Rapidly changing high-latitude seasonality: implications for the 21st century carbon cycle in Alaska

Seasonal variations in high-latitude terrestrial carbon (C) fluxes are predominantly driven by air temperature and radiation. At present, high-latitude net C uptake is largest during the summer. Recent observations and modeling studies have demonstrated that ongoing and projected climate change will increase plant productivity, microbial respiration, and growing season lengths at high-latitudes, but impacts on high-latitude C cycle seasonality (and potential feedbacks to the climate system) remain uncertain. Here we use ecosys, a well-tested and process-rich mechanistic ecosystem model that we evaluate further in this study, to explore how climate warming under an RCP8.5 scenario will shift C cycle seasonality in Alaska throughout the 21st century. The model successfully reproduced recently reported large high-latitude C losses during the fall and winter and yet still predicts a high-latitude C sink, pointing to a resolution of the current conflict between process-model and observation-based estimates of high-latitude C balance. We find that warming will result in surprisingly large changes in net ecosystem exchange (NEE; defined as negative for uptake) seasonality, with spring net C uptake overtaking summer net C uptake by year 2100. This shift is driven by a factor of 3 relaxation of spring temperature limitation to plant productivity that results in earlier C uptake and a corresponding increase in magnitude of spring NEE from −19 to −144 gC m−2 season−1 by the end of the century. Although a similar relaxation of temperature limitation will occur in the fall, radiation limitation during those months will limit increases in C fixation. Additionally, warmer soil temperatures and increased carbon inputs from plants lead to combined fall and winter C losses (163 gC m−2) that are larger than summer net uptake (123 gC m−2 season−1) by year 2100. However, this increase in microbial activity leads to more rapid N cycling and increased plant N uptake during the fall and winter months that supports large increases in spring NPP. Due to the large increases in spring net C uptake, the high-latitude atmospheric C sink is projected to sustain throughout this century. Our analysis disentangles the effects of key environmental drivers of high-latitude seasonal C balances as climate changes over the 21st century.


Introduction
Surface air temperature and solar radiation exhibit strong seasonality and shape seasonal and annual cycles of plant and microbial activities in highlatitude ecosystems (Ernakovich et al 2014). Late snowmelt, cool summers, and short autumn days lead to short and relatively unproductive growing seasons (Billings 1973, Ernakovich et al 2014, and frozen soils and harsh winters inhibit organic matter decomposition (Mikan et al 2002). The high latitudes are particularly susceptible to anthropogenic climate warming (Serreze et al 2009), and recent rapid increases in air temperature are projected to Figure 1. 21st century climate warming will shift the seasonality of the C cycle across Alaska. As depicted in this schematic, there will be important changes to the seasonality of high-latitude NEE throughout the 21st century. Relaxation of temperature limitation to plant productivity will cause large increases in spring net C uptake. Warming air and soil temperatures, coincident with persistent radiation limitation to plant productivity, will cause large increases in fall and winter net C losses. Summer net C uptake also increases with increasing air temperatures and CO2 concentrations, but will be smaller in magnitude than spring C uptake and combined fall and winter C losses by year 2100. Also depicted are changes to snow, soil freeze and thaw, and vegetation dynamics that result from and contribute to changing C cycle seasonality. Changes to soil biogeochemical processes during fall and winter, including N mineralization and uptake, are implied in the changes to soil freeze and thaw state. The seasons are equinox based (i.e. spring is the period 21 March-20 June, summer is the period 21 June-20 September, fall is the period 21 September-20 December, and winter is the period 21 December-20 March). accelerate throughout the 21st century Barry 2011, Box et al 2019). This climate warming will shift the relative effects of temperature and radiation limitations on biological activity, and therefore the C cycle (figure 1). While previous modeling and observation-based studies have demonstrated that climate warming will induce increased C fixation and a longer growing season at high-latitudes (Arora and Boer 2014, Ito et al 2016, Gallego-Sala et al 2018, Tharammal et al 2019, the impacts of climate change on the seasonality of high-latitude C cycling remain uncertain.
The amplitude of the seasonal cycle of atmospheric CO 2 concentrations in northern latitudes has been increasing steadily over the past 50 years and has been explained by changes in the seasonality of the terrestrial C cycle (Graven et al 2013). Earlier leaf-out in the spring (Commane et al 2017, Xu et al 2018, Winkler et al 2019 and higher productivity and allocation to woody biomass during the summer (Leffler et al 2016, Mekonnen et al 2018) increase the regional C sink strength. Fall and winter soil warming increases regional C losses due to higher soil microbial respiration rates (Commane et al 2017, Parazoo et al 2018, Natali et al 2019. The sum of these seasonal changes, which represents the balance between climate induced changes in vegetation productivity and microbial activity, has important implications for the global C cycle as the large organic C stocks stored in permafrost soils (Schuur et al 2015) have the potential to drive important climate feedbacks (Gallego-Sala et al 2018, Rogelj et al 2019. Model predictions of current high-latitude ecosystems mostly suggest that they are net C sinks (McGuire et al 2012, Arora and Boer 2014, Ito et al 2016, while several recent observation-based studies indicate the opposite. For example, Natali et al (2019), using machine learning to spatially and temporally extrapolate high-latitude measurements, predicted higher fall and winter regional C losses than did a suite of process-models for the years 2003-2017. The non-growing season losses predicted by Natali et al (2019) are also larger than the growing season uptake predicted by the process-models. Commane et al (2017), using aircraft observations and upscaled eddy covariance measurements, argued that high rates of fall respiration caused Alaska to be a C source between 2012 and 2014. These discrepancies between processmodel predictions and observation-based estimates raise concerns that missing or misrepresented coldseason mechanisms could bias process-model predictions of high-latitude ecosystem responses to climate change. Consideration of seasonal changes in C fluxes is needed to disentangle mismatches between modeling and observation-based studies, to provide insight into driving forces behind model results, and to identify important measurements needed to evaluate and build confidence in model predictions.
Here, we examine climate change impacts on Alaska C cycle seasonality using a well-tested mechanistic ecosystem model, ecosys. This study is also motivated by the large reported differences in models and observation-based assessments of C cycle seasonality dynamics, particularly during the fall and winter (Commane et al 2017, Xu et al 2018, Natali et al 2019, Winkler et al 2019. Because of its rich process representations (e.g. process-specific temperature cutoffs and activation energies that can represent low-temperature biogeochemical activity and climate-change acclimation, mineralization and plant nutrient uptake that is driven by availability and capability rather than photosynthetic activity), ecosys is well-suited to address these questions. After further evaluating the model and showing it is broadly consistent with recent site-and regional-scale observations (including those mentioned above), we apply it to analyze processes that control seasonality of plant and microbial activity, and explore how these controls are expected to change over the 21st century and how these changes will affect regional ecosystem C budgets.

Model description
Ecosys is an hourly time-step ecosystem model with multiple canopy, snow, and soil layers. The tightly coupled C, energy, nutrient, and water cycles operate across a wide range of temporal, and spatial scales. Key model processes are outlined below, and a comprehensive description is given in the supplementary material of Mekonnen et al (2019).
Each grid cell is forced with meteorological inputs and the model is initialized with prescribed seed densities for five plant functional types (PFTs) (deciduous, evergreen, sedge, moss, lichen). PFT-specific functional traits (i.e. CO 2 fixation kinetics, leaf optical properties, phenology, morphology, and root traits) result in emergent PFT variation in phenology, irradiance, CO 2 fixation rate, and water uptake. Functional traits differ for PFTs seeded in the boreal forest compared with those seeded in the tundra (figure 2). The model represents a multi-layer canopy, a residue layer, and multi-layer soil column. Leaf properties (including azimuth and angle), solar incidence angle, and light availability dictate propagation and absorption of direct and diffuse shortwave radiation in the canopy (Grant and Baldocchi 1992). Canopy energy balance, calculated using first-order closure schemes and by setting the sum of heat fluxes to zero, is tightly coupled to the water cycle through evapotranspiration. Heat and water transfers between the atmosphere, canopy, and soil column determine the temperature, water content, and ice content of each soil layer.
Coupling between carboxylation and CO 2 diffusion determines canopy CO 2 fixation (Grant et al 2001). The Farquhar biogeochemical growth model (Farquhar et al 1980) is used to calculate carboxylation as the lesser of the light-limited and CO 2 limited carboxylation rates. Stomatal conductance balances water availability and maintenance of a fixed internal-to-ambient CO 2 ratio (Grant and Flanagan 2007). In the light limited carboxylation rate, potential electron transport rate is a hyperbolic function of radiation. Leaf temperature directly controls fixation rates through a modified Arrhenius temperature dependence of maximum rate constants, Michaelis-Menten coefficients, light-saturated electron transport rates, and an exponential temperature dependence of CO 2 and O 2 aqueous solubility. The modified Arrhenius temperature dependencies include low and high deactivation temperatures and a constant offset to account for thermal adaptation of each PFT. Air temperatures indirectly impact CO 2 fixation by modifying nutrient mineralization and uptake rates, boundary layer resistances, and evapotranspiration rates.
Photosynthesized sugars are allocated to nonstructural C pools and oxidized to support first maintenance respiration and then shoot and root growth and active nutrient uptake. Oxidation rates and maintenance respiration requirements depend on a modified Arrhenius function of temperature that includes low and high temperature cutoffs and a thermal acclimation offset.
For winter deciduous PFTs, temperature-driven leaf onset (leaf offset) occurs after accumulated hours above (below) a set canopy temperature cross a set threshold. For evergreen PFTs, leaf dehardening (hardening) occurs after accumulated hours in increasing (decreasing) photoperiods crosses a set threshold. Leaf and root senescence for all PFTs occurs when non-structural C oxidation cannot meet maintenance respiration demands.
Pools of soil organic matter (woody litter, nonwoody litter, particulate organic matter, humus, microbial biomass) are partitioned into subsets of varying susceptibility to hydrolysis (Grant 2001). Decomposition of these pools produces dissolved organic C, which drives microbial growth when oxidized. Substrate availability, soil temperature (according to an Arrhenius functional form with a constant offset and low and high temperatures of deactivation), and soil water content control oxidation rates. Oxygen and nutrient availability may impose additional constraints. Plant nitrogen (N) and phosphorus (P) uptake rates are affected by nutrient mineralization and immobilization rates driven by microbial biomass C:N:P ratios (Grant 2014).

Model forcing and simulation design
The model was run at a 0.25 • × 0.25 • grid that covers Alaska. Clay and sand fraction, pH, cation exchange capacity, and bulk density were extracted from the Unified North America Soil Map (Liu et al 2013) and values for initial soil organic C content were extracted from the Northern Circumpolar Soil Carbon Database (Hugelius et al 2013). Surface air temperature, precipitation, incoming shortwave radiation, relative humidity and wind speed were taken from the North American Regional Reanalysis (NARR; Wei et al 2014) for the years 1979-2019. The first decade of the NARR record was used to spin-up the model over the years 1800-1978. NARR weather forcing for 2020-2100 was modified using seasonal anomalies from a CCSM4 ensemble member under the Representative Concentration Pathway 8.5 (RCP8.5). Since global carbon emissions are increasing at a rate consistent with RCP8.5, use of this high emissions scenario is common practice (

Model evaluation
Ecosys representation of ecosystem C, nutrients, energy, and hydrological dynamics has been tested in many high-latitude sites. For example, modeled active layer depth matched long-term measurements at 28 Circumpolar Active Layer Monitoring sites (R 2 = 0.63; RMSE = 10 cm; Mekonnen et al 2021), modeled North American tundra gross primary production (GPP) matched upscaled EC tower measurements (geographically weighted regression, R 2 = 0.78; Mekonnen et al 2018), modeled tree composition of the Alaskan boreal forest agreed well with LANDFIRE-FCCS maps (Mekonnen et al 2019), and modeled NEE agreed well with EC tower measurements at 12 North American tundra and boreal sites (0.6 < R 2 < 0.9; Grant et al 2009Grant et al , 2011Grant et al , 2015Grant et al , 2017. Additionally, ecosys accurately captured thermal and biological dynamics of short-term soil warming experiments at 4 sites across Alaska (Bouskill et al 2020). Fifteen studies of ecosys performance in high-latitude systems are described in the supplementary material (available online at stacks.iop.org/ERL/17/014032/mmedia).
Here we perform further validation of the ecosys model. Simulated soil temperatures at 24 locations were compared to data from the Soil Climate Analysis Network (SCAN; table S1; Schaefer et al 2007) and the Snow Telemetry (SNOTEL) Network (USDA Natural Resources Conservation Service 2020). Simulated NEE at 8 sites in Alaska was compared to data from Ameriflux EC towers (table S2). Where available, weather observations, rather than NARR forcings, were used to force the model for each Ameriflux site. At the regional scale, we compared ecosys outputs with 7 observation-based estimates of monthly NEE across Alaska: (1) (7) an alternate machine-learning upscaling of global EC tower measurements using ERA5 weather forcing produced by Zeng et al (2020).

Calculation of temperature and radiation limitation to net primary production (NPP)
Temperature and radiation limitations to plant productivity were quantified according to the methodology outlined in Donohue et al (2013), Keenan and Riley (2018), and Ukkola et al (2016). All daily modeled NPP values for the years 2010-2019 and 2090-2099 were combined and grouped by air temperature (incoming shortwave radiation) into bins of 1 • C (0.3 kWh m −2 d −1 ). For each bin the 99th percentile NPP was calculated. Using breakpoint regression analysis, air temperatures and radiation levels that limit NPP, and the lowest air temperatures and radiation levels that do not limit NPP, were calculated. Temperature and radiation scalars were linearly interpolated between these values.

Model evaluation and present day C cycle seasonality
In addition to the extensive validation of model performance in high-latitude ecosystems discussed above and listed in table S4, we also compared simulations of soil temperatures and NEE to site observations and regional observation-based estimates. At the site scale, ecosys soil temperatures at 5 cm depth agreed very well with measurements at 8 Soil Climate Analysis Network (SCAN) and 15 Snow Telemetry (SNOTEL) Network locations that are broadly representative of Alaskan climatic zones and land cover types (mean R 2 = 0.70 ± 0.11, mean bias = 0.09 ± 1.46 • C, and RMSE = 4.22 ± 1.41 • C; figure S4, table S1). We also found excellent agreement with ecosys monthly NEE and EC tower measurements at eight Alaskan Ameriflux sites (mean R 2 = 0.67 ± 0.11, mean bias = −0.09 ± 0.19 gC m −2 d −1 , and Nash-Sutcliffe coefficient = 0.41 ± 0.56 gC m −2 d 1 ; figure 2, table S2). At the regional scale, we evaluated ecosys NEE against seven observation-based products (Commane et  . These products were generated by others, either through machine learning upscaling of site measurements or through estimation of land surface flux contributions to measured atmospheric CO 2 gradients using atmospheric transport models. We modeled an annual average NEE of −28 ± 25 gC m −2 yr −1 across Alaska for the years 2000-2019 (i.e. a net CO 2 sink from the atmosphere; uncertainty is expressed as standard deviation across years). The long-term mean modeled NEE is in excellent agreement with four of the six observation-based products that produced annual NEE estimates (figure 2(c)). Previous studies have expressed concern that process-model underestimation of fall and winter high-latitude C fluxes (Commane et al 2017, Natali et al 2019) has led to an incorrect characterization of the region as a C sink. However, ecosys predicted a 27 gC m −2 (50%) larger combined fall and winter CO 2 flux than the average of the observation-based products, while still predicting that Alaska is currently a net C sink.
Under current climate conditions, modeled Alaska NEE for years 2000-2019 is largest in magnitude during summer (figure 3). Modeled summer dominance of the Alaskan C cycle is corroborated by the site measurements and 4 of the 6 observationbased products discussed above (figures 2(b) and (d)). The NEE seasonality of the observation-based products is broadly consistent with modeled NEE seasonality (table S3). These comparisons, and those described in the supplementary material, give confidence that ecosys is reasonably capturing the C cycle seasonality across our study domain.

Future changes to the spring C cycle
To assess how C cycle seasonality will change across Alaska with climate warming, we ran ecosys through the year 2100 using a CCSM4 RCP8.5 scenario. In spring (21 March-20 June), modeled NPP increases from 66 to 260 gC m −2 season −1 by year 2100 (positive NPP signifies positive plant growth; figure 3). This large increase in spring C fixation is driven by increases in air temperature (on average 6.7 • C by year 2100, figure S5) that lead to enhanced C fixation rates and earlier C uptake, particularly since temperature sensitivity of fixation rates is larger at lower temperatures (Kirschbaum 1995). Growing season onset (defined as the first day that modeled NPP is positive) in Alaska advances by 39 d by the year 2100 (5.8 d We quantified temperature and radiation limitations to modeled plant productivity using breakpoint regression analysis of daily air temperatures, incoming SW radiation, and modeled NPP (figure S6, Methods, Keenan and Riley 2018). According to this method, NPP is considered to be limited by, e.g. cold temperatures, if outlying NPP values increase at warmer temperatures. Under current Alaska climate conditions, modeled NPP experiences a 61% limitation due to air temperature in the spring. By year 2100, however, spring temperature limitation to NPP will relax by more than a factor of 3 (to 19%; figure S6). Since incoming shortwave radiation during these months is high, photosynthetic activity can respond positively to warmer spring temperatures. The geographically weighted regression coefficient (R 2 ) between spring temperature limitation and spring NPP remains high (0.83-0.85) throughout the century for the study domain (figure 4), providing confirmation that temperature remains a primary control of spring NPP throughout the century.
Modeled spring microbial respiration (denoted as R h in figure 3) increases from 48 to 116 gC m −2 season −1 throughout the century in response to earlier snowmelt and warmer soil temperatures. Soil temperatures increase more slowly than air temperatures in the spring (figure S5), so the increase in microbial respiration is much smaller than the projected increase in NPP. This difference results in a very large increase in the magnitude of spring NEE by year 2100 (−19 to −144 g C m −2 season −1 ; figure 3(a)).

Future changes to the summer C cycle
In summer (21 June-20 September), modeled NPP increases from 201 to 344 gC m −2 season −1 throughout the century ( figure 3(a)). Similar increases in spring and summer air temperature (∼6 • C-7 • C; figure S5) lead to similar increases in spring and summer Alaska GPP by year 2100 (326 vs. 331 gC m −2 ; figure 3). However, higher baseline air temperatures during summer lead to larger increases in autotrophic respiration (187 gC m −2 in summer vs. 132 gC m −2 in spring; denoted as R a in figure 3), resulting in smaller increases in NPP compared to spring (figures 3 and S5).
Some studies have hypothesized that increases in growing season water stress may lead to a reduction in summer GPP (Buermann et al 2013, Liu et al 2020. Consistent with these studies, the impact of warming on summer water stress has been demonstrated using ecosys in an analysis of boreal forest dynamics (Grant et al 2009). In this study, we find that increasing summer water stress is buffered by increasing precipitation and increasing atmospheric CO 2 concentrations that reduce transpiration. The RCP8.5 climate forcing we applied here (Methods) predicts a 22% increase in summer precipitation across Alaska (figure S5), consistent with studies that suggest rainfall is projected to increase with warming at high latitudes (Bintanja and Andry 2017). As a result, we predicted slightly wetter summer soils, almost no change in summer evapotranspiration (0.4% decrease), and a small increase (∼16%) in summer water stress (calculated as the number of hours that the canopy water potential drops below a threshold value; Methods) across Alaska by year 2100.
Modeled summer microbial respiration increases from 114 to 221 gC m −2 season −1 throughout the century ( figure 3). This increase is larger than that during spring because there are larger plant litter inputs and warmer and drier soils in summer than in spring. Summer net C uptake is projected to increase by only 36 gC m −2 , which is much smaller than the increase in spring net C uptake. By year 2100, summer net C uptake (123 gC m −2 season −1 ) is less than spring net C uptake (144 gC m −2 season −1 ) across Alaska (figures 3 and 5).

Future changes to the fall and winter C cycle
In fall (21 September-20 December) and winter , there is only a small change in NPP throughout the century (21 gC m −2 season −1 for fall and 16 gC m −2 season −1 for winter). In fall, the current complete temperature limitation reduces to a partial limitation by year 2100 (figure S6). However, day lengths during fall in Alaska are very short, and there is not enough sunlight to drive photosynthesis. Since incoming shortwave radiation seasonality is driven primarily by earth-sun geometry (i.e. not climate change), the extreme radiation limitation to fall and winter NPP is not expected to change significantly over the coming century (Holland and Landrum 2015). In fall, temperature and radiation are each currently strongly limiting, but by year 2100 radiation will become the dominant limiting factor to fall C fixation. This prediction is consistent with observed large-scale increases in radiation limitation across northern latitudes (Zhang et al 2020).
By year 2100, average fall soil temperatures increase from −1.3 • C to 0.8 • C, average winter soil temperatures increase from −3.0 • C to −0.9 • C, and the first frost (defined as the date when surface soil temperature first drops below −0.2 • C) is delayed by one month on average. Modeled microbial respiration by year 2100 increases in response to warming soil temperatures from 25 to 61 gC m −2 season −1 in fall, and from 15 to 31 gC m −2 season −1 in winter. Net C loss during fall and winter is projected to shift from 76 gC m −2 season −1 (88% of summer net C uptake) to 163 gC m −2 season −1 (133% of summer net C uptake) over the course of the century (figures 3 and 5). Adding in the large increase in spring net C uptake, modeled annual Alaska NEE will increase in magnitude from −30 gC m −2 y −1 in the current decade to −108 gC m −2 y −1 by year 2100 (figure 5).

Fall and winter N cycle is linked to spring C cycle
Whereas most large-scale land models link plant nutrient acquisition with instantaneous photosynthetic demand, ecosys allows plants to use nonstructural C reserves to uptake and store nutrients whenever they are available (Riley et al 2018, 2021). In the model, rates of mineralization and N fixation (symbiotic and non-symbiotic) control soil N availability and depend on soil temperature and liquid water availability. During fall and winter, modeled plant N uptake varies with the number of days that soil temperatures at 5 cm depth remain above freezing (R 2 = 0.60). Throughout the 21st century, the number of days above freezing increases from 21 to 60 d, synchronous with an increase in fall and winter plant N uptake from 0.26 to 0.69 gN m −2 (figure 6(a)).
Nutrient acquisition during the fall and winter has been shown to strongly influence year-round vegetation growth and competitive dynamics in northern ecosystems (Chapin and Bloom 1976, Larsen et al 2012, Malyshev and Henry 2012. By the end of the century, 22.5% of modeled plant N acquisition occurs during fall and winter, and it is likely that the large increase in spring productivity discussed above would not be possible without this source of N. Indeed, we find that modeled increases in spring NPP throughout the century are more strongly correlated with increases in fall and winter plant N uptake (R 2 = 0.69) than with increases in spring plant N uptake (R 2 = 0.47; figure 6). This result highlights the importance of accounting for fall and winter plant nutrient uptake in predictions of seasonal and annual high-latitude ecosystem response to climate change.

Caveats and uncertainties
Some processes important to ecosystem C balance and export, such as topography, landscape-scale hydrology, thermokarst, and other geomorphological dynamics, are not represented in these model runs. Additionally, since boreal forest PFT species are not initialized in regions of present-day tundra, boreal treeline advance, which has been observed across the high-latitudes (Harsch et al 2009), does not occur in these simulations. However, trends in the seasonality of terrestrial ecosystem C exchange identified here are expected to be robust since they are attributed primarily to seasonal light availability driven by earth-sun geometry and large-scale shifts in seasonal temperature driven by climate change. As is the case for all model analyses of ecosystem dynamics, there is uncertainty associated with the gridded climate data and soil information we used to force ecosys (Mekonnen et al 2016, Wang andClow 2021). Biases in temperature forcing data would affect the rates of processes (e.g. maximum fixation rates, electron transport rates) that drive modeled plant productivity and microbial respiration. While this uncertainty has an impact on comparisons between ecosys and observation-based products, it does not affect our conclusions, as we show using a sensitivity analysis (supplementary material).

Conclusions
We show that 21st century climate warming will shift C cycle seasonality across Alaska. Spring C sink strength will become larger than summer C sink strength by year 2100 due to relaxation of temperature limitations to plant productivity and nutrient availability. This result represents a striking, and to our knowledge, previously unreported shift in the timing of high-latitude net C uptake. Severe radiation limitation to NPP in fall and winter is not projected to change, so increased temperatures during these months will not benefit plant C uptake. Instead, warming soils and increased plant inputs will lead to higher rates of autotrophic and microbial respiration, and net C loss during the fall and winter will become larger than net C uptake during the summer by year 2100. Further investigation is needed to ascertain the impacts of shifting C cycle seasonality, and associated changes in energy and water fluxes, on climate.
Our results address the conflict between modeled and observation-based assessments of high-latitude ecosystem C balance. Our model predictions of large and increasing fall and winter C losses are consistent with observation-based estimates produced by Natali et al (2019) and Commane et al (2017), unlike most process models referenced in those studies. However, our results also agree with the process model consensus that high-latitudes will remain a C sink throughout the 21st century. This result is attributable in part to increased N mineralization and plant nutrient uptake coincident with fall and winter C losses. Nevertheless, data used to build observationbased products and to parameterize and validate process models is very sparse at high-latitudes. Increased spatial and temporal coverage of measured ecosystem C fluxes would be very helpful to verify the trends predicted here, and to further close the gap between mechanistically modeled and observationbased estimates of seasonal C fluxes.

Data availability statement
The data that support the findings of this study are openly available at the following URL/DOI: https://doi.org/10.5440/1861071.