Effects of Soil Freezing and Thawing on Vegetation Carbon Density in Siberia

GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 21, GB1012, doi:10.1029/2006GB002760, 2007 Click Here for Full Article

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) C. Beer,1,2 W. Lucht,3 D. Gerten,3 K. Thonicke,4 and C. Schmullius1 Received 18 May 2006; revised 8 November 2006; accepted 16 November 2006; published 20 February 2007.

[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. Citation: Beer, C., W. Lucht, D. Gerten, K. Thonicke, and C. Schmullius (2007), 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), Global Biogeochem. Cycles, 21, GB1012, doi:10.1029/2006GB002760.

1. Introduction respired from carbon that is currently blocked in permanently frozen grounds [Goulden et al., 1998]. [2] High-latitude ecosystems currently experience various [3] The ability of increasing carbon uptake by expanding changes due to warming, for example, increasing net forests to partly counteract these carbon emissions is un- primary production (NPP) [Lucht et al., 2002; Nemani et clear. To assess a potential carbon uptake by vegetation in al., 2003] that is attended by northward migration of the tree the high latitudes, an understanding of the environmental line [Serreze et al., 2000; Esper and Schweingruber, 2004], factors and processes limiting the current amount of bio- but also increasing heterotrophic soil respiration [Oechel et mass as well as their relative importance is required. The al., 1993; Zimov et al., 1993]. Owing to rising temperature, observed gradient in vegetation carbon density (VCD) from a large fraction of the current extend of permafrost soils of 0.6 kg carbon per m2 (kgC/m2) in the Russian tundra to about half of the territories of Canada and the former Soviet 3.6 kgC/m2 in the southern taiga [Shvidenko et al., 2000] Union [Bonan and Shugart, 1989] is assumed to degrade suggests a climatic driven limitation of biomass in northern until 2100 [Anisimov and Nelson, 1997; Stendel and taiga and tundra. The relationship of biomass to the depth of Christensen, 2002]. This accompanied a risk of an addi- the active layer [Bonan, 1989], which is the uppermost part tional radiative forcing of the climate system by CO2 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 1Institute of Geography, Friedrich Schiller University, Jena, Germany. 2Now at Max Planck Institute for Biogeochemistry, Jena, Germany. radiation and supply of nutrient and water are reported to 3Potsdam Institute for Climate Impact Research, Potsdam, Germany. limit generally the establishment and survive of seedlings, 4School of Geographical Sciences, University of Bristol, Bristol, UK. carbon assimilation and plant growth [e.g., Bonan and Shugart, 1989; Woodward, 1995; Ko¨rner, 2003]. However, Copyright 2007 by the American Geophysical Union. 0886-6236/07/2006GB002760$12.00 Briffa et al. [1998] found that tree growth was less sensitive

GB1012 1of14 GB1012 BEER ET AL.: FREEZE-THAW EFFECTS ON BIOMASS GB1012 to increasing temperature during the last decades, and that resentations of terrestrial vegetation dynamics and land- this could not be explained readily from soil moisture data. atmosphere carbon and water exchanges in a modular In addition, disturbances like fire and damage by insects or framework. For a detailed description and evaluation of wind had the capability to counteract an increasing produc- the model see Sitch et al. [2003]. LPJ explicitly considers tivity due to warming. Fire is the major disturbance agent in key ecosystem processes such as photosynthesis, carbon the boreal zone, where between 3 and 23 million ha are allocation, mortality, resource competition, fire disturbance burned annually mainly as crown fires in boreal North and soil heterotrophic respiration. To account for the variety America, but apart from extreme fire years as surface fires of structure and functioning among plants, four plant in boreal Eurasia [Kasischke et al., 2005], thereby releasing functional types (PFTs) are distinguished for the boreal approximately 106–209 TgC/a. zone (see auxiliary material1). Gross primary production is [4] The processes controlling biomass take place in par- calculated on the basis of a coupled photosynthesis-water allel or are even coupled, especially in regions with perma- balance scheme after Farquhar et al. [1980] with leaf-level nently frozen grounds. For instance, low temperatures optimized nitrogen allocation [Haxeltine and Prentice, during the growing season limit not only photosynthesis 1996]. The underlying model of light use efficiency but also active-layer thickness (ALT), which impacts water assumes no limitation of nitrogen uptake [Haxeltine and availability [Benninghoff, 1952]. Both effects of tempera- Prentice, 1996], thus no nitrogen cycle is represented in the ture impact carbon assimilation by the vegetation. More- model. Fire disturbance is mainly driven by litter moisture over, low temperatures lead to slow litter decomposition, and dead fuel load as well as PFT specific fire resistances hence nutrient limitation [Bonan and Shugart, 1989]. The Thonicke et al. [2001]. deep litter layer also isolates the underlying soil leading to a [7] The LPJ-DGVM used in this study has been inten- low thaw depth [Hinzman et al., 1991], thus low water sively validated against observations of key ecosystem availability in summer again. In addition, litter moisture parameters on different scales including land-atmosphere determines the probability of fire. Such linkages make it CO2 exchange [McGuire et al., 2001; Sitch et al., 2003; impossible to separate the even most important effects of Peylin et al., 2005], dominant vegetation distribution environmental influences on the amount of biomass by field [Cramer et al., 2001; Sitch et al., 2003; Beer, 2005], fluxes studies alone, in particular because biomass is the result of of water [Gerten et al., 2004, 2005], global patterns of soil different processes over decades. Experiments and observa- moisture [Wagner et al., 2003], NPP enhancement due to tions are critical to providing the inputs and functions for carbon fertilization [Gerten et al., 2005], and satellite- models, but it would probably be impossible to set up an observed northern latitude leaf area index anomalies [Lucht experiment to answer the questions posed here. et al., 2002]. 2 [5] In this paper, we investigate the effects of the alter- [8] In this study specific leaf area (SLA,inm/kg) is ations in the soil water balance due to freeze-thaw processes related to leaf longevity (aleaf, in years) after Reich et al. in the soil on the amount of VCD in permafrost-dominated [1997] as follows: regions of Siberia. The impact of soil water content which is controlled by phase change on the productivity and fire SLA ¼ 0:22 Á exp 5:6 À 0:46 Á ln aleaf Á 12 : ð1Þ return interval is studied. For this purpose, we perform two Moreover, parameters of boreal vegetation are slightly simulation experiments with the LPJ-DGVM, in which adjusted in this study (see auxiliary material). With these freeze-thaw processes are excluded in the control run. Both adjustments, a more valid distribution of dominant woody simulations are driven by the same observations of climatic vegetation in Siberia is achieved [Beer, 2005]. variables and atmospheric CO2 content. The comparison of VCD results by these simulation experiments with forest 2.2. Input Data and Modeling Protocol inventory estimates over a large transect swath in central [9] LPJ was driven by gridded monthly fields of air Siberia allows for the investigation of the working hypoth- temperature, precipitation, number of rainy days, and cloud esis that besides direct influences of temperature, solar cover at a 0.5° pixel size. This station-based data set was radiation and nutrient availability on the productivity, also provided by the Climatic Research Unit (CRU) of the limitations in liquid soil water availability during the year University of East Anglia, UK [Mitchell and Jones, 2005] and the disturbance dynamics substantially contribute to but revised and extended by the Potsdam Institute for low VCD values in high-latitude permafrost regions, i.e., Climate Impact Research (PIK), Germany [Oesterle et al., that soil thermal dynamics are crucial for the changeover of 2003]. As a nongridded global input, annual CO2 concen- VCD from taiga to tundra. In order to isolate better the trations were used that were derived from ice-core measure- influence of fire disturbance and water stress, the results of ments and atmospheric observations, provided by the these experiments are further compared to results of two Carbon Cycle Model Linkage Project [McGuire et al., LPJ-P model runs excluding the simulation of fire and water 2001]. In addition, eight soil texture classes (see auxiliary stress. material) were used which are also available globally on a 0.5° grid [Food and Agriculture Organization, 1991]. LPJ 2. Methods was run from 1901 to 2003 with a pseudo-daily time step, 2.1. LPJ-DGVM Overview preceded by a 1000-year spinup period that brought the [6] The LPJ-DGVM is a coupled dynamic biogeography- biogeochemistry model which combines process-based rep- 1Auxiliary material data sets are available at ftp://ftp.agu.org/apend/gb/ 2006gb002760. Other auxiliary material files are in the HTML.

2of14 GB1012 BEER ET AL.: FREEZE-THAW EFFECTS ON BIOMASS GB1012 carbon pools and vegetation cover in equilibrium with [15] 2. Water is only allowed to percolate through a soil climate. layer if it is completely thawed. This will increase soil [10] Two model experiments were performed for the land moisture of the upper layer after precipitation if the transi- area of Northern Eurasia. In both, the same model version is tion to the underlying layer remains frozen. used but (1) with (LPJ-P) and (2) without permafrost (LPJ- [16] 3. In addition to w a new vector stores frozen water noP). In order to isolate the influence of fire disturbance and for both layers also as a fraction of plant available soil water stress, the results of these experiments are further water. Thus precipitation in autumn is conserved until the compared to results of LPJ-P model runs excluding fire next spring. (LPJ-noFire), and excluding water stress (LPJ-noStress). [17] Water supply to plants is the product of the plant root-weighted soil moisture availability and a maximum 2.3. Soil Water Balance transpiration rate [Sitch et al., 2003]. The fractions of fine [11] Soil hydrology is modeled as described by Sitch et al. roots in the upper and lower soil layer, z1 and z2 are [2003] and Gerten et al. [2004] following a semi-empirical optimized daily for boreal needleleaved summergreen veg- approach. There are two soil layers of constant depth etation (BoNS) as a function of mm thawed water (w1, (0.5 and 1 m) and spatially varying soil texture. Plant upper layer; w2, lower layer) to give BoNS the opportunity available soil moisture w is expressed as the fraction of to access as much water from both layers as possible, available water holding capacity Wplant, which is defined as À1 the difference between volumetric soil water content at field z1 ¼ w1ðÞw1 þ w2 ; capacity and permanent wilting point Wpwp. For calculating z2 ¼ 1 À z1 soil thermal properties, the whole water content is required, while z and z are set to 0 if w + w = 0. This mechanism hence W is added to W (see auxiliary material). 1 2 1 2 pwp plant gives BoNS an advantage over other woody PFTs in [12] The water content of each layer is updated daily by permafrost regions by allowing them to better manage taking into account rainfall, snowmelt, canopy interception droughts (e.g., in East Siberia). Larix gmelinii uses soil of precipitation, percolation, soil evaporation, transpiration water of the upper 15 cm under wet conditions, while roots and runoff. Monthly values of precipitation, P are distrib- observed at 30–70 cm depth are possibly essential for the uted stochastically to provide pseudo-daily values. Below a uptake of thawed water during drought [Sugimoto et al., threshold value of 0°C precipitation is defined as snow. 2002]. Snowmelt (M, mm/d) is calculated using an index equation [18] Links between site hydrology and the carbon cycle [Choudhury and DiGirolamo, 1998]: are simulated by the LPJ-DGVM on several timescales. The fast processes, photosynthesis and transpiration are coupled M ¼ ðÞÁ1:5 þ 0:007 Á P Ta; ð2Þ by the dynamics of canopy conductance after Farquhar et al. [1980], which strongly depend on soil moisture [Gerten where Ta is the air temperature above 0°C. Percolation rate et al., 2005]. On a longer timescale, moisture-related adjust- ( p, mm/d) through the soil layers depends on soil texture ments in the allocation of carbon to different plant compo- and layer thickness, nents [Bongarten and Teskey, 1987; Mencuccini and Grace,

p ¼ kperc Á w1; ð3Þ 1995] lead to moisture-dependent leaf area [Grier and Running, 1977; Gholz, 1982]. The ratio between atmo- where w1 is the plant available soil moisture of the upper spheric demand and supply of water (water stress, w)is layer and kperc the texture-dependent conductivity, varying modeled to influence the carbon allocation between leaves from 5 mm/d for sandy soils to 0.2 mm/d for vertisols. and fine roots [Sitch et al., 2003], Surface runoff and drainage are calculated as the excess water above field capacity in both soil layers [Gerten et al., cleaf ¼ w Á croot: ð4Þ 2004]; that is, all water above field capacity that does not percolate to the respective underlying layer will run off. 2.4. Fire [13] Under conditions of permafrost, the water balance is altered as follows. [19] A full description of the LPJ fire module Glob-FIRM is given by Thonicke et al. [2001]. The probability p of a [14] 1. Thaw depth within the active layer changes dynamically for each simulation day. W is adjusted fire increases with decreasing litter moisture content as plant described by daily in accordance, which directly influences runoff gen- eration and water availability. For instance, if only a very À1 pðÞ¼w exp ÀpwÁ we ; ð5Þ thin part of the soil is already thawed in spring, the storage capacity of the soil will be small hence most of snow melt w is the PFT-weighted moisture of extinction, above which or rainfall will run off. Lateral flow of water, which may litter cannot ignite, because all the ignition energy is play an important role in permafrost regions, is not consid- consumed to vaporize the water it contains [see Sitch et al., ered in the present model. This may be a shortcoming for 2003, Table 3]. The fire season s is calculated as the annual small-scale investigations, but implies that lateral flows are arithmetic mean of the daily fire probability. A constant canceled out at the large scale considered here (0.5°), which nonlinear relationship between s and annual area burned A, is not an unrealistic assumption. Lateral flow among grid assuming that the fractional area burned increases the longer cells, however, will be enabled in a forthcoming version of the burning conditions persist, is applied [Thonicke et al., the LPJ model (Stefanie Jachner, unpublished data, 2006). 2001]. Fire effects are described by fire resistances, which

3of14 GB1012 BEER ET AL.: FREEZE-THAW EFFECTS ON BIOMASS GB1012 combine fire intensity and fire severity in a PFT-specific where Tave is the mean annual temperature above the parameter (see auxiliary material). respective layer andpffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiA is the amplitude of this temperature fluctuation. d = 2k Á WÀ1 represents the damping depth 2.5. Freeze-Thaw Processes in the Active Layer Over with thermal diffusivity k = l Á cÀ1, where c is volumetric Permanently Frozen Ground heat capacity and l thermal conductivity. W =2p Á tÀ1 [20] In a first step we determine whether a given geo- represents the angular frequency of oscillation with graphic location is characterized by permanently frozen oscillation period t. The method of solving equation (7) ground. Permafrost status is applied to the whole of a grid for each simulation day is described in the appendix of cell, neglecting discontinuous permafrost, since DGVMs do [Sitch et al., 2003]. The concept of a running mean is not resolve subgrid localizations of spatial heterogeneity applied for calculating Tave; that is, the model is not required (this is a tradeoff with their continental-scale applicability in to have knowledge of future climate. Snow cover and a the absence of detailed spatial data). Surface temperature near-surface carbon layers (aboveground litter and grass below snow and litter (including near-surface carbon, e.g., carbon density) are considered to be above the soil in this of grass) is calculated by treating snow and vegetation study. The temperature above snow corresponds to the air layers separately. If a particular grid cell is determined to temperature. Equation 7 is applied to calculate the following be located in a permafrost region, thaw depth is computed temperatures for each simulation day step by step: by using this altered surface temperature. The simulation of temperature below snow, temperature below near-surface hydrological processes follows as described in section 2.3. carbon, and temperature in 0.25 m soil depth. Each of these 2.5.1. Permafrost Distribution results is taken as input for calculating the subsequent value. [21] Spatial permafrost distribution is modeled at annual Mean depth of snow and near-surface carbon layers of the time steps using the normalized frost index F [Nelson and last 31 days as well as their mean thermal diffusivity are Outcalt, 1987], updated daily (see auxiliary material). [24] Once temperature below snow or near-surface carbon pffiffiffiffiffiffi pffiffiffiffiffi pffiffiffiffiffiffi À1 layers is estimated, soil temperature in 0.25 m depth and F ¼ Df Dt þ Df : ð6Þ thaw depth can be calculated in parallel. In doing so, soil volumetric heat capacity c and thermal conductivity l are Its reliability can be assumed to also hold in a changing required for each day, which are derived from mean climate [Anisimov and Nelson, 1997; Christensen and volumetric water content of the thawed fraction of the soil Kuhry, 2000; Stendel and Christensen, 2002]. Df and Dt of the last 31 days, w, after Campbell and Norman [2000]. represent the annual freezing and thawing degree-days (sum The nonlinear relationship l = l(w) is approximated line- of pseudo-daily temperatures that are derived from inter- arly for w 2 [0..0.1), w 2 [0.1..0.25) and w 2 [0.25..1), polated monthly data). In case of snow coverage the respectively. While a phase change the latent heat Q is temperature below snow is used for the calculation of Dt considered as apparent heat capacity [Jumikis, 1966] in the and Df (see section 2.5.2). Thus vegetation has an impact on exponential damping term of equation (7), the calculation of the frost index since canopy interception of snow is taken into account in LPJ [Gerten et al., 2004]. Q ¼ k Á wthaw Á Qw: ð8Þ

Values of F that represent the threshold boundaries of wthaw represents the amount of thawed soil water content 3 continuous, discontinuous and sporadic permafrost are 0.67, averaged over the last 31 days and Qw = 334.94 kJ/m is the 0.6 and 0.5, respectively [Nelson and Outcalt, 1987; heat of fusion of water. We approximate k to 0.5 by Christensen and Kuhry, 2000]. In this study F = 0.6 is assuming a nearly linear change of the 0°C isotherm during applied to distinguish between permafrost and nonperma- the short interval of a 1-day time step. frost situations. [25] Figure 1 summarizes the scheme of the thaw depth 2.5.2. Daily Thaw Depth module and visualizes feedbacks of vegetation on thaw [22] In this work ‘thaw depth’ represents the soil depth z depth through soil moisture and insulating snow and near- of the 0°C isotherm. We assume only one isotherm instead surface carbon layers. Thaw depth is finally computed by of two phase interfaces in autumn since the effects of the finding the null of equation (7) numerically via Newton’s precise discrimination of the location of the interfaces method for each simulation day, between the two soil layers on the carbon fluxes are small 0 À1 while air temperature is below the freezing point. In other znþ1 ¼ zn þ TzðÞn; t ðÞT ðÞzn; t : ð9Þ words, in autumn our variable ‘thaw depth’ corresponds to If the calculated thaw depth indicates a thawing or freezing the thawed water column length. During the growing season event, the associated soil water will be shifted between the when air temperature is above 0°C simulated thaw depth separate pools for water and ice for each layer, and a new matches the definition used for the temperature-measured value for soil moisture is derived by calculating the site variable in the field. hydrology. In doing so, soil thermal properties of the next [23] Soil temperature in °C is assumed to follow an simulation day are influenced. annual sinusoidal cycle of air temperature with a damped oscillation [Campbell and Norman, 2000], 2.6. Freeze-Thaw Processes Outside the Permafrost Zone TzðÞ¼; t T þ A Á exp Àz Á dÀ1 sin W Á Dt À z Á dÀ1 ; ð7Þ ave [26] Outside of the permafrost zone (determined as described in section 2.5.1) the soil freezes during winter

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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 under- estimates 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 under- estimation 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 Moni- toring (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% dis- crepancy to the observations, the coefficient of determina- Figure 1. Principal steps in calculating daily thaw depth in tion and root mean square error are 0.3 and 21 cm, LPJ. 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 to a certain depth and thaws in spring at two phase interfaces by coarse climate and soil data are considered reasonable, until the whole soil column is thawed. In contrast to the thaw particularly as this study is focused not on the details of depth over permafrost soils during summer, the winter frost permafrost dynamics but on general effects of permafrost on depth has less of an importance for the vegetation function- the vegetation growing on permafrost soils. ing in the boreal zone. Trees are hardly able to make use of [29] In addition to the seasonal cycle of thaw depth and its water from deeper soils while the xylem is frozen in winter maximum, Figure 4 provides an evaluation of the interan- [Woodward, 1995]. In addition, photosynthesis can be nual variability of ALT at two stations in Alaska and two in neglected while temperature is clearly below the freezing Russia. As can be seen, changes in simulated ALT from point. Thus we do not attempt to estimate frost depth during year to year correspond well to observations. This demon- winter but consider the whole of the soil column to be frozen strates the ability of the algorithm to simulate the impacts of when the temperature (as calculated by equation (7)) falls air temperature, soil moisture and insulations on soil tem- below 0°C in 10 cm depth, following Zhuang et al. [2003]. perature, which act on a daily basis or even finer temporal In doing so, we make sure that snow melt water in spring resolution but are here recovered from a coarser resolution does not infiltrate immediately into the soil, that is, the water model. pool available to vegetation. [30] Spatial details of simulated ALT within permafrost areas are shown in the auxiliary material. Continental-scale 2.7. Study Region for Evaluation of Vegetation observations of ALT are not available, hence spatial pattern Carbon Density of ALT are compared to results from a state-of-the-art [27] Simulated VCD values are compared to a recently freeze-thaw model, the Goodrich model [Oelke et al., updated inventory-based VCD data from an ecosystem 2003]. Although, in contrast to LPJ, this model is driven information system on a 1:1 million polygon basis, that by NCEP/NCAR reanalysis climate data with a topography- was made available by the International Institute for Applied based correction of surface air temperature and a radar- Systems Analysis (IIASA), Laxenburg, Austria. It covers a based information on snow water equivalent, both model 3 million km2 broad transect swath through central Siberia results match in their general pattern (see auxiliary material). reaching from the Arctic Ocean to the southern steppe and They agree in the increase of ALT from north to south as the Yenisey river in the West to Lake Baikal in the east well as the decrease from west to east in Northern Eurasia (approx. 79°E–119°E, 51°N–78°N). This ecosystem data that is a consequence of a more continental climate in base [Nilsson et al., 2005] was created as part of the Eastern Siberia. LPJ, however, shows a stronger spatial European Commission’s SIBERIA-II project [Schmullius gradient in the northern taiga. ALT mostly ranges from and Hese, 2002]. 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 3. Results by LPJ is situated more to the north than is the case for the 3.1. Permafrost Model Evaluation Goodrich model, especially in Western Eurasia. In addition, [28] For site-specific evaluation, LPJ was run with the soil ALT as computed by LPJ is lower in the Far East, with textures of the observation points. Simulated daily thaw values between 25 and 50 cm. The strong impact of soil depth is compared to seasonal thaw depth at four stations in parameters on ALT can be seen on the border between Alaska from 1995 until 1997 [Nelson, 1996] and one site in different soil texture classes, for example, around 75°N, the Siberian Far East from 1981 until 1985 (S. Zimov, Thaw 95°E.

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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).

[31] By comparing the simulated permafrost distribution Reasons for this could also be the usage of different climate against the permafrost map by Brown et al. [1998] the input data or different formulations for snow properties like general applicability of the frost index by Nelson and density and thermal conductivity. Outcalt [1987] can be confirmed (see auxiliary material). [32] Given that the discrepancies noted are discrepancies However, by setting F in equation (6) to 0.6, which was between models, that the comparison with observations is found to represent the border between discontinuous and on average satisfactory though with large deviations in sporadic permafrost [Nelson and Outcalt, 1987; Christensen particular cases, and that the spatial patterns between the and Kuhry, 2000], it seems that rather the zonation of models are mostly similar, we conclude that the model is continuous permafrost was delineated, as can be seen from sufficiently accurate for the present study, which is focused discrepancies south of the Ob Bay (see auxiliary material). on the changes caused in vegetation by changes permafrost-

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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 water- sheds (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 [Vo¨ro¨smarty 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 Figure 3. Comparison of simulated maximum thaw depth boreal zone. Without the consideration of these processes with observations at 29 stations from Alaska, Canada, and (LPJ-noP model) runoff is clearly underestimated in spring Russia from 1992 until 2002 [Brown et al., 2003]. by 30–60% because the assumed high available water Observations represent mean values of measurements holding capacity of the soil does not reflect the reality where within 100 m2 to 1 km2 large areas with standard deviations partly frozen grounds additionally constrain the holding in the range of 5–20 cm [Brown et al., 2003]. 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 4. Time series of active-layer thickness anomalies at four CALM [Brown et al., 2003] stations in comparison to LPJ-P model results.

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Figure 5. Comparison of simulated monthly long-term means of fresh water runoff in Siberian Arctic river basins with observations of river discharge [Vo¨ro¨smarty 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 model results (most notably Figure 6a and Figure 6d). As summer and autumn. Additional analysis (see auxiliary for the thaw depth calculation, highest uncertainty in material) revealed that this is due to an overestimation of modeled soil moisture is due to assumed homogenous soil the amount of water transpired or intercepted by vegetation properties with depth. In comparison to mean observations in the summer and in autumn, which is a consequence of the in the first meter of soil, LPJ seems to overestimate soil DGVM simulating dense forest cover (foliage cover 95%) water content in spring at Bayelva, Svalbard (Figure 6d). throughout the region when the actual foliage cover is less The comparison to the observation at 6 cm soil depth, dense (20–75% following Hansen et al. [2003]) and however, clarifies this fact (Figure 6d). During this time spatially heterogeneous. Model runs with an improved period, LPJ results represent only the water content of the lower vegetation density showed little change in the spring upper thin layer that is thawed. runoff but correctly increased runoff in autumn, in accordance with observations [Beer et al., 2006]. 3.3. Consequences for the Fire Return Interval [35] Despite the analysis of simulated river runoff, which [36] As can be seen in Figure 7, simulation results of the allows for conclusions about impacts of freeze-thaw pro- fire return interval (FRI) notably differ between the both cesses on the water balance at continental scale, an evalu- model versions LPJ-P and LPJ-noP. FRI values simulated ation of simulated soil moisture dynamics at stations located by LPJ-noP are considerably too long. With the consider- in various ecosystems within the permafrost zone (Figure 6) ation of freeze-thaw processes (LPJ-P) FRI is mostly is required to assess the reliability of the hydrological reduced to values of 100 to 200 years in Northern Eurasia, scheme. During the first days of soil thawing modeled in accordance with observations [Bonan and Shugart, 1989; relative water content oscillate highly in contrast to obser- Kharuk et al., 2005]. vations. This is due to a very thin layer of thawed soil [37] Shorter FRI values are associated with higher carbon simulated by LPJ during this period while observations are emissions. The LPJ-P model estimates twofold emissions in considered after some centimeter of soil has already thawed. central Siberia compared to LPJ-noP results, which are up Except for this fact, modeled soil moisture is within the to fourfold in Western Siberia. Values ranges from 25 to range of standard deviation of different observations at the 45 gC/m2/a on average between 1993 and 2003. same site (Figure 6b) and its dynamics is represented by

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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).

[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 con- tributes 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 Figure 7. Mean fire return interval in Northern Eurasia NPPinatwofoldmanner.Asafastprocess,GPPis 1993–2003 as simulated by (middle) LPJ-noP and (bottom) constrained by stomatal conductance [Farquhar et al., LPJ-P. (top) The result by the original LPJ model [Sitch et 1980]. On the other hand, water availability influences the al., 2003] is shown for comparison.

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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).

allocation of carbon to the components of plants [Bongarten [42] During the simulation period, fire also has a direct and Teskey, 1987; Mencuccini and Grace, 1995] leading to impact on VCD, as can be seen by the comparison of LPJ-P lower leaf area under drier conditions [Grier and Running, and LPJ-noFire results in Figure 8d. However, the results 1977; Gholz, 1982]. shown in Figure 8 also demonstrate the impact of water [40] To differentiate between these effects, two additional availability on growth through canopy conductance and simulations excluding fire, LPJ-noFire, or excluding water- carbon allocation to roots. For the same location, Figure 9 stress impacts on carbon allocation (equation (4)), clarifies the impact of plant available soil water content LPJ-noStress, are compared to LPJ-noP and LPJ-P for a (SWC) on NPP that is mediated by thaw depth. It shows larch-dominated permafrost area around 68.25°Nand LPJ-noP and LPJ-P simulation results of NPP, thaw depth 120.25°E in the first 300 simulation years (Figure 8). and SWC on a daily time step for the simulation year 50. LPJ-P results of leaf area index (LAI), foliage projected Without the representation of freeze-thaw processes, a very cover (FPC), NPP and finally VCD are lowest and best deep and saturated soil would be assumed with SWC of comparable to independent estimates. Without the consid- about 200 mm throughout the whole year. By contrast, eration of permafrost, these parameters acquire considerably SWC is constrained by thaw depth in the LPJ-P experiment higher (unrealistic) values. LPJ-noFire results in only slightly leading to reduced NPP. In addition, the timing of the first higher values than LPJ-P during the first 100 simulation day with photosynthesis is altered forward in time. years, revealing that the direct impacts of fires are not the dominant effect. 3.5. Vegetation Carbon Density [41] By contrast, LPJ-noStress shows higher values than [43] Simulated VCD approximates inventory estimates LPJ-P already after about 50 years since no extra investment much better in the LPJ-P simulation as compared to LPJ- in roots is assumed (equation (4)). This illustrates the impact noP (Figure 10a), where the deviation is considerable. VCD of higher carbon allocation to roots on LAI and NPP, thus is estimated by LPJ-P to range between 6 and 7 kgC/m2 VCD in permafrost regions. However, LPJ-noStress still south of 63°N decreasing to 1–2 kgC/m2 north of 69°N. produces lower LAI, NPP and VCD than LPJ-noP (Figure 8). Freeze-thaw processes account for a 1.5 to 2.5-fold reduc- This is due to the fact that lower water availability also tion in VCD in the South (Figure 10b) which increased with directly impacts productivity [e.g., Gerten et al., 2005].

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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]. None- theless, such a comparison allows to demonstrate the reliability of computed first-order patterns. Our evaluation Figure 9. Daily estimates of woody NPP simulated by the of simulated thaw depth seasonality and spatial patterns of LPJ-noP and LPJ-P models, thaw depth simulated by LPJ-P, ALT with observations and the results of a recent permafrost and soil water content (Wplant) simulated by LPJ-noP and model, the Goodrich model, shows the capability of our LPJ-P around 68.25°N, 120.25°E (larch forest, northern method to generically represent freeze-thaw processes. taiga) in 2003. Discrepancies to observations are within the range of variability of thaw depth that occurs naturally on a much latitude to a fivefold reduction in high latitudes. This smaller spatial scale than applied in this study. A limitation demonstrates the impact of freeze-thaw processes on bio- of the method applied is the assumption of homogenous soil mass through disturbances and water availability. Soil properties with depth, a limitation that equally applies to all thermal dynamics are primary processes of boreal ecosys- other large-scale permafrost models. The comparison of tems that cannot be neglected, as has commonly been the results obtained from different approaches is a problem that case, in DGVM simulations. Besides the fact that VCD is can currently not be resolved satisfactorily and places the lower in magnitude, its changeover between 60 and 70°Eis strongest constraints on our current ability to demonstrate smoother in LPJ-P (Figure 10a) than in LPJ-noP owing to and validate results. the effect of a decreasing ALT on biomass [Bonan, 1989]. [46] It should be noted that the current snow module embedded into the LPJ model does not take into account 4. Discussion vegetation effects on solar radiation extinction and atmospheric turbulence which are a far greater influence on snow [44] Freeze-thaw processes strongly codetermine the sea- cover than interception. This could lead to an underestima- sonality of soil moisture in the boreal zone, particularly in tion of snow depth in forest areas with effects on the soil the permafrost zone, where the thermal dynamics in the thermal regime. In addition to this, DGVMs do not resolve active layer are significantly altered. Owing to the intrinsic subgrid localizations of spatial heterogeneity hence are not physiological coupling of the carbon and water cycles in able to represent discontinuous permafrost. We acknowl- vegetation, with an additional important soil moisture edge that the greatest change in permafrost is likely to feedback on vegetation mediated through fire, the most

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.

11 of 14 GB1012 BEER ET AL.: FREEZE-THAW EFFECTS ON BIOMASS GB1012 happen in this zone. Further, regional-scale studies are thus ALT and the disappearance of permafrost under warming desirable to explore these effects in more detail. conditions [Anisimov and Nelson, 1997; Stendel and [47] Given the permafrost module, our evaluation of Christensen, 2002] will lead to a substantial alteration of runoff, fire return interval and VCD as computed in re- the site hydrology [Kane et al., 1992]. The consideration of sponse to its dynamics demonstrates the importance of the most important consequences of freeze-thaw processes freeze-thaw processes for the functioning of boreal ecosys- on the hydrological regime in high latitudes by the LPJ-P tems. High runoff of snow melt and precipitation on a still model give us an opportunity to arrive at more reliable frozen ground in spring subsequently leads to dry soil estimates of the future dynamics of the global carbon and conditions. In permafrost regions, this effect is amplified water balances, which are both strongly influenced by the by the fact that vegetation is able to draw water only from high latitudes. Uncertainties exist with respect to the the shallow fraction of the active layer which had been dynamics, for example, of peat, cryoturbation, methano- already thawed in spring and early summer. The ensuing genesis and methanotropy, of which large-scale observations low soil water content, which rapidly translates into low are not currently available. In addition, the impact of fire on litter moisture, strongly increases fire probability reducing nutrient availability should be considered for projections of the fire return interval. Our model results show that freeze- the future carbon balance in the boreal zone. Furthermore, thaw processes and the related site hydrology are essential feedbacks between changing vegetation coverage and cli- for realistic fire disturbance modeling on a continental scale mate through albedo and carbon and water fluxes can be in the boreal zone. The actual area burned, and its spatial large [e.g., Betts, 2000; Chapin et al., 2005]. Nonetheless, and temporal variability, however, depend on additional the inclusion of freeze-thaw processes is a significant step factors such as ignition, tree allometry and structure, and toward a more comprehensive understanding of ecosystem litter quality. In addition to disturbances, NPP is the main processes in the boreal zone and Arctic tundra. Our freeze- determinant of biomass as it quantifies vegetation growth thaw algorithm of medium complexity also is suitable for and background mortality. The relatively low soil water application in the land surface scheme of a General Circu- availability during the boreal summer causes low values of lation Model since additional computation cost is minimal. NPP, hence plant growth depressed owing to low LAI and canopy conductance in permafrost regions. This remains the 5. Conclusion case despite various adaptations of boreal vegetation to permafrost conditions, represented in our model, such as a [50] The combined evaluation of river runoff, fire return rapid greenup and shallow rooting systems. Even if the interval and vegetation carbon density, as simulated by the resulting low foliage cover would be partly compensated on permafrost-enhanced LPJ-DGVM, demonstrates that both the ecosystem level by more seedlings during the time, stem limitation in water availability during the growing season density increased, also contributing to lower VCD values. and the disturbance dynamics of boreal vegetation substan- Our results support the fact that plant growth in the northern tially contribute to the low vegetation carbon density observed taiga is limited by water availability in addition to other in permafrost regions of the boreal zone. This supports the factors, such as nitrogen uptake. conclusion that the availability of liquid water and soil [48] The dynamic ecosystem model still simulates too moisture seasonality are also major limitations of biomass high VCD north of 60°N, where values may be up to twice within the permafrost zone, in addition to temperature and as large as independent estimates produced by IIASA from nitrogen availability. Since moisture dynamics are strongly forest inventory data (Figure 10). A number of limitations to determined by the thermal dynamics in these regions, freeze- tree establishment and growth in high latitudes that are not thaw processes are found to be ecosystem processes of first represented in the simulation model are a likely explanation order. This conclusion demonstrates the potential of vegeta- for this overestimation, for example temperature depen- tion in high latitudes to sequester more carbon than it dence of cell division [Ko¨rner, 2003] or of seed quality currently does owing to warming that leads to increasing and quantity [Black and Bliss, 1980; Sveinbjo¨rnsson et al., ALT or even the complete disappearance of permafrost. 2002], limitations by nutrient availability [Bonan and Shugart, 1989] or limitation of tree establishment by stand- [51] Acknowledgments. The authors thank B. Smith, S. Schaphoff, and S. Sitch for support regarding the LPJ-DGVM code, and M. Fink, ing water on the permafrost [Sveinbjo¨rnsson et al., 2002]. In D. Knorr, W. Cramer, V. Romanovsky, D. Riseborough, O. Anisimov, and the boreal zone, a considerable amount of heterotrophic P. Ciais for valuable notes. A. Shvidenko and I. McCallum from the respiration was detected during winter [Monson et al., 2006] International Institute for Applied Systems Analysis, Austria made the forest with consequences on nitrogen mineralization, hence nitro- inventory data available. We are grateful to G. and S. Zimov who provided seasonal thaw depth and soil moisture measurements, to P. Overduin, gen availability during the growing season. This positive A. Rodionov, and J. Boike, who provided seasonal soil moisture observa- effect of winter soil respiration on productivity is not tions, and to all members of the SIBERIA-II consortium for discussions. considered in this study but LPJ even assumes no limitation Financial support was provided by the European Commission under the SIBERIA-II project, contract EVG2-2001-00008. W. L. and D. G. were of nitrogen uptake. Future modeling studies which aim at funded by the German Ministry of Education and Research under the clarifying the nutrient limitation of VCD will have to DEKLIM project CVECA. We thank Nigel Roulet and an anonymous consider this positive effect of winter soil respiration on reviewer for helpful comments on an earlier manuscript of this paper. nutrient availability. References [49] However, our results clearly point out that freeze- thaw processes are of first order and should not be neglected Anisimov, O. A., and F. E. Nelson (1997), Permafrost zonation and climate change in the Northern Hemisphere: Results from the transient general in a process-based modeling framework. The increase of circulation models, Clim. Change, 35, 241–258.

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