Northeastern China (NEC) is the largest grain base in China. Improving understanding of the effect of climate change on grain production over NEC is conducive to providing immediate response strategies for grain production. In this study, the relationships of the maize production with the dry state during the different maize growth stage have been investigated using the year-to-year increment method. Results showed that the severe drought that occurred from the jointing to maturity period have exerted severe effects on the maize growth. Further analysis indicated that the sea surface temperature (SST) anomalies over North Atlantic and Maritime Continent in later spring are the important factors affecting the summer droughts over NEC. The late spring SST anomaly over North Atlantic can excite the Rossby waves from the western North Atlantic and propagate eastward to NEC. The snow anomaly over western Siberia in late spring and the soil moisture anomaly over NEC in summer are key factors linking the SST anomaly to drought over the NEC. On the other hand, the Maritime Continent SST anomaly in late spring can modulate the activity of the East Asian jet stream via the East AsiaPacific (EAP) teleconnection, which can provide the favorable conditions for the soil moisture reduction over NEC. Eventually, a predictive model for maize yield over NEC is successfully developed by using the predictive indices of the North Atlantic and the Maritime Continental SST during late spring. Both the cross-validation and independent sample tests show that the calibrated prediction model is robust and exhibits high skill in predicting maize yield over NEC.

Freeze-thaw cycles (FTC) alter soil function through changes to physical organization of the soil matrix and biogeochemical processes. Understanding how dynamic climate and soil properties influence FTC may enable better prediction of ecosystem response to changing climate patterns. In this study, we quantified FTC occurrence and frequency across 40 National Ecological Observatory Network (NEON) sites. We used site mean annual precipitation (MAP) and mean annual temperature (MAT) to define warm and wet, warm and dry, and cold and dry climate groupings. Site and soil properties, including MAT, MAP, maximum-minimum temperature difference, aridity index, precipitation as snow (PAS), and organic mat thickness, were used to characterize climate groups and investigate relationships between site properties and FTC occurrence and frequency. Ecosystem-specific drivers of FTC provided insight into potential changes to FTC dynamics with climate warming. Warm and dry sites had the most FTC, driven by rapid diurnal FTC close to the soil surface in winter. Cold and dry sites were characterized by fewer, but longer-duration FTC, which mainly occurred in spring and increased in number with higher organic mat thickness (Spearman's rho = 0.97, p < 0.01). The influence of PAS and MAT on the occurrence of FTC depended on climate group (binomial model interaction p (chi(2)) < 0.05), highlighting the role of a persistent snowpack in buffering soil temperature fluctuations. Integrating ecosystem type and season-specific FTC patterns identified here into predictive models may increase predictive accuracy for dynamic system response to climate change.

This study analyzes the forest flammability hazard in the south of Tyumen Oblast (Western Siberia, Russia) and identifies variation patterns in fire areas depending on weather and climate characteristics in 2008-2023. Using correlation analysis, we proved that the area of forest fires is primarily affected by maximum temperature, relative air humidity, and the amount of precipitation, as well as by global climate change associated with an increase in carbon dioxide in the atmosphere and the maximum height of snow cover. As a rule, a year before the period of severe forest fires in the south of Tyumen Oblast, the height of snow cover is insignificant, which leads to insufficient soil moisture in the following spring, less or no time for the vegetation to enter the vegetative phase, and the forest leaf floor remaining dry and easily flammable, which contributes to an increase in the fire area. According to the estimates of the CMIP6 project climate models under the SSP2-4.5 scenario, by the end of the 21st century, a gradual increase in the number of summer temperatures above 35 degrees C is expected, whereas the extreme SSP5-8.5 scenario forecasts the tripling in the number of such hot days. The forecast shows an increase of fire hazardous conditions in the south of Tyumen Oblast by the late 21st century, which should be taken into account in the territory's economic development.

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.

Generally, with increasing elevation, there is a corresponding decrease in annual mean air and soil temperatures, resulting in an overall decrease in ecosystem carbon dioxide (CO2) exchange. However, there is a lack of knowledge on the variations in CO2 exchange along elevation gradients in tundra ecosystems. Aiming to quantify CO2 exchange along elevation gradients in tundra ecosystems, we measured ecosystem CO2 exchange in the peak growing season along an elevation gradient (9-387 m above sea level, m.a.s.l) in an arctic heath tundra, West Greenland. We also performed an ex-situ incubation experiment based on soil samples collected along the elevation gradient, to assess the sensitivity of soil respiration to changes in temperature and soil moisture. There was no apparent temperature gradient along the elevation gradient, with the lowest air and soil temperatures at the second lowest elevation site (83 m). The lowest elevation site exhibited the highest net ecosystem exchange (NEE), ecosystem respiration (ER) and gross ecosystem production (GEP) rates, while the other three sites generally showed intercomparable CO2 exchange rates. Topography aspect-induced soil microclimate differences rather than the elevation were the primary drivers for the soil nutrient status and ecosystem CO2 exchange. The temperature sensitivity of soil respiration above 0 degrees C increased with elevation, while elevation did not regulate the temperature sensitivity below 0 degrees C or the moisture sensitivity. Soil total nitrogen, carbon, and ammonium contents were the controls of temperature sensitivity below 0 degrees C. Overall, our results emphasize the significance of considering elevation and microclimate when predicting the response of CO2 balance to climate change or upscaling to regional scales, particularly during the growing season. However, outside the growing season, other factors such as soil nutrient dynamics, play a more influential role in driving ecosystem CO2 fluxes. To accurately upscale or predict annual CO2 fluxes in arctic tundra regions, it is crucial to incorporate elevation-specific microclimate conditions into ecosystem models.

Permafrost in the Northern Hemisphere has been degrading under climate change, affecting climatic, hydrological, and ecological systems. To reveal the temporal and spatial characteristics of permafrost degradation under climate change, we quantified permafrost thermal states and active layer thicknesses using observational data covering various periods and different areas of the Northern Hemisphere. The soil temperatures at 20 cm depth in the circumpolar Arctic permafrost regions were much lower than in the Qinghai-Tibet Plateau. The thaw period is 114 days in the circumpolar permafrost regions compared to 167 days in the Qinghai-Tibet Plateau. The active layer thickness (ALT) was largest in transitional permafrost regions and sporadic permafrost regions, and lowest in the high latitude permafrost regions and continuous permafrost regions, and the ALT generally exhibited an increasing trend. The average ALT was 1.7 m, and increased by 3.6 cm per year in the Northern Hemisphere. The mean annual ground temperature (MAGT) was largest in the high-altitude permafrost regions and isolated permafrost regions, and lowest in the high latitude permafrost regions and continuous permafrost regions. The warming rate of the MAGT was largest in the high latitude regions and lowest in the high altitude regions, and gradually increased from isolated permafrost regions to continuous permafrost regions, with an average warming rate of 0.3 degrees C per decade for the whole Northern Hemisphere. These findings provide important information for understanding the variability in permafrost degradation processes across different regions under climate change.

In Central Asia, the ground thermal regime is strongly affected by the interplay between topographic factors and ecosystem properties. In this study, we investigate the governing factors of the ground thermal regime in an area in Central Mongolia, which features discontinuous permafrost and is characterized by grassland and forest ecosystems. Miniature temperature dataloggers were used to measure near-surface temperatures at c. 100 locations throughout the 6 km2 large study area, with the goal to obtain a sample of sites that can represent the variability of different topographic and ecosystem properties. Mean annual near-surface ground temperatures showed a strong variability, with differences of up to 8 K. The coldest sites were all located in forests on north-facing slopes, while the warmest sites are located on steep south-facing slopes with sparse steppe vegetation. Sites in forests show generally colder near-surface temperatures in spring, summer and fall compared to grassland sites, but they are warmer during the winter season. The altitude of the measurement sites did not play a significant role in determining the near-surface temperatures, while especially solar radiation was highly correlated. In addition, we investigated the suitability of different hyperspectral indices calculated from Sentinel-2 as predictors for annual average near-surface ground temperatures. We found that especially indices sensitive to vegetation properties, such as the Normalized Difference Vegetation Index (NDVI), show a strong correlation. The presented observations provide baseline data on the spatiotemporal patterns of the ground thermal regime which can be used to train or validate modelling and remote sensing approaches targeting the impacts of climate change.

This study utilized electrical resistivity imaging (ERI) to investigate subsurface characteristics near Nicolaus Copernicus University Polar Station on the western Spitsbergen-Kaffi & oslash;yra Plain island in the Svalbard archipelago. Surveys along two lines, LN (148 m) collected in 2022 and 2023, and ST (40 m) collected in 2023, were conducted to assess resistivity and its correlation with ground temperatures. The LN line revealed a 1- to 2-m-thick resistive unsaturated outwash sediment layer, potentially indicative of permafrost. Comparing the LN resistivity result between 2022 and 2023, a 600 Ohm.m decrease in the unsaturated active layer in 2023 was observed, attributed to a 5.8 degrees C temperature increase, suggesting a link to global warming. ERI along the ST line depicted resistivity, reaching its minimum at approximately 1.6 m, rising to over 200 Ohm.m at 4 m, and slightly decreasing to around 150 Ohm.m at 7 m. Temperature measurements from the ST line's monitoring strongly confirmed that the active layer extends to around 1.6 m, with permafrost located at greater depths. Additionally, water content distribution in the ST line was estimated after temperature correction, revealing a groundwater depth of approximately 1.06 m, consistent with measurements from the S4 borehole on the ST line. This study provides valuable insights into Arctic subsurface dynamics, emphasizing the sensitivity of resistivity patterns to climate change and offering a comprehensive understanding of permafrost behavior in the region.

Water temperature extremes can pose serious threats to the aquatic ecosystems of mountain rivers. These rivers are influenced by snow and glaciermelt, which change with climate. As a result, the frequency and severity of water temperature extremes may change. While previous studies have documented changes in non-extreme water temperature, it is yet unclear how extreme water temperatures change in a warming climate and how their hydro-meteorological drivers differ from those of non-extremes. This study aims to assess temporal changes and spatial variability in water temperature extremes and enhance our understanding of the driving processes across European mountain rivers in the current climate, at both a regional and continental scale. First, we describe the characteristics of extreme events and explore their relationships with catchment characteristics. Second, we assess trends in water temperature extremes and compare them with trends in mean water temperature. Third, we use random forest models to identify the main driving processes of water temperature extremes. Last, we conduct a co-occurrence analysis to examine the relationship between water temperature extremes and hydro-climatic extremes. Our results show that mean water temperature has increased by +0.38 +/- 0.14 ${+}0.38\pm 0.14$degrees C per decade, leading to more extreme events at high elevations in spring and summer. While non-extreme water temperatures are mainly driven by air temperature, water temperature extremes are also importantly influenced by soil moisture, baseflow, and meltwater. Our study highlights the complexity of water temperature dynamics in mountain rivers at the regional and continental scale, especially during water temperature extremes.

The comprehensive understanding of the occurred changes of permafrost, including the changes of mean annual ground temperature (MAGT) and active layer thickness (ALT), on the Qinghai-Tibet Plateau (QTP) is critical to project permafrost changes due to climate change. Here, we use statistical and machine learning (ML) modeling approaches to simulate the present and future changes of MAGT and ALT in the permafrost regions of the QTP. The results show that the combination of statistical and ML method is reliable to simulate the MAGT and ALT, with the root-mean-square error of 0.53 degrees C and 0.69 m for the MAGT and ALT, respectively. The results show that the present (2000-2015) permafrost area on the QTP is 1.04 x 10(6) km(2) (0.80-1.28 x 10(6) km(2)), and the average MAGT and ALT are -1.35 +/- 0.42 degrees C and 2.3 +/- 0.60 m, respectively. According to the classification system of permafrost stability, 37.3% of the QTP permafrost is suffering from the risk of disappearance. In the future (2061-2080), the near-surface permafrost area will shrink significantly under different Representative Concentration Pathway scenarios (RCPs). It is predicted that the permafrost area will be reduced to 42% of the present area under RCP8.5. Overall, the future changes of MAGT and ALT are pronounced and region-specific. As a result, the combined statistical method with ML requires less parameters and input variables for simulation permafrost thermal regimes and could present an efficient way to figure out the response of permafrost to climatic changes on the QTP.