Abstract

Abstract. This study investigates the impacts of fire emission, convection, various climate conditions and transport pathways on the interannual variation of carbon monoxide (CO) in the tropical upper troposphere (UT), by evaluating the field correlation between these fields using multi-satellite observations and principle component analysis, and the transport pathway auto-identification method developed in our previous study. The rotated empirical orthogonal function (REOF) and singular value decomposition (SVD) methods are used to identify the dominant modes of CO interannual variation in the tropical UT and to study the coupled relationship between UT CO and its governing factors. Both REOF and SVD results confirm that Indonesia is the most significant land region that affects the interannual variation of CO in the tropical UT, and El Niño–Southern Oscillation (ENSO) is the dominant climate condition that affects the relationships between surface CO emission, convection and UT CO. In addition, our results also show that the impact of El Niño on the anomalous CO pattern in the tropical UT varies strongly, primarily due to different anomalous emission and convection patterns associated with different El Niño events. In contrast, the anomalous CO pattern in the tropical UT during La Niña period appears to be less variable among different events. Transport pathway analysis suggests that the average CO transported by the "local convection" pathway (ΔCOlocal) accounts for the differences of UT CO between different ENSO phases over the tropical continents during biomass burning season. ΔCOlocal is generally higher over Indonesia–Australia and lower over South America during El Niño years than during La Niña years. The other pathway ("advection within the lower troposphere followed by convective vertical transport") occurs more frequently over the west-central Pacific during El Niño years than during La Niña years, which may account for the UT CO differences over this region between different ENSO phases.

Highlights

  • Carbon monoxide (CO), which is a byproduct of the incomplete combustion of carbon-based fuels, plays an important role in atmospheric chemistry and radiation balance

  • Both rotated empirical orthogonal function (REOF) and singular value decomposition (SVD) results confirm that Indonesia is the most significant land region that affects the interannual variation of CO in the tropical upper troposphere (UT), and El Niño–Southern Oscillation (ENSO) is the dominant climate condition that affects the relationships between surface CO emission, convection and UT CO

  • Over SE Asia (Fig. 2d), both CO emission and ice water content (IWC) anomalies are significantly correlated with UT CO anomaly (0.77 and −0.66, respectively), suggesting that both CO emission and convective transport are important in determining the interannual variation of UT CO over this region

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Summary

Introduction

Carbon monoxide (CO), which is a byproduct of the incomplete combustion of carbon-based fuels, plays an important role in atmospheric chemistry and radiation balance. L. Huang et al.: Impacts of fire emissions and transport pathways interannual variation of biomass burning, especially those major burning events in Indonesia associated with El Niño, is the main driver of large-scale CO variability in the tropics. The relative importance between changes of surface emission and transport pathways on the interannual changes of UT CO and how such relative importance varies with, for example, different ENSO phases, is still an open question. Huang et al (2012) developed a method to automate the identification of these pathways through a joint use of A-Train multi-satellite measurements This approach allows us to more efficiently evaluate the relationships between changes of CO emission, transport pathways and CO in the UT. To homogenize the horizontal resolutions of the MLS CO, IWC, SST and GFED CO emission, we averaged these data into 4◦ latitude × 8◦ longitude grid boxes. The MLS CO and IWC data were averaged over each month to get the monthly mean

Methodology
Findings
Conclusions

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