Forests are increasingly impacted by climate change, affecting tree growth and carbon sequestration. Tree-ring width, closely related to tree growth, is a key climate proxy, yet models describing ring width or growth often lack comprehensive environmental data. This study assesses ERA5-Land data for tree-ring width prediction compared to automatic weather station observations, emphasizing the value of extended and global climate data. We analyzed 723 site-averaged and detrended tree-ring chronologies from two broadleaved and two gymnosperm species across Europe, integrating them with ERA5-Land climate data, CO2 concentration, and a drought index (SPEI12). A subset was compared with weather station data. For modelling interannual variations of tree-ring width we used linear models to assess parameter importance. ERA5-Land and weather-station-based models performed similarly, maintaining stable correlations and consistent errors. Models based on meteorological data from weather stations highlighted SPEI12, sunshine duration, and temperature extremes, while ERA5-Land models emphasized SPEI12, dew-point temperature (humidity), and total precipitation. CO2 positively influenced the growth of gymnosperm species. ERA5-Land facilitated broader spatial analysis and incorporated additional factors like evaporation, snow cover, and soil moisture. Monthly assessments revealed the importance of parameters for each species. Our findings confirm that ERA5-Land is a reliable alternative for modeling tree growth, offering new insights into climate-vegetation interactions. The ready availability of underutilized parameters, such as air humidity, soil moisture and temperature, and runoff, enables their inclusion in future growth models. Using ERA5-Land can therefore deepen our understanding of forest responses to diverse environmental drivers on a global scale.

The snowpack regime influences the timing of soil water available for transpiration and synchrony with the evapotranspiration (ET) energy demand (air temperature, VPD, and shortwave radiation). Variability of snowmelt timing, soil water availability, and the energy demand results in heterogeneous ET rates throughout a watershed. In this study, we assessed how ET and growing season length vary across five sites on an elevational gradient in the Dry Creek Watershed, ID, USA. We compared trends of daily and annual ET between 2012 and 2017 to environmental parameters of soil moisture, air temperature, vapour pressure deficit, snow cover, and precipitation. Trends between parameters and ET were evaluated at each site and compared between sites. We observed three trends in ET across the watershed. The first trend is at the low elevation site where the snow cover is not continuous throughout the winter and rain is the dominant precipitation form. The first day of the growing season and ET occurs early in the season when the energy demand is low and soil water is available. Annual ET at the low elevation site is a balance between spring precipitation providing soil water into the summer season and limiting the ET energy demand. The second trend occurs at the middle elevation site located in the rain-snow transition. At this site, ET increases with snow depth and spring precipitation extending the soil water availability into the summer season. At the higher elevation sites, ET is aligned with the energy demand and limited by growing season length. At the high elevation sites, decreasing snow depth and spring precipitation and increasing spring air temperatures result in greater annual ET rates. The observations from this study highlight the influence of environmental parameters and the potential sensitivity of ET to climate change.

The impact of high latitude climate warming on Arctic snow cover and its insulating properties has key implications for the surface and soil energy balance. Few studies have investigated specific trends in Arctic snowpack properties because there is a lack of long-term in situ observations and current detailed snow models fail to represent the main traits of Arctic snowpacks. This results in high uncertainty in modeling snow feedbacks on ground thermal regime due to induced changes in snow insulation. To better simulate Arctic snow structure and snow thermal properties, we implemented new parameterizations of several snow physical processes-including the effect of Arctic low vegetation and wind on snowpack-in the Crocus detailed snowpack model. Significant improvements compared to standard Crocus snow simulations and ERA-Interim (ERAi) reanalysis snow outputs were observed for a large set of in-situ snow data over Siberia and North America. Arctic Crocus simulations produced improved Arctic snow density profiles over the initial Crocus version, leading to a soil surface temperature bias of -0.5 K with RMSE of 2.5 K. We performed Crocus simulations over the past 39 years (1979-2018) for circumpolar taiga (open forest) and pan-Arctic areas at a resolution of 0.5 degrees, driven by ERAi meteorological data. Snowpack properties over that period feature significant increase in spring snow bulk density (mainly in May and June), a downward trend in snow cover duration and an upward trend in wet snow (mainly in spring and fall). The pan-Arctic maximum snow water equivalent shows a decrease of -0.33 cm dec(-1). With the ERAi air temperature trend of +0.84 K dec(-1) featuring Arctic winter warming, these snow property changes have led to an upward trend in soil surface temperature (Tss) at a rate of +0.41 K dec(-1) in winter. We show that the implemented snowpack property changes increased the Tss trend by 36% compared to the standard simulation. Winter induced changes in Tss led to a significant increase of 16% (+4 cm dec(-1)) in the estimated active layer thickness (ALT) over the past 39 years. An increase in ALT could have a significant impact on permafrost evolution, Arctic erosion and hydrology.