Data from: Monthly microclimate models in a managed boreal forest landscape

The majority of microclimate studies have been done in topographically
complex landscapes to quantify and predict how near-ground temperatures
vary as a function of terrain properties. However, in forests understory
temperatures can be strongly influenced also by vegetation. We quantified
the relative influence of vegetation features and physiography (topography
and moisture-related variables) on understory temperatures in managed
boreal forests in central Sweden. We used a multivariate regression
approach to relate near-ground temperature of 203 loggers over the
snow-free seasons in an area of ∼16,000 km2 to remotely sensed and on-site
measured variables of forest structure and physiography. We produced
climate grids of monthly minimum and maximum temperatures at 25 m
resolution by using only remotely sensed and mapped predictors. The
quality and predictions of the models containing only remotely sensed
predictors (MAP models) were compared with the models containing also
on-site measured predictors (OS models). Our data suggest that during the
warm season, where landscape microclimate variability is largest, canopy
cover and basal area were the most important microclimatic drivers for
both minimum and maximum temperatures, while physiographic drivers (mainly
elevation) dominated maximum temperatures during autumn and early winter.
The MAP models were able to reproduce findings from the OS models but
tended to underestimate high and overestimate low temperatures. Including
important microclimatic drivers, particularly soil moisture, that are yet
lacking in a mapped form should improve the microclimate maps. Because of
the dynamic nature of managed forests, continuous updates of mapped forest
structure parameters are needed to accurately predict temperatures. Our
results suggest that forest management (e.g. stand size, structure and
composition) and conservation may play a key role in amplifying or
impeding the effects of climate-forcing factors on near-ground temperature
and may locally modify the impact of global warming.