Benchmarking Chemistry–Climate Model Top-of-Atmosphere Flux in the 9.6-µm Infrared Ozone Absorption Band with Comparisons to Satellite Observations

The 9.6 micron infrared absorption band of ozone is the primary contributor to long wave radiative forcing by tropospheric ozone, the 3rd most important greenhouse gas. Biases of present day top-of-atmosphere (TOA) flux for the 9.6 micron ozone band, which is predicted by IPCC chemistry-climate models, have never been evaluated with measurements and these biases impact estimates of ozone radiative forcing (RF) from pre-industrial to present day. Satellite measurements of TOA spectral radiances and ozone profiles allow evaluation of the performance of chemistry-climate models for both TOA flux and Instantaneous Radiative Kernels (IRK), which give the sensitivity of TOA flux to changes in the vertical distribution of atmospheric state, e.g. tropospheric ozone, water or temperature. Changes in the atmospheric state can then have a feedback effect on ozone radiative forcing by altering the sensitivity of TOA flux to ozone.

We compute the ozone band flux and the IRK, from Aura-TES and MetOP-IASI and SNPP-CrIS Fourier Transform spectrometer (FTS) measurements. The IRKs from spectrally resolved radiances explicitly account for more dominant radiative processes such as clouds and water vapor, due to the spectrally resolved absorption features, and allow attribution of changes in ozone RF to vertical changes in ozone and ozone precursor emissions. The continuation of the TES record of infrared ozone spectra and corresponding IRK with long-term IASI and CrIS data will allow accurate predictions of future ozone forcing and an assessment of the feedback from changes in the hydrological cycle on ozone RF. We use these IRK to evaluate the TOA flux bias in a suite of chemistry-climate models in terms of ozone, temperature, and water vapor. We show that the spatial pattern and the sign of these biases differs significantly between models. This evaluation suggests opportunities for model improvement in climate sensitive regions.