Sam Vanden Broucke

New insights in the capability of climate models to simulate the impact of LUC based on temperature decomposition of paired site observations

In this study, we present a new methodology for evaluating the biogeophysical impact of land use change (LUC) in regional climate models. For this, we use observational data from paired eddy covariance flux towers in Europe, representing a LUC from forest to open land (deforestation). Two model simulations with the regional climate model COSMO-CLM2 (The Consortium for Small-Scale Modelling model in climate mode COSMO-CLM coupled to the Community Land Model CLM) are performed which differ only in prescribed land use for site pair locations. The model is evaluated by comparing the observed and simulated difference in surface temperature (Ts) between open land and forests. Next, we identify the biogeophysical mechanisms responsible for Ts differences by applying a decomposition method to both observations and model simulations. This allows us to determine which LUC-related mechanisms were well represented in COSMO-CLM2, and which were not. Results from observations show that deforestation leads to a significant cooling at night, which is severely underestimated by COSMO-CLM2. It appears that the model is missing one crucial impact of deforestation on the nighttime surface energy budget: a reduction in downwelling longwave radiation. Results are better for daytime, as the model is able to simulate the increase in albedo and associated surface cooling following deforestation reasonably well. Also well simulated, albeit underestimated slightly, is the decrease in sensible heat flux caused by reduced surface roughness. Overall, these results stress the importance of differentiating between daytime and nighttime climate when discussing the effect of LUC on climate. Finally, we believe that they provide new insights supporting a wider application of the methodology (to other regional climate models).

Do convection-permitting models improve the representation of the impact of LUC?

In this study we assess the added value of convection permitting scale (CPS) simulations in studies using regional climate models to quantify the bio-geophysical climate impact of land-use change (LUC). To accomplish this, a comprehensive model evaluation methodology is applied to both non-CPS and CPS simulations. The main characteristics of the evaluation methodology are (1) the use of paired eddy-covariance site observations (forest vs open land) and (2) a simultaneous evaluation of all surface energy budget components. Results show that although generally satisfactory, non-CPS simulations fall short of completely reproducing the observed LUC signal because of three key biases. CPS scale simulations succeed at significantly reducing two of these biases, namely, those in daytime shortwave radiation and daytime sensible heat flux. Also, CPS slightly reduces a third bias in nighttime incoming longwave radiation. The daytime improvements can be attributed partially to the switch from parameterized to explicit convection, the associated improvement in the simulation of afternoon convective clouds, and resulting surface energy budget and atmospheric feedbacks. Also responsible for the improvements during daytime is a better representation of surface heterogeneity and thus, surface roughness. Meanwhile, the modest nighttime longwave improvement can be attributed to increased vertical atmospheric resolution. However, the model still fails at reproducing the magnitude of the observed nighttime longwave difference. One possible explanation for this persistent bias is the nighttime radiative effect of biogenic volatile organic compound emissions over the forest site. A correlation between estimated emission rates and the observed nighttime longwave difference, as well as the persistence of the longwave bias provide support for this hypothesis. However, more research is needed to conclusively determine if the effect indeed exists.

Annual impact of wind-farm gravity waves on the Belgian–Dutch offshore wind-farm cluster

While research on wind-farm–atmospheric boundary layer interaction has primarily focused on local effects inside and above the farm, recent studies found that wind farms may affect the wind conditions several kilometres upstream of the farm via the excitation of atmospheric gravity waves. Such non-local effects can have strong implications for the windfarm energy extraction but are currently overlooked in wind-farm design and operation and control strategies. In the present study, we employ a fast wind-farm boundary-layer model in combination with ERA5 reanalysis data to assess the potential impact of wind-farm induced gravity waves on the annual energy production of the Belgian–Dutch offshore wind-farm cluster in the North Sea. We estimate the annual energy loss due to the effect of self-induced gravity waves to be of the order of 4 to 6 %.