Abstract

Spatial patterns of climate response to changes in anthropogenic aerosols and well-mixed greenhouse gases (GHGs) are investigated using climate model simulations for the twentieth century. The climate response shows both similarities and differences in spatial pattern between aerosol and GHG runs. Common climate response between aerosol and GHG runs tends to be symmetric about the equator. This work focuses on the distinctive patterns that are unique to the anthropogenic aerosol forcing. The tropospheric cooling induced by anthropogenic aerosols is locally enhanced in the midlatitude Northern Hemisphere with a deep vertical structure around 40°N, anchoring a westerly acceleration in thermal wind balance. The aerosol-induced negative radiative forcing in the Northern Hemisphere requires a cross-equatorial Hadley circulation to compensate interhemispheric energy imbalance in the atmosphere. Associated with a southward shift of the intertropical convergence zone, this interhemispheric asymmetric mode is unique to aerosol forcing and absent in GHG runs. Comparison of key climate response pattern indices indicates that the aerosol forcing dominates the interhemispheric asymmetric climate response in historical all-forcing simulations, as well as regional precipitation change such as the drying trend over the East Asian monsoon region. While GHG forcing dominates global mean surface temperature change, its effect is on par with and often opposes the aerosol effect on precipitation, making it difficult to detect anthropogenic change in rainfall from historical observations.

1. Introduction

Over the past century, rapid industrialization has resulted in a dramatic increase in emissions of anthropogenic aerosols and greenhouse gases (GHGs). Both the global aerosol optical depth (AOD) and CO2 (a key GHG) mass show a rapid increase after the 1950s due to postwar economic development (Fig. 1a). After the 1990s, the aerosol increase gradually levels off as a result of the clean air acts, while GHGs continue their fast increase.

Fig. 1.

(a) Global mean total atmospheric mass of CO2 (red line; 1015 kg) and global mean AOD (ambient aerosol optical thickness at 550 nm, blue line; 1 × 10−1) from 1900 to 1999; (b) global trend of AOD (1 × 10−1) from 1900 to 1999. Data are from GFDL CM3.

Fig. 1.

(a) Global mean total atmospheric mass of CO2 (red line; 1015 kg) and global mean AOD (ambient aerosol optical thickness at 550 nm, blue line; 1 × 10−1) from 1900 to 1999; (b) global trend of AOD (1 × 10−1) from 1900 to 1999. Data are from GFDL CM3.

Anthropogenic change in aerosols is a major driver of twentieth-century climate change (Kaufman et al. 2002; Bao et al. 2009; Lamarque et al. 2010; Donner et al. 2011; Boucher et al. 2013), masking a considerable fraction of the GHG-induced warming since the industrial revolution (Hegerl et al. 2007; Boucher et al. 2013). While the direct fast atmospheric circulation response to radiative forcing contributes to regional precipitation (Bony et al. 2013), patterns of sea surface temperature (SST) change under global warming dominate the rainfall change (Chadwick et al. 2014) following the “warmer get wetter” mechanism (Xie et al. 2010). Climate responses to radiative forcing consist of both fast and slow components. The fast response to aerosol forcing relates to atmospheric change without SST mediation and consists of direct and indirect effects. Aerosols can absorb, reflect, and scatter radiation to change the energy budget of the climate system (direct effect; Menon et al. 2002; Bollasina et al. 2011). Furthermore, aerosol particles such as sulfate serve as cloud condensation nuclei or ice nuclei and interact with clouds to influence cloud albedo and lifetime as well as precipitation efficiency (indirect effect; Penner et al. 2001; Rotstayn and Lohmann 2002; Lau et al. 2006; Rosenfeld et al. 2008; Levy et al. 2013). The slow response to aerosol forcing relates to the SST mediation effect and dominates the long-term large-scale patterns through coupled ocean–atmospheric processes (Xie et al. 2013; Xu and Xie 2015).

Unlike well-mixed GHGs, the anthropogenic aerosols are geographically distributed (Fig. 1b) because of localized emission sources and the short atmospheric residence time. Over the twentieth century, changes in anthropogenic aerosols are mostly concentrated over Asia, Africa, and to a lesser extent over Europe and the Americas. Overall, large anthropogenic aerosol increases are located in the Northern Hemisphere (NH).

The spatially distributed anthropogenic aerosols can cause changes in large-scale atmospheric circulation and regional climate change especially where the aerosol concentrations are high (Shindell et al. 2013; Xie et al. 2015)—for example, in eastern Asia (Menon et al. 2002; Lau et al. 2006; Bollasina et al. 2011; Ganguly et al. 2012a,b; Wu et al. 2013; Song et al. 2014). Phase 5 of the Coupled Model Intercomparison Project (CMIP5) historical anthropogenic aerosol single-forcing and all anthropogenic and natural forcing (all forcing) experiments show that over the East Asian (EA) summer monsoon region, aerosol forcing dominates over the GHG effect on rainfall change during the twentieth century, leading to a general drying trend in the historical all-forcing simulations (Li et al. 2015). The EA monsoon rainfall response is mostly due to the aerosol-induced change in monsoon circulation (Song et al. 2014; Li et al. 2015). Another striking regional manifestation of the aerosol effect is the sustained decline in Sahel rainfall during 1950–80. Research has linked the Sahel droughts prior to the 1980s with the atmospheric circulation and tropical Atlantic SST changes induced by anthropogenic aerosols (Held et al. 2005; Biasutti and Giannini 2006; Chang et al. 2011; Ackerley et al. 2011; Booth et al. 2012; Dong and Sutton 2015).

Aerosol direct and indirect effects decrease the downward solar radiation and create an interhemispheric radiative imbalance at the top of the atmosphere. The aerosol-induced cooling in the midlatitude NH spreads into the tropics by baroclinic eddies and further induces an anomalous cross-equatorial Hadley circulation to balance the cross-equatorial energy transport between the ocean and atmosphere (Kang et al. 2008, 2014). This weakens (enhances) atmospheric convection north (south) of the equator and gives rise to a southward shift of the ITCZ (Chang et al. 2011; Ming and Ramaswamy 2011; Chiang and Friedman 2012; Hwang et al. 2013; Wilcox et al. 2013; Xu and Xie 2015; Allen et al. 2015; Wang et al. 2016).

In climate models, the aerosol-induced cooling in the midlatitude NH strengthens the meridional temperature gradient on the equatorward side of the westerly jet and weakens it on the poleward side (Xu and Xie 2015). In the extratropical Southern Hemisphere (SH), the annual mean surface westerly winds are weakened in the Southern Ocean even though the local aerosol forcing is small (Collier et al. 2013; Xie et al. 2013), possibly as a result of the effect of the interhemispheric anomalous Hadley circulation (Ceppi et al. 2013). The aerosol-induced weakening and equatorward shift of the eddy-driven jet in the SH resemble the inverse of the GHG effects (Fyfe et al. 1999; Kushner et al. 2001).

Although the anthropogenic aerosol and GHG forcings have different spatial distributions, they induce similar spatial patterns of climate change in SST and precipitation with the reversed sign (Xie et al. 2013), especially in the tropical oceans. This suggests a global ocean–atmosphere mode common to radiatively induced climate change and relatively insensitive to forcing distribution. This global radiative-induced climate change mode arises because a common set of ocean–atmosphere feedbacks imprint characteristic patterns.

The present study extends Xie et al. (2013) by investigating the sensitivity of regional climate patterns to different forcings due to anthropogenic aerosols and GHGs during the twentieth century. Like Xie et al. (2013), we use historical anthropogenic aerosol or GHG single forcing and all anthropogenic and natural forcing (all forcing) simulations from CMIP5. Unlike Xie et al. (2013), the present study focuses on climate response patterns that are distinct between aerosol and GHG runs. By comparing the single-forcing simulations, we diagnose the climate effects of anthropogenic aerosols and GHGs separately and evaluate their relative contributions in the historical all-forcing simulations. The focus of this study is on annually averaged patterns of SST, rainfall, atmospheric temperature, and circulation. We aim to identify the broad, large-scale response patterns that are common among climate models.

The rest of the paper is organized as follows. Section 2 describes the model simulations and methods used in this study. Section 3 discusses the principal climate response patterns under different forcing simulations. Section 4 investigates the rainfall pattern variations in response to anthropogenic aerosols. Section 5 develops pattern indices to quantify distinct patterns of climate response to external forcing distributions. Section 6 is a summary with discussion.

2. Models and methods

This study uses a multimodel ensemble of simulations from the WCRP CMIP5 archive (Taylor et al. 2012). To eliminate the internal variability, we first obtain the multimember average for each model and then construct the multimodel ensemble mean. We chose eight models that performed the following three experiments: historical single-forcing (anthropogenic aerosol or well-mixed GHGs only) simulations and historical simulations with all anthropogenic and natural forcings. For the historical single-forcing simulations, the anthropogenic aerosols or the well-mixed GHGs are the only time-varying forcing agent with the other forcing fixed at the preindustrial level. This yields an estimate of the contribution of anthropogenic aerosol– (mainly consisting of black carbon and sulfate) or GHG-induced (mainly consisting of CO2) radiative forcing to recent climate change. The emissions are the same in all models. All the models consider the direct aerosol radiative effect. Some models include part of the aerosol indirect effects (e.g., GISS models only include the first aerosol indirect effect, which is known as cloud-albedo effect), and some do not include the aerosol indirect effects at all (e.g., IPSL model). Therefore, the uncertainties in aerosol forcing are considerable. Details of the forcing agents used in the anthropogenic aerosol–forcing simulations are described by Collins et al. (2013). The historical all-forcing simulation imposes both the anthropogenic (e.g., GHG, sulfate and black carbon aerosol, and ozone) and the natural (e.g., solar irradiance and volcanic aerosols) forcings to replicate climate change during 1850–2005. Comparisons of the single-forcing and all-forcing simulations help quantify the relative contribution of an individual radiative forcing to the twentieth-century climate change. Details of the CMIP5 models used in this study are listed in Table 1.

Table 1.

List of CMIP5 models and the number of realizations used in this study. (Expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList.)

List of CMIP5 models and the number of realizations used in this study. (Expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList.)
List of CMIP5 models and the number of realizations used in this study. (Expansions of acronyms are available online at http://www.ametsoc.org/PubsAcronymList.)

The climatology is defined as the 1900–99 average. Annual anomalies are calculated, and an 11-yr running average is used for analysis. We apply a joint empirical orthogonal function (EOF) analysis based on the 1900–99 period on the multimodel ensemble mean fields to derive the principal components and climate response patterns under different forcing agents. The joint EOF (also known as combined EOF) is designed to investigate the covariability of two or more fields (Deser and Blackmon 1993). In joint EOF analysis, the data matrix is constructed with vectors of two or more variables concatenated one after another. As in the conventional EOF analysis, we solve the eigenproblem on the normalized concatenated matrix. The eigenvalues of the new matrix measure the fraction of the covariance among the input fields. Therefore, the fraction of variance explained applies to all the variables jointly. The spatial patterns are then computed by projecting each original field onto the eigenvector. We normalize the spatial patterns of the joint EOF results by the tropical mean SST (25°S–25°N) change to highlight the relative changes of atmospheric circulation and precipitation in the leading EOF mode. Climate change in the twentieth century is expressed using Sen’s trend (Sen 1968) to eliminate the effect of extreme points. Trends in each member are normalized by its tropical mean (25°S–25°N) SST change before calculating the ensemble mean results. A Student’s t test is used to estimate the statistical significance of the ensemble of the normalized trend among different models to check the intermodel consistency. The null hypothesis is no trend. The test evaluates if the change is significant in magnitude among different models. To facilitate comparison, we interpolate all model outputs onto a common grid of 128 (zonal) × 64 (meridional) grid points (about 2.8° in the horizontal) and 22 vertical layers.

3. Principal climate response patterns

a. Surface response

We apply the joint EOF analysis to SST, precipitation, and surface wind anomalies, separately for anthropogenic aerosol and GHG single-forcing runs (Fig. 2). All the variables are normalized by the tropical mean SST change for easy comparison, and the tropical mean is removed from the SST mode. For the single-forcing results, the leading EOF mode explains about 74% in aerosol and 92% in GHG runs of the total variance. In the tropics, rainfall increases where the SST change exceeds the tropical mean in GHG runs. The collocated equatorial peak in relative SST and rainfall change is an example (Fig. 2b). A similar response pattern with intensified cooling SST and corresponding rainfall decrease in the equatorial Pacific can be seen in aerosol-forcing runs (Fig. 2a). In the southeastern Pacific, reduced cooling (warming) in aerosol (GHG) forcing is associated with the weakened (intensified) southeasterly trade winds, following the wind–evaporation–SST (WES) feedback (Xie 1996). In the SH, surface westerly winds weakened (intensified) in the Southern Ocean in response to aerosol (GHG) forcing. The regional patterns of SST response in the deep tropics, North Atlantic, and Southern Ocean along with the equatorial precipitation change and the SH westerly wind change are similar between aerosol and GHG runs. The precipitation response is mediated by SST change following the warmer-get-wetter pattern (Xie et al. 2010). Similarities in response patterns between aerosol and GHG forcing indicate that the radiative-induced global climate change mode is not very sensitive to external forcing distribution (Xie et al. 2013).

Fig. 2.

Leading EOF modes in (a) anthropogenic aerosol and (b) GHG runs: precipitation (shadings; mm day−1) for SST (contours at 0.2°C interval, with 0°C contour omitted; red denotes positive, and blue denotes negative values) and surface wind (vectors, with scale at top right; wind speed < 0.3 m s−1 omitted). The tropical mean (25°S–25°N) has been removed from the SST modes. All variables are normalized by the tropical mean (25°S–25°N) SST change. (c) The PC1s normalized by the respective standard deviation.

Fig. 2.

Leading EOF modes in (a) anthropogenic aerosol and (b) GHG runs: precipitation (shadings; mm day−1) for SST (contours at 0.2°C interval, with 0°C contour omitted; red denotes positive, and blue denotes negative values) and surface wind (vectors, with scale at top right; wind speed < 0.3 m s−1 omitted). The tropical mean (25°S–25°N) has been removed from the SST modes. All variables are normalized by the tropical mean (25°S–25°N) SST change. (c) The PC1s normalized by the respective standard deviation.

Aside from these similarities, Xie et al. (2013) also indicates that there are apparent differences in regional climate response patterns between aerosol and GHG runs. In the NH, there is an enhanced SST cooling and intensified Aleutian low (Ming et al. 2011; Wang et al. 2013) in the midlatitude North Pacific in aerosol runs, while the SST response is much weaker in GHG runs. This difference is linked to the distinct atmospheric circulation response under different forcings (Ming et al. 2011). Notable differences are also found in the rainfall change. Over the ocean, the common precipitation change to aerosol and GHG runs is mediated by SST change according to the warmer-get-wetter mechanism. In Xie et al. (2013), the pattern correlation of rainfall response between aerosol and GHG runs is 0.67 over ocean, suggesting the similarity induced by radiative-induced climate change mode. However, when we expand the analysis of rainfall over land, the spatial correlation of the global precipitation response in Fig. 2 reduces to 0.42 between aerosol and GHG runs. The precipitation change over land between aerosol and GHG runs is correlated in space only at 0.35, indicating distinct climate response patterns between the two different forcing types. Over the EA (5°–45°N, 90°–130°E) and the African Sahel (10°S–10°N, 0°–40°E), rainfall decrease is larger in aerosol than the weak increase in GHG runs (Figs. 2a,b). The significant drying trend over EA is due to the atmospheric circulation change induced by the aerosol forcing (Song et al. 2014). By evaluating the Asian summer monsoon change, Li et al. (2015) further show that aerosol forcing dominates the drying trend over the Asian summer monsoon region in the twentieth century by regulating the dynamic change in moisture convergence.

We compare the leading joint EOF pattern between the single-forcing and all-forcing simulations. The leading joint EOF mode explains a high percentage (76%) of the total variance in the all-forcing runs (Fig. 3a). The sum of the results from the two single-forcing runs correlates with the historical all-forcing simulations at 0.68 and 0.57 for precipitation and SST changes, respectively. In the tropical Pacific, the equatorial peak of the SST and rainfall increase largely follows the pattern of GHG runs (Fig. 2b). The SST pattern in historical all-forcing simulations also shows a cross-equatorial gradient, accompanied by a rainfall decrease (increase) north (south) of the equator and the intensification of the South Pacific convergence zone, changes that are in line with the aerosol-induced response (Fig. 2a). The southward shift of the tropical rain belt is associated with southward cross-equatorial surface wind. On land, rainfall decreases over the Asian summer monsoon and the African Sahel in all-forcing runs (Fig. 3a). Generally, the historical all-forcing simulations feature an interhemispheric asymmetric pattern in climate response as a result of the radiative imbalance induced by anthropogenic aerosols.

Fig. 3.

Leading EOF modes in historical all-forcing simulations: (a) precipitation (shadings; mm day−1) for SST (contours at 0.2°C interval, with 0°C omitted; red denotes positive, and blue denotes negative values) and surface wind (vectors, with scale at top right; wind speed < 0.3 m s−1 omitted). The tropical mean (25°S–25°N) has been removed from the SST modes. (b) Zonal mean air temperature (shadings; °C), meridional streamfunction (white contours at 2 × 109 kg s−1 interval, with 0 kg s−1 contour omitted; positive indicates clockwise circulation), and zonal wind speed (black contours at 0.3 m s−1 interval, with 0 m s−1 contour omitted; positive indicates westerly). All variables are normalized by the tropical mean (25°S–25°N) SST change. (c) The PC1s normalized by the respective standard deviation.

Fig. 3.

Leading EOF modes in historical all-forcing simulations: (a) precipitation (shadings; mm day−1) for SST (contours at 0.2°C interval, with 0°C omitted; red denotes positive, and blue denotes negative values) and surface wind (vectors, with scale at top right; wind speed < 0.3 m s−1 omitted). The tropical mean (25°S–25°N) has been removed from the SST modes. (b) Zonal mean air temperature (shadings; °C), meridional streamfunction (white contours at 2 × 109 kg s−1 interval, with 0 kg s−1 contour omitted; positive indicates clockwise circulation), and zonal wind speed (black contours at 0.3 m s−1 interval, with 0 m s−1 contour omitted; positive indicates westerly). All variables are normalized by the tropical mean (25°S–25°N) SST change. (c) The PC1s normalized by the respective standard deviation.

The leading principal components (PC1s; Figs. 2c and 3c) track the evolution of aerosol and GHG concentrations (Fig. 1a) very well, capturing the physical effect of both the aerosol and GHG-induced climate changes with a pronounced trend in single and all-forcing simulations. In aerosol runs, PC1 is flat before the 1950s but shows a sharp rise afterward with the postwar economic growth and then levels off in the 1990s resulting from air pollution control. The GHG-induced PC1 features a robust rising trend after the 1950s. For the all-forcing simulations, PC1 represents the response to both the anthropogenic aerosol and the GHG forcing and shows a sharp increase after the 1970s with the rapid increase of GHG.

b. Atmospheric circulation response

This section examines how the zonal mean atmospheric circulation responds to external radiative forcing. Figure 4 compares the leading joint EOF modes of zonal mean tropospheric (925–100 hPa) temperature, zonal wind, and meridional streamfunction in the anthropogenic aerosol– and GHG-forcing runs. The PC1s (Fig. 4c) all show an obvious increase trend after the 1950s, with the PC for the aerosol forcing leveling off after the 1990s.

Fig. 4.

Leading EOF modes in (a) anthropogenic aerosol and (b) GHG runs for zonal mean air temperature (shadings; °C), zonal mean meridional streamfunction (white contours at 2 × 109 kg s−1 interval, with 0 kg s−1 contour omitted; positive indicates clockwise circulation), and zonal wind speed (black contours at 0.3 m s−1 interval, with 0 m s−1 contour omitted; positive indicates westerly). All variables are normalized by the tropical mean (25°S–25°N) SST change. (c) The PC1s normalized by the respective standard deviation.

Fig. 4.

Leading EOF modes in (a) anthropogenic aerosol and (b) GHG runs for zonal mean air temperature (shadings; °C), zonal mean meridional streamfunction (white contours at 2 × 109 kg s−1 interval, with 0 kg s−1 contour omitted; positive indicates clockwise circulation), and zonal wind speed (black contours at 0.3 m s−1 interval, with 0 m s−1 contour omitted; positive indicates westerly). All variables are normalized by the tropical mean (25°S–25°N) SST change. (c) The PC1s normalized by the respective standard deviation.

The leading joint EOF mode explains 89% and 93% of the total variance in aerosol and GHG runs, respectively. The response patterns (Figs. 4a,b) show similarities between two single-forcing runs within the tropics, as pointed out by Xie et al. (2013). The aerosol-induced upward amplified cooling in the tropics bears some resemblance to the effect of increasing GHG with sign reversed. This equatorial symmetric pattern is insensitive to forcing location and is flattened by fast equatorial waves (Sobel et al. 2002). The corresponding upper-tropospheric westerly deceleration (acceleration) in the tropics in aerosol (GHG) forcing simulations is another response pattern similar between aerosol and GHG changes. Unlike in the tropics, the aerosol-induced response shows a pronounced interhemispheric asymmetry with a deep temperature decrease around 40°N (Fig. 4a), which is different from the GHG-induced interhemispheric symmetric response pattern. Xu and Xie (2015) shows that the deep cooling structure is due to the slow response of the ocean. This is related to the aforementioned results that the North Pacific is notably sensitive to the forcing type (Ming et al. 2011; Wang et al. 2013; Xie et al. 2013), with a much larger response in aerosol than in GHG runs (Figs. 2a,b and 3a). Large meridional SST gradient induced by aerosols drives an anomalous interhemispheric Hadley circulation in the deep tropics (white contours in Fig. 4a) and further induces atmospheric eddy adjustments in the NH midlatitudes. Through the thermal wind relation, the NH deep cooling anchors a westerly acceleration to its south (around 30°N). In addition, at the poleward terminus of the anomalous Hadley cell, the angular momentum advection accelerates (decelerates) the subtropical jet at 300 hPa in the NH (SH) in response to the NH midlatitude cooling (Ceppi et al. 2013). These interhemispheric asymmetric response patterns are unique to aerosol forcing and absent in the GHG-forced response.

In the all-forcing simulation (Fig. 3b), the leading EOF pattern (91% explained variance) features a reduced warming structure around 40°N in the middle and lower troposphere, a westerly acceleration (20°–40°N; 500–200 hPa), and a strong interhemispheric anomalous Hadley circulation in the tropics. All these are the signatures of aerosol effects. In the SH, where aerosol forcing is weak, the response patterns are dominated by the GHG forcing with a dipolar change in the westerlies: an increase at 60°S and decrease at 40°S. In addition, other forcing agents in the historical all-forcing simulations such as the ozone forcing may also significantly regulate the SH mid- and high-latitude circulation changes (Polvani et al. 2011).

4. Distinctive patterns induced by anthropogenic aerosols

a. Interhemispheric asymmetry

The temperature warming in the historical all-forcing simulations is dominated by GHG instead of aerosol forcing. The tropical SST increase induced by GHG is nearly twice as large as the aerosol-induced cooling (0.77° vs −0.42°C). Since the GHG radiative forcing is relatively uniform spatially, it masks a considerable fraction of the aerosol-induced cooling and makes it difficult to detect the climate response patterns induced by the inhomogeneous aerosol forcing. To isolate the pattern difference between the aerosol- and GHG-induced response, we take the GHG-forced climate response as the reference and represent the difference of the aerosol-induced response from the reference:

 
formula

where the asterisk denotes the ensemble-mean trend normalized by tropical mean SST change.

Figure 5a shows the zonal mean Rdiff for temperature and circulation. In the NH, the aerosol-induced cooling far outweighs the GHG-induced warming from the surface to the upper troposphere around 40°N. In the SH, by comparison, the GHG effect dominates over the aerosol effect for temperature change. In thermal wind balance with meridional temperature gradient, the zonal wind trend shows a westerly (black contours in Fig. 5a) acceleration centered at 25°N, 200 hPa south of the deep cooling structure. The aerosol-induced interhemispheric thermal contrast also gives rise to a clockwise Hadley circulation (gray contours in Fig. 5a) across the equator and spans the deep tropic (20°S–20°N). The associated anomalous circulation indicates a southward shift of the ITCZ and a corresponding surface northerly across the equator.

Fig. 5.

Difference in climate response pattern between aerosol- and GHG-forcing runs [see Eq. (1)]. (a) Zonal mean air temperature [shadings; °C (100 yr)−1], zonal wind [black contours at 0.2 m s−1 (100 yr)−1 interval, with 0 m s−1 (100 yr)−1 contour omitted; positive indicates westerly] and zonal mean meridional streamfunction [gray contours at 1 × 109 kg s−1 (100 yr)−1 interval, with 0 kg s−1 (100 yr)−1 contour omitted; positive indicates clockwise circulation]; and (b) 500-hPa air temperature [shadings; °C (100 yr)−1] and zonal wind [black contours at 0.3 m s−1 (100 yr) interval, with 0 m s−1 (100 yr) contour omitted]. Stippling indicates regions exceeding 95% statistical confidence among ensemble members for zonal mean and 500-hPa air temperature in (a) and (b), respectively.

Fig. 5.

Difference in climate response pattern between aerosol- and GHG-forcing runs [see Eq. (1)]. (a) Zonal mean air temperature [shadings; °C (100 yr)−1], zonal wind [black contours at 0.2 m s−1 (100 yr)−1 interval, with 0 m s−1 (100 yr)−1 contour omitted; positive indicates westerly] and zonal mean meridional streamfunction [gray contours at 1 × 109 kg s−1 (100 yr)−1 interval, with 0 kg s−1 (100 yr)−1 contour omitted; positive indicates clockwise circulation]; and (b) 500-hPa air temperature [shadings; °C (100 yr)−1] and zonal wind [black contours at 0.3 m s−1 (100 yr) interval, with 0 m s−1 (100 yr) contour omitted]. Stippling indicates regions exceeding 95% statistical confidence among ensemble members for zonal mean and 500-hPa air temperature in (a) and (b), respectively.

In the midtroposphere (500 hPa), we can distinguish the cooling band over the NH induced by aerosol forcing, while the SH temperature is warmed by GHG (Fig. 5b). Corresponding to the temperature cooling band near 40°N, the zonal wind speed decreases to its north and increases to its south, generating the southward shift of the EA westerly jet. In the single-forcing runs, the jet intensity index (defined as 500-hPa zonal wind speed averaged over 30°–35°N, 120°E–180°) shows a much larger response in aerosol [0.14 m s−1 (100 yr)−1] than that in GHG runs [0.01 m s−1 (100 yr) −1]. The change of the subtropical jet in the Southern Ocean is regulated by the GHG-induced warming. The interhemispheric asymmetric response pattern is robust among models (stippled temperature changes in Fig. 5).

In the Rdiff field, the zonal-mean surface air temperature (SAT) change follows the aerosol-induced interhemispheric thermal gradient with cooling in the NH midlatitudes and relative warming in the SH (Fig. 6). Negative radiative forcing induced by aerosols in the NH requires an anomalous interhemispheric Hadley circulation (Fig. 5a) to compensate the interhemispheric energy imbalance (Chiang and Friedman 2012; Frierson and Hwang 2012; Hwang et al. 2013). This energy theory applies only to the zonal mean. The surface manifestation of this interhemispheric Hadley circulation includes the southward cross-equatorial flow (Wang et al. 2016), accompanied by a southward shift of the tropical rain belt and a consistent anomalous meridional SST gradient as shown in Fig. 6 (Rotstayn and Lohmann 2002; Yoshimori and Broccoli 2008; Ming and Ramaswamy 2011). In observations, this global zonal-mean mode follows the evolution of global aerosol emissions, which is distinct from the regional change in the Atlantic sector (Wang et al. 2016).

Fig. 6.

Difference in climate response pattern between aerosol- and GHG-forcing runs [see Eq. (1)]. SST [red; °C (100 yr)−1], SAT [black; °C (100 yr)−1], and precipitation [green; mm day−1 (100 yr)−1]. Rainfall trends are multiplied by a factor of 4.

Fig. 6.

Difference in climate response pattern between aerosol- and GHG-forcing runs [see Eq. (1)]. SST [red; °C (100 yr)−1], SAT [black; °C (100 yr)−1], and precipitation [green; mm day−1 (100 yr)−1]. Rainfall trends are multiplied by a factor of 4.

Spatial patterns of Rdiff highlight the distinct regional climate response patterns induced by aerosol and GHG. Within the tropics, Rdiff is weak for SST and SAT and lacks intermodel agreement compared with the midlatitude response (Fig. 7). In the extratropics, Rdiff displays a strong north–south asymmetry. GHGs warm the SH more than aerosols cool, and this interhemispheric asymmetry triggers an ocean–atmosphere coupled response, including the meridional displacement of the ITCZ with rainfall decreasing (increasing) north (south) of the equator. Another pattern difference between aerosol and GHG is the reduced cooling (warming) over the southeastern Pacific in aerosol (GHG) runs. There, the Rdiff fields show a coupled pattern of a warm tongue, weakened trade winds, and the corresponding rainfall increase (Fig. 7), as the mean condition is favorable for WES feedback (Zhang et al. 2014). In addition, there are regional climate response patterns unique to anthropogenic aerosol forcing, including the drying trend over southern and eastern Asia (Li et al. 2015). In addition, the sustained decline in Amazonian and African Sahel rainfall during the latter half of the twentieth century is due to the increased anthropogenic aerosol emissions from North America and Europe (Held et al. 2005; Biasutti and Giannini 2006; Chang et al. 2011; Ackerley et al. 2011; Booth et al. 2012; Dong and Sutton 2015).

Fig. 7.

Difference in climate response pattern between aerosol- and GHG-forcing runs [see Eq. (1)]. (a) SST [contours at 0.3°C (100 yr)−1 interval, with 0°C (100 yr)−1 contour omitted; red denotes positive, and blue denotes negative values] and precipitation [shadings; mm day−1 (100 yr)−1]. (b) SAT [shadings; °C (100yr)−1], surface scalar wind [contours at 0.1 m s−1 (100 yr)−1 interval, with 0 m s−1 (100 yr)−1 contour omitted; red denotes positive, and blue denotes negative values], and surface wind [vectors, with scale at top right; wind speed < 0.2 m s−1 (100 yr)−1 omitted]. Stippling indicates regions exceeding 95% statistical confidence among ensemble members for SST and SAT in (a) and (b), respectively.

Fig. 7.

Difference in climate response pattern between aerosol- and GHG-forcing runs [see Eq. (1)]. (a) SST [contours at 0.3°C (100 yr)−1 interval, with 0°C (100 yr)−1 contour omitted; red denotes positive, and blue denotes negative values] and precipitation [shadings; mm day−1 (100 yr)−1]. (b) SAT [shadings; °C (100yr)−1], surface scalar wind [contours at 0.1 m s−1 (100 yr)−1 interval, with 0 m s−1 (100 yr)−1 contour omitted; red denotes positive, and blue denotes negative values], and surface wind [vectors, with scale at top right; wind speed < 0.2 m s−1 (100 yr)−1 omitted]. Stippling indicates regions exceeding 95% statistical confidence among ensemble members for SST and SAT in (a) and (b), respectively.

b. Rainfall pattern variation

To measure the magnitude of the precipitation response, we define the efficiency (EF) of radiative forcing–induced (aerosol or GHG) spatial rainfall change as follows:

 
formula

where σxyP) denotes the spatial standard deviation of rainfall change over the globe, and GMT and TMT indicate the global mean and tropical mean surface air temperature change, respectively. The EF ratio between aerosol and GHG is about 2:1 (Table 2), which indicates that the aerosols are twice as effective in inducing pattern variation of precipitation change as the GHGs per unit temperature change.

Table 2.

Spatial standard deviation of precipitation change per degree Celsius global mean and tropical (25°S–25°N) mean surface air temperature change. The EF ratio between aerosol and GHG is in parentheses [Eq. (2)].

Spatial standard deviation of precipitation change per degree Celsius global mean and tropical (25°S–25°N) mean surface air temperature change. The EF ratio between aerosol and GHG is in parentheses [Eq. (2)].
Spatial standard deviation of precipitation change per degree Celsius global mean and tropical (25°S–25°N) mean surface air temperature change. The EF ratio between aerosol and GHG is in parentheses [Eq. (2)].

As has been discussed, the aerosol forcing dominates the drying trend over EA, while GHG dominates rainfall change elsewhere. To explore the relative importance of aerosols and GHGs in precipitation change, we multiply the normalized precipitation trend in all-forcing runs by that in the single-forcing runs. The positive values highlight the regions where ΔP is similar to that resulting from the given single forcing. Results (Fig. 8a) confirm that the drying trend from southeastern China to southern Asia is due to aerosol forcing in historical all-forcing runs. The rainfall change in the off-equatorial Pacific is also regulated by aerosol forcing via the cross-equatorial anomalous Hadley circulation. Anthropogenic aerosols shift the ITCZ southward and increase rainfall in the southeastern Pacific (Fig. 2a). The GHG contribution dominates the rainfall change over the equatorial Pacific where the SST warming peaks (Fig. 8b). Somewhat surprisingly, both the aerosol and GHG forcings contribute constructively to the historical rainfall change in Central America and the tropical Atlantic (Villarini and Vecchi 2013).

Fig. 8.

Single-forcing contribution to historical all-forcing rainfall changes. The ΔP represents rainfall changes [mm day−1 (100 yr)−1] normalized by the tropical mean (25°S–25°N) SST changes.

Fig. 8.

Single-forcing contribution to historical all-forcing rainfall changes. The ΔP represents rainfall changes [mm day−1 (100 yr)−1] normalized by the tropical mean (25°S–25°N) SST changes.

We calculate spatial correlation in rainfall change between the all-forcing and single-forcing runs within 20° latitudinal bands to identify regions where a forcing dominates in rainfall change. The GHGs dominate the rainfall change over the tropics (15°S–17°N) and the SH high latitudes (south of 25°S), while anthropogenic aerosols dominate rainfall change in the NH midlatitudes (17°–55°N), especially over the EA. Aerosol contributes more than GHG does to the rainfall change over 15°–25°S by increasing the southeastern Pacific rainfall in historical all-forcing simulations via the anomalous Hadley circulation (Figs. 2a and 9).

Fig. 9.

Spatial correlation in 20° running latitude bands for precipitation change between aerosol forcing (blue) or GHG forcing (red) and all-forcing runs.

Fig. 9.

Spatial correlation in 20° running latitude bands for precipitation change between aerosol forcing (blue) or GHG forcing (red) and all-forcing runs.

5. Climate response pattern indices

Figure 4 shows that the tropospheric temperature change includes both symmetric and asymmetric patterns about the equator. In the tropical upper troposphere, the temperature change shows a remarkable equatorial symmetric pattern in both aerosol and GHG runs. Xie et al. (2013) suggests that the horizontal homogenization of tropospheric temperature perturbations in the tropics helps form a global climate change mode that is insensitive to external forcing distribution. In addition to this mode, there is an extratropical interhemispheric asymmetric response pattern about the equator in aerosol-forcing runs, which highlights the sensitivity of the climate response to the inhomogeneous forcing.

Table 3 defines several indices to quantify key patterns of climate response using anomalous SST, precipitation, sea level pressure (SLP), and surface meridional wind normalized by the tropical mean SST change (V_EQ). Patterns that are insensitive to the forcing type include the Pacific equatorial peak of SST [for aerosol 0.96°C (°C)−1 and GHG 0.89°C (°C)−1] and rainfall [for aerosol 0.42 mm day−1 (°C)−1 and GHG 0.48 mm day−1 (°C)−1] changes, as well as the Indian Ocean dipole [for aerosol 0.16°C (°C)−1 and GHG 0.15°C (°C)−1]. These patterns are insensitive to the spatial distribution of the external forcing and regulated by ocean dynamical processes. The enhancement of the SST on the equator is due to the evaporative damping that minimizes in the equatorial cold tongue and peaks in the subtropics (Liu et al. 2005; Xie et al. 2010). Precipitation change is regulated by the SST pattern in a manner consistent with the warmer-get-wetter view.

Table 3.

Pattern indices based on normalized [by the tropical mean (25°S–25°N) SST changes] trends (for aerosol, and GHG in parentheses). The first column denotes the variables used in the calculations. The pattern indices include from columns 2 to 6: the cross-equatorial gradient (Cross_EQ; between 0°–20°N and 0°–20°S) of SST, precipitation, SLP changes, and cross-equatorial (5°S–5°N) surface wind changes; the equatorial Pacific peak (EQ_Peak; 5°S–5°N, 130°E–80°W) of SST and precipitation changes; southeastern Pacific (SE_Pacific) SST (10°–30°S, 120°–80°W) change and the convergence zone precipitation (10°–30°S, 160°–100°W) change; Indian Ocean dipole (IOD) SST (between 10°S–10°N, 50°–70°E and 10°S–0°, 90°–110°E) change; and East Asian land (EA_Land) precipitation (5°–45°N, 70°–140°E) change. The tropical (25°S–25°N) mean has been removed from the SST modes. Boldface numbers indicate that the two responses are statistically different.

Pattern indices based on normalized [by the tropical mean (25°S–25°N) SST changes] trends (for aerosol, and GHG in parentheses). The first column denotes the variables used in the calculations. The pattern indices include from columns 2 to 6: the cross-equatorial gradient (Cross_EQ; between 0°–20°N and 0°–20°S) of SST, precipitation, SLP changes, and cross-equatorial (5°S–5°N) surface wind changes; the equatorial Pacific peak (EQ_Peak; 5°S–5°N, 130°E–80°W) of SST and precipitation changes; southeastern Pacific (SE_Pacific) SST (10°–30°S, 120°–80°W) change and the convergence zone precipitation (10°–30°S, 160°–100°W) change; Indian Ocean dipole (IOD) SST (between 10°S–10°N, 50°–70°E and 10°S–0°, 90°–110°E) change; and East Asian land (EA_Land) precipitation (5°–45°N, 70°–140°E) change. The tropical (25°S–25°N) mean has been removed from the SST modes. Boldface numbers indicate that the two responses are statistically different.
Pattern indices based on normalized [by the tropical mean (25°S–25°N) SST changes] trends (for aerosol, and GHG in parentheses). The first column denotes the variables used in the calculations. The pattern indices include from columns 2 to 6: the cross-equatorial gradient (Cross_EQ; between 0°–20°N and 0°–20°S) of SST, precipitation, SLP changes, and cross-equatorial (5°S–5°N) surface wind changes; the equatorial Pacific peak (EQ_Peak; 5°S–5°N, 130°E–80°W) of SST and precipitation changes; southeastern Pacific (SE_Pacific) SST (10°–30°S, 120°–80°W) change and the convergence zone precipitation (10°–30°S, 160°–100°W) change; Indian Ocean dipole (IOD) SST (between 10°S–10°N, 50°–70°E and 10°S–0°, 90°–110°E) change; and East Asian land (EA_Land) precipitation (5°–45°N, 70°–140°E) change. The tropical (25°S–25°N) mean has been removed from the SST modes. Boldface numbers indicate that the two responses are statistically different.

There are patterns of climate response that are sensitive to forcing distributions. The cross-equatorial thermal gradient is distinct between GHG- and aerosol-induced responses. The aerosol-to-GHG ratio for both cross-equatorial gradient of SST and precipitation is about 4.5:1 (Table 3), indicating a significant southward shift of ITCZ in aerosol runs, which is absent in GHG simulations. For the drying trend over the EA land region (Figs. 2a and 3a), the corresponding pattern index for the rainfall change over 5°–45°N, 70°–140°E is 0.49 mm day−1 (°C)−1 in aerosol and 0.12 mm day−1 (°C)−1 in GHG runs.

Both the cross-equatorial SLP gradient and the corresponding meridional surface wind at the equator show larger responses in aerosol than in GHG runs (Table 3). Figure 7b shows that the most pronounced southward cross-equatorial wind anomaly takes place over the southeastern Pacific and is associated with the weakened climatological trade winds, consistent with observations (Tokinaga et al. 2012; Wang et al. 2016). Corresponding to the wind decrease in the southeastern Pacific (10°–30°S, 120°–80°W), the relative SST increases following the WES mechanism. Rainfall change related to the South Pacific convergence zone (10°–30°S, 160°–100°W) also increases following the warmer-get-wetter pattern. This can also be identified in Fig. 8; rainfall change over the southeastern Pacific is sensitive to the forcing type. This result is consistent with Ming and Ramaswamy (2011), who note the strengthened ascent over the SH convergence zones and a corresponding shift of the South Pacific convergence zone.

The anomalous cross-equatorial Hadley circulation is unique to anthropogenic aerosol forcing and apparent in historical all-forcing simulations but absent in GHG runs. As the surface manifestation of this interhemispheric asymmetric pattern, cross-equatorial wind at the surface serves as a good fingerprint of the climate response patterns to anthropogenic aerosol forcing (Wang et al. 2016). In Fig. 10, the southward cross-equatorial flow in historical all-forcing simulations tracks the changes in the aerosol single-forcing very well (correlation coefficient is 0.91), following the evolution of global aerosol concentration (Fig. 1a). Anthropogenic aerosols dominate the interhemispheric asymmetric climate response patterns.

Fig. 10.

Changes of zonal mean cross-equatorial (averaged over 5°S–5°N) surface wind (m s−1) in historical all-forcing simulations (green), historical anthropogenic aerosol–forcing simulations (blue), and historical GHG-forcing simulations (red) during the twentieth century. A 10-yr low-pass filter is applied.

Fig. 10.

Changes of zonal mean cross-equatorial (averaged over 5°S–5°N) surface wind (m s−1) in historical all-forcing simulations (green), historical anthropogenic aerosol–forcing simulations (blue), and historical GHG-forcing simulations (red) during the twentieth century. A 10-yr low-pass filter is applied.

6. Summary and discussion

We have compared the patterns of climate response to the anthropogenic aerosol and the GHG changes in the twentieth century, using historical single- and all-forcing simulations from eight CMIP5 models. Similar climate response patterns between aerosol and GHG runs include the equatorial peak in SST response and associated rainfall change over the Pacific as well as vertically amplified temperature change in the tropical troposphere. These patterns are symmetric about the equator and are part of a global ocean–atmosphere mode that is insensitive to the external forcing distribution (Xie et al. 2013). Here, we focus on the climate response patterns unique to anthropogenic aerosols that have not been systematically examined previously.

The leading EOF modes identify the distinct climate response between aerosol and GHG runs. We have developed a method to highlight the distinct patterns of climate response to anthropogenic aerosols versus the GHG changes. In the tropics, the aerosol-induced negative radiative forcing in the NH midlatitudes requires an anomalous Hadley circulation across the equator according to the cross-equatorial energy transport theory. This is also an important mechanism to propagate the aerosol effect to the extratropical SH. Eddy–mean flow interactions are important in the adjustment (Ceppi et al. 2013; Xu and Xie 2015). In the NH midlatitudes, patterns unique to anthropogenic aerosols include the North Pacific cooling, the drying trend over eastern Asia, and the southward shift of the NH westerly jet. Comparisons of climate response pattern indices quantify the distinct regional climate change between anthropogenic aerosol and GHG runs. The interhemispheric asymmetric mode, represented by the anomalous cross-equatorial gradient of SST, SLP, and precipitation, shows a much larger response to aerosol than to GHG forcing.

GHG forcing dominates over the aerosol cooling effect on temperature in the historical all-forcing simulations. The tropical SST increase induced by GHG is twice as large as the aerosol-induced cooling (0.77° vs −0.42°C). As a regional forcing, aerosols are more effective in inducing spatial precipitation change (Table 2). While the GHG-induced warming is already detectable, the opposing effect of aerosol and GHG forcings on precipitation renders the detection of rainfall change difficult in observations. A robust fingerprint of aerosols is detected from shipboard observations in the southward cross-equatorial surface wind (Wang et al. 2016).

Anthropogenic aerosols and GHGs are the major external radiative forcings for the twentieth-century climate change. Indeed, the sum of the results from the aerosol and GHG single-forcing runs correlates in space with those from the all-forcing runs at 0.68 and 0.57 for precipitation and SST changes, respectively. In the SH high-latitude regions, the ozone forcing also contributes to the change in atmospheric circulation (Polvani et al. 2011). Thus, much still needs to be learned about the mechanisms for radiative-induced atmospheric circulation change. Furthermore, large uncertainties in aerosol radiative forcing by anthropogenic aerosols remain both for the global mean and on the regional scale (Myhre et al. 2013, their Figs. 8.23 and 8.24), affecting the interpretation of the climate change over the twentieth century. This topic deserves further studies.

Acknowledgments

We acknowledge the WCRP Working Group on Coupled Modelling, which is responsible for CMIP5, and the climate modeling groups for producing and making available the model outputs. This work is supported by the National Basic Research Program of China (2012CB955603), NSFC–Shandong Joint Fund for Marine Science Research Centers (U1406401), the National Natural Science Foundation of China (41490643), and the U.S. National Science Foundation (1305719 and 1249145). H. W. is also supported by the China Scholarship Council (201406330005).

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