To date, the assessment of hydrological climate change impacts, not least on pluvial flooding, has been severely limited by i) the insufficient spatial resolution of regional climate models (RCMs) as well as ii) the simplified description of key processes, e.g., convective rainfall generation. Therefore, expectations have been high on the recent generation of high-resolution convection-permitting regional climate models (CPRCMs), to reproduce the small-scale features of observed (extreme) rainfall that are driving small-scale hydrological hazards. Are they living up to these expectations? In this study, we zoom in on southern Sweden and investigate to which extent two climate models, a 3-km resolution CPRCM (HCLIM3) and a 12-km non-convection permitting RCM (HCLIM12), are able to reproduce the rainfall climate with focus on short-duration extremes. We use three types of evaluation–intensity-based, time-based and event-based–which have been designed to provide an added value to users of high-intensity rainfall information, as compared with the ways climate models are generally evaluated. In particular, in the event-based evaluation we explore the prospect of bringing climate model evaluation closer to the user by investigating whether the models are able to reproduce a well-known historical high-intensity rainfall event in the city of Malmö 2014. The results very clearly point at a substantially reduced bias in HCLIM3 as compared with HCLIM12, especially for short-duration extremes, as well as an overall better reproduction of the diurnal cycles. Furthermore, the HCLIM3 model proved able to generate events similar to the one in Malmö 2014. The results imply that CPRCMs offer a clear potential for increased confidence in future projections of small-scale hydrological climate change impacts, which is crucial for climate-proofing, e.g., our cities, as well as climate modeling in general.

Abstract. This paper presents a new version of HCLIM, a regional climate modelling system based on the ALADIN-HIRLAM numerical weather prediction (NWP) system. HCLIM uses atmospheric physics packages from three NWP model configurations, HARMONIE-AROME, ALARO and ALADIN, which are designed for use at different horizontal resolutions. The main focus of HCLIM is convection permitting climate modelling, i.e. developing the climate version of HARMONIE-AROME. In HCLIM, the ALADIN and ALARO physics packages are used for coarser resolutions where convection needs to be parameterized. Here we describe the structure, development and performance of the current recommended HCLIM version, cycle 38. HCLIM38 is a new system for regional climate modelling and it is being used in a number of national and international projects over different domains and climates, ranging from equatorial to polar regions. Our initial evaluation indicates that HCLIM38 is applicable in different conditions and provides satisfactory results without additional region-specific tuning. HCLIM is developed by a consortium of national meteorological institutes in close collaboration with the ALADIN-HIRLAM NWP model development. While the current HCLIM cycle has considerable differences in model setup compared to the NWP version (primarily in the description of the surface), it is planned for the next cycle release that the two versions will use a very similar setup. This will ensure a feasible and timely climate model development and updates in the future and provide evaluation of long-term model biases to both NWP and climate model developers.

Extreme precipitation events have a major impact upon our society. Although many studies have indicated that it is likely that the frequency of such events will increase in a warmer climate, little has been done to assess changes in extreme precipitation at a sub-daily scale. Recently, there is more and more evidence that high-resolution convection-permitting models (CPMs) (grid-mesh typically < 4 km) can represent especially short-duration precipitation extremes more accurately when compared with coarser-resolution regional climate models (RCMs). This study investigates sub-daily and daily precipitation characteristics based on hourly output data from the HARMONIE-Climate model at 3-km and 12-km grid-mesh resolution over the Nordic region between 1998 and 2018. The RCM modelling chain uses the ERA-Interim reanalysis to drive a 12-km grid-mesh simulation which is further downscaled to 3-km grid-mesh resolution using a non-hydrostatic model set-up. The statistical properties of the modeled extreme precipitation are compared to several sub-daily and daily observational products, including gridded and in-situ gauge data, from April to September. We investigate the skill of the model to represent different aspects of the frequency and intensity of extreme precipitation as well as intensity–duration–frequency (IDF) curves that are commonly used to investigate short duration extremes from an urban planning perspective. The high grid resolution combined with the 20-year-long simulation period allows for a robust assessment at a climatological time scale and enables us to examine the added value of high-resolution CPM in reproducing precipitation extremes over the Nordic region. Based on the tentative results, the high-resolution CPM can realistically capture the characteristics of precipitation extremes, for instance, in terms of improved diurnal cycle and maximum intensities of sub-daily precipitation.

Convection-permitting climate models have shown superior performance in simulating important aspects of the precipitation climate including extremes and also to give partly different climate change signals compared to coarser-scale models. Here, we present the first long-term (1998–2018) simulation with a regional convection-permitting climate model for Fenno-Scandinavia. We use the HARMONIE-Climate (HCLIM) model on two nested grids; one covering Europe at 12 km resolution (HCLIM12) using parameterized convection, and one covering Fenno-Scandinavia with 3 km resolution (HCLIM3) with explicit deep convection. HCLIM12 uses lateral boundaries from ERA-Interim reanalysis. Model results are evaluated against reanalysis and various observational data sets, some at high resolutions. HCLIM3 strongly improves the representation of precipitation compared to HCLIM12, most evident through reduced “drizzle” and increased occurrence of higher intensity events as well as improved timing and amplitude of the diurnal cycle. This is the case even though the model exhibits a cold bias in near-surface temperature, particularly for daily maximum temperatures in summer. Simulated winter precipitation is biased high, primarily over complex terrain. Considerable undercatchment in observations may partly explain the wet bias. Examining instead the relative occurrence of snowfall versus rain, which is sensitive to variance in topographic heights it is shown that HCLIM3 provides added value compared to HCLIM12 also for winter precipitation. These results, indicating clear benefits of convection-permitting models, are encouraging motivating further exploration of added value in this region, and provide a valuable basis for impact studies.

Current literature strongly indicates large benefits of convection permitting models for subdaily summer precipitation extremes. There has been less insight about other variables, seasons and weather conditions. We examine new climate simulations over the Nordic region, performed with the HCLIM38 regional climate model at both convection permitting and coarser scales, searching for benefits of using convection permitting resolutions. The Nordic climate is influenced by the North Atlantic storm track and characterised by large seasonal contrasts in temperature and precipitation. It is also in rapid change, most notably in the winter season when feedback processes involving retreating snow and ice lead to larger warming than in many other regions. This makes the area an ideal testbed for regional climate models. We explore the effects of higher resolution and better reproduction of convection on various aspects of the climate, such as snow in the mountains, coastal and other thermal circulations, convective storms and precipitation with a special focus on extreme events. We investigate how the benefits of convection permitting models change with different variables and seasons, and also their sensitivity to different circulation regimes.

In general terms, climate adaptation in cities is highly complicated by the very high required spatial and temporal resolution. The high resolution is needed to capture both the full variability of small-scale high-impact weather phenomena and the associated response from the mosaic of land uses and buildings in urban environments. Most commonly available climate model simulations and projections are too spatially coarse (≥10 km) for a proper assessment of many important urban climate impacts. In terms of water-related impacts, a key issue concerns the reproduction of local short-duration rainfall extremes (cloudbursts) that may cause pluvial flooding. An accurate reproduction of the convective generation of such extremes requires a spatial resolution of at least 5 km, preferably even higher, in convection-permitting regional climate models (CPRCM). Conceivably, estimates of future changes in cloudburst characteristics and associated statistics based on CPRCM simulations will be more reliable than today’s estimates based on non-CP RCMs. Because of the extreme computational demand, however, the number of CPRCM simulations made is still rather low and generally limited to small domains and/or short time slices. But many efforts are currently being made in this direction and the main focus of this presentation will be a case study evaluation of hourly rainfall extremes from 3×3 km² convection-permitting simulations with the HARMONIE-climate model over the Nordic region. The case study will focus on the region around the Öresund strait, that connects southern Sweden and eastern Denmark. This region contains the cities Malmö and Copenhagen that were both hit by heavy cloudburst in the last decade, that caused severe flooding and substantial damage to infrastructure. The presentation will include different aspects of the simulations and their applicability: * Historical performance. Evaluation of reference period simulations, with both ERA-Interim and GCM boundaries, against high-resolution observations, focusing at the reproduction of short-duration (sub-daily) extremes but also e.g. diurnal cycle and spatial variability. * Future changes. Assessment in terms of climate factors for different durations, return periods and future time horizons. A comparison is made with climate factors estimated from lower-resolution, non-convection permitting downscalings based on the same GCM projections. * End-user practices. A discussion of what resolution that is needed in order to meet different stakeholders’ needs in the light of climate adaptation. The key question is how the output from CPRCM simulations can be processed and interpreted to provide an added value. Besides the above analyses, two additional related investigations will be presented: * Lessons learnt from experiments of tailored “urban downscaling” of climate projections down to 1×1 km² and 15 min over selected European urban regions (Stockholm, Bologna, Amsterdam) performed in the Urban SIS project. * An evaluation of hourly rainfall extremes over selected European countries in a 11×11 km² EURO-CORDEX ensemble, including spatial patterns and temperature scaling of the estimated future changes.

There is an increased need for more detailed climate information from impact researchers, stakeholders and policy makers for regional-to-local climate change assessments. In order to design relevant and informative planning strategies on these scales it is important to have reliable climate data and information on high spatial O(1km) and temporal (daily to sub-daily) scales. Such high-resolution data is also beneficial for climate impact modellers as input to their models, e.g. hydrological or urban models that operate on regional to local scales. It has been established that regional climate models (RCMs) provide added value compared to coarser global climate models (GCMs) or re-analysis (e.g. ERA-Interim). However, RCMs with standard spatial resolution O(10 − 50km) still suffer from inadequacies in representing important regional-to-local climate phenomena and characteristics, both from the implied ”smoothening” effect within each grid cell which limits the representation of fine scale surface forcings, and the need to parameterize small-scale processes like atmospheric convection. The latter particularly invokes uncertainties in future climate responses of short-duration precipitation extremes such as flash-floods. Here, we compare 20-year simulations with a very high resolution (3 km grid spacing) convection permitting regional climate model (CPRCM) with a standard high-resolution (12 km grid spacing) convection parameterized RCM and their abilities to simulate the climate characteristics of the Nordic region in Europe, with particular focus on precipitation extremes. The study covers both recent past (with boundary data from ERA-Interim and the EC-Earth GCM) and the end of the 21st century (boundary data from EC-Earth using the RCP8.5 radiative forcing scenario). The high model grid resolution combined with the extensive simulated time period which enables assessment on climatological time scales makes this study one of very few for this region.

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.

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.

Thermal infrared sensing of evapotranspiration (E) through surface energy balance (SEB) models is challenging due to uncertainties in determining the aerodynamic conductance (g(A)) and due to inequalities between radiometric (T-R) and aerodynamic temperatures (T-0). We evaluated a novel analytical model, the Surface Temperature Initiated Closure (STIC1.2), that physically integrates T-R observations into a combined Penman-Monteith Shuttleworth-Wallace (PM-SW) framework for directly estimating E, and overcoming the uncertainties associated with T0 and gA determination. An evaluation of STIC1.2 against high temporal frequency SEB flux measurements across an aridity gradient in Australia revealed a systematic error of 10-52% in E from mesic to arid ecosystem, and low systematic error in sensible heat fluxes (H) (12-25%) in all ecosystems. Uncertainty in TR versus moisture availability relationship, stationarity assumption in surface emissivity, and SEB closure corrections in E were predominantly responsible for systematic E errors in arid and semi-arid ecosystems. A discrete correlation (r) of the model errors with observed soil moisture variance (r = 0.33-0.43), evaporative index (r = 0.77-0.90), and climatological dryness (r = 0.60-0.77) explained a strong association between ecohydrological extremes and T-R in determining the error structure of STIC1.2 predicted fluxes. Being independent of any leaf-scale biophysical parameterization, the model might be an important value addition in working group (WG2) of the Australian Energy and Water Exchange (OzEWEX) research initiative which focuses on observations to evaluate and compare biophysical models of energy and water cycle components. Plain Language Summary Evapotranspiration modeling and mapping in arid and semi-arid ecosystems are uncertain due to empirical approximation of surface and atmospheric conductances. Here we demonstrate the performance of a fully analytical model which is independent of any leaf-scale empirical parameterization of the conductances and can be potentially used for continental scale mapping of ecosystem water use as well as water stress using thermal remote sensing satellite data.d