Currently, little is known on how volcanic eruptions impact large-scale climate phenomena such as South American paleo-Intertropical Convergence Zone (ITCZ) position and summer monsoon behavior. In this paper, an analysis of observations and model simulations is employed to assess the influence of large volcanic eruptions on the climate of tropical South America. This problem is first considered for historically recent volcanic episodes for which more observations are available but where fewer events exist and the confounding effects of El Niño–Southern Oscillation (ENSO) lead to inconclusive interpretation of the impact of volcanic eruptions at the continental scale. Therefore, we also examine a greater number of reconstructed volcanic events for the period 850 CE to present that are incorporated into the NASA GISS ModelE2-R simulation of the last millennium.
An advantage of this model is its ability to explicitly track water isotopologues throughout the hydrologic cycle and simulating the isotopic imprint following a large eruption. This effectively removes a degree of uncertainty associated with error-prone conversion of isotopic signals into climate variables, and allows for a direct comparison between GISS simulations and paleoclimate proxy records.
Our analysis reveals that both precipitation and oxygen isotope variability respond with a distinct seasonal and spatial structure across tropical South America following an eruption. During austral winter, the heavy oxygen isotope in precipitation is enriched, likely due to reduced moisture convergence in the ITCZ domain and reduced rainfall over northern South America. During austral summer, however, more negative values of the precipitation isotopic composition are simulated over Amazonia, despite reductions in rainfall, suggesting that the isotopic response is not a simple function of the “amount effect”. During the South American monsoon season, the amplitude of the temperature response to volcanic forcing is larger than the rather weak and spatially less coherent precipitation signal, complicating the isotopic response to changes in the hydrologic cycle.
Plinian (large, explosive) volcanic eruptions are a dominant driver of naturally forced climate variability during the last millennium (LM, taken here to be 850 CE to present; e.g., Stothers and Rampino, 1983; Hansen et al., 1992; Crowley et al., 2000; Robock et al., 2000, 2003; Goosse et al., 2005; Yoshimori et al., 2005; Emile-Geay et al., 2008; Cole-Dai, 2010; Timmreck, 2012; Iles et al., 2013; Schurer et al., 2014). In addition to their importance for 20th century climate, they are the largest magnitude external forcing during last 1000 years of the preindustrial period, the most recent key interval identified by the Paleoclimate Modelling Intercomparison Project Phase III (PMIP3). As such, these eruptions serve as a natural test bed to assess the skill of climate models in simulating how climate responds to external perturbations.
Although the most significant climate impacts of eruptions are realized over
just a few years following the eruption, they provide the source of the
largest amplitude perturbations to Earth's energy budget during the LM. For
example, the eruption of Mt. Pinatubo in June 1991, although transitory,
exerted a radiative forcing comparable to an instantaneous halving of
atmospheric CO
Aerosol optical depth (AOD) used to force the
NASA GISS ModelE2-R over the last millennium and (bottom) zoomed in on the
period 1950–1999 (Sato et al., 1993; Crowley and Unterman, 2013) as discussed in the text. AOD is the
vertically integrated (15–35 km) and latitudinal average from 30
The principle climate impact from volcanic eruptions results from the
liberation of subsurface sulfur-containing gases such as sulfur dioxide,
which are injected into the stratosphere and react with water to form
sulfate aerosols (e.g., Harshvardhan and Cess, 1976; Coakley and Grams,
1976; Pollack et al., 1976, 1981; Lacis et al., 1992). The most pronounced
impact of large tropical eruptions includes a radiatively cooled troposphere
and heated stratosphere (e.g., Lacis et al., 1992; Robock and Mao, 1995;
Stenchikov et al., 1998). Sulfate aerosols from the Mt. Pinatubo eruption
grew from a background effective radius of
Studies on the impacts of volcanic eruptions have generally focused on global or Northern Hemisphere metrics (e.g., Lucht et al., 2002; Gillett et al., 2004; Shindell et al., 2004; Oman et al., 2005, 2006; Anchukaitis et al., 2010; Peng et al., 2010; Evan et al., 2012; Zhang et al., 2013; Man et al., 2014; Stoffel et al., 2015), for instance in examining responses to the East Asian monsoon or the Arctic Oscillation (e.g., Ortega et al., 2015). Comparatively little attention has been given to the Southern Hemisphere, or to South America specifically (although see Joseph and Zeng, 2011, and Wilmes et al., 2012). Some previous work has focused on the Southern Annular Mode in the ERA-40 and NCEP/NCAR reanalysis, in addition to a previous version of NASA Goddard Institute for Space Studies (GISS) Model-E (Robock et al., 2007) and in a subset of CMIP3 models (Karpechko et al., 2010) or in CMIP5 (Gillett and Fyfe, 2013).
How volcanic forcing is expressed over South America remains an important target question for several reasons. First, recognition of the South American monsoon system (SAMS) as an actual monsoon system is less than two decades old (Zhou and Lau, 1998), and thus study of SAMS dynamics is still relatively young (Sect. 1.3) and very little work has been done specifically focused on volcanic eruptions. For instance, should we expect to see a reduction in austral summer rainfall? Secondly, the largest volcanic eruptions during the late 20th century (e.g., Mt. Agung, 1963, Indonesia; El Chichón, 1982, Mexico; Mt. Pinatubo, 1991, island of Luzon in the Philippines – hereafter, these three events are referred to as L20 eruptions) occur quasi-simultaneously with an anomalous El Niño–Southern Oscillation (ENSO) state, and in general represent a small sample size in a noisy system. This limits the prospect of robust hypothesis testing and guidance for what impacts ought to be expected following large eruptions at the continental scale. Finally, South America offers promise for a comparatively dense network of high-resolution proxy locations relative to other tropical regions (see below), offering the potential to detect whether South American hydroclimate signals to large eruptions are borne out paleoclimatically.
In this study, we explore the post-volcanic response of South American
climate operating through the vehicle of unique model simulations (spanning
the LM) using the recently developed GISS ModelE2-R (LeGrande et al., 2016;
Schmidt et al., 2014a), which allows for the sampling of a greater
number of events than is possible over the instrumental period. Emphasis is
placed on temperature and precipitation, but a novel part of this study
extends to the response of water isotopologues (e.g., H
The aim of this paper is to create a potentially falsifiable prediction for the isotopic imprint that a volcanic eruption should tend to produce across the South American continent. The ability to explicitly model the isotopic response allows for a less ambiguous comparison of simulations and paleoclimate records and for hypothesis testing. It is unclear whether or not the current proxy archives are suitable to test such a prediction with high confidence, given dating uncertainties (in both proxies and in the actual timing of eruptions), or the level of noise in proxy data and the real world. Additionally, the prevailing high-resolution archives in South America only feature a few tropical records (Vimeux et al., 2009; Neukom and Gergis, 2012; Vuille et al., 2012). Nonetheless, the growing number of high-resolution records offers hope that testing the modeled response to high-frequency volcanic signals will be an avenue for future research. This can also better inform debate centered on the inverse problem in interpreting isotopic signals (i.e., what do observed changes in proxy data imply about past climate changes?), which remains contentious (Sect. 1.4).
The structure of this article is as follows. In the remaining part of Sect. 1, we summarize previous literature on the impact of large volcanic eruptions on paleoclimate, in addition to a discussion of South American climate. Section 2 presents data and methodology, including how volcanic forcing is implemented in ModelE2-R. Section 3 discusses our results and we end with conclusions in Sect. 4.
Volcanic forcing has had a very large influence on the climate of the LM (Crowley, 2000; Hegerl et al., 2003, 2006; Shindell et al., 2004; Mann et al., 2005; Fischer et al., 2007; D'Arrigo et al., 2009; Timmreck, 2012; Esper et al., 2013; Ludlow et al., 2013; Schurer et al., 2014). Several studies (Miller et al., 2012; Schurer et al., 2014; Atwood et al., 2016; McGregor et al., 2015) collectively provide a compelling case that volcanic forcing may be substantially more important than solar forcing on a hemispheric to global scale during the LM, in addition to driving a large portion of the interannual to multi-decadal variability in LM simulations (Schmidt et al., 2014b).
Two volcanic forcing data sets (Gao et al., 2008; Crowley and Unterman, 2013) relying on ice core reconstructions of volcanism are used as input in the LM ModelE2-R simulations (and are the CMIP5/PMIP3 LM standard), as discussed in Sect. 2.
South America is home to nearly 390 million people. The continent spans a
vast meridional extent (from
The most prominent climatic feature of tropical and subtropical South America is the South American monsoon system (Zhou and Lau, 1998; Marengo et al., 2001, 2012; Vera et al., 2006; Garreaud et al., 2009). Much of South America is in a monsoon regime, with tropical/subtropical rainfall over the continent exhibiting a pronounced seasonal cycle. Unlike other monsoon systems such as that in Asia, low-level easterly winds prevail during the entire year in tropical South America, although the wind anomalies do change direction when the annual mean wind field is removed from winter and summer composites (Zhou and Lau, 1998).
During austral winter, the maximum in continental precipitation is largely restricted to north of the Equator, in a band-like pattern associated with the oceanic Intertropical Convergence Zone (ITCZ). During austral summer, convection is displaced from northwestern South America, and a band of heavy precipitation covers much of the continent, from the southern Amazon Basin to central Brazil and northern Argentina. A distinctive feature of the SAMS is the South Atlantic Convergence Zone (SACZ), a band of cloudiness and precipitation sourced primarily from the tropical Atlantic that extends diagonally (southeastward) from the Amazon towards southeastern Brazil (Fig. 2).
Top: observed Climatological Precipitation for
DJF (shading, in mm day
The SAMS onset occurs around the end of October and the demise between the end of March and April (e.g., Nogués-Paegle et al., 2002; Vera et al., 2006; Silva and Carvalho, 2007). The dominant mode of intraseasonal precipitation variability over South America during summer exhibits a dipole pattern (Nogués-Paegle and Mo, 1997), seesawing between the SACZ region and southeastern South America, the latter including the densely populated La Plata Basin with local economies strongly dependent on agricultural activities.
The SAMS is strongly modulated by ENSO behavior on interannual timescales (Vuille and Werner, 2005; Garreaud et al., 2009). In general, SAMS-affected regions of tropical South America tend to experience drier than normal conditions during El Niño, while conditions in subtropical latitudes are anomalously humid, including the southeastern part of the continent. Surface air temperatures tend to be anomalously warm in tropical and subtropical South America during El Niño events. These relationships depend somewhat on the time of year, and during La Niña events, the pattern is essentially reversed.
SAMS variability spanning most of the Holocene has been diagnosed from speleothem records in the Peruvian Andes (Kanner et al., 2013) and a review focused on the last 1000–2000 years was given in Bird et al. (2011) and Vuille et al. (2012). In all cases, a critical piece of information that is required to properly diagnose paleo-SAMS variability is the ability to translate oxygen isotope variability from natural recorders into a physical climate signal of interest.
Early work on isotopes in ice core records from the tropical Andes detected a Little Ice Age (LIA) signal in the oxygen isotope composition of the ice, with results initially interpreted to reflect variations in local temperature due to their resemblance to ice core records from Greenland (e.g., Thompson et al., 1995, 1998) and due to their isotopic enrichment over the past 150 years, in parallel with rising global mean temperatures (Thompson et al., 2006). A temperature-dependence to oxygen isotope variability has been long known and is particularly important in mid- to high latitudes (Dansgaard, 1964) and is most directly related to the ratio of initial and final water vapor content of a parcel that is transported horizontally, rather than the temperature-dependence of fractionation itself (Hoffman and Heimann, 1997).
This interpretation in the tropics has been challenged through a number of observational and modeling efforts (Hardy et al., 2003; Vuille and Werner 2005; Vimeux et al., 2005, 2009; Kanner et al., 2012) which suggest that the isotopic signal is more closely related to the degree of rainout upstream in regions of intense convection (in the case of South America, over the Amazon Basin). Additionally, since sea surface temperatures (SST) in the Pacific have a large influence on SAMS intensity on interannual timescales in the present, oxygen isotope variability over much of tropical South America is linked to the state of the equatorial Pacific (Bradley et al., 2003; Vuille et al., 2003a, b).
In regimes that are highly convective in nature as in tropical South
America, empirical evidence shows that the amount of precipitation (the
so-called “amount effect”, Dansgaard, 1964) rather than the condensation
temperature correlates most strongly with
The influence of precipitation amount on
Nonetheless, oxygen isotopes respond in unique ways depending on the climate forcing of interest. Indeed, a unique, quantitative local relationship between an isotope record and any particular climate variable of interest is unlikely to hold for all timescales and prospective forcing agents (Schmidt et al., 2007) thus motivating the use of forward modeling to work in conjunction with proxy-based field data. For the remainder of this paper, we focus specifically on the volcanic forcing response.
The primary tool used in this study is the water-isotope-enabled GISS
ModelE2-R. ModelE2-R is a fully coupled atmosphere–ocean general circulation model (GCM) (LeGrande et
al., 2016; Schmidt et al., 2014a) that explicitly tracks stable
water isotopes. The version used here is the same as the non-interactive
atmospheric composition (NINT) physics version used in the CMIP5 experiments
(Miller et al., 2014). The current model features 2
Due to uncertainties in past radiative forcing, a suite of LM simulations
using ModelE2-R have been run with different combinations of plausible
solar, volcanic, and anthropogenic land-use histories (Schmidt et al., 2011,
2012) but with identical greenhouse gas and orbital evolution. These
simulations span the period 850–2005 CE. There are two reconstructions of
past volcanic activity (Gao et al., 2008; Crowley and Unterman, 2013) that
are used in six combinations of the ModelE2-R LM simulations (see the
“past1000” experimental design at
For the LM, three forcing combinations are available in the GISS ModelE2-R simulations that use the Crowley reconstruction for volcanic perturbations. These include Pongratz et al. (2008; land)/Krivova et al. (2007; solar), Kaplan et al. (2010; land)/Krivova et al. (2007; solar), and Pongratz et al. (2008; land)/Steinhilber et al. (2009; solar) (see Schmidt et al., 2011, 2012). We focus only on results from the Crowley reconstruction prior to 1850 CE due to a mis-scaling of the Gao forcing in the model that roughly doubled the appropriate radiative forcing. For the historical period (1850–present), the volcanic forcing history is based on Sato et al. (1993) and is equivalent among the different (six) simulation members.
Crowley and Unterman (2013) discuss the details behind the LM aerosol
optical depth (AOD) reconstruction that defines the volcanic forcing
time series in ModelE2-R (Fig. 1). This estimate is derived from sulfate
peaks in ice cores, which are relatively well dated and referenced to the
historical record during the satellite era. Crowley and Unterman (2013)
provide an AOD history over four latitude bands (from 0 to 30 and
30 to 90
We note that there are more recent volcanic reconstructions available (e.g., Sigl et al., 2015) suggesting modifications to the timing or magnitude of LM eruptions, as well as developments of data sets focusing on sulfur injection and microphysics-based evolution of the aerosol forcing (e.g., Arfeuille et al., 2014). In this contribution, we are agnostic concerning the veracity of the forcing data sets that were standard for CMIP5/PMIP3 but stress that timing of eruptions is irrelevant in our modeling context and that the model results should be interpreted as a self-consistent response to the imposed AOD and particle size.
Water isotope tracers are incorporated into the model's atmosphere, land surface, sea ice, and ocean. These isotopes are advected and tracked through every stage of the hydrologic cycle. At each phase change (including precipitation, evaporation, ice formation or melting) an appropriate fractionation factor is applied (Schmidt et al., 2005) and all freshwater fluxes are tagged isotopically. Stable isotope results from the lineage of GISS models have a long history of being tested against observations and proxy records (e.g., Schmidt et al., 2007; LeGrande and Schmidt, 2008, 2009; Lewis et al., 2010, 2013, 2014; Field et al., 2014).
In addition to the model, we briefly explore the observed instrumental
record to assess responses to eruptions occurring during the 20th century.
To do this, we take advantage of the NASA GISS Surface Temperature analysis
(GISTEMP) land–ocean index (Hansen et al., 1999), and Global Precipitation
Climatology Centre (GPCC) v6, a monthly precipitation data set over land
(Schneider et al., 2011). For Figs. 2 and 3m where ocean climatological
data are shown, we use the Global Precipitation Climatology Project (GPCP)
version 2.2 (Adler et al., 2003), a combined land station and satellite
product available since 1979. These data sets are called upon to gauge the
tropical climate response following the three L20 eruptions. We use the
2.5
Seasonal cycle (DJF minus JJA) of precipitation in the
Finally, in Sect. 3.1 we present data from the Global Network of Isotopes
in Precipitation (GNIP) accessible from the International Atomic Energy
Agency (IAEA) for
We present the spatial pattern of observed and simulated response for
temperature and precipitation over land for two L20 eruptions (El
Chichón and Mt. Pinatubo). Results are shown for annual means in 1983
and 1992. We choose only two for brevity, as our argument that assessing the
signal in any specific region is difficult in a small sample of eruptions is
unaffected. Because of the dominant influence of unforced variability on
tropical South American climate (Garreaud et al., 2009) overriding the
volcanic signal during the L20 eruptions, we instead present a superposed
epoch anomaly composite of the tropical-mean temperature anomaly, zonally
averaged from 30
For the full LM spatial composites, we use only eruptions where vertically
integrated (15 to 35 km) stratospheric AOD averaged from 30
Time of eruptions and global aerosol optical depth (AOD) from Crowley and Unterman (2013). List of eruptions used in study. List of LM and L20 eruptions.
For the LM “non-eruption” fields, we use 15 years prior to the eruption as
a reference period to calculate the anomaly for each event, unless another
event occurs during that time (overlap occurs only once for eruptions in
1809 and 1815), in which case the pre-1809 climatology is used twice. The
exception is for Mt. Pinatubo, which again uses the previous 5 years to
calculate the anomaly. When constructing seasonal averages of
Since each post-eruption difference field is computed using the immediate response minus a local 15-year climatology, time is not relevant in this analysis and so we use all three members with the Crowley forcing (representing over 3000 years of simulation time) to generate a composite that features 45 volcanic “events” (15 eruptions in each of the three members). In the historical (post-1850) extension of these runs, the coding error that resulted in a mis-implementation of the Gao forcing is not an issue, and so we use six ensemble members each (three volcanic events in six ensemble members) for the L20 results.
The ensemble-mean composite results displayed for the LM eruptions include contributions from three members that differ not just in the internal variability but also in their solar and land-use forcing. Similarly, the L20 results are from model runs that also include other transient historical forcings occurring at the time of the eruption, including greenhouse gas increases throughout the duration of the event (although these forcings are the same among all ensemble members). However, in all cases we focus only on the immediate years after the eruption. Since the primary signal of interests is expected to be large compared to the impact of more slowly varying and smaller-amplitude forcings, the ensemble spread for a given eruption can be interpreted as a sampling of the model internal variability coincident with the event. We have tested our composite results using the same dates as our volcanic events in simulations with other varying forcings with no volcanoes (there are no volcano-only runs with this model version for the LM), and the results are indistinguishable from noise (not shown). The LM composite results are discussed in Sect. 3.2.
Finally, it is now well appreciated that any climate response under
investigation will be shackled to the spatial structure of the forcing
imposed on a model. For example, preferential heating/cooling of one
hemisphere will induce different tropical precipitation responses than a
well-mixed gas that behaves CO
For the L20 volcanic events, El Niño events are occurring quasi-simultaneously with the eruption. This introduces a pervasive issue when attempting to isolate the volcanic signal (e.g., Robock, 2003; Trenberth and Dai, 2007; Joseph and Zeng, 2011) and is particularly important over South America (e.g., Garreaud et al., 2009).
In order to remove the effects of ENSO from the superposed epoch and spatial
composite analyses described above in the GISTEMP and GPCC data, we first
perform a multiple regression with the variable of interest over the period
1951–2005 using a linear time trend and the Niño 3 index as predictors
(5
For each of the six ensemble members used in the model L20 composite, a similar procedure is performed in which the Niño 3 index (consistent with the realization of the Niño 3 domain SSTs in that model simulation) is calculated and regressed out in the same manner. For the full LM computations, the number of larger-amplitude events in the three-ensemble member composite should help average out the influence of Pacific SST variability, and no ENSO removal procedure is applied.
Figure 3 illustrates that ModelE2-R reproduces the seasonal cycle of
climatological rainfall (comparing Fig. 3a with b) and oxygen isotope
distribution (comparing Fig. 3c with d) with some fidelity over South
America. This includes a meridional migration of the ITCZ toward the summer
hemisphere and an intensification of the South American monsoon during DJF.
Where data permit (Fig. 3c), there is good agreement between model and
observations, both displaying oxygen isotope DJF enrichment relative to JJA
in the tropics north of the Equator and the higher latitudes south of
30
Figure 4 shows the ENSO-removed superposed epoch analysis for tropical temperature associated with the recent three L20 eruptions. There is good agreement between the observed and modeled temperature response, both in amplitude and recovery timescale. The tropical-mean cooling is on the order of several tenths of a degree, and larger after Mt. Pinatubo (not shown individually).
Composite tropical (30
The spatial structure of the post-El Chichón and Pinatubo events in land observations and the individual model realizations are shown in Figs. 5 and 6, respectively. Observations exhibit cooling over much of the globe, especially after Mt. Pinatubo, that is largely reproduced by the model. However, there is considerable spread among the individual ensemble members and between the two events, indicating a large role for internal variability in dictating the observed spatial pattern following these events. This is also true over South America.
Annual-mean temperature change (
As in Fig. 5, except for precipitation change
(mm day
In GISTEMP, the high latitudes of South America cool more than the tropical region of the continent after Mt. Pinatubo. There is still a residual signal from ENSO in tropical South America following both L20 eruptions that is not reproduced by the model. This is not unexpected, since ENSO events comparable to the magnitude of the historic realizations do not occur coincident with the volcanic forcing in the individual ensemble members. The magnitude of this signal is sensitive to the Niño index used in the regression method described above. Without ENSO removal, tropical South America warms following the two eruptions (not shown). The influence of ENSO appears minimal over the higher-latitude sectors of the continent.
Last millennium post-volcanic temperature composite
(
The precipitation pattern following the L20 eruptions exhibits substantial variability in space and across eruptions, with a general drying pattern over land in tropical latitudes. South America experiences less precipitation near the Equator after Mt. Pinatubo (see also Trenberth and Dai, 2007), a pattern reproduced in some of the ensemble realizations.
It should be noted that model–observation comparison is hindered not just by internal variability but also by the specified historical volcanic forcing in the model. In fact, the Stratospheric Aerosol and Gas Experiment (or SAGE) II satellite sensor was saturated by the aerosol cloud after Mt. Pinatubo; subsequent work (Santer et al., 2014; Schmidt et al., 2014c) suggests that the forcing following Pinatubo is too large in the CMIP5 generation of models.
Because of the considerable variability seen in observations (following historical eruptions) and also across ensemble members, it is evident that a larger signal-to-noise ratio than is available from the L20 eruptions alone is required to help isolate any volcanic signal. ModelE2-R is the laboratory from which we proceed to sample a larger number of events, some of which contain larger amplitude than the L20 eruptions.
Figure 7 shows the LM post-volcanic temperature composite for all 45 events.
During both seasons, cooling is statistically significant over virtually the
entire continent (stippling indicates significance at the 90 % level,
The precipitation anomalies for the LM composite are shown in Fig. 8. As expected, there is a distinct seasonal structure in the response, with the largest anomaly concentrated in a narrow region north of the Equator during austral winter, coincident with the location of climatological rainfall maxima in the region. During JJA, precipitation increases in the North Atlantic region following volcanic eruptions, while very strong and statistically significant precipitation reductions occur just north of the Equator (including over northern Brazil, Ecuador, Venezuela, Colombia, and Guyana) and encompassing the northern Amazon Basin. This signal is consistent with a weakening of the moisture flux owing to the decrease in saturation vapor pressure due to cooling that is demanded by the Clausius–Clapeyron relationship (Held and Soden, 2006). During this season, the precipitation response is significant virtually everywhere in northern South America. Figure S5 further illustrates that the JJA precipitation response is remarkably robust to all eruptions that enter into the composite.
Last millennium post-volcanic precipitation composite
(mm day
Figure 9b illustrates the relationship between area-averaged precipitation
from 20 to 0
The precipitation response during austral summer is more difficult to interpret (Fig. 8a). During this season, the zonally oriented Atlantic ITCZ migrates southward and the SACZ becomes more intense as it is connected with the area of convection over the central and southeastern part of the continent. It is noteworthy that the land cools substantially more than the surrounding ocean (Fig. 7), which one could expect to weaken the monsoon-sourced precipitation during DJF. While precipitation is indeed reduced over the tropical continent, the response is weaker than in JJA and less spatially coherent, with many areas failing to meet statistical significance. An analysis of the individual responses reveals that the signal is more eruption-dependent during DJF than during JJA (see Fig. S4), with a few events actually exhibiting modest increases in precipitation. Nonetheless, there is a clear tendency for reduced DJF precipitation within the SAMS region, although there is little to no dependence of the mean rainfall anomaly on the magnitude of the AOD perturbation, at least above the 0.1 threshold used in this study (Fig. 9b), unlike for equatorial South America during JJA. Conversely, the temperature response (Fig. 9a) depends on the size of the eruption in both seasons, as is expected given its dependence on the size of the radiative forcing.
Since the South American climate is intimately linked to large-scale
tropical dynamics, the global precipitation composite is shown in Fig. S6
to better inform the model response. The most robust signal is characterized
by a reduction in tropically averaged precipitation and the tendency for wet
regions to become drier, and dry regions to become wetter (see also Iles et
al., 2013; Iles and Hegerl, 2014), in contrast to the anticipated hydrologic
response in a future, higher-CO
Last millennium post-volcanic oxygen isotope in
precipitation (
Last millennium Hovmöller diagram (10 years, time
moving forward going upward, with year number labeled next to each month)
for
This pattern is a thermodynamic effect linked to reduced moisture
convergence within the convergence zones and to reduced moisture divergence
in the descending zones of the Hadley cell, which reduces the contrast in
values of precipitation minus evaporation (
The tendency for modest precipitation anomalies over the continent during DJF appears to be part of a pattern that spans a broad swath of longitudes across the entire deep tropics in association with the seasonal cycle. Nonetheless, the response during DJF is weaker over land.
In order to relate the responses discussed in the previous sections back to
a potentially observable paleoclimate metric, we show the composite
During the JJA season, there is a strong enrichment of the
During the austral summer, volcanic eruptions lead to a clear negative
excursion in
The austral summer
The correlation between
Taken together, these results suggest that the primary controls on oxygen isotope variability may vary by forcing agent, rather than being determined inherently by the latitude of interest (e.g., “precipitation-driven” in the tropics and “temperature-driven” in the extratropics). This conclusion is compelled by the fact that the precipitation production and distribution in proxy records are the result of an interaction between multiple scales of motion in the atmosphere, the temperature of air in which the condensate was embedded, and exchange processes operating from source to sink of the parcel deposited at a site. Thus, a consistent description of how to interpret oxygen isotopes into a useful climate signal cannot be given without considering all of these processes and the target process of interest.
Frequency distribution of 100 random 45-event composites in LM control simulation of ModelE2-R (blue) for temperature (top row), precipitation (middle), and oxygen isotopes in precipitation (bottom) for DJF (left column) and JJA (right column). Results averaged over same domains as in Fig. 9. Normal distribution with a mean and standard deviation equal to that of the data shown in red. Purple square shows the single 45-event composite used in this study, with the distribution of individual 15 volcanic eruptions (each averaged over three ensemble members) in black dots.
To further complement the spatial analysis, a composite Hovmöller
diagram is utilized (Fig. 11) in order to illustrate the time evolution of
the temperature, precipitation, and oxygen isotope response. For this plot,
the start of each eruption is defined as the closest January to the first
month in which AOD reaches 0.1 in order to illustrate the seasonal evolution
(rather than compositing by “month from each eruption” as in Fig. 3).
Therefore, for all 45 events in the composite, the local AOD may reach this
threshold within 5 months (before or after) of the January baseline point
(eruptions in June are rounded up to the following January). The
Hovmöller composites are plotted for 10 years (beginning January 3
years prior to the eruption). The closest January point to the start of each
eruption occurs in the 37th month of the Hovmöller (solid black
line in Fig. 11a, b, d). Results are zonally averaged from 77.5
to 45
Figure 11a demonstrates a substantial temperature anomaly that peaks south
of 10
Figure 12 provides additional statistical insight into the magnitude of the excursions described in this section. Here, we sampled 100 random 45-event composites in a control simulation with no external forcing (each “event”, two seasons in length, is defined as an anomaly expressed relative to a pre-eruption climatology as done previously). The anomalies were averaged over the same areas as in Fig. 9, with different domains for DJF and JJA. Notably, for both seasons and for all three variables examined, the single 45-event post-volcanic composite (purple square) lies outside the distribution of all sampled 45-event composites constructed with no external forcing. Nonetheless, the distribution for a smaller sample of events (black circles denote the data for each (15) eruption, each averaged over the three ensemble members) shows considerable spread.
The
In this study, we have analyzed the response of temperature, precipitation,
and
However, the immediate post-volcanic impact over South America has a complex seasonal and spatial structure. During the austral winter, the precipitation response over the continent is slaved to the response of the large-scale circulation, including a weakening of rainfall intensity within the ITCZ that is migrating northward. In the extratropics, the continent cools and exhibits slight precipitation declines nearly everywhere. Our results suggest the seasonal monsoon precipitation (during DJF) in ModelE2-R exhibits a fairly weak response that is scattered across the continent. It appears that volcanic forcing preconditions the tropical rainfall over the continent to decline during the wet season but that this response is likely to be eruption-dependent and may be overwhelmed by internal variability.
A unique aspect of this study was to probe the
Unfortunately, validation of our model results is hindered by the paucity of observational stable isotope data and by the coincidence of volcanic eruptions with ENSO events over the 20th century. Nonetheless, our results may provide some guidance in the search of volcanic signals in high-resolution isotopic or other temperature- and precipitation-sensitive proxy data from South America. Given the importance of volcanic forcing for climate variability over the past millennium, and in particular the LIA period, which has been identified as a period of significant climatic perturbation in isotopic proxies from South America, a better understanding of the climatic response to volcanic forcing over this region is urgently needed.
The observed ENSO time series can be obtained from
This study was funded by NOAA C2D2 NA10OAR4310126 and NSF awards AGS-1003690
and AGS-1303828. We would like to thank NASA GISS for institutional support,
the editor, Valerie Masson-Delmotte, for handling the review process of our
paper, and Raphael Neukom and an anonymous reviewer for the constructive
comments that helped improve the manuscript. Computing resources supporting
this work were provided by the NASA High-End Computing (HEC) Program through
the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight
Center. GPCP/GPCC data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado,
USA, from their website at