# Biomass offsets little or none of permafrost carbon release from soils, streams, and wild_re: an expert assessment

Benjamin W. Abbott, Jeremy Jones, Edward Schuur, F. Stuart Chapin, William Bowden, M. Syndonia Bret-Harte, Howard Epstein, Michael Flannigan, Tamara K. Harms, Teresa Hollingsworth, et al.

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Benjamin W. Abbott, Jeremy Jones, Edward Schuur, F. Stuart Chapin, William Bowden, et al. Biomass offsets little or none of permafrost carbon release from soils, streams, and wild_re: an expert assessment. Environmental Research Letters, 2016, 11 (3), pp.1-13. 10.1088/1748-9326/11/3/034014. hal-01288782

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Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire: an expert assessment

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2016 Environ. Res. Lett. 11 034014

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LETTER

Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire: an expert assessment

Benjamin W Abbott¹,², Jeremy B Jones², Edward A G Schuur³, F Stuart Chapin III², William B Bowden⁴,
M Syndonia Bret-Harte³, Howard E Epstein⁵, Michael D Flannigan⁶, Tamara K Harms²,
Teresa N Hollingsworth⁷, Michelle C Mack³, A David McGuire⁸, Susan M Natali⁹, Adrian V Rocha¹⁰,
Suzanne E Tank¹¹, Merritt R Turetsky¹², Jorien E Vonk¹³, Kimberly P Wickland¹⁴, George R Aiken¹⁴,
Heather D Alexander¹⁵, Rainer M W Amon¹⁶, Brian W Benscoter¹⁷, Yves Bergeron¹⁸, Kevin Bishop¹⁹,²⁰,
Olivier Blarquez²¹, Ben Bond-Lamberty²², Amy L Breen²³, Ishi Buffam²⁴, Yihua Cai²⁵,
Christopher Carcaillet²⁶, Sean K Carey²⁷, Jing M Chen²⁸, Han Y H Chen²⁹, Torben R Christensen³⁰,³¹,
Lee W Cooper³², J Hans C Cornelissen³³, William J de Groot³⁴, Thomas H DeLuca³⁵, Ellen Dorrepaal³⁶,
Ned Fetcher³⁷, Jacques C Finlay³⁸, Bruce C Forbes³⁹, Nancy H F French⁴⁰, Sylvie Gauthier⁴¹,
Martin P Girardin⁴¹, Scott J Goetz⁹, Johann G Goldammer⁴², Laura Gough⁴³, Paul Grogan⁴⁴, Laodong Guo⁴⁵,
Philip E Higuera⁴⁶, Larry Hinzman⁴⁷, Feng Sheng Hu⁴⁸, Gustaf Hugelius⁴⁹, Elchin E Jafarov⁵⁰, Randi Jandt⁵¹,
Jill F Johnstone⁵², Jan Karlsson⁴⁶, Eric S Kasischke⁵³, Gerhard Kattner⁵⁴, Ryan Kelly⁵⁵, Frida Keuper³⁶,⁵⁶,
George W Kling⁵⁷, Pirkko Kortelainen⁵⁸, Jari Kouki⁵⁹, Peter Kuhry⁶⁰, Hjalmar Laudon⁶¹, Isabelle Laurion⁶²,
Robie W Macdonald⁶³, Paul J Mann⁶⁴, Pertti J Martikainen⁶⁵, James W McClelland⁶⁶, Ulf Molau⁶⁷,
Steven F Oberbauer⁶⁸, David Olefeldt⁶⁹, David Paré⁴¹, Marc-André Parisien⁷⁰, Serge Payette⁷¹,
Changhui Peng⁷²,⁷³, Oleg S Pokrovsky⁷⁴,⁷⁵, Edward B Rastetter⁷⁶, Peter A Raymond⁷⁷, Martha K Raynolds⁷⁸,
Guillermo Rein⁷⁹, James F Reynolds⁸⁰,⁸¹, Martin Robards⁸², Brendan M Rogers⁹, Christina Schädel³,
Kevin Schaefer⁸³, Inger K Schmidt⁸⁴, Anatoly Shvidenko⁸⁵,⁸⁶, Jasper Sky⁸⁷, Robert G M Spencer⁸⁸,
Gregory Starr⁸⁹, Robert G Striegl¹⁴, Roman Teisserenc⁹⁰, Lars J Tranvik⁹¹, Tarmo Virtanen⁹²,
Jeffrey M Welker⁹³ and Sergei Zimov⁹⁴

¹ Université de Rennes 1, OSUR, CNRS, UMR 6553 ECOBIO, France

² Institute of Arctic Biology and Department of Biology & Wildlife, University of Alaska Fairbanks, USA

³ Center for Ecosystem Science and Society, Northern Arizona University, USA

⁴ The Rubenstein School of Environment and Natural Resources, University of Vermont, USA

⁵ Department of Environmental Sciences, University of Virginia, USA

⁶ Department of Renewable Resources, University of Alberta, Canada

⁷ USDA Forest Service, PNW Research Station, University of Alaska Fairbanks, USA

⁸ US Geological Survey, Alaska Cooperative Fish and Wildlife Research Unit, University of Alaska Fairbanks, USA

⁹ Woods Hole Research Center, USA

¹⁰ Department of Biological Sciences and the Environmental Change Initiative, University of Notre Dame, USA

¹¹ Department of Biological Sciences, University of Alberta, Canada

¹² Department of Integrative Biology, University of Guelph, Canada

¹³ Department of Earth Sciences, VU University Amsterdam, The Netherlands

¹⁴ US Geological Survey, National Research Program, Boulder, CO, USA

¹⁵ Mississippi State University, Forest and Wildlife Research Center, MS 39762, USA

¹⁶ Texas A&M University at Galveston, USA

¹⁷ Florida Atlantic University, USA

¹⁸ Forest Research Institute, Université du Québec en Abitibi-Témiscamingue, Canada

¹⁹ Department of Earth Sciences, Uppsala University, Sweden

²⁰ Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Sweden

²¹ Département de Géographie, Université de Montréal, Canada

²² Pacific Northwest National Lab, USA

²³ Scenarios Network for Alaska & Arctic Planning, International Arctic Research Center, University of Alaska Fairbanks, USA

²⁴ University of Cincinnati, USA

²⁵ State Key Laboratory of Marine Environmental Science, Xiamen University, People's Republic of China

²⁶ Ecole Pratique des Hautes Etudes, UMR5023 CNRS Lyon 1, France

²⁷ McMaster University, Canada

²⁸ University of Toronto, Canada

²⁹ Faculty of Natural Resources Management, Lakehead University, Canada

³⁰ Lund University, Arctic Research Centre, Sweden

³¹ Aarhus University, Denmark

³² University of Maryland Center for Environmental Science, USA

³³ Systems Ecology, VU University, The Netherlands

³⁴ Canadian Forest Service, Natural Resources Canada, Canada
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35 School of Environmental and Forest Sciences, University of Washington, USA

36 Climate Impacts Research Centre, Department of Ecology and Environmental Science, Umeå University, Sweden

37 Institute for Environmental Science and Sustainability, Wilkes University, USA

38 Department of Ecology, Evolution and Behavior, University of Minnesota, USA

39 Arctic Centre, University of Lapland, Finland

40 Michigan Tech Research Institute, Michigan Technological University, USA

41 Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Canada

42 Global Fire Monitoring Center, Max Planck Institute for Chemistry, Germany

43 Department of Biological Sciences, Towson University, USA

44 Department of Biology, Queen's University, Canada

45 University of Wisconsin-Milwaukee, School of Freshwater Sciences, USA

46 Department of Ecosystem and Conservation Sciences, University of Montana, USA

47 University of Alaska Fairbanks, USA

48 Department of Plant Biology and Department of Geology, University of Illinois, USA

49 Department of Physical Geography, Stockholm University, Sweden

50 Institute of Arctic and Alpine Research, University of Colorado Boulder, USA

51 Alaska Fire Science Consortium, University of Alaska Fairbanks, USA

52 Biology Department, University of Saskatchewan, Canada

53 Department of Geographical Sciences, University of Maryland, USA

54 Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Germany

55 Neptune and Company Inc., USA

56 INRA, AgroImpact UPR1158, France

57 University of Michigan, USA

58 Finnish Environment Institute, Finland

59 School of Forest Sciences, University of Eastern Finland, Finland

60 Department of Physical Geography, Stockholm University, Sweden

61 Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden

62 Centre Eau Terre Environnement, Institut national de la recherche scientifique, Canada

63 Department of Fisheries and Oceans, Institute of Ocean Sciences, Canada

64 Department of Geography, Northumbria University, UK

65 Department of Environmental and Biological Sciences, University of Eastern Finland, Finland

66 University of Texas at Austin, Marine Science Institute, USA

67 Department of Biological and Environmental Sciences, University of Gothenburg, Sweden

68 Department of Biological Sciences, Florida International University, USA

69 Department of Renewable Resources, University of Alberta, Canada

70 Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Canada

71 Centre d'études Nordiques, Université Laval, Canada

72 Center of CEF/ESCR, University of Quebec at Montreal, Canada

73 State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Forestry, Northwest A&F University, People's Republic of China

74 Georesources and Environment, CNRS, Toulouse, France

75 BIO-GEO-CLIM Laboratory, Tomsk State University, Russia

76 The Ecosystems Center, Marine Biological Laboratory, Woods Hole, USA

77 Yale School of Forestry and Environmental Studies, USA

78 Institute of Arctic Biology, University of Alaska Fairbanks, USA

79 Department of Mechanical Engineering, Imperial College London, UK

80 School of Life Sciences, Lanzhou University, People's Republic of China

81 Nicholas School of the Environment, Duke University, USA

82 Wildlife Conservation Society, Arctic Beringia Program, USA

83 National Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, USA

84 Department of Geosciences and Natural Resource Management, University of Copenhagen, Denmark

85 International Institute for Applied Systems Analysis, Laxenburg, Austria

86 Sukachev Institute of Forest, Russia

87 Cambridge Centre for Climate Change Research, UK

88 Department of Earth, Ocean & Atmospheric Science, Florida State University, USA

89 Department of Biological Sciences, University of Alabama, USA

90 ECOLAB, Université de Toulouse, CNRS, INPT, UPS, Toulouse, France

91 Limnology, Department of Ecology and Genetics, Uppsala University, Sweden

92 Department of Environmental Sciences, University of Helsinki, Finland

93 University of Alaska Anchorage, USA

94 Northeast Science Station of the Russian Academy of Sciences, Russia

E-mail: benabbo@gmail.com

Keywords: permafrost carbon, Arctic, boreal, wildfire, dissolved organic carbon, particulate organic carbon, coastal erosion

Supplementary material for this article is available online
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## Abstract

As the permafrost region warms, its large organic carbon pool will be increasingly vulnerable to decomposition, combustion, and hydrologic export. Models predict that some portion of this release will be offset by increased production of Arctic and boreal biomass; however, the lack of robust estimates of net carbon balance increases the risk of further overshooting international emissions targets. Precise empirical or model-based assessments of the critical factors driving carbon balance are unlikely in the near future, so to address this gap, we present estimates from 98 permafrost-region experts of the response of biomass, wildfire, and hydrologic carbon flux to climate change. Results suggest that contrary to model projections, total permafrost-region biomass could decrease due to water stress and disturbance, factors that are not adequately incorporated in current models.

Assessments indicate that end-of-the-century organic carbon release from Arctic rivers and collapsing coastlines could increase by 75% while carbon loss via burning could increase four-fold. Experts identified water balance, shifts in vegetation community, and permafrost degradation as the key sources of uncertainty in predicting future system response. In combination with previous findings, results suggest the permafrost region will become a carbon source to the atmosphere by 2100 regardless of warming scenario but that 65%-85% of permafrost carbon release can still be avoided if human emissions are actively reduced.

## Introduction

### Permafrost zone carbon balance

The United Nations has set a target of limiting warming to 2 °C above pre-industrial temperatures to mitigate risk of the most damaging consequences of climate change (UNEP 2013). Maintaining global climate within this target depends on understanding ecosystem feedbacks to climate change so that adequate limits on human emissions can be set. As high latitudes warm, more of the large permafrost carbon pool will be exposed to decomposition, combustion, and hydrologic export (Harden et al 2012, Schuur et al 2015). Up to 220 Petagrams (Pg) carbon could be released from permafrost-region soil by 2100, and 500 Pg by 2300 (MacDougall et al 2012, Schuur et al 2013), representing 10%-30% of greenhouse gas emissions required to push the global climate system beyond the 2 °C target (Schaefer et al 2014). Models project that some permafrost carbon release will be offset by increases in Arctic and boreal primary productivity due to extended growing season, CO₂ fertilization, and nutrient release from decomposing soil organic matter. However, many processes and dynamics known to influence biomass accumulation, such as ecosystem disturbance and nutrient limitation, are incompletely represented or absent in current models (Qian et al 2010, Koven et al 2011, Schaefer et al 2011, Koven et al 2015b). Likewise, only a few models projecting future permafrost carbon release consider wildfire emissions, and none include hydrologic carbon flux (Qian et al 2010, Koven et al 2011, Schaefer et al 2011, MacDougall et al 2012, Schaefer et al 2014), though past hydrologic flux has been simulated (McGuire et al 2010, Laudon et al 2012, Kicklighter et al 2013). Despite clear policy implications of this climate feedback, considerable uncertainty of both carbon inputs

and outputs limits our ability to model carbon balance of the permafrost region. To bring to bear the best available quantitative and qualitative scientific information (Joly et al 2010) on this climate feedback, we present results from expert assessment surveys indicating that there is little consensus on the magnitude and even sign of change in high-latitude biomass, whereas most researchers expect fire emissions and hydrologic organic carbon flux to substantially increase by the end of the century.

### Expert assessment

When data are sparse but management decisions are pressing, expert judgements have long been used to constrain possible system response and risk of dangerous or undesired outcomes (Zickfeld et al 2010, Morgan 2014). There are multiple methods for collecting and combining expert opinion including formal expert elicitation interviews, interactive software, and surveys (Aspinall 2010, Javeline et al 2013, Morgan 2014). While expert assessment cannot definitively answer questions of future system response, it complements modeling and empirical approaches by allowing the synthesis of formal and informal system information and by identifying research priorities (figure 1; Sutherland et al 2013, Morgan 2014). The approach is similar to the concept of ensemble models where multiple estimates built on different assumptions and data provide a more robust estimate and measure of variance. Because the experimental unit is an individual researcher, each data point represents an integration of quantitative knowledge from modeling, field, and laboratory studies as well as qualitative information based on professional opinion and personal experience with the system. Expert assessment has been used in risk assessment and forecasting of natural disasters, human impacts on ecosystems, and tipping points in the climate system
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 1.** Conceptual model of the role of expert assessment in generating and communicating scientific understanding. Modeling and field research generate quantitative and qualitative understanding of the system (in this case the permafrost zone). Expert assessment synthesizes current understanding including qualitative information not yet included in numerical models or field studies. These syntheses provide perspective to the scientific community and wholistic summaries of the state of the knowledge to the non-scientific community with the goal of improving management of the system.</figcaption></figure>

(Halpern *et al* 2008, Lenton *et al* 2008, Aspinall 2010). In a data-limited environment such as the permafrost region, expert assessment allows formal consideration of a range of factors known to affect carbon balance but insufficiently quantified for inclusion in models. For permafrost carbon balance, these factors include nutrient dynamics, nonlinear shifts in vegetation community, human disturbance, land-water interactions, and the relationship of permafrost degradation with water balance.

Because precise empirical or model-based assessments of the critical factors driving permafrost-region carbon balance are unlikely in the near future (Harden *et al* 2012), we collected estimates of the components of net ecosystem carbon balance from 98 permafrost-region experts (table 1). We had two major goals: (1) Assess current understanding of the timing and magnitude of non-soil biomass accumulation, hydrologic organic carbon flux, and wildfire carbon emissions, and (2) Identify major sources of uncertainty in high-latitude carbon balance to inform future research.

## Methods

### Survey development and design

In the fall of 2013 we administered three expert assessments to address knowledge gaps concerning the response of permafrost-region biomass, wildfire, and hydrologic carbon flux to climate change. Development of assessment methodology began in early 2009 as a part of the Dangerous Climate Change Assessment Project administered by the University of Oxford. We iteratively revised questions, response format, and background information based on four rounds of input from participants, including at the Vulnerability

<table><caption>**Table 1.** Composition and characteristics of participant group.</caption><thead><tr><th></th><th>Biomass</th><th>Wildfire</th><th>Hydrologic flux</th></tr></thead><tbody><tr><td>Number of respondents</td><td>46</td><td>34</td><td>35</td></tr><tr><td>Average responses per question<sup>a</sup></td><td>41</td><td>28</td><td>32</td></tr><tr><td>Primary region of study</td><td></td><td></td><td></td></tr><tr><td>Asia</td><td>10</td><td>3</td><td>8</td></tr><tr><td>Europe</td><td>12</td><td>5</td><td>9</td></tr><tr><td>North America</td><td>27</td><td>27</td><td>18</td></tr><tr><td>Circumpolar</td><td>12</td><td>6</td><td>9</td></tr><tr><td>Primary biome of study</td><td></td><td></td><td></td></tr><tr><td>Arctic</td><td>31</td><td>13</td><td>27</td></tr><tr><td>Boreal</td><td>27</td><td>29</td><td>18</td></tr><tr><td>Both</td><td>14</td><td>9</td><td>12</td></tr><tr><td>Average modeling/ field self rating<sup>b</sup></td><td>3.6</td><td>3.7</td><td>4.1</td></tr><tr><td>Combined years of experience</td><td>762</td><td>533</td><td>521</td></tr><tr><td>Ratio male:female</td><td>2.6</td><td>2.8</td><td>4.9</td></tr></tbody></table>

Background information on survey participants. Experts could indicate multiple regions and biomes of study.

of Permafrost Carbon Research Coordination Network meeting in Seattle 2011 (Schuur *et al* 2013). To help survey participants consider all of the evidence available from field and modeling studies, we distributed a system summary document for each questionnaire including regional and pan-Arctic estimates of current carbon pools and fluxes, a brief treatment of historical trends, and a summary of model projections

<footnote><sup>a</sup> Not all experts provided estimates for all questions.</footnote>

<footnote><sup>b</sup> Experts rated themselves on a 1–5 scale where 1 = exclusive modeler and 5 = exclusive field researcher.</footnote>
<figure><img src="image_9.png" /><figcaption>**Table 2.** Estimates of current permafrost region organic carbon pools and fluxes. Literature-based estimates of belowground biomass were calculated from aboveground or total biomass with ratios from Saugier *et al* (2001). POC delivery to freshwater ecosystems was calculated from ocean POC delivery with downscaled global ratio of 0.75 for sedimentation. POC from coastal erosion is the sum of Vonk *et al* (2012) and McGuire *et al* (2009). Considerable uncertainty remains around many of these estimates.</figcaption></figure>

<table><thead><tr><th></th><th>Aboveground biomass</th><th>Belowground biomass<sup>a</sup></th><th>Dead wood<sup>b</sup></th><th>Litter</th><th>Total non-soil biomass</th></tr></thead><tbody><tr><td colspan="6"><strong>Biomass</strong></td></tr><tr><td>Boreal forest (Pg C)</td><td>43.6<sup>c</sup></td><td>16.1</td><td>16</td><td>27<sup>b</sup></td><td>102.7</td></tr><tr><td>Arctic Tundra (Pg C)</td><td>2.4<sup>d</sup></td><td>4.0</td><td></td><td>2<sup>e</sup></td><td>8.4</td></tr><tr><td colspan="6"><strong>Wildfire</strong></td></tr><tr><td></td><td>Boreal forest (Eurasia)</td><td>Boreal forest (N. America)</td><td>Total Boreal forest<sup>f</sup></td><td>Total Tundra</td><td></td></tr><tr><td>Area burned (km<sup>2</sup> yr<sup>-1</sup>)</td><td>62 100</td><td>22 500</td><td>84 600</td><td>4200<sup>g</sup></td><td></td></tr><tr><td>CO<sub>2</sub> emissions from fire (Tg C yr<sup>-1</sup>)</td><td>194</td><td>56</td><td>250</td><td>8<sup>h</sup></td><td></td></tr><tr><td colspan="6"><strong>Hydrologic organic carbon flux</strong></td></tr><tr><td></td><td>DOC</td><td>POC (Riverine)</td><td>POC (coastal)</td><td>Total OC</td><td></td></tr><tr><td>Delivery to freshwater ecosystems (Tg yr<sup>-1</sup>)</td><td>100<sup>c</sup></td><td>20<sup>i</sup></td><td>na</td><td>120</td><td></td></tr><tr><td>Delivery to Arctic Ocean and surrounding seas (Tg yr<sup>-1</sup>)</td><td>36<sup>j</sup></td><td>6<sup>c</sup></td><td>18<sup>ck</sup></td><td>60</td><td></td></tr></tbody></table>

where available (table 2; supplementary information questionnaires and system summaries).

Participants were selected based on contribution to peer-reviewed literature or referrals from other experts and had experience in all major boreal and Arctic regions (table 1). We identified potential participants by querying Thomas Reuters Web of Science (webofknowledge.com) with applicable search terms (e.g. Arctic, boreal, biomass, dissolved organic carbon, fire, permafrost). To reach researchers with applicable expertise who were underrepresented in the literature, we supplemented the list with personal referrals from lead experts and all participants. In total 256 experts were invited to participate. We distributed the surveys and system summaries via email with a two-week deadline. After sending out three reminders and accepting responses for three months after initial invitation, we received 115 responses from 98 experts (38% response rate), with 15 experts participating in more than one survey (supplementary information list

of experts). Experts who provided estimates and input to this paper are coauthors.

Experts provided quantitative estimates of change in biomass, hydrologic flux, or wildfire for three time points (2040, 2100, and 2300), and four regional warming scenarios based on representative concentration pathway (RCP) scenarios from the IPCC Fifth Assessment Report (Moss *et al* 2010). Warming scenarios ranged from cessation of human emissions before 2100 (RCP2.6) to sustained human emissions (RCP8.5) and corresponded to permafrost-region mean annual warming of 2 °C–7.5 °C by 2100. All surveys were driven by the same scenarios of high-latitude warming generated from RCP2.6, 4.5, 6.0, and 8.5 with the National Center for Atmospheric Research's Community Climate System Model 4 (Lawrence *et al* 2012). For the purposes of this survey, warming was assumed to stabilize at 2100 levels for all scenarios so that estimates for the 2300 time point would account for lags in ecosystem responses to climate drivers. While climate scenarios were defined by

<footnote><sup>a</sup> Saugier *et al* (2001).</footnote>

<footnote><sup>b</sup> Pan *et al* (2011).</footnote>

<footnote><sup>c</sup> McGuire *et al* (2009).</footnote>

<footnote><sup>d</sup> Epstein *et al* (2012).</footnote>

<footnote><sup>e</sup> Potter and Klooster (1997).</footnote>

<footnote><sup>f</sup> Balshi *et al* (2007), Giglio *et al* (2010), Hayes *et al* (2011), van der Werf *et al* (2010).</footnote>

<footnote><sup>g</sup> Rocha *et al* (2012).</footnote>

<footnote><sup>h</sup> Mack *et al* (2011).</footnote>

<footnote><sup>i</sup> Aufdenkampe *et al* (2011), Battin *et al* (2009).</footnote>

<footnote><sup>j</sup> Holmes *et al* (2012).</footnote>

<footnote><sup>k</sup> Vonk *et al* (2012).</footnote>
<figure><img src="image_10.png" /></figure>

temperature, we asked experts to consider all accom-
panying direct climate effects (e.g. temperature, pre-
cipitation, and atmospheric CO2) and indirect effects
(e.g. vegetation shifts, permafrost degradation, inva-
sive species, and disturbance). Experts were encour-
aged to consider all available formal and informal
information when generating their estimates includ-
ing published and unpublished modeled and empiri-
cal data as well as professional judgment. Participants
listed the major sources of uncertainty in their esti-
mates, self-rated their confidence and expertise for
each question, described rationale for their estimates,
and provided background information (table 1
and S1).

The biomass survey consisted of a single question
asking for cumulative change in tundra and boreal
non-soil biomass including above and belowground
living biomass, standing deadwood, and litter. The
wildfire survey asked for estimates of change in wild-
fire extent and CO2 emissions for the boreal and tun-
dra regions to assess changes in both fire extent and
severity. The hydrologic flux survey asked for esti-
mates of dissolved and particulate organic carbon
(DOC and POC, respectively) delivery to freshwater
ecosystems in the pan-Arctic watershed and delivery
to the Arctic Ocean and surrounding seas via riverine
flux and coastal erosion, allowing the calculation of
losses during transport due to burial or mineraliza-
tion. Dissolved inorganic carbon fluxes were not
included in this survey.

The original questionnaires in 2009 asked for par-
ticipants to estimate subjective 95% confidence inter-
vals of the whole system response (e.g. total change in
high-latitude biomass). Based on expert input during
subsequent testing we disaggregated the system into
different components to encourage detailed con-
sideration of possibly competing dynamics (e.g. asking
for separate estimates of boreal forest and Arctic tun-
dra response; Morgan 2014). This resulted in a large
response table for each question (72–102 quantitative
estimates), which we found caused respondent fatigue
and decreased the number of experts willing to partici-
pate. As a compromise, we asked respondents to pro-
vide a single best estimate and indicate confidence
with a five-point scale (table S1). While analysis of best
estimates can return narrower uncertainty ranges than
subjective probability distributions (Morgan 2014),
we believe this tradeoff resulted in broader expert par-
ticipation, better representing diversity of opinion
across disciplines and compensating for possible
underestimation of variability and uncertainty.

Analysis and calculations

We calculated basic summary statistics, using median
values to estimate center and interquartile ranges
(IQR) to estimate spread. To calculate the portion of
permafrost carbon release offset by biomass accumu-
lation, we combined estimates from this study with

reanalyzed data from Schuur et al (2013). The low IQR
for carbon release offset by biomass growth was
calculated by dividing the low IQR of uptake by the
upper IQR of carbon release and conversely for the
high IQR (figure 3). All analyses were performed in R
3.0.2. The complete dataset of quantitative estimates
and comments from survey participants stripped of
personal identifiers is available at www.aoncadis.org/
dataset/PermafrostCarbonig_surrvey.html.

Results

Carbon pools and fluxes

Expert estimates revealed diverging views on the
response of boreal biomass to warming, with over a
third of estimates predicting a decrease or no change
in boreal biomass across scenarios and time periods
(figure 2). While median change in boreal biomass was
similar across warming scenarios for each time step
(3%, 9%, and 11% increases by 2040, 2100, and 2300,
respectively; figure 2 and S1), variability was much
higher for warmer scenarios. Consequently, all of the
IQR of change in boreal biomass for RCP6.0 and
RCP8.5 included zero. Experts projecting a decrease in
boreal biomass attributed their estimates primarily to
water-stress and disturbance such as fire and perma-
frost degradation. In contrast, there was general
agreement that tundra biomass would respond posi-
tively to warming, with end-of-century increases of
6%–30% projected for RCP2.6 and 10%–90% for
RCP8.5. Because of these contrasting responses to
increased warming, tundra accounted for 40% of total
biomass gain by 2300 for RCP8.5, though it currently
constitutes less than 10% of total permafrost region
biomass (based on median values in figures 2, 3(a) and
table 2). Estimates of boreal biomass were generally
symmetrically distributed while tundra biomass esti-
mates were right-skewed, and most datasets had 1–4
estimates beyond 1.5 times the interquartile range
(figure S2). Self-rated confidence was higher for tundra
than for boreal forest, but was below 3 (moderately
confident) in both cases (table S1), highlighting
considerable uncertainty of individual estimates in
addition to variability among respondents.

Experts projected major shifts in both fire and
hydrologic carbon regimes, with up to a 75% increase
of riverine organic carbon flux to the ocean and a four-
fold increase in fire emissions by 2100 for RCP8.5
based on IQR (figure 2 and S1). Fire and hydrologic
carbon release estimates peaked at 2100, followed by a
10%-40% decrease by 2300. In contrast to biomass,
the response of both fire-driven and hydrologic car-
bon flux varied strongly by warming scenario, with
RCP8.5 resulting in 2-6 times more carbon release
than RCP2.6. While the boreal forest dominated total
wildfire emissions, the relative change in tundra fire
emissions was 1.5- and 2-fold greater than the relative
boreal response for 2100 and 2300, respectively (figure
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>**Figure 2.** Estimates of change in non-soil biomass, wildfire emissions, and hydrologic carbon flux from the permafrost region for four warming scenarios at three time points. All values represent change from current pools or fluxes reported in table 2. Biomass includes above and belowground living biomass, standing deadwood, and litter. Dissolved and particulate organic carbon (DOC and POC respectively) fluxes represent transfer of carbon from terrestrial to aquatic ecosystems. 'Coast' represents POC released by coastal erosion. Representative concentration pathway (RCP) scenarios range from aggressive emissions reductions (RCP2.6) to sustained human emissions (RCP8.5). Box plots represent median, quartiles, and minimum and maximum within 1.5 times the interquartile range. Relative change (percent change from current state) is presented in figure S1 and full distributions are presented in figures S2–S4.</figcaption></figure>

S1). Increases in fire emissions were attributed to changes in fire extent rather than severity, which varied less than 5% among scenarios and time periods. Though dissolved organic carbon (DOC) represented the majority of total hydrologic organic carbon release, experts projected higher relative increases for coastal POC, with end-of-the-century increases of 6%–50% for RCP2.6 and 13%–190% for RCP8.5. There was a lack of consensus on the response of DOC delivery to the ocean, with 21% of estimates predicting a decrease or no change. Experts predicting a decrease attributed their estimates to increased mineralization, changes in hydrologic flowpath, and changes in DOC photo- and bio-lability (Cory et al 2014, Abbott et al 2014). Responses indicated no change in the

proportion of organic carbon mineralized or trapped in sediment before reaching the ocean, with 63%–69% of DOC and 68%–74% of POC lost in transport. Fire and hydrologic carbon flux estimates were strongly right-skewed with a few experts projecting extreme change well beyond 1.5 times the interquartile range for each timestep and warming scenario combination (figures S3 and S4). Average self-rated confidence was between 2 and 3 for all questions except tundra fire emissions which had average confidence of 2.0 and 1.7 (table S1).

## Sources of uncertainty

Along with quantitative estimates of carbon balance, experts identified sources of uncertainty currently
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>**Figure 3.** Total change in non-soil biomass (a) and percentage of permafrost region carbon release offset by change in non-soil biomass (b). Estimates of permafrost carbon release used in estimating percentage offset are recalculated from data presented in Schuur *et al* (2013). See figure 2 for definition of RCP scenarios and symbology. Error bars represent propagated error between the interquartile ranges of carbon release from permafrost soil and carbon uptake by biomass (see methods).</figcaption></figure>

<table><caption>**Table 3.** Sources of uncertainty in system response to climate change.</caption><thead><tr><th colspan="2">Biomass</th><th colspan="2">Wildfire</th><th colspan="2">Hydrologic OC flux</th></tr><tr><th>Source of uncertainty</th><th>%</th><th>Source of uncertainty</th><th>%</th><th>Source of uncertainty</th><th>%</th></tr></thead><tbody><tr><td>Water balance</td><td>56</td><td>Vegetation shift</td><td>73</td><td>Water balance</td><td>41</td></tr><tr><td>Wildfire</td><td>47</td><td>Water balance</td><td>58</td><td>Hydrologic flowpath</td><td>39</td></tr><tr><td>Permafrost degradation</td><td>40</td><td>Human disturbance</td><td>27</td><td>Permafrost degradation</td><td>24</td></tr><tr><td>Human disturbance</td><td>29</td><td>Permafrost degradation</td><td>18</td><td>Photo and bio-lability</td><td>24</td></tr><tr><td>Insect damage</td><td>27</td><td>Seasonality</td><td>15</td><td>Vegetation shift</td><td>20</td></tr><tr><td>Vegetation shift</td><td>24</td><td>Regional differences</td><td>12</td><td>Fluvial erosion</td><td>11</td></tr><tr><td>Treeline dynamics</td><td>16</td><td></td><td></td><td></td><td></td></tr><tr><td>Nutrient availability</td><td>13</td><td></td><td></td><td></td><td></td></tr><tr><td>Non-insect herbivores</td><td>11</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Major factors contributing uncertainty to projections of future system response based on expert comments. Rank is based on percent of experts who listed each factor in their responses. All sources listed by 10% or more of each group are included here. Water balance includes comments mentioning precipitation, soil moisture, runoff, infiltration, or discharge. Permafrost degradation includes comments referring to permafrost collapse (thermokarst) and active layer deepening.

limiting the prediction of system response to climate change (table 3). Water balance, including precipitation, soil moisture, runoff, infiltration, and discharge, was the most frequently mentioned source of uncertainty for both biomass and hydrologic organic carbon

flux, and the second most mentioned for wildfire. Many experts noted that water balance is as or more important than temperature in controlling future carbon balance, yet projections of water balance are less well constrained (Zhang *et al* 2013, Bintanja and
<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /><figcaption>**Figure 4.** A comparison of soil carbon release recalculated from Schuur *et al* (2013) and non-soil biomass uptake in the permafrost region from this study for the business as usual scenario (RCP8.5) and the active reduction of human emissions scenario (RCP2.6). Polygons represent median cumulative change and dotted lines represent the interquartile range. Biomass carbon uptake is overlayed on soil carbon release to show the proportion of carbon release potentially offset by biomass. Linear rates of change were assumed between the three dates where estimates were provided.</figcaption></figure>

Selten 2014). Almost three-quarters of wildfire experts identified the future distribution of vegetation as the primary source of uncertainty in projecting wildfire, noting strong differences in flammability between different boreal and tundra species. Permafrost degradation was identified as an important source of uncertainty for biomass, hydrologic flux, and wildfire, due to both disturbance from ground collapse (thermokarst) and interactions with water-table dynamics and surface soil moisture as deeper thaw affects soil drainage.

## Discussion

### Carbon balance

Arctic tundra and boreal forest have accumulated a vast pool of organic carbon, twice as large as the atmospheric carbon pool and three times as large as the carbon contained by all living things (Hugelius *et al* 2014, Schuur *et al* 2015). Over the past several decades, the permafrost region has removed an average of 500 Tg carbon yr⁻¹ from the atmosphere (McGuire *et al* 2009, Pan *et al* 2011, Hayes *et al* 2011). Combining our estimates of biomass uptake with a recent projection of permafrost soil carbon release

(Schuur *et al* 2013) suggests that the permafrost region will become a carbon source to the atmosphere by 2100 for all warming scenarios (figure 3(b)). Experts predicted that boreal and Arctic biomass could respond more quickly to warming than soil carbon release, offsetting -33% to 200% of mid-century emissions from permafrost-region soil (figure 3(b)). However, because estimates of change in biomass are similar across warming scenarios but permafrost carbon release is strongly temperature-sensitive, the emissions gap widens for warmer scenarios, resulting in five-times more net carbon release under RCP8.5 than RCP2.6. This suggests that 65 to 85% of permafrost carbon release could be avoided if human emissions are actively reduced—i.e. if emissions follow RCP2.6 instead of RCP8.5 (figure 4).

### Comparison with quantitative models

Model projections of future boreal and Arctic biomass agree in sign but vary widely in magnitude, with increases of 9–61 Pg carbon projected by 2100 (Qian *et al* 2010, Koven *et al* 2011, Schaefer *et al* 2011, Falloon *et al* 2012). While some of these models fall within the range estimated here of -20 to 28 Pg carbon by 2100, none include zero or negative change in biomass as
<figure><img src="image_17.png" /></figure>

predicted by over a third of participants in our expert
assessment. Two potential reasons for this disagree-
ment are an overestimation of the effect of CO2
fertilization or an underestimation of the role of
disturbance in some models. Firstly, CO2 fertilization
exerts a larger effect on carbon balance than all other
climate effects in many models (Balshi et al 2009), with
up to 88 Pg carbon difference between model runs
with and without CO2 fertilization effects for some
models (Koven et al 2011). However, there is little field
evidence that CO2 fertilization results in long-term
biomass accumulation in tundra and boreal ecosys-
tems (Hickler et al 2008, Gedalof and Berg 2010,
Peñuelas et al 2011). Additionally, many models with
large CO2 effects do not include other limiting factors,
such as nutrients and water, known to interact with
CO2 fertilization (Hyvonen et al 2007, Thornton
et al 2007, Yarie and Van Cleve 2010, Maaroufi
et al 2015, Koven et al 2015a). Secondly, models that
do not account for disturbance such as wildfire,
permafrost collapse, insect damage, and human
resource extraction likely overestimate the positive
response of biomass to climate change (Kurz
et al 2008, Abbott and Jones 2015, Hewitt et al 2015).

Considering the scenario of a complete biome shift
is useful in evaluating both model projections of
change and estimates from our expert assessment. If
all boreal forest became temperate forest, living bio-
mass would increase by 27%, resulting in the uptake of
16 Pg carbon based on average carbon densities from
both ecosystems (Pan et al 2011). However, 22 Pg car-
bon would be lost due to decreases in dead wood and
litter, resulting in a net circumboreal loss of 6 Pg car-
bon. If all tundra became boreal forest, non-soil bio-
mass would increase by 205% (Saugier et al 2001,
Epstein et al 2012, Raynolds et al 2012), taking up
17 Pg carbon. This scenario may not represent the
upper limit of possible carbon uptake if other unfore-
seen shifts in C allocation take place; however, it high-
lights the relatively modest carbon gains probable on
century timescales.

While regional projections from models of boreal
wildfire vary in sign and magnitude (supplementary
information system summaries), most models agree
that at the circumboreal scale, fire emissions will
increase several-fold, with increases of 200%–560%
projected by the end of the century (Flannigan
et al 2009, Kloster et al 2012). IQR from our study are
somewhat lower (40% to 300%, median 170%), but
participant confidence in these estimates was low, sug-
gesting considerable uncertainty in the future
response of boreal fire. The 60%–480% increase in
tundra fire projected by our study would represent an
even larger ecological shift than experienced by the
boreal forest, with implications for regional biomass,
habitat, and carbon balance, though there are few
models that project changes in tundra fire (Rupp
et al 2000) and none at a circumarctic scale (Mack
et al 2011).

The production of Arctic DOC and POC depends on abundance of carbon sources in terrestrial ecosystems (influenced by biomass, wildfire, temperature, and permafrost degradation) and the ability of hydrologic flow to transport that carbon (determined by factors such as precipitation, runoff, depth of flow through soil, and coastal erosion; Guo et al 2007, Kicklighter et al 2013, Abbott et al 2015, Larouche et al 2015). Due to these complexities and others, there are currently no quantitative projections of future DOC and POC flux from the circumarctic. However, estimates from our study suggest a substantial departure from historical rates of change. For RCP8.5, hydrologic organic carbon loading would increase 4–20 times faster in the 21st century than it did in the 20th (Kicklighter et al 2013), representing a nonlinear response to high-latitude warming. The lack of consensus on the response of DOC, the largest component of hydrologic organic carbon flux, highlights the importance of developing and testing conceptual frameworks to be incorporated into models (Laudon et al 2012).

An alternative explanation for differences between expert estimates and modeled projections is the possibility of bias in the group of experts. Participants in our assessment tended to have more field than modeling experience (table 1) and may have therefore been skeptical of simulated ecosystem responses that have not been observed in the field such as CO2 fertilization and rapid migration of treeline (McGuire et al 2009). Because future dynamics cannot always reliably be predicted on the basis of past system behavior, this bias may or may not result in overly conservative estimates. Furthermore, because experts are likely to base projections on the study areas with which they are most familiar, regional differences could be a source of bias. Fundamental differences among regions in the response of DOC flux and fire-regime to warming have been observed (Kicklighter et al 2013, de Groot et al 2013; supplementary information system summaries). Asia, which represents more than half of the total permafrost region, was under-represented in all three surveys, particularly wildfire (table 1). However, the regional bias in this study may not be greater than that of model projections, which depend on observational and experimental data that are not evenly distributed throughout the permafrost region.

Reducing uncertainty surrounding the permafrost
carbon feedback

Experts identified water balance, vegetation distribu-
tion, and permafrost degradation as the most impor-
tant sources of uncertainty in predicting the timing
and magnitude of the permafrost carbon feedback
(table 3). These three processes are closely intercon-
nected by several internal feedbacks (Anisimov and
Reneva 2006, Shur and Jorgenson 2007, Jorgenson
et al 2013, Girardin et al 2016). For example, wildfire
<figure><img src="image_18.png" /></figure>

or drought can trigger a transition from coniferous to deciduous dominance, warming permafrost by up to 7 °C due to loss of insulating moss and associated changes (Sturm et al. 2001, Shur and Jorgenson 2007, Yarie and Van Cleve 2010). The subsequent recovery trajectories of vegetation and permafrost, as well as the proportion of thawed carbon released CO₂ or CH₄, then depend largely on near-surface hydrologic conditions (Payette et al. 2004, Myers-Smith et al. 2008, Jorgenson et al. 2010, Chapin et al. 2010, O'Donnell et al. 2011, Lawrence et al. 2015). These interdependencies mean that improving projections of the permafrost carbon feedback will require conceptualizing these parameters together. The question of water balance is additionally important in Arctic and boreal ecosystems where hydrologic carbon flux can be the determining factor causing net carbon uptake or release (Kling et al. 1991, Aufdenkampe et al. 2011, Raymond et al. 2013). The lack of model projections of hydrologic carbon fluxes is a major gap in our ability to estimate the permafrost carbon feedback.

The permafrost region has responded differently to various climatic perturbations in the past, representing another tool to constrain possible future response (Zachos et al. 2008). During the Paleocene–Eocene thermal maximum, high-latitude temperature warmed more than 10 °C, causing almost complete loss of permafrost and the mineralization of most permafrost soil organic matter (Bowen and Zachos 2010, DeConto et al. 2012). More recently, the 2 °C–4 °C warming at high-latitudes during the early Holocene caused active-layer deepening throughout the permafrost region but did not trigger complete permafrost loss or widespread carbon release (French 1999, Schirrmeister et al. 2002, Jorgenson et al. 2013). While there are many differences between the Paleozoic and Holocene warming events, one clear distinction is the degree of warming. There may have been a threshold between 4 °C and 10 °C high-latitude warming due to positive feedbacks such as a shift from a coniferous to a deciduous dominated system or an abrupt change in hydrology. If a tipping point does exist between 4 and 10 °C high-latitude warming, it would fall between scenarios RCP4.5 and RCP8.5, representing maximum atmospheric CO₂ of 650 ppm and 850 ppm, respectively (Moss et al. 2010, Lawrence et al. 2012). RCP4.5 is still widely accepted as politically and technically attainable, though it assumes global CO₂ emissions peak before 2050 and decrease by half by 2080 (Moss et al. 2010).

## Conclusions

The permafrost climate feedback has been portrayed in popular media (and to a lesser extent in peer-reviewed literature) as an all-or-nothing scenario. Permafrost greenhouse gas release has been described as a tipping point, a runaway climate feedback, and,

most dramatically, a time bomb (Wieczorek et al. 2011, Treat and Frolking, 2013, Whiteman et al. 2013). On the other extreme, some have dismissed the importance of this feedback, asserting that increases in biomass will offset any carbon losses from soil, or that changes will occur too slowly to concern current governments (Idso et al. 2014). Our study highlights that Arctic and boreal biomass should not be counted on to offset permafrost carbon release and suggests that the permafrost region will become a carbon source to the atmosphere by 2100 regardless of warming scenario. Perhaps more importantly, our results indicate a 5-fold difference in emissions between the business as usual scenario (RCP8.5) and active reduction of human emissions (RCP2.6), suggesting that up to 85% of carbon release from the permafrost region can still be avoided, though the window of opportunity for keeping that carbon in the ground is rapidly closing. Models projecting a strong boreal carbon sink and models that do not consider hydrologic and fire emissions may substantially underestimate net carbon release from the permafrost region. If such projections are used as the basis for emissions negotiations, climate targets are likely to be overshot.

## Acknowledgments

Peter J Fix provided valuable input on the methods and manuscript. This work was supported by the National Science Foundation ARCSS program and Vulnerability of Permafrost Carbon Research Coordination Network (grants OPP-0806465, OPP-0806394, and 955713) with additional funding from SITES (Swedish Science Foundation), Future Forest (Mistra), and a Marie Curie International Reintegration Grant (TOMCAR-Permafrost #277059) within the 7th European Community Framework Programme. This work complies with the US HHS Policy for Protection of Human Research Subjects. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

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