Evaluation of Temperature and Precipitation Trends and Long-Term Persistence in CMIP5 Twentieth-Century Climate Simulations

Author:

Kumar Sanjiv1,Merwade Venkatesh2,Kinter James L.3,Niyogi Dev4

Affiliation:

1. Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland

2. School of Civil Engineering, Purdue University, West Lafayette, Indiana

3. Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland, and Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, Virginia

4. Department of Agronomy, and Department of Earth, Atmospheric and Planetary Sciences, Purdue University, West Lafayette, Indiana

Abstract

Abstract The authors have analyzed twentieth-century temperature and precipitation trends and long-term persistence from 19 climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This study is focused on continental areas (60°S–60°N) during 1930–2004 to ensure higher reliability in the observations. A nonparametric trend detection method is employed, and long-term persistence is quantified using the Hurst coefficient, taken from the hydrology literature. The authors found that the multimodel ensemble–mean global land–average temperature trend (0.07°C decade−1) captures the corresponding observed trend well (0.08°C decade−1). Globally, precipitation trends are distributed (spatially) at about zero in both the models and in the observations. There are large uncertainties in the simulation of regional-/local-scale temperature and precipitation trends. The models’ relative performances are different for temperature and precipitation trends. The models capture the long-term persistence in temperature reasonably well. The areal coverage of observed long-term persistence in precipitation is 60% less (32% of land area) than that of temperature (78%). The models have limited capability to capture the long-term persistence in precipitation. Most climate models underestimate the spatial variability in temperature trends. The multimodel ensemble–average trend generally provides a conservative estimate of local/regional trends. The results of this study are generally not biased by the choice of observation datasets used, including Climatic Research Unit Time Series 3.1; temperature data from Hadley Centre/Climatic Research Unit, version 4; and precipitation data from Global Historical Climatology Network, version 2.

Publisher

American Meteorological Society

Subject

Atmospheric Science

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