Understanding the dynamics of soil respiration (Rs) in response to freeze-thaw cycles is crucial due to permafrost degradation on the Qinghai-Tibet Plateau (QTP). We conducted continuous in situ observations of Rs using an Li-8150 automated soil CO2 flux system, categorizing the freeze-thaw cycle into four stages: completely thawed (CT), autumn freeze-thaw (AFT), completely frozen (CF), and spring freeze-thaw (SFT). Our results revealed distinct differences in Rs magnitudes, diurnal patterns, and controlling factors across these stages, attributed to varying thermal regimes. The mean Rs values were as follows: 2.51 (1.10) mu mol center dot m(-2)center dot s(-1) (CT), 0.37 (0.04) mu mol center dot m(-2)center dot s(-1) (AFT), 0.19 (0.06) mu mol center dot m(-2)center dot s(-1) (CF), and 0.68 (0.19) mu mol center dot m(-2)center dot s(-1) (SFT). Cumulatively, the Rs contributions to annual totals were 89.32% (CT), 0.79% (AFT), 5.01% (CF), and 4.88% (SFT). Notably, the temperature sensitivity (Q10) value during SFT was 2.79 times greater than that in CT (4.63), underscoring the significance of CO2 emissions during spring warming. Soil temperature was the primary driver of Rs in the CT stage, while soil moisture at 5 cm depth and solar radiation significantly influenced Rs during SFT. Our findings suggest that global warming will alter seasonal Rs patterns as freeze-thaw phases evolve, emphasizing the need to monitor CO2 emissions from alpine meadow ecosystems during spring.

Ice-wedge ice is the most widespread type of massive ice found in the continuous permafrost zone. Polygonal networks of ice-wedges drive environmental changes and feedback that will likely be exacerbated with future climate change. Recent decadal-scale observations have shown that ice-wedges are degrading rapidly within the entire Circum-Arctic Region but observations of feedback associated with ground temperature regimes are still lacking in many areas. We present over a year's worth of field observations from an area with cold (-16.5 degrees C), thick (>500 m) continuous permafrost and a mean annual air temperature of -19.7 degrees C in the Canadian high Arctic. Topographic surveys, thaw depths, vegetation cover, soil moisture, and annual shallow (12 cm) ground temperature measurements were collected for seven ice-wedge troughs and two polygon centers in a high-centered polygon system. We show that geomorphic changes caused by ice-wedge degradation generate new responses in soil moisture, vegetation cover, and snow distribution that create a mosaic of ground temperatures that range by 5.1 degrees C for mean annual, 2.5 degrees C in summer, and 15.2 degrees C in winter between polygon-centers and ice-wedge troughs. Our results show that snow redistribution due to wind induces the cooling of polygon centers, thus promoting new thermal contraction cracking and ice-wedge formation. We provide an example based on high-resolution remote sensing data on how these ice-wedge trough densities vary spatially in our study area. Capturing these fine scale geomorphic differences and resulting ground temperatures will be critical to accurately assess future changes of these common Arctic landscapes.

Landscape attributes that vary with microtopography, such as active layer thickness (ALT), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization, initialization, and evaluation of Arctic terrestrial models. In this work, we demonstrate a new approach using high-resolution remotely sensed data for estimating centimeter-scale ALT in a 5 km(2) area of ice-wedge polygon terrain in Barrow, Alaska. We use a simple regression-based, machine learning data-fusion algorithm that uses topographic and spectral metrics derived from multisensor data (LiDAR and WorldView-2) to estimate ALT (2 m spatial resolution) across the study area. Comparison of the ALT estimates with ground-based measurements, indicates the accuracy (r(2)=0.76, RMSE 4.4 cm) of the approach. While it is generally accepted that broad climatic variability associated with increasing air temperature will govern the regional averages of ALT, consistent with prior studies, our findings using high-resolution LiDAR and WorldView-2 data, show that smaller-scale variability in ALT is controlled by local eco-hydro-geomorphic factors. This work demonstrates a path forward for mapping ALT at high spatial resolution and across sufficiently large regions for improved understanding and predictions of coupled dynamics among permafrost, hydrology, and land-surface processes from readily available remote sensing data. Key Points First effort to map the ALT using fine resolution remotely sensed data A blended methodology incorporating RS data and statistical manipulation Smaller-scale ALT is controlled by eco-hydro-geo variables