This paper investigates the spatiotemporal dynamics and their changes of the southern limit of latitudinal permafrost (SLLP) and the lower limit of mountain permafrost (LLMP) in Northeast China, emphasizing the roles of climate change and human activities. Permafrost in this region is primarily distributed in the northern parts of the Da and Xiao Xing'anling mountain ranges and in the upper parts of the Changbai Mountains and at the summits of the Huanggangliang Mountains in the southern part of the Da Xing'anling Mountain Range. Permafrost degradation, ongoing since at least the local Holocene Megathermal Period (8.5-6.0 ka BP), has intermittently reversed during cooler climatic intervals but continues to exert significant impacts on regional environments, infrastructure stability, and carbon storage. Notably, the northward retreats of the SLLP since the mid-19th century underscore the sustained nature of this degradation, especially in southern patchy permafrost zones increasingly sensitive to warming and anthropogenic influences. LLMP variability is similarly shaped by a combination of climatic, hydrometeorological, ecological, and topographic factors. The distributions of SLLP and LLMP are further complicated by the presence of relict and sporadic permafrost, as well as the hydrothermal effects of vegetation and snow cover. Addressing the challenges of mapping and modeling boreal permafrost in Northeast China requires comprehensive field investigations, long-term in situ monitoring via station networks, and advanced numerical modeling. Emerging technologies, including satellite and airborne remote sensing (RS), geographic information systems (GIS), unmanned aerial vehicles (UAVs), surface geophysical methods, and big data analytics, offer new possibilities for enhancing permafrost monitoring and mapping. Integrating these tools with conventional field studies can significantly improve our understanding of permafrost dynamics. Continued efforts in monitoring, technological innovation, multidisciplinary collaboration, and international cooperation are essential to meet the challenges posed by permafrost degradation in a changing climate.

Ecosystems at the southern edge of the permafrost distribution are highly sensitive to global warming. Changes in soil freeze-thaw cycles can influence vegetation growth in permafrost regions. Extant studies mainly focused on analyzing the differences of vegetation dynamics in different permafrost regions. However, the intrinsic drivers of permafrost degradation on vegetation growth remain elusive yet. Based on the top temperature of permafrost (TTOP) model, we simulated the spatial distribution of permafrost in Northeast China (NEC) from 2001 to 2020. Using the data of the vegetation Net Primary Productivity (NPP), vegetation phenology, climate and permafrost phenology, and analytical methods including partial correlation, multiple linear regression, and path analysis, we explored the response of vegetation growth and phenology to soil freeze-thaw changes and climate change under different degrees of permafrost degradation. Overall, the start date of the growing season (SOS) was very sensitive to the start date of soil thaw (SOT) changes, and multiple regression analyses showed that SOT was the main factor influencing SOS in 41.8% of the NEC region. Climatic factors remain the main factors affecting vegetation NPP in NEC, and the results of partial correlation analysis showed that only 9.7% of the regional duration of soil thaw (DOT) had a strong correlation with vegetation NPP. Therefore, we determined the mechanism responsible for the soil freeze-thaw changes and vegetation growth relationship using the path analysis. The results indicated that there is a potential inhibitory effect of persistent permafrost degradation on vegetation growth. Our findings would contribute to the improvement of process-based models of forest dynamics in the boreal region, which would help to plan sustainable development and conservation strategies in permafrost areas.

The high-resolution permafrost distribution maps have a closer relationship with engineering applications in cold regions because they are more relative to the real situation compared with the traditional permafrost zoning mapping. A particle swarm optimization algorithm was used to obtain the index eta with 30 m resolution and to characterize the distribution probability of permafrost at the field scale. The index consists of five environmental variables: slope position, slope, deviation from mean elevation, topographic diversity, and soil bulk density. The downscaling process of the surface frost number from a resolution of 1000 m to 30 m is achieved by using the spatial weight decomposition method and index eta. We established the regression statistical relationship between the surface frost number after downscaling and the temperature at the freezing layer that is below the permafrost active layer base. We simulated permafrost temperature distribution maps with 30 m resolution in the four periods of 2003-2007, 2008-2012, 2013-2017, and 2018-2021, and the permafrost area is, respectively, 28.35 x 10(4) km(2), 35.14 x 10(4) km(2), 28.96 x 10(4) km(2), and 25.21 x 10(4) km(2). The proportion of extremely stable permafrost (< -5.0 degrees C), stable permafrost (-3.0 similar to -5.0 degrees C), sub-stable permafrost (-1.5 similar to -3.0 degrees C), transitional permafrost (-0.5 similar to -1.5 degrees C), and unstable permafrost (0 similar to -0.5 degrees C) is 0.50-1.27%, 6.77-12.45%, 29.08-33.94%, 34.52-39.50%, and 19.87-26.79%, respectively, with sub-stable, transitional, and unstable permafrost mainly distributed. Direct and indirect verification shows that the permafrost temperature distribution maps after downscaling still have high reliability, with 83.2% of the residual controlled within the range of +/- 1 degrees C and the consistency ranges from 83.17% to 96.47%, with the identification of permafrost sections in the highway engineering geological investigation reports of six highway projects. The maps are of fundamental importance for engineering planning and design, ecosystem management, and evaluation of the permafrost change in the future in Northeast China.

The size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important for passive microwave remote sensing, snow physics and hydrological modelling. Snow models would benefit from more observations of surface grain size. This paper uses an asymptotic radiative transfer model (ART model) based on MOD09GA ground reflectance data. A simulation of snow grain size (SGS) in northeast China from 2001 to 2019 was carried out using a two-channel algorithm. We verified the accuracy of the inversion results by using ground-based observations to obtain stratified snow grain sizes at 48 collection sites in northeastern China. Furthermore, we analysed the spatial and temporal trends of snow grain size in Northeastern China. The results show that the ART model has good accuracy in inverting snow grain size, with an RMSD of 65 mu m, which showed a non-significant increasing trend from 2001 to 2019 in northeast China. The annual average SGS distribution ranged from 430.83 to 452.38 mu m in northeast China, 2001-2019. The mean value was 441.78 mu m, with an annual increase of 0.26 mu m/a, showing a non-significant increasing trend and a coefficient of variation of 0.014. The simulations show that there is also intermonth variation in SGS, with December having the largest snow grain size with a mean value of 453.92 mu m, followed by January and February with 450.77 mu m and 417.78 mu m, respectively. The overall spatial distribution of SGS in the northeastern region shows the characteristics of being high in the north and low in the south, with values ranging from 380.248 mu m to 497.141 mu m. Overall, we clarified the size and distribution of snow grains over a long time series in the northeast. The results are key to an accurate evaluation of their effect on snow-ice albedo and their radiative forcing effect.

Brown carbon (BrC) represents not only a major component of haze pollution but also a non-negligible contributor to positive radiative forcing, making it a key species for coordinating air quality and climate policies. In China, field observations on BrC remain limited given the highly variable emission sources and meteorological conditions across different regions. Here we focused on the optical properties of BrC in a distinct but rarely studied megacity in Northeast China, which is within a major agricultural region and experiences extremely cold winter. Agricultural fires were evident in April of 2021 and the fall of 2020, although open burning was strictly prohibited. Such emissions enhanced BrC's mass absorption efficiency at 365 nm (MAE365), more efficiently by the fall fires which were inferred to have relatively high combustion efficiencies (CE). After taking CE into consideration, the relationships between MAE365 and the levoglucosan to organic carbon ratio (a measure of the significance of agricultural fire influence) roughly converged for the fire episodes in different seasons, including those identified in February and March of 2019 by a previous campaign. Agricultural fires also influenced the determination of absorption & ANGS;ngstrom exponent (AAE), by resulting in non-linearity for BrC's absorption spectra shown on ln-ln scale. Based on three indicators developed by this study, the non-linearity was inferred to be caused by similar chromophores although the fires were characterized by various CE levels in different seasons. In addition, for the samples without significant influence of open burning, coal combustion emissions were identified as the dominant influencing factor for MAE365, whereas none solid link was found between the solution-based AAE and aerosol source.

Climate warming has significantly changed the near-surface soil freeze state, significantly impacting terrestrial ecosystems and regional agroforestry production. As Northeast China (NEC) is highly sensitive to climate change, this study introduces the concept of velocity to analyze the spatial pattern of frozen days (F-DAY), onset date of soil freeze (F-ON), offset date of soil freeze (F-OFF), and number of soil freeze/thaw cycles in spring (F-TC) in NEC from 1979 to 2020. We observed that the velocity changes of F-DAY, F-ON, and F-TC in croplands were significantly higher than those in forests (difference > 1 km yr(-1)), with the fastest velocity changes found in the cropland of the Songnen Plain. The highest velocity of FOFF was found in the forests of the Greater Khingan Range. In most study areas (> 60%), the isoline of F-DAY/F-ON/F-OFF/F-TC showed a northward movement. The isoline of F-DAY/F-ON/F-OFF/ F-TC moved in the cold direction in each cropland region (Sanjiang, Songnen, and Liaohe River Plains) and forest regions (Greater Khingan and Lesser Khingan Ranges, and the Changbai Mountains). The results of the quantitative analysis indicate that air temperature (T-A) had a more significant effect on the velocity change of F-DAY and F-ON in cropland, whereas snowpack is the dominant factor in forests. In both forests and croplands, the main factor affecting the velocity of F-OFF was snowpack, and T(A )mainly affected the F-TC. This study is significant for formulating regional climate change countermeasures and maintaining ecological security in cold regions.

Boreal forest and wetland have important influences on the development and protection of the ecosystem-dominated Xing'an permafrost. However, the responses of different ecosystems to climate change and the impacts on the underlying permafrost are still unclear. Here, based on the multi-period land use/land cover (LULC) data and long-time series of air temperature, combined with the ordinary least squares (OLS) and ordinary kriging (OK) methods, the effects of land use and cover change (LUCC) on the distribution of mean annual air temperature (MAAT) and permafrost in Northeast China were analyzed. From 1980s to 2010s, MAAT showed an upward trend (0.025 degrees C per yr) and extents of permafrost showed a decreasing trend (-3668 km(2)yr(-1)) in Northeast China. Permafrost degradation mainly occurred in forested land and grassland, with areal reductions of 4.0106 x 10(4) and 3.8754 x 10(4) km(2), respectively. The transformation of LULC aggravates the degradation of permafrost. The conversions of forested land and grassland to cultivated land and forested land to grassland resulted in the shrinkage of permafrost extent by 6233 km(2) from 1980s to 2010s . Our results confirm the significant impacts of LUCC on the Xing'an permafrost resulting in its degradation. Additionally, they can provide a scientific basis for ecological environment protection and restoration and sustainable development of boreal forest and wetland ecosystems in permafrost regions of Northeast China.

Against the background of global warming, environmental and ecological problems caused by frozen ground degradation have become a focus of attention for the scientific community. As the temperature rises, the permafrost is degrading significantly in the frozen ground region of northeast China (FGRN China). At present, research on FGRN China is based mainly on data from meteorological stations, and the research period has been short. In this study, we analyzed spatial and temporal variation in the ground surface freezing index (GFI) and ground surface thawing index (GTI) from 1900 to 2017 for FGRN China, with the air freezing index (AFI) and air thawing index (ATI) using the University of Delaware (UDEL) monthly gridded air temperature dataset. The turning point year for annual mean air temperature (AMAT) was identified as 1985, and the turning point years for GFI and GTI were 1977 and 1996. The air temperature increased by 0.01 degrees C per year during 1900-2017, and the GFI and GTI increased at rates of -0.4 and 0.5 degrees C d per year before the turning point year; after the turning point, these rates were -0.7 and -2.1 degrees C d per year. We utilized a surface frost number model to study the distribution of frozen ground in FGRN China from 1900 to 2017. When the empirical coefficient E value is 0.57, the simulated frozen ground distribution is basically consistent with the existing frozen ground maps. The total area of permafrost in FGRN China decreased by 22.66x10(4) km(2) from 1900 to 2017, and the permafrost boundary moved northward with obvious degradation. The results of this study demonstrate the trend in permafrost boundary degradation in FGRN China, and provide basic data for research on the hydrological, climate, and ecological changes caused by permafrost degradation.

As an important agriculture production area in the world and a flood prone area, future hydro-climate changes in winter and spring in Northeast China could have remarkable influence on spring water resources and flood. Yet, studies on future hydro-climate variations with special considerations of snow influences remain limited so far in the region. Here, we studied future winter and spring hydro-climate changes to the end of the century with special considerations of snow variations under the RCP2.6, RCP6.0 and RCP8.5 scenarios in Northeast China. A water and energy budget-based distributed biosphere hydrological model with improved snow physics was implemented. We find that winter and spring are warming 0.08 degrees C and 0.06 degrees C annually under 30 years moving average in RCP8.5. Air temperature increasing rate in winter is approximately two times higher than that in spring in 2030-2059. In this period, snow melt contribution to spring average runoff and maximum runoff decrease by at least 39% and 23%, respectively, and spring soil moisture decreases by 7%. The spring snow melt runoff peak will move from April to March under the warmest climate condition (i.e., 2070-2099 in RCP8.5). The earlier snow melt renders the snow melt contribution to total runoff decrease to almost zero in May, which could increase drought severity. This study sheds some lights on changes in hydrological regimes under climate change with a focus on snow melt and the influences in the entire Northeast China for the first time, and is helpful for adaption to future climate change.

Severe haze hovered over Harbin during the heating season of 2019-2020, making it one of the ten most polluted Chinese cities in January of 2020. Here we focused on the optical properties and sources of brown carbon (BrC) during the extreme atmospheric pollution periods. Enhanced formation of secondary BrC (BrCsec) was evident as relative humidity (RH) became higher, accompanied with a decrease of ozone but concurrent increases of aerosol water content and secondary inorganic aerosols. These features were generally similar to the characteristics of haze chemistry observed during winter haze events in the North China Plain, and indicated that heterogeneous reactions involving aerosol water might be at play in the formation of BrCsec despite the low temperatures in Harbin. Although BrCsec accounted for a substantial fraction of brown carbon mass, its contribution to BrC absorption was much smaller (6 vs. 28%), pointing to a lower mass absorption efficiency (MAE) of BrCsec compared to primary BrC. In addition, emissions of biomass burning BrC (BrCBB) were inferred to increase with increasing RH, coinciding with a large drop of temperature. Since both the less absorbing BrCsec and the more absorbing BrCBB increased as RH became higher, the MAE of total BrC were largely unchanged throughout the measurement period. This study unfolded the contrast in the source apportionment results of BrC mass and absorption, and could have implications for the simulation of radiative forcing by brown carbon.