Abstract
The surface air temperature increase in the southwestern United States was much larger during the last few decades than the increase in the global mean. While the global temperature increased by about 0.5 °C from 1975 to 2000, the southwestern US temperature increased by about 2 °C. If such an enhanced warming persisted for the next few decades, the southwestern US would suffer devastating consequences. To identify major drivers of southwestern climate change we perform a multiple-linear regression of the past 100 years of the southwestern US temperature and precipitation. We find that in the early twentieth century the warming was dominated by a positive phase of the Atlantic multi-decadal oscillation (AMO) with minor contributions from increasing solar irradiance and concentration of greenhouse gases. The late twentieth century warming was about equally influenced by increasing concentration of atmospheric greenhouse gases (GHGs) and a positive phase of the AMO. The current southwestern US drought is associated with a near maximum AMO index occurring nearly simultaneously with a minimum in the Pacific decadal oscillation (PDO) index. A similar situation occurred in mid-1950s when precipitation reached its minimum within the instrumental records. If future atmospheric concentrations of GHGs increase according to the IPCC scenarios (Solomon et al. in Climate change 2007: working group I. The Physical Science Basis, Cambridge, 996 pp, 2007), climate models project a fast rate of southwestern warming accompanied by devastating droughts (Seager et al. in Science 316:1181–1184, 2007; Williams et al. in Nat Clim Chang, 2012). However, the current climate models have not been able to predict the behavior of the AMO and PDO indices. The regression model does support the climate models (CMIP3 and CMIP5 AOGCMs) projections of a much warmer and drier southwestern US only if the AMO changes its 1,000 years cyclic behavior and instead continues to rise close to its 1975–2000 rate. If the AMO continues its quasi-cyclic behavior the US SW temperature should remain stable and the precipitation should significantly increase during the next few decades.
Highlights
Climate change in the southwestern US is of concern because a slight increase in temperature and decrease in precipitation can transform the semi-arid land into a desert-like landscape (Seager et al 2007; MacDonald 2010; Cayan et al 2010; Williams et al 2012)
We find that in the early twentieth century the warming was dominated by a positive phase of the Atlantic multi-decadal oscillation (AMO) with minor contributions from increasing solar irradiance and concentration of greenhouse gases
About a half of the recent US SW warming trend can be attributed to the anthropogenic influences of increasing atmospheric concentration of greenhouse gases and aerosol variability (GHGA), with the remaining half being due to a positive phase of the AMO
Summary
Climate change in the southwestern US is of concern because a slight increase in temperature and decrease in precipitation can transform the semi-arid land into a desert-like landscape (Seager et al 2007; MacDonald 2010; Cayan et al 2010; Williams et al 2012). The global climate change is driven by increasing atmospheric concentration of greenhouse gases (GHGs) as well as by natural climate variability (Wu et al 2007, 2011a; Tung and Zhou 2013). In the following we perform multiple linear regression analysis (Wilks 2006; Lean and Rind 2008; Zhou and Tung 2013) of the southwestern US surface air temperature and precipitation records using historical radiative forcing and natural variability indices as predictors. To assemble a set of potential predictors for regression analysis we consider first the forcing used in the Coupled Model Inter-comparison Project phase 5 (CMIP5) These include anthropogenic radiative forcing by increasing atmospheric concentration of greenhouse gases and aerosols (GHGA), total solar irradiance (SOL), volcanic aerosols (VOLC), and El Nino Southern Oscillation (ENSO). There are several different ways how to characterize the Atlantic Ocean variability (Zanchettin et al 2013) for our analysis we employ the NOAA unsmoothed AMO long series from http://www.esrl.noaa. gov/psd/data/timeseries/AMO/
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