How climate modelling can help us better understand the historical temperature evolution

By: Andrea Dittus

Figure 1: Annual global mean surface temperatures from NASA GISTempNOAA GlobalTempHadley/UEA HadCRUT4Berkeley EarthCowtan and WayCopernicus/ECMWF and Carbon Brief’s raw temperature record. Anomalies plotted with respect to a 1981-2010 baseline. Figure and caption from Carbon Brief (

Earth’s climate has warmed by approximately 0.85 degrees over the period from 1880 to 2012 [IPCC, 2013] due to anthropogenic emissions of greenhouse gases. However, the rate of warming throughout the twentieth and early twenty-first centuries has not been uniform, with periods of accelerated warming and cooling (Figure 1). A key player in determining the historical evolution of global temperatures besides greenhouse gases are anthropogenic aerosols. Aerosols are airborne particles that scatter or absorb incoming solar radiation, and affect cloud properties, therefore altering the surface energy budget. Different aerosols species have different properties and climate impacts, but perhaps the most important aerosols in the context of global climate variability are sulphate aerosols, which account for a large proportion of anthropogenic aerosol. As a scattering aerosol, sulphate has a cooling effect on global climate and has partially offset some of the warming induced by emissions of greenhouse gases. Although we know that aerosols play an important role for global climate, the magnitude of historical aerosol forcing remains uncertain [e.g. Stevens, 2015; Kretzschmar et al., 2017; Booth et al., 2018].

In climate models, the representation of aerosol processes is very diverse, resulting in a wide spread in the magnitude of aerosol forcing across different climate models [Wilcox et al., 2015]. Consequently, the climate effects of aerosols are also very different from model to model. Studies have suggested that aerosol forcing can influence the phasing of key modes of multi-decadal variability such as the Atlantic Multidecadal Variability [Booth et al., 2012] and Pacific Decadal Oscillation [Smith et al., 2016], although the degree of influence is still unclear [e.g. Zhang et al., 2013; Oudar et al., 2018]. Key open questions are whether these findings are model dependent, influenced by the magnitude of simulated aerosol forcing, ensemble size, or a combination of these.

Figure 2: Simulated temperatures for each ensemble member across the different aerosol scalings for the period 1941 to 1970. The numbers 0.2 to 1.5 indicate the scaling factor that was applied to the anthropogenic aerosol emissions. Blue indicates that temperatures are cooler than the reference temperature defined as the 1.0 scaling ensemble mean 1850-2014 climatology, red indicates warmer temperatures.

The SMURPHS Project (Securing Multidisciplinary Understanding of Hiatus and Surge Events, is a multi-disciplinary project whose aim is to improve our understanding of the causes of variations in the observed rate of warming. As part of this project, we have designed an ensemble of historical climate simulations with the HadGEM3-GC3.1 climate model, where anthropogenic aerosol emissions were scaled up or down to sample a wide range in historical aerosol forcing. The emergence of large ensembles in the climate modelling community has highlighted the importance of sampling a large number of realisations, to better estimate the forced response (common to all members run with the same forcings) and magnitude of internal variability (individual to each member). As a compromise between the need to sample a wide range of aerosol forcing and multiple initial condition members, we have opted to run four different initial condition members for five different aerosol scalings. Figure 2 illustrates the effect of aerosol forcing on temperature in the SMURPHS ensemble for the period from 1941 to 1970, a period particularly sensitive to aerosol forcing (not shown). Along the x-axis, different magnitudes of aerosol forcing represent the sensitivity of climate model simulations to aerosol forcing. On the y-axis, each line represents a single realisation to highlight the role of internal variability. The simulations with higher aerosol emissions are systematically colder than the simulations with lower aerosol emissions, consistent with the expected response to increasing aerosol forcing across the ensemble. 

Going forward, these simulations will allow us to investigate how variations in historical aerosol forcing have shaped climate variability in the twentieth and early twenty-first century, from global mean surface temperatures to multi-decadal modes of variability and beyond.


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