Journal article

Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry-climate model

Laura E Revell, Andrea Stenke, Fiona Tummon, Aryeh Feinberg, Eugene Rozanov, Thomas Peter, N Luke Abraham, Hideharu Akiyoshi, Alexander T Archibald, Neal Butchart, Makoto Deushi, Patrick Joeckel, Douglas Kinnison, Martine Michou, Olaf Morgenstern, Fiona M O'Connor, Luke D Oman, Giovanni Pitari, David A Plummer, Robyn Schofield Show all

Atmospheric Chemistry and Physics | Copernicus Publications | Published : 2018

Abstract

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..

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University of Melbourne Researchers

Grants

Awarded by New Zealand Royal Society Marsden Fund


Awarded by SNSF


Awarded by Australian Research Council's Centre of Excellence for Climate System Science


Awarded by Australian government's National Computational Merit Allocation Scheme


Awarded by Australian Antarctic science grant program


Awarded by Joint UK BEIS-Defra Met Office Hadley Centre Climate Programme


Awarded by European Commission's Seventh Framework Programme StratoClim project


Awarded by Environment Research and Technology Development Fund of the Ministry of the Environment, Japan


Funding Acknowledgements

We acknowledge the modelling groups for making their simulations available for this analysis, the joint WCRP SPARC-IGAC Chemistry-Climate Model Initiative (CCMI) for organizing and coordinating the model data analysis activity and the British Atmospheric Data Centre (BADC) for collecting and archiving the CCMI model output. The EMAC simulations were performed at the German Climate Computing Centre (DKRZ) through support from the Bundesministerium fur Bildung und Forschung (BMBF). DKRZ and its scientific steering committee are gratefully acknowledged for providing the HPC and data archiving resources for this consortial project ESCiMo (Earth System Chemistry integrated Modelling). We acknowledge the UK Met Office for use of the MetUM. This research was partially supported by the New Zealand government's Strategic Science Investment Fund (SSIF) through the NIWA programme CACV. Olaf Morgenstern acknowledges funding by the New Zealand Royal Society Marsden Fund (grant 12-NIW-006). The authors wish to acknowledge the contribution of NeSI high-performance computing facilities to the results of this research. New Zealand's national facilities are provided by the New Zealand eScience Infrastructure (NeSI) and funded jointly by NeSI's collaborator institutions and through the Ministry of Business, Innovation and Employment's Research Infrastructure programme (https://www.nesi.org.nz, last access: 11 November 2018). Fiona Tummon was supported by SNSF grant number 20F121_138017. ACCESS-CCM runs were supported by the Australian Research Council's Centre of Excellence for Climate System Science (CE110001028), the Australian government's National Computational Merit Allocation Scheme (q90) and the Australian Antarctic science grant program (FoRCES 4012). The HadGEM3-ES simulations from the Met Office were supported by the Joint UK BEIS-Defra Met Office Hadley Centre Climate Programme (GA01101) and the European Commission's Seventh Framework Programme StratoClim project (grant agreement 603557). CCSRNIES research was supported by the Environment Research and Technology Development Fund (2-1303 and 2-1709) of the Ministry of the Environment, Japan, and computations were performed on NEC-SX9/A(ECO) computers at the CGER, NIES. UMUKCA-UCAM model integrations were performed using the ARCHER UK National Supercomputing Service and MONSooN system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, which is a strategic partnership between the UK Met Office and the Natural Environment Research Council. Laura E. Revell thanks China Southern for partial support. The authors thank Edmund Ryan and one anonymous reviewer for their helpful and constructive comments.