scholarly journals Long-term analysis of carbon dioxide and methane column-averaged mole fractions retrieved from SCIAMACHY

2010 ◽  
Vol 10 (11) ◽  
pp. 27479-27522 ◽  
Author(s):  
O. Schneising ◽  
M. Buchwitz ◽  
M. Reuter ◽  
J. Heymann ◽  
H. Bovensmann ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Large Scale ◽  
Near Infrared ◽  
Retrieval Algorithm ◽  
Steady Increase ◽  
Absorption Bands ◽  
Longitudinal Gradients ◽  
Surface Measurements ◽  
Concentration Changes

Abstract. Carbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases contributing to global climate change. SCIAMACHY onboard ENVISAT (launch 2002) was the first and is now together with TANSO onboard GOSAT (launch 2009) the only satellite instrument currently in space whose measurements are sensitive to CO2 and CH4 concentration changes in the lowest atmospheric layers where the variability due to sources and sinks is largest. We present long-term SCIAMACHY retrievals (2003–2009) of column-averaged mole fractions of both gases (denoted XCO2 and XCH4) derived from absorption bands in the near-infrared/shortwave-infrared (NIR/SWIR) spectral region focusing on large-scale features. The results are obtained using an upgraded version (v2) of the retrieval algorithm WFM-DOAS including several improvements, while simultaneously maintaining its high processing speed. The retrieved mole fractions are compared to global model simulations (CarbonTracker XCO2 and TM5 XCH4) being optimised by assimilating highly accurate surface measurements from the NOAA/ESRL network and taking the SCIAMACHY averaging kernels into account. The comparisons address seasonal variations and long-term characteristics. The steady increase of atmospheric carbon dioxide primarily caused by the burning of fossil fuels can be clearly observed with SCIAMACHY globally. The retrieved annual mean XCO2 increase over both hemispheres agrees with CarbonTracker within the error bars but is on average somewhat smaller (1.8 ppm yr−1 compared to 1.9 ppm yr−1). The amplitude of the XCO2 seasonal cycle as retrieved by SCIAMACHY, which is 4.3 ppm for the Northern Hemisphere and 1.4 ppm for the Southern Hemisphere, is on average about 1 ppm larger than for CarbonTracker. An investigation of the boreal forest carbon uptake during the growing season via the analysis of longitudinal gradients shows good agreement between SCIAMACHY and CarbonTracker concerning the overall magnitude of the gradients and their annual variations. The retrieved XCH4 results show that after years of stability, atmospheric methane has started to rise again in recent years which is consistent with surface measurements. The largest increase is observed for the tropics and northern mid- and high-latitudes amounting to about 8 ppb yr−1 since 2007. Due care has been exercised to minimise the influence of detector degradation on the quantitative estimate of this anomaly.

scholarly journals Long-term analysis of carbon dioxide and methane column-averaged mole fractions retrieved from SCIAMACHY

2011 ◽  
Vol 11 (6) ◽  
pp. 2863-2880 ◽  
Author(s):  
O. Schneising ◽  
M. Buchwitz ◽  
M. Reuter ◽  
J. Heymann ◽  
H. Bovensmann ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Boreal Forest ◽  
Large Scale ◽  
Retrieval Algorithm ◽  
Steady Increase ◽  
Absorption Bands ◽  
Longitudinal Gradients ◽  
Surface Measurements ◽  
Concentration Changes

Abstract. Carbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases contributing to global climate change. SCIAMACHY onboard ENVISAT (launch 2002) was the first and is now with TANSO onboard GOSAT (launch 2009) one of only two satellite instruments currently in space whose measurements are sensitive to CO2 and CH4 concentration changes in the lowest atmospheric layers where the variability due to sources and sinks is largest. We present long-term SCIAMACHY retrievals (2003–2009) of column-averaged dry air mole fractions of both gases (denoted XCO2 and XCH4) derived from absorption bands in the near-infrared/shortwave-infrared (NIR/SWIR) spectral region focusing on large-scale features. The results are obtained using an upgraded version (v2) of the retrieval algorithm WFM-DOAS including several improvements, while simultaneously maintaining its high processing speed. The retrieved mole fractions are compared to global model simulations (CarbonTracker XCO2 and TM5 XCH4) being optimised by assimilating highly accurate surface measurements from the NOAA/ESRL network and taking the SCIAMACHY averaging kernels into account. The comparisons address seasonal variations and long-term characteristics. The steady increase of atmospheric carbon dioxide primarily caused by the burning of fossil fuels can be clearly observed with SCIAMACHY globally. The retrieved global annual mean XCO2 increase agrees with CarbonTracker within the error bars (1.80±0.13 ppm yr−1 compared to 1.81±0.09 ppm yr−1). The amplitude of the XCO2 seasonal cycle as retrieved by SCIAMACHY, which is 4.3±0.2 ppm for the Northern Hemisphere and 1.4±0.2 ppm for the Southern Hemisphere, is on average about 1 ppm larger than for CarbonTracker. An investigation of the boreal forest carbon uptake during the growing season via the analysis of longitudinal gradients shows good agreement between SCIAMACHY and CarbonTracker concerning the overall magnitude of the gradients and their annual variations. The analysis includes a discussion of the relative uptake strengths of the Russian and North American boreal forest regions. The retrieved XCH4 results show that after years of stability, atmospheric methane has started to rise again in recent years which is consistent with surface measurements. The largest increase is observed for the tropics and northern mid- and high-latitudes amounting to about 7.5±1.5 ppb yr−1 since 2007. Due care has been exercised to minimise the influence of detector degradation on the quantitative estimate of this anomaly.

scholarly journals Update on GOSAT TANSO-FTS performance, operations, and data products after more than 6 years in space

2016 ◽  
Vol 9 (6) ◽  
pp. 2445-2461 ◽  
Author(s):  
Akihiko Kuze ◽  
Hiroshi Suto ◽  
Kei Shiomi ◽  
Shuji Kawakami ◽  
Makoto Tanaka ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Near Infrared ◽  
Satellite System ◽  
Fourier Transform Spectrometer ◽  
Infrared Sensor ◽  
Data Set ◽  
Dry Air ◽  
Retrieval Accuracy ◽  
Carbon Dioxide Co2

Abstract. A data set containing more than 6 years (February 2009 to present) of radiance spectra for carbon dioxide (CO2) and methane (CH4) observations has been acquired by the Greenhouse gases Observing SATellite (GOSAT, available at http://data.gosat.nies.go.jp/GosatUserInterfaceGateway/guig/GuigPage/open.do), nicknamed “Ibuki”, Thermal And Near infrared Sensor for carbon Observation Fourier Transform Spectrometer (TANSO-FTS). This paper provides updates on the performance of the satellite and TANSO-FTS sensor and describes important changes to the data product, which has recently been made available to users. With these changes the typical accuracy of retrieved column-averaged dry air mole fractions of CO2 and CH4 (XCO2 and XCH4, respectively) are 2 ppm or 0.5 % and 13 ppb or 0.7 %, respectively. Three major anomalies of the satellite system affecting TANSO-FTS are reported: a failure of one of the two solar paddles in May 2014, a switch to the secondary pointing system in January 2015, and most recently a cryocooler shutdown and restart in August 2015. The Level 1A (L1A) (raw interferogram) and the Level 1B (L1B) (radiance spectra) of version V201 described here have long-term uniform quality and provide consistent retrieval accuracy even after the satellite system anomalies. In addition, we discuss the unique observation abilities of GOSAT made possible by an agile pointing mechanism, which allows for optimization of global sampling patterns.

scholarly journals Space-based geoengineering: Challenges and requirements

2009 ◽  
Vol 224 (3) ◽  
pp. 571-580 ◽  
Author(s):  
C R McInnes
Keyword(s):  
Climate Change ◽  
Carbon Dioxide ◽  
Large Scale ◽  
Climate Model ◽  
Carbon Dioxide Concentration ◽  
Complex Solution ◽  
Lagrange Point ◽  
Simple Climate Model ◽  
Earth’S Climate

The prospect of engineering the Earth's climate (geoengineering) raises a multitude of issues associated with climatology, engineering on macroscopic scales, and indeed the ethics of such ventures. Depending on personal views, such large-scale engineering is either an obvious necessity for the deep future, or yet another example of human conceit. In this article a simple climate model will be used to estimate requirements for engineering the Earth's climate, principally using space-based geoengineering. Active cooling of the climate to mitigate anthropogenic climate change due to a doubling of the carbon dioxide concentration in the Earth's atmosphere is considered. This representative scenario will allow the scale of the engineering challenge to be determined. It will be argued that simple occulting discs at the interior Lagrange point may represent a less complex solution than concepts for highly engineered refracting discs proposed recently. While engineering on macroscopic scales can appear formidable, emerging capabilities may allow such ventures to be seriously considered in the long term. This article is not an exhaustive review of geoengineering, but aims to provide a foretaste of the future opportunities, challenges, and requirements for space-based geoengineering ventures.

scholarly journals The Impact of Improved Aerosol Priors on Near-Infrared Measurements of Carbon Dioxide

2018 ◽  
Author(s):  
Robert R. Nelson ◽  
Christopher W. O'Dell
Keyword(s):  
Carbon Dioxide ◽  
Near Infrared ◽  
Carbon Sources ◽  
Total Carbon ◽  
Primary Sources ◽  
Retrieval Algorithm ◽  
Sources And Sinks ◽  
Earth Observing System ◽  
Infrared Measurements ◽  
The Impact

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) was launched in 2014 with the goal of measuring the column-averaged dry-air mole fraction of carbon dioxide (XCO2) with sufficient precision and accuracy to infer regional carbon sources and sinks. One of the primary sources of error in near-infrared measurements of XCO2 is the scattering effects of cloud and aerosol layers. In this work, we study the impact of ingesting intelligent aerosol priors from the Goddard Earth Observing System Model, Version 5 (GEOS-5) into the OCO-2 ACOS V8 retrieval algorithm with the objective of reducing the error in XCO2 from real measurements. Multiple levels of both aerosol setup complexity and uncertainty on the aerosol priors were tested, ranging from a mostly unconstrained aerosol optical depth (AOD) setup to ingesting full aerosol profiles with high confidence. We find that using co-located GEOS-5 aerosol types and AODs with low uncertainty results in a small improvement in the retrieved XCO2 against the Total Carbon Column Observing Network relative to V8. In contrast, attempting to use modeled vertical information in the aerosol prior to improve the XCO2 retrieval generally gives poor results, as aerosol models struggle with the vertical placement of aerosol layers. To assess regional differences in XCO2, we compare our results to a global CO2 model validation suite. We find that the GEOS-5 setup performs better than V8 over Northern Africa and Central Asia, with the standard deviation of the XCO2 error reduced from 2.12 ppm to 1.83 ppm, due to a combination of smaller prior AODs and lower prior uncertainty. In general, the use of more intelligent aerosol priors shows promise but is currently restricted by the accuracy of aerosol models.

scholarly journals Atmospheric methane and carbon dioxide from SCIAMACHY satellite data: initial comparison with chemistry and transport models

2004 ◽  
Vol 4 (6) ◽  
pp. 7217-7279 ◽  
Author(s):  
M. Buchwitz ◽  
R. de Beek ◽  
J. P. Burrows ◽  
H. Bovensmann ◽  
T. Warneke ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Reasonable Agreement ◽  
Near Infrared ◽  
Weighting Function ◽  
Lower Surface ◽  
Retrieval Algorithm ◽  
Atmospheric Methane ◽  
Mixing Ratios ◽  
Data Products ◽  
Carbon Dioxide Co2

Abstract. The remote sensing of the atmospheric greenhouse gases methane (CH4) and carbon dioxide (CO2) in the troposphere from instrumentation aboard satellites is a new area of research. In this manuscript, results obtained from observations of the up-welling radiation in the near-infrared by SCIAMACHY (Scanning Imaging Absorption spectroMeter for Atmospheric CHartographY), which flies on board ENVISAT, are presented. Vertical columns of CH4, CO2 and oxygen (O2) have been retrieved and the (air or) O2-normalized CH4 and CO2 column amounts, the dry air column averaged mixing ratios XCH4 and XCO2 derived. In this manuscript the first results, obtained by using the version 0.4 of the Weighting Function Modified (WFM) DOAS retrieval algorithm applied to SCIAMACHY data, are described and compared with global models. This is an important step in assessing the quality and information content of the data products derived from SCIAMACHY observations. This study investigates the behaviour of CO2 and CH4 in the period from January to October 2003. The SCIAMACHY greenhouse gas column amounts and their mixing ratios for cloud free scenes over land are shown to be in reasonable agreement with models. Over the ocean, as a result of the lower surface spectral reflectance and resultant low signal to noise with the exception of sun glint conditions, the accuracy of the individual data products is poorer. The measured methane column amounts agree with the model columns within a few percent. The inter-hemispheric difference of the methane mixing ratios, determined from single day cloud free measurements over land, is in the range 30–110 ppbv and in reasonable agreement with the corresponding model data (48–71 ppbv). For the set of individual measurements the standard deviations of the difference with respect to the models are in the range ~100–200 ppbv (5–10%) and ±14.4 ppmv (3.9%) for XCH

scholarly journals The use of relaxation methods in the WRF model for the analysis of meteorological conditions in Ukraine over a long period

2020 ◽  
Vol 2 ◽  
pp. 30-42
Author(s):  
O.V. Khalchenkov ◽  
◽  
I.V. Kovalets ◽  
Keyword(s):  
Large Scale ◽  
Mesoscale Model ◽  
Soil Layer ◽  
Meteorological Conditions ◽  
Optimal Combination ◽  
Relaxation Methods ◽  
Advantages And Disadvantages ◽  
Long Period ◽  
Surface Measurements

The possibility of using grid and spectral relaxation methods and other options in the WRF mesoscale model for long-term continuous calculations has been investigated. Results of comparison of selected me-teorological parameters with surface measurements are presented. The basic recommendations for select-ing the optimal combination of long-term calculation parameters are given. The use of the selected param-eters allowed to obtain continuous meteorological fields over a long period (several months), which are well consistent with surface measurements, retain large scale synoptic structures and have a deviation from measurements commensurate with the results of short-term simulations over corresponding time peri-od. The selected optimal combination of parameters allowed us to perform continuous calculation for the period from January 1, 2019 to November 6, 2019 without accumulating errors. In a long-run calculation of meteorological conditions in Ukraine with spatial resolution 0.15 deg. for a temperature at a height of 2 meters was obtained a mean absolute error of MAE=2,05 ºC, a correlation coefficient of Corr=0,97, for a wind speed at a height of 10 meters of MAE=1.4 m/s, of Corr=0,75, and for a wind direction at a height of 10 meters of MAE=24,6 degrees, Corr=0,66. The influence of the parametrizations of the underlying sur-face and the active soil layer on the quality of calculation of meteorological fields is studied. Using the option to update the water surface temperature allowed to reduce the MAE for the temperature from 2,17 ºС to 2,05 ºС. Each of the investigated surface models showed its advantages and disadvantages. The pa-rameterizations RUC and NOAH LSM showed good agreement with the measurements for all studied pa-rameters and can be recommended for use in long-term continuous calculations. A long calculation made it possible to describe the process of accumulation and melting of snow correctly, and made it possible to reproduce the temperature of the upper soil layer correctly as well. The paper shows that the disadvantage of long- term calculations is the inability to determine the temperature of the lower layers of the soil cor-rectly.

scholarly journals Evaluation of the MODIS LAI/FPAR Algorithm Based on 3D-RTM Simulations: A Case Study of Grassland

2020 ◽  
Vol 12 (20) ◽  
pp. 3391
Author(s):  
Jiabin Pu ◽  
Kai Yan ◽  
Guohuan Zhou ◽  
Yongqiao Lei ◽  
Yingxin Zhu ◽  
...  
Keyword(s):  
Model Uncertainty ◽  
Large Scale ◽  
Near Infrared ◽  
Evaluation Method ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Scale Dependency ◽  
Area Index ◽  
Input Uncertainty

Uncertainty assessment of the moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) retrieval algorithm can provide a scientific basis for the usage and improvement of this widely-used product. Previous evaluations generally depended on the intercomparison with other datasets as well as direct validation using ground measurements, which mix the uncertainties from the model, inputs, and assessment method. In this study, we adopted the evaluation method based on three-dimensional radiative transfer model (3D RTM) simulations, which helps to separate model uncertainty and other factors. We used the well-validated 3D RTM LESS (large-scale remote sensing data and image simulation framework) for a grassland scene simulation and calculated bidirectional reflectance factors (BRFs) as inputs for the LAI/FPAR retrieval. The dependency between LAI/FPAR truth and model estimation serves as the algorithm uncertainty indicator. This paper analyzed the LAI/FPAR uncertainty caused by inherent model uncertainty, input uncertainty (BRF and biome classification), clumping effect, and scale dependency. We found that the uncertainties of different algorithm paths vary greatly (−6.61% and +84.85% bias for main and backup algorithm, respectively) and the “hotspot” geometry results in greatest retrieval uncertainty. For the input uncertainty, the BRF of the near-infrared (NIR) band has greater impacts than that of the red band, and the biome misclassification also leads to nonnegligible LAI/FPAR bias. Moreover, the clumping effect leads to a significant LAI underestimation (−0.846 and −0.525 LAI difference for two clumping types), but the scale dependency (pixel size ranges from 100 m to 1000 m) has little impact on LAI/FPAR uncertainty. Overall, this study provides a new perspective on the evaluation of LAI/FPAR retrieval algorithms.

Improvements in XCO2 accuracy from OCO-2 with the latest ACOS v10 product

2021 ◽  
Author(s):  
Christopher ODell ◽  
Annmarie Eldering ◽  
Michael Gunson ◽  
David Crisp ◽  
Brendan Fisher ◽  
...  
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Regional Scale ◽  
Surface Fluxes ◽  
Retrieval Algorithm ◽  
The Past ◽  
Tropical Oceans ◽  
Desert Areas ◽  
Global Carbon

<p>While initial plans for measuring carbon dioxide from space hoped for 1-2 ppm levels of accuracy (bias) and precision in the CO<sub>2</sub> column mean dry air mole fraction (XCO<sub>2</sub>), in the past few years it has become clear that accuracies better than 0.5 ppm are required for most current science applications.  These include measuring continental (1000+ km) and regional scale (100s of km) surface fluxes of CO<sub>2</sub> at monthly-average timescales.  Considering the 400+ ppm background, this translates to an accuracy of roughly 0.1%, an incredibly challenging target to hit. </p><p>Improvements in both instrument calibration and retrieval algorithms have led to significant improvements in satellite XCO<sub>2</sub> accuracies over the past decade.  The Atmospheric Carbon Observations from Space (ACOS) retrieval algorithm, including post-retrieval filtering and bias correction, has demonstrated unprecedented accuracy with our latest algorithm version as applied to the Orbiting Carbon Observatory-2 (OCO-2) satellite sensor.   This presentation will discuss the performance of the v10 XCO<sub>2</sub> product by comparisons to TCCON and models, and showcase its performance with some recent examples, from the potential to infer large-scale fluxes to its performance on individual power plants.  The v10 product yields better agreement with TCCON over land and ocean, plus reduced biases over tropical oceans and desert areas as compared to a median of multiple global carbon inversion models, allowing better accuracy and faith in inferred regional-scale fluxes.  More specifically, OCO-2 has single sounding precision of ~0.8 ppm over land and ~0.5 ppm over water, and RMS biases of 0.5-0.7 ppm over both land and water.  Given the six-year and growing length of the OCO-2 data record, this also enables new studies on carbon interannual variability, while at the same time allowing identification of more subtle and temporally-dependent errors.  Finally, we will discuss the prospects of future improvements in the next planned version (v11), and the long-term prospects of greenhouse gas retrievals in the coming years. </p><p> </p>

scholarly journals Bias assessment of lower and middle tropospheric CO<sub>2</sub> concentrations of GOSAT/TANSO-FTS TIR version 1 product

2017 ◽  
Vol 10 (10) ◽  
pp. 3877-3892 ◽  
Author(s):  
Naoko Saitoh ◽  
Shuhei Kimoto ◽  
Ryo Sugimura ◽  
Ryoichi Imasu ◽  
Kei Shiomi ◽  
...  
Keyword(s):  
Bias Correction ◽  
Near Infrared ◽  
Transport Model ◽  
Lower Troposphere ◽  
Upper Atmosphere ◽  
Retrieval Algorithm ◽  
Co2 Absorption ◽  
Free Troposphere ◽  
Absorption Bands ◽  
Co2 Concentrations

Abstract. CO2 observations in the free troposphere can be useful for constraining CO2 source and sink estimates at the surface since they represent CO2 concentrations away from point source emissions. The thermal infrared (TIR) band of the Thermal and Near Infrared Sensor for Carbon Observation (TANSO) Fourier transform spectrometer (FTS) on board the Greenhouse Gases Observing Satellite (GOSAT) has been observing global CO2 concentrations in the free troposphere for about 8 years and thus could provide a dataset with which to evaluate the vertical transport of CO2 from the surface to the upper atmosphere. This study evaluated biases in the TIR version 1 (V1) CO2 product in the lower troposphere (LT) and the middle troposphere (MT) (736–287 hPa), on the basis of comparisons with CO2 profiles obtained over airports using Continuous CO2 Measuring Equipment (CME) in the Comprehensive Observation Network for Trace gases by AIrLiner (CONTRAIL) project. Bias-correction values are presented for TIR CO2 data for each pressure layer in the LT and MT regions during each season and in each latitude band: 40–20° S, 20° S–20° N, 20–40° N, and 40–60° N. TIR V1 CO2 data had consistent negative biases of 1–1.5 % compared with CME CO2 data in the LT and MT regions, with the largest negative biases at 541–398 hPa, partly due to the use of 10 µm CO2 absorption band in conjunction with 15 and 9 µm absorption bands in the V1 retrieval algorithm. Global comparisons between TIR CO2 data to which the bias-correction values were applied and CO2 data simulated by a transport model based on the Nonhydrostatic ICosahedral Atmospheric Model (NICAM-TM) confirmed the validity of the bias-correction values evaluated over airports in limited areas. In low latitudes in the upper MT region (398–287 hPa), however, TIR CO2 data in northern summer were overcorrected by these bias-correction values; this is because the bias-correction values were determined using comparisons mainly over airports in Southeast Asia, where CO2 concentrations in the upper atmosphere display relatively large variations due to strong updrafts.

scholarly journals The impact of improved aerosol priors on near-infrared measurements of carbon dioxide

2019 ◽  
Vol 12 (3) ◽  
pp. 1495-1512 ◽  
Author(s):  
Robert R. Nelson ◽  
Christopher W. O'Dell
Keyword(s):  
Carbon Dioxide ◽  
Near Infrared ◽  
Carbon Sources ◽  
Total Carbon ◽  
Primary Sources ◽  
Retrieval Algorithm ◽  
Sources And Sinks ◽  
Earth Observing System ◽  
Infrared Measurements ◽  
The Impact

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) was launched in 2014 with the goal of measuring the column-averaged dry-air mole fraction of carbon dioxide (XCO2) with sufficient precision and accuracy to infer regional carbon sources and sinks. One of the primary sources of error in near-infrared measurements of XCO2 is the scattering effects of cloud and aerosol layers. In this work, we study the impact of ingesting better informed aerosol priors from the Goddard Earth Observing System Model, Version 5 (GEOS-5) into the OCO-2 ACOS V8 retrieval algorithm with the objective of reducing the error in XCO2 from real measurements. Multiple levels of both aerosol setup complexity and uncertainty on the aerosol priors were tested, ranging from a mostly unconstrained aerosol optical depth (AOD) setup to ingesting full aerosol profiles with high confidence. We find that using co-located GEOS-5 aerosol types and AODs with low uncertainty results in a small improvement in the retrieved XCO2 against the Total Carbon Column Observing Network relative to V8. In contrast, attempting to use modeled vertical information in the aerosol prior to improve the XCO2 retrieval generally gives poor results, as aerosol models struggle with the vertical placement of aerosol layers. To assess regional differences in XCO2, we compare our results to a global CO2 model validation suite. We find that the GEOS-5 setup performs better than V8 over northern Africa and central Asia, with the standard deviation of the XCO2 error reduced from 2.12 to 1.83 ppm, due to a combination of smaller prior AODs and lower prior uncertainty. In general, the use of better informed aerosol priors shows promise but may be restricted by the current accuracy of aerosol models.

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