Hydrologic-land surface models (H-LSMs) offer a physically-based framework for representing and predicting the present and future states of the extensive high-latitude permafrost areas worldwide. Their primary challenge, however, is that soil temperature data are severely limited, and traditional model validation, based only on streamflow, can show the right fit to these data for the wrong reasons. Here, we address this challenge by (1) collecting existing data in various forms including in-situ borehole data and different large-scale permafrost maps in addition to streamflow data, (2) comprehensively evaluating the performance of an H-LSM with a wide range of possible process parametrizations and initializations, and (3) assessing possible trade-offs in model performance in concurrently representing hydrologic and permafrost dynamics, thereby pointing to the possible model deficiencies that require improvement. As a case study, we focus on the sub-arctic Liard River Basin in Canada, which typifies vast northern sporadic and discontinuous permafrost regions. Our findings reveal that different process parameterizations tend to align with different data sources or variables, which largely exhibit inconsistencies among themselves. We further observe that a model may fail to represent permafrost occurrence yet seemingly fit streamflows adequately. Nonetheless, we demonstrate that accurately representing essential permafrost dynamics, including the active soil layer and insulation effects from snow cover and soil organic matter, is crucial for developing high-fidelity models in these regions. Given the complexity of processes and the incompatibility among different data sources/variables, we conclude that employing an ensemble of carefully designed model parameterizations is essential to provide a reliable picture of the current conditions and future spatio-temporal co-evolution of hydrology and permafrost.

The climate in Northwest China (NWC) has undergone a warming and wetting trend (WWT) since the 1980s, which has attracted considerable attention from the scientific and policy communities. However, the majority of previous studies have focused on overall effects of WWT, and very few have examined how land surface system responds to climate warming or wetting trend, respectively. For this purpose, this study uses the Community Land Model (CLM5) driven by the Chinese Meteorological Forcing Dataset (CMFD) to conduct four modeling experiments: a control experiment (CTRL) and three sensitivity experiments, in which the annual trend of air temperature (NonWarm), precipitation (NonWet), and both (NonWWT) are removed from the CMFD from 1979 to 2018. Compared to CTRL, the land hydrological variables (i.e. soil moisture, runoff and evapotranspiration) show a visible reduction in magnitude, interannual variability, as well as annual trend in NonWet, while they are enhanced in NonWarm. In both NonWarm and NonWet, the magnitude and trend of both net radiation and sensible heat fluxes increase, with a more pronounced change in NonWWT. Further analysis indicates that the land surface processes are more sensitive to wetting trend than to warming trend. Among all land surface hydrological variables and energy variables, runoff and snow cover fraction are the most susceptible to climate change. Overall, the effects of climate change in Ta and Pr on surface hydrological variables are non-linearly offsetting, while the effects on surface energy budgets are non-linearly superimposed. Compared to warming trend, wetting trend plays a larger impact on the variability of land surface processes in NWC.

Arctic land is characterized by a high surface and subsurface heterogeneity on different scales. However, the effects of land surface model resolution on fluxes and soil state variables in the Arctic have never been systematically studied, even though smaller scale heterogeneities are resolved in high-resolution land boundary condition datasets. Here, we compare 210 km and 5 km setups of the land surface model JSBACH3 for an idealized case study in eastern Siberia to investigate the effects of high versus low-resolution land boundary conditions on simulating the interactions of soil physics, hydrology and vegetation. We show for the first time that there are differences in the spatial averages of the simulated fluxes and soil state variables between resolution setups. Most differences are small in the summer mean, but larger within individual months. Heterogeneous soil properties induce large parts of the differences while vegetation characteristics play a minor role. Active layer depth shows a statistically significant increase of +20% in the 5 km setup relative to the 210 km setup for the summer mean and +43% for August. The differences are due to the nonlinear vertical discretization of the soil column amplifying the impact of the heterogeneous distributions of soil organic matter content and supercooled water. Resolution-induced differences in evaporation fluxes amount to +43% in July and are statistically significant. Our results show that spatial resolution significantly affects model outcomes due to nonlinear processes in heterogenous land surfaces. This suggests that resolution needs to be accounted in simulations of land surface models in the Arctic.

The Tibetan Plateau (TP) is distributed with large areas of permafrost, which have received increasing attention as the climate warms. Accurately modeling the extent of permafrost and permafrost changes is now an important challenge for climate change research and climate modeling in this region. Uncertainty in land use and land cover (LULC), which is important information characterizing surface conditions, directly affects the accuracy of the simulation of permafrost changes in land surface models. In order to investigate the effect of LULC uncertainty on permafrost simulation, we conducted simulation experiments on the TP using the Community Land Model, version 5 (CLM5) with five high-resolution LULC products in this study. Firstly, we evaluated the simulation results using shallow soil temperature data and deep borehole data at several sites. The results show that the model performs well in simulating shallow soil temperatures and deep soil temperature profiles. The effect of different land use products on the shallow soil temperature and deep soil temperature contours is not obvious due to the small differences in land use products at these sites. Although there is little difference in the simulating results of different land use products when compared to the permafrost distribution map, the differences are noticeable for the simulation of the active layer. Land cover had a greater impact on soil temperature simulations in regions with greater land use inconsistency, such as at the junction of bare soil and grassland in the northwestern part of the TP, as well as in the southeast region with complex topography. The main way in which this effect occurs is that land cover affects the net surface radiation, which in turn causes differences in soil temperature simulations. In addition, we discuss other factors affecting permafrost simulation results and point out that increasing the model plant function types as well as carefully selecting LULC products is one of the most important ways to improve the simulation performance of land-surface models in permafrost regions.

Downward solar radiation (DSR) and air temperature (Ta) have significant influences on the thermal state of frozen ground. These parameters are also important forcing terms for physically based land surface models (LSMs). However, the quantitative influences of inaccuracies in DSR and Ta products on simulated frozen ground temperatures remain unclear. In this study, three DSR products (CMFD-SR, Tang-SR, and GLDAS-SR) and two Ta products (CMFD-Ta and GLDAS-Ta) were used to force an LSM model in an alpine watershed in Northwest China, to investigate the sensitivity of simulated ground temperatures to different DSR and Ta products. Compared to a control model (CTRL) forced by in situ observed DSR, ground temperatures simulated by the experimental model forced by GLDAS-SR are obviously decreased because GLDAS-SR is much lower than in situ observations. Instead, simulation results in models forced by CMFD-SR and Tang-SR are much closer to those of CTRL. Ta products led to significant errors in simulated ground temperatures. In conclusion, both CMFD-SR and Tang-SR could be used as good alternatives to in situ observed DSR for forcing a model, with acceptable errors in simulation results. However, more care need to be paid for models forced by Ta products instead of Ta observations, and conclusions should be carefully drawn.

Freezing/thawing indices are important indicators of the dynamics of frozen ground on the Qinghai-Tibet Plateau (QTP), especially in areas with limited observations. Based on the numerical outputs of Community Land Surface Model version 4.5 (CLM4.5) from 1961 to 2010, this study compared the spatial and temporal variations between air freezing/thawing indices (2 m above the ground) and ground surface freezing/thawing indices in permafrost and seasonally frozen ground (SFG) across the QTP after presenting changes in frozen ground distribution in each decade in the context of warming and wetting. The results indicate that an area of 0.60 x 10(6) km(2) of permafrost in the QTP degraded to SFG in the 1960s-2000s, and the primary shrinkage period occurred in the 2000s. The air freezing index (AFI) and ground freezing index (GFI) decreased dramatically at rates of 71.00 & DEG;C & BULL;d/decade and 34.33 & DEG;C & BULL;d/decade from 1961 to 2010, respectively. In contrast, the air thawing index (ATI) and ground thawing index (GTI) increased strikingly, with values of 48.13 & DEG;C & BULL;d/decade and 40.37 & DEG;C & BULL;d/decade in the past five decades, respectively. Permafrost showed more pronounced changes in freezing/thawing indices since the 1990s compared to SFG. The changes in thermal regimes in frozen ground showed close relations to air warming until the late 1990s, especially in 1998, when the QTP underwent the most progressive warming. However, a sharp increase in the annual precipitation from 1998 began to play a more controlling role in thermal degradation in frozen ground than the air warming in the 2000s. Meanwhile, the following vegetation expansion hiatus further promotes the thermal instability of frozen ground in this highly wet period.

Due to an imbalance between incoming and outgoing radiation at the top of the atmosphere, excess heat has accumulated in Earth's climate system in recent decades, driving global warming and climatic changes. To date, it has not been quantified how much of this excess heat is used to melt ground ice in permafrost. Here, we diagnose changes in sensible and latent ground heat contents in the northern terrestrial permafrost region from ensemble-simulations of a tailored land surface model. We find that between 1980 and 2018, about 3.9+1.4-1.6 $3.9\genfrac{}{}{0pt}{}{+1.4}{-1.6}$ ZJ of heat, of which 1.7+1.3-1.4 $1.7\genfrac{}{}{0pt}{}{+1.3}{-1.4}$ ZJ (44%) were used to melt ground ice, were absorbed by permafrost. Our estimate, which does not yet account for the potentially increased heat uptake due to thermokarst processes in ice-rich terrain, suggests that permafrost is a persistent heat sink comparable in magnitude to other components of the cryosphere and must be explicitly considered when assessing Earth's energy imbalance.

Permafrost thaw/degradation in the Northern Hemisphere due to global warming is projected to accelerate in coming decades. Assessment of this trend requires improved understanding of the evolution and dynamics of permafrost areas. Land surface models (LSMs) are well-suited for this due to their physical basis and large-scale applicability. However, LSM application is challenging because (a) LSMs demand extensive and accurate meteorological forcing data, which are not readily available for historic conditions and only available with significant biases for future climate, (b) LSMs possess a large number of model parameters, and (c) observations of thermal/hydraulic regimes to constrain those parameters are severely limited. This study addresses these challenges by applying the MESH-CLASS modeling framework (Modelisation Environmenntale communautaire-Surface et Hydrology embedding the Canadian Land Surface Scheme) to three regions within the Mackenzie River Basin, Canada, under various meteorological forcing data sets, using the variogram analysis of response surfaces framework for sensitivity analysis and threshold-based identifiability analysis. The study shows that the modeler may face complex trade-offs when choosing a forcing data set; for current and future scenarios, forcing data require multi-variate bias correction, and some data sets enable the representation of some aspects of permafrost dynamics, but are inadequate for others. The results identify the most influential model parameters and show that permafrost simulation is most sensitive to parameters controlling surface insulation and runoff generation. But the identifiability analysis reveals that many of the most influential parameters are unidentifiable. These conclusions can inform future efforts for data collection and model parameterization.

Permafrost thaw has been observed in recent decades in the Northern Hemisphere and is expected to accelerate with continued global warming. Predicting the future of permafrost requires proper representation of the interrelated surface/subsurface thermal and hydrologic regimes. Land surface models (LSMs) are well suited for such predictions, as they couple heat and water interactions across soil-vegetation-atmosphere interfaces and can be applied over large scales. LSMs, however, are challenged by the long-term thermal and hydraulic memories of permafrost and the paucity of historical records to represent permafrost dynamics under transient climate conditions. In this study, we aim to understand better how LSMs function under different spin-up states, which facilitates addressing the challenge of model initialization by characterizing the impact of initial climate conditions and initial soil frozen and liquid water contents on the simulation length required to reach equilibrium. Further, we quantify how the uncertainty in model initialization propagates to simulated permafrost dynamics. Modelling experiments are conducted with the Modelisation Environmentale Communautaire-Surface and Hydrology (MESH) framework and its embedded Canadian land surface scheme (CLASS). The study area is in the Liard River basin in the Northwest Territories of Canada with sporadic and discontinuous regions. Results show that uncertainty in model initialization controls various attributes of simulated permafrost, especially the active layer thickness, which could change by 0.5-1.5 m depending on the initial condition chosen. The least number of spin-up cycles is achieved with near field capacity condition, but the number of cycles varies depending on the spin-up year climate. We advise an extended spin-up of 200-1000 cycles to ensure proper model initialization under different climatic conditions and initial soil moisture contents.

Spin-up is essential to provide initial conditions for land surface models (LSM) when they cannot be given reliably as in the application to regional permafrost change studies. In this study, the impacts of spin-up strategy including total spin-up length and cycling scheme on modeling of permafrost dynamics on the Qinghai-Tibet Plateau (QTP) were evaluated through two groups of experiments using a modified Noah LSM. The first group aims to test different total spin-up lengths and the second group for different cycling schemes. The results show that the presence of permafrost prolongs the convergence of the model. Vertically, the slowest convergence is observed at the permafrost table. The insufficiency of total spin-up length is prone to underestimate permafrost area and overestimate the degradation rate. Different cycling schemes considerably affect the resulting initial thermal fields and result in degradation rates with a difference of 3.37 x 10(3) km(2)/a on the QTP, which exceeds the difference (2.92 x 10(3) km(2)/a) in the degradation rates reported in existing studies. The multi-year cycling scheme is generally preferred, but overlong cycle length should be avoided to prevent the introduction of climate change trends in the spin-up period. We recommend a spin-up strategy of a 500-year cycling with the first 5- to 10-year of forcing for modeling permafrost on the QTP with the Noah LSM. Our findings highlight the importance of the spin-up strategy, which is usually neglected in present LSM-based permafrost modeling studies.