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Sensitivity of mesoscale modeling of smoke direct radiative effect to the emission inventory: a case study in northern sub-Saharan African region
An ensemble approach is used to examine the sensitivity of smoke loading and smoke direct radiative effect in the atmosphere to uncertainties in smoke emission estimates. Seven different fire emission inventories are applied independently to WRF-Chem model (v3.5) with the same model configuration (e...
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Published in: | Environmental research letters 2014-07, Vol.9 (7), p.75002-14 |
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creator | Zhang, Feng Wang, Jun Ichoku, Charles Hyer, Edward J Yang, Zhifeng Ge, Cui Su, Shenjian Zhang, Xiaoyang Kondragunta, Shobha Kaiser, Johannes W Wiedinmyer, Christine da Silva, Arlindo |
description | An ensemble approach is used to examine the sensitivity of smoke loading and smoke direct radiative effect in the atmosphere to uncertainties in smoke emission estimates. Seven different fire emission inventories are applied independently to WRF-Chem model (v3.5) with the same model configuration (excluding dust and other emission sources) over the northern sub-Saharan African (NSSA) biomass-burning region. Results for November and February 2010 are analyzed, respectively representing the start and end of the biomass burning season in the study region. For February 2010, estimates of total smoke emission vary by a factor of 12, but only differences by factors of 7 or less are found in the simulated regional (15°W-42°E, 13°S-17°N) and monthly averages of column PM2.5 loading, surface PM2.5 concentration, aerosol optical depth (AOD), smoke radiative forcing at the top-of-atmosphere and at the surface, and air temperature at 2 m and at 700 hPa. The smaller differences in these simulated variables may reflect the atmospheric diffusion and deposition effects to dampen the large difference in smoke emissions that are highly concentrated in areas much smaller than the regional domain of the study. Indeed, at the local scale, large differences (up to a factor of 33) persist in simulated smoke-related variables and radiative effects including semi-direct effect. Similar results are also found for November 2010, despite differences in meteorology and fire activity. Hence, biomass burning emission uncertainties have a large influence on the reliability of model simulations of atmospheric aerosol loading, transport, and radiative impacts, and this influence is largest at local and hourly-to-daily scales. Accurate quantification of smoke effects on regional climate and air quality requires further reduction of emission uncertainties, particularly for regions of high fire concentrations such as NSSA. |
doi_str_mv | 10.1088/1748-9326/9/7/075002 |
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Seven different fire emission inventories are applied independently to WRF-Chem model (v3.5) with the same model configuration (excluding dust and other emission sources) over the northern sub-Saharan African (NSSA) biomass-burning region. Results for November and February 2010 are analyzed, respectively representing the start and end of the biomass burning season in the study region. For February 2010, estimates of total smoke emission vary by a factor of 12, but only differences by factors of 7 or less are found in the simulated regional (15°W-42°E, 13°S-17°N) and monthly averages of column PM2.5 loading, surface PM2.5 concentration, aerosol optical depth (AOD), smoke radiative forcing at the top-of-atmosphere and at the surface, and air temperature at 2 m and at 700 hPa. The smaller differences in these simulated variables may reflect the atmospheric diffusion and deposition effects to dampen the large difference in smoke emissions that are highly concentrated in areas much smaller than the regional domain of the study. Indeed, at the local scale, large differences (up to a factor of 33) persist in simulated smoke-related variables and radiative effects including semi-direct effect. Similar results are also found for November 2010, despite differences in meteorology and fire activity. Hence, biomass burning emission uncertainties have a large influence on the reliability of model simulations of atmospheric aerosol loading, transport, and radiative impacts, and this influence is largest at local and hourly-to-daily scales. Accurate quantification of smoke effects on regional climate and air quality requires further reduction of emission uncertainties, particularly for regions of high fire concentrations such as NSSA.</description><identifier>ISSN: 1748-9326</identifier><identifier>EISSN: 1748-9326</identifier><identifier>DOI: 10.1088/1748-9326/9/7/075002</identifier><identifier>CODEN: ERLNAL</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Aerosols ; Air pollution ; Air quality ; Air temperature ; Atmosphere ; Atmospheric aerosols ; Atmospheric diffusion ; Atmospheric models ; Biomass ; Biomass burning ; Burning ; Climate effects ; Diffusion effects ; Emission analysis ; Emission inventories ; Emissions control ; Estimates ; fire emission inventory ; mesoscale modeling ; Meteorology ; Optical analysis ; Optical thickness ; Particulate matter ; Radiative forcing ; Sahel and northern sub-Saharan African region ; Sensitivity ; Simulation ; Smoke ; smoke radiative effect ; Uncertainty</subject><ispartof>Environmental research letters, 2014-07, Vol.9 (7), p.75002-14</ispartof><rights>2014 IOP Publishing Ltd</rights><rights>2014. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c562t-f04c4a29fa7797b48631bf76ecc3981d180a737865dbdcb438476da53b6b24213</citedby><cites>FETCH-LOGICAL-c562t-f04c4a29fa7797b48631bf76ecc3981d180a737865dbdcb438476da53b6b24213</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2550673678?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,37013,44590</link.rule.ids></links><search><creatorcontrib>Zhang, Feng</creatorcontrib><creatorcontrib>Wang, Jun</creatorcontrib><creatorcontrib>Ichoku, Charles</creatorcontrib><creatorcontrib>Hyer, Edward J</creatorcontrib><creatorcontrib>Yang, Zhifeng</creatorcontrib><creatorcontrib>Ge, Cui</creatorcontrib><creatorcontrib>Su, Shenjian</creatorcontrib><creatorcontrib>Zhang, Xiaoyang</creatorcontrib><creatorcontrib>Kondragunta, Shobha</creatorcontrib><creatorcontrib>Kaiser, Johannes W</creatorcontrib><creatorcontrib>Wiedinmyer, Christine</creatorcontrib><creatorcontrib>da Silva, Arlindo</creatorcontrib><title>Sensitivity of mesoscale modeling of smoke direct radiative effect to the emission inventory: a case study in northern sub-Saharan African region</title><title>Environmental research letters</title><addtitle>ERL</addtitle><addtitle>Environ. Res. Lett</addtitle><description>An ensemble approach is used to examine the sensitivity of smoke loading and smoke direct radiative effect in the atmosphere to uncertainties in smoke emission estimates. Seven different fire emission inventories are applied independently to WRF-Chem model (v3.5) with the same model configuration (excluding dust and other emission sources) over the northern sub-Saharan African (NSSA) biomass-burning region. Results for November and February 2010 are analyzed, respectively representing the start and end of the biomass burning season in the study region. For February 2010, estimates of total smoke emission vary by a factor of 12, but only differences by factors of 7 or less are found in the simulated regional (15°W-42°E, 13°S-17°N) and monthly averages of column PM2.5 loading, surface PM2.5 concentration, aerosol optical depth (AOD), smoke radiative forcing at the top-of-atmosphere and at the surface, and air temperature at 2 m and at 700 hPa. The smaller differences in these simulated variables may reflect the atmospheric diffusion and deposition effects to dampen the large difference in smoke emissions that are highly concentrated in areas much smaller than the regional domain of the study. Indeed, at the local scale, large differences (up to a factor of 33) persist in simulated smoke-related variables and radiative effects including semi-direct effect. Similar results are also found for November 2010, despite differences in meteorology and fire activity. Hence, biomass burning emission uncertainties have a large influence on the reliability of model simulations of atmospheric aerosol loading, transport, and radiative impacts, and this influence is largest at local and hourly-to-daily scales. Accurate quantification of smoke effects on regional climate and air quality requires further reduction of emission uncertainties, particularly for regions of high fire concentrations such as NSSA.</description><subject>Aerosols</subject><subject>Air pollution</subject><subject>Air quality</subject><subject>Air temperature</subject><subject>Atmosphere</subject><subject>Atmospheric aerosols</subject><subject>Atmospheric diffusion</subject><subject>Atmospheric models</subject><subject>Biomass</subject><subject>Biomass burning</subject><subject>Burning</subject><subject>Climate effects</subject><subject>Diffusion effects</subject><subject>Emission analysis</subject><subject>Emission inventories</subject><subject>Emissions control</subject><subject>Estimates</subject><subject>fire emission inventory</subject><subject>mesoscale modeling</subject><subject>Meteorology</subject><subject>Optical analysis</subject><subject>Optical thickness</subject><subject>Particulate matter</subject><subject>Radiative forcing</subject><subject>Sahel and northern sub-Saharan African region</subject><subject>Sensitivity</subject><subject>Simulation</subject><subject>Smoke</subject><subject>smoke radiative effect</subject><subject>Uncertainty</subject><issn>1748-9326</issn><issn>1748-9326</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp9kd9qFDEYxQdRsFbfwIuAIN6Mm8xkkox3pdRaWBBsex0yyZdt1plkTTKFfQzfuBlHahExN0kOv3Py51TVW4I_EizEhnAq6r5t2Kbf8A3mHcbNs-rkUX7-ZP2yepXSHuOOdlycVD-vwSeX3b3LRxQsmiCFpNUIaAoGRud3i5qm8B2QcRF0RlEZp4oDEFi7CDmgfFd2k0vJBY-cvwefQzx-QgpplQClPJtj0ZEPsaDRozQP9bW6U1F5dGaj02WOsCv219ULq8YEb37Pp9Xt54ub8y_19uvl1fnZttYda3JtMdVUNb1VnPd8oIK1ZLCcgdZtL4ghAivecsE6Mxg90FZQzozq2oENDW1Ie1pdrbkmqL08RDepeJRBOflLCHEnVcxOjyAHDqLDBDTHlGJdDtUUWInSZWAylKwPa9Yhhh8zpCzLX2gYR-UhzEkSjnvCRNOzgr77C92HOfryUtl0HWa8ZVwUiq6UjiGlCPbxggTLpXO5FCqXQmUvuVw7L7b3q82Fw5_ci2_bJ5A8GFtA_A_wv9kPHw-7rw</recordid><startdate>20140701</startdate><enddate>20140701</enddate><creator>Zhang, Feng</creator><creator>Wang, Jun</creator><creator>Ichoku, Charles</creator><creator>Hyer, Edward J</creator><creator>Yang, Zhifeng</creator><creator>Ge, Cui</creator><creator>Su, Shenjian</creator><creator>Zhang, Xiaoyang</creator><creator>Kondragunta, Shobha</creator><creator>Kaiser, Johannes W</creator><creator>Wiedinmyer, Christine</creator><creator>da Silva, Arlindo</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>7ST</scope><scope>7TG</scope><scope>7TV</scope><scope>C1K</scope><scope>KL.</scope><scope>SOI</scope><scope>DOA</scope></search><sort><creationdate>20140701</creationdate><title>Sensitivity of mesoscale modeling of smoke direct radiative effect to the emission inventory: a case study in northern sub-Saharan African region</title><author>Zhang, Feng ; Wang, Jun ; Ichoku, Charles ; Hyer, Edward J ; Yang, Zhifeng ; Ge, Cui ; Su, Shenjian ; Zhang, Xiaoyang ; Kondragunta, Shobha ; Kaiser, Johannes W ; Wiedinmyer, Christine ; da Silva, Arlindo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c562t-f04c4a29fa7797b48631bf76ecc3981d180a737865dbdcb438476da53b6b24213</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Aerosols</topic><topic>Air pollution</topic><topic>Air quality</topic><topic>Air temperature</topic><topic>Atmosphere</topic><topic>Atmospheric aerosols</topic><topic>Atmospheric diffusion</topic><topic>Atmospheric models</topic><topic>Biomass</topic><topic>Biomass burning</topic><topic>Burning</topic><topic>Climate effects</topic><topic>Diffusion effects</topic><topic>Emission analysis</topic><topic>Emission inventories</topic><topic>Emissions control</topic><topic>Estimates</topic><topic>fire emission inventory</topic><topic>mesoscale modeling</topic><topic>Meteorology</topic><topic>Optical analysis</topic><topic>Optical thickness</topic><topic>Particulate matter</topic><topic>Radiative forcing</topic><topic>Sahel and northern sub-Saharan African region</topic><topic>Sensitivity</topic><topic>Simulation</topic><topic>Smoke</topic><topic>smoke radiative effect</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Feng</creatorcontrib><creatorcontrib>Wang, Jun</creatorcontrib><creatorcontrib>Ichoku, Charles</creatorcontrib><creatorcontrib>Hyer, Edward J</creatorcontrib><creatorcontrib>Yang, Zhifeng</creatorcontrib><creatorcontrib>Ge, Cui</creatorcontrib><creatorcontrib>Su, Shenjian</creatorcontrib><creatorcontrib>Zhang, Xiaoyang</creatorcontrib><creatorcontrib>Kondragunta, Shobha</creatorcontrib><creatorcontrib>Kaiser, Johannes W</creatorcontrib><creatorcontrib>Wiedinmyer, Christine</creatorcontrib><creatorcontrib>da Silva, Arlindo</creatorcontrib><collection>Open Access: IOP Publishing Free Content</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Pollution Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Environment Abstracts</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Environmental research letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Feng</au><au>Wang, Jun</au><au>Ichoku, Charles</au><au>Hyer, Edward J</au><au>Yang, Zhifeng</au><au>Ge, Cui</au><au>Su, Shenjian</au><au>Zhang, Xiaoyang</au><au>Kondragunta, Shobha</au><au>Kaiser, Johannes W</au><au>Wiedinmyer, Christine</au><au>da Silva, Arlindo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sensitivity of mesoscale modeling of smoke direct radiative effect to the emission inventory: a case study in northern sub-Saharan African region</atitle><jtitle>Environmental research letters</jtitle><stitle>ERL</stitle><addtitle>Environ. Res. Lett</addtitle><date>2014-07-01</date><risdate>2014</risdate><volume>9</volume><issue>7</issue><spage>75002</spage><epage>14</epage><pages>75002-14</pages><issn>1748-9326</issn><eissn>1748-9326</eissn><coden>ERLNAL</coden><abstract>An ensemble approach is used to examine the sensitivity of smoke loading and smoke direct radiative effect in the atmosphere to uncertainties in smoke emission estimates. Seven different fire emission inventories are applied independently to WRF-Chem model (v3.5) with the same model configuration (excluding dust and other emission sources) over the northern sub-Saharan African (NSSA) biomass-burning region. Results for November and February 2010 are analyzed, respectively representing the start and end of the biomass burning season in the study region. For February 2010, estimates of total smoke emission vary by a factor of 12, but only differences by factors of 7 or less are found in the simulated regional (15°W-42°E, 13°S-17°N) and monthly averages of column PM2.5 loading, surface PM2.5 concentration, aerosol optical depth (AOD), smoke radiative forcing at the top-of-atmosphere and at the surface, and air temperature at 2 m and at 700 hPa. The smaller differences in these simulated variables may reflect the atmospheric diffusion and deposition effects to dampen the large difference in smoke emissions that are highly concentrated in areas much smaller than the regional domain of the study. Indeed, at the local scale, large differences (up to a factor of 33) persist in simulated smoke-related variables and radiative effects including semi-direct effect. Similar results are also found for November 2010, despite differences in meteorology and fire activity. Hence, biomass burning emission uncertainties have a large influence on the reliability of model simulations of atmospheric aerosol loading, transport, and radiative impacts, and this influence is largest at local and hourly-to-daily scales. Accurate quantification of smoke effects on regional climate and air quality requires further reduction of emission uncertainties, particularly for regions of high fire concentrations such as NSSA.</abstract><cop>Bristol</cop><pub>IOP Publishing</pub><doi>10.1088/1748-9326/9/7/075002</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Aerosols Air pollution Air quality Air temperature Atmosphere Atmospheric aerosols Atmospheric diffusion Atmospheric models Biomass Biomass burning Burning Climate effects Diffusion effects Emission analysis Emission inventories Emissions control Estimates fire emission inventory mesoscale modeling Meteorology Optical analysis Optical thickness Particulate matter Radiative forcing Sahel and northern sub-Saharan African region Sensitivity Simulation Smoke smoke radiative effect Uncertainty |
title | Sensitivity of mesoscale modeling of smoke direct radiative effect to the emission inventory: a case study in northern sub-Saharan African region |
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