1. Introduction
Explosive volcanic eruptions have major impacts on the climate system, on time scales of a few months to a few years. Upon a strong eruption, sulfur dioxide (SO2) may reach the lower stratosphere, where it is converted into aqueous sulfuric acid droplets that scatter shortwave and absorb infrared radiation and overall reduce the global mean surface air temperature. The volcanic aerosol may affect the formation of precipitation in several ways. Decreases in surface air temperatures lead to reduced evaporation and decreases in tropospheric column-integrated water vapor (Randel et al. 1996). Since precipitation is directly linked to evaporation, global mean precipitation decreases after a strong volcanic eruption (Robock and Liu 1994). Once the stratospheric volcanic aerosol has been advected or sedimented into the upper troposphere, it may also influence cloud microphysical processes (Kübbeler et al. 2012; Cirisan et al. 2013) and consequently precipitation. Stratospheric aerosol particles that form after a large volcanic eruption have typical stratospheric residence times on the order of a year. After returning to the troposphere, they may reside there for days up to a few weeks (Thomason and Peter 2006) and during this time alter the lifetime and properties of clouds in the upper and midtroposphere. The response to radiative forcings is physically less constrained for precipitation than for temperature (Allen and Ingram 2002). Accordingly, the signal-to-noise ratio of precipitation responses to volcanic eruptions is thought to be lower than for surface air temperature (Robock and Liu 1994). In addition, a variety of dynamical feedback processes complicate matters further. The volcanic aerosol may induce vertical and horizontal heating gradients. These can affect stratospheric and tropospheric dynamical processes (see, e.g., Graf et al. 1993; Ramachandran et al. 2000; Stenchikov et al. 2002), which may affect the distribution of precipitation.
Observational studies indicate, for example, significant decreases in global and tropical-land-area precipitation following the June 1991 eruption of Mount Pinatubo (Trenberth and Dai 2007; Gu and Adler 2011). No significant reduction was observed after the eruption of El Chichón in April 1982, during which an estimated 40% of the amount of SO2 released during the Pinatubo eruption (18.5 ± 4 Mt) was emitted into the lower stratosphere (Guo et al. 2004; Krueger et al. 2008). Climate simulation studies suggest significant decreases in global and tropical-land-area mean precipitation following the eruptions of Toba about 74 000 years ago (Robock et al. 2009; Timmreck et al. 2012) and of Pinatubo in 1991 (Broccoli et al. 2003), as well as based on composites of several volcanic eruptions (Robock and Liu 1994; Joseph and Zeng 2011; Schneider et al. 2009). Geoengineering has taken an interest in these violent events as observable, potential proxies to estimate the consequences of climate engineering via injection of sulfate into the stratosphere. In this overall context, it is of interest to examine the response of current-generation climate models to the eruptions of Pinatubo and El Chichón.
Iles and Hegerl (2014) studied the responses to volcanic eruptions simulated by atmosphere–ocean coupled climate models in phase 5 of the Coupled Model Intercomparison Project (CMIP5). They found that the models simulate significant global precipitation reductions and that the reductions are largest in the tropics.
The aim of our study is to analyze how well the 17 atmosphere-only models in phase 5 of the Atmospheric Model Intercomparison Project (AMIP5) simulate the observed surface air temperature and precipitation responses to the eruptions of El Chichón (17°N) in 1982 and Mount Pinatubo (15°N) in 1991. The study is complementary to that of Iles and Hegerl (2014) in two respects: first, in terms of the simulation data examined (AMIP instead of CMIP—i.e., using prescribed, observation-based sea surface temperature data) and second, in that we remove the ENSO signal interfering with the volcano signal (see below) from both observations and model data. Iles and Hegerl (2014) removed it only from the observations and relied on statistical averaging out for the model data.
We focus on the El Chichón and Pinatubo events as they were by far the strongest eruptions after 1979, when satellite-based estimates of precipitation became available for global analysis. We focus on tropical land areas as precipitation reductions after low-latitude volcanic eruptions have been found to affect the tropical regions (20°N–20°S) in particular, which has been related to a weakening or contraction of the Hadley circulation (Robock and Liu 1994; Trenberth and Dai 2007; Schneider et al. 2009).
Identifying potential surface climate effects of volcanic aerosol is complicated by the fact that recent eruptions took place concurrently with warm phases of El Niño–Southern Oscillation (ENSO). During such a warm phase, heat is transported from the ocean to the atmosphere in volcanically quiescent times, so surface air temperatures are enhanced while precipitation over tropical land regions decreases, as precipitation shifts from the land to the ocean (Trenberth et al. 2002). Thus, the influence of ENSO partially masks the effects of the volcanic aerosol in surface temperature and precipitation time series. Over tropical land regions, for example, an El Niño phase and a strong volcanic eruption will each induce a precipitation reduction while having counteracting effects on surface air temperatures. When studying the surface climate effects of large volcanic eruptions, it is necessary, therefore, to disentangle the volcanic and the ENSO influences.
In our AMIP5 data, we achieve this separation by applying the statistical lag-correlation/regression analysis method presented by Gu and Adler (2011), who investigated volcanic and ENSO signatures in the Global Precipitation Climatology Project (GPCP), version 2.1 (Adler et al. 2003).
The objectives of our study are 1) to determine the time lags of the temperature and precipitation responses to changes in the ENSO phase based on observations and AMIP5 model simulations, 2) to investigate the observed and simulated sensitivity of the surface climate responses to the ENSO phase, and 3) to compare the magnitudes of the observed and the simulated posteruptive temperature and precipitation anomalies corrected for the ENSO contribution. We go beyond the work of Gu and Adler (2011) by investigating these questions for AMIP model simulations and for three different observational datasets of surface air temperature and precipitation.
2. Data and methods
a. Temperature observations
Observed 2-m surface air temperatures were obtained from 1) the Global Historical Climatology Network (GHCN) and the Climate Anomaly Monitoring System (CAMS), version 3.01 (Fan and van den Dool 2008); 2) the University of Delaware’s air temperature dataset (UDel), version 3.01 (Willmott and Matsuura 1995); and 3) the University of East Anglia Climatic Research Unit temperature dataset (CRU TS), version 3.20 (Harris et al. 2014). These datasets contain global monthly station data interpolated to a 0.5° × 0.5° grid. Because there are no satellite data of precipitation available prior to 1979, this year was chosen as the start year for the present study. We chose 2005 as the last assessed year for reasons explained below.
b. Precipitation observations
Observational data of tropical precipitation over land for years 1979–2005 are taken from three sources: 1) the GPCP, version 2.2 (Adler et al. 2003); 2) the Climate Prediction Center Merged Analysis of Precipitation (CMAP), version 1201 (Xie and Arkin 1997); and 3) the January 2011 version of the Precipitation Reconstruction Over Land dataset (PRECL; Chen et al. 2002). All three datasets provide gridded global monthly mean precipitation at 2.5° × 2.5° spatial resolution. The GPCP and CMAP datasets are merged satellite and surface rain gauge estimates, while the PRECL is rain gauge based only.
c. Model data
All simulated data come from the atmosphere-only twentieth-century CMIP5 simulations which have been run with observed sea surface temperatures (SSTs) and sea ice concentrations (CMIP5 experiment 3.3, referred to as AMIP5; Taylor et al. 2012). This ensures that the GCMs are subject to historically correct El Niño/La Niña phases.
Table 1 provides an overview of the 17 models whose simulations we analyzed in this study. We considered only GCMs for which at least two ensemble members are available and that account for volcanic forcing. Ensemble sizes range from 2 to 6 members, as shown in Table 1. Altogether, we have analyzed 64 ensemble members. For many of the models considered here, the atmosphere-only simulations have been run only for years after 1979 or before 2005. For that reason, our study is based on the years 1979–2005.
CMIP5 models evaluated in this study including their resolution (number of degrees in longitude nlon by number of degrees in latitude nlat). CESM1(CAM5), GISS-E2-R (p3), MIROC5, and MRI-CGCM3 calculate gas-to-aerosol conversion and aerosol heating as a function of the simulated atmospheric state variables (“online”).
Different stratospheric aerosol forcing datasets have been used by the modeling groups, as CMIP5 does not provide emission data for volcanic aerosols. We have compared the three stratospheric aerosol optical depth (AOD) datasets most commonly used in the 17 models. The one by Sato et al. (1993) contains the stratospheric AOD at 550 nm. The Stenchikov et al. (1998) dataset provides stratospheric AOD in the 442–625-nm solar band, while the stratospheric AOD of Ammann et al. (2003) is provided at 500 nm. The Ammann et al. (2003) and Stenchikov et al. (1998) datasets only extend until December 1999. The stratospheric AOD time series of Sato et al. (1993) and Stenchikov et al. (1998) are very similar in the tropical mean, whereas clear differences exist in comparison to Ammann et al. (2003), whose peak tropical mean stratospheric AOD is about 150% that of the former two datasets after both eruptions and whose stratospheric AOD decay times exceed those of the other two datasets.
d. Choice of posteruptive periods
We studied 1-yr posteruptive periods because the volcanic aerosol had a stratospheric residence time on the order of a year in the case of the El Chichón and Pinatubo events. Posteruptive periods from June 1982 to May 1983 (El Chichón) and from August 1991 to July 1992 (Mt. Pinatubo) were chosen to account for the time needed for the formation of aqueous H2SO4 aerosol droplets from the SO2 that gathered in the lower stratosphere after the eruptions (Thomason and Peter 2006). We repeated the analysis also for a 2-yr posteruptive period but obtained largely the same results (not shown in the following).
e. ENSO removal
Gu and Adler (2011) suggested that the El Niño/La Niña signal may be removed in the tropics by making use of its strong correlation with tropical land 2-m surface air temperatures 
In practical terms, we proceed as follows, for both our three observational and 64 AMIP5 model datasets. The Niño-3.4 index, which is the time series of monthly mean SST anomalies averaged over the tropical Pacific (5°N–5°S, 120°–170°W), was used to represent the ENSO phase. The index was computed from observed SSTs of Taylor et al. (2000). We interpolated all datasets bilinearly to a T63 grid (1.9° × 1.9°) for comparability. Then, at each grid point, we constructed time series of surface air temperature anomalies. We computed the tropical mean as a 20°N–20°S area-weighted average, detrended the time series, and removed the seasonal cycle by subtracting monthly climatologies based on volcanically largely unperturbed times (from April 1979 to March 1982, from April 1985 to March 1991, and from April 1995 to March 2005). We will refer to the detrended and deseasonalized time series as the filtered 
In an analogous manner, the lag-correlation/regression analysis was applied to the three precipitation observation time series and to all of the 64 ensemble members individually.
We note that nonlinear ENSO effects remain poorly understood. By adopting a linear model for the relationship between the ENSO phase and filtered 
3. Results
a. Lags and correlations (tropical-land-area mean)
Shown in Fig. 1 are the filtered 
Filtered 
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Filtered
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Filtered
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Figure 2 quantifies the correlation strength (i.e., R) as a function of the number of months by which the respective temperature time series is shifted relative to the ENSO index. The lags are well defined, as each of the correlation functions is monotonously increasing toward a single maximum.
Shown on the y axis is the Pearson correlation coefficient between filtered 
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
Shown on the y axis is the Pearson correlation coefficient between filtered
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
Shown on the y axis is the Pearson correlation coefficient between filtered
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
The lags of the observation-based time series are 4 months (GHCN CAMS and UDel) and 5 months (CRU TS). Those of the model-based time series range from 2 to 7 months with a mean of 4.3 months. Figure 2 also illustrates that for most models the scatter in time lag is small among the ensemble members.
The correlation coefficients between observed lag-shifted filtered temperature and the ENSO index are 0.74 (UDel) and 0.75 (GHCN CAMS and CRU TS). The simulated correlation coefficients range from 0.58 to 0.84 among the 64 ensemble members with a mean simulated correlation coefficient of 0.71. The strongest temperature–ENSO correlation is simulated by CanAM4 (ensemble mean of 0.83; model index 16), while the weakest correlation is simulated by MRI-CGCM3 and FGOALS (ensemble mean of 0.61 in each model; model indices 11 and 14, respectively).
Figure 3 illustrates the correlation between the Niño-3.4 index and the filtered precipitation. When comparing the onset times of ENSO events, such as the developing phase of the 1982/83 El Niño event at the beginning of 1982 or the 1997/98 El Niño developing phase at the beginning of 1997, it becomes clear that the observed and the simulated precipitation responses occur with a near-zero time lag. In several models, such as MIROC5 and the IPSL-CM5A-LR (model indices 8 and 13, respectively), the precipitation over tropical land areas seems to respond more sensitively to changes in the ENSO phase than is observed, in particular during El Niño onset phases. These models simulate precipitation minima before the ENSO index reaches its maximum value during the 1991/92 El Niño period. This may imply a too-rapid shift of the simulated convective activity from the tropical land regions toward the ocean.
As in Fig. 1, but for filtered precipitation. The time series variance s2 [(mm day−1)2] is provided as an ensemble mean for each model and as a mean over the observational datasets.
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
As in Fig. 1, but for filtered precipitation. The time series variance s2 [(mm day−1)2] is provided as an ensemble mean for each model and as a mean over the observational datasets.
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
As in Fig. 1, but for filtered precipitation. The time series variance s2 [(mm day−1)2] is provided as an ensemble mean for each model and as a mean over the observational datasets.
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
As shown in Fig. 4, the observed and simulated filtered precipitation time series are significantly negatively correlated with the ENSO phase. Among the 64 ensemble members, the simulated lags range from −4 to +2 months with a mean lag of −1.8 months. The observed precipitation responds to the ENSO phase with a time lag of zero months (GPCP and PRECL) or one month (CMAP). This finding is in agreement with Gu and Adler (2011), who also determined a zero lag using an earlier version of the GPCP dataset. The observed mean precipitation reacts much faster to ENSO phase changes than 
As in Fig. 2, but for filtered precipitation.
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
As in Fig. 2, but for filtered precipitation.
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
As in Fig. 2, but for filtered precipitation.
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
The correlation of the lag-shifted filtered observed precipitation with the Niño-3.4 index is −0.63, −0.61, and −0.54, respectively, in the GPCP, CMAP, and PRECL datasets. The mean correlation over the 64 ensemble members is −0.53 (maximum and minimum values of −0.68 and −0.26), so AMIP5 tends to underestimate the ENSO–precipitation correlation strength over tropical land regions.
The relationship between lag-shifted filtered 
Regression of lag-shifted filtered 
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Regression of lag-shifted filtered
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Regression of lag-shifted filtered
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The observed negative relationship between precipitation and the ENSO phase can be estimated well by a linear regression line, as shown in Fig. 6. According to the three observational datasets of precipitation, the filtered precipitation is reduced by 0.1 mm day−1 per unit of Niño-3.4 index. The majority of the models successfully simulate a similar ratio. About 35% of the observed variance in filtered precipitation is explained by the regression. The simulated R2 values range from 7% to 46% among the ensemble members.
Regression of lag-shifted filtered tropical mean precipitation over land on the Niño-3.4 index. To identify the models, the numbers in parentheses indicate the model indices as provided in Table 1.
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
Regression of lag-shifted filtered tropical mean precipitation over land on the Niño-3.4 index. To identify the models, the numbers in parentheses indicate the model indices as provided in Table 1.
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
Regression of lag-shifted filtered tropical mean precipitation over land on the Niño-3.4 index. To identify the models, the numbers in parentheses indicate the model indices as provided in Table 1.
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
b. ENSO-removed temperature and precipitation (tropical-land-area mean)
Figure 7 provides the 
Residual ENSO-removed 
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Residual ENSO-removed
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Residual ENSO-removed
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
As can be seen, removing the ENSO contributions significantly reduced the variance of the filtered 
Focusing now on our original goal, the impact of the El Chichón and Pinatubo eruptions as seen in appropriately treated time series, mean temperature reductions of about 0.4 and 0.6 K are observed over tropical land areas after the El Chichón and Pinatubo eruptions in late 1982 and the second half of 1992, respectively, in agreement with Gu and Adler (2011, their Figs. 3b and 8b).
The variances in the filtered precipitation time series were likewise significantly reduced by the ENSO removal for all models and the observational datasets, as shown in Fig. 8. The mean precipitation observed over tropical land areas is clearly reduced by up to 0.3 mm day−1 following the Pinatubo eruption, whereas the observed precipitation response to the eruption of El Chichón is more ambiguous, in agreement with Gu and Adler (2011, their Figs. 2b and 7b).
Residual ENSO-removed precipitation. Model indices, volcanic eruption times, and time series variances are indicated as in Fig. 1.
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
Residual ENSO-removed precipitation. Model indices, volcanic eruption times, and time series variances are indicated as in Fig. 1.
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
Residual ENSO-removed precipitation. Model indices, volcanic eruption times, and time series variances are indicated as in Fig. 1.
Citation: Journal of Climate 29, 4; 10.1175/JCLI-D-15-0034.1
Figure 9 shows the ranges of 
Simulated anomalies in 
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Simulated anomalies in
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Simulated anomalies in
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Before determining the significance of the observed and simulated posteruptive 
The anomalies shown in Fig. 9 are means over the above 12-month posteruptive periods. As shown in Fig. 9, the observed 
The AMIP5 models generally simulate 
Figure 10 shows the simulated and observed anomalies of all ensemble members. The mean and standard deviation of the simulated surface temperature and precipitation anomalies, taken over all ensemble members of all 17 models, are −0.08 ± 0.11 K and −0.12 ± 0.09 mm day−1 for the El Chichón eruption and −0.29 ± 0.11 K and −0.26 ± 0.10 mm day−1 for Pinatubo. This corresponds to signal-to-noise ratios of 0.7 and 1.3 for the anomalies in 
Simulated anomalies in 
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Simulated anomalies in
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Simulated anomalies in
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The ranges (maximum–minimum) of the simulated 
It is interesting to note that there is no apparent relation between the strength of the 
The Pinatubo eruption was 2–3 times larger than the eruption of El Chichón in terms of the stratospheric SO2 input (Thomason and Peter 2006). Figure 10 suggests that the stratospheric AOD increase and the reduction in evaporation following the El Chichón eruption were not large enough to cause a response in 
4. Conclusions
We have investigated surface air temperature and precipitation responses to the eruptions of El Chichón in April 1982 and Mount Pinatubo in June 1991 over tropical land areas, as simulated by 17 atmosphere-only GCMs in phase 5 of the Coupled Model Intercomparison Project (CMIP5).
The objectives of our study were to validate the time lags and coupling strengths of the simulated surface climate responses to changes in the ENSO phase and to validate the observed and the simulated posteruptive temperature and precipitation anomalies over tropical land areas by comparison to observed temperature and precipitation.
Focusing on the responses over tropical land regions, we found the following:
All models successfully simulate surface air temperature responses delayed by a few months relative to the ENSO phase (on average 4.3 months), which agrees well with the observed 4–5-month delay.
The strong positive correlation observed between mean temperatures and the ENSO phase (correlation coefficient of 0.75) is generally captured well by the models (simulated correlation of 0.71). There is, however, considerable scatter in the simulated correlation strength across models (ensemble means of 0.61–0.83).
The observed precipitation response lags the ENSO phase by 0–1 months. Most of the models appear to simulate a somewhat too-fast precipitation response during the El Niño onset (mean simulated lag of −1.8 months). This may be related to a too-rapid shift of simulated convective activity toward the ocean.
The models tend to underestimate the observed correlation strength between precipitation and ENSO phase (mean correlation of −0.59). The mean simulated correlation coefficient is −0.53. Simulated correlations of ensemble members range as low as −0.26.
The observed relationship between ENSO phase, as measured by the Niño-3.4 index, and temperature (or precipitation) can be considered linear to a reasonably good approximation for 1979–2005. The models successfully capture this linearity, though in the case of temperature typically at lower values of R2.
The observed mean temperature and precipitation increase by 0.16°C and 0.1 mm day−1 per unit of Niño-3.4 index. Many but not all models simulate temperature and precipitation sensitivities in agreement with this finding.
Observed ENSO-removed temperature and precipitation decreased by about 0.35 K and 0.25 mm day−1 after the Pinatubo eruption, whereas no significant decrease in either variable was observed after El Chichón. The models generally capture this behavior, though with large scatter. They appear to somewhat overestimate the precipitation response to El Chichón.
The stratospheric AOD increase and associated reduction in evaporation after the El Chichón eruption seem to not have been large enough to result in temperature or precipitation responses beyond the level of natural variability.
Obviously, the present study is only a first step. An area we did not touch upon is the model physics behind the good or bad agreement between observations and one particular model—or, similarly, the reason for the considerable spread across ensemble members for at least some models. Another question concerns the quality of the ENSO removal. One possible way forward here could be a dedicated modeling study: perform two sets of atmosphere-only model simulations that differ only in the presence or absence of Pinatubo-like eruptions and check whether the ENSO removal procedure applied here can indeed retrieve the difference signal of the two sets of simulations.
Acknowledgments
We thank Christoph Frei for helpful discussions and are grateful to Urs Beyerle for downloading and archiving the CMIP5 datasets. We also thank three anonymous reviewers for their constructive comments on our manuscript. The CMAP, PRECL, and GPCP precipitation data as well as the GHCN and UDel surface air temperature data were provided by NOAA/OAR/ESRL PSD, Boulder, Colorado (http://www.esrl.noaa.gov/psd/data/gridded/). The CRU TS air temperature data were provided by the NCAS British Atmospheric Data Centre (http://browse.ceda.ac.uk/browse/badc/cru/data/cru_ts/cru_ts_3.20/data/). The dataset of Sato et al. (1993) is available online (http://data.giss.nasa.gov/modelforce/strataer/). The Stenchikov et al. (1998) dataset is contained in the ECHAM6 archive. The dataset of Ammann et al. (2003) is provided online (http://www.ncdc.noaa.gov/paleo/pubs/ammann2003/ammann2003.html). All of the aforementioned datasets are available for free download. This work was supported by ETH Research Grant CH2-01 11-1 and the Center for Climate Systems Modeling (C2SM).
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