Sensitivity of European Temperature to Albedo Parameterization in the Regional Climate Model COSMO-CLM Linked to Extreme Land Use Changes - Tölle, Merja H.; Breil, Marcus; Radtke, Kai; Panitz, Hans-Jürgen (Frontiers in Environmental Science, 2018)
Previous studies based on observations and models are uncertain about the biophysical impact of af- and deforestation in the northern hemisphere mid-latitude summers, and show either a cooling or warming. The magnitude and direction is still uncertain. In this study, the effect of three different albedo parameterizations in the regional climate model COSMO-CLM (v5.09) is examined performing afforestation experiments at 0.44° horizontal resolution across the EURO-CORDEX domain during 1986-2015. Idealized de- and af-forestation simulations are compared to a simulation with no land cover change. Emphasis is put on the impact of changes in radiation and turbulent fluxes. A clear latitudinal pattern is found, which results partly due to the strong land cover conversion from forest- to grassland in the high latitudes and open land to forest conversion in mid-latitudes. Afforestation warms the climate in winter, and strongest in mid-latitudes. Results are indifferent in summer owing to opposing albedo and evapotranspiration effects of comparable size but different sign. Thus, the net effect is small for summer. Depending on the albedo parameterization in the model, the temperature effect can turn from cooling to warming in mid-latitude summers. The summer warming due to deforestation to grassland is up to 3 °C higher than due to afforestation. The cooling by grass or warming by forest is in magnitude comparable and small in winter. The strength of the described near-surface temperature changes depends on the magnitude of the individual biophysical changes in the specific background climate conditions of the region. Thus, the albedo parameterization need to account for different vegetation types. Furthermore, we found that, depending on the region, the land use change effect is more important than the model uncertainty due to albedo parameterization. This is important information for model development.