Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry–climate model
<p>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 Processe...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-11-01
|
| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/18/16155/2018/acp-18-16155-2018.pdf |
| _version_ | 1819041006995111936 |
|---|---|
| author | L. E. Revell L. E. Revell L. E. Revell A. Stenke F. Tummon F. Tummon A. Feinberg E. Rozanov E. Rozanov T. Peter N. L. Abraham N. L. Abraham H. Akiyoshi A. T. Archibald A. T. Archibald N. Butchart M. Deushi P. Jöckel D. Kinnison M. Michou O. Morgenstern F. M. O'Connor L. D. Oman G. Pitari D. A. Plummer R. Schofield R. Schofield K. Stone K. Stone K. Stone S. Tilmes D. Visioni D. Visioni Y. Yamashita Y. Yamashita G. Zeng |
| author_facet | L. E. Revell L. E. Revell L. E. Revell A. Stenke F. Tummon F. Tummon A. Feinberg E. Rozanov E. Rozanov T. Peter N. L. Abraham N. L. Abraham H. Akiyoshi A. T. Archibald A. T. Archibald N. Butchart M. Deushi P. Jöckel D. Kinnison M. Michou O. Morgenstern F. M. O'Connor L. D. Oman G. Pitari D. A. Plummer R. Schofield R. Schofield K. Stone K. Stone K. Stone S. Tilmes D. Visioni D. Visioni Y. Yamashita Y. Yamashita G. Zeng |
| author_sort | L. E. Revell |
| collection | DOAJ |
| description | <p>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, <q>SOCOLv3.1</q>, 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
N<sub>2</sub>O<sub>5</sub> 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<sub><i>x</i></sub>), carbon monoxide, methane and other volatile organic
compounds, VOCs) 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 6 of the Coupled Model
Intercomparison Project includes approximately 20 % more NO<sub><i>x</i></sub>
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.</p> |
| first_indexed | 2024-12-21T09:18:08Z |
| format | Article |
| id | doaj.art-2339ba77581f497d91767aa260bff63c |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-12-21T09:18:08Z |
| publishDate | 2018-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-2339ba77581f497d91767aa260bff63c2022-12-21T19:09:05ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242018-11-0118161551617210.5194/acp-18-16155-2018Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry–climate modelL. E. Revell0L. E. Revell1L. E. Revell2A. Stenke3F. Tummon4F. Tummon5A. Feinberg6E. Rozanov7E. Rozanov8T. Peter9N. L. Abraham10N. L. Abraham11H. Akiyoshi12A. T. Archibald13A. T. Archibald14N. Butchart15M. Deushi16P. Jöckel17D. Kinnison18M. Michou19O. Morgenstern20F. M. O'Connor21L. D. Oman22G. Pitari23D. A. Plummer24R. Schofield25R. Schofield26K. Stone27K. Stone28K. Stone29S. Tilmes30D. Visioni31D. Visioni32Y. Yamashita33Y. Yamashita34G. Zeng35School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New ZealandInstitute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandBodeker Scientific, Christchurch, New ZealandInstitute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandInstitute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerlandnow at: Biosciences, Fisheries, and Economics Faculty, University of Tromsø, Tromsø, NorwayInstitute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandInstitute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandPhysical-Meteorological Observatory/World Radiation Center, Davos, SwitzerlandInstitute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandDepartment of Chemistry, University of Cambridge, Cambridge, UKNational Centre for Atmospheric Science (NCAS), Cambridge, UKNational Institute of Environmental Studies (NIES), Tsukuba, JapanDepartment of Chemistry, University of Cambridge, Cambridge, UKNational Centre for Atmospheric Science (NCAS), Cambridge, UKMet Office Hadley Centre (MOHC), Exeter, UKMeteorological Research Institute (MRI), Tsukuba, JapanInstitut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, GermanyNational Center for Atmospheric Research (NCAR), Boulder, Colorado, USACNRM UMR 3589, Météo-France/CNRS, Toulouse, FranceNational Institute of Water and Atmospheric Research (NIWA), Wellington, New ZealandMet Office Hadley Centre (MOHC), Exeter, UKNational Aeronautics and Space Administration Goddard Space Flight Center (NASA GSFC), Greenbelt, Maryland, USADepartment of Physical and Chemical Sciences, Universitá dell'Aquila, L'Aquila, ItalyEnvironment and Climate Change Canada, Montréal, CanadaSchool of Earth Sciences, University of Melbourne, Melbourne, Victoria, AustraliaARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, AustraliaSchool of Earth Sciences, University of Melbourne, Melbourne, Victoria, AustraliaARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, Australianow at: Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USANational Center for Atmospheric Research (NCAR), Boulder, Colorado, USADepartment of Physical and Chemical Sciences, Universitá dell'Aquila, L'Aquila, Italynow at: Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York, USANational Institute of Environmental Studies (NIES), Tsukuba, Japannow at: Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, JapanNational Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand<p>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, <q>SOCOLv3.1</q>, 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 N<sub>2</sub>O<sub>5</sub> 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<sub><i>x</i></sub>), carbon monoxide, methane and other volatile organic compounds, VOCs) 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 6 of the Coupled Model Intercomparison Project includes approximately 20 % more NO<sub><i>x</i></sub> 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.</p>https://www.atmos-chem-phys.net/18/16155/2018/acp-18-16155-2018.pdf |
| spellingShingle | L. E. Revell L. E. Revell L. E. Revell A. Stenke F. Tummon F. Tummon A. Feinberg E. Rozanov E. Rozanov T. Peter N. L. Abraham N. L. Abraham H. Akiyoshi A. T. Archibald A. T. Archibald N. Butchart M. Deushi P. Jöckel D. Kinnison M. Michou O. Morgenstern F. M. O'Connor L. D. Oman G. Pitari D. A. Plummer R. Schofield R. Schofield K. Stone K. Stone K. Stone S. Tilmes D. Visioni D. Visioni Y. Yamashita Y. Yamashita G. Zeng Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry–climate model Atmospheric Chemistry and Physics |
| title | Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry–climate model |
| title_full | Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry–climate model |
| title_fullStr | Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry–climate model |
| title_full_unstemmed | Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry–climate model |
| title_short | Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry–climate model |
| title_sort | tropospheric ozone in ccmi models and gaussian process emulation to understand biases in the socolv3 chemistry climate model |
| url | https://www.atmos-chem-phys.net/18/16155/2018/acp-18-16155-2018.pdf |
| work_keys_str_mv | AT lerevell troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT lerevell troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT lerevell troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT astenke troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT ftummon troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT ftummon troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT afeinberg troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT erozanov troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT erozanov troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT tpeter troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT nlabraham troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT nlabraham troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT hakiyoshi troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT atarchibald troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT atarchibald troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT nbutchart troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT mdeushi troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT pjockel troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT dkinnison troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT mmichou troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT omorgenstern troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT fmoconnor troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT ldoman troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT gpitari troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT daplummer troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT rschofield troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT rschofield troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT kstone troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT kstone troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT kstone troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT stilmes troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT dvisioni troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT dvisioni troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT yyamashita troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT yyamashita troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel AT gzeng troposphericozoneinccmimodelsandgaussianprocessemulationtounderstandbiasesinthesocolv3chemistryclimatemodel |