Attribution of Chemistry-Climate Model Initiative (CCMI) ozone radiative flux bias from satellites

摘要: Abstract. The top-of-atmosphere (TOA) outgoing longwave flux over the 9.6  µm ozone band is a fundamental quantity for understanding chemistry–climate coupling. However, observed TOA fluxes are hard to estimate as they exhibit considerable variability in space and time that depend on distributions of clouds, ozone ( O3 ), water vapor H2O air temperature ( Ta surface temperature Ts ). Benchmarking present-day fluxes quantifying relative influence of their drivers first step estimating climate feedbacks from radiative forcing and predicting evolution. To end, we constructed observational instantaneous kernels (IRKs) under clear-sky conditions, representing sensitivities TOA flux vertical distribution of geophysical variables, including , based upon Aura Tropospheric Emission Spectrometer (TES) measurements. Applying these kernels present-day simulations Chemistry-Climate Model Initiative (CCMI) project compared 2006 reanalysis assimilating satellite observations, show models have large differences flux, attributable different geophysical variables. In particular, model continue diverge observations the tropics, reported previous studies Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP) simulations. principal culprits tropical middle upper tropospheric followed by tropical lower . Five out eight studied here have TOA biases exceeding 100 mW m −2 ozone bias. Another set five 50 mW m due to On other hand, bias negligible all models (no more than 30 mW m We found atmospheric component (AM3) Geophysical Fluid Dynamics Laboratory (GFDL) general circulation Canadian Middle Atmosphere (CMAM) the lowest globally but result cancellation opposite biases due processes. Overall, multi-model ensemble mean bias - 133 ± 98  mW m indicating too atmospherically opaque trapping too much radiation atmosphere by overestimated Having much troposphere would impacts the sensitivity competing effects add more uncertainties forcing. find inter-model TOA (OLR) difference well anti-correlated with bias. This suggests there significant radiative compensation calculation longwave radiation.