Supplementary Material
1 Evaluation of modeled ENSO-like δ18O variability
The evaluation of the GISS ModelE-R isotopic response for the ENSO-like
simulations is inherently more problematic than evaluating modeled precipitation
changes. In particular, the observed Global Network for Isotopes in Precipitation
(GNIP), developed by the International Atomic Energy Agency (IAEA), is spatially
limited (Fig. S1) and measurements are largely temporally sporadic. In the maritime
region, for example, where numerous valuable paleoclimatic studies have been
conducted (e.g. Cobb et al., 2007; Griffiths et al., 2010), there are GNIP monitoring
stations only available for Darwin, Australia, and Jakarta, Indonesia. As such, in
instances where the GNIP database has been gridded, the resulting interpolation is of
coarse resolution and contains large areas of missing data in regions of interest for
paleoclimatic reconstructions (Fig. S1). In the maritime continent, no interpolated
GNIP product is provided for large parts of this region.
Over the comparatively data-rich Northwest Pacific region (30-60°N, 110140°N), there are only four GNIP stations (stations 7179900, 724940, 7257206 and
7269800) that provide records encompassing the strong El Niño event occurring in
1982 and hence the possible opportunity for comparison with model output. However,
during 1982, two of these stations provide no δ18Op data and the other two contain at
least 6 missing months of δ18Op data. No stations within this region provide monthly
isotopic data for the following strong ENSO event that occurred in 1997. Hence, the
GNIP database is not suitable for monthly-scale regional comparisons with model
output from the idealized ENSO experiments.
1.1 ENSO-like δ18Op variability in the maritime continent
As regional or global mean comparisons of model output with gridded GNIP
data are not considered necessarily reliable (Schmidt et al., 2005), we instead compare
simulated ENSO-like δ18O anomalies with observed values at particular sites on a
case study basis, in order to assess the applicability of the idealized ENSO-like
simulation to paleoclimatic reconstructions. In particular, we evaluate these
simulations for the western Pacific region, using both station-based observed δ18Op
variability in the western Pacific (Cobb et al., 2007) and GNIP data from nearby
monitoring sites.
The climates of the western Pacific region, encompassing the islands of
northern Papua New Guinea (PNG) and Borneo, are dominated by ENSO variability
on interannual timescales and the site of numerous proxy reconstructions (e.g.
McGregor and Gagan, 2004; Griffiths et al., 2009; Lewis et al., 2011). Generally,
during El Niño events, convective activity in the Warm Pool region shifts eastward
and regional drought ensues (Ropelewski and Halpert, 1996; Lau and Nath, 2003).
Over northern Borneo, for example, sustained positive precipitation anomalies and
lighter δ18Op values are associated with La Niña events, through the rainfall ‘amount
effect’ (Cobb et al., 2007). Further analyses show that interannual δ18Op anomalies of
up to 6-8‰ are significantly correlated with indices of ENSO conditions (Moerman et
al., 2013). Specifically, increased rainfall δ18O is associated with dry, El Niño
conditions and conversely, decreased δ18Op values with wet, La Niña conditions. In
addition, as the Borneo Mulu rainfall δ18O and amount record is considered a useful
indicator of broader, basin-scale climate variability, we use this record to assess
simulated mean ENSOWARM and ENSOCOOL anomalies.
Over the western Pacific region, there is a simulated increase in δ18Op (2.2‰)
in the ENSOWARM experiment, associated with an average decrease in precipitation in
the region of ~2.3 mm/day. Conversely, in the ENSOCOOL experiment, there is a
simulated increase in precipitation over the western Pacific, compared to the preindustrial, of ~1.3 mm/day and a decrease in δ18Op of -0.2‰. By comparison, at the
Mulu site δ18Op anomalies on the order of ~2-4‰ were associated with high NINO3.4
indices over the period of rainwater collection (Moerman et al., 2013). A direct
comparison of regional average gridded modeled precipitation and δ18Op data with
point-specific values determined from a temporally limited monitoring program is not
attempted here. Nonetheless, the sign of δ18Op and precipitation amount changes
simulated for both the ENSOWARM and ENSOCOOL match those recorded over several
years at Mulu in the western Pacific (Cobb et al., 2007) (Moerman et al., in press). As
such, the ENSOWARM and ENSOCOOL experiments, though idealized and forced by
strong Pacific surface temperature anomalies, are considered generally useful for
investigating the impact of ENSO-like variability on the hydrological cycle under
varying boundary conditions.
1.2 SWING comparisons
Given that the GNIP network is limited by poor spatial and sporadic temporal
coverage (Werner et al., 2011) and that single site rainwater monitoring programs
generally provide short, disparate records, we also utilise the Stable Water Isotope
Intercomparison
Group
(SWING:
www.bgc-jena.mpg.de/bgc-
synthesis/projects/SWING/index.shtml) standard simulations a complement to
evaluating the GISS ModelE-R model performance for present-day climates. We
consider the SWING simulations as an additional tool for evaluating model
performance of broad spatial scales. In the SWING experiment (S1A), a 134-year
simulation is undertaken with varying sea surface temperatures (SSTs) from the
HadISST dataset from the period of 1870 to 2003. We compare isotopic anomalies
(relative to the 1980-1999 climatology) in the SWING ensemble mean (encompassing
ECHAM4, GISSE and MUGCM models) for the composite of 1982 and 1997
(wherein observed sea surface temperature anomalies in the NINO3.4 region
exceeded two standard deviations above the 1961-1990 average for 6 consecutive
months and represent strong El Niño events) with mean simulated ENSOWARM
isotopic anomalies, relative for the pre-industrial (Fig. S2).
The spatial pattern of δ18Op anomalies in the strongly forced ENSOWARM
experiment is broadly similar to that of the anomalies occurring during strong El Niño
years in the SWING dataset. However, the magnitude of anomalies is greater in the
ENSOWARM experiment, particularly in the extra-tropics. Both model results indicate
enrichment in δ18Op over the maritime continents during ENSOWARM (El Niño)
conditions. Similarly, both datasets display a δ18Op depletion in the equatorial Pacific
under these conditions. The impact of ENSOWARM conditions (under a large sea
surface temperature forcing in the tropical Pacific) is notably more discernible in the
higher latitudes (such as over the northwest Pacific) than that occurring in the strong
El Niño years in the SWING models.
Overall, the comparison of the GISS ModelE-R ENSOWARM experiments against
gridded precipitation datasets, δ18Op site monitoring data and SWING models
demonstrates that these experiments are broadly insightful about the impact of
ENSONEGATIVE related sea surface temperature forcing on the hydrological cycle.
These experiments are forced by comparatively strong SST anomalies in order that
the impact of this type of climate change is discernible in the hydrological cycle in
remote, teleconnected regions. As such, these experiments are not intended to be
representative of any particular El Niño event, but rather are used in this analysis to
investigate both changes in boundary conditions on the hydrological characteristics of
the various ENSO phases and the relative impact of ENSO conditions on various
regions that are valuable for paleoclimatic reconstructions.
2 Regions of interest
Model data are investigated in five regions that are identified as sensitive to
contemporary ENSO variability. Regions were selected to provide coverage of
simulated hydrological change across the Pacific basin and to encompass the location
of numerous proxy archives utilised in paleoclimate reconstructions of the Holocene.
Regions were intended to provide insight into ENSO-like changes, rather than to
provide a comprehensive analysis of areas containing long paleoclimate archives that
record potential ENSO signals.
Supplementary Figure Captions
Supplementary Figure 1. Comparison of annual mean δ18Op in the pre-industrial
control simulation with the GNIP database (IAEA, 2001), indicating the limited
spatial extent of GNIP records.
Supplementary Figure 2. Comparison of annual mean δ18Op anomalies in the
ENSOWARM experiment (relative to the pre-industrial control simulation, A) with
SWING model output (B). SWING data is shown as the multi-model mean δ18Op
anomaly for the composite of 1982 and 1997 years (wherein sea surface temperature
anomalies in the NINO3.4 region exceeded two standard deviations above average for
6 consecutive months and thus represents a strong El Niño event), relative to 19611990 climatology. Note that different scale bars are used for each plot.
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