42 results on '"Whaley, Cynthia H."'
Search Results
2. Canada's wildfire future: climate change below a 2°C global target avoids large increases in burned area by the end of the century
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Curasi, Salvatore, primary, Melton, Joe, additional, Arora, Vivek, additional, Humphreys, Elyn, additional, and Whaley, Cynthia H., additional
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- 2024
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3. Clean air policies are key for successfully mitigating Arctic warming
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von Salzen, Knut, Whaley, Cynthia H., Anenberg, Susan C., Van Dingenen, Rita, Klimont, Zbigniew, Flanner, Mark G., Mahmood, Rashed, Arnold, Stephen R., Beagley, Stephen, Chien, Rong-You, Christensen, Jesper H., Eckhardt, Sabine, Ekman, Annica M. L., Evangeliou, Nikolaos, Faluvegi, Greg, Fu, Joshua S., Gauss, Michael, Gong, Wanmin, Hjorth, Jens L., Im, Ulas, Krishnan, Srinath, Kupiainen, Kaarle, Kühn, Thomas, Langner, Joakim, Law, Kathy S., Marelle, Louis, Olivié, Dirk, Onishi, Tatsuo, Oshima, Naga, Paunu, Ville-Veikko, Peng, Yiran, Plummer, David, Pozzoli, Luca, Rao, Shilpa, Raut, Jean-Christophe, Sand, Maria, Schmale, Julia, Sigmond, Michael, Thomas, Manu A., Tsigaridis, Kostas, Tsyro, Svetlana, Turnock, Steven T., Wang, Minqi, and Winter, Barbara
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- 2022
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4. Arctic Tropospheric Ozone Trends
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Law, Kathy S., primary, Hjorth, Jens L., additional, Pernov, Jakob B., additional, Whaley, Cynthia H., additional, Skov, Henrik, additional, Collaud Coen, Martine, additional, Langner, Joakim, additional, Arnold, Stephen R., additional, Tarasick, David, additional, Christensen, Jesper, additional, Deushi, Makoto, additional, Effertz, Peter, additional, Faluvegi, Greg, additional, Gauss, Michael, additional, Im, Ulas, additional, Oshima, Naga, additional, Petropavlovskikh, Irina, additional, Plummer, David, additional, Tsigaridis, Kostas, additional, Tsyro, Svetlana, additional, Solberg, Sverre, additional, and Turnock, Stephen, additional
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- 2023
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5. Network for the Detection of Atmospheric Composition Change (NDACC) Fourier transform infrared (FTIR) trace gas measurements at the University of Toronto Atmospheric Observatory from 2002 to 2020
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Yamanouchi, Shoma, primary, Conway, Stephanie, additional, Strong, Kimberly, additional, Colebatch, Orfeo, additional, Lutsch, Erik, additional, Roche, Sébastien, additional, Taylor, Jeffrey, additional, Whaley, Cynthia H., additional, and Wiacek, Aldona, additional
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- 2023
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6. Evaluating modelled tropospheric columns of CH4, CO and O3 in the Arctic using ground-based FTIR measurements
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Flood, Victoria A., primary, Strong, Kimberly, additional, Whaley, Cynthia H., additional, Walker, Kaley A., additional, Blumenstock, Thomas, additional, Hannigan, James W., additional, Mellqvist, Johan, additional, Notholt, Justus, additional, Palm, Mathias, additional, Röhling, Amelie N., additional, Arnold, Stephen, additional, Beagley, Stephen, additional, Chien, Rong-You, additional, Christensen, Jesper, additional, Deushi, Makoto, additional, Dobricic, Srdjan, additional, Dong, Xinyi, additional, Fu, Joshua S., additional, Gauss, Michael, additional, Gong, Wanmin, additional, Langner, Joakim, additional, Law, Kathy S., additional, Marelle, Louis, additional, Onishi, Tatsuo, additional, Oshima, Naga, additional, Plummer, David A., additional, Pozzoli, Luca, additional, Raut, Jean-Christophe, additional, Thomas, Manu A., additional, Tsyro, Svetlana, additional, and Turnock, Steven, additional
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- 2023
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7. Evaluating modelled tropospheric columns of CH4, CO, and O3 in the Arctic using ground-based Fourier transform infrared (FTIR) measurements.
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Flood, Victoria A., Strong, Kimberly, Whaley, Cynthia H., Walker, Kaley A., Blumenstock, Thomas, Hannigan, James W., Mellqvist, Johan, Notholt, Justus, Palm, Mathias, Röhling, Amelie N., Arnold, Stephen, Beagley, Stephen, Chien, Rong-You, Christensen, Jesper, Deushi, Makoto, Dobricic, Srdjan, Dong, Xinyi, Fu, Joshua S., Gauss, Michael, and Gong, Wanmin
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FOURIER transforms ,AIR pollution measurement ,ATMOSPHERIC composition ,METHANE ,TRACE gases ,ATMOSPHERIC methane ,CARBON monoxide - Abstract
This study evaluates tropospheric columns of methane, carbon monoxide, and ozone in the Arctic simulated by 11 models. The Arctic is warming at nearly 4 times the global average rate, and with changing emissions in and near the region, it is important to understand Arctic atmospheric composition and how it is changing. Both measurements and modelling of air pollution in the Arctic are difficult, making model validation with local measurements valuable. Evaluations are performed using data from five high-latitude ground-based Fourier transform infrared (FTIR) spectrometers in the Network for the Detection of Atmospheric Composition Change (NDACC). The models were selected as part of the 2021 Arctic Monitoring and Assessment Programme (AMAP) report on short-lived climate forcers. This work augments the model–measurement comparisons presented in that report by including a new data source: column-integrated FTIR measurements, whose spatial and temporal footprint is more representative of the free troposphere than in situ and satellite measurements. Mixing ratios of trace gases are modelled at 3-hourly intervals by CESM, CMAM, DEHM, EMEP MSC-W, GEM-MACH, GEOS-Chem, MATCH, MATCH-SALSA, MRI-ESM2, UKESM1, and WRF-Chem for the years 2008, 2009, 2014, and 2015. The comparisons focus on the troposphere (0–7 km partial columns) at Eureka, Canada; Thule, Greenland; Ny Ålesund, Norway; Kiruna, Sweden; and Harestua, Norway. Overall, the models are biased low in the tropospheric column, on average by - 9.7 % for CH 4 , - 21 % for CO, and - 18 % for O 3. Results for CH 4 are relatively consistent across the 4 years, whereas CO has a maximum negative bias in the spring and minimum in the summer and O 3 has a maximum difference centered around the summer. The average differences for the models are within the FTIR uncertainties for approximately 15 % of the model–location comparisons. [ABSTRACT FROM AUTHOR]
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- 2024
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8. Evaluating modelled tropospheric columns of CH4, CO and O3 in the Arctic using ground-based FTIR measurements
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Flood, Victoria A., Strong, Kimberly, Whaley, Cynthia H., Walker, Kaley A., Blumenstock, Thomas, Hannigan, James W., Mellqvist, Johan, Notholt, Justus, Palm, Mathias, Röhling, Amelie N., Arnold, Stephen, Beagley, Stephen, Chien, Rong-You, Christensen, Jesper, Deushi, Makoto, Dobricic, Srdjan, Dong, Xinyi, Fu, Joshua S., Gauss, Michael, Gong, Wanmin, Langner, Joakim, Law, Kathy S., Marelle, Louis, Onishi, Tatsuo, Oshima, Naga, Plummer, David A., Pozzoli, Luca, Raut, Jean-Christophe, Thomas, Manu A., Tsyro, Svetlana, and Turnock, Steven
- Abstract
Both measurements and modelling of air pollution in the Arctic are difficult. Yet with the Arctic warming at nearly four times the global average rate, and changing emissions in and near the region, it is important to understand Arctic atmospheric composition and how it is changing. This study examines the simulations of atmospheric concentrations of methane, carbon monoxide and ozone in the Arctic by 11 models. Evaluations are performed using data from five high-latitude ground-based Fourier transform infrared (FTIR) spectrometers in the Network for the Detection of Atmospheric Composition Change (NDACC). Mixing ratios of trace gases are modelled at three-hourly intervals by CESM, CMAM, DEHM, EMEP MSC-W, GEM-MACH, GEOS-Chem, MATCH, MATCH-SALSA, MRI-ESM2, UKESM1 and WRF-Chem for the years 2008, 2009, 2014, and 2015. The comparisons focus on the troposphere (0–7 km partial columns) at Eureka, Canada; Thule, Greenland; Ny Ålesund, Norway; Kiruna, Sweden; and Harestua, Norway. Overall, the models are biased low in the tropospheric column, on average by -9.6 % for CH4, -21 % for CO and -18 % for O3. Results for CH4 are relatively consistent across the four years, whereas CO has a maximum negative bias in the spring and minimum in the summer, and O3 has a maximum difference centred around the summer. The average differences for the models are within the FTIR uncertainties for approximately 15 % of the model-location comparisons.
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- 2023
9. Arctic tropospheric ozone : assessment of current knowledge and modelperformance
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Whaley, Cynthia H., Law, Kathy S., Hjorth, Jens Liengaard, Skov, Henrik, Arnold, Stephen R., Langner, Joakim, Pernov, Jakob Boyd, Bergeron, Garance, Bourgeois, Ilann, Christensen, Jesper H., Chien, Rong-You, Deushi, Makoto, Dong, Xinyi, Effertz, Peter, Faluvegi, Gregory, Flanner, Mark, Fu, Joshua S., Gauss, Michael, Huey, Greg, Im, Ulas, Kivi, Rigel, Marelle, Louis, Onishi, Tatsuo, Oshima, Naga, Petropavlovskikh, Irina, Peischl, Jeff, Plummer, David A., Pozzoli, Luca, Raut, Jean-Christophe, Ryerson, Tom, Skeie, Ragnhild, Solberg, Sverre, Thomas, Manu, Thompson, Chelsea, Tsigaridis, Kostas, Tsyro, Svetlana, Turnock, Steven T., von Salzen, Knut, Tarasick, David W., Whaley, Cynthia H., Law, Kathy S., Hjorth, Jens Liengaard, Skov, Henrik, Arnold, Stephen R., Langner, Joakim, Pernov, Jakob Boyd, Bergeron, Garance, Bourgeois, Ilann, Christensen, Jesper H., Chien, Rong-You, Deushi, Makoto, Dong, Xinyi, Effertz, Peter, Faluvegi, Gregory, Flanner, Mark, Fu, Joshua S., Gauss, Michael, Huey, Greg, Im, Ulas, Kivi, Rigel, Marelle, Louis, Onishi, Tatsuo, Oshima, Naga, Petropavlovskikh, Irina, Peischl, Jeff, Plummer, David A., Pozzoli, Luca, Raut, Jean-Christophe, Ryerson, Tom, Skeie, Ragnhild, Solberg, Sverre, Thomas, Manu, Thompson, Chelsea, Tsigaridis, Kostas, Tsyro, Svetlana, Turnock, Steven T., von Salzen, Knut, and Tarasick, David W.
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- 2023
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10. NDACC FTIR trace gas measurements at the University of Toronto Atmospheric Observatory from 2002 to 2020
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Yamanouchi, Shoma, primary, Conway, Stephanie, additional, Strong, Kimberly, additional, Colebatch, Orfeo, additional, Lutsch, Erik, additional, Roche, Sebastien, additional, Taylor, Jeffrey, additional, Whaley, Cynthia H., additional, and Wiacek, Aldona, additional
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- 2023
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11. Arctic tropospheric ozone: assessment of current knowledge and model performance
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Whaley, Cynthia H., primary, Law, Kathy S., additional, Hjorth, Jens Liengaard, additional, Skov, Henrik, additional, Arnold, Stephen R., additional, Langner, Joakim, additional, Pernov, Jakob Boyd, additional, Bergeron, Garance, additional, Bourgeois, Ilann, additional, Christensen, Jesper H., additional, Chien, Rong-You, additional, Deushi, Makoto, additional, Dong, Xinyi, additional, Effertz, Peter, additional, Faluvegi, Gregory, additional, Flanner, Mark, additional, Fu, Joshua S., additional, Gauss, Michael, additional, Huey, Greg, additional, Im, Ulas, additional, Kivi, Rigel, additional, Marelle, Louis, additional, Onishi, Tatsuo, additional, Oshima, Naga, additional, Petropavlovskikh, Irina, additional, Peischl, Jeff, additional, Plummer, David A., additional, Pozzoli, Luca, additional, Raut, Jean-Christophe, additional, Ryerson, Tom, additional, Skeie, Ragnhild, additional, Solberg, Sverre, additional, Thomas, Manu A., additional, Thompson, Chelsea, additional, Tsigaridis, Kostas, additional, Tsyro, Svetlana, additional, Turnock, Steven T., additional, von Salzen, Knut, additional, and Tarasick, David W., additional
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- 2023
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12. Evaluating modelled tropospheric columns of CH4, CO and O3 in the Arctic using ground-based FTIR measurements.
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Flood, Victoria A., Strong, Kimberly, Whaley, Cynthia H., Walker, Kaley A., Blumenstock, Thomas, Hannigan, James W., Mellqvist, Johan, Notholt, Justus, Palm, Mathias, Röhling, Amelie N., Arnold, Stephen, Beagley, Stephen, Chien, Rong-You, Christensen, Jesper, Deushi, Makoto, Dobricic, Srdjan, Dong, Xinyi, Fu, Joshua S., Gauss, Michael, and Gong, Wanmin
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AIR pollution measurement ,ATMOSPHERIC composition ,ATMOSPHERIC methane ,TRACE gases ,SPRING ,CARBON monoxide ,TROPOSPHERIC aerosols - Abstract
Both measurements and modelling of air pollution in the Arctic are difficult. Yet with the Arctic warming at nearly four times the global average rate, and changing emissions in and near the region, it is important to understand Arctic atmospheric composition and how it is changing. This study examines the simulations of atmospheric concentrations of methane, carbon monoxide and ozone in the Arctic by 11 models. Evaluations are performed using data from five high-latitude ground-based Fourier transform infrared (FTIR) spectrometers in the Network for the Detection of Atmospheric Composition Change (NDACC). Mixing ratios of trace gases are modelled at three-hourly intervals by CESM, CMAM, DEHM, EMEP MSC-W, GEM-MACH, GEOS-Chem, MATCH, MATCH-SALSA, MRI-ESM2, UKESM1 and WRF-Chem for the years 2008, 2009, 2014, and 2015. The comparisons focus on the troposphere (0–7 km partial columns) at Eureka, Canada; Thule, Greenland; Ny Ålesund, Norway; Kiruna, Sweden; and Harestua, Norway. Overall, the models are biased low in the tropospheric column, on average by -9.6 % for CH
4 , -21 % for CO and -18 % for O3 . Results for CH4 are relatively consistent across the four years, whereas CO has a maximum negative bias in the spring and minimum in the summer, and O3 has a maximum difference centred around the summer. The average differences for the models are within the FTIR uncertainties for approximately 15 % of the model-location comparisons. [ABSTRACT FROM AUTHOR]- Published
- 2023
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13. Arctic tropospheric ozone: assessment of current knowledge and model performance
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Whaley, Cynthia H., primary, Law, Kathy S., additional, Hjorth, Jens Liengaard, additional, Skov, Henrik, additional, Arnold, Stephen R., additional, Langner, Joakim, additional, Pernov, Jakob Boyd, additional, Chien, Rong-You, additional, Christensen, Jesper H., additional, Deushi, Makoto, additional, Dong, Xinyi, additional, Faluvegi, Gregory, additional, Flanner, Mark, additional, Fu, Joshua S., additional, Gauss, Michael, additional, Im, Ulas, additional, Marelle, Louis, additional, Onishi, Tatsuo, additional, Oshima, Naga, additional, Plummer, David A., additional, Pozzoli, Luca, additional, Raut, Jean-Christophe, additional, Skeie, Ragnhild, additional, Thomas, Manu A., additional, Tsigaridis, Kostas, additional, Tsyro, Svetlana, additional, Turnock, Steven T., additional, von Salzen, Knut, additional, and Tarasick, David W., additional
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- 2022
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14. Supplementary material to "Arctic tropospheric ozone: assessment of current knowledge and model performance"
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Whaley, Cynthia H., primary, Law, Kathy S., additional, Hjorth, Jens Liengaard, additional, Skov, Henrik, additional, Arnold, Stephen R., additional, Langner, Joakim, additional, Pernov, Jakob Boyd, additional, Chien, Rong-You, additional, Christensen, Jesper H., additional, Deushi, Makoto, additional, Dong, Xinyi, additional, Faluvegi, Gregory, additional, Flanner, Mark, additional, Fu, Joshua S., additional, Gauss, Michael, additional, Im, Ulas, additional, Marelle, Louis, additional, Onishi, Tatsuo, additional, Oshima, Naga, additional, Plummer, David A., additional, Pozzoli, Luca, additional, Raut, Jean-Christophe, additional, Skeie, Ragnhild, additional, Thomas, Manu A., additional, Tsigaridis, Kostas, additional, Tsyro, Svetlana, additional, Turnock, Steven T., additional, von Salzen, Knut, additional, and Tarasick, David W., additional
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- 2022
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15. Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study
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Whaley, Cynthia H., primary, Mahmood, Rashed, additional, von Salzen, Knut, additional, Winter, Barbara, additional, Eckhardt, Sabine, additional, Arnold, Stephen, additional, Beagley, Stephen, additional, Becagli, Silvia, additional, Chien, Rong-You, additional, Christensen, Jesper, additional, Damani, Sujay Manish, additional, Dong, Xinyi, additional, Eleftheriadis, Konstantinos, additional, Evangeliou, Nikolaos, additional, Faluvegi, Gregory, additional, Flanner, Mark, additional, Fu, Joshua S., additional, Gauss, Michael, additional, Giardi, Fabio, additional, Gong, Wanmin, additional, Hjorth, Jens Liengaard, additional, Huang, Lin, additional, Im, Ulas, additional, Kanaya, Yugo, additional, Krishnan, Srinath, additional, Klimont, Zbigniew, additional, Kühn, Thomas, additional, Langner, Joakim, additional, Law, Kathy S., additional, Marelle, Louis, additional, Massling, Andreas, additional, Olivié, Dirk, additional, Onishi, Tatsuo, additional, Oshima, Naga, additional, Peng, Yiran, additional, Plummer, David A., additional, Popovicheva, Olga, additional, Pozzoli, Luca, additional, Raut, Jean-Christophe, additional, Sand, Maria, additional, Saunders, Laura N., additional, Schmale, Julia, additional, Sharma, Sangeeta, additional, Skeie, Ragnhild Bieltvedt, additional, Skov, Henrik, additional, Taketani, Fumikazu, additional, Thomas, Manu A., additional, Traversi, Rita, additional, Tsigaridis, Kostas, additional, Tsyro, Svetlana, additional, Turnock, Steven, additional, Vitale, Vito, additional, Walker, Kaley A., additional, Wang, Minqi, additional, Watson-Parris, Duncan, additional, and Weiss-Gibbons, Tahya, additional
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- 2022
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16. Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme : a multi-species, multi-model study
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Whaley, Cynthia H., Mahmood, Rashed, von 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, Kuhn, Thomas, Langner, Joakim, Law, Kathy S., Marelle, Louis, Massling, Andreas, Olivie, 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, Whaley, Cynthia H., Mahmood, Rashed, von 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, Kuhn, Thomas, Langner, Joakim, Law, Kathy S., Marelle, Louis, Massling, Andreas, Olivie, 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, and Weiss-Gibbons, Tahya
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- 2022
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17. Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study
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Barcelona Supercomputing Center, Whaley, Cynthia H., Mahmood, Rashed, von Salzen, Knut, Winter, Barbara, Eckhardt, Sabine, Barcelona Supercomputing Center, Whaley, Cynthia H., Mahmood, Rashed, von Salzen, Knut, Winter, Barbara, and Eckhardt, Sabine
- Abstract
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 (CH4, O3, BC, and SO), 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 wer, Assessments from the Russian ship-based campaign were performed with the support of RFBR project no. 20-55-12001 and according to the development program of the Interdisciplinary Scientific and Educational School of M.V. Lomonosov Moscow State University “Future Planet and Global Environmental Change”. Development of the methodology for aethalometric data treatment was supported by RSF project no. 19-77-30004. The BC observations on R/V Mirai were supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan (Arctic Challenge for Sustainability (ArCS) project). Contributions by SMHI were funded by the Swedish Environmental Protection Agency under contract NV-03174-20 and the Swedish Climate and Clean Air Research program (SCAC) as well as partly by the Swedish National Space Board (NORD-SLCP, grant agreement ID: 94/16) and the EU Horizon 2020 project Integrated Arctic Observing System (INTAROS, grant agreement ID: 727890). Work on ACE-FTS analysis was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). Julia Schmale received funding from the Swiss National Science Foundation (project no. 200021_188478). Duncan Watson-Parris received funding from NERC projects NE/P013406/1 (A-CURE) and NE/S005390/1 (ACRUISE) as well as funding from the European Union's Horizon 2020 research and innovation program iMIRACLI under Marie Skłodowska-Curie grant agreement no. 860100. LATMOS has been supported by the EU iCUPE (Integrating and Comprehensive Understanding on Polar Environments) project (grant agreement no. 689443) under the European Network for Observing our Changing Planet (ERA-Planet), as well as access to IDRIS HPC resources (GENCI allocation A009017141) and the IPSL mesoscale computing center (CICLAD: Calcul Intensif pour le CLimat, l’Atmosphère et la Dynamique) for model simulations. Naga Oshima was supported by the Japan Society for the Promotion of Science KAKENHI (grant nos. JP18H03363, JP18H05292, and, Peer Reviewed, "Article signat per més de 50 autors/es: Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay Manish Damani, Xinyi Dong, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbigniew Klimont, Thomas Kühn, Joakim Langner, Kathy S. Law, Louis Marelle, Andreas Massling, Dirk Olivié, Tatsuo Onishi, Naga Oshima, Yiran Peng, David A. Plummer, Olga Popovicheva, Luca Pozzoli, Jean-Christophe Raut, Maria Sand, Laura N. Saunders, Julia Schmale, Sangeeta Sharma, Ragnhild Bieltvedt Skeie, Henrik Skov, Fumikazu Taketani, Manu A. Thomas, Rita Traversi, Kostas Tsigaridis, Svetlana Tsyro, Steven Turnock, Vito Vitale, Kaley A. Walker, Minqi Wang, Duncan Watson-Parris, and Tahya Weiss-Gibbons ", Postprint (published version)
- Published
- 2022
18. Using Ground-Based Fourier TransformInfrared Spectroscopy to Evaluate Model Concentrations of Short-Lived Climate Forcers
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Flood, Victoria, Strong, Kimberly, Walker, Kaley, Whaley, Cynthia H., Raut, Jean-Christophe, and Cardon, Catherine
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[SDU.STU.CL] Sciences of the Universe [physics]/Earth Sciences/Climatology - Abstract
This work presents an evaluation of modeled atmospheric concentrations of O3, CO and CH4 from eleven models, as presented in the most recent assessment report by the Arctic Monitoring and Assessment Programme (AMAP) on short-lived climate forcers. AMAP is a scientific working group that was created to advise the Arctic Council on matters of Arctic pollution, climate change and the associated threats to local ecosystems and health. This framework is then used to inform policy and decision making through science-based assessments. The current report focuses on the impacts of Short-Lived Climate Forcers (SLCFs) on the Arctic climate, atmospheric chemistry, and human health. The report presents model-measurement comparisons to assess the performance of atmospheric modelling of SLCFs in the Arctic for the years 2008, 2009, 2014 and 2015. The 3-hourly mixing ratios of select SLCFs and related gases are modelled by CESM, CMAM, DEHM, EMEP-MSC-W, GEM-MACH, GEOS-Chem, MATCH, MATCH-SALSA, MRI-ESM2, UKESM1 and WRF-Chem. This presentation will compare these outputs to corresponding trace gas measurements from ground-based Fourier Transform Infrared (FTIR) spectrometers. The FTIR instruments used are part of the Network for the Detection of Atmospheric Composition Change (NDACC) Infrared Working Group, with emphasis on results from the Canadian High Arctic site at the Polar Environment Atmospheric Research Laboratory, in Eureka, Nunavut (80.05ºN, 86.42ºW). Analyses are performed by converting model outputs into smoothed partial columns of O3, CO and CH4, at the locations of the FTIR instruments. Comparisons include seasonal cycle analysis, percent differences and regression analysis.
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- 2022
19. Arctic tropospheric ozone : integratedobservations, modelling, and analysis
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Whaley, Cynthia H., Law, Kathy S., Jens Liengaard Hjorth, Henrik Skov, Arnold, Stephen R., Joakim Langner, Jakob Boyd Pernov, Rong-You Chien, Christensen, Jesper H., Makoto Deushi, Xinyi Dong, Gregory Faluvegi, Mark Flanner, Fu, Joshua S., Michael Gauss, Ulas Im, Louis Marelle, Tatsuo Onishi, Naga Oshima, Plummer, David A., Luca Pozzoli, Jean-Christophe Raut, Ragnhild Skeie, Thomas, Manu A., Kostas Tsigaridis, Svetlana Tsyro, Turnock, Steven T., Knut von Salzen, Tarasick, David W., and Cardon, Catherine
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[SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere ,[PHYS.PHYS.PHYS-AO-PH] Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph] - Published
- 2022
20. Validation of Short-Lived Climate Forcer Modelling by Ground-Based Near-Infrared Fourier Transform Spectroscopy
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Erin Mcgee, Kimberly Strong, Walker, Kaley A., Whaley, Cynthia H., Rigel Kivi, Justus Notholt, Jean-Christophe Raut, and Cardon, Catherine
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[SDU.STU.CL] Sciences of the Universe [physics]/Earth Sciences/Climatology - Abstract
The Arctic Monitoring and Assessment Programme (AMAP), a working group of the Arctic Council, studies and documents the effects of climate change and pollution on Arctic climate, with the intent of informing policy recommendations. One subject of interest is the impact of Short-Lived Climate Forcers (SLCFs), atmospheric components with lifetimes shorter than that of carbon dioxide; the 2021 AMAP Assessment Report is focused on the climate and health effects of SLCFs in the Arctic and globally. AMAP uses multiple models to determine levels of SLCFs in the Arctic. These models include CESM, CMAM, DEHM, EMEP-MSC-W, GEM-MACH, GEOS-Chem, MATCH, MATCH-SALSA, MRI-ESM2, UKESM1, and WRF-Chem. This work compares outputs from these models for carbon monoxide and, where possible, methane, to data from ground-based Fourier Transform Infrared (FTIR) Spectrometers focused on the near-infrared spectral region. These spectrometers are part of the Total Carbon Column Observing Network (TCCON) and are located in Eureka (Nunavut, Canada), Ny Ålesund (Spitzbergen, Norway), and Sodankylä (Finland). The model outputs are mixing ratios given at three-hour intervals for the years 2009, 2014 and 2015; these are transformed as necessary to be compared to the TCCON column-averaged dry air mole fraction (Xgas) data product. TCCON has been used for many validation studies in the past and these stations in particular provide an essential high Arctic data set with very low site-to-site bias. We will assess the ability of the AMAP models to simulate high Arctic CO and CH4 in order to better understand their suitability to inform SLCF policies.
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- 2022
21. Supplementary material to "Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study"
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Whaley, Cynthia H., primary, Mahmood, Rashed, additional, von Salzen, Knut, additional, Winter, Barbara, additional, Eckhardt, Sabine, additional, Arnold, Stephen, additional, Beagley, Stephen, additional, Becagli, Silvia, additional, Chien, Rong-You, additional, Christensen, Jesper, additional, Damani, Sujay M., additional, Eleftheriadis, Kostas, additional, Evangeliou, Nikolaos, additional, Faluvegi, Gregory S., additional, Flanner, Mark, additional, Fu, Joshua S., additional, Gauss, Michael, additional, Giardi, Fabio, additional, Gong, Wanmin, additional, Hjorth, Jens Liengaard, additional, Huang, Lin, additional, Im, Ulas, additional, Kanaya, Yugo, additional, Krishnan, Srinath, additional, Klimont, Zbigniew, additional, Kühn, Thomas, additional, Langner, Joakim, additional, Law, Kathy S., additional, Marelle, Louis, additional, Massling, Andreas, additional, Olivié, Dirk, additional, Onishi, Tatsuo, additional, Oshima, Naga, additional, Peng, Yiran, additional, Plummer, David A., additional, Popovicheva, Olga, additional, Pozzoli, Luca, additional, Raut, Jean-Christophe, additional, Sand, Maria, additional, Saunders, Laura N., additional, Schmale, Julia, additional, Sharma, Sangeeta, additional, Skov, Henrik, additional, Taketani, Fumikazu, additional, Thomas, Manu A., additional, Traversi, Rita, additional, Tsigaridis, Kostas, additional, Tsyro, Svetlana, additional, Turnock, Steven, additional, Vitale, Vito, additional, Walker, Kaley A., additional, Wang, Minqi, additional, Watson-Parris, Duncan, additional, and Weiss-Gibbons, Tahya, additional
- Published
- 2021
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22. Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study
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Whaley, Cynthia H., primary, Mahmood, Rashed, additional, von Salzen, Knut, additional, Winter, Barbara, additional, Eckhardt, Sabine, additional, Arnold, Stephen, additional, Beagley, Stephen, additional, Becagli, Silvia, additional, Chien, Rong-You, additional, Christensen, Jesper, additional, Damani, Sujay M., additional, Eleftheriadis, Kostas, additional, Evangeliou, Nikolaos, additional, Faluvegi, Gregory S., additional, Flanner, Mark, additional, Fu, Joshua S., additional, Gauss, Michael, additional, Giardi, Fabio, additional, Gong, Wanmin, additional, Hjorth, Jens Liengaard, additional, Huang, Lin, additional, Im, Ulas, additional, Kanaya, Yugo, additional, Krishnan, Srinath, additional, Klimont, Zbigniew, additional, Kühn, Thomas, additional, Langner, Joakim, additional, Law, Kathy S., additional, Marelle, Louis, additional, Massling, Andreas, additional, Olivié, Dirk, additional, Onishi, Tatsuo, additional, Oshima, Naga, additional, Peng, Yiran, additional, Plummer, David A., additional, Popovicheva, Olga, additional, Pozzoli, Luca, additional, Raut, Jean-Christophe, additional, Sand, Maria, additional, Saunders, Laura N., additional, Schmale, Julia, additional, Sharma, Sangeeta, additional, Skov, Henrik, additional, Taketani, Fumikazu, additional, Thomas, Manu A., additional, Traversi, Rita, additional, Tsigaridis, Kostas, additional, Tsyro, Svetlana, additional, Turnock, Steven, additional, Vitale, Vito, additional, Walker, Kaley A., additional, Wang, Minqi, additional, Watson-Parris, Duncan, additional, and Weiss-Gibbons, Tahya, additional
- Published
- 2021
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23. NDACC FTIR trace gas measurements at the University of Toronto Atmospheric Observatory from 2002 to 2020.
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Yamanouchi, Shoma, Conway, Stephanie, Strong, Kimberly, Colebatch, Orfeo, Lutsch, Erik, Roche, Sébastien, Taylor, Jeffrey, Whaley, Cynthia H., and Wiacek, Aldona
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TRACE gases ,DATA libraries ,SOLAR spectra ,ATMOSPHERIC composition ,OBSERVATORIES - Abstract
Nineteen years of atmospheric composition measurements made at the University of Toronto Atmospheric Observatory (TAO, 43.66°N, 79.40°W, 174 m.a.s.l.) are presented. These are retrieved from Fourier Transform InfraRed (FTIR) solar absorption spectra recorded with an ABB Bomem DA8 spectrometer from May 2002 to December 2020. The retrievals have been optimized for fourteen species: O3, HCl, HF, HNO3, CH4, C2H6, CO, HCN, N2O, C2H2, H2CO, CH3OH, HCOOH and NH3 using the SFIT4 algorithm. The measurements have been archived in the Network for Detection of Atmospheric Composition Change (NDACC) data repository in Hierarchical Data Format version 4 (HDF4) files following the Generic Earth Observation Metadata Standard (GEOMS) and are also publicly available on Borealis, the Canadian Dataverse Repository. In this paper, we describe the instrumentation, the retrieval strategy, the vertical sensitivity of the retrievals, the quality assurance process, and error analysis of the TAO FTIR measurements, and present the current version of the time series. [ABSTRACT FROM AUTHOR]
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- 2023
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24. Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model
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Im, Ulas, primary, Tsigaridis, Kostas, additional, Faluvegi, Gregory, additional, Langen, Peter L., additional, French, Joshua P., additional, Mahmood, Rashed, additional, Thomas, Manu A., additional, von Salzen, Knut, additional, Thomas, Daniel C., additional, Whaley, Cynthia H., additional, Klimont, Zbigniew, additional, Skov, Henrik, additional, and Brandt, Jørgen, additional
- Published
- 2021
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25. Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model
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Im, Ulas, Tsigaridis, Kostas, Faluvegi, Gregory, Langen, Peter L., French, Joshua P., Mahmood, Rashed, Thomas, Manu, von Salzen, Knut, Thomas, Daniel C., Whaley, Cynthia H., Klimont, Zbigniew, Skov, Henrik, Brandt, Jorgen, Im, Ulas, Tsigaridis, Kostas, Faluvegi, Gregory, Langen, Peter L., French, Joshua P., Mahmood, Rashed, Thomas, Manu, von Salzen, Knut, Thomas, Daniel C., Whaley, Cynthia H., Klimont, Zbigniew, Skov, Henrik, and Brandt, Jorgen
- Abstract
The Arctic is warming 2 to 3 times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990-2014) and future (2015-2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (> 60 degrees N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Inter-comparison Project Phase 6 (CMIP6) databases, while global annual mean greenhouse gas concentrations were prescribed and kept fixed in all simulations. Results showed that the simulations have underestimated observed surface aerosol levels, in particular black carbon (BC) and sulfate (SO42-), by more than 50 %, with the smallest biases calculated for the atmosphere-only simulations, where winds are nudged to reanalysis data. CMIP6 simulations performed slightly better in reproducing the observed surface aerosol concentrations and climate parameters, compared to the Eclipse simulations. In addition, simulations where atmosphere and ocean are fully coupled had slightly smaller biases in aerosol levels compared to atmosphere-only simulations without nudging. Arctic BC, organic aerosol (OA), and SO(4)(2-)burdens decrease significantly in all simulations by 10 %-60% following the reductions of 7 %-78% in emission projections, with the Eclipse ensemble showing larger reductions in Arctic aerosol burdens compared to the CMIP6 ensemble. For the 2030-2050 period, the Eclipse ensemble simulated a radiative forcing due to aerosol-radiation interactions (RFARI) of -0.39 +/- 0.01Wm(-2), which is -0.08Wm(-2) larger than the 1990-2010 mean forcing (-0.32Wm(-2)), of which -0.24 +/- 0.01Wm(-2) was attributed to the anthropogenic aerosols. The CMIP6 ensemble simulated a RFARI of --0.35 to -0.40Wm(-2) for the same period, which is -0.01 to -0.06Wm(-2) larger than the 199
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- 2021
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26. Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model
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Barcelona Supercomputing Center, Im, Ulas, Tsigaridis, Kostas, Faluvegi, Gregory, Langen, Peter L., French, Joshua P., Mahmood, Rashed, Thomas, Manu A., Salzen, Knut von, Thomas, Daniel C., Whaley, Cynthia H., Klimont, Zbigniew, Skov, Henrik, Brandt, Jørgen, Barcelona Supercomputing Center, Im, Ulas, Tsigaridis, Kostas, Faluvegi, Gregory, Langen, Peter L., French, Joshua P., Mahmood, Rashed, Thomas, Manu A., Salzen, Knut von, Thomas, Daniel C., Whaley, Cynthia H., Klimont, Zbigniew, Skov, Henrik, and Brandt, Jørgen
- Abstract
The Arctic is warming 2 to 3 times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990–2014) and future (2015–2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (>60∘ N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases, while global annual mean greenhouse gas concentrations were prescribed and kept fixed in all simulations. Results showed that the simulations have underestimated observed surface aerosol levels, in particular black carbon (BC) and sulfate (SO2−4), by more than 50 %, with the smallest biases calculated for the atmosphere-only simulations, where winds are nudged to reanalysis data. CMIP6 simulations performed slightly better in reproducing the observed surface aerosol concentrations and climate parameters, compared to the Eclipse simulations. In addition, simulations where atmosphere and ocean are fully coupled had slightly smaller biases in aerosol levels compared to atmosphere-only simulations without nudging. Arctic BC, organic aerosol (OA), and SO2−4 burdens decrease significantly in all simulations by 10 %–60 % following the reductions of 7 %–78 % in emission projections, with the Eclipse ensemble showing larger reductions in Arctic aerosol burdens compared to the CMIP6 ensemble. For the 2030–2050 period, the Eclipse ensemble simulated a radiative forcing due to aerosol–radiation interactions (RFARI) of −0.39±0.01 W m−2, which is −0.08 W m−2 larger than the 1990–2010 mean forcing (−0.32 W m−2), of which −0.24±0.01 W m−2 was attributed to the anthropogenic aerosols. The CMIP6 ensemble simulated a RFARI of −0.35 to −0.40 W m−2 for the same period, which is −0.01 to −0.06 W m−2 larger than the 1990–2010 mean forcing of, This research has been supported by the Aarhus University Interdisciplinary Centre for Climate Change (iClimate) OH fund (no. 2020-0162731), the FREYA project funded by the Nordic Council of Ministers (grant agreement nos. MST-227-00036 and MFVM-2019-13476), and the EVAM-SLCF funded by the Danish Environmental Agency (grant agreement no. MST-112-00298). Kostas Tsigaridis and Gregory Faluvegi thank the NASA Modeling, Analysis and Prediction program (MAP) for support. Zbigniew Klimont was financially supported by the EU-funded Action on Black Carbon in the Arctic (EUA-BCA) under the EU Partnership Instrument. Joshua P. French was partially supported by NSF award 1915277., Peer Reviewed, Postprint (published version)
- Published
- 2021
27. Arctic tropospheric ozone: assessment of current knowledge and model performance.
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Whaley, Cynthia H., Law, Kathy S., Hjorth, Jens Liengaard, Skov, Henrik, Arnold, Stephen R., Langner, Joakim, Pernov, Jakob Boyd, Rong-You Chien, Christensen, Jesper H., Deushi, Makoto, Xinyi Dong, Flanner, Gregory Faluvegi7,8Mark, Fu, Joshua S., Gauss, Michael, Im, Ulas, Marelle, Louis, Onishi, Tatsuo, Oshima, Naga, Plummer, David A., and Pozzoli, Luca
- Abstract
As the third most important greenhouse gas (GHG) after CO
2 and methane, tropospheric ozone (O3 ) is also an air pollutant causing damage to human health and ecosystems. This study brings together recent research on observations and modeling of tropospheric O3 in the Arctic, a rapidly warming and sensitive environment. At different locations in the Arctic, the observed surface O3 seasonal cycles are quite different. Coastal Arctic locations, for example, have a minimum in the springtime due to O3 depletion events resulting from surface bromine chemistry. In contrast, other Arctic locations have a maximum in the spring. The 12 state-of-the-art models used in this study lack the surface halogen chemistry needed to simulate coastal Arctic surface O3 depletion in the springtime, however, the multi-model median (MMM) has accurate seasonal cycles at non-coastal Arctic locations. There is a large amount of variability among models, which has been reported previously, and we show that there continues to be no convergence among models, nor improved accuracy in simulating tropospheric O3 and its precursor species. The MMM underestimates Arctic surface O3 by 5% to 15% depending on the location. The vertical distribution of tropospheric O3 is studied from recent ozonesonde measurements and the models. The models are highly variable, simulating free-tropospheric O3 within a range of +/- 50% depending on the model and the altitude. The MMM performs best, within +/- 8% at most locations and seasons. However, nearly all models overestimate O3 near the tropopause (~300 hPa or ~8 km), likely due to ongoing issues with underestimating the altitude of the tropopause and excessive downward transport of stratospheric O3 at high latitudes. For example, the MMM is biased high by about 20% at Eureka. Observed and simulated O3 precursors (CO, NOx and reservoir PAN) are evaluated throughout the troposphere. Models underestimate wintertime CO everywhere, likely due to a combination of underestimating CO emissions and possibly overestimating OH. Throughout the vertical profile (compared to aircraft measurements), the MMM underestimates both CO and NOx but overestimates PAN. Perhaps as a result of competing deficiencies, the MMM O3 matches the observed O3 reasonably well. Our findings suggest that despite model updates over the last decade, model results are as highly variable as ever, and have not increased in accuracy for representing Arctic tropospheric. [ABSTRACT FROM AUTHOR]- Published
- 2022
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28. Aerosols and their impacts on future Arctic climate change under different emission projections in the GISS-E2.1 Earth system model
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Im, Ulas, primary, Tsigaridis, Kostas, additional, Faluvegi, Gregory S., additional, Langen, Peter L., additional, French, Joshua P., additional, Mahmood, Rashed, additional, Manu, Thomas, additional, von Salzen, Knut, additional, Thomas, Daniel C., additional, Whaley, Cynthia H., additional, Klimont, Zbigniew, additional, Skov, Henrik, additional, and Brandt, Jørgen, additional
- Published
- 2021
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29. Supplementary material to "Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model"
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Im, Ulas, primary, Tsigaridis, Kostas, additional, Faluvegi, Gregory, additional, Langen, Peter L., additional, French, Joshua P., additional, Mahmood, Rashed, additional, Manu, Thomas, additional, von Salzen, Knut, additional, Thomas, Daniel C., additional, Whaley, Cynthia H., additional, Klimont, Zbigniew, additional, Skov, Henrik, additional, and Brandt, Jørgen, additional
- Published
- 2021
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30. Short-Lived Climate Forcers over the Arctic between 1995 and 2015 as simulated by the GISS modelE2.1
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Im, Ulas, primary, Tsigaridis, Kostas, additional, Whaley, Cynthia H., additional, Faluvegi, Gregory S., additional, Klimont, Zbigniew, additional, and von Salzen, Knut, additional
- Published
- 2020
- Full Text
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31. How much does traffic contribute to benzene and polycyclic aromatic hydrocarbon air pollution? Results from a high-resolution North American air quality model centred on Toronto, Canada
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Whaley, Cynthia H., primary, Galarneau, Elisabeth, additional, Makar, Paul A., additional, Moran, Michael D., additional, and Zhang, Junhua, additional
- Published
- 2020
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32. Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study.
- Author
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Whaley, Cynthia H., Mahmood, Rashed, Salzen, Knut von, Winter, Barbara, Eckhardt, Sabine, Arnold, Stephen, Beagley, Stephen, Becagli, Silvia, Chien, Rong-You, Christensen, Jesper, Damani, Sujay Manish, Eleftheriadis, Kostas, Evangeliou, Nikolaos, Faluvegi, Greg, Flanner, Mark, Fu, Joshua S., Gauss, Michael, Giardi, Fabio, Gong, Wanmin, and Hjorth, Jens Liengaard
- Abstract
The Arctic atmosphere is warming rapidly and its relatively pristine environment is sensitive to the long-range transport of atmospheric pollutants. While carbon dioxide is the main cause for global warming, short-lived climate forcers (SLCFs) such as methane, ozone, and particles also play a role in Arctic climate on near-term time scales. Atmospheric modelling is critical for understanding the abundance and distribution of SLCFs throughout the Arctic atmosphere, and is used as a tool towards determining 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, 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 four years (2008-2009 and 2014-2015) conducted for the 2021 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship and aircraft-based observations. 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 was able to represent the general features of SLCFs in the Arctic, though vertical mixing, long-range transport, deposition, and wildfire emissions remain highly uncertain processes. These need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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33. How much does traffic contribute to benzene and PAH air pollution? Results from a high-resolution North American air quality model centered on Toronto, Canada
- Author
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Whaley, Cynthia H., primary, Galarneau, Elisabeth, additional, Makar, Paul A., additional, Moran, Michael D., additional, and Zhang, Junhua, additional
- Published
- 2019
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34. Supplementary material to "How much does traffic contribute to benzene and PAH air pollution? Results from a high-resolution North American air quality model centered on Toronto, Canada"
- Author
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Whaley, Cynthia H., primary, Galarneau, Elisabeth, additional, Makar, Paul A., additional, Moran, Michael D., additional, and Zhang, Junhua, additional
- Published
- 2019
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- View/download PDF
35. Present and future aerosol impacts on Arctic climate change in 1 n the GISS-E2.1 Earth system model.
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Ulas Im, Kostas Tsigaridis, Faluvegi, Gregory, Langen, Peter L., French, Joshua P., Mahmood, Rashed, Manu, Thomas, von Salzen, Knut, Thomas, Daniel C., Whaley, Cynthia H., Zbigniew Klimont, Skov, Henrik, and Brandt, Jørgen
- Abstract
The Arctic is warming two to three times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990-2014) and future (2015-2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (>60 °N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases. Surface aerosol levels, in particular black carbon (BC) and sulfate (SO
4 2- ), have been significantly underestimated by more than 50%, with the smallest biases calculated for the nudged atmosphere-only simulations. CMIP6 simulations performed slightly better in simulating both surface concentrations of aerosols and climate parameters, compared to the Eclipse simulations. In addition, fully-coupled simulations had slightly smaller biases in aerosol levels compared to atmosphere only simulations without nudging. Arctic BC, organic carbon (OC) and SO4 2- burdens decrease significantly in all simulations following the emission projections, with the CMIP6 ensemble showing larger reductions in Arctic aerosol burdens compared to the Eclipse ensemble. For the 2030-2050 period, both the Eclipse Current Legislation (CLE) and the Maximum Feasible Reduction (MFR) ensembles simulated an aerosol top of the atmosphere (TOA) forcing of -0.39±0.01 W m-2 , of which - 0.24±0.01 W m-2 were attributed to the anthropogenic aerosols. The CMIP6 SSP3-7.0 scenario simulated a TOA aerosol forcing of -0.35 W m-2 for the same period, while SSP1-2.6 and SSP2-4.5 scenarios simulated a slightly more negative TOA forcing (-0.40 W m-2 ), of which the anthropogenic aerosols accounted for -0.26 W m-2 . Finally, all simulations showed an 46 increase in the Arctic surface air temperatures both throughout the simulation period. In 2050, surface air temperatures are projected to increase by 2.4 °C to 2.6 °C in the Eclipse ensemble and 1.9 °C to 2.6 °C in the CMIP6 ensemble, compared to the 1990-2010 mean. Overall, results show that even the scenarios with largest emission reductions lead to similar impact on the future Arctic surface air temperatures compared to scenarios with smaller emission reductions, while scenarios no or little mitigation leads to much larger sea-ice loss, implying that even though the magnitude of aerosol reductions lead to similar responses in surface air temperatures, high mitigation of aerosols are still necessary to limit sea-ice loss. [ABSTRACT FROM AUTHOR]- Published
- 2021
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36. GEM-MACH-PAH (rev2488): a new high-resolution chemical transport model for North American polycyclic aromatic hydrocarbons and benzene
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Whaley, Cynthia H., primary, Galarneau, Elisabeth, additional, Makar, Paul A., additional, Akingunola, Ayodeji, additional, Gong, Wanmin, additional, Gravel, Sylvie, additional, Moran, Michael D., additional, Stroud, Craig, additional, Zhang, Junhua, additional, and Zheng, Qiong, additional
- Published
- 2018
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37. Contributions of natural and anthropogenic sources to ambient ammonia in the Athabasca Oil Sands and north-western Canada
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Whaley, Cynthia H., primary, Makar, Paul A., additional, Shephard, Mark W., additional, Zhang, Leiming, additional, Zhang, Junhua, additional, Zheng, Qiong, additional, Akingunola, Ayodeji, additional, Wentworth, Gregory R., additional, Murphy, Jennifer G., additional, Kharol, Shailesh K., additional, and Cady-Pereira, Karen E., additional
- Published
- 2018
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- View/download PDF
38. Supplementary material to "GEM-MACH-PAH (rev2488): a new high-resolution chemical transport model for North American PAHs and benzene"
- Author
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Whaley, Cynthia H., primary, Galarneau, Elisabeth, additional, Makar, Paul A., additional, Akingunola, Ayodeji, additional, Gong, Wanmin, additional, Gravel, Sylvie, additional, Moran, Michael D., additional, Stroud, Craig, additional, Zhang, Junhua, additional, and Zheng, Qiong, additional
- Published
- 2018
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39. GEM-MACH-PAH (rev2488): a new high-resolution chemical transport model for North American PAHs and benzene
- Author
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Whaley, Cynthia H., primary, Galarneau, Elisabeth, additional, Makar, Paul A., additional, Akingunola, Ayodeji, additional, Gong, Wanmin, additional, Gravel, Sylvie, additional, Moran, Michael D., additional, Stroud, Craig, additional, Zhang, Junhua, additional, and Zheng, Qiong, additional
- Published
- 2018
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- View/download PDF
40. Toronto-area ozone: Long-term measurements and modelled sources of poor air quality events
- Author
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Whaley, Cynthia H., Strong, K., Jones, D. B. A., Walker, T. W., Jiang, Z., Henze, D. K., Cooke, M. A., Mclinden, C. A., Mittermeier, R. L., Pommier, Matthieu, Fogal, P. F., Department of Physics [Toronto], University of Toronto, Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), Department of Mechanical Engineering, University of Colorado [Boulder], Air Quality Research Division [Toronto], Environment and Climate Change Canada, TROPO - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
tropospheric ozone ,adjoint model ,[PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph] ,[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere ,FTIR ,GEOS-Chem ,Toronto ,air quality - Abstract
International audience; The University of Toronto Atmospheric Observatory and Environment Canada's Centre for Atmospheric Research Experiments each has over a decade of ground-based Fourier transform infrared (FTIR) spectroscopy measurements in southern Ontario. We present the Toronto area FTIR time series from 2002 to 2013 of two tropospheric trace gases—ozone and carbon monoxide—along with surface in situ measurements taken by government monitoring programs. We interpret their variability with the GEOS-Chem chemical transport model and determine the atmospheric conditions that cause pollution events in the time series. Our analysis includes a regionally tagged O3 model of the 2004–2007 time period, which quantifies the geographical contributions to Toronto area O3. The important emission types for 15 pollution events are then determined with a high-resolution adjoint model. Toronto O3, during pollution events, is most sensitive to southern Ontario and U.S. fossil fuel NOx emissions and natural isoprene emissions. The sources of Toronto pollution events are found to be highly variable, and this is demonstrated in four case studies representing local, short-, middle-, and long-range transport scenarios. This suggests that continental-scale emission reductions could improve air quality in the Toronto region. We also find that abnormally high temperatures and high-pressure systems are common to all pollution events studied, suggesting that climate change may impact Toronto O3. Finally, we quantitatively compare the sensitivity of the surface and column measurements to anthropogenic NOx emissions and show that they are remarkably similar. This work thus demonstrates the usefulness of FTIR measurements in an urban area to assess air quality.
- Published
- 2015
41. How much does traffic contribute to benzene and PAH air pollution? Results from a high-resolution North American air quality model centered on Toronto, Canada.
- Author
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Whaley, Cynthia H., Galarneau, Elisabeth, Makar, Paul A., Moran, Michael D., and Zhang, Junhua
- Abstract
Benzene and polycyclic aromatic hydrocarbons (PAHs) are toxic air pollutants that have long been associated with motor vehicle emissions, though the importance of such emissions has never been quantified over an extended domain using a chemical transport model. Herein we present the first application of such a model (GEM-MACH-PAH) to examine the contribution of motor vehicles to benzene and PAHs in ambient air. We have applied the model over a region that is centered on Toronto, Canada, and includes much of southern Ontario and the northeastern United States. The resolution (2.5 km) was the highest ever employed by a model for these compounds in North America, and the model domain was the largest at this resolution in the world to date. Using paired model simulations that were run with vehicle emissions turned on and off (while all other emissions were left on), we estimated the absolute and relative contributions of motor vehicles to ambient pollutant concentrations. Our results provide estimates of motor vehicle contributions that are realistic as a result of the inclusion of atmospheric processing, whereas assessing changes in benzene and PAH emissions alone would neglect effects caused by shifts in atmospheric oxidation and particle/gas partitioning. A secondary benefit of our scenario approach is in its utility in representing a fleet of zero emission vehicles (ZEV), whose adoption is being encouraged in a variety of jurisdictions. Our simulations predicted domain-average on-road vehicle contributions to benzene and PAH concentrations of 4-21% and 14-24% in the spring-summer and fall-winter periods, respectively, depending on the aromatic compound. Contributions to PAH concentrations up to 50% were predicted for the Greater Toronto Area, with a domain maximum of 91%. Such contributions are substantially higher than those reported in national emissions inventories, and they also differ from inventory estimates at the sub-national scale because those do not account for the physico-chemical processing that alters pollutant concentrations in the atmosphere. The removal of on-road vehicle emissions generally led to decreases in benzene and PAH concentrations during both periods that were studied, though atmospheric processing (such as chemical reactions and changes to gas/particle partitioning) contributed to non-linear behaviour at some locations or times of year. Such results demonstrate the added value associated with regional air quality modelling relative to examinations of emissions inventories alone. We also found that removing on-road vehicle emissions reduced spring-summertime surface O
3 volume mixing ratios and fall-wintertime PM10 concentrations each by ~ 10% in the model domain, providing further air quality benefits. Toxic equivalents contributed by vehicle emissions of PAHs were found to be substantial (20-60% depending on location), and this finding is particularly relevant to the study of public health in the urban areas of our model area where human population, ambient concentrations, and traffic volumes tend to be high. [ABSTRACT FROM AUTHOR]- Published
- 2019
- Full Text
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42. GEM-MACH-PAH (rev2488): a new high-resolution chemical transport model for North American PAHs and benzene.
- Author
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Whaley, Cynthia H., Galarneau, Elisabeth, Makar, Paul A., Akingunola, Ayodeji, Wanmin Gong, Gravel, Sylvie, Moran, Michael D., Stroud, Craig, Junhua Zhang, and Qiong Zheng
- Subjects
- *
POLYCYCLIC aromatic hydrocarbons & the environment , *AIR quality - Abstract
Environment and Climate Change Canada's online air quality forecasting model, GEMMACH, was extended to simulate atmospheric concentrations of benzene and seven polycyclic aromatic hydrocarbons (PAHs): phenanthrene, anthracene, fluoranthene, pyrene, benz(a)anthracene, chrysene, and benzo(a)pyrene (BaP). In the expanded model, benzene and PAHs are emitted from major point, area, and mobile sources, with emissions based on recent emission factors. Modelled PAHs undergo gas-particle partitioning (whereas benzene is only in the gas phase), atmospheric transport, oxidation, cloud processing, and dry and wet deposition. To represent PAH gas-particle partitioning, the Dachs-Eisenreich scheme was used, and we have improved gas-particle partitioning parameters based on an empirical analysis to get significantly better gas-particle partitioning results than the previous North American PAH model, AURAMS-PAH. Other added process parameterizations include the particle phase benzo(a)pyrene reaction with ozone via the Kwamena scheme and gas-phase scavenging of PAHs by snow via vapor sorption to the snow surface. The resulting GEM-MACH-PAH model was used to generate the first online model simulations of PAH emissions, transport, chemical transformation and deposition for a high resolution domain (2.5-km grid cell spacing) in North America, centered on the PAH-data-rich region of southern Ontario, Canada and the north-eastern United States. Model output for two seasons was compared to measurements from three monitoring networks spanning Canada and the U.S. Average summertime model results were found to be statistically indistinguishable from measurements of benzene and all seven PAHs. The same was true for the winter seasonal mean, except for BaP, which had a statistically significant positive bias. We present evidence that the benzo(a)pyrene results may be ameliorated via further improvements to PM and oxidant processes and transport. Our analysis focused on four key components to the prediction of atmospheric PAH levels: spatial variability; sensitivity to mobile emissions; gas-particle partitioning; and wet deposition. Spatial variability of PAHs/PM2.5 at 2.5-km resolution was found to be comparable to measurements. Predicted ambient surface concentrations of benzene and the PAHs were found to be critically dependent on mobile emission factors, indicating the mobile emissions sector has a significant influence on ambient PAH levels in the study region. PAH wet deposition was overestimated due to additive precipitation biases in the model and the measurements. Our overall performance evaluation suggests that GEM-MACHPAH can provide seasonal estimates for benzene and PAHs and be suitable for emissions scenario simulations. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
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