CMIP5 Models’ Ability to Capture Observed Trends under the Influence of Shifts and Persistence: An In-Depth Study on the Colorado River Basin

Abstract

AbstractThis study evaluated the ability of phase 5 of the Coupled Model Intercomparison Project (CMIP5) to capture observed trends under the influence of shifts and persistence in their data distributions. A total of 41 temperature and 25 precipitation CMIP5 simulation models across 22 grid cells (2.5° × 2.5° squares) within the Colorado River basin were analyzed and compared with the Climate Research Unit Time Series (CRU-TS) observed datasets over a study period of 104 years (from 1901 to 2004). Both the modeled simulations and observations were tested for shifts, and the time series before and after the shifts were analyzed separately for trend detection and quantification. Effects of several types of persistence were accounted for prior to both the trend and shift detection tests. The mean significant shift points (SPs) of the CMIP5 temperature models across the grid cells were found to be within a narrower range (between 1957 and 1968) relative to the CRU-TS observed SPs (between 1924 and 1985). Precipitation time series, especially the CRU-TS dataset, had a lack of significant SPs, which led to an inconsistency between the models and observations since the number of grid cells with a significant SP was not comparable. The CMIP5 temperature trends, under the influence of shifts and persistence, were able to match the observed trends very satisfactorily (within the same order of magnitude and consistent direction). Unlike the temperature models, the CMIP5 precipitation models detected SPs that were earlier than the observed SPs found in the CRU-TS data. The direction (as well as the magnitude) of trends, before and after significant shifts, was found to be inconsistent between the modeled simulations and observed precipitation data. Shifts, based on their direction, were found either to strengthen or to neutralize the preexisting trends in both the model simulations and the observations. The results also suggest that the temperature and precipitation data distributions were sensitive to different types of persistence—such sensitivity was found to be consistent between the modeled and observed datasets. The study detected certain biases in the CMIP5 models in detecting the SPs (tendency of detecting shifts earlier for precipitation and later for temperature than the observed shifts) and also in quantifying the trends (overestimating the trend slopes)—such insights may be helpful in evaluating the efficacy of the simulation models in capturing observed trends under uncertainties and natural variabilities.