The goal of this study is to determine how H2O and HDO measurements in water vapor can be used to detect and diagnose biases in the representation of processes controlling tropospheric humidity in ...atmospheric general circulation models (GCMs). We analyze a large number of isotopic data sets (four satellite, sixteen ground‐based remote‐sensing, five surface in situ and three aircraft data sets) that are sensitive to different altitudes throughout the free troposphere. Despite significant differences between data sets, we identify some observed HDO/H2O characteristics that are robust across data sets and that can be used to evaluate models. We evaluate the isotopic GCM LMDZ, accounting for the effects of spatiotemporal sampling and instrument sensitivity. We find that LMDZ reproduces the spatial patterns in the lower and mid troposphere remarkably well. However, it underestimates the amplitude of seasonal variations in isotopic composition at all levels in the subtropics and in midlatitudes, and this bias is consistent across all data sets. LMDZ also underestimates the observed meridional isotopic gradient and the contrast between dry and convective tropical regions compared to satellite data sets. Comparison with six other isotope‐enabled GCMs from the SWING2 project shows that biases exhibited by LMDZ are common to all models. The SWING2 GCMs show a very large spread in isotopic behavior that is not obviously related to that of humidity, suggesting water vapor isotopic measurements could be used to expose model shortcomings. In a companion paper, the isotopic differences between models are interpreted in terms of biases in the representation of processes controlling humidity.
Key Points
Isotopic evaluation with in situ, satellite, ground‐based remote‐sensing data
Consistent features and model‐data differences across data sets
Isotopic GCMs share common biases
Methane emissions inventories for Southern California's South Coast Air Basin (SoCAB) have underestimated emissions from atmospheric measurements. To provide insight into the sources of the ...discrepancy, we analyze records of atmospheric trace gas total column abundances in the SoCAB starting in the late 1980s to produce annual estimates of the ethane emissions from 1989 to 2015 and methane emissions from 2007 to 2015. The first decade of measurements shows a rapid decline in ethane emissions coincident with decreasing natural gas and crude oil production in the basin. Between 2010 and 2015, however, ethane emissions have grown gradually from about 13 ± 5 to about 23 ± 3 Gg yr−1, despite the steady production of natural gas and oil over that time period. The methane emissions record begins with 1 year of measurements in 2007 and continuous measurements from 2011 to 2016 and shows little trend over time, with an average emission rate of 413 ± 86 Gg yr−1. Since 2012, ethane to methane ratios in the natural gas withdrawn from a storage facility within the SoCAB have been increasing by 0.62 ± 0.05 % yr−1, consistent with the ratios measured in the delivered gas. Our atmospheric measurements also show an increase in these ratios but with a slope of 0.36 ± 0.08 % yr−1, or 58 ± 13 % of the slope calculated from the withdrawn gas. From this, we infer that more than half of the excess methane in the SoCAB between 2012 and 2015 is attributable to losses from the natural gas infrastructure.
We examined biases in the global GEOS-Chem chemical transport model for the period of February–May 2010 using weak-constraint (WC) four-dimensional variational (4D-Var) data assimilation and dry-air ...mole fractions of CH4 (XCH4) from the Greenhouse gases Observing SATellite (GOSAT). The ability of the observations and the WC 4D-Var method to mitigate model errors in CH4 concentrations was first investigated in a set of observing system simulation experiments (OSSEs). We then assimilated the GOSAT XCH4 retrievals and found that they were capable of providing information on the vertical structure of model errors and of removing a significant portion of biases in the modeled CH4 state. In the WC 4D-Var assimilation, corrections were added to the modeled CH4 state at each model time step to account for model errors and improve the model fit to the assimilated observations. Compared to the conventional strong-constraint (SC) 4D-Var assimilation, the WC method was able to significantly improve the model fit to independent observations. Examination of the WC state corrections suggested that a significant source of model errors was associated with discrepancies in the model CH4 in the stratosphere. The WC state corrections also suggested that the model vertical transport in the troposphere at middle and high latitudes is too weak. The problem was traced back to biases in the uplift of CH4 over the source regions in eastern China and North America. In the tropics, the WC assimilation pointed to the possibility of biased CH4 outflow from the African continent to the Atlantic in the mid-troposphere. The WC assimilation in this region would greatly benefit from glint observations over the ocean to provide additional constraints on the vertical structure of the model errors in the tropics. We also compared the WC assimilation at 4∘ × 5∘ and 2∘ × 2.5∘ horizontal resolutions and found that the WC corrections to mitigate the model errors were significantly larger at 4∘ × 5∘ than at 2∘ × 2.5∘ resolution, indicating the presence of resolution-dependent model errors. Our results illustrate the potential utility of the WC 4D-Var approach for characterizing model errors. However, a major limitation of this approach is the need to better characterize the specified model error covariance in the assimilation scheme.
Satellite retrievals of XCO2 at northern high latitudes currently have sparser coverage and lower data quality than most other regions of
the world. We use a neural network (NN) to filter Orbiting ...Carbon
Observatory 2 (OCO-2) B10 bias-corrected XCO2 retrievals and compare the quality of the filtered data to
the quality of the data filtered with the standard B10 quality control filter. To assess the performance of the NN filter, we use Total Carbon
Column Observing Network (TCCON) data at selected northern high latitude sites as a truth proxy. We found that the NN filter decreases the overall
bias by 0.25 ppm (∼ 50 %), improves the precision by 0.18 ppm (∼ 12 %), and increases the throughput by 16 %
at these sites when compared to the standard B10 quality control filter. Most of the increased throughput was due to an increase in throughput
during the spring, fall, and winter seasons. There was a decrease in throughput during the summer, but as a result the bias and precision were
improved during the summer months. The main drawback of using the NN filter is that it lets through fewer retrievals at the highest-latitude Arctic
TCCON sites compared to the B10 quality control filter, but the lower throughput improves the bias and precision.
We describe an approach for determining limited information about the vertical distribution of carbon monoxide (CO) and carbon dioxide
(CO2) from total column ground-based Total Carbon Column ...Observation Network (TCCON) observations. For CO and CO2, it has been difficult to retrieve information
about their vertical distribution from spectral line shapes because of the errors in the spectroscopy and the atmospheric temperature profile that
mask the effects of variations in their mixing ratio with altitude. For CO2 the challenge is especially difficult given that these
variations are typically 2 % or less. Nevertheless, if sufficient accuracy can be obtained, such information would be highly valuable for
evaluation of retrievals from satellites and more generally for improving the estimate of surface sources and sinks of these trace gases. We present here the Temporal Atmospheric Retrieval Determining Information from Secondary Scaling (TARDISS) retrieval algorithm. TARDISS uses
several simultaneously obtained total column observations of the same gas from different absorption bands with distinctly different vertical
averaging kernels. The different total column retrievals are combined in TARDISS using a Bayesian approach where the weights and temporal covariance
applied to the different retrievals include additional constraints on the diurnal variation in the vertical distribution for these gases. We assume
that the near-surface part of the column varies rapidly over the course of a day (from surface sources and sinks, for example) and that the upper part of
the column has a larger temporal covariance over the course of a day. Using measurements from the five North American TCCON sites, we find that the retrieved lower partial column (between the surface and
∼ 800 hPa) of the CO and CO2 dry mole fractions (DMFs) have slopes of 0.999 ± 0.002 and 1.001 ± 0.003 with
respect to lower column DMF from integrated in situ data measured directly from aircraft and in AirCores. The average error for our lower column
CO retrieval is 1.51 ppb (∼ 2 %) while the average error for our CO2 retrieval is 5.09 ppm
(∼ 1.25 %). Compared with classical line-shape-derived vertical profile retrievals, our algorithm reduces the influence of forward model
errors such as imprecision in spectroscopy (line shapes and intensities) and in the instrument line shape. In addition, because TARDISS uses the
existing retrieved column abundances from TCCON (which themselves are computationally much less intensive than profile retrieval algorithms), it is
very fast and processes years of data in minutes. We anticipate that this approach will find broad application for use in carbon cycle science.
We describe an approach for determining limited information about the vertical distribution of carbon monoxide (CO) and carbon dioxide (CO.sub.2) from total column ground-based Total Carbon Column ...Observation Network (TCCON) observations. For CO and CO.sub.2, it has been difficult to retrieve information about their vertical distribution from spectral line shapes because of the errors in the spectroscopy and the atmospheric temperature profile that mask the effects of variations in their mixing ratio with altitude. For CO.sub.2 the challenge is especially difficult given that these variations are typically 2 % or less. Nevertheless, if sufficient accuracy can be obtained, such information would be highly valuable for evaluation of retrievals from satellites and more generally for improving the estimate of surface sources and sinks of these trace gases.
We use historical and new atmospheric trace gas observations to refine the estimated source of methane (CH4) emitted into California’s South Coast Air Basin (the larger Los Angeles metropolitan ...region). Referenced to the California Air Resources Board (CARB) CO emissions inventory, total CH4 emissions are 0.44 ± 0.15 Tg each year. To investigate the possible contribution of fossil fuel emissions, we use ambient air observations of methane (CH4), ethane (C2H6), and carbon monoxide (CO), together with measured C2H6 to CH4 enhancement ratios in the Los Angeles natural gas supply. The observed atmospheric C2H6 to CH4 ratio during the ARCTAS (2008) and CalNex (2010) aircraft campaigns is similar to the ratio of these gases in the natural gas supplied to the basin during both these campaigns. Thus, at the upper limit (assuming that the only major source of atmospheric C2H6 is fugitive emissions from the natural gas infrastructure) these data are consistent with the attribution of most (0.39 ± 0.15 Tg yr–1) of the excess CH4 in the basin to uncombusted losses from the natural gas system (approximately 2.5–6% of natural gas delivered to basin customers). However, there are other sources of C2H6 in the region. In particular, emissions of C2H6 (and CH4) from natural gas seeps as well as those associated with petroleum production, both of which are poorly known, will reduce the inferred contribution of the natural gas infrastructure to the total CH4 emissions, potentially significantly. This study highlights both the value and challenges associated with the use of ethane as a tracer for fugitive emissions from the natural gas production and distribution system.
To fight climate change, it is crucial to have a precise knowledge of greenhouse gas (GHG) concentrations in the atmosphere and to monitor sources and sinks of GHGs. On global scales, satellites are ...an appropriate monitoring tool. For the validation of the satellite measurements and to tie them to the World Meteorological Organization (WMO) trace gas scale, ground-based Fourier transform infrared (FTIR) networks are used, which provide reference data. To ensure the highest-quality validation data, the network must be scaled to the WMO trace gas scale and have a very small site-to-site bias. Currently, the Total Carbon Column Observing Network (TCCON) is the de facto standard FTIR network for providing reference data. Ensuring a small site-to-site bias is a major challenge for the TCCON. In this work, we describe the development and application of a new method to evaluate the site-to-site bias by using a remotely controlled portable FTIR spectrometer as a travel standard (TS) for evaluating the consistency of columnar GHG measurements performed at different TCCON stations, and we describe campaign results for the TCCON sites in Tsukuba (Japan), East Trout Lake (Canada) and Wollongong (Australia). The TS is based on a characterized portable EM27/SUN FTIR spectrometer equipped with an accurate pressure sensor which is operated in an automated enclosure. The EM27/SUN is the standard instrument of the Collaborative Carbon Column Observing Network (COCCON). The COCCON is designed such that all spectrometers are referenced to a common reference unit located in Karlsruhe, Germany. To evaluate the long-term stability of the TS instrument, it is placed side-by-side with the TCCON instrument in Karlsruhe (KA) and the COCCON reference unit (the EM27/SUN spectrometer SN37, which is operated permanently next to the TCCON-KA site) between deployments to collect comparing measurements.
The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO2) with the accuracy, ...resolution, and coverage needed to quantify CO2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014 and has gathered more than 2 years of observations. The v7/v7r operational data products from September 2014 to January 2016 are discussed here. On monthly timescales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO2 dry air mole fraction, XCO2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of XCO2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes XCO2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north–south XCO2 gradient is small. Enhanced XCO2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north–south XCO2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in XCO2 across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart XCO2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset.
Abstract
In order to better manage anthropogenic CO
2
emissions, improved methods of quantifying emissions are needed at all spatial scales from the national level down to the facility level. ...Although the Orbiting Carbon Observatory 2 (OCO‐2) satellite was not designed for monitoring power plant emissions, we show that in some cases, CO
2
observations from OCO‐2 can be used to quantify daily CO
2
emissions from individual middle‐ to large‐sized coal power plants by fitting the data to plume model simulations. Emission estimates for U.S. power plants are within 1–17% of reported daily emission values, enabling application of the approach to international sites that lack detailed emission information. This affirms that a constellation of future CO
2
imaging satellites, optimized for point sources, could monitor emissions from individual power plants to support the implementation of climate policies.
Plain Language Summary
Burning coal for electricity generation accounts for more than 40% of humanity's current global CO
2
emissions. To better manage CO
2
emissions, improved methods of quantifying emissions are needed at all spatial scales. Although the Orbiting Carbon Observatory 2 (OCO‐2) satellite was not designed for monitoring power plant emissions, we show that in select cases, CO
2
observations from OCO‐2 can be used to quantify daily CO
2
emissions from individual middle‐ to large‐sized coal power plants by fitting the data to a simple model. Demonstrating the method on U.S. power plants with reliable reported emission data enabled application of the approach to international sites that have less or lower quality information available on emissions. Space agencies around the world are currently exploring how to design satellite missions to help address climate change and support Monitoring, Reporting and Verification (MRV) of CO
2
emissions for climate agreements. This work affirms that a constellation of CO
2
imaging satellites, with a design optimized for point sources, could monitor CO
2
emissions from individual fossil fuel burning power plants to support that objective.
Key Points
The combustion of coal for electricity generation accounts for more than 40% of global anthropogenic CO
2
emissions
Orbiting Carbon Observatory 2 observations can be used to quantify CO
2
emissions from individual coal power plants, in selected cases
This work suggests that a future constellation of CO
2
imaging satellites could monitor fossil fuel power plant CO
2
emissions to support climate policy
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