Heterogeneous topography and vegetation structure both lead to mosaics of buffered and decoupled microclimates in a Mediterranean massif

Studying climate at fine spatial scales reveals critical differences between regional and local conditions. Quantifying and understanding the drivers of microclimate is essential for assessing climate change impacts. Microclimates can be colder or warmer than the macroclimate ( i.e. buffering effect); but they can also deviate from macroclimatic trends, a phenomenon termed decoupling. We quantify spatial variations in both buffering and decoupling effects in a rugged Mediterranean landscape in south-eastern France in response to heterogeneous topography and vegetation structure. Based on 15 months of data, collected from a network of 60 microclimatic sensors deployed across 30 ha, we highlight fine scale spatial mosaics of contrasting buffering and decoupling effects that vary seasonally and depend on the microclimatic variable considered: maximum temperature (Tmax), vapor pressure deficit, or minimum temperature. Observations reveal large microclimatic contrasts within a few meters in terms of buffering (from -8.26°C to +10.77°C locally compared to macroclimate for Tmax in summer) and decoupling (from 0.35°C to 1.84°C increase locally for a 1°C increase in macroclimate, for Tmax in summer). Analyses further reveal that the main drivers of microclimate shift seasonally from topographic factors, particularly relative elevation and solar radiation, during the cold season, to vegetation characteristics, e.g. height, during the growing season. Although climatic conditions are often expected to vary linearly across space ( e.g. cooling with altitude or latitude), heterogeneous landscapes may support mosaics of microclimates that can locally attenuate or amplify broad-scale climatic trends. However, while attenuation processes are relatively well documented in forest systems, both attenuation and amplification remain insufficiently quantified in complex terrains, limiting our ability to predict local ecosystem trajectories under climate change.