Increase in the concentrations of the atmospheric greenhouse gases (i.e., carbon dioxide (CO2
), methane [CH4
], nitrous oxide [N2
O], etc.) trigger the rise in temperature that eventually changes the frequency of occurrence of extreme precipitation events (e.g., flood, drought and hurricane) in many regions across the globe [1
]. Industrial growth and deforestations are among many factors that are playing a great role in raising the concentration of the atmospheric CO2
that intensify the change in the mean climatic states [2
]. As a result, the frequency of occurrence of extreme events (i.e., floods, droughts, sea level rise, etc.) has increased in recent decades and caused an impact on the socio-economic and environmental sectors at large [3
]. The available global climate projections indicate the changes in average climate and to some extent about extreme events. The impacts of the extreme events are becoming even worse and could continue to worsen in the future unless remarkable and proper measures are taken to reduce the current greenhouse gas emissions [2
]. The trends of future climate extreme events can be projected and analyzed through the use of climate projection data from the global and regional climate models [9
] under different emission scenarios.
Regional climate models (RCMs) driven by the global climate models (GCMs) are increasingly used to assess potential changes in climatic states by various studies [10
]. The North America COordinated Regional Climate Downscaling Experiment (NA-CORDEX) provides output from several RCM simulations using different boundary conditions from multiple GCMs across the majority of North America [15
]. The RCM products have the potential added value of capturing detailed spatial variability, as well as non-linear effects at local and regional scales associated with their finer spatial resolutions, as compared to the GCMs [16
]. However, the accuracy and performance of each RCM may vary from region to region since RCMs were derived by considering different boundary conditions (GCMs), unique physical principles and downscaling approaches [17
]. Despite the increasing use of RCMs to study the impact of extreme events, considerable systematic errors and bias remain to be challenging for their efficient use and wide application [18
]. Thus, several evaluation and bias correction approaches have been developed and applied in various studies to improve the accuracy and quality of the RCMs outputs [19
The evaluation of the RCMs is crucial to measure their skills and accuracy in reproducing the observed data during the reference period [19
]. A positive outcome of the evaluation process increases confidence in the potential applications of the RCMs for trend and other analyses of extreme events for future scenarios [9
]. The evaluation process involves the computation of several climatic indices derived from the original datasets. These climatic indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), mainly measure the exceedance of the fundamental characteristics (e.g., duration, intensity) of the climatic variables from certain threshold values [23
]. In addition, to using climatic indices, the Standardized Precipitation Index (SPI) and Standardized Precipitation and Evaporation Index (SPEI) are used to evaluate the RCMs [25
]. These indices are widely used to measure the deviation of a given climate event from the long-term mean value and assess the frequency of occurrence of very dry (drought) and very wet (flood) conditions.
Several studies have evaluated RCMs at continental, country and regional scales using different evaluation techniques [16
]. However, more evaluation studies of the RCMs need to be conducted at smaller/local scales (e.g., watershed, basin and state level) to build confidence in the capability of RCM simulations to capture the detailed characteristics of the climatic patterns at local scales. In Florida, there are limited studies of evaluations of the climate models despite the fact that the state is vulnerable to the impacts of climate change of unprecedented magnitude [27
]. Some studies follow a quantitative evaluation and bias correction approach to assess the performance of a single RCM in reproducing the variability of the observed climatic variables [28
], whereas the majority of the other studies focused on the assessment of climate change impacts on agricultural productivity [29
], rainfall intensity–duration–frequency (IDF) curves [30
], stream flow simulation [31
] and socio-economic sectors [32
]. Nevertheless, several RCM evaluations studies are still limited in Florida. These evaluation studies are crucial for identifying RCMs that perform relatively better in reproducing the observed precipitation and temperature during the reference period and their potential application in the impact assessment of climate change in the future.
Thus, this study evaluates precipitation and temperature simulations from four RCMs (i.e., Canadian Regional Climate Model version 5 Université du Québec à Montréal (CRCM5-UQAM), Canadian Regional Climate Model version 4 (CanRCM4), Rossby Center Regional atmospheric model (RCA4) and HIRHAM5, a combination of High Resolution Limited Area Model (HIRLAM) and European Centre Hamburg Model (ECHAM) forced by three GCMs (i.e., Canadian Centre for Climate Modelling and Analysis (CanESM2), European community Earth-System Model (EC-EARTH) and Max Planck Institute Earth System Mode (MPI-ESM-LR) across Florida. The ensemble mean of the four RCM simulations are also calculated and compared with each RCM in the evaluation process to identify whether any added value that is obtained could be from the combinations of the four RCMs. The evaluation is based on the climatological mean (31 years average) of the climate indices and SPEI time series data derived from the daily precipitation and temperature.
This study evaluates four RCM models (CRCM5-UQUAM, CanRCM4, RCA4 and HIRHAM5) driven by three GCMs (EC-EARTH, CanESM2 and MPI-ESM-LR) and the ensemble mean (averaging values of the climate models climatology) by considering four evaluation techniques (absolute bias, pattern correlation, reduction of variance and SPEI) for Florida. Several climatic indices suggested by the international Joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) were derived from the daily time series of each climate model and used in the evaluation process. The main objective of this evaluation is to identify the climate model with the best skill in reproducing the climatic variables during the reference period of 31 years (1975–2005) and to use the future projection data for our imminent impact study and trend analysis of extreme events.
Each climate model has shown a unique skill in reproducing some of the climatic indices considered in this study and their skill, however, varies from one evaluation technique to the other. EC-EARTH.RCA4 relatively reproduced precipitation based climatic indices with minimum absolute biases as compared to other RCMs. Majority of the RCMs including the ensemble mean showed comparable performance in reproducing temperature based climatic indices under the same evaluation criteria. Based on the pattern correlation criteria, the ensemble mean, EC-EARTH.HIRHAM5 RCM and MPI-ESM-LR.CRCM5-UQAM RCMs showed relatively better skill in reproducing most of the climatic indices as compared to the other climate models. The ensemble mean, EC-EARTH.RCA4, CanESM2.RCA4 and MPI-ESM-LR.CRCM5-UQAM RCMs showed good skill when evaluated using the reduction of variance and absolute biases. The ensemble mean showed relatively better skill in reproducing the temperature-based climatic indices compared to its skill in reproducing the precipitation-based climatic indices. There were no remarkable differences among the performances of the climate models compared to the SPEI. However, CanESM2.CRCM5-UQAM, EC-EARTH.RCA4 and the ensemble mean performed relatively better than the other model simulations. The better performances of these RCMs under different criteria have a positive implication for their potential use/application in climate change impact studies and future trend analysis of extreme events. This result could help in identifying better information to understand, analyze and mitigate possible future impacts of climate change across Florida.
Even though the climate models have shown reasonable skill in reproducing the observed climate variable, their performance may further improve through applying a bias correction approach (in addition to the evaluation efforts) that is planned for our future studies. Moreover, the coarser spatial resolution of the climate models might contribute to the lesser accuracy of the skills of each climate model, particularly when they are evaluated relatively in a smaller study region. Hence, downscaling the climate models to a finer spatial resolution using robust dynamical or statistical downscaling approaches may improve the accuracy of each RCM and this is recommended for future studies. The evaluation of the RCMs was carried out in this study using the data for the reference period 1975–2005. The performance of the RCMs may be altered when using different reference periods.