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Satellite-based observations of atmospheric carbon dioxide (CO2) provide measurements in remote regions, such as the biologically sensitive but under sampled northern high latitudes, and are progressing toward true global data coverage. Recent improvements in satellite retrievals of total column-averaged dry air mole fractions of CO2 (XCO2) from the NASA Orbiting Carbon Observatory 2 (OCO-2) have allowed for unprecedented data coverage of northern high latitude regions, while maintaining acceptable accuracy and consistency relative to ground-based observations, and finally providing sufficient data in spring and autumn for analysis of the satellite-observed XCO2 seasonal cycles across a majority of terrestrial northern high latitude regions. Here, we present an analysis of XCO2 seasonal cycles calculated from OCO-2 data for temperate, boreal, and tundra regions, subdivided into 5° latitude by 20° longitude zones. We quantify the seasonal cycle amplitudes (SCA) and the annual half drawdown day (HDD). OCO-2 SCA is in good agreement with ground-based observations at five high latitude sites and OCO-2 SCA show very close agreement with SCA calculated for model estimates of XCO2 from the Copernicus Atmospheric Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1. Model estimates of XCO2 from the GEOS-Chem CO2 simulation version 12.7.2 with underlying biospheric fluxes from CarbonTracker2019 yield SCA of larger magnitude and spread over a larger range than those from CAMS and OCO-2; however, GEOS-Chem SCA still exhibit a very similar spatial distribution across northern high latitude regions to that from CAMS and OCO-2. Zones in the Asian Boreal Forest were found to have exceptionally large SCA and early HDD, and both OCO-2 data and model estimates yield a distinct longitudinal gradient of increasing SCA from west to east across the Eurasian continent. Longitudinal gradients in both SCA and HDD are at least as pronounced as meridional gradients (with respect to latitude), suggesting an essential role for global atmospheric transport patterns in defining XCO2 seasonality. GEOS-Chem surface contact tracers show that the largest XCO2 SCA occurs in areas with the greatest contact with land surfaces, integrated over 15-30 days. The correlation of XCO2 SCA with these land contact tracers are stronger than the correlation of XCO2 SCA with the SCA of CO2 fluxes within each 5° latitude by 20° longitude zone. This indicates that accumulation of terrestrial CO2 flux during atmospheric transport is a major driver of regional variations in XCO2 SCA. [ABSTRACT FROM AUTHOR]

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 differentiating the vertical distribution of model errors and of removing a significant portion of biases in the modelled 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 the 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 mid- 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 the 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. [ABSTRACT FROM AUTHOR]

Inverse modeling is a useful tool to retrieve CH4 fluxes; however, evaluation of the applied chemical transport model is an important step before using the inverted emissions. For inversions using column data one concern is how well the model represents stratospheric and tropospheric CH4 respectively when assimilating total column measurements. In this study atmospheric CH4 from three inverse models is compared to FTS (Fourier Transform Spectrometry), satellite and in situ measurements. Using the FTS measurements the model biases are separated into stratospheric and tropospheric contributions. When averaged over all FTS sites the model bias amplitudes (absolute model to FTS differences) are 7.4 ± 5.1 ppb, 6.7 ± 4.8 ppb, and 8.1 ± 5.5 ppb in the troposphere for the models TM3, TM5-4DVAR, LMDz-PYVAR, respectively, and 4.3 ± 9.9 ppb, 4.7 ± 9.9 ppb, and 6.2 ± 11.2 ppb in the stratosphere. The tropospheric model biases show a latitudinal gradient for all models, however there are no clear latitudinal dependencies for stratospheric model biases visible except with the LMDz-PYVAR model. The latitudinal gradient is not present in a comparison with in situ measurements, which is attributed to the different longitudinal coverage of FTS and in situ measurements. Similarly, a latitudinal pattern exists in model biases in vertical CH4 gradients in the troposphere, which indicates vertical transports of tropospheric CH4 is not represented correctly in the models. [ABSTRACT FROM AUTHOR]

Global distributions of atmospheric ammonia (NH3) measured with satellite instruments such as the Infrared Atmospheric Sounding Interferometer (IASI) contain valuable information on NH3 concentrations and variability in regions not yet covered by ground based instruments. Due to their large spatial coverage and daily observations, the satellite observations have the potential to increase our knowledge of the distribution of NH3 emissions, and associated seasonal cycles. However the observations remain poorly validated, with only a handful of available studies often using only surface observations without any vertical information. In this study, we present the first validation of the IASI-NH3 product using ground-based Fourier Transform InfraRed (FTIR) observations. Using a recently developed consistent retrieval strategy, NH3 concentration profiles have been retrieved using observations from nine Network for the Detection of Atmospheric Composition Change (NDACC) stations around the world between 2008-2015. We demonstrate the importance of strict spatio-temporal collocation criteria for the comparison. Large differences in the regression results are observed for changing intervals of spatial criteria, mostly due to terrain characteristics and the short lifetime of NH3 in the atmosphere. The seasonal variations of both datasets are consistent for most sites. Correlations are found to be high at sites in areas with considerable NH3 levels, whereas correlations are lower at sites with low atmospheric NH3 levels close to the detection limit of the IASI instrument. A combination of the observations from all sites (Nobs = 547) give a MRD of -32.4 ± (56.3) %, a correlation r of 0.8 with a slope of 0.73. These results indicate that the IASI-NH3 product performs better than previous upper bound estimates (-50% - +100 %). [ABSTRACT FROM AUTHOR]

Global distributions of atmospheric ammonia (NH3) measured with satellite instruments such as the Infrared Atmospheric Sounding Interferometer (IASI) contain valuable information on NH3 concentrations and variability in regions not yet covered by ground based instruments. Due to their large spatial coverage and daily observations, the satellite observations have the potential to increase our knowledge of the distribution of NH3 emissions, and associated seasonal cycles. However the observations remain poorly validated, with only a handful of available studies often using only surface observations without any vertical information. In this study, we present the first validation of the IASI-NH3 product using ground-based Fourier Transform InfraRed (FTIR) observations. Using a recently developed consistent retrieval strategy, NH3 concentration profiles have been retrieved using observations from nine Network for the Detection of Atmospheric Composition Change (NDACC) stations around the world between 2008-2015. We demonstrate the importance of strict spatio-temporal collocation criteria for the comparison. Large differences in the regression results are observed for changing intervals of spatial criteria, mostly due to terrain characteristics and the short lifetime of NH3 in the atmosphere. The seasonal variations of both datasets are consistent for most sites. Correlations are found to be high at sites in areas with considerable NH3 levels, whereas correlations are lower at sites with low atmospheric NH3 levels close to the detection limit of the IASI instrument. A combination of the observations from all sites (Nobs = 547) give a MRD of -32.4 ± (56.3)%, a correlation r of 0.8 with a slope of 0.73. These results indicate that the IASI-NH3 product performs better than previous upper bound estimates (-50% - +100%). [ABSTRACT FROM AUTHOR]