1 Introduction

Since preindustrial times, global mean surface temperatures have increased by approximately 1 K. An examination of the global surface temperature record reveals that the rate of warming has varied substantially through time, with periods of accelerated warming punctuated by periods with weak or negligible temperature trend (Hartmann et al., 2013). There is now broad consensus within the climate science community on the root cause of the overall warming, namely, rising levels of atmospheric CO 2 , now at 50% above preindustrial levels, driven predominantly by anthropogenic burning of fossil fuels (Cook et al., 2016). Understanding exactly how sensitive Earth's climate is to CO 2 (and other greenhouse gas [GHG]) emissions is critically important for efforts to mitigate future climate change and particularly for efforts to limit warming to less than 1.5 °C, a goal now shared by most countries as stated in the Paris Agreement (United Nations, 2016). Despite recognition of the importance of quantifying Earth's climate sensitivity, that is, the global mean surface temperature increase for a given atmospheric CO 2 increase, this quantity remains elusive. Lack of progress on this issue can be partly attributed to the difficulty of deducing climate sensitivity to CO 2 based on observations. Such efforts have been plagued by the fact that aerosol particles, which have a net cooling effect on climate, have been increasing along with CO 2 and have therefore masked some unknown proportion of CO 2 ‐induced warming to date (Andreae et al., 2005; Millar & Friedlingstein, 2018). Two apparent pauses in global warming (in the 1960s and in the 2000s) have both been attributed to aerosol changes (D. M. Smith et al., 2016; Wilcox et al., 2013; Wild, 2016). Representing the cooling effect of aerosol particles in global climate models (GCMs) has proven notoriously challenging, and GCM estimates of aerosol cooling continue to diverge (Boucher et al., 2013).

The overall aerosol effect on climate is often quantified in terms of its effective radiative forcing (ERF; Myhre et al., 2013). ERF can be defined in this context as the perturbation to Earth's radiation balance at the top‐of‐the‐atmosphere (TOA) associated with a given change in atmospheric composition—it is negative for anthropogenic aerosols, which cause more solar radiation to be reflected back to space, but positive for CO 2 and other GHGs, which trap more infrared radiation in the Earth system. The underlying mechanisms responsible for the aerosol ERF are not well understood, and there have thus far been few observational constraints on models incorporating these mechanism. One implication of the lack of observational constraints concerns the current generation of state‐of‐the‐art GCMs. These models must be able to broadly capture the surface temperature evolution of the last century in order to have any credibility. Yet they all reasonably do so despite having vastly different reported climate sensitivities (Forster et al., 2013; Kiehl, 2007; Knutti, 2008). Among the GCMs that participated in the Coupled Model Intercomparison Project Version 5 (CMIP5), for instance, the reported equilibrium climate sensitivity (ECS) estimates ranged from 2.0 to 4.5 °C. Clearly, observational constraints that embody aerosol forcing elements are urgently needed to enhance model realism and to help narrow the wide range of current ECS estimates.

For this purpose, we here make use of a frequently used proxy for aerosol forcing, namely, perturbations to the incoming solar radiation at the surface (Cherian et al., 2014). Downward fluxes of solar radiation at the surface (SDSR) have been measured extensively at hundreds of stations worldwide since the midtwentieth century and have been recorded as monthly averages in the Global Energy Balance Archive (GEBA; Wild et al., 2017). Because of atmospheric absorption of solar radiation, aerosol forcing evaluated at the surface differs from that evaluated at the TOA. As such, SDSR changes represent an imperfect proxy for ERF, but the GEBA data set is nevertheless a unique and invaluable data set in this context because of its length and relative consistency. Satellite observations of changes to the net solar radiation at the TOA would be preferable, but reliable long‐term records are unfortunately not available. Most GEBA stations do not record separate clear‐sky and all‐sky SDSR records, so a caveat to the analysis presented here is that cloud changes that are unrelated to aerosol changes could to some extent be responsible for the observed trends. However, previous papers have analyzed SDSR trends from a subset of the GEBA stations that do record separate clear‐sky and all‐sky data, and they consistently do not find support for the idea that the trends are dominated by cloud changes (Wild, 2012, and references therein). In the following, we will therefore assume that the reported SDSR changes are reliable aerosol ERF proxies. The GEBA data set will be described in more detail in the following section (section 2.1), along with the observational data set used for surface temperature in this study (section 2.2). More details on the CMIP5 model output that we utilized for this study are presented in section 2.3. Thereafter follows a comparison of simulated and observed temperature and SDSR trends globally (section 3.1) and regionally (section 3.2). In section 4 we discuss the implications of our findings and conclude the paper.