The road weather model RoadSurf (v6.60b) driven by the regional climate model HCLIM38: evaluation over Finland

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.

Characteristics of precipitation extremes over the Nordic region: added value of convection-permitting modeling

“…Overall, the average relative bias over the domain is positive: 23 % for HCLIM12 and even greater in HCLIM3 with 44 %. The results are in line with previous studies in which HCLIM38 has been shown to overestimate mean daily precipitation at 12 km resolution by 25 %-28 % during the summer period over the Nordic region (Toivonen et al, 2019;Lind et al, 2020). In addition, Lind et al (2020) showed that HCLIM3 improved the representation of the mean daily precipitation in the area.…”

“…For instance, an organized convective system occurred on 31 August 2014 in the Malmö basin in southern Sweden, generating very intense rainfall and leading to severe flooding (Olsson et al, 2017). The accumulated rainfall reached ∼ 150 mm within 6 h. Although such extreme events are rare, previous studies have shown that precipitation extremes have become more frequent globally and in Europe over recent decades (van den Besselaar et al, 2013;Westra et al, 2013;Fowler et al, 2021). There is also evidence that the past trends in annual maximum daily precipitation are predominantly positive over the Nordic-Baltic region (Du et al, 2019;Dyrrdal et al, 2021).…”

Climate change impacts on future driving and walking conditions in Finland, Norway and Sweden

“…Driving road weather models with climate models therefore can help road management and authorities to better plan ahead for the arising challenges and opportunities of road management in the far future (Matthews et al, 2017;McSweeney et al, 2016). Our predecessor study by Toivonen et al (2019) confirmed that the Finnish road weather model RoadSurf (Finnish Meteorological Institute, Kangas et al, 2015) produced accurate historical road temperature estimates for Finland when driven with the cycle 38 of the regional climate model HARMO-NIE-Climate (HCLIM38) and thereby set the grounds for this study.…”

HCLIM38: a flexible regional climate model applicable for different climate zones from coarse to convection-permitting scales

“…However, the coupling of ALARO with SURFEX as used in HCLIM38 resulted in evaporation from oceans that is too weak in low latitudes, causing considerable underestimation of atmospheric moisture content and consequently a lack of precipitation in long climate simulations. Similar issues have not been observed at high latitudes (Toivonen et al, 2019), imply- Boone 2000and Decharme et al (2011) Vegetation and carbon-related processes Jarvis-based stomatal resistance; soil organic carbon for soil thermal and hydrological properties Noilhan and Planton (1989) and Decharme et al (2016) Subgrid hydrology Subgrid runoff, Horton runoff Dümenil and Todini (1992) and Decharme and Douville (2006) Snow ISBA-ES explicit snow scheme (12 layers) Boone and Etchevers (2001) and Decharme et al (2016) Town Town Energy Balance (TEB) Masson (2000) Inland water Freshwater lake model (FLake) Mironov et al (2010) ing that ALARO can be used there. However, it is not the default option in HCLIM38 because the modelling system should be applicable at all latitudes.…”

“…More in-depth evaluation studies are left for separate papers for each of the domains analysed below as well as for other domains (e.g. Crespi et al, 2019;Toivonen et al, 2019;Wu et al, 2019). The model experiments are summarized in Table S1.…”