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.

In contrast to boreal winter when extratropical seasonal predictions benefit greatly from ENSO-related teleconnections, our understanding of forecast skill and sources of predictability in summer is limited. Based on 40 years of hindcasts of the Canadian Seasonal to Interannual Prediction System, version 3 (CanSIPSv3), this study shows that predictions for the Northern Hemisphere summer surface air temperature are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe-Middle East, central Siberia-Mongolia-North China, and the western United States. These midlatitude regions of statistically significant predictive skill appear to be connected to each other through an upper-tropospheric circumglobal wave train. Although a large part of the forecast skill for the surface air temperature and 500-hPa geopotential height is attributable to the linear trend associated with global warming, there is signifi- cant long-lead seasonal forecast skill related to interannual variability. Two additional idealized hindcast experiments are performed to help shed light on sources of the long-lead forecast skill using one of the CanSIPSv3 models and its uncoupled version. It is found that tropical ENSO-related sea surface temperature (SST) anomalies contribute to the forecast skill in the western United States, while land surface conditions in winter, including snow cover and soil moisture, in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill in these regions. This implies that improving land surface initial conditions and model representation of land surface processes is crucial for the further development of a seasonal forecasting system.

In this study, a global variable-resolution modeling framework of atmospheric dust and its radiative feedback is established and evaluated. In this model, atmospheric dust is simulated simultaneously with meteorological fields, and dust-radiation interactions are included. Five configurations of global mesh with refinement at different resolutions and over different regions are used to explore the impacts of regional refinement on modeling dust lifecycle at regional and global scales. The model reasonably produces the overall magnitudes and spatial variabilities of global dust metrics such as surface mass concentration, deposition, aerosol optical depth, and radiative forcing compared to observations and previous modeling results. Two global variable-resolution simulations with mesh refinement over major deserts of North Africa (V16 km-NA) and East Asia (V16 km-EA) simulate less dust emissions and smaller dry deposition rates inside the refined regions due to the weakened near-surface wind speed caused by better resolved topographic complexity at higher resolution. The dust mass loadings over North Africa are close to each other between V16 km-NA and the quasi-uniform resolution (similar to 120 km) (U120 km), while over East Asia, V16 km-EA simulates higher dust mass loading. Over the non-refined areas with the same resolution, the difference between global variable-resolution and uniform-resolution experiments also exists, which is partly related to their difference in dynamic time-step and the coefficient for horizontal diffusion. Refinement at convection-permitting resolution around the Tibetan Plateau (TP) simulates less dust due to its more efficient wet scavenging from resolved convective precipitation around the TP against coarse resolution. Mineral dust plays an important role in Earth's climate system. Numerical simulation of dust and its impacts on a regional scale still has large uncertainties, partly due to the relatively coarse horizontal resolution. Limited-area simulation at relatively high resolution can generally better characterize dust and its impacts on a regional scale; however, lateral boundary conditions may introduce some numerical issues and constrain regional feedback, such as dust-cloud and dust-radiation interactions, to large-scale circulation. In this study, a novel modeling framework of atmospheric dust and its climatic feedbacks with the capability of global variable-resolution simulation is established and evaluated. The model produces reasonable global spatial distributions of dust compared to observations and previous studies. The difference between the simulations at global quasi-uniform resolution and global variable resolution with regional refinement over East Asia and North Africa is significant, particularly with refinement at convection-permitting resolution. This model may be used in the future to provide new insights into the impacts of dust on regional and global climate systems. A modeling framework of atmospheric dust with the capability of global variable-resolution simulation is introduced and evaluatedExperiments with regional refinement produce less dust emissions and mass loading and smaller dry deposition due to weaker surface windRefinement at convection-permitting resolution simulates stronger wet scavenging and less dust mass compared to coarse resolution

1. Factors shaping arthropod and plant community structure at fine spatial scales are poorly understood. This includes microclimate, which likely plays a large role in shaping local community patterns, especially in heterogeneous landscapes characterised by high microclimatic variability in space and in time.2. We explored differences in local microclimatic conditions and regional species pools in two subarctic regions: Kilpisj & auml;rvi in north-west Finland and Varanger in north-east Norway. We then investigated the relationship between fine-scale climatic variation and local community characteristics (species richness and abundance) among plants and arthropods, differentiating the latter into two groups: flying and ground-dwelling arthropods collected by Malaise and pitfall traps, respectively. Arthropod taxa were identified through DNA metabarcoding. Finally, we examined if plant richness can be used to predict patterns in arthropod communities.3. Variation in soil temperature, moisture and snow depth proved similar between regions, despite differences in absolute elevation. For each group of organisms, we found that about half of the species were shared between Kilpisj & auml;rvi and Varanger, with a quarter unique to each region.4. Plants and arthropods responded largely to the same drivers. The richness and abun-dance of both groups decreased as elevation increased and were positively correlated with higher soil moisture and temperature values. Plant species richness was a poor predictor of local arthropod richness, in particular for ground-dwelling arthropods.5. Our results reveal how microclimatic variation within each region carves pro-nounced, yet consistent patterns in local community richness and abundance out of a joint species pool.

This study examines the Arctic surface air temperature response to regional aerosol emissions reductions using three fully coupled chemistry-climate models: National Center for Atmospheric Research-Community Earth System Model version 1, Geophysical Fluid Dynamics Laboratory-Coupled Climate Model version 3 (GFDL-CM3) and Goddard Institute for Space Studies-ModelE version 2. Each of these models was used to perform a series of aerosol perturbation experiments, in which emissions of different aerosol types (sulfate, black carbon (BC), and organic carbon) in different northern mid-latitude source regions, and of biomass burning aerosol over South America and Africa, were substantially reduced or eliminated. We find that the Arctic warms in nearly every experiment, the only exceptions being the U.S. and Europe BC experiments in GFDL-CM3 in which there is a weak and insignificant cooling. The Arctic warming is generally larger than the global mean warming (i.e. Arctic amplification occurs), particularly during non-summer months. The models agree that changes in the poleward atmospheric moisture transport are the most important factor explaining the spread in Arctic warming across experiments: the largest warming tends to coincide with the largest increases in moisture transport into the Arctic. In contrast, there is an inconsistent relationship (correlation) across experiments between the local radiative forcing over the Arctic and the simulated Arctic warming, with this relationship being positive in one model (GFDL-CM3) and negative in the other two. Our results thus highlight the prominent role of poleward energy transport in driving Arctic warming and amplification, and suggest that the relative importance of poleward energy transport and local forcing/feedbacks is likely to be model dependent.

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.

Since China implemented the Air Pollution Prevention and Control Action Plan in 2013, the aerosol emis-sions in East Asia have been greatly reduced, while emissions in South Asia have continued to increase. This has led to a dipole pattern of aerosol emissions between South Asia and East Asia. Here, the East Asian summer monsoon (EASM) responses to the dipole changes in aerosol emissions during 2013-17 are investigated using the atmosphere model of Com-munity Earth System Model version 2 (CESM2). We show that decreases in East Asian emissions alone lead to a positive aerosol effective radiative forcing (ERF) of 1.59 (+/- 0.97) W m-2 over central-eastern China (25 degrees-40 degrees N, 105 degrees-122.5 degrees E), along with a 0.09 (+/- 0.07)degrees C warming in summer during 2013-17. The warming intensified the land-sea thermal contrast and increased the rainfall by 0.32 (+/- 0.16) mm day-1. When considering both the emission reductions in East Asia and in-creases in South Asia, the ERF is increased to 3.39 (+/- 0.89) W m-2, along with an enhanced warming of 0.20 (+/- 0.08)degrees C over central-eastern China, while the rainfall insignificant decreased by 0.07 (+/- 0.16) mm day-1. It is due to the westward shift of the strengthened western Pacific subtropical high, linked to the increase in black carbon in South Asia. Based on multiple EASM indices, the reductions in aerosol emissions from East Asia alone increased the EASM strength by almost 5%. Considering the effect of the westward shift of WPSH, the dipole changes in emissions together increased the EASM by 5%-15% during 2013-17, revealing an important role of South Asian aerosols in changing the East Asian climate.

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.

The freezing front depth (z(ff)) of annual freeze-thaw cycles is critical for monitoring the dynamics of the cryosphere under climate change because z(ff) is a sensitive indicator of the heat balance over the atmosphere-cryosphere interface. Meanwhile, although it is very promising for acquiring global soil moisture distribution, the L-band microwave remote sensing products over seasonally frozen grounds and permafrost is much less than in wet soil. This study develops an algorithm, i.e., the brightness temperature inferred freezing front (BT-FF) model, for retrieving the interannual z(ff) with the diurnal amplitude variation of L-band brightness temperature (?T-B) during the freezing period. The new algorithm assumes first, the daily-scale solar radiation heating/cooling effect causes the daily surface thawing depth (z(tf)) variation, which leads further to ?T-B; second, ?T-B can be captured by an L-band radiometer; third, z(tf) and z(ff) are negatively linear correlated and their relation can be quantified using the Stefan equation. In this study, the modeled soil temperature profiles from the land surface model (STEMMUS-FT, i.e., simultaneous transfer of energy, mass, and momentum in unsaturated soil with freeze and thaw) and T-B observations from a tower-based L-band radiometer (ELBARA-III) at Maqu are used to validate the BT-FF model. It shows that, first, ?T-B can be precisely estimated from z(tf) during the daytime; second, the decreasing of z(tf) is linearly related to the increase of z(ff) with the Stefan equation; third, the accuracy of retrieved z(ff) is about 5-25 cm; fourth, the proposed model is applicable during the freezing period. The study is expected to extend the application of L-band T-B data in cryosphere/meteorology and construct global freezing depth dataset in the future.

Climate warming has aggravated the occurrence of thaw settlement in permafrost region, but the associated risk has not been precisely assessed or understood. This study applied four machine learning models to explore and compare the spatial distribution of thaw settlement risk in the Wudaoliang-Tuotuohe region, Qinghai-Tibet Plateau, namely, naive Bayesian, k-nearest neighbor, logistic model tree and random forest models. A total of 853 thaw settlement locations and 12 conditioning factors were used to train and validate the above four models. The results indicated that random forest model performed best with the highest accuracy. The risk map produced by random forest model implied that about 76.55% of thaw settlements were located in very high-risk regions, which only occupied 6.85% of study area. The volume ice content, active layer thickness and thawing degree days were the main factors leading thaw settlement. By further comparing the performances between random forest model and other three models, the overestimated and underestimated risk regions (Beiluhe and Tuotuohe basins), and imbalanced conditioning factors (altitude and slope angle) were determined. In contrast with similar studies, this research performed better in model construction and accuracy. The results can help designers to implement precautionary measures in thaw settlement risk management.