Improved simulation of the terrestrial ... - Wiley Online Library

JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, VOL. 4, M08002. doi:10.1029/2012MS000165, 2012

Improved simulation of the terrestrial hydrological cycle in permafrost regions by the Community Land Model S. C. Swenson,1 D. M. Lawrence,1 and Hanna Lee1 Received 20 April 2012; revised 3 July 2012; accepted 23 July 2012; published 30 August 2012.

Plausible predictions of future climate require realistic representations of past and current climate. Simulations of the distribution of permafrost in the 21st century made with the Community Climate System Model (CCSM4) indicate that substantial decreases in permafrost extent can be expected, especially under high emissions scenarios. One of the implications of permafrost loss is the potential release of carbon from newly thawed soils into the atmosphere, thus raising its concentration of greenhouse gases and amplifying the initial warming trend. However, the biogeochemical cycle simulated by CCSM4 presents significant biases in carbon fluxes such as gross primary production, net primary production, and vegetation carbon storage in permafrost regions. The biases in the carbon cycle simulated by CCSM4 are in part due to excessively dry soils in permafrost regions. In this study, we show that the CCSM4 dry soil bias results from the model’s formulation of soil hydraulic permeability when soil ice is present. The calculation of the hydraulic properties of frozen soils is first modified by replacing their dependence on total water content with liquid water content only. Then an ice impedance function having a power-law form is incorporated. When the parameterization of the hydraulic properties of frozen soil is corrected, the model simulates significantly higher moisture contents in near-surface soils in permafrost regions, especially during spring. This result is validated qualitatively by comparing soil moisture profiles to descriptions based on field studies, and quantitatively by comparing simulated hydrographs of two large Siberian rivers to observed hydrographs. After the dry soil bias is reduced, the vegetation productivity simulated by the model is improved, which is manifested in leaf area indices that at some locations are twice as large as in the original model.

[1]

Citation: Swenson, S. C., D. M. Lawrence, and H. Lee (2012), Improved simulation of the terrestrial hydrological cycle in permafrost regions by the Community Land Model, J. Adv. Model. Earth Syst., 4, M08002, doi:10.1029/2012MS000165.

1. Introduction [2] In recent decades, numerous changes to the Arctic and sub-Arctic environment have been observed: increasing air temperatures, rapidly decreasing sea ice extent, mass loss from glaciers and ice sheets, warming permafrost, and changes in the distributions of flora and fauna [Richter-Menge and Overland, 2010; Hinzman et al., 2005; Drobot et al., 2008; Romanovsky et al., 2010; Velicogna, 2009; Meier et al., 2007]. Projections of future climate using the Community Climate System Model (CCSM4) indicate that warming in the highlatitudes will continue through the 21st century with sustained anthropogenic greenhouse gas emissions [Lawrence et al., 2012]. Permafrost soils contain large 1 Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado, USA.

’2012. American Geophysical Union. All Rights Reserved. 1942-2466/12/2012MS000165

amounts of soil carbon that has accumulated since the end of the last glacial period, which may become vulnerable to increased microbial activity as temperatures warm [Schuur et al., 2008; Zimov et al., 2006]. Because the thermal and hydrological states of the land surface and subsurface are tightly coupled in areas where seasonally and permanently frozen soils occur, and because soil biogeochemical cycling is sensitive to these physical states, an accurate prediction of the fate of permafrost carbon depends on a realistic representation of the hydrological cycle in climate and Earth System models. [3] Nearly 24 % of the Northern Hemisphere has been estimated to contain permafrost, and over three-quarters of that region is underlain with continuous permafrost [Zhang et al., 2000]. Figure 1 shows the areas of the northern high latitudes in which permafrost is present, as estimated by the International Permafrost Association Circum-Arctic Map of Permafrost and Ground Ice Conditions [Brown et al., 2001] (data provided by the National Snow and Ice Data Center).

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SWENSON ET AL.: IMPROVED CLM COLD-REGION HYDROLOGY

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Figure 1. (left) Map showing permafrost extent above 45N. Non-permafrost areas are colored brown, while permafrost regions are shaded blue. Lighter shades of blue indicate areas characterized by isolated (0–10%) and sporadic (10–50%) permafrost, while darker blues indicate areas of discontinuous (50–90%) and continuous (90– 100%) permafrost. (right) Map of northern Asia showing locations of Yenisey and Lena river basins. Rivers are shown with gray lines, basin boundaries are shown with black lines. Locations where river discharge measurements are made are indicated by red circles; Igarka for the Yenisey and Kusur for the Lena. [4] In this study, we identify significant biases in the hydrological state of permafrost regions simulated by the Community Land Model (CLM4), the land component of both CCSM4 and the Community Earth System Model (CESM1) (Table 1). First, river discharge from basins containing extensive permafrost areas is shown to agree poorly with observations. As examples, we focus on two large Siberian rivers, the Yenisey and the Lena. These are two of the world’s largest river basins, draining approximately 2,500,000 km2 and 2,400,000 km2, respectively. Permafrost underlies 36–55% of the surface area of the Yenisey River [Yang et al., 2004], while 78–93% of the Lena River is underlain by permafrost [Ye et al., 2003]. Hydrographs from CLM4 simulations underpredict peak discharge and overpredict winter low flows by over 50% for these rivers. [5] Second, soil moisture profiles and the movement of water through frozen soil in CLM4 simulations are not consistent with field and laboratory studies. The relative impermeability of frozen soil plays a central role in the seasonal evolution of soil moisture within the Table 1.

active layer. In CLM4 simulations, however, the effect of ice on the flow of water through frozen soil is minor. This deficiency leads to excessively dry soils in permafrost regions as water drains from the active layer into deeper frozen layers before contributing to subsurface runoff. It also contributes to the poor hydrograph comparison through its effect on the partitioning of surface inputs of water between infiltration and surface runoff. The dry bias in permafrost soils influences the simulated carbon cycle, leading to very low predicted gross primary production, net primary production, and vegetation carbon storage in permafrost regions [Lawrence et al., 2011; Thornton and Zimmermann, 2007]. After modifying the manner in which the presence of soil ice influences the calculation of the model soil hydraulic properties, the CLM4 simulation of both the hydrographs and soil moisture profiles is improved. In some locations, the higher near-surface soil moisture enhances vegetation growth when the model is run with a prognostic carbon cycle, leading to more realistic leaf area indices.

Comparison of River Discharge Statistics for Observations and CLM4 Simulations

a

Discharge Statistics Peak [103

Mean Annual (2004–2007) [km3]

Observed CONTROL FV_VARY HP_LIQ IMPED

m3 ] s

Winter (JFM) [103

m3 ] s

Yenisey

Lena

Yenisey

Lena

Yenisey

Lena

642 630 630 631 617

642 642 642 648 621

102 61 72 138 129

94 39 48 97 89

10.7 (4.5) 8.5 7.2 4.2 3.5

5.0 (1.9) 11.9 9.0 3.7 3.8

a

Values in parentheses indicate observed values prior to the construction of large dams.

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2. The Community Land Model [6] The Community Land Model (CLM4) [Lawrence et al., 2011] is the land component of the Community Climate System Model [Gent et al., 2011]. CLM4 simulates the partitioning of mass and energy from the atmosphere, the redistribution of mass and energy within the land surface, and the export of fresh water and heat to the oceans. To realistically simulate these interactions, CLM4 includes terrestrial hydrological processes such as interception of precipitation by the vegetation canopy, throughfall, infiltration, surface and subsurface runoff, snow and soil moisture evolution, evaporation from soil and vegetation and transpiration [Oleson et al., 2008]. [7] CLM4 also contains a streamflow routing submodel called the River Transport Model (RTM). Gridcell runoff determined by CLM4 is transported to the oceans by RTM using a cell-to-cell routing scheme in which rivers are modeled as linear reservoirs [Oleson et al., 2010], thus facilitating the comparison of modeled runoff to observed river discharge. [8] In this study, we use CLM4 in offline mode, in which the atmospheric boundary conditions are specified. The observed forcing data, which provides precipitation, air temperature and pressure, specific humidity, shortwave radiation, and wind speed, was obtained from NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA). MERRA was generated with version 5.2.0 of the Goddard Earth Observing System (GEOS) atmospheric model and data assimilation system o(DAS), and has a 1o 2 spatial resolution of latitude and longitude with 72 2 3 vertical levels, from the surface to 0.01 hPa [Rienecker et al., 2011]. Land surface state variables were initialized from the land surface state at the end of a multi-decade offline run, and a simulation was generated for the period 2000–2010 at a spatial resolution 0f 0.9o latitude and 1.25o longitude.

3. CLM4 Hydrological Biases in Permafrost Regions 3.1. River Discharge [9] The presence of frozen soil can greatly influence the generation of runoff. Where soil temperatures fall below freezing, near-surface soils are often ice-rich due to the migration of water to the freezing front during fall and winter and refreezing of infiltrating water in the spring [Kane, 1980; Sta¨hli et al., 1999]. Infiltration rates of soils with high ice contents are very low, and much of the input snowmelt water can be converted to runoff [McNamara et al., 1998; Quinton and Marsh, 1999]. In many highlatitude locations, accumulated snowfall may provide half or more of the annual input of water to the land surface [Kane et al., 2004]. There is a strong asymmetry in the lengths of the snow accumulation and ablation periods; whereas accumulation occurs over a period of months, the ablation period is typically two weeks or less [Wang et al., 2008]. Thus, the hydrograph of high-latitude river basins can be relatively sharply peaked.

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[10] The Yenisey and Lena Rivers, shown in Figure 1, both demonstrate this behavior. Figure 2 shows the hydrographs of both basins averaged over the period 2004–2007, using discharge data obtained from R-ArcticNet (www.r-arcticnet.sr.unh.edu/) [Lammers et al., 2001]. The black line in the left panel shows the Yenisey River daily discharge observed at Igarka, while the black line in the right panel shows the Lena River daily discharge observed at Kusur. Both discharge records show qualitatively similar behavior, with an abrupt rise in spring, a somewhat more gradual decline during summer, followed by very low winter flows. Peak m3 m3 discharge exceeds 100,000 (Yenisey) and 80,000 s s (Lena) in nearly all years during this time period (Table 1), with 48% of the annual Yenisey flow and 36% of the annual Lena flow occurring during the three months March, April, and May. [11] The hydrographs simulated by CLM4 (Figure 2, gray lines) are significantly different from the observed hydrographs. The CLM4 simulation agrees to within 2% with the average observed annual discharge for the simulation period from the Yenisey (Obs 5 642 km3, CLM4 5 630 km3), but the timing of the CLM4 hydrograph is incorrect. The average observed peak flow for the Yenisey during this period is about m3 102,000 while that of the CLM4 simulation, which s m3 . A occurs about one month later, is only 61,000 s similar discrepancy is found for the Lena River, where CLM4 simulates only an average peak discharge of m3 m3 compared to the observed 94,000 , despite 39,000 s s having nearly equal annual discharge (642 km3). Furthermore, the peak of the simulated hydrograph occurs about three months later than observed. [12] The simulation of river discharge is influenced by three main factors: the atmospheric forcing data, the partitioning of inputs (i.e., precipitation) into runoff, and the routing of runoff through the stream network. The latter two functions are performed by CLM4 and its streamflow routing submodel, the River Transport Model (RTM), respectively. Before examining how parametrizations within CLM4 affect the simulated hydrographs, we first look at the contribution of RTM. [13] Runoff at each model gridcell is determined by CLM4, and then passed to RTM to be routed to the ocean. A key parameter in a runoff routing scheme is the flow velocity. The river flow velocity in RTM has a m globally constant value of 0.35 . A number of studies s [e.g., Schulze et al., 2005; Ngo-Duc et al., 2007, Decharme et al., 2010] have sought to simulate flow velocities more realistically by implementing variable flow velocity parameterizations based on the Manning or Chezy equations. However, these parameterizations require knowledge of hydraulic quantities that are unavailable globally, such as channel cross-sectional area, wetted perimeter, and roughness [Dingman, 2002]. A flow velocity parameterization of intermediate

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Figure 2. Hydrographs for (a) the Yenisey River and (b) the Lena River. Orange line represents observed discharge, blue line represents discharge simulated by the control model. complexity was described by Ducharne et al. [2003], who used a slope-dependent flow velocity. To examine the effects of varying river flow flow velocity, we performed a simulation using a flow velocity proportional to the square root of the gridcell average slope h i 1 v~max 0:35,0:7 b2 ð1Þ [14] where v is the gridcell river flow velocity (m s21), and b is the gridcell average slope in degrees. We refer to this experiment as ‘‘FV_VARY’’. [15] Figure 3 shows the results of including this parameterization in RTM. Simulated hydrographs for both river basins are more sharply peaked during the spring, which is consistent with the results of Decharme et al. [2010], who showed that increasing the flow velocity in the Total Runoff Integrating Pathways (TRIP) river routing model generally led to earlier and more sharply peaked hydrographs. Peak discharge increases in the m3 FV_VARY simulation, to about 72,000 for the s m3 for the Lena. Yenisey and 48,000 s

[16] While the inclusion of a spatially variable flow velocity increases the simulated peak discharge, it is still too low by roughly 30–50%. Furthermore, simulated winter discharge from the Lena River is typically more than double that which is observed. Because RTM does not model the effects of managed reservoirs on streamflow, one could argue that the simulated Yenisey River winter discharge is also too large based on the results of Yang et al. [2004], who noted that after the construction of large dams during the middle of the 20th century, winter discharge from the Yenisey was increased by 45–85%. A similar increase in winter river discharge in the Lena River was described by Ye et al. [2003]. [17] These results show that making the flow velocity in RTM spatially variable can reduce some of the biases in simulated river discharge but it cannot eliminate them, implying that CLM4 hydrological parameterizations also contribute to the poor hydrograph simulations. In the next sections, we examine the parameterizations within CLM4 that control the movement of water into and through frozen soil, the biases in soil moisture that they produce, and how improving these parameterizations

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Figure 3.

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Same as Figure 2, except that simulated discharge is from FV_VARY.

improves the simulation of soil moisture and river discharge simultaneously. 3.2. Active Layer Soil Moisture [18] In high-latitude and high-elevation regions underlain by permafrost, the near-surface volume of soil that thaws each summer and freezes each winter is called the active layer [Woo, 1986]. Within this relatively thin layer of soil, nearly all of the biological activity of such ecosystems takes place [Hinzman et al., 1991]. [19] While the temperature and soil moisture profiles of the active layer at a particular location will depend on the soils, topography, vegetation, and climate of the site, certain general characteristics can be gleaned from a synthesis of the available field studies reported in the literature. In this section, we provide a qualitative description, based on field/observational studies, of the seasonal evolution of the active layer. Then we examine the temperature and soil moisture profiles simulated by CLM4, and assess the extent to which the model represents the general features of the active layer seen in nature.

[20] The hydrological and thermal states of the soil in the active layer are tightly coupled. Both the thermal conductivity and heat capacity of the soil depend on its water content as well as the phase [Woo and Xia, 1996]. Furthermore, the permeability of the soil may decrease by orders of magnitude upon freezing, again depending on the water content of the soil [Burt and Williams, 1976; Kane, 1980; Kane and Stein, 1983; Andersland et al., 1996; McCauley et al., 2002]. Although snowmelt provides a large pulse of water to the land surface during spring, infiltration rates into icy soils are low, and much of this water may be converted to runoff. However, in addition to reducing moisture inputs, the presence of soil ice limits the drainage of water from the bottom of the active layer [Shur et al., 2005]. As the soil continues to thaw, the active layer deepens, and water storage capacity increases. A perched saturated zone can form above the frozen soil at the base of the active layer [Hinzman et al., 1991; Carey and Woo, 2001; Woo and Marsh, 1990]. Thus, the active layer can have high moisture levels. This perched saturated zone can provide moisture to vegetation later in the summer, maintaining

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Figure 4. CLM4 simulation averaged over the time period 2004–2007 at gridcells located at (left) 84E/70N and (right) 210E/69N. (top) Daily averaged soil moisture, expressed as relative saturation. (bottom) Daily averaged soil temperature, in degrees Celsius. Stippled regions show temperatures below freezing, thin black line represents zero degree isotherm, thick black line represents water table depth. evapotranspiration even when precipitation inputs are low [Ohta et al., 2001; Kane et al., 1990]. [21] Figure 4, shows the soil moisture and soil temperature as a function of depth, in meters, at two permafrost locations (84E/70N and 210E/69N) simulated by CLM4. Figure 4 (top) shows soil moisture, expressed as relative saturation, temporally averaged over the period 2004–2007. Figure 4 shows that little or no coupling exists between the thermal and hydrologic states of the soil in CLM4. Frozen soils in the model, even with high moisture contents, do not impede the flow of water. Instead, water moves relatively freely. In the spring, for example, infiltrating water can be seen to increase soil moisture content at all depths at the west Siberian location (84E/70N), raising the water table. The water table, which resides in soil layers that are frozen throughout the entire simulation period, declines from summer to winter, providing the baseflow responsible for the high bias in winter river discharge shown in Figures 2 and 3. The

near-surface soil layers shallower than about 10–20 cm depth, which have high organic matter content, are quite dry at all times. These characteristics are typical of gridcells throughout the permafrost zone in the control simulation.

4. Current Model Formulation [22] The rapid movement of water through the frozen layers of the soil column implied by Figure 4 is a result of the way in which the soil hydraulic properties are calculated in CLM4. CLM4 uses Darcy’s law to describe the vertical flux of water between layers of the soil column. Darcy’s law depends on the hydraulic conductivity k and the soil matric potential Y, both of which are functions of soil moisture content:

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k~ksat

2Bz3 h w

ð2Þ

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{B h Y~Ysat w

ð3Þ

[23] where ksat is the saturated hydraulic conductivity (mm s21), Ysat is the saturated soil matric potential (mm), B is an exponent based on soil texture, w is porosity, and h is volumetric soil moisture content (mm3 mm23) [Oleson et al., 2010]. Typically, the soil moisture used in such calculations is the liquid water, i.e., h5hliq [Zhang et al., 2007; Zhao et al., 1997; Flerchinger and Saxton, 1989; Jame and Norum, 1980; Harlan, 1973]. CLM4 appears to be unique due to its use of total soil moisture content, (h5hliq+hice), rather than liquid water content, in the calculation of hydraulic conductivity and the soil matric potential. Because the soil hydraulic properties are based on the total soil moisture content, they do not directly respond to changes in phase of soil water. Thus, the gradient in soil matric potential is not coupled to the soil thermal gradient, and the migration of water towards a freezing front observed in field studies [Kane, 1980; Sta¨hli et al., 1999] and laboratory experiments [Jame and Norum, 1980; Hansson et al., 2004] cannot be simulated. To account for the observed reduction in hydraulic conductivity in the presence of soil ice, CLM4 includes an empirical factor ffrz [Oleson et al., 2008]: hliq zhice 2Bz3 k~(1{ffrz ) ksat ð4Þ w [24] This factor also controls infiltration into the model soil column. [25] ffrz is a function of ice content, whereby the fraction of the land surface or soil layer that is impermeable increases with increasing ice content according to wice exp {a 1{ {exp({a) wliq zwice ð5Þ ffrz ~ 1{exp({a) [26] where ffrz is the soil ice impermeable fraction, wliq and wice are the liquid and solid water contents of the top soil layer (mm), and a is an adjustable parameter whose value in the current model is 3 [Oleson et al., 2010]. This ad hoc function is slightly sub-linear [Niu and Yang, 2006], and in principle ranges from 0 to 1. In practice, however, the dependence of ffrz on wliq limits the maximum value to less than 1 due to the presence of supercooled liquid water. Furthermore, the nearly linear nature of this function does not capture the variation in hydraulic conductivity, which is typically orders of magnitude smaller in frozen soils than in unfrozen soils having the same total moisture content [Burt and Williams, 1976; Kane, 1980; Kane and Stein, 1983; Andersland et al., 1996; McCauley et al., 2002]. When snowmelt begins infiltrating, ffrz rapidly decreases, as wliq increases from both surface inputs and continued melting of wice.

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[27] Figure 5 shows the simulated snowmelt (blue line) and surface runoff (orange line) fluxes during the spring for the gridcell at 84E/70N. The partitioning of water at the surface between infiltration and runoff is determined by the gridcell impermeable fraction (black line), which accounts for the effects of both ice and the saturated fraction of the gridcell. While the impermeable fraction is close to 1 at the beginning of the melt season, it rapidly decreases as melting progresses, resulting in large infiltration rates. At this location, about 54% of the snowmelt is converted to surface runoff, the remainder infiltrates into the soil. Averaged over each river basin, only 33% and 42% of snowmelt for the Yenisey and Lena Rivers, respectively, is converted to runoff in the control simulation. [28] The preceding analysis implies that additional water, in the form of snow, is available for runoff production, and that modifying the soil ice impermeable fraction, ffrz, could result in more runoff, and thus a hydrograph that more closely matches the observations. However, increasing ffrz during the melt season will not improve the near-surface soil moisture dry bias shown in Figure 4. On the contrary, simply increasing surface runoff at the expense of infiltration will further reduce the soil moisture content. Instead, we will show in the next section that a solution to both the discharge and soil moisture biases can be obtained by calculating the soil hydraulic properties based on liquid soil water content, rather than total soil water content.

5. New Model Formulation [29] To examine the effect of the soil hydraulic properties on the simulation of river discharge and the vertical distribution of soil moisture, we performed a simulation (‘‘HP_LIQ’’) using liquid soil water content in the calculation of hydraulic conductivity and soil matric potential, as has been done in numerous other studies [Zhang et al., 2007; Zhao et al., 1997; Flerchinger and Saxton, 1989; Jame and Norum, 1980; Harlan, 1973]. [30] Figure 6 compares the hydrographs for the HP_LIQ simulation to the R-ArcticNet observations. Total annual runoff for the Yenisey River is about the same as for the variable flow velocity run (631 km3 for HP_LIQ; 630 km3 for FV_VARY), but peak discharge m3 for HP_LIQ; is increased by over 90% (138,000 s m3 72,000 for FV_VARY). The total annual runoff for s the Lena River is similar (648/642 km3), but peak m3 for HP_LIQ; discharge is about doubled (97,000 s m3 for FV_VARY). The simulation of winter 48,000 s low flows is also improved. Average flow rates during January, February, and March for FV_VARY are m3 m3 about 7200 for the Yenisey and 9000 for the s s Lena. In the HP_LIQ simulation, winter flows are about m3 m3 and 3700 . The observed values during the 4200 s s m3 period of the simulation are about 10,700 and 5000 s

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Figure 5. Simulated daily snowmelt and surface runoff at a gridcell located at 84E/70N. Black line is gridcell total impermeable fraction, blue line is snowmelt, orange line is surface runoff. m3 . However, the observed discharge rates reflect the s influence of managed reservoirs. Low flows in the observed Yenisey discharge values are much higher after the mid-1960’s, and the Lena River discharge record increases during the 1970’s. Prior to the construction of dams on these rivers, average winter flows were about m3 m3 4500 and 1900 , respectively. s s [31] The increased peak flows and the reduction in winter baseflow in the HP_LIQ simulation are due to significant changes in soil moisture relative to the control. Figure 7 shows the vertical distributions of soil moisture and temperature as functions of time for the HP_LIQ gridcells at (84E/70N) and (210E/69N). Soil moisture, shown in the top panel, remains relatively constant during the winter months, because the hydraulic conductivity is calculated from liquid water content, and the movement of water through frozen soils is greatly decreased. When near-surface soils are icy, infiltration is also reduced, leading to higher surface runoff rates during snowmelt. In the HP_LIQ simulation, the ffrz function (equation (5)) is not used to limit infiltration into the soil column. Instead, infiltration is reduced when soil pore space becomes filled with ice. At the 84E/70N location, for example, about 93% of the snowmelt is converted to surface runoff, compared to only 54% in the control simulation. Averaged over each

river basin, only 33% and 42% of snowmelt for the Yenisey and Lena Rivers, respectively, is converted to runoff in the control simulation, while the HP_LIQ simulation has conversion rates of 83% and 78% (Table 2). As soil ice melts at progressively deeper depths, water infiltrates further into the active layer. [32] The relatively impermeable frozen soils below the thaw front inhibit further soil drainage, leading to the buildup of an ice-rich zone at the base of the active layer; this has been observed in field studies, and is sometimes called the ‘‘transient layer’’ [Shur et al., 2005]. Because drainage from the bottom of the active layer is inhibited, groundwater recharge is minimal, and the modeled water table now resides well below the base of the soil column. Baseflow, which is a function of water table depth, is therefore negligible for this and other permafrost gridcells; as shown in Figure 6, this greatly reduces winter discharge in both river basins, with a greater reduction for the Lena, which has a greater permafrost area. [33] As the active layer develops and the thaw front moves downward, soil moisture values rise. However, the water table remains at a depth at which subsurface flow is minimal, and there is no other mechanism within the model to generate subsurface flow. As a result, many permafrost gridcells become nearly saturated throughout the summer. Baseflow from near-surface soils has been observed in field studies, particularly through highly permeable organic-rich soil layers. [Metcalfe

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Figure 6. Same as Figure 2, except that simulated discharge is from HP_LIQ. and Buttle, 2001; Hinzman et al., 1991] To simulate lateral subsurface flow occurring in the active layer, the following parameterization is added to the HP_LIQ model Qperched ~a Kp zfrost {zperched sin b ð6Þ [34] where b is the gridcell mean topographic slope, Kp is the mean saturated hydraulic conductivity within the perched saturated zone (mm s21), zfrost is the depth to the permafrost table and zperched is the depth to the perched water table (m). a is an adjustable parameter (m21), whose value in these simulations is 0.6. Equation (6) can be derived from the application of Darcy’s law to a uniform saturated layer overlying an impermeable layer of mean slope b [Childs, 1971]. [35] The perched water table is defined as the shallowest layer above the frost table having a saturation of greater than 0.95. Kp is calculated as the layer thickness weighted average P Ksat Dz Kp ~ P , ð7Þ Dz

[36] where the sum is taken over the layers between zfrost and zperched. Thus, for similar thicknesses of perched saturated regions, regions having greater topographic slope will more effectively drain the active layer. The addition of a lateral runoff parameterization based on the perched water table calculation leads to active layers that are typically wetter than the control run, but not saturated throughout the summer (Figure 7). [37] Another feature of the HP_LIQ simulation is the redistribution of water in response to temperature gradients. During the summer months, the uppermost soil layers dry out, while the lower active layer is relatively wet (Figure 7). When temperatures drop below freezing in the fall, however, a rapid transition takes place. As near-surface soils freeze and their liquid water content decreases, a strong gradient in matric potential develops that drives water from deeper layers towards the surface. This leads to an inversion of the summer moisture profile, with a more ice-rich layer overlying a dessicated middle layer. When the soil thaws in spring, the ice melts and returns to lower layers as the active layer deepens. [38] More closely than the control or FV_VARY simulations, the HP_LIQ simulation resembles the river

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Figure 7. Same as Figure 4, except that soil hydraulic properties are calculated with liquid water content instead of total water content (HP_LIQ). discharge measurements, the movement of water through frozen soils reported in field and laboratory studies, and the qualitative description of the active layer outlined in section 3.2. Still, certain aspects of the simulation can be improved. For example, infiltration at the 210E/69N gridcell (Figure 7, right) is still high, with only 43% of snowmelt converted to surface runoff (Table 2). This occurs because near-surface soils in the HP_LIQ simulation are relatively dry during winter, and adequate pore space exists to allow large Table 2. Comparison of Surface Runoff to Snowmelt Ratio a for CLM4 Simulations Runoff Ratio Statistics

CONTROL HP_LIQ IMPED

84E/70N

210E/69N

Yenisey

Lena

0.54 0.93 0.89

0.38 0.43 0.64

0.33 0.83 0.83

0.42 0.78 0.82

a The second and third columns are single gridcell values, and the third and fourth columns are basin-averaged values.

infiltration rates. There is enough latent heat associated with this water that it does not freeze in the near-surface soil layers, and therefore does not inhibit subsequent infiltration. Another feature of the HP_LIQ simulation is the upward movement of water towards the freezing front in the fall and winter leads to overly dry middle soil layers. From Figure 7, one can see that the relative saturation values at 40 cm depth are about 50% less than the near-surface values. Both field [Hansson et al., 2004] and laboratory [Jame and Norum, 1980] studies have measured differences of at most 20 to 25% in relative saturation due to thermal gradient induced soil moisture redistribution. Both studies found that to obtain good agreement between simulated and observed moisture profiles, a reduction in hydraulic conductivity due to the presence of soil ice was necessary. 5.1. Soil Ice Impedance [39] A number of studies have modified the calculation of the hydraulic conductivity in frozen soils to account for the effects of ice within the soil matrix by using an ice impedance factor [Zhang et al., 2009, 2007;

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Figure 8.

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Same as Figure 2, except that simulated discharge is from IMPED.

Hansson et al., 2004; Zhao et al., 1997; Lundin, 1990; Jame and Norum, 1980]. The ice impedance factor is a function of ice content, and is meant to quantify the increased tortuosity of the water flow when part of the pore space is filled with ice [Lundin, 1990]. Following Hansson et al. [2004] and Lundin [1990], we calculate the hydraulic conductivity using an ice impedance factor having a power law form 2Bz3 hliq {VFice ksat ð8Þ k~10 w hice is the ice-filled fraction of the w pore space, and V is the ice impedance factor. In this [40] where Fice ~

simulation (‘‘IMPED’’), V56, the value used by Lundin [1990]. [41] Figure 8 shows the effects of including the ice impedance factor on the simulation of river discharge. Total annual runoff is slightly reduced for the Yenisey (617 km3 for IMPED; 631 km3 for HP_LIQ) and the Lena (621 km3 for IMPED; 648 km3 for HP_LIQ)

m3 for IMPED; Rivers, as is peak discharge (129000 s m3 m3 138000 for HP_LIQ) for the Yenisey and (89000 s s m3 for HP_LIQ) for the Lena. for IMPED; 97000 s m3 Winter flows are similar for the Yensiey (3500 for s m3 IMPED; 4200 for HP_LIQ) and for the Lena (3800 s 3 m m3 for IMPED; 3700 for HP_LIQ), both of which s s are closer to the respective observed pre-dam average m3 values (4500 and 1900 ) than the control simulation. s [42] The ice impedance factor has a greater impact on the simulation of the vertical distribution of soil moisture. Figure 9 shows the soil moisture and temperature profiles for the IMPED simulation at the two grid points shown in Figures 4 and 7. The upward migration of water toward the freezing front is reduced in this simulation relative to HP_LIQ, leading to the presence of less ice in the top 20 cm during the winter. At the 84E/ 70N gridcell, the highest moisture contents during

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Figure 9. The same as Figure 7, except that an ice impedance factor is applied to the calculation of hydraulic conductivity. winter are located at a depth of about 20–30 cm, which is just below the interface between the organic-rich nearsurface layers and the deeper mineral soil layers. The frost-induced upward flux of moisture also leads to a drier region at about 50 cm depth, which is deeper and less dessicated than HP_LIQ. The difference in relative saturation between the near-surface layers and the drier layers between 60 and 100 cm depth is about 20–25%, similar to the values observed by Hansson et al. [2004] and Jame and Norum [1980]. [43] While the active layer thickness is similar in HP_LIQ and IMPED, the active layer is wetter in the latter simulation. This condition arises because of the greater soil wetness at depth in the IMPED simulation. As the soil thaws and the active layer deepens each spring, this soil ice melts and can remain in the active layer until freezing again in the fall. In the HP_LIQ simulation, the more rapid movement of water towards the freezing front can create an impermeable surface layer despite relatively drier conditions. In the IMPED simulation, an overall wetter soil column must develop before ice-rich near-surface layers can form.

[44] In contrast, the 210E/69N grid point in the IMPED simulation is drier in the HP_LIQ simulation. In this case, the addition of the ice impedance factor raises the runoff to snowmelt ratio from 43% to 64% (Table 2). During spring, when the active layer thickness is less than about 50 cm, moisture content is not fully saturated, but still 50% greater than in the control run. With less available moisture, a more modest transient layer forms. Averaged over each river basin, 83% and 82% of snowmelt for the Yenisey and Lena Rivers, respectively, is converted to runoff in the IMPED simulation.

6. Summary and Discussion [45] The current version of the Community Land Model, CLM4, appears to have a novel approach to the calculation of soil hydraulic properties of frozen soils, leading to unrealistic simulations of the land hydrologic state. Because soil hydraulic properties are based on total, rather than liquid, water content, moisture redistribution is decoupled from the thermal state

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Figure 10. Climatology of Leaf Area Index (LAI) simulated at two permafrost locations. Left panel: LAI at 84E/ 70N, blue line is control model, red line is modified model. Right panel: LAI at 210E/69N. of the soil column. This decoupling manifests itself in the following ways. First, the majority of snowmelt infiltrates into the soil and contributes to river discharge as baseflow. The resulting peak flows are lower than observed, and winter low flows are higher than observed when anthropogenic influences are taken into account. Second, the water that infiltrates passes through the active layer. Ice-rich transient layers at the base of the active layer do not form, perched saturated layers cannot be sustained, and the upward migration of water towards the freezing front cannot be simulated. The dry conditions in the active layer adversely affect the simulation of carbon and nutrient cycling in permafrost regions, resulting in simulations having extremely low gross primary production, net primary production, and vegetation carbon storage in permafrost affected areas [Lawrence et al., 2011; Thornton and Zimmermann, 2007]. [46] In this study, we have shown that parameterizing soil hydraulic properties in terms of liquid water content, in conjunction with an impedance factor due to the presence of soil ice, can ameliorate many of the described biases. The hydrographs simulated using the IMPED parameterizations agree better with observations. Furthermore, the remaining biases in the IMPED hydrographs, i.e., higher peak discharge and lower winter flows, are consistent with conditions prior to dam construction on the Yenisey and Lena rivers. This is expected because CLM4 presently does not model the effects of human management of rivers. The addition of an ice impedance factor yields a consistent treatment of infiltration and soil water redistribution, without the need for a separate infiltration parameterization such as equation (5). Despite the reduced infiltration of snowmelt into frozen soils, the active layers in the IMPED simulations are generally wetter than those in the control run. This is possible because an ice-rich transient layer builds at the base of the active layer, minimizing drainage to the regional groundwater system. Higher moisture levels,

especially during spring, can impact the simulated carbon cycle by facilitating vegetation growth. Figure 10 shows the time-averaged seasonal cycle of leaf area index (LAI) for two permafrost locations. At the 84E/70N gridcell, LAI is only slightly changed because the active layer in the control simulation is adequately wet. At the 210E/ 69N gridcell, peak LAI is nearly double in the IMPED simulation relative to the control. [47] Although the modifications responsible for the IMPED simulation significantly improve the hydrographs of the Yenisey and Lena rivers, hydrographs of other river basins are degraded, most notably the Ob River in western Siberia and the Mackenzie River in northwestern Canada (not shown). Although these two river basins are partially underlain by permafrost, their observed hydrographs are less sharply peaked than the Yenisey or Lena hydrographs. The Ob and Mackenzie river basins contain extensive wetland areas [Frey and Smith, 2007; Emmerton et al., 2007]. In a subsequent study, we show that by adding surface water storage from wetlands and small water bodies to model, CLM4 can simulate well the hydrographs of all four of these large Arctic drainage basins. [48] CLM4 and CCSM4 are currently being used to project permafrost extent in high latitude regions and to study and evaluate both the forcing mechanisms for permafrost thaw and feedbacks that permafrost thaw can exert on the Arctic and global climate system (e.g., permafrost degradation in CCSM4 [Lawrence et al., 2012]; rapid sea ice loss increases permafrost vulnerability [Lawrence et al., 2008a, 2008b]; shrubs area expansion increases permafrost vulnerability [Lawrence and Swenson, 2011]; amplification of soil warming in a projected wetter soil environment due to increased precipitation or plant water use efficiency (Z. M. Subin et al., Could future hydrological forcings warm permafrost soils?, submitted to Journal of Climate, 2012); methane emissions associated with soil warming and changes in wetland distribution, [Riley et al. 2011]). Efforts to examine the feedbacks related to changing

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vegetation dynamics and the carbon balance in permafrost regions have been hindered in part by the poor simulation of the cold region hydrological cycle. Preliminary results (e.g., Figure 10) indicate that the more realistic hydrologic cycle simulated using the revised cold region hydrology parameterizations described in this study will contribute to a more realistic carbon cycle simulation in the next version of CLM. Other ongoing developments to the model will build on the parameterizations described here by adding prognostically varying surface water storage (i.e., wetlands and small water bodies), flooding, and vertically resolved soil carbon dynamics. These model advancements will facilitate future research examining the interactions between permafrost, lakes and wetlands, and soil carbon in high-latitude regions. [49] Acknowledgments. We wish to thank two anonymous reviewers and the associate editor for their constructive comments. NCAR and the CESM project are supported by the National Science Foundation. This research was supported by funding from the US Department of Energy, Office of Biological and Environmental Research, as part of its Climate Change Prediction Program, cooperative agreement DE-FC03-97ER62402/A010 and NSF grant 108711.

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