Abstract
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 cycle 38 of the high-resolution regional climate model (RCM) HARMONIE-Climate (HCLIM38) with ALARO physics (HCLIM38-ALARO) and ERA-Interim forcing in the lateral boundaries. 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 simulated road surface temperatures with a mean monthly bias of −0.3 ∘C and mean absolute error of 0.9 ∘C. The RoadSurf's output bias most probably stemmed from the absence of road maintenance operations in the model, such as snow plowing 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 HCLIM38-ALARO overestimated precipitation and had a warm bias in near-surface air temperatures during the winter season. Moreover, the variability in the biases of air temperature was found to explain on average 57 % of the variability in the biases of road surface temperature. On the other hand, the absence 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 in Finland by forcing RoadSurf with future climate projections from RCMs, such as HCLIM.
Highlights
The road traffic sector is one field benefiting from improved regional weather and climate information, especially at northern high latitudes
The main goal of this paper is to evaluate the skill of RoadSurf to reproduce present-day road weather conditions in Finland when driven by a state-of-the-art highresolution regional climate model (RCM), cycle 38 of the HIRLAM-ALADIN Regional Mesoscale Operational Numerical Weather Prediction (NWP) In Europe (HARMONIE) Climate (HCLIM) (Lindstedt et al, 2015)
This study described the performance of the HCLIM38ALARO regional climate model over Finland and, further, evaluated the skill of the HCLIM38-ALARO-driven road weather model RoadSurf to reproduce the present-day road weather conditions in Finland
Summary
The road traffic sector is one field benefiting from improved regional weather and climate information, especially at northern high latitudes. These regions do experience frequent wintertime snow and ice conditions and rapidly changing road weather due to, for instance, the onset of snowfall (Juga et al, 2012) or during temperature variations around the freezing point (Kangas et al, 2015). Jaroszweski et al, 2014) throughout the inhabited northern high latitudes This region is strongly impacted by the Arctic amplification of climate warming (Screen, 2014), which can clearly be seen, for instance, in the Finnish temperature records of the past 170 years (Mikkonen at al., 2015).
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