Just two explanatory variables (GHG and AMO) still account for 93% of the temperature variance.
Dr. Leif Svalgaard sends word of this article in Geophysical Research letters by Petr Chylek, James D. Klett, Glen Lesins, Manvendra K. Dubey and Nicolas Hengartner Article first published online: 5 MAR 2014 DOI: 10.1002/2014GL059274
A multiple linear regression analysis of global annual mean near-surface air temperature (1900–2012) using the known radiative forcing and the El Niño–Southern Oscillation index as explanatory variables account for 89% of the observed temperature variance. When the Atlantic Multidecadal Oscillation (AMO) index is added to the set of explanatory variables, the fraction of accounted for temperature variance increases to 94%. The anthropogenic effects account for about two thirds of the post-1975 global warming with one third being due to the positive phase of the AMO. In comparison, the Coupled Models Intercomparison Project Phase 5 (CMIP5) ensemble mean accounts for 87% of the observed global mean temperature variance. Some of the CMIP5 models mimic the AMO-like oscillation by a strong aerosol effect. These models simulate the twentieth century AMO-like cycle with correct timing in each individual simulation. An inverse structural analysis suggests that these models generally overestimate the greenhouse gases-induced warming, which is then compensated by an overestimate of anthropogenic aerosol cooling.
During the past century the Earth has experienced considerable warming due to anthropogenic as well as natural causes. Although a substantial body of research suggests that most of the warming has been due to an increasing atmospheric concentration of CO2 and other greenhouse gases, an exact partitioning of the magnitude of the global warming due to the natural and anthropogenic causes remains uncertain. Most climate research has centered on the use of coupled AOGCMs (atmosphere-ocean general circulation models) to elucidate the climate system from near first principles representing physical, chemical, and biological processes.
Empirical statistical models have been used recently [Lean and Rind, 2008; Foster and Rahmstorf, 2011; Mascioli et al., 2012; Zhou and Tung, 2013; Canty et al., 2013; Chylek et al., 2013] to complement physics-based models and are contributing to our understanding of anthropogenic and natural components of climate variability. The method assumes a linear relation between the observed temperature and a set of selected physically plausible explanatory variables (predictors). A typical set of explanatory variables includes the known radiative forcing and an additional factor characterizing the oceanic influence on climate [Compo and Sardeshmukh, 2009; Zhou and Tung, 2013].
Major radiative forcing includes solar variability (SOL), volcanic eruptions (VOLC) [Douglass and Clader, 2002; Haigh, 2003; Scafetta and West, 2006; Camp and Tung, 2007; Lean and Rind, 2008], anthropogenic greenhouse gases (GHG), and anthropogenic aerosols (AER). The oceanic influence is usually characterized by the El Niño–Southern Oscillation (ENSO) index [Lean and Rind, 2008; Foster and Rahmstorf, 2011]. However, the AMO (Atlantic Multidecadal Oscillation) [Schlesinger and Ramankutty, 1994; Delworth and Mann, 2000; Gray et al., 2004] also exerts a considerable influence on the global and regional climate [Polyakov and Johnson, 2000; Chylek et al., 2006, 2009; Chylek et al., 2010; Chylek et al., 2013; Zhang et al., 2007; Mahajan et al., 2011; Frankcombe and Djikstra, 2011; Zhou and Tung, 2013; Canty et al., 2013; Muller et al., 2013; Kavvada et al., 2013].
In this note we show that the observed annual mean global temperature variability is captured more fully by a regression model when the AMO is added to the set of explanatory variables. Considering a compromise between accuracy and complexity, the minimal regression model that accounts for 93% of the observed annual mean global temperature variance contains only two explanatory variables: anthropogenic greenhouse gases (GHG) and the AMO. Adding all other predictors increases the fraction of accounted for global temperature variance to 94%.
Summary and Discussion
A multiple linear regression model that uses the set of explanatory variables composed of radiative forcing due to anthropogenic greenhouse gases and aerosols, solar variability, volcanic eruptions, and ENSO accounts for 89 ± 1% of the global annual mean temperature variance. When AMO is added to the set of explanatory variables, the fraction of explained temperature variance increases to 94%. Just two explanatory variables (GHG and AMO) still account for 93% of the temperature variance. The improvement of the regression model by including the AMO is highly statistically significant (p < 0.01). For comparison, the CMIP5 ensemble mean of all simulations accounts for 87% of the observed temperature variance.
Our analysis suggests that about two thirds of the late twentieth century warming has been due to anthropogenic influences and about one third due to the AMO. This is a robust result independent of the parameterization of the anthropogenic aerosol radiative forcing used or of the considered regression model, as long as the AMO is among the explanatory variables.
An inverse structural analysis shows that all considered climate models (GFDL-CM3, HadGEM-ES, CCSM4, CanESM2, and GISS-E2) overestimate GHG warming that is then compensated by an overestimated aerosol cooling. The overestimates are especially large in models with an indirect aerosol effect. In these models a strong aerosol effect generates the AMO-like 20th century temperature variability. The apparent agreement with the observed temperature variability is achieved by two compensating errors: overestimation of GHG warming and aerosol cooling. This raises a question of reliability of these models’ projections of future global temperature. The inverse structural analysis underscores the significance of the AMO-like oscillation and therefore the need to establish its origin and to better simulate it in future climate models.
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Also of interest:
CMIP5 multi-model hindcasts for the mid-1970s shift and early 2000s hiatus and predictions for 2016–2035
Gerald A. Meehl* and Haiyan Teng
Article first published online: 7 MAR 2014 DOI: 10.1002/2014GL059256
Compared to uninitialized climate change projections, a multi-model ensemble from the CMIP5 10 year decadal prediction experiments produces more warming during the mid-1970s climate shift and less warming in the early 2000s hiatus in both the tropical Indo-Pacific region and globally averaged surface air temperature (TAS) in closer agreement with observations. Assuming bias in TAS has stabilized in the 10 year predictions, after bias adjustment, TAS anomalies for the 2016–2035 period in the 30 year predictions initialized in 2006 are about 16% less than the uninitialized projections. One contributing factor for the improved climate simulation is the bias adjustment, which corrects the models’ systematic errors and higher-than-observed decadal warming trend. Another important factor is the initialization with observations which constrains the ocean such that the starting points of the initialized simulations are close to the observed initial states.