Missing pieces to modeling the Arctic-Boreal puzzle

Environmental Research Letters

ACCEPTED MANUSCRIPT • OPEN ACCESS

Missing pieces to modeling the Arctic-Boreal puzzle To cite this article before publication: Joshua Fisher et al 2017 Environ. Res. Lett. in press https://doi.org/10.1088/1748-9326/aa9d9a

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Missing pieces to modeling the Arctic-Boreal puzzle

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Joshua B. Fisher1,*, Daniel J. Hayes2, Christopher R. Schwalm3, Deborah N. Huntzinger4, Eric

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Stofferahn1, Kevin Schaefer5, Yiqi Luo6, Stan D. Wullschleger7, Scott Goetz8, Charles E. Miller1,

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Peter Griffith 9 , Sarah Chadburn 10 , Abhishek Chatterjee9, 11 , Philippe Ciais 12 , Thomas A.

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Douglas13, Hélène Genet14, Akihiko Ito15, Christopher S.R. Neigh9, Benjamin Poulter9, Brendan

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M. Rogers3, Oliver Sonnentag16, Hanqin Tian17, Weile Wang18,19, Yongkang Xue20, Zong-Liang

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Yang21, Ning Zeng22

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Corresponding author. E-mail: [email protected]

Author contributions: JBF formulated idea; JBF designed research; JBF performed research; YL, SC, PC, HG, AI,

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BP, HT, WW, YX, Z-LY, and NZ provided data; all authors contributed to the writing of the paper.

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Submission: Environmental Research Letters (ABoVE Special Issue)

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Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA, 91109, USA School of Forest Resources, University of Maine, 233 Nutting Hall, Orono, ME, 04469-5755, USA 3 Woods Hole Research Center, 149 Woods Hole Road, Falmouth, MA, 02540-1644, USA 4 School of Earth Sciences & Environmental Sustainability, Northern Arizona University, PO Box 5694, Flagstaff, AZ, 860115697, USA 5 National Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, 1540 30th Street #376, Boulder, CO, 80303, USA 6 Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, 73019, USA 7 Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6301, USA 8 School of Informatics, Computing and Cyber Systems, Northern Arizona University, PO Box 5693, Flagstaff, AZ, 86011-5693, USA 9 NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA 10 University of Leeds, School of Earth and Environment, Leeds, LS2 9JT, UK 11 Universities Space Research Association, 7178 Columbia Gateway Drive, Columbia, MD, 21046, USA 12 Laboratoire des Sciences du Climat et l’Environnement, Orme des Merisiers, bat. 701 – Point courier 129, 91191 Gif Sur Yvette, France 13 U.S. Army Cold Regions Research and Engineering Laboratory, Fort Wainwright, AK, 99703, USA 14 Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, 99775, USA 15 National Institute for Environmental Studies, Tsukuba, 3058506, Japan 16 Université de Montréal, Département de géographie & Centre d’études Nordiques, 520 Chemin de la Côte Sainte-Catherine, Montréal, QC, H2V 2B8, Canada 17 International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, 602 Duncan Drive, Auburn, AL, 36849, USA 18 NASA Ames Research Center, Moffett Field, CA, 94035, USA 19 California State University Monterey Bay, Seaside, CA, 93955, USA 20 Department of Geography, University of California, Los Angeles, CA, 900945, USA 21 Department of Geological Sciences, Jackson School of Geosciences, 1 University Station #C1100, University of Texas at Austin, Austin, TX, 78712-0254, USA 22 Department of Atmospheric and Oceanic Science, University of Maryland, 2417 Computer and Space Sciences Building, College Park, MD, 20742-2425, USA

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AUTHOR SUBMITTED MANUSCRIPT - ERL-104308

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Abstract NASA has launched the decade-long Arctic-Boreal Vulnerability Experiment (ABoVE). While the initial phases focus on field and airborne data collection, early integration with modeling activities is important to benefit future modeling syntheses. We compiled feedback from ecosystem modeling teams on key data needs, which encompass carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance dynamics. A suite of variables was identified as part of this activity with a critical requirement that they are collected concurrently and representatively over space and time. Individual projects in ABoVE may not capture all these needs, and thus there is both demand and opportunity for augmentation of field observations, and synthesis of the observations that are collected, to ensure that science questions and integrated modeling activities are successfully implemented.

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Keywords: ABoVE; Arctic; Arctic Boreal Vulnerability Experiment; Boreal; gaps; model; requirements; uncertainty Social Media Abstract: Linking international ecosystem modeling teams to field teams in the Arctic/Boreal #ABoVE #NASA

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Climate is changing worldwide, but temperatures are rising disproportionately in the high northern latitudes, i.e., the Arctic-Boreal Region—home to the largest biome in the world [Chapman and Walsh, 2007; Hinzman et al., 2005; IPCC, 2007; 2014; McGuire et al., 2006; Overpeck et al., 1997; Screen and Simmonds, 2010; Serreze and Barry, 2011; Winton, 2006]. Warming temperatures in such cold environments may benefit plants, improve productivity, enable a green-up of new areas, accelerate nutrient cycling, and increase CO2 uptake from the atmosphere [Euskirchen et al., 2009; Forkel et al., 2016; Jia et al., 2003; Mack et al., 2004; Myneni et al., 1997; Natali et al., 2012; Qian et al., 2010]. However, rising temperatures are also thawing permafrost, altering hydrology and ecology, changing albedo, browning and decreasing productivity in some areas, increasing fire frequency/severity and disease infestations, and exposing enormous amounts of previously preserved soil organic carbon to the atmosphere [Beck and Goetz, 2011; Goetz et al., 2005; Koven et al., 2011; Lloyd and Bunn, 2007; McGuire et al., 2009; Olefeldt et al., 2013; Schädel et al., 2016; Schaefer et al., 2011; Schuur et al., 2009; Zimov et al., 2006]. This stored soil carbon has accumulated over millennia, and its exposure and mobilization is tipping the historical carbon sink of the Arctic-Boreal Region into a volatile source of increasing carbon to the atmosphere [Belshe et al., 2013; Hayes et al., 2011; Oechel et al., 1993; Schaefer et al., 2014; Schuur et al., 2013; Turetsky et al., 2011; Zona et al., 2016].

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Our predictive ecosystem modeling capabilities for the region have substantial uncertainties due to the complexity of these interacting ecosystem components, tipping carbon sink/source dynamics, large and remote area, extreme environment, and consequent dearth of measurements. As a result, carbon cycle dynamics in the Arctic-Boreal Region are among the largest sources of identified uncertainties to global climate projections [Chapin et al., 2000; IPCC, 2014; Ito et al., 2016; Koven et al., 2011; McGuire et al., 2006; Parmentier et al., 2015; Schaefer et al., 2014; Snyder and Liess, 2014; Zhang et al., 2017]. These uncertainties can be conceptually considered as missing pieces to a modeling ‘puzzle’ that can inform ecosystem function and dynamics with changing climate. Models are challenged in how to initialize current conditions and carbon pools, determine the precise sensitivities of soil and vegetation responses to changing temperature and hydrological regimes, and scale highly heterogeneous processes to large grid sizes [Fisher et al., 2014a; Fisher et al., 2014b; Hayes et al., 2014; Loranty et al., 2014; McGuire et al., 2012; Melton et al., 2013; Rogers et al., 2017; Schuur et al., 2015; Sitch et al., 2007]. The lack of observational data has limited model improvements, testing, and evaluation for the Arctic-Boreal Region: evidence of this is seen in the fact that models have exhibited nearly every possible combination of carbon sink/source dynamics with orders of magnitude differences in carbon stocks [Fisher et al., 2014b; McGuire et al., 2006; McGuire et al., 2012; Melton et al., 2013; Schuur et al., 2015; Sitch et al., 2007].

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In 2015, NASA launched the decade-long Arctic-Boreal Vulnerability Experiment (ABoVE) focused in Alaska and Western Canada to study the ecosystems in response to a changing environment (above.nasa.gov). NASA is able to leverage its remote sensing strengths to combine airborne and satellite observations with in situ measurements to capture ecosystem dynamics across large scales [Goetz et al., 2011; Griffith et al., 2012; Kasischke et al., 2013]. ABoVE is partitioned into three phases, with the first two phases focused predominantly on science-driven intensive data collection from field studies and airborne campaigns; the last phase is focused on

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analysis and synthesis of these data, including integration with modeling. Although the last phase is reserved for model integration, with foresight NASA included a Model–Data Integration Framework in Phase I [Stofferahn et al., 2016]. This Framework lays the foundation for the modeling activities, connects modeling efforts to the field activities early on, and aims to ensure that the data collected meet the needs of the modeling community. This is a lesson learned from previous large-scale NASA campaigns. For example, in the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) an extensive network of flux towers was installed throughout Amazonia, but did not include sensors for downwelling longwave radiation, a crucial input for modelers [de Gonçalves et al., 2013]. Including the instruments during installation would have been relatively cheap and easy, but doing so after the fact proved very difficult and timeconsuming. Although many ABoVE projects are primarily field- and remote sensing-based studies targeting individual science questions with specific data collection requirements, opportunities exist for ABoVE-sponsored projects and/or ABoVE-affiliated projects to include additional data needed by the modeling community. However, the modeling community must define their data requirements now so that NASA and the ABoVE project teams can augment their implementation plans in time to collect the critical observations.

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We surveyed 18 modeling teams from around the world on data needs for modeling terrestrial ecosystem dynamics in the Arctic-Boreal Region. Our focus was on global terrestrial biosphere models used within global climate projections, and whose inter-model variabilities define global uncertainties [Friedlingstein et al., 2006; Friedlingstein et al., 2014; IPCC, 2007; 2014]. The 18 models included: CABLE [Wang et al., 2010], Biome-BGC [Thornton et al., 2002], CLM [Koven et al., 2015], CLM4-VIC [Lei et al., 2014], DLEM [Tian et al., 2014], ECOSYS [Grant et al., 2009], ISAM [Jain and Yang, 2005], JeDI [Pavlick et al., 2013], JULES [Clark et al., 2011] , LPJ [Zhang et al., 2016], MC2 [Bachelet et al., 2015], Noah-MP [Niu et al., 2011], ORCHIDEE [Krinner et al., 2005], SiB4 [Baker et al., 2008], SSiB [Xue et al., 1991], TEM [Hayes et al., 2011], VEGAS [Zeng et al., 2005], and VISIT [Ito, 2010]. Some models represent more processes than others with respect to Arctic-Boreal ecosystem dynamics, but all show divergent results in terms of carbon pools and fluxes [Fisher et al., 2014b]. Our survey centered on ecosystem dynamics, building on previous similar inquiries focused specifically on soil carbon dynamics [Luo et al., 2016; Tian et al., 2015]. Further, we used a set of 20 models featured in a previous analysis of Alaskan carbon dynamics [Fisher et al., 2014b] to calculate the inter-model variability for the ABoVE domain (western North America) as an indicator of modeling community disagreement, or uncertainty; these models were also included in the TRENDY [Sitch et al., 2015] and North American Carbon Program (NACP) Regional Synthesis [Huntzinger et al., 2012]. The modeling teams provided a wide range of responses, which we grouped into common categorical phrases for analysis. There was a total of 115 unique phrases, which, for illustration we plotted as a ‘Wordle’ (wordle.net), where the font size of the word is proportional to the frequency of the response (Figure 1). By far the most common response was soil carbon, followed by net primary productivity (NPP), plant biomass, soil moisture, plant functional types (PFTs), and gross primary productivity (GPP). The next tier of modeling needs included soil respiration, litter biomass, active layer thickness, freeze/thaw dynamics, net ecosystem exchange (NEE), soil temperature, evapotranspiration (ET), water table, permafrost, soil vertical profile, and leaf area index (LAI). We note that other types of data, such as meteorology, are critical

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model inputs, but are more commonly available so are less in demand. We also note that some of these variables are somewhat ambiguously defined or not directly aligned with exact measurements. Some key variables to modelers may be overlooked due to inherent biases or lack of knowledge of arctic-boreal processes. Still, the diversity of responses contributed by modelers points to the overall lack of observational data, which must be addressed, but also highlights that the very fundamental processes governing terrestrial carbon cycling are poorly understood and constrained in Arctic-Boreal ecosystems. Indeed, this list would likely mirror modeling needs for most global biomes [Fisher et al., 2014a]—but, with additional key requirements related to permafrost, active layer thickness, and freeze/thaw dynamics, reinforcing the top priority of understanding the magnitude and fate of soil carbon, particular to northern high latitude terrestrial ecosystems [Koven et al., 2017].

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It is important to emphasize that many of these variables are needed concurrently, and such that they sufficiently represent variability over space and time. Concurrency forms the basis of the response functions that structures models (e.g., temperature versus respiration)—variables collected in isolation may lack the spatiotemporal robustness needed to inform and improve the model as a whole. These concurrency requirements enable modelers to extrapolate spatially beyond existing intensive but sparse study sites, as well as refine sensitivities and tipping points/thresholds temporally. This is particularly acute for residence time and turnover of soil and plant carbon stocks; implicit here is turnover related to disturbance with respect to accurate quantification of fuels, fates, and frequencies. A particular strength of ABoVE for modeling is that there is a concerted effort to scale up site level data through airborne and satellite observations (see: above.nasa.gov/images/Scaling%20Diagram_169.jpg). This allows an improved direct comparison between the coarse model pixels and the ground data. Spatially, we identify where these variables should be collected based on uncertainty in modeled soil carbon, NEE, NPP, GPP, heterotrophic respiration, and autotrophic respiration (Figure 2). We show absolute uncertainty for transparency and direct connection to measurements, though other statistical metrics, such as interannual variability, can readily be derived. Low uncertainty regions may be classed as such due to our uncertainty definition, but models may have converged due to equifinality or other shared assumptions, while uncertainty by other definitions may be large. Much of the carbon flux uncertainty is co-located in the southwest areas of Alaska and the Canadian part of the ABoVE domain (roughly congruent with boreal biome extent), while the soil carbon uncertainty is located throughout tundra regions of northern Canada and Alaska, and the Yukon area (areas with high soil carbon concentration). Generally, the survey results align with five of ABoVE’s overarching science themes—carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance. ABoVE’s field and airborne campaigns have targeted known geographic areas of interest and uncertainty, though our uncertainty maps in Figure 2 provide direct and quantitative guidance to these campaigns specifically where models most need data. For instance, it may be that the feedbacks between ecosystem dynamics and atmospheric conditions unique to particular locations expose particular model sensitivities, thereby causing large divergence; data specifically from these areas may help both to constrain these sensitivities as well as to provide benchmark data to assess the accuracy of models. Multiple field-based projects are funded by or affiliated with ABoVE within each of these categories, so this alignment may bode well for data capture for modeling requirements. A live list of measurements being collected in ABoVE as of this writing can be found online

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(above.nasa.gov/cgi-bin/above_meas.pl). Synthesis activities across projects, especially, can help integrate datasets within modeling frameworks. However, many of the variables required by models may be absent, non-concurrent with other variables, or lacking the spatial or temporal resolutions and domains from the field campaigns needed to sufficiently refine model performance. For instance, soil carbon dynamics, such as stocks and change trajectories/sensitivities to various forcing variables, were clearly the highest demand by the modelers. At the time of this writing there were 20 projects listed under the Carbon Dynamics category within ABoVE. Nonetheless, most of these projects were not focused on soil carbon (due in part, for example, to available proposals, solicitation wording, and technical difficulty). Rather, they focus predominantly on carbon fluxes between the land surface and the atmosphere, which while critically important to the modeling community, may overlook some of the key data needs for modelers, presenting a potentially worrisome gap for model–data integration.

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There is an enormous wealth of complementary data and information existing or in development by programs outside of ABoVE that are relevant to modelers. These include, for example: DOE’s NGEE Arctic [Wullschleger et al., 2011], ESA’s GlobPermafrost [Bartsch et al., 2017], the Permafrost Carbon Network (PCN) [Schuur and Abbott, 2011], the International Soil Carbon Network (ISCN) [Jandl et al., 2014], the Northern Circumpolar Soil Carbon Database (NCSCD) [Hugelius et al., 2013], the Study of Environmental Arctic Change (SEARCH) [Bromwich et al., 2010], the Arctic System Reanalysis (ASR) [Bromwich et al., 2016], the Polar Geospatial Center [Noh and Howat, 2015], the National Ecological Observatory Network (NEON) [Keller et al., 2008], the Long-Term Ecological Research (LTER) network [Hobbie et al., 2003], and individual AmeriFlux/FLUXNET sites [Oechel et al., 2014]. Other agencies such as the Interagency Arctic Research Policy Committee (IARPC) coordinates among some of these networks [Arctic Research and Policy Act, 1984]; but, a stronger international cooperative effort is still greatly needed, especially in the face of international politics that may present barriers to scientific collaboration. ABoVE has been coordinating with each of these programs and it may be that some potential gaps in ABoVE’s data collection will be filled by these other efforts. However, while such datasets will be useful for model initialization, benchmarking, and evaluation, they may not meet the equally critical demand for variables to be collected concurrently, which is essential for advancing model development and performance. Moreover, these data are primarily focused in N. America, whereas there is an even greater data dearth in the larger pan-Arctic and Boreal region across the globe. The modeling community additionally needs infrastructure to allow repeatable evaluation of model performance compared to benchmark datasets. The benchmark datasets, constructed from a suite of observations, must thoroughly confront and challenge models against the processes and response functions important to Arctic-Boreal ecosystem dynamics for models to improve. The ABoVE Model–Data Integration Framework facilitates the construction, integration, connection, and flow of the valuable data collected by the ABoVE science teams and other data networks to the modeling community [Stofferahn et al., 2016]. Through the ABoVE Science Cloud central data repository (daac.ornl.gov/cgi-bin/dataset_lister.pl?p=34), the framework provides a backend database link to a front-end web user interface to access the ABoVE data. These data can and should be used by the modeling community to update and refine model parameterization and structural process representation, especially where data highlight key gaps in process representation in models [e.g., Li et al., 2010]. In turn, as model versions advance, the

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framework can be used as a benchmarking system to test improvement in model performance against key ABoVE indicators and science questions related specifically to important ArcticBoreal Region ecosystem dynamics. Moreover, the integrated framework readily identifies key missing datasets or uncertainties required to test and advance models across the ABoVE indicators—highly useful for feedback to ground campaigns. The benchmarking system is based on the International Land Model Benchmarking (ILaMB) project [Collier et al., 2016; Hoffman et al., 2016; Luo et al., 2012], and affiliated with the Permafrost Benchmarking System [Schaefer et al., 2016] (i.e., some shared datasets and statistical metrics). Additionally, the Model–Data Integration Framework provides relatively high-quality and high-resolution model driver data for regional-scale runs, critical for modeling high latitudes [Guimberteau et al., 2017]. In sum, this Framework helps ease the workload of connecting to all of these disparate but related databases for the modeling community.

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There is tremendous and well-justified interest, effort, and activity in understanding the ArcticBoreal Region. Much of the current focus is on identifying what is happening now under a changing climate, but there is particular interest in and concern for what changes may occur in the future. Mathematical and computational models of the terrestrial biosphere are essential for understanding potential future changes, and are ultimately the great integrators of our information now. With large and long-term investments such as NASA’s ABoVE currently underway, it is critical that a community-wide modeling framework is incorporated into data collection early so that we can add substantially to the value of assimilating currently available data assets. The challenge is in connecting the wide array of datasets and focused science interests to a cohesive and coherent integrated larger picture. A coordinated and supported effort across these field and modeling components will help complete the missing pieces to modeling the complex dynamics and feedbacks of the Arctic-Boreal puzzle.

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Acknowledgements

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We thank D. Bachelet, N. French, R. Grant, A. Harper, K. Haynes, M. Huang, A. Jain, C. Koven,

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J. Metsaranta, M. Sikka, R. Pavlick, and S. Veraverbeke for survey inputs and/or paper

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comments. This work was supported by NASA’s Arctic Boreal Vulnerability Experiment

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(ABoVE); NNN13D504T (JBF), NNX17AE44G (SJG). TAD was supported by the U.S. Army

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Engineer Research and Development Center Basic Research (6.1) Program. Computing

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resources supporting this work were provided by the NASA High-End Computing (HEC)

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Program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight

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Center. The paper was led from the Jet Propulsion Laboratory, California Institute of Technology,

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under a contract with the National Aeronautics and Space Administration. California Institute of

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Technology. Government sponsorship acknowledged. Copyright 2017. All rights reserved.

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References

249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292

Arctic Research and Policy Act (1984), Arctic Research and Policy Act of 1984 (amended 1990), Public Law, 98, 373. Bachelet, D., K. Ferschweiler, T. J. Sheehan, B. M. Sleeter, and Z. Zhu (2015), Projected carbon stocks in the conterminous USA with land use and variable fire regimes, Global Change Biology, 21(12), 4548-4560. Baker, I. T., L. Prihodko, A. S. Denning, M. Goulden, S. Miller, and H. R. da Rocha (2008), Seasonal drought stress in the Amazon: Reconciling models and observations, J. Geophys. Res., 113(G1), G00B01, doi:10.1029/2007jg000644. Bartsch, A., G. Grosse, A. Kääb, S. Westermann, T. Strozzi, A. Wiesmann, C. Duguay, F. M. Seifert, J. Obu, and I. Nitze (2017), Examining Environmental Gradients with Remotely Sensed Data-the ESA GlobPermafrost project, paper presented at EGU General Assembly Conference Abstracts. Beck, P. S., and S. J. Goetz (2011), Satellite observations of high northern latitude vegetation productivity changes between 1982 and 2008: ecological variability and regional differences, Environmental Research Letters, 6(4), 045501. Belshe, E. F., E. A. G. Schuur, and B. M. Bolker (2013), Tundra ecosystems observed to be CO2 sources due to differential amplification of the carbon cycle, Ecology Letters, 16(10), 1307-1315, doi:10.1111/ele.12164. Bromwich, D. H., Y. H. Kuo, M. Serreze, J. Walsh, L. S. Bai, M. Barlage, K. Hines, and A. Slater (2010), Arctic system reanalysis: call for community involvement, Eos, Transactions American Geophysical Union, 91(2), 13-14. Bromwich, D. H., A. B. Wilson, L.-S. Bai, G. W. Moore, and P. Bauer (2016), A comparison of the regional Arctic System Reanalysis and the global ERA-Interim Reanalysis for the Arctic, QJR Meteorol. Soc, 142, 644-658. Chapin, F., A. McGuire, J. Randerson, R. Pielke, D. Baldocchi, S. Hobbie, N. Roulet, W. Eugster, E. Kasischke, and E. Rastetter (2000), Arctic and boreal ecosystems of western North America as components of the climate system, Global Change Biology, 6(S1), 211-223. Chapman, W. L., and J. E. Walsh (2007), Simulations of Arctic temperature and pressure by global coupled models, Journal of Climate, 20(4), 609-632, doi:10.1175/jcli4026.1. Clark, D. B., L. M. Mercado, S. Sitch, C. D. Jones, N. Gedney, M. J. Best, M. Pryor, G. G. Rooney, R. L. H. Essery, E. Blyth, O. Boucher, R. J. Harding, C. Huntingford, and P. M. Cox (2011), The Joint UK Land Environment Simulator (JULES), model description -Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4(3), 701-722, doi:10.5194/gmd-4-701-2011. Collier, N., F. M. Hoffman, M. Mu, J. T. Randerson, and W. J. Riley (2016), International land Model Benchmarking (ILAMB) Package v002. 00Rep., BGCF-DATA (Biogeochemistry (BGC) Feedbacks);. de Gonçalves, L. G. G., J. S. Borak, M. H. Costa, S. R. Saleska, I. Baker, N. Restrepo-Coupe, M. N. Muza, B. Poulter, H. Verbeeck, J. B. Fisher, M. A. Arain, P. Arkin, B. P. Cestaro, B. Christoffersen, D. Galbraith, X. Guan, B. J. J. M. van den Hurk, K. Ichii, H. M. A. Imbuzeiro, A. K. Jain, N. Levine, C. Lu, G. Miguez-Macho, D. R. Roberti, A. Sahoo, K. Sakaguchi, K. Schaefer, M. Shi, W. J. Shuttleworth, H. Tian, Z.-L. Yang, and X. Zeng (2013), Overview of the Large-Scale Biosphere–Atmosphere Experiment in Amazonia

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us cri

pt

Data Model Intercomparison Project (LBA-DMIP), Agricultural and Forest Meteorology, 182–183(0), 111-127, doi:http://dx.doi.org/10.1016/j.agrformet.2013.04.030. Euskirchen, E. S., A. D. McGuire, F. S. Chapin, S. Yi, and C. C. Thompson (2009), Changes in vegetation in northern Alaska under scenarios of climate change, 2003–2100: implications for climate feedbacks, Ecological Applications, 19(4), 1022-1043. Fisher, J. B., D. N. Huntzinger, C. R. Schwalm, and S. Sitch (2014a), Modeling the terrestrial biosphere, Annual Review of Environment and Resources, 39, 91-123. Fisher, J. B., M. Sikka, W. C. Oechel, D. N. Huntzinger, J. R. Melton, C. D. Koven, A. Ahlström, M. A. Arain, I. Baker, J. M. Chen, P. Ciais, C. Davidson, M. Dietze, B. El-Masri, D. Hayes, C. Huntingford, A. K. Jain, P. E. Levy, M. R. Lomas, B. Poulter, D. Price, A. K. Sahoo, K. Schaefer, H. Tian, E. Tomelleri, H. Verbeeck, N. Viovy, R. Wania, N. Zeng, and C. E. Miller (2014b), Carbon cycle uncertainty in the Alaskan Arctic, Biogeosciences, 11(15), 4271-4288, doi:10.5194/bg-11-4271-2014. Forkel, M., N. Carvalhais, C. Rödenbeck, R. Keeling, M. Heimann, K. Thonicke, S. Zaehle, and M. Reichstein (2016), Enhanced seasonal CO2 exchange caused by amplified plant productivity in northern ecosystems, Science, 351(6274), 696-699. Friedlingstein, P., P. Cox, R. Betts, L. Bopp, W. von Bloh, V. Brovkin, P. Cadule, S. Doney, M. Eby, I. Fung, G. Bala, J. John, C. Jones, F. Joos, T. Kato, M. Kawamiya, W. Knorr, K. Lindsay, H. D. Matthews, T. Raddatz, P. Rayner, C. Reick, E. Roeckner, K. G. Schnitzler, R. Schnur, K. Strassmann, A. J. Weaver, C. Yoshikawa, and N. Zeng (2006), Climatecarbon cycle feedback analysis: Results from the C4MIP model intercomparison, Journal of Climate, 19(14), 3337-3353. Friedlingstein, P., M. Meinshausen, V. K. Arora, C. D. Jones, A. Anav, S. K. Liddicoat, and R. Knutti (2014), Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks, Journal of Climate, 27(2), 511-526. Goetz, S. J., A. G. Bunn, G. J. Fiske, and R. Houghton (2005), Satellite-observed photosynthetic trends across boreal North America associated with climate and fire disturbance, Proceedings of the National Academy of Sciences of the United States of America, 102(38), 13521-13525. Goetz, S. J., J. Kimball, M. Mack, and E. Kasischke (2011), Scoping Completed for an Experiment to Assess Vulnerability of Arctic and Boreal Ecosystems, Eos Trans. AGU, 92(18), doi:10.1029/2011eo180002. Grant, R. F., A. G. Barr, T. A. Black, H. A. Margolis, A. L. Dunn, J. Metsaranta, S. Wang, J. H. McCaughey, and C. A. Bourque (2009), Interannual variation in net ecosystem productivity of Canadian forests as affected by regional weather patterns – A FluxnetCanada synthesis, Agricultural and Forest Meteorology, 149(11), 2022-2039, doi:10.1016/j.agrformet.2009.07.010. Griffith, P., S. Goetz, E. Kasischke, M. Mack, and D. Wickland (2012), The Arctic-Boreal Vulnerability Experiment: a NASA Terrestrial Ecology Field Campaign, paper presented at AGU Fall Meeting Abstracts. Guimberteau, M., D. Zhu, F. Maignan, Y. Huang, C. Yue, S. Dantec-Nédélec, C. Ottlé, A. Jornet-Puig, A. Bastos, P. Laurent, D. Goll, S. Bowring, J. Chang, B. Guenet, M. Tifafi, S. Peng, G. Krinner, A. Ducharne, F. Wang, T. Wang, X. Wang, Y. Wang, Z. Yin, R. Lauerwald, E. Joetzjer, C. Qiu, H. Kim, and P. Ciais (2017), ORCHIDEE-MICT (revision 4126), a land surface model for the high-latitudes: model description and validation, Geosci. Model Dev. Discuss., 2017, 1-65, doi:10.5194/gmd-2017-122.

ce

293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 10 of 18

10

Page 11 of 18

pte

dM

an

us cri

pt

Hayes, D. J., D. W. Kicklighter, A. D. McGuire, M. Chen, Q. Zhuang, F. Yuan, J. M. Melillo, and S. D. Wullschleger (2014), The impacts of recent permafrost thaw on land– atmosphere greenhouse gas exchange, Environmental Research Letters, 9(4), 045005. Hayes, D. J., A. D. McGuire, D. W. Kicklighter, K. R. Gurney, T. J. Burnside, and J. M. Melillo (2011), Is the northern high-latitude land-based CO2 sink weakening?, Global Biogeochemical Cycles, 25(GB3018), doi:doi:10.1029/2010GB003813. Hinzman, L. D., N. D. Bettez, W. R. Bolton, F. S. Chapin, M. B. Dyurgerov, C. L. Fastie, B. Griffith, R. D. Hollister, A. Hope, and H. P. Huntington (2005), Evidence and implications of recent climate change in northern Alaska and other arctic regions, Climatic Change, 72(3), 251-298. Hobbie, J. E., S. R. Carpenter, N. B. Grimm, J. R. Gosz, and T. R. Seastedt (2003), The US long term ecological research program, BioScience, 53(1), 21-32. Hoffman, F. M., W. J. Riley, J. T. Randerson, G. Keppel-Aleks, D. M. Lawrence, and C. D. Koven (2016), 2016 International Land Model Benchmarking (ILAMB) Workshop ReportRep., USDOE Office of Science, Washington, DC (United States). Hugelius, G., C. Tarnocai, G. Broll, J. Canadell, P. Kuhry, and D. Swanson (2013), The Northern Circumpolar Soil Carbon Database: spatially distributed datasets of soil coverage and soil carbon storage in the northern permafrost regions, Earth System Science Data, 5(1), 3. Huntzinger, D. N., W. M. Post, Y. Wei, A. M. Michalak, T. O. West, A. R. Jacobson, I. T. Baker, J. M. Chen, K. J. Davis, D. J. Hayes, F. M. Hoffman, A. K. Jain, S. Liu, A. D. McGuire, R. P. Neilson, C. Potter, B. Poulter, D. Price, B. M. Raczka, H. Q. Tian, P. Thornton, E. Tomelleri, N. Viovy, J. Xiao, W. Yuan, N. Zeng, M. Zhao, and R. Cook (2012), North American Carbon Program (NACP) regional interim synthesis: terrestrial biospheric model intercomparison, Ecological Modelling, 232, 144-157, doi:10.1016/j.ecolmodel.2012.02.004. IPCC (2007), Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, in Climate Change 2007: The Physical Science Basis, edited by S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller, p. 996, Cambridge University Press, Cambridge, United Kingdrom and New York, NY, USA. IPCC (2014), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 1132 pp., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Ito, A. (2010), Changing ecophysiological processes and carbon budget in East Asian ecosystems under near-future changes in climate: implications for long-term monitoring from a process-based model, Journal of Plant Research, 123(4), 577-588. Ito, A., K. Nishina, and H. M. Noda (2016), Impacts of future climate change on the carbon budget of northern high-latitude terrestrial ecosystems: An analysis using ISI-MIP data, Polar Science, 10(3), 346-355. Jain, A. K., and X. Yang (2005), Modeling the effects of two different land cover change data sets on the carbon stocks of plants and soils in concert with CO2 and climate change, Global Biogeochem. Cycles, 19(2), GB2015, doi:10.1029/2004gb002349. Jandl, R., M. Rodeghiero, C. Martinez, M. F. Cotrufo, F. Bampa, B. van Wesemael, R. B. Harrison, I. A. Guerrini, D. deB Richter, and L. Rustad (2014), Current status,

ce

339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


11


pte

dM

an

us cri

pt

uncertainty and future needs in soil organic carbon monitoring, Science of the Total Environment, 468, 376-383. Jia, G. J., H. E. Epstein, and D. A. Walker (2003), Greening of arctic Alaska, 1981–2001, Geophysical Research Letters, 30(20). Kasischke, E., D. Hayes, P. Griffith, E. Larson, and D. Wickland (2013), NASA's Arctic-Boreal Vulnerability Experiment: A large-scale study of environmental change in Western North America and its implications for ecological systems and society, paper presented at AGU Fall Meeting Abstracts. Keller, M., D. S. Schimel, W. W. Hargrove, and F. M. Hoffman (2008), A continental strategy for the National Ecological Observatory Network, Frontiers in Ecology and the Environment, 6(5), 282-284. Koven, C. D., G. Hugelius, D. M. Lawrence, and W. R. Wieder (2017), Higher climatological temperature sensitivity of soil carbon in cold than warm climates, Nature Climate Change, 7, 817, doi:10.1038/nclimate3421. Koven, C. D., D. M. Lawrence, and W. J. Riley (2015), Permafrost carbon−climate feedback is sensitive to deep soil carbon decomposability but not deep soil nitrogen dynamics, Proceedings of the National Academy of Sciences, 201415123. Koven, C. D., B. Ringeval, P. Friedlingstein, P. Ciais, P. Cadule, D. Khvorostyanov, G. Krinner, and C. Tarnocai (2011), Permafrost carbon-climate feedbacks accelerate global warming, Proceedings of the National Academy of Sciences, 108(36), 14769-14774, doi:10.1073/pnas.1103910108. Krinner, G., N. Viovy, N. de Noblet-DucoudrÈ, J. OgÈe, J. Polcher, P. Friedlingstein, P. Ciais, S. Sitch, and I. C. Prentice (2005), A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochem. Cycles, 19(1), GB1015, doi:10.1029/2003gb002199. Lei, H., M. Huang, L. R. Leung, D. Yang, X. Shi, J. Mao, D. J. Hayes, C. R. Schwalm, Y. Wei, and S. Liu (2014), Sensitivity of global terrestrial gross primary production to hydrologic states simulated by the Community Land Model using two runoff parameterizations, Journal of Advances in Modeling Earth Systems, 6(3), 658-679. Li, Q., S. Sun, and Y. Xue (2010), Analyses and development of a hierarchy of frozen soil models for cold region study, Journal of Geophysical Research: Atmospheres, 115(D3). Lloyd, A. H., and A. G. Bunn (2007), Responses of the circumpolar boreal forest to 20th century climate variability, Environmental Research Letters, 2(4), 045013. Loranty, M. M., L. T. Berner, S. J. Goetz, Y. Jin, and J. T. Randerson (2014), Vegetation controls on northern high latitude snow‐albedo feedback: observations and CMIP5 model simulations, Global Change Biology, 20(2), 594-606. Luo, Y., A. Ahlström, S. D. Allison, N. H. Batjes, V. Brovkin, N. Carvalhais, A. Chappell, P. Ciais, E. A. Davidson, and A. Finzi (2016), Toward more realistic projections of soil carbon dynamics by Earth system models, Global Biogeochemical Cycles. Luo, Y., J. T. Randerson, G. Abramowitz, C. Bacour, E. Blyth, N. Carvalhais, P. Ciais, D. Dalmonech, J. B. Fisher, R. Fisher, P. Friedlingstein, K. Hibbard, F. Hoffman, D. Huntzinger, C. D. Jones, C. Koven, D. Lawrence, D. J. Li, M. Mahecha, S. L. Niu, R. Norby, S. L. Piao, X. Qi, P. Peylin, I. C. Prentice, W. Riley, M. Reichstein, C. Schwalm, Y. P. Wang, J. Y. Xia, S. Zaehle, and X. H. Zhou (2012), A framework for benchmarking land models, Biogeosciences, 9(10), 3857-3874, doi:10.5194/bg-9-3857-2012.

ce

384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 18

12

Page 13 of 18

pte

dM

an

us cri

pt

Mack, M. C., E. A. G. Schuur, M. S. Bret-Harte, G. R. Shaver, and F. S. Chapin (2004), Ecosystem carbon storage in arctic tundra reduced by long-term nutrient fertilization, Nature, 431(7007), 440-443, doi:doi:10.1038/nature02887. McGuire, A., L. G. Anderson, T. R. Christensen, S. Dallimore, L. Guo, D. J. Hayes, M. Heimann, T. D. Lorenson, R. W. Macdonald, and N. Roulet (2009), Sensitivity of the carbon cycle in the Arctic to climate change, Ecological Monographs, 79(4), 523-555, doi:10.1890/082025.1. McGuire, A., F. S. Chapin, J. E. Walsh, and C. Wirth (2006), Integrated regional changes in Arctic climate feedbacks: implications for the global climate system, Annual Review of Environment and Resources, 31(1), 61-91, doi:doi:10.1146/annurev.energy.31.020105.100253. McGuire, A., T. R. Christensen, D. Hayes, A. Heroult, E. Euskirchen, J. S. Kimball, C. Koven, P. Lafleur, P. A. Miller, W. Oechel, P. Peylin, and M. Williams (2012), An assessment of the carbon balance of arctic tundra: comparisons among observations, process models, and atmospheric inversions, Biogeosciences, 9, 3185-3204, doi:doi:10.5194/bg- 9-31852012. Melton, J. R., R. Wania, E. L. Hodson, B. Poulter, B. Ringeval, R. Spahni, T. Bohn, C. A. Avis, D. J. Beerling, G. Chen, A. V. Eliseev, S. N. Denisov, P. O. Hopcroft, D. P. Lettenmaier, W. J. Riley, J. S. Singarayer, Z. M. Subin, H. Tian, S. Zürcher, V. Brovkin, P. M. van Bodegom, T. Kleinen, Z. C. Yu, and J. O. Kaplan (2013), Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP), Biogeosciences, 10(2), 753-788, doi:10.5194/bg-10-753-2013. Myneni, R. B., C. D. Keeling, C. J. Tucker, G. Asrar, and R. R. Nemani (1997), Increased plant growth in the northern high latitudes between 1981-1991, Nature, 386, 1380-1393. Natali, S. M., E. A. G. Schuur, and R. L. Rubin (2012), Increased plant productivity in Alaskan tundra as a result of experimental warming of soil and permafrost, Journal of Ecology, 100(2), 488-498, doi:10.1111/j.1365-2745.2011.01925.x. Niu, G. Y., Z. L. Yang, K. E. Mitchell, F. Chen, M. B. Ek, M. Barlage, A. Kumar, K. Manning, D. Niyogi, and E. Rosero (2011), The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements, Journal of Geophysical Research: Atmospheres (1984–2012), 116(D12). Noh, M.-J., and I. M. Howat (2015), Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions, GIScience & Remote Sensing, 52(2), 198-217. Oechel, W. C., S. J. Hastings, G. Vourlrtis, M. Jenkins, G. Riechers, and N. Grulke (1993), Recent change of Arctic tundra ecosystems from a net carbon dioxide sink to a source, Nature, 361(6412), 520-523. Oechel, W. C., C. A. Laskowski, G. Burba, B. Gioli, and A. A. Kalhori (2014), Annual patterns and budget of CO2 flux in an Arctic tussock tundra ecosystem, Journal of Geophysical Research: Biogeosciences, 119(3), 323-339. Olefeldt, D., M. R. Turetsky, P. M. Crill, and A. D. McGuire (2013), Environmental and physical controls on northern terrestrial methane emissions across permafrost zones, Global Change Biology, 19(2), 589-603.

ce

429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


13


pte

dM

an

us cri

pt

Overpeck, J., K. Hughen, D. Hardy, R. Bradley, R. Case, M. Douglas, B. Finney, K. Gajewski, G. Jacoby, A. Jennings, S. Lamoureux, A. Lasca, G. MacDonald, J. Moore, M. Retelle, S. Smith, A. Wolfe, and G. Zielinski (1997), Arctic environmental change of the last four centuries, Science, 278(5341), 1251-1256, doi:10.1126/science.278.5341.1251. Parmentier, F. J. W., W. Zhang, Y. Mi, X. Zhu, J. Huissteden, D. J. Hayes, Q. Zhuang, T. R. Christensen, and A. D. McGuire (2015), Rising methane emissions from northern wetlands associated with sea ice decline, Geophysical Research Letters, 42(17), 72147222. Pavlick, R., D. T. Drewry, K. Bohn, B. Reu, and A. Kleidon (2013), The Jena DiversityDynamic Global Vegetation Model (JeDi-DGVM): a diverse approach to representing terrestrial biogeography and biogeochemistry based on plant functional trade-offs, Biogeosciences, 10(6), 4137-4177, doi:10.5194/bg-10-4137-2013. Qian, H., R. Joseph, and N. Zeng (2010), Enhanced terrestrial carbon uptake in the Northern High Latitudes in the 21st century from the Coupled Carbon Cycle Climate Model Intercomparison Project model projections, Global Change Biology, 16(2), 641-656, doi:10.1111/j.1365-2486.2009.01989.x. Rogers, A., S. P. Serbin, K. S. Ely, V. L. Sloan, and S. D. Wullschleger (2017), Terrestrial biosphere models underestimate photosynthetic capacity and CO2 assimilation in the Arctic, New Phytologist. Schädel, C., M. K.-F. Bader, E. A. Schuur, C. Biasi, R. Bracho, P. Čapek, S. De Baets, K. Diáková, J. Ernakovich, and C. Estop-Aragones (2016), Potential carbon emissions dominated by carbon dioxide from thawed permafrost soils, Nature Climate Change, 6(10), 950-953. Schaefer, K., E. Jafarov, M. Piper, and C. Schwalm (2016), A Permafrost Benchmark System to evaluate permafrost models, in ILaMB, edited. Schaefer, K., H. Lantuit, V. E. Romanovsky, E. A. Schuur, and R. Witt (2014), The impact of the permafrost carbon feedback on global climate, Environmental Research Letters, 9(8), 085003. Schaefer, K., T. Zhang, L. Bruhwiler, and A. P. Barrett (2011), Amount and timing of permafrost carbon release in response to climate warming, Tellus B, 63(2), 165-180, doi:10.1111/j.1600-0889.2011.00527.x. Schuur, E., and B. Abbott (2011), Climate change: high risk of permafrost thaw, Nature, 480(7375), 32-33. Schuur, E., B. W. Abbott, W. B. Bowden, V. Brovkin, P. Camill, J. G. Canadell, J. P. Chanton, F. S. Chapin, III, T. R. Christensen, P. Ciais, B. T. Crosby, C. I. Czimczik, G. Grosse, J. Harden, D. J. Hayes, G. Hugelius, J. D. Jastrow, J. B. Jones, T. Kleinen, C. D. Koven, G. Krinner, P. Kuhry, D. M. Lawrence, A. D. McGuire, S. M. Natali, J. A. O’Donnell, C. L. Ping, W. J. Riley, A. Rinke, V. E. Romanovsky, A. B. K. Sannel, C. Schädel, K. Schaefer, J. Sky, Z. M. Subin, C. Tarnocai, M. R. Turetsky, M. P. Waldrop, K. M. Walter Anthony, K. P. Wickland, C. J. Wilson, and S. A. Zimov (2013), Expert assessment of vulnerability of permafrost carbon to climate change, Climatic Change, 119(2), 359-374, doi:10.1007/s10584-013-0730-7. Schuur, E., A. McGuire, C. Schädel, G. Grosse, J. Harden, D. Hayes, G. Hugelius, C. Koven, P. Kuhry, and D. Lawrence (2015), Climate change and the permafrost carbon feedback, Nature, 520(7546), 171.

ce

474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 18

14

Page 15 of 18

pte

dM

an

us cri

pt

Schuur, E., J. G. Vogel, K. G. Crummer, H. Lee, J. O. Sickman, and T. E. Osterkamp (2009), The effect of permafrost thaw on old carbon release and net carbon exchange from tundra, Nature, 459(7246), 556-559, doi:http://www.nature.com/nature/journal/v459/n7246/suppinfo/nature08031_S1.html. Screen, J. A., and I. Simmonds (2010), The central role of diminishing sea ice in recent Arctic temperature amplification, Nature, 464(7293), 1334. Serreze, M. C., and R. G. Barry (2011), Processes and impacts of Arctic amplification: A research synthesis, Global and Planetary Change, 77(1), 85-96. Sitch, S., P. Friedlingstein, N. Gruber, S. Jones, G. Murray-Tortarolo, A. Ahlström, S. C. Doney, H. Graven, C. Heinze, and C. Huntingford (2015), Recent trends and drivers of regional sources and sinks of carbon dioxide, Biogeosciences, 12(3), 653-679. Sitch, S., A. D. McGuire, J. Kimball, N. Gedney, J. Gamon, R. Engstrom, A. Wolf, Q. Zhuang, J. Clein, and K. C. McDonald (2007), ASSESSING THE CARBON BALANCE OF CIRCUMPOLAR ARCTIC TUNDRA USING REMOTE SENSING AND PROCESS MODELING, Ecological Applications, 17(1), 213-234, doi:10.1890/10510761(2007)017[0213:ATCBOC]2.0.CO;2. Snyder, P. K., and S. Liess (2014), The simulated atmospheric response to expansion of the Arctic boreal forest biome, Climate Dynamics, 42(1-2), 487-503. Stofferahn, E., J. B. Fisher, D. J. Hayes, D. N. Huntzinger, and C. Schwalm (2016), How well does your model capture the terrestrial ecosystem dynamics of the Arctic-Boreal Region, in American Geophysical Union Fall Meeting, edited, San Francisco. Thornton, P. E., B. E. Law, H. L. Gholz, K. L. Clark, E. Falge, D. S. Ellsworth, A. H. Goldstein, R. K. Monson, D. Hollinger, J. C. Paw U, and J. P. Sparks (2002), Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests, Agricultural and Forest Meteorology, 113, 185-222. Tian, H., G. Chen, C. Lu, X. Xu, D. J. Hayes, W. Ren, S. Pan, D. N. Huntzinger, and S. C. Wofsy (2014), North American terrestrial CO2 uptake largely offset by CH4 and N2O emissions: toward a full accounting of the greenhouse gas budget, Climatic Change, 1-14. Tian, H., C. Lu, J. Yang, K. Banger, D. N. Huntzinger, C. R. Schwalm, A. M. Michalak, R. Cook, P. Ciais, and D. Hayes (2015), Global patterns and controls of soil organic carbon dynamics as simulated by multiple terrestrial biosphere models: Current status and future directions, Global Biogeochemical Cycles, 29(6), 775-792. Turetsky, M. R., E. S. Kane, J. W. Harden, R. D. Ottmar, K. L. Manies, E. Hoy, and E. S. Kasischke (2011), Recent acceleration of biomass burning and carbon losses in Alaskan forests and peatlands, Nature Geoscience, 4(1), 27. Wang, Y. P., R. M. Law, and B. Pak (2010), A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere, Biogeosciences, 7, 2261-2282. Winton, M. (2006), Amplified Arctic climate change: What does surface albedo feedback have to do with it?, Geophysical Research Letters, 33(3). Wullschleger, S. D., L. D. Hinzman, and C. J. Wilson (2011), Planning the Next Generation of Arctic Ecosystem Experiments, Eos Trans. AGU, 92(17), 145, doi:10.1029/2011eo170006. Xue, Y., P. J. Sellers, J. L. Kinter, and J. Shukla (1991), A simplified biosphere model for global climate studies, Journal of Climate, 4, 345-364.

ce

519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562

Ac

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


15


pte

dM

an

us cri

pt

Zeng, N., H. Qian, C. Roedenbeck, and M. Heimann (2005), Impact of 1998-2002 midlatitude drought and warming on terrestrial ecosystem and the global carbon cycle, Geophys. Res. Lett., 32(22), L22709, doi:10.1029/2005gl024607. Zhang, Z., N. E. Zimmermann, J. O. Kaplan, and B. Poulter (2016), Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties, Biogeosciences, 13(5), 1387-1408, doi:10.5194/bg13-1387-2016. Zhang, Z., N. E. Zimmermann, A. Stenke, X. Li, E. L. Hodson, G. Zhu, C. Huang, and B. Poulter (2017), Emerging role of wetland methane emissions in driving 21st century climate change, Proceedings of the National Academy of Sciences, 201618765. Zimov, S. A., E. A. G. Schuur, and F. S. Chapin (2006), Permafrost and the global carbon budget, Science, 312(5780), 1612-1613, doi:10.1126/science.1128908. Zona, D., B. Gioli, R. Commane, J. Lindaas, S. C. Wofsy, C. E. Miller, S. J. Dinardo, S. Dengel, C. Sweeney, and A. Karion (2016), Cold season emissions dominate the Arctic tundra methane budget, Proceedings of the National Academy of Sciences, 113(1), 40-45.

ce

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Figures

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Figure 1. Terrestrial biosphere modeling needs for the Arctic-Boreal Region highlight soil and

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vegetation dynamics, as illustrated by font size proportional to frequency of response from 18

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modeling groups.

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0.025

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Uncertainty in Heterotrophic Respiration ( kg C m−2 month−1 )

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Uncertainty in Net Primary Production ( kg C m−2 month−1 )

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0

Uncertainty in Autotrophic Respiration ( kg C m−2 month−1 )

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Uncertainty in Gross Primary Production ( kg C m−2 month−1 )

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Uncertainty in Net Ecosystem Exchange ( kg C m−2 month−1 )

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Uncertainty in Total Soil Carbon ( kg C m−2 )

Figure 2. Uncertainty in ecosystem carbon stocks and fluxes across NASA’s Arctic-Boreal

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Vulnerability Experiment (ABoVE) domain. Maps are calculated from multi-model (n=20)

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disagreement, i.e., standard deviation, from the TRENDY [Sitch et al., 2015] and the North

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American Carbon Program (NACP) Regional Synthesis [Huntzinger et al., 2012] averaged to

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annual means (reference year 2003). Variables included: Total Soil Carbon, Net Ecosystem

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Exchange, Gross Primary Production, Autotrophic Respiration, Net Primary Production, and

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Heterotrophic Respiration. Flux units are in kg C m-2 month-1; stock units are in kg C m-2.

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