Addressing the role of land-cover change in regional climate observation datasets: an example over Belgium (1900-2015)

The significant land-cover changes that occurred in many countries during the past century have driven strong temperature trends, either exacerbating or tempering the impacts of global climate change on public health, infrastructure, and ecosystems. These changes are generally not included in climate reconstructions, such that temperature changes may solely be attributed to large-scale changes. Indeed, it remains challenging to include land-cover change effects in high-resolution climate reconstructions due to the scarcity of historical meteorological and physiographic data. 

We address these challenges by proposing a novel approach for historical climate reconstruction, explicitly integrating urbanization and forest-cover changes into gridded observational fields. The approach is demonstrated through the reconstruction of 1-km resolution monthly fields of near-surface daily minimum and maximum temperature starting in 1900 for Belgium, which saw pronounced changes in urban sprawl, as well as agricultural and forest cover. We rely on long homogenized series of air-temperature observations, global gridded temperature products, a dynamic land-cover map, and complementary information on the local impact of the land cover from models and local observations. Various sources of input data are employed in the generation of an ensemble dataset, providing a measure of the uncertainty on the estimates and the sensitivity to data source. For the recent decades, the new reconstruction is benchmarked against a country-wide reference gridded observational dataset spanning a shorter temporal extent.

To our knowledge, this reconstruction is the first century-long gridded temperature product at kilometric resolution including the effects of land-cover changes. The presented method provides a promising framework for disentangling land-cover change effects from background climate warming, and enhancing understanding of observed temperature trends.