Revell Laura E, Stenke Andrea, Tummon Fiona, Feinberg Aryeh, Rozanov Eugene, Peter Thomas, Abraham N Luke, Akiyoshi Hideharu, Archibald Alexander T, Butchart Neal, Deushi Makoto, Jöckel Patrick, Kinnison Douglas, Michou Martine, Morgenstern Olaf, O'Connor Fiona M, Oman Luke D, Pitari Giovanni, Plummer David A, Schofield Robyn, Stone Kane, Tilmes Simone, Visioni Daniele, Yamashita Yousuke, Zeng Guang
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand.
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland.
Atmos Chem Phys. 2018 Nov;18(21):16155-16172. doi: 10.5194/acp-18-16155-2018. Epub 2018 Nov 13.
Previous multi-model intercomparisons have shown that chemistry-climate models exhibit significant biases in tropospheric ozone compared with observations. We investigate annual-mean tropospheric column ozone in 15 models participating in the SPARC/IGAC (Stratosphere-troposphere Processes and their Role in Climate/International Global Atmospheric Chemistry) Chemistry-Climate Model Initiative (CCMI). These models exhibit a positive bias, on average, of up to 40-50% in the Northern Hemisphere compared with observations derived from the Ozone Monitoring Instrument and Microwave Limb Sounder (OMI/MLS), and a negative bias of up to ~30% in the Southern Hemisphere. SOCOLv3.0 (version 3 of the Solar-Climate Ozone Links CCM), which participated in CCMI, simulates global-mean tropospheric ozone columns of 40.2 DU - approximately 33% larger than the CCMI multi-model mean. Here we introduce an updated version of SOCOLv3.0, "SOCOLv3.1", which includes an improved treatment of ozone sink processes, and results in a reduction in the tropospheric column ozone bias of up to 8 DU, mostly due to the inclusion of NO hydrolysis on tropospheric aerosols. As a result of these developments, tropospheric column ozone amounts simulated by SOCOLv3.1 are comparable with several other CCMI models. We apply Gaussian process emulation and sensitivity analysis to understand the remaining ozone bias in SOCOLv3.1. This shows that ozone precursors (nitrogen oxides (NO), carbon monoxide, methane and other volatile organic compounds) are responsible for more than 90% of the variance in tropospheric ozone. However, it may not be the emissions inventories themselves that result in the bias, but how the emissions are handled in SOCOLv3.1, and we discuss this in the wider context of the other CCMI models. Given that the emissions data set to be used for phase of the Coupled Model Intercomparison Project includes approximately 20% more NO than the data set used for CCMI, further work is urgently needed to address the challenges of simulating sub-grid processes of importance to tropospheric ozone in the current generation of chemistry-climate models.
以往的多模式对比研究表明，与观测结果相比，化学气候模型在对流层臭氧方面存在显著偏差。我们调查了参与平流层 - 对流层过程及其在气候中的作用/国际全球大气化学（SPARC/IGAC）化学气候模型倡议（CCMI）的15个模型中的年平均对流层柱臭氧。与来自臭氧监测仪器和微波临边探测仪（OMI/MLS）的观测结果相比，这些模型在北半球平均表现出高达40 - 50%的正偏差，在南半球则表现出高达约30%的负偏差。参与CCMI的SOCOLv3.0（太阳 - 气候臭氧联系CCM的第3版）模拟的全球平均对流层臭氧柱为40.2多布森单位，比CCMI多模式平均值大约大33%。在此，我们介绍SOCOLv3.0的更新版本“SOCOLv3.1”，它改进了对臭氧汇过程的处理，使对流层柱臭氧偏差减少了多达8多布森单位，这主要是由于考虑了对流层气溶胶上的NO水解作用。由于这些改进，SOCOLv3.1模拟的对流层柱臭氧量与其他几个CCMI模型相当。我们应用高斯过程仿真和敏感性分析来了解SOCOLv3.1中剩余的臭氧偏差。结果表明，臭氧前体（氮氧化物（NO）、一氧化碳、甲烷和其他挥发性有机化合物）导致了对流层臭氧中超过90%的变化。然而，导致偏差的可能并非排放清单本身，而是SOCOLv3.1中对排放的处理方式，我们将在其他CCMI模型的更广泛背景下对此进行讨论。鉴于耦合模式对比计划第6阶段将使用的排放数据集比CCMI使用的数据集包含多约20%的NO，当前一代化学气候模型迫切需要开展进一步工作，以应对模拟对对流层臭氧至关重要的亚网格过程所面临的挑战。
