Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study
While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmosphe...
Published in: | Atmospheric Chemistry and Physics |
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Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Language: | English |
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2022
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Online Access: | https://doi.org/10.5194/acp-22-5775-2022 https://acp.copernicus.org/articles/22/5775/2022/ |
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Copernicus Publications: E-Journals |
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While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over 4 years (2008–2009 and 2014–2015) conducted for the 2022 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, seasonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percent model biases and correlation coefficients. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean (mmm) was able to represent the general features of SLCFs in the Arctic and had the best overall performance. For the SLCFs with the greatest radiative impact (CH 4 , O 3 , BC, and SO <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi/><mn mathvariant="normal">4</mn><mrow><mn mathvariant="normal">2</mn><mo>-</mo></mrow></msubsup></mrow></math> <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="13pt" height="17pt" class="svg-formula" dspmath="mathimg" md5hash="ae2ede098a116a7d88438e298a87d51c"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-5775-2022-ie00001.svg" width="13pt" height="17pt" src="acp-22-5775-2022-ie00001.png"/></svg:svg> ), the mmm was within ± 25 % of the measurements across the Northern Hemisphere. Therefore, we recommend a multi-model ensemble be used for simulating climate and health impacts of SLCFs. Of the SLCFs in our study, model biases were smallest for CH 4 and greatest for OA. For most SLCFs, model biases skewed from positive to negative with increasing latitude. Our analysis suggests that vertical mixing, long-range transport, deposition, and wildfires remain highly uncertain processes. These processes need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. As model development proceeds in these areas, we highly recommend that the vertical and 3-D distribution of SLCFs be evaluated, as that information is critical to improving the uncertain processes in models. |
format |
Text |
author |
Whaley, Cynthia H. Mahmood, Rashed Salzen, Knut Winter, Barbara Eckhardt, Sabine Arnold, Stephen Beagley, Stephen Becagli, Silvia Chien, Rong-You Christensen, Jesper Damani, Sujay Manish Dong, Xinyi Eleftheriadis, Konstantinos Evangeliou, Nikolaos Faluvegi, Gregory Flanner, Mark Fu, Joshua S. Gauss, Michael Giardi, Fabio Gong, Wanmin Hjorth, Jens Liengaard Huang, Lin Im, Ulas Kanaya, Yugo Krishnan, Srinath Klimont, Zbigniew Kühn, Thomas Langner, Joakim Law, Kathy S. Marelle, Louis Massling, Andreas Olivié, Dirk Onishi, Tatsuo Oshima, Naga Peng, Yiran Plummer, David A. Popovicheva, Olga Pozzoli, Luca Raut, Jean-Christophe Sand, Maria Saunders, Laura N. Schmale, Julia Sharma, Sangeeta Skeie, Ragnhild Bieltvedt Skov, Henrik Taketani, Fumikazu Thomas, Manu A. Traversi, Rita Tsigaridis, Kostas Tsyro, Svetlana Turnock, Steven Vitale, Vito Walker, Kaley A. Wang, Minqi Watson-Parris, Duncan Weiss-Gibbons, Tahya |
spellingShingle |
Whaley, Cynthia H. Mahmood, Rashed Salzen, Knut Winter, Barbara Eckhardt, Sabine Arnold, Stephen Beagley, Stephen Becagli, Silvia Chien, Rong-You Christensen, Jesper Damani, Sujay Manish Dong, Xinyi Eleftheriadis, Konstantinos Evangeliou, Nikolaos Faluvegi, Gregory Flanner, Mark Fu, Joshua S. Gauss, Michael Giardi, Fabio Gong, Wanmin Hjorth, Jens Liengaard Huang, Lin Im, Ulas Kanaya, Yugo Krishnan, Srinath Klimont, Zbigniew Kühn, Thomas Langner, Joakim Law, Kathy S. Marelle, Louis Massling, Andreas Olivié, Dirk Onishi, Tatsuo Oshima, Naga Peng, Yiran Plummer, David A. Popovicheva, Olga Pozzoli, Luca Raut, Jean-Christophe Sand, Maria Saunders, Laura N. Schmale, Julia Sharma, Sangeeta Skeie, Ragnhild Bieltvedt Skov, Henrik Taketani, Fumikazu Thomas, Manu A. Traversi, Rita Tsigaridis, Kostas Tsyro, Svetlana Turnock, Steven Vitale, Vito Walker, Kaley A. Wang, Minqi Watson-Parris, Duncan Weiss-Gibbons, Tahya Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study |
author_facet |
Whaley, Cynthia H. Mahmood, Rashed Salzen, Knut Winter, Barbara Eckhardt, Sabine Arnold, Stephen Beagley, Stephen Becagli, Silvia Chien, Rong-You Christensen, Jesper Damani, Sujay Manish Dong, Xinyi Eleftheriadis, Konstantinos Evangeliou, Nikolaos Faluvegi, Gregory Flanner, Mark Fu, Joshua S. Gauss, Michael Giardi, Fabio Gong, Wanmin Hjorth, Jens Liengaard Huang, Lin Im, Ulas Kanaya, Yugo Krishnan, Srinath Klimont, Zbigniew Kühn, Thomas Langner, Joakim Law, Kathy S. Marelle, Louis Massling, Andreas Olivié, Dirk Onishi, Tatsuo Oshima, Naga Peng, Yiran Plummer, David A. Popovicheva, Olga Pozzoli, Luca Raut, Jean-Christophe Sand, Maria Saunders, Laura N. Schmale, Julia Sharma, Sangeeta Skeie, Ragnhild Bieltvedt Skov, Henrik Taketani, Fumikazu Thomas, Manu A. Traversi, Rita Tsigaridis, Kostas Tsyro, Svetlana Turnock, Steven Vitale, Vito Walker, Kaley A. Wang, Minqi Watson-Parris, Duncan Weiss-Gibbons, Tahya |
author_sort |
Whaley, Cynthia H. |
title |
Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study |
title_short |
Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study |
title_full |
Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study |
title_fullStr |
Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study |
title_full_unstemmed |
Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study |
title_sort |
model evaluation of short-lived climate forcers for the arctic monitoring and assessment programme: a multi-species, multi-model study |
publishDate |
2022 |
url |
https://doi.org/10.5194/acp-22-5775-2022 https://acp.copernicus.org/articles/22/5775/2022/ |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
AMAP Arctic black carbon Global warming |
genre_facet |
AMAP Arctic black carbon Global warming |
op_source |
eISSN: 1680-7324 |
op_relation |
doi:10.5194/acp-22-5775-2022 https://acp.copernicus.org/articles/22/5775/2022/ |
op_doi |
https://doi.org/10.5194/acp-22-5775-2022 |
container_title |
Atmospheric Chemistry and Physics |
container_volume |
22 |
container_issue |
9 |
container_start_page |
5775 |
op_container_end_page |
5828 |
_version_ |
1766360538983432192 |
spelling |
ftcopernicus:oai:publications.copernicus.org:acp99357 2023-05-15T13:21:36+02:00 Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study Whaley, Cynthia H. Mahmood, Rashed Salzen, Knut Winter, Barbara Eckhardt, Sabine Arnold, Stephen Beagley, Stephen Becagli, Silvia Chien, Rong-You Christensen, Jesper Damani, Sujay Manish Dong, Xinyi Eleftheriadis, Konstantinos Evangeliou, Nikolaos Faluvegi, Gregory Flanner, Mark Fu, Joshua S. Gauss, Michael Giardi, Fabio Gong, Wanmin Hjorth, Jens Liengaard Huang, Lin Im, Ulas Kanaya, Yugo Krishnan, Srinath Klimont, Zbigniew Kühn, Thomas Langner, Joakim Law, Kathy S. Marelle, Louis Massling, Andreas Olivié, Dirk Onishi, Tatsuo Oshima, Naga Peng, Yiran Plummer, David A. Popovicheva, Olga Pozzoli, Luca Raut, Jean-Christophe Sand, Maria Saunders, Laura N. Schmale, Julia Sharma, Sangeeta Skeie, Ragnhild Bieltvedt Skov, Henrik Taketani, Fumikazu Thomas, Manu A. Traversi, Rita Tsigaridis, Kostas Tsyro, Svetlana Turnock, Steven Vitale, Vito Walker, Kaley A. Wang, Minqi Watson-Parris, Duncan Weiss-Gibbons, Tahya 2022-05-04 application/pdf https://doi.org/10.5194/acp-22-5775-2022 https://acp.copernicus.org/articles/22/5775/2022/ eng eng doi:10.5194/acp-22-5775-2022 https://acp.copernicus.org/articles/22/5775/2022/ eISSN: 1680-7324 Text 2022 ftcopernicus https://doi.org/10.5194/acp-22-5775-2022 2022-05-09T16:22:28Z While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over 4 years (2008–2009 and 2014–2015) conducted for the 2022 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, seasonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percent model biases and correlation coefficients. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean (mmm) was able to represent the general features of SLCFs in the Arctic and had the best overall performance. For the SLCFs with the greatest radiative impact (CH 4 , O 3 , BC, and SO <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi/><mn mathvariant="normal">4</mn><mrow><mn mathvariant="normal">2</mn><mo>-</mo></mrow></msubsup></mrow></math> <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="13pt" height="17pt" class="svg-formula" dspmath="mathimg" md5hash="ae2ede098a116a7d88438e298a87d51c"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-5775-2022-ie00001.svg" width="13pt" height="17pt" src="acp-22-5775-2022-ie00001.png"/></svg:svg> ), the mmm was within ± 25 % of the measurements across the Northern Hemisphere. Therefore, we recommend a multi-model ensemble be used for simulating climate and health impacts of SLCFs. Of the SLCFs in our study, model biases were smallest for CH 4 and greatest for OA. For most SLCFs, model biases skewed from positive to negative with increasing latitude. Our analysis suggests that vertical mixing, long-range transport, deposition, and wildfires remain highly uncertain processes. These processes need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. As model development proceeds in these areas, we highly recommend that the vertical and 3-D distribution of SLCFs be evaluated, as that information is critical to improving the uncertain processes in models. Text AMAP Arctic black carbon Global warming Copernicus Publications: E-Journals Arctic Atmospheric Chemistry and Physics 22 9 5775 5828 |