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.

The Granger Causality (GC) statistical test explores the causal relationships between different time series variables. By employing the GC method, the underlying causal links between environmental drivers and global vegetation properties can be untangled, which opens possibilities to forecast the increasing strain on ecosystems by droughts, global warming, and climate change. This study aimed to quantify the spatial distribution of four distinct satellite vegetation products' (VPs) sensitivities to four environmental land variables (ELVs) at the global scale given the GC method. The GC analysis assessed the spatially explicit response of the VPs: (i) the fraction of absorbed photosynthetically active radiation (FAPAR), (ii) the leaf area index (LAI), (iii) solar-induced fluorescence (SIF), and, finally, (iv) the normalized difference vegetation index (NDVI) to the ELVs. These ELVs can be categorized as water availability assessing root zone soil moisture (SM) and accumulated precipitation (P), as well as, energy availability considering the effect of air temperature (T) and solar shortwave (R) radiation. The results indicate SM and P are key drivers, particularly causing changes in the LAI. SM alone accounts for 43%, while P accounts for 41%, of the explicitly caused areas over arid biomes. SM further significantly influences the LAI at northern latitudes, covering 44% of cold and 50% of polar biome areas. These areas exhibit a predominant response to R, which is a possible trigger for snowmelt, showing more than 40% caused by both cold and polar biomes for all VPs. Finally, T's causality is evenly distributed amongst all biomes with fractional covers between similar to 10 and 20%. By using the GC method, the analysis presents a novel way to monitor the planet's ecosystem, based on solely two years as input data, with four VPs acquired by the synergy of Sentinel-3 (S3) and 5P (S5P) satellite data streams. The findings indicated unique, biome-specific responses of vegetation to distinct environmental drivers.

Diurnal variation of land surface temperature (LST) is essential for land surface energy and water balance at regional or global scale. Diurnal temperature cycle (DTC) model with least parameters and high accuracy is the key issue in estimating the spatial-temporal variation of DTC. The alpine meadow is the main land cover in the Tibetan Plateau (TP). However, few studies have been reported on the performance of different DTC models over alpine meadows in the TP. Four semi-empirical types of DTC models were used to generate nine 4-parameter (4-para) models by fixing some of free parameters. The performance of the nine 4-para DTC models were evaluated with four in situ and MODIS observations. All models except GOT09-dT-t(s) (dT means the temperature residual between T-0 and T (t ->infinity); t(s) means the time when free attenuation begins) had higher correlation with in situ data (R-2 > 0.9), while the INA08-t(s) model performed best with NSE of 0.99 and RMSE of 2.04 K at all sites. The GOT09-t(s)-tau (tau is the total optical thickness), VAN06-t(s)-omega(1) (omega(1) means the half-width of the cosine term in the morning), and GOT01-t(s) models had better performance, followed by GOT09-dT-tau, GOT01-dT, and VAN06-t(s)-omega(2) (omega(2) means the half-width of the cosine term in the afternoon) models. All models had higher accuracy in summer than in other seasons, while poorer performance was produced in winter. The INA08-t(s) model showed best performance among all seasons. Models with fixing t(s) could produce higher accuracy results than that with fixing dT. The comparison of INA08-ts model driven by in situ and Moderate Resolution Imaging Spectroradiometer (MODIS) data indicated that the simulation accuracy mainly depended on the accuracy of MODIS LST. The daily maximum temperature generated by the nine models had high accuracy when compared with in situ data. The sensitivity analysis indicated that the INA08-dT and GOT09-dT-t(s) models were more sensitive to parameter dT, while all models were insensitive to parameter t(s), and all models had weak relationship with parameters omega and tau. This study provides a reference for exploring suitable DTC model in the TP.

We estimate snow albedo feedback effects of anthropogenic increases in global radiative forcing, which includes carbon dioxide, methane, nitrous oxide, CFC11, CFC12, black carbon, anthropogenic sulfur emissions, total solar irradiance, and local sulfur emissions by compiling annual observations (1972-2008) for radiative forcing, temperature, snow cover, sulfur emissions, and various teleconnections for 255 5 degrees x 5 degrees grid cells in the Northern Hemisphere. Panel DOLS estimates of the long-run relations indicate that the effect of radiative forcing on temperature increases with latitude (consistent with polar amplification), eliminating snow cover increases local temperature by about 2.8 degrees C, and a 1 degrees C temperature increase reduces snow cover by about 1%. These values create a snow albedo feedback (SAF) that amplifies the temperature increase of higher forcing by about 3.4% relative to its direct effect while an increase in sulfur emissions increases the temperature reduction by about 0.4% relative to its direct effect. The 3.4% SAF is smaller than values generated by process-based climate models and may be associated with the empirical estimates for snowmelt sensitivity Delta S-c/Delta T-s To narrow estimates for the SAF from climate models, we conclude with suggestions for a new experimental design that controls for the simultaneous relation between temperature and snow cover.

Permafrost thaw/degradation in the Northern Hemisphere due to global warming is projected to accelerate in coming decades. Assessment of this trend requires improved understanding of the evolution and dynamics of permafrost areas. Land surface models (LSMs) are well-suited for this due to their physical basis and large-scale applicability. However, LSM application is challenging because (a) LSMs demand extensive and accurate meteorological forcing data, which are not readily available for historic conditions and only available with significant biases for future climate, (b) LSMs possess a large number of model parameters, and (c) observations of thermal/hydraulic regimes to constrain those parameters are severely limited. This study addresses these challenges by applying the MESH-CLASS modeling framework (Modelisation Environmenntale communautaire-Surface et Hydrology embedding the Canadian Land Surface Scheme) to three regions within the Mackenzie River Basin, Canada, under various meteorological forcing data sets, using the variogram analysis of response surfaces framework for sensitivity analysis and threshold-based identifiability analysis. The study shows that the modeler may face complex trade-offs when choosing a forcing data set; for current and future scenarios, forcing data require multi-variate bias correction, and some data sets enable the representation of some aspects of permafrost dynamics, but are inadequate for others. The results identify the most influential model parameters and show that permafrost simulation is most sensitive to parameters controlling surface insulation and runoff generation. But the identifiability analysis reveals that many of the most influential parameters are unidentifiable. These conclusions can inform future efforts for data collection and model parameterization.

In this study, we compiled a high-quality, in situ observational dataset to evaluate snow depth simulations from 22 CMIP6 models across high-latitude regions of the Northern Hemisphere over the period 1955-2014. Simulated snow depths have low accuracy (RMSE = 17-36 cm) and are biased high, exceeding the observed baseline (1976-2005) on average (18 +/- 16 cm) across the study area. Spatial climatological patterns based on observations are modestly reproduced by the models (normalized root-mean-square deviations of 0.77 +/- 0.20). Observed snow depth during the cold season increased by about 2.0 cm over the study period, which is approximately 11% relative to the baseline. The models reproduce decreasing snow depth trends that contradict the observations, but they all indicate a precipitation increase during the cold season. The modeled snow depths are insensitive to precipitation but too sensitive to air temperature; these inaccurate sensitivities could explain the discrepancies between the observed and simulated snow depth trends. Based on our findings, we recommend caution when using and interpreting simulated changes in snow depth and associated impacts.

Hydrological conditions in cold regions have been shown to be sensitive to climate change. However, a detailed understanding of how regional climate and basin landscape conditions independently influence the current hydrology and its climate sensitivity is currently lacking. This study, therefore, compares the climate sensitivity of the hydrology of two basins with contrasted landscape and meteorological characteristics typical of eastern Canada: a forested boreal climate basin (Montmorency) versus an agricultural hemiboreal climate basin (Aca-die). The physically based Cold Regions Hydrological Modelling (CRHM) platform was used to simulate the current and future hydrological processes. Both basin landscape and regional climate drove differences in hy-drological sensitivities to climate change. Projected peak SWE were highly sensitive to warming, particularly for milder baseline climate conditions and moderately influenced by differences in landscape conditions. Landscape conditions mediated a wide range of differing hydrological processes and streamflow responses to climate change. The effective precipitation was more sensitive to warming in the forested basin than in the agricultural one, due to reductions in forest canopy interception losses with warming. Under present climate, precipitation and discharge were found to be more synchronized in the greater relief and slopes of the forested basin, whereas under climate change, they are more synchronized in the agricultural basin due to reduced infiltration and storage capacities. Flow through and over agricultural soils translated the increase in water availability under a warmer and wetter climate into higher peak discharges, whereas the porous forest soils dampened the response of peak discharge to increased available water. These findings help diagnose the mechanisms controlling hy-drological response to climate change in cold regions forested and agricultural basins.

Understanding the balance between methane (CH4) production (methanogenesis) and its oxidation is important for predicting carbon emissions from thermokarst lakes under global warming. However, the response of thermokarst lake methanogenesis and the anaerobic oxidation of methane (AOM) to warming, especially from Qinghai-Tibetan Plateau (QTP), is still not quantified. In this study, sediments were collected from 11 thermokarst lakes on the QTP. These lakes are surrounded with different vegetation types, including alpine desert (AD), alpine steppe (AS), alpine meadow (AM) and alpine swamp meadow (ASM). The results showed that methanogenesis and AOM rates exponentially increased with temperature, while the temperature sensitivity (Q10, average Q10 values of methanogenesis and AOM were 0.69-30 and 0.54-16.9 respectively) of methanogenesis were larger than AOM, but not significant, showing a similar temperature dependence of methanogenesis and AOM in thermokarst lake sediments. Thermokarst lake sediments in the ASM had higher methanogenesis and anaerobic oxidation potential, matching its higher NDVI and relative abundances of methanogens and SBM (syntrophic bacteria with methanogens). Although the thermokarst lake sediments AOM depleted 15 %-27.8 % of the total CH4 production, the AOM rate was lower than methanogenesis in thermokarst lake sediments, it did not offset increased CH4 production under anaerobic conditions. The increase in CH4 production in thermokarst lake sediments will likely lead to higher emissions within a warming world. These findings indicate that methanogenesis and AOM in thermokarst lake sediments are sensitive to climate change. Models should consider the Q10 values of methanogenesis and AOM and vegetation types when predicting carbon cycle in thermokarst lakes under global warming.

Permafrost peatlands, as large soil carbon pools, are sensitive to global warming. However, the effects of temperature, moisture, and their interactions on carbon emissions in the permafrost peatlands remain unclear, when considering the availability of soil matrixes. The permafrost peatland (0-50 cm soil) in the Great Xing'an Mountains was selected to explore the deficiency. The cumulative carbon dioxide (CO2) and methane (CH4) emissions from soil were measured under different temperatures (5 C, 10 C, and 15 C) and moisture content (130%, 100%, and 70%) treatments by the indoor incubation. The results showed that the soil carbon and nitrogen matrix determined soil carbon emissions. Warming affected the availability of soil carbon and nitrogen substrates, thus stimulating microbial activity and increasing soil carbon emissions. With soil temperature increasing by 10 C, soil CO2 and CH4 emission rates increased by 5.1-9.4 and 3.8-6.4 times respectively. Warming promoted soil carbon emissions, and the decrease of moisture content promoted CO2 emissions but inhibited CH4 emissions in the permafrost peatland. Soil moisture and the carbon and nitrogen matrix determined the intensity of CO2 and CH4 emissions. The results were important to assess soil carbon emissions from permafrost peatlands under the impact of future climate warming and to formulate carbon emission reduction policies.

Arctic permafrost surface freeze-thaw (FT) changes related to warming could regulate the magnitude of global warming by altering the terrestrial carbon cycle and energy balances. This study investigated the sensitivity of surface FT changes to warming over Arctic permafrost regions by analyzing long-term changes in surface FT phenology from satellite remote sensing and meteorological variables from the climate data for the period from 1979 to 2017. Averaging over the entire Arctic permafrost regions, spring thawed date apparently advanced by -2.05 days decade-1, whereas autumn frozen date showed weak delaying trend of 0.83 days decade-1, implying the lengthening of the thawed season. Dividing the regions by permafrost types, advancing trends of thawed dates in continuous and high ice content permafrost areas (-2.57 and -2.70 days decade-1) were stronger than those over the discontinuous and low ice content permafrost areas (-1.61 and -1.73 days decade-1). The difference in changes in spring thawed dates between the regions is attributed to the difference in absolute magnitude of warming trends (e.g., 0.72 degrees C decade- 1 for continuous vs. 0.44 degrees C decade- 1 for discontinuous). However, the temperature sensitivity over discontinuous (low ice content) permafrost areas was 23% (10%) stronger than that over continuous (high ice content) permafrost areas for thawed date. In case of autumn, delaying trends of frozen dates were smaller over continuous and high ice content areas (0.69 and 0.74 days decade-1) than those over discontinuous and low ice content areas (1.01 and 0.88 days decade-1). This is mainly explained by the difference in temperature sensitivity (e.g., 1.57 days degrees C- 1 for continuous vs. 2.18 days degrees C- 1 for discontinuous) to warming between the regions rather than the difference in the absolute warming trends between the regions (e.g., 0.91 degrees C decade- 1 for continuous vs. 0.51 degrees C decade- 1 for discontinuous). The stronger temperature sensitivity of discontinuous and low ice content permafrost could be related to the lower demand of latent heat for the phase change of ground ice (or water). Overall, our results suggest that discontinuous and low ice content permafrost are more vulnerable to atmospheric warming. In addition to the magnitude of warming, the sensitivity to warming also needs to be considered when predicting permafrost FT changes.