scholarly journals SCIAMACHY WFM-DOAS <i>X</i>CO<sub>2</sub>: reduction of scattering related errors

2012 ◽  
Vol 5 (10) ◽  
pp. 2375-2390 ◽  
Author(s):  
J. Heymann ◽  
H. Bovensmann ◽  
M. Buchwitz ◽  
J. P. Burrows ◽  
N. M. Deutscher ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Regional Scale ◽  
Correction Method ◽  
Surface Fluxes ◽  
Total Carbon ◽  
Set Covering ◽  
Retrieval Algorithm ◽  
Data Set ◽  
Data Product ◽  
Carbon Dioxide Co2

Abstract. Global observations of column-averaged dry air mole fractions of carbon dioxide (CO2), denoted by XCO2 , retrieved from SCIAMACHY on-board ENVISAT can provide important and missing global information on the distribution and magnitude of regional CO2 surface fluxes. This application has challenging precision and accuracy requirements. In a previous publication (Heymann et al., 2012), it has been shown by analysing seven years of SCIAMACHY WFM-DOAS XCO2 (WFMDv2.1) that unaccounted thin cirrus clouds can result in significant errors. In order to enhance the quality of the SCIAMACHY XCO2 data product, we have developed a new version of the retrieval algorithm (WFMDv2.2), which is described in this manuscript. It is based on an improved cloud filtering and correction method using the 1.4 μm strong water vapour absorption and 0.76 μm O2-A bands. The new algorithm has been used to generate a SCIAMACHY XCO2 data set covering the years 2003–2009. The new XCO2 data set has been validated using ground-based observations from the Total Carbon Column Observing Network (TCCON). The validation shows a significant improvement of the new product (v2.2) in comparison to the previous product (v2.1). For example, the standard deviation of the difference to TCCON at Darwin, Australia, has been reduced from 4 ppm to 2 ppm. The monthly regional-scale scatter of the data (defined as the mean intra-monthly standard deviation of all quality filtered XCO2 retrievals within a radius of 350 km around various locations) has also been reduced, typically by a factor of about 1.5. Overall, the validation of the new WFMDv2.2 XCO2 data product can be summarised by a single measurement precision of 3.8 ppm, an estimated regional-scale (radius of 500 km) precision of monthly averages of 1.6 ppm and an estimated regional-scale relative accuracy of 0.8 ppm. In addition to the comparison with the limited number of TCCON sites, we also present a comparison with NOAA's global CO2 modelling and assimilation system CarbonTracker. This comparison also shows significant improvements especially over the Southern Hemisphere.

scholarly journals SCIAMACHY WFM-DOAS XCO<sub>2</sub>: reduction of scattering related errors

2012 ◽  
Vol 5 (3) ◽  
pp. 4285-4320 ◽  
Author(s):  
J. Heymann ◽  
H. Bovensmann ◽  
M. Buchwitz ◽  
J. P. Burrows ◽  
N. M. Deutscher ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Regional Scale ◽  
Correction Method ◽  
Surface Fluxes ◽  
Total Carbon ◽  
Set Covering ◽  
Retrieval Algorithm ◽  
Data Set ◽  
Near Surface ◽  
Data Product

Abstract. Global observations of column-averaged dry air mole fractions of carbon dioxide (CO2), denoted by XCO2, retrieved from passive remote sensing instruments on Earth orbiting satellites can provide important and missing global information on the distribution and magnitude of regional CO2 surface fluxes. This application has challenging precision and accuracy requirements. SCIAMACHY on-board ENVISAT is the first satellite instrument, which measures the upwelling electromagnetic radiation in the near and short wave infrared at an adequate spectral and spatial resolution to yield near-surface sensitive XCO2. In a previous publication (Heymann et al., 2012), it has been shown by analysing seven years of SCIAMACHY WFM-DOAS XCO2 (WFMDv2.1) that unaccounted thin cirrus clouds can result in significant errors. In order to enhance the quality of the SCIAMACHY XCO2 data product, we have developed a new version of the retrieval algorithm (WFMDv2.2), which is described in this manuscript. It is based on an improved cloud filtering and correction method using the 1.4 μm strong water vapour absorption and 0.76 μm O2-A bands. The new algorithm has been used to generate a SCIAMACHY XCO2 data set covering the years 2003–2009. The new XCO2 data set has been validated using ground-based observations from the Total Carbon Column Observing Network (TCCON). The validation shows a significant improvement of the new product (v2.2) in comparison to the previous product (v2.1). For example, the standard deviation of the difference to TCCON at Darwin, Australia, has been reduced from 4 ppm to 2 ppm. The monthly regional-scale scatter of the data (defined as the mean inner monthly standard deviation of all quality filtered XCO2 retrievals within a radius of 350 km around various locations) has also been reduced, typically by a factor of about 1.5. Overall, the validation of the new WFMDv2.2 XCO2 data product can be summarised by a single measurement precision of 3.8 ppm, an estimated regional-scale (radius of 500 km) precision of monthly averages of 1.6 ppm and an estimated regional-scale relative accuracy of 0.8 ppm. In addition to the comparison with the limited number of TCCON sites, we also present a comparison with NOAA's global CO2 modelling and assimilation system CarbonTracker. This comparison also shows significant improvements especially over the Southern Hemisphere.

scholarly journals The ACOS CO<sub>2</sub> retrieval algorithm – Part II: Global X<sub>CO<sub>2</sub></sub> data characterization

2012 ◽  
Vol 5 (4) ◽  
pp. 687-707 ◽  
Author(s):  
D. Crisp ◽  
B. M. Fisher ◽  
C. O'Dell ◽  
C. Frankenberg ◽  
R. Basilio ◽  
...  
Keyword(s):  
Cross Sections ◽  
Regional Scale ◽  
Lessons Learned ◽  
Total Carbon ◽  
Quality Data ◽  
Retrieval Algorithm ◽  
Co2 Absorption ◽  
Absorption Cross ◽  
Dry Air ◽  
Carbon Dioxide Co2

Abstract. Here, we report preliminary estimates of the column averaged carbon dioxide (CO2) dry air mole fraction, XCO2, retrieved from spectra recorded over land by the Greenhouse gases Observing Satellite, GOSAT (nicknamed "Ibuki"), using retrieval methods originally developed for the NASA Orbiting Carbon Observatory (OCO) mission. After screening for clouds and other known error sources, these retrievals reproduce much of the expected structure in the global XCO2 field, including its variation with latitude and season. However, low yields of retrieved XCO2 over persistently cloudy areas and ice covered surfaces at high latitudes limit the coverage of some geographic regions, even on seasonal time scales. Comparisons of early GOSAT XCO2 retrievals with XCO2 estimates from the Total Carbon Column Observing Network (TCCON) revealed a global, −2% (7–8 parts per million, ppm, with respect to dry air) XCO2 bias and 2 to 3 times more variance in the GOSAT retrievals. About half of the global XCO2 bias is associated with a systematic, 1% overestimate in the retrieved air mass, first identified as a global +10 hPa bias in the retrieved surface pressure. This error has been attributed to errors in the O2 A-band absorption cross sections. Much of the remaining bias and spurious variance in the GOSAT XCO2 retrievals has been traced to uncertainties in the instrument's calibration, oversimplified methods for generating O2 and CO2 absorption cross sections, and other subtle errors in the implementation of the retrieval algorithm. Many of these deficiencies have been addressed in the most recent version (Build 2.9) of the retrieval algorithm, which produces negligible bias in XCO2 on global scales as well as a ~30% reduction in variance. Comparisons with TCCON measurements indicate that regional scale biases remain, but these could be reduced by applying empirical corrections like those described by Wunch et al. (2011b). We recommend that such corrections be applied before these data are used in source sink inversion studies to minimize spurious fluxes associated with known biases. These and other lessons learned from the analysis of GOSAT data are expected to accelerate the delivery of high quality data products from the Orbiting Carbon Observatory-2 (OCO-2), once that satellite is successfully launched and inserted into orbit.

scholarly journals Calibration of the SBUV version 8.6 ozone data product

2012 ◽  
Vol 5 (4) ◽  
pp. 5151-5203 ◽  
Author(s):  
M. T. DeLand ◽  
S. L. Taylor ◽  
L. K. Huang ◽  
B. L. Fisher
Keyword(s):  
Set Covering ◽  
Retrieval Algorithm ◽  
Data Set ◽  
Data Product ◽  
Ozone Data ◽  
Ozone Trends ◽  
Calibration Techniques ◽  
Radiance Data

Abstract. This paper describes the calibration process for the Solar Backscatter Ultraviolet (SBUV) Version 8.6 (V8.6) ozone data product. Eight SBUV instruments have flown on NASA and NOAA satellites since 1970, and a continuous data record is available since November 1978. The accuracy of ozone trends determined from these data depends on the calibration and long-term characterization of each instrument. V8.6 calibration adjustments are determined at the radiance level, and do not rely on comparison of retrieved ozone products with other instruments. The primary SBUV instrument characterization is based on prelaunch laboratory tests and dedicated on-orbit calibration measurements. We supplement these results with "soft" calibration techniques using carefully chosen subsets of radiance data and information from the retrieval algorithm output to validate each instrument's calibration. The estimated long-term uncertainty in albedo is approximately ±0.8–1.2% (1σ) for most of the instruments. The overlap between these instruments and the Shuttle SBUV (SSBUV) data allows us to intercalibrate the SBUV instruments to produce a coherent V8.6 data set covering more than 32 yr. The estimated long-term uncertainty in albedo is less than 3% over this period.

scholarly journals Calibration of the SBUV version 8.6 ozone data product

2012 ◽  
Vol 5 (11) ◽  
pp. 2951-2967 ◽  
Author(s):  
M. T. DeLand ◽  
S. L. Taylor ◽  
L. K. Huang ◽  
B. L. Fisher
Keyword(s):  
Set Covering ◽  
Retrieval Algorithm ◽  
Data Set ◽  
Data Product ◽  
Ozone Data ◽  
Ozone Trends ◽  
Calibration Techniques ◽  
Radiance Data

Abstract. This paper describes the calibration process for the Solar Backscatter Ultraviolet (SBUV) Version 8.6 (V8.6) ozone data product. Eight SBUV instruments have flown on NASA and NOAA satellites since 1970, and a continuous data record is available since November 1978. The accuracy of ozone trends determined from these data depends on the calibration and long-term characterization of each instrument. V8.6 calibration adjustments are determined at the radiance level, and do not rely on comparison of retrieved ozone products with other instruments. The primary SBUV instrument characterization is based on prelaunch laboratory tests and dedicated on-orbit calibration measurements. We supplement these results with "soft" calibration techniques using carefully chosen subsets of radiance data and information from the retrieval algorithm output to validate each instrument's calibration. The estimated long-term uncertainty in albedo is approximately ±0.8–1.2% (1σ) for most of the instruments. The overlap between these instruments and the Shuttle SBUV (SSBUV) data allows us to intercalibrate the SBUV instruments to produce a coherent V8.6 data set covering more than 32 yr. The estimated long-term uncertainty in albedo is less than 3% over this period.

scholarly journals The ACOS X<sub>CO<sub>2</sub></sub> retrieval algorithm, Part 2: Global X<sub>CO<sub>2</sub></sub> data characterization

2012 ◽  
Vol 5 (1) ◽  
pp. 1-60 ◽  
Author(s):  
D. Crisp ◽  
B. M. Fisher ◽  
C. O'Dell ◽  
C. Frankenberg ◽  
R. Basilio ◽  
...  
Keyword(s):  
Cross Sections ◽  
Regional Scale ◽  
Lessons Learned ◽  
Total Carbon ◽  
Quality Data ◽  
Retrieval Algorithm ◽  
Co2 Absorption ◽  
Absorption Cross ◽  
Dry Air ◽  
Carbon Dioxide Co2

Abstract. Here, we report preliminary estimates of the column averaged carbon dioxide (CO2) dry air mole fraction, XCO2, retrieved from spectra recorded over land by the Greenhouse gases Observing Satellite, GOSAT (nicknamed "Ibuki"), using retrieval methods originally developed for the NASA Orbiting Carbon Observatory (OCO) mission. After screening for clouds and other known error sources, these retrievals reproduce much of the expected structure in the global XCO2 field, including its variation with latitude and season. However, low yields of retrieved XCO2 over persistently cloudy areas and ice covered surfaces at high latitudes limit the coverage of some geographic regions, even on seasonal time scales. Comparisons of early GOSAT XCO2 retrievals with XCO2 estimates from the Total Carbon Column Observing Network (TCCON) revealed a global, −2% (7–8 parts per million, ppm, with respect to dry air) XCO2 bias and 2 to 3 times more variance in the GOSAT retrievals. About half of the global XCO2 bias is associated with a systematic, 1% overestimate in the retrieved air mass, first identified as a global +10 hPa bias in the retrieved surface pressure. This error has been attributed to errors in the O2 A-band absorption cross sections. Much of the remaining bias and spurious variance in the GOSAT XCO2 retrievals has been traced to uncertainties in the instrument's calibration, oversimplified methods for generating O2 and CO2 absorption cross sections, and other subtle errors in the implementation of the retrieval algorithm. Many of these deficiencies have been addressed in the most recent version (Build 2.9) of the retrieval algorithm, which produces negligible bias in XCO2 on global scales as well as a ∼30% reduction in variance. Comparisons with TCCON measurements indicate that regional scale biases remain, but these could be reduced by applying empirical corrections like those described by Wunch et al. (2011). We recommend that such corrections be applied before these data are used in source sink inversion studies to minimize spurious fluxes associated with known biases. These and other lessons learned from the analysis of GOSAT data are expected to accelerate the delivery of high quality data products from the Orbiting Carbon Observeratory-2 (OCO-2), once that satellite is successfully launched and inserted into orbit.

scholarly journals Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

2015 ◽  
Vol 8 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
J. Heymann ◽  
M. Reuter ◽  
M. Hilker ◽  
M. Buchwitz ◽  
O. Schneising ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Detailed Comparison ◽  
Mean Difference ◽  
Total Carbon ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Time Period ◽  
Comparison Results ◽  
The Future

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY onboard ENVISAT (March 2002–April 2012) and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.

scholarly journals Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms; validation against collocated MODIS and CALIOP data

2015 ◽  
Vol 8 (12) ◽  
pp. 12663-12707 ◽  
Author(s):  
T. E. Taylor ◽  
C. W. O'Dell ◽  
C. Frankenberg ◽  
P. Partain ◽  
H. Q. Cronk ◽  
...  
Keyword(s):  
Surface Pressure ◽  
Near Infrared ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Orthogonal Polarization ◽  
Screening Methods ◽  
Data Set ◽  
Atmospheric Scattering ◽  
Carbon Dioxide Co2 ◽  
Cloud Screening

Abstract. The objective of the National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2) mission is to retrieve the column-averaged carbon dioxide (CO2) dry air mole fraction (XCO2) from satellite measurements of reflected sunlight in the near-infrared. These estimates can be biased by clouds and aerosols within the instrument's field of view (FOV). Screening of the most contaminated soundings minimizes unnecessary calls to the computationally expensive Level 2 (L2) XCO2 retrieval algorithm. Hence, robust cloud screening methods have been an important focus of the OCO-2 algorithm development team. Two distinct, computationally inexpensive cloud screening algorithms have been developed for this application. The A-Band Preprocessor (ABP) retrieves the surface pressure using measurements in the 0.76 μm O2 A-band, neglecting scattering by clouds and aerosols, which introduce photon path-length (PPL) differences that can cause large deviations between the expected and retrieved surface pressure. The Iterative Maximum A-Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) retrieves independent estimates of the CO2 and H2O column abundances using observations taken at 1.61 μm (weak CO2 band) and 2.06 μm (strong CO2 band), while neglecting atmospheric scattering. The CO2 and H2O column abundances retrieved in these two spectral regions differ significantly in the presence of cloud and scattering aerosols. The combination of these two algorithms, which key off of different features in the spectra, provides the basis for cloud screening of the OCO-2 data set. To validate the OCO-2 cloud screening approach, collocated measurements from NASA's Moderate Resolution Imaging Spectrometer (MODIS), aboard the Aqua platform, were compared to results from the two OCO-2 cloud screening algorithms. With tuning to allow throughputs of &amp;simeq; 30 %, agreement between the OCO-2 and MODIS cloud screening methods is found to be &amp;simeq; 85 % over four 16-day orbit repeat cycles in both the winter (December) and spring (April–May) for OCO-2 nadir-land, glint-land and glint-water observations. No major, systematic, spatial or temporal dependencies were found, although slight differences in the seasonal data sets do exist and validation is more problematic with increasing solar zenith angle and when surfaces are covered in snow and ice and have complex topography. To further analyze the performance of the cloud screening algorithms, an initial comparison of OCO-2 observations was made to collocated measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). These comparisons highlight the strength of the OCO-2 cloud screening algorithms in identifying high, thin clouds but suggest some difficulty in identifying some clouds near the surface, even when the optical thicknesses are greater than 1.

scholarly journals MODIS observed increase in duration and spatial extent of sediment plumes in Greenland fjords

2014 ◽  
Vol 8 (4) ◽  
pp. 1161-1176 ◽  
Author(s):  
B. Hudson ◽  
I. Overeem ◽  
D. McGrath ◽  
J. P. M. Syvitski ◽  
A. Mikkelsen ◽  
...  
Keyword(s):  
North Atlantic Ocean ◽  
Regional Scale ◽  
Greenland Ice Sheet ◽  
Sediment Concentration ◽  
Retrieval Algorithm ◽  
Freshwater Flux ◽  
River Plumes ◽  
Data Set ◽  
In Situ Data ◽  
The North

Abstract. The freshwater flux from the Greenland Ice Sheet (GrIS) to the North Atlantic Ocean carries extensive but poorly documented volumes of sediment. We develop a suspended sediment concentration (SSC) retrieval algorithm using a large Greenland specific in situ data set. This algorithm is applied to all cloud-free NASA Moderate Resolution Imaging Spectrometer (MODIS) Terra images from 2000 to 2012 to monitor SSC dynamics at six river plumes in three fjords in southwest Greenland. Melt-season mean plume SSC increased at all but one site, although these trends were primarily not statistically significant. Zones of sediment concentration > 50 mg L−1 expanded in three river plumes, with potential consequences for biological productivity. The high SSC cores of sediment plumes ( > 250 mg L−1 expanded in one-third of study locations. At a regional scale, higher volumes of runoff were associated with higher melt-season mean plume SSC values, but this relationship did not hold for individual rivers. High spatial variability between proximal plumes highlights the complex processes operating in Greenland's glacio–fluvial–fjord systems.

scholarly journals Validation of SCIAMACHY HDO/H<sub>2</sub>O measurements using the TCCON and NDACC-MUSICA networks

2015 ◽  
Vol 8 (4) ◽  
pp. 1799-1818 ◽  
Author(s):  
R. A. Scheepmaker ◽  
C. Frankenberg ◽  
N. M. Deutscher ◽  
M. Schneider ◽  
S. Barthlott ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Hydrological Cycle ◽  
Source Region ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
The Tibetan Plateau ◽  
Wet Season ◽  
Data Set ◽  
The Mean ◽  
The Impact

Abstract. Measurements of the atmospheric HDO/H2O ratio help us to better understand the hydrological cycle and improve models to correctly simulate tropospheric humidity and therefore climate change. We present an updated version of the column-averaged HDO/H2O ratio data set from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The data set is extended with 2 additional years, now covering 2003–2007, and is validated against co-located ground-based total column δD measurements from Fourier transform spectrometers (FTS) of the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC, produced within the framework of the MUSICA project). Even though the time overlap among the available data is not yet ideal, we determined a mean negative bias in SCIAMACHY δD of −35 ± 30‰ compared to TCCON and −69 ± 15‰ compared to MUSICA (the uncertainty indicating the station-to-station standard deviation). The bias shows a latitudinal dependency, being largest (∼ −60 to −80‰) at the highest latitudes and smallest (∼ −20 to −30‰) at the lowest latitudes. We have tested the impact of an offset correction to the SCIAMACHY HDO and H2O columns. This correction leads to a humidity- and latitude-dependent shift in δD and an improvement of the bias by 27‰, although it does not lead to an improved correlation with the FTS measurements nor to a strong reduction of the latitudinal dependency of the bias. The correction might be an improvement for dry, high-altitude areas, such as the Tibetan Plateau and the Andes region. For these areas, however, validation is currently impossible due to a lack of ground stations. The mean standard deviation of single-sounding SCIAMACHY–FTS differences is ∼ 115‰, which is reduced by a factor ∼ 2 when we consider monthly means. When we relax the strict matching of individual measurements and focus on the mean seasonalities using all available FTS data, we find that the correlation coefficients between SCIAMACHY and the FTS networks improve from 0.2 to 0.7–0.8. Certain ground stations show a clear asymmetry in δD during the transition from the dry to the wet season and back, which is also detected by SCIAMACHY. This asymmetry points to a transition in the source region temperature or location of the water vapour and shows the added information that HDO/H2O measurements provide when used in combination with variations in humidity.

scholarly journals Atmospheric Simulations of Total Column CO2 Mole Fractions from Global to Mesoscale within the Carbon Monitoring System Flux Inversion Framework

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 787 ◽  
Author(s):  
Martha P. Butler ◽  
Thomas Lauvaux ◽  
Sha Feng ◽  
Junjie Liu ◽  
Kevin W. Bowman ◽  
...  
Keyword(s):  
North America ◽  
Monitoring System ◽  
Atmospheric Transport ◽  
Regional Scale ◽  
Wrf Model ◽  
Horizontal Distribution ◽  
Surface Fluxes ◽  
Coupling Scheme ◽  
Northern Half ◽  
Carbon Dioxide Co2

Quantifying the uncertainty of inversion-derived CO2 surface fluxes and attributing the uncertainty to errors in either flux or atmospheric transport simulations continue to be challenges in the characterization of surface sources and sinks of carbon dioxide (CO2). Despite recent studies inferring fluxes while using higher-resolution modeling systems, the utility of regional-scale models remains unclear when compared to existing coarse-resolution global systems. Here, we present an off-line coupling of the mesoscale Weather Research and Forecasting (WRF) model to optimized biogenic CO2 fluxes and mole fractions from the global Carbon Monitoring System inversion system (CMS-Flux). The coupling framework consists of methods to constrain the mass of CO2 introduced into WRF, effectively nesting our regional domain covering most of North America (except the northern half of Canada) within the CMS global model. We test the coupling by simulating Greenhouse gases Observing SATellite (GOSAT) column-averaged dry-air mole fractions (XCO2) over North America for 2010. We find mean model-model differences in summer of ∼0.12 ppm, significantly lower than the original coupling scheme (from 0.5 to 1.5 ppm, depending on the boundary). While 85% of the XCO2 values are due to long-range transport from outside our North American domain, most of the model-model differences appear to be due to transport differences in the fraction of the troposphere below 850 hPa. Satellite data from GOSAT and tower and aircraft data are used to show that vertical transport above the Planetary Boundary Layer is responsible for significant model-model differences in the horizontal distribution of column XCO2 across North America.

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