scholarly journals Evaluating consistency between total column CO<sub>2</sub> retrievals from OCO-2 and the in situ network over North America: implications for carbon flux estimation

2021 ◽  
Vol 21 (18) ◽  
pp. 14385-14401
Author(s):  
Bharat Rastogi ◽  
John B. Miller ◽  
Micheal Trudeau ◽  
Arlyn E. Andrews ◽  
Lei Hu ◽  
...  
Keyword(s):  
North America ◽  
Carbon Cycle ◽  
Bias Correction ◽  
Surface Flux ◽  
Systematic Errors ◽  
Surface Fluxes ◽  
Satellite Retrievals ◽  
Flux Estimation ◽  
Total Column

Abstract. Feedbacks between the climate system and the carbon cycle represent a key source of uncertainty in model projections of Earth's climate, in part due to our inability to directly measure large-scale biosphere–atmosphere carbon fluxes. In situ measurements of the CO2 mole fraction from surface flasks, towers, and aircraft are used in inverse models to infer fluxes, but measurement networks remain sparse, with limited or no coverage over large parts of the planet. Satellite retrievals of total column CO2 (XCO2), such as those from NASA's Orbiting Carbon Observatory-2 (OCO-2), can potentially provide unprecedented global information about CO2 spatiotemporal variability. However, for use in inverse modeling, data need to be extremely stable, highly precise, and unbiased to distinguish abundance changes emanating from surface fluxes from those associated with variability in weather. Systematic errors in XCO2 have been identified and, while bias correction algorithms are applied globally, inconsistencies persist at regional and smaller scales that may complicate or confound flux estimation. To evaluate XCO2 retrievals and assess potential biases, we compare OCO-2 v10 retrievals with in situ data-constrained XCO2 simulations over North America estimated using surface fluxes and boundary conditions optimized with observations that are rigorously calibrated relative to the World Meteorological Organization X2007 CO2 scale. Systematic errors in simulated atmospheric transport are independently evaluated using unassimilated aircraft and AirCore profiles. We find that the global OCO-2 v10 bias correction shifts the distribution of retrievals closer to the simulated XCO2, as intended. Comparisons between bias-corrected and simulated XCO2 reveal differences that vary seasonally. Importantly, the difference between simulations and retrievals is of the same magnitude as the imprint of recent surface flux in the total column. This work demonstrates that systematic errors in OCO-2 v10 retrievals of XCO2 over land can be large enough to confound reliable surface flux estimation and that further improvements in retrieval and bias correction techniques are essential. Finally, we show that independent observations, especially vertical profile data, such as those from the National Oceanic and Atmospheric Administration aircraft and AirCore programs are critical for evaluating errors in both satellite retrievals and carbon cycle models.

scholarly journals Evaluating consistency between total column CO<sub>2</sub> retrievals from OCO-2 and the in-situ network over North America: Implications for carbon flux estimation

2021 ◽  
Author(s):  
Bharat Rastogi ◽  
John B. Miller ◽  
Micheal Trudeau ◽  
Arlyn E. Andrews ◽  
Lei Hu ◽  
...  
Keyword(s):  
North America ◽  
Carbon Cycle ◽  
Bias Correction ◽  
Surface Flux ◽  
Systematic Errors ◽  
Surface Fluxes ◽  
Satellite Retrievals ◽  
Flux Estimation ◽  
Total Column

Abstract. Feedbacks between the climate system and the carbon cycle represent a key source of uncertainty in model projections of Earth’s climate, in part due to our inability to directly measure large scale biosphere-atmosphere carbon fluxes. In-situ measurements of CO2 mole fraction from surface flasks, towers and aircraft are used in inverse models to infer fluxes, but measurement networks remain sparse, with limited or no coverage over large parts of the planet. Satellite retrievals of total column CO2 (XCO2), such as those from NASA’s Orbiting Carbon Observatory-2 (OCO-2), can potentially provide unprecedented global information about CO2 spatiotemporal variability. However, for use in inverse modeling, data need to be extremely stable, highly precise and unbiased to distinguish abundance changes emanating from surface fluxes from those associated with variability in weather. Systematic errors in XCO2 have been identified and, while bias correction algorithms are applied globally, inconsistencies persist at regional and smaller scales that may complicate or confound flux estimation. To evaluate XCO2 retrievals and assess potential biases, we compare OCO-2 v10 retrievals with in-situ data-constrained XCO2 simulations over North America estimated using surface fluxes and boundary conditions optimized with observations that are rigorously calibrated relative to the WMO X2007 CO2 scale. Systematic errors in simulated atmospheric transport are independently evaluated using unassimilated aircraft and AirCore profiles. We find that the global OCO-2 v10 bias correction shifts the distribution of retrievals closer to the simulated XCO2, as intended. Comparisons between bias corrected and simulated XCO2 reveal differences that vary seasonally. Importantly, the difference between simulations and retrievals is of the same magnitude as the imprint of recent surface flux in the total column. This work demonstrates that systematic errors in OCO-2v10 retrievals of XCO2 over land can be large enough to confound reliable surface flux estimation and that further improvements in retrieval and bias correction techniques are essential. Finally, we show that independent observations, especially vertical profile data, such as from NOAA’s aircraft and AirCore programs are critical for evaluating errors in both satellite retrievals and carbon-cycle models.

scholarly journals Gradients of column CO<sub>2</sub> across North America from the NOAA Global Greenhouse Gas Reference Network

2017 ◽  
Vol 17 (24) ◽  
pp. 15151-15165 ◽  
Author(s):  
Xin Lan ◽  
Pieter Tans ◽  
Colm Sweeney ◽  
Arlyn Andrews ◽  
Andrew Jacobson ◽  
...  
Keyword(s):  
North America ◽  
Greenhouse Gas ◽  
Spatial Patterns ◽  
Reference Network ◽  
Spatial Gradients ◽  
Continental Scale ◽  
Satellite Retrievals ◽  
The Impact ◽  
Total Column

Abstract. This study analyzes seasonal and spatial patterns of column carbon dioxide (CO2) over North America, calculated from aircraft and tall tower measurements from the NOAA Global Greenhouse Gas Reference Network from 2004 to 2014. Consistent with expectations, gradients between the eight regions studied are larger below 2 km than above 5 km. The 11-year mean CO2 dry mole fraction (XCO2) in the column below  ∼  330 hPa ( ∼  8 km above sea level) from NOAA's CO2 data assimilation model, CarbonTracker (CT2015), demonstrates good agreement with those calculated from calibrated measurements on aircraft and towers. Total column XCO2 was attained by combining modeled CO2 above 330 hPa from CT2015 with the measurements. We find large spatial gradients of total column XCO2 from June to August, with north and northeast regions having  ∼  3 ppm stronger summer drawdown (peak-to-valley amplitude in seasonal cycle) than the south and southwest regions. The long-term averaged spatial gradients of total column XCO2 across North America show a smooth pattern that mainly reflects the large-scale circulation. We have conducted a CarbonTracker experiment to investigate the impact of Eurasian long-range transport. The result suggests that the large summertime Eurasian boreal flux contributes about half of the north–south column XCO2 gradient across North America. Our results confirm that continental-scale total column XCO2 gradients simulated by CarbonTracker are realistic and can be used to evaluate the credibility of some spatial patterns from satellite retrievals, such as the long-term average of growing-season spatial patterns from satellite retrievals reported for Europe which show a larger spatial difference ( ∼  6 ppm) and scattered hot spots.

Ground-based CO2 monitoring network design over India to constrain the regional terrestrial fluxes

2020 ◽  
Author(s):  
Nalini Krishnankutty ◽  
Sijikumar Sivaraman ◽  
Vinu Valsala ◽  
Yogesh Tiwari ◽  
Radhika Ramachandran
Keyword(s):  
Dispersion Model ◽  
Monitoring Network ◽  
Surface Flux ◽  
Northeast India ◽  
Particle Dispersion ◽  
Surface Fluxes ◽  
Monitoring Network Design ◽  
North East ◽  
Observational Uncertainty ◽  
Flux Estimation

&lt;p&gt;The present study aims to design an optimal CO&lt;sub&gt;2&lt;/sub&gt; monitoring network over India to better constrain the Indian terrestrial surface fluxes using Lagrangian Particle Dispersion Model FLEXPART and Bayesian inversion methods. Prior and posterior cost functions are calculated using potential emission sensitivity from FLEXPART, prior flux uncertainties from CASA-GFED biosphere fluxes and CDIAC fossil fuel fluxes, and assumed uniform observational uncertainty of 2 ppm. A total of 73 regular grid cells are identified over the Indian land mass in 2&amp;#176;x2&amp;#176; latitude by longitude resolution assuming each cell can hold a potential site. Further, using incremental optimization methodology, the effectiveness of CO&lt;sub&gt;2&lt;/sub&gt; observations from these locations to reduce the Indian terrestrial flux uncertainty is quantified. The study is carried out in three parts. Firstly, we evaluated the existing stations over India in terms of reduction in uncertainty brought out by them in the surface flux estimation over the Indian landmass. This provides a unique opportunity for the representative stations to restart the observational programs based on their role in the flux estimation. In second part, we devised a methodology to design an extended network by adding a few more potential stations to the existing stations. Thirdly, we identified a completely new set of optimal stations for measuring atmospheric CO&lt;sub&gt;2&lt;/sub&gt; over India, which do not have any liabilities of pre-existing stations. The study depicts that the existing stations could bring down the uncertainty in the range of 18% to 36%. Among the existing stations, Kharagpur, Sagar, Shadnagar, Kodaikanal and Pondicherry are the best stations, which are indeed adding value to the CO&lt;sub&gt;2&lt;/sub&gt; flux inversions by reducing the uncertainty in the range of 4% to 13%. Addition of five new stations to the base network formed an extended network, which could reduce the uncertainty by an additional 15% for all the seasons reaching up to 45%. The new stations are mainly located over the east and north-east India with few exceptions during post-monsoon where stations are identified over the west and south India as well. The study identified 12 stations for each season and formed a &amp;#8216;new network&amp;#8217; that could achieve the equivalent uncertainty reduction as compared with the 14 stations in the &amp;#8216;extended network&amp;#8217;. From this, an &amp;#8216;optimal network&amp;#8217; and the best network consisting of 17 stations were identified that could best represent flux scenario and transport over India in all the four seasons. In northeast India, flux uncertainty is quite large, also the prevailing westerly wind in most parts of the year contributes to the surface CO&lt;sub&gt;2&lt;/sub&gt; signature of India to that location, demanding requirement of CO&lt;sub&gt;2 &lt;/sub&gt;observations throughout the year. The study highlights a major zone of CO&lt;sub&gt;2&lt;/sub&gt; &amp;#8216;observational void&amp;#8217; that exists in potential locations near east and northeast parts of India. Immediate requirement of CO&lt;sub&gt;2&lt;/sub&gt; monitoring initiative in these areas is highly recommended.&lt;/p&gt;

scholarly journals Bias correction of long-path CO<sub>2</sub> observations in a complex urban environment for carbon cycle model inter-comparison and data assimilation

2019 ◽  
Vol 12 (11) ◽  
pp. 5791-5800 ◽  
Author(s):  
T. Scott Zaccheo ◽  
Nathan Blume ◽  
Timothy Pernini ◽  
Jeremy Dobler ◽  
Jinghui Lian
Keyword(s):  
Carbon Cycle ◽  
Urban Environment ◽  
Bias Correction ◽  
Environmental Research ◽  
Cycle Model ◽  
Carbon Cycle Model ◽  
Co2 Concentrations ◽  
Paris Area ◽  
Gas Measurement

Abstract. The Greenhouse gas Laser Imaging Tomography Experiment (GreenLITE™) trace gas measurement system, jointly designed and developed by Atmospheric and Environmental Research, Inc. and Spectral Sensor Solutions LLC, provides high-precision, long-path measurements of atmospheric trace gases including CO2 and CH4 over extended (0.04–25 km2) areas of interest. In 2015, a prototype unit was deployed in Paris, France, to demonstrate its ability to provide continuous observations of CO2 concentrations along horizontal air segments and two-dimensional (2-D) maps of time-varying CO2 concentrations over a complex urban environment. Subsequently, these data have been adapted to create a physically consistent set of horizontal segment mean concentrations for (1) comparisons to highly accurate in situ point measurements obtained for coincident times within the Greater Paris area, (2) inter-comparisons with results from high spatial and temporal regional carbon cycle model data, and (3) potential assimilation of these data to constrain and inform regional carbon cycle modeling frameworks. To achieve these ends, the GreenLITE™ data are calibrated against precise in situ point measurements to reconcile constant systematic as well as slowly varying temporal differences that exist between in situ and GreenLITE™ measurements to provide unbiased comparisons, and the potential for long-term co-assimilation of both measurements into urban-scale emission models. While both the constant systematic biases and the slowly varying differences may have different impacts on the measurement accuracy and/or precisions, they are in part due to a number of potential common terms that include limitation in the instrument design, uncertainties in spectroscopy and imprecise knowledge of the atmospheric state. This work provides a brief overview of the system design and the current gas concentration retrieval and 2-D reconstruction approaches, a description of the bias-correction approach, the results as applied to data collected in Paris, France, and an analysis of the inter-comparison between collocated in situ measurements and GreenLITE™ observations.

scholarly journals Reviews and syntheses: guiding the evolution of the observing system for the carbon cycle through quantitative network design

2017 ◽  
Vol 14 (20) ◽  
pp. 4755-4766 ◽  
Author(s):  
Thomas Kaminski ◽  
Peter Julian Rayner
Keyword(s):  
Carbon Cycle ◽  
Network Design ◽  
Observational Data ◽  
Data Streams ◽  
Surface Fluxes ◽  
Modelling Framework ◽  
Independent Information ◽  
The Impact ◽  
Global Carbon

Abstract. Various observational data streams have been shown to provide valuable constraints on the state and evolution of the global carbon cycle. These observations have the potential to reduce uncertainties in past, current, and predicted natural and anthropogenic surface fluxes. In particular such observations provide independent information for verification of actions as requested by the Paris Agreement. It is, however, difficult to decide which variables to sample, and how, where, and when to sample them, in order to achieve an optimal use of the observational capabilities. Quantitative network design (QND) assesses the impact of a given set of existing or hypothetical observations in a modelling framework. QND has been used to optimise in situ networks and assess the benefit to be expected from planned space missions. This paper describes recent progress and highlights aspects that are not yet sufficiently addressed. It demonstrates the advantage of an integrated QND system that can simultaneously evaluate a multitude of observational data streams and assess their complementarity and redundancy.

scholarly journals Global CO<sub>2</sub> fluxes estimated from GOSAT retrievals of total column CO<sub>2</sub>

2013 ◽  
Vol 13 (17) ◽  
pp. 8695-8717 ◽  
Author(s):  
S. Basu ◽  
S. Guerlet ◽  
A. Butz ◽  
S. Houweling ◽  
O. Hasekamp ◽  
...  
Keyword(s):  
Bias Correction ◽  
Seasonal Cycle ◽  
Joint Inversion ◽  
Surface Fluxes ◽  
Global Land ◽  
Net Uptake ◽  
Surface Measurements ◽  
Source Sink ◽  
The Tropics ◽  
Total Column

Abstract. We present one of the first estimates of the global distribution of CO2 surface fluxes using total column CO2 measurements retrieved by the SRON-KIT RemoTeC algorithm from the Greenhouse gases Observing SATellite (GOSAT). We derive optimized fluxes from June 2009 to December 2010. We estimate fluxes from surface CO2 measurements to use as baselines for comparing GOSAT data-derived fluxes. Assimilating only GOSAT data, we can reproduce the observed CO2 time series at surface and TCCON sites in the tropics and the northern extra-tropics. In contrast, in the southern extra-tropics GOSAT XCO2 leads to enhanced seasonal cycle amplitudes compared to independent measurements, and we identify it as the result of a land–sea bias in our GOSAT XCO2 retrievals. A bias correction in the form of a global offset between GOSAT land and sea pixels in a joint inversion of satellite and surface measurements of CO2 yields plausible global flux estimates which are more tightly constrained than in an inversion using surface CO2 data alone. We show that assimilating the bias-corrected GOSAT data on top of surface CO2 data (a) reduces the estimated global land sink of CO2, and (b) shifts the terrestrial net uptake of carbon from the tropics to the extra-tropics. It is concluded that while GOSAT total column CO2 provide useful constraints for source–sink inversions, small spatiotemporal biases – beyond what can be detected using current validation techniques – have serious consequences for optimized fluxes, even aggregated over continental scales.

scholarly journals Averaging kernels for DOAS total-column satellite retrievals

2003 ◽  
Vol 3 (1) ◽  
pp. 895-910
Author(s):  
H. J. Eskes ◽  
K. F. Boersma
Keyword(s):  
Optical Absorption ◽  
Trace Gases ◽  
A Priori ◽  
Systematic Errors ◽  
Optical Absorption Spectroscopy ◽  
Additional Information ◽  
Satellite Validation ◽  
Satellite Retrievals ◽  
Uv Visible ◽  
Total Column

Abstract. The Differential Optical Absorption Spectroscopy (DOAS) method is used extensively to retrieve total column amounts of trace gases based on UV-visible measurements of satellite spectrometers, such as ERS-2 GOME. In practice the sensitivity of the instrument to the tracer density is strongly height dependent, especially in the troposphere. The resulting tracer profile dependence may introduce large systematic errors in the retrieved columns that are difficult to quantify without proper additional information, as provided by the averaging kernel (AK). In this paper we generalise the AK concept to total column retrievals, and derive an explicit expression for the DOAS AK. It is shown that the additional AK information corrects for the a priori dependence of the retrieval. The availability of averaging kernel information as part of the total column retrieval product is essential for the interpretation of the observations, and for applications like chemical data assimilation and detailed satellite validation studies.

scholarly journals Bias Correction of Long-Path CO<sub>2</sub> Observations in a Complex Urban Environment for Carbon Cycle Model Inter-Comparison and Data Assimilation

2019 ◽  
Author(s):  
T. Scott Zaccheo ◽  
Nathan Blume ◽  
Timothy Pernini ◽  
Jeremy Dobler ◽  
Jinghui Lian
Keyword(s):  
Carbon Cycle ◽  
Urban Environment ◽  
Bias Correction ◽  
Environmental Research ◽  
Cycle Model ◽  
Carbon Cycle Model ◽  
Co2 Concentrations ◽  
Paris Area ◽  
Gas Measurement

Abstract. The Greenhouse gas Laser Imaging Tomography Experiment (GreenLITE™) trace gas measurement system, jointly designed and developed by Atmospheric and Environmental Research, Inc. and Spectral Sensor Solutions LLC, provides high-precision, long-path measurements of atmospheric trace gases including CO2 and CH4 over extended (0.04 km2–25 km2) areas of interest. In 2015, a prototype unit was deployed in Paris, France to demonstrate its ability to provide continuous observations of CO2 concentrations along horizontal air segments and two-dimensional (2-D) maps of time-varying CO2 concentrations over a complex urban environment. Subsequently, these data have been adapted to create a physically consistent set of horizontal segment mean concentrations for: 1) Comparisons to highly accurate in situ point measurements obtained for coincident times within the Greater Paris area, 2) Inter-comparisons with results from high-spatial and temporal regional carbon cycle model data, and 3) Potential assimilation of these data to constrain and inform regional carbon cycle modeling frameworks. To achieve these ends, the GreenLITE™ data are calibrated against precise in situ point measurements to reconcile constant systematic as well as slowly varying temporal differences that exist between in situ and GreenLITE™ measurements to provide unbiased comparisons, and the potential for long-term co-assimilation of both measurements into urban-scale emission models. While both the constant systematic biases and the slowly varying differences may have different impacts on the measurement accuracy and/or precisions, they are in part due to a number of potential common terms that include limitation in the instrument design, uncertainties in spectroscopy and imprecise knowledge of the atmospheric state. This work provides a brief overview of the system design and the current gas concentration retrieval and 2-D reconstruction approaches, a description of the bias correction approach, the results as applied to data collected in Paris, France, and an analysis of the inter-comparison between collocated in situ measurements and GreenLITE™ observations.

scholarly journals Gradients of Column CO<sub>2</sub> across North America from the NOAA Global Greenhouse Gas Reference Network

2017 ◽  
Author(s):  
Xin Lan ◽  
Pieter Tans ◽  
Colm Sweeney ◽  
Arlyn Andrews ◽  
Andrew Jacobson ◽  
...  
Keyword(s):  
North America ◽  
Greenhouse Gas ◽  
Spatial Patterns ◽  
Reference Network ◽  
Spatial Gradients ◽  
Continental Scale ◽  
The North ◽  
Satellite Retrievals ◽  
The Impact ◽  
Total Column

Abstract. This study analyzes seasonal and spatial patterns of column carbon dioxide (CO2) over North America calculated from aircraft and tall tower measurements from the NOAA Global Greenhouse Gas Reference Network from 2004 to 2014. Consistent with expectations, gradients between the eight regions studied are larger below 2 km than above 5 km. The 11-year mean CO2 dry mole fraction (XCO2) in the column below ~ 330 hPa (~ 8 km above sea level) from NOAA's CO2 data assimilation model, CarbonTracker (CT2015), demonstrates good agreement with those calculated from calibrated measurements on aircraft and towers. Total column XCO2 was attained by combining modeled CO2 above 330 hPa from CT2015 with the measurements. We find large spatial gradients of total column XCO2 during June to August, and the north and northeast regions have ~ 3 ppm stronger summer drawdown than the south and southwest regions. The spatial gradients of total column XCO2 across North America mainly reflect large-scale circulation patterns rather than regional surface sources and sinks. We have conducted a CarbonTracker experiment to investigate the impact of Eurasian long-range transport. The result suggests that the large summer time Eurasian boreal flux contributes about half of the north-south column XCO2 gradient across North America. Our results confirm that continental-scale total column XCO2 gradients simulated by CarbonTracker are realistic and can be used to evaluate the credibility of spatial patterns from satellite retrievals, such as the long term average spatial patterns from satellite retrievals reported for Europe which show larger spatial difference (~ 6 ppm) and scattered hot spots.

scholarly journals Averaging kernels for DOAS total-column satellite retrievals

2003 ◽  
Vol 3 (5) ◽  
pp. 1285-1291 ◽  
Author(s):  
H. J. Eskes ◽  
K. F. Boersma
Keyword(s):  
A Priori ◽  
Systematic Errors ◽  
Optical Absorption Spectroscopy ◽  
Transport Models ◽  
Retrieval Method ◽  
Additional Information ◽  
Satellite Validation ◽  
Satellite Retrievals ◽  
Uv Visible ◽  
Total Column

Abstract. The Differential Optical Absorption Spectroscopy (DOAS) method is used extensively to retrieve total column amounts of trace gases based on UV-visible measurements of satellite spectrometers, such as ERS-2 GOME. In practice the sensitivity of the instrument to the tracer density is strongly height dependent, especially in the troposphere. The resulting tracer profile dependence may introduce large systematic errors in the retrieved columns that are difficult to quantify without proper additional information, as provided by the averaging kernel (AK). In this paper we discuss the DOAS retrieval method in the context of the general retrieval theory as developed by Rodgers. An expression is derived for the DOAS AK for optically thin absorbers. It is shown that the comparison with 3D chemistry-transport models and independent profile measurements, based on averaging kernels, is no longer influenced by errors resulting from a priori profile assumptions. The availability of averaging kernel information as part of the total column retrieval product is important for the interpretation of the observations, and for applications like chemical data assimilation and detailed satellite validation studies.

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