Re-evaluating the role of solar variability on Northern Hemisphere temperature trends since the 19th century

Abstract

Debate over what influence (if any) solar variability has had on surface air temperature trends since the 19th century has been controversial. In this paper, we consider two factors which may have contributed to this controversy:

• Several different solar variability datasets exist. While each of these datasets is constructed on plausible grounds, they often imply contradictory estimates for the trends in solar activity since the 19th century.

• Although attempts have been made to account for non-climatic biases in previous estimates of surface air temperature trends, recent research by two of the authors has shown that current estimates are likely still affected by non-climatic biases, particularly urbanization bias.

With these points in mind, we first review the debate over solar variability. We summarise the points of general agreement between most groups and the aspects which still remain controversial. We discuss possible future research which may help resolve the controversy of these aspects. Then, in order to account for the problem of urbanization bias, we compile a new estimate of Northern Hemisphere surface air temperature trends since 1881, using records from predominantly rural stations in the monthly Global Historical Climatology Network dataset. Like previous weather station-based estimates, our new estimate suggests that surface air temperatures warmed during the 1880s–1940s and 1980s–2000s. However, this new estimate suggests these two warming periods were separated by a pronounced cooling period during the 1950s–1970s and that the relative warmth of the mid-20th century warm period was comparable to the recent warm period.

We then compare our weather station-based temperature trend estimate to several other independent estimates. This new record is found to be consistent with estimates of Northern Hemisphere Sea Surface Temperature (SST) trends, as well as temperature proxy-based estimates derived from glacier length records and from tree ring widths. However, the multi-model means of the recent Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model hindcasts were unable to adequately reproduce the new estimate — although the modelling of certain volcanic eruptions did seem to be reasonably well reproduced.

Finally, we compare our new composite to one of the solar variability datasets not considered by the CMIP5 climate models, i.e., Scafetta and Willson, 2014's update to the Hoyt and Schatten, 1993 dataset. A strong correlation is found between these two datasets, implying that solar variability has been the dominant influence on Northern Hemisphere temperature trends since at least 1881. We discuss the significance of this apparent correlation, and its implications for previous studies which have instead suggested that increasing atmospheric carbon dioxide has been the dominant influence.

Introduction

In recent years, there has been considerable debate over what influence (if any) solar variability has had on global and regional surface air temperature trends¹ since the 19th century. Some authors have argued for a large role (e.g., Soon, 2005, Svensmark et al., 2009, Le Mouël et al., 2010, Vahrenholt and Lüning, 2013, Scafetta and Willson, 2014); others have argued that it has only played a minor role in recent decades (e.g., Solanki and Krivova, 2003, Balling and Roy, 2005, Gray et al., 2010, Bindoff et al., 2013); while others have argued it has played little (if any) role (e.g., Foukal et al., 2006, Clette et al., 2014, Tsonis et al., 2015). One of us (WS) has been an active participant in this debate (e.g., Zhang et al., 1994, Soon and Yaskell, 2003, Soon, 2005, Soon, 2009, Soon, 2014, Soon et al., 2011, Soon and Legates, 2013, Yan et al., 2015), which has become particularly significant lately, since the latest Global Climate Model hindcasts² used by the Intergovernmental Panel on Climate Change (IPCC) reports have indicated that solar variability has only had a modest influence on recent temperature trends. As a result, the latest IPCC reports concluded that temperature trends since 1951 are mostly “…due to the observed anthropogenic increase in greenhouse gas (GHG) concentrations” (Bindoff et al., 2013).

One reason for the lack of resolution of the debate is that the available information on solar variability is still rather limited, and as a result different estimates for solar trends are often contradictory. For instance, of the three satellite-based estimates for the Total Solar Irradiance (TSI) activity since 1978, one suggests there has been a general decrease (e.g., Fröhlich, 2006, Fröhlich, 2012, Fröhlich, 2013); one suggests there has been no discernible trend (e.g., Mekaoui and Dewitte, 2008, Mekaoui et al., 2010); and the third suggests an increase until about 2000 followed by a decrease (e.g., Willson, 2014, Scafetta and Willson, 2014). The Total Solar Irradiance (sometimes referred to as “solar activity”) is the aspect of solar variability which is most likely to directly influence climate. Therefore, in this paper we will usually treat the terms synonymously — although we will briefly consider in Section 2.3 other aspects of solar variability which may indirectly influence the climate, e.g., the strength of the solar wind.

Another problem is that debate exists over the extent to which non-climatic biases in the instrumental records have biased current global temperature trend estimates, e.g., see the debate between Le Mouël et al., 2009, Le Mouël et al., 2011 and Legras et al. (2010). In particular, for many years, there has been concern that the development and expansion of “urban heat islands” (e.g., Stewart and Oke, 2012) around many weather stations may have introduced a warming “urbanization bias” into regional (and possibly global) temperature trend estimates (e.g., Mitchell, 1953, Karl et al., 1988, Balling and Idso, 1989, Ren et al., 2008, Ren and Ren, 2011, Yang et al., 2011, Yang et al., 2013, Li et al., 2013, Symmons, 2014).

Several studies have claimed that urbanization bias has not substantially biased current estimates (e.g., Peterson et al., 1999, Parker, 2006, Wickham et al., 2013) and/or that data homogenization has effectively removed the problem (e.g., Menne et al., 2009, Hansen et al., 2010, Lawrimore et al., 2011, Hausfather et al., 2013). However, in a series of three companion papers, two of us (RC & MC) have recently shown that there were flaws in each of these earlier studies, and that urbanization bias is indeed a substantial (and insidious) problem for the current weather station-based temperature estimates (Connolly and Connolly, 2014a, Connolly and Connolly, 2014b, Connolly and Connolly, 2014c).

With this in mind, we have tried in this collaborative paper to address both problems simultaneously. First, we review the reasons for the ongoing solar variability debate. Second, we construct and assess a new Northern Hemisphere temperature trend estimate derived from predominantly rural stations taken from the widely-used Global Historical Climatology Network (GHCN) dataset (Lawrimore et al., 2011). We then present evidence which suggests that Northern Hemisphere temperature trends since the 19th century have actually been heavily influenced by changes in solar variability. However, before we do so, it may be helpful to briefly discuss the caveats associated with analyses that rely on apparent correlations between datasets.

Much of the research into the possible influence of solar variability on the Earth's climate has relied on the presence (or absence) of apparent correlations between various solar variability datasets and climatic datasets. However, correlation does not necessarily imply causation. That is, there are at least four types of correlations:

• Causal correlation. One of the variables directly influences the other, and so changes in that variable over time will tend to cause a corresponding change in the other variable. Sometimes both variables can influence each other, in which case changes in one of the variables can sometimes trigger a feedback loop. However, if one variable can cause a change in the other, but not vice versa, then we say that the “direction of causation” lies from the former to the latter.

• Commensal correlation. Both variables are influenced by a common factor. So, changes in that common (“parent”) factor over time will induce corresponding changes in both variables.

• Coincidental correlation. The two datasets are completely independent of each other. However, due to the variability within both datasets, over a short period of time the trends of both variables temporarily appear to be correlated. Often, when these datasets are updated with further data, the apparent correlation will start to break down.

• Constructional correlation. When one of the datasets was being constructed, it might have been assumed that it should be related to the other. When subjective decisions are made, researchers may mistakenly allow confirmation bias (e.g., Nickerson, 1998) to affect their decision. As a result, this could have artificially introduced an apparent correlation between the two datasets.

For a given correlation, it is possible that more than one factor might be at play. For instance, one variable might genuinely be causally or commensally correlated to the other, but the apparent strength of the correlation might have been exaggerated by a coincidental or constructional correlation. On the other hand, one variable might directly influence the other, but if the second variable is also influenced by other factors, this could reduce the apparent strength of the correlation over short periods of time (i.e., whenever more than one factor is influencing the second variable).

In the case of an apparent correlation between a given solar variability dataset and a climatic dataset, if the correlation is causal then it seems reasonable to assume that the direction of causation lies from the former to the latter. That is, it seems safe to assume that solar variability would be influencing the Earth's climate, rather than the other way around. In some cases, changes in a climatic dataset may appear to precede the changes in solar variability. While this may often indicate that the apparent correlation is spurious, we must caution that this is not always the case. For instance, the variable being used as a solar proxy might lag the actual solar variability. Or, if there are cyclical patterns in both the solar and climate variables, then differences in phase might incorrectly create the impression that the effect precedes the cause.

In many cases, it should not make too much difference to our conclusions whether the correlation is causal or commensal. If a given solar-climate correlation were commensal, then this would indicate that some (possibly unknown) factor which is influencing the Earth's climate is also influencing a particular aspect of solar variability. However, if that factor was influencing some aspect of solar variability, it would presumably be some other form of solar variability, and therefore the correlation would still be with solar variability.

It follows that our primary concern should be the possibility that either of the other two types of correlation is involved. For if the apparent correlations are either coincidental or constructional in nature, then an apparently strong link between surface air temperatures (for example) and solar variability can be considered spurious.

The format of this article is as follows:

• In Section 2, we review the solar variability debate and discuss the evidence for and against various different estimates of solar trends since the 19th century

• In Section 3, we look in detail at four regions in the Northern Hemisphere (China, U.S., Ireland and the Arctic) and determine the regional temperature trends for these areas using data from predominantly rural stations

• In Section 4, we combine our four regional estimates into a single Northern Hemisphere composite covering the period 1881–2014. We then compare and contrast this composite with several other estimates of Northern Hemisphere temperature trends.

• In Section 5, we identify and discuss an apparently strong relationship between our new Northern Hemisphere composite and the updated version of Hoyt & Schatten's estimate of solar activity trends (Hoyt and Schatten, 1993; updated by Scafetta and Willson, 2014).

• Finally, in Section 6, we offer some concluding remarks.