Evaluation of summer precipitation from EURO‐CORDEX fine‐scale RCM simulations over Norway

Abstract

Regional climate models represent a valuable tool in climate impact analyses. Their ability to accurately estimate current and future climate conditions is increasingly important. In Norway precipitation is of special interest. Heavy precipitation, particularly over short durations, is responsible for enormous damages to infrastructure such as roads and railways, hence information on a fine spatial and temporal scale is crucial. We evaluate the ability of seven regional climate model simulations of 0.11° resolution from the CORDEX ensemble in reproducing 3‐h and 24‐h accumulated summer precipitation characteristics in Norway. The two‐step evaluation includes comparison of modelled precipitation to gridded observation‐based datasets and to station measurements in terms of the following indices: summer maxima, summer wet event frequency, and total summer precipitation. We find a general overestimation by the models for all indices, with only few exceptions. Country‐wide spatial averages show, however, that simulated summer maxima are mainly within the uncertainty interval of the gridded reference dataset. This might also be true for summer wet event frequency, although the comparison to station measurements indicates that the positive bias is significant. We find the largest deviation between models in the evaluation of summer totals. The spatial distribution of the different precipitation indices is fairly well simulated, although the precipitation gradients evident in observation‐based datasets appear weak in the models. We believe the high spatial resolution improves the simulations of extreme precipitation in Norway, especially in areas of orographic enhancement.

High‐resolution monthly precipitation climatologies over Norway (1981–2010): Joining numerical model data sets and in situ observations

“…The Norwegian Meteorological Institute (MET) provided two versions of the seNorge gridded data set for daily precipitation over Norway at 1 km resolution: one is based on a triangulation procedure with altitude corrections (Tveito et al, 2005;Mohr, 2008; and the other is based on a multi-scale optimal interpolation approach (Lussana et al, 2018). The distribution of Norwegian station network is very sparse over the northern inland and the scarcely sampled high-elevation areas significantly limit the ability of observation-based models to capture the actual conditions occurring over these regions.…”

“…The distribution of Norwegian station network is very sparse over the northern inland and the scarcely sampled high-elevation areas significantly limit the ability of observation-based models to capture the actual conditions occurring over these regions. In particular, remarkable underestimations in gridded precipitation values are found to occur over the mountainous inland where the complex environment prevents the management of manual stations and limits the availability of dense observations (Lussana et al, 2018). Only in recent years, some new automatic raingauges have been established in these areas and their data have improved the monitoring applications even though the short length of the new records is not yet suitable for climatological studies.…”

“…In this study, we aim to combine two data sources, such as in situ observations and numerical model outputs in order to obtain as accurate as possible monthly precipitation climatologies even in data-sparse areas over complex terrains. The advancements in the accuracy and spatial resolution of numerical models in fact provided new data and information in the last years, which started to be used in the intercomparisons with observation-based gridded data sets (Haylock et al, 2008;Dyrrdal et al, 2018) as well as in climatological studies (Karger et al, 2017;Berthou et al, 2018). In particular, regional model reanalyses are primarily relevant for climatological applications because of the high spatial resolution (almost comparable to operational numerical weather prediction systems [NWP]) of their long-term gridded data sets (Isotta et al, 2015;Jermey and Renshaw, 2016).…”

Ensemble Spatial Precipitation Analysis From Rain Gauge Data: Methodology and Application in the European Alps

“…The example catchments in our analysis are clearly larger than the scale of station spacing. The uncertainties for the Lammer catchment are representative for a 20‐km grid pixel and should therefore be considered conservative for applications with an interest in even finer scales (e.g., Dyrrdal et al, ; Fantini et al, ). Moreover, the uncertainties in our analyses are likely too optimistic, in general, considering that the observational coverage is not as favorable in other regions.…”

Assessing Harvested Sites in a Forested Boreal Mountain Catchment through Global Forest Watch

“…Mountain catchments play an increasingly important role in the provision of ecosystems services, of which supporting functions such as physical protection against avalanches [1], rock-falls and landand debris slides [2] are perhaps some of the most obvious. Regulation of water flow during spring thaw [3] and extreme precipitation events [4], as well as the supply of clean drinking water make up some of the better known provisional services [5]. Cultural services are also prevalent in mountain catchments, manifested through hiking, mountain biking, fishing and hunting [6,7] as are provisioning services such as the supply of timber and wood products [8].…”