The start of frozen dates over northern permafrost regions with the changing climate.

The soil freeze-thaw cycle in the permafrost regions has a significant impact on regional surface energy and water balance. Although increasing efforts have been made to understand the responses of spring thawing to climate change, the mechanisms controlling the global interannual variability of the start date of permafrost frozen (SOF) remain unclear. Using long-term SOF from the combinations of multiple satellite microwave sensors between 1979 and 2020, and analytical techniques, including partial correlation, ridge regression, path analysis, and machine learning, we explored the responses of SOF to multiple climate change factors, including warming (surface and air temperature), start date of permafrost thawing (SOT), soil properties (soil temperature and volume of water), and the snow depth water equivalent (SDWE). Overall, climate warming exhibited the maximum control on SOF, but SOT in spring was also an important driver of SOF variability; among the 65.9% significant SOT and SOF correlations, 79.3% were positive, indicating an overall earlier thawing would contribute to an earlier frozen in winter. The machine learning analysis also suggested that apart from warming, SOT ranked as the second most important determinant of SOF. Therefore, we identified the mechanism responsible for the SOT-SOF relationship using the SEM analysis, which revealed that soil temperature change exhibited the maximum effect on this relationship, irrespective of the permafrost type. Finally, we analyzed the temporal changes in these responses using the moving window approach and found increased effect of soil warming on SOF. In conclusion, these results provide important insights into understanding and predicting SOF variations with future climate change.

Remotely sensed lake area changes in permafrost regions of the Arctic and the Tibetan Plateau between 1987 and 2017.

Both gradual and abrupt changes in lake surface area in permafrost regions are crucial for understanding the water cycles in cold regions under climate change. However, seasonal changes in lake area in permafrost regions are not available, and their occurrence conditions are still unclear. Based on remotely sensed water body products at a 30 m resolution, this study provides a detailed comparison of lake area changes across seven basins characterized by clear gradients in climatic, topographic and permafrost conditions in the Arctic and Tibetan Plateau between 1987 and 2017. The results show that the maximum surface area of all lakes net increased by 13.45 %. Among them, the seasonal lake area net increased by 28.66 %, but there was also a 2.48 % loss. The permanent lake area net increased by 6.39 %, and the area loss was approximately 3.22 %. The total permanent lake area generally decreased in the Arctic but increased in the Tibetan Plateau. At lake region scale (0.1° grid), the changes in permanent area of contained lakes were divided into four types including no change, homogeneous changes (only expansion or only shrinkage), heterogeneous changes (expansion neighboring shrinkage) and abrupt changes (newforming or vanishing). The lake regions with heterogeneous changes accounted for over one-quarter of all lake regions. All types of changes in lake regions, especially the heterogeneous changes and abrupt changes (e.g., vanishing), occurred more extensively and intensely on low and flat terrain, in high-density lake regions and in warm permafrost regions. These findings indicate that, considering the increase in surface water balance in these river basins, surface water balance alone cannot fully explain changes in permanent lake area in the permafrost region, and the thawing or disappearance of permafrost plays a tipping point effect on the lake changes.

Convergence and divergence emerging in climatic controls of polynomial trends for northern ecosystem productivity over 2000-2018.

Recent rapid warming has caused uneven impacts on the composition, structure, and functioning of northern ecosystems. It remains unknown how climatic drivers control linear and non-linear trends in ecosystem productivity. Based on a plant phenology index (PPI) product at a spatial resolution of 0.05° over 2000-2018, we used an automated polynomial fitting scheme to detect and characterize trend types (i.e., polynomial trends and no-trends) in the yearly-integrated PPI (PPIINT) for northern (> 30°N) ecosystems and their dependence on climatic drivers and ecosystem types. The averaged slope for the linear trends (p < 0.05) of PPIINT was positive across all the ecosystems, among which deciduous broadleaved forests and evergreen needle-leaved forests (ENF) showed the highest and lowest mean slopes, respectively. More than 50% of the pixels in ENF, arctic and boreal shrublands, and permanent wetlands (PW) had linear trends. A large fraction of PW also showed quadratic and cubic trends. These trend patterns agreed well with estimates of global vegetation productivity based on solar-induced chlorophyll fluorescence. Across all the biomes, PPIINT in pixels with linear trends showed lower mean values and higher partial correlation coefficients with temperature or precipitation than in pixels without linear trends. Overall, our study revealed the emergence of latitudinal convergence and divergence in climatic controls on the linear and non-linear trends of PPIINT, implying that northern shifts of vegetation and climate change may potentially increase the non-linear nature of climatic controls on ecosystem productivity. These results can improve our understanding and prediction of climate-induced changes in plant phenology and productivity and facilitate sustainable management of ecosystems by accounting for their resilience and vulnerability to future climate change.

Global spatiotemporally continuous MODIS land surface temperature dataset.

Land surface temperature (LST) plays a critical role in land surface processes. However, as one of the effective means for obtaining global LST observations, remote sensing observations are inherently affected by cloud cover, resulting in varying degrees of missing data in satellite-derived LST products. Here, we propose a solution. First, the data interpolating empirical orthogonal functions (DINEOF) method is used to reconstruct invalid LSTs in cloud-contaminated areas into ideal, clear-sky LSTs. Then, a cumulative distribution function (CDF) matching-based method is developed to correct the ideal, clear-sky LSTs to the real LSTs. Experimental results prove that this method can effectively reconstruct missing LST data and guarantee acceptable accuracy in most regions of the world, with RMSEs of 1-2 K and R values of 0.820-0.996 under ideal, clear-sky conditions and RMSEs of 4-7 K and R values of 0.811-0.933 under all weather conditions. Finally, a spatiotemporally continuous MODIS LST dataset at 0.05° latitude/longitude grids is produced based on the above method.