Potential for added value in precipitation simulated by high-resolution nested Regional Climate Models and observations

Abstract

Regional Climate Models (RCMs) constitute the most often used method to perform affordable highresolution regional climate simulations. The key issue in the evaluation of nested regional models is to determine whether RCM simulations improve the representation of climatic statistics compared to the driving data, that is, whether RCMs add value. In this study we examine a necessary condition that some climate statistics derived from the precipitation field must satisfy in order that the RCM technique can generate some added value: we focus on whether the climate statistics of interest contain some fine spatial-scale variability that would be absent on a coarser grid. The presence and magnitude of fine-scale precipitation variance required to adequately describe a given climate statistics will then be used to quantify the potential added value (PAV) of RCMs. Our results show that the PAV of RCMs is much higher for short temporal scales (e.g., 3-hourly data) than for long temporal scales (16-day average data) due to the filtering resulting from the time-averaging process. PAV is higher in warm season compared to cold season due to the higher proportion of precipitation falling from small-scale weather systems in the warm season. In regions of complex topography, the orographic forcing induces an extra component of PAV, no matter the season or the temporal scale considered. The PAV is also estimated using high-resolution datasets based on observations allowing the evaluation of the sensitivity of changing resolution in the real climate system. The results show that RCMs tend to reproduce relatively well the PAV compared to observations although showing an overestimation of the PAV in warm season and mountainous regions.

Projected Change—Models and Methodology

“…Di Luca et al (2012) used a diagram, adapted from Orlanski (1975) andvon Storch (2005), to illustrate the concept of AV for the range of scales represented by global and regional models, relative to the characteristic temporal and spatial scales of atmospheric processes (Fig. 10.9).…”

“…10.9. Because separation of scales is usually made while assessing AV, it is convenient to analyse both types of AV separately (Di Luca et al 2012). RCMs operate in a limited domain, and two-way interactions between the regional domain and the rest of the globe do not usually occur.…”

“…and GCMs (red line). Red and blue shaded regions represent the added value of type 1 (AV1) and 2 (AV2), respectively (redrawn from Di Luca et al 2012) As RCMs can resolve mesoscale atmospheric features explicitly, they do add small-scale structures to the largescale circulation provided by the driving model (Feser 2006). This explicit treatment of small-scale atmospheric features leads, for many variables, to an AV with respect to the driving model.…”

“…Di Luca et al (2012) also introduced the concept of potential AV, as a type of necessary condition for AV. It is a fine spatial scale variability that would be present in regional climate statistics but absent on a coarser grid.…”

“…The presence of potential AV in RCM simulations indicates the possible existence of AV but does not prove it. Di Luca et al (2012) investigated the existence of potential AV at the temporal scale for different regions and seasons. They showed that for precipitation, potential AV increases for short temporal scales, the summer season and in regions with complex Fig.…”