Effects of soil freezing and thawing on vegetation carbon density in Siberia: A modeling analysis with the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM)

Authors


Abstract

[1] The current latitudinal gradient in biomass suggests a climate-driven limitation of biomass in high latitudes. Understanding of the underlying processes, and quantification of their relative importance, is required to assess the potential carbon uptake of the biosphere in response to anticipated warming and related changes in tree growth and forest extent in these regions. We analyze the hydrological effects of thawing and freezing of soil on vegetation carbon density (VCD) in permafrost-dominated regions of Siberia using a process-based biogeochemistry-biogeography model, the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). The analysis is based on spatially explicit simulations of coupled daily thaw depth, site hydrology, vegetation distribution, and carbon fluxes influencing VCD subject to climate, soil texture, and atmospheric CO2 concentration. LPJ represents the observed high spring peak of runoff of large Arctic rivers, and simulates a realistic fire return interval of 100 to 200 years in Siberia. The simulated VCD changeover from taiga to tundra is comparable to inventory-based information. Without the consideration of freeze-thaw processes VCD would be overestimated by a factor of 2 in southern taiga to a factor of 5 in northern forest tundra, mainly because available soil water would be overestimated with major effects on fire occurrence and net primary productivity. This suggests that forest growth in high latitudes is not only limited by temperature, radiation, and nutrient availability but also by the availability of liquid soil water.

1. Introduction

[2] High-latitude ecosystems currently experience various changes due to warming, for example, increasing net primary production (NPP) [Lucht et al., 2002; Nemani et al., 2003] that is attended by northward migration of the tree line [Serreze et al., 2000; Esper and Schweingruber, 2004], but also increasing heterotrophic soil respiration [Oechel et al., 1993; Zimov et al., 1993]. Owing to rising temperature, a large fraction of the current extend of permafrost soils of about half of the territories of Canada and the former Soviet Union [Bonan and Shugart, 1989] is assumed to degrade until 2100 [Anisimov and Nelson, 1997; Stendel and Christensen, 2002]. This accompanied a risk of an additional radiative forcing of the climate system by CO2 respired from carbon that is currently blocked in permanently frozen grounds [Goulden et al., 1998].

[3] The ability of increasing carbon uptake by expanding forests to partly counteract these carbon emissions is unclear. To assess a potential carbon uptake by vegetation in the high latitudes, an understanding of the environmental factors and processes limiting the current amount of biomass as well as their relative importance is required. The observed gradient in vegetation carbon density (VCD) from 0.6 kg carbon per m2 (kgC/m2) in the Russian tundra to 3.6 kgC/m2 in the southern taiga [Shvidenko et al., 2000] suggests a climatic driven limitation of biomass in northern taiga and tundra. The relationship of biomass to the depth of the active layer [Bonan, 1989], which is the uppermost part of the soil in permafrost areas that is thawing each summer and therefore provides plants with liquid water and nutrients, supports this assumption. Temperature, solar radiation and supply of nutrient and water are reported to limit generally the establishment and survive of seedlings, carbon assimilation and plant growth [e.g., Bonan and Shugart, 1989; Woodward, 1995; Körner, 2003]. However, Briffa et al. [1998] found that tree growth was less sensitive to increasing temperature during the last decades, and that this could not be explained readily from soil moisture data. In addition, disturbances like fire and damage by insects or wind had the capability to counteract an increasing productivity due to warming. Fire is the major disturbance agent in the boreal zone, where between 3 and 23 million ha are burned annually mainly as crown fires in boreal North America, but apart from extreme fire years as surface fires in boreal Eurasia [Kasischke et al., 2005], thereby releasing approximately 106–209 TgC/a.

[4] The processes controlling biomass take place in parallel or are even coupled, especially in regions with permanently frozen grounds. For instance, low temperatures during the growing season limit not only photosynthesis but also active-layer thickness (ALT), which impacts water availability [Benninghoff, 1952]. Both effects of temperature impact carbon assimilation by the vegetation. Moreover, low temperatures lead to slow litter decomposition, hence nutrient limitation [Bonan and Shugart, 1989]. The deep litter layer also isolates the underlying soil leading to a low thaw depth [Hinzman et al., 1991], thus low water availability in summer again. In addition, litter moisture determines the probability of fire. Such linkages make it impossible to separate the even most important effects of environmental influences on the amount of biomass by field studies alone, in particular because biomass is the result of different processes over decades. Experiments and observations are critical to providing the inputs and functions for models, but it would probably be impossible to set up an experiment to answer the questions posed here.

[5] In this paper, we investigate the effects of the alterations in the soil water balance due to freeze-thaw processes in the soil on the amount of VCD in permafrost-dominated regions of Siberia. The impact of soil water content which is controlled by phase change on the productivity and fire return interval is studied. For this purpose, we perform two simulation experiments with the LPJ-DGVM, in which freeze-thaw processes are excluded in the control run. Both simulations are driven by the same observations of climatic variables and atmospheric CO2 content. The comparison of VCD results by these simulation experiments with forest inventory estimates over a large transect swath in central Siberia allows for the investigation of the working hypothesis that besides direct influences of temperature, solar radiation and nutrient availability on the productivity, also limitations in liquid soil water availability during the year and the disturbance dynamics substantially contribute to low VCD values in high-latitude permafrost regions, i.e., that soil thermal dynamics are crucial for the changeover of VCD from taiga to tundra. In order to isolate better the influence of fire disturbance and water stress, the results of these experiments are further compared to results of two LPJ-P model runs excluding the simulation of fire and water stress.

2. Methods

2.1. LPJ-DGVM Overview

[6] The LPJ-DGVM is a coupled dynamic biogeography-biogeochemistry model which combines process-based representations of terrestrial vegetation dynamics and land-atmosphere carbon and water exchanges in a modular framework. For a detailed description and evaluation of the model see Sitch et al. [2003]. LPJ explicitly considers key ecosystem processes such as photosynthesis, carbon allocation, mortality, resource competition, fire disturbance and soil heterotrophic respiration. To account for the variety of structure and functioning among plants, four plant functional types (PFTs) are distinguished for the boreal zone (see auxiliary material). Gross primary production is calculated on the basis of a coupled photosynthesis-water balance scheme after Farquhar et al. [1980] with leaf-level optimized nitrogen allocation [Haxeltine and Prentice, 1996]. The underlying model of light use efficiency assumes no limitation of nitrogen uptake [Haxeltine and Prentice, 1996], thus no nitrogen cycle is represented in the model. Fire disturbance is mainly driven by litter moisture and dead fuel load as well as PFT specific fire resistances Thonicke et al. [2001].

[7] The LPJ-DGVM used in this study has been intensively validated against observations of key ecosystem parameters on different scales including land-atmosphere CO2 exchange [McGuire et al., 2001; Sitch et al., 2003; Peylin et al., 2005], dominant vegetation distribution [Cramer et al., 2001; Sitch et al., 2003; Beer, 2005], fluxes of water [Gerten et al., 2004, 2005], global patterns of soil moisture [Wagner et al., 2003], NPP enhancement due to carbon fertilization [Gerten et al., 2005], and satellite-observed northern latitude leaf area index anomalies [Lucht et al., 2002].

[8] In this study specific leaf area (SLA, in m2/kg) is related to leaf longevity (aleaf, in years) after Reich et al. [1997] as follows:

equation image

Moreover, parameters of boreal vegetation are slightly adjusted in this study (see auxiliary material). With these adjustments, a more valid distribution of dominant woody vegetation in Siberia is achieved [Beer, 2005].

2.2. Input Data and Modeling Protocol

[9] LPJ was driven by gridded monthly fields of air temperature, precipitation, number of rainy days, and cloud cover at a 0.5° pixel size. This station-based data set was provided by the Climatic Research Unit (CRU) of the University of East Anglia, UK [Mitchell and Jones, 2005] but revised and extended by the Potsdam Institute for Climate Impact Research (PIK), Germany [Oesterle et al., 2003]. As a nongridded global input, annual CO2 concentrations were used that were derived from ice-core measurements and atmospheric observations, provided by the Carbon Cycle Model Linkage Project [McGuire et al., 2001]. In addition, eight soil texture classes (see auxiliary material) were used which are also available globally on a 0.5° grid [Food and Agriculture Organization, 1991]. LPJ was run from 1901 to 2003 with a pseudo-daily time step, preceded by a 1000-year spinup period that brought the carbon pools and vegetation cover in equilibrium with climate.

[10] Two model experiments were performed for the land area of Northern Eurasia. In both, the same model version is used but (1) with (LPJ-P) and (2) without permafrost (LPJ-noP). In order to isolate the influence of fire disturbance and water stress, the results of these experiments are further compared to results of LPJ-P model runs excluding fire (LPJ-noFire), and excluding water stress (LPJ-noStress).

2.3. Soil Water Balance

[11] Soil hydrology is modeled as described by Sitch et al. [2003] and Gerten et al. [2004] following a semi-empirical approach. There are two soil layers of constant depth (0.5 and 1 m) and spatially varying soil texture. Plant available soil moisture w is expressed as the fraction of available water holding capacity Wplant, which is defined as the difference between volumetric soil water content at field capacity and permanent wilting point Wpwp. For calculating soil thermal properties, the whole water content is required, hence Wpwp is added to Wplant (see auxiliary material).

[12] The water content of each layer is updated daily by taking into account rainfall, snowmelt, canopy interception of precipitation, percolation, soil evaporation, transpiration and runoff. Monthly values of precipitation, P are distributed stochastically to provide pseudo-daily values. Below a threshold value of 0°C precipitation is defined as snow. Snowmelt (M, mm/d) is calculated using an index equation [Choudhury and DiGirolamo, 1998]:

equation image

where Ta is the air temperature above 0°C. Percolation rate (p, mm/d) through the soil layers depends on soil texture and layer thickness,

equation image

where w1 is the plant available soil moisture of the upper layer and kperc the texture-dependent conductivity, varying from 5 mm/d for sandy soils to 0.2 mm/d for vertisols. Surface runoff and drainage are calculated as the excess water above field capacity in both soil layers [Gerten et al., 2004]; that is, all water above field capacity that does not percolate to the respective underlying layer will run off.

[13] Under conditions of permafrost, the water balance is altered as follows.

[14] 1. Thaw depth within the active layer changes dynamically for each simulation day. Wplant is adjusted daily in accordance, which directly influences runoff generation and water availability. For instance, if only a very thin part of the soil is already thawed in spring, the storage capacity of the soil will be small hence most of snow melt or rainfall will run off. Lateral flow of water, which may play an important role in permafrost regions, is not considered in the present model. This may be a shortcoming for small-scale investigations, but implies that lateral flows are canceled out at the large scale considered here (0.5°), which is not an unrealistic assumption. Lateral flow among grid cells, however, will be enabled in a forthcoming version of the LPJ model (Stefanie Jachner, unpublished data, 2006).

[15] 2. Water is only allowed to percolate through a soil layer if it is completely thawed. This will increase soil moisture of the upper layer after precipitation if the transition to the underlying layer remains frozen.

[16] 3. In addition to w a new vector stores frozen water for both layers also as a fraction of plant available soil water. Thus precipitation in autumn is conserved until the next spring.

[17] Water supply to plants is the product of the plant root-weighted soil moisture availability and a maximum transpiration rate [Sitch et al., 2003]. The fractions of fine roots in the upper and lower soil layer, z1 and z2 are optimized daily for boreal needleleaved summergreen vegetation (BoNS) as a function of mm thawed water (w1, upper layer; w2, lower layer) to give BoNS the opportunity to access as much water from both layers as possible,

equation image

while z1 and z2 are set to 0 if w1 + w2 = 0. This mechanism gives BoNS an advantage over other woody PFTs in permafrost regions by allowing them to better manage droughts (e.g., in East Siberia). Larix gmelinii uses soil water of the upper 15 cm under wet conditions, while roots observed at 30–70 cm depth are possibly essential for the uptake of thawed water during drought [Sugimoto et al., 2002].

[18] Links between site hydrology and the carbon cycle are simulated by the LPJ-DGVM on several timescales. The fast processes, photosynthesis and transpiration are coupled by the dynamics of canopy conductance after Farquhar et al. [1980], which strongly depend on soil moisture [Gerten et al., 2005]. On a longer timescale, moisture-related adjustments in the allocation of carbon to different plant components [Bongarten and Teskey, 1987; Mencuccini and Grace, 1995] lead to moisture-dependent leaf area [Grier and Running, 1977; Gholz, 1982]. The ratio between atmospheric demand and supply of water (water stress, ω) is modeled to influence the carbon allocation between leaves and fine roots [Sitch et al., 2003],

equation image

2.4. Fire

[19] A full description of the LPJ fire module Glob-FIRM is given by Thonicke et al. [2001]. The probability p of a fire increases with decreasing litter moisture content as described by

equation image

ω is the PFT-weighted moisture of extinction, above which litter cannot ignite, because all the ignition energy is consumed to vaporize the water it contains [see Sitch et al., 2003, Table 3]. The fire season s is calculated as the annual arithmetic mean of the daily fire probability. A constant nonlinear relationship between s and annual area burned A, assuming that the fractional area burned increases the longer the burning conditions persist, is applied [Thonicke et al., 2001]. Fire effects are described by fire resistances, which combine fire intensity and fire severity in a PFT-specific parameter (see auxiliary material).

2.5. Freeze-Thaw Processes in the Active Layer Over Permanently Frozen Ground

[20] In a first step we determine whether a given geographic location is characterized by permanently frozen ground. Permafrost status is applied to the whole of a grid cell, neglecting discontinuous permafrost, since DGVMs do not resolve subgrid localizations of spatial heterogeneity (this is a tradeoff with their continental-scale applicability in the absence of detailed spatial data). Surface temperature below snow and litter (including near-surface carbon, e.g., of grass) is calculated by treating snow and vegetation layers separately. If a particular grid cell is determined to be located in a permafrost region, thaw depth is computed by using this altered surface temperature. The simulation of hydrological processes follows as described in section 2.3.

2.5.1. Permafrost Distribution

[21] Spatial permafrost distribution is modeled at annual time steps using the normalized frost index F [Nelson and Outcalt, 1987],

equation image

Its reliability can be assumed to also hold in a changing climate [Anisimov and Nelson, 1997; Christensen and Kuhry, 2000; Stendel and Christensen, 2002]. Df and Dt represent the annual freezing and thawing degree-days (sum of pseudo-daily temperatures that are derived from interpolated monthly data). In case of snow coverage the temperature below snow is used for the calculation of Dt and Df (see section 2.5.2). Thus vegetation has an impact on the calculation of the frost index since canopy interception of snow is taken into account in LPJ [Gerten et al., 2004]. Values of F that represent the threshold boundaries of continuous, discontinuous and sporadic permafrost are 0.67, 0.6 and 0.5, respectively [Nelson and Outcalt, 1987; Christensen and Kuhry, 2000]. In this study F = 0.6 is applied to distinguish between permafrost and nonpermafrost situations.

2.5.2. Daily Thaw Depth

[22] In this work ‘thaw depth’ represents the soil depth z of the 0°C isotherm. We assume only one isotherm instead of two phase interfaces in autumn since the effects of the precise discrimination of the location of the interfaces between the two soil layers on the carbon fluxes are small while air temperature is below the freezing point. In other words, in autumn our variable ‘thaw depth’ corresponds to the thawed water column length. During the growing season when air temperature is above 0°C simulated thaw depth matches the definition used for the temperature-measured variable in the field.

[23] Soil temperature in °C is assumed to follow an annual sinusoidal cycle of air temperature with a damped oscillation [Campbell and Norman, 2000],

equation image

where Tave is the mean annual temperature above the respective layer and A is the amplitude of this temperature fluctuation. d = equation image represents the damping depth with thermal diffusivity k = λ · c−1, where c is volumetric heat capacity and λ thermal conductivity. Ω = 2π · τ−1 represents the angular frequency of oscillation with oscillation period τ. The method of solving equation (7) for each simulation day is described in the appendix of [Sitch et al., 2003]. The concept of a running mean is applied for calculating Tave; that is, the model is not required to have knowledge of future climate. Snow cover and a near-surface carbon layers (aboveground litter and grass carbon density) are considered to be above the soil in this study. The temperature above snow corresponds to the air temperature. Equation 7 is applied to calculate the following temperatures for each simulation day step by step: temperature below snow, temperature below near-surface carbon, and temperature in 0.25 m soil depth. Each of these results is taken as input for calculating the subsequent value. Mean depth of snow and near-surface carbon layers of the last 31 days as well as their mean thermal diffusivity are updated daily (see auxiliary material).

[24] Once temperature below snow or near-surface carbon layers is estimated, soil temperature in 0.25 m depth and thaw depth can be calculated in parallel. In doing so, soil volumetric heat capacity c and thermal conductivity λ are required for each day, which are derived from mean volumetric water content of the thawed fraction of the soil of the last 31 days, w, after Campbell and Norman [2000]. The nonlinear relationship λ = λ(w) is approximated linearly for w ∈ [0.0.1), w ∈ [0.1.0.25) and w ∈ [0.25.1), respectively. While a phase change the latent heat Q is considered as apparent heat capacity [Jumikis, 1966] in the exponential damping term of equation (7),

equation image

wthaw represents the amount of thawed soil water content averaged over the last 31 days and Qw = 334.94 kJ/m3 is the heat of fusion of water. We approximate k to 0.5 by assuming a nearly linear change of the 0°C isotherm during the short interval of a 1-day time step.

[25] Figure 1 summarizes the scheme of the thaw depth module and visualizes feedbacks of vegetation on thaw depth through soil moisture and insulating snow and near-surface carbon layers. Thaw depth is finally computed by finding the null of equation (7) numerically via Newton's method for each simulation day,

equation image

If the calculated thaw depth indicates a thawing or freezing event, the associated soil water will be shifted between the separate pools for water and ice for each layer, and a new value for soil moisture is derived by calculating the site hydrology. In doing so, soil thermal properties of the next simulation day are influenced.

Figure 1.

Principal steps in calculating daily thaw depth in LPJ.

2.6. Freeze-Thaw Processes Outside the Permafrost Zone

[26] Outside of the permafrost zone (determined as described in section 2.5.1) the soil freezes during winter to a certain depth and thaws in spring at two phase interfaces until the whole soil column is thawed. In contrast to the thaw depth over permafrost soils during summer, the winter frost depth has less of an importance for the vegetation functioning in the boreal zone. Trees are hardly able to make use of water from deeper soils while the xylem is frozen in winter [Woodward, 1995]. In addition, photosynthesis can be neglected while temperature is clearly below the freezing point. Thus we do not attempt to estimate frost depth during winter but consider the whole of the soil column to be frozen when the temperature (as calculated by equation (7)) falls below 0°C in 10 cm depth, following Zhuang et al. [2003]. In doing so, we make sure that snow melt water in spring does not infiltrate immediately into the soil, that is, the water pool available to vegetation.

2.7. Study Region for Evaluation of Vegetation Carbon Density

[27] Simulated VCD values are compared to a recently updated inventory-based VCD data from an ecosystem information system on a 1:1 million polygon basis, that was made available by the International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria. It covers a 3 million km2 broad transect swath through central Siberia reaching from the Arctic Ocean to the southern steppe and the Yenisey river in the West to Lake Baikal in the east (approx. 79°E–119°E, 51°N–78°N). This ecosystem data base [Nilsson et al., 2005] was created as part of the European Commission's SIBERIA-II project [Schmullius and Hese, 2002].

3. Results

3.1. Permafrost Model Evaluation

[28] For site-specific evaluation, LPJ was run with the soil textures of the observation points. Simulated daily thaw depth is compared to seasonal thaw depth at four stations in Alaska from 1995 until 1997 [Nelson, 1996] and one site in the Siberian Far East from 1981 until 1985 (S. Zimov, Thaw depth measurements near River Kolyma 1981–1985, personal communication, 2005). Thaw depth simulated by LPJ follows the seasonal cycle of measured thaw depth values fairly well (Figure 2). Discrepancies between modeled and observed values are within the range of standard deviations of observations. In many cases the model slightly underestimates the maximum thaw depth. However, this is obviously the case only for the selection of locations shown here for which seasonal thaw depth are available. Other locations show as many cases of overestimation as underestimation of maximal thaw depth, as can be seen from Figure 3. It compares LPJ-simulated ALT with 178 observations collected by the Circumpolar Active Layer Monitoring (CALM) project [Brown et al., 2003] from 29 stations in Alaska, Canada and Russia between 1992 and 2002. Most simulation results range between 50 and 200% discrepancy to the observations, the coefficient of determination and root mean square error are 0.3 and 21 cm, respectively. These discrepancies are of the same magnitude as the natural variability of ALT over only tens of meters [Nelson et al., 1997]. Thus results by LPJ that were driven by coarse climate and soil data are considered reasonable, particularly as this study is focused not on the details of permafrost dynamics but on general effects of permafrost on the vegetation growing on permafrost soils.

Figure 2.

Mean seasonal thaw depth (crosses) and their standard deviation (bars) at five stations in Alaska between 1995 and 1997 [Nelson, 1996] and one station in the Russian Far East between 1981 and 1985 (S. Zimov, personal communication, 2005) in comparison to LPJ simulation results of daily thaw depth (line).

Figure 3.

Comparison of simulated maximum thaw depth with observations at 29 stations from Alaska, Canada, and Russia from 1992 until 2002 [Brown et al., 2003]. Observations represent mean values of measurements within 100 m2 to 1 km2 large areas with standard deviations in the range of 5–20 cm [Brown et al., 2003].

[29] In addition to the seasonal cycle of thaw depth and its maximum, Figure 4 provides an evaluation of the interannual variability of ALT at two stations in Alaska and two in Russia. As can be seen, changes in simulated ALT from year to year correspond well to observations. This demonstrates the ability of the algorithm to simulate the impacts of air temperature, soil moisture and insulations on soil temperature, which act on a daily basis or even finer temporal resolution but are here recovered from a coarser resolution model.

Figure 4.

Time series of active-layer thickness anomalies at four CALM [Brown et al., 2003] stations in comparison to LPJ-P model results.

[30] Spatial details of simulated ALT within permafrost areas are shown in the auxiliary material. Continental-scale observations of ALT are not available, hence spatial pattern of ALT are compared to results from a state-of-the-art freeze-thaw model, the Goodrich model [Oelke et al., 2003]. Although, in contrast to LPJ, this model is driven by NCEP/NCAR reanalysis climate data with a topography-based correction of surface air temperature and a radar-based information on snow water equivalent, both model results match in their general pattern (see auxiliary material). They agree in the increase of ALT from north to south as well as the decrease from west to east in Northern Eurasia that is a consequence of a more continental climate in Eastern Siberia. LPJ, however, shows a stronger spatial gradient in the northern taiga. ALT mostly ranges from 25 cm in the north or east up to 100 cm in the south or west. In a few regions in Western Siberia ALT was computed to reach 250 cm. A band of 75–250 cm values as computed by LPJ is situated more to the north than is the case for the Goodrich model, especially in Western Eurasia. In addition, ALT as computed by LPJ is lower in the Far East, with values between 25 and 50 cm. The strong impact of soil parameters on ALT can be seen on the border between different soil texture classes, for example, around 75°N, 95°E.

[31] By comparing the simulated permafrost distribution against the permafrost map by Brown et al. [1998] the general applicability of the frost index by Nelson and Outcalt [1987] can be confirmed (see auxiliary material). However, by setting F in equation (6) to 0.6, which was found to represent the border between discontinuous and sporadic permafrost [Nelson and Outcalt, 1987; Christensen and Kuhry, 2000], it seems that rather the zonation of continuous permafrost was delineated, as can be seen from discrepancies south of the Ob Bay (see auxiliary material). Reasons for this could also be the usage of different climate input data or different formulations for snow properties like density and thermal conductivity.

[32] Given that the discrepancies noted are discrepancies between models, that the comparison with observations is on average satisfactory though with large deviations in particular cases, and that the spatial patterns between the models are mostly similar, we conclude that the model is sufficiently accurate for the present study, which is focused on the changes caused in vegetation by changes permafrost-induced changes in site hydrology. In the absence of more detailed data and considerably more expensive computational effort, both of which are currently unrealistic for the continental scale, we argue that the present model is sufficient for use in a DGVM.

3.2. Impacts of Freeze-Thaw Processes on the Water Balance

[33] Effects of permafrost on the water balance are quantified by analyzing runoff simulated by the LPJ-noP and LPJ-P model runs for a number of large river watersheds (more than 200,000 km2) that are located (partially) within the permafrost zone, and for which discharge measurements over a period of more than 10 years were available [Vörösmarty et al., 1996]. The model results shown in Figure 5 demonstrate the importance of freeze-thaw processes on a continental scale for the water balance in the boreal zone. Without the consideration of these processes (LPJ-noP model) runoff is clearly underestimated in spring by 30–60% because the assumed high available water holding capacity of the soil does not reflect the reality where partly frozen grounds additionally constrain the holding capacity (see section 2.3). By contrast, the enhanced model version (LPJ-P model) leads to runoff results over basins of large Arctic rivers that compares well with the observations in spring. Snow melt and precipitation over a still frozen ground during spring cause the observed runoff peak. This process is represented by the model now.

Figure 5.

Comparison of simulated monthly long-term means of fresh water runoff in Siberian Arctic river basins with observations of river discharge [Vörösmarty et al., 1996] between 1936 and 1984. Simulated runoff values are shifted by 1 month for the two large rivers Yenisey and Lena and by 0.5 months for the mesoscale river basins Yana and Indirgirka since no river routing scheme is embedded in the LPJ-DGVM. LPJ-PIK denotes to results by the original LPJ model [Sitch et al., 2003].

[34] However, simulated runoff remains too low in late summer and autumn. Additional analysis (see auxiliary material) revealed that this is due to an overestimation of the amount of water transpired or intercepted by vegetation in the summer and in autumn, which is a consequence of the DGVM simulating dense forest cover (foliage cover ∼95%) throughout the region when the actual foliage cover is less dense (20–75% following Hansen et al. [2003]) and spatially heterogeneous. Model runs with an improved lower vegetation density showed little change in the spring runoff but correctly increased runoff in autumn, in accordance with observations [Beer et al., 2006].

[35] Despite the analysis of simulated river runoff, which allows for conclusions about impacts of freeze-thaw processes on the water balance at continental scale, an evaluation of simulated soil moisture dynamics at stations located in various ecosystems within the permafrost zone (Figure 6) is required to assess the reliability of the hydrological scheme. During the first days of soil thawing modeled relative water content oscillate highly in contrast to observations. This is due to a very thin layer of thawed soil simulated by LPJ during this period while observations are considered after some centimeter of soil has already thawed. Except for this fact, modeled soil moisture is within the range of standard deviation of different observations at the same site (Figure 6b) and its dynamics is represented by model results (most notably Figure 6a and Figure 6d). As for the thaw depth calculation, highest uncertainty in modeled soil moisture is due to assumed homogenous soil properties with depth. In comparison to mean observations in the first meter of soil, LPJ seems to overestimate soil water content in spring at Bayelva, Svalbard (Figure 6d). The comparison to the observation at 6 cm soil depth, however, clarifies this fact (Figure 6d). During this time period, LPJ results represent only the water content of the upper thin layer that is thawed.

Figure 6.

Dynamics of volumetric soil water content for the year (a–c) 2003 or (d) 1999. The LPJ-P model is driven by local soil texture information and precipitation (except for Figure 6c) for this comparison. Asterisks denote daily precipitation (mm). In Figure 6b, means and standard deviations of four plots are shown. Data were provided by Overduin [2005] (a), A. Rodionov, personal communication, 2006 (Figure 6b), S. Zimov, personal communication, 2006 (Figure 6c), and Roth and Boike [2001] (Figure 6d).

3.3. Consequences for the Fire Return Interval

[36] As can be seen in Figure 7, simulation results of the fire return interval (FRI) notably differ between the both model versions LPJ-P and LPJ-noP. FRI values simulated by LPJ-noP are considerably too long. With the consideration of freeze-thaw processes (LPJ-P) FRI is mostly reduced to values of 100 to 200 years in Northern Eurasia, in accordance with observations [Bonan and Shugart, 1989; Kharuk et al., 2005].

Figure 7.

Mean fire return interval in Northern Eurasia 1993–2003 as simulated by (middle) LPJ-noP and (bottom) LPJ-P. (top) The result by the original LPJ model [Sitch et al., 2003] is shown for comparison.

[37] Shorter FRI values are associated with higher carbon emissions. The LPJ-P model estimates twofold emissions in central Siberia compared to LPJ-noP results, which are up to fourfold in Western Siberia. Values ranges from 25 to 45 gC/m2/a on average between 1993 and 2003.

[38] The reason for this increase in fire activity is as follows. Increased high runoff of water from snow melt and precipitation on the surface of predominantly frozen grounds during spring leads to dry soils. Vegetation contributes to this drying by rapidly taking up water after spring greenup from that thin fraction of the active layer that is already thawed. As a consequence, fire probability and thus fire occurrence in Siberia is highest in spring [Korovin, 1996]. The global fire module embedded in the LPJ-DGVM [Thonicke et al., 2001] uses a statistical approach that does not resolve the interannual variability of fire occurrence, but the demonstrated combined effect of permafrost on runoff (see section 3.2) and FRI emphasizes the strong link between permafrost, water balance and fire probability, a link represented by the model LPJ-P with consequences for the carbon density of standing vegetation.

3.4. Permafrost Effects on Growth

[39] In permafrost regions, a large amount of water is lost as runoff during spring (section 3.2) and is hence not available for vegetation later in the growing season. In addition, soil water content is constrained by thaw depth during early summer [Benninghoff, 1952]. Besides the effects of soil moisture on fire, water availability limits NPP in a twofold manner. As a fast process, GPP is constrained by stomatal conductance [Farquhar et al., 1980]. On the other hand, water availability influences the allocation of carbon to the components of plants [Bongarten and Teskey, 1987; Mencuccini and Grace, 1995] leading to lower leaf area under drier conditions [Grier and Running, 1977; Gholz, 1982].

[40] To differentiate between these effects, two additional simulations excluding fire, LPJ-noFire, or excluding water-stress impacts on carbon allocation (equation (4)), LPJ-noStress, are compared to LPJ-noP and LPJ-P for a larch-dominated permafrost area around 68.25°N and 120.25°E in the first 300 simulation years (Figure 8). LPJ-P results of leaf area index (LAI), foliage projected cover (FPC), NPP and finally VCD are lowest and best comparable to independent estimates. Without the consideration of permafrost, these parameters acquire considerably higher (unrealistic) values. LPJ-noFire results in only slightly higher values than LPJ-P during the first 100 simulation years, revealing that the direct impacts of fires are not the dominant effect.

Figure 8.

Ecosystem variables in the region around 68.25°N, 120.25°E (larch forest, northern taiga) for the first 300 simulation years. Simulation results are with (LPJ-P) or without (LPJ-noP) the consideration of freeze-thaw processes, or with it but without the consideration of fire (LPJ-P-noFire) or water stress (LPJ-P-noStress). Independent estimates are shown in grey and are from (a) Schulze et al. [1999], (b) Hansen et al. [2003], and (c, d) Stolbovoi and McCallum [2002]. The width of grey bars indicates the range of observations (Figure 8a), the standard deviation (Figure 8b), and the reported uncertainty range [see Shvidenko et al., 2001] of forest inventory estimates (Figures 8c and 8d).

[41] By contrast, LPJ-noStress shows higher values than LPJ-P already after about 50 years since no extra investment in roots is assumed (equation (4)). This illustrates the impact of higher carbon allocation to roots on LAI and NPP, thus VCD in permafrost regions. However, LPJ-noStress still produces lower LAI, NPP and VCD than LPJ-noP (Figure 8). This is due to the fact that lower water availability also directly impacts productivity [e.g., Gerten et al., 2005].

[42] During the simulation period, fire also has a direct impact on VCD, as can be seen by the comparison of LPJ-P and LPJ-noFire results in Figure 8d. However, the results shown in Figure 8 also demonstrate the impact of water availability on growth through canopy conductance and carbon allocation to roots. For the same location, Figure 9 clarifies the impact of plant available soil water content (SWC) on NPP that is mediated by thaw depth. It shows LPJ-noP and LPJ-P simulation results of NPP, thaw depth and SWC on a daily time step for the simulation year 50. Without the representation of freeze-thaw processes, a very deep and saturated soil would be assumed with SWC of about 200 mm throughout the whole year. By contrast, SWC is constrained by thaw depth in the LPJ-P experiment leading to reduced NPP. In addition, the timing of the first day with photosynthesis is altered forward in time.

Figure 9.

Daily estimates of woody NPP simulated by the LPJ-noP and LPJ-P models, thaw depth simulated by LPJ-P, and soil water content (Wplant) simulated by LPJ-noP and LPJ-P around 68.25°N, 120.25°E (larch forest, northern taiga) in 2003.

3.5. Vegetation Carbon Density

[43] Simulated VCD approximates inventory estimates much better in the LPJ-P simulation as compared to LPJ-noP (Figure 10a), where the deviation is considerable. VCD is estimated by LPJ-P to range between 6 and 7 kgC/m2 south of 63°N decreasing to 1–2 kgC/m2 north of 69°N. Freeze-thaw processes account for a 1.5 to 2.5-fold reduction in VCD in the South (Figure 10b) which increased with latitude to a fivefold reduction in high latitudes. This demonstrates the impact of freeze-thaw processes on biomass through disturbances and water availability. Soil thermal dynamics are primary processes of boreal ecosystems that cannot be neglected, as has commonly been the case, in DGVM simulations. Besides the fact that VCD is lower in magnitude, its changeover between 60 and 70°E is smoother in LPJ-P (Figure 10a) than in LPJ-noP owing to the effect of a decreasing ALT on biomass [Bonan, 1989].

Figure 10.

(a) Vegetation carbon density as a function of latitude in the SIBERIA-II study transect swath in 2003 as derived by the land-ecosystem inventory approach of the International Institute for Applied Systems Analysis, Austria [Nilsson et al., 2005], and as simulated by LPJ-noP and LPJ-P. LPJ-PIK denotes to the result by the original LPJ model [Sitch et al., 2003]. (b) Ratio between LPJ-noP and LPJ-P results shown in Figure 10a.

4. Discussion

[44] Freeze-thaw processes strongly codetermine the seasonality of soil moisture in the boreal zone, particularly in the permafrost zone, where the thermal dynamics in the active layer are significantly altered. Owing to the intrinsic physiological coupling of the carbon and water cycles in vegetation, with an additional important soil moisture feedback on vegetation mediated through fire, the most important disturbance in the boreal forest, freeze-thaw processes cannot be neglected in process-based analyzes of the continental-scale carbon and water balance at boreal latitudes.

[45] The permafrost module we therefore implemented into the LPJ-DGVM reflects the general seasonal cycle of thawing depth and the large-scale spatial patterns of its maximum. We are aware of the limited significance of comparing simulated thaw depth based on 0.5° × 0.5° averages of climate and soil data with observations at much smaller scales (1 ha or 1 km2) because of the spatially highly heterogeneous nature of thaw depth processes, which are influenced by variations in soil properties, slope and slope aspect [Washburn, 1979; Nelson et al., 1997]. Nonetheless, such a comparison allows to demonstrate the reliability of computed first-order patterns. Our evaluation of simulated thaw depth seasonality and spatial patterns of ALT with observations and the results of a recent permafrost model, the Goodrich model, shows the capability of our method to generically represent freeze-thaw processes. Discrepancies to observations are within the range of variability of thaw depth that occurs naturally on a much smaller spatial scale than applied in this study. A limitation of the method applied is the assumption of homogenous soil properties with depth, a limitation that equally applies to all other large-scale permafrost models. The comparison of results obtained from different approaches is a problem that can currently not be resolved satisfactorily and places the strongest constraints on our current ability to demonstrate and validate results.

[46] It should be noted that the current snow module embedded into the LPJ model does not take into account vegetation effects on solar radiation extinction and atmospheric turbulence which are a far greater influence on snow cover than interception. This could lead to an underestimation of snow depth in forest areas with effects on the soil thermal regime. In addition to this, DGVMs do not resolve subgrid localizations of spatial heterogeneity hence are not able to represent discontinuous permafrost. We acknowledge that the greatest change in permafrost is likely to happen in this zone. Further, regional-scale studies are thus desirable to explore these effects in more detail.

[47] Given the permafrost module, our evaluation of runoff, fire return interval and VCD as computed in response to its dynamics demonstrates the importance of freeze-thaw processes for the functioning of boreal ecosystems. High runoff of snow melt and precipitation on a still frozen ground in spring subsequently leads to dry soil conditions. In permafrost regions, this effect is amplified by the fact that vegetation is able to draw water only from the shallow fraction of the active layer which had been already thawed in spring and early summer. The ensuing low soil water content, which rapidly translates into low litter moisture, strongly increases fire probability reducing the fire return interval. Our model results show that freeze-thaw processes and the related site hydrology are essential for realistic fire disturbance modeling on a continental scale in the boreal zone. The actual area burned, and its spatial and temporal variability, however, depend on additional factors such as ignition, tree allometry and structure, and litter quality. In addition to disturbances, NPP is the main determinant of biomass as it quantifies vegetation growth and background mortality. The relatively low soil water availability during the boreal summer causes low values of NPP, hence plant growth depressed owing to low LAI and canopy conductance in permafrost regions. This remains the case despite various adaptations of boreal vegetation to permafrost conditions, represented in our model, such as a rapid greenup and shallow rooting systems. Even if the resulting low foliage cover would be partly compensated on the ecosystem level by more seedlings during the time, stem density increased, also contributing to lower VCD values. Our results support the fact that plant growth in the northern taiga is limited by water availability in addition to other factors, such as nitrogen uptake.

[48] The dynamic ecosystem model still simulates too high VCD north of 60°N, where values may be up to twice as large as independent estimates produced by IIASA from forest inventory data (Figure 10). A number of limitations to tree establishment and growth in high latitudes that are not represented in the simulation model are a likely explanation for this overestimation, for example temperature dependence of cell division [Körner, 2003] or of seed quality and quantity [Black and Bliss, 1980; Sveinbjörnsson et al., 2002], limitations by nutrient availability [Bonan and Shugart, 1989] or limitation of tree establishment by standing water on the permafrost [Sveinbjörnsson et al., 2002]. In the boreal zone, a considerable amount of heterotrophic respiration was detected during winter [Monson et al., 2006] with consequences on nitrogen mineralization, hence nitrogen availability during the growing season. This positive effect of winter soil respiration on productivity is not considered in this study but LPJ even assumes no limitation of nitrogen uptake. Future modeling studies which aim at clarifying the nutrient limitation of VCD will have to consider this positive effect of winter soil respiration on nutrient availability.

[49] However, our results clearly point out that freeze-thaw processes are of first order and should not be neglected in a process-based modeling framework. The increase of ALT and the disappearance of permafrost under warming conditions [Anisimov and Nelson, 1997; Stendel and Christensen, 2002] will lead to a substantial alteration of the site hydrology [Kane et al., 1992]. The consideration of the most important consequences of freeze-thaw processes on the hydrological regime in high latitudes by the LPJ-P model give us an opportunity to arrive at more reliable estimates of the future dynamics of the global carbon and water balances, which are both strongly influenced by the high latitudes. Uncertainties exist with respect to the dynamics, for example, of peat, cryoturbation, methanogenesis and methanotropy, of which large-scale observations are not currently available. In addition, the impact of fire on nutrient availability should be considered for projections of the future carbon balance in the boreal zone. Furthermore, feedbacks between changing vegetation coverage and climate through albedo and carbon and water fluxes can be large [e.g., Betts, 2000; Chapin et al., 2005]. Nonetheless, the inclusion of freeze-thaw processes is a significant step toward a more comprehensive understanding of ecosystem processes in the boreal zone and Arctic tundra. Our freeze-thaw algorithm of medium complexity also is suitable for application in the land surface scheme of a General Circulation Model since additional computation cost is minimal.

5. Conclusion

[50] The combined evaluation of river runoff, fire return interval and vegetation carbon density, as simulated by the permafrost-enhanced LPJ-DGVM, demonstrates that both limitation in water availability during the growing season and the disturbance dynamics of boreal vegetation substantially contribute to the low vegetation carbon density observed in permafrost regions of the boreal zone. This supports the conclusion that the availability of liquid water and soil moisture seasonality are also major limitations of biomass within the permafrost zone, in addition to temperature and nitrogen availability. Since moisture dynamics are strongly determined by the thermal dynamics in these regions, freeze-thaw processes are found to be ecosystem processes of first order. This conclusion demonstrates the potential of vegetation in high latitudes to sequester more carbon than it currently does owing to warming that leads to increasing ALT or even the complete disappearance of permafrost.

Acknowledgments

[51] The authors thank B. Smith, S. Schaphoff, and S. Sitch for support regarding the LPJ-DGVM code, and M. Fink, D. Knorr, W. Cramer, V. Romanovsky, D. Riseborough, O. Anisimov, and P. Ciais for valuable notes. A. Shvidenko and I. McCallum from the International Institute for Applied Systems Analysis, Austria made the forest inventory data available. We are grateful to G. and S. Zimov who provided seasonal thaw depth and soil moisture measurements, to P. Overduin, A. Rodionov, and J. Boike, who provided seasonal soil moisture observations, and to all members of the SIBERIA-II consortium for discussions. Financial support was provided by the European Commission under the SIBERIA-II project, contract EVG2-2001-00008. W. L. and D. G. were funded by the German Ministry of Education and Research under the DEKLIM project CVECA. We thank Nigel Roulet and an anonymous reviewer for helpful comments on an earlier manuscript of this paper.

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