Reply on RC1

Abstract

Data from ozonesondes launched at ARIES Nainital (29.40° N, 79.50° E, and 1793 m elevation) are used to evaluate the Atmospheric Infrared Sounder (AIRS) version 6 ozone profiles and total column ozone during the period 2011–2017 over the central Himalaya. The AIRS ozone products are analyzed in terms of retrieval sensitivity, retrieval biases/errors, and ability to retrieve the natural variability of columnar ozone, which has not been done so far from the Himalayan region having complex topography. For a direct comparison, averaging kernels information is used to account for the sensitivity difference between the AIRS and ozonesonde data. We show that AIRS can provide quality data of ozone in the lower and middle troposphere and stratosphere with nominal underestimation (<20 %). However, in the upper troposphere and lower stratosphere (UTLS), we observe a considerable overestimation of the magnitude as high as 102 %. The weighted statistical error analysis of AIRS ozone shows higher positive bias, root mean squared error, and standard deviation in the upper troposphere of about 65 %, 65 %, and 25 %, respectively. Similar to AIRS, Infrared Atmospheric Sounding Interferometer (IASI) and Cross-track Infrared Sounder (CrIS) are also able to produce ozone peaks and gradients successfully. However, the statistical errors are again higher in the UTLS region that are likely related to larger variability of ozone, lower ozone partial pressure and inadequate retrieval information on the surface parameters. The monthly variations of columnar ozone (total, UTLS, and tropospheric) are captured well by AIRS, except the total columnar ozone, which shows a strong bimodal variation, unlike unimodal variation seen in ozonesonde and Ozone Monitoring Instrument (OMI). Increases in ozone of 5–20 % (in 2–6 km altitude) after the biomass burning and during events of downward transport (in 2–16 km altitude) are captured well by AIRS. Ozone radiative forcing (RF) derived from total column ozone matches well between ozonesonde (4.86 mW/m2) and OMI (4.04 mW/m2), while significant RF underestimation is seen in AIRS (2.96 mW/m2). The fragile and complex landscapes of the Himalayas are more sensitive to global climate change, and establishing such biases and error analysis of space-borne sensors will help study the long-term trends and estimate accurate radiative budgets.