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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. For. Glob. Change</journal-id>
<journal-title>Frontiers in Forests and Global Change</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. For. Glob. Change</abbrev-journal-title>
<issn pub-type="epub">2624-893X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/ffgc.2022.867112</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Forests and Global Change</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Forest Carbon Emission Sources Are Not Equal: Putting Fire, Harvest, and Fossil Fuel Emissions in Context</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Bartowitz</surname> <given-names>Kristina J.</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1578118/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Walsh</surname> <given-names>Eric S.</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Stenzel</surname> <given-names>Jeffrey E.</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Kolden</surname> <given-names>Crystal A.</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Hudiburg</surname> <given-names>Tara W.</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1326181/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Department of Forest, Rangeland, and Fire Sciences, University of Idaho</institution>, <addr-line>Moscow, ID</addr-line>, <country>United States</country></aff>
<aff id="aff2"><sup>2</sup><institution>Management of Complex Systems, University of California, Merced</institution>, <addr-line>Merced, CA</addr-line>, <country>United States</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Eduardo Maeda, University of Helsinki, Finland</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Francis Edward Putz, University of Florida, United States; Lori D. Daniels, University of British Columbia, Canada</p></fn>
<corresp id="c001">&#x002A;Correspondence: Kristina J. Bartowitz, <email>kbartowitz@uidaho.edu</email></corresp>
<fn fn-type="other" id="fn004"><p>This article was submitted to Forests and the Atmosphere, a section of the journal Frontiers in Forests and Global Change</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>09</day>
<month>05</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>5</volume>
<elocation-id>867112</elocation-id>
<history>
<date date-type="received">
<day>31</day>
<month>01</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>04</day>
<month>04</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2022 Bartowitz, Walsh, Stenzel, Kolden and Hudiburg.</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>Bartowitz, Walsh, Stenzel, Kolden and Hudiburg</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract>
<p>Climate change has intensified the scale of global wildfire impacts in recent decades. In order to reduce fire impacts, management policies are being proposed in the western United States to lower fire risk that focus on harvesting trees, including large-diameter trees. Many policies already do not include diameter limits and some recent policies have proposed diameter increases in fuel reduction strategies. While the primary goal is fire risk reduction, these policies have been interpreted as strategies that can be used to save trees from being killed by fire, thus preventing carbon emissions and feedbacks to climate warming. This interpretation has already resulted in cutting down trees that likely would have survived fire, resulting in forest carbon losses that are greater than if a wildfire had occurred. To help policymakers and managers avoid these unintended carbon consequences and to present carbon emission sources in the same context, we calculate western United States forest fire carbon emissions and compare them with harvest and fossil fuel emissions (FFE) over the same timeframe. We find that forest fire carbon emissions are on average only 6% of anthropogenic FFE over the past decade. While wildfire occurrence and area burned have increased over the last three decades, per area fire emissions for extreme fire events are relatively constant. In contrast, harvest of mature trees releases a higher density of carbon emissions (e.g., per unit area) relative to wildfire (150&#x2013;800%) because harvest causes a higher rate of tree mortality than wildfire. Our results show that increasing harvest of mature trees to save them from fire increases emissions rather than preventing them. Shown in context, our results demonstrate that reducing FFEs will do more for climate mitigation potential (and subsequent reduction of fire) than increasing extractive harvest to prevent fire emissions. On public lands, management aimed at less-intensive fuels reduction (such as removal of &#x201C;ladder&#x201D; fuels, i.e., shrubs and small-diameter trees) will help to balance reducing catastrophic fire and leave live mature trees on the landscape to continue carbon uptake.</p>
</abstract>
<kwd-group>
<kwd>carbon</kwd>
<kwd>forests</kwd>
<kwd>fire</kwd>
<kwd>climate change mitigation</kwd>
<kwd>GHG emissions</kwd>
</kwd-group>
<contract-sponsor id="cn001">National Science Foundation <named-content content-type="fundref-id">10.13039/100000001</named-content></contract-sponsor>
<contract-sponsor id="cn002">National Science Foundation <named-content content-type="fundref-id">10.13039/100000001</named-content></contract-sponsor>
<contract-sponsor id="cn003">National Science Foundation <named-content content-type="fundref-id">10.13039/100000001</named-content></contract-sponsor>
<contract-sponsor id="cn004">National Science Foundation <named-content content-type="fundref-id">10.13039/100000001</named-content></contract-sponsor>
<contract-sponsor id="cn005">National Institute of Food and Agriculture <named-content content-type="fundref-id">10.13039/100005825</named-content></contract-sponsor>
<contract-sponsor id="cn006">National Institute of Food and Agriculture <named-content content-type="fundref-id">10.13039/100005825</named-content></contract-sponsor>
<contract-sponsor id="cn007">Bureau of Indian Affairs <named-content content-type="fundref-id">10.13039/100013174</named-content></contract-sponsor>
<counts>
<fig-count count="5"/>
<table-count count="4"/>
<equation-count count="1"/>
<ref-count count="63"/>
<page-count count="11"/>
<word-count count="7705"/>
</counts>
</article-meta>
</front>
<body>
<sec id="S1" sec-type="intro">
<title>Introduction</title>
<p>Climate change has intensified and increased the scale of global wildfire impacts in recent decades (<xref ref-type="bibr" rid="B5">Bowman et al., 2020</xref>). The western United States 2020 fire season exemplified intensifying fire impacts (<xref ref-type="bibr" rid="B20">Higuera and Abatzoglou, 2020</xref>), including high loss of life and property, and the record area burned in the last century in California, Oregon, and Colorado (<xref ref-type="bibr" rid="B20">Higuera and Abatzoglou, 2020</xref>). Historically, similar catastrophic wildfires events (i.e., the 1910 Big Burn) instigated development of management policies to prevent and contain wildfire, including a century of fire suppression. In the western United States, climate change is now amplifying the negative effects of these management practices, resulting in unprecedented catastrophic wildfire outcomes (<xref ref-type="bibr" rid="B42">Parks and Abatzoglou, 2020</xref>).</p>
<p>Forests provide many ecosystem services such as wildlife habitat, hydrologic benefits, recreation opportunities, and wood harvest (<xref ref-type="bibr" rid="B35">Lawler et al., 2014</xref>), and also serve as a critical &#x201C;natural climate solution&#x201D;; they act as extensive and persistent carbon sinks that sequester large amounts of carbon from the atmosphere (<xref ref-type="bibr" rid="B54">Turner et al., 2011</xref>; <xref ref-type="bibr" rid="B16">Fargione et al., 2018</xref>). Increases in climate change-driven wildfire events (<xref ref-type="bibr" rid="B58">Westerling et al., 2006</xref>) have led to proposals to increase extractive forest harvest (i.e., the removal of large, mature trees, including altering policy to increase diameter limits to remove even larger trees; <xref ref-type="table" rid="T1">Table 1</xref>) in areas at high-risk of wildfire to decrease fire risk (<xref ref-type="fig" rid="F1">Figure 1</xref>; <xref ref-type="bibr" rid="B15">Executive Order, 2018</xref>; <xref ref-type="bibr" rid="B24">Infrastructure Investment and Jobs Act, 2021</xref>). Public opinion and policies have been shaped by the misconception that harvest can reduce fire risk (or save other trees), or that harvest of a singular tree can save that tree from &#x201C;burning down&#x201D; (<xref ref-type="table" rid="T2">Table 2</xref>). These beliefs are widespread (<xref ref-type="table" rid="T2">Table 2</xref>), but their impact on policy and subsequent impact on on-the-ground harvest has not been quantified. While prescribed fire has been shown to decrease fire risk (<xref ref-type="bibr" rid="B31">Kolden, 2019</xref>) and increase carbon storage (<xref ref-type="bibr" rid="B59">Wiedinmyer and Hurteau, 2010</xref>), removal of biomass through large-diameter tree thinning or logging produces mixed outcomes for fire risk mitigation and forest resilience (<xref ref-type="bibr" rid="B49">Sohn et al., 2016</xref>) and reduces forest carbon storage and sequestration for decades to centuries (<xref ref-type="bibr" rid="B8">Campbell et al., 2012</xref>; <xref ref-type="bibr" rid="B3">Bartowitz et al., 2019</xref>; <xref ref-type="bibr" rid="B51">Stenzel et al., 2021</xref>). The misconception that trees need to be saved from wildfire through harvest (<xref ref-type="bibr" rid="B63">Zinke, 2018</xref>; <xref ref-type="bibr" rid="B24">Infrastructure Investment and Jobs Act, 2021</xref>; <xref ref-type="table" rid="T2">Table 2</xref>) may lead to unintended consequences through increased logging. These consequences include increased fire risk, a decreased forest carbon sink, decreased forest resiliency, and loss of the forest as a natural climate solution (<xref ref-type="bibr" rid="B22">Hudiburg et al., 2013</xref>; <xref ref-type="bibr" rid="B34">Law et al., 2018</xref>; <xref ref-type="bibr" rid="B62">Zald and Dunn, 2018</xref>; <xref ref-type="bibr" rid="B53">Stephens et al., 2020</xref>).</p>
<table-wrap position="float" id="T1">
<label>TABLE 1</label>
<caption><p>Common management scenario types in western United States forests.</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left">Management scenario</td>
<td valign="top" align="left">Description</td>
<td valign="top" align="center">Calculation scenario (<xref ref-type="fig" rid="F4">Figure 4</xref>)</td>
<td valign="top" align="center">Extractive management?</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Thin-from-below</td>
<td valign="top" align="left">Removal of understory brush and small-diameter trees. No tree sales.</td>
<td valign="top" align="center">30% harvest</td>
<td valign="top" align="center">No</td>
</tr>
<tr>
<td valign="top" align="left">Commercial thin</td>
<td valign="top" align="left">Removal of understory brush, small-diameter trees, and some larger, mature trees for sale.</td>
<td valign="top" align="center">50% harvest</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Clear-cut removal</td>
<td valign="top" align="left">Removal of all trees (small and mature) for sale.</td>
<td valign="top" align="center">100% harvest</td>
<td valign="top" align="center">Yes</td>
</tr>
<tr>
<td valign="top" align="left">Prescribed burn</td>
<td valign="top" align="left">Intentionally set fire to remove ground fuels. Often coupled with a restoration or commercial thin.</td>
<td valign="top" align="center">Not included in calculations</td>
<td valign="top" align="center">No</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn><p><italic>Tree removal scenarios (thins and clear-cut) were used in the hypothetical harvest carbon loss calculations (<xref ref-type="fig" rid="F4">Figure 4</xref>). Extractive management (i.e., if there is also a financial sale from the management rather than just for restoration or fuels reduction) is noted for each scenario.</italic></p></fn>
</table-wrap-foot>
</table-wrap>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption><p>Conceptual figure describing the misconception about extractive forest management (Column 1) and how it can lead to unintended and unwanted consequences with forest resilience and the forest carbon sink. Column 2 describes how we can correct that misconception and develop policies that enhance forest resilience and the forest carbon sink.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-05-867112-g001.tif"/>
</fig>
<table-wrap position="float" id="T2">
<label>TABLE 2</label>
<caption><p>Examples of recent public opinions surrounding the logging-forest-carbon misconceptions and how they lead to policy that increases harvest.</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left">Source</td>
<td valign="top" align="left">Year</td>
<td valign="top" align="left">Author</td>
<td valign="top" align="left">Quote or summary</td>
<td valign="top" align="left">Description</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Public</td>
<td valign="top" align="left">2020</td>
<td valign="top" align="left">Logging and grazing organizations (<xref ref-type="bibr" rid="B46">Radke, 2020</xref>)</td>
<td valign="top" align="left">&#x201C;Log it, graze it, or watch it burn it&#x201D;</td>
<td valign="top" align="left">Logging and grazing orgs believe logging and grazing will solve the fire problem</td>
</tr>
<tr>
<td valign="top" align="left">Public</td>
<td valign="top" align="left">2020</td>
<td valign="top" align="left">Consortium for Research on Renewable Industrial Materials (<xref ref-type="bibr" rid="B10">CORRIM, 2020</xref>)</td>
<td valign="top" align="left">&#x201C;Sustainably harvesting forest carbon not only provides significant opportunities for carbon storage&#x201D;</td>
<td valign="top" align="left">Logging industry promotion trying to show &#x201C;reducing carbon emissions by using wood products&#x201D;</td>
</tr>
<tr>
<td valign="top" align="left">Public</td>
<td valign="top" align="left">2018</td>
<td valign="top" align="left">Former United States Cabinet Member Ryan Zinke (<xref ref-type="bibr" rid="B63">Zinke, 2018</xref>)</td>
<td valign="top" align="left">&#x201C;When an entire forest burns to the ground&#x201D;</td>
<td valign="top" align="left">Advocates for logging to prevent wildfires, boost the economy, and to save lives.</td>
</tr>
<tr>
<td valign="top" align="left">Public</td>
<td valign="top" align="left">2017</td>
<td valign="top" align="left">Catherine mater (<xref ref-type="bibr" rid="B37">Mater, 2017</xref>)</td>
<td valign="top" align="left">&#x201C;Half of those emissions are due to tree mortality&#x201D;</td>
<td valign="top" align="left">Misconception that tree mortality equals direct emissions</td>
</tr>
<tr>
<td valign="top" align="left">Policy</td>
<td valign="top" align="left">2021</td>
<td valign="top" align="left">United States Government &#x2013; Infrastructure Investment and Jobs Act (<xref ref-type="bibr" rid="B24">Infrastructure Investment and Jobs Act, 2021</xref>)</td>
<td valign="top" align="left">Significant increase in project funds to increase logging and commercial thinning on public lands for fire risk reduction</td>
<td valign="top" align="left">&#x0024;3.3 billion allocated to hazard fuels reduction with no diameter limits set. 12 million ha opened to logging on public lands.</td>
</tr>
<tr>
<td valign="top" align="left">Policy</td>
<td valign="top" align="left">2020</td>
<td valign="top" align="left">United States Government &#x2013; Twisp River Restoration Project (<xref ref-type="bibr" rid="B56">USFS, 2020</xref>)</td>
<td valign="top" align="left">Increase diameter limits on trees harvest to cut down larger trees for fire risk reduction and restoration</td>
<td valign="top" align="left">&#x003E;30,000 ha forest management project in fire-prone forest in Washington state</td>
</tr>
</tbody>
</table></table-wrap>
<p>Although high intensity fire combusts less than 5% of mature, live tree biomass (<xref ref-type="bibr" rid="B28">Knorr et al., 2016</xref>), discussions of fire policy and forest management have framed tree biomass combustion as an undesirable outcome requiring mitigation through extractive forest management (i.e., harvest of mature trees for timber sales; <xref ref-type="bibr" rid="B37">Mater, 2017</xref>; <xref ref-type="bibr" rid="B63">Zinke, 2018</xref>; <xref ref-type="bibr" rid="B40">Newhouse, 2021</xref>; <xref ref-type="bibr" rid="B48">Senate Bill 762, 2021</xref>). Increasing, i.e., extractive forest management (<xref ref-type="table" rid="T1">Table 1</xref>), to &#x201C;lock&#x201D; carbon into man-made structures, to increase forest productivity (<xref ref-type="bibr" rid="B10">CORRIM, 2020</xref>), or reduce fire risk ignores the volume of forest fire emissions relative to the direct emissions of such strategies (<xref ref-type="bibr" rid="B21">Hudiburg et al., 2019</xref>; <xref ref-type="bibr" rid="B50">Stenzel et al., 2019</xref>). Previous studies have shown that timber harvest directly kills more trees than forest fire in the western United States (<xref ref-type="bibr" rid="B4">Berner et al., 2017</xref>), but it remains unclear how much fire and harvest are contributing to regional total carbon emissions in the western United States, especially in the context of how these emissions compare with anthropogenic FFE (<xref ref-type="bibr" rid="B21">Hudiburg et al., 2019</xref>; <xref ref-type="bibr" rid="B50">Stenzel et al., 2019</xref>).</p>
<p>Here, we calculate forest fire emissions (average for the last decade; large historical events, and the record 2020 fire season) and compare those to (1) average and hypothetical timber harvest emissions, and (2) average decadal FFE. Our comparisons clarify the relative contributions of extractive forest management, fire, and fossil fuels to atmospheric carbon dioxide concentrations and help provide clarity for future management scenarios intended to reduce carbon emissions and/or increase carbon uptake and the scientific observations that show the opposite occurs. We further show how misrepresentation of fire and management impacts on forest carbon cycling leads to discussions and policies that overestimates benefits to carbon stocks and sequestration, or downplays carbon consequences (<xref ref-type="fig" rid="F1">Figure 1</xref>). Finally, we discuss how policy and management based on carbon cycle science and observations could be used to both reduce fire risk and to increase and maintain carbon storage.</p>
</sec>
<sec id="S2" sec-type="materials|methods">
<title>Materials and Methods</title>
<sec id="S2.SS1">
<title>Study Area: Forest Fires Across the Western United States</title>
<p>We calculated carbon emissions from the forest fires in the western United States (<xref ref-type="fig" rid="F1">Figure 1</xref>) between 1984 and 2020 and the largest fire in the continental United States, the 1910 Big Burn. Here, we group the &#x201C;western United States&#x201D; as the 11 states in the contiguous United States West (i.e., Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming). While there were other extreme forest fires in the 20th century (e.g., 1902 Yacoult Burn in Washington, 1933 Tillamook Burn), historic records of forest attributes were not available for analysis.</p>
<p>Extreme fires have continued to occur in recent decades (<xref ref-type="fig" rid="F2">Figure 2</xref>). Availability of high-resolution fire perimeter and burn severity data allows for analysis of fires since 1984 (<xref ref-type="bibr" rid="B12">Eidenshink et al., 2007</xref>) through 2020. All wildfires &#x003E;526 ha (1,000 acres) with &#x003E;50% forest area within the burn perimeter were included in this analysis. In addition, we selected large, notable forest fires (or complexes of individual fires; referred to as &#x201C;extreme&#x201D; fires throughout the manuscript) that occurred between 1984 and 2020. Fires were selected based on how notable they were at the time for size, duration, volatile fire behavior and legacy of impact in the subsequent years. Wildfires included in the &#x201C;extreme fires&#x201D; list (<xref ref-type="table" rid="T1">Table 1</xref>) were chosen from the created emissions database based on high area burned (i.e., &#x003E;40,000 ha), and overall significance of fire event (i.e., most were record events in some way such as: highest area burned in that state or human impacts).</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption><p>Extreme forest wildfires in the western United States. <bold>(A)</bold> Perimeters of forest fires in 2020, a selection of extreme forest fire events 1984&#x2013;2018, and of the 1910 Big Burn fires. <bold>(B)</bold> Fire statistics of the 1910 Big Burn and contemporary fires (1984&#x2013;2020) within the Western United States.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-05-867112-g002.tif"/>
</fig>
<p>Emissions have not been previously calculated for the Big Burn. We have calculated an estimate from the Big Burn not only because it is the largest known fire to have occurred in the continental United States, but also to serve as a baseline or reference for the range of emissions possible in the absence of fire suppression. While the Big Burn emissions estimate is calculated differently from modern fires due to lack of forest data from that time, the comparison between modern fire emissions and the Big Burn is still useful and has been completed with the best possible methodology given data availability. The 1910 Big Burn encompassed an area throughout Washington, Montana, and Idaho (<xref ref-type="fig" rid="F2">Figure 2</xref>). We calculated fire-induced carbon emissions of the Big Burn using historical accounts and records (<xref ref-type="bibr" rid="B29">Koch, 1942</xref>). The fire perimeters used in this study were a cross-reference between Koch&#x2019;s account and the 1910 Fire perimeters (<xref ref-type="bibr" rid="B55">USFS, 1978</xref>; <xref ref-type="bibr" rid="B18">Gibson, 2005</xref>).</p>
</sec>
<sec id="S2.SS2">
<title>Forest Fire Emissions Calculations</title>
<p>Direct carbon emissions for contemporary forest fires (1984&#x2013;2020) were calculated using, fire severity and area burned from the Monitoring Trends in Burn Severity database (MTBS; <xref ref-type="bibr" rid="B12">Eidenshink et al., 2007</xref>) for forest fires between 1984 and 2019, and Burned Area Emergency Response (BAER; <xref ref-type="bibr" rid="B43">Parsons, 2003</xref>) and National Interagency Fire Coordination (<xref ref-type="bibr" rid="B41">NIFC, 2020</xref>) data products for 2020 fires. Carbon emissions for the 1910 Big Burn were calculated using area burned from the Northern Rockies Fire atlas for the Big Burn (<xref ref-type="bibr" rid="B18">Gibson, 2005</xref>). All carbon stock calculations were from forest type and ecoregion-specific carbon data (<xref ref-type="supplementary-material" rid="TS1">Supplementary Figure 1</xref>; <xref ref-type="bibr" rid="B7">Buotte et al., 2019</xref>; <xref ref-type="bibr" rid="B50">Stenzel et al., 2019</xref>), and severity-specific combustion factors (<xref ref-type="supplementary-material" rid="TS1">Supplementary Table 1</xref>; <xref ref-type="bibr" rid="B50">Stenzel et al., 2019</xref>). Only fires that burned &#x003E; 526 ha and in 50% or greater forested area within the burn perimeter (<xref ref-type="bibr" rid="B47">Ruefenacht et al., 2008</xref>) were used in this analysis. Aboveground carbon stocks were calculated for each forest fire area based on average carbon stocks for the forest type and ecoregion and area of the specific forest type within the burn perimeter (<xref ref-type="supplementary-material" rid="TS1">Supplementary Table 1</xref>). Aboveground carbon stocks were multiplied by the appropriate combustion factor for the fire severity value of that forested area to obtain carbon losses (<xref ref-type="supplementary-material" rid="TS1">Supplementary Table 1</xref>). Fires between 1984 and 2019 were calculated using MTBS severity classes. Smaller forest fires in 2020 were calculated using an average combustion factor. Big Burn carbon emissions were calculated using a range for the moderate-severe combustion factor which gives us a range (uncertainty) of emissions for this fire. Extreme 2020 forest fire emissions were calculated using BAER severity classes (which are precursors to MTBS severity classes). While we used contemporary forest structure data to calculate emissions from the Big Burn, our range of carbon emissions (<xref ref-type="table" rid="T3">Table 3</xref>) from this fire are robust because we used stem tree biomass and demographic data from pre-1910 timber cruise inventories (<xref ref-type="bibr" rid="B29">Koch, 1942</xref>) to identify FIA plots of similar structure (diameter and heights) and age classes to the 1910 inventory.</p>
<table-wrap position="float" id="T3">
<label>TABLE 3</label>
<caption><p>Carbon emissions from 1910 Big Burn and extreme contemporary (1984&#x2013;2020) forest fires in the western United States.</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left">Complex</td>
<td valign="top" align="center">Year</td>
<td valign="top" align="center">Area burned (ha)</td>
<td valign="top" align="center">Tg C</td>
<td valign="top" align="center">Mg C ha<sup>&#x2013;1</sup></td>
<td valign="top" align="center">Tg CO2e</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Big Burn</td>
<td valign="top" align="center">1910</td>
<td valign="top" align="center">966,564&#x2013;1,257,690<xref ref-type="table-fn" rid="t3fns1">&#x002A;</xref></td>
<td valign="top" align="center">29.79&#x2013;49.87<xref ref-type="table-fn" rid="t3fns2">&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">23.69&#x2013;39.65<xref ref-type="table-fn" rid="t3fns2">&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">94.60&#x2013;158.51<xref ref-type="table-fn" rid="t3fns2">&#x002A;&#x002A;</xref></td>
</tr>
<tr>
<td valign="top" align="left">Siege of &#x2019;87</td>
<td valign="top" align="center">1987</td>
<td valign="top" align="center">151,339</td>
<td valign="top" align="center">3.58 &#x00B1; 0.20</td>
<td valign="top" align="center">23.70 &#x00B1; 1.32</td>
<td valign="top" align="center">13.10 &#x00B1; 0.73</td>
</tr>
<tr>
<td valign="top" align="left">Yellowstone</td>
<td valign="top" align="center">1988</td>
<td valign="top" align="center">672,509</td>
<td valign="top" align="center">16.3 &#x00B1; 0.91</td>
<td valign="top" align="center">24.30 &#x00B1; 1.36</td>
<td valign="top" align="center">59.90 &#x00B1; 3.35</td>
</tr>
<tr>
<td valign="top" align="left">Big Bar</td>
<td valign="top" align="center">1999</td>
<td valign="top" align="center">57,158</td>
<td valign="top" align="center">1.40 &#x00B1; 0.08</td>
<td valign="top" align="center">24.60 &#x00B1; 1.38</td>
<td valign="top" align="center">5.10 &#x00B1; 0.29</td>
</tr>
<tr>
<td valign="top" align="left">Biscuit</td>
<td valign="top" align="center">2002</td>
<td valign="top" align="center">200,444</td>
<td valign="top" align="center">4.40 &#x00B1; 0.25</td>
<td valign="top" align="center">22.0 &#x00B1; 1.23</td>
<td valign="top" align="center">16.10 &#x00B1; 0.90</td>
</tr>
<tr>
<td valign="top" align="left">Hayman</td>
<td valign="top" align="center">2002</td>
<td valign="top" align="center">52,373</td>
<td valign="top" align="center">1.53 &#x00B1; 0.09</td>
<td valign="top" align="center">29.20 &#x00B1; 1.63</td>
<td valign="top" align="center">5.60 &#x00B1; 0.31</td>
</tr>
<tr>
<td valign="top" align="left">Tripod</td>
<td valign="top" align="center">2006</td>
<td valign="top" align="center">70,753</td>
<td valign="top" align="center">1.31 &#x00B1; 0.07</td>
<td valign="top" align="center">18.60 &#x00B1; 1.04</td>
<td valign="top" align="center">4.80 &#x00B1; 0.27</td>
</tr>
<tr>
<td valign="top" align="left">Central Idaho</td>
<td valign="top" align="center">2007</td>
<td valign="top" align="center">298,821</td>
<td valign="top" align="center">5.96 &#x00B1; 0.33</td>
<td valign="top" align="center">20.0 &#x00B1; 1.12</td>
<td valign="top" align="center">21.90 &#x00B1; 1.22</td>
</tr>
<tr>
<td valign="top" align="left">Klamath Theater</td>
<td valign="top" align="center">2008</td>
<td valign="top" align="center">86,795</td>
<td valign="top" align="center">2.16 &#x00B1; 0.12</td>
<td valign="top" align="center">25.0 &#x00B1; 1.40</td>
<td valign="top" align="center">7.90 &#x00B1; 0.44</td>
</tr>
<tr>
<td valign="top" align="left">Wallow</td>
<td valign="top" align="center">2011</td>
<td valign="top" align="center">228,106</td>
<td valign="top" align="center">3.39 &#x00B1; 0.19</td>
<td valign="top" align="center">14.90 &#x00B1; 0.83</td>
<td valign="top" align="center">12.40 &#x00B1; 0.69</td>
</tr>
<tr>
<td valign="top" align="left">August</td>
<td valign="top" align="center">2020</td>
<td valign="top" align="center">417,891</td>
<td valign="top" align="center">9.02 &#x00B1; 0.50</td>
<td valign="top" align="center">24.60 &#x00B1; 1.38</td>
<td valign="top" align="center">33.10 &#x00B1; 1.85</td>
</tr>
<tr>
<td valign="top" align="left">Creek<xref ref-type="table-fn" rid="t3fns3">&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">2020</td>
<td valign="top" align="center">153,700</td>
<td valign="top" align="center">4.56 &#x00B1; 0.25</td>
<td valign="top" align="center">29.70 &#x00B1; 1.66</td>
<td valign="top" align="center">16.70 &#x00B1; 0.93</td>
</tr>
<tr>
<td valign="top" align="left">North</td>
<td valign="top" align="center">2020</td>
<td valign="top" align="center">126,220</td>
<td valign="top" align="center">2.35 &#x00B1; 0.13</td>
<td valign="top" align="center">18.60 &#x00B1; 1.04</td>
<td valign="top" align="center">8.60 &#x00B1; 0.48</td>
</tr>
<tr>
<td valign="top" align="left">Oregon<xref ref-type="table-fn" rid="t3fns4">&#x002A;&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">2020</td>
<td valign="top" align="center">340,702</td>
<td valign="top" align="center">8.18 &#x00B1; 0.46</td>
<td valign="top" align="center">24.0 &#x00B1; 1.34</td>
<td valign="top" align="center">30.0 &#x00B1; 1.68</td>
</tr>
<tr>
<td valign="top" align="left">Colorado<xref ref-type="table-fn" rid="t3fns4">&#x002A;&#x002A;&#x002A;&#x002A;</xref></td>
<td valign="top" align="center">2020</td>
<td valign="top" align="center">153,294</td>
<td valign="top" align="center">4.23 &#x00B1; 0.24</td>
<td valign="top" align="center">27.60 &#x00B1; 1.54</td>
<td valign="top" align="center">16.90 &#x00B1; 0.94</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="t3fns1"><p><italic>&#x002A;The area estimates are from <xref ref-type="bibr" rid="B29">Koch (1942)</xref> and <xref ref-type="bibr" rid="B18">Gibson (2005)</xref>.</italic></p></fn>
<fn id="t3fns2"><p><italic>&#x002A;&#x002A;The emissions from Big Burn were calculated using present day USFS Forest Inventory and Analysis data. Here we provide a range of carbon emissions values, rather than uncertainty ranges, because we use a range of combustion factors as we do not have detailed severity data.</italic></p></fn>
<fn id="t3fns3"><p><italic>&#x002A;&#x002A;&#x002A;Prior to the 2020 Creek Fire, this forest had a proportion killed by the 2012&#x2013;2017 drought and was subsequently salvaged logged. Our emissions calculation is likely an overestimate due to the large amount of biomass already removed from salvage logging, and slash left behind from salvage logging driving higher fire severity.</italic></p></fn>
<fn id="t3fns4"><p><italic>&#x002A;&#x002A;&#x002A;&#x002A;Oregon and Colorado forest fire area and emissions are aggregated for the 2020 extreme forest fires.</italic></p></fn>
</table-wrap-foot>
</table-wrap>
<p>We calculated mean annual forest fire emissions for the western United States and each western United States state based on the 2009&#x2013;2018 decade, to best represent the observed trends toward increased area burned under climate change (<xref ref-type="bibr" rid="B1">Abatzoglou and Williams, 2016</xref>). These calculations were completed using MTBS (<xref ref-type="bibr" rid="B12">Eidenshink et al., 2007</xref>) perimeter and severity data, carbon stock data, and combustion factors. All fire carbon losses were converted to Tg CO<sub>2</sub>e (i.e., tera-grams CO<sub>2</sub> equivalent) for comparison with fossil fuel emissions (FFE). To normalize emissions on a per area basis, we calculated megagrams of carbon lost per hectare burned.</p>
<p>To calculate uncertainties for contemporary forest fire emissions, we used a propagation of error approach. We combined uncertainty estimates of each emissions calculation component to calculate total uncertainty for each individual extreme fire event (<xref ref-type="table" rid="T4">Table 4</xref>). We used the Combining Uncertainties Propagation of Error estimates from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (Eq. 1; <xref ref-type="bibr" rid="B25">IPCC, 2006</xref>).</p>
<table-wrap position="float" id="T4">
<label>TABLE 4</label>
<caption><p>Average fossil fuel emissions, forest fire emissions, and harvest emissions, 2020 fire emissions, and record year fire emissions (2008&#x2013;2020) for all western United States and for the entire western United States region.</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left">State</td>
<td valign="top" align="center">10-year average fossil fuel emissions (TgCO<sub>2e</sub>)</td>
<td valign="top" align="center">10-year average forest fire emissions (TgCO<sub>2e</sub>)</td>
<td valign="top" align="center">10-year average harvest emissions (TgCO<sub>2e</sub>)</td>
<td valign="top" align="center">2020 forest fire emissions (TgCO<sub>2e</sub>)</td>
<td valign="top" align="center">Record year forest fire emissions (TgCO<sub>2e</sub>)</td>
<td valign="top" align="center">Record year</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Arizona</td>
<td valign="top" align="center">92.76 &#x00B1; 1.57</td>
<td valign="top" align="center">7.02 &#x00B1; 0.79</td>
<td valign="top" align="center">0.41 &#x00B1; 0.07</td>
<td valign="top" align="center">8.28 &#x00B1; 0.93</td>
<td valign="top" align="center">21.34 &#x00B1; 2.39</td>
<td valign="top" align="center">2011</td>
</tr>
<tr>
<td valign="top" align="left">California</td>
<td valign="top" align="center">370.71 &#x00B1; 4.98</td>
<td valign="top" align="center">14.09 &#x00B1; 1.58</td>
<td valign="top" align="center">7.38 &#x00B1; 1.27</td>
<td valign="top" align="center">121.92 &#x00B1; 13.63</td>
<td valign="top" align="center">121.92 &#x00B1; 13.63</td>
<td valign="top" align="center">2020</td>
</tr>
<tr>
<td valign="top" align="left">Colorado</td>
<td valign="top" align="center">91.84 &#x00B1; 1.04</td>
<td valign="top" align="center">4.92 &#x00B1; 0.55</td>
<td valign="top" align="center">0.36 &#x00B1; 0.06</td>
<td valign="top" align="center">33.35 &#x00B1; 3.73</td>
<td valign="top" align="center">33.35 &#x00B1; 1.91</td>
<td valign="top" align="center">2020</td>
</tr>
<tr>
<td valign="top" align="left">Idaho</td>
<td valign="top" align="center">17.66 &#x00B1; 0.32</td>
<td valign="top" align="center">9.81 &#x00B1; 1.09</td>
<td valign="top" align="center">5.03 &#x00B1; 0.86</td>
<td valign="top" align="center">5.06 &#x00B1; 0.56</td>
<td valign="top" align="center">31.37 &#x00B1; 3.51</td>
<td valign="top" align="center">2012</td>
</tr>
<tr>
<td valign="top" align="left">Montana</td>
<td valign="top" align="center">32.86 &#x00B1; 0.71</td>
<td valign="top" align="center">8.95 &#x00B1; 1.00</td>
<td valign="top" align="center">2.03 &#x00B1; 0.35</td>
<td valign="top" align="center">2.20 &#x00B1; 0.25</td>
<td valign="top" align="center">35.30 &#x00B1; 3.95</td>
<td valign="top" align="center">2017</td>
</tr>
<tr>
<td valign="top" align="left">Nevada</td>
<td valign="top" align="center">37.24 &#x00B1; 1.46</td>
<td valign="top" align="center">0.64 &#x00B1; 0.07</td>
<td valign="top" align="center">0.03 &#x00B1; 0.01</td>
<td valign="top" align="center">0.59 &#x00B1; 0.07</td>
<td valign="top" align="center">2.46 &#x00B1; 0.28</td>
<td valign="top" align="center">2018</td>
</tr>
<tr>
<td valign="top" align="left">New Mexico</td>
<td valign="top" align="center">52.68 &#x00B1; 1.06</td>
<td valign="top" align="center">0.16 &#x00B1; 0.02</td>
<td valign="top" align="center">0.17 &#x00B1; 0.03</td>
<td valign="top" align="center">1.33 &#x00B1; 0.15</td>
<td valign="top" align="center">1.33 &#x00B1; 0.15</td>
<td valign="top" align="center">2020</td>
</tr>
<tr>
<td valign="top" align="left">Oregon</td>
<td valign="top" align="center">39.58 &#x00B1; 0.60</td>
<td valign="top" align="center">7.36 &#x00B1; 0.82</td>
<td valign="top" align="center">19.38 &#x00B1; 3.33</td>
<td valign="top" align="center">35.78 &#x00B1; 4.00</td>
<td valign="top" align="center">35.78 &#x00B1; 4.00</td>
<td valign="top" align="center">2020</td>
</tr>
<tr>
<td valign="top" align="left">Utah</td>
<td valign="top" align="center">63.01 &#x00B1; 0.89</td>
<td valign="top" align="center">1.85 &#x00B1; 0.21</td>
<td valign="top" align="center">0.14 &#x00B1; 0.03</td>
<td valign="top" align="center">3.21 &#x00B1; 0.36</td>
<td valign="top" align="center">7.01 &#x00B1; 0.78</td>
<td valign="top" align="center">2018</td>
</tr>
<tr>
<td valign="top" align="left">Washington</td>
<td valign="top" align="center">77.70 &#x00B1; 1.21</td>
<td valign="top" align="center">4.10 &#x00B1; 0.45</td>
<td valign="top" align="center">14.72 &#x00B1; 2.53</td>
<td valign="top" align="center">2.11 &#x00B1; 0.24</td>
<td valign="top" align="center">18.68 &#x00B1; 2.09</td>
<td valign="top" align="center">2015</td>
</tr>
<tr>
<td valign="top" align="left">Wyoming</td>
<td valign="top" align="center">64.91 &#x00B1; 0.60</td>
<td valign="top" align="center">0.94 &#x00B1; 0.01</td>
<td valign="top" align="center">0.18 &#x00B1; 0.03</td>
<td valign="top" align="center">10.25 &#x00B1; 1.15</td>
<td valign="top" align="center">10.25 &#x00B1; 1.15</td>
<td valign="top" align="center">2020</td>
</tr>
<tr>
<td valign="top" align="left">Total WUS</td>
<td valign="top" align="center">941.00 &#x00B1; 10.74</td>
<td valign="top" align="center">59.95 &#x00B1; 6.70</td>
<td valign="top" align="center">49.88 &#x00B1; 8.56</td>
<td valign="top" align="center">224.09 &#x00B1; 25.05</td>
<td valign="top" align="center">224.09 &#x00B1; 25.05</td>
<td valign="top" align="center">2020</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Equation 1: Combining Uncertainties (individual associated uncertainties):</p>
<disp-formula id="S2.Ex1">
<mml:math id="M1" display="block">
<mml:mrow>
<mml:mpadded width="+3.3pt">
<mml:msub>
<mml:mi>U</mml:mi>
<mml:mrow>
<mml:mtext>total</mml:mtext>
</mml:mrow>
</mml:msub>
</mml:mpadded>
<mml:mo rspace="10.8pt">=</mml:mo>
<mml:mfrac>
<mml:msqrt>
<mml:mrow>
<mml:msup>
<mml:mrow>
<mml:mo>(</mml:mo>
<mml:mrow>
<mml:mpadded>
<mml:msub>
<mml:mi>U</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
</mml:mpadded>
<mml:mo>&#x002A;</mml:mo>
<mml:msub>
<mml:mi>x</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
</mml:mrow>
<mml:mo>)</mml:mo>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msup>
<mml:mo rspace="5.8pt">+</mml:mo>
<mml:msup>
<mml:mrow>
<mml:mo>(</mml:mo>
<mml:mrow>
<mml:mpadded>
<mml:msub>
<mml:mi>U</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
</mml:mpadded>
<mml:mo>&#x002A;</mml:mo>
<mml:msub>
<mml:mi>x</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
</mml:mrow>
<mml:mo>)</mml:mo>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mrow>
</mml:msqrt>
<mml:mrow>
<mml:mo>|</mml:mo>
<mml:mrow>
<mml:mpadded>
<mml:msub>
<mml:mi>x</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
</mml:mpadded>
<mml:mo>+</mml:mo>
<mml:msub>
<mml:mi>x</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
</mml:mrow>
<mml:mo>|</mml:mo>
</mml:mrow>
</mml:mfrac>
</mml:mrow>
</mml:math>
</disp-formula>
<p>Where <italic>U</italic><sub><italic>total</italic></sub> = the percentage uncertainty in the sum of the quantities (half the 95 percent confidence interval divided by the total, i.e., mean and expressed as a percentage).</p>
<p><italic>x</italic><sub><italic>i</italic></sub> and <italic>U</italic><sub><italic>i</italic></sub> = the uncertain quantities and the percentage uncertainties associated with them. <italic>x</italic><sub><italic>i</italic></sub> refers to the specific fire emission calculation. <italic>U</italic><sub><italic>1</italic></sub> refers to the uncertainty in biomass calculations (0.05) and <italic>U</italic><sub><italic>2</italic></sub> refers to remote sensing uncertainty in area burned calculations (0.10).</p>
<p>Calculations and spatial analyses were conducted using R (<xref ref-type="bibr" rid="B45">R Core Team, 2017</xref>) and ESRI software (<xref ref-type="bibr" rid="B14">ESRI, 2020</xref>).</p>
</sec>
<sec id="S2.SS3">
<title>Timber Harvest and Wood Product Emissions</title>
<sec id="S2.SS3.SSS1">
<title>Hypothetical Harvest Carbon Losses</title>
<p>Hypothetical harvest carbon losses were calculated for all states for burned areas between 2009 and 2019; these are the exact burned areas and pre-fire carbon stocks used to calculate forest fire emissions for this study. This hypothetical calculation allows us to directly compare fire carbon losses to harvest carbon losses on a per area basis. Here, we calculated three scenarios for standing tree carbon (both live trees and snags): 30% harvest, 50% harvest, and 100% harvest. Both 30% and 50% harvests are meant to represent different levels of thinning (thinning-from-below and commercial, respectively), while 100% harvest is akin to a clear-cut harvest (<xref ref-type="table" rid="T1">Table 1</xref>). For these scenarios, the fraction (30, 50, or 100%) of aboveground carbon for standing live and dead trees was calculated and counted as carbon loss, and then converted to a per area basis. For the hypothetical thinning scenarios we did not include carbon stored in wood products because very little to no long-term wood products would be created from the smaller-diameter trees removed from these types of thinning. These smaller-diameter trees will most likely be used in short-term wood products such as paper (<xref ref-type="bibr" rid="B21">Hudiburg et al., 2019</xref>). The hypothetical clear-cut used a static 60% emission from aboveground tree carbon stocks, with 40% remaining in long-term wood products pools. To normalize harvest carbon losses on a per area basis, we calculated megagrams of carbon lost per hectare harvested.</p>
</sec>
<sec id="S2.SS3.SSS2">
<title>Actual Timber Harvest Carbon Losses</title>
<p>Actual timber harvest calculations were aggregated from publicly-available state and federal historical harvest sources (<xref ref-type="bibr" rid="B21">Hudiburg et al., 2019</xref>), including privately-owned lands. Detailed methodology can be found in <xref ref-type="bibr" rid="B21">Hudiburg et al. (2019)</xref>. We calculated a mean annual harvest loss for the most recent available harvest data for each state (2007&#x2013;2016) as well as an annual average for the entire western United States. Reported harvest volumes (merchantable) were converted to grams carbon using board feet to cubic volume estimates from Keegan (<xref ref-type="bibr" rid="B27">Keegan et al., 2010</xref>). Our calculations include the carbon stored (and released from at end of life) in wood products for the years of this analysis. Wood was assumed to enter short-term (1 to 10 years before emissions return to the atmosphere; includes wood waste at the mills) and long-term (50-year half-life) product pools at rates of 60 and 40%, respectively, (<xref ref-type="bibr" rid="B19">Heath et al., 2010</xref>; <xref ref-type="bibr" rid="B21">Hudiburg et al., 2019</xref>). All timber harvest carbon losses were converted to Tg CO<sub>2</sub>e for comparison with FFE.</p>
</sec>
</sec>
<sec id="S2.SS4">
<title>Fossil Fuel Emissions</title>
<p>Fossil fuel emission numbers were sourced from the Environmental Protection Agency (<xref ref-type="bibr" rid="B13">EPA, 2020</xref>). The EPA provides yearly, state-by-state FFE from nearly all emissions sources (i.e., commercial, industrial, residential, transportation, and electric power; <xref ref-type="table" rid="T4">Table 4</xref>). We calculated an annual average for the most recent available data for each state (2009&#x2013;2018), as well as an annual average for the entire western United States.</p>
</sec>
<sec id="S2.SS5">
<title>Forest Fire and Fossil Fuel Emission Comparisons</title>
<p>Carbon emissions from forest fires and FFE were normalized on a state-by-state basis by normalizing both average FFE (2009&#x2013;2018) and record year fire emissions (1984&#x2013;2020) with average fire emissions (2009&#x2013;2018). A factor of both average FFE and record year fire emissions over the average fire emissions was calculated for each state.</p>
</sec>
</sec>
<sec id="S3" sec-type="results">
<title>Results</title>
<sec id="S3.SS1">
<title>Forest Fire Carbon Emissions</title>
<p>Carbon emissions from 1984 to 2020 wildfire events varied considerably by fire severity (<xref ref-type="supplementary-material" rid="TS1">Supplementary Figure 1</xref>), forest type (e.g., varied carbon density), and size (<xref ref-type="fig" rid="F2">Figure 2</xref>). As forest fire carbon emissions are a product of forest type (pre-fire aboveground carbon density per area, <xref ref-type="fig" rid="F3">Figure 3</xref>) and fire severity (<xref ref-type="table" rid="T4">Table 4</xref>), it is notable that Colorado fires generally exhibit higher emissions per unit area (27.60 Mg C ha<sup>&#x2013;1</sup>) compared to other 2020 fire events in Oregon and California, although the 2020 Creek Fire in California had the highest emissions per unit area for a single contemporary fire (<xref ref-type="table" rid="T4">Table 4</xref>). This highlights how severely Colorado wildfires have burned in recent decades, given their lower pre-fire carbon density. By contrast, Oregon forests have much higher pre-fire carbon density and slightly lower area-normalized emissions compared to other western fires because they burned, on average, at a lower severity (<xref ref-type="fig" rid="F3">Figure 3</xref>). When normalized by area burned to control for size, there is notable variation amongst the contemporary extreme fires in emissions per hectare (<xref ref-type="table" rid="T4">Table 4</xref>). For example, the 2020 Creek Fire in California had the highest emissions per hectare (29.7 Mg C ha<sup>&#x2013;1</sup>), and fires in Colorado all exceed the Idaho, Montana, and Wyoming fires, and many in the carbon-dense forests of Oregon when normalized by area. In addition, &#x003E;90% of the burn area of extreme forest fires in 2020 were in low-to-moderate severity classes (<xref ref-type="supplementary-material" rid="TS1">Supplementary Figure 1</xref>).</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption><p>Fire perimeters and forest carbon density losses (Mg C ha<sup>&#x2013;1</sup>) from 2020 extreme, large forest fires in CA, CO, and OR (&#x003E;100,000 acres). Green background indicates aboveground carbon forest layer, where darker green forest cover denotes higher density of aboveground carbon. Detailed maps display both fire severity (multicolored fire area), and per area carbon losses (red fire area).</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-05-867112-g003.tif"/>
</fig>
</sec>
<sec id="S3.SS2">
<title>Harvest Carbon Losses</title>
<p>Total average annual western United States total harvest emissions were lower than total average forest fire emissions (<xref ref-type="table" rid="T4">Table 4</xref>), however, actual harvest area is much lower than area burned (<xref ref-type="bibr" rid="B4">Berner et al., 2017</xref>). Actual harvest carbon losses vary greatly by state, with carbon-dense Oregon and Washington having the highest biomass harvest removals (<xref ref-type="table" rid="T4">Table 4</xref>). However, on a per unit area basis, hypothetical 100% harvest is 2&#x2013;8 times greater than fire for the same perimeters across the entire region (<xref ref-type="fig" rid="F4">Figure 4</xref>). We calculated hypothetical harvest carbon losses for the exact burn areas in each state to compare per area harvest losses. We found that for all states a 50&#x2013;100% harvest would have led to greater carbon losses than fire for those burned areas, and even a 30% harvest led to greater carbon losses than fire for all but four of the western United States. Hypothetical harvest carbon losses continue to outpace fire carbon losses on a per unit area basis for most scenarios (<xref ref-type="fig" rid="F4">Figure 4</xref>).</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption><p>Comparison of per area (Mg C ha<sup>&#x2013;1</sup>) hypothetical harvest scenario carbon losses to actual fire emissions. Harvest scenarios were calculated for the exact burn area in these states for 2009&#x2013;2018. Harvest scenarios are based on 30, 50, and 100% aboveground tree removal rates. Here, a 30% is showing a thin-from-below, 50% harvest is akin to a commercial thin, while 100% would be representative of a clear-cut removal. Fire emissions are based on the fire perimeters of forest fires used in this study. Error bars represent standard error.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-05-867112-g004.tif"/>
</fig>
</sec>
<sec id="S3.SS3">
<title>Anthropogenic Fossil Fuel Emissions</title>
<p>Anthropogenic fossil fuel emissions (AFFE) for each western United States and for the total western United States substantially exceed forest fire carbon emissions (mean annual and 2020), and average actual timber harvest (<xref ref-type="fig" rid="F5">Figure 5</xref> and <xref ref-type="table" rid="T4">Table 4</xref>). Mean annual AFFE in the western United States were over 15&#x00D7; higher than mean annual forest fire emissions and mean annual AFFE were 420% higher than forest fire emissions from the 2020 record fires across the west (<xref ref-type="fig" rid="F5">Figure 5</xref>). Emissions vary widely by state, primarily due to population size (i.e., population and FFE are positively correlated) and large-scale, high-emissions industries within the state. Total western United States 2020 fire emissions were higher than the mean annual fire emissions (2009&#x2013;2018), driven by large fire events in California, Oregon, and Colorado (<xref ref-type="table" rid="T4">Table 4</xref>). California, Oregon, Colorado, and New Mexico all had record-high forest fire emissions in 2020.</p>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption><p>Fossil fuel and record-year forest fire emissions as a factor of mean annual forest fire emissions for the baseline decade 2009&#x2013;2018. This comparison shows <bold>(A)</bold> mean annual fire emissions (Tg CO<sub>2</sub>e) calculated per state for a baseline 10-year (2009&#x2013;2018) period, and factors from mean annual forest fire emissions (i.e., the number of times higher, or the proportion of those emissions relative to mean annual forest fire emissions) for <bold>(B)</bold> record-year forest fire year emissions (i.e., the year with the highest forest fire emissions for that state, <xref ref-type="table" rid="T4">Table 4</xref>), and <bold>(C)</bold> mean annual fossil fuel emissions.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="ffgc-05-867112-g005.tif"/>
</fig>
</sec>
</sec>
<sec id="S4" sec-type="discussion">
<title>Discussion</title>
<sec id="S4.SS1">
<title>Forest Fire, Harvest, and Fossils Fuels: Putting Emissions in Context</title>
<p>Public perception and existing overestimates of forest mortality and carbon emissions from wildfire feeds into the misconception that wildfire kills all live forest cover and combusts all forest carbon (<xref ref-type="bibr" rid="B60">Wiedinmyer and Neff, 2007</xref>; <xref ref-type="bibr" rid="B37">Mater, 2017</xref>; <xref ref-type="bibr" rid="B63">Zinke, 2018</xref>). The reality of actual fire emissions calculated from mixed-severity combustion rather than overestimates calculated from the false high-severity narrative highlights the need to disentangle ecological impacts of wildfire from societal impacts (i.e., loss of lives and houses). This will help to ensure that risk-reduction solutions can decrease wildfire disasters while still maintaining ecosystem services, such as live tree carbon uptake and wildlife habitat (<xref ref-type="bibr" rid="B30">Kolden, 2020</xref>).</p>
<p>As wildfire policy discussions increasingly include extractive forest harvest to mitigate forest fires (<xref ref-type="bibr" rid="B15">Executive Order, 2018</xref>; <xref ref-type="bibr" rid="B40">Newhouse, 2021</xref>; <xref ref-type="bibr" rid="B48">Senate Bill 762, 2021</xref>), a comparison of emissions from forest fire, timber harvest, and fossil fuels provides a more complete understanding of the relative contributions of emissions sources to anthropogenic climate change. Despite increasing area burned trends across the western United States (<xref ref-type="bibr" rid="B42">Parks and Abatzoglou, 2020</xref>), FFE still greatly outpace forest fire emissions in the last decade, including 2020. FFE are also significantly higher than intensive and large-scale land management operations like timber harvest in many United States (i.e., California).</p>
</sec>
<sec id="S4.SS2">
<title>Policy Implications and Ways Forward</title>
<p>Much of United States fire management and policy has been shaped by specific, previously unprecedented wildfire events. The Big Burn of 1910 was the first massive fire event for the fledgling United States Forest Service (<xref ref-type="bibr" rid="B29">Koch, 1942</xref>), and is still the largest wildfire complex that has occurred in the contiguous United States. Fire suppression as the main form of fire control persisted until the late 20th century, when ecological restoration efforts began seeking to reduce hazardous fuels and increase ecologically beneficial fire effects (<xref ref-type="bibr" rid="B44">Parsons et al., 1986</xref>). However, these efforts have not yet altered the fire suppression culture instilled by 1910 (<xref ref-type="bibr" rid="B52">Stephens and Ruth, 2005</xref>; <xref ref-type="bibr" rid="B31">Kolden, 2019</xref>; <xref ref-type="bibr" rid="B38">McWethy et al., 2019</xref>).</p>
<p>Like past extreme fire events, the 2020 and 2021 fire seasons have accelerated fire policy and forest management discussions at all levels of government &#x2013; federal, state, and local &#x2013; including recent bills introduced in the United States Senate (S.4625, S.4331). Many new policy discussions on fire and forest management are being based upon the misconception that harvest will protect forests from mortality and carbon loss (<xref ref-type="bibr" rid="B15">Executive Order, 2018</xref>; <xref ref-type="bibr" rid="B63">Zinke, 2018</xref>; <xref ref-type="bibr" rid="B24">Infrastructure Investment and Jobs Act, 2021</xref>; <xref ref-type="bibr" rid="B40">Newhouse, 2021</xref>), and decrease fire risk (<xref ref-type="bibr" rid="B17">Forest Climate Action Team, 2018</xref>; <xref ref-type="fig" rid="F1">Figure 1</xref>) despite substantial uncertainty over long-term impacts to forest climate resilience (i.e., forest treatments may decrease forest resilience in the era of climate change). Our results and the majority of full-carbon accounting studies conclude that any type of harvest (logging or commercial thinning) decreases forest carbon storage (<xref ref-type="bibr" rid="B33">Law et al., 2013</xref>), and this research shows harvest emits more carbon per unit area than fire at all scales (<xref ref-type="fig" rid="F5">Figure 5</xref>).</p>
<p>To mitigate climate change, it is key we understand exactly where emissions are originating. While increased intensity and size of fires are increasing overall fire emissions, these emissions are still substantially less than FFE. This is true even in record forest fire years (<xref ref-type="fig" rid="F5">Figure 5</xref>). While the 2020 fire season was unprecedented in many ways (record forest area burned in California, Oregon, and Colorado; societal devastation including fatalities, thousands of homes consumed, dramatic evacuations, and regional hazardous air quality events), ecologically, most of the forested area burned in extreme fires was in a low-to-moderate severity class (&#x003E;90%, <xref ref-type="supplementary-material" rid="TS1">Supplementary Figure 1</xref>). Moreover, while there is assumed high tree mortality in these forest fire perimeters, many of these burns were mixed-severity fires (<xref ref-type="supplementary-material" rid="TS1">Supplementary Figure 1</xref> and <xref ref-type="fig" rid="F3">Figure 3</xref>) meaning many live trees will persist across most of the low-to-moderate severity burned areas. Locations with high-harvest rates and carbon-dense forests, such as the Pacific Northwest United States, see higher carbon losses from harvest than fire compared to areas in the Southwest United States with low harvest rates and carbon-sparse forests (e.g., Oregon versus Arizona). Forest management needs to be specific to forest type and region; old-growth and wet forests in the Northwest are best left preserved while dry, fire-prone forests or areas in the Wildland Urban Interface benefit from fire risk-reduction strategies like small-diameter thinning and prescribed fires (<xref ref-type="bibr" rid="B34">Law et al., 2018</xref>; <xref ref-type="bibr" rid="B9">Case et al., 2021</xref>). Inclusion of specific diameter limits in policy for public lands could help prevent large-diameter tree removal and subsequent unintended consequences.</p>
<p>Forest management strategies that are site-specific and balance the immediate protection of life and property with long-term preservation of existing and potential carbon stocks in forests are critical to mitigating the negative impacts of climate change. The most effective forest management strategy to protect forest carbon stocks on public lands is to preserve forests through decreased harvest and thinning, lengthened harvest rotations, increased proportion of long-term wood products, reduced harvest and mill waste, and working toward afforestation and reforestation (<xref ref-type="bibr" rid="B22">Hudiburg et al., 2013</xref>; <xref ref-type="bibr" rid="B34">Law et al., 2018</xref>; <xref ref-type="bibr" rid="B6">Buotte et al., 2020</xref>; <xref ref-type="fig" rid="F1">Figure 1</xref>). Prescribed burns reduce fire risk while minimizing carbon losses and amplify tree growth and carbon sequestration in large-diameter trees in fire-adapted forests (<xref ref-type="bibr" rid="B23">Hurteau and North, 2009</xref>). In western United States forests, 33 to 46% of aboveground live biomass is stored in the large diameter trees (&#x003E;60 cm; <xref ref-type="bibr" rid="B36">Lutz et al., 2018</xref>; <xref ref-type="bibr" rid="B39">Mildrexler et al., 2020</xref>). Carbon-smart treatments on public lands need to be specific about diameter limits to avoid large-diameter tree removal.</p>
<p>Here, we have shown that FFE for the western United States are 7 times greater than emissions associated with timber harvest and fire (<xref ref-type="fig" rid="F5">Figure 5</xref> and <xref ref-type="table" rid="T4">Table 4</xref>). As more forest-fire policy and management plans are expanded, written, and discussed following extreme fires of the recent decades, and especially the extreme forest fires of 2020 (<xref ref-type="bibr" rid="B11">DNR, 2020</xref>), it is crucial that these policy changes focus on the largest driving factor of these fires &#x2013; anthropogenic climate change. In practice, large-scale extractive forest management efforts will hamper climate mitigation and may be futile for decreasing fire risk. To be most effective, policy will need to focus on fire-wise adaptations for homes and property and disentangle ecologically-good fire from destructive fires (<xref ref-type="bibr" rid="B30">Kolden, 2020</xref>). Protecting forests with ecologically sound principles, rather than increasing extractive management, may be the best scenario for the mitigation of climate change (<xref ref-type="bibr" rid="B34">Law et al., 2018</xref>), and protecting humans, biodiversity, and forests (<xref ref-type="bibr" rid="B57">Walsh et al., 2019</xref>; <xref ref-type="bibr" rid="B6">Buotte et al., 2020</xref>; <xref ref-type="bibr" rid="B32">Law et al., 2021</xref>). The continued escalation of fires throughout the 21st century is evidence of climate change-mediated intensification of fire regimes in the United States (<xref ref-type="bibr" rid="B61">Williams et al., 2019</xref>). Fire catastrophes will continue to occur and worsen if we do not focus on decreasing FFE, the primary driver of climate change (<xref ref-type="bibr" rid="B26">IPCC, 2018</xref>).</p>
</sec>
</sec>
<sec id="S5" sec-type="data-availability">
<title>Data Availability Statement</title>
<p>The data that support the findings of this study are openly available from the University of Idaho at <ext-link ext-link-type="uri" xlink:href="http://doi.org/10.7923/VVZE-7642">http://doi.org/10.7923/VVZE-7642</ext-link> (<xref ref-type="bibr" rid="B2">Bartowitz et al., 2022</xref>).</p>
</sec>
<sec id="S6">
<title>Author Contributions</title>
<p>KB and TH: conceptualization, formal analysis, investigation, and writing&#x2014;original draft preparation. KB, JS, EW, CK, and TH: methodology and writing&#x2014;review and editing. All authors contributed to the article and approved the submitted version.</p>
</sec>
<sec id="conf1" sec-type="COI-statement">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="pudiscl1" sec-type="disclaimer">
<title>Publisher&#x2019;s Note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
</body>
<back>
<sec id="S7" sec-type="funding-information">
<title>Funding</title>
<p>KB was supported by National Science Foundation award number DEB-1655183 and the National Institute of Food and Agriculture award number 2020-10162. EW was supported by the Bureau of Indian Affairs under award number A19AC00014 and the National Institute of Food and Agriculture under award number 2021-67020-33419. JS was supported by National Science Foundation award numbers DEB-155304 and DEB-2052571. CK was supported by the National Science Foundation under award number DMS-1520873. TH was supported by National Science Foundation award numbers DEB-1655183, DEB-155304 and DEB-2052571.</p>
</sec>
<ack>
<p>We thank two reviewers for helpful comments that improved and clarified the manuscript.</p>
</ack>
<sec id="S9" sec-type="supplementary-material">
<title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/ffgc.2022.867112/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/ffgc.2022.867112/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Table_1.DOCX" id="TS1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
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