The interannual variability of snow cover in alpine areas is increasing, which may affect the tightly coupled cycles of carbon and water through snow–vegetation–atmosphere interactions across a range of spatio-temporal scales. To explore the role of snow cover for the land–atmosphere exchange of CO₂ and water vapor in alpine tundra ecosystems, we combined 3 years (2019–2021) of continuous eddy covariance flux measurements of the net ecosystem exchange of CO₂ (NEE) and evapotranspiration (ET) from the Finse site in alpine Norway (1210 m a.s.l.) with a ground-based ecosystem-type classification and satellite imagery from Sentinel-2, Landsat 8, and MODIS. While the snow conditions in 2019 and 2021 can be described as site typical, 2020 features an extreme snow accumulation associated with a strong negative phase of the Scandinavian pattern of the synoptic atmospheric circulation during spring. This extreme snow accumulation caused a 1-month delay in melt-out date, which falls in the 92nd percentile in the distribution of yearly melt-out dates in the period 2001–2021. The melt-out dates follow a consistent fine-scale spatial relationship with ecosystem types across years. Mountain and lichen heathlands melt out more heterogeneously than fens and flood plains, while late snowbeds melt out up to 1 month later than the other ecosystem types. While the summertime average normalized difference vegetation index (NDVI) was reduced considerably during the extreme-snow year 2020, it reached the same maximum as in the other years for all but one of the ecosystem types (late snowbeds), indicating that the delayed onset of vegetation growth is compensated to the same maximum productivity. Eddy covariance estimates of NEE and ET are gap-filled separately for two wind sectors using a random forest regression model to account for complex and nonlinear ecohydrological interactions. While the two wind sectors differ markedly in vegetation composition and flux magnitudes, their flux response is controlled by the same drivers as estimated by the predictor importance of the random forest model, as well as by the high correlation of flux magnitudes (correlation coefficient r=0.92 for NEE and r=0.89 for ET) between both areas. The 1-month delay of the start of the snow-free season in 2020 reduced the total annual ET by 50 % compared to 2019 and 2021 and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink (−31 to −6 gC m⁻² yr⁻¹) to a source (34 to 20 gC m⁻² yr⁻¹). These results underpin the strong dependence of ecosystem structure and functioning on snow dynamics, whose anomalies can result in important ecological extreme events for alpine ecosystems.

The snow-fed river basins of the Near East region are facing an urgent threat in the form of declining water resources. In this study, we analyzed several remote sensing products (optical, passive microwave, and gravimetric) and outputs of a meteorological reanalysis data set to understand the relationship between the terrestrial water storage anomalies and the mountain snowpack. The results from different satellite retrievals show a clear signal of a depletion of both water storage and the seasonal snowpack in four basins in the region. We find a strong reduction in terrestrial water storage over the Gravity Recovery and Climate Experiment (GRACE) observational period, particularly over the higher elevations. Snow-cover duration estimates from Moderate Resolution Imaging Spectroradiometer (MODIS) products point towards negative and significant trends up to one month per decade in the current era. These numbers are a clear indicator of the partial disappearance of the seasonal snow-cover in the region which has been projected to occur by the end of the century. The spatial patterns of changes in the snow-cover duration are positively correlated with both GRACE terrestrial water storage decline and peak snow water equivalent (SWE) depletion from the ERA5 reanalysis. Possible drivers of the snowpack depletion are a significant reduction in the snowfall ratio and an earlier snowmelt. A continued depletion of the montane snowpack in the Near East paints a bleak picture for future water availability in this water-stressed region.

Dynamic Global Vegetation Models (DGVMs) provide a state‐of‐the‐art process‐based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single‐site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version‐controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site‐level simulations. We provide input data for 20 established geo‐ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines.

Complex Land Surface Models (LSMs) rely on a plethora of parameters. These parameters and the associated process formulations are often poorly constrained, which hampers reliable predictions of ecosystem dynamics and climate feedbacks. Robust and uncertainty‐aware parameter estimation with observations is complicated by, for example, the high dimensionality of the model parameter space and the computational cost of LSM simulations. Herein, we adapt a novel Bayesian data assimilation (DA) and machine learning framework termed “calibrate, emulate, sample” (CES) to infer parameters in a widely‐used LSM coupled with a demographic vegetation model (CLM‐FATES). First, an iterative ensemble Kalman smoother provides an initial estimate of the posterior distribution (“calibrate”). Subsequently, a machine‐learning‐based emulator is trained on the resulting model‐observation mismatches to predict outcomes for unseen parameter combinations (“emulate”). Finally, this emulator replaces CLM‐FATES simulations in an adaptive Markov Chain Monte Carlo approach enabling computationally feasible posterior sampling with enhanced uncertainty quantification (“sample”). We test our implementation with synthetic and real observations representing a boreal forest site in southern Finland. We estimate a total of six plant‐functional‐type‐specific photosynthetic parameters by assimilating evapotranspiration (ET) and gross primary production (GPP) flux data. CES provided the best estimates of the synthetic truth parameters when compared to data‐blind emulator sampling designs while all approaches reduced model‐observation errors compared to a default parameter simulation (GPP: % to %, ET: % to %). Although errors were also consistently reduced with real data, comparing the emulator designs was less conclusive, which we mainly attribute to equifinality, structural uncertainty within CLM‐FATES, and/or unknown errors in the data that are not accounted for.