Evaluation of precipitation indices in suites of dynamically and statistically downscaled regional climate models over Florida

Abstract

The present work evaluates historical precipitation and its indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) in suites of dynamically and statistically downscaled regional climate models (RCMs) against NOAA’s Global Historical Climatology Network Daily (GHCN-Daily) dataset over Florida. The models examined here are: (1) nested RCMs involved in the North American CORDEX (NA-CORDEX) program, (2) variable resolution Community Earth System Models (VR-CESM), (3) Coupled Model Intercomparison Project phase 5 (CMIP5) models statistically downscaled using localized constructed analogs (LOCA) technique. To quantify observational uncertainty, three in situ-based (PRISM, Livneh, CPC) and three reanalysis (ERA5, MERRA2, NARR) datasets are also evaluated against the station data. The reanalyses and dynamically downscaled RCMs generally underestimate the magnitude of the monthly precipitation and the frequency of the extreme rainfall in summer. The models forced with CanESM2 miss the phase of the seasonality of extreme precipitation. All models and reanalyses severely underestimate both the mean and interannual variability of mean wet-day precipitation (SDII), consecutive dry days (CDD), and overestimate consecutive wet days (CWD). Metric analysis suggests large uncertainty across NA-CORDEX models. Both the LOCA and VR-CESM models perform better than the majority of models. Overall, RegCM4 and WRF models perform poorer than the median model performance. The performance uncertainty across models is comparable to that in the reanalyses. Specifically, NARR performs poorer than the median model performance in simulating the mean indices and MERRA2 performs worse than the majority of models in capturing the interannual variability of the indices.

Assessment of NA-CORDEX regional climate models, reanalysis, and in-situ gridded-observational against U.S. Climate Reference Network datasets

“…As a third step, the spatial-temporal variability throughout the subregions was assessed using Taylor diagrams (Taylor, 2001). Please, note that the Taylor diagram summarizes the main scores skills: correlation coefficient, standard deviation, and root mean square deviation and has been already employed in the ranking of models and reanalysis products in many studies over the CONUS regions (e.g., Gibson et al, 2019;Srivastava et al, 2022). Furthermore, to estimate correlations, a modified Taylor diagram using robust non-parametric Kendall rank correlation test τ (Croux & Dehon, 2010) is used.…”

“…Nevertheless, the use of these methods requires a dense spatial coverage of observations. An alternative solution for evaluating climate model, widely used in literature, is to use reanalysis or gridded surface observational datasets (e.g., Gibson et al, 2019;Srivastava et al, 2020Srivastava et al, , 2022). An increasing number of studies evaluated regional climate models in the presence of observational uncertainties by considering different gridded observational datasets as references to rank their reliability (e.g., Gibson et al, 2019;Herold et al, 2016;.…”

“…An increasing number of studies evaluated regional climate models in the presence of observational uncertainties by considering different gridded observational datasets as references to rank their reliability (e.g., Gibson et al, 2019;Herold et al, 2016;. Consequently, different conclusions are often drawn altering model performance in nontrivial ways due to the contribution of observational uncertainties (Gibson et al, 2019;Gómez-Navarro et al, 2012;Kotlarski et al, 2019;Prein & Gobiet, 2017;Srivastava et al, 2022). A debate related to the uncertainties related to the usage of reanalysis and gridded surface observational datasets in the model evaluation is ongoing since several years, and it is a topic of several papers in the literature (e.g., Diaconescu et al, 2018;Dieng et al, 2022;Dosio et al, 2022;Flaounas et al, 2012;Gibson et al, 2019;Srivastava et al, 2020Srivastava et al, , 2022.…”

“…Consequently, different conclusions are often drawn altering model performance in nontrivial ways due to the contribution of observational uncertainties (Gibson et al, 2019;Gómez-Navarro et al, 2012;Kotlarski et al, 2019;Prein & Gobiet, 2017;Srivastava et al, 2022). A debate related to the uncertainties related to the usage of reanalysis and gridded surface observational datasets in the model evaluation is ongoing since several years, and it is a topic of several papers in the literature (e.g., Diaconescu et al, 2018;Dieng et al, 2022;Dosio et al, 2022;Flaounas et al, 2012;Gibson et al, 2019;Srivastava et al, 2020Srivastava et al, , 2022. The uncertainties are influenced by several factors, such as: i) the use of interpolation methods/technics on sparse station networks (e.g.…”

“…In this work, the ability of regional climate models (RCMs) participating to the North American Coordinated Regional climate Downscaling Experiment (NA-CORDEX, Mearns et al, 2017) to simulate the daily mean temperature and precipitation over the Contiguous United States (CONUS) is evaluated, along with performances of atmospheric reanalysis and gridded surface observational datasets. To address these issues, differently from previous studies available in the literature (e.g., Gibson et al, 2019;Srivastava et al, 2020Srivastava et al, , 2022, observations from the USCRN network are used as reference for comparison. This paper focuses on the three main scientific questions: i)…”