Global climate models simulate a robust increase of global mean precipitation of about 1.5 to 2% per kelvin surface warming in response to greenhouse gas (GHG) forcing. Here, it is shown that the sensitivity to aerosol cooling is robust as well, albeit roughly twice as large. This larger sensitivity is consistent with energy budget arguments. At the same time, it is still considerably lower than the 6.5 to 7% K −1 decrease of the water vapor concentration with cooling from anthropogenic aerosol because the water vapor radiative feedback lowers the hydrological sensitivity to anthropogenic forcings. When GHG and aerosol forcings are combined, the climate models with a realistic 20th century warming indicate that the global mean precipitation increase due to GHG warming has, until recently, been completely masked by aerosol drying. This explains the apparent lack of sensitivity of the global mean precipitation to the net global warming recently found in observations. As the importance of GHG warming increases in the future, a clear signal will emerge.

Keywords

Climate model simulations suggest that the global mean precipitation will increase by 1.5 to 2% K −1 surface warming in response to greenhouse gas (GHG) forcing ( 1 , 2 ). However, this expected increase is not yet generally supported by observations ( 3 , 4 ), and it has recently been suggested that the hydrological sensitivity might be lower than expected because of a cloud radiative feedback that is not represented by climate models ( 5 ). Meanwhile, an issue that has received little attention is the hydrological sensitivity associated with an increase in anthropogenic aerosol. On the one hand, it is well understood that the effect of anthropogenic aerosol is net cooling and drying, that aerosol cooling has reduced the overall anthropogenic warming ( 6 – 8 ), and that a reduction in solar radiation yields a stronger hydrological response than GHG warming ( 9 , 10 ). Various studies ( 11 – 14 ) have shown that aerosol has to be taken into account for explaining observed precipitation trends and that it is important for determining the overall hydrological sensitivity. On the other hand, it is still very often implicitly assumed that the global mean hydrological sensitivity to aerosol cooling is the same as that to GHG warming. However, the hydrological sensitivity to GHG forcing is lowered by the long-wave (infrared) radiative effect of GHGs, which tends to prevent condensation heat from escaping to space: whereas the water vapor concentration in the boundary layer increases by about 6.5 to 7% K −1 surface warming, precipitation increases only by 1.5 to 2% K −1 surface warming for GHG forcing ( 1 , 2 , 12 , 15 , 16 ). Anthropogenic aerosols, such as sulfates that primarily scatter sunlight, on the other hand, have a comparatively small long-wave radiative effect. They therefore exhibit larger precipitation sensitivity and a weaker damping effect (although absorbing and scattering aerosols can also induce damping or compounding effects on precipitation sensitivity).

RESULTS

The “historicalGHG” model sensitivity experiment from the Coupled Model Intercomparison Project Phase 5 (CMIP5) (17) yields a multimodel mean sensitivity of 1.7 ± 0.4% K−1 (mean ± 1 SD) to well-mixed GHGs, as expected. The multimodel mean sensitivity to aerosol forcing, on the other hand, is roughly twice as large and also rather robust across different models (3.6 ± 0.5% K−1, based on eight CMIP5 models for which aerosol-only runs are available; see the Supplementary Materials for details). However, this sensitivity to aerosol cooling is still significantly lower than the 6.5 to 7% K−1 change of the lower tropospheric water vapor content, which is simulated in global climate models in response to surface temperature changes and which coincides with the expectations based on the Clausius-Clapeyron relation, under the assumption of constant relative humidity (2). Instead, it is similar to the sensitivity that is obtained by comparing two CMIP5 experiments with fixed sea surface temperatures (SSTs), in one of which the SST is increased by 4 K everywhere (see table S1 and fig. S1) at constant forcing, allowing only atmospheric feedbacks. This suggests a significant contribution from the water vapor long-wave radiative feedback and from long-wave cloud feedbacks to the overall damping, in agreement with previous studies (12, 13). The magnitude of this damping (roughly half the GHG damping) is compatible with the contribution of the water vapor feedback to the overall increased greenhouse effect [roughly a doubling (18)].

Here, the hydrological sensitivities have been estimated from differences in surface air temperature ΔT and precipitation ΔP between the years 1850–1869 and 1986–2005 from climate model runs with only GHG, only aerosol, and all forcings (Fig. 1), and the models have been grouped according to the magnitude of their 20th century warming in the all-forcing runs (fig. S2). Because the natural forcing is comparatively small, ΔT and ΔP in the all-forcing runs are given by the sums of ΔT and ΔP from the GHG and aerosol runs, that is, ΔT = ΔT G + ΔT A and ΔP = ΔP G + ΔP A to a very good approximation (fig. S3). Consequently, the overall hydrological sensitivity can be computed from (1)which yields a rather accurate estimate according to fig. S3. Therefore, the global mean precipitation change is related to the individual hydrological sensitivities as follows (2)where and are the hydrological sensitivities from the single-forcing experiments. Because ΔT G > 0 and ΔT A < 0 and also , the actual (all-forcing) hydrological sensitivity is lower than the known and often discussed sensitivity to GHGs (compare schematic in fig. S4). Overall, the simulated multimodel mean hydrological sensitivity is −0.4 ± 1.7% K−1 in the standard historical experiment that combines all forcings.

Fig. 1 Response to GHG, aerosol, and all forcings. Multimodel mean difference between years 1850–1869 and 1986–2005 from climate model runs with only GHG (red), only aerosol (gray), and all forcings (blue) for global mean near-surface air temperature (top), precipitation (middle), and hydrological sensitivity (bottom). The models are grouped into cold, medium, and warm models based on 20th century warming in the historical (all-forcing) runs according to fig. S2. Boxes indicate medians and quartiles. The ranges indicate averages ± 1 SD.

Figure 1 together with fig. S2 shows that the models that simulate a fairly realistic 20th century warming (“medium”) tend to yield particularly small overall hydrological sensitivities, although it must be noted that on average, the medium models slightly underestimate the observed warming, whereas the “warm” models yield several individual runs with only a rather small overestimate of the global mean temperature increase. This suggests that the overall hydrological sensitivity is still much smaller than the hydrological sensitivity to GHGs and also still within the range of internal climate variability given by the spread between individual model runs in fig. S3. It also explains the absence of a strong hydrological sensitivity in observations (4) and suggests that global mean precipitation has not yet increased significantly despite global warming simply because the hydrological sensitivity to aerosol cooling is larger than that to GHG warming. This lack of observed response in global precipitation to GHG warming is consistent with energy budget arguments and the analysis of historical trends in previous studies that have taken into account aerosol effects (14, 16).

However, locally, the changes due to GHGs and aerosol do not balance (Fig. 2 and figs. S5 to S7) because the aerosol forcing is highly nonuniform (19), which makes the detection of anthropogenic changes possible (20). Yet, to completely understand observed regional patterns of multidecadal precipitation trends, one has to take into account not only anthropogenic forcings but also internal climate variability (21–23). Although anthropogenic aerosol can have large influences on local circulation (24, 25), the overall effect on the global mean atmospheric vertical overturning circulation strength is much weaker than that of GHGs (figs. S8 and S9). This is in line with the weaker damping and higher hydrological sensitivity, because the low sensitivity to GHG warming is associated with a weakening of the circulation under GHG warming (2). Ultimately, the local response of precipitation to anthropogenic forcing is determined not only by the temperature-dependent water vapor availability and the strengthening or weakening of the overturning circulation but also by geographical shifts of precipitation patterns (16). The impacts depend strongly on changes of precipitation intensity (26, 27) and seasonal cycle (28). Furthermore, the type of aerosol is important (11, 16, 29, 30), and also, the treatment of aerosol effects differs in global models (table S2). This strongly influences the simulated changes in temperature and precipitation. At the same time, the aerosol hydrological sensitivity is found to be fairly robust (Fig. 1). Many features of the spatial patterns simulated in response to anthropogenic aerosol (Fig. 2B) are fairly robust as well (31), even across different models (figs. S5 to S7).

Fig. 2 Regional precipitation response and additivity of responses to individual forcings. Multimodel averages of simulated surface precipitation change between years 1850–1869 and 1986–2005 in millimeters per day for GHG, aerosol, and all forcings, as well as the sum of the GHG- and aerosol-forcing experiments for models for which at least one aerosol run is available. Stippling indicates that six of seven models (where two very similar models have been considered as a single model) agree on the sign of the change.

Because long-lived GHGs accumulate in the atmosphere while the atmospheric residence time of tropospheric aerosol is rather short, and because aerosol emissions are expected to decrease in the future, eventually ΔT G will overwhelm ΔT A (32, 33), and thus the overall hydrological sensitivity will be dominated by GHGs. This is confirmed by analyzing results from CMIP5 future scenario runs in Fig. 3.