Permafrost degradation is a growing direct impact of climate change. Detecting permafrost shrinkage, in terms of extension, depth reduction and active layer shift is fundamental to capture the magnitude of trends and address actions and warnings. Temperature profiles in permafrost allow direct understanding of the status of the frozen ground layer and its evolution in time. The Sommeiller Pass permafrost monitoring station, at about 3000 m of elevation, is the key site of the regional network installed in 2009 during the European Project PermaNET in the Piedmont Alps (NW Italy). The station consists of three vertical boreholes with different characteristics, equipped with a total of 36 thermistors distributed in three different chains. The collected raw data shows a degradation of the permafrost base at approximately 60 m of depth since 2014, corresponding to about 0.03 degrees C/yr. In order to verify and better quantify this potential degradation, three on-site sensor calibration campaigns were carried out to understand the reliability of these measurements. By repeating calibrations in different years, two key results have been achieved: the profiles have been corrected for errors and the re-calibration allowed to distinguish the effective change of permafrost temperatures during the years, from possible drifts of the sensors, which can be of the same order of magnitude of the investigated thermal change. The warming of permafrost base at a depth of similar to 60 m has been confirmed, with a rate of (4.2 +/- 0.5)center dot 10(-2) degrees C/yr. This paper reports the implementation and installation of the on-site metrology laboratory, the dedicated calibration procedure adopted, the calibration results and the resulting adjusted data, profiles and their evolution with time. It is intended as a further contribution to the ongoing studies and definition of best practices, to improve data traceability and comparability, as prescribed by the World Meteorological Organization Global Cryosphere Watch programme.

Uncertainty plays a key role in hydrological modeling and forecasting, which can have tremendous environmental, economic, and social impacts. Therefore, it is crucial to comprehend the nature of this uncertainty and identify its scope and effects in a way that enhances hydrological modeling and forecasting. During recent decades, hydrological researchers investigated several approaches for reducing inherent uncertainty considering the limitations of sensor measurement, calibration, parameter setting, model conceptualization, and validation. Nevertheless, the scope and diversity of applications and methodologies, sometimes brought from other disciplines, call for an extensive review of the state-of-the-art in this field in a way that promotes a holistic view of the proposed concepts and provides textbook-like guidelines to hydrology researchers and the community. This paper contributes to this goal where a systematic review of the last decade's research (2010 onward) is carried out. It aims to synthesize the theories and tools for uncertainty reduction in surface hydrological forecasting, providing insights into the limitations of the current state-of-the-art and laying down foundations for future research. A special focus on remote sensing and multi-criteria-based approaches has been considered. In addition, the paper reviews the current state of uncertainty ontology in hydrological studies and provides new categorizations of the reviewed techniques. Finally, a set of freely accessible remotely sensed data and tools useful for uncertainty handling and hydrological forecasting are reviewed and pointed out.

Aerosols are liquid and solid particles suspended in the atmosphere and have a broad size range; they can cool the Earth by scattering radiation back to space or warm the Earth by absorbing radiation directly. Since the industrial revolution, the loading of aerosols in the Earth's atmosphere has increased significantly, yielding modifications to the Earth's energy budget and further affecting the climate state. Aerosol direct radiative forcing (ADRF), defined as the difference in radiation with and without total or specific aerosols, is an important concept used to describe the direct impact of aerosols on radiation. Accurate quantification of ADRF is the premise for understanding and predicting the Earth's climate state. To improve the estimation and evaluation of ADRF, numerous researchers have dedicated their efforts to developing a series of observations and models in recent decades. However, due to the limited availability of wide spatial and high-precision observations of aerosol optical characteristics, as well as an insufficient model description of aerosol properties and physical and chemical processes, the ADRF uncertainty is still high. This paper first reviews the spatio-temporal distribution of aerosol optical depth (AOD), single scattering albedo (SSA) and corresponding ADRF by using observations and models. The aerosol optical properties and ADRF show distinct discrepancies among various regions due to the impact of anthropogenic emissions and meteorological and climate conditions. In regions with rapid economic development, such as India, AOD demonstrates a long-term increasing trend with higher average values. However, regions influenced by environmental protection policies, such as North America and Europe, show a long-term decreasing trend in AOD, accompanied by lower average values. Based on site observations, most of Europe, North America, Africa, and Asia exhibit a significant long-term increasing trend in SSA. However, in seasons with biomass burning or dust outbreaks, specific regions, such as southern and southwestern China in late autumn and early spring, and northern and northwestern China in spring, exhibit a reduction in SSA. In the future, with the global and regional emissions of aerosols and precursors declining, ADRF is expected to weaken, highlighting the warming effect of greenhouse gases. However, the ADRF trend is closely linked to the present development level and trajectory of each region. Second, we systematically summarize the impacts of the influential factors on the ADRF, considering the AOD, SSA, surface albedo (SA), solar zenith angle (SZA), asymmetry factor (ASY), relative altitude between aerosols and clouds, and relative altitude between different types of aerosols. Subsequently, we proceed to review the sensitivities of ADRF to different influential factors, as well as the contributions of these factors to the overall uncertainty of ADRF, which indicate that ADRF is more sensitive to AOD and SSA while SSA emerges as the most significant source of uncertainty in ADRF due to the larger errors associated with its measurement. It should be noted that the uncertainty caused by SA and ASY cannot be ignored in polluted regions. Finally, from the perspective of observations and models, a brief outlook on improving the accuracy of ADRF evaluation is provided. In the future, advanced observation technologies, such as multi-angle, hyperspectral, polarized remote sensing observations, and precise in-situ measurements, should be developed to obtain more accurate information about the aerosols and environment. Furthermore, we need to properly combine various observations and models, including Earth system models, to improve the simulation of aerosols and their precursors. With improved understanding of aerosol-radiation interactions and refining techniques in observations and model simulations, the evaluation of ADRF will be more accurate.

The Tarim River, the largest inland river in China, sits in the Tarim River Basin (TRB), which is an arid area with the ecosystem primarily sustained by water from melting snow and glaciers in the headstream area. To evaluate the pressures of natural disasters in this climate-change-sensitive basin, this study projected flash droughts in the headstream area of the TRB. We used the variable infiltration capacity (VIC) model to describe the hydrological processes of the study area, Markov chain Monte Carlo to quantify the parameter uncertainty of the VIC model. Ten downscaled general circulation models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to drive the VIC model, and the standardized evaporative stress ratio was applied to identify flash droughts. The results demonstrated that the VIC model after Bayesian parameters uncertainty analysis can efficiently describe the hydrological processes of the study area. In the future (2021-2100), compared with the plain region, the alpine region has higher flash drought frequency and intensity. Compared with the historical period (1961-2014), the frequency, duration, and intensity of flash droughts tend to increase throughout the study area, especially for the alpine area. Moreover, based on variance decomposition, CMIP6 model is the most important uncertainty source for flash drought projection, followed by the shared socioeconomic pathway of climate change scenario and VIC model parameters.

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.

The global aerosol direct and indirect radiative effect remains the largest source of uncertainty in estimations of the global energy budget in climate models. Black carbon (BC) is the largest contributor to aerosol atmospheric radiative absorption, and its contribution to radiative forcing must be better constrained to reduce uncertainties in the overall aerosol radiative effect. This paper reviews the advancement in the understanding of BC radiative forcing, highlighting improved constraints for major sources of model uncertainty as described by Bond et al. (J Geophys Res Atmos 118:5380-5552, 2013), a fundamental review of the climate effects of BC which served as a primary source of the BC uncertainty analysis in the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). Bond identified five primary sources of forcing uncertainty: altitude and removal rates of BC, BC emissions rates, individualized attribution of radiative forcing to absorbing aerosols species, BC effects on clouds, and accuracy of climate models in representing components of the Earth system, such as clouds and sea ice absent of BC. Improved constraints in each of these areas of forcing uncertainty-particularly BC impacts on clouds and atmospheric water vapor-have achieved a narrower uncertainty range in the IPCC Sixth Assessment Report (AR6). This paper excludes a review of the accuracy of the representation of climate models, rather focusing on the interaction of anthropogenic emissions of BC in the climate system. Future research should both expand upon the progress detailed in this paper and address the impacts of BC in the cryosphere, with particular focus on the contribution of BC to observed rapid warming of the Arctic.

This study investigates the impacts of climate change on the hydrology and soil thermal regime of 10 sub-arctic watersheds (northern Manitoba, Canada) using the Variable Infiltration Capacity (VIC) model. We utilize statistically downscaled and biascorrected forcing datasets based on 17 general circulation model (GCM) - representative concentration pathways (RCPs) scenarios from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to run the VIC model for three 30-year periods: a historical baseline (1981-2010: 1990s), and future projections (2021-2050: 2030s and 2041-2070: 2050s), under RCPs 4.5 and 8.5. Future warming increases the average soil column temperature by similar to 2.2 C in the 2050s and further analyses of soil temperature trends at three different depths show the most pronounced warming in the top soil layer (1.6 degrees C 30-year(-1) in the 2050s). Trend estimates of mean annual frozen soil moisture fraction in the soil column show considerable changes from 0.02 30-year(-1) (1990s) to 0.11 30-year(-1) (2050s) across the study area. Soil column water residence time decreases significantly (by 5 years) during the 2050s when compared with the 1990s as soil thawing intensifies the infiltration process thereby contributing to faster conversion to baseflow. Future warming results in 40%-50% more baseflow by the 2050s, where it increases substantially by 19.7% and 46.3% during the 2030s and 2050s, respectively. These results provide crucial information on the potential future impacts of warming soil temperatures on the hydrology of sub-arctic watersheds in north-central Canada and similar hydro-climatic regimes.

Rising global temperatures are a threat to the current state of the Arctic. In particular, permafrost degradation has been impacting the terrestrial cryosphere in many ways, including effects on carbon cycling and the global climate, regional hydrological connectivity and ecosystem dynamics, as well as human health and infrastructure. However, the ability to simulate permafrost dynamics under future climate projections is limited, and model outputs are often associated with large uncertainties. A model structured on a Bayesian Network is presented to address existing limitations in the representation of physically complex processes and the limited availability of observational data. A strength of Bayesian methods over more traditional modeling methods is the ability to integrate various types of evidence (i.e., observations, model outputs, expert assessments) into a single model by mapping the evidence into probability distributions. Here, we outline PermaBN, a new modeling framework, to simulate permafrost thaw in the continuous permafrost region of the Arctic. Pre-validation and expert assessment validation results show that the model produces estimations of permafrost thaw depth that are consistent with current research, i.e., thaw depth increases during the snow-free season under initial conditions favoring warming temperatures, lowered soil moisture conditions, and low active layer ice content. Using a case study from northwestern Canada to evaluate PermaBN, we show that model performance is enhanced when certainty about the system components increases for known scenarios described by observations directly integrated into the model; in this case, insulation properties from vegetation were integrated to the model. Overall, PermaBN could provide informative predictions about permafrost dynamics without high computational cost and with the ability to integrate multiple types of evidence that traditional physics-based models sometimes do not account for, allowing PermaBN to be applied to carbon modeling studies, infrastructure hazard assessments, and policy decisions aimed at mitigation of, and adaptation to, permafrost degradation.

Permafrost thaw has been observed in recent decades in the Northern Hemisphere and is expected to accelerate with continued global warming. Predicting the future of permafrost requires proper representation of the interrelated surface/subsurface thermal and hydrologic regimes. Land surface models (LSMs) are well suited for such predictions, as they couple heat and water interactions across soil-vegetation-atmosphere interfaces and can be applied over large scales. LSMs, however, are challenged by the long-term thermal and hydraulic memories of permafrost and the paucity of historical records to represent permafrost dynamics under transient climate conditions. In this study, we aim to understand better how LSMs function under different spin-up states, which facilitates addressing the challenge of model initialization by characterizing the impact of initial climate conditions and initial soil frozen and liquid water contents on the simulation length required to reach equilibrium. Further, we quantify how the uncertainty in model initialization propagates to simulated permafrost dynamics. Modelling experiments are conducted with the Modelisation Environmentale Communautaire-Surface and Hydrology (MESH) framework and its embedded Canadian land surface scheme (CLASS). The study area is in the Liard River basin in the Northwest Territories of Canada with sporadic and discontinuous regions. Results show that uncertainty in model initialization controls various attributes of simulated permafrost, especially the active layer thickness, which could change by 0.5-1.5 m depending on the initial condition chosen. The least number of spin-up cycles is achieved with near field capacity condition, but the number of cycles varies depending on the spin-up year climate. We advise an extended spin-up of 200-1000 cycles to ensure proper model initialization under different climatic conditions and initial soil moisture contents.

Soil moisture is an important driver of growth in boreal Alaska, but estimating soil hydraulic parameters can be challenging in this data-sparse region. Parameter estimation is further complicated in regions with rapidly warming climate, where there is a need to minimize model error dependence on interannual climate variations. To better identify soil hydraulic parameters and quantify energy and water balance and soil moisture dynamics, we applied the physically based, one-dimensional ecohydrological Simultaneous Heat and Water (SHAW) model, loosely coupled with the Geophysical Institute of Permafrost Laboratory (GIPL) model, to an upland deciduous forest stand in interior Alaska over a 13-year period. Using a Generalized Likelihood Uncertainty Estimation parameterisation, SHAW reproduced interannual and vertical spatial variability of soil moisture during a five-year validation period quite well, with root mean squared error (RMSE) of volumetric water content at 0.5 m as low as 0.020 cm(3)/cm(3). Many parameter sets reproduced reasonable soil moisture dynamics, suggesting considerable equifinality. Model performance generally declined in the eight-year validation period, indicating some overfitting and demonstrating the importance of interannual variability in model evaluation. We compared the performance of parameter sets selected based on traditional performance measures such as the RMSE that minimize error in soil moisture simulation, with one that is designed to minimize the dependence of model error on interannual climate variability using a new diagnostic approach we call CSMP, which stands for Climate Sensitivity of Model Performance. Use of the CSMP approach moderately decreases traditional model performance but may be more suitable for climate change applications, for which it is important that model error is independent from climate variability. These findings illustrate (1) that the SHAW model, coupled with GIPL, can adequately simulate soil moisture dynamics in this boreal deciduous region, (2) the importance of interannual variability in model parameterisation, and (3) a novel objective function for parameter selection to improve applicability in non-stationary climates.