Top-down Atmospheric Inventories of CO2 and CH4 to Support the Global Stocktakes
No abstract provided
Tanimoto, Hiroshi↗
Engineering topics
Oda, Tomohiro
Publications and source records attributed to Oda, Tomohiro.
Urban-Focused Satellite CO2 Observations From the Orbiting Carbon Observatory-3: A First Look at the Los Angeles Megacity
No abstract provided
Blavier, Jean-Francois↗
Prototype for Monitoring Carbon Flux Anomalies in near Real Time Using NASA's GEOS System: Progress and Challenges
No abstract available
Ott, Lesley E.↗
A Suite of High-Resolution Atmospheric Carbon Dioxide Simulations in Support of the OCO-3 Snapshot Area Mapping (SAM) Mode Observation: PSU-WRF, CSU-OLAM and NASA GEOS
Cities and power plants are responsible more than 70% of GHG emissions. The emissions from the subnational localized sources need to be accurately quantified and properly managed in order to achieve the Paris Climate Agreement goals. The accurate estimation of these emission is also crucial for assessing the capacity of natural sinks to uptake the carbon released into the atmosphere that ultimately defines our emission allowance for the 1.5 or 2.0 degree temperature goals. New data collected by the Orbiting Carbon Observatory 3 (OCO-3) Snapshot Area Mapping (SAM) observations should provide a tremendous new opportunity for us to study CO2 emissions from targeted large localized sources, such as cities, power plants and beyond. Since 2018 (prior to the OCO-3 launch), we have studied the observational strategies for the SAM mode observation in order to collect the useful data for estimating CO2 emissions from target sources. To maximize the benefit of the SAM mode observation data for quantifying CO2 emission, it is important to define how to observe the localized sources depending on the local environmental and emission specificities. We employ a suite of state-of-the-art CO2 modeling systems, such as PSU's WRF-CO2, CSU's OLAM and NASA's GEOS models. All of these CO2 modeling systems are prescribedwith the high-resolution fuel CO2 emission estimates from the ODIAC data product to achieve realistic urban CO2 variations. We focus on cities with established ground-based observation networks, such as Los Angeles, Indianapolis, and Paris. We have examined the urban emission signal detectability in response to the influence of local background conditions that can observed by the SAM and biospheric contributions that will be a new challenge for urban emission inverse estimation. Based on the results of our simulation experiments, we plan to propose city-specific observation strategies. Upon the availability of the OCO-3 data, we will attempt to estimate city emissions using inverse models. We also developed synthetic OCO-3 data using NASA's GEOS5 model, which should be useful to assess the utility of the OCO-3 data in combination with data collected by carbon satellites in other orbits, such as NASA's OCO-2 and Japanese GOSAT-1/2. The synthetic data also provide an opportunity to study the errors due to clouds and aerosols, which have been not fully studied in the past.
Oda, Tomohiro↗
Quantifying Contributions of Land Carbon Fluxes Variability and Atmospheric Transport Variability to Atmospheric CO2 Variability
Much of the uncertainty in predicting variations in greenhouse gases originates in the complex dynamics of the land ecosystem and the atmosphere. To reduce the uncertainties, it is useful to decouple the contributions of land carbon fluxes and atmospheric transport to atmospheric carbon variability. Here we isolate these contributions using a version of the NASA GEOS model that couples carbon, energy and water cycles between the land and the atmosphere. Our current study is a follow-on to a preliminary analysis that suggested that an extreme event (e.g., imposed drought) in a free running AGCM simulation affects atmospheric CO2 more through its impact on atmospheric transport than through a modification of land carbon fluxes. In order to more carefully isolate the effects of the land carbon variability and atmospheric transport variability on atmospheric CO2 variability, we conducted two coupled-AGCM simulations in replay mode, a mode that forces the model's evolution of weather to match that of the MERRA-2 reanalysis. In the control simulation, the land carbon fluxes and the atmospheric CO2 concentrations, as well as the meteorology, are simulated over 2001-2015. We compute the climatological seasonal cycles of net land carbon production from this control simulation and then prescribe, in a second simulation, these climatological carbon fluxes to the atmosphere in the same replay mode. By comparing the atmospheric CO2 variability produced in the two simulations, we fully isolate the part of this variability associated with land surface fluxes. Relative contributions of land flux variability and atmospheric transport variability to CO2 variability are quantified both on a regional basis and as a function of height into the atmosphere to support interpretation of both ground-based and satellite observations.
Lee, Eunjee↗
Impacts of Land Carbon Flux Variability on Variations in Atmospheric CO2
To better understand the role of atmospheric CO2 in the global carbon cycle, it is important to understand the factors that control its variability in time and space. Here we investigate these factors using a coupled land-atmosphere modeling system (the NASA GEOS-5 model) fitted with land carbon physics and atmospheric CO2 transport. To separate the influences of land carbon flux variability and atmospheric transport variability on the variability of CO2 concentrations, we conducted two carbon-fitted GEOS-5 AGCM simulations run in replay mode (a technique that guides the model's weather to match that of the MERRA-2 reanalysis): (i) a control simulation of 15-year carbon cycle dynamics and climate, and (ii) a simulation in which the climatological seasonal cycles of net biosphere production (NBP), as determined from diagnostics produced in the control experiment, are applied at the surface to the atmosphere instead. The impact of land flux variability on atmospheric CO2 variability is then isolated by subtracting the variability inherent in the second simulation (as induced, e.g., by strong El Nino conditions) from that in the control. These results are also presented in the context of supplemental simulations that have examined the impact of imposed drought in a free running AGCM simulation on carbon fluxes and atmospheric CO2 variability.
Lee, Eunjee↗
The Use of Gridded Fossil Fuel CO2 Emissions (FFCO2) Inventory for Climate Mitigation Applications: Errors, Uncertainties, and Current and Future Challenges
Emission Inventory (EI) is a fundamental tool to monitor global compliance of greenhouse gases (GHGs) emissions reduction actions. Inventory guidelines provide a best practice to help EI compilers to make comparable national emission estimates, in spite of the differences in data availability across countries and regions. There are a variety of sources of errors and uncertainties, however, that originate beyond what the inventory guidelines define. For example, spatially-explicit EIs, which are a key product for atmospheric modeling applications, are often developed for research purposes, and there are no specific guidelines to disaggregate emission estimates from country scale. On top of that, EIs are fundamentally prone to systematic biases due to the simple calculation methodology and thus an objective evaluation (e.g. atmospheric top-down estimates) is needed to assure the accuracy of the estimates. ODIAC is a global high-resolution (1x1 km) fossil fuel carbon dioxide (CO2) gridded EI that is now often used in atmospheric CO2 modeling. ODIAC is based on disaggregation of national emission estimates made by CDIAC, which is the well accepted standard in the community. The ODIAC emission data product is updated on an annual basis using best available statistical data. Subnational spatial emission patterns are estimated using power plant profiles and satellite-observations of nighttime lights. In addition to the conventional CDIAC gridded data product, ODIAC carries international bunker emissions (shipping and aviation), which allows flux inversion modelers to accurately impose the global total fossil fuel emissions and their horizontal and vertical distribution. We have extensively evaluated ODIAC emissions using fine-grained EIs as well as a high-resolution atmospheric model simulation across different scales (national, subnational/regional, and urban policy relevant) with a focus on the uncertainties associated with the emission disaggregation. We have examined the use of NASA's Black Marble Suomi-NPP/VIIRS nightlight data.
Oda, Tomohiro↗
Southern California Megacity CO2, CH4, and CO Flux Estimates Using Ground- and Space-Based Remote Sensing and a Lagrangian Model
We estimate the overall CO2, CH4, and CO flux from the South Coast Air Basin using an inversion that couples Total Carbon Column Observing Network (TCCON) and Orbiting Carbon Observatory-2 (OCO-2) observations, with the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the Open-source Data Inventory for Anthropogenic CO2 (ODIAC). Using TCCON data we estimate the direct net CO2 flux from the So-CAB to be 104±26 Tg CO2 yr(exp -1) for the study period of July 2013–August 2016. We obtain a slightly higher estimate of 120±30 Tg CO2 yr(exp -1) using OCO-2 data. These CO2 emission estimates are on the low end of previous work. Our net CH4 (360±90 Gg CH4 y(exp -1)) flux estimate is in agreement with central values from previous top-down studies going back to 2010 (342–440 Gg CH4 yr(exp -1)). CO emissions are estimated at 487±122 Gg CO yr(exp -1), much lower than previous top-down estimates (1440 Gg CO yr(exp -1)). Given the decreasing emissions of CO, this finding is not unexpected. We perform sensitivity tests to estimate how much errors in the prior, errors in the covariance, different inversion schemes, or a coarser dynamical model influence the emission estimates. Overall, the uncertainty is estimated to be 25%, with the largest contribution from the dynamical model. Lessons learned here may help in future inversions of satellite data over urban areas.
Total Carbon Column Observing Network (TCCON)↗
NASA's Black Marble Nighttime Lights Product Suite
NASA's Black Marble nighttime lights product suite (VNP46) is available at 500 meters resolution since January 2012 with data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP). The retrieval algorithm, developed and implemented for routine global processing at NASA's Land Science Investigator-led Processing System (SIPS), utilizes all high-quality, cloud-free, atmospheric-, terrain-, vegetation-, snow-, lunar-, and stray light-corrected radiances to estimate daily nighttime lights (NTL) and other intrinsic surface optical properties. Key algorithm enhancements include: (1) lunar irradiance modeling to resolve non-linear changes in phase and libration; (2) vector radiative transfer and lunar bidirectional surface anisotropic reflectance modeling to correct for atmospheric and BRDF (Bidirectional Reflectance Distribution Function) effects; (3) geometric-optical and canopy radiative transfer modeling to account for seasonal variations in NTL; and (4) temporal gap-filling to reduce persistent data gaps. Extensive benchmark tests at representative spatial and temporal scales were conducted on the VNP46 time series record to characterize the uncertainties stemming from upstream data sources. Initial validation results are presented together with example case studies illustrating the scientific utility of the products. This includes an evaluation of temporal patterns of NTL dynamics associated with urbanization, socioeconomic variability, cultural characteristics, and displaced populations affected by conflict. Current and planned activities under the Group on Earth Observations (GEO) Human Planet Initiative are aimed at evaluating the products at different geographic locations and time periods representing the full range of retrieval conditions.
Suomi-NPP↗
Comparing GOSAT Observations of Localized CO2 Enhancements by Large Emitters with Inventory-Based Estimates
We employed an atmospheric transport model to attribute column-averaged CO2 mixing ratios (XCO2) observed by Greenhouse gases Observing SATellite (GOSAT) to emissions due to large sources such as megacities and power plants. XCO2 enhancements estimated from observations were compared to model simulations implemented at the spatial resolution of the satellite observation footprint (0.1deg × 0.1deg). We found that the simulated XCO2 enhancements agree with the observed over several continental regions across the globe, for example, for North America with an observation to simulation ratio of 1.05 +/- 0.38 (p<0.1), but with a larger ratio over East Asia (1.22 +/- 0.32; p<0.05). The obtained observation-model discrepancy (22%) for East Asia is comparable to the uncertainties in Chinese emission inventories (approx.15%) suggested by recent reports. Our results suggest that by increasing the number of observations around emission sources, satellite instruments like GOSAT can provide a tool for detecting biases in reported emission inventories.
CO2↗
Consistent Evaluation of ACOS-GOSAT, BESD-SCIAMACHY, CarbonTracker, and MACC Through Comparisons to TCCON
Consistent validation of satellite CO2 estimates is a prerequisite for using multiple satellite CO2 measurements for joint flux inversion, and for establishing an accurate long-term atmospheric CO2 data record. Harmonizing satellite CO2 measurements is particularly important since the differences in instruments, observing geometries, sampling strategies, etc. imbue different measurement characteristics in the various satellite CO2 data products. We focus on validating model and satellite observation attributes that impact flux estimates and CO2 assimilation, including accurate error estimates, correlated and random errors, overall biases, biases by season and latitude, the impact of coincidence criteria, validation of seasonal cycle phase and amplitude, yearly growth, and daily variability. We evaluate dry-air mole fraction (X(sub CO2)) for Greenhouse gases Observing SATellite (GOSAT) (Atmospheric CO2 Observations from Space, ACOS b3.5) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) (Bremen Optimal Estimation DOAS, BESD v2.00.08) as well as the CarbonTracker (CT2013b) simulated CO2 mole fraction fields and the Monitoring Atmospheric Composition and Climate (MACC) CO2 inversion system (v13.1) and compare these to Total Carbon Column Observing Network (TCCON) observations (GGG2012/2014). We find standard deviations of 0.9, 0.9, 1.7, and 2.1 parts per million vs. TCCON for CT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the single observation errors 1.9 and 0.9 times the predicted errors for GOSAT and SCIAMACHY, respectively. We quantify how satellite error drops with data averaging by interpreting according to (error(sup 2) equals a(sup 2) plus b(sup 2) divided by n (with n being the number of observations averaged, a the systematic (correlated) errors, and b the random (uncorrelated) errors). a and b are estimated by satellites, coincidence criteria, and hemisphere. Biases at individual stations have year-to-year variability of 0.3 parts per million, with biases larger than the TCCON predicted bias uncertainty of 0.4 parts per million at many stations. We find that GOSAT and CT2013b under-predict the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46 and 53 degrees North latitude, MACC over-predicts between 26 and 37 degrees North latitude, and CT2013b under-predicts the seasonal cycle amplitude in the Southern Hemisphere (SH). The seasonal cycle phase indicates whether a data set or model lags another data set in time. We find that the GOSAT measurements improve the seasonal cycle phase substantially over the prior while SCIAMACHY measurements improve the phase significantly for just two of seven sites. The models reproduce the measured seasonal cycle phase well except for at Lauder_125HR (CT2013b) and Darwin (MACC). We compare the variability within 1 day between TCCON and models in June-July-August; there is correlation between 0.2 and 0.8 in the NH, with models showing 10-50 percent the variability of TCCON at different stations and CT2013b showing more variability than MACC. This paper highlights findings that provide inputs to estimate flux errors in model assimilations, and places where models and satellites need further investigation, e.g., the SH for models and 45-67 degrees North latitude for GOSAT and CT2013b.
ACOS-GOSAT↗