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    (left to right) Seasonal mean 2-m air temperature bias maps for the period 1980–99 for (top) CCSM4 and (bottom) CCSM3 for Arctic land areas. Observational data are the Climatic Research Unit 0.5° × 0.5° TS2.1 (CRU TS2.1) dataset (Mitchell and Jones 2005). All model and observed data are regridded to the CCSM4 grid prior to plotting.

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    Bias map for climatological NDJFMA precipitation for (top) CCSM4 and (bottom) CCSM3 compared against the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) 1979–2009 “standard” (no reanalysis data) monthly data at 2.5° × 2.5° (Xie and Arkin 1997). Model data are the ensemble average for the period 1980–99. All model and observed data are regridded to the CCSM4 grid prior to plotting.

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    Permafrost distribution and active-layer thickness for the period 1980–99: (left) (top) CLM3 and (bottom) CCSM3; (middle) (top) CLM4 and (bottom) CCSM4; and (right) observed. Observed distribution is from the International Permafrost Association (Brown et al. 1998). Note that only continuous and discontinuous permafrost areas are shown.

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    Scatterplot of ALT for CALM sites vs model for CLM3, CLM4, CCSM3, and CCSM4. Model data are from ensemble mean climatological average for the period 1980–99. CALM site data are climatological average, but with each site containing a different number of years of data. See text for further details.

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    Scatterplot of deep ground temperature for borehole sites (observed) vs model for (left) CCSM4 and (middle) CLM4. Model data are from ensemble mean year 2005 for CCSM4 and 2004 for CLM4. Observed data are from 2007/2008. (right) Borehole locations used. See text for further details.

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    Time series of Northern Hemisphere near-surface permafrost extent for CCSM3 and CCSM4 for historical and projection periods. Near-surface permafrost extent is the integrated area of grid cells with at least one soil layer within the top 10 soil layers (3.5 m in CCSM3, 3.8 m in CCSM4) that remains frozen throughout the year. Frozen ground underneath glaciers is not included in the near-surface permafrost extent. The greenhouse gas concentration in CO2 equivalents (ppm) for the year 2100 are listed in parentheses for each SRES and RCP scenario. Shading indicates the ensemble spread.

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    Fractional soil ice content with depth for a subjectively chosen illustrative grid point in northern European Russia (68.3°N, 62.5°E) showing rapid soil ice melt after talik formation. At this location, talik first occurs at 1.5–2-m depth at year 2008, refreezes in 2011, and then reforms in 2015.

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    Maps of the ensemble mean year of first occurrence of talik (that lasts at least three years) at all grid cells where permafrost is present at the year 1850 in CCSM4. Results from RCP8.5 and RCP2.6 are shown. Magenta color is plotted if a talik does not form by year 2100 in one or more of the ensemble members.

  • View in gallery

    Maps showing depth of seasonal soil freeze for (top) present day and (bottom) 2080–99 for (left) RCP 8.5 and (right) RCP 2.6. Purple color denotes grid cells that are either permafrost or glacier. Integrated areas are 34.2, 35.5, and 36.0 × 106 km2, for present day, RCP8.5 and RCP2.6, respectively, which includes newly formed seasonally frozen ground in grid cells that have experienced permafrost thaw. When original present-day permafrost area is excluded from the 2080–99 seasonally frozen ground area, the area is 26.9 and 32.3 × 106 km2 for RCP8.5 and RCP2.6, respectively.

  • View in gallery

    Time series of Northern Hemisphere globally integrated frozen ground in millions of km3 months for CCSM4.

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    Climatological annual cycle time series (1980–99) for ensemble mean from CCSM3 and CCSM4 compared against observations averaged over eastern Alaska–western Canada region (62°–72°N, 125°–145°W). Snow depth as simulated in CLM3 and CLM4 is also shown. Observations for (top) Tair are from Matsuura and Willmott (2009b). (bottom) Snow depth observations are from Canadian Meteorological Center (1980–96) (Brown et al. 2003).

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    Climatological annual cycle-depth plots of soil temperature for CCSM3, CCSM4, CLM3, and CLM4 averaged over same eastern Alaska/western Canada domain as shown in Fig. 11.

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    Time series of Northern Hemisphere near-surface permafrost extent for a single CCSM4 twentieth century and RCP8.5 (solid lines) and RCP2.6 (dashed lines) ensemble member and for offline CLM4 simulations forced with climate bias-corrected data from the same CCSM4 simulation (see text for details).

  • View in gallery

    Time series of (top) Tair and (middle) Tsoil15 (layer-15 soil temperature, ~35-m deep layer midpoint) and (bottom) near-surface permafrost extent from LM simulation (years 850–1850) and the twentieth-century extension (solid line) and from the CCSM4 twentieth-century ensemble (dotted line with gray shading showing ensemble range). Here, Tair and Tsoil15 are averaged over Siberian region (55°–85°N, 90°E–180°). A 7-yr running average is used to smooth the Tair and Tsoil15 time series for clarity of presentation.

  • View in gallery

    Map of July–August (JA) (left) GH/RNET for 1980–99 and for (right) Δ(GH/RNET) for 2080–99 minus 1980–99 for RCP8.5.

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Simulation of Present-Day and Future Permafrost and Seasonally Frozen Ground Conditions in CCSM4

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  • 1 Earth System Laboratory, Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado
  • | 2 Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado
  • | 3 Earth System Laboratory, Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado
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Abstract

The representation of permafrost and seasonally frozen ground and their projected twenty-first century trends is assessed in the Community Climate System Model, version 4 (CCSM4) and the Community Land Model version 4 (CLM4). The combined impact of advances in CLM and a better Arctic climate simulation, especially for air temperature, improve the permafrost simulation in CCSM4 compared to CCSM3. Present-day continuous plus discontinuous permafrost extent is comparable to that observed [12.5 × 106 versus (11.8–14.6) × 106 km2], but active-layer thickness (ALT) is generally too thick and deep ground (>15 m) temperatures are too warm in CCSM4. Present-day seasonally frozen ground area is well simulated (47.5 × 106 versus 48.1 × 106 km2). ALT and deep ground temperatures are much better simulated in offline CLM4 (i.e., forced with observed climate), which indicates that the remaining climate biases, particularly excessive high-latitude snowfall biases, degrade the CCSM4 permafrost simulation.

Near-surface permafrost (NSP) and seasonally frozen ground (SFG) area are projected to decline substantially during the twenty-first century [representative concentration projections (RCPs); RCP8.5: NSP by 9.0 × 106 km2, 72%, SFG by 7.1 × 106, 15%; RCP2.6: NSP by 4.1 × 106, 33%, SFG by 2.1 × 106, 4%]. The permafrost degradation rate is slower (2000–50) than in CCSM3 by ~35% because of the improved soil physics. Under the low RCP2.6 emissions pathway, permafrost state stabilizes by 2100, suggesting that permafrost related feedbacks could be minimized if greenhouse emissions could be reduced. The trajectory of permafrost degradation is affected by CCSM4 climate biases. In simulations with this climate bias ameliorated, permafrost degradation in RCP8.5 is lower by ~29%. Further reductions of Arctic climate biases will increase the reliability of permafrost projections and feedback studies in earth system models.

Corresponding author address: David M. Lawrence, NCAR, P.O. Box 3000, Boulder, CO 80307-3000. E-mail: dlawren@ucar.edu

This article is included in the CCSM4 Special Collection special collection.

Abstract

The representation of permafrost and seasonally frozen ground and their projected twenty-first century trends is assessed in the Community Climate System Model, version 4 (CCSM4) and the Community Land Model version 4 (CLM4). The combined impact of advances in CLM and a better Arctic climate simulation, especially for air temperature, improve the permafrost simulation in CCSM4 compared to CCSM3. Present-day continuous plus discontinuous permafrost extent is comparable to that observed [12.5 × 106 versus (11.8–14.6) × 106 km2], but active-layer thickness (ALT) is generally too thick and deep ground (>15 m) temperatures are too warm in CCSM4. Present-day seasonally frozen ground area is well simulated (47.5 × 106 versus 48.1 × 106 km2). ALT and deep ground temperatures are much better simulated in offline CLM4 (i.e., forced with observed climate), which indicates that the remaining climate biases, particularly excessive high-latitude snowfall biases, degrade the CCSM4 permafrost simulation.

Near-surface permafrost (NSP) and seasonally frozen ground (SFG) area are projected to decline substantially during the twenty-first century [representative concentration projections (RCPs); RCP8.5: NSP by 9.0 × 106 km2, 72%, SFG by 7.1 × 106, 15%; RCP2.6: NSP by 4.1 × 106, 33%, SFG by 2.1 × 106, 4%]. The permafrost degradation rate is slower (2000–50) than in CCSM3 by ~35% because of the improved soil physics. Under the low RCP2.6 emissions pathway, permafrost state stabilizes by 2100, suggesting that permafrost related feedbacks could be minimized if greenhouse emissions could be reduced. The trajectory of permafrost degradation is affected by CCSM4 climate biases. In simulations with this climate bias ameliorated, permafrost degradation in RCP8.5 is lower by ~29%. Further reductions of Arctic climate biases will increase the reliability of permafrost projections and feedback studies in earth system models.

Corresponding author address: David M. Lawrence, NCAR, P.O. Box 3000, Boulder, CO 80307-3000. E-mail: dlawren@ucar.edu

This article is included in the CCSM4 Special Collection special collection.

1. Introduction

Permafrost and seasonally frozen ground are key components of the Arctic and global climate system because of their influence on energy, water, and carbon cycles. The freeze–thaw status of the ground is a critical threshold in the terrestrial system that is closely linked to the timing and length of the vegetation growing season (Black et al. 2000), boreal plant productivity (Kimball et al. 2006), the seasonal evolution of land–atmosphere carbon dioxide (Goulden et al. 1998) and methane (Mastepanov et al. 2008) exchange, and the amplitude and timing of spring snowmelt river discharge peaks (Rawlins et al. 2005; Kane et al. 2008). Frozen ground conditions are currently experiencing rapid change in response to late twentieth-century warming and associated climatic changes. Observed terrestrial Arctic changes (see Hinzman et al. 2005 for review) include warming and degrading of permafrost (Romanovsky and Osterkamp 1997; Camill 2005; Åkerman and Johansson 2008; Thibault and Payette 2009), advances in spring thaw and growing season initiation (Kimball et al. 2006; McDonald et al. 2009), and changes in vegetation productivity (Myneni et al. 1997) and composition (Tape et al. 2006), biogeochemical cycling (Schuur et al. 2009), and Arctic hydrology (Peterson et al. 2002; Smith et al. 2005; Quinton et al. 2011).

Earth system models (ESMs) project that Arctic warming will continue and potentially accelerate (Chapman and Walsh 2007; Lawrence et al. 2008b) during the twenty-first century under various scenarios of continued anthropogenic greenhouse gas emissions. Ecological model studies suggest that the interplay of snow season length, soil thaw, and growing season will govern the carbon balance in high latitudes (Euskirchen et al. 2006), yet the present generation ESMs typically fall short of capturing the full range of hydrological, ecological, and biogeochemical feedbacks that can occur as soils thaw and permafrost degrades. This is relevant because it is thought that on balance these feedbacks are positive and therefore would act together to amplify Arctic and global climate change (see McGuire et al. 2006 for review). The biggest concern and biggest unknown is the degree of vulnerability of the large and historically inert permafrost soil carbon pool that could be exposed to decomposition as permafrost thaws (Zimov et al. 2006; Schuur et al. 2008) and the role that permafrost thaw will have on local hydrological conditions, which strongly control the fate of the freshly thawed carbon.

Consequently, an improved and more expansive representation of permafrost and its vulnerability along with associated feedbacks that could be instigated by permafrost degradation or changes in seasonally frozen ground is an important challenge for climate change science. Until recently, however, relatively little specific effort has been made to incorporate permafrost dynamics and permafrost-related feedbacks into ESMs (Riseborough et al. 2008). Incorporating and improving permafrost dynamics in ESMs is not only required to improve the fidelity of climate projections in general but also will provide an additional powerful tool that can be used to increase our understanding of the complex interactions between terrestrial cryospheric change and Arctic and global climate.

Several efforts to incorporate and/or improve the representation permafrost in process-based land models used in global ESMs or regional climate models are underway. Examples include global models such as the Community Land Model (CLM; Lawrence et al. 2008a), Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE; Koven et al. 2009), the Joint U.K. Land Environment Simulator (JULES; Dankers et al. 2011), and the regional climate model HIRHAM (Rinke et al. 2008).

In this study, we report on the representation of near-surface permafrost in the latest version of the Community Climate System Model, version 4 (CCSM4). The characteristics of permafrost under the simulated present-day climate are assessed and compared to available observations. Projections of permafrost conditions out to 2100 are shown and the results are compared and contrasted to those obtained with CCSM3 (Lawrence and Slater 2005). There is some a priori expectation that the representation of permafrost will be superior in CCSM4 compared to CCSM3 because of improvements in the land model [CLM version 4 (CLM4)]—including principally an extension of the ground column to a depth of ~50 m (Lawrence et al. 2008a) and an explicit treatment of the thermal and hydrologic properties of organic soil (Lawrence and Slater 2008)—in conjunction with moderate reductions in Arctic climate biases in CCSM4. We also evaluate how biases in the simulated Arctic climate and initial conditions affect the present-day and future permafrost conditions. In addition, we include an assessment of projected changes in seasonally frozen ground. Finally, based on these results, we identify priorities for further model improvement.

2. Description of models and simulations

a. CCSM4 and CLM4

CCSM4 is a global climate model consisting of individual models of the atmosphere, land, ocean, and sea ice that are coupled together. An overview of CCSM4 and its performance relative to CCSM3 is provided in Gent et al. (2011). Assessments of CCSM4 that are relevant to this study and that are included in the CCSM4 Journal of Climate Special Collection include assessments of the land surface climate (Lawrence et al. 2012), the Arctic atmosphere (de Boer et al. 2012), and Arctic climate change (Vavrus et al. 2012). Note that despite a higher climate sensitivity (Bitz et al. 2012) and more twentieth-century global warming in CCSM4, Arctic changes are generally slightly smaller than in CCSM3. For example, Arctic amplification is about 16% weaker and the date by which there is a seasonally ice-free Arctic Ocean occurs about 20 years later in CCSM4 (Vavrus et al. 2011).

The land component of CCSM4 is CLM4 (Oleson et al. 2010; Lawrence et al. 2011). Biogeophysical processes simulated by CLM include solar and longwave radiation interactions with vegetation canopy and soil, momentum and turbulent fluxes from canopy and soil, heat transfer in soil and snow, hydrology of canopy, soil, and snow, and stomatal physiology and photosynthesis. CLM4 includes a 5-layer snow model that simulates processes such as accumulation, melt, compaction, snow aging, and water transfer across layers and also considers the radiative impact of aerosol deposition onto snow (Flanner et al. 2007). Improvements to CLM that were included in CLM4 that were specifically aimed at improving the representation of permafrost in CCSM4 include an extension of the ground column to a depth of ~50 m by adding 5 bedrock layers below the original 10 soil layers (Lawrence et al. 2008a) and an explicit accounting of the thermal and hydrologic properties of organic soil (Lawrence and Slater 2008), which due to the insulating properties of organic soil tended to cool the soils across much of the organic rich Arctic. Heat conduction in the soil is determined by numerically solving the second law of heat conduction equation, which requires the volumetric heat capacity and the thermal conductivity. The thermal and hydrologic properties of each soil layer are functions of soil liquid and ice water content, soil texture (sand, silt, clay, organic), and soil temperature. The boundary conditions for the solution of the heat conduction equation are the heat flux into the soil–snow at the top and zero heat flux at the bottom of the soil column. Heat advection associated with water infiltrating into and through the soil is not considered.

b. Simulations

The CCSM4 simulations analyzed here include five-member ensembles of historical (1850–2005, referred to as twentieth century) and future (2006–2100, referred to as twenty-first century) simulations. The twentieth-century simulations are described and assessed in Gent et al. (2011). For the twenty-first-century simulation, ensembles were completed for four different representative concentration projections (RCPs; Moss et al. 2010): RCP2.6, RCP4.5, RCP6.0, and RCP8.5, where the numerical value of each RCP indicates the approximate radiative forcing in the year 2100 (i.e., RCP8.5 has specified greenhouse gases and aerosol trajectories consistent with a radiative forcing of 8.5 W m−2 in the year 2100). The atmosphere and land resolution for these simulations is 0.9375° latitude × 1.25° longitude. The carbon and nitrogen (CN) cycle component for the land is active in all the CCSM4 simulations, which means that the vegetation state (phenology, canopy height) is prognostic, though the carbon and nitrogen fluxes are purely diagnostic (Lawrence et al. 2012). Twentieth century and RCP forcings include time-varying CO2 and other greenhouse gases, solar irradiance, atmospheric aerosol burden, aerosol deposition (black carbon and dust) onto snow, nitrogen deposition, and land cover and land use change. Further details about the configuration and forcing fields for these simulations are described in Gent et al. (2011) and Meehl et al. (2012). The CCSM4 simulations are compared to T85 resolution CCSM3 simulations [five-member ensembles of 1870–1999 historical simulations and the Special Report on Emissions Scenarios (SRES) (Collins et al. 2006; Meehl et al. 2006)]. Note that vegetation phenology is prescribed and land cover is static at present-day distributions for the CCSM3 simulations.

Additionally, results from offline simulations in which the land model is forced with observed meteorological data (Qian et al. 2006) are assessed for CLM4 (at the same resolution as CCSM4) and CLM3 (at T42 resolution) (Oleson et al. 2004).

We also analyze permafrost as represented in a CCSM4 last millennium (LM) simulation, which starts at a.d. 850 and runs to 2005. This simulation, including the forcings and boundary conditions for the period 850–1850, is described in Landrum et al. (2011, manuscript submitted to J. Climate). Among the LM forcings and boundary conditions included in this simulation are changes in solar irradiance, greenhouse gases, land cover, and volcanic forcing. The volcanic forcing includes several volcanic eruptions that are estimated to be much larger than those that have occurred since 1850 including major eruptions in the years 1258, 1452, and 1815. For the period 1850–2005, the same forcing and boundary conditions used in the CCSM4 twentieth-century simulations is used in the LM extension.

3. Permafrost and seasonally frozen ground in CCSM and CLM

a. Arctic land surface climate simulation in CCSM4

To first order, the simulation of permafrost and seasonally frozen ground conditions in a coupled climate model is dependent on the quality of the surface climate simulation. Here, we include assessments against observations of several variables that directly affect the soil temperature simulation: the terrestrial surface air temperature (Tair), precipitation (P), snow depth (SDP), and snow water equivalent (SWE). Figure 1 shows seasonal maps of Tair biases against Climatic Research Unit (CRU) TS2.1 temperature data (Mitchell and Jones 2005) for CCSM4 and CCSM3. Area mean biases and centered root-mean-square error (RMSE) statistics are listed in Table 1. Though the annual mean Tair biases are similar in CCSM4 and CCSM3, the CCSM4 simulation is substantially improved in winter and summer and to a lesser extent in spring, with lower area mean biases in each of these seasons. In winter, the large warm bias seen in CCSM3 is weaker and less extensive in CCSM4. In summer, the large and extensive cold bias in CCSM3 is essentially absent in CCSM4. Combined, the winter and summer bias reductions yield a more realistic annual temperature range in CCSM4. Though it is difficult to ascribe improvements to specific changes in CCSM4, the changes seen here are consistent with what one would expect along with the reduction in Arctic cloud fraction seen in CCSM4 (de Boer et al. 2012) that is related to a “freezedry” cloud parameterization included in CCSM4 (Vavrus and Waliser 2008).

Fig. 1.
Fig. 1.

(left to right) Seasonal mean 2-m air temperature bias maps for the period 1980–99 for (top) CCSM4 and (bottom) CCSM3 for Arctic land areas. Observational data are the Climatic Research Unit 0.5° × 0.5° TS2.1 (CRU TS2.1) dataset (Mitchell and Jones 2005). All model and observed data are regridded to the CCSM4 grid prior to plotting.

Citation: Journal of Climate 25, 7; 10.1175/JCLI-D-11-00334.1

Table 1.

Arctic (>55°N, 60°E–360° and excluding grid cells where glacier fraction ≠ 0%) land seasonal biases and centered RMSE for the years 1980–99 for Tair, P, SDP, and SWE (SDP and SWE for North America only; >55°N, 180°–360°). CRU is CRU TS2.1 (Mitchell and Jones 2005). MW is the version 2.01 0.5° × 0.5° monthly time series from Matsuura and Willmott (2009b; 2009a). CMAP is CPC Merged Analysis of Precipitation (Xie and Arkin 1997). CMC is snow climatology from the Canadian Meteorological Center (1980–1996) (Brown et al. 2003). For snow fields: Cpl refers to statistics for coupled CCSM simulations, and Off refers to statistics from offline CLM simulations forced with observed meteorology. Centered RMSE is calculated from climatological annual cycle time series where the annual mean of the model data and the observation data, respectively, is removed prior to the RMSE calculation. Observational data are interpolated to model grid prior to bias and RMSE calculations.

Table 1.

Precipitation is excessive in both CCSM3 and CCSM4 in all seasons (Table 1), though the bias in CCSM4 is about 10%–15% lower than in CCSM3 [see also de Boer et al. (2012) for additional analysis]. The cold season [November–April (NDJFMA)] snowfall bias, which is strongest over Alaska and western Canada (Fig. 2), corresponds with deeper than observed snow packs (Table 1) that, because of the highly insulating nature of snow, prevent the ground from cooling off as much as it should in winter, presumably leading to warm winter soil temperature biases. Both the precipitation and snow biases are substantial with cold season precipitation that is on average 79% too high across the terrestrial Arctic and snow depths that are on average 73% too deep across North America. The high-latitude precipitation bias is thought to be related to excessive poleward heat transport in CCSM4 (and CCSM3), which may lead to too much high-latitude evaporation from the oceans and high atmospheric moisture content that could then be advected over land and precipitated out; cloud physics that are too simplistic are also a potential culprit (R. Neale 2011, personal communication). This interpretation would explain why the snowfall bias is larger in Alaska and western Canada, downwind of the North Pacific Ocean, and Scandinavia, downwind of the Baltic Sea, than in the continental interiors of Asia and North America.

Fig. 2.
Fig. 2.

Bias map for climatological NDJFMA precipitation for (top) CCSM4 and (bottom) CCSM3 compared against the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) 1979–2009 “standard” (no reanalysis data) monthly data at 2.5° × 2.5° (Xie and Arkin 1997). Model data are the ensemble average for the period 1980–99. All model and observed data are regridded to the CCSM4 grid prior to plotting.

Citation: Journal of Climate 25, 7; 10.1175/JCLI-D-11-00334.1

The snow depth biases are much lower when CLM is forced offline with observed precipitation (Table 1), which indicates that it is predominantly the excessive CCSM precipitation rather than any potential inadequacies with the snow model that is generating the snow depth biases. Nonetheless, there are still biases in the offline snow depths, which suggests that the snow model may require further development, possibly to treat blowing snow and temperature gradient metamorphism that is specific to cold regions.

Other aspects of the CCSM4 Arctic simulation (with a focus on the Arctic Ocean) are assessed in Jahn et al. (2012), who find that present-day Arctic sea ice thickness and distribution is well simulated in CCSM4 and in de Boer et al. (2012) who find, among many Arctic climate features assessed, that the atmospheric boundary layer is generally too stable in CCSM4, particularly in spring, and that while summer low cloud fraction is reduced and in better agreement with observations, winter low cloud fraction is underpredicted in CCSM4.

For reference, we include annual and seasonal climate change projections (2080–99 minus 1980–99) for the CCSM4 RCP8.5 ensemble and the CCSM3 SRES A2 ensemble for Tair, snowfall and snow depth in Table 2. The changes, averaged over a domain dominated by permafrost (55°–85°N, 60°–360°E), are comparable in the two models but are not identical because of the differing climate sensitivities and climate feedbacks as well as differences in the greenhouse gas concentration scenarios.

Table 2.

Area-weighted ensemble mean change (2080–99 minus 1980–99) for selected quantities for CCSM4 RCP 8.5 and CCSM3 SRES A2. Region is 55°–85°N, 60°–360°E. Glacier points are excluded. Percentage change for snowfall and SDP are shown in parentheses.

Table 2.

b. Present-day permafrost in the model

The simulated present-day (1980–1999) near-surface permafrost distribution and active-layer thickness (ALT) is shown in Fig. 3 for both offline CLM4 and CLM3 and coupled CCSM4 and CCSM3 experiments. A model grid cell is identified as containing near-surface permafrost if there is at least one soil layer within the upper 10 soil layers that remain frozen throughout the year (10 layers equates to 3.8-m depth in CCSM4–CLM4 and 3.5-m depth in CCSM3–CLM3). The ALT in each near-surface permafrost grid cell is calculated based on 20-yr climatological mean (and ensemble mean for coupled simulations) monthly data. For the month of maximum soil temperature (typically August or September), the soil temperature at the 10 discrete soil levels is linearly interpolated to 200 evenly spaced levels. The ALT is defined as the depth where the vertically interpolated soil temperature crosses the 0°C threshold. Overall, the simulated near-surface permafrost distribution corresponds reasonably with observed permafrost distribution estimates (Brown et al. 1998). Note that the observed distribution does not discriminate between near-surface and deeper permafrost, though most of the area identified as permafrost in the observed estimate is likely to contain permafrost within the upper 3.5 m of the ground. The CLM3 and CCSM3 simulations miss the permafrost in southern and western Siberia and in general the southern margin of permafrost extent is a few degrees latitude too far north in these simulations. The integrated area for CCSM3 and CLM3 is 11.7 and 11.1 × 106 km2 for the period 1970–89, which is below the 15.8 (11.8–14.6) × 106 km2 estimate for the Northern Hemisphere area of continuous (90%–100% coverage) and discontinuous permafrost (50%–90% coverage) combined (Zhang et al. 2000). Note that the modeled and observed area includes permafrost in Tibet, which is not shown in Fig. 3. Note that the comparison between simulated and observed permafrost extent is not exact as the modeled and observed quantities are slightly different. The model simulates only one soil temperature column for each grid cell and therefore each grid cell either contains near-surface permafrost or it does not. This means that the model cannot discriminate between continuous and discontinuous permafrost; however, because discontinuous permafrost is defined as occupying more than 50% of a given area (which means that the mean soil temperature should be below 0°C), the model should in principle be able to capture the discontinuous area. When the global integral of near-surface permafrost area is performed, the subgrid area within a grid cell that might not actually be permafrost if, for example, a higher-resolution simulation was performed is counted as permafrost. Consequently, the integrated modeled near-surface permafrost area is likely biased high (compared, for example, to a hypothetical much higher-resolution simulation). Based on this reasoning, the upper 14.6 × 106 km2 estimate of observed continuous plus discontinuous permafrost area is the more appropriate target.

Fig. 3.
Fig. 3.

Permafrost distribution and active-layer thickness for the period 1980–99: (left) (top) CLM3 and (bottom) CCSM3; (middle) (top) CLM4 and (bottom) CCSM4; and (right) observed. Observed distribution is from the International Permafrost Association (Brown et al. 1998). Note that only continuous and discontinuous permafrost areas are shown.

Citation: Journal of Climate 25, 7; 10.1175/JCLI-D-11-00334.1

The permafrost distribution is improved in CCSM4 and CLM4. The CLM4 simulation matches the observed distribution reasonably with the southern edge of permafrost accurately captured across Siberia, Alaska, and Canada. Active-layer thicknesses are generally shallower in CLM4 and CCSM4 compared to CLM3 and CCSM3 and also exhibit more spatial variations. The shallower ALTs are primarily due to the inclusion of a spatially explicit representation of the impact of organic matter on the thermal and hydrologic properties of the soil (Lawrence and Slater 2008) and to a lesser extent the extension of the ground column to ~50-m depth (Lawrence et al. 2008a). We note that, although CLM4 does prognostically calculate soil carbon content, for these simulations the simulated soil carbon content is not used to set the thermal and hydrologic soil properties because of an extreme low bias in simulated Arctic soil carbon stocks in the CLM4 soil biogeochemistry model. The low soil carbon bias is likely at least partially due to the lack of a representation in CLM of critical soil processes that govern the accumulation of soil organic material in the cold, moist high-latitude climate regime such as decomposition constraints due to anoxia and the mixing of organic matter into the soil through cryoturbation (Koven et al. 2009).

In Fig. 4, we show ALT from the model compared against ALT measurements from the Circumpolar Active Layer Monitoring (CALM) dataset (Brown et al. 2000). To generate these scatterplots, for each of the 202 CALM sites we calculate the average ALT (individual sites have between 1 and 20 years of data) and then plot this value against the climatological (1980–99) ALT from the model in the grid cell that overlaps the CALM site. If more than one CALM site is located within a model grid cell, then we average the data from the CALM sites together. This comparison of observed to modeled suffers from a scale mismatch: CALM sites report ALT at a particular location while the model represents a gridcell mean ALT. Furthermore, measured ALT varies substantially over small distances. At many CALM sites, data are collected over a 100 m × 100 m domain at 10-m intervals or 1000 m × 1000 m domain at 100-m intervals yielding up to 121 data points at each site. The standard deviation of ALT measurements at a site typically varies from 10%–30% of the mean. Despite these issues, the differences in the CALM–model comparisons across model versions is enlightening. In both CLM3 and CCSM3, the ALT is too thick compared to nearly all the CALM measurements. For CLM4 and CCSM4, though the scatter is still substantial the mean bias is reduced (note that several CALM sites near coastlines were not used in the analysis because of an error in the organic matter content in the CLM4 surface dataset for some coastal grid points). For CLM4 in particular, the ALT is actually too shallow compared to CALM for a substantial fraction of sites/grid cells. The very shallow ALTs in CLM4 may be caused in part by what appears to be excessively dry simulated soil conditions in the active layer in organic rich regions (Lawrence et al. 2011). Dry near-surface organic soils have very low thermal conductivity (O’Donnell et al. 2009), which restricts heat from penetrating into the soil during summer, leading to the cool soil temperatures and shallow active layer in CLM4. The dry active layer is unrealistic and is indicative of limitations of the soil hydrology scheme in frozen or partially frozen conditions. Research and model development is underway to improve cold region hydrological processes in CLM, the results of which will be reported in forthcoming papers.

Fig. 4.
Fig. 4.

Scatterplot of ALT for CALM sites vs model for CLM3, CLM4, CCSM3, and CCSM4. Model data are from ensemble mean climatological average for the period 1980–99. CALM site data are climatological average, but with each site containing a different number of years of data. See text for further details.

Citation: Journal of Climate 25, 7; 10.1175/JCLI-D-11-00334.1

With the inclusion of extra ground layers in CLM4, we can now evaluate deep ground temperatures against borehole observations. We compare borehole measurements that were taken during the International Polar Year (2007/2008, Romanovsky et al. 2010) against model data for regions north of 65°N and depths greater than 15 m. Model values are from the end of the historical simulations (2005 in CCSM4 and 2004 in CLM4). All borehole sites within a single model grid box are averaged as are observations at different depths. This “gridcell observation” is compared to the corresponding model layer 14 (21 m deep midpoint) and 15 (35 m deep midpoint) Tsoil average. As with the model–data ALT comparison, there are limitations in the model–data borehole analysis related to point versus area comparisons and the limited spatial coverage of observations. Nonetheless, the overall biases are easily discernable (Fig. 5). CCSM4 exhibits a warm bias, averaged across all sites, of almost 3°C with almost all points warmer than observations; this is perhaps not surprising given that over a third of compared grid boxes (23 out of 60) are in Alaska where the excess snowfall bias is prominent. When the climate biases are eliminated through offline CLM4 simulations, the comparison is much better with the modeled Tsoil14–15 scattered above and below the borehole observations and a smaller mean cold bias of −1.3°C (the fact that the mean bias is cold is consistent with the hypothesis raised above that dry active layers are leading to excessively cool soil temperatures).

Fig. 5.
Fig. 5.

Scatterplot of deep ground temperature for borehole sites (observed) vs model for (left) CCSM4 and (middle) CLM4. Model data are from ensemble mean year 2005 for CCSM4 and 2004 for CLM4. Observed data are from 2007/2008. (right) Borehole locations used. See text for further details.

Citation: Journal of Climate 25, 7; 10.1175/JCLI-D-11-00334.1

c. Projections of near-surface permafrost degradation

Historical and future projections for each of the SRES and RCP scenarios of near-surface permafrost extent for CCSM3 and CCSM4 are shown in Fig. 6. We define near-surface permafrost degradation as a contraction of the integrated simulated area with near-surface permafrost. For a particular grid cell, permafrost degradation occurs when the permafrost table drops below the 10th soil layer. In CCSM4, the main result, as it was with CCSM3 (Lawrence and Slater 2005; Lawrence et al. 2008a), is that large-scale degradation of near-surface permafrost is projected for the twenty-first century, especially under the more severe greenhouse gas emission scenarios. For RCP 8.5, for example, near-surface permafrost extent drops from 12.5 × 106 km2 in the late twentieth century to only 3.5 × 106 km2 by the end of the twenty-first century (see Table 3). The less severe emission scenarios show progressively less permafrost degradation. In RCP2.6, near-surface permafrost extent has nearly stabilized by 2100 in response to a stabilizing Arctic climate.

Fig. 6.
Fig. 6.

Time series of Northern Hemisphere near-surface permafrost extent for CCSM3 and CCSM4 for historical and projection periods. Near-surface permafrost extent is the integrated area of grid cells with at least one soil layer within the top 10 soil layers (3.5 m in CCSM3, 3.8 m in CCSM4) that remains frozen throughout the year. Frozen ground underneath glaciers is not included in the near-surface permafrost extent. The greenhouse gas concentration in CO2 equivalents (ppm) for the year 2100 are listed in parentheses for each SRES and RCP scenario. Shading indicates the ensemble spread.

Citation: Journal of Climate 25, 7; 10.1175/JCLI-D-11-00334.1

Table 3.

Total Northern Hemisphere area containing near-surface permafrost (×106 km2, ensemble mean), or total area containing continuous and discontinuous permafrost in the case of the observed estimate for selected periods. Percentage loss relative to 1970–89 period is listed in parentheses. Climate bias correction results are italicized, see section 3f.

Table 3.

Despite the broad similarities in the results in CCSM3 and CCSM4, there are several important differences between the projections in these two versions of the model. First, there is considerably less variability in near-surface permafrost extent in CCSM4 (cf. oscillations and ensemble spread over historical period). Interannual variability in Tair and P is similar in the CCSM3 and CCSM4 historical simulations (within about 10% of each other), but Tsoil1m interannual variability is about 35% lower in CCSM4 owing to the damping effects of soil organic matter. Second, the rate of degradation during the first half of the twenty-first century is approximately 35% slower in CCSM4, which is similar to the 27% slower degradation rate seen when organic soil and a deeper soil column were introduced into CLM 3.5 (Lawrence et al. 2008a). The slower degradation rate in CCSM4 means that much less permafrost is degraded by 2050 than in CCSM3 (Table 3).

Once permafrost begins to thaw in an individual model grid cell, it often proceeds rapidly under continued warming. Lawrence et al. (2008b) showed that active-layer deepening and permafrost thaw in CLM proceeds in a nonlinear fashion even under a linear warming scenario, with near-surface permafrost disappearing quickly once a talik forms (a talik is a layer of perpetually unfrozen ground above the permafrost table). Similar features are seen in the CCSM4 simulations. As an example, we show the vertically resolved time series of fractional soil ice content for an illustrative grid cell in northern European Russia (Fig. 7). The depth of seasonal soil thaw, that is, the active layer, varies from year to year in step with interannual climate variations, but it does not exhibit a clear deepening trend until around 1985 (only 1970 onward is shown). In the year 2008, a talik forms that lasts for a few years, before a sequence of relatively cool summers along with low snow depths in the early to mid-2010s temporarily refreezes the talik. By 2015, however, a perpetual talik forms and permafrost rapidly degrades thereafter leaving only seasonally frozen ground. The rate of talik thickness expansion from 2008 to 2020 is about 0.2 m yr−1 at this location, which is within the observed range of talik thickness expansion rates (0.02–0.8 m yr−1) for that region in northern European Russia (Oberman 2008).

Fig. 7.
Fig. 7.

Fractional soil ice content with depth for a subjectively chosen illustrative grid point in northern European Russia (68.3°N, 62.5°E) showing rapid soil ice melt after talik formation. At this location, talik first occurs at 1.5–2-m depth at year 2008, refreezes in 2011, and then reforms in 2015.

Citation: Journal of Climate 25, 7; 10.1175/JCLI-D-11-00334.1

Effectively, talik formation heralds the beginning of the end for permafrost at a particular location as permafrost is no longer sustainable though degradation at depth may proceed on much longer time scales. In Fig. 8 we present maps of the year of the first occurrence of talik for RCP2.6 and RCP8.5. Since talik can appear and disappear in response to climate variability, we require that the talik persist for three years. We define talik in the model as the occurrence in a permafrost grid cell of a soil layer in which the soil ice fraction does not exceed 0.25 in any month of the year. For RCP8.5, a large portion of the permafrost domain experiences talik formation between 2040 and 2060, with the colder regions further north developing talik only by the end of the twenty-first century or not at all. In RCP2.6, talik formation occurs later or not at all at most locations.

Fig. 8.
Fig. 8.

Maps of the ensemble mean year of first occurrence of talik (that lasts at least three years) at all grid cells where permafrost is present at the year 1850 in CCSM4. Results from RCP8.5 and RCP2.6 are shown. Magenta color is plotted if a talik does not form by year 2100 in one or more of the ensemble members.

Citation: Journal of Climate 25, 7; 10.1175/JCLI-D-11-00334.1

d. Seasonally frozen ground

We also examine the simulation of seasonally frozen ground. The total estimated area experiencing seasonally frozen ground (including the permafrost area), based on a simple 0°C isotherm, is 48.1 × 106 km2 (Zhang et al. 2003). In the CCSM and CLM simulations, we identify a grid cell as experiencing seasonally frozen ground for a particular time period when we identify at least one monthly frozen soil layer during the climatological ensemble mean annual cycle. In both CLM4 and CCSM4, the seasonally frozen ground area is reasonably captured (48.5 and 47.1 × 106 km2 in CLM4 and CCSM4, respectively, Table 4). The good correspondence in CCSM4 mainly indicates a reasonable wintertime climate simulation. Figure 9 shows the maximum depth of seasonal soil freeze in CCSM4. The depth of soil freeze varies substantially and penetrates as far as 2.5 m in areas of Tibet. Throughout much of arid western China the depth reaches 2 m.

Table 4.

Total Northern Hemisphere area with seasonally frozen ground, including permafrost area (106 km2) for selected periods. Percentage loss relative to 1970–89 period is listed in parentheses.

Table 4.
Fig. 9.
Fig. 9.

Maps showing depth of seasonal soil freeze for (top) present day and (bottom) 2080–99 for (left) RCP 8.5 and (right) RCP 2.6. Purple color denotes grid cells that are either permafrost or glacier. Integrated areas are 34.2, 35.5, and 36.0 × 106 km2, for present day, RCP8.5 and RCP2.6, respectively, which includes newly formed seasonally frozen ground in grid cells that have experienced permafrost thaw. When original present-day permafrost area is excluded from the 2080–99 seasonally frozen ground area, the area is 26.9 and 32.3 × 106 km2 for RCP8.5 and RCP2.6, respectively.

Citation: Journal of Climate 25, 7; 10.1175/JCLI-D-11-00334.1

e. Projections of changes in seasonally frozen ground

Not surprisingly, the depth of seasonal ground freeze shallows substantially virtually everywhere under RCP8.5 (Fig. 9). Decreases of ~1 m are seen over vast portions of Asia. In addition, extensive regions that contained near-surface permafrost at the end of the twentieth century are characterized as seasonally frozen by the end of the twenty-first century in RCP8.5. Though it is difficult to see clearly in Fig. 9, the area that experiences seasonal or perpetual soil freeze drops considerably in RCP8.5, from 47.5 × 106 km2 at the end of the twentieth century to 40.4 × 106 km2 at the end of the twenty-first century (Table 4). Another way of looking at this is to examine the change in the integrated volume of frozen soil over the calendar year, expressed in units of km3 months. Over the Northern Hemisphere, CCSM4 simulates about (0.25–0.30) × 106 km3 months of frozen soil in the preindustrial era (Fig. 10, this number does not include the volume of frozen ground in simulated permafrost regions). By the year 2000, this number has dropped to about 0.20 × 106 km3 months. By 2100 in RCP8.5 it drops a further 0.08 × 106 km3 months to 0.12 × 106 km3 months.

Fig. 10.
Fig. 10.

Time series of Northern Hemisphere globally integrated frozen ground in millions of km3 months for CCSM4.

Citation: Journal of Climate 25, 7; 10.1175/JCLI-D-11-00334.1

f. Impact of simulated climate biases on present-day and future permafrost

Here, we evaluate the impact of climate biases on the representation of present-day and future permafrost conditions. Differences in the representation of permafrost in coupled versus offline simulations are a result of climate model biases and their influence on subsurface thermal and hydrologic states. Though the distribution of near-surface permafrost simulated in CCSM4 is fairly reasonable, it exhibits about 10%–15% lower areal extent than is found in offline CLM4 simulations that are forced with observed climate. The eastern Alaska and western Canada region (62°–72°N, 125°–145°W) presents an interesting illustration of the impact that climate biases can have. In this region, permafrost is present in CCSM3 (Fig. 3), as it should be based on the IPA permafrost distribution map (Brown et al. 1998), but it is not found in CCSM4 despite the superior soil physics, especially including a representation of the thermal and hydrologic properties of organic soil, in CLM4 and an improved Tair simulation in this region (see Fig. 1). It appears that permafrost is sustained in CCSM3 mainly because of a substantial summer cold bias (5°C averaged over this domain, see Fig. 11) that limits summertime soil heating. In CCSM4, this cold bias has been eliminated and therefore the soils experience much warmer and more realistic summertime conditions. The impact of the Tair bias is clear in the Tsoil annual cycle-depth plots for this region that are shown in Fig. 12 for CCSM3, CCSM4, CLM3, and CLM4. The maximum near-surface Tsoil is 5°C cooler in CCSM3 than in CCSM4, which results in far less heat penetration into the ground. The problem in CCSM4 is that excessive snowfall leads to an overly deep wintertime snowpack (which occurs in both models, though the bias is smaller in CCSM4, Fig. 11). The deeper than observed snowpack insulates the ground from cold winter atmospheric conditions, thereby preventing it from cooling down by the end of winter as much as it should. In CLM4, when observed precipitation is used, the snow depth is much more accurately simulated (Lawrence et al. 2012) and consequently the ground cools more during the winter, which helps maintain perpetually frozen conditions at depth through the summer. The cooling impact of organic soil and the improved snow model can be seen by comparing the annual cycle-depth soil temperature plots for CLM3 and CLM4.

Fig. 11.
Fig. 11.

Climatological annual cycle time series (1980–99) for ensemble mean from CCSM3 and CCSM4 compared against observations averaged over eastern Alaska–western Canada region (62°–72°N, 125°–145°W). Snow depth as simulated in CLM3 and CLM4 is also shown. Observations for (top) Tair are from Matsuura and Willmott (2009b). (bottom) Snow depth observations are from Canadian Meteorological Center (1980–96) (Brown et al. 2003).

Citation: Journal of Climate 25, 7; 10.1175/JCLI-D-11-00334.1

Fig. 12.
Fig. 12.

Climatological annual cycle-depth plots of soil temperature for CCSM3, CCSM4, CLM3, and CLM4 averaged over same eastern Alaska/western Canada domain as shown in Fig. 11.

Citation: Journal of Climate 25, 7; 10.1175/JCLI-D-11-00334.1

Clearly, the simulated climate biases have a strong influence on the simulation of present-day permafrost. It is also interesting to consider to what extent these climate biases influence the projected trajectory of permafrost conditions. To investigate this question, we performed a set of offline CLM4 simulations that were forced with data that were archived from one of the CCSM4 twentieth century and RCP ensemble members. A bias-corrected forcing dataset was generated from the CCSM4 data by calculating monthly climatologies from 20 years (1980–99) of data from the standard CLM4 offline forcing dataset (Qian et al. 2006) as well as from the CCSM4 ensemble member output. State variables such as Tair were corrected by adding the difference between the observed climatology and the CCSM4 climatology at each grid point. Flux variables, such as P, were corrected using scale factors, computed as the ratio of the observed climatology to the CCSM4 climatology. These adjustments were then applied to the CCSM4 forcing data for an 1850–2100 offline CLM4 simulation. Note that snow depth was not adjusted explicitly. Instead, precipitation is adjusted to match observed precipitation estimates and then the CLM4 snow model calculates snow depth based on processes such as accumulation, sublimation, and snow compaction due to wind, snow metamorphism, and melt-freeze processes. The bias-correction method effectively removes the present-day climate bias but retains the simulated climate change signal. It assumes that the climate bias is constant throughout the simulation, though some studies suggest that the climate bias may not actually be constant (Reifen and Toumi 2009).

The results of this exercise are shown in Fig. 13 in the form of Northern Hemisphere near-surface permafrost extent time series (1850–2100 for RCP8.5) for a coupled CCSM4 simulation (i.e., including the CCSM4 climate bias) and a simulation with the climate bias removed by the method described above. Mean permafrost extent values for several 20-yr periods are also reported in Table 3. Throughout the twentieth century, there is about 1 × 106 km2 more near-surface permafrost, which brings the amount of permafrost in line with that seen in control offline CLM4 simulations forced with observed data. This agreement indicates that the method to remove the climate bias from the CCSM4 data has worked. Deep soil temperatures and active-layer thicknesses in the bias-corrected simulation are also qualitatively similar to those in control offline CLM4 simulations (not shown). During the twenty-first century, the rate of near-surface permafrost degradation is about 29% slower in the bias-corrected simulation compared to the CCSM4 simulation presumably due to colder initial permafrost conditions.

Fig. 13.
Fig. 13.

Time series of Northern Hemisphere near-surface permafrost extent for a single CCSM4 twentieth century and RCP8.5 (solid lines) and RCP2.6 (dashed lines) ensemble member and for offline CLM4 simulations forced with climate bias-corrected data from the same CCSM4 simulation (see text for details).

Citation: Journal of Climate 25, 7; 10.1175/JCLI-D-11-00334.1

g. Last millennium simulation

Lastly, we examine the trajectory of near-surface permafrost extent and deep Tsoil in a simulation of the LM that runs from 850 to 1850 with an extension to 2005. Time series of annual mean regionally averaged (Siberia, 65°–85°N, 90°E–180°) Tair and Tsoil15 (layer-15 soil temperature), and near-surface permafrost extent from the LM simulation and its twentieth-century extension as well as for the standard CCSM4 twentieth-century simulations are shown in Fig. 14. There is a slight cooling trend in this region over the period 850–1850 of ~1.0°C, which is larger than the global cooling of ~0.3°C [see Landrum et al. (2011, manuscript submitted to J. Climate) for further discussion]. Near-surface permafrost extent increases by about 1.5 × 106 km2 over the period 850–1850, reflecting the cooling. Spikes in near-surface permafrost extent occur directly after several of the major volcanic eruptions, most notably after the eruption in 1258. The increase in near-surface permafrost extent is a response to the cooler posteruption-simulated climate, which can generate fresh permafrost if summer Tair is cool enough not to thaw a particular CLM ground layer. This fresh permafrost is thin and is vulnerable to thaw once the climate returns back to prevolcanic eruption conditions. The cold period during 1600–1850 similarly increases near-surface permafrost extent.

Fig. 14.
Fig. 14.

Time series of (top) Tair and (middle) Tsoil15 (layer-15 soil temperature, ~35-m deep layer midpoint) and (bottom) near-surface permafrost extent from LM simulation (years 850–1850) and the twentieth-century extension (solid line) and from the CCSM4 twentieth-century ensemble (dotted line with gray shading showing ensemble range). Here, Tair and Tsoil15 are averaged over Siberian region (55°–85°N, 90°E–180°). A 7-yr running average is used to smooth the Tair and Tsoil15 time series for clarity of presentation.

Citation: Journal of Climate 25, 7; 10.1175/JCLI-D-11-00334.1

In the LM 1850–2005 extension, Tsoil15 warms and near-surface permafrost extent decreases at approximately the same rate as it does in the CCSM4 twentieth-century ensemble. Though it is difficult to establish through these simulations alone, the implication is that the cooler 1850 “initial conditions” that are seen at the end of the LM simulation (Tair and Tsoil15 are ~0.8°C cooler for Siberia, 1850–70) do not appear to substantively affect permafrost thaw rates during the twentieth century, though since the whole climate system begins at a cooler state, the more extensive 1850 permafrost results in slightly more near-surface permafrost at 2000 (~0.7 × 106 km2 more). Whether additional near-surface permafrost would exist by 2100, compared to the standard CCSM4 twenty-first-century simulations, would depend upon the particular climate trajectory as the difference in extent decreases from 1850–2000. Regardless of potential differences by 2100, the potential for feedbacks like the hypothesized permafrost–carbon feedback is most dependent upon the amount of permafrost thaw that occurs over the twenty-first century, not the amount of permafrost that remains at the end of the twenty-first century.

4. Discussion

Though the representation of permafrost in CCSM4 is better than it was in CCSM3, further improvements are still desirable. The simulated Arctic climate in CCSM4 is better especially with respect to temperature, but the remaining biases are substantial and these biases adversely affect the permafrost simulation. Excessive wintertime snowfall and the associated deeper-than-observed snowpack lead to too much insulation of the ground, thereby preventing the soil from cooling off in winter as much as it should. We showed that permafrost projections with the climate biases removed significantly altered the projected rates of degradation. The climate bias-corrected projection is probably more reliable, but there are limitations to studies that rely solely on offline simulations, especially if one is interested in permafrost thaw feedbacks onto carbon, water, and energy cycles, which can only fully be studied in the context of a coupled model. The results presented here indicate that to enhance the utility of coupled model permafrost studies with CCSM, additional efforts to reduce simulated Arctic climate biases are required. Preliminary analysis of snowfall rates in the next version of CAM (CAM5) suggests an improvement over those in CAM4 with about 0–1 mm day−1 lower snowfall rates seen over most of the terrestrial Arctic region. The improvement is possibly due to a more physically based cloud microphysics scheme and improved precipitation processes (Morrison and Gettelman 2008).

In addition to improvements in CCSM, there are several desirable improvements to CLM that once implemented should also increase the realism and utility of the model for permafrost studies. Principally, a correction for the apparent dry near-surface soil moisture bias (see Lawrence et al. 2011 section 4.6 for further discussion) is critical. The dry near-surface organic soils simulated in CLM4 have very low thermal conductivity which severely restricts heat penetration into the soil during summer, leading to cool soil temperatures and a shallow active layer (see Fig. 12). Cold region hydrology in general remains a weakness in the model and is an active area of model development, the results of which will be reported in a forthcoming study. Additional areas of planned development include the incorporation of a representation of excess soil ice [pockets of ice that are commonly found in permafrost ground that are present in excess of the available soil pore space (Zhang et al. 2000)], improvements to the CLM soil biogeochemistry to resolve the low northern high-latitude soil carbon bias, and an explicit treatment of peatlands, which are likely to respond in a different fashion to climate change than upland regions (Wisser et al. 2011). A process-based terrestrial methane emissions model, which will permit the investigation of permafrost thaw–methane emissions feedbacks, has also recently been incorporated in a developmental version of CLM (Riley et al. 2011).

It is important to acknowledge that the local response of permafrost to climate warming will be sensitive to subgrid-scale properties such as aspect, slope, soil properties, and vegetation cover as well as by the dynamic and often very local evolution of surface characteristics such as ground microtopographical transformation due to subsidence or thermal erosion, vegetation community evolution, and lake and wetland area–distribution change in response to climate change or disturbances such as fire or thermokarst (Rowland et al. 2010; Grosse et al. 2011). Representing these subgrid-scale surface changes in a large-scale model remains a significant unresolved challenge for spatially explicit models.

The availability of data that can be used to assess permafrost in large-scale models has improved since the 2007/2008 IPY, with the number of available ALT measurements and borehole temperatures increasing substantially. However, many limitations such as spatial sparsity and relatively short time series still exist. Highly variable local responses to the factors mentioned above and observations that pockets of relict permafrost can persist under climates in which permafrost would appear to be unsustainable (Froese et al. 2008) further confound the process of model–data comparisons.

Lastly, we note that there are indications in the CCSM simulations of a potentially climatically important energy feedback that is related to permafrost thaw. The presence of permafrost and soil ice acts as an ice box in the summertime Arctic system. In permafrost zones, a higher proportion (compared to nonpermafrost zones) of available surface energy is devoted to heating the ground and melting soil ice, rather than heating the atmosphere. This effect is apparent in Fig. 15, which shows the fraction of surface net radiation (net shortwave plus net longwave radiation) that goes into ground heat flux (GH/RNET) for July and August at present day (1980–99). GH/RNET is highest over permafrost regions, reaching 0.15 (meaning that 15% of surface net radiation goes into ground heat flux while the remainder goes into sensible and latent heat fluxes) in selected locations. Over most of the nonpermafrost domain, GH/RNET is about 0.04–0.08. By the end of the twenty-first century, when the bulk of the soil ice has melted, midsummer GH/RNET in most permafrost zones decreases by 0.02–0.08, which corresponds to about 2–8 W m−2. This effectively means that more energy is available to go into sensible and latent heat fluxes that will warm and moisten the atmosphere. In effect, this is a reduction in the cooling effect of permafrost on near-surface climate. Put another way, the summertime permafrost icebox effect is strongly reduced by the end of the twenty-first century for most regions. The climatic impact of this projected change in the surface energy balance bears further investigation.

Fig. 15.
Fig. 15.

Map of July–August (JA) (left) GH/RNET for 1980–99 and for (right) Δ(GH/RNET) for 2080–99 minus 1980–99 for RCP8.5.

Citation: Journal of Climate 25, 7; 10.1175/JCLI-D-11-00334.1

5. Summary

The combined impact of improvements to CLM and a more accurate climate simulation improve the representation of the permafrost in CCSM4 compared to CCSM3. Though evaluation of the permafrost simulation is limited by the availability of observations and the scale mismatch between point measurements and gridcell quantities, comparisons of model data against available observations indicate improvements in the permafrost distribution, active-layer thickness, and permafrost temperatures. The area containing near-surface permafrost is projected to decline substantially during the twenty-first century in CCSM4, as in CCSM3. The rate of degradation, however, is slower by about 35% during the first half of the twenty-first century in CCSM4 than it was in CCSM3 because of improved soil physics. Under the highest emission scenario (RCP8.5), the majority of present-day permafrost is projected to undergo significant active-layer deepening and permafrost degradation. The total projected loss of near-surface permafrost from the end of the twentieth century to the end of the twenty-first century is 9.0 × 106 km2 (note that this total does not include the probable degradation of sporadic and isolated permafrost, which the model does not resolve). Under the low emissions pathway (RCP2.6), the projected near-surface permafrost area contraction is much less severe (loss of 4.1 × 106 km2) and the permafrost state effectively stabilizes by 2100 in response to a stabilizing climate. This result implies that the permafrost–carbon feedback could be largely constrained if a low enough emissions pathway is followed. Simulations of the last millennium show variations in permafrost extent of about 1 × 106 km2 in response to major volcanic eruptions and other climate variations. Total permafrost extent rises very slowly up to 1850 before beginning to decline during the twentieth century.

Although there is a general improvement in the simulated terrestrial Arctic climate in CCSM4, the remaining biases are substantial and these biases degrade the permafrost simulation, as evidenced by the poorer agreement with observations of permafrost distribution, active-layer thickness, and permafrost temperature in coupled CCSM4 versus offline CLM4 simulations. When CLM4 is forced with CCSM4 data that have been corrected to remove the CCSM4 climate bias, the present-day permafrost temperatures are cooler, better matching borehole data. For the RCP8.5 emissions scenario, the bias correction leads to ~29% less contraction of near-surface permafrost extent than in the corresponding CCSM4 simulation (near-surface permafrost extent reduction of 6.4 × 106 km2 in bias-corrected simulation versus 9.0 × 106 km2 in coupled simulation, 2080–99 minus 1980–99). The substantial difference in permafrost projections in the biased and debiased simulations suggests that a further reduction of Arctic climate biases should be a high priority for the CCSM development community.

In addition to contractions in permafrost area, large reductions in the area experiencing seasonally frozen ground are projected for the twenty-first century with a reduction of 7.1 × 106 km2 (15%, 47.5 to 40.4, 2080–99 minus 1970–89) projected for RCP8.5 and 2.1 × 106 km2 (4%) for RCP2.6. The integrated volume of ground frost (penetration depth and frost season length) decreases by 40% (RCP 8.5). Changes in frozen ground can alter the hydrologic cycle and transition-season surface energy fluxes as well as induce shifts in vegetation phenology and affect agricultural practices and productivity.

Permafrost degradation of the magnitude simulated in CCSM4 is likely to initiate several hydrological, biogeochemical, and ecological feedbacks in the Arctic system that remain poorly represented in CLM and CCSM specifically and global land and Earth System models in general. The broad scientific challenge is to increase our understanding and representation of the complex hydrological, biogeophysical, and biogeochemical feedbacks that are anticipated in the Arctic. Future work of the CLM and CCSM development community will focus on advances in land model biogeophysics (e.g., prognostic wetland distribution, dynamic vegetation, cold region hydrology, subgrid-scale permafrost, thermokarst) and biogeochemistry (vertically resolved soil biogeochemistry, aerobic versus anoxic decomposition) as well as the CCSM representation of Arctic climate, all of which are required to better represent the impact of climate change on permafrost and the feedbacks of permafrost degradation on regional and global climate.

Acknowledgments

NCAR is sponsored by the National Science Foundation (NSF). The CCSM/CESM project is supported by the NSF and the Office of Science (BER) of the U.S. Department of Energy. Computing resources were provided by the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory (CISL), sponsored by NSF and other agencies. This research was enabled by CISL compute and storage resources. Bluefire, a 4064-processor IBM Power6 resource with a peak of 77 TeraFLOPS provided more than 7.5 × 106 computing hours, the GLADE high-speed disk resources provided 0.4 PetaBytes of dedicated disk, and CISL’s 12-PB HPSS archive provided over 1 PetaByte of storage in support of this research project. We thank the NCAR software engineers, without which the development of CCSM4 would not be possible. In particular, we thank E. Kluzek, M. Vertenstein, T. Craig, and B. Kaufmann for their invaluable contributions to the development of CLM4 and Bette Otto-Bliesner and Laura Landrum for useful input on the last millennium simulations. DML and SCS are supported by funding from the U.S. Department of Energy BER, as part of its Climate Change Prediction Program, Cooperative Agreement DE-FC03-97ER62402/A010. AGS acknowledges support from NSF Grants ARC0902057 and ARC0901962. Lastly, we thank the three anonymous reviewers whose constructive comments helped improve the paper.

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