The road weather model RoadSurf driven by the HARMONIE-Climate regional climate model: evaluation over Finland

Abstract. In this paper, we evaluate the skill of the road weather model RoadSurf to reproduce present-day road weather conditions in Finland. RoadSurf was driven by meteorological input data from a high-resolution regional climate model (RCM) HARMONIE-Climate (HCLIM) utilizing ALARO physics (HCLIM-ALARO). Simulated road surface temperatures and road surface conditions were compared to observations between 2002 and 2014 at 25 road weather stations located in different parts of Finland. The main characteristics of road weather conditions were accurately captured by RoadSurf in the study area. For example, the model precisely simulated road surface temperatures with a mean bias of −0.3 °C, RMSE of 2.1 °C, and Pearson's correlation coefficient of 0.93. The RoadSurf's output bias most probably stemmed from the lack of road maintenance operations in the model, such as snow ploughing and salting, and the biases in the input meteorological data. The biases in the input data were most evident in northern parts of Finland, where the regional climate model HCLIM-ALARO overestimated precipitation and had a warm bias in simulated air temperatures during the winter season. In turn, these input data biases seemed to result in a warm bias in simulated road surface temperatures. Furthermore, the lack of road maintenance operations in the model might have affected RoadSurf's ability to simulate road surface conditions: the model tended to overestimate icy and snowy road surfaces and underestimate the occurrence of water on the road. However, the overall good performance of RoadSurf implies that this approach can be used to study the impacts of climate change on road weather conditions by forcing RoadSurf by future climate projections from RCMs, such as HCLIM.