In situ and satellite observations have unambiguously indicated the hastening rate of global glacier decay over the past two decades. In the region affected by the Karakoram Anomaly, however, the near-zero mass change and relatively high uncertainty from satellite observations combine with complex glacier dynamics to make glacier mass balances difficult to interpret, yet very few direct observations are available to confirm glacier mass changes. A pressing question for this region is therefore whether this glacier mass stability has already ended, or how long it will persist. Our observations over the past several years in the Pamir mountains, located on the periphery of this anomalous zone, have highlighted glaciers suffering from small accumulation areas at the end of the balance year, due to a combination of reduced winter snowfall and increased summer melt. In this study, we draw together a variety of field and remote sensing observations to assess the severity of Pamir glacier changes in recent years as compared to the historical baseline.We first examine historic climatic records and reanalyses from the region to examine the degree to which recent years fit within the observed historic seasonal and annual ranges. We compare the recent period to historic in situ and remote sensing glacier mass balance measurements recorded at Abramov Glacier, the single long-term monitoring reference glacier for the region, and to the historic network of Soviet meteorological measurements. We then consider regional changes to glacier surface albedo and surface temperature over the past 23 years based on satellite measurements. Taken together, these data sources enable us to link direct meteorological and glaciological conditions to broad spatial and temporal patterns of change across the Pamir mountains.Our results highlight progressively worsening conditions for glaciers since 2000, as indicated by warming air temperatures, decreasing precipitation, and decreasing albedo. 2021 and 2022 were likely the worst two years for glaciers at the regional scale, experiencing the hottest air temperatures and land surface temperatures in the 21st century, but poor conditions also occurred in 2006-2008. Our results highlight that Pamir surface albedos in these years were the lowest of the 21st century, excepting in the East Pamir, which also shows the least negative mass balances and the most moderated climatic changes. Satellite albedo and thinning measurements agree with both reanalysis data and in situ measurements at Abramov Glacier that mass losses have accelerated. However, historic glaciological measurements at Abramov and regional meteorological stations both highlight that similar periods in terms of hot air temperatures, low precipitation and rapid glacier mass loss occurred in the 1970s, and likely the 1940s, across much of the Pamirs. Consequently, although observations and projections suggest trends towards hotter and drier conditions with increased mass loss, it may be too soon to draw the curtains on the 40-year mass stability of the Karakoram Anomaly.

In absence of the high-frequency measurements of wind components, sonic temperature and water vapour required by the eddy covariance (EC) method, Monin–Obukhov similarity theory (MOST) is often used to calculate heat fluxes. However, MOST requires assumptions of stability corrections and roughness lengths. In most environments and weather situations, roughness length and stability corrections have high uncertainty. Here, we revisit the modified Bowen-ratio method, which we call C-method, to calculate the latent heat flux over snow. In the absence of high-frequency water vapour measurements, we use sonic anemometer data, which have become much more standard. This method uses the exchange coefficient for sensible heat flux to estimate latent-heat flux. Theory predicts the two exchange coefficients to be equal and the method avoids assuming roughness lengths and stability corrections. We apply this method to two datasets from high mountain (Alps) and polar (Antarctica) environments and compare it with MOST and the three-layer model (3LM). We show that roughness length has a great impact on heat fluxes calculated using MOST and that different calculation methods over snow lead to very different results. Instead, the 3LM leads to good results, in part due to the fact that it avoids roughness length assumptions to calculate heat fluxes. The C-method presented performs overall better or comparable to established MOST with different stability corrections and provides results comparable to the direct EC method. An application of this method is provided for a new station installed in the Pamir mountains.

Global climate reanalyses and projections are available worldwide for the past and coming century.However, model grids remain too coarse to be directly relevant at the hillslope-scale (Fan et al., 2019), requiring adapted downscaling tools to account for the effects of local topography.Mountain regions are experiencing accelerated warming with the cryosphere rapidly responding to climate change.To understand and study geomorphological, hydrological, and glaciological changes, we need tools to downscale meteorological timeseries for the basic atmospheric variables used to solve surface energy and mass balance in process-based models.Advanced dynamical downscaling methods exist, though they come with a high computational cost and complex technical setup (Kruyt et al., 2022).TopoPyScale uses a pragmatic approach to downscaling by minimizing complexity, reducing computational cost, simplifying interoperability with land surface models, while retaining physical coherence and allowing the primary drivers of land surface-atmosphere interaction to be considered.The toolbox is designed to be flexible in its usage and development.

Climate change modifies the water and energy fluxes between the atmosphere and the surface in mountainous regions such as the Qinghai–Tibet Plateau (QTP), which has shown substantial hydrological changes over the last decades, including rapid lake level variations. The ground across the QTP hosts either permafrost or is seasonally frozen, and, in this environment, the ground thermal regime influences liquid water availability, evaporation and runoff. Consequently, climate-induced changes in the ground thermal regime may contribute to variations in lake levels, but the validity of this hypothesis has yet to be established. This study focuses on the cryo-hydrology of the catchment of Lake Paiku (southern Tibet) for the 1980–2019 period. We process ERA5 data with downscaling and clustering tools (TopoSCALE, TopoSUB) to account for the spatial variability of the climate in our forcing data (Fiddes and Gruber, 2012, 2014). We use a distributed setup of the CryoGrid community model (version 1.0) to quantify thermo-hydrological changes in the ground during this period. Forcing data and simulation outputs are validated with data from a weather station, surface temperature loggers and observations of lake level variations. Our lake budget reconstruction shows that the main water input to the lake is direct precipitation (310 mm yr−1), followed by glacier runoff (280 mm yr−1) and land runoff (180 mm yr−1). However, altogether these components do not offset evaporation (860 mm yr−1). Our results show that both seasonal frozen ground and permafrost have warmed (0.17 ∘C per decade 2 m deep), increasing the availability of liquid water in the ground and the duration of seasonal thaw. Correlations with annual values suggest that both phenomena promote evaporation and runoff. Yet, ground warming drives a strong increase in subsurface runoff so that the runoff/(evaporation + runoff) ratio increases over time. This increase likely contributed to stabilizing the lake level decrease after 2010. Summer evaporation is an important energy sink, and we find active-layer deepening only where evaporation is limited. The presence of permafrost is found to promote evaporation at the expense of runoff, consistently with recent studies suggesting that a shallow active layer maintains higher water contents close to the surface. However, this relationship seems to be climate dependent, and we show that a colder and wetter climate produces the opposite effect. Although the present study was performed at the catchment scale, we suggest that this ambivalent influence of permafrost may help to understand the contrasting lake level variations observed between the south and north of the QTP, opening new perspectives for future investigations.

Abstract. Several regional studies identified heterogeneous mass changes in western High Mountain Asia over the last decade. Causes for these mass change patterns are still not fully understood. Modelling the physical interactions between glacier surface and atmosphere over several decades can provide insight into relevant processes. Such model applications, however, have data needs which are usually not met in these data scarce regions. Unique glaciological and meteorological data exist for the Abramov glacier in the Pamir Alay range. In this study, we use weather station measurements in combination with downscaled reanalysis data to force a coupled surface energy balance–multilayer subsurface model for Abramov glacier for 52 years. Available in situ data are used for model calibration and validation. We find an overall negative mass balance of −0.27m w.e. a−1 for 1968/1969–2019/2020 and a loss of firn pore space causing a reduction of internal accumulation. Despite increasing air temperatures, we do not find an acceleration of glacier-wide mass loss over time. Such an acceleration is compensated by increasing precipitation rates (+0.0022 m w.e. a−1, significant at a 90 % confidence level). Our results indicate a significant correlation between annual mass balance and precipitation (R2 = 0.72).

Spatially distributed meteorological information at the slope scale is relevant for many processes in complex terrain, yet information at this sub-km spatial resolution is difficult to obtain. While downscaling to kilometer resolutions is well described in literature, moving beyond the kilometer scale is not. In this work, we present a methodical comparison of three downscaling methods of varying complexity, that are used to downscale data from the Numerical Weather Prediction model COSMO-1 at 1.1 km horizontal resolution to 250 and 50 m over a domain of highly complex terrain in the Swiss Alps. We compare WRF, a dynamical atmospheric model; ICAR, a model of intermediate complexity; and TopoSCALE, an efficient topography-based downscaling scheme. Point-scale comparisons show similar results amongst all three models w.r.t. mean-error statistics, but underlying dynamics are different. Ridge-flow interactions show reasonable agreement between WRF and ICAR at 250 m model resolution. However, at 50 m resolution WRF is able to simulate complex flow patterns that ICAR cannot. Validation against Lidar data suggests that only WRF is able to capture preferential deposition of snow. Based on these findings and the significant reduction in computational costs, ICAR is a cost efficient alternative to WRF at the 250 m resolution. TopoScale performs very well in point-scale comparisons, but it is unclear if this can be attributed to the model itself or to the forcing data and the observations assimilated therein. Further study is required to quantify this effect.

Ground thermal regime of high mountain catchments impacts the partition between infiltration and runoff, latent and sensible heat fluxes, frozen and liquid subsurface water and the presence (or absence) of permafrost. In the context of global warming, hydrological modifications associated to ground thermal changes are of critical importance for extensive headwater regions such as the Qinghai-Tibet Plateau (QTP) and the Himalayas, which are major water towers of the world. Improving our ability to quantify these changes is therefore a key scientific challenge both regarding basic science and continental-scale water resource management. Many watersheds of the QTP have seen their hydrologic budget modified over the last decades as evidenced by strong lake level variations observed in endorheic basins. Yet, the role of ground thermal changes in these variations has not been assessed. Lake Paiku (central Himalayas, southern TP) has exhibited important level decreases since the 70s and thus offers the possibility to test the potential role of ground thermal changes and permafrost thaw on these hydrologic changes. We present distributed ground thermo-hydric simulations covering the watershed over the last four decades to discuss their implications on the lake level changes. We use the Cryogrid model to simulate the surface energy balance, snow pack dynamics and the ground thermo-hydric regime while accounting for the phase changes and the soil water budget. Because the surface radiative, sensible and latent heat fluxes in alpine environments are strongly dependent on the physiography, the model is forced with distributed downscaled forcing data produced with the TOPOSCALE model to account for this spatial variability. Simulated surface conditions are evaluated against meteorological data acquired within the basin, ground surface temperature loggers and remotely sensed surface temperatures. The simulations show that, contrary to large scale estimates of permafrost occurrence probability, an significant part of the basin is underlaid by permafrost (>20%). We also show that over the 1980-2020 period, ground temperature warmed up by 1.5 to 2°C per centuries. The permafrost limit rose from 5100 to 5300 m asl (in 40 years). Unfrozen surface conditions increased by around 25 days per century and evaporation increasing by +22% over the period. To represent the impact of these changes on the lake level, we included them in a simple hydrological budget calculation including the contribution of glacier melt and lake evaporation. This approach shows that ground thermo-hydric changes in the catchment have significantly contributed to the lake level changes. These first results highlight the potential of thermo-hydric simulation to better quantify hydrological changes to come in the QTP.

Abstract. Accurate knowledge of the seasonal snow distribution is vital in several domains including ecology, water resources management, and tourism. Current spaceborne sensors provide a useful but incomplete description of the snowpack. Many studies suggest that the assimilation of remotely sensed products in physically based snowpack models is a promising path forward to estimate the spatial distribution of snow water equivalent (SWE). However, to date there is no standalone, open source software dedicated to snow data assimilation. Here we introduce a new data assimilation toolbox, the Multiscale Snow Data Assimilation System (MuSA). MuSA was developed to fuse remotely sensed information with the energy and mass balance Flexible Snow Model (FSM2). MuSA was designed to be user-friendly and scalable. It enables assimilation of different state variables such as the snow depth, SWE, snow surface temperature, binary or fractional snow-covered area, and snow albedo and could be easily upgraded to assimilate other variables such as liquid water content or snow density in the future. MuSA allows the joint assimilation of an arbitrary number of these variables, through the generation of an ensemble of FSM2 simulations. The characteristics of the ensemble (i.e. the number of particles and their covariance) may be controlled by the user, and it is generated by perturbing the meteorological forcing of FSM2. The observational variables may be assimilated using different algorithms including the particle filters and smoothers as well as ensemble Kalman filters and smoothers along with their iterative variants. We demonstrate the wide capabilities of MuSA through two snow data assimilation experiments. First, 5 m resolution snow depth maps derived from drone surveys are assimilated in a distributed fashion in the Izas catchment (Central Pyrenees). Furthermore, we conducted a joint assimilation experiment, fusing MODIS land surface temperature and fractional snow-covered area with FSM2 in a single cell experiment. In light of these experiments, we discuss the pros and cons of assimilation algorithms, including their computational cost.

Accurate knowledge of the seasonal snow distribution is vital in several domains including ecology, water resources management, and tourism. Current spaceborne sensors provide a useful but incomplete description of the snowpack. Many studies suggest that the assimilation of remotely sensed products in physically based snowpack models is a promising path forward to estimate the spatial distribution of snow water equivalent (SWE). However, to date there is no standalone, open-source, community-driven project dedicated to snow data assimilation, which makes it difficult to compare existing algorithms and fragments development efforts. Here we introduce a new data assimilation toolbox, the Multiple Snow Data Assimilation System (MuSA), to help fill this gap. MuSA was developed to fuse remotely sensed information that is available at different timescales with the energy and mass balance Flexible Snow Model (FSM2). MuSA was designed to be user-friendly and scalable. It enables assimilation of different state variables such as the snow depth, SWE, snow surface temperature, binary or fractional snow-covered area, and snow albedo and could be easily upgraded to assimilate other variables such as liquid water content or snow density in the future. MuSA allows the joint assimilation of an arbitrary number of these variables, through the generation of an ensemble of FSM2 simulations. The characteristics of the ensemble (i.e., the number of particles and their prior covariance) may be controlled by the user, and it is generated by perturbing the meteorological forcing of FSM2. The observational variables may be assimilated using different algorithms including particle filters and smoothers as well as ensemble Kalman filters and smoothers along with their iterative variants. We demonstrate the wide capabilities of MuSA through two snow data assimilation experiments. First, 5 m resolution snow depth maps derived from drone surveys are assimilated in a distributed fashion in the Izas catchment (central Pyrenees). Furthermore, we conducted a joint-assimilation experiment, fusing MODIS land surface temperature and fractional snow-covered area with FSM2 in a single-cell experiment. In light of these experiments, we discuss the pros and cons of the assimilation algorithms, including their computational cost.

Abstract. This study describes and evaluates a new downscaling scheme that specifically addresses the need for hillslope-scale atmospheric-forcing time series for modelling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope in that it does not rely directly on surface observations and is able to generate the full suite of model forcing variables required for hydrological and land surface modelling in hourly time steps. It achieves this by utilizing the previously published TopoSCALE scheme to generate synthetic observations of the current climate at the hillslope scale while accounting for a broad range of surface–atmosphere interactions. These synthetic observations are then used to debias (downscale) CORDEX climate variables using the quantile mapping method. A further temporal disaggregation step produces sub-daily fields. This approach has the advantages of other empirical–statistical methods, including speed of use, while it avoids the need for ground data, which are often limited. It is therefore a suitable method for a wide range of remote regions where ground data is absent, incomplete, or not of sufficient length. The approach is evaluated using a network of high-elevation stations across the Swiss Alps, and a test application in which the impacts of climate change on Alpine snow cover are modelled.

Permafrost has a crucial influence on sub-surface water flow and thus on the hydrology of catchments. Its thawing drives the release of frozen water and a transition from surface-water-dominated systems to groundwater-dominated systems. In the context of global warming, these hydrological modifications are of critical importance for extensive headwater regions such as the Qinghai-Tibet Plateau (QTP) and the Himalayas. Permafrost covers a significant proportion of these regions (40% of the QTP), which are major water towers of the world. Therefore, improving our understanding and ability to quantify these changes are a key scientific challenge. Many watersheds of the QTP have seen their hydrologic budget modified over the last decades as evidenced by strong lake level variations observed in endorheic basins. Yet, the possible contribution of permafrost thaw to these variations has not been assessed. The Paiku basin (central Himalayas, southern TP) finds itself in a similar situation. The Paiku lake at the lowest point of this endorheic basin has exhibited important level decreases since the 70’s and thus offers the possibility to test the potential role of permafrost thaw on these hydrologic changes. We present permafrost simulations at the scale of the basin over the last four decades that reproduce its degradation as result of regional climatic change. We use the Cryogrid model to simulate the surface energy balance, snow pack dynamics and the ground thermal regime while accounting for the phase changes and the soil water budget. Because the surface radiative, sensitive and latent heat fluxes in alpine environments are strongly dependent on the physiography the model is forced with distributed downscaled forcing data produced with the TOPOSCALE model to account for this spatial variability. Simulated surface conditions are evaluated against meteorological data acquired within the basin and remotely sensed surface temperatures. The simulations show that, contrary to large scale estimates of permafrost occurrence probability, an important part of the basin is underlaid by permafrost. During the simulated period, permafrost distribution and active layer exhibit limited variations (active layer deepening neighboring 10 cm) yet deeper ground temperatures (7-8 m) show a warming close to 0.8 degree (0.2 degree per decade). These first results tend to indicate a limited contribution of permafrost to the catchment hydrology over the last decades, a trend that could be significantly modified in the future if the simulated warming rates persist and lead to increased permafrost thawing.

The alpine cryosphere, including snow, glaciers and permafrost, is critical to water management in the Aral Sea Basin (ASB) and larger Central Asia (CA) under the changing climate, as it stores large amounts of water in its solid forms. Most cryospheric components in the Aral Sea Basin are close to melting point, and hence very vulnerable to a slight increase in air temperature with significant consequences to long-term water availability and to water resources variability and extremes. Current knowledge about different components of the cryosphere and their connection to climate in the Basin and in the entire Central Asia region varies. While it is advanced in the topics of snow and glaciers, knowledge on permafrost is rather limited. Observed trends in runoff point in the direction of increasing water availability in July and August at least until mid-century and increasing possibility for water storage in reservoirs and aquifers. However, eventually this will change as glaciers waste away. Future runoff may change considerably after mid-century and start to decline if not compensated by increasing precipitation. Cryosphere monitoring systems are the basis for sound estimates of water availability and water-related hazards associated with snow, glaciers and permafrost. They require a well-distributed observational network for all cryospheric variables. Such systems need to be re-established in the Basin after the breakup of the Soviet Union in the early 1990s. This process is slowly emerging in the region. Collaboration between local operational hydro-meteorological services and the academic sector, and with international research networks, may improve the observational capabilities in high-mountain regions of CA in general and in the ASB in particular.

The Global Cryosphere Watch (GCW) was initiated by the World Meteorological Organization (WMO) as a mechanism to support the delivery of Earth System monitoring, modelling and prediction services focused on the cryosphere. GCW fosters international coordination and partnerships with the goal of providing authoritative, clear and usable data, information and analyses on the past, current and future state of the cryosphere. It fosters sustained and mutually beneficial partnerships between research and operational institutions, by linking research and operations as well as scientists and practitioners. This is important as most available cryospheric data come from the scientific community. It is generally managed by research institutes which often do not have the infrastructure, the resources, nor the mandate to enable FAIR data management, which is necessary for interoperability and discovery at data level. This implies that data do not fit into standardized systems or dataflows for broader data access and exchange (as exists at the WMO) and thus have been unavailable for operational meteorological and climate applications. This lack of standardization also impairs the reuse of data within the scientific community. GCW is bridging this gap through a data portal and software stack enabling the transformation of sparsely documented and highly variable data into standardized and well documented data suitable for downstream applications with data level interoperability. A processing engine converts raw data provided by the data producers into NetCDF-CF standard files with NetCDF Attribute Convention for Dataset Discovery (ACDD) metadata. The data portal web front end harvests the metadata necessary for its search engine through an OPeNDAP server so no manual editing of the medatadata is necessary. When a user downloads some data from the web portal, it gets the requested data through the OPeNDAP server.

Knowledge about changes in ground temperatures under a changing climate is important for many environmental, economic, and infrastructure applications and can be estimated by transient numerical simulations. However, a full annual cycle of precipitation data is needed to achieve this, yet is often unavailable in high alpine regions where a lack of infrastructure precludes installation of heated instruments capable of measuring the solid precipitation component. This paper presents a method to reconstruct a full year precipitation dataset at high alpine weather stations, which is then used to model ground temperature and snow depth for 16 alpine sites in Switzerland for the past and three climate scenarios. Differences in the possible temperature trajectories are highlighted with a focus on elevation and regional climatic differences within Switzerland. Snow height and ground temperatures under a changing climate are modeled with the one-dimensional physical model SNOWPACK by applying a delta change signal to the meteorological data set obtained from the CH2011 climate scenarios of Switzerland. All sites showed a decrease of snow cover, a shortening of the snow season and an increase in ground temperature to the end of the century. Sites in the inner alpine regions of Grisons were found to be less sensitive to climate change than sites in the western Alps. The magnitude of reduction of mean snow height depends mainly on location, whereas for the contraction of the snow season elevation is the key factor. It could be shown that the temperature–precipitation combination as expressed in the snow dynamics explain changes in ground temperatures more than the individual changes in either parameter. Alpine meadow and thin snow cover appear to delay warming of the ground.

Accurately estimating the snow water equivalent (SWE) that is stored in the worlds mountains remains a challenging and important unsolved problem. The SWE reconstruction approach, where the remotely sensed seasonal depletion of fractional snow-covered area (fSCA) is used with a snow model to build up the snowpack in reverse, has been used for decades to help tackle this problem retrospectively. Despite some success, this deterministic approach ignores uncertainties in the snow model, the meteorological forcing, and the remotely sensed fSCA. A trade-off has also existed between the desired temporal and spatial resolution of the satellite-retrieved fSCA depletion. Recently, ensemble-based data assimilation techniques that can account for the uncertainties inherent in the reconstruction exercise have allowed for probabilistic snow reanalyses. In addition, new higher resolution optical satellite constellations such as Sentinel-2 and the PlanetScope cubesats have been launched into polar orbit, potentially eliminating the aforementioned trade-off. We combine these two developments, namely ensemble-based data assimilation and the emerging remotely sensed data streams, to see if snow reanalyses can be improved at the hillslope (100 m) scale in complex terrain. As a first step, we develop accurate high-resolution binary snow-cover maps using a terrestrial automatic camera system installed on a mountaintop near Ny-Ålesund (Svalbard, Norway). These maps are used to validate fSCA retrieved from various satellite sensors (MODIS, Sentinel-2 MSI, and Landsat 8 OLI) using algorithms ranging from simple thresholding of the normalized difference snow index to spectral unmixing. Through the validation, we demonstrate that the spectral unmixing technique can obtain unbiased fSCA retrievals at the hillslope scale. Next, we move to the Mammoth Lakes basin in the Californian Sierra Nevada, USA, where we have access to independent validation data retrieved from several Airborne Snow Observatory (ASO) and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) flights. Using these airborne retrievals as a reference, we show that fSCA can be retrieved at the hillslope scale with reasonable accuracy at an unprecedented near daily revisit period using a combination of the Landsat, Sentinel-2 MSI, RapidEye, and PlanetScope satellite constellations. In a series of data assimilation experiments we show how the combination of these constellations can lead to significant improvements in hillslope scale snow reanalyses as gauged by various evaluation metrics. Furthermore, it is suggested that an iterative ensemble smoother data assimilation scheme can provide more robust SWE estimates than other smoothers that have previously been proposed for snow reanalysis. We briefly conclude with thoughts as to the current impediments to conducting a global hillslope scale snow reanalysis and propose avenues for further research, such as how snow reanalyses can help in the prediction exercise.

Models that consume meteorological data have often requirements that are quite incompatible with the realities of continuously measured data: they require gapless data when datasets have gaps, they require sampling rate matching the phenomena of interest when datasets use a sampling rate dictated by energy and storage capacities, they require ‘perfect’ data when sensors have flaws. The MeteoIO library [1] has been designed to solve this discrepancy as a meteorological data pre-processing library for numerical models (as well as other applications consuming such data), able to read measured data from a variety of sources and to standardize it into a unique representation (parameters naming and units) as well as filter, correct, resample and spatially interpolate it according to the end user’s configuration. From its very beginning, it aimed to be a toolbox that allows the user to choose from a large panel of published methods for each of the processing steps Unfortunately, until now there has been no systematic assessment of the performance of the available methods nor recommendations on best strategies. Based on an extensive network of Automatic Weather Stations (AWS) located around Davos, Switzerland, we present our preliminary recommendations for data reconstruction and corrections. Artificially degraded data allow us to compare the reconstruction with the original data, either exclusively based on the local data or by using neighboring stations. The high quality instruments available at Davos Weissfluhjoch (2536m a.s.l.) similarly allow us to compare various correction methods applied to the simpler kind of sensors normally found on regular AWS. [1] Bavay, M. and Egger, T., "MeteoIO 2.4.2: a preprocessing library for meteorological data", Geosci. Model Dev., 7, 3135-3151, doi:10.5194/gmd-7-3135-2014, 2014.

•Cryosphere plays a key role for future water availability in Central Asia.•Cryosphere monitoring has to be combined with remote sensing and modelling.•New snow monitoring tools are essential for sound quantification of snow resources.•Resumed glacier monitoring data improves understanding of the glacier role in runoff.•Continuous permafrost warming for the past 9 years.

More than half of all tropical forests are degraded by human impacts, leaving them threatened with conversion to agricultural plantations and risking substantial biodiversity and carbon losses. Restoration could accelerate recovery of aboveground carbon density (ACD), but adoption of restoration is constrained by cost and uncertainties over effectiveness. We report a long-term comparison of ACD recovery rates between naturally regenerating and actively restored logged tropical forests. Restoration enhanced decadal ACD recovery by more than 50%, from 2.9 to 4.4 megagrams per hectare per year. This magnitude of response, coupled with modal values of restoration costs globally, would require higher carbon prices to justify investment in restoration. However, carbon prices required to fulfill the 2016 Paris climate agreement [$40 to $80 (USD) per tonne carbon dioxide equivalent] would provide an economic justification for tropical forest restoration.

Spatial variability in high-relief landscapes is immense, and grid-based models cannot be run at spatial resolutions to explicitly represent important physical processes. This hampers the assessment of the current and future evolution of important issues such as water availability or mass movement hazards. Here, we present a new processing chain that couples an efficient sub-grid method with a downscaling tool and a data assimilation method with the purpose of improving numerical simulation of surface processes at multiple spatial and temporal scales in ungauged basins. The novelty of the approach is that while we add 1–2 orders of magnitude of computational cost due to ensemble simulations, we save 4–5 orders of magnitude over explicitly simulating a high-resolution grid. This approach makes data assimilation at large spatio-temporal scales feasible. In addition, this approach utilizes only freely available global datasets and is therefore able to run globally. We demonstrate marked improvements in estimating snow height and snow water equivalent at various scales using this approach that assimilates retrievals from a MODIS snow cover product. We propose that this as a suitable method for a wide variety of operational and research applications where surface models need to be run at large scales with sparse to non-existent ground observations and with the flexibility to assimilate diverse variables retrieved by Earth observation missions.