Divergent magnitude and distribution of temperature effects

Our simulations reveal substantial inequality in the global-mean temperature response to identical total annual aerosol emissions in each region (Fig. 1b, y-axis). The largest global mean temperature change (−0.29 ± 0.01 °C, induced in our simulations by emissions from Western Europe) is approximately 14 times larger than the smallest global mean temperature changes (−0.02 ± 0.01 °C induced by emissions from India). Global-mean temperature effects correlate roughly with latitude, with higher latitude emitting regions typically generating stronger temperature effects than lower latitude regions, a behavior also seen in previous studies of the response to aerosol forcing in different latitude bands19. However, substantial differentiation occurs within latitude bands. For example, emissions sourced from East Africa generate approximately four times more cooling (−0.06 ± 0.01 °C) than emissions from India (−0.02 ± 0.02 °C) at similar latitudes—demonstrating the importance of our region-by-region focus.

There are also important regional distinctions in the degree to which the temperature changes due to aerosol emissions are likely to be concentrated in the emitting region versus being felt globally (Fig. 1b, placement relative to diagonals). Because aerosols generally remain concentrated near their source and most strongly influence surface temperature by attenuating incoming solar radiation to the regions they overlie, they cool the emitting region more strongly than the global mean in all cases (Figs. 1b, 2). However, although Indian emissions produce the smallest global-mean cooling effects, their impacts are much more strongly concentrated within the emitting region than for other regions. India experiences cooling from its own emissions at a level ~21 times greater than the global mean (−0.42 ± 0.03 K versus −0.02 ± 0.01 K), while Western European, and Indonesian emissions generate localized cooling (−0.50 ± 0.06, and −0.14 ± 0.05 K, respectively) that is less than two times as great as the respective global-mean cooling (−0.29 ± 0.01, and −0.07 ± 0.01 K, respectively). As a result, even though Western European emissions produce ~14 times the global mean cooling effect of Indian emissions, India and Western Europe experience comparable regional-mean cooling from their own emissions. In other words, regions like Western Europe, Indonesia, and (to a slightly lesser extent) the United States strongly export the climate impacts of their emissions, while regions like India more strongly experience the cooling effects of their own emissions.

Fig. 2 Patterns of surface air temperature response to identical aerosol emissions from eight regions. Spatial patterns of surface air temperature change in response to identical aerosol emission in each the eight regions are shown. Global mean temperature change (K) with standard error is shown in bottom left. Grid markings indicate regions that are not statistically significant at the 95% confidence level via t-test Full size image

Role of of aerosol burden and radiative forcing differences

We find that differences in global-mean temperature response resulting from identical emissions emerge at each of several steps between initial emission and eventual temperature effect. Aerosol emissions impact temperature when their resulting atmospheric concentrations interact with atmospheric radiation, both directly and through aerosols’ rapid effects on cloud radiative properties and amount. These radiative interactions then modify the top-of-atmosphere energy balance, which creates a global-mean temperature change mediated by a series of feedback processes. Differences in the temperature change induced by each regional emission can emerge at each step in this process: from emission to atmospheric burden, from burden to top-of-atmosphere radiative forcing, and from top-of-atmosphere radiative forcing to temperature change.

The total atmospheric aerosol burdens generated by the identical regional emissions are spread between 128–233 Gg of sulfate aerosol, 9.29–15.7 Gg of black carbon aerosol, and 23.1–54.2 Gg of organic carbon aerosol (Supplementary Table 1; spatial distributions shown in Supplementary Figs. 1, 2, and 3, respectively). However, the atmospheric burdens of the individual aerosol species generated by each regional emission are largely uncorrelated with the regional emissions’ relative potency at changing global-mean temperature (Supplementary Fig. 4). Thus, the disparity in temperature effects does not arise solely from a disparity in the atmospheric lifetime and resulting atmospheric burden of aerosols emitted from each region, but also through the differential generation of climate forcing and feedbacks by those burdens.

How do the aerosol burdens from each region’s emissions translate into radiative forcing, which in turn drives the global mean temperature change? The radiative effects of sulfate (a global-mean cooling agent), black carbon (a global-mean warming agent), and organic carbon (a global-mean cooling agent, though with minor shortwave absorbing properties) will counteract each other in driving the radiative forcing. This cancellation can be accommodated by using an aggregate measure of aerosol optical properties, such as aerosol optical depth. In order to capture the cancellation between the absorbing and scattering aerosol burdens, we calculate this as the ratio between the change in global-mean total aerosol optical depth and global-mean absorbing aerosol optical depth caused by each regional emission.

The ratio of total to absorbing aerosol optical depth explains approximately 60% of the variance (R = 0.60, 0.44–0.69) in global-mean top-of-atmosphere effective radiative forcing (ERF) from each regional emission (Fig. 3a). We calculate the ERF as the top-of-atmosphere radiative imbalance induced by the regional emission after the atmosphere and land surface has been allowed to respond (see Methods). The variance in ERF is thus largely explained by the rate of global-mean cancellation between the absorbing and scattering aerosol burdens resulting from each regional emission, arising from differences in aerosol mixing rates, relative altitudes, and other microphysical and radiative factors30,31. The remaining variance may be explained by (1) the radiative environment in which the aerosol population is interacting, created by regional differences in climatological cloud cover or background aerosol; or (2) the particular cloud/convective environment in which the aerosols are placed and the influence this has on the manifestation of the aerosols’ indirect and semidirect effects on clouds32,33. We find that the ratio of total aerosol optical depth to absorbing aerosol optical depth is approximately as well correlated with clear-sky ERF (R = 0.58, 0.31–0.61) as it is with the total ERF, indicating that the presence or absence of climatological cloud does not substantially impact the translation of the atmospheric aerosol burdens into radiative forcing.

Fig. 3 Relationships between aerosol burden, radiative forcing, and temperature change. The ratio of aerosol optical depth produced by the atmospheric burden of sulfate, black carbon, and organic carbon to its absorbing component (AOD/AAOD) (a, y-axis) explains 60% of variance in effective radiative forcing (a, b, x-axis), which in turn has differing efficacy (b, diagonals) at producing global mean temperature changes (b, y-axis). Error bars capture the standard error Full size image

Drastic inequalities in forcing efficacy

Although the rate of global-mean cancellation between the absorbing and scattering aerosol burdens is somewhat correlated with the top-of-atmosphere radiative forcing generated by each emitting region, the relative top-of-atmosphere effective radiative forcings are not well correlated with the relative global mean temperature change (Fig. 3b), indicating a substantial divergence in forcing efficacy depending on emitting region. The forcing efficacy of each regional emission—i.e., the global-mean temperature change per unit top-of-atmosphere effective radiative forcing (captured in Fig. 4a and by placement relative to the diagonal lines in Fig. 3b)—range from 0.24 ± 0.14 K(Wm−2)−1 to 1.3 ± 0.06 K(Wm−2)−1. This constitutes a factor of five range in forcing efficacy between emitting regions, roughly twice the relative range in estimated forcing efficacy between global historical aerosols and greenhouse gases7,34. Emissions from the U.S. and Western Europe have the largest forcing efficacies (1.32 ± 0.06 and 1.09 ± 0.07 K(Wm−2)−1, respectively), producing outsized temperature responses for the effective radiative forcing they generate. Emissions from regions like Brazil, meanwhile, produce a comparable effective radiative forcing to emissions from Western Europe or the U.S., but generate much smaller global-mean temperature change (Fig. 3b). These efficacy differences highlight the shortcomings of using global-mean radiative forcing to estimate the climate effects of highly regionalized forcings.

Fig. 4 Radiative feedbacks versus forcing efficacy. Differences in forcing efficacy (a) are largely explained by variance in the global-mean radiative gain from surface albedo and cloud feedbacks (b)—i.e., the additional top-of-atmosphere flux change from the feedback due to a unit of effective radiative forcing—across emissions regions. Error bars capture the standard error Full size image

Differential excitement of feedbacks

Differing forcing efficacy is fundamentally driven by differences in how the particular spatiotemporal distribution of global- and annual-mean top-of-atmosphere radiative forcing excites feedback processes that contribute to eventual temperature change. This forcing-feedback framework can be represented mathematically as:

$${\mathrm{d}}F{\, \mathrm{ = }}\, \frac{{\partial R}}{{\partial T}}{\mathrm{d}}T + \mathop {\sum }\limits_i \frac{{\partial R}}{{\partial X_i}}\frac{{\partial X_i}}{{\partial T}}{\mathrm{d}}T$$

The climate system balances an initial radiative forcing (dF) through the radiative effects of a change in temperature (\(\frac{{\partial R}}{{\partial T}}{\mathrm{d}}T\)), which is either amplified or damped by the top-of-atmosphere radiative effects of temperature-sensitive changes in factors like surface albedo, clouds, and water vapor (\(\mathop {\sum }\limits_i \frac{{\partial R}}{{\partial X_i}}\frac{{\partial X_i}}{{\partial T}}{\mathrm{d}}T\)). The degree to which a given initial radiative forcing excites these feedbacks will determine the extent to which the temperature must change to achieve re-equilibration. The differing spatial distributions of the radiative forcing (Supplementary Fig. 5) versus the surface temperature responses (Fig. 2) demonstrates a role for these remote feedback processes in setting the temperature responses to each regional emission.

We find that the differences in forcing efficacy across emitting region can be largely explained by differences in the degree to which the top-of-atmosphere radiative forcing from each regional emission excites top-of-atmosphere surface albedo radiative feedbacks and cloud radiative feedbacks. For each feedback process, we characterize its relative rate of excitement by a given region’s emissions as the radiative gain: the ratio of radiative perturbation from the feedback (\(\frac{{\partial R}}{{\partial X_i}}\frac{{\partial X_i}}{{\partial T}}{\mathrm{d}}T\)) to the initial radiative forcing generated by the emission (dF). Water vapor feedbacks show relatively small and uncorrelated differences in radiative gain across emitting region (Supplementary Fig. 6). However, surface albedo feedbacks—driven primarily by sea ice changes—and cloud feedbacks vary in correspondence with the differences in efficacy (Fig. 4 and Supplementary Fig. 6). The regional differences in the combined radiative gain from the cloud and surface albedo feedbacks (Fig. 4b) explains 84% (R = 0.84, 0.71–0.91) of the variance in efficacy across regional emissions (Fig. 4a).

The differences in radiative gain from surface albedo feedbacks and the associated forcing efficacies sort roughly by latitude of emissions. The surface albedo feedback is dominantly driven by sea ice changes (Supplementary Fig. 5) in both the Arctic and Antarctic, and manifests in increased spatial extent and temporal duration of the sea ice. Forcing from Western European and Chinese emissions strongly increases sea ice in both the Arctic and Antarctic, inducing a strong surface albedo feedback radiative gain, while forcing induced by Indian and Brazilian emissions has relatively little effect. The sea ice responses are not strongly spatially collocated with the respective radiative forcings (Supplementary Fig. 5) or atmospheric aerosol burdens (Supplementary Figs. 1, 2, and 3), indicating that the polar effects primarily result from changes in atmospheric circulations that control the energy balance of and/or sea ice dynamics in the polar regions, rather than in situ forcing or aerosol deposition onto the ice. This aligns well with previous studies that show strong dependence of sea-ice albedo feedbacks on the meridional placement of forcings34,35.

The inter-regional differences in radiative gain associated with cloud feedbacks also help to explain the inter-regional differences in forcing efficacy, and are partially driven by the climatological cloud environment with which each region’s aerosol emissions interact. Aerosols emitted in India, Indonesia, and Brazil produce large localized cloud changes (Supplementary Fig. 7), associated with the dynamical and thermodynamical effects of the localized aerosol forcing acting on the strongly convective cloud environment in these regions36,37,38,39. The cloud changes in response to all regional emissions are primarily dynamical or thermodynamical—rather than microphysical—in nature, as aerosol indirect effects (captured by the change in cloud droplet number concentration, Supplementary Fig. 7) are locally confined and relatively uncorrelated with the maxima in cloud change. India’s strong cloud feedback gain and weak surface albedo feedback gain counteract each other in setting the relative overall efficacy of the radiative forcing from Indian emissions.

In our simulations, the cloud feedbacks generated in several regions largely manifest through a north–south shift in tropical cloud cover (Supplementary Fig. 7) associated with the intertropical convergence zone (ITCZ). This meridional ITCZ shift results from the large-scale atmospheric circulation adjusting to compensate for the hemispheric radiative imbalance induced by the localized aerosol forcing and its climate effects40,41,42,43. This is likely amplified by the surface albedo feedback to each regional emission, which will generate its own hemispheric energy imbalance44. Even in the presence of Southern Hemispheric emissions, Arctic sea ice increases more strongly than Antarctic sea ice in all cases (Supplementary Fig. 8). This is likely attributable to the stronger overall regional climate sensitivity of the Arctic relative to the Antarctic45,46,47. This common hemispheric imbalance in sea ice response contributes to the comparable ITCZ shifts seen in response to many of the regional aerosol emissions.