Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain : regional patterns and uncertainties

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain : regional patterns and uncertainties © 2021 the Authors Accepted version (Final draft) Virkkala, Anna‐Maria; Aalto, Juha; Rogers, Brendan M.; Tagesson, Torbern; Treat, Claire C.; Natali, Susan M.; Watts, Jennifer D.; Potter, Stefano; Lehtonen, Aleksi; Mauritz, Marguerite; Schuur, Edward A.G.; Kochendorfer, John; Zona, Donatella; Oechel, Walter; Kobayashi, Hideki; Humphreys, Elyn; Goeckede, Mathias; Iwata, Hiroki; Lafleur, Peter M.; Euskirchen, Eugenie S.; Bokhorst, Stef; Marushchak, Maija; Martikainen, Pertti J.; Elberling, Bo; Voigt, Carolina; Biasi, Christina; Sonnentag, Oliver; Parmentier, Frans‐Jan W.; Ueyama, Masahito; Celis, Gerardo; St.Loius, Vincent L.; Emmerton, Craig A.; Peichl, Matthias; Chi, Jinshu; Järveoja, Järvi; Nilsson, Mats.B.; Oberbauer, Steven F.; Torn, Margaret S.; Park, Sang‐Jong; Dolman, Han; Mammarella, Ivan; Chae, Namyi; Poyatos, Rafael; López‐Blanco, Efrén; Røjle, Christensen Torben; Jung, Kwon Min; Sachs, Torsten; Holl, David; Luoto, Miska Virkkala, A., Aalto, J., Rogers, B. M., Tagesson, T., Treat, C. C., Natali, S. M., Watts, J. D., Potter, S., Lehtonen, A., Mauritz, M., Schuur, E. A., Kochendorfer, J., Zona, D., Oechel, W., Kobayashi, H., Humphreys, E., Goeckede, M., Iwata, H., Lafleur, P. M., . . . Luoto, M. (2021). Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain : regional patterns and uncertainties. Global Change Biology, 27(17), 4040-4059. https://doi.org/10.1111/gcb.15659 2021 4040 | Glob Change Biol. 2021;27:4040–4059.wileyonlinelibrary.com/journal/gcb Received: 10 February 2021 | Accepted: 5 March 2021 DOI: 10.1111/gcb.15659 PRIMARY RESEARCH ARTICLE Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties AnnaMaria Virkkala1,2 | Juha Aalto1,3 | Brendan M. Rogers2 | Torbern Tagesson4,5 | Claire C. Treat6 | Susan M. Natali2 | Jennifer D. Watts2 | Stefano Potter2 | Aleksi Lehtonen7 | Marguerite Mauritz8 | Edward A. G. Schuur9 | John Kochendorfer10 | Donatella Zona11,12 | Walter Oechel11,13 | Hideki Kobayashi14 | Elyn Humphreys15 | Mathias Goeckede16 | Hiroki Iwata17 | Peter M. Lafleur18 | Eugenie S. Euskirchen19 | Stef Bokhorst20 | Maija Marushchak21,22 | Pertti J. Martikainen22 | Bo Elberling23 | Carolina Voigt22,24 | Christina Biasi22 | Oliver Sonnentag24 | FransJan W. Parmentier4,25 | Masahito Ueyama26 | Gerardo Celis27 | Vincent L. St.Louis28 | Craig A. Emmerton28 | Matthias Peichl29 | Jinshu Chi29 | Järvi Järveoja29 | Mats B. Nilsson29 | Steven F. Oberbauer30 | Margaret S. Torn31 | SangJong Park32 | Han Dolman33 | Ivan Mammarella34 | Namyi Chae35 | Rafael Poyatos36,37 | Efrén LópezBlanco38,39 | Torben Røjle Christensen39 | Min Jung Kwon40,41 | Torsten Sachs42 | David Holl43 | Miska Luoto1 1Department of Geosciences and Geography, Faculty of Science, University of Helsinki, Helsinki, Finland 2Woodwell Climate Research Center, Falmouth, MA, USA 3Weather and Climate Change Impact Research, Finnish Meteorological Institute, Helsinki, Finland 4Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden 5Department of Geosciences and Natural Resource Management, Copenhagen University, Copenhagen, Denmark 6Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Potsdam, Germany 7Natural Resources Institute Finland, Helsinki, Finland 8University of Texas at El Paso, El Paso, TX, USA 9Center for Ecosystem Science and Society, Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA 10Atmosperic Turbulence and Diffusion Division of NOAA's Air Resources Laboratory, Oak Ridge, TN, USA 11San Diego State University, San Diego, CA, USA 12University of Sheffield, Sheffield, UK 13University of Exeter, Exeter, UK 14Research Institute for Global Change, Japan Agency for MarineEarth Science and Technology, Yokoama, Japan 15Carleton University, Ottawa, ON, Canada 16Dept. Biogeochemical Signals, Max Planck Institute for Biogeochemistry, Jena, Germany 17Department of Environmental Science, Shinshu University, Matsumoto, Japan 18School of the Environment, Trent University, Peterborough, ON, Canada This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2021 The Authors. Global Change Biology published by John Wiley & Sons Ltd. | 4041 VIRKKALA et AL. 19Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA 20Vrije Universiteit Amsterdam, Amsterdam, the Netherlands 21Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland 22Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, Finland 23Center for Permafrost, Department of Geoscience and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark 24Département de géographie, Université de Montréal, Montréal, QC, Canada 25Centre for Biogeochemistry in the Anthropocene, Department of Geosciences, University of Oslo, Oslo, Norway 26Graduate School of Life and Environmental Sciences, Osaka Prefecture University, Sakai, Japan 27Agronomy Department, University of Florida, Gainesville, FL, USA 28Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada 29Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden 30Department of Biological Sciences, Florida International University, Miami, FL, USA 31Berkeley Lab and UC Berkeley, Berkeley, CA, USA 32Division of Atmospheric Sciences, Korea Polar Research Institute, Incheon, Republic of Korea 33Department of Earth Sciences, Free University Amsterdam, Amsterdam, the Netherlands 34Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland 35Institute of Life Science and Natural Resources, Korea University, Seoul, Republic of Korea 36CREAF, Catalonia, Spain 37Universitat Autònoma de Barcelona, Catalonia, Spain 38Department of Environment and Minerals, Greenland Institute of Natural Resources, Nuuk, Greenland 39Department of Bioscience, Arctic Research Center, Aarhus University, Roskilde, Denmark 40Laboratoire des Sciences du Climat et de l'Environnement, GifsurYvette, France 41Division of Life Sciences, Korea Polar Research Institute, Incheon, Republic of Korea 42GFZ German Research Centre for Geosciences, Potsdam, Germany 43Center for Earth System Research and Sustainability (CEN), University of Hamburg, Hamburg, Germany Correspondence AnnaMaria Virkkala, Woodwell Climate Research Center, Falmouth, MA, USA. Email: avirk[email protected] Funding information Humboldt Fellowship for Experienced Researchers; European Commission, Grant/Award Number: H2020BG092016 and 727890; Helsingin Yliopisto; Jenny ja Antti Wihurin Rahasto; Väisälä fund; Natural Sciences and Engineering Research Council of Canada; Alfred Kordelinin Säätiö; Arctic Challenge for Sustainability, Grant/ Award Number: JPMXD1420318865; Suomen Kulttuurirahasto; Netherlands Earth System Science Centre; Korean government, Grant/ Award Number: KOPRIPN21011, NRF2021M1A5A1065679, NRF2021M1A5A1065425 and NRF2018R1D1A1B07047778; Gordon and Betty Moore Foundation, Grant/Award Number: 8414; Skogssällskapet, Grant/ Award Number: 2018485Steg 2 2017; Office of Biological and Environmental Research; Natural Sciences and Engineering Research Council; Svenska Forskningsrådet Formas, Grant/Award Number: 201601289 and 942201549; Vetenskapsrådet, Grant/Award Number: 201705268; Norges Forskningsråd, Grant/Award Number: 274711; NASA, Abstract The regional variability in tundra and boreal carbon dioxide (CO2) fluxes can be high, complicating efforts to quantify sinksource patterns across the entire region. Statistical models are increasingly used to predict (i.e., upscale) CO2 fluxes across large spatial domains, but the reliability of different modeling techniques, each with different specifications and assumptions, has not been assessed in detail. Here, we compile eddy covariance and chamber measurements of annual and growing season CO2 fluxes of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) during 1990– 2015 from 148 terrestrial highlatitude (i.e., tundra and boreal) sites to analyze the spatial patterns and drivers of CO2 fluxes and test the accuracy and uncertainty of different statistical models. CO2 fluxes were upscaled at relatively high spatial resolution (1 km2) across the highlatitude region using five commonly used statistical models and their ensemble, that is, the median of all five models, using climatic, vegetation, and soil predictors. We found the performance of machine learning and ensemble predictions to outperform traditional regression methods. We also found the predictive performance of NEEfocused models to be low, relative to models predicting GPP and ER. Our data compilation and ensemble predictions showed that CO2 sink strength was larger in the boreal biome (observed and predicted average annual NEE −46 and −29 g C m−2 yr−1, respectively) compared to tundra (average annual NEE +10 and −2 g C m−2 yr−1). This pattern was associated with large spatial variability, reflecting local heterogeneity in soil organic carbon stocks, climate, and vegetation productivity. The terrestrial ecosystem CO2 budget, estimated 4042 | VIRKKALA et AL. 1 | INTRODUCTION The terrestrial ecosystem carbon dioxide (CO2) balance is one of the largest uncertainties in the global carbon budget (Friedlingstein et al., 2020), with high latitudes (i.e., tundra and boreal biomes) representing one of the leastconstrained budgets (LópezBlanco et al., 2019; Schuur et al., 2015; Zscheischler et al., 2017). Moreover, due to polar amplification and large carbon stocks, the high latitudes have the potential for substantial positive feedbacks to climate warming (Abbott et al., 2016; Gasser et al., 2018; Schuur et al., 2008; Turetsky et al., 2020). Currently, in the absence of major disturbances (e.g., fire), boreal forests are generally CO2 sinks (Bradshaw & Warkentin, 2015; Pan et al., 2011), while regional estimates of tundra vary from sinks (McGuire et al., 2009, 2012, 2016) to sources (Belshe et al., 2013). Both the growing and nongrowing seasons are important for these annual budget estimates. A recent synthesis found that nongrowing season soil CO2 emissions from the northern permafrost region are larger than previously estimated (Natali et al., 2019). However, CO2 uptake by plants over the growing season can be substantial and is often the dominant component of the annual CO2 budget (Alekseychik et al., 2017; Kolari et al., 2009; Lafleur et al., 2012). The current state of the annual terrestrial highlatitude CO2 budget (net sink or source) remains highly uncertain. A key research priority is to develop robust datadriven quantitative frameworks to constrain regional boreal and tundra CO2 budgets at annual and seasonal time scales. Estimating highlatitude CO2 fluxes across large areas and over long timescales is challenging due to their high spatiotemporal variability (Ai et al., 2018; Wilkman et al., 2018) that is controlled by a range of environmental variables (CampsValls et al., 2015; Lund et al., 2010). The ecosystem CO2 balance (i.e., net ecosystem CO2 exchange; NEE) is the relatively small difference between the two large CO2 fluxes of photosynthesis (gross primary production; GPP) and ecosystem respiration (ER; comprising autotrophic and heterotrophic respiration). Although NEE can be measured with the eddy covariance (EC) and chamber techniques (Baldocchi et al., 1988; Lundegårdh, 1927), GPP and ER are estimated indirectly using environmental light and temperature measurements for EC sites (Lasslop et al., 2010; Reichstein et al., 2005) and dark chamber measurements for chamber sites (Shaver et al., 2007). Field studies have shown that GPP, ER, and NEE depend on climatic conditions (e.g., temperature, precipitation, and radiation) (LópezBlanco et al., 2017; Nobrega & Grogan, 2008; Zhang et al., 2018), vegetation (Cahoon et al., 2012; Fox et al., 2008; Järveoja et al., 2018), using the annual NEE ensemble prediction, suggests the highlatitude region was on average an annual CO2 sink during 1990– 2015, although uncertainty remains high. KEYWORDS Arctic, CO2 balance, empirical, greenhouse gas, land, permafrost, remote sensing Grant/Award Number: NNH17ZDA001N, NNX15AT81A and NNX17AE13G; Danmarks Grundforskningsfond, Grant/ Award Number: CENPERM DNRF100; Nordic Center of Excellence; Arctic Data Center; EU FP7ENV, Grant/Award Number: 282700; Natural Sciences and Engineering Research Council Discovery Grants; Suomen Akatemia, Grant/Award Number: 286950, 312912, 314630, 317054, 325680, 332196, 337549, 33761 and 337552; Greenland Research Council, Grant/Award Number: 80.35; Canada Research Chairs; US Geological Survey; Nordenskiöldsamfundet; Swedish National Space Board; NSF Research, Synthesis, and Knowledge Transfer in a Changing Arctic: Science Support for the Study of Environmental Arctic Change, Grant/Award Number: 1331083; NSF PLR Arctic System Science Research Networking Activities (Permafrost Carbon Network: Synthesizing Flux Observations for Benchmarking Model Projections of Permafrost Carbon Exchange), Grant/ Award Number: 1931333; NSF grant, Grant/Award Number: 1203583, 1204263, 1702797, 1702798, DEB1636476, PLR1504381, PLR1836898, AON 856864, 1304271, 0632264 and 1107892; EU 6th Framework Programme, Grant/Award Number: 036993; Danish National Research Foundation, Grant/ Award Number: DNRF100; Research Council of Norway; Swedish Research Council, Grant/Award Number: contract #201803966; the national research infrastructures SITES and ICOS, funded by VR and partner institutes | 4043 VIRKKALA et AL. and soil properties (e.g., soil nutrients and moisture) (Arens et al., 2008; Dagg & Lafleur, 2011; Lund et al., 2009). However, our understanding of the influence of these drivers on GPP and ER, and particularly on NEE, across the entire highlatitude region remains limited (see e.g., Belshe et al., 2013; Lund et al., 2010). Knowledge of the contemporary highlatitude terrestrial CO2 budget is further limited by an increasing, but still relatively sparse, flux measurement network (Alton, 2020; Chu et al., 2017; Virkkala et al., 2018). The majority of flux sites are concentrated within a few intensively studied regions, particularly Alaska and Fennoscandia (Metcalfe et al., 2018; Pastorello et al., 2020; Virkkala et al., 2019), with just a few sites in other large regions such as Siberia and northern Canada. Consequently, issues related to the temporal, geographical and environmental representativeness of the measurements need to be considered to accurately estimate highlatitude carbon budgets and their uncertainties. Previous studies have used a variety of synthesis approaches (Belshe et al., 2013; McGuire et al., 2012), and statistical (Natali et al., 2019), processbased (LópezBlanco et al., 2019; McGuire et al., 2018; Rawlins et al., 2015; Wania et al., 2009) and atmospheric inversion models (McGuire et al., 2012), yielding highly different CO2 budgets. Most of these modeling studies have been conducted at coarse spatial resolutions (25– 100 km; Natali et al., 2019; Rawlins et al., 2015; LópezBlanco et al., 2019) that do not fully capture the heterogeneity in highlatitude environments despite their importance for the regional CO2 budgets (Raynolds et al., 2019; Treat et al., 2018). New efforts synthesizing the current distribution of flux data and developing models at high spatial resolution are required to improve our understanding on the spatial patterns and magnitudes of CO2 fluxes. Models that rely on the statistical relationships between CO2 flux and predictor variables have been increasingly employed to constrain global and highlatitude CO2 budgets (e.g., Jung et al., 2020; Natali et al., 2019; Warner et al., 2019). These statistical models are useful for predicting fluxes across larger areas (i.e., upscaling) because they directly draw upon relationships between fluxes and environmental variables, can account for environmental variability across space and time at high resolutions, and are able to handle biases in the geographic representation of the data (Jung et al., 2020; Natali et al., 2019; Warner et al., 2019). A broad range of statistical models and data sources are available for upscaling, but not all of these have been fully utilized. For example, many past studies have upscaled highlatitude fluxes using a single model (Natali et al., 2019; Peltola et al., 2019; Ueyama, Ichii, et al., 2013), but how different models compare with each other is not well known (with exception of Jung et al., 2017 and Tramontana et al., 2016). Further, most of these studies have primarily used machine learning models due to their ability to capture nonlinear relationships and interactions in data (Elith et al., 2008). However, traditional regression methods can be a powerful tool in upscaling highlatitude ground conditions due to their ability to extrapolate beyond the range of data used for training, and due to their generalizability and ease of interpretation (Aalto et al., 2018). Finally, many of the recent upscaling studies have relied on EC flux measurements only, neglecting chamber measurements despite their importance as additional data sources (with exception of Natali et al., 2019). Chambers are useful especially in remote, sparsely measured treeless tundra where they can capture the entire ecosystem CO2 balance and directly measure NEE and ER (Sørensen et al., 2019). Thus, a compilation of both EC and chamber flux measurements and the comparison of available modeling techniques is clearly required to ensure accurate CO2 flux estimates from existing data and models. Here, we synthesize annual and growing season CO2 fluxes from EC and chamber measurements across the highlatitude terrestrial tundra and boreal region. We then use this new database to upscale annual average ecosystem CO2 fluxes at relatively high spatial resolution (1 km2) across the highlatitude domain using several statistical models. We compare our new database of in situ CO2 fluxes to past tundra syntheses (Belshe et al., 2013; McGuire et al., 2012), provide a detailed assessment of model performance, analyze the spatial patterns and drivers of CO2 fluxes, and discuss the resulting CO2 budget estimates and recommendations for future work. We focus on understanding the spatial variability in average CO2 fluxes instead of a temporal analysis of CO2 flux change; however, our modeling framework also considers the interannual variability in fluxes. 2 | MATERIAL AND METHODS 2.1 | Data collection 2.1.1 | Collection of CO2 flux data Our study area was defined by the highlatitude tundra and boreal biomes (>45°N) based on global ecoregions (20.6 × 106 km2; Figure 1; Dinerstein et al., 2017). We first conducted a literature survey to identify existing EC and chamberbased terrestrial CO2 flux observations of GPP, ER, and NEE over annual and growing season periods across the domain. Potential sites were identified from previous studies (Ichii et al., 2017; Marushchak et al., 2013; McCallum et al., 2013; Watts et al., 2014) and prior synthesis efforts (Belshe et al., 2013; McGuire et al., 2012; Virkkala et al., 2018). We augmented the resulting site list using a Web of Science search with key words (“tundra” or “boreal” or “arctic”) and (“CO2 flux” or “CO2 exchange” or “CO2 budget”). Additionally, a community call was solicited through a CO2 flux synthesis workshop (Parmentier et al., 2019), whereby investigators contributed their most current unpublished data. Additional EC data were downloaded from FLUXNET2015 (Pastorello et al., 2020). The compiled dataset represents all natural terrestrial vegetation types (categorized by needleor broadleaf forest, shrubland, grassland, wetland, and sparse vegetation) present in the highlatitude region. We included studies and sites with NEE, GPP, and ER estimates over a full growing season or calendar year (i.e., cumulative flux). Growing season flux measurements are provided by EC and chambers. Nongrowing season flux measurements include a variety of methods in addition to EC and chambers (e.g., a gas diffusion method by Björkman et al., 2010, soda lime by Welker et al., 2004, or an 4044 | VIRKKALA et AL. empirical model by Vogel et al., 2009). Growing season length and measurement period were defined in multiple ways at individual sites. To allow intersite comparison, we filtered out measurements that did not represent the entire growing season and standardized the remaining measurements (see Supplementary Text Section 1.1 and a similar approach in Belshe et al., 2013). From this filtered dataset, we calculated average growing season daily flux rates based on the reported measurement length and standardized the fluxes based on a common growing season length. The final list of sites having representative annual or growing season measurements is provided in Table S1, sites that were excluded from our analysis are in Table S2. The resulting dataset included 148 sites with CO2 fluxes from 1990 to 2015 from variable measurement periods (Figure 1). We compiled 1448 cumulative annual and growing season flux values (when chamber measurements were aggregated per site; Figure 1); 82% of the aggregated observations are from EC and 18% are from chambers. Annual and growing season NEE were the most widely reported fluxes in the dataset (Figure 1). Unlike McGuire et al. (2012) FIGURE 1 Measured median annual (a– c) and growing season (d– f) fluxes of GPP (gross primary production), ER (ecosystem respiration), and NEE (net ecosystem exchange) in the study domain (>45°N). The color of the point defines the median flux of the site (i.e., a sampling location), and the size of the point the number of observations that was measured (i.e., number of years). The background map represents the highlatitude region (dark gray = boreal biome, light gray = tundra biome). In all panels, sites that had only eddy covariance measurements are shown with black outline color around the point, and chamber measurements are without outline. One site had both eddy covariance and chamber measurements, but this is shown with black outline color. Positive numbers for NEE indicate net ecosystem CO2 loss to the atmosphere (i.e., CO2 source) and negative numbers indicate net ecosystem CO2 gain (i.e., CO2 sink) [Colour figure can be viewed at wileyonlinelibrary.com] | 4045 VIRKKALA et AL. and Belshe et al. (2013) we also included data from the boreal biome, additional tundra sites, and wetlands (not synthesized in Belshe et al., 2013; Figure S1). Similar to McGuire et al. (2012) and Belshe et al. (2013), our database primarily represents undisturbed environments. However, it also includes measurements from ca. 10 sites that have experienced high natural, anthropogenic or anthropogenically induced disturbances, such as permafrost thaw (Bäckstrand et al., 2010; Cassidy et al., 2016; Trucco et al., 2012), fires (Iwata et al., 2011; Ueyama et al., 2019), insect outbreaks (Heliasz et al., 2011; LópezBlanco et al., 2017; Lund et al., 2017), or extensive harvesting practices (Coursolle et al., 2012; Machimura et al., 2005). Throughout the text, positive numbers for NEE indicate net CO2 loss to the atmosphere (i.e., CO2 source) and negative numbers indicate net CO2 gain (i.e., CO2 sink). GPP and ER are always given as positive numbers. 2.1.2 | Gridded predictors and reference flux data We acquired 10 ecophysiologically relevant predictors at 1km2 resolution (0.0083°) representing climate, vegetation, topographic, and soil conditions: growing degree days (GDD3; °C), freezing degree days (FDD; °C), water balance (WAB; mm), maximum growing season normalized difference vegetation index (NDVI), topographic wetness index (TWI), potential incoming direct annual solar radiation (RAD; MJ cm−2 yr−1), soil organic carbon stocks in the upper 2 meters (SOC; tons per ha), topsoil (0– 5 cm) pH, topsoil clay content (CLAY; %), and land cover (LC; classes were mixed or broadleaved forest, needleleaved forest, grassland and shrubland, wetland, sparse vegetation; see Supplementary Text Section 1.2 and Figure S2 for more information about the predictors). These predictors characterize previously identified key relationships between CO2 fluxes and summer and winter temperatures, radiation, precipitation, local hydrology and soil conditions, soil carbon stocks, and vegetation properties (i.e., see Beer et al., 2010; Belshe et al., 2013; Lund et al., 2010; Natali et al., 2019; Ueyama, Iwata, et al., 2013). NDVI further reflects disturbances as it can show spectral browning signals related to drought, harvesting, or fires (MyersSmith et al., 2020; Figure S3; Supplementary Text Section 2.5). We recognize that GPP and ER partitioning and gap filling rely on supporting environmental data (e.g., temperature and radiation), and consequently these fluxes already include some information about variables that we also used as predictors in our statistical models. We used annual (1990– 2015) data for GDD3, FDD, WAB, and NDVI; the remaining predictors were considered to be static. All predictor datasets were masked to only include highlatitude tundra and boreal biomes (Dinerstein et al., 2017), and to exclude permanent water bodies, urban areas, and croplands based on a land cover dataset developed by ESA (2017). We compared our annual ecosystem NEE predictions and budgets (see Section 2.2.1) with FLUXCOM, a global product derived from FLUXNET EC towers and machine learning at 0.5° resolution (Baldocchi et al., 2001; Jung et al., 2017; Tramontana et al., 2016) and an ensemble of global Earth system models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) at 1.92 × 1.5° resolution (Taylor et al., 2012) (Supplementary Text Section 1.3). 2.2 | Data analysis 2.2.1 | Statistical modeling Our main response variables were annual and growing season cumulative GPP, ER, and NEE, but we also modeled daily average GPP, ER, and NEE during the growing season. Annual and growing season CO2 fluxes were linked to the environmental predictors using a range of different statistical modeling methods (Figure S4). We used five statistical models; two were extensions of linear regression models, and three were based on machinelearning. All of these models have been widely used in empirical CO2 flux upscaling studies (BondLamberty & Thomson, 2010; Hursh et al., 2016; Tramontana et al., 2016; Ueyama, Ichii, et al., 2013). Specifically, we examined generalized linear models (GLMs); generalized additive models (GAMs); generalized boosted regression trees (GBMs); random forest (RF models); and support vector machines (SVMs). We used several model approaches because individual models have inherent strengths and weaknesses (Supplementary Text Section 2). For example, machine learning methods might suffer from overfitting, whereas regression methods might result in unrealistic values when extrapolated outside the model data range. Further, individual models may detect different patterns in the data, and the best performing models are not always the same for different response variables (Segurado & Araújo, 2004). We also produced an ensemble prediction by calculating a median prediction over the five predictions from the individual modeling methods (see also Tramontana et al., 2016). We used the median instead of the mean to avoid extreme predicted values inflating the ensemble prediction. In this procedure, the uncertainty of the ensemble is expected to be lower than the uncertainty of a single model (Aalto et al., 2018). Consequently, we produced six model predictions for each of our response variables. To determine the main drivers of the spatial patterns of response variables, the relative contribution of predictors in the models was assessed using a prediction reshuffling approach (Niittynen & Luoto, 2018). We first fit the model and developed predictions using the original data, and then repeated this procedure with the values for one predictor randomly permuted. The contribution of a variable was calculated as a correlation between these two predictions (i.e., original model and the model with a shuffled predictor) subtracted from one: Values close to 1 indicate that the two predictions were different, indicating high variable importance of the predictor variable. Relativecontribution = 1 − correlation ( Prediction original data , Prediction Randomly permuted data) 4046 | VIRKKALA et AL. Each predictor was randomly permuted 100 times for each flux with each of the modelling methods, and an ensemble contribution was derived by taking a mean of the values. To visualize a predictor's effect on a response variable after controlling for the effects of other predictors, partial dependence plots were derived from the random forest model. For both variable importance and partial dependence plot analyses, we used daily average growing season fluxes because the growing season length estimates that were used to calculate growing season fluxes are not independent from GDD3. We found that the daily average fluxes correlated strongly with the growing season fluxes (Pearson's correlation 0.93– 0.94), so they can be assumed to reflect the same relationships with the predictors. To extrapolate across the study domain, we fit the models using the entire dataset to produce annual flux predictions and their ensembles that were subsequently averaged to 1990– 2015 mean values. Because the ensemble predictions were among the most accurate and least uncertain predictions across all response variables, and because their use is generally recommended in predictive efforts (Araújo & New, 2007), our final flux maps and budgets were based on the flux ensemble. In addition to annual and growing season budgets, we also calculated a nongrowing season budget (see Table S4). We had different numbers of observations and sites available for each flux and model, and consequently observed and predicted ER and GPP fluxes and budgets do not sum up to NEE. 2.2.2 | Model fit, predictive performance and uncertainty To evaluate model fit, we predicted fluxes over the entire model training data. To assess the predictive performance of the models, we used a leaveonesiteout cross validation scheme in which each site was iteratively left out from the dataset, and the remaining data were used to predict fluxes for the excluded site (Bodesheim et al., 2018). For both model fit and predictive performance, we calculated bias as an average of the absolute error between prediction and actual observations, Pearson correlation (r) to determine the strength of the linear relationship between the observed and predicted fluxes, and root mean squared error (RMSE) to estimate the model error. We use the terms “observed” and “predicted” to distinguish between field measurements and model predictions but acknowledge that some of these observed values represent indirect estimates of fluxes (e.g., GPP). We evaluated the prediction uncertainty of all flux models and the budget uncertainty of annual and growing season NEE models using a repeated random resampling procedure (Aalto et al., 2018). Prediction uncertainty was calculated to characterize the spatial variability in flux predictions across the highlatitude region, whereas budget uncertainty quantified the range of potential NEE budget values. We used bootstrapping (fractional resampling with replacement based on LC classes) to subset the model training data into 200 different datasets, all of which had the same number of observations as the original flux data itself. These 200 datasets were then used to produce 200 individual predictions with all five statistical models and their ensemble for each flux and for each year from 1990 to 2015 to assess prediction uncertainty which was summarized using the prediction interval (PI; 95th percentile – 5th percentile). Uncertainty for annual and growing season NEE budgets was estimated by calculating the range of budgets from the 50 first ensemble predictions out of the 200 predictions for each year from 1990 to 2015, due to computational constraints. For more details, see Supplementary Text Section 2.4 and Figure S5. 3 | RESULTS 3.1 | Observed flux variation Flux measurements showed considerable variation in magnitudes and signs (CO2 sink vs source) across the highlatitude environments (Figure 1 and Table 1). Observed annual NEE (no upscaling) was on average a small source of CO2 in the most northern parts of the study domain (tundra: +10 g C m−2 yr−1, 42 sites; northern permafrost region: +6 g C m−2 yr−1, 63 sites) and in drier environments (tundra upland: +16 g C m−2 yr−1, 36 sites), whereas the boreal biome (−46 g C m−2 yr−1, 41 sites), and in particular boreal uplands (−47 C m−2 yr−1, 34 sites), and nonpermafrost regions (−90 g C m−2 yr−1, 20 sites) were net ecosystem CO2 sinks. All environmental categories were, on average, net CO2 sinks during the growing season, with the average NEE ranging from −37 to −115 g C m−2 period−1 (Table 1). Tundra upland and nonpermafrost regions had the lowest average growing season sink strength. The nonpermafrost region sink was greatly reduced by one disturbed site that had large source values up to +600 g C m−2 period−1 (Petrone et al., 2014), but this was not apparent in the annual averages because the same site did not report annual fluxes. Although the environmental conditions at the sites were fairly representative of the entire highlatitude region (Figure S6), colder environments with low NDVI and GDD3 as well as high FDD were less well represented (e.g., large areas of Siberia; Figure 1). Some chamber sites were located in conditions that would have otherwise remained undersampled (Figure S6). These included sites with relatively high soil organic carbon stocks in Hudson Bay Lowland and northwestern Canada, and wet climates in Greenland and northern Fennoscandia. 3.2 | Predictive performance of the models The model fit and predictive performance analyses indicated that the GBM, RF and SVM (machine learning) methods outperformed the GLM and GAM (regression model) approaches across most of the response variables (in particular with NEE, but also with GPP and ER; model fit of annual machine learning models: r = 0.69– 0.99 vs. regression models: r = 0.6– 0.92; predictive performance of annual machine learning methods: r = 0.2– 0.73 vs. regression models: | 4047 VIRKKALA et AL. r = 0.12– 0.72; Figure 2gi). We found that the machine learningbased methods were less uncertain (Figure S7) and predicted values within the range of the observed fluxes as opposed to regression models. However, the machine learning method that performed best and had the least uncertainties varied depending on the flux response variable. Ensemble predictions were among the best performing models (model fit of annual and growing season ensemble models: r = 0.68– 0.94; predictive performance of annual and growing season ensemble models: r = 0.21– 0.73; Figure 2 and Figure S8). However, similar to the individual models, model fit and predictive performance was lower for annual and growing season NEE compared to GPP and ER (model fit for GPP and ER: r = 0.89– 0.94 vs. NEE: r = 0.68– 0.77; predictive performance for GPP and ER: r = 0.53– 0.71 vs. NEE: r = 0.21– 0.27; Figure 2 and Figure S8). Annual models for ER and NEE exhibited a better fit and predictive performance than the growing season models (based on r), whereas the opposite was true for GPP (Figure 2 and Figure S8). The growing season GPP model fit and predictive performance was higher than that of the ER models, but annual GPP and ER models performed equally well. Model fit and predictive performance were similar in models trained with and without chambers (Table S3). In most predictive performance analyses, the lowest and highest observed fluxes were overand underestimated, respectively, indicating overall poor predictive performance at the extremes (Figures S9 and S10). Average predicted and observed fluxes were of similar magnitude (Table 1). However, there was a tendency for the average predicted values to have slightly larger GPP and ER values (e.g., observed and predicted annual GPP in the tundra: 250 g C m−2 yr−1 and 378 g C m−2 yr−1, respectively) and stronger net CO2 sink values than what was observed (e.g., observed and predicted annual NEE in the tundra: +10 g C m−2 yr−1 and −2 g C m−2 yr−1, respectively). Our crosscomparison of annual and growing season flux ensemble predictions showed there was a mismatch between annual and growing season component fluxes in approximately 2% of the pixels (growing season GPP/ER larger than annual GPP/ER) and that unrealistic flux values (negative GPP or ER) were found in less than 0.01% of the pixels in the ensemble predictions. TABLE 1 Summary statistics of observed and predicted (using the average ensemble prediction) annual and growing season GPP (gross primary productivity), ER (ecosystem respiration), and NEE (net ecosystem exchange) fluxes (g C m−2 yr−1 for annual and g C m−2 period−1 for growing season fluxes) in different environments across the highlatitude region over 1990– 2015. The timeseries of the sites were averaged prior to calculating the observed mean flux (i.e., one flux value from one site was used when the regional averages were calculated). Positive numbers for NEE indicate net CO2 loss to the atmosphere (i.e., CO2 source) and negative numbers indicate net CO2 gain (i.e., CO2 sink). Note that ER and GPP do not sum up to NEE as different numbers of observations and sites were available for each flux and model. Moreover, some plant uptake occurs outside of our defined growing season, and consequently growing season GPP and annual GPP do not equal to each other. The average fluxes were calculated based on the extent of the highlatitude tundra and boreal biomes (Dinerstein et al., 2017), permafrost zones (Brown et al., 2002), and land cover (i.e., wetlands, and everything else is upland; ESA, 2017). The confidence intervals for the observed fluxes and the uncertainty ranges for the predicted fluxes can be found in the Table S6 Category Annual GPP Annual ER Annual NEE Growing season GPP Growing season ER Growing season NEE Observed mean flux Highlatitude 482 456 −17 317 262 −63 Boreal 624 605 −46 420 347 −87 Tundra 250 259 10 232 192 −44 Boreal upland 676 647 −47 432 350 −84 Boreal wetland 406 381 −38 347 330 −102 Tundra upland 250 259 16 232 192 −37 Tundra wetland −24 −115 No permafrost 831 773 −90 405 370 −37 Permafrost 342 350 6302 241 −67 Predicted mean flux Highlatitude 554 508 −20 343 283 −50 Boreal 638 594 −29 396 327 −52 Tundra 378 326 −2 230 192 −46 Boreal upland 653 604 −30 399 328 −51 Boreal wetland 437 458 −18 358 303 −64 Tundra upland 378 326 −1 229 191 −45 Tundra wetland 367 347 −29 281 242 −71 No permafrost 805 736 −56 447 375 −53 Permafrost 489 448 −11 315 259 −49 4054 | VIRKKALA et AL. 2014). Our tundra budget is within the range (though on average more positive, indicating stronger source) of the one comprising process and inversion models, and fieldbased estimates by McGuire et al. (2012) (−103 Tg C yr−1, from −297 to +89 Tg C yr−1). However, it differs from the source budget (+462 Tg C yr−1, from +94 to +840 Tg C yr−1; 10.5 × 106 km2; wetlands not included) estimated by Belshe et al. (2013). The divergence of average annual NEE across our and Belshe et al. (2013) study is likely explained by our inclusion of fluxes from wetlands, which were on average annual net ecosystem CO2 sinks (Table 1). The discrepancy between our and the McGuire et al. (2012) study can be explained by a 50% increase in new annual tundra source observations in our dataset (see e.g., Celis et al., 2017; Euskirchen et al., 2014), which were not included in the McGuire et al. (2012) analysis. Further, there are some differences in the study domain boundaries (e.g., the tundra domain in Belshe et al., 2013 was larger than in this study) which might explain some of the discrepancies between these studies, although the general patterns of these boundaries were rather similar (see e.g., Figure 1 in McGuire et al., 2012 vs. our tundra domain in Figure 1). Our findings suggest that both the boreal and tundra biomes were relatively strong CO2 sinks during the growing season. Our growing season CO2 budgets estimated for the same seasons as in previous studies (see Supplementary Text Section 2.3), derived both by predicting NEE as well as subtracting GPP from ER suggest that the growing season net uptake is stronger than or similar to the estimates in Belshe et al. (2013) and Natali et al. (2019). The growing season NEE budget calculated for 100 days in the tundra was −296 Tg C yr−1 in this study, compared to −137 ± 80 Tg C yr−1 in Belshe et al. (2013). The growing season NEE budget estimated for 153 days in the northern permafrost region in this study was −1122 Tg C yr−1, whereas the process model estimates varied between −687 and −1647 Tg C yr−1 in Natali et al. (2019). Further, the observed daily average growing season NEE in tundra demonstrated a stronger sink strength than the average growing season NEE reported in McGuire et al. (2012) and Belshe et al. (2013) (−0.6, −0.3, and −0.2 g C m−2 d−1, respectively). Even though we acknowledge that some plant uptake and CO2 emissions occur outside of our defined growing season (i.e., our growing season estimates did not capture the spring and autumn seasons), our results demonstrate that growing season CO2 uptake might be larger than previously thought. 4.4 | Summary and next steps in highlatitude CO2 flux upscaling Overall, our findings suggest that statistical predictions aimed at describing highlatitude CO2 flux patterns provide new insights into the understanding of broad GPP and ER patterns but have uncertainty with NEE. Furthermore, this study demonstrates that machine learning models are a robust and accurate empirical approach to predicting highlatitude terrestrial CO2 fluxes, and that no individual machine learning model outperformed the others. This therefore supports the use of ensemble predictions to reduce uncertainties associated with a single method and to produce more robust predictions. Nevertheless, the building of better models with an improved flux measurement network remains the highest research priority. Our results suggest that the next steps for future highlatitude upscaling efforts are to (a) measure fluxes over the entire year in as many sites as possible, (b) establish new sites in datapoor regions and regions where CO2 predictions were most uncertain, such as in European Russia, Siberia, eastern Canada, and Canadian Arctic Archipelago, and specifically in disturbed and highArctic conditions, (c) develop better geospatial predictors (e.g., describing soil moisture and nutrients or permafrost thaw) to explain fluxes, (d) conduct detailed sensitivity tests of the importance of the flux measurement method, data distribution, and different predictor datasets influencing the budgets, and (e) build models at a finer temporal resolution than annual and growing season, to capture rapidly changing transition periods and bypass issues associated with temporal aggregation and varying definitions of seasons. Highlatitude specific models are needed to more accurately monitor current emissions and improve understanding of the role of highlatitude regions in the global carbon cycle, as large changes in carbon cycling are likely in the near future. ACKNOWLEDGEMENTS AMV was supported by Nordenskiöldsamfundet, The Finnish Cultural Foundation, Alfred Kordelin Foundation, Väisälä fund, and Jenny and Antti Wihuri Foundation. AMV and ML were also funded by the Academy of Finland (grant 286950). JA acknowledges the funding by Academy of Finland (grants 33761, 337552), while AL acknowledges strategic research funding by the Academy of Finland for SOMPA project (grants 312912 and 325680). TT was funded by the Swedish National Space Board (SNSB Dnr 95/16). BR was supported by the NASA Carbon Cycle Science and ArcticBoreal Vulnerability Experiment programs (ABoVE grant NNX17AE13G), SMN by NASA ABoVE (grant NNX15AT81A) and JDW by NNX15AT81A and NASA NIP grant NNH17ZDA001N. AMV, BR, SN, and JDW were also funded by the Gordon and Betty Moore foundation (grant #8414). EAGS acknowledges NSF Research, Synthesis, and Knowledge Transfer in a Changing Arctic: Science Support for the Study of Environmental Arctic Change (grant #1331083) and NSF PLR Arctic System Science Research Networking Activities (Permafrost Carbon Network: Synthesizing Flux Observations for Benchmarking Model Projections of Permafrost Carbon Exchange; grant #1931333). JK acknowledges NSF grant 1203583, DZ NSF 1204263 and 1702797 and WO NSF 1204263, and 1702798. WO and DZ further acknowledge NOAA NA16SEC4810008, NASA NNX15AT74A and NNX16AF94A, EU Horizon 2020 727890, and UK NERC NE/ P002552/1. HK, MU, and HI were funded by Arctic Challenge for Sustainability II grant JPMXD1420318865, and EH and PL by Natural Sciences and Engineering Research Council. MG acknowledges European Commission (INTAROS project, H2020BG092016, project 727890) and ESE NSF grants DEB1636476, AON 856864, 1304271, 0632264, and 1107892, and the US Geological Survey. MM was funded by Academy of Finland (grant 317054) and MM and PJM were funded by the EU 6th Framework Programme project | 4055 VIRKKALA et AL. CARBONorth (grant 036993). CB and CV were funded by the EU FP7ENV project PAGE21 (grant 282700) and CB, CV, and PJM by the Nordic Center of Excellence project DEFROST. CB was further funded by the Academy of Finland (grant 314630), and CV by Academy of Finland (grant 332196). BE acknowledges Danish National Research Foundation (CENPERM DNRF100) and FJWP Research Council of Norway (Winterproof, grant 274711) and Swedish Research Council (WinterGap, project 201705268). JC was funded by FORMAS (grant 942201549). VLSL and CE were funded by the Natural Sciences and Engineering Research Council and MP by FORMAS #201601289. JJ was funded by the Swedish Forest Society Foundation (2018485Steg 2 2017) and SFO by NSF grants PLR1504381 and PLR1836898. MST acknowledges Office of Biological and Environmental Research, DOE Office of Science; SJP the Korean government (NRF2021M1A5A1 065425,KOPRIPN21011); and NC the Korean government (MSIP) (NRF2018R1D1A1B07047778 and NRF2021M1A5A1065679). HS was funded by Netherlands Earth System Science Centre (NESSC), and IM by Academy of Finland Flagship funding (project 337549) and ICOSFinland by University of Helsinki funding. RP was funded by Humboldt Fellowship for Experienced Researchers, MBN by Swedish Research Council, contract #201803966 and the national research infrastructures SITES and ICOS, funded by VR and partner institutes, and ELB by Greenland Research Council grant number 80.35, financed by the “Danish Program for Arctic Research”. OS was supported through the Canada Research Chairs and Natural Sciences and Engineering Research Council Discovery Grants programs. The authors would also like to acknowledge Liangzhi Chen for his help with the literature review. Funding for the CO2 flux synthesis workshop was provided by the Arctic Data Center. AUTHOR CONTRIBUTIONS AMV and ML designed the study. AMV extracted the flux data from the literature and the data from the community call was designed and gathered by MM, TS et al. AMV, JA, and SP prepared the gridded datasets. ML, JA, and AMV developed the modeling framework. TT, CT, BR, JDW, and SMN commented on the analysis and AMV, with the help of JA and ML, conducted the analysis. Other authors contributed data and all authors were involved in the writing. DATA AVAILABILITY STATEMENT Data are archived and freely available at Zenodo. The synthesis dataset is available at http://doi.org/10.5281/zenodo.4519583. Annual and averaged flux predictions are available at http://doi.org/10.5281/ zenodo.4521852. The codes to run the statistical models and predictions together with the uncertainty estimation can be found in an R Markdown file as a supplement (Virkkalaetal_CO2flux_upscaling.pdf). ORCID AnnaMaria Virkkala https://orcid.org/0000-0003-4877-2918 Juha Aalto https://orcid.org/0000-0001-6819-4911 Brendan M. Rogers https://orcid.org/0000-0001-6711-8466 Torbern Tagesson https://orcid.org/0000-0003-3011-1775 Claire C. Treat https://orcid.org/0000-0002-1225-8178 Susan M. Natali https://orcid.org/0000-0002-3010-2994 Aleksi Lehtonen https://orcid.org/0000-0003-1388-0388 Marguerite Mauritz https://orcid.org/0000-0001-8733-9119 Edward A. G. Schuur https://orcid.org/0000-0002-1096-2436 Mathias Goeckede https://orcid.org/0000-0003-2833-8401 Hiroki Iwata https://orcid.org/0000-0002-8962-8982 Peter M. Lafleur https://orcid.org/0000-0003-0347-9128 Stef Bokhorst https://orcid.org/0000-0003-0184-1162 Maija Marushchak https://orcid.org/0000-0002-2308-5049 Pertti J. Martikainen https://orcid.org/0000-0003-0415-8449 Bo Elberling https://orcid.org/0000-0002-6023-885X Carolina Voigt https://orcid.org/0000-0001-8589-1428 Christina Biasi https://orcid.org/0000-0002-7413-3354 Oliver Sonnentag https://orcid.org/0000-0001-9333-9721 FransJan W. Parmentier https://orcid.org/0000-0003-2952-7706 Masahito Ueyama https://orcid.org/0000-0002-4000-4888 Vincent L. St.Louis https://orcid.org/0000-0001-5405-1522 Craig A. Emmerton https://orcid.org/0000-0001-9511-9191 Matthias Peichl https://orcid.org/0000-0002-9940-5846 Jinshu Chi https://orcid.org/0000-0001-5688-8895 Järvi Järveoja https://orcid.org/0000-0001-6317-660X Mats B. Nilsson https://orcid.org/0000-0003-3765-6399 SangJong Park https://orcid.org/0000-0002-6944-6962 Ivan Mammarella https://orcid.org/0000-0002-8516-3356 Rafael Poyatos https://orcid.org/0000-0003-0521-2523 Efrén LópezBlanco https://orcid.org/0000-0002-3796-8408 Torben Røjle Christensen https://orcid.org/0000-0002-4917-148X Min Jung Kwon https://orcid.org/0000-0002-7330-2320 Torsten Sachs https://orcid.org/0000-0002-9959-4771 David Holl https://orcid.org/0000-0002-9269-7030 Miska Luoto https://orcid.org/0000-0001-6203-5143 REFERENCES Aalto, J., Karjalainen, O., Hjort, J., & Luoto, M. (2018). Statistical forecasting of current and future circumarctic ground temperatures and active layer thickness. Geophysical Research Letters, 45, 4889– 4898. https://doi.org/10.1029/2018G L078007 Abbott, B. W., Jones, J. B., Schuur, E. A. G., Chapin III, F. S., Bowden, W. B., BretHarte, M. S., Epstein, H. E., Flannigan, M. D., Harms, T. K., Hollingsworth, T. N., Mack, M. C., McGuire, A. D., Natali, S. M., Rocha, A. V., Tank, S. E., Turetsky, M. R., Vonk, J. E., Wickland, K. P., Aiken, G. R., … Zimov, S. (2016). Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire: An expert assessment. Environmental Research Letters, 11(3), 1– 13. https://doi.org/10.1088/17489326/11/3/034014 Ai, J., Jia, G., Epstein, H. E., Wang, H., Zhang, A., & Hu, Y. (2018). MODISbased estimates of global terrestrial ecosystem respiration. Journal of Geophysical Research: Biogeosciences, 123(2), 326– 352. https:// doi.org/10.1002/2017J G004107 Alekseychik, P., Mammarella, I., Karpov, D., Dengel, S., Terentieva, I., Sabrekov, A., & Lapshina, E. (2017). Net ecosystem exchange and energy fluxes in a West Siberian bog. Atmospheric Chemistry and Physics Discussions, 17, 9333– 9345. https://doi.org/10.5194/ a c p - 1 7 - 9 3 3 3 - 2 0 1 7 Alton, P. B. (2020). Representativeness of global climate and vegetation by carbonmonitoring networks; implications for estimates of gross and net primary productivity at biome and global levels. 4056 | VIRKKALA et AL. Agricultural and Forest Meteorology, 290(April), 108017. https://doi. org/10.1016/j.agrfo rmet.2020.108017 Araújo, M. B., & New, M. (2007). Ensemble forecasting of species distributions. Trends in Ecology and Evolution, 22(1), 42– 47. https://doi. org/10.1016/j.tree.2006.09.010 Arens, S. J. T., Sullivan, P. F., & Welker, J. M. (2008). Nonlinear responses to nitrogen and strong interactions with nitrogen and phosphorus additions drastically alter the structure and function of a high arctic ecosystem. Journal of Geophysical ResearchBiogeosciences, 113(G3), 10. https://doi.org/10.1029/2007j g000508 Bäckstrand, K., Crill, P. M., JackowiczKorczyński, M., Mastepanov, M., Christensen, T. R., & Bastviken, D. (2010). Annual carbon gas budget for a subarctic peatland. Biogeosciences, 7(1), 95– 108. https:// d o i . o r g / 1 0 . 5 1 9 4 / b g - 7 - 9 5 - 2 0 1 0 Baldocchi, D., Chu, H., & Reichstein, M. (2018). Interannual variability of net and gross ecosystem carbon fluxes: A review. Agricultural and Forest Meteorology, 249, 520– 533. https://doi.org/10.1016/j.agrfo rmet.2017.05.015 Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C. H., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W., … Wofsy, S. (2001). FLUXNET: A new tool to study the temporal and spatial variability of ecosystemscale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society, 82(11), 2415– 2434. Baldocchi, D. D., Hicks, B. B., & Meyers, T. P. (1988). Measuring biosphereatmosphere exchanges of biologically related gases with micrometeorological methods. Ecology, 69(5), 1331– 1340. https:// doi.org/10.2307/1941631 Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rodenbeck, C., Arain, M. A., Baldocchi, D., Bonan, G. B., Bondeau, A., Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis, H., Oleson, K. W., Roupsard, O., … Papale, D. (2010). Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate. Science, 329(5993), 834– 838. https://doi. org/10.1126/scien ce.1184984 Belshe, E. F., Schuur, E. A. G., & Bolker, B. M. (2013). Tundra ecosystems observed to be CO2 sources due to differential amplification of the carbon cycle. Ecology Letters, 16(10), 1307– 1315. https://doi. org/10.1111/ele.12164 Berner, L. T., Massey, R., Jantz, P., Forbes, B. C., MaciasFauria, M., MyersSmith, I., Kumpula, T., Gauthier, G., AndreuHayles, L., Gaglioti, B. V., Burns, P., Zetterberg, P., D'Arrigo, R., & Goetz, S. J. (2020). Summer warming explains widespread but not uniform greening in the Arctic tundra biome. Nature Communications, 11(1), 1 – 1 2 . h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 4 6 7 - 0 2 0 - 1 8 4 7 9 - 5 Björkman, M. P., Morgner, E., Björk, R. G., Cooper, E. J., Elberling, B., & Klemedtsson, L. (2010). A comparison of annual and seasonal carbon dioxide effluxes between subArctic Sweden and HighArctic Svalbard. Polar Research, 29(1), 75– 84. https://doi. org/10.1111/j.17518369.2010.00150.x Bodesheim, P., Jung, M., Gans, F., Mahecha, M. D., & Reichstein, M. (2018). Upscaled diurnal cycles of landAtmosphere fluxes: A new global halfhourly data product. Earth System Science Data, 10(3), 1 3 2 7 – 1 3 6 5 . h t t p s : / / d o i . o r g / 1 0 . 5 1 9 4 / e s s d - 1 0 - 1 3 2 7 - 2 0 1 8 BondLamberty, B., & Thomson, A. (2010). Temperatureassociated increases in the global soil respiration record. Nature, 464, 579– 582. https://doi.org/10.1038/natur e08930 Bradshaw, C. J. A., & Warkentin, I. G. (2015). Global estimates of boreal forest carbon stocks and flux. Global and Planetary Change, 128, 24– 30. https://doi.org/10.1016/j.glopl acha.2015.02.004 Brown, J., Ferrians, O., Heginbottom, J. A., & Melnikov, E. (2002). Circumarctic map of permafrost and groundice conditions, version 2. NSIDC National Snow and Ice Data Center. Cahoon, S. M. P., Sullivan, P. F., Shaver, G. R., Welker, J. M., & Post, E. (2012). Interactions among shrub cover and the soil microclimate may determine future Arctic carbon budgets. Ecology Letters, 15, 1415– 1422. https://doi.org/10.1111/j.14610248.2012.01865.x CampsValls, G., Jung, M., Ichii, K., Papale, D., Tramontana, G., Bodesheim, P., & Reichstein, M. (2015). Ranking drivers of global carbon and energy fluxes over land. International Geoscience and Remote Sensing Symposium, 4416– 4419. https://doi.org/10.1109/ IGARSS.2015.7326806 Cassidy, A. E., Christen, A., & Henry, G. H. R. (2016). The effect of a permafrost disturbance on growingseason carbondioxide fluxes in a high Arctic tundra ecosystem. Biogeosciences, 13(8), 2291– 2303. h t t p s : / / d o i . o r g / 1 0 . 5 1 9 4 / b g - 1 3 - 2 2 9 1 - 2 0 1 6 Celis, G., Mauritz, M., Bracho, R., Salmon, V. G., Webb, E. E., Hutchings, J., & Schuur, E. A. G. (2017). Tundra is a consistent source of CO2 at a site with progressive permafrost thaw during 6 years of chamber and eddy covariance measurements. Journal of Geophysical Research: Biogeosciences, 122(6), 1471– 1485. https://doi.org/10.1002/2016J G003671 Chu, H., Baldocchi, D. D., John, R., Wolf, S., & Reichstein, M. (2017). Fluxes all of the time? A primer on the temporal representativeness of FLUXNET. Journal of Geophysical Research: Biogeosciences, 122(2), 289– 307. https://doi.org/10.1002/2016J G003576 Coursolle, C., Giasson, M.- A., Margolis, H. A., & Bernier, P. Y. (2012). Moving towards carbon neutrality: CO2 exchange of a black spruce forest ecosystem during the first 10 years of recovery after harvest. Canadian Journal of Forest Research, 42(11), 1908– 1918. https://doi. o r g / 1 0 . 1 1 3 9 / x 2 0 1 2 - 1 3 3 Dagg, J., & Lafleur, P. (2011). Vegetation community, foliar nitrogen, and temperature effects on tundra CO2 exchange across a soil moisture gradient. Arctic Antarctic and Alpine Research, 43(2), 189– 197. https://doi.org/10.1657/1938424643.2.189 Davidson, E. A. J. E. A., Janssens, I. A. I. A., Marks, D., Murdock, M. D. M., Ahl, R. S., Woods, S. W., & Loffler, J. (2006). Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature, 440(7081), 165– 173. https://doi.org/10.1038/ n a t u r e 0 4 5 1 4 Dinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N. D., Wikramanayake, E., Hahn, N., Palminteri, S., Hedao, P., Noss, R., Hansen, M., Locke, H., Ellis, E. C., Jones, B., Barber, C. V., Hayes, R., Kormos, C., Martin, V., Crist, E., … Saleem, M. (2017). An ecoregionbased approach to protecting half the terrestrial realm. BioScience, 67(6), 534– 545. https://doi.org/10.1093/biosc i/bix014 Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77(4), 802– 813. https:// doi.org/10.1111/j.13652656.2008.01390.x Emmerton, C. A., St. Louis, V. L., Humphreys, E. R., Gamon, J. A., Barker, J. D., & Pastorello, G. Z. (2016). Net ecosystem exchange of CO2 with rapidly changing high Arctic landscapes. Global Change Biology, 22(3), 1185– 1200. https://doi.org/10.1111/gcb.13064 ESA (2017). Land cover CCI product user guide version 2. Tech. Rep. Retrieved from http://maps.elie.ucl.ac.be/CCI/viewe r/downl oad/ E S A C C I - L C - P h 2 - P U G v 2 _ 2 . 0 . p d f Euskirchen, E. S., Edgar, C. W., Turetsky, M. R., Waldrop, M. P., & Harden, J. W. (2014). Differential response of carbon fluxes to climate in three peatland ecosystems that vary in the presence and stability of permafrost. Journal of Geophysical Research G: Biogeosciences, 119(8), 1576– 1595. https://doi.org/10.1002/2014J G002683 Fox, A. M., Huntley, B., Lloyd, C. R., Williams, M., & Baxter, R. (2008). Net ecosystem exchange over heterogeneous Arctic tundra: Scaling between chamber and eddy covariance measurements. Global Biogeochemical Cycles, 22(2), 1– 15. https://doi.org/10.1029/2007G B003027 Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Hauck, J., Olsen, A., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Le Quéré, C., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S., Aragão, L. E. O. C., Arneth, A., Arora, V., Bates, N. R., … Zaehle, S. (2020). Global carbon budget 2020. Earth System Science Data, 12, 3269– 3340. | 4057 VIRKKALA et AL. Gasser, T., Kechiar, M., Ciais, P., Burke, E. J., Kleinen, T., Zhu, D., Huang, Y., Ekici, A., & Obersteiner, M. (2018). Pathdependent reductions in CO2 emission budgets caused by permafrost carbon release. Nature Geoscience, 11(11), 830– 835. https://doi.org/10.1038/ s 4 1 5 6 1 - 0 1 8 - 0 2 2 7 - 0 Heliasz, M., Johansson, T., Lindroth, A., Mölder, M., Mastepanov, M., Friborg, T., Callaghan, T. V., & Christensen, T. R. (2011). Quantification of C uptake in subarctic birch forest after setback by an extreme insect outbreak. Geophysical Research Letters, 38(1), 1– 5. https://doi.org/10.1029/2010G L044733 Hugelius, G., Strauss, J., Zubrzycki, S., Harden, J. W., Schuur, E. A. G., Ping, C.- L., Schirrmeister, L., Grosse, G., Michaelson, G. J., Koven, C. D., O'Donnell, J. A., Elberling, B., Mishra, U., Camill, P., Yu, Z., Palmtag, J., & Kuhry, P. (2014). Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps. Biogeosciences, 11(23), 6573– 6593. https://doi. o r g / 1 0 . 5 1 9 4 / b g - 1 1 - 6 5 7 3 - 2 0 1 4 Hursh, A., Ballantyne, A., Cooper, L., Maneta, M., Kimball, J., & Watts, J. (2016). The sensitivity of soil respiration to soil temperature, moisture, and carbon supply at the global scale. Global Change Biology, 23(5), 2090– 2103. https://doi.org/10.1111/gcb.13489 Ichii, K., Ueyama, M., Kondo, M., Saigusa, N., Kim, J., Alberto, M. C., & Zhao, F. (2017). New datadriven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression. Journal of Geophysical Research: Biogeosciences, 122(4), 767– 795. https://doi.org/10.1002/2016J G003640 Illeris, L., Michelsen, A., & Jonasson, S. (2003). Soil plus root respiration and microbial biomass following water, nitrogen, and phosphorus application at a high arctic semi desert. Biogeochemistry, 65(1), 15– 29. https://doi.org/10.1023/a:10260 34523499 Iwata, H., Ueyama, M., Harazono, Y., Tsuyuzaki, S., Kondo, M., & Uchida, M. (2011). Quick recovery of carbon dioxide exchanges in a burned black spruce forest in Interior Alaska. Scientific Online Letters on the Atmosphere, 7(1), 105– 108. https://doi.org/10.2151/sola.2011027 Järveoja, J., Nilsson, M. B., Gažovič, M., Crill, P. M., & Peichl, M. (2018). Partitioning of the net CO2 exchange using an automated chamber system reveals plant phenology as key control of production and respiration fluxes in a boreal peatland. Global Change Biology, 24(8), 3436– 3451. https://doi.org/10.1111/gcb.14292 Jung, M., Reichstein, M., Schwalm, C. R., Huntingford, C., Sitch, S., Ahlström, A., Arneth, A., CampsValls, G., Ciais, P., Friedlingstein, P., Gans, F., Ichii, K., Jain, A. K., Kato, E., Papale, D., Poulter, B., Raduly, B., Rödenbeck, C., Tramontana, G., … Zeng, N. (2017). Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature, 541(7638), 516– 520. https://doi. org/10.1038/natur e20780 Jung, M., Schwalm, C., Migliavacca, M., Walther, S., CampsValls, G., Koirala, S., & Walker, A. (2020). Scaling carbon fluxes from eddy covariance sites to globe: Synthesis and evaluation of the FLUXCOM approach. Biogeosciences, 17, 1343– 1365. https://doi.org/10.5194/ b g - 2 0 1 9 - 3 6 8 Karelin, D. V., Zamolodchikov, D. G., Zukert, N. V., Chestnykh, O. V., Pochikalov, A. V., & Krayev, G. N. (2013). Interannual changes in PAR and soil moisture during the warm season may be more important for directing of annual carbon balance in tundra than temperature fluctuations. Zhurnal Obshchei Biologii, 74(1), 3– 22. https:// doi.org/10.1134/S2079 08641 3050058 Kittler, F., Heimann, M., Kolle, O., Zimov, N., Zimov, S., & Göckede, M. (2017). Longterm drainage reduces CO2 uptake and CH4 emissions in a Siberian permafrost ecosystem. Global Biogeochemical Cycles, 31(12), 1704– 1717. https://doi.org/10.1002/2017G B005774 Kolari, P., Kulmala, L., Pumpanen, J., Launiainen, S., Ilvesniemi, H., Hari, P., & Nikinmaa, E. (2009). CO2 exchange and component CO2 fluxes of a boreal Scots pine forest. Boreal Environment Research, 14, 761– 783. La Puma, I. P., Philippi, T. E., & Oberbauer, S. F. (2007). Relating NDVI to ecosystem CO2 exchange patterns in response to season length and soil warming manipulations in arctic Alaska. Remote Sensing of Environment, 109(2), 225– 236. https://doi.org/10.1016/j.rse. 2007.01.001 Lafleur, P. M., Humphreys, E. R., St. Louis, V. L., Myklebust, M. C., Papakyriakou, T., Poissant, L., Barker, J. D., Pilote, M., & Swystun, K. A. (2012). Variation in peak growing season net ecosystem production across the Canadian arctic. Environmental Science and Technology, 46(15), 7971– 7977. https://doi.org/10.1021/es300 500m Lasslop, G., Reichstein, M., Papale, D., Richardson, A. D., Arneth, A., Barr, A., Stoy, P., & Wohlfahrt, G. (2010). Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: Critical issues and global evaluation. Global Change Biology, 16(1), 187– 208. https://doi.org/10.1111/ j.13652486.2009.02041.x LópezBlanco, E., Exbrayat, J.- F., Lund, M., Christensen, T. R., Tamstorf, M. P., Slevin, D., Hugelius, G., Bloom, A. A., & Williams, M. (2019). Evaluation of terrestrial panArctic carbon cycling using a dataassimilation system. Earth System Dynamics, 10(2), 233– 255. h t t p s : / / d o i . o r g / 1 0 . 5 1 9 4 / e s d - 1 0 - 2 3 3 - 2 0 1 9 LópezBlanco, E., Lund, M., Williams, M., Tamstorf, M. P., WestergaardNielsen, A., Exbrayat, J. F., & Christensen, T. R. (2017). Exchange of CO2 in Arctic tundra: Impacts of meteorological variations and biological disturbance. Biogeosciences, 14(19), 4467– 4483. https:// d o i . o r g / 1 0 . 5 1 9 4 / b g - 1 4 - 4 4 6 7 - 2 0 1 7 Lund, M., Christensen, T. R., Mastepanov, M., Lindroth, A., & Ström, L. (2009). Effects of N and P fertilization on the greenhouse gas exchange in two northern peatlands with contrasting N deposition rates. Biogeosciences, 6(10), 2135– 2144. https://doi.org/10.5194/ b g - 6 - 2 1 3 5 - 2 0 0 9 Lund, M., Lafleur, P. M., Roulet, N. T., Lindroth, A., Christensen, T. R., Aurela, M., Chojnicki, B. H., Flanagan, L. B., Humphreys, E. R., Laurila, T., Oechel, W. C., Olejnik, J., Rinne, J., Schubert, P., & Nilsson, M. B. (2010). Variability in exchange of CO2 across 12 northern peatland and tundra sites. Global Change Biology, 16(9), 2436– 2448. https://doi.org/10.1111/j.13652486.2009.02104.x Lund, M., Raundrup, K., WestergaardNielsen, A., LópezBlanco, E., Nymand, J., & Aastrup, P. (2017). Larval outbreaks in West Greenland: Instant and subsequent effects on tundra ecosystem productivity and CO2 exchange. Ambio, 46, 26– 38. https://doi. o r g / 1 0 . 1 0 0 7 / s 1 3 2 8 0 - 0 1 6 - 0 8 6 3 - 9 Lundegårdh, H. (1927). Carbon dioxide evolution of soil and crop growth. Soil Science, 23(6), 417– 453. Machimura, T., Kobayashi, Y., Iwahana, G., Hirano, T., Lopez, L., Fukuda, M., & Fedorov, A. N. (2005). Change of carbon dioxide budget during three years after deforestation in eastern Siberian larch forest. Journal of Agricultural Meteorology, 60(5), 653– 656. https://doi. org/10.2480/agrmet.653 Marushchak, M. E., Kiepe, I., Biasi, C., Elsakov, V., Friborg, T., Johansson, T., Soegaard, H., Virtanen, T., & Martikainen, P. J. (2013). Carbon dioxide balance of subarctic tundra from plot to regional scales. Biogeosciences, 10(1), 437– 452. https://doi.org/10.5194/ b g - 1 0 - 4 3 7 - 2 0 1 3 McCallum, I., Franklin, O., Moltchanova, E., Merbold, L., Schmullius, C., Shvidenko, A., Schepaschenko, D., & Fritz, S. (2013). Improved light and temperature responses for lightuseefficiencybased GPP models. Biogeosciences, 10(10), 6577– 6590. https://doi. o r g / 1 0 . 5 1 9 4 / b g - 1 0 - 6 5 7 7 - 2 0 1 3 McGuire, A. D., Anderson, L. G., Christensen, T. R., Dallimore, S., Guo, L., Hayes, D. J., Heimann, M., Lorenson, T. D., Macdonald, R. W., & Roulet, N. (2009). Sensitivity of the carbon cycle in the Arctic to climate change. Ecological Monographs, 79(4), 523– 555. McGuire, A. D., Christensen, T. R., Hayes, D., Heroult, A., Euskirchen, E., Kimball, J. S., Koven, C., Lafleur, P., Miller, P. A., Oechel, W., Peylin, 4058 | VIRKKALA et AL. P., Williams, M., & Yi, Y. (2012). An assessment of the carbon balance of Arctic tundra: comparisons among observations, process models, and atmospheric inversions. Biogeosciences, 9(8), 3185– 3204. https://doi.org/10.5194/bg931852012 McGuire, A. D., Koven, C., Lawrence, D. M., Clein, J. S., Xia, J., Beer, C., Burke, E., Chen, G., Chen, X., Delire, C., Jafarov, E., MacDougall, A. H., Marchenko, S., Nicolsky, D., Peng, S., Rinke, A., Saito, K., Zhang, W., Alkama, R., … Zhuang, Q. (2016). Variability in the sensitivity among model simulations of permafrost and carbon dynamics in the permafrost region between 1960 and 2009. Global Biogeochemical Cycles, 30, 1015– 1037. https://doi.org/10.1002/2016G B005405 McGuire, A. D., Lawrence, D. M., Koven, C., Clein, J. S., Burke, E., Chen, G., Jafarov, E., MacDougall, A. H., Marchenko, S., Nicolsky, D., Peng, S., Rinke, A., Ciais, P., Gouttevin, I., Hayes, D. J., Ji, D., Krinner, G., Moore, J. C., Romanovsky, V., … Zhuang, Q. (2018). Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change. Proceedings of the National Academy of Sciences of the United States of America, 115(15), 3882– 3887. https://doi.org/10.1073/pnas.17199 03115 Metcalfe, D. B., Hermans, T. D. G., Ahlstrand, J., Becker, M., Berggren, M., Björk, R. G., Björkman, M. P., Blok, D., Chaudhary, N., Chisholm, C., Classen, A. T., Hasselquist, N. J., Jonsson, M., Kristensen, J. A., Kumordzi, B. B., Lee, H., Mayor, J. R., Prevéy, J., Pantazatou, K., … Abdi, A. M. (2018). Patchy field sampling biases understanding of climate change impacts across the Arctic. Nature Ecology and Evolution, 2(9), 1443– 1448. https://doi.org/10.1038/s4155 9 - 0 1 8 - 0 6 1 2 - 5 MyersSmith, I. H., Kerby, J. T., Phoenix, G. K., Bjerke, J. W., Epstein, H. E., Assmann, J. J., John, C., AndreuHayles, L., AngersBlondin, S., Beck, P. S. A., Berner, L. T., Bhatt, U. S., Bjorkman, A. D., Blok, D., Bryn, A., Christiansen, C. T., Cornelissen, J. H. C., Cunliffe, A. M., Elmendorf, S. C., … Wipf, S. (2020). Complexity revealed in the greening of the Arctic. Nature Climate Change, 10, 106– 117. https:// d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 5 5 8 - 0 1 9 - 0 6 8 8 - 1 Natali, S. M., Watts, J. D., Rogers, B. M., Potter, S., Ludwig, S. M., Selbmann, A.- K., Sullivan, P. F., Abbott, B. W., Arndt, K. A., Birch, L., Björkman, M. P., Bloom, A. A., Celis, G., Christensen, T. R., Christiansen, C. T., Commane, R., Cooper, E. J., Crill, P., Czimczik, C., … Zona, D. (2019). Large loss of CO2 in winter observed across the northern permafrost region. Nature Climate Change, 9, 852– 857. h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 5 5 8 - 0 1 9 - 0 5 9 2 - 8 Niittynen, P., & Luoto, M. (2018). The importance of snow in species distribution models of arctic vegetation. Ecography, 41(6), 1024– 1037. https://doi.org/10.1111/ecog.03348 Nobrega, S., & Grogan, P. (2007). Deeper snow enhances winter respiration from both plantassociated and bulk soil carbon pools in birch hummock tundra. Ecosystems, 10(3), 419– 431. https://doi. o r g / 1 0 . 1 0 0 7 / s 1 0 0 2 1 - 0 0 7 - 9 0 3 3 - z Nobrega, S., & Grogan, P. (2008). Landscape and ecosystemlevel controls on net carbon dioxide exchange along a natural moisture gradient in Canadian low arctic tundra. Ecosystems, 11(3), 377– 396. h t t p s : / / d o i . o r g / 1 0 . 1 0 0 7 / s 1 0 0 2 1 - 0 0 8 - 9 1 2 8 - 1 Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A., & Hayes, S. (2011). A large and persistent carbon sink in the world's forests. Science, 333, 1239– 1243. https://doi.org/10.1002/ clen.20072 0011 Parmentier, F.- J.- W., Sonnentag, O., Mauritz, M., Virkkala, A.- M., & Schuur, E. A. G. (2019). Is the northern permafrost zone a source or a sink for carbon? EOS, 100. Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y.- W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Isaac, P., Polidori, D., Reichstein, M., Ribeca, A., van Ingen, C., Vuichard, N., Zhang, L., Amiro, B., Ammann, C., … Papale, D. (2020). The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Scientific Data, 7(1), 225. https://doi. o r g / 1 0 . 1 0 3 8 / s 4 1 5 9 7 - 0 2 0 - 0 5 3 4 - 3 Peltola, O., Vesala, T., Gao, Y., Räty, O., Alekseychik, P., Aurela, M., Chojnicki, B., Desai, A. R., Dolman, A. J., Euskirchen, E. S., Friborg, T., Göckede, M., Helbig, M., Humphreys, E., Jackson, R. B., Jocher, G., Joos, F., Klatt, J., Knox, S. H., … Aalto, T. (2019). Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations. Earth System Science Data, 11, 1 2 6 3 – 1 2 8 9 . h t t p s : / / d o i . o r g / 1 0 . 5 1 9 4 / e s s d - 1 1 - 1 2 6 3 - 2 0 1 9 Petrone, R. M., Chasmer, L., Hopkinson, C., Silins, U., Landhäusser, S. M., Kljun, N., & Devito, K. J. (2014). Effects of harvesting and drought on CO2 and H2O fluxes in an aspendominated western boreal plain forest: Early chronosequence recovery. Canadian Journal of Forest Research, 45(1), 87– 100. https://doi.org/10.1139/cjfr20140253 Rawlins, M. A., McGuire, A. D., Kimball, J. S., Dass, P., Lawrence, D., Burke, E., & Sueyoshi, T. (2015). Assessment of model estimates of landatmosphere CO2 exchange across Northern Eurasia. Biogeosciences, 12(14), 4385– 4405. https://doi.org/10.5194/bg1243852015 Raynolds, M. K., Walker, D. A., Balser, A., Bay, C., Campbell, M., Cherosov, M. M., Daniëls, F. J. A., Eidesen, P. B., Ermokhina, K. A., Frost, G. V., Jedrzejek, B., Jorgenson, M. T., Kennedy, B. E., Kholod, S. S., Lavrinenko, I. A., Lavrinenko, O. V., Magnússon, B., Matveyeva, N. V., Metúsalemsson, S., … Troeva, E. (2019). A raster version of the Circumpolar Arctic Vegetation Map (CAVM). Remote Sensing of Environment, 232, 111297. https://doi.org/10.1016/ j.rse.2019.111297 Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., … Valentini, R. (2005). On the separation of net ecosystem exchange into assimilation and ecosystem respiration: Review and improved algorithm. Global Change Biology, 11(9), 1424– 1439. https://doi. org/10.1111/j.13652486.2005.001002.x Schuur, E. A. G., Bockheim, J., Canadell, J. G., Euskirchen, E., Field, C. B., Goryachkin, S. V., Hagemann, S., Kuhry, P., Lafleur, P. M., Lee, H., Mazhitova, G., Nelson, F. E., Rinke, A., Romanovsky, V. E., Shiklomanov, N., Tarnocai, C., Venevsky, S., Vogel, J. G., & Zimov, S. A. (2008). Vulnerability of permafrost carbon to climate change: Implications for the global carbon cycle. BioScience, 58(8), 701. https://doi.org/10.1641/B580807 Schuur, E. A. G., McGuire, A. D., Schädel, C., Grosse, G., Harden, J. W., Hayes, D. J., Hugelius, G., Koven, C. D., Kuhry, P., Lawrence, D. M., Natali, S. M., Olefeldt, D., Romanovsky, V. E., Schaefer, K., Turetsky, M. R., Treat, C. C., & Vonk, J. E. (2015). Climate change and the permafrost carbon feedback. Nature, 520(7546), 171– 179. https://doi. org/10.1038/natur e14338 Segurado, P., & Araújo, M. B. (2004). An evaluation of methods for modelling species distributions. Journal of Biogeography, 31(10), 1555– 1568. Shaver, G. R., Street, L. E., Rastetter, E. B., Van Wijk, M. T., & Williams, M. (2007). Functional convergence in regulation of net CO2 flux in heterogeneous tundra landscapes in Alaska and Sweden. Journal of Ecology, 95(4), 802– 817. https://doi.org/10.1111/ j.13652745. 2007.01259.x Sørensen, M. V., Graae, B. J., Classen, A., Enquist, B. J., & Strimbeck, R. (2019). Drivers of C cycling in three arcticalpine plant communities. Arctic, Antarctic, and Alpine Research, 51(1), 128– 147. https:// doi.org/10.1080/15230 430.2019.1592649 Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012). An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93(4), 485– 498. https://doi.org/10.1175/ B A M S - D - 1 1 - 0 0 0 9 4 . 1 Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., CampsValls, G., Ráduly, B., & Papale, D. (2016). Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences, 13(14), 4291– 4313. https://doi.org/10.5194/ b g - 1 3 - 4 2 9 1 - 2 0 1 6 | 4059 VIRKKALA et AL. Treat, C. C., Marushchak, M. E., Voigt, C., Zhang, Y. U., Tan, Z., Zhuang, Q., Virtanen, T. A., Räsänen, A., Biasi, C., Hugelius, G., Kaverin, D., Miller, P. A., Stendel, M., Romanovsky, V., Rivkin, F., Martikainen, P. J., & Shurpali, N. J. (2018). Tundra landscape heterogeneity, not interannual variability, controls the decadal regional carbon balance in the Western Russian Arctic. Global Change Biology, 24(11), 5188– 5204. https://doi.org/10.1111/ gcb.14421 Trucco, C., Schuur, E. A. G. G., Natali, S. M., Belshe, E. F., Bracho, R., & Vogel, J. (2012). Sevenyear trends of CO2 exchange in a tundra ecosystem affected by longterm permafrost thaw. Journal of Geophysical Research: Biogeosciences, 117(2), 1– 12. https://doi. org/10.1029/2011J G001907 Turetsky, M. R., Abbott, B. W., Jones, M. C., Anthony, K. W., Olefeldt, D., Schuur, E. A. G., Grosse, G., Kuhry, P., Hugelius, G., Koven, C., Lawrence, D. M., Gibson, C., Sannel, A. B. K., & McGuire, A. D. (2020). Carbon release through abrupt permafrost thaw. Nature Geoscience, 13(2), 138– 143. https://doi.org/10.1038/s4156 1 - 0 1 9 - 0 5 2 6 - 0 Ueyama, M., Ichii, K., Iwata, H., Euskirchen, E. S., Zona, D., Rocha, A. V., Harazono, Y., Iwama, C., Nakai, T., & Oechel, W. C. (2013). Upscaling terrestrial carbon dioxide fluxes in Alaska with satellite remote sensing and support vector regression. Journal of Geophysical Research: Biogeosciences, 118(3), 1266– 1281. https:// doi.org/10.1002/jgrg.20095 Ueyama, M., Iwata, H., Harazono, Y., Euskirchen, E. S., Oechel, W. C., & Zona, D. (2013). Growing season and spatial variations of carbon fluxes of Arctic and boreal ecosystems in Alaska (USA). Ecological Applications, 23(8), 1798– 1816. https://doi. o r g / 1 0 . 1 8 9 0 / 1 1 - 0 8 7 5 . 1 Ueyama, M., Iwata, H., Nagano, H., Tahara, N., Iwama, C., & Harazono, Y. (2019). Carbon dioxide balance in earlysuccessional forests after forest fires in interior Alaska. Agricultural and Forest Meteorology, 275, 196– 207. https://doi.org/10.1016/j.agrfo rmet. 2019.05.020 Virkkala, A.- M., Abdi, A. M., Luoto, M., & Metcalfe, D. (2019). Identifying multidisciplinary research gaps across Arctic terrestrial gradients. Environmental Research Letters, 14, 124061. https://doi. org/10.1088/17489326/ab4291 Virkkala, A.- M., Virtanen, T., Lehtonen, A., Rinne, J., & Luoto, M. (2018). The current state of CO2 flux chamber studies in the Arctic tundra: A review. Progress in Physical Geography, 42(2), 162– 184. https://doi. org/10.1177/03091 33317 745784 Vogel, J., Schuur, E. A. G. G., Trucco, C., & Lee, H. (2009). Response of CO2 exchange in a tussock tundra ecosystem to permafrost thaw and thermokarst development. Journal of Geophysical ResearchBiogeosciences, 114(G4), 1– 14. https://doi.org/10.1029/2008J G000901 Voigt, C., Marushchak, M. E., Mastepanov, M., Lamprecht, R. E., Christensen, T. R., Dorodnikov, M., JackowiczKorczyński, M., Lindgren, A., Lohila, A., Nykänen, H., Oinonen, M., Oksanen, T., Palonen, V., Treat, C. C., Martikainen, P. J., & Biasi, C. (2019). Ecosystem carbon response of an Arctic peatland to simulated permafrost thaw. Global Change Biology, 25(5), 1746– 1764. https://doi. org/10.1111/gcb.14574 Walker, X. J., Baltzer, J. L., Cumming, S. G., Day, N. J., Ebert, C., Goetz, S., Johnstone, J. F., Potter, S., Rogers, B. M., Schuur, E. A. G., Turetsky, M. R., & Mack, M. C. (2019). Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature, 572(7770), 520– 523. h t t p s : / / d o i . o r g / 1 0 . 1 0 3 8 / s 4 1 5 8 6 - 0 1 9 - 1 4 7 4 - y Wania, R., Ross, I., & Prentice, I. C. (2009). Integrating peatlands and permafrost into a dynamic global vegetation model: 2. Evaluation and sensitivity of vegetation and carbon cycle processes. Global Biogeochemical Cycles, 23, GB3015. https://doi.org/10.1029/2008G B003413 Warner, D. L., BondLamberty, B., Jian, J., Stell, E., & Vargas, R. (2019). Spatial predictions and associated uncertainty of annual soil respiration at the global scale. Global Biogeochemical Cycles, 33(12), 1733– 1745. https://doi.org/10.1029/2019G B006264 Watts, J. D., Kimball, J. S., Parmentier, F. J. W., Sachs, T., Rinne, J., Zona, D., & Aurela, M. (2014). A satellite data driven biophysical modeling approach for estimating northern peatland and tundra CO2 and CH4 fluxes. Biogeosciences, 11(7), 1961– 1980. https://doi.org/10.5194/ b g - 1 1 - 1 9 6 1 - 2 0 1 4 Welker, J. M., Fahnestock, J. T., Henry, G. H. R., O'Dea, K. W., & Chimner, R. A. (2004). CO2 exchange in three Canadian High Arctic ecosystems: Response to longterm experimental warming. Global Change Biology, 10(12), 1981– 1995. https://doi.org/10.1111/j.13652486. 2004.00857.x Wilkman, E., Zona, D., Tang, Y., Gioli, B., Lipson, D. A., & Oechel, W. (2018). Temperature response of respiration across the heterogeneous landscape of the Alaskan arctic tundra. Journal of Geophysical Research: Biogeosciences Research, 123, 2287– 2302. https://doi. org/10.1029/2017J G004227 Zhang, W., Jansson, P. E., Schurgers, G., Hollesen, J., Lund, M., Abermann, J., & Elberling, B. (2018). Processoriented modeling of a high arctic tundra ecosystem: Longterm carbon budget and ecosystem responses to interannual variations of climate. Journal of Geophysical Research: Biogeosciences, 123(4), 1178– 1196. https:// doi.org/10.1002/2017J G003956 Zscheischler, J., Mahecha, M. D., Avitabile, V., Calle, L., Carcalhais, N., Ciais, P., & Reichstein, M. (2017). Reviews and syntheses: An empirical spatiotemporal description of the global surface – Atmosphere carbon fluxes: Opportunities and data limitations. Biogeosciences, 14, 3685– 3703. https://doi.org/10.5194/bg1436852017 SUPPORTING INFORMATION Additional supporting information may be found online in the Supporting Information section. How to cite this article: Virkkala A-M, Aalto J, Rogers BM, et al. Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties. Glob Change Biol. 2021;27:4040–4059. https:// doi.org/10.1111/gcb.15659