Comparison of improved Aura Tropospheric Emission Spectrometer CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; with HIPPO and SGP aircraft profile measurements

S S Kulawik,D B A Jones,E T Olsen,S Park,S C Wofsy,B C Daube,R Jimenez,E A Kort,S C Biraud,G B Osterman,J R Worden,J V Pittman,G W Santoni,B B Stephens,R Nassar

doi:10.5194/acp-13-3205-2013

Abstract

Abstract. Thermal infrared radiances from the Tropospheric Emission Spectrometer (TES) between 10 and 15 μm contain significant carbon dioxide (CO2) information, however the CO2 signal must be separated from radiative interference from temperature, surface and cloud parameters, water, and other trace gases. Validation requires data sources spanning the range of TES CO2 sensitivity, which is approximately 2.5 to 12 km with peak sensitivity at about 5 km and the range of TES observations in latitude (40° S to 40° N) and time (2005–2011). We therefore characterize Tropospheric Emission Spectrometer (TES) CO2 version 5 biases and errors through comparisons to ocean and land-based aircraft profiles and to the CarbonTracker assimilation system. We compare to ocean profiles from the first three Hiaper Pole-to-Pole Observations (HIPPO) campaigns between 40° S and 40° N with measurements between the surface and 14 km and find that TES CO2 estimates capture the seasonal and latitudinal gradients observed by HIPPO CO2 measurements. Actual errors range from 0.8–1.8 ppm, depending on the campaign and pressure level, and are approximately 1.6–2 times larger than the predicted errors. The bias of TES versus HIPPO is within 1 ppm for all pressures and datasets; however, several of the sub-tropical TES CO2 estimates are lower than expected based on the calculated errors. Comparisons to land aircraft profiles from the United States Southern Great Plains (SGP) Atmospheric Radiation Measurement (ARM) between 2005 and 2011 measured from the surface to 5 km to TES CO2 show good agreement with an overall bias of −0.3 ppm to 0.1 ppm and standard deviations of 0.8 to 1.0 ppm at different pressure levels. Extending the SGP aircraft profiles above 5 km using AIRS or CONTRAIL measurements improves comparisons with TES. Comparisons to CarbonTracker (version CT2011) show a persistent spatially dependent bias pattern and comparisons to SGP show a time-dependent bias of −0.2 ppm yr−1. We also find that the predicted sensitivity of the TES CO2 estimates is too high, which results from using a multi-step retrieval for CO2 and temperature. We find that the averaging kernel in the TES product corrected by a pressure-dependent factor accurately reflects the sensitivity of the TES CO2 product.

Highlights

Over the past decade, measurements of carbon dioxide (CO2 ) from space have become increasingly prevalent, withCO2 measurements from SCIAMACHY, Atmospheric Infrared Sounder (AIRS), Tropospheric Emission Spectrometer (TES), IASI, ACE, and GOSAT (e.g. Reuter et al, 2011; Chahine et al, 2008; Kulawik et al, 2010; Crevosier et al, 2009; Foucher et al, 2011; Yoshida et al, 2011; Crisp et al, 2012; Butz et al, 2011)
The improved TES CO2 estimates described in this work capture the latitudinal gradients and seasonal patterns found in the HIPPO and SGP aircraft data
TES CO2 product are remarkable over land, and both land and ocean data for all pressure levels in this version of TES

Summary

Introduction

Measurements of carbon dioxide (CO2 ) from space have become increasingly prevalent, with. Fit of years 2005–2009 of monthly averages of TES or SGP aircraft data with the TES observation operator find a difference in the fitted yearly increase of −0.20 ppm yr−1 in the mid-troposphere. We compare results using these two different priors, linearly transforming the variable prior results to use the constant prior using Eq (6), to determine whether the TES CO2 retrieval strategy is linear and that the predicted sensitivity is correct, as well as verify that TES can capture the seasonal and yearly trends in the absence of a priori knowledge of CO2. Calculated correlations (“corr”), predicted (“pred”) and actual (”actl”) errors and biases (in ppm) when averaging within 5◦ , 10◦ , and 20◦ longitude for each of the datasets at 900 hPa near the surface (“surf”) and 500 hPa in the mid-troposphere (“trop”). Temperature-based coincidence improves correlations and reduces the bias and variability of the bias from −0.41 ± 0.47 ppm to −0.23 ± 0.27 ppm

Conclusions

Findings

A High-Precision Fast-Response Airborne CO2 Analyzer for