As the global climate warms (Hartmann et al., 2013), changes are not only observed in the daily mean temperature but also in a variety of parameters relevant to society. One such parameter is the diurnal temperature range (DTR), which is a measure of the difference between the maximum and the minimum temperature over a 24 h period. Variations in the magnitude of the DTR have been found to influence mortality and morbidity (Cheng et al., 2014; Kim et al., 2016; Lim et al., 2012), parasite infection and transmission (Paaijmans et al., 2010), and crop failure (Hernandez-Barrera et al., 2017; Lobell, 2007). Future changes in DTR are therefore a potential driver of climate impacts, especially in vulnerable regions, affecting risk assessments associated with health and agriculture.

A range of geophysical processes contribute to the land surface DTR of a given region. Ultimately, DTR changes are driven by differential changes to daily maximum and minimum temperatures. Maximum temperatures (Tmax) are reached during daytime, due to the excess of incoming shortwave (SW or solar) radiation. Minimum temperatures (Tmin) occur at night, primarily due to cooling by longwave (LW or heat) radiation. As LW cooling is active during both daytime and nighttime, factors affecting primarily LW radiation will have an effect on both Tmin and Tmax, reducing the potential influence on DTR. Thus, greenhouse gases such as CO2 or water vapor, which have a particularly strong effect on LW radiation fluxes throughout the day (e.g., Lagouarde and Brunet, 1993), are not initially expected to have the strongest direct radiative influence on DTR. Dai et al. (1999) showed that changes in water vapor had a relatively small effect on DTR. Aerosols, on the other hand, primarily have climate interactions affecting the shortwave (SW) spectrum. They tend to lower the amount of downwelling SW radiation at the surface through scattering and absorption, initially reducing the daytime Tmax and thus reducing DTR.

In addition to the direct interactions with SW and, to a lesser extent, LW radiation, greenhouse gases and aerosols alike have a range of indirect (radiative and nonradiative) influences on climate. These effects can cause further changes to Tmin and Tmax. For instance, sulfate aerosols can interact microphysically with clouds to make them more reflective (Twomey, 1974) or increase the general cloud cover by increasing cloud lifetime (Albrecht, 1989). Cloud changes have been shown to have a strong influence on DTR, mainly by blocking SW radiation and hence reducing Tmax (e.g., Dai et al., 1999). Increased cloud thickness or cloud cover will also affect the surface energy budget by increasing downwelling LW radiation. This effect operates during both day and night.

The strong atmospheric absorption by BC and CO2 can cause rapid adjustments in both cloudiness and precipitation through their influence on atmospheric stability (Hansen et al., 1997; Richardson et al., 2018; Stjern et al., 2017). An increase in precipitation, for instance, may induce changes in soil moisture, which could in turn influence DTR through a reduced Tmin due to enhanced evaporation (Zhou et al., 2007). On a longer timescale, feedback responses following a warming climate can cause changes to DTR via associated changes in cloud cover (Dai et al., 1999), atmospheric circulation, precipitation (Karl et al., 1993), soil moisture (Zhou et al., 2007), surface heat storage capacity (Kleidon and Renner, 2017), land use (Mohan and Kandya, 2015), and the turbulent fluxes of sensible and latent heat in the atmospheric boundary layer (Davy et al., 2017). Finally, each process and its effect on DTR may be modified by nonlinear effects such as, for example, local hydrological conditions or atmospheric stratification.

Observations show a general reduction in DTR over the twentieth century, typically mediated by a stronger increase in the daily minimum temperature than in the daily maximum temperature (Dai et al., 1999; Karl et al., 1993; Vose et al., 2005). This trend in DTR has been linked to anthropogenic emissions, but whether greenhouse gases or aerosols are the dominating influence, as well as what roles these respective climate drivers will play in future DTR changes, is not clear. For instance, Vose et al. (2005) showed that while the overall trend in DTR was negative for western US and central Europe for the period of 1950–2005, it reverses to a positive trend in these regions when considering the later 1979–2005 period which saw reductions in aerosol emissions. China, however, saw a DTR reduction also for this later period – but it is also located at lower latitudes.

Over the coming decades, we can expect continued emissions of both greenhouse gases and aerosols but with amounts and a relative balance that are determined by future socioeconomic and political developments. The global backdrop of increased greenhouse-gas-induced forcing will be combined with an aerosol influence that has regionally heterogeneous patterns and potentially strong trends. As an example, the global burden of aerosol loading has recently shifted from Europe to Asia (Myhre et al., 2017b). These aerosol trends have been designated as potential causes of the ongoing drying of the Mediterranean region (Tang et al., 2017) and of changes to the Southeast Asian Monsoon circulations (Wilcox et al., 2020). However, the future balance between the different climate forcers is highly uncertain, and it differs markedly between the various Shared Socioeconomic Pathways currently in use by the projection and climate impact communities (Lund et al., 2019; Rao et al., 2017). In particular, they include a wide range of possible emission combinations of BC and SO4 from India and China, some of which lead to a strong dipole pattern in regional aerosol-induced radiative forcing over the coming decades (Samset et al., 2019).

Given the uncertainty in future emission trends, disentangling the individual responses of DTR to these two aerosol species and understanding how their influence differs from that of CO2, when taking into account both direct and indirect effects and their climate feedbacks, are of high relevance. Such understanding is an important prerequisite for understanding how regional DTR will evolve over the coming decades. The purpose of this work is to contribute to such an understanding, based on a sample of common, idealized experiments performed by nine coupled climate models. Model studies investigating effects of greenhouse gases and aerosols on DTR have typically used historical simulations (Lewis and Karoly, 2013; Liu et al., 2016). However, such simulations include trends in greenhouse gases as well as trends in both scattering and absorbing aerosols, with opposite effects on global mean temperature and, possibly, on DTR. To disentangle the role of different climate drivers in the DTR changes, model responses to idealized experiments where individual drivers are perturbed separately provide a separate line of evidence.

In the present study we compare idealized instantaneous perturbations of CO2, BC and SO4 in nine global climate models from the Precipitation Driver Response Model Intercomparison Project (PDRMIP) (Myhre et al., 2017a). This unique data set allows us to investigate whether differing changes to DTR can be expected from trends in greenhouse gases, sulfate or black carbon, and it can shed light on results from more comprehensive multi-forcer simulations, such as those in the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Eyring et al., 2016). While the size of the data set precludes detailed process-level investigations of the output from each model, any significant changes found based on the median response of the model sample should represent physically robust expectations based on the geophysical understanding underlying the generation of climate models participating here (which are mostly similar to their CMIP5 configurations; Myhre et al., 2017a).

In the next section, we give a brief overview of data and methods used in this paper. Section 3 describes the main results of this study, starting with a comparison between the PDRMIP baseline DTR values and observations to show how the specific PDRMIP models capture regional DTR. The results are summarized in Sect. 4.

In the Precipitation Driver and Response Multimodel Intercomparison Project (PDRMIP), nine global climate models have performed idealized simulations of instantaneous perturbations in different climate drivers. Here, we analyze experiments involving a doubling of CO2 (denoted CO2× 2), a 10-fold increase in black carbon (denoted BC × 10) and a fivefold increase in sulfate (denoted SO4× 5) relative to a climatology consistent with year 2000 conditions. See Table 1 and (Myhre et al., 2017a; Samset et al., 2016; Stjern et al., 2017) for details and a list of models. The geographical distribution of the baseline BC and SO4 aerosol burden fields can be found in Fig. 1, which shows that India and eastern China are regions of particularly high current aerosol loading.

Using step perturbations rather than transient simulations means that climate responses will be different to those seen in the real world. The advantage is that signals more rapidly emerge from the noise of internal variability, provided that the forcing applied is of sufficient strength. In PDRMIP, the experiments were designed to produce such clear and robust climate signals. The experiments are, however, not identical in effective radiative forcing, which necessitates some normalization if the results are to be fully comparable. Here, we have chosen to divide climate responses (e.g., the DTR change) by the global annual mean temperature change for each driver and model. Our comparisons therefore show the response expected for a 1 ∘C surface warming due to perturbations in the given climate driver.

Model median global temperature change and model spread (given in the first parentheses) for the three drivers are 2.6 K (1.5 to 3.7 K) (CO2× 2), 0.7 K (0.2 to 1.7 K) (BC × 10) and -1.65 K (-0.9 to -6.6 K) (SO4×5) (see Samset et al., 2016, for core analysis of all PDRMIP experiments and models). For SO4, which cools the climate, normalization by a negative global mean temperature change switches the sign of the change and shows in principle the result of a reduced SO4 level as opposed to the other drivers. Note that even a 10-fold increase in BC yielded a weak impact on global temperatures (Stjern et al., 2017). This has the implication that normalization leads to particularly large normalized changes for the BC × 10 experiment. However, as seen by comparing absolute DTR changes for BC × 10 in Fig. S2 to those of CO2× 2 and SO4× 5 (Figs. S1 and S3), the absolute DTR change for BC × 10 is also large in itself: an annual mean model median DTR change of -0.03 K (compared to -0.05 K for CO2× 2) is substantial given than the doubling of CO2 causes a 4 times stronger response in the global mean temperature.

CO2 concentrations were prescribed in all models. For the aerosol perturbations, 4 of the 10 models perturbed concentrations, while the rest changed their emissions. This leads to some additional intermodel differences in forcing and response patterns. For instance, in concentration-driven simulations, climate dynamics (e.g., a change in precipitation and thus wet deposition) will not influence BC concentrations, while feedbacks between BC and other climate processes can operate in emission-driven simulations. However, a previous PDRMIP study found the difference between climate responses in emission-driven versus concentration-driven experiments to be highly model dependent (Stjern et al., 2017). At least for the BC × 10 simulations, two of the emission-driven models (CESM-CAM5 and MIROC-SPRINTARS) showed responses very similar to the concentration-driven models, while the two others (HadGEM2-ES and CanESM2) had slightly stronger responses that might be related to the nature of the experiment setup.

All the simulations were 100 years long. Data for the simulation year nos. 51–100 were used in the analyses, and changes were defined as the average of these years for a perturbed simulation minus the corresponding average for the baseline simulation. In a comparison between PDRMIP data and gridded observational data from the Climate Research Unit (CRU) TS v. 4.03 (Harris et al., 2014), we compare baseline PDRMIP values (averaged over simulation years nos. 51–100) to observational data averaged over years 1991–2010.

DTR was calculated based on daily minimum and maximum temperature values and averaged into monthly and seasonal means. To determine whether a given DTR change is significantly different from zero, regional mean monthly-mean DTR values over a 50-year period, for perturbed versus baseline climates, were tested for each model and experiment using a Student's t test (p<0.05). As the multimodel but single-realization simulations performed here will be sensitive to the timing of internal variability among model simulations, this will likely cause some of the intermodel differences. However, the model spread is not sensitive to the exact time period used. As a sensitivity test, we picked out 20-year periods from the 50 years of the baseline simulations, moving 5 years at a time (giving seven 20-year periods within the 50 years of data), and we found that intermodel standard deviations of DTR for these periods ranged between 2.555 and 2.564 K. While this indicates that model differences are more likely related to actual differences in model formulations and parameterizations, we note that internal variations in regional clouds and precipitation – which strongly influence DTR – can affect trends over periods up to 60 years (Deser et al., 2012), making it difficult to compare changes in DTR among both models and between models and observations.

We present results for all land regions aggregated (LND) as well as the populated high-aerosol-emission regions (present or previous) of the continental United States (USA), central Europe (EUR), India (IND) and eastern China (CHI). In addition, we study changes in the Arctic (ARC), which is a region known to be sensitive to remote emissions but where the mediating processes are not fully explored. As an example, potential drivers of regional impacts such as melt ponds and sea ice loss may depend on summertime Arctic DTR, which may in turn depend on diurnal variations in, for example, photochemical particle production or transport into the region (Deshpande and Kamra, 2014). Our main focus is, however, on the major aerosol emission regions.

This section presents the global, annual overland mean modeled DTR changes in response to the PDRMIP perturbations, as well as regionally and seasonally resolved results. As earlier work has demonstrated a tendency in CMIP5-generated models to underestimate DTR relative to observations, with a bias that differs strongly between models and regions (Lindvall and Svensson, 2015), we also compare the PDRMIP baseline DTR values to surface temperature observations.

We have analyzed a multimodel set of idealized simulations to investigate how changes to the atmospheric levels of CO2, BC and SO4 influence the diurnal temperature range, through alterations of global mean surface temperature, cloud cover and other climate parameters. For northern mid- and high-latitude regions, we find DTR changes that are broadly similar between drivers. The cause of the DTR change, as apparent from patterns of Tmin and Tmax changes, is not always the same for all drivers. However, the resulting change is consistently an increase in DTR in summer, in EUR, USA and ARC, and a decrease in winter. This similarity may partly be the result of general atmospheric response to changes in surface temperature rather than the distinct processes through which the drivers operate. Thus, while the strong DTR reductions over Europe have been linked to the massive mitigation effort of SO4 over the past decades, our similar responses of SO4 perturbations to perturbations of CO2 and BC indicate that this is not necessarily an aerosol-specific response.

Over India and China there is less agreement between drivers, with BC causing a strong DTR reduction in both regions in all seasons. The intermodel spread is large, but all models agree on the sign of this change. Although the strong shortwave atmospheric absorption induced by BC particles is predominantly active in daytime, thus impacting the maximum (daytime) temperature more than the minimum (nighttime) temperature, we find that the direct aerosol effect is likely not the leading cause of the DTR response. Rather, it is the strong cloud response to BC in these regions, shown in previous studies (Stjern et al., 2017) to result from aerosol-induced changes to atmospheric stability and relative humidity, that drive the response in DTR. All models have stronger correlations to cloud-related variables than to clear-sky radiative fluxes or changes in BC burden. Hence, the very high BC concentrations in this region have a strong influence on clouds and thus on DTR.

Although these high-emission regions seem to have driver-specific responses in the DTR, in some seasons, e.g., during autumn over India, CO2 and SO4 produce DTR changes of the same sign as BC, again indicating the existence of an underlying driver-independent DTR response tied to the general warming of the climate. This supports the work of Vinnarasi et al. (2017), who stressed that observed DTR changes over India are a result of both local and global factors working in tandem.

Disentangling the role of aerosols and greenhouse gases to DTR changes is a crucial step towards prediction of future changes in regional DTR. This is particularly true in regions such as India and East Asia (Vinnarasi et al., 2017), in which risk factors are aggravated by agriculture-dependent economies and dense populations and where future trends in aerosol emissions are highly uncertain but likely to be strong. Understanding how greenhouse gases, absorbing aerosols and scattering aerosols individually influence the DTR may help these regions prepare for future changes.