coarser-resolution data as forcing data to drive a model. For example, linear interpolation methods were used to transform the monthly CRU climate dataset into daily intervals to study the carbon dynamics of Alaska wetlands (Lyu et al., 2018). The Arctic region generally lacks year-round field observations, in particular due to logistical and practical difficulties in making continuous measurements during the snow-covered/winter period. Interpolated meteorological data may be the only available data for the cold season, and importantly, interpolations may not capture rapid responses to abrupt environmental changes. Year-round meteorological data (with hourly to daily resolution) are now increasingly available at high-latitude sites (Belokopytova et al., 2021; Boike et al., 2018, 2019; Hartl et al., 2020), providing suitable forcing data to models. This improved forcing data, combined with suitably fine temporal resolution in models, will likely bring about improvements to modelling seasonal changes in carbon and microbial activity.

As a word of caution, it is important to note that imposing a higher temporal resolution in modelling incurs a higher computational cost, and thus the trade-offs between model resolution and computational cost must also be considered.

### 3.4.2. Spatial dimensions: Vertical

Many soil-biogeochemical models are 0-D (i.e. not spatially resolved), with a box-model structure that does not discern any vertical structure or differences (Koven et al., 2013). Some simplified and earlier soil biogeochemical models consider only the top organic layer as a single fixed SOC profile and assume that the deeper mineral layer does not significantly impact biogeochemical reactions (Baudena et al., 2007; Brandes and Den Hollander, 1996; Klepper and den Hollander, 1999). Beyond single layer box models, discrete layers of soil may be represented in multi-layer box models, where each vertically-stacked box contains prescribed initial conditions and the reactions unfold without any vertical exchange of carbon (Kuc, 2005). Vertical heterogeneity in soil characteristics and resulting carbon cycling is considered in the Community Land Model (CLM, Koven et al., 2013; Lawrence et al., 2011) and RothC model (Coleman and Jenkinson, 1996; Jenkinson and Coleman, 2008), capturing vertical advection and diffusion to at least 3 m depth (Koven et al., 2013; Tang and Riley, 2015). Currently, vertical SOC distribution is generally defined according to the root distribution, and trade-offs between the vertical allocation and root nutrient uptake are not considered (Fisher et al., 2012; Koven et al., 2013).

The vertical characteristics of Arctic soils are physically, chemically and biologically heterogeneous, both at discrete timepoints and also considering seasonal and long-term change. For example, deeper layers of some permafrost soils may contain vast quantities of SOC which are largely buffered from seasonal temperature changes, but may become more dynamic and play an active role in soil biogeochemical cycling under future climate warming as permafrost thaw depth increases (Koarashi et al., 2012; Tarnocai et al., 2009). Past vertically resolved applications of microbially-implicit soil models to the Arctic have assessed how changes in simulated soil temperature affected the active-layer depth, and have predicted variations in overall soil carbon stores and fluxes as a result of built-in SOC-temperature sensitivities (Genet et al., 2013; Burke et al., 2017; Lyu et al., 2018; Lyu and Zhuang, 2018).

Vertically-resolved model schemes will enable seasonal changes in the depth of the active layer, the thawing and/or freezing fronts, and subsequent changes to microbial activity in the soil depth-profile to be captured. Since the upper-most layers of soil profiles are typically the most biologically active (due to the abundant microorganisms and substrates) and responsive to environmental variation, it might be advantageous to apply a finer vertical resolution in the uppermost layers and a relatively coarser vertical resolution beneath these layers to the bottom mineral layer, in order to reduce computational costs.

### 3.4.3. Spatial dimensions: Horizontal

Heterogeneity in soil biological, physical and chemical characteristics occurs across a landscape (i.e. laterally/horizontally) across a range

of scales from individual soil particles to plots and landscapes. These heterogeneities arise due to numerous interacting factors including geomorphological, climatological, hydrological, chemical and biological processes, often mediated by the soil, that have occurred in the past and are presently shaping the environment. Seasonal processes affecting Arctic soils may also manifest differently across spatial scales, for instance due to (micro-)topographic features, local hydrological processes including variations in the depth of the water table, and snow cover. For instance, hydrological heterogeneities are highly pronounced at the Samoylov research site in the Lena River delta, northeast Siberia, where 58% of the land area is classified as dry tundra and 17% is classified as wet tundra (Boike et al., 2019; Nitzbon et al., 2019). Seasonal permafrost thawing further contributes to horizontal hydrological heterogeneities through its dual impact on ground permeability. The increased permeability resulting from permafrost thaw facilitates lateral water movement across the landscape, influencing lateral flow patterns and fostering the creation of features such as thaw ponds and channels (Bense et al., 2012). Changes in lateral water movement impacts hydrological connectivity, enhancing the transport of suspended sediment and particulate organic matter (Lafrenière and Lamoureux, 2019; Walvoord and Striegl, 2021). On the other hand, water released from thawing can strengthen surface or deep runoff rather than storage, causing alterations to seasonal runoff patterns (Bring et al., 2016). Both of these processes can ultimately affect microbial community dynamics and biogeochemical processes (Blaud et al., 2015).

When applying a model to a regional scale and beyond, the spatial resolution of the model simulation may be limited to a large extent by the spatial coverage and resolution of the available forcing data, particularly in regions which are highly heterogeneous in soil characteristics, hydrology, and topography. Meteorological and hydrological data are usually available either through reanalysis or remote sensing data collected by airborne platforms or satellites. These data are generally gridded at 1.0 km or 0.5° resolution, with some products available at a finer resolution of 250 m (Justice et al., 1998; Shao et al., 2011). Therefore, these are also the most common spatial resolutions used by regional soil biogeochemical models. Nevertheless, considering the horizontal scale and resolution that is resolved in soil biogeochemical models is especially important when extrapolating parameters that are calibrated at the site-level to regional scales and beyond.

Differences in the spatial resolution of soil biogeochemical models can result in different output characteristics. For example, 11 different Earth system models were driven with the same set of meteorological data and run to compare the SOC change between the end and the beginning of the 21st century in the Coupled Model Intercomparison Project Phase 5 (CMIP5) (Todd-Brown et al., 2013, 2014). These models are originally designed with different modelling grid resolution for different geographies and ecosystems, and were extrapolated to be applied to the global scale. The different models produced a wide range of simulated SOC changes ranging from carbon loss of 72 Pg C over a century, to carbon gain of 253 Pg C. In particular, high-latitude regions were subject to large discrepancies between model outputs, from carbon loss of 37 Pg C to carbon gain of 146 Pg C (Todd-Brown et al., 2014). This indicates that many models especially lack accurate simulation of high-latitude soil processes, and poorly represent heterogenous permafrost dynamics across the northernmost land masses.

### 3.4.4. Validation data

The application of land surface models is critically reliant on data: used both as forcing data for model initialization and utilization, and for calibration and validation. The quantity and quality of data that is available may greatly influence the design, behavior, and performance of numerical models, and the confidence entrusted in their outputs. Both in situ and remotely sensed data may be used for forcing, calibration and validation purposes (Liu et al., 2016; Lyu and Zhuang, 2018; Tan et al., 2016; Chadburn et al., 2017). For instance, climatic data collected on-
coarser-resolution data as forcing data to drive a model. For example, linear interpolation methods were used to transform the monthly CRU climate dataset into daily intervals to study the carbon dynamics of Alaska wetlands (Lyu et al., 2018). The Arctic region generally lacks year-round field observations, in particular due to logistical and practical difficulties in making continuous measurements during the snow-covered/winter period. Interpolated meteorological data may be the only available data for the cold season, and importantly, interpolations may not capture rapid responses to abrupt environmental changes. Year-round meteorological data (with hourly to daily resolution) are now increasingly available at high-latitude sites (Belokopytova et al., 2021; Boike et al., 2018, 2019; Hartl et al., 2020), providing suitable forcing data to models. This improved forcing data, combined with suitably fine temporal resolution in models, will likely bring about improvements to modelling seasonal changes in carbon and microbial activity.

As a word of caution, it is important to note that imposing a higher temporal resolution in modelling incurs a higher computational cost, and thus the trade-offs between model resolution and computational cost must also be considered.

### 3.4.2. Spatial dimensions: Vertical

Many soil-biogeochemical models are 0-D (i.e. not spatially resolved), with a box-model structure that does not discern any vertical structure or differences (Koven et al., 2013). Some simplified and earlier soil biogeochemical models consider only the top organic layer as a single fixed SOC profile and assume that the deeper mineral layer does not significantly impact biogeochemical reactions (Baudena et al., 2007; Brandes and Den Hollander, 1996; Klepper and den Hollander, 1999). Beyond single layer box models, discrete layers of soil may be represented in multi-layer box models, where each vertically-stacked box contains prescribed initial conditions and the reactions unfold without any vertical exchange of carbon (Kuc, 2005). Vertical heterogeneity in soil characteristics and resulting carbon cycling is considered in the Community Land Model (CLM, Koven et al., 2013; Lawrence et al., 2011) and RothC model (Coleman and Jenkinson, 1996; Jenkinson and Coleman, 2008), capturing vertical advection and diffusion to at least 3 m depth (Koven et al., 2013; Tang and Riley, 2015). Currently, vertical SOC distribution is generally defined according to the root distribution, and trade-offs between the vertical allocation and root nutrient uptake are not considered (Fisher et al., 2012; Koven et al., 2013).

The vertical characteristics of Arctic soils are physically, chemically and biologically heterogeneous, both at discrete timepoints and also considering seasonal and long-term change. For example, deeper layers of some permafrost soils may contain vast quantities of SOC which are largely buffered from seasonal temperature changes, but may become more dynamic and play an active role in soil biogeochemical cycling under future climate warming as permafrost thaw depth increases (Koarashi et al., 2012; Tarnocai et al., 2009). Past vertically resolved applications of microbially-implicit soil models to the Arctic have assessed how changes in simulated soil temperature affected the active-layer depth, and have predicted variations in overall soil carbon stores and fluxes as a result of built-in SOC-temperature sensitivities (Genet et al., 2013; Burke et al., 2017; Lyu et al., 2018; Lyu and Zhuang, 2018).

Vertically-resolved model schemes will enable seasonal changes in the depth of the active layer, the thawing and/or freezing fronts, and subsequent changes to microbial activity in the soil depth-profile to be captured. Since the upper-most layers of soil profiles are typically the most biologically active (due to the abundant microorganisms and substrates) and responsive to environmental variation, it might be advantageous to apply a finer vertical resolution in the uppermost layers and a relatively coarser vertical resolution beneath these layers to the bottom mineral layer, in order to reduce computational costs.

### 3.4.3. Spatial dimensions: Horizontal

Heterogeneity in soil biological, physical and chemical characteristics occurs across a landscape (i.e. laterally/horizontally) across a range

of scales from individual soil particles to plots and landscapes. These heterogeneities arise due to numerous interacting factors including geomorphological, climatological, hydrological, chemical and biological processes, often mediated by the soil, that have occurred in the past and are presently shaping the environment. Seasonal processes affecting Arctic soils may also manifest differently across spatial scales, for instance due to (micro-)topographic features, local hydrological processes including variations in the depth of the water table, and snow cover. For instance, hydrological heterogeneities are highly pronounced at the Samoylov research site in the Lena River delta, northeast Siberia, where 58% of the land area is classified as dry tundra and 17% is classified as wet tundra (Boike et al., 2019; Nitzbon et al., 2019). Seasonal permafrost thawing further contributes to horizontal hydrological heterogeneities through its dual impact on ground permeability. The increased permeability resulting from permafrost thaw facilitates lateral water movement across the landscape, influencing lateral flow patterns and fostering the creation of features such as thaw ponds and channels (Bense et al., 2012). Changes in lateral water movement impacts hydrological connectivity, enhancing the transport of suspended sediment and particulate organic matter (Lafrenière and Lamoureux, 2019; Walvoord and Striegl, 2021). On the other hand, water released from thawing can strengthen surface or deep runoff rather than storage, causing alterations to seasonal runoff patterns (Bring et al., 2016). Both of these processes can ultimately affect microbial community dynamics and biogeochemical processes (Blaud et al., 2015).

When applying a model to a regional scale and beyond, the spatial resolution of the model simulation may be limited to a large extent by the spatial coverage and resolution of the available forcing data, particularly in regions which are highly heterogeneous in soil characteristics, hydrology, and topography. Meteorological and hydrological data are usually available either through reanalysis or remote sensing data collected by airborne platforms or satellites. These data are generally gridded at 1.0 km or 0.5° resolution, with some products available at a finer resolution of 250 m (Justice et al., 1998; Shao et al., 2011). Therefore, these are also the most common spatial resolutions used by regional soil biogeochemical models. Nevertheless, considering the horizontal scale and resolution that is resolved in soil biogeochemical models is especially important when extrapolating parameters that are calibrated at the site-level to regional scales and beyond.

Differences in the spatial resolution of soil biogeochemical models can result in different output characteristics. For example, 11 different Earth system models were driven with the same set of meteorological data and run to compare the SOC change between the end and the beginning of the 21st century in the Coupled Model Intercomparison Project Phase 5 (CMIP5) (Todd-Brown et al., 2013, 2014). These models are originally designed with different modelling grid resolution for different geographies and ecosystems, and were extrapolated to be applied to the global scale. The different models produced a wide range of simulated SOC changes ranging from carbon loss of 72 Pg C over a century, to carbon gain of 253 Pg C. In particular, high-latitude regions were subject to large discrepancies between model outputs, from carbon loss of 37 Pg C to carbon gain of 146 Pg C (Todd-Brown et al., 2014). This indicates that many models especially lack accurate simulation of high-latitude soil processes, and poorly represent heterogenous permafrost dynamics across the northernmost land masses.

### 3.4.4. Validation data

The application of land surface models is critically reliant on data: used both as forcing data for model initialization and utilization, and for calibration and validation. The quantity and quality of data that is available may greatly influence the design, behavior, and performance of numerical models, and the confidence entrusted in their outputs. Both in situ and remotely sensed data may be used for forcing, calibration and validation purposes (Liu et al., 2016; Lyu and Zhuang, 2018; Tan et al., 2016; Chadburn et al., 2017). For instance, climatic data collected on-
site was combined with remotely-sensed data on snow prevalence (of the same temporal and spatial resolution) to run a biogeochemical model to quantify northern high latitude region carbon responses to soil thermal changes (Lyu and Zhuang, 2018). In NASA's ABoVE project, an array of 18 land surface models guided the identification of target variables, as well as the spatial and temporal resolution of monitoring trough a suite of field, airborne and satellite measurements (Fisher et al., 2018). Interpolation or downscaling methods can also be used to accommodate the integration of multiple data types of different resolutions for model applications (Lyu et al., 2018; McGuire et al., 2018b).

Future applications of soil biogeochemical models must, in particular, consider how data can be effectively collected and used year-round to calibrate model simulations. Consideration must be given not only to which variables might be critical to measure, but also to the timespan and temporal resolution of this data. For model applications to Arctic soils, continuous vertically-resolved data on soil temperature and water content will be especially important in order to predict soil microbial activity and carbon degradation year-round. In addition, records of snow depth and snow characteristics would benefit the representation of both soil temperature and emission timing. When incorporating meteorological data, consideration should be given to seasonal variability but also inter-annual variability, including extreme weather conditions such as periodic winter-warming, or summer drought (Zhu and Zhuang, 2013). For Arctic regions, extreme events could greatly affect the soil thermal and hydrological conditions, driving subsequent changes to soil microbial activities and carbon fluxes. The use of forcing and validation datasets of past extreme events may enable a better constraint of model parameters and sensitivity.

## 4. Conclusions and recommendations

A better understanding of longer-term interactions between Arctic soils and permafrost, climate, microorganisms, vegetation, soil organic matter and nutrients, and snow cover, alongside a refined model depiction of subsurface conditions such as ground ice, will contribute significantly to improved modelling of soil biogeochemical processes. Here we describe the seasonal changes in Arctic soil microbiology and biogeochemistry, and the various factors that should be considered for the development and application of soil biogeochemical models year-round in Arctic settings to improve understanding of processes in Arctic soils and permafrost, and reduce uncertainty regarding the thermo-hydraulic state of permafrost and its future climate feedbacks. Ultimately, model complexity and structure should be guided by available knowledge, data, and the overarching research questions. Therefore, in practice, model development may need to be largely bespoke to each specific application. The most complex model, whilst potentially more closely resembling the complexities of the natural environment, is not necessarily the most useful. In general, a model should be as simple as possible, but not any simpler than that – a simple model may produce equally beneficial and constructive results (e.g. Manzoni and Porporato, 2009). Simple mechanistic models applied to different locations with high temporal and spatial resolution may help in the identification of the most relevant processes to be implemented and tested in global models (e.g. Todd-Brown et al., 2012), as well as explore the importance of representing interspecies and interkingdom (plant-microbe) interactions (Naylor et al., 2020). These simple mechanistic models are promising for bridging the gap between data-driven models aimed at identifying the drivers of carbon dynamics in field studies, and complex land surface models, which often fail at accurately representing practical observations despite being process-oriented. With a simpler model, the opportunity to learn about microbiome-climate feedbacks and their role on shaping seasonal dynamics may be limited. The application of any model should be viewed as a work in progress, to be constantly re-evaluated and tested in the context of the evolving data availability and mechanistic understanding. Over time, these tools will be incrementally improved and better constrained such that they become more

useful in analytically testing hypotheses, extrapolating, interpolating and constructing budgets of processes and rates, and enabling predictive capabilities.

The emergence of year-round data and the integration of this data and understanding of seasonal processes into soil biogeochemical models will be critical to understand and predict the year-round functioning and fate of Arctic soil biology and biogeochemistry, particularly under a changing climate. Year-round measurements of microbial activity rates, organic carbon uptake and remineralization, carbon dioxide and methane production and consumption, and soil thermal and hydrological characteristics will particularly help to constrain the understanding of seasonal processes and simulations of microbially-explicit models. Moreover, improvements to models and understanding rely especially on consideration of how the carbon pool magnitude, fate and cycling dynamics varies with the microbial community's taxonomic and functional trait composition, and ways in which the carbon dynamics depart from idealized models due to spatial and temporal heterogeneity of biological, geological, physical and geochemical factors. Incorporating this complexity into models, over temporal and spatial scales that are appropriate to resolve potentially important abiotic constraints on biological processes (such as the zero-curtain effect and the progression of thawing and freezing fronts with soil depth) is a formidable but vital challenge, which, if overcome, will yield vast improvements in the understanding and predictive capabilities of the year-round functioning of Arctic soils and carbon stocks.

## CRediT authorship contribution statement

Zhou Lyu: Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Pacifica Sommers: Writing – review & editing, Funding acquisition. Steven K. Schmidt: Writing – review & editing, Funding acquisition. Marta Magnani: Writing – review & editing. Mihai Cimpoiasu: Writing – review & editing. Oliver Kuras: Writing – review & editing, Funding acquisition. Qianlai Zhuang: Writing – review & editing. Youmi Oh: Writing – review & editing. Maria De La Fuente: Writing – review & editing. Margaret Cramm: Writing – review & editing. James A. Bradley: Conceptualization, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

No data was used for the research described in the article.

## Acknowledgements

This work was funded by the NSF-NERC (UKRI) thematic program "Signals in the Soil" (NERC grants: NE/T010967/1, NE/T010568/1; NSF grant: 1935689). JB also acknowledges support from the CNRS Chaires de Professeur Junior (CPJ) and NERC grant NE/V012991/1 during the writing of this manuscript. QZ acknowledges support from NSF grant: 1802832. MM acknowledges the Italian National Biodiversity Future Center (NBFC): National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 of the Ministry of University and Research; funded by the European Union – NextGenerationEU (Project code CN00000033).
site was combined with remotely-sensed data on snow prevalence (of the same temporal and spatial resolution) to run a biogeochemical model to quantify northern high latitude region carbon responses to soil thermal changes (Lyu and Zhuang, 2018). In NASA's ABoVE project, an array of 18 land surface models guided the identification of target variables, as well as the spatial and temporal resolution of monitoring trough a suite of field, airborne and satellite measurements (Fisher et al., 2018). Interpolation or downscaling methods can also be used to accommodate the integration of multiple data types of different resolutions for model applications (Lyu et al., 2018; McGuire et al., 2018b).

Future applications of soil biogeochemical models must, in particular, consider how data can be effectively collected and used year-round to calibrate model simulations. Consideration must be given not only to which variables might be critical to measure, but also to the timespan and temporal resolution of this data. For model applications to Arctic soils, continuous vertically-resolved data on soil temperature and water content will be especially important in order to predict soil microbial activity and carbon degradation year-round. In addition, records of snow depth and snow characteristics would benefit the representation of both soil temperature and emission timing. When incorporating meteorological data, consideration should be given to seasonal variability but also inter-annual variability, including extreme weather conditions such as periodic winter-warming, or summer drought (Zhu and Zhuang, 2013). For Arctic regions, extreme events could greatly affect the soil thermal and hydrological conditions, driving subsequent changes to soil microbial activities and carbon fluxes. The use of forcing and validation datasets of past extreme events may enable a better constraint of model parameters and sensitivity.

## 4. Conclusions and recommendations

A better understanding of longer-term interactions between Arctic soils and permafrost, climate, microorganisms, vegetation, soil organic matter and nutrients, and snow cover, alongside a refined model depiction of subsurface conditions such as ground ice, will contribute significantly to improved modelling of soil biogeochemical processes. Here we describe the seasonal changes in Arctic soil microbiology and biogeochemistry, and the various factors that should be considered for the development and application of soil biogeochemical models year-round in Arctic settings to improve understanding of processes in Arctic soils and permafrost, and reduce uncertainty regarding the thermo-hydraulic state of permafrost and its future climate feedbacks. Ultimately, model complexity and structure should be guided by available knowledge, data, and the overarching research questions. Therefore, in practice, model development may need to be largely bespoke to each specific application. The most complex model, whilst potentially more closely resembling the complexities of the natural environment, is not necessarily the most useful. In general, a model should be as simple as possible, but not any simpler than that – a simple model may produce equally beneficial and constructive results (e.g. Manzoni and Porporato, 2009). Simple mechanistic models applied to different locations with high temporal and spatial resolution may help in the identification of the most relevant processes to be implemented and tested in global models (e.g. Todd-Brown et al., 2012), as well as explore the importance of representing interspecies and interkingdom (plant-microbe) interactions (Naylor et al., 2020). These simple mechanistic models are promising for bridging the gap between data-driven models aimed at identifying the drivers of carbon dynamics in field studies, and complex land surface models, which often fail at accurately representing practical observations despite being process-oriented. With a simpler model, the opportunity to learn about microbiome-climate feedbacks and their role on shaping seasonal dynamics may be limited. The application of any model should be viewed as a work in progress, to be constantly re-evaluated and tested in the context of the evolving data availability and mechanistic understanding. Over time, these tools will be incrementally improved and better constrained such that they become more

useful in analytically testing hypotheses, extrapolating, interpolating and constructing budgets of processes and rates, and enabling predictive capabilities.

The emergence of year-round data and the integration of this data and understanding of seasonal processes into soil biogeochemical models will be critical to understand and predict the year-round functioning and fate of Arctic soil biology and biogeochemistry, particularly under a changing climate. Year-round measurements of microbial activity rates, organic carbon uptake and remineralization, carbon dioxide and methane production and consumption, and soil thermal and hydrological characteristics will particularly help to constrain the understanding of seasonal processes and simulations of microbially-explicit models. Moreover, improvements to models and understanding rely especially on consideration of how the carbon pool magnitude, fate and cycling dynamics varies with the microbial community's taxonomic and functional trait composition, and ways in which the carbon dynamics depart from idealized models due to spatial and temporal heterogeneity of biological, geological, physical and geochemical factors. Incorporating this complexity into models, over temporal and spatial scales that are appropriate to resolve potentially important abiotic constraints on biological processes (such as the zero-curtain effect and the progression of thawing and freezing fronts with soil depth) is a formidable but vital challenge, which, if overcome, will yield vast improvements in the understanding and predictive capabilities of the year-round functioning of Arctic soils and carbon stocks.

## CRediT authorship contribution statement

Zhou Lyu: Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Pacifica Sommers: Writing – review & editing, Funding acquisition. Steven K. Schmidt: Writing – review & editing, Funding acquisition. Marta Magnani: Writing – review & editing. Mihai Cimpoiasu: Writing – review & editing. Oliver Kuras: Writing – review & editing, Funding acquisition. Qianlai Zhuang: Writing – review & editing. Youmi Oh: Writing – review & editing. Maria De La Fuente: Writing – review & editing. Margaret Cramm: Writing – review & editing. James A. Bradley: Conceptualization, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

No data was used for the research described in the article.

## Acknowledgements

This work was funded by the NSF-NERC (UKRI) thematic program "Signals in the Soil" (NERC grants: NE/T010967/1, NE/T010568/1; NSF grant: 1935689). JB also acknowledges support from the CNRS Chaires de Professeur Junior (CPJ) and NERC grant NE/V012991/1 during the writing of this manuscript. QZ acknowledges support from NSF grant: 1802832. MM acknowledges the Italian National Biodiversity Future Center (NBFC): National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 of the Ministry of University and Research; funded by the European Union – NextGenerationEU (Project code CN00000033).
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Boike, J., Nitzbon, J., Anders, K., Grigoriev, M., Bolshiyanov, D., Langer, M., Lange, S., Bornemann, N., Morgenstern, A., Schreiber, P., Wille, C., 2019. A 16-year record (2002–2017) of permafrost, active-layer, and meteorological conditions at the Samoylov Island Arctic permafrost research site, Lena River delta, northern Siberia: an opportunity to validate remote-sensing data and land surface, snow, and permafrost models. Earth Syst. Sci. Data 11 (1), 261–299.

Bormann, K.J., Westra, S., Evans, J.P., McCabe, M.F., 2013. Spatial and temporal variability in seasonal snow density. J. Hydrol. 484, 63–73.

Bormann, K.J., Brown, R.D., Derksen, C., Painter, T.H., 2018. Estimating snow-cover trends from space. Nat. Clim. Chang. 8 (11), 924–928.

Box, J.E., Colgan, W.T., Christensen, T.R., Schmidt, N.M., Lund, M., Parmentier, F.J.W., Brown, R., Bhatt, U.S., Euskirchen, E.S., Romanovsky, V.E., Walsh, J.E., 2019. Key indicators of Arctic climate change: 1971–2017. Environ. Lett. 14 (4), 045010.

Bradley, J.A., Singarayer, J.S., Anesio, A.M., 2014. Microbial community dynamics in the forefield of glaciers. Proc. R. Soc. B Biol. Sci. 281 (1795), 20140882.

Bradley, J.A., Anesio, A., Singarayer, J., Heath, M., Arndt, S., 2015. SHIMMER (1.0): a novel mathematical model for microbial and biogasification dynamics in glacier forefield ecosystems. Geosci. Model Dev. 8, 3441–3470. https://doi.org/10.5194/gmd-8-3441-2015.

Bradley, J.A., Anesio, A., Arndt, S., 2016a. Bridging the divide: a model-data approach to Polar & Alpine Microbiology. FEMS Microbiol. Ecol. 92.

Bradley, J.A., Arndt, S., Sabacká, M., Benning, L.G., Barker, G.L., Blacker, J.J., Yallop, M. L., Wright, K.E., Bellas, C.M., Telling, J., Tranter, M., 2016b. Microbial dynamics in a High Arctic glacier forefield: a combined field, laboratory, and modelling approach. Biogeosciences 13 (19), 5677–5696.

Brandes, L.J., Den Hollander, H., 1996. SimpleBox 2.0: a nested multimedia fate model for evaluating the environmental fate of chemicals.

Bring, A., Fedorova, I., Dibike, Y., Hinzman, L., Mård, J., Mernild, S.H., Prowse, T., Semenova, O., Stuefer, S.L., Woo, M.-K., 2016. Arctic terrestrial hydrology: a synthesis of processes, regional effects, and research challenges. Eur. J. Vasc. Endovasc. Surg. 121, 621–649. https://doi.org/10.1002/2015JG003131.

Brooks, P.D., Schmidt, S.K., Williams, M.W., 1997. Winter production of CO₂ and N₂O from alpine tundra: environmental controls and relationship to inter-system C and N fluxes. Oecologia 110 (3), 403–413.

Brown, R., Barrette, C., Brown, L., Chaumont, D., Grenier, P., Howell, S., Sharp, M., 2018. Climate variability, trends and projected change. In: From Science to Policy in the Eastern Canadian Arctic: An Integrated Regional Impact Study (IRIS) of Climate Change and Modernization. ArcticNet, Quebec City, 560 pp., p.55.

Bruhwiler, L., Parmentier, F.J.W., Crill, P., Leonard, M., Palmer, P.I., 2021. The Arctic carbon cycle and its response to changing climate. Curr. Clim. Chang. Rep. 7, 14–34.

Bruin, J., Bense, V., Ploeg, M., 2023. Soil thermal properties during freeze–thaw dynamics as function of variable organic carbon and grain size distribution. Vadose Zone J. 22.

Buckeridge, K.M., Grogan, P., 2008. Deepened snow alters soil microbial nutrient limitations in arctic birch hummock tundra. Appl. Soil Ecol. 39 (2), 210–222.

Buckeridge, K.M., Banerjee, S., Siciliano, S.D., Grogan, P., 2013. The seasonal pattern of soil microbial community structure in Mesic low arctic tundra. Soil Biol. Biochem. 65, 338–347.

Bulygina, O.N., Razuvaev, V.N., Korshunova, N.N., 2009. Changes in snow cover over Northern Eurasia in the last few decades. Environ. Res. Lett. 4 (4), 045026.

Burke, E.J., Ekici, A., Huang, Y., Chadburn, S.E., Huntingford, C., Ciais, P., Krinner, G., 2017. Quantifying uncertainties of permafrost carbon–climate feedbacks. Biogeosciences 14 (12), 3051–3066.

Burns, R.G., DeForest, J.L., Marxsen, J., Sinsabaugh, R.L., Stromberge, M.E., Wallenstein, M.D., Weintraub, M.N., Zoppini, A., 2013. Soil enzymes in a changing environment: current knowledge and future directions. Soil Biol. Biochem. 58, 216–234.

Cannone, N., Augusti, A., Malfasi, P., Pallozzi, E., Calfapietra, C., Brugnoli, E., 2016. The interaction of biotic and abiotic factors at multiple spatial scales affects the variability of CO₂ fluxes in polar environments. Polar Biol. 39, 1581–1596.

Cannone, N., Ponti, S., Christiansen, H.H., Christensen, T.R., Pirk, N., Guglielmin, M., 2019. Effects of active layer seasonal dynamics and plant phenology on CO₂ land-atmosphere fluxes at polygonal tundra in the High Arctic, Svalbard. Catena 174, 142–153.

Cao, M., Dent, J.B., Heal, O.W., 1995. Modeling methane emissions from rice paddies. Glob. Biogeochem. Cycles 9 (2), 183–195.

Chadburn, S., Burke, E., Essery, R., Boike, J., Langer, M., Heikenfeld, M., Cox, P., Friedlingstein, P., 2015. An improved representation of physical permafrost dynamics in the JULES land-surface model. Geosci. Model Dev. 8 (5), 1493–1508.

Chadburn, S.E., Krinner, G., Porada, P., Bartsch, A., Beer, C., Belelli Marchesini, L., Boike, J., Ekici, A., Elberling, B., Friborg, T., Hugelius, G., 2017. Carbon stocks and fluxes in the high latitudes: using site-level data to evaluate Earth system models. Biogeosciences 14 (22), 5143–5169.

Chandel, A.K., Jiang, L., Luo, Y., 2023. Microbial models for simulating soil carbon dynamics: a review. J. Geophys. Res. Biogeosci. 128 e2023JG007436.

Chapin III, F.S., Sturm, S., Serreze, M.C., McFadden, J.P., Key, J.R., Lloyd, A.H., McGuire, A.D., Rupp, T.S., Lynch, A.H., Schimel, J.P., Beringer, J., 2005. Role of land-surface changes in Arctic summer warming. Science 310 (5748), 657–660.

Christensen, T.R., Prentice, I.C., Kaplan, J., Haxeltine, A., Sitch, M., 1996. Methane flux from northern wetlands and tundra. Tellus B 48 (5), 652–661.

Chuvilin, E., Bukhanov, B., Grebenkin, S., Tumskoy, V., Shakhova, N., Dudarev, O., Semiletov, I., Spasennykh, M., 2021. Thermal properties of sediments in the east Siberian Arctic seas: a case study in the Buor-Khaya Bay. Mar. Pet. Geol. 123, 104672.

Clein, J.S., Schimel, J.P., 1995. Microbial activity of tundra and taiga soils at sub-zero temperatures. Soil Biol. Biochem. 27 (9), 1231–1234.

Coleman, K., Jenkinson, D.S., 1996. RothC-26.3-a Model for the turnover of carbon in soil. In: Evaluation of Soil Organic Matter Models: Using Existing Long-Term Datasets. Springer, Berlin Heidelberg, pp. 237–246.

Commane, R., Lindaas, J., Benmergui, J., Luus, K.A., Chang, R.Y.W., Daube, B.C., Euskirchen, E.S., Henderson, J.M., Karion, A., Miller, J.B., Miller, S.M., 2017. Carbon dioxide sources from Alaska driven by increasing early winter respiration from Arctic tundra. Proc. Natl. Acad. Sci. 114 (21), 5361–5366.

Comyn-Platt, E., et al., 2018. Carbon budgets for 1.5 and 2 °C targets lowered by natural wetland and permafrost feedbacks. Nat. Geosci. 11, 568–573.
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Fan, Z., David McGuire, A., Turetsky, M.R., Harden, J.W., Michael Waddington, J., Kane, E.S., 2013. The response of soil organic carbon of a rich fen peatland in interior Alaska to projected climate change. Glob. Chang. Biol. 19 (2), 604–620.

Farquharson, L.M., Romanovsky, V.E., Kholodov, A., Nicolsky, D., 2022. Sub-aerial talik formation observed across the discontinuous permafrost zone of Alaska. Nat. Geosci. 15, 475–481.

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Finestone, J., Templem, P.H., Bhatnagar, J.M., 2022. Soil fungi exposed to warming temperatures and shrinking snowpack in a northern forest have lower capacity for growth and nutrient cycling. Front. Global Change 5. https://doi.org/10.3389/fggc.2022.800335.

Fisher, J.B., Badgley, G., Blyth, E., 2012. Global nutrient limitation in terrestrial vegetation. Glob. Biogeochem. Cycles 26 (3).

Fisher, J.B., Sikka, M., Oechel, W.C., Huntzinger, D.N., Melton, J.R., Koven, C.D., Ahlström, A., Arain, M.A., Baker, I., Chen, J.M., Ciais, P., 2014. Carbon cycle uncertainty in the Alaskan Arctic. Biogeosciences 11 (15), 4271–4288.

Fisher, J.B., Hayes, D.J., Schwalm, C.R., Huntzinger, D.N., Stofferahn, E., Schaefer, K., Luo, Y., Wullschleger, S.D., Goetz, S., Miller, C.E., Griffith, P., 2018. Missing pieces to modeling the Arctic-Boreal puzzle. Environ. Res. Lett. 13 (2), 020202.

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Frey, S.D., Drijber, R., Smith, H., Melillo, J., 2008. Microbial biomass, functional capacity, and community structure after 12 years of soil warming. Soil Biol. Biochem. 40 (11), 2904–2907.

Fu, Z., Wu, Q., Zhang, W., He, H., Wang, L., 2022. Water migration and segregated ice formation in frozen ground: current advances and future perspectives. Front. Earth Sci. 10, 826961.

Geml, J., Morgado, L.N., Semenova-Nelsen, T.A., 2021. Tundra Type Drives Distinct Trajectories of Functional and Taxonomic Composition of Arctic Fungal Communities in Response to climate Change – results from Long-Term Experimental Summer Warming and increased Snow Depth. Front. Microbiol. 12, 628746.

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Graham, R.M., Cohen, L., Petty, A.A., Boisvert, L.N., Rinke, A., Hudson, S.R., Nicolaus, M., Granskog, M.A., 2017. Increasing frequency and duration of Arctic winter warming events. Geophys. Res. Lett. 44 (13), 6974–6983.

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Grant, R.F., Juma, N.G., Robertson, J.A., Izaurralde, R.C., McGill, W.B., 2001. Long-term changes in soil carbon under different fertilizer, manure, and rotation: Testing the mathematical model ecosys with data from the Breton plots. Soil Sci. Soc. Am. J. 65 (1), 205–214.

Guo, X., Gao, Q., Yuan, M., Wang, G., Zhou, X., Feng, J., Shi, Z., Hale, L., Wu, L., Zhou, A., Tian, R., 2020. Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming. Nat. Commun. 11 (1), 1–12.

Harris, R.F., 1981. Effect of water potential on microbial growth and activity. Water Potent. Relat. Soil Microbiol. 9, 23–95.

Hartl, J., Stuefer, M., Saito, T., Okura, Y., 2020. History and Data Records of the Automatic Weather Station on Denali Pass (5715 m), 1990–2007. J. Appl. Meteorol. Climatol. 59 (12), 2113–2127.

He, Y., Zhuang, Q., Harden, J.W., McGuire, A.D., Fan, Z., Liu, Y., Wickland, K.P., 2014. The implications of microbial and substrate limitation for the fates of carbon in different organic soil horizon types of boreal forest ecosystems: a mechanistically based model analysis. Biogeosciences 11 (16), 4477–4491.

He, Y., Yang, J., Zhuang, Q., Harden, J.W., McGuire, A.D., Liu, Y., Wang, G., Gu, L., 2015. Incorporating microbial dormancy dynamics into soil decomposition models to improve quantification of soil carbon dynamics of northern temperate forests. J. Geophys. Res. Biogeosci. 120 (12), 2596–2611.

He, L., Lai, C.T., Mayes, M.A., Murayama, S., Xu, X., 2021. Microbial seasonality promotes soil respiratory carbon emission in natural ecosystems: a modeling study. Glob. Chang. Biol. 27 (13), 3035–3051.

Heinze, T., 2021. A multi-phase heat transfer model for water infiltration into frozen soil. Water Resour. Res. 57 e2021WR030067.

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Hobbs, J.K., Jiao, W., Easter, A.D., Parker, E.J., Schipper, L.A., Arcus, V.L., 2013. Change in heat capacity for enzyme catalysis determines temperature dependence of enzyme catalyzed rates. ACS Chem. Biol. 8 (11), 2388–2393.

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Guo, X., Gao, Q., Yuan, M., Wang, G., Zhou, X., Feng, J., Shi, Z., Hale, L., Wu, L., Zhou, A., Tian, R., 2020. Gene-informed decomposition model predicts lower soil carbon loss due to persistent microbial adaptation to warming. Nat. Commun. 11 (1), 1–12.

Harris, R.F., 1981. Effect of water potential on microbial growth and activity. Water Potent. Relat. Soil Microbiol. 9, 23–95.

Hartl, J., Stuefer, M., Saito, T., Okura, Y., 2020. History and Data Records of the Automatic Weather Station on Denali Pass (5715 m), 1990–2007. J. Appl. Meteorol. Climatol. 59 (12), 2113–2127.

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He, L., Lai, C.T., Mayes, M.A., Murayama, S., Xu, X., 2021. Microbial seasonality promotes soil respiratory carbon emission in natural ecosystems: a modeling study. Glob. Chang. Biol. 27 (13), 3035–3051.

Heinze, T., 2021. A multi-phase heat transfer model for water infiltration into frozen soil. Water Resour. Res. 57 e2021WR030067.

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Schuur, E.A., 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., 2015. Climate change and the permafrost carbon feedback. Nature 520 (7546), 171-179.

Segers, R., 1998. Methane production and methane consumption: a review of processes underlying wetland methane fluxes. Biogeochemistry 41, 23-51.

Segers, R., Kengen, S.W.M., 1998. Methane production as a function of anaerobic carbon mineralization: a process model. Soil Biol. Biochem. 30 (8), 1107-1117.

Shao, Y., Taff, G.N., Lunetta, R.S., Weng, Q., 2011. A review of selected moderate-resolution imaging spectroradiometer algorithms, data products, and applications. Adv. Remote Sensing 31-55.

Shastri, A., Sanchez, M., Gai, X., Lee, M.Y., Dewers, T., 2021. Mechanical behavior of frozen soils: Experimental investigation and numerical modeling. Comput. Geotech. 138, 104361.

Shirley, I.A., Mekonnen, Z.A., Grant, R.F., Dafflon, B., Hubbard, S.S., Riley, W.J., 2022. Rapidly changing high-latitude seasonality: implications for the 21st century carbon cycle in Alaska. Environ. Res. Lett. 17 (1), 014032.

Sierra, C.A., Harmon, M.E., Perakis, S.S., 2011. Decomposition of heterogeneous organic matter and its long-term stabilization in soils. Ecol. Monogr. 81 (4), 619-634.

Singh, J.S., Gupta, S.R., 1977. Plant decomposition and soil respiration in terrestrial ecosystems. Bot. Rev. 43 (4), 449-528.

Singh, S.M., Singh, S.K., Yadav, L.S., Singh, P.N., Ravindra, R., 2012. Filamentous soil fungi from Ny-Ålesund, Spitsbergen, and screening for extracellular enzymes. Arctic 65 (1), 45-55. http://www.jstor.org/stable/23187223.

Sipes, K., Paul, R., Fine, A., Li, P., Liang, R., Boike, J., Onstott, T.C., Vishnivetskaya, T.A., Schaeffer, S., Lloyd, K.G., 2022. Permafrost active Layer Microbes from Ny Ålesund, Svalbard (79° N) show Autotrophic and Heterotrophic Metabolisms with Diverse Carbon-Degrading Enzymes. Front. Microbiol. 12.

Sitch, S., Smith, B., Prentice, I.C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J.O., Levis, S., Lucht, W., Sykes, M.T., Thonicke, K., 2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Chang. Biol. 9 (2), 161-185.

Six, J., Frey, S.D., Thiet, R.K., Batten, K.M., 2006. Bacterial and fungal contributions to carbon sequestration in agroecosystems. Soil Sci. Soc. Am. J. 70 (2), 555-569.

Stieglitz, M., Déry, S.J., Romanovsky, V.E., Osterkamp, T.E., 2003. The role of snow cover in the warming of arctic permafrost. Geophys. Res. Lett. 30 (13).

Stolpovsky, K., Martinez-Lavanchy, P., Heipieper, H.J., Van Cappellen, P., Thullner, M., 2011. Incorporating dormancy in dynamic microbial community models. Ecol. Model. 222 (17), 3092-3102.

Sturm, M., Schimel, J., Michaelson, G., Welker, J.M., Oberbauer, S.F., Liston, G.E., Fahnestock, J., Romanovsky, V.E., 2005. Winter biological processes could help convert arctic tundra to shrubland. Bioscience 55 (1), 17-26.

Sulman, B.N., Phillips, R.P., Oishi, A.C., Shevliakova, E., Pacala, S.W., 2014. Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated $CO_2$. Nat. Clim. Chang. 4 (12), 1099-1102.

Tan, Z., Zhuang, Q., Henze, D.K., Frankenberg, C., Dlugokencky, E., Sweeney, C., Turner, A.J., Sasakawa, M., Machida, T., 2016. Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models? Atmos. Chem. Phys. 16 (19), 12649-12666.

Tang, J., Riley, W.J., 2015. Weaker soil carbon-climate feedbacks resulting from microbial and abiotic interactions. Nat. Clim. Chang. 5 (1), 56-60.

Tang, J., Riley, W.J., 2019. A theory of effective microbial substrate affinity parameters in variably saturated soils and an example application to aerobic soil heterotrophic respiration. J. Geophys. Res. Biogeosci. 124 (4), 918-940.

Tarnocai, C., Canadell, J.G., Schuur, E.A., Kuhry, P., Mazhitova, G., Zimov, S., 2009. Soil organic carbon pools in the northern circumpolar permafrost region. Glob. Biogeochem. Cycles 23 (2).

Teefe, R., Ludwig, B., 2004. Variability of $CO_2$ and $N_2O$ emissions during freeze-thaw cycles: results of model experiments on undisturbed forest-soil cores. J. Plant Nutr. Soil Sci. 167 (2), 153-159.

Thomas, H.R., Cleall, P., Li, Y.C., et al., 2009. Modeling of cryogenic processes in permafrost and seasonally frozen soils. Geotechnique 59 (3), 173-184.

Thormann, M.N., Currah, R.S., Bayley, S.E., 2003. Succession of microfungal assemblages in decomposing peatland plants. Plant Soil 250, 323-333.

Tian, H., Xu, X., Liu, M., Ren, W., Zhang, C., Chen, G., Lu, C., 2010. Spatial and temporal patterns of $CH_4$ and $N_2O$ fluxes in terrestrial ecosystems of North America during

1979-2008: application of a global biogeochemistry model. Biogeosciences 7 (9), 2673.

Todd-Brown, K.E., Hopkins, F.M., Kivlin, S.N., Talbot, J.M., Allison, S.D., 2012. A framework for representing microbial decomposition in coupled climate models. Biogeochemistry 109, 19-33.

Todd-Brown, K.E., Randerson, J.T., Post, W.M., Hoffman, F.M., Tarnocai, C., Schuur, E. A., Allison, S.D., 2013. Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations. Biogeosciences 10 (3), 1717-1736.

Todd-Brown, K.E.O., Randerson, J.T., Hopkins, F., Arora, V., Hajima, T., Jones, C., Shevliakova, E., Tjiputra, J., Volodin, E., Wu, T., Zhang, Q., 2014. Changes in soil organic carbon storage predicted by Earth system models during the 21st century. Biogeosciences 11 (8), 2341-2356.

Tojo, M., Newsham, K.K., 2012. Snow moulds in polar environments. Fungal Ecol. 5, 395-402.

Tomczyk, A.M., Bednorz, E., Szyga-Pluta, K., 2021. Changes in air temperature and snow cover in winter in Poland. Atmosphere 12 (1), 68.

Treat, C.C., Bloom, A.A., Marushchak, M.E., 2018. Nongrowing season methane emissions—a significant component of annual emissions across northern ecosystems. Glob. Chang. Biol. 24 (8), 3331-3343.

Treseder, K.K., Balser, T.C., Bradford, M.A., Brodie, E.L., Dubinsky, E.A., Eviner, V.T., Hofmockel, K.S., Lennon, J.T., Levine, U.Y., MacGregor, B.J., Pett-Ridge, J., 2012. Integrating microbial ecology into ecosystem models: challenges and priorities. Biogeochemistry 109, 7-18.

Turetsky, M.R., Abbott, B.W., Jones, M.C., Anthony, K.W., Olefeldt, D., Schuur, E.A., Grosse, G., Kuhry, P., Hugelius, G., Koven, C., Lawrence, D.M., 2020. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13 (2), 138-143.

Uchida, M., Kishimoto, A., Muraoka, H., Nakatsubo, T., Kanda, H., Koizumi, H., 2010. Seasonal shift in factors controlling net ecosystem production in a high Arctic terrestrial ecosystem. J. Plant Res. 123 (1), 79-85.

Vepraskas, M.J., Faulkner, S.P., 2001. In: Richardson, J.L., Vepraskas, M.J. (Eds.), Redox chemistry of hydric soils. Wetland Soils CRC Press, Boca Raton, FL, p. 85 to 105.

Villarreal, S., Hollister, R.D., Johnson, D.R., Lara, M.J., Webber, P.J., Tweedie, C.E., 2012. Tundra vegetation change near Barrow, Alaska (1972-2010). Environ. Res. Lett. 7 (1), 015508.

Waldrop, M.P., McFarland, W., Manies, K.L., Leewis, M.C., Blazewicz, S.J., Jones, M.C., Neumann, R.B., Keller, J.K., Cohen, L., Euskirchen, E.S., Edgar, C., 2021. Carbon fluxes and microbial activities from boreal peatlands experiencing permafrost thaw. J. Geophys. Res. Biogeosci. 126 (3) p.e2020JG005869.

Wallenstein, M.D., McMahon, S., Schimel, J., 2007. Bacterial and fungal community structure in Arctic tundra tussock and shrub soils. FEMS Microbiol. Ecol. 59 (2), 428-435.

Walvoord, M.A., Striegl, R.G., 2021. Complex Vulnerabilities of the Water and Aquatic Carbon Cycles to Permafrost Thaw. Front. Clim. 3, 730402 https://doi.org/10.3389/fclim.2021.730402.

Wang, Y.P., Law, R.M., Pak, B., 2010. A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere. Biogeosciences 7 (7), 2261-2282.

Wang, G., Mayes, M.A., Gu, L., Schadt, C.W., 2014. Representation of dormant and active microbial dynamics for ecosystem modeling. PLoS One 9 (2), e89252.

Wania, R., 2007. Modelling Northern Peatland Land Surface Processes, Vegetation Dynamics and Methane Emissions (Doctoral dissertation. University of Bristol.

Wania, R., Ross, I., Prentice, I.C., 2010. Implementation and evaluation of a new methane model within a dynamic global vegetation model: LPJ-WHYMe v1.3.1. Geosci. Model Dev. 3 (2), 565-584.

Waring, B.G., Averill, C., Hawkes, C.V., 2013. Differences in fungal and bacterial physiology alter soil carbon and nitrogen cycling: insights from meta-analysis and theoretical models. Ecol. Lett. 16 (7), 887-894.

Watanabe, K., Kugisaki, Y., 2017. Effect of macropores on soil freezing and thawing with infiltration. Hydrol. Process. 31, 270-278.

Webb, E.E., Schuur, E.A., Natali, S.M., Oken, K.L., Bracho, R., Krapek, J.P., Risk, D., Nickerson, N.R., 2016. Increased wintertime $CO_2$ loss as a result of sustained tundra warming. J. Geophys. Res. Biogeosci. 121 (2), 249-265.

Wei, D., et al., 2021. Plant uptake of $CO_2$ outpaces losses from permafrost and plant respiration on the Tibetan Plateau. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.2015283118.

Weintraub, M.N., Scott-Denton, L.E., et al., 2007. The effects of tree rhizodeposition on soil exoenzyme activity, dissolved organic carbon, and nutrient availability in a subalpine forest ecosystem. Oecologia 154, 327-338.

Wieder, W.R., Grandy, A.S., Kallenbach, C.M., Taylor, P.G., Bonan, G.B., 2015. Representing life in the Earth system with soil microbial functional traits in the MIMICS model. Geosci. Model Dev. 8 (6), 1789-1808.

Wieder, W.R., Sulman, B.N., Hartman, M.D., Koven, C.D., Bradford, M.A., 2019. Arctic soil governs whether climate change drives global losses or gains in soil carbon. Geophys. Res. Lett. 46 (24), 14486-14495.

Wik, M., Varner, R.K., Anthony, K.W., MacIntyre, S., Bastviken, D., 2016. Climate-sensitive northern lakes and ponds are critical components of methane release. Nat. Geosci. 9 (2), 99-105. https://doi.org/10.1038/ngeo2578.

Xu, X., Tian, H., 2012. Methane exchange between marshland and the atmosphere over China during 1949-2008. Glob. Biogeochem. Cycles 26 (2).

Xu, X., Yuan, F., Hanson, P.J., Wullschleger, S.D., Thornton, P.E., Riley, W.J., Graham, D. E., 2016. Reviews and syntheses: four decades of modeling methane cycling in terrestrial ecosystems. Biogeosciences 13 (12), 3735.

Yang, K., Wang, C., Li, S., 2018. Improved simulation of frozen-thawing process in land surface model (CLM4.5). J. Geophys. Res. Atmos. 123 (23), 13-238.
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Schmidt, S.K., Wilson, K.L., Monson, R.K., Lipson, D.A., 2009. Exponential growth of "snow molds" at sub-zero temperatures: an explanation for high beneath-snow respiration rates and $Q_10$ values. Biogeochemistry 95, 13-21.

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Schnecker, J., Wild, B., Hofansl, F., et al., 2014. Effects of soil organic matter properties and microbial community composition on enzyme activities in cryoturbated arctic soils. PLoS One 9, e94076.

Schneegurt, M.A., Dore, S.Y., Kulpa, C.F., 2003. Direct extraction of DNA from soils for studies in microbial ecology. Curr. Issues Mol. Biol. 5 (1), 1-8.

Schostag, M., Stibal, M., Jacobsen, C.S., Bælum, J., Taş, N., Elberling, B., Jansson, J.K., Semenchuk, P., Priemé, A., 2015. Distinct summer and winter bacterial communities in the active layer of Svalbard permafrost revealed by DNA-and RNA-based analyses. Front. Microbiol. 6, 399.

Schuur, E.A., 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., 2015. Climate change and the permafrost carbon feedback. Nature 520 (7546), 171-179.

Segers, R., 1998. Methane production and methane consumption: a review of processes underlying wetland methane fluxes. Biogeochemistry 41, 23-51.

Segers, R., Kengen, S.W.M., 1998. Methane production as a function of anaerobic carbon mineralization: a process model. Soil Biol. Biochem. 30 (8), 1107-1117.

Shao, Y., Taff, G.N., Lunetta, R.S., Weng, Q., 2011. A review of selected moderate-resolution imaging spectroradiometer algorithms, data products, and applications. Adv. Remote Sensing 31-55.

Shastri, A., Sanchez, M., Gai, X., Lee, M.Y., Dewers, T., 2021. Mechanical behavior of frozen soils: Experimental investigation and numerical modeling. Comput. Geotech. 138, 104361.

Shirley, I.A., Mekonnen, Z.A., Grant, R.F., Dafflon, B., Hubbard, S.S., Riley, W.J., 2022. Rapidly changing high-latitude seasonality: implications for the 21st century carbon cycle in Alaska. Environ. Res. Lett. 17 (1), 014032.

Sierra, C.A., Harmon, M.E., Perakis, S.S., 2011. Decomposition of heterogeneous organic matter and its long-term stabilization in soils. Ecol. Monogr. 81 (4), 619-634.

Singh, J.S., Gupta, S.R., 1977. Plant decomposition and soil respiration in terrestrial ecosystems. Bot. Rev. 43 (4), 449-528.

Singh, S.M., Singh, S.K., Yadav, L.S., Singh, P.N., Ravindra, R., 2012. Filamentous soil fungi from Ny-Ålesund, Spitsbergen, and screening for extracellular enzymes. Arctic 65 (1), 45-55. http://www.jstor.org/stable/23187223.

Sipes, K., Paul, R., Fine, A., Li, P., Liang, R., Boike, J., Onstott, T.C., Vishnivetskaya, T.A., Schaeffer, S., Lloyd, K.G., 2022. Permafrost active Layer Microbes from Ny Ålesund, Svalbard (79° N) show Autotrophic and Heterotrophic Metabolisms with Diverse Carbon-Degrading Enzymes. Front. Microbiol. 12.

Sitch, S., Smith, B., Prentice, I.C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J.O., Levis, S., Lucht, W., Sykes, M.T., Thonicke, K., 2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Chang. Biol. 9 (2), 161-185.

Six, J., Frey, S.D., Thiet, R.K., Batten, K.M., 2006. Bacterial and fungal contributions to carbon sequestration in agroecosystems. Soil Sci. Soc. Am. J. 70 (2), 555-569.

Stieglitz, M., Déry, S.J., Romanovsky, V.E., Osterkamp, T.E., 2003. The role of snow cover in the warming of arctic permafrost. Geophys. Res. Lett. 30 (13).

Stolpovsky, K., Martinez-Lavanchy, P., Heipieper, H.J., Van Cappellen, P., Thullner, M., 2011. Incorporating dormancy in dynamic microbial community models. Ecol. Model. 222 (17), 3092-3102.

Sturm, M., Schimel, J., Michaelson, G., Welker, J.M., Oberbauer, S.F., Liston, G.E., Fahnestock, J., Romanovsky, V.E., 2005. Winter biological processes could help convert arctic tundra to shrubland. Bioscience 55 (1), 17-26.

Sulman, B.N., Phillips, R.P., Oishi, A.C., Shevliakova, E., Pacala, S.W., 2014. Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated $CO_2$. Nat. Clim. Chang. 4 (12), 1099-1102.

Tan, Z., Zhuang, Q., Henze, D.K., Frankenberg, C., Dlugokencky, E., Sweeney, C., Turner, A.J., Sasakawa, M., Machida, T., 2016. Inverse modeling of pan-Arctic methane emissions at high spatial resolution: what can we learn from assimilating satellite retrievals and using different process-based wetland and lake biogeochemical models? Atmos. Chem. Phys. 16 (19), 12649-12666.

Tang, J., Riley, W.J., 2015. Weaker soil carbon-climate feedbacks resulting from microbial and abiotic interactions. Nat. Clim. Chang. 5 (1), 56-60.

Tang, J., Riley, W.J., 2019. A theory of effective microbial substrate affinity parameters in variably saturated soils and an example application to aerobic soil heterotrophic respiration. J. Geophys. Res. Biogeosci. 124 (4), 918-940.

Tarnocai, C., Canadell, J.G., Schuur, E.A., Kuhry, P., Mazhitova, G., Zimov, S., 2009. Soil organic carbon pools in the northern circumpolar permafrost region. Glob. Biogeochem. Cycles 23 (2).

Teefe, R., Ludwig, B., 2004. Variability of $CO_2$ and $N_2O$ emissions during freeze-thaw cycles: results of model experiments on undisturbed forest-soil cores. J. Plant Nutr. Soil Sci. 167 (2), 153-159.

Thomas, H.R., Cleall, P., Li, Y.C., et al., 2009. Modeling of cryogenic processes in permafrost and seasonally frozen soils. Geotechnique 59 (3), 173-184.

Thormann, M.N., Currah, R.S., Bayley, S.E., 2003. Succession of microfungal assemblages in decomposing peatland plants. Plant Soil 250, 323-333.

Tian, H., Xu, X., Liu, M., Ren, W., Zhang, C., Chen, G., Lu, C., 2010. Spatial and temporal patterns of $CH_4$ and $N_2O$ fluxes in terrestrial ecosystems of North America during

1979-2008: application of a global biogeochemistry model. Biogeosciences 7 (9), 2673.

Todd-Brown, K.E., Hopkins, F.M., Kivlin, S.N., Talbot, J.M., Allison, S.D., 2012. A framework for representing microbial decomposition in coupled climate models. Biogeochemistry 109, 19-33.

Todd-Brown, K.E., Randerson, J.T., Post, W.M., Hoffman, F.M., Tarnocai, C., Schuur, E. A., Allison, S.D., 2013. Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations. Biogeosciences 10 (3), 1717-1736.

Todd-Brown, K.E.O., Randerson, J.T., Hopkins, F., Arora, V., Hajima, T., Jones, C., Shevliakova, E., Tjiputra, J., Volodin, E., Wu, T., Zhang, Q., 2014. Changes in soil organic carbon storage predicted by Earth system models during the 21st century. Biogeosciences 11 (8), 2341-2356.

Tojo, M., Newsham, K.K., 2012. Snow moulds in polar environments. Fungal Ecol. 5, 395-402.

Tomczyk, A.M., Bednorz, E., Szyga-Pluta, K., 2021. Changes in air temperature and snow cover in winter in Poland. Atmosphere 12 (1), 68.

Treat, C.C., Bloom, A.A., Marushchak, M.E., 2018. Nongrowing season methane emissions—a significant component of annual emissions across northern ecosystems. Glob. Chang. Biol. 24 (8), 3331-3343.

Treseder, K.K., Balser, T.C., Bradford, M.A., Brodie, E.L., Dubinsky, E.A., Eviner, V.T., Hofmockel, K.S., Lennon, J.T., Levine, U.Y., MacGregor, B.J., Pett-Ridge, J., 2012. Integrating microbial ecology into ecosystem models: challenges and priorities. Biogeochemistry 109, 7-18.

Turetsky, M.R., Abbott, B.W., Jones, M.C., Anthony, K.W., Olefeldt, D., Schuur, E.A., Grosse, G., Kuhry, P., Hugelius, G., Koven, C., Lawrence, D.M., 2020. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13 (2), 138-143.

Uchida, M., Kishimoto, A., Muraoka, H., Nakatsubo, T., Kanda, H., Koizumi, H., 2010. Seasonal shift in factors controlling net ecosystem production in a high Arctic terrestrial ecosystem. J. Plant Res. 123 (1), 79-85.

Vepraskas, M.J., Faulkner, S.P., 2001. In: Richardson, J.L., Vepraskas, M.J. (Eds.), Redox chemistry of hydric soils. Wetland Soils CRC Press, Boca Raton, FL, p. 85 to 105.

Villarreal, S., Hollister, R.D., Johnson, D.R., Lara, M.J., Webber, P.J., Tweedie, C.E., 2012. Tundra vegetation change near Barrow, Alaska (1972-2010). Environ. Res. Lett. 7 (1), 015508.

Waldrop, M.P., McFarland, W., Manies, K.L., Leewis, M.C., Blazewicz, S.J., Jones, M.C., Neumann, R.B., Keller, J.K., Cohen, L., Euskirchen, E.S., Edgar, C., 2021. Carbon fluxes and microbial activities from boreal peatlands experiencing permafrost thaw. J. Geophys. Res. Biogeosci. 126 (3) p.e2020JG005869.

Wallenstein, M.D., McMahon, S., Schimel, J., 2007. Bacterial and fungal community structure in Arctic tundra tussock and shrub soils. FEMS Microbiol. Ecol. 59 (2), 428-435.

Walvoord, M.A., Striegl, R.G., 2021. Complex Vulnerabilities of the Water and Aquatic Carbon Cycles to Permafrost Thaw. Front. Clim. 3, 730402 https://doi.org/10.3389/fclim.2021.730402.

Wang, Y.P., Law, R.M., Pak, B., 2010. A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere. Biogeosciences 7 (7), 2261-2282.

Wang, G., Mayes, M.A., Gu, L., Schadt, C.W., 2014. Representation of dormant and active microbial dynamics for ecosystem modeling. PLoS One 9 (2), e89252.

Wania, R., 2007. Modelling Northern Peatland Land Surface Processes, Vegetation Dynamics and Methane Emissions (Doctoral dissertation. University of Bristol.

Wania, R., Ross, I., Prentice, I.C., 2010. Implementation and evaluation of a new methane model within a dynamic global vegetation model: LPJ-WHYMe v1. 3.1. Geosci. Model Dev. 3 (2), 565-584.

Waring, B.G., Averill, C., Hawkes, C.V., 2013. Differences in fungal and bacterial physiology alter soil carbon and nitrogen cycling: insights from meta-analysis and theoretical models. Ecol. Lett. 16 (7), 887-894.

Watanabe, K., Kugisaki, Y., 2017. Effect of macropores on soil freezing and thawing with infiltration. Hydrol. Process. 31, 270-278.

Webb, E.E., Schuur, E.A., Natali, S.M., Oken, K.L., Bracho, R., Krapek, J.P., Risk, D., Nickerson, N.R., 2016. Increased wintertime $CO_2$ loss as a result of sustained tundra warming. J. Geophys. Res. Biogeosci. 121 (2), 249-265.

Wei, D., et al., 2021. Plant uptake of $CO_2$ outpaces losses from permafrost and plant respiration on the Tibetan Plateau. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.2015283118.

Weintraub, M.N., Scott-Denton, L.E., et al., 2007. The effects of tree rhizodeposition on soil exoenzyme activity, dissolved organic carbon, and nutrient availability in a subalpine forest ecosystem. Oecologia 154, 327-338.

Wieder, W.R., Grandy, A.S., Kallenbach, C.M., Taylor, P.G., Bonan, G.B., 2015. Representing life in the Earth system with soil microbial functional traits in the MIMICS model. Geosci. Model Dev. 8 (6), 1789-1808.

Wieder, W.R., Sulman, B.N., Hartman, M.D., Koven, C.D., Bradford, M.A., 2019. Arctic soil governs whether climate change drives global losses or gains in soil carbon. Geophys. Res. Lett. 46 (24), 14486-14495.

Wik, M., Varner, R.K., Anthony, K.W., MacIntyre, S., Bastviken, D., 2016. Climate-sensitive northern lakes and ponds are critical components of methane release. Nat. Geosci. 9 (2), 99-105. https://doi.org/10.1038/ngeo2578.

Xu, X., Tian, H., 2012. Methane exchange between marshland and the atmosphere over China during 1949-2008. Glob. Biogeochem. Cycles 26 (2).

Xu, X., Yuan, F., Hanson, P.J., Wullschleger, S.D., Thornton, P.E., Riley, W.J., Graham, D. E., 2016. Reviews and syntheses: four decades of modeling methane cycling in terrestrial ecosystems. Biogeosciences 13 (12), 3735.

Yang, K., Wang, C., Li, S., 2018. Improved simulation of frozen-thawing process in land surface model (CLM4. 5). J. Geophys. Res. Atmos. 123 (23), 13-238.
Ye, X., Yu, Z., Zhang, Y., Kang, J., Wu, S., Yang, T., Gao, P., 2022. Mineral Composition Impact on the thermal Conductivity of Granites based on Geothermal Field experiments in the Songliao and Gonghe Basins, China. Minerals 12, 247.

Yi, Y., Kimball, J.S., Watts, J.D., Natali, S.M., Zona, D., Liu, J., Ueyama, M., Kobayashi, H., Oechel, W., Miller, C.E., 2020. Investigating the sensitivity of soil heterotrophic respiration to recent snow cover changes in Alaska using a satellite-based permafrost carbon model. Biogeosciences 17 (22), 5861–5882.

Yokohata, T., Saito, K., Takata, K., et al., 2020. Model improvement and future projection of permafrost processes in a global land surface model. Prog Earth Planet Sci 7, 69.

Yoshitake, S., Uchida, M., Koizumi, H., Nakatsubo, T., 2007. Carbon and nitrogen limitation of soil microbial respiration in a High Arctic successional glacier foreland near Ny-Ålesund. Svalbard. Polar Res. 26 (1), 22–30.

Yu, L., Ahrens, B., Wutzler, T., Schrumpf, M., Zaehle, S., 2020. Jena Soil Model (JSM v1.0; revision 1934): a microbial soil organic carbon model integrated with nitrogen and phosphorus processes. Geosci. Model Dev. 13 (2), 783–803.

Zha, J., Zhuang, Q., 2018. Microbial decomposition processes and vulnerable arctic soil organic carbon in the 21st century. Biogeosciences 15 (18), 5621–5634.

Zhang, T., 2005. Influence of the seasonal snow cover on the ground thermal regime: an overview. Rev. Geophys. 43 (4).

Zhang, W., Jansson, P.E., Sigsgaard, C., McConnell, A., Jammet, M.M., Westergaard-Nielsen, A., Lund, M., Friborg, T., Michelsen, A., Elberling, B., 2019. Model-data fusion to assess year-round CO₂ fluxes for an arctic heath ecosystem in West Greenland (69°N). Agric. For. Meteorol. 272, 176–186.

Zhou, J., Wei, C., Lai, Y., Wei, H., Tian, H., 2018. Application of the Generalized Clapeyron Equation to Freezing Point Depression and Unfrozen Water Content. Water Resour. Res. 54 (11), 9412–9431.

Zhu, Q., Zhuang, Q., 2013. Influences of calibration data length and data period on model parameterization and quantification of terrestrial ecosystem carbon dynamics. Geosci. Model Dev. Discuss. 6 (4), 6835–6865.

Zhu, Q., Liu, J., Peng, C., Chen, H., Fang, X., Jiang, H., Yang, G., Zhu, D., Wang, W., Zhou, X., 2014. Modelling methane emissions from natural wetlands by development and application of the TRIPLEX-GHG model. Geosci. Model Dev. 7 (3), 981–999.

Zhu, D., Ciais, P., Krinner, G., et al., 2019. Controls of soil organic matter on soil thermal dynamics in the northern high latitudes. Nat. Commun. 10, 3172.

Zhuang, Q., Romanovsky, V.E., McGuire, A.D., 2001. Incorporation of a permafrost model into a large-scale ecosystem model: Evaluation of temporal and spatial scaling issues in simulating soil thermal dynamics. J. Geophys. Res. Atmos. 106 (D24), 33649–33670.

Zhuang, Q., Melillo, J.M., Kicklighter, D.W., Prinn, R.G., McGuire, A.D., Steudler, P.A., Felzer, B.S., Hu, S., 2004. Methane fluxes between terrestrial ecosystems and the atmosphere at northern high latitudes during the past century: a retrospective analysis with a process-based biogeochemistry model. Glob. Biogeochem. Cycles 18 (3).

Zimov, S.A., Davidov, S.P., Voropaev, Y.V., Prosiannikov, S.F., Semiletov, I.P., Chapin, M.C., Chapin, F.S., 1996. Siberian CO₂ efflux in winter as a CO₂ source and cause of seasonality in atmospheric CO₂. Clim. Chang. 33, 111–120.

Zinger, L., Shahnavaz, B., Baptist, F., Geremia, R.A., Choler, P., 2009. Microbial diversity in alpine tundra soils correlates with snow cover dynamics. ISME J. 3 (7), 850–859.

Zona, D., Gioli, B., Commane, R., Lindaas, J., Wofsy, S.C., Miller, C.E., Dinardo, S.J., Dengel, S., Sweeney, C., Karion, A., Chang, R.Y.W., 2016. Cold season emissions dominate the Arctic tundra methane budget. Proc. Natl. Acad. Sci. 113 (1), 40–45.
Ye, X., Yu, Z., Zhang, Y., Kang, J., Wu, S., Yang, T., Gao, P., 2022. Mineral Composition Impact on the thermal Conductivity of Granites based on Geothermal Field experiments in the Songliao and Gonghe Basins, China. Minerals 12, 247.

Yi, Y., Kimball, J.S., Watts, J.D., Natali, S.M., Zona, D., Liu, J., Ueyama, M., Kobayashi, H., Oechel, W., Miller, C.E., 2020. Investigating the sensitivity of soil heterotrophic respiration to recent snow cover changes in Alaska using a satellite-based permafrost carbon model. Biogeosciences 17 (22), 5861–5882.

Yokohata, T., Saito, K., Takata, K., et al., 2020. Model improvement and future projection of permafrost processes in a global land surface model. Prog Earth Planet Sci 7, 69.

Yoshitake, S., Uchida, M., Koizumi, H., Nakatsubo, T., 2007. Carbon and nitrogen limitation of soil microbial respiration in a High Arctic successional glacier foreland near Ny-Ålesund. Svalbard. Polar Res. 26 (1), 22–30.

Yu, L., Ahrens, B., Wutzler, T., Schrumpf, M., Zaehle, S., 2020. Jena Soil Model (JSM v1.0; revision 1934): a microbial soil organic carbon model integrated with nitrogen and phosphorus processes. Geosci. Model Dev. 13 (2), 783–803.

Zha, J., Zhuang, Q., 2018. Microbial decomposition processes and vulnerable arctic soil organic carbon in the 21st century. Biogeosciences 15 (18), 5621–5634.

Zhang, T., 2005. Influence of the seasonal snow cover on the ground thermal regime: an overview. Rev. Geophys. 43 (4).

Zhang, W., Jansson, P.E., Sigsgaard, C., McConnell, A., Jammet, M.M., Westergaard-Nielsen, A., Lund, M., Friborg, T., Michelsen, A., Elberling, B., 2019. Model-data fusion to assess year-round CO₂ fluxes for an arctic heath ecosystem in West Greenland (69°N). Agric. For. Meteorol. 272, 176–186.

Zhou, J., Wei, C., Lai, Y., Wei, H., Tian, H., 2018. Application of the Generalized Clapeyron Equation to Freezing Point Depression and Unfrozen Water Content. Water Resour. Res. 54 (11), 9412–9431.

Zhu, Q., Zhuang, Q., 2013. Influences of calibration data length and data period on model parameterization and quantification of terrestrial ecosystem carbon dynamics. Geosci. Model Dev. Discuss. 6 (4), 6835–6865.

Zhu, Q., Liu, J., Peng, C., Chen, H., Fang, X., Jiang, H., Yang, G., Zhu, D., Wang, W., Zhou, X., 2014. Modelling methane emissions from natural wetlands by development and application of the TRIPLEX-GHG model. Geosci. Model Dev. 7 (3), 981–999.

Zhu, D., Ciais, P., Krinner, G., et al., 2019. Controls of soil organic matter on soil thermal dynamics in the northern high latitudes. Nat. Commun. 10, 3172.

Zhuang, Q., Romanovsky, V.E., McGuire, A.D., 2001. Incorporation of a permafrost model into a large-scale ecosystem model: Evaluation of temporal and spatial scaling issues in simulating soil thermal dynamics. J. Geophys. Res. Atmos. 106 (D24), 33649–33670.

Zhuang, Q., Melillo, J.M., Kicklighter, D.W., Prinn, R.G., McGuire, A.D., Steudler, P.A., Felzer, B.S., Hu, S., 2004. Methane fluxes between terrestrial ecosystems and the atmosphere at northern high latitudes during the past century: a retrospective analysis with a process-based biogeochemistry model. Glob. Biogeochem. Cycles 18 (3).

Zimov, S.A., Davidov, S.P., Voropaev, Y.V., Prosiannikov, S.F., Semiletov, I.P., Chapin, M.C., Chapin, F.S., 1996. Siberian CO₂ efflux in winter as a CO₂ source and cause of seasonality in atmospheric CO₂. Clim. Chang. 33, 111–120.

Zinger, L., Shahnavaz, B., Baptist, F., Geremia, R.A., Choler, P., 2009. Microbial diversity in alpine tundra soils correlates with snow cover dynamics. ISME J. 3 (7), 850–859.

Zona, D., Gioli, B., Commane, R., Lindaas, J., Wofsy, S.C., Miller, C.E., Dinardo, S.J., Dengel, S., Sweeney, C., Karion, A., Chang, R.Y.W., 2016. Cold season emissions dominate the Arctic tundra methane budget. Proc. Natl. Acad. Sci. 113 (1), 40–45.