Dr. Clare Webster

Climate data matching the scales at which organisms experience climatic conditions are often missing. Yet, such data on microclimatic conditions are required to better understand climate change impacts on biodiversity and ecosystem functioning. Here we combine a network of microclimate temperature measurements across different habitats and vertical heights with a novel radiative transfer model to map daily temperatures during the vegetation period at 10 m spatial resolution across Switzerland. Our results reveal strong horizontal and vertical variability in microclimate temperature, particularly for maximum temperatures at 5 cm above the ground and within the topsoil. Compared to macroclimate conditions as measured by weather stations outside forests, diurnal air and topsoil temperature ranges inside forests were reduced by up to 3.0 and 7.8 ^∘C, respectively, while below trees outside forests, e.g. in hedges and below solitary trees, this buffering effect was 1.8 and 7.2 ^∘C, respectively. We also found that, in open grasslands, maximum temperatures at 5 cm above ground are, on average, 3.4 ^∘C warmer than those of the macroclimate, suggesting that, in such habitats, heat exposure close to the ground is often underestimated when using macroclimatic data. Spatial interpolation was achieved by using a hybrid approach based on linear mixed-effect models with input from detailed radiation estimates from radiative transfer models that account for topographic and vegetation shading, as well as other predictor variables related to the macroclimate, topography, and vegetation height. After accounting for macroclimate effects, microclimate patterns were primarily driven by radiation, with particularly strong effects on maximum temperatures. Results from spatial block cross-validation revealed predictive accuracies as measured by root mean squared errors ranging from 1.18 to 3.43 ^∘C, with minimum temperatures being predicted more accurately overall than maximum temperatures. The microclimate-mapping methodology presented here enables a biologically relevant perspective when analysing climate–species interactions, which is expected to lead to a better understanding of biotic and ecosystem responses to climate and land use change.

In mountain regions, forests that overlap with seasonal snow mostly reside in complex terrain. Due to persisting major observational challenges in these environments, the combined impact of forest structure and topography on seasonal snow cover dynamics is still poorly understood. Recent advances in forest snow process representation and increasing availability of detailed canopy structure datasets, however, now allow for hyper-resolution (<5m) snow model simulations capable of resolving tree-scale processes. These can shed light on the complex process interactions that govern forest snow dynamics. We present multi-year simulations at 2m resolution obtained with FSM2, a mass- and energy-balance-based forest snow model specifically developed and validated for metre-scale applications. We simulate an ∼3km2 model domain encompassing forested slopes of a sub-alpine valley in the eastern Swiss Alps and six snow seasons. Simulations thus span a wide range of canopy structures, terrain characteristics, and meteorological conditions. We analyse spatial and temporal variations in forest snow energy balance partitioning, aiming to quantify and understand the contribution of individual energy exchange processes at different locations and times. Our results suggest that snow cover evolution is equally affected by canopy structure, terrain characteristics, and meteorological conditions. We show that the interaction of these three factors can lead to snow accumulation and ablation patterns that vary between years. We further identify higher snow distribution variability and complexity in slopes that receive solar radiation early in winter. Our process-level insights corroborate and complement existing empirical findings that are largely based on snow distribution datasets only. Hyper-resolution simulations as presented here thus help to better understand how snowpacks and ecohydrological regimes in sub-alpine regions may evolve due to forest disturbances and a warming climate. They could further support the development of process-based sub-grid forest snow cover parameterizations or tiling approaches for coarse-resolution modelling applications.

The seasonal evolution of snow cover has significant impacts on the hydrological cycle and microclimate in mountainous regions. However, snow processes also play a crucial role in triggering alpine mass movements and flooding, posing risks to people and infrastructure. To mitigate these risks, many countries use operational forecast systems for snow distribution and melt. This paper presents the Swiss Operational Snow-hydrological (OSHD) model system, developed to provide daily analysis and forecasts on snow cover dynamics throughout Switzerland. The OSHD system is a sophisticated snow hydrological model designed specifically for the high-alpine terrain of the Swiss Alps. It leverages exceptional station data and high-resolution meteorological forcing data, as well as various reanalysis products to combine snow modeling with advanced data assimilation and meteorological downscaling methods. The system offers models of varying complexity, each tailored to specific modeling strategies and applications. For snowmelt runoff forecasting, monitoring snow water resources, and research-grade purposes, the OSHD system employs physics-based modeling chains. For snow climatological assessments, a conceptual model chain is available. We are pleased to present two comprehensive datasets from the conceptual and physics-based models that cover the entirety of Switzerland. The first dataset comprises a snow water equivalent climatology spanning 1998-2022, with a spatial resolution of 1 km. The second dataset includes snow distribution and snow melt data spanning 2016-2022 at a high spatial resolution of 250 m. To meet the needs of a multi-purpose snow hydrological model framework, the OSHD system employs various strategies for process representation and sub-grid parameterizations at the snow-canopy-atmosphere interface, particularly in complex terrain. Recent and ongoing model developments are aimed at accounting for complex forest snow processes, representing slope and ridge-scale precipitation and snow redistribution processes, as well as improving probabilistic snow forecasts and data assimilation procedures based on remote sensing products.

Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Recently, snow routines in hydrological and land surface models were improved to incorporate more accurate representations of forest snow processes, but model intercomparison projects have identified deficiencies, partly due to incomplete knowledge of the processes controlling snow cover in forests. The Snow Under Forest (SnoUF) project was initiated to enhance knowledge of the complex interactions between snow and vegetation. Two field campaigns, during the winters 2016–2017 and 2017–2018, were conducted in a coniferous forest bordering the snow study at Col de Porte (1325 m a.s.l., French Alps) to document the snow accumulation and ablation processes. This paper presents the field site, the instrumentation and the collection and postprocessing methods. The observations include distributed forest characteristics (tree inventory, lidar measurements of forest structure, subcanopy hemispherical photographs), meteorology (automatic weather station and an array of radiometers), snow cover and depth (snow pole transect and laser scan) and snow interception by the canopy during precipitation events. The weather station installed under dense canopy during the first campaign has been maintained since then and has provided continuous measurements throughout the year since 2018. Data are publicly available from the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) data center at https://doi.org/10.17178/SNOUF.2022 (Sicart et al., 2022).

This study presents a new model for calculating canopy shortwave radiation transmissivity from synthetic hemispheric images using only information contained within a canopy height model (CHM) - CanopyHeightModel2Radiation (C2R). The enhanced version calculates synthetic hemispherical images based on the geometric arrangement of the surrounding canopy while applying a statistical correction for canopy transmissivity using canopy thickness and tree species leaf area. The simple input data and statistical correction make this model suitable for estimating canopy transmissivity across large spatial extents typical of land surface models for which canopy transmissivity or radiation is a primary input variable. Performance of C2R-enhanced is assessed against hemispherical photographs, and compared to a basic version of C2R without transmissivity correction, and two versions of a Lidar2Radiation model (L2R-enhanced, L2R-basic) with either a basic representation of canopy structure or an enhanced representation including trunks and branches within tree crowns. The two enhanced models (L2R-enhanced and C2R-enhanced) perform best compared to hemispherical photographs, while the L2R-basic and C2R-basic models over- and underestimate canopy transmissivity, respectively. At 1-meter and 10-minute resolution, the two enhanced models perform similarly, but exact timing and location of transmissivity controlled by canopy structure is better represented in the physically explicit L2R-enhanced model. Across hourly and 25 × 25 m grid-averaged scales, both enhanced models achieve similar estimates of canopy transmissivity. Based on these results, it is recommended that the purely physically-based representation in the L2R-enhanced model is used when estimates of canopy transmissivity at high spatial and temporal (meter and minute) resolutions are necessary, while the computationally more efficient C2R-enhanced model is used when calculating canopy transmissivity within spatially aggregated grid cells, for example, as input into coarser-resolution land surface models. Incorporating C2R-enhanced into existing forest energy balance models creates exciting opportunities for investigating forest structure changes on forest hydrology and ecosystems across previously impossible spatial extents.

Mapping snow in forests is important for understanding the snow cover dynamics in these environments in view of hydrological applications and water resources management. Today, Uncrewed Aerial Vehicles (UAVs) are widely used for snow studies due to their rather cheap and flexible operation. UAV-borne Light detection and ranging (LiDAR) systems are a promising technology for sub-canopy snow mapping at high temporal resolutions, concurrently providing information on both the canopy and the below-canopy snow surface. In this pilot study, we used a UAV-LiDAR system to investigate the snow cover dynamics within two steep forested slopes of opposing aspects in the Swiss Alps at both high spatial resolution and unprecedented temporal resolution. Using a Distance to Canopy Edge (DCE) algorithm to characterize local forest structure, snow depth was analyzed in terms of relative position within variable forest cover. The north-exposed site had higher mean snow depths throughout the season compared to the south-exposed site, especially in canopy gaps. Whereas snow depletion rate was consistent throughout the north-exposed site, snow depletion was much faster in the gaps at the south-exposed slope. Correlation coefficients between snow depths and local canopy closure were weaker at the south-exposed (between -0.5 and - 0.7) than at the north-exposed site (between -0.7 and - 0.9), and rapidly deteriorated right after the peak of winter at the south-exposed slope. This indicates shortwave radiation dominates snowmelt processes at this site, which was thought to be spatially uncorrelated to local canopy cover, unlike accumulation and melt processes on the north-exposed slope that generated snow patterns with a high spatial correlation to local canopy cover throughout the entire season. Calculations of incoming sub-canopy shortwave radiation (SWR) for both sites confirmed this assumption. While our findings encourage the use of UAV-borne LiDAR for further investigations of snow cover dynamics in steep forested slopes, we also outline and discuss technical challenges specific to this application. Our insights allow deriving useful recommendations for future studies using UAV-borne LiDAR over a similar environment.

Aim: Pleistocene erratic boulders are rocks that were relocated by glaciers during the Ice Ages. When their geology differs from the geology of the landscape that surrounds them, erratic boulders form habitat islands for regionally rare, edaphically specialised, rock-dwelling cryptogams (bryophytes, ferns and lichens). Such boulders constitute terrestrial model systems for exploring island biogeographic predictions and the effect of environmental variables on species diversity and community composition, which we studied in order to provide basic knowledge of the ecology, with relevance for the conservation, of these unusual island systems.
Location: Siliceous erratic boulders in the calcareous Swiss Plateau and Jura Mountains.
Methods: For 160 erratic boulders we recorded all bryophyte species and a diverse set of environmental variables. For all species and for specialist species (acidophile rock-dwellers) separately, we analysed species–area relationships and nestedness, and explored relationships between environmental variables, species diversity and community composition.
Results: We found 138 bryophyte species, 19 of which were specialists of erratic boulders. A steeper species–area curve for boulder specialists than for total species richness underlined the island properties of boulders for specialist species. Large boulders were more likely to harbour numerous boulder specialists and communities on small boulders were nested within the communities present on large boulders. However, at the landscape level small boulders contributed more specialist species than a few large boulders of the same surface area. Erratic boulders near settlements were less likely to harbour boulder specialists. Boulders in open land harboured different and more specialist species than boulders in forests.
Conclusions: Large undisturbed erratic boulders in open land harbour rare bryophyte communities with a large number of specialist species. Conservation should thus prioritise this type of boulders. Furthermore, conserving large boulders is logistically easier, and they may function as flagships for small boulders that also contribute to the biodiversity within landscapes.

Land Surface Albedo (LSA) of forested environments continues to be a source of uncertainty in land surface modeling, especially across seasonally snow covered domains. Assessment and improvement of global scale model performance has been hampered by the contrasting spatial scales of model resolution and in‐situ LSA measurements. In this study, point‐scale simulations of the Community Land Model 5.0 (CLM5) were evaluated across a large range of forest structures and solar angles at two climatically different locations. LSA measurements, using an uncrewed aerial vehicle with up and down‐looking shortwave radiation sensors, showed canopy structural shading of the snow surface exerted a primary control on LSA. Diurnal patterns of measured LSA revealed strong effects of both azimuth and zenith angles, neither of which were adequately represented in simulations. In sparse forest environments, LSA were overestimated by up to 66%. Further analysis revealed a lack of correlation between Plant Area Index (PAI), the primary canopy descriptor in CLM5, and measured LSA. Instead, measured LSA showed considerable correlation with the fraction of snow visible in the sensor's field of view, a correlation which increased further when only considering the sunlit fraction of visible snow. The use of effective PAI values as a simple first‐order correction for the discrepancy between measured and simulated LSA in sparse forest environments substantially improved model results (64%-76% RMSE reduction). However, the large biases suggest the need for a more generic solution, for example, by introducing a canopy metric that represents canopy gap fraction rather than assuming a spatially homogeneous canopy.

Processes shaping forest snow cover evolution often vary at small spatial scales which are not resolved by most model applications. Representing this variability at larger scales and coarser model resolutions constitutes a major challenge for model developers. In this study, we use a well‐validated hyper‐resolution forest snow model that explicitly resolves the spatial variability of canopy‐snow interactions at the meter scale to explore adequate representation of forest‐snow processes at coarser resolutions (50m). For this purpose, we assess coarser‐resolution runs against spatially averaged results from corresponding hyper‐resolution simulations over a 150 000 m² model domain. For the coarser‐resolution simulations, we tested alternative upscaling strategies. Our results reveal considerable discrepancies between strategies that utilize generalized canopy metrics versus strategies that apply a more detailed set of process‐specific canopy descriptors. Particularly, the inclusion of canopy descriptors that represent the various scales and perspectives relevant to the individual processes leads to accurate simulation of forest snow cover dynamics at coarse resolutions. Our results further demonstrate that a realistic representation of snow‐covered fraction in snowmelt calculations is important even for relatively small (∼50 m) grid cells. Ultimately, this work provides recommendations for modelling forest‐snow processes in large‐scale applications, which allow coarse‐resolution simulations to approximate spatially averaged results of corresponding hyper‐resolution simulations.

Forested environments feature a highly complex radiation regime. The three-dimensional canopy structure masks solar radiation as it penetrates through the forest. This entails sub-canopy radiation to vary in space, over time, season, and with weather conditions, which challenges both measurements and modelling. Here we combine the experimental acquisition of forest structure information with radiation transfer modelling to achieve efficient and accurate estimation of sub-canopy shortwave radiation at very high temporal resolution. For this purpose we have developed a Matlab-based software tool to analyse digital hemispherical imagery for directional canopy transmissivity which yields, in combination with measured or estimated above-canopy shortwave radiation, respective below-canopy radiation. We demonstrate the utility of the tool to accurately model shortwave radiation in comparison to pyranometer measurements at both the point scale and along a transect under heterogeneous canopy cover. Optimized for computational efficiency the software even enables fully-distributed simulations. Without necessitating high performance computing or complex ray-tracing, this tool enables easy access to detailed sub-canopy radiation information. These should be valuable for validating site-scale radiation transfer models, as input to eco-hydrological and snowmelt models, and for researching light-dependant bio-physiological processes.

It has been widely reported that Arctic sea ice has decreased in both extent and thickness, coupled with steadily rising mean annual temperatures. These trends have been particularly severe along the rock coast of southern Svalbard. Concerns have been raised over the potential for higher energy storms and longer ice-free open water seasons to increase the exposure of Arctic coasts, and consequently the concentration of infrastructure critical to Arctic community survival, to enhanced rates of erosion. Here we present and apply innovative remote sensing, monitoring and process analyses to assess the impact of recent coastal climatic changes. High resolution analyses demonstrate that the small scale (<0.001 m³) changes that are rarely considered quantitatively exhibit geomorphic responses distinct from those of larger, more readily detected cliff failures. We monitor temperature depth profiles in both the shore platform and the cliff face to show rock sensitivity over time to both global and local influences. The results demonstrate the efficacy of thermal processes on Arctic rock cliffs relative to platforms, and may hold implications for understanding strandflat development rates. New three-dimensional thermography (thermal mapping) and process zone characterisation has been used to spatially assess the sensitivity of Arctic rock coast responses to contemporary processes on deglaciating coasts. Through the spatial and temporal analyses of key geomorphic behaviour zones and comparison over a range of sites, the complex and changing interplay between subaerial weathering and cryogenic and intertidal processes has been highlighted. These data challenge long standing assumptions over the future of Arctic rock coasts and identify new, focused lines of enquiry on the decline in cryogenic processes and understanding the sensitivity of Arctic rock coasts to climatic changes.

The complex dynamics of snow accumulation and melt processes under forest canopies entail major observational and modeling challenges, as they vary strongly in space and time. In this study, we present novel data sets acquired with mobile multisensor platforms in subalpine and boreal forest stands. These data sets include spatially and temporally resolved measurements of shortwave and longwave irradiance, air and snow surface temperatures, wind speed, and snow depth, all coregistered to canopy structure information. We then apply the energy balance snow model FSM2 to obtain concurrent, distributed simulations of the forest snowpack at very high ("hyper") resolution (2 m). Our data sets allow us to assess the performance of alternative canopy representation strategies within FSM2 at the level of individual snow energy balance components and in a spatially explicit manner. We demonstrate the benefit of accounting for detailed spatial patterns of shortwave and longwave radiation transfer through the canopy and show the importance of describing wind attenuation by the canopy using stand‐scale metrics. With the proposed canopy representation, snowmelt dynamics in discontinuous forest stands were successfully reproduced. Hyper‐resolution simulations resolving these effects provide an optimal basis for assessing the snow‐hydrological impacts of forest disturbances and for validating and improving the representation of forest snow processes in land surface models intended for coarser‐scale applications.

Forest canopies act as intermediaries in radiation energy exchange between the atmosphere and the snow surface. The size, location and distribution of forest discontinuities are important controls on forest shortwave radiation transmission and subsequent snow surface shading and radiation energy exchange between the atmosphere and the canopy, but challenges arise when accounting for these vegetation characteristics at large spatial scales. Airborne LiDAR datasets contain detailed information about canopy structure across large spatial scales which can be exploited within 2D transmission models. However, airborne LiDAR data typically does not resolve lower canopy elements, leading to unrealistic depictions of individual trees. We present a methodology to enhance airborne LiDAR data by calculating additional trunk and branch points based on segmentation of a canopy height model, allowing more accurate estimates of canopy shortwave transmissivity. To demonstrate this, we deployed a computationally efficient 2D radiation transfer modelling framework that calculates direct and diffuse radiation from a set of distributed synthetic hemispherical images. The model can predict incoming direct and diffuse solar radiation at the snow surface at high spatial (meter-scale) and temporal (minute-scale) resolutions. Comparison between synthetic and real hemispherical photographs showed that synthetic images, if based on enhanced LiDAR data, featured canopy and individual tree crowns that were much denser than the original LiDAR portrays, improving the representation of vegetation structure especially within dense environments and along canopy edges. Corresponding modelled total shortwave radiation matched well with spatially gridded measurements from a moving pyranometer at two sites, where model RMSE was reduced to 59 and 29 W m^-2 from 181 and 138 W m^-2, respectively, compared to the same transmission model with the original LiDAR data. Maps of snow surface shading patterns corresponded well to those seen in aerial photographs, showing the enhanced LiDAR data can be used to solve complex spatiotemporal patterns of sub-canopy incoming radiation. This work demonstrates that canopy structure information from the lower canopy is an important aspect for accurate radiation transfer modelling, and methods presented here can successfully mitigate problems inherent in many airborne LiDAR datasets to improve spatially distributed estimates of sub-canopy shortwave radiation.

Uncooled thermal infrared (TIR) imagers, commonly used on aircraft and small unmanned aircraft systems (UAS, "drones"), can provide high-resolution surface temperature maps, but their accuracy is dependent on reliable calibration sources. A novel method for correcting surface temperature observations made by uncooled TIR imagers uses observations over melting snow, which provides a constant 0 °C reference temperature. This bias correction method is applied to remotely sensed surface temperature observations of forests and snow over two mountain study sites: Laret, Davos, Switzerland (27 March 2017) in the Alps, and Sagehen Creek, California, USA (21 April 2017) in the Sierra Nevada. Surface temperature retrieval errors that arise from temperature-induced instrument bias, differences in image resolution, retrieval of mixed pixels, and variable view angles were evaluated for these forest snow scenes. Applying the melting snow-based bias correction decreased the root-mean-square error by about 1 °C for retrieving snow, water, and forest canopy temperatures from airborne TIR observations. The influence of mixed pixels on surface temperature retrievals over forest snow scenes was found to depend on image resolution and the spatial distribution of forest stands. Airborne observations over the forests at Sagehen showed that near the edges of TIR images, at more than 20° from nadir, the snow surface within forest gaps smaller than 10 m was obscured by the surrounding trees. These off-nadir views, with fewer mixed pixels, could allow more accurate airborne and satellite-based observations of canopy surface temperatures.

Boreal forests cover about a fifth of seasonally snow-covered land over the Northern Hemisphere. Enhancement of longwave radiation beneath coniferous forests has been found to impact the surface energy balance and rates of snowmelt. Although the skill of model-simulated snowmelt has been shown to be lower for forests than for open areas, model intercomparisons and evaluations of model parameterizations have not yet focused on longwave enhancement. This study uses stand-scale forcing for the simulation of subcanopy longwave radiation by Community Land Model version 4.5 (CLM4.5) and to drive SNOWPACK, a snow model featuring more complex canopy structure, as a benchmark model for CLM4.5. Simulated subcanopy longwave radiation and longwave enhancement are assessed using measurements from forest stands located within perennially snow-covered regions. These forest stands, of varying canopy density, cover the range of boreal plant functional types in CLM4.5. CLM4.5 is found to overestimate the diurnal range of subcanopy longwave radiation and longwave enhancement, and simulation errors increase with decreasing cloudiness and increasing vegetation density. Implementation of a parameterization of heat storage by biomass reduces simulation errors but only marginally affects the amplitude of diurnal ranges. These results reaffirm previous findings that simulation of subcanopy longwave radiation can be improved by partitioning the vegetation canopy into two layers. Moreover, this study reveals the variations of simulation errors across meteorological conditions and vegetation density, the latter of which is the most important parameter for longwave enhancement independent of vegetation type.

The presence of a forest canopy above highly reflective snow results in overall lower surface albedo, even when snow is intercepted by the forest canopy. The effective forest snow albedo (FSA) explains the overall upwelling radiation from the forest relative to the incoming radiation. FSA is strongly influenced by the complex pathways of radiation as it travels through the 3D canopy structure. Current errors in calculations of FSA arise due to uncertainties in how models should treat masking of snow by vegetation. Improvement of distributed models is currently limited by a lack of measurements that demonstrate both spatial and temporal variability over forests. We present above-canopy measurements of winter-time effective forest snow albedo using up- and down-looking radiometers mounted on an octocopter UAV for a total of fifteen flights on eight different days. Ground-view fractions across the flight path were between 0.12 and 0.81. Correlations between FSA and both ground-view fraction and maximum canopy height were statistically significant during 14 out of 15 flights, but correlation strength varied between flights as a function of solar angle and snow cover. Measured effective albedo across the flight path differed by up to 0.33 during snow-on canopy conditions. A subsequent comparison between maximum interception and no interception showed effective albedo differed by up 0.27. A similar variation (0.26) in effective FSA was measured during low (44°) and high (67°) solar zenith angles. This study therefore demonstrates that temporal and spatial variations in effective albedo caused by canopy shading of the snow surface are therefore as important as temporal variations caused by interception of snow by the canopy. Calculation of effective albedo of forested areas requires careful consideration of canopy height, canopy coverage, solar angle and interception coverage. The results of this study should be used to inform snow albedo and canopy structure parametrizations in local and larger scale land surface models.

Measurements of vegetation structure have become a valuable tool for ecological research and environmental management. However, data describing the thermal 3D structure of canopies and how they vary both spatially and temporally remain sparse. Coincident RGB and thermal imagery from a UAV platform were collected of both a standalone tree and a relatively dense forest stand in the sub-alpine Eastern Swiss Alps. For the first time, SfM-MVS methods were used to develop 3D RGB and thermal point clouds of the two sites with point densities of 35,245 and 776 points per m², respectively, compared to 78 points per m² for an airborne LiDAR dataset of the same area. Despite the low resolution of the thermal imagery compared to RGB photosets, forest structural elements were accurately resolved in both point clouds. Improvements in the quality of the thermal 3D data were gained through the application of a distance filter based on the proximity of these data to the RGB 3D point data. Vertical temperature gradients of trees were negative with increasing height at the standalone tree, but were positive in the dense stand largely due to increased self-shading of incoming shortwave energy. Repeat surveys across a single morning during the snowmelt period revealed changes in the spatial distribution of canopy temperatures which are consistent with canopy warming from direct solar radiation. This is the first time that coincidentally acquired RGB and thermal imagery have been combined to generate separate RGB and thermal point clouds of 3D structures. These methods and findings demonstrate important implications for atmospheric, hydrological and ecological modeling, and have wide application for effective thermal measurements of remote environmental landscapes.

A comprehensive analysis of canopy surface temperatures was conducted around a small and large gap at a forested alpine site in the Swiss Alps during the 2015 and 2016 snowmelt seasons (March–April). Canopy surface temperatures within the small gap were within 2–3°C of measured reference air temperature. Vertical and horizontal variations in canopy surface temperatures were greatest around the large gap, varying up to 18°C above measured reference air temperature during clear-sky days. Nighttime canopy surface temperatures around the study site were up to 3°C cooler than reference air temperature. These measurements were used to develop a simple parameterization for correcting reference air temperature for elevated canopy surface temperatures during (1) nighttime conditions (subcanopy shortwave radiation is 0 W m^-2) and (2) periods of increased subcanopy shortwave radiation5gt;400 W m^-2 representing penetration of shortwave radiation through the canopy. Subcanopy shortwave and longwave radiation collected at a single point in the subcanopy over a 24 h clear-sky period was used to calculate a nighttime bulk offset of 3°C for scenario 1 and develop a multiple linear regression model for scenario 2 using reference air temperature and subcanopy shortwave radiation to predict canopy surface temperature with a root-mean-square error (RMSE) of 0.7°C. Outside of these two scenarios, reference air temperature was used to predict subcanopy incoming longwave radiation. Modeling at 20 radiometer locations throughout two snowmelt seasons using these parameterizations reduced the mean bias and RMSE to below 10 W m s−2^-2 at all locations.

A simple statistical model to predict the number of aerosols which activate to form cloud droplets in warm clouds has been established, based on regression analysis of data from four summertime Cloud and Aerosol Characterisation Experiments (CLACE) at the high-altitude site Jungfraujoch (JFJ). It is shown that 79 % of the observed variance in droplet numbers can be represented by a model accounting only for the number of potential cloud condensation nuclei (defined as number of particles larger than 80 nm in diameter), while the mean errors in the model representation may be reduced by the addition of further explanatory variables, such as the mixing ratios of O₃, CO, and the height of the measurements above cloud base. The statistical model has a similar ability to represent the observed droplet numbers in each of the individual years, as well as for the two predominant local wind directions at the JFJ (northwest and southeast). Given the central European location of the JFJ, with air masses in summer being representative of the free troposphere with regular boundary layer in-mixing via convection, we expect that this statistical model is generally applicable to warm clouds under conditions where droplet formation is aerosol limited (i.e. at relatively high updraught velocities and/or relatively low aerosol number concentrations). A comparison between the statistical model and an established microphysical parametrization shows good agreement between the two and supports the conclusion that cloud droplet formation at the JFJ is predominantly controlled by the number concentration of aerosol particles.

Ground-based, subcanopy measurements of incoming shortwave and longwave radiation are frequently used to drive and validate energy balance and snowmelt models. These subcanopy measurements are frequently obtained using different configurations (linear or distributed; stationary or moving) of radiometer arrays that are installed to capture the spatial and temporal variability of longwave and shortwave radiation. Three different radiometer configurations (stationary distributed, stationary linear, and moving linear) were deployed in a spruce forest in the eastern Swiss Alps during a 9-month period, capturing the annual range of sun angles and sky conditions. Results showed a strong seasonal variation in differences between measurements of shortwave transmissivity between the three configurations, whereas differences in longwave enhancement appeared to be seasonally independent. Shortwave transmissivity showed a larger spatial variation in the subcanopy than longwave enhancement at this field site. The two linear configurations showed the greatest similarity in shortwave transmissivity measurements, and the measurements of longwave enhancement were largely similar between all three configurations. A reduction in the number of radiometers in each array reduced the similarities between each stationary configuration. The differences presented here are taken to reflect the natural threshold of spatial noise in subcanopy measurements that can be expected between the three configurations.