气候变化领域集成服务门户(CCPortal): Attribution of Chemistry-Climate Model Initiative (CCMI) ozone radiative flux bias from satellites

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DOI	10.5194/acp-20-281-2020
	Attribution of Chemistry-Climate Model Initiative (CCMI) ozone radiative flux bias from satellites
	Kuai L.; Bowman K.W.; Miyazaki K.; Deushi M.; Revell L.; Rozanov E.; Paulot F.; Strode S.; Conley A.; Jöckel P.; Plummer D.A.; Oman L.D.; Worden H.; Kulawik S.; Paynter D.; Stenke A.; Kunze M.
发表日期	2020
ISSN	16807316
起始页码	281
结束页码	301
卷号	20 期号:1
英文摘要	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 the distributions of clouds, ozone (O3), water vapor (H2O), air temperature (Ta), and surface temperature (Ts). Benchmarking present-day fluxes and quantifying the relative influence of their drivers is the first step for estimating climate feedbacks from ozone radiative forcing and predicting radiative forcing evolution. To that end, we constructed observational instantaneous radiative kernels (IRKs) under clear-sky conditions, representing the sensitivities of the TOA flux in the 9.6 μm ozone band to the vertical distribution of geophysical variables, including O3, H2O, Ta, and Ts based upon the Aura Tropospheric Emission Spectrometer (TES) measurements. Applying these kernels to present-day simulations from the Chemistry-Climate Model Initiative (CCMI) project as compared to a 2006 reanalysis assimilating satellite observations, we show that the models have large differences in TOA flux, attributable to different geophysical variables. In particular, model simulations continue to diverge from observations in the tropics, as reported in previous studies of the Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP) simulations. The principal culprits are tropical middle and upper tropospheric ozone followed by tropical lower tropospheric H2O. Five models out of the eight studied here have TOA flux biases exceeding 100 mW m-2 attributable to tropospheric ozone bias. Another set of five models have flux biases over 50 mW m-2 due to H2O. On the other hand, Ta radiative bias is negligible in all models (no more than 30 mW m-2). We found that the atmospheric component (AM3) of the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model and Canadian Middle Atmosphere Model (CMAM) have the lowest TOA flux biases globally but are a result of cancellation of opposite biases due to different processes. Overall, the multi-model ensemble mean bias is $- 133 \pm 98$ mW m-2, indicating that they are too atmospherically opaque due to trapping too much radiation in the atmosphere by overestimated tropical tropospheric O3 and H2O. Having too much O3 and H2O in the troposphere would have different impacts on the sensitivity of TOA flux to O3 and these competing effects add more uncertainties on the ozone radiative forcing. We find that the inter-model TOA outgoing longwave radiation (OLR) difference is well anti-correlated with their ozone band flux bias. This suggests that there is significant radiative compensation in the calculation of model outgoing longwave radiation. © 2020. This work is distributed under the Creative Commons Attribution 4.0 License.
关键词	air temperature atmospheric chemistry atmospheric modeling climate feedback climate modeling longwave radiation ozone radiative forcing satellite imagery surface temperature
语种	英语
来源机构	Atmospheric Chemistry and Physics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/132312
推荐引用方式 GB/T 7714	Kuai L.,Bowman K.W.,Miyazaki K.,et al. Attribution of Chemistry-Climate Model Initiative (CCMI) ozone radiative flux bias from satellites[J]. Atmospheric Chemistry and Physics,2020,20(1).
APA	Kuai L..,Bowman K.W..,Miyazaki K..,Deushi M..,Revell L..,...&Kunze M..(2020).Attribution of Chemistry-Climate Model Initiative (CCMI) ozone radiative flux bias from satellites.,20(1).
MLA	Kuai L.,et al."Attribution of Chemistry-Climate Model Initiative (CCMI) ozone radiative flux bias from satellites".20.1(2020).