Aviation emissions contribute to climate change and local air pollution, with important contributions from non-CO2 emissions. These exhibit diverse impacts on atmospheric chemistry and radiative forcing (RF), varying with location, altitude, and time. Assessments of local mitigation strategies with global emission metrics may overlook this variability, but detailed studies of aviation emissions in areas smaller than continents are scarce. Integrating the AviTeam emission model and OsloCTM3, we quantify CO2, NOx, BC, OC, and SOx emissions, tropospheric concentration changes, RF, region-specific metrics, and assess alternative fuels for Norwegian domestic aviation. Mitigation potentials fora fuel switch to LH2 differ by up to 3.1 x 108 kgCO2-equivalents (GWP20) when using region-specific compared to global metrics. These differences result from a lower, region- specific contribution of non-CO2 emissions, particularly related to NOx. This study underscores the importance of accounting for non-CO2 variability in regional assessments, whether through region-specific metrics or advanced atmospheric modelling techniques.

Freeze-thaw (FT) events profoundly perturb the biochemical processes of soil and water in mid- and high-latitude regions, especially the riparian zones that are often recognized as the hotspots of soil-water interactions and thus one of the most sensitive ecosystems to future climate change. However, it remains largely unknown how the heterogeneously composed and progressively discharged meltwater affect the biochemical cycling of the neighbor soil. In this study, stream water from a valley in the Chinese Loess Plateau was frozen at -10 degrees C for 12 hours, and the meltwater (at +10 degrees C) progressively discharged at three stages (T1 similar to T3) was respectively added to rewet the soil collected from the same stream bed (Soil+T1 similar to Soil+T3). Our results show that: (1) Approximately 65% of the total dissolved organic carbon and 53% of the total NO3--N were preferentially discharged at the first stage T1, with enrichment ratios of 1.60 similar to 1.94. (2) The dissolved organic matter discharged at T1 was noticeably more biodegradable with significantly lower SUVA(254) but higher HIX, and also predominated with humic-like, dissolved microbial metabolite-like, and fulvic acid-like components. (3) After added to the soil, the meltwater discharged at T1 (e.g., Soil+T1) significantly accelerated the mineralization of soil organic carbon with 2.4 similar to 8.07-folded k factor after fitted into the first-order kinetics equation, triggering 125 similar to 152% more total CO2 emissions. Adding T1 also promoted significantly more accumulation of soil microbial biomass carbon after 15 days of incubation, especially on the FT soil. Overall, the preferential discharge of the nutrient-enriched meltwater with more biodegradable DOM components at the initial melting stage significantly promoted the microbial growth and respiratory activities in the recipient soil, and triggered sizable CO2 emission pulses. This reveals a common but long-ignored phenomenon in cold riparian zones, where progressive freeze-thaw can partition and thus shift the DOM compositions in stream water over melting time, and in turn profoundly perturb the biochemical cycles of the neighbor soil body.

Soil organic carbon (SOC) rapidly accumulates during ecosystem primary succession in glacier foreland. This makes it an ideal model for studying soil carbon sequestration and stabilization, which are urgently needed to mitigate climate change. Here, we investigated SOC dynamics in the Kuoqionggangri glacier foreland on the Tibetan Plateau. The study area along a deglaciation chronosequence of 170-year comprising three ecosystem succession stages, including barren ground, herb steppe, and legume steppe. We quantified amino sugars, lignin phenols, and relative expression of genes associated with carbon degradation to assess the contributions of microbial and plant residues to SOC, and used FT-ICR mass spectroscopy to analyze the composition of dissolved organic matter. We found that herbal plant colonization increased SOC by enhancing ecosystem gross primary productivity, while subsequent legumes development decreased SOC, due to increased ecosystem respiration from labile organic carbon inputs. Plant residues were a greater contributor to SOC than microbial residues in the vegetated soils, but they were susceptible to microbial degradation compared to the more persistent and continuously accumulating microbial residues. Our findings revealed the organic carbon accumulation and stabilization process in early soil development, which provides mechanism insights into carbon sequestration during ecosystem restoration under climate change.

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.

Permafrost temperature is a vital indicator of climate and permafrost changes, benefiting ecosystem development and informing local climate strategies. Alpine grasslands impact moisture and heat exchange between the surface and atmosphere, thereby affecting the thermal state of underlying permafrost. This study analyzed permafrost temperatures (2004-2019) from various alpine grasslands (including alpine meadow, alpine steppe, alpine desert grassland, and barren land) in the Beiluhe region of the Tibetan Plateau and revealed their connections to climate change and controlling factors, using time-frequency analysis. The findings revealed that in the time-frequency domain, permafrost temperatures exhibited multiple time scales characteristics, driven by climate fluctuations. Changes in the active layer closely followed monthly climate variations, while permafrost dynamics responded to annual climate changes. Significant oscillations with periods of 10-11, 8-9, and 14 years were observed in the surface, permafrost table, and deep permafrost layers, respectively. Among the different types of alpine grasslands, alpine meadows proved to be the most sensitive to climate change, with the intensity of periodic fluctuations initially decreasing and then increasing with depth in alpine meadows, while it consistently decreased with depth in the other three alpine grasslands. The impact of air temperature, precipitation, and wind speed on permafrost dynamics exhibited depth-dependent variations in the time-frequency domain, contrasting with the time domain where permafrost temperature changes were predominantly associated with air temperature across all depths.

Aboveground biomass (AGB) serves as a crucial measure of ecosystem productivity and carbon storage in alpine grasslands, playing a pivotal role in understanding the dynamics of the carbon cycle and the impacts of climate change on the Qinghai-Xizang Plateau. This study utilized Google Earth Engine to amalgamate Landsat 8 and Sentinel-2 satellite imagery and applied the Random Forest algorithm to estimate the spatial distribution of AGB in the alpine grasslands of the Beiliu River Basin in the Qinghai-Xizang Plateau permafrost zone during the 2022 growing season. Additionally, the geodetector technique was employed to identify the primary drivers of AGB distribution. The results indicated that the random forest model, which incorporated the normalized vegetation index (NDVI), the enhanced vegetation index (EVI), the soil-adjusted vegetation index (SAVI), and the normalized burn ratio index (NBR2), demonstrated robust performance in regards to AGB estimation, achieving an average coefficient of determination (R2) of 0.76 and a root mean square error (RMSE) of 70 g/m2. The average AGB for alpine meadows was determined to be 285 g/m2, while for alpine steppes, it was 204 g/m2, both surpassing the regional averages in the Qinghai-Xizang Plateau. The spatial pattern of AGB was primarily driven by grassland type and soil moisture, with q-values of 0.63 and 0.52, and the active layer thickness (ALT) also played a important role in AGB change, with a q-value of 0.38, demonstrating that the influences of ALT should not be neglected in regards to grassland change.

Litter decomposition represents a major path for atmospheric carbon influx into Arctic soils, thereby controlling below-ground carbon accumulation. Yet, little is known about how tundra litter decomposition varies with microenvironmental conditions, hindering accurate projections of tundra soil carbon dynamics with future climate change. Over 14 months, we measured landscape-scale decomposition of two contrasting standard litter types (Green tea and Rooibos tea) in 90 plots covering gradients of micro-climate and -topography, vegetation cover and traits, and soil characteristics in Western Greenland. We used the tea bag index (TBI) protocol to estimate relative variation in litter mass loss, decomposition rate (k) and stabilisation factor (S) across space, and structural equation modelling (SEM) to identify relationships among environmental factors and decomposition. Contrasting our expectations, microenvironmental factors explained little of the observed variation in both litter mass loss, as well as k and S, suggesting that the variables included in our study were not the major controls of decomposer activity in the soil across the studied tundra landscape. We use these unexpected findings of our study combined with findings from the current literature to discuss future avenues for improving our understanding of the drivers of tundra decomposition and, ultimately, carbon cycling across the warming Arctic.

Our understanding of tundra fire effects in Northern Alaska is limited because fires have been relatively rare. We sampled a 70+ year -old burn visible in a 1948 aerial photograph for vegetation composition and structure, soil attributes, terrain rugosity, and thermokarst pit density. Between 1948 and 2017 the burn initially became wetter as ice wedges melted but then drained and dried as the troughs became hydrologically connected. The reference tundra has become wetter over the last few decades and appears to be lagging through a similar sequence. The burn averaged 2.5 degrees C warmer than the reference tundra at 30 cm depth. Thinning of organic soil following fire appears to dramatically accelerate the background degradation of ground-ice features in response to climate change and promotes a plant community that is distinct in terms of taxa and structure, dominated by tall willows and other competitive, rather than cold-tolerant, species. The cover of sedges and mosses is low while that of willows and grass is high relative to the reference tundra. The changes in plant community composition and structure, increasing ground temperature, and thermokarst lead us to expect the observed biophysical changes to the tundra will persist centuries into the future.

Extensive, detailed information on the spatial distribution of active layer thickness (ALT) in northern Alaska and how it evolves over time could greatly aid efforts to assess the effects of climate change on the region and also help to quantify greenhouse gas emissions generated due to permafrost thaw. For this reason, we have been developing high-resolution maps of ALT throughout northern Alaska. The maps are produced by upscaling from high-resolution swaths of estimated ALT retrieved from airborne P-band synthetic aperture radar (SAR) images collected for three different years. The upscaling was accomplished by using hundreds of thousands of randomly selected samples from the SAR-derived swaths of ALT to train a machine learning regression algorithm supported by numerous spatial data layers. In order to validate the maps, thousands of randomly selected samples of SAR-derived ALT were excluded from the training in order to serve as validation pixels; error performance calculations relative to these samples yielded root-mean-square errors (RMSEs) of 7.5-9.1 cm, with bias errors of magnitude under 0.1 cm. The maps were also compared to ALT measurements collected at a number of in situ test sites; error performance relative to the site measurements yielded RMSEs of approximately 11-12 cm and bias of 2.7-6.5 cm. These data are being used to investigate regional patterns and underlying physical controls affecting permafrost degradation in the tundra biome.

Surface albedo is a quantitative indicator for land surface processes and climate modeling, and plays an important role in surface radiation balance and climate change. In this study, by means of the MCD43A3 surface albedo product developed on the basis of Moderate Resolution Imaging Spectroradiometer (MODIS), we analyzed the spatiotemporal variation, persistence status, land cover type differences, and annual and seasonal differences of surface albedo, as well as the relationship between surface albedo and various influencing factors (including Normalized Difference Snow Index (NDSI), precipitation, Normalized Difference Vegetation Index (NDVI), land surface temperature, soil moisture, air temperature, and digital elevation model (DEM)) in the north of Xinjiang Uygur Autonomous Region (northern Xinjiang) of Northwest China from 2010 to 2020 based on the unary linear regression, Hurst index, and Pearson's correlation coefficient analyses. Combined with the random forest (RF) model and geographical detector (Geodetector), the importance of the above-mentioned influencing factors as well as their interactions on surface albedo were quantitatively evaluated. The results showed that the seasonal average surface albedo in northern Xinjiang was the highest in winter and the lowest in summer. The annual average surface albedo from 2010 to 2020 was high in the west and north and low in the east and south, showing a weak decreasing trend and a small and stable overall variation. Land cover types had a significant impact on the variation of surface albedo. The annual average surface albedo in most regions of northern Xinjiang was positively correlated with NDSI and precipitation, and negatively correlated with NDVI, land surface temperature, soil moisture, and air temperature. In addition, the correlations between surface albedo and various influencing factors showed significant differences for different land cover types and in different seasons. To be specific, NDSI had the largest influence on surface albedo, followed by precipitation, land surface temperature, and soil moisture; whereas NDVI, air temperature, and DEM showed relatively weak influences. However, the interactions of any two influencing factors on surface albedo were enhanced, especially the interaction of air temperature and DEM. NDVI showed a nonlinear enhancement of influence on surface albedo when interacted with land surface temperature or precipitation, with an explanatory power greater than 92.00%. This study has a guiding significance in correctly understanding the land-atmosphere interactions in northern Xinjiang and improving the regional land-surface process simulation and climate prediction.