Tropospheric carbon monoxide variability from AIRS under clear and cloudy conditions

J Warner,F Carminati,Z Wei,W Lahoz,J.-L Attié

doi:10.5194/acp-13-12469-2013

Abstract

Abstract. We study the carbon monoxide (CO) variability in the last decade measured by NASA's Atmospheric InfraRed Sounder (AIRS) on the Earth Observing System (EOS)/Aqua satellite. The focus of this study is to analyze CO variability and short-term trends separately for background CO and fresh CO emissions based on a new statistical approach. The AIRS Level 2 (L2) retrieval algorithm utilizes cloud clearing to treat cloud contaminations in the signals, and this increases the data coverage significantly to a yield of more than 50% of the total measurements. We first study if the cloud clearing affects CO retrievals and the subsequent trend studies by using the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask to identify AIRS clear sky scenes. We then carry out a science analysis using AIRS CO data individually for the clear and cloud-cleared scenes to identify any potential effects due to cloud clearing. We also introduce a new technique to separate background and recently emitted CO observations, which aims to constrain emissions using only satellite CO data. We validate the CO variability of the recent emissions estimated from AIRS against other emission inventory databases (i.e., Global Fire Emissions Database – GFED3 and the MACC/CityZEN UE – MACCity) and calculate that the correlation coefficients between the AIRS CO recently emitted and the emission inventory databases are 0.726 for the Northern Hemisphere (NH) and 0.915 for the Southern Hemisphere (SH). The high degree of agreement between emissions identified using only AIRS CO and independent inventory sources demonstrates the validity of this approach to separate recent emissions from the background CO using one satellite data set.

Highlights

Global long-term measurements of tropospheric carbon monoxide (CO) from space-borne instruments have been possible since year 2000 with the launch of the Measurement Of Pollution In The Troposphere (MOPITT) (Drummond, 1989) on the Earth Observing System (EOS) Terra satellite, followed by the Atmospheric InfraRed Sounder (AIRS) on Aqua (Aumman et al, 2003), the Tropospheric Emission Spectrometer (TES) on Aura (Beer, 2006), the Infrared Atmospheric Sounder Interferometer (IASI) on the European MetOp platform (Clerbaux et al, 2010), and future CO products from the Cross-track Infrared Sensor (CrIS) on SuomiNPP satellite
This study points out that, under at lower CO values are generally associated with the backpure clear sky conditions, it is possible for AIRS to retrieve, ground (BG) CO, whereas the peaks at the higher CO values over land, Southern Hemisphere (SH) clean background CO values of approximately are associated with the recent emissions (RE)
We have demonstrated that this technique works well by showing high correlation between the AIRS CO emissions we obtained and the established inventory database (i.e., GFED3 and MACCity) with correlation coefficients of 0.726 in the Northern Hemisphere (NH) and 0.915 in the SH

Summary

Introduction

Global long-term measurements of tropospheric carbon monoxide (CO) from space-borne instruments have been possible since year 2000 with the launch of the Measurement Of Pollution In The Troposphere (MOPITT) (Drummond, 1989) on the Earth Observing System (EOS) Terra satellite, followed by the Atmospheric InfraRed Sounder (AIRS) on Aqua (Aumman et al, 2003), the Tropospheric Emission Spectrometer (TES) on Aura (Beer, 2006), the Infrared Atmospheric Sounder Interferometer (IASI) on the European MetOp platform (Clerbaux et al, 2010), and future CO products from the Cross-track Infrared Sensor (CrIS) on SuomiNPP satellite These measurements have advanced our understanding in many areas of science such as air quality and transport studies (Heald et al, 2003; Lin et al, 2012); field campaign support and validation (Fisher et al, 2010; Warner et al, 2007; Emmons et al, 2004, 2007); and model chemistry, transport, and data assimilation studies (Kim et al, 2013; Arellano et al, 2007; Pradier et al, 2006; Lamarque et al, 2004) that aim to improve the capability of air quality forecasts.

Identifying AIRS clear-sky coverage

AIRS CO variability for clear sky and cloud-cleared scenes

Findings

Summary