer. Enhanced vertical mixing due to strong winds leads to increasing BrO values and reduced ozone depletion. We find that BrO enhancements show two modes differentiated by local wind direction and air mass history. Longer time spent by the airmass in first-year sea ice areas corresponds to increased BrO for one of these modes only. Furthermore, we argue that snow on multiyear ice might also contribute to bromine release. High aerosol optical depth is required to maintain lofted BrO, and we show that the presence of coarse-mode aerosols (&gt;0.5 μm, likely SSA) is a necessary and sufficient condition for observing BrO at our elevated measurement site in Eureka. This work highlights the importance of considering variable local conditions when characterizing bromine activation.

Research areas: Air Quality
Land-use harmonization datasets for annual global carbon budgets (2021)

Abstract. Land-use change has been the dominant source of anthropogenic carbon emissions for most of the historical period and is currently one of the largest and most uncertain components of the global carbon cycle. Advancing the scientific understanding on this topic requires that the best data be used as input to state-of-the-art models in well-organized scientific assessments. The Land-Use Harmonization 2 dataset (LUH2), previously developed and used as input for simulations of the 6th Coupled Model Intercomparison Project (CMIP6), has been updated annually to provide required input to land models in the annual Global Carbon Budget (GCB) assessments. Here we discuss the methodology for producing these annual LUH2-GCB updates and extensions which incorporate annual wood harvest data updates from the Food and Agriculture Organization (FAO) of the United Nations for dataset years after 2015 and the History Database of the Global Environment (HYDE) gridded cropland and grazing area data updates (based on annual FAO cropland and grazing area data updates) for dataset years after 2012, along with extrapolations to the current year due to a lag of 1 or more years in the FAO data releases. The resulting updated LUH2-GCB datasets have provided global, annual gridded land-use and land-use-change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, regrowth and afforestation, crop rotations, and pasture management and are used by both bookkeeping models and dynamic global vegetation models (DGVMs) for the GCB. For GCB 2019, a more significant update to LUH2 was produced, LUH2-GCB2019 (https://doi.org/10.3334/ORNLDAAC/1851, Chini et al., 2020b), to take advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil as far back as 1950. From 1951 to 2012 the LUH2-GCB2019 dataset begins to diverge from the version of LUH2 used for the World Climate Research Programme's CMIP6, with peak differences in Brazil in the year 2000 for grazing land (difference of 100 000 km2) and in the year 2009 for cropland (difference of 77 000 km2), along with significant sub-national reorganization of agricultural land-use patterns within Brazil. The LUH2-GCB2019 dataset provides the base for future LUH2-GCB updates, including the recent LUH2-GCB2020 dataset, and presents a starting point for operationalizing the creation of these datasets to reduce time lags due to the multiple input dataset and model latencies.

Research areas: Habitat Conversion/Fragmentation
Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16 (2022)

Abstract. A new dynamical core, known as the Finite-Volume Cubed-Sphere (FV3) and developed at both NASA and NOAA, is used in NOAA's Global Forecast System (GFS) and in limited-area models for regional weather and air quality applications. NOAA has also upgraded the operational FV3GFS to version 16 (GFSv16), which includes a number of significant developmental advances to the model configuration, data assimilation, and underlying model physics, particularly for atmospheric composition to weather feedback. Concurrent with the GFSv16 upgrade, we couple the GFSv16 with the Community Multiscale Air Quality (CMAQ) model to form an advanced version of the National Air Quality Forecasting Capability (NAQFC) that will continue to protect human and ecosystem health in the US. Here we describe the development of the FV3GFSv16 coupling with a state-of-the-science CMAQ model version 5.3.1. The GFSCMAQ coupling is made possible by the seminal version of the NOAA-EPA AtmosphereChemistry Coupler (NACC), which became a major piece of the next operational NAQFC system (i.e., NACC-CMAQ) on 20 July 2021. NACC-CMAQ has a number of scientific advancements that include satellite-based data acquisition technology to improve land cover and soil characteristics and inline wildfire smoke and dust predictions that are vital to predictions of fine particulate matter (PM2.5) concentrations during hazardous events affecting society, ecosystems, and human health. The GFS-driven NACC-CMAQ model has significantly different meteorological and chemical predictions compared to the previous operational NAQFC, where evaluation of NACC-CMAQ shows generally improved near-surface ozone and PM2.5 predictions and diurnal patterns, both of which are extended to a 72 h (3 d) forecast with this system.

Research areas: Air Quality
Short Black Carbon lifetime inferred from a global set of aircraft observations (2018)

Black Carbon (BC) aerosols substantially affect the global climate. However, accurate simulation of BC atmospheric transport remains elusive, due to shortcomings in modeling and a shortage of constraining measurements. Recently, several studies have compared simulations with observed vertical concentration profiles, and diagnosed a global-mean BC atmospheric residence time of <5 days. These studies have, however, been focused on limited geographical regions, and used temporally and spatially coarse model information. Here we expand on previous results by comparing a wide range of recent aircraft measurements from multiple regions, including the Arctic and the Atlantic and Pacific oceans, to simulated distributions obtained at varying spatial and temporal resolution. By perturbing BC removal processes and using current best-estimate emissions, we confirm a constraint on the global-mean BC lifetime of <5.5 days, shorter than in many current global models, over a broader geographical range than has so far been possible. Sampling resolution influences the results, although generally without introducing major bias. However, we uncover large regional differences in the diagnosed lifetime, in particular in the Arctic. We also find that only a weak constraint can be placed in the African outflow region over the South Atlantic, indicating inaccurate emission sources or model representation of transport and microphysical processes. While our results confirm that BC lifetime is shorter than predicted by most recent climate models, they also cast doubt on the usability of the concept of a global-mean BC lifetime for climate impact studies, or as an indicator of model skill.

Research areas: Air Quality
The influence of African air pollution on regional and global tropospheric ozone (2007)

Abstract. We investigate the influence of African biomass burning, biogenic, lightning and anthropogenic emissions on the tropospheric ozone over Africa and globally using a coupled global chemistry climate model. Our model studies indicate that surface ozone concentration may rise by up to 50 ppbv in the burning region during the biomass burning seasons. Biogenic emissions yield between 530 ppbv increase in the near surface ozone concentration over tropical Africa. The impact of lightning on surface ozone is negligible, while anthropogenic emissions yield a maximum of 7 ppbv increase in the annual-mean surface ozone concentration over Nigeria, South Africa and Egypt. Our results show that biogenic emissions are the most important African emission source affecting total tropospheric ozone. The influence of each of the African emissions on the global tropospheric ozone burden (TOB) of 384 Tg yields about 9.5 Tg, 19.6 Tg, 9.0 Tg and 4.7 Tg for biomass burning, biogenic, lightning and anthropogenic emissions emitted in Africa respectively. The impact of each of these emission categories on African TOB of 33 Tg is 2.5 Tg, 4.1 Tg, 1.75 Tg and 0.89 Tg respectively, which together represents about 28% of the total TOB calculated over Africa. Our model calculations also suggest that more than 70% of the tropospheric ozone produced by each of the African emissions is found outside the continent, thus exerting a noticeable influence on a large part of the tropical troposphere. Apart from the Atlantic and Indian Ocean, Latin America experiences the largest impact of African emissions, followed by Oceania, the Middle East, Southeast and south-central Asia, northern North America (i.e. the United States and Canada), Europe and north-central Asia, for all the emission categories.

Research areas: Air Quality
Using MAIAC AOD to verify the PM2. 5 spatial patterns of a land use regression model (2018)

Accurate spatial information of PM2.5 is critical for air pollution control and epidemiological studies. Land use regression (LUR) models have been widely used for predicting spatial distribution of ground PM2.5. However, the predicted PM2.5 spatial patterns of a LUR model has not been adequately examined due to limited ground observations. The increasing aerosol optical depth (AOD) products might be an approximation of spatially continuous observation across large areas. This study established the relationship between seasonal 1 km  1 km MAIAC AOD and observed ground PM2.5 in Beijing, and then seasonal PM2.5 maps were predicted based on AOD. Seasonal LUR models were also developed, and both the AOD and LUR models were validated by hold-out monitoring sites. Finally, the spatial patterns of LUR models were comprehensively verified by the above AOD PM2.5 maps. The results showed that AOD alone could be used directly to predict the spatial distribution of ground PM2.5 concentration at seasonal level (R2  0.53 in model fitting and testing), which was comparable with the capability of LUR models (R2  0.81 in model fitting and testing). PM2.5 maps derived from the two methods showed similar spatial trend and coordinated variations near traffic roads. Large discrepancies could be observed at urban-rural transition areas where land use characters varied quickly. Variable and buffer size selection was critical for LUR model as they dominated the spatial patterns of predicted PM2.5. Incorporating AOD into LUR model could improve model performance in spring season and provide more reliable results during testing.

Research areas: Air Quality
Determining the contribution of environmental factors in controlling dust pollution during cold and warm months of western Iran using different data mining … (2021)

Dust pollution is one of the major environmental crises in the arid regions of Iran and there is a need to predict dust pollution and identify its controlling factors to help reduce its adverse effects on the livelihood of residents of these areas. Although deep neural networks (DNN) are powerful tools in the modelling of environmental phenomena, they are recognized as being challenging to interpret due to their black-box nature. To address this issue and understand the importance of each environmental control on dust pollution, game theory (i.e., Shapley values) was used to better understand the performance and interpretability of DNN models. Here, monthly mean values of precipitation, air temperature, surface wind speed, potential evapotranspiration, normalized difference vegetation index, normalized difference salinity index, Palmer drought severity index, soil heat flux, and surface pressure were selected as explanatory variables. The dust storm index (DSI), an indicator of dust pollution, was the predicted response variable for the cold and warm months. The results showed that the accuracies of the DNN model in predicting cold months DSI (CMDSI) and warm months DSI (WMDSI) were higher compared to other traditional machine learning algorithms. DNN model increased the R2 by 13% and 15% for predicting CMDSI and WMDSI, respectively, compared to the Random Forest model, which was the second most effective approach. According to the Shapley values, the most important controls on the occurrence of dust storms during the cold months of the study period (20002018) were wind speed, soil heat flux, and precipitation. During the warm months, wind speed was the most important controlling factor and was followed by precipitation, soil heat flux, and potential evapotranspiration. Overall, the results demonstrate the effectiveness of the DNN model and game theory in identifying the factors affecting dust pollution, which may help mitigate its impacts on the residents of western Iran.

Research areas: Air Quality
The Vulcan Version 3.0 High‐Resolution Fossil Fuel CO2 Emissions for the United States (2020)

Estimates of high-resolution greenhouse gas (GHG) emissions have become a critical component of climate change research and an aid to decision makers considering GHG mitigation opportunities. The "Vulcan Project" is an effort to estimate bottom-up carbon dioxide emissions from fossil fuel combustion and cement production (FFCO<SUB>2</SUB>) for the U.S. landscape at space and time scales that satisfy both scientific and policy needs. Here, we report on the Vulcan version 3.0 which quantifies emissions at a resolution of 1 km<SUP>2</SUP>/hr for the 2010-2015 time period. We estimate 2011 FFCO<SUB>2</SUB> emissions of 1,589.9 TgC with a 95% confidence interval of 1,367/1,853 TgC (-14.0%/+16.6%), implying a one-sigma uncertainty of ~ ±8%. Per capita emissions are larger in states dominated by electricity production and industrial activity and smaller where onroad and building emissions dominate. The U.S. FFCO<SUB>2</SUB> emissions center of mass (CoM) is located in the state of Missouri with mean seasonality that moves on a near-elliptical NE/SW path. Comparison to ODIAC, a global gridded FFCO<SUB>2</SUB> emissions estimate, shows large total emissions differences (100.4 TgC for year 2011), a spatial correlation of 0.68 (R<SUP>2</SUP>), and a mean absolute relative difference at the 1 km<SUP>2</SUP> scale of 104.3%. The Vulcan data product offers a high-resolution estimate of FFCO<SUB>2</SUB> emissions in every U.S. city, obviating costly development of self-reported urban inventories. The Vulcan v3.0 annual gridded emissions data product can be downloaded from the Oak Ridge National Laboratory Distributed Active Archive Center (Gurney, Liang, et al., 2019, <A href="https://doi.org/10.3334/ORNLDAAC/1741">https://doi.org/10.3334/ORNLDAAC/1741</A>).

Research areas: Greenhouse Gases
PM2. 5 over North China based on MODIS AOD and effect of meteorological elements during 2003–2015 (2020)

Over the past 40 years, PM2.5 pollution in North China has become increasingly serious and progressively exposes the densely populated areas to pollutants However, due to limited ground data, it is challenging to estimate accurate PM2.5 exposure levels, further making it unfavorable for the prediction and prevention of PM2.5 pollutions. This paper therefore uses the mixed effect model to estimate daily PM2.5 concentrations of North China between 2003 and 2015 with ground observation data and MODIS AOD satellite data. The tempo-spatial characteristics of PM2.5 and the influence of meteorological elements on PM2.5 is discussed with EOF and canonical correlation analysis respectively. Results show that overall R2 is 0.36 and the root mean squared predicted error was 30.1 g/m3 for the model prediction. Our time series analysis showed that, the Taihang Mountains acted as a boundary between the high and low pollution areas in North China; while the northern part of Henan Province, the southern part of Hebei Province and the western part of Shandong Province were the most polluted areas. Although, in 2004, 2009 and dates after 2013, PM2.5 concentrations were relatively low. Meteorological/topography conditions, that include high surface humidity of area in the range of 3440N and 119124E, relatively low boundary layer heights, and southerly and easterly winds from the east and north area were common factors attributed to haze in the most polluted area. Overall, the spatial distribution of increasingly concentrated PM2.5 pollution in North China are consistent with the local emission level, unfavorable meteorological conditions and topographic changes.

Research areas: Air Quality
Estimating the Daily NO2 Concentration with High Spatial Resolution in the Beijing–Tianjin–Hebei Region Using an Ensemble Learning Model (2021)

Nitrogen dioxide (NO2) is an important pollutant related to human activities, which has short-term and long-term effects on human health. An ensemble learning model was constructed and applied to estimate daily NO2 concentrations in the BeijingTianjinHebei region between 2010 and 2016. A variety of predictive variables included satellite-based troposphere NO2 vertical column concentration, meteorology, elevation, gross domestic product (GDP), population, land-use variables, and road network. The ensemble learning model achieved two things: a 0.01  0.01 grid resolution and the estimation of historical data for the years 20102013. The ensemble model showed good performance, whereby the R2 of tenfold cross-validation was 0.72 and the R2 of test validation was 0.71. Meteorological hysteretic effects were incorporated into the model, where the one-day lagged boundary layer height contributed the most. The annual NO2 estimation showed little change from 2010 to 2016. The seasonal NO2 estimation from highest to lowest occurred in winter, autumn, spring, and summer. In the annual maps and seasonal maps, the NO2 estimations in the northwest region were lower than those in the southeast region, and there was a heavily polluted band in the south of the Taihang Mountains. In coastal areas, the annual NO2 estimations were higher than the NO2 monitored values. The drawback of the model is underestimation at high values and overestimation at low values. This study indicates that the ensemble learning model has excellent performance in the simulation of NO2 with high spatial and temporal resolution. Furthermore, the research framework in this study can be a generally applied for drawing implications for other regions, especially for other cities in China.

Research areas: Air Quality
1.5 years of TROPOMI CO measurements: comparisons to MOPITT and ATom (2020)

Abstract. We have analyzed TROPOspheric Monitoring Instrument (TROPOMI) carbon monoxide (CO) data acquired between November 2017 and March 2019 with respect to other satellite (MOPITT, Measurement Of Pollution In The Troposphere) and airborne (ATom, Atmospheric Tomography mission) datasets to better understand TROPOMI's contribution to the global tropospheric CO record (2000 to present). MOPITT and TROPOMI are two of only a few satellite instruments to ever derive CO from solar-reflected radiances. Therefore, it is particularly important to understand how these two datasets compare. Our results indicate that TROPOMI CO retrievals over land show excellent agreement with respect to MOPITT: relative biases and their SD (i.e., accuracy and precision) are on average -3.73%11.51%, -2.24%12.38%, and -3.22%11.13% compared to the MOPITT TIR (thermal infrared), NIR (near infrared), and TIR + NIR (multispectral) products, respectively. TROPOMI and MOPITT data also show good agreement in terms of temporal and spatial patterns. Despite depending on solar-reflected radiances for its measurements, TROPOMI can also retrieve CO over bodies of water if clouds are present by approximating partial columns under cloud tops using scaled, model-based reference CO profiles. We quantify the bias of TROPOMI total column retrievals over bodies of water with respect to colocated in situ ATom CO profiles after smoothing the latter with the TROPOMI column averaging kernels (AKs), which account for signal attenuation under clouds (relative bias and its SD =3.25%11.46 %). In addition, we quantify enull (the null-space error), which accounts for differences between the shape of the TROPOMI reference profile and that of the ATom true profile (enull=2.16%2.23 %). For comparisons of TROPOMI and MOPITT retrievals over open water we compare TROPOMI total CO columns to their colocated MOPITT TIR counterparts. Relative bias and its SD are 2.98 %15.71 % on average. We investigate the impact of discrepancies between the a priori and reference CO profiles (used by MOPITT and TROPOMI, respectively) on CO retrieval biases by applying a null-space adjustment (based on the MOPITT a priori) to the TROPOMI total column values. The effect of this adjustment on MOPITT and TROPOMI biases is minor, typically 12 percentage points.

Research areas: Validation