Impact of physical parameterizations and initial conditions on simulated atmospheric transport and CO₂ mole fractions in the US Midwest

Atmospheric transport model errors are one of the main contributors to the uncertainty affecting CO2 inverse flux estimates. In this study, we determine the leading causes of transport errors over the US upper Midwest with a large set of simulations generated with the Weather Research and Forecasting (WRF) mesoscale model. The various WRF simulations are performed using different meteorological driver datasets and physical parameterizations including planetary boundary layer (PBL) schemes, land surface models (LSMs), cumulus parameterizations and microphysics parameterizations. All the different model configurations were coupled to CO2 fluxes and lateral boundary conditions from the CarbonTracker inversion system to simulate atmospheric CO2 mole fractions. PBL height, wind speed, wind direction, and atmospheric CO2 mole fractions are compared to observations during a month in the summer of 2008, and statistical analyses were performed to evaluate the impact of both physics parameterizations and meteorological datasets on these variables. All of the physical parameterizations and the meteorological initial and boundary conditions contribute 3 to 4&thinsp;ppm to the model-to-model variability in daytime PBL CO2 except for the microphysics parameterization which has a smaller contribution. PBL height varies across ensemble members by 300 to 400&thinsp;m, and this variability is controlled by the same physics parameterizations. Daily PBL CO2 mole fraction errors are correlated with errors in the PBL height. We show that specific model configurations systematically overestimate or underestimate the PBL height averaged across the region with biases closely correlated with the choice of LSM, PBL scheme, and cumulus parameterization (CP). Domain average PBL wind speed is overestimated in nearly every model configuration. Both planetary boundary layer height (PBLH) and PBL wind speed biases show coherent spatial variations across the Midwest, with PBLH overestimated averaged across configurations by 300–400&thinsp;m in the west, and PBL winds overestimated by about 1&thinsp;m&thinsp;s−1 on average in the east. We find model configurations with lower biases averaged across the domain, but no single configuration is optimal across the entire region and for all meteorological variables. We conclude that model ensembles that include multiple physics parameterizations and meteorological initial conditions are likely to be necessary to encompass the atmospheric conditions most important to the transport of CO2 in the PBL, but that construction of such an ensemble will be challenging due to ensemble biases that vary across the region.</p

Analysis of errors introduced by geographic coordinate systems on weather numeric prediction modeling

Most atmospheric models, including the Weather Research and Forecasting (WRF) model, use a spherical geographic coordinate system to internally represent input data and perform computations. However, most geographic information system (GIS) input data used by the models are based on a spheroid datum because it better represents the actual geometry of the earth. WRF and other atmospheric models use these GIS input layers as if they were in a spherical coordinate system without accounting for the difference in datum. When GIS layers are not properly reprojected, latitudinal errors of up to 21 km in the midlatitudes are introduced. Recent studies have suggested that for very high-resolution applications, the difference in datum in the GIS input data (e.g., terrain land use, orography) should be taken into account. However, the magnitude of errors introduced by the difference in coordinate systems remains unclear. This research quantifies the effect of using a spherical vs. a spheroid datum for the input GIS layers used by WRF to study greenhouse gas transport and dispersion in northeast Pennsylvania

Calibration of a multi-physics ensemble for estimating the uncertainty of a greenhouse gas atmospheric transport model

Atmospheric inversions have been used to assess biosphere–atmosphere CO2 surface exchanges at various scales, but variability among inverse flux estimates remains significant, especially at continental scales. Atmospheric transport errors are one of the main contributors to this variability. To characterize transport errors and their spatiotemporal structures, we present an objective method to generate a calibrated ensemble adjusted with meteorological measurements collected across a region, here the upper US Midwest in midsummer. Using multiple model configurations of the Weather Research and Forecasting (WRF) model, we show that a reduced number of simulations (less than 10 members) reproduces the transport error characteristics of a 45-member ensemble while minimizing the size of the ensemble. The large ensemble of 45 members was constructed using different physics parameterization (i.e., land surface models (LSMs), planetary boundary layer (PBL) schemes, cumulus parameterizations and microphysics parameterizations) and meteorological initial/boundary conditions. All the different models were coupled to CO2 fluxes and lateral boundary conditions from CarbonTracker to simulate CO2 mole fractions. Observed meteorological variables critical to inverse flux estimates, PBL wind speed, PBL wind direction and PBL height are used to calibrate our ensemble over the region. Two optimization techniques (i.e., simulated annealing and a genetic algorithm) are used for the selection of the optimal ensemble using the flatness of the rank histograms as the main criterion. We also choose model configurations that minimize the systematic errors (i.e., monthly biases) in the ensemble. We evaluate the impact of transport errors on atmospheric CO2 mole fraction to represent up to 40&thinsp;% of the model–data mismatch (fraction of the total variance). We conclude that a carefully chosen subset of the physics ensemble can represent the uncertainties in the full ensemble, and that transport ensembles calibrated with relevant meteorological variables provide a promising path forward for improving the treatment of transport uncertainties in atmospheric inverse flux estimates.</p

Mesoscale modelling of the CO2 interactions between the surface and the atmosphere applied to the April 2007 CERES field experiment

This paper describes a numerical interpretation of the April 2007, CarboEurope Regional Experiment Strategy (CERES) campaign, devoted to the study of the CO2 cycle at the regional scale. Four consecutive clear sky days with intensive observations of CO2 concentration, fluxes at the surface and in the boundary layer have been simulated with the Meso-NH mesoscale model, coupled to ISBA-A-gs land surface model. The main result of this paper is to show how aircraft observations of CO2 concentration have been used to identify surface model errors and to calibrate the CO2 driving component of the surface model. In fact, the comparisons between modelled and observed CO2 concentrations within the Atmospheric Boundary Layer (ABL) allow to calibrate and correct not only the parameterization of respired CO2 fluxes by the ecosystem but also the Leaf Area Index (LAI) of the dominating land cover. After this calibration, the paper describes systematic comparisons of the model outputs with numerous data collected during the CERES campaign, in April 2007. For instance, the originality of this paper is the spatial integration of the comparisons. In fact, the aircraft observations of CO2 concentration and fluxes and energy fluxes are used for the model validation from the local to the regional scale. As a conclusion, the CO2 budgeting approach from the mesoscale model shows that the winter croplands are assimilating more CO2 than the pine forest, at this stage of the year and this case study

Bridging the gap between atmospheric concentrations and local ecosystem measurements

This paper demonstrates that atmospheric inversions of CO₂ are a reliable tool for estimating regional fluxes. We compare results of an inversion over 18 days and a 300 x 300 km 2 domain in southwest France against independent measurements of fluxes from aircraft and towers. The inversion used concentration measurements from 2 towers while the independent data included 27 aircraft transects and 5 flux towers. The inversion reduces the mismatch between prior and independent fluxes, improving both spatial and temporal structures. The present mesoscale atmospheric inversion improves by 30% the CO₂ fluxes over distances of few hundreds of km around the atmospheric measurement locations. Citation: Lauvaux, T., et al. (2009), Bridging the gap between atmospheric concentrations and local ecosystem measurements, Geophys. Res. Lett., 36, L19809, doi: 10.1029/2009GL039574

Methane emissions from dairies in the Los Angeles Basin

We estimate the amount of methane (CH_4) emitted by the largest dairies in the southern California region by combining measurements from four mobile solar-viewing ground-based spectrometers (EM27/SUN), in situ isotopic ^(13∕12)CH_4 measurements from a CRDS analyzer (Picarro), and a high-resolution atmospheric transport simulation with a Weather Research and Forecasting model in large-eddy simulation mode (WRF-LES). The remote sensing spectrometers measure the total column-averaged dry-air mole fractions of CH_4 and CO_2 (X_(CH)_4 and X_(CO)_2) in the near infrared region, providing information on total emissions of the dairies at Chino. Differences measured between the four EM27/SUN ranged from 0.2 to 22 ppb (part per billion) and from 0.7 to 3 ppm (part per million) for X_(CH)_4 and X_(CO)_2, respectively. To assess the fluxes of the dairies, these differential measurements are used in conjunction with the local atmospheric dynamics from wind measurements at two local airports and from the WRF-LES simulations at 111 m resolution. Our top-down CH_4 emissions derived using the Fourier transform spectrometers (FTS) observations of 1.4 to 4.8 ppt s^(−1) are in the low end of previous top-down estimates, consistent with reductions of the dairy farms and urbanization in the domain. However, the wide range of inferred fluxes points to the challenges posed by the heterogeneity of the sources and meteorology. Inverse modeling from WRF-LES is utilized to resolve the spatial distribution of CH_4 emissions in the domain. Both the model and the measurements indicate heterogeneous emissions, with contributions from anthropogenic and biogenic sources at Chino. A Bayesian inversion and a Monte Carlo approach are used to provide the CH_4 emissions of 2.2 to 3.5 ppt s^(−1) at Chino

Emissions and topographic effects on column CO_2 (XCO_2) variations, with a focus on the Southern California Megacity

Within the California South Coast Air Basin (SoCAB), X_(CO)_2 varies significantly due to atmospheric dynamics and the nonuniform distribution of sources. X_(CO)_2 measurements within the basin have seasonal variation compared to the “background” due primarily to dynamics, or the origins of air masses coming into the basin. We observe basin-background differences that are in close agreement for three observing systems: Total Carbon Column Observing Network (TCCON) 2.3 ± 1.2 ppm, Orbiting Carbon Observatory-2 (OCO-2) 2.4 ± 1.5 ppm, and Greenhouse gases Observing Satellite 2.4 ± 1.6 ppm (errors are 1σ). We further observe persistent significant differences (∼0.9 ppm) in X_(CO)_2 between two TCCON sites located only 9 km apart within the SoCAB. We estimate that 20% (±1σ confidence interval (CI): 0%, 58%) of the variance is explained by a difference in elevation using a full physics and emissions model and 36% (±1σ CI: 10%, 101%) using a simple, fixed mixed layer model. This effect arises in the presence of a sharp gradient in any species (here we focus on CO_2) between the mixed layer (ML) and free troposphere. Column differences between nearby locations arise when the change in elevation is greater than the change in ML height. This affects the fraction of atmosphere that is in the ML above each site. We show that such topographic effects produce significant variation in X_(CO)_2 across the SoCAB as well

Emissions and topographic effects on column CO2 (XCO2) variations, with a focus on the Southern California Megacity

Within the California South Coast Air Basin (SoCAB), XCO2 varies significantly due to atmospheric dynamics and the nonuniform distribution of sources. XCO2 measurements within the basin have seasonal variation compared to the “background” due primarily to dynamics, or the origins of air masses coming into the basin. We observe basin‐background differences that are in close agreement for three observing systems: Total Carbon Column Observing Network (TCCON) 2.3 ± 1.2 ppm, Orbiting Carbon Observatory‐2 (OCO‐2) 2.4 ± 1.5 ppm, and Greenhouse gases Observing Satellite 2.4 ± 1.6 ppm (errors are 1σ). We further observe persistent significant differences (∼0.9 ppm) in XCO2 between two TCCON sites located only 9 km apart within the SoCAB. We estimate that 20% (±1σ confidence interval (CI): 0%, 58%) of the variance is explained by a difference in elevation using a full physics and emissions model and 36% (±1σ CI: 10%, 101%) using a simple, fixed mixed layer model. This effect arises in the presence of a sharp gradient in any species (here we focus on CO2) between the mixed layer (ML) and free troposphere. Column differences between nearby locations arise when the change in elevation is greater than the change in ML height. This affects the fraction of atmosphere that is in the ML above each site. We show that such topographic effects produce significant variation in XCO2 across the SoCAB as well.Plain Language SummaryCities persistently have elevated carbon dioxide (CO2) levels as compared to surrounding regions. Within a city CO2 levels can also vary significantly at different locations for reasons such as more CO2 being emitted in some parts than others. Elevated column CO2 levels in the South Coast Air Basin (SoCAB) are in agreement for three observation systems (two satellite and one ground‐based) systems and vary with regional wind patterns throughout the year. In Pasadena, California, within the SoCAB, a significant fraction (about 25%) of variation in the column‐averaged CO2 can be explained by differences in surface altitude. This is important to understand so that all variations in column CO2 within an urban region are not mistakenly interpreted as being from CO2 surface fluxes.Key PointsIn the SoCAB, 20–36% of spatial variance in XCO2 is explained by topography on scales ≲10 kmIn Pasadena, XCO2 is enhanced by 2.3 ± 1.2 (1σ) ppm above background levels, at 1300 (UTC 8) with seasonal variationThe SoCAB XCO2 enhancement is in agreement for 3 different observation sets (TCCON, GOSAT, and OCO‐2)Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137737/1/jgrd53887.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137737/2/jgrd53887_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137737/3/jgrd53887-sup-0001-supinfo.pd

Errors and uncertainties in a gridded carbon dioxide emissions inventory

Emission inventories (EIs) are the fundamental tool to monitor compliance with greenhouse gas (GHG) emissions and emission reduction commitments. Inventory accounting guidelines provide the best practices to help EI compilers across different countries and regions make comparable, national emission estimates regardless of differences in data availability. However, there are a variety of sources of error and uncertainty that originate beyond what the inventory guidelines can define. 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 achieve spatial emission estimates. The errors and uncertainties associated with the spatial estimates are unique to the approaches employed and are often difficult to assess. This study compares the global, high-resolution (1 km), fossil fuel, carbon dioxide (CO2), gridded EI Open-source Data Inventory for Anthropogenic CO2 (ODIAC) with the multi-resolution, spatially explicit bottom-up EI geoinformation technologies, spatio-temporal approaches, and full carbon account for improving the accuracy of GHG inventories (GESAPU) over the domain of Poland. By taking full advantage of the data granularity that bottom-up EI offers, this study characterized the potential biases in spatial disaggregation by emission sector (point and non-point emissions) across different scales (national, subnational/regional, and urban policy-relevant scales) and identified the root causes. While two EIs are in agreement in total and sectoral emissions (2.2% for the total emissions), the emission spatial patterns showed large differences (10~100% relative differences at 1 km) especially at the urban-rural transitioning areas (90–100%). We however found that the agreement of emissions over urban areas is surprisingly good compared with the estimates previously reported for US cities. This paper also discusses the use of spatially explicit EIs for climate mitigation applications beyond the common use in atmospheric modeling. We conclude with a discussion of current and future challenges of EIs in support of successful implementation of GHG emission monitoring and mitigation activity under the Paris Climate Agreement from the United Nations Framework Convention on Climate Change (UNFCCC) 21st Conference of the Parties (COP21). We highlight the importance of capacity building for EI development and coordinated research efforts of EI, atmospheric observations, and modeling to overcome the challenges

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