The role of different land cover input data on local climate and its extremes

The spatio-temporal heterogeneity in surface characteristics is considered to play a key role in terrestrial surface processes. Its characterization is essential for adaptation strategies. Here, we conducted regional climate simulations with COSMO-CLM (v5.0_clm16) with different land cover input data driven by ERA5 reanalysis over Germany at convection-permitting horizontal resolution of 3 km from 2000 to 2011. The difference between the land cover data of GLC2000, CCI_LC and ECOCLIMAP and the operational used GLOBCOVER2009 dataset on temperature and its extremes is investigated. The results reveal that the differences in turbulent fluxes and temperature are related to land cover classes. Even though the land cover class fractional differences are small among the land cover maps, some land cover types, such as croplands and urban areas, have greatly changed over the years. These distribution changes can be seen in the temperature differences. Simulations based on the CCI_LC retrieved in 2000 and 2015 revealed no accreditable difference in the climate variables as the land cover changes that occurred between these years are marginal, and thus, the influence is small over Germany. Increasing the land cover types as in ECOCLIMAP leads to higher temperature variability. The largest differences among the simulations occur in maximum temperature and from spring to autumn, which is the main vegetation period. The temperature differences seen among the simulations relate to changes in the leaf area index, plant coverage, roughness length, latent and sensible heat fluxes due to differences in land cover types. The vegetation fraction was the main parameter affecting the seasonal evolution of the latent heat fluxes based on linear regression analysis, followed by roughness length and leaf area index. If the same natural vegetation (e.g. forest) or pasture grid cells changed into urban types in another land cover map, daily maximum temperatures increased accordingly. Similarly, differences in climate extreme indices (e.g., SU or TR) are strongest for any land cover type change to urban areas. The uncertainties in regional temperature due to different land cover datasets were overall lower than the uncertainties associated with climate projections. Although the impact and their implications are different on different spatial and temporal scales as shown for urban area differences in the land cover maps. Thus, to realistically simulate land use/cover change effects on regional and local climate and draw conclusions for management strategies, numerical models would benefit from land surface characteristics, which are as accurate as possible in retrieval year, number of land cover classes, their distribution and fractions and have a high spatial resolution.