Global warming has shown an Arctic amplification effect in recent decades, leading to pronounced changes in pan-Arctic soil surface temperature (SST). SST plays a direct role in energy exchange between soil and atmosphere and serves as an indicator of the land-atmosphere energy balance. Remote sensing land surface temperature (LST) data is able to indicate near-surface temperature, but influences from environment factors, such as vegetation and snow, can introduce biases between LST and SST. In this study, the importances of five environment factors (vegetation, snow, surface soil composition, topography, and solar radiation) to monthly mean SST estimation from MODIS LST in pan-Arctic were analyzed. Then a method for pan-Arctic monthly mean SST estimation from MODIS LST by incorporating these environment factors and monthly-based modeling based on random forest (RF) algorithm was proposed. The results reveal that all the selected environment factors contribute to monthly-based modeling, with vegetation exerting the greatest importance from May to October and snow in March and April. The root mean square error (RMSE) of pan-Arctic monthly SST estimated by the proposed method from 2003 to 2022 ranges from 0.89 to 1.88 degrees C, which is a 42.95---53.35 % reduction compared to the widely used season-based multivariate linear regression (MLR) models based solely on LST (RMSE between 1.56 and 4.03 degrees C). The accuracy is notably improved in areas with lower and no vegetation (grassy woodlands, grasslands, permanent wetlands, and barrens) in the cold season (September to the following April), and in higher vegetation (forests) areas in the warm season (May to August). The proposed method can contribute to producing high-precision monthly mean SST data from LST, estimating permafrost extent and active layer thickness, and understanding the land-atmosphere energy balance in pan-Arctic.

Northern high-latitude permafrost holds the largest soil carbon pool in the world. Understanding the responses of permafrost to wildfire is crucial for improving our ability to predict permafrost degradation and further carbon emissions. Recently, studies have demonstrated that wildfires in the pan-Arctic region induced the thickening of the active layer based on site or fire event observations. However, how this induced thickening is influenced by vegetation and permafrost types remains not fully understood due to the lack of wall-to-wall analysis. Therefore, this study employed remotely sensed fire data and modelled active layer thickness (ALT) to identify the fireinduced ALT change (& UDelta;ALT) for the pan-Arctic region, and the contributions of vegetation and permafrost were quantified using the random forest (RF) model. Our results showed that the average & UDelta;ALT and the sensitivity of & UDelta;ALT to burn severity both increased with decreasing ground ice content in permafrost. The largest values were detected in thick permafrost with low ground ice content. Regarding vegetation, the average and sensitivity of & UDelta;ALT in tundra were highest, followed by those in forest and shrub. When the individual environmental factors were all taken into account, the results showed that the contribution of vegetation types was much higher than that of permafrost types (20.2 % vs. 3.5 %). Our findings highlighted the importance of environmental factors in regulating the responses of permafrost to fire.

Land evapotranspiration (ET) is a key factor in the hydrological cycle and is particularly sensitive to climate change. In the past few decades, the Pan-Arctic region has witnessed a strong warming trend, causing substantial changes in ground surface conditions. However, it remains poorly understood how ET varies across such a region. Here, we investigated the spatial-temporal changes in ET over six large Pan-Arctic river basins using corrected ET estimates from GLEAM and ERA5-Land during 1981-2020. The 40-year mean annual ET in the Ob, Yenisei, Lena, Kolyma, Yukon, and Mackenzie River basins were approximately 366 mm.yr(-1), 271 mm.yr(-1), 228 mm.yr(-1), 239 mm.yr(-1), 278 mm.yr(-1), and 295 mm.yr(-1), respectively. Climatologically, permafrost-dominated basins tend to have lower ET than those with less permafrost. Temporally, the annual ET in permafrost-dominated basins has increased faster than that in less permafrost-developed basins over the past four decades. This increase is particularly prominent in the Kolyma River basin (completely covered by permafrost), where the significant trend in ET (7.5 mm.decade(-1), p < 0.001) is the highest among the six basins. Further analyses show that intensified permafrost thawing is likely the key factor contributing to increased ET in permafrost-dominated basins, as seen from the positive relationship between the thickening in the active layer (ALT) and the change in ET. Under an amplified warming background, ET in permafrost-dominated regions is expected to continue to increase. The results reported in this study improve our understanding of the variations in ET and the associated mechanisms in the changing Arctic.

Recent efforts have been made to monitor the seasonal metrics of plant canopy variations globally from space, using optical remote sensing. However, phenological estimations based on vegetation indices (VIs) in high-latitude regions such as the pan-Arctic remain challenging and are rarely validated. Nevertheless, pan-Arctic ecosystems are vulnerable and also crucial in the context of climate change. We reported the limitations and challenges of using MODerate-resolution Imaging Spectroradiometer (MODIS) measurements, a widely exploited set of satellite measurements, to estimate phenological transition dates in pan-Arctic regions. Four indices including normalized vegetation difference index (NDVI), enhanced vegetation index (EVI), phenology index (PI), plant phenological index (PPI) and a MODIS Land Cover Dynamics Product MCD12Q2, were evaluated and compared against eddy covariance (EC) estimates at 11 flux sites of 102 site-years during the period from 2000 to 2014. All the indices were influenced by snow cover and soil moisture during the transition dates. While relationships existed between VI-based and EC-estimated phenological transition dates, the R-2 values were generally low (0.01-0.68). Among the VIs, PPI-estimated metrics showed an inter-annual pattern that was mostly closely related to the EC-based estimations. Thus, further studies are needed to develop region-specific indices to provide more reliable estimates of phenological transition dates.

There is mounting evidence that permafrost degradation has occurred over the past century. However, the amount of permafrost lost is uncertain because permafrost is not readily observable over long time periods and large scales. This paper uses JULES, the land surface component of the Hadley Centre global climate model, driven by different realisations of twentieth century meteorology to estimate the pan-arctic changes in near-surface permafrost. Model simulations of permafrost are strongly dependent on the amount of snow both in the driving meteorology and the way it is treated once it reaches the ground. The multi-layer snow scheme recently adopted by JULES significantly improves its estimates of soil temperatures and permafrost extent. Therefore JULES, despite still having a small cold bias in soil temperatures, can now simulate a near-surface permafrost extent which is comparable to that observed. Changes in snow cover have been shown to contribute to changes in permafrost and JULES simulates a significant decrease in late twentieth century pan-Arctic spring snow cover extent. In addition, large-scale modelled changes in the active layer are comparable with those observed over northern Russia. Simulations over the period 1967-2000 show a significant loss of near-surface permafrost-between 0.55 and 0.81 million km(2) per decade with this spread caused by differences in the driving meteorology. These runs also show that, for the grid cells where the active layer has increased significantly, the mean increase is similar to 10 cm per decade. The permafrost degradation discussed here is mainly caused by an increase in the active layer thickness driven by changes in the large scale atmospheric forcing. However, other processes such as thermokarst development and river and coastal erosion may also occur enhancing permafrost loss.

The potential impacts of climate change on northern groundwater supplies were examined at a fractured-marble mountain aquifer near Nome, Alaska. Well water surface elevations (WSE) were monitored from 2004-2009 and analyzed with local meteorological data. Future aquifer response was simulated with the Pan-Arctic Water Balance Model (PWBM) using forcings (air temperature and precipitation) derived from fifthgeneration European Centre Hamburg Model (ECHAM5) global circulation model climate scenarios for extreme and modest increases in greenhouse gases. We observed changes in WSE due to the onset of spring snowmelt, low intensity and high intensity rainfall events, and aquifer head recession during the winter freeze period. Observed WSE and snow depth compared well with PWBM-simulated groundwater recharge and snow storage. Using ECHAM5-simulated increases in mean annual temperature of 4-8 degrees C by 2099, the PWBM predicted that by 2099 later freeze-up and earlier snowmelt will decrease seasonal snow cover by one to two months. Annual evapotranspiration and precipitation are predicted to increase 27-40% (55-81 mm) and 33-42% (81-102 mm), respectively, with the proportion of snowfall in annual precipitation decreasing on average 9-25% (p < 0.05). The amount of snowmelt is not predicted to change significantly by 2099; however, a decreasing trend is evident from 2060 in the extreme ECHAM5 greenhouse gas scenario. Increases in effective precipitation were predicted to be great enough to sustain sufficient groundwater recharge.