Mediterranean Precipitation Response to Greenhouse Gases and Aerosols

Atmospheric aerosols and greenhouse gases affect cloud properties, radiative balance and thus, the hydrological cycle. Observations show that precipitation has decreased in the Mediterranean since the 20th century, and many studies have investigated possible mechanisms. So far, however, the effects of aerosol forcing on 25 Mediterranean precipitation remain largely unknown. Here we compare Mediterranean precipitation responses to individual forcing agents in a set of state-of-the-art global climate models (GCMs). Our analyses show that both greenhouse gases and aerosols can cause drying in the Mediterranean, and that precipitation is more sensitive to black carbon (BC) forcing than to well-mixed greenhouse gases (WMGHGs) or sulfate aerosol. In addition to local heating, BC appears to reduce precipitation by causing an enhanced positive North Atlantic Oscillation (NAO)/Arctic 30 Oscillation (AO)-like sea level pressure (SLP) pattern, characterized by higher SLP at mid-latitudes and lower SLP at high-latitudes. WMGHGs cause a similar SLP change, and both are associated with a northward diversion of the jet stream and storm tracks, reducing precipitation in the Mediterranean while increasing precipitation in Northern Europe. Though the applied forcings were much larger, if forcings are scaled to those of the historical period of 19012010, roughly one-third (31±17%) of the precipitation decrease would be attributable to global BC forcing with the 35 remainder largely attributable to WMGHGs whereas global scattering sulfate aerosols have negligible impacts. The results from this study suggest that future BC emissions may significantly affect regional water resources, agricultural practices, ecosystems, and the economy in the Mediterranean region. Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-56 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 30 January 2018 c © Author(s) 2018. CC BY 4.0 License.

Abstract. Atmospheric aerosols and greenhouse gases affect cloud properties, radiative balance and thus, the hydrological cycle. Observations show that precipitation has decreased in the Mediterranean since the 20th century, and many studies have investigated possible mechanisms. So far, however, the effects of aerosol forcing on Mediterranean precipitation remain largely unknown. Here we compare Mediterranean precipitation responses to individual forcing agents in a set of state-of-the-art global climate models (GCMs). Our analyses show that both 5 greenhouse gases and aerosols can cause drying in the Mediterranean, and that precipitation is more sensitive to black carbon (BC) forcing than to well-mixed greenhouse gases (WMGHGs) or sulfate aerosol. In addition to local heating, BC appears to reduce precipitation by causing an enhanced positive North Atlantic Oscillation (NAO)/Arctic Oscillation (AO)-like sea level pressure (SLP) pattern, characterized by higher SLP at mid-latitudes and lower SLP at high-latitudes. WMGHGs cause a similar SLP change, and both are associated with a northward diversion of the jet 10 stream and storm tracks, reducing precipitation in the Mediterranean while increasing precipitation in Northern Europe. Though the applied forcings were much larger, if forcings are scaled to those of the historical period of 1901-2010, roughly one-third (31±17%) of the precipitation decrease would be attributable to global BC forcing with the remainder largely attributable to WMGHGs whereas global scattering sulfate aerosols have negligible impacts. The results from this study suggest that future BC emissions may significantly affect regional water resources, agricultural 15 practices, ecosystems, and the economy in the Mediterranean region.

Introduction
Aerosols, fine particles in the atmosphere produced by both natural processes and anthropogenic activities, impact the Earth's climate by scattering and absorbing solar radiation (direct effect), or by modifying the properties of clouds (indirect effects) through a variety of mechanisms including atmospheric heating and changes in ice nuclei and cloud condensation nuclei (CCN), including their size, location and concentration. These changes may significantly affect 5 solar radiation and precipitation (Ramanathan et al., 2001;Kaufman et al., 2002;Shindell et al., 2012;Bond et al., 2013;Boucher et al., 2013). The effects of aerosols on climate have been widely studied both on global and regional scales (Ramanathan and Carmichael, 2008;Shindell and Faluvegi, 2009). For example, Menon et al. (2002) reported slight cooling and drying trends in the northern part of China in the 2nd half of the 20th century, and attributed such trends to the emissions of BC aerosols based on model simulations. Similarly, Hodnebrog et al. (2016) reported a 10 precipitation decrease in southern Africa due to local biomass burning aerosols based on analyses of model simulations and local energy budget. On the other hand, Koren et al. (2012) argued that aerosols could intensify rainfall events in the lower and mid-latitudes by analyzing satellite observations. However, Stevens and Feingold (2009) contended that the effects of aerosols on clouds and precipitation are very limited due to the buffering effects of the climate system itself. In addition to their influence on temperature and precipitation, aerosols may also affect large-scale atmospheric 15 circulation. For example, Takahashi and Watanabe (2016) suggested that the Pacific trade winds were accelerated partially by sulfate aerosols during the past two decades. Jacobson and Kaufman (2006) suggested a surface wind reduction due to aerosol particles in California and China, which may also impact air pollution and wind energy. Dunstone et al. (2013) also reported that aerosols could modulate Atlantic tropical storm frequency due to aerosolinduced shifts in the Hadley circulation. These differing results suggest that aerosol effects on regional climate may 20 depend on the aerosol types, seasons, and regions of interest.
A decreasing precipitation trend in the Mediterranean area since the 20 th century has been reported and its possible causes have been investigated in many studies (Piervitali et al., 1998;Buffoni et al., 1999;Mariotti et al., 2002;Dünkeloh and Jacobeit, 2003;Xoplaki et al., 2004). For instance, Quadrelli et al. (2001) observed a strong correlation 25 between winter Mediterranean precipitation and the NAO (Hurrell et al., 2001). Krichak and Alpert (2005) suggested that the East Atlantic-West Russia (EA-WR) pattern may also play an important role in modulating the precipitation in the Mediterranean. Hence, the responses of Mediterranean precipitation to these large-scale variability patterns (e.g., NAO, EA-WR), and to some extent how these patterns might be responding to external drivers, are fairly wellunderstood (Black et al., 2010). However, prior studies included all the drivers at once, so cannot discern the relative 30 roles of WMGHGs and other agents. Anthropogenic aerosols have been reported to greatly influence the temperature in the Mediterranean (Nabat et al., 2014), but the effects of aerosols on Mediterranean precipitation have not been carefully examined. Since precipitation impacts water availability for both ecosystems and human societies, it is crucial to understand the different impacts of the climate drivers that are responsible for the Mediterranean precipitation trend. To bridge this knowledge gap, here we analyze Mediterranean precipitation changes based on a 35 group of state-of-the-art GCMs that examined the precipitation response to individual climate drivers, which could help inform management of water resources, regional societal activities such as agriculture, and even emissions mitigation.

Data
This study employed output from nine models participating in the Precipitation Driver and Response Model 5 Intercomparison Project (PDRMIP), utilizing simulations examining the individual responses to CO2, sulfate and BC aerosols. In these simulations, perturbations were performed with each model at global scale: a doubling of CO2 concentration (CO2×2), 10 times present-day BC concentration (BC×10), and 5 times present-day SO4 concentration (SO4×5). All perturbations were abrupt. CO2×2 perturbations were applied relative to the models' own baseline values.
For aerosol perturbations, monthly present-day concentrations were derived from the AeroCom Phase II initiative 10 (Myhre et al., 2013a). The concentrations were multiplied by the stated factors (concentration-driven). A few models instead perturbed aerosol emissions (emission-driven), in most cases again using AeroCom Phase II data. Hence many of the models were run using climatological aerosols as a way to examine the similarity in model responses when driven with the same aerosol concentrations rather than including differences in both concentrations and responses.
This leads to less realism in the physics, however, particularly of aerosol-cloud interactions, and hence this study 15 focuses on aspects of the response that appear to be less sensitive to those interactions as they are relatively robust across the models (despite some using interactive aerosols while others used climatological fields). Many PDRMIP studies have taken this approach Myhre et al., 2017;Stjern et al., 2017;Liu et al., 2018;Richardson et al., 2018;Samset et al., 2018), though further work with models incorporating more realistic aerosolcloud interactions would of course be valuable in determining the veracity of all conclusions from the project. 20 Each perturbation was run in two configurations, a 15-yr fixed sea surface temperature (SST) simulation and a 100yr coupled simulation. Each fixed-SST simulation is compared with its fixed-SST control simulation to diagnose the effective radiative forcing (ERF) due to each perturbation (Myhre et al., 2013b), whereas each coupled run is compared with its coupled control run to examine climate response. All models include dust among the aerosols, but changes in 25 Saharan dust are not considered here since we are investigating anthropogenic impacts in the current study. The nine models used in this study and their configurations, as well as the aerosol treatment are listed in Table 1.

Method
In addition to direct analysis of meteorological fields (e.g. precipitation, sea-level pressure) in the models, we also analyse the energy budget associated with the hydrological cycle. Following Hodnebrog et al. (2016) and Muller and 30 O'Gorman (2011), the precipitation change is related to diabatic cooling and the horizontal transport of dry static energy as follows: Here Lc is the latent heat of condensation of water vapor, which is 29 W m -2 mm -1 day. ∆P is the precipitation change.
∆H is the column-integrated dry static energy flux divergence and ∆Q is the column-integrated diabatic cooling, which is calculated as: where ∆LW is the change of longwave radiation in the atmospheric column and ∆SW is the change of shortwave radiation in the atmospheric column. ∆SH is the change of upward sensible heat flux.

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Since most of the precipitation events occur in the wet season (Oct-Mar) in the Mediterranean, roughly 70% of total annual precipitation (Mariotti et al., 2002;Kottek et al., 2006), the analysis was restricted to the wet season in the current study unless noted otherwise. All of the data used in this study were re-gridded into 2.5°× 2.5°horizontal resolution for analyses. nine models again show drying trends, with the MMM value -0.12±0.07 mm/day per W/m 2 , which is four times as large as that of CO2. When it comes to SO4 (Fig. 2c), the model results even differ in the sign of change and the MMM value is small (-0.01±0.04 mm/day per W/m 2 ). These analyses show that the precipitation response is more sensitive 25 to BC forcing than to CO2 and SO4 in this region.
In order to investigate the mechanisms governing the precipitation response, we performed an energy budget analysis for this region (Fig. 3). For CO2, the drying is dominated by horizontal energy transport (gray box in the CO2 panel), albeit some offset by diabatic cooling (pink box in the CO2 panel). For BC, the net radiation change, which is primarily 30 SW (red box in the BC panel), has a larger impact than the horizontal energy change (gray box in the BC panel), but the latter is nonetheless a substantial fraction of the net change. When it comes to SO4, the small precipitation response results from the offsetting of net radiation change (pink box in the SO4 panel) and horizontal energy transport (gray box in the SO4 panel). The energy budget analysis implies that the dynamical responses to CO2 and BC played a crucial role in modulating the precipitation in this region. 35 We then analyzed the ∆SLP from the model output (Fig. 4). Specifically, it is seen that CO2 induced strong SLP changes. The SLP increased at mid-latitudes, with increases extending from the North Atlantic to Southern Europe, and decreased at high latitudes (Fig. 4a). BC led to a similar pattern of SLP change, but with increased magnitude ( Fig. 4b), characterized by two increases centered in Europe and the Western North Atlantic. Compared with CO2 and BC, SO4 caused an opposite change (Fig. 4c). The CO2 and BC forcings appear to induce a pattern similar to the 5 positive phase of the NAO /AO (Lorenz, 1951), in which the jet streams and storm tracks are displaced northward, leading to a drier Mediterranean and precipitation increases in Northern Europe ( Fig. 1a-b). Such a shift in response to forcings is more clearly seen in the changes of zonal winds (Fig. 5). The CO2 caused a strengthening of zonal winds in the whole upper atmosphere and a strengthening around 60°N from the near-surface to the top of the atmosphere, as well as weakening around 30°N from the near-surface to the mid-troposphere (Fig. 5a), as in prior studies (Shindell 10 et al., 2001) The strengthening around 60°N is more apparent for BC (Fig. 5b). Similar results were seen in response to aerosol forcing in a prior study (Allen and Sherwood, 2011). This shift is possibly due to the enhancement of the tropospheric temperature gradient between mid-latitudes and high-latitudes, as suggested by Allen et al. (2012).
Our analyses illustrate that BC aerosols may modulate regional precipitation in part via modifying large-scale 15 circulation patterns. Many previous studies suggest that BC could impact regional precipitation by changing the local vertical temperature profile, in which BC aerosols absorb solar radiation and heat the atmosphere, thus suppressing convection and cloud formation (Kaufman et al., 2002;Meehl et al., 2008;Ramanathan and Carmichael, 2008;Hodnebrog et al., 2016). Our results (analyses of the energy budget, SLP and zonal winds) suggest that a portion of the drying is also associated with large-scale circulation responses. In addition, our pattern of jet stream/storm track 20 changes (Fig. 4 & 5) is also in agreement with the projections from the latest IPCC report (Collins et al., 2013) based on a set of CMIP5 models, with increasing storm activities in Northern Europe and decreasing storms in the Mediterranean. Such a shift of storm tracks may further reduce the precipitation in the Mediterranean, though reductions in WMGHG or BC emissions may help to mitigate the projected drying.

Case Study -Historical Observations and Scaled Model Results 25
The above analyses demonstrated how the precipitation and circulation responded to each forcing both qualitatively and quantitatively. In order to explore their potential relative contributions to the total precipitation change, here we apply linear scaling to the model output. Since PDRMIP utilized large aerosol and greenhouse gas changes in order to achieve strong signals that could be statistically significant with a relatively modest amount of computational time, the precipitation change from those model outputs needs to be scaled in order to compare with observations. 30 Uncertainties related to this approach are discussed further in section 5.
In this study, we focus on the period from 1901 to 2010. The scaled precipitation change for each individual forcing is defined as: In equation (3), ∆P is the precipitation change over Mediterranean in the model during the last 50 years in the coupled run, since the model has reached near-equilibrium state after 30 years. ERF1901-2010 is the historical global ERF for the period of 1901-2010. The values were obtained from the latest Intergovernmental Panel on Climate Change (IPCC) assessment report (Myhre et al., 2013b). The ERF1901-2010 value used for CO2 is 2.33 W/m 2 , which is larger than the 5 CO2 value from the IPCC report as CO2 was used to represent all WMGHGs in this case study. ERF1901-2010 values for BC and SO4 are 0.28 W/m 2 and -0.33 W/m 2 , respectively. ERFmodel is the global ERF in the PDRMIP models, which was obtained by calculating the energy flux change at the top of the atmosphere from years 6 to 15 of the fixed-SST simulations, since present models largely equilibrate within 5 years of fixed-SST running (Kvalevå g et al., 2013). In addition to the direct effects of the aerosols, the indirect effects of aerosols were also included in most of the models 10 and thus, in the ERFmodel. The value of (ERF1901-2010 / ERFmodel) is the scaling factor applied to model precipitation output to match historical forcing levels.

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∆Pscaled is calculated for CO2, BC, and SO4 separately. The total ∆Pscaled is the combination of the three, assuming their responses to those forcings can be added linearly. It should be noted that in this analysis, we use the combined responses to WMGHGs, BC and SO4 to approximate the total historical response over 1901-2010. Several additional factors may have also played a role, including natural forcing (solar and volcanic activities), land use/land cover change, contrails, ozone (O3) (both tropospheric and stratospheric) and stratospheric water vapor, which have forcings 20 of -0.03, -0.09, 0.05, 0.26 and 0.06 W/m 2 , respectively (Myhre et al., 2013b). As all these forcings are fairly small, simulations to isolate their impacts would be extremely computationally expensive and hence were not performed but to first order we expect their exclusion is unlikely to greatly affect our results. Characterization of the influence of these other drivers merits future study, particularly as some operate via different physical processes (e.g. tropospheric ozone is both a greenhouse gas and an absorber of incoming solar radiation). Similar analyses were also performed to 25 obtain scaled SLP change (∆SLPscaled), zonal wind change and energy budget change in the atmospheric column.
Several observational and reanalysis datasets were also employed in this part of our study. For precipitation, Global Precipitation Climatology Center (GPCC) monthly precipitation data (Schneider et al., 2011), provided by NOAA/OAR/ESRL from their website (https://www.esrl.noaa.gov/psd/data/gridded/data.gpcc.html#detail), is 30 employed. It is a high quality gridded dataset that is mainly terrestrial station-derived. For SLP, we use HadSLP2 data (Allan and Ansell, 2006), which is created by combining marine observations from ICOADS data (Worley et al., 2005) and land (terrestrial and island) observations (available at https://www.esrl.noaa.gov/psd/data/gridded/data.hadslp2.html). We also use NCEP/NCAR reanalysis data (Kalnay et al., 1996), downloaded from https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.derived.surface.html, 35 for the comparisons of zonal wind. All these datasets have undergone rigorous quality control and have been widely used in the climate community, including the IPCC 2013 assessment report (Hartmann et al., 2013). The trends of the observed and reanalysis data were estimated by a simple linear regression applied to the same period of the datasets.
The combination of WMGHGs, BC and SO4 exerted a strong drying trend in the Mediterranean (Fig. 6a). The drying trends shown here are statistically significant and consistent with the observations (Fig. 6b), as well as previous studies 5 (Buffoni et al., 1999;Mariotti et al., 2002;Dünkeloh and Jacobeit, 2003). When averaged over the whole domain, the scaled drying trends caused by WMGHGs, BC and SO4 are -1.28±1.21 mm/decade, -0.58±0.34 mm/decade and -0.03±0.21 mm/decade, respectively (not shown here). When combined (Fig. 6c), all nine models show decreased precipitation, with MMM value of -1.89±1.39 mm/decade, which is roughly a 5% decrease relative to the climatology of the control simulations. Such a decreasing trend is indistinguishable from the observations (-2.78±1.10 mm/decade, 10 a 10% decrease compared with its 110-yr climatology). In spite of the dominant role of WMGHGs in the drying of the Mediterranean, BC contributed roughly one-third (31±17%) of the total forced precipitation decrease in this region whereas the contribution of the scattering aerosol-SO4 is negligible (~1.6%). We also examined the trend of precipitation in the control simulations and found only very weak responses (Fig. 6c), with a mean value of 0.004±0.03 mm/decade and maximum value of 0.03 mm/decade in any individual model. Since current GCMs are able to capture 15 the broad spatial and temporal features of internal variability (Flato et al., 2013), and the forced drying signal is almost equal to the total signal (Fig. 6a-c), the consistent drying trend in the models is very unlikely to be attributable to unforced variability and appears realistic. The energy budget change (Fig. 6d) clearly shows that the net precipitation decrease is mainly due to horizontal energy transport (gray box) rather than diabatic cooling (pink box), because the absorption of SW radiation (red box) and LW radiative cooling (green box) offset one another in total. 20 Fig. 7a shows the overall response of SLP to these forcings, with strong SLP increases at mid-latitudes and strong decreases at higher latitudes. Such patterns of SLP changes are also found in the observed datasets (Fig. 7b), albeit with a larger magnitude. The combined pattern of zonal wind responses shows winds intensified at the northern edge of the jet stream and weakened at the southern edge (Fig. 7c). The NCEP dataset depicts a similar pattern of changes, 25 with winds intensifying at 60°N and weakening at 30°N, but as with SLP, with a stronger magnitude (Fig. 7d). Some previous studies have pointed out that current GCMs significantly underestimate the tropical expansion and jet stream shift, which could be related to the short observational record, large internal variability or model deficiencies (Johanson and Fu, 2009;Allen et al., 2012). Despite the underestimations, our analyses clearly demonstrate the shift of the jet stream in response to these forcings that appears qualitatively consistent with observations. 30 Based on the model simulations in the current study, the pattern of climate response to BC forcing over the past ~110 years is similar to the response to WMGHGs over Europe and the North Atlantic, including precipitation, SLP and zonal winds. At the same time, our results suggest that SO4 played a very limited role in modulating Mediterranean precipitation trends and North Atlantic storm tracks. In other words, the precipitation trends during the past 110 years 35 in the Mediterranean are likely to be only weakly sensitive to scattering aerosols that were not modeled (e.g., organic carbon, nitrate) or the uncertainties in aerosol negative forcing (probably not even for indirect forcing, as they were included in sulfate simulations for most models). The small sensitivity of SO4 is likely due to compensation between local and remote effects (Liu et al., 2018). Combined with its small ERF, the role of SO4 appears to be negligible during this period. However, the simulations examined here were not designed to determine whether the aerosol effects are due to local or remote emissions from the models. Initial analysis from PDRMIP regional experiments (in which BC over Asia only is multiplied by 10, with everything else being held at present-day levels) indicate that BC from 5 Asia contributes as much as 60% to the drying signal in the Mediterranean, and in fact a larger average rainfall change in the Mediterranean than averaged over Asia itself. This suggests that the remote effects of BC may have dominated the Mediterranean precipitation changes. Hence the response to global BC increases may be a reasonable proxy for the 20 th century changes, although it would be useful to explore the effects of local reductions from Europe itself in the late 20 th century. The relative impacts of local versus remote forcing will be further explored in forthcoming 10 PDRMIP analyses.

Discussion and Conclusion
Since PDRMIP experiments are equilibrium simulations while the real-world is transient, and we scaled PDRMIP forcing to match historical levels, we examined related experiments to test both these aspects of the methodology used in our comparison with historical observations. Historical GHG-only simulations using the same CMIP5 models 15 (Taylor et al., 2012) that participated in the PDRMIP project were collected and analyzed (data available at http://strega.ldeo.columbia.edu:81/CMIP5/.monthly/.byModel/). Six models are available and each model has 1-5 ensemble members. All of the six models show drying trends (Fig. 8), with a MMM value of -1.32±1.65 mm/decade (-1.29 mm/decade when weighted by ensemble size) which is quite close to the WMGHGs result of our scaled equilibrium PDRMIP output (-1.28±1.21 mm/decade). In fact, the overlap of their probability density functions is 20 0.85, assuming a normal distribution. This comparison indicates that our methodology does not appear to be a large source of uncertainty in the current study, though response to other agents may not be as linear as those to WMGHGs (unfortunately, simulations are not currently available to evaluate other forcers, and, given the enormous expense of running enough ensemble members to isolate the relatively small signals for individual aerosols, are unlike to be anytime soon). Similar analyses were also performed for SLP and zonal winds, and again there is no appreciable 25 difference between the historical transients and the scaled equilibrium responses. The consistent results suggest that the methodology works surprisingly well.
In addition to the wet season, precipitation during the dry season (Apr-Sep) for the PDRMIP model was also analyzed.
The modelled dry season precipitation trends, however, do not match the observations well (not shown). The modelled 30 results also show a statistically strong drying trend while the observations do not show significant changes. Two possible reasons may be responsible for the apparent discrepancies. One is that only 30% of the total precipitation occurs during the dry season (boreal summer months) and it is difficult to simulate the uneven distribution of infrequent rainfall events. The other is that there are large uncertainties in the observational data itself. Unlike the wet season, in which nearly half of the grid boxes show statistically significant trends (Fig. 6b), almost none of the grid 35 boxes show statistically significant trends in the dry season, undermining the robustness of the observational results.
The drying influence of WMGHGs will be more prominent in the future due to their projected continued growth. In contrast, many studies suggest that aerosol concentrations may decrease rapidly in the future due to air quality and climate policies along with their relatively short lifetime compared with WMGHGs (Andreae et al., 2005;Myhre et al., 2013b;Shindell et al., 2013). Reductions of BC could, to some extent, slow down the drying trend in the 5 Mediterranean. Overall, a drier Mediterranean region is expected owing to increasing WMGHGs, but the pace of change in global BC emissions may substantially modify the drying rate in the near term.
Some limitations and uncertainties still exist in our current study. First, it is important to keep in mind that the case study in Section 4 is not a formal attribution analysis, despite the estimation of BC contribution. Our aim is to give a 10 first grasp of the effects of aerosol on regional precipitation in the Mediterranean. Second, although our comparison of scaled equilibrium and unscaled transient simulations indicates that our methodology works well at least for WMGHGs, there is no systematic study so far exploring the linearity (or non-linearity) of the precipitation responses to BC or the linearity of responses to multiple versus individual forcings on regional scales. Third is that the ERF1901-2010 of BC represents direct effects only (Myhre et al., 2013b). Semidirect and indirect effects, however, are included 15 in many of our PDRMIP models, and thus in ERFmodel. We did not include these effects in the scaling in this study for two reasons: first, the indirect effects of BC in the PDRMIP models do not include ice particles, as well as internal cloud absorption (Jacobson, 2012) and are difficult to evaluate as BC concentrations were prescribed in several of the models so that they cannot interact fully with clouds, indicating that they are not fully resolved, and second, the net ERF1901-2010 of semidirect plus indirect effects is likely small (-0.1 to +0.2 W/m 2 ) with a very large overall uncertainty 20 range (-0.4 to +0.9 W/m 2 ) (Bond et al., 2013). If the semi-direct and indirect effects of BC (-0.1 to +0.2 W/m 2 ) are considered in the scaling, the ∆Pscaled of BC aerosol would be -0.44 to -0.87 mm/decade and still contribute a substantial part (25 to 40 %) to the drying. The situation is similar for sulfate aerosol, for which indirect effects are included in ERFmodel, but not in ERF1901-2010. We did not include indirect effects in our scaling as these were not attributed to individual aerosol species in the IPCC AR5 (Boucher et al., 2013). If the indirect effects are considered, 25 the negative ERF1901-2010 could increase roughly by a factor of two (assuming indirect effects are largely associated with sulfate). However, the ∆Pscaled of sulfate aerosol would still be very small compared with WMGHGs or BC, which would not impact our conclusions. As noted previously, the use of prescribed concentrations will also limit the ability of models to capture aerosol-cloud interactions realistically, affecting precipitation responses as well as ERF estimates. Since the responses do not obviously vary systematically between concentration-driven and emissions-30 driven models, such effects may be relatively small but merit future study. The final issue is related to the design of the model simulations. The perturbations are 5× or 10× present-day aerosol concentrations, which are time-invariant.
The aerosols, however, have significant spatial and temporal variations. For instance, aerosol concentrations have been increasing in Asia continuously since 1950, but decreasing in Europe since the 1970s (Allen et al., 2013). As noted, further work is needed to determine how much of the Mediterranean trends result from local relative to remote 35 forcing. To the extent that the trends are driven by remote forcing the potential influence of such spatio-temporal variations will be small. This will be explored in future PDRMIP simulations.
Our analyses show that both WMGHGs and BC influence wet season Mediterranean rainfall by causing an enhanced positive NAO/AO-like SLP pattern as well as by some local heating due to SW absorption. The SLP pattern is characterized by higher SLP in the North Atlantic and Mediterranean and lower SLP in the Northern part of Europe, which diverts the jet stream and storm tracks further northward, reducing the precipitation in the Mediterranean and increasing precipitation in Northern Europe. In contrast, global perturbations of the scattering aerosol SO4 have a 5 negligible impact. The results from this study may have important implications to the management of regional water resources, agricultural practice, ecosystems, environment, and economics as well as social development and behavior in a warming climate. They also stress the importance of accounting for the aerosols (and generally short-lived forcers) for short-term (e.g., decadal) regional climate prediction.

Acknowledgement 10
All model results used for this study are available to the public through the Norwegian NORSRORE data storage facility. We thank the three reviewers for their insightful comments. We also acknowledge the NASA High-End