nasa-gesdisc/es-publications-researchareas huggingface dataset:
Greenhouse gas fluxes from Alaska's North Slope inferred from the Airborne Carbon Measurements campaign (ACME-V) (2021)

Northern high latitudes are likely to be heavily impacted by climate change, changes that will undoubtedly alter carbon cycling across these regions. An understanding of both the magnitude and drivers of current CO2 and CH4 fluxes are a prerequisite for making robust projections of future changes. In this study, we use observations from the recent Airborne Carbon Measurements (ACME-V) aircraft campaign to estimate the magnitude and environmental predictors CO2 and CH4 fluxes in Alaska. ACME-V consisted of 38 flights across the North Slope between late May and mid-September 2015, making it the most detailed airborne survey of northern Alaska to date. These data, combined with a geostatistical inverse model, provide a unique lens into fluxes across the region. Using this approach, we estimate a large CO2 flux to the atmosphere from the North Slope of Alaska in early summer that is counterbalanced by CO2 uptake in late summer; this balance between early season respiration and late-summer photosynthesis drives the total summer CO2 flux across northern Alaska during the study period. We further compare our results to process-based flux estimates (the Terrestrial Model Intercomparison Project, MsTMIP and the Wetland and Wetland CH4 Inter-comparison of Models Project, WETCHIMP) and several recent studies of the same spatial domain. We observe a similarity in CO2 flux totals between MsTMIP suite of models and our study (June 20150.86  0.57 vs 0.96, July 2015 MsTMIP 1.01  0.00 vs 0.14 and August 2915 MsTMIP 0.73  0.29 vs 0.50 mol m2 s1, MsTMIP and our study, respectively, averaged across the North Slope). However, we find significantly higher CH4 fluxes from the North Slope than any of the WETCHIMP models. Specifically, we estimate total CH4 fluxes from the North Slope of Alaska of +0.64  0.13 Tg (95% confidence), during June through August 2015 while the WETCHIMP model estimates range from 0.004 to 0.1 Tg, depending upon the model. Furthermore, the contribution of the North Slope tundra to the overall Alaskan CH4 fluxes during the entire period was ~27%, compared to 3% in state-of-the-art process-based models.

Research areas: Greenhouse Gases
Neural-network-based estimation of regional-scale anthropogenic CO2 emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and … (2021)

Abstract. Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of CO2, i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2), and the net primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the anthropogenic CO2 emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 XCO2, MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (20152019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variability. The XCO2 anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic CO2 emissions were estimated for 2019 based on the NPP and XCO2 anomalies derived for the same year. The estimated and the ODIAC CO2 emissions were compared, and the results showed good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional-scale anthropogenic CO2 emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets.

Research areas: Greenhouse Gases
Sensitivity of the recent methane budget to LMDz sub-grid-scale physical parameterizations (2015)

Abstract. With the densification of surface observing networks and the development of remote sensing of greenhouse gases from space, estimations of methane (CH4) sources and sinks by inverse modeling are gaining additional constraining data but facing new challenges. The chemical transport model (CTM) linking the flux space to methane mixing ratio space must be able to represent these different types of atmospheric constraints for providing consistent flux estimations.  Here we quantify the impact of sub-grid-scale physical parameterization errors on the global methane budget inferred by inverse modeling. We use the same inversion setup but different physical parameterizations within one CTM. Two different schemes for vertical diffusion, two others for deep convection, and one additional for thermals in the planetary boundary layer (PBL) are tested. Different atmospheric methane data sets are used as constraints (surface observations or satellite retrievals).  At the global scale, methane emissions differ, on average, from 4.1 Tg CH4 per year due to the use of different sub-grid-scale parameterizations. Inversions using satellite total-column mixing ratios retrieved by GOSAT are less impacted, at the global scale, by errors in physical parameterizations. Focusing on large-scale atmospheric transport, we show that inversions using the deep convection scheme of Emanuel (1991) derive smaller interhemispheric gradients in methane emissions, indicating a slower interhemispheric exchange. At regional scale, the use of different sub-grid-scale parameterizations induces uncertainties ranging from 1.2 % (2.7 %) to 9.4 % (14.2 %) of methane emissions when using only surface measurements from a background (or an extended) surface network. Moreover, spatial distribution of methane emissions at regional scale can be very different, depending on both the physical parameterizations used for the modeling of the atmospheric transport and the observation data sets used to constrain the inverse system.  When using only satellite data from GOSAT, we show that the small biases found in inversions using a coarser version of the transport model are actually masking a poor representation of the stratospheretroposphere methane gradient in the model. Improving the stratospheretroposphere gradient reveals a larger bias in GOSAT CH4 satellite data, which largely amplifies inconsistencies between the surface and satellite inversions. A simple bias correction is proposed. The results of this work provide the level of confidence one can have for recent methane inversions relative to physical parameterizations included in CTMs.

Research areas: Greenhouse Gases
Evaluating Cropland N2O Emissions and Fertilizer Plant Greenhouse Gas (2020)

Agricultural activity is a significant source of greenhouse gas emissions. The fertilizer production process emits N<SUB>2</SUB>O, CO<SUB>2</SUB>, and CH<SUB>4</SUB>, and fertilized croplands emit N<SUB>2</SUB>O. We present continuous airborne observations of these trace gases in the Lower Mississippi River Basin to quantify emissions from both fertilizer plants and croplands during the early growing season. Observed hourly emission rates from two fertilizer plants are compared with reported inventory values, showing agreement for N<SUB>2</SUB>O and CO<SUB>2</SUB> emissions but large underestimation in reported CH<SUB>4</SUB> emissions by up to a factor of 100. These CH<SUB>4</SUB> emissions are consistent with loss rates of 0.6-1.2%. We quantify regional emission fluxes (100 km) of N<SUB>2</SUB>O using the airborne mass balance technique, a first application for N<SUB>2</SUB>O, and explore linkages to controlling processes. Finally, we demonstrate the ability to use airborne measurements to distinguish N<SUB>2</SUB>O emission differences between neighboring fields, determining we can distinguish different emission behaviors of regions on the order of 2.5 km<SUP>2</SUP> with emissions differences of approximately 0.026 μmol m<SUP>-2</SUP> s<SUP>-1</SUP>. This suggests airborne approaches such as outlined here could be used to evaluate the impact of different agricultural practices at critical field-size spatial scales.

Research areas: Greenhouse Gases
Design and evaluation of CO2 observation network to optimize surface CO2 fluxes in Asia using observation system simulation experiments (2020)

Abstract. Continuous efforts have been made to monitor atmospheric CO2 mole fractions as it is one of the most influential greenhouse gases in Earth's atmosphere. The atmospheric CO2 mole fractions are mostly determined by CO2 exchanges at the Earth's surface (i.e., surface CO2 flux). Inverse modeling, which is a method to estimate the CO2 exchanges at the Earth's surface, derives surface CO2 fluxes using modeled and observed atmospheric CO2 mole fraction data. Although observation data are crucial for successful modeling, comparatively fewer in situ observation sites are located in Asia compared to Europe or North America. Based on the importance of the terrestrial ecosystem of Asia for global carbon exchanges, more observation stations and an effective observation network design are required. In this paper, several observation network experiments were conducted to optimize the surface CO2 flux of Asia using CarbonTracker and observation system simulation experiments (OSSEs). The impacts of the redistribution of and additions to the existing observation network of Asia were evaluated using hypothetical in situ observation sites. In the case of the addition experiments, 10 observation stations, which is a practical number for real implementation, were added through three strategies: random addition, the influence matrix (i.e., self-sensitivity), and ecoregion information within the model. The simulated surface CO2 flux in Asia in summer can be improved by redistributing the existing observation network. The addition experiments revealed that considering both the distribution of normalized self-sensitivity and ecoregion information can yield better simulated surface CO2 fluxes compared to random addition, regardless of the season. This study provides a diagnosis of the existing observation network and useful information for future observation network design in Asia to estimate the surface CO2 flux and also suggests the use of an influence matrix for designing CO2 observation networks. Unlike other previous observation network studies with many numerical experiments for optimization, comparatively fewer experiments were required in this study. Thus, the methodology used in this study may be used for designing observation networks for monitoring greenhouse gases at both continental and global scales.

Research areas: Greenhouse Gases
Doubling of U.S. Population Exposure to Climate Extremes by 2050 (2020)

We quantify historical and projected trends in the population exposure to climate extremes as measured by the United States National Center for Environmental Information Climate Extremes Index (CEI). Based on the analyses of the historical observations, we find that the U.S. has already experienced a rise in the occurrence of aggregated extremes in recent decades, consistent with the climate response to historical increases in radiative forcing. Additionally, we find that exposure can be expected to intensify under the Representative Concentration Pathway 8.5, with all counties permanently exceeding the baseline variability in the occurrence of extreme hot days, warm nights, and drought conditions by 2050. As a result, every county in the U.S. is projected to permanently exceed the historical CEI variability (as measured by one standard deviation during the 1981-2005 period). Based on the current population distribution, this unprecedented change implies a yearly exposure to extreme conditions for one in every three people. We find that the increasing trend in exposure to the aggregated extremes is already detectable over much of the U.S., and particularly in the central and eastern U.S. The high correspondence between the pattern of trends in our simulations and observations increases confidence in the projected amplification of population exposure to unprecedented combinations of extreme climate conditions, should greenhouse gas concentrations continue to escalate along their current trajectory.

Research areas: Atmospheric/Ocean Indicators, Public Health
An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data (2019)

Abstract. Emissions of methane (CH4) from tropical ecosystems, and how they respond to changes in climate, represent one of the biggest uncertainties associated with the global CH4 budget. Historically, this has been due to the dearth of pan-tropical in situ measurements, which is particularly acute in Africa. By virtue of their superior spatial coverage, satellite observations of atmospheric CH4 columns can help to narrow down some of the uncertainties in the tropical CH4 emission budget. We use proxy column retrievals of atmospheric CH4 (XCH4) from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the nested version of the GEOS-Chem atmospheric chemistry and transport model (0.50.625) to infer emissions from tropical Africa between 2010 and 2016. Proxy retrievals of XCH4 are less sensitive to scattering due to clouds and aerosol than full physics retrievals, but the method assumes that the global distribution of carbon dioxide (CO2) is known. We explore the sensitivity of inferred a posteriori emissions to this source of systematic error by using two different XCH4 data products that are determined using different model CO2 fields. We infer monthly emissions from GOSAT XCH4 data using a hierarchical Bayesian framework, allowing us to report seasonal cycles and trends in annual mean values. We find mean tropical African emissions between 2010 and 2016 range from 76 (7478) to 80 (7882) Tg yr1, depending on the proxy XCH4 data used, with larger differences in Northern Hemisphere Africa than Southern Hemisphere Africa. We find a robust positive linear trend in tropical African CH4 emissions for our 7-year study period, with values of 1.5 (1.11.9) Tg yr1 or 2.1 (1.72.5) Tg yr1, depending on the CO2 data product used in the proxy retrieval. This linear emissions trend accounts for around a third of the global emissions growth rate during this period. A substantial portion of this increase is due to a short-term increase in emissions of 3 Tg yr1 between 2011 and 2015 from the Sudd in South Sudan. Using satellite land surface temperature anomalies and altimetry data, we find this increase in CH4 emissions is consistent with an increase in wetland extent due to increased inflow from the White Nile, although the data indicate that the Sudd was anomalously dry at the start of our inversion period. We find a strong seasonality in emissions across Northern Hemisphere Africa, with the timing of the seasonal emissions peak coincident with the seasonal peak in ground water storage. In contrast, we find that a posteriori CH4 emissions from the wetland area of the Congo Basin are approximately constant throughout the year, consistent with less temporal variability in wetland extent, and significantly smaller than a priori estimates.

Research areas: Greenhouse Gases
Modeling carbon dioxide exchange in a single-layer urban canopy model (2020)

Carbon dioxide (CO2) is the primary greenhouse gas that drives the global climate change in past centuries. Much effort of carbon mitigation as a countermeasure to global changes has focused on the urban areas  the hotspots of fossil fuel and concentrated emission and pollutants. Despite their critical role in CO2 exchange in urban ecosystem, emission from vegetation and soils are largely overlooked in existing urban land surface models. In this study, we parameterized the biogenic CO2 exchange in cities using an advanced single-layer urban canopy model, by incorporating plant physiological functions in the built environment. In addition, the proposed model also includes the anthropogenic CO2 fluxes especially that from traffic emissions based on gridded dataset. We evaluate the proposed model using field measurements from an eddy covariance flux tower located at west Phoenix, Arizona, USA. The model results are in good agreement with the observed carbon flux over the built terrain, with a root mean squared error of 0.21 mg m2s1. Furthermore, our simulations show that the abiotic traffic-emitted CO2 amounts the largest source in cities, as expected. Nevertheless, the biogenic carbon exchange can be significantly enhanced in the built environment, making an equally important contributor to the total carbon emission especially in sub-urban areas.

Research areas: Greenhouse Gases
Hadley cell expansion in CMIP6 models (2020)

Abstract. In response to increasing greenhouse gases, the subtropical edges of Earth's Hadley circulation shift poleward in global climate models. Recent studies have found that reanalysis trends in the Hadley cell edge over the past 3040 years are within the range of trends simulated by Coupled Model Intercomparison Project Phase 5 (CMIP5) models and have documented seasonal and hemispheric asymmetries in these trends. In this study, we evaluate whether these conclusions hold for the newest generation of models (CMIP6). Overall, we find similar characteristics of Hadley cell expansion in CMIP5 and CMIP6 models. In both CMIP5 and CMIP6 models, the poleward shift of the Hadley cell edge in response to increasing greenhouse gases is 23 times larger in the Southern Hemisphere (SH), except during SeptemberNovember. The trends from CMIP5 and CMIP6 models agree well with reanalyses, although prescribing observed coupled atmosphereocean variability allows the models to better capture reanalysis trends in the Northern Hemisphere (NH). We find two notable differences between CMIP5 and CMIP6 models. First, while both CMIP5 and CMIP6 models contract the NH summertime Hadley circulation equatorward (particularly over the Pacific sector), this contraction is larger in CMIP6 models due to their higher average climate sensitivity. Second, in recent decades, the poleward shift of the NH annual-mean Hadley cell edge is slightly larger in CMIP6 models. Increasing greenhouse gases drive similar trends in CMIP5 and CMIP6 models, so the larger recent NH trends in CMIP6 models point to the role of other forcings, such as aerosols.

Research areas: Atmospheric/Ocean Indicators, Greenhouse Gases
Temporal evolution of mid-tropospheric CO2 over the Indian Ocean (2021)

The rapid increase in carbon dioxide (CO2) in the atmosphere due to anthropogenic activities aids global warming that demands immediate attention and policy actions. The changes in weather patterns, such as winds, temperature and precipitation, leading to floods and droughts are linked to the increasing greenhouse gases including CO2. This situation warrants close and continuous monitoring of atmospheric CO2. Here, the mid-tropospheric CO2 from Atmospheric Infrared Sounder (AIRS) is analysed for the past 14 years over Indian Ocean. The northern hemisphere has a higher CO2 concentration than the southern hemisphere, as most of the landmasses with significant CO2 sources are present in the former region. Seasonal variations show the highest CO2 concentration over the north Indian Ocean (NIO) during MAM (March, April and May), about 388.55 ppm, and lowest during ON (October and November), about 386.92 ppm. In contrast, the south Indian Ocean shows the highest, but the magnitude is smaller than that over NIO, during December, January and February. Arabian Sea (AS) exhibits smaller CO2 concentrations compared to that over Bay of Bengal (BoB). However, AS shows the highest trend of 2.13 ppm/year during the post-monsoon season (ON). The seasonal correlation analysis of the temperature at 10 m and sea surface temperature against mid-tropospheric CO2 shows a higher positive correlation for BoB. These analyses, therefore, provide a detailed understanding of the basin-wide seasonal and inter-annual trends of mid-tropospheric CO2 over Indian Ocean.

Research areas: Greenhouse Gases
Introducing Satellite Data Based Biosphere Model Beams to Improve Regional Transport Model Aist-Mm for Estimating Carbon Dioxide Emission from Mega-City … (2019)

Remote sensing data from GOSAT (Greenhouse gases Observing SATellite) and GOSAT-2 in the future ameliorate inversion analysis of greenhouse gas (GHG) emissions[1],[2]. Meso-scale atmospheric transport model AIST-MM (National Institute of Advanced Industrial Science and Technology-Mesoscale Model)[3],[4] and global-scale transport model NICAM-TM (Nonhydrostatic ICosahedral Atmospheric Model-Transport Model)[5] have been coupled for data assimilation in order to estimate CO2 emission from mega-city Tokyo. However, forests west and north of Tokyo Metropolis in the Kanto plain generate significant biogenic CO2 fluxes and such atmosphere-biosphere gas exchange remains to be properly calculated during the modeling processes. In this study, we use MODIS products[6],[7] to simulate regional gross primary production (GPP), vegetational and soil respirations based on Biosphere model integrating Eco-physiological And Mechanistic approaches using Satellite data (BEAMS) algorithms[8],[9]. By integrating this atmosphere-terrestrial ecosystem carbon balance module to our regional inversion analysis, we aim at more precise estimation of CO2 emission from Tokyo.

Research areas: Greenhouse Gases
Factors controlling the variability and emissions of greenhouse gases (CO2, CH4 and N2O) in three estuaries of the Southern Iberian Atlantic Basin during July 2017 (2020)

The partial pressure of CO2 (pCO2) and concentration of dissolved CH4 and N2O were studied during July 2017 in three estuaries of the southern Iberian Atlantic basin: the Guadalquivir, Tinto - Odiel and Guadiana. Two different sampling methods were adopted for each estuary: a longitudinal sampling along the course of the river, and a tidal cycle sampling of 24 h in the mouth of the estuary. In general, there is an increase in the concentration of gases towards the inner part of the river, in all three systems. Daily variations coupled to the tidal cycles were also observed in the three estuaries. The highest concentrations were measured during ebb, which suggests that the estuaries export CO2, CH4 and N2O to the Gulf of Cadiz. These three gases showed the highest value and range of variation in the Guadalquivir (pCO2: 959  1009 atm, CH4: 54.1  74.5 nM, N2O: 14.1  16.1 nM), followed by the Guadiana (pCO2: 595  155 atm, CH4: 53.5  62.9 nM, N2O: 7.1  0.5 nM), and the Tinto - Odiel (pCO2: 581  204 atm, CH4: 39.6  43.5 nM, N2O: 6.8  0.3 nM). Changes in pCO2 values and CH4 and N2O concentrations were affected by several factors: temperature, salinity, exchange with the atmosphere, reactivity of the gases and various physical and biological processes taking place in the estuaries object of study. The water - atmosphere fluxes of pCO2, CH4 and N2O resulted positive, revealing that the study area acts as a source of these gases to the atmosphere.

Research areas: Greenhouse Gases