Earth's cryosphere and biosphere are extremely sensitive to climate changes, and transitions in states could alter the carbon emission rate to the atmosphere. However, little is known about the climate sensitivities of frozen soil and vegetation production. Moreover, how does climate heterogeneity control the spatial patterns of such sensitivities, and influence regional vulnerability of both frozen soil and vegetation production? Such questions are critical to be answered. We compiled long-time-series dataset including frozen soil depth (FD), normalized difference vegetation index (NDVI), and temperature and precipitation across Tibetan Plateau to quantify their sensitivities. Results reveal large spatial heterogeneity in FD and NDVI sensitivities. Precipitation alleviated FD sensitivities to warming in the cold northeast zone but accelerated FD sensitivities to precipitation in the warm south and southeast. Meanwhile, the positive warming effect on the NDVI was largely offset by slow increase of precipitation. Areas with high FD decreasing rate were found in northeast, inland, and south and southeast zones. Predominate area across the nine eco-regions are characterized as medium FD decreasing rate, and are synchronized with positive NDVI response in inland and west Himalayas, but negative in northeast and south and southeast. Precipitation restriction on NDVI would be pronounced in moist south and southeast. Our study provides new information that makes a much-needed contribution to advancing our understandings of the effects of global climate change on cryosphere and biosphere, which has important implications for global climate and our ability to predict, and therefore prepare for, future global climatic changes. Our attempt confirms that the method we used could be used to identify climate sensitivity of permafrost based on substantial observation data on active layer dynamics in future.

Mountain ecosystems are experiencing rapid warming resulting in ecological changes worldwide. Projecting the response of these ecosystems to climate change is thus crucial, but also uncertain due to complex interactions between topography, climate, and vegetation. Here, we performed numerical simulations in a real and a synthetic spatial domain covering a range of contrasting climatic conditions and vegetation characteristics representative of the European Alps. Simulations were run with the mechanistic ecohydrological model Tethys-Chloris to quantify the drivers of ecosystem functioning and to explore the vulnerability of Alpine ecosystems to climate change. We correlated the spatial distribution of ecohydrological responses with that of meteorological and topographic attributes and computed spatially explicit sensitivities of net primary productivity, transpiration, and snow cover to air temperature, radiation, and water availability. We also quantified how the variance in several ecohydrological processes, such as transpiration, quickly diminishes with increasing spatial aggregation, which highlights the importance of fine spatial resolution for resolving patterns in complex topographies. We conducted controlled numerical experiments in the synthetic domain to disentangle the effect of catchment orientation on ecohydrological variables, such as streamflow. Our results support previous studies reporting an altitude threshold below which Alpine ecosystems are water-limited in the drier inner-Alpine valleys and confirm that the wetter areas are temperature-limited. High-resolution simulations of mountainous areas can improve our understanding of ecosystem functioning across spatial scales. They can also locate the areas that are the most vulnerable to climate change and guide future measurement campaigns.

Soil properties such as soil organic carbon (SOC) stocks and active-layer thickness are used in earth system models (ESMs) to predict anthropogenic and climatic impacts on soil carbon dynamics, future changes in atmospheric greenhouse gas concentrations, and associated climate changes in the permafrost regions. Accurate representation of spatial and vertical distribution of these soil properties in ESMs is a prerequisite for reducing existing uncertainty in predicting carbon-climate feedbacks. We compared the spatial representation of SOC stocks and active-layer thicknesses predicted by the coupled Model Intercomparison Project Phase 5 (CMIP5) ESMs with those predicted from geospatial predictions, based on observation data for the state of Alaska, USA. For the geospatial modeling, we used soil profile observations (585 for SOC stocks and 153 for active-layer thickness) and environmental variables (climate, topography, land cover, and surficial geology types) and generated fine-resolution (50-m spatial resolution) predictions of SOC stocks (to 1-m depth) and active-layer thickness across Alaska. We found large inter-quartile range (2.5-5.5 m) in predicted active-layer thickness of CMIP5 modeled results and small inter-quartile range (11.5-22 kg m(-2)) in predicted SOC stocks. The spatial coefficient of variability of active-layer thickness and SOC stocks were lower in CMIP5 predictions compared to our geospatial estimates when gridded at similar spatial resolutions (24.7 compared to 30% and 29 compared to 38%, respectively). However, prediction errors, when calculated for independent validation sites, were several times larger in ESM predictions compared to geospatial predictions. Primary factors leading to observed differences were (1) lack of spatial heterogeneity in ESM predictions, (2) differences in assumptions concerning environmental controls, and (3) the absence of pedogenic processes in ESM model structures. Our results suggest that efforts to incorporate these factors in ESMs should reduce current uncertainties associated with ESM predictions of carbon-climate feedbacks. (C) 2016 The Authors. Published by Elsevier B.V.