Abstract
Abstract. There is strong and constant demand from various sectors (research, industry and government) for long-term, high-resolution (both temporal and spatial), gridded climate datasets. To address this demand, the Irish Centre for High-End Computing (ICHEC) has recently performed two high-resolution simulations of the Irish climate, utilising the Regional Climate Models (RCMs) COSMO-CLM5 and WRF v3.7.1. The datasets produced contain hourly outputs for an array of sub-surface, surface and atmospheric fields for the entire 36-year period 1981–2016. In this work, we list the climate variables that have been archived at ICHEC. We present preliminary uncertainty estimates (error, standard deviation, mean absolute error) based on Met Éireann station observations, for several of the more commonly used variables: 2 m temperature, 10 m wind speeds and mean sea level pressure at the hourly time scale; and precipitation at hourly and daily time scales. Additionally, analyses of 10 cm soil temperatures, CAPE 3 km, Showalter index and surface lifted index are presented.
Highlights
Gridded climate datasets are invaluable aids to studies in observed climate change trends and variability
Daily and monthly gridded datasets of precipitation have been created for Ireland (Walsh, 2012, 2016) and are based on station data from Met Éireann’s rainfall network – the identification of changes in Irish precipitation patterns, whether they be driven by natural variability or man-made climate change, is important to the country with recent projections pointing to an increased likelihood of summer droughts and winter flooding (Nolan et al, 2013a, b)
Gridded datasets based on station observations come with numerous caveats as detailed by Prein and Gobiet (2017): they may not be representative in regions with few stations; station data are prone to error and/or missing values; precipitation under-catch and excessive smoothing
Summary
Gridded climate datasets are invaluable aids to studies in observed climate change trends and variability. Flanagan et al.: Towards a definitive historical high-resolution climate dataset for Ireland former being HIRLAM (1979–2014; 22 km; Dahlgreen et al, 2016) and COSMO-REA6 (1997–2004; 6 km; Bollmeyer et al, 2015) whilst two examples of the latter are described in Lucas-Picher et al (2012) and Dasari and Challa (2015) There are both advantages and disadvantages to the downscaling approach: downscaling can offer both finer detail and less computational cost than regional reanalysis (Kanamitsu and Kanamaru, 2007); errors are cascaded with new errors introduced through the flow of information at the boundaries.
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