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An Examination of the Long-Term CO Records from MOPITT and IASI

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An Examination of the Long-Term CO Records from MOPITT and IASI An examination of the long-term CO records from MOPITT and IASI: comparison of retrieval methodology Maya George, Cathy Clerbaux, Idir Bouarar, Pierre-Fran¸coisCoheur, Merritt N. Deeter, David P. Edwards, G. Francis, John C. Gille, Juliette Hadji-Lazaro, Daniel Hurtmans, et al. To cite this version: Maya George, Cathy Clerbaux, Idir Bouarar, Pierre-Fran¸coisCoheur, Merritt N. Deeter, et al.. An examination of the long-term CO records from MOPITT and IASI: comparison of retrieval methodology. Atmospheric Measurement Techniques, European Geosciences Union, 2015, 8 (10), pp.4313-4328. <10.5194/amt-8-4313-2015>. <insu-01145299> HAL Id: insu-01145299 https://hal-insu.archives-ouvertes.fr/insu-01145299 Submitted on 15 Oct 2015 HAL is a multi-disciplinary open access L'archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destin´eeau d´ep^otet `ala diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publi´esou non, lished or not. The documents may come from ´emanant des ´etablissements d'enseignement et de teaching and research institutions in France or recherche fran¸caisou ´etrangers,des laboratoires abroad, or from public or private research centers. publics ou priv´es. Atmos. Meas. Tech., 8, 4313–4328, 2015 www.atmos-meas-tech.net/8/4313/2015/ doi:10.5194/amt-8-4313-2015 © Author(s) 2015. CC Attribution 3.0 License. An examination of the long-term CO records from MOPITT and IASI: comparison of retrieval methodology M. George1, C. Clerbaux1,2, I. Bouarar3, P.-F. Coheur2, M. N. Deeter4, D. P. Edwards4, G. Francis4, J. C. Gille4, J. Hadji-Lazaro1, D. Hurtmans2, A. Inness5, D. Mao4, and H. M. Worden4 1Sorbonne Universités, UPMC Univ. Paris 06, Université Versailles St-Quentin, CNRS/INSU, LATMOS-IPSL, Paris, France 2Spectroscopie de l’Atmosphère, Chimie Quantique et Photophysique, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium 3Max Planck Institute for Meteorology, Hamburg, Germany 4Atmospheric Chemistry Observations and Modeling, National Center for Atmospheric Research, Boulder, CO, USA 5European Centre for Medium-Range Weather Forecasts, Reading, UK Correspondence to: M. George ([email protected]) Received: 12 February 2015 – Published in Atmos. Meas. Tech. Discuss.: 23 April 2015 Revised: 14 August 2015 – Accepted: 8 September 2015 – Published: 15 October 2015 Abstract. Carbon monoxide (CO) is a key atmospheric com- stant vs. a variable a priori. On one hand, MOPITT agrees pound that can be remotely sensed by satellite on the global better with the aircraft profiles for observations with persist- scale. Fifteen years of continuous observations are now avail- ing high levels of CO throughout the year due to pollution or able from the MOPITT/Terra mission (2000 to present). An- seasonal fire activity (because the climatology-based a pri- other 15 and more years of observations will be provided ori is supposed to be closer to the real atmospheric state). On by the IASI/MetOp instrument series (2007–2023 >). In or- the other hand, IASI performs better when unexpected events der to study long-term variability and trends, a homogeneous leading to high levels of CO occur, due to a larger variability record is required, which is not straightforward as the re- associated with the a priori. trieved quantities are instrument and processing dependent. The present study aims at evaluating the consistency between the CO products derived from the MOPITT and IASI mis- sions, both for total columns and vertical profiles, during 1 Introduction a 6-year overlap period (2008–2013). The analysis is per- formed by first comparing the available 2013 versions of the Measuring the variability and trends in carbon monox- retrieval algorithms (v5T for MOPITT and v20100815 for ide (CO) on the global scale is essential as it is an ozone and IASI), and second using a dedicated reprocessing of MO- carbon dioxide precursor, and it regulates the oxidizing ca- PITT CO profiles and columns using the same a priori in- pacity of the troposphere through its destruction cycle involv- formation as the IASI product. MOPITT total columns are ing the hydroxyl radical (OH) (Duncan and Logan, 2008). generally slightly higher over land (bias ranging from 0 to The background CO atmospheric loading varies as a func- 13 %) than IASI data. When IASI and MOPITT data are re- tion of season and latitude and is significantly perturbed by trieved with the same a priori constraints, correlation coeffi- human activities related to combustion processes: car traffic, cients are slightly improved. Large discrepancies (total col- heating/cooking systems, industrial activities, etc. CO accu- umn bias over 15 %) observed in the Northern Hemisphere mulates in the Northern Hemisphere (NH) during the win- during the winter months are reduced by a factor of 2 to 2.5. ter months due to low solar insolation corresponding to less The detailed analysis of retrieved vertical profiles compared chemical destruction, and concentrations peak in early spring with collocated aircraft data from the MOZAIC-IAGOS net- each year. Natural and human-induced fires also affect the work, illustrates the advantages and disadvantages of a con- CO budget, in particular in boreal areas where intense fires occur during the dry season and in the tropics where large Published by Copernicus Publications on behalf of the European Geosciences Union. 4314 M. George et al.: Long-term CO records from MOPITT and IASI emissions are linked to agricultural practices (Edwards et al., retrieval process is the choice of the a priori, which con- 2006). CO emissions inventories still present large uncertain- sists of an expected profile (xa/ and its associated variance- ties (Streets et al., 2013), and separating anthropogenic and covariance matrix (Sa/, to constrain the retrieved CO profile biomass burning contributions is essential for attributing CO to fall within the range of physically realistic solutions (based long-term trends (Strode and Pawson, 2013). on the known variability of this species). Due to its moderate lifetime (1–3 months), CO is an excel- Previous studies have inter-compared CO retrieved lent tracer of tropospheric pollution, which can often travel columns or profiles over specific areas and limited time pe- far downwind, even between continents (HTAP, 2010). CO riods. Clerbaux et al. (2002) made a first comparison of the can easily be measured by infrared remote sensing as it com- TES, MOPITT, and IASI retrieval algorithms to retrieve CO bines high variability and significant perturbations over back- columns from a common nadir radiance data set, provided ground concentration levels with a strong infrared absorption by the IMG/ADEOS thermal infrared instrument. Luo et signature. Over the last 2 decades, Earth-observing satellites al. (2007) compared, for 2 days in September 2004, TES- have revolutionized our ability to map CO and to understand retrieved CO profiles adjusted to the MOPITT a priori with its evolving concentration on regional and global scales. At the MOPITT retrievals and also the adjusted TES CO profiles the moment several satellite missions using the thermal in- with the MOPITT profiles vertically smoothed by the TES frared (TIR) spectral range to sound the atmosphere are de- averaging kernels. Warner et al. (2007) used the MOPITT livering CO data, including MOPITT on EOS/Terra launched a priori profile as AIRS first guess and showed global im- at the end of 1999 (Drummond and Mand, 1996; Deeter et provements to the agreements between CO at 500 hPa from al., 2003), AIRS on the EOS/Aqua satellite launched in 2002 these two instruments, for the 2-month time period of the (Aumann et al., 2003; McMillan et al., 2005), TES on the INTEX-A campaign. Ho et al. (2009) applied TES a priori EOS/Aura satellite launched in 2003 (Beer, 2006; Rinsland et profiles and covariance matrix to a modified MOPITT re- al., 2006), and IASI on the EPS/MetOp-A satellite launched trieval algorithm, for a 1-month study. George et al. (2009) in 2006 (Clerbaux et al., 2009; George et al., 2009). All these compared the IASI CO columns with MOPITT, AIRS and missions are maturing and have exceeded their foreseen life- TES CO columns, adjusted with the IASI a priori assump- times. More recently, the CrIS (Gambacorta et al., 2014) tions, for three different months (August 2008, November and IASI/MetOp-B instruments were launched onboard the 2008 and February 2009) and on the global scale. Illingworth SNPP and MetOp-B satellites, in 2011 and 2012, respec- et al. (2011) compared IASI CO with MOPITT CO data over tively. a localized region of Africa, for 1 day. They first retrieved the Each of these thermal infrared sensors has a dedicated CO MOPITT profiles using IASI a priori assumptions and then retrieval algorithm that was improved over time and has ben- applied the averaging kernels resulting from these new MO- efited from cross comparisons with other products. The opti- PITT retrievals to the IASI CO profiles. Finally, Worden et mal estimation (OE) retrieval approach (Rodgers, 2000) is a al. (2013) examined hemispheric and regional trends for CO widely used inverse method in atmospheric sciences to derive from all four missions, from 2000 through 2011. geophysical products from instrument measurements (e.g., The present study compares the CO record from MOPITT radiances). It regularizes the under-determined inverse prob- and IASI on the global scale, in order to setup a framework lem and provides the best estimates given the observations for building a consistent long-term data set. These two sen- and some prior knowledge of the atmospheric state. For MO- sors together already provided a 15-year record of data, in- PITT and IASI, one CO vertical profile and its associated cluding 6 years of common observation (2008–2013). The integrated total column are retrieved at each sounding loca- analysis is performed on both retrieved total columns and tion and the OE provides useful diagnostic variables such as vertical profiles, and focuses on identifying differences in the averaging kernel matrix (the sensitivity of both the in- the retrievals due to a priori assumptions.
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