Impact of Strong Tropical Volcanic Eruptions on ENSO Simulated in a Coupled GCM

Similar documents
An Introduction to Coupled Models of the Atmosphere Ocean System

The Formation of Precipitation Anomaly Patterns during the Developing and Decaying Phases of ENSO

Different impacts of Northern, Tropical and Southern volcanic eruptions on the tropical Pacific SST in the last millennium

the 2 past three decades

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 23 April 2012

East-west SST contrast over the tropical oceans and the post El Niño western North Pacific summer monsoon

Predictability of the duration of La Niña

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 5 August 2013

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 24 September 2012

ENSO Outlook by JMA. Hiroyuki Sugimoto. El Niño Monitoring and Prediction Group Climate Prediction Division Japan Meteorological Agency

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 25 February 2013

ENSO: Recent Evolution, Current Status and Predictions. Update prepared by: Climate Prediction Center / NCEP 9 November 2015

JP1.7 A NEAR-ANNUAL COUPLED OCEAN-ATMOSPHERE MODE IN THE EQUATORIAL PACIFIC OCEAN

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 15 July 2013

ENSO: Recent Evolution, Current Status and Predictions. Update prepared by: Climate Prediction Center / NCEP 30 October 2017

Oceanic origin of the interannual and interdecadal variability of the summertime western Pacific subtropical high

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 11 November 2013

Potential of Equatorial Atlantic Variability to Enhance El Niño Prediction

Climate Feedbacks from ERBE Data

The Two Types of ENSO in CMIP5 Models

KUALA LUMPUR MONSOON ACTIVITY CENT

ENSO and April SAT in MSA. This link is critical for our regression analysis where ENSO and

Twentieth-Century Sea Surface Temperature Trends M.A. Cane, et al., Science 275, pp (1997) Jason P. Criscio GEOS Apr 2006

3. Carbon Dioxide (CO 2 )

2013 ATLANTIC HURRICANE SEASON OUTLOOK. June RMS Cat Response

The ENSO s Effect on Eastern China Rainfall in the Following Early Summer

TROPICAL METEOROLOGY Ocean-Atmosphere Interaction and Tropical Climate Shang-Ping Xie OCEAN-ATMOSPHERE INTERACTION AND TROPICAL CLIMATE

Patterns and impacts of ocean warming and heat uptake

Robust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades

The Coupled Model Predictability of the Western North Pacific Summer Monsoon with Different Leading Times

Moist static energy budget diagnostics for. monsoon research. H. Annamalai

Significant changes to ENSO strength and impacts in the twenty-first century: Results from CMIP5

The 1970 s shift in ENSO dynamics: A linear inverse model perspective

8B.3 THE RESPONSE OF THE EQUATORIAL PACIFIC OCEAN TO GLOBAL WARMING

Interhemispheric climate connections: What can the atmosphere do?

lecture 10 El Niño and the Southern Oscillation (ENSO) Part I sea surface height anomalies as measured by satellite altimetry

The Spring Predictability Barrier Phenomenon of ENSO Predictions Generated with the FGOALS-g Model

North Pacific Climate Overview N. Bond (UW/JISAO), J. Overland (NOAA/PMEL) Contact: Last updated: September 2008

Relationships between Extratropical Sea Level Pressure Variations and the Central- Pacific and Eastern-Pacific Types of ENSO

A Coupled Atmosphere Ocean GCM Study of the ENSO Cycle

The Role of Indian Ocean Sea Surface Temperature in Forcing East African Rainfall Anomalies during December January 1997/98

Development of a Coupled Atmosphere-Ocean-Land General Circulation Model (GCM) at the Frontier Research Center for Global Change

Tropical Pacific modula;ons of global climate

Lecture 1. Amplitude of the seasonal cycle in temperature

SUPPLEMENTARY INFORMATION

High initial time sensitivity of medium range forecasting observed for a stratospheric sudden warming

Atmospheric Sciences 321. Science of Climate. Lecture 20: More Ocean: Chapter 7

The Planetary Circulation System

FUTURE PROJECTIONS OF PRECIPITATION CHARACTERISTICS IN ASIA

Lecture 8: Natural Climate Variability

Contents of this file

The Australian Summer Monsoon

The two types of ENSO in CMIP5 models

UC Irvine Faculty Publications

TROPICAL-EXTRATROPICAL INTERACTIONS

ENSO, AO, and climate in Japan. 15 November 2016 Yoshinori Oikawa, Tokyo Climate Center, Japan Meteorological Agency

Will a warmer world change Queensland s rainfall?

THE INFLUENCE OF CLIMATE TELECONNECTIONS ON WINTER TEMPERATURES IN WESTERN NEW YORK INTRODUCTION

Global Ocean Monitoring: Recent Evolution, Current Status, and Predictions

Impact of the Atlantic Multidecadal Oscillation on the Asian summer monsoon

The Impact of increasing greenhouse gases on El Nino/Southern Oscillation (ENSO)

Lecture 28. El Nino Southern Oscillation (ENSO) part 5

Why the Atlantic was surprisingly quiet in 2013

LETTERS. The cause of the fragile relationship between the Pacific El Niño and the Atlantic Niño

Assessing the Quality of Regional Ocean Reanalysis Data from ENSO Signals

Supplementary Figure 1 Observed change in wind and vertical motion. Anomalies are regime differences between periods and obtained

ENSO Amplitude Change in Observation and Coupled Models

NORTH ATLANTIC DECADAL-TO- MULTIDECADAL VARIABILITY - MECHANISMS AND PREDICTABILITY

Interannual Biases Induced by Freshwater Flux and Coupled Feedback in the Tropical Pacific

North Pacific Climate Overview N. Bond (UW/JISAO), J. Overland (NOAA/PMEL) Contact: Last updated: August 2009

1. Header Land-Atmosphere Predictability Using a Multi-Model Strategy Paul A. Dirmeyer (PI) Zhichang Guo (Co-I) Final Report

1. Introduction. 2. Verification of the 2010 forecasts. Research Brief 2011/ February 2011

Introduction of Seasonal Forecast Guidance. TCC Training Seminar on Seasonal Prediction Products November 2013

Theoretical and Modeling Issues Related to ISO/MJO

Simple Mathematical, Dynamical Stochastic Models Capturing the Observed Diversity of the El Niño Southern Oscillation (ENSO)

The feature of atmospheric circulation in the extremely warm winter 2006/2007

lecture 11 El Niño/Southern Oscillation (ENSO) Part II

Fossil coral snapshots of ENSO and tropical Pacific climate over the late Holocene

PRMS WHITE PAPER 2014 NORTH ATLANTIC HURRICANE SEASON OUTLOOK. June RMS Event Response

Lecture 2 ENSO toy models

Which Climate Model is Best?

Sensitivity of summer precipitation to tropical sea surface temperatures over East Asia in the GRIMs GMP

Impact of overestimated ENSO variability in the relationship between ENSO and East Asian summer rainfall

Development Processes of the Tropical Pacific Meridional Mode

Climate Forecast Applications Network (CFAN)

Impact of Eurasian spring snow decrement on East Asian summer precipitation

Analysis Links Pacific Decadal Variability to Drought and Streamflow in United States

How Will Low Clouds Respond to Global Warming?

Effect of anomalous warming in the central Pacific on the Australian monsoon

Chapter outline. Reference 12/13/2016

The North Atlantic Oscillation: Climatic Significance and Environmental Impact

Inactive Period of Western North Pacific Tropical Cyclone Activity in

ENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP July 26, 2004

Externally forced and internal variability in multi-decadal climate evolution

ENSO amplitude changes in climate change commitment to atmospheric CO 2 doubling

Influence of reducing weather noise on ENSO prediction

Ocean Mixing and Climate Change

Local versus non-local atmospheric weather noise and the North Pacific SST variability

SUPPLEMENTARY INFORMATION

Introduction of climate monitoring and analysis products for one-month forecast

Transcription:

15 JULY 2013 O H B A E T A L. 5169 Impact of Strong Tropical Volcanic Eruptions on ENSO Simulated in a Coupled GCM MASAMICHI OHBA Central Research Institute of Electric Power Industry, Abiko, Japan HIDEO SHIOGAMA AND TOKUTA YOKOHATA National Institute for Environmental Studies, Tsukuba, Japan MASAHIRO WATANABE Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Japan (Manuscript received 16 July 2012, in final form 9 January 2013) ABSTRACT The impact of strong tropical volcanic eruptions (SVEs) on the El Ni~no Southern Oscillation (ENSO) and its phase dependency is investigated using a coupled general circulation model (CGCM). This paper investigates the response of ENSO to an idealized SVE forcing, producing a peak perturbation of global-mean surface shortwave radiation larger than 26.5 W m 22. Radiative forcing due to volcanic aerosols injected into the stratosphere induces tropical surface cooling around the volcanic forcing peak. Identical-twin forecast experiments of an ENSO-neutral year in response to an SVE forcing show an El Ni~no like warming lagging one year behind the peak forcing. In addition to a reduced role of the mean subsurface water upwelling (known as the dynamical thermostat mechanism), the rapid land surface cooling around the Maritime Continent weakens the equatorial Walker circulation, contributing to the positive zonal gradient of sea surface temperature (SST) and precipitation anomalies over the equatorial Pacific. Since the warm and cold phases of ENSO exhibit significant asymmetry in their transition and duration, the impact of a SVE forcing on El Ni~no and La Ni~na is also investigated. In the warm phase of ENSO, the prediction skill of the SVE-forced experiments rapidly drops approximately six months after the volcanic peak. Since the SVE significantly facilitates the duration of El Ni~no, the following transition from warm to cold ENSO is disrupted. The impact of SVE forcing on La Ni~na is, however, relatively weak. These results imply that the intensity of a dynamical thermostat-like response to a SVE could be dependent on the phase of ENSO. 1. Introduction Explosive strong tropical volcanic eruptions (SVEs) such as Mount Pinatubo (1991) significantly affect the climate by injecting sulfur-rich gases into the stratosphere. The resulting increased concentration of stratospheric sulfate aerosols acts to scatter and absorb incoming solar radiation, which leads to a temporary reduction in the amount of solar radiation reaching the surface. The relatively short persistence of volcanic aerosols in the stratosphere (2 3 yr) identifies Corresponding author address: Masamichi Ohba, Central Research Institute of Electric Power Industry, Environmental Science Research Laboratory, 1646 Abiko, Abiko-shi, Chiba 270-1194, Japan. E-mail: oba-m@criepi.denken.or.jp SVE forcing as a narrow-peak-type perturbation of the climate system (Robock 2000). However, volcanically induced cooling at the surface could penetrate deeper into the ocean where it may persist for several years (e.g., Church et al. 2005; Gleckler et al. 2006). The effects of SVEs are not, therefore, limited to direct changes in the radiative energy balance, but may also alter the atmospheric and oceanic circulation and modulate interannual to decadal climate variations (e.g., Mann et al. 2005; McGregor et al. 2010; Shiogama et al. 2010; Zanchettin et al. 2012). The El Ni~no Southern Oscillation (ENSO), which consists of a quasiperiodic (3 7-yr time scale) warming (El Ni~no) and cooling (La Ni~na) of the tropical central and eastern Pacific (CEP) Ocean, forms the main pattern of the earth s interannual climate variability. The prediction of ENSO is of practical and scientific interest DOI: 10.1175/JCLI-D-12-00471.1 Ó 2013 American Meteorological Society

5170 J O U R N A L O F C L I M A T E VOLUME 26 as it has large environmental and societal impacts. Volcanically induced cooling in the tropics alters the surface climate, which then rapidly reduces the predictability of ENSO. As ENSO affects the global climate, it is of considerable importance to determine how its phases are altered by the impact of volcanic forcing. At the end of the twentieth century, several articles discussed (using limited observed episodes) whether a SVE can alter ENSO (Handler 1984) or whether it does not affect it (Nicholls 1988; Self et al. 1997; Robock 2000). The largest eruptions of the last 50 years (Agung in 1963, El Chichon in 1982, and Pinatubo in 1991) have occurred in conjunction with the warm phase of ENSO, and it is therefore possible that contributions of a SVE have been hidden. However, recent analysis of much longer-term paleoclimate records derived from multiple proxy data suggests that the radiative effects of a tropical SVE can lead to an El Ni~no like state and increase the probability of El Ni~no occurrences (e.g., Adams et al. 2003; McGregor et al. 2010). These studies document the fact that volcanic forcing exerts a relatively weak, but discernible, influence on ENSO. The process by which volcanic forcing influences ENSO is not well understood. The relationship between ENSO and explosive volcanism has previously been studied (Mann et al. 2005; Emile-Geay et al. 2008) using an intermediate air sea coupled model (Zebiak and Cane 1987). Mann et al. and Emile-Geay et al. demonstrate a warming in the CEP in response to a uniform reduction of model surface heat fluxes, which can be explained by the dynamical thermostat hypothesis (Clement et al. 1996). In this hypothesis, the mean oceanic advection makes it harder for radiative forcing to change sea surface temperature (SST) in the eastern equatorial Pacific. Given a uniform reduction of incoming surface solar radiation, the SST therefore cools faster in the west, initially reducing the climatological zonal SST gradient. The resultant positive zonal gradient of SST initiates El Ni~no and this effect is subsequently amplified by the Bjerknes feedback (Bjerknes 1969). However, some studies using coupled general circulation models (CGCMs) show that feedback from tropical cooling can initially lead to a strong negative phase of ENSO (e.g., McGregor and Timmermann 2011; Zanchettin et al. 2012). Details of the short-term dynamical responses of the climate to SVEs still remain unclear. Because the air sea coupled system in the Pacific includes a strong nonlinearity (Ohba and Ueda 2009), it is possible that the ENSO response to volcanic forcing may be different depending on its phase. The aim of this study was to examine the ENSO response to tropical SVEs and its dependency on the ENSO phase using a CGCM. To further the understanding of SVE effects on ENSO, we performed climate simulations with and without volcanic forcing. This paper is organized in the following manner. Section 2 presents a brief description of our CGCM and experimental design. Section 3 examines the volcanic impact on ENSO in the CGCM when initialized at different ENSO phases. Section 4 investigates the physical causes of the El Ni~no like response in the CGCM, and section 5 presents the discussion and a summary of the conclusions. 2. Model and experimental design a. MIROC5i In the present study, we use an interim version of the Model for Interdisciplinary Research on Climate (MIROC) (Watanabe et al. 2010), named MIROC5i, which contains several minor differences from the official fifth version (MIROC5, see Watanabe et al. 2011 for details). This version will also be employed in the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). The design of MIROC5 is based on MIROC3 (Hasumi and Emori 2004), which employs a global spectral dynamical core and implements a standard physics package for the atmosphere. The ocean and sea ice models within MIROC5 are taken from the updated Center for Climate System Research ocean component model (Hasumi 2006). In addition, a land model (which includes a river module) is coupled to the system. In MIROC5, many of the schemes have been replaced with recent ones. A preindustrial control experiment showed a remarkable improvement in the ENSO amplitude, spatiotemporal structure, and their asymmetry between El Ni~no and La Ni~na in comparison to that recorded by MIROC3 (Watanabe et al. 2010; Ohba and Watanabe 2012). The standard resolution for MIROC5 is T85L40 for the atmosphere. Because the model s mean state and variability are not seriously altered when the horizontal resolution of the atmosphere is reduced, we decided to use a coarser resolution of T42L40 for MIROC5i in this study. Our decision was based solely on the increased computational burden of the new physics package in MIROC5. To implement volcanic forcing, we simply incorporated a variation in the stratospheric aerosol optical thickness at 0.55 mm in the radiation code. This implementation enables the CGCM to reproduce the observed climate cooling after a significant volcanic eruption (Yokohata et al. 2005; Shiogama et al. 2006). b. Volcanic forcing experiments We initially performed a 200-yr control simulation with no-volcanic activity (hereafter referred to as Ctrl). Regarding Ctrl as a benchmark, three sets of idealized

15 JULY 2013 O H B A E T A L. 5171 FIG. 1. The scatterplot of the simulated Ni~no-3.4 index (8C) during DJF 0/11 against the following year, DJF 11/2.TheselectedENSOneutral, El Ni~no, and La Ni~na cases for the identical-twin experiments are denoted by green, red, and blue dots, respectively. experiments (or so-called perfect model studies) were conducted with MIROC5i to examine the effects of volcanic forcing on the tropical Pacific and to help understand the mechanisms underlying the MIROC5 response. Each of these experiments used the same model configuration and the only difference between the simulations was the phase of ENSO used. Three different ENSO conditions were extracted from Ctrl (neutral, peak of El Ni~no, and La Ni~na). Figure 1 shows a scatter diagram of the simulated December February (DJF) Ni~no-3.4 index with that recorded in the following year. The Ni~no-3.4 index is defined as the average SST anomaly in the 58S 58N, 1708 1208W region. We see that the air sea coupled system over the Pacific remains in a weak La Ni~na state for up to two years, while El Ni~no tends to turn rapidly into La Ni~na after the mature phase (e.g., Ohba and Ueda 2009; Ohba et al. 2010). The wellknown nonlinear relationship between ENSO and the SST in the following year was well reproduced by the model, and moderate El Ni~no events persisted into the following year (Fig. 1). Because of the computational burden, we selected five initial conditions (1 July) during no-enso (i.e., neutral) and strong El Ni~no (La Ni~na) events that showed peculiar transition (duration) features in Ctrl. The selected cases were plotted using green, red, and blue dots, which represent ENSO-neutral, El Ni~no, and La Ni~na years, respectively. The volcanic forcing in each of these ensembles was at a level where volcanic stratospheric aerosol concentrations increase six months after initialization and reach their peak in the boreal winter (Fig. 2a). We make the assumption, for computational simplicity, that the eruption occurs in this season. The spatiotemporal structure of the FIG. 2. (a) Global-mean optical depth forcing used in the SVE run and (b) its meridional distribution. forcing was derived from the lag regression against the tropical subtropical mean optical thickness (308S 308N) in the stratosphere given by Sato et al. (1993). In this temporal profile, the volcanically induced tropical stratospheric aerosols persisted for approximately 2 3 yr. The idealized forcing provides suitable test beds for examining the potential of climate forecasting. The amplitude of volcanic forcing is about 1.5 times larger than that of the forcing of the 1991 eruption of Mount Pinatubo (Sato et al. 1993), which resulted in a clear-sky surface radiative forcing of approximately 212 W m 22 around the SVE peak that is comparable to the magnitude of the 1258 eruption, undoubtedly the largest in the past millennium and by some accounts, the third largest of the Holocene (Stothers 2000). Months in the SVE developing years are denoted by a superscript 0, and those in the succeeding years are represented by 11 and 12. Ensemble forecasts using five members over 30 months were performed from 1 July 0 until the end of December 12 with (SVE run) and without (nosve run) SVE forcing, respectively. In addition to the CGCM integration, similar SVE experiments using the same atmospheric general circulation model (AGCM) with prescribed climatological SST were additionally conducted to evaluate the effect of air sea coupling. 3. Impact of volcanic eruptions on ENSO a. Neutral year experiment In this section, we present the results of the normal year experiments to illustrate the impact of a SVE on

5172 J O U R N A L O F C L I M A T E VOLUME 26 FIG. 3. (a) Evolution of the composited Ni~no-3.4 index (8C) for Ctrl (black) and ensemble mean of SVE (red) overlaid from January 0 to December 12 for ENSO-neutral cases. Global mean surface shortwave radiation anomaly (W m 22 ) in the SVE run is denoted by a green line. The orange shading illustrate how each ensemble is distributed (i.e., probability density function). Longitude time section of (b) SST (8C), (c) zonal wind (m s 21 ), and (d) precipitation (mm day 21 ) anomalies along the equator (28S 28N) from January 0 to December 12 in the SVE run. The contour interval of SST (zonal wind) is 0.58C(0.6 m s 21 ). Peak SVE forcing is represented by a brown triangle. ENSO. Figure 3a shows the composited ensemble mean of the Ni~no-3.4 index and the global mean surface shortwave radiation anomaly derived from ENSOneutral year cases. Corresponding with the SVE forcing (Fig. 1a), we find a reduction of incoming surface shortwave radiation at about 26.5 W m 22 around the SVE peak. In agreement with the observed findings from paleoclimate records (Adams et al. 2003), the ensemble simulations in response to the volcanic shortwave forcing show an El Ni~no like warming that lags one year behind the peak SVE forcing. The difference between the ensemble mean in SVE and Ctrl exceeds 18C around the end of year (11). The SVE run also exhibited widespread variability among the ensemble members (shaded orange) around the El Ni~no peak, implying that the trajectories of the simulated ENSO in the CGCM are affected by chaotic or stochastic forcing, irrespective of forcing by SVEs. To describe the time evolution of the ENSO transition in relation to variability in wind forcing, we also plotted a longitude time section of the simulated SST (Fig. 3b), zonal wind (Fig. 3c), and precipitation (Fig. 3d) anomalies over the equatorial Indo-Pacific. A strong anomalous westerly wind at the surface (i.e., a weakening of the trade winds) is recognized around the peak of SVE (Fig. 3c), corresponding to weak surface warming (cooling) over the CEP (Indo-western Pacific, Fig. 3b). Lagging behind the peak of the westerly wind anomalies by about one-half to one year, the SST anomalies in the CEP reveal remarkably positive values, which expand westward.

15 JULY 2013 O H B A E T A L. 5173 As a linear response of the ocean to wind forcing, the anomalous surface westerlies act to deepen the equatorial Pacific thermocline and advect warm surface water from the western to the eastern Pacific. The resultant decreased cold water upwelling and zonal advection further amplify the CEP warming. It appears evident that the air sea coupled feedback can significantly amplify the initial SVE response. The SST anomalies evolve rapidly from the succeeding summer 11 to winter 11/ 2 to ultimately show a strong warming with westerly wind anomalies. In the following seasons, the SST anomalies gradually decrease to zero, indicating that the termination of El Ni~no is established in the winter 12/3. Figure 4 presents the spatial distribution of the simulated surface air temperature, precipitation, and wind anomalies over the Indo-Pacific Ocean. In the early stage (i.e., around the SVE peak), an anomalous cooling of the surface is seen, particularly over continental areas. Previous studies have reported the Indian Ocean basin as being one of the most sensitive regions to radiative forcing (e.g., Guemas et al. 2013), and we note that cooling (and reduced precipitation; Fig. 3d) in the Indo-Maritime Continent (MC) is robust in comparison with that of the Pacific Ocean. The resultant zonal temperature gradient over the equator can, therefore, be expected to contribute to the reduction of the Walker circulation via an increase (decrease) in the local convective instability (as seen in Fig. 3d) and decrease (increase) in the sea level pressure (e.g., Lindzen and Nigam 1987). Actually, we found surface westerly wind anomalies over the MC at around the SVE peak, while the warm anomalies over the CEP are much weaker. The SVE-related change in the surface temperature can also reduce (intensify) the precipitation over the MC (western-central Pacific), which can drive the anomalous equatorial westerly wind. The surface cooling with the reduced evaporation in the tropics tends to increase atmospheric stability and reduce the convective activity over the MC, which could result in enhanced convergence at the east of the region by an atmospheric Kelvin wave like response. This response can further amplify the equatorial westerly wind anomalies. Therefore both the surface pressure gradient and indirect effect through the precipitation tend to cause the westerly anomalies that can be enhanced by the air sea feedback. Such a surface response is also found in other studies of SVEs using different models (Robock et al. 2008). In the following summer [June August 11 (JJA 11 )], significant warm anomalies are seen associated with the onset of El Ni~no, which enters its mature phase in the subsequent winter. The simulated characteristics of the wind and SST closely resemble observations of El Ni~no. Also of note is an initial cooling in the equatorial eastern Pacific FIG. 4. Simulated surface temperature (8C, color shading), precipitation (mm day 21, green and purple contours) and surface wind (m s 21, vectors) anomalies for the SVE run of ENSOneutral cases from September 0 to February 12. The contour interval is 1 mm day 21. preceding the large-scale warming in the CEP, which could occur in relation to land surface cooling in the vicinity of the South American continent. The initial warming pattern is relatively similar to that of the central Pacific El Ni~no (e.g., Kug et al. 2009; Newman et al. 2011) when the following development of El Ni~no switches off the signal. In global warming simulations, we find characteristic features of land sea contrast with a stronger warming over land than over oceans, implying that land surface temperature is more sensitive to radiative forcing. This is largely due to the different surface and atmospheric feedback that occurs over land in comparison to over oceans (e.g., Sutton et al. 2007; Joshi et al. 2007. The land sea response ratio of the surface air temperature to an increase in CO 2 exceeds 1 in the tropics and subtropics, which is largely consistent between models (Sutton et al. 2007). Such a land sea contrast is also seen in the SVE experiments. The cooling of the land surface over the

5174 J O U R N A L O F C L I M A T E VOLUME 26 tropical subtropical region is about two times faster and stronger than that of the ocean surface (not shown). b. SVE impact during the warm and cold phase of ENSO Because of the probabilistic nature of climate forecast, it is not sufficient to perform the simulations only during the ENSO-neutral year. To assess its impact on climate and its potential for forecasting, an ensemble of many simulations initiated from various phases of ENSO is required. Figures 5a and 5b show the composited time evolution of the Ni~no-3.4 index for the SVE experiments during El Ni~no and La Ni~na, respectively. The results presented here are based on ensemble averages over the 25 individual integrations. We found that the SVE forcing contributes to the relative CEP warming during both El Ni~no and La Ni~na, but the effect is much stronger in the warm phase. The SVE forcing during El Ni~no prevents the transition of El Ni~no to La Ni~na (Figs. 5a,b), while during La Ni~na it weakens the duration of cold events. To quantitatively measure the difference between the sensitivity of El Ni~no and La Ni~na to a SVE, we calculated the root-mean-square error (RMSE) from nosve using the ensemble mean SST in the tropical Pacific region (208S 208N, 1208E 908W) for the respective warm and cold ENSO phases (Fig. 5c). In the El Ni~no phase, the RMSE deteriorated rapidly and exceeded 1.08C near the boreal spring summer 11, which is approximately triple that of the La Ni~na phase. Such asymmetry in the sensitivity of ENSO to the SVE forcing of El Ni~no and La Ni~na, can be attributed to the difference in subsequent air sea process. To better describe the asymmetry in the ENSO responses, we adopted one each El Ni~no and La Ni~na case that showed a marked response (Figs. 6a,b). The results presented here are based on ensemble averages over the five individual integrations. We find a significant difference between the SVE and Ctrl in the winter 11/2 in Ni~no-3.4 (in the ENSO phase), while no significant difference is seen between nosve and Ctrl. Figures 6c and 6e (Figs. 6d,f) present the longitude time section, highlighting the differences in the simulated surface zonal wind (precipitation) between SVE and nosve near the equator. When the model is integrated without SVE forcing (Fig. 6c, black contour), a close examination of the mature phase reveals surface easterly anomalies along the equator within 1208 1608E. As described in previous studies (e.g., Kug and Kang 2006; Ohba and Ueda 2007), the easterly wind anomalies are known to be associated with a warming of the Indian Ocean, which can accelerate the El Ni~no transition. Lagging behind the easterly anomalies by a few months, FIG. 5. Evolution of the composited Ni~no-3.4 index (8C) for the ensemble mean of nosve (solid) and SVE (dash) overlaid from January 0 to December 12 for (a) El Ni~no and (b) La Ni~na cases. (c) RMSE using ensemble mean SST in the tropical Pacific region (208S 208N, 1208E 908W) between SVE and nosve for El Ni~no (red), La Ni~na (blue), and ENSO-neutral (black dashed) cases. Peak SVE forcing is represented by a brown triangle. the Ni~no-3.4 index drops rapidly between the succeeding summer 11 and winter 11/2 to ultimately exhibit negative values (Fig. 6a, black line), indicating that the transition from El Ni~no to La Ni~na is established when the model is run without volcanic forcing. The differences between the results in the presence or absence of SVEs highlight the impact of a SVE during ENSO events (Figs. 6c,e, shaded). In accordance with ENSO-neutral cases (Fig. 3c), the difference in zonal winds in the central Pacific reveals remarkably positive values that start to strengthen around the SVE peak (Fig. 6c). The SVE-enhanced anomalous westerly wind stress can be a significant contributor in preventing the El Ni~no transition (Fig. 6a) by exciting a downwelling oceanic Kelvin wave. The anomalous westerly wind anomalies are highly collaborated with the simulated precipitation anomalies in both phases (Figs. 6d,f). Figure 7 presents the wind anomalies and spatial distribution of the simulated precipitation and surface temperature during the El Ni~no phase for nosve and SVE around the SVE peak. In the nosve run, the simulated anomalies show a largescale structure: the Indian Ocean and CEP warming and

15 JULY 2013 O H B A E T A L. 5175 FIG. 6. As in Fig. 3a but for simulation of one (a) El Ni~no and (b) La Ni~na case. Longitude time section of the difference in zonal wind (m s 21 ) between SVE and nosve (shaded, green contours) along the equator (28S 28N) from January 0 to December 12 for simulation of one (c) El Ni~no and (e) La Ni~na case. Zonal wind anomaly in nosve is overlaid by black contours: contour interval 1 m s 21. (d),(f) As in (c),(e), but for the difference in precipitation (mm day 21 ): contour interval 2 mm day 21. cooling in the northern western Pacific (WP). The precipitation and SST anomalies are accompanied by an anomalous anticyclonic circulation centered over the northern WP, which induces enhanced equatorial trade winds. It is widely accepted that ENSO variability exerts a significant impact on the Indian Ocean (e.g., Klein et al. 1999). Positive SST anomalies are known to appear over the Indian Ocean around the mature phase of warm ENSO events, which then persist through the following summer. The increase in incoming solar radiation is mainly responsible for the warming of the Indian Ocean (Klein et al. 1999), with ocean dynamics also playing an important role in the southwestern part of the basin (Xie et al. 2002). However, in the SVE run, the warming (cooling) is suppressed (enhanced) over the tropical Indian Ocean (WP) by a reduction in the incoming shortwave radiation. We find an enhanced surface temperature gradient between the equatorial WP and CEP with the eastward shift in convective anomalies in the SVE run that can be a main factor in enhancing CEP westerly wind anomalies. The strengthened Bjerknes feedback can dominate and weaken the transition process and contribute to the regeneration of El Ni~no. In contrast to El Ni~no, the SVE forcing in the La Ni~na phase reduces the easterly wind anomalies around the peak of forcing (Fig. 6e). The reduced easterly results in the termination of the La Ni~na duration and therefore the CEP SST anomalies at the end of year (12) are in an approximately neutral condition (Fig. 6b). Of significant interest is the stronger warming seen in the El Ni~no case than that in the La Ni~na case with SVE forcing (Fig. 5.). This feature is not significant in the comparison between the El Ni~no and neutral cases (Figs. 3a and 5a). This nonlinearity of the response could be related to the direction of the feedback. In neutral- ENSO and El Ni~no phases, positive feedback of El Ni~no significantly amplifies the SVE impact. However, during the La Ni~na phase, the effect of the SVE forcing is regarded as a damping against the positive feedback of La Ni~na. Much stronger SVE forcing may be needed to cause the breaking of La Ni~na. To investigate the sensitivity of ENSO to changes in the amplitude of SVE forcing, similar experiments were conducted by scaling the SVE intensity to between half and double the times (Fig. 1c). Five ensemble members were used for SVE, along with the additional integration of one member each for 0.5, 1.5, and 2.0 times the value (Fig. 8a). This experimental design allowed us to determine how SVEs of various amplitudes contribute to the CEP warming, regardless of the ENSO phase. We

5176 J O U R N A L O F C L I M A T E VOLUME 26 FIG. 7. As in Fig. 4 but for one El Ni~no case in the (a) nosve and (b) SVE run during DJF 0/11. The contour interval of the precipitation is 2 mm day 21. adopted the experiments from one of the El Ni~no and La Ni~na cases (Figs. 6a,b). Figures 8b and 8c show the RMSE between SVE and nosve over the Pacific during DJF 11/2 (squares) and the difference in the Ni~no-3.4 index between DJF 0/11 and the following DJF 11/2 (circles) of the following year, derived from the ensemble mean of the experiments for El Ni~no and La Ni~na phases. In this scaling range, the warming response of the CEP to SVE forcing is relatively linear for both phases. The RMSE and the warming of the Ni~no-3.4 index are found to be larger for a larger SVE. Compared with La Ni~na, the sensitivity of El Ni~no to a change in SVE amplitude is relatively high. This indicates that the coupled feedback greatly amplifies the differences during El Ni~no, while the effect in the opposite phase is weak. It is also worth noting that the CEP warming (in the 1.5 times and 2.0 times runs in the La Ni~na phase) overcame the La Ni~na related cooling anomalies. Clearsky surface radiative forcing of approximately 218 to 224 W m 22 could therefore be a threshold point of the CGCM that allows warming of the CEP, despite the La Ni~na cooling. 4. Physical causes of the El Ni~no like response In this section, we examine the physical causes of the El Ni~no like response in the CGCM. It is significant that SVE forcing induces the anomalous surface westerly wind stress over the equatorial WP (Fig. 3c). As also seen in SVE experiments using different CGCMs (Robock et al. 2008), the SVE forcing effectively reduces the simulated precipitation around the MC, which could potentially contribute to causing the anomalous surface westerly wind over the equatorial WP. As documented in previous studies (Ohba and Ueda 2006; Xie et al. 2009), a reduced surface temperature around the Indo-MC can contribute to a reduction of in situ precipitation. Such precipitation around the Indo-MC region can be amplified by the intensified precipitation and cyclonic circulations over the western North Pacific. Similar to the CGCM experiment, an anomalous westerly wind is also evident when the SVE experiments are conducted using the AGCM only (Fig. 9), implying that the incipient wind response can be attributed to the response of the atmosphere land system to the SVE forcing. To examine whether the equatorial wind response can contribute to the CEP warming ahead of the thermostat mechanism of Clement et al. (1996), we show the time depth section of ocean temperature in the equatorial central Pacific derived from the ENSO-neutral case (Fig. 10a). In the early stage of a SVE, the surface temperature warming is preceded by the subsurface warming, which implies that the surface warming could be mainly due to a deepening of the thermocline in relation to the initial equatorial westerly wind anomaly.

15 JULY 2013 O H B A E T A L. 5177 FIG. 8. (a) Global mean optical depth forcing used in the additional SVE run: 2 (red line), 1.5 (orange line), and 0.5 times the amplitude (blue line) are denoted in addition to the normal SVE forcing (black dashed line). Scatterplot between the SVE amplitude (squares) vs the RMSE between SVE and nosve, using ensemble mean SST in the tropical Pacific region (208S 208N, 1208E 908W), and the difference in simulated Ni~no-3.4 index (8C, circles) between DJF 0/11 minus DJF 11/2 for (b) El Ni~no and (c) La Ni~na. The spread of individual ensemble members for the Ni~no-3.4 index is denoted by the error bar. To further understand the importance of the thermostat hypothesis in relation to other factors, a heat budget analysis of the ocean mixed layer (Vialard et al. 2001) is conducted. From the analysis, only three dominant terms are plotted in Fig. 10b to facilitate visualization. The anomalous mixed layer temperature in the Ni~no-3.4 region (T 0 nino, a proxy for SST) in relation to ENSO can be simplified as Tnino 0 O 2 w T0 t z 2 u0 T x 1 Q0 sw rc p H. (1) Here Q 0 sw is volcanically induced surface solar radiation anomalies, (u, w) are the zonal and vertical components of ocean currents, r is the density of seawater, C p the specific heat of seawater at constant pressure, and H the depth of the oceanic mixed layer. The time evolution of vertical advection of the anomalous subsurface temperature by the climatological-mean upwelling (2w T 0 / z: thermocline feedback) and the zonal advection of climatological-mean SST by anomalous current (2u 0 T/ x: zonal advective feedback) are known to be essential for the growth and decay of the ENSOrelated equatorial SST anomaly (e.g., An et al. 1999; Jin and An 1999). The effect of the SVE forcing (negative radiative forcing) on the mixed layer temperature is also plotted [Q 0 sw/(rc p H); green solid line in Fig. 10b]. Note that the surface shortwave radiation anomaly averaged over the whole tropics, globally between 308S and 308N, is used to remove the effect of an enhanced cloud shortwave reflection by the El Ni~no related convective anomaly. From this figure, we find a warming by the thermocline and zonal advective feedback around the time of SVE forcing. Although cooling by the net surface heat flux follows the dynamical warming, its effect at around DJF 11/2 is about one-third of the warming by oceanic advection. While the effect of the thermostat mechanism of Clement et al. (1996) is included in the thermocline feedback (2w T 0 / z: blue dashed line in Fig. 10b), we can roughly estimate the maximum potential effect of the thermostat mechanism (hereafter referred to as E c ) from the reduced mixed layer temperature (SST 0 SVE )by the negative radiative forcing and climatological-mean condition of the ocean surface/subsurface, that is, where E c 52w SST0 SVE, (2) z SST 0 SVE 5 Q0 sw t rc p H 2 D. As the dissipation (D) of anomalous surface cooling, the Newtonian damping is represented by a linear drag, which has a time scale of (3 month) 21. The advection of SST 0 SVE by the ocean currents and feedback of E c on SST 0 SVE are neglected here for simplicity. The effect of the thermostat mechanism averaged in the Ni~no-3.4 region (black dashed line in Fig. 10b) shows the relative anomalous warming. This is regarded as the mitigation effect to the SVE forcing, which lags behind peak of the forcing by 6 months. However, this effect is much weaker than that in the thermocline and zonal advective

5178 J O U R N A L O F C L I M A T E VOLUME 26 FIG. 9. Simulated precipitation (mm day 21, color shading) and surface wind (m s 21, vectors) anomalies for September 0 November 0 (SON 0 ) derived from the SVE run by using the AGCM. feedback and less than 20% of the thermocline feedback. The E c alone may be difficult to excite El Ni~no in the model that represents strongly nonlinear processes, such as turbulent fluids. It is, therefore, hard to conclude that the negative radiative forcing directly works well to excite El Ni~no like CEP warming. If the cooling by reduced insolation is not prevented by oceanic advective warming, we wonder how prevalent the thermostat hypothesis is in comparison to all other factors. It is interesting to evaluate the extent to which the El Ni~no like warming can be caused by the thermostat mechanism. However, the anomalous westerly wind simulated in our CGCM co-occurs with the CEP warming, so it is difficult to separate the effects of each physical process from the CGCM simulations alone. To roughly quantify the effects of such SVErelated changes in the atmospheric and oceanic states on the following El Ni~no amplitude, we used a modified version of the simplified coupled model called the Zebiak Cane (ZC) model (Zebiak and Cane 1987), also used by Mann et al. (2005) and Emile-Geay et al. (2008). In this anomaly model, it is possible to easily reproduce the El Ni~no onset by imposing the air sea boundary condition and evaluate the role of each atmospheric factor [such as the surface shortwave radiation as conducted in Clement et al. (1996)]. To make a simple representation of the effect of change in the atmospheric state, the atmospheric component of the ZC model was substituted (Ohba and Ueda 2009) by the following empirical formula for the surface wind (U 0 ): U 0 (x, y) 5 R(x, y)t diff, where T diff is the difference in the surface temperature between the Ni~no-3.4 minus the MC region (58S 58N, 1008 1508E) and R is the monthly wind stress anomaly obtained from the regression analysis in the CGCM. To describe the impact of the SVE on the onset of El Ni~no, the surface heat flux anomalies derived from the ENSOneutral cases were imposed on the model ocean surface. The oceanic component of the model is a 1 1 / 2 -layer reduced gravity ocean model including a grid with a horizontal FIG. 10. (a) Time depth section of the ocean temperature anomaly (8C) in the equatorial central Pacific (28S 28N, 1908 2208E) derived from the ENSO-neutral case. (b) Time evolution of the mixed layer heat budget terms (8C month 21 ). Each line denotes the vertical advection of anomalous subsurface temperature by the climatological-mean upwelling (blue) and the zonal advection of mean SST by anomalous current (red) in the Ni~no-3.4 region that are essential for the equatorial SST anomaly associated with ENSO. The green line represents the effect of the negative radiation anomaly averaged globally over the tropics between 308S and 308N. Its thermostat effect (reduced vertical advection of subsurface temperature by the climatological mean upwelling) in the Ni~no-3.4 region is denoted by a black dashed line.

15 JULY 2013 O H B A E T A L. 5179 resolution of 0.58 latitude by 18 longitude (Cane and Patton 1984). The SST was determined by a balance between the surface heat fluxes, horizontal advection due to imposed winds, horizontal diffusion, and entrainment from below the mixed layer (Zebiak and Cane 1987). Using the intermediate hybrid coupled model, we made five ensembles, each of which consisted of a 2.5-yr integration. To separately evaluate the effect of a direct (oceanic) and indirect (atmospheric) response on ENSO, the surface heat flux anomaly, which consists of the surface shortwave and longwave radiation, and the latent and sensible heat fluxes derived from the CGCM ensembles, were forced for an initial 1-yr (Jul 0 Jun 11 ) period. The indirect effect of the MC cooling was represented by including the CGCM s MC cooling in T diff, which indirectly affects the model ocean via the surface wind response U 0. A simulation, imposing both surface heat flux anomalies and MC cooling, was also conducted (denoted as ALL in Fig. 11). The results of the experiments, indicating that the simplified model can reproduce the CGCM-simulated El Ni~no onset, are presented in Fig. 11. Two possible variables of importance are identified; one is the relatively weakened effect of the reduced incoming solar radiation on the surface cooling in the CEP (known as the dynamical thermostat) and the other is the rapid surface cooling around the MC. Consistent with the mechanism in Clement et al. (1996), the surface radiative cooling contributes to reduce the zonal SST contrast, as a direct oceanic response to SVE, which could then result in CEP warming. Since the simulated surface shortwave radiation is not spatiotemporally uniform, the effect is significantly reduced when compared with the previous studies (Mann et al. 2005; Emile-Geay et al. 2008), as discussed in McGregor and Timmermann (2011).Inadditiontotheradiativecooling,oursimulations additionally present the important role of the anomalous zonal gradients of surface temperature on the El Ni~no onset (via modulation of the precipitation), especially after the SVE peak. In the model experiment, the direct effect of the reduced solar radiation explains about 30% of the total effect in the model, while there is an indirect effect of 50%. However, the thermostat mechanism is arguably the only physical mechanism at play in the Zebiak and Cane (1987) model on those time scales, and it is therefore not surprising that it should show up relatively strongly in experiments using its oceanic component. 5. Discussion and summary The motivation for our study was to systematically examine the ENSO response to a tropical SVE using a FIG. 11. Evolution of the ensemble-mean Ni~no-3.4 index (8C) for the intermediate coupled model experiment overlaid from January 0 to December 12. The effect of surface shortwave radiation (SSW) anomaly and the MC cooling on ENSO are respectively denoted by orange and blue lines. The ALL forcing run (i.e., both the surface heat flux anomalies and MC cooling) and its spread of individual forecast members are denoted by a black line with gray shading. The forced period is represented by yellow shading. CGCM, conditional on ENSO phase. We found that the radiative forcing of volcanic aerosols in the stratosphere initially creates an El Ni~no like response that can be significantly amplified by air sea interactions in seasons following the SVE. The peak of this equatorial response follows the time of the volcanic forcing by about one year. The results obtained from the CGCM experiments are in excellent agreement with those obtained from the recent proxy evidence of Adams et al. (2003) and McGregor et al. (2010). We therefore conclude that the SVE response of the air sea coupled dynamics in the Pacific can increase the probability of an El Ni~no event, in particular one year after the forcing peak during neutral, or El Ni~no, years. This also implies that a rapid change in radiative forcing could create the additional risk of other events, such as widespread drought and reduced freshwater resources, via a modulation of ENSO. We also investigated the response to SVE forcing during El Ni~no and La Ni~na, because ENSO exhibits a significant asymmetry. The SVE forcing during El Ni~no significantly prevents the transition to a cold state. Because of self-amplification by air sea coupled dynamics, the response of the CEP during El Ni~no is larger than that during La Ni~na. Therefore, the intensity of the dynamical thermostat-like response of ENSO to a SVE is clearly dependent the ENSO phase. We suggest that it could plausibly be the case in nature as well. In addition to simple model studies (e.g., Mann et al. 2005), the dynamical thermostat-like response documented in Clement et al. (1996) is also seen in the full CGCM. To diagnose which component excites the El Ni~no like response, we analyzed the mixed layer heat budget and an intermediate coupled model. Our model experiments revealed a new mechanism: that the effect

5180 J O U R N A L O F C L I M A T E VOLUME 26 of a land sea cooling contrast (and a relatively rapid cooling of the Indian Ocean) is the dominant mechanism, instead of the direct response of oceanic dynamics to the radiative forcing proposed in Clement et al. (1996). We need to verify that this mechanism is also at play in other coupled GCMs. We note that this study assumes the same temporal evolution of volcanically induced stratospheric aerosols in all experiments, with magnitude as the only variable (Fig. 8a). We are interested in studying the model response for when the SVE begins at a more rapid rate or occurs in the extratropics, which can potentially alter the sensitivity of ENSO to a SVE. In addition, our experiments also fix the volcanic eruption peak in the boreal winter. Because of the seasonal change in the instability of ENSO, the sensitivity of ENSO to the SVE forcing would, therefore, possibly be different in each season. McGregor and Timmermann (2011) provide a different explanation for the influence of volcanic eruptions on the ENSO. The SVEs in the CCSM3 model induce enhanced trade winds, which then lead to a deepened thermocline and SST warming after a period of several months, via a recharge process (Jin 1997). The contrast in the response between the models may be due to the difference in the initial response of low-level cloud response in the CEP, or to the intensity of the following feedback process (such as the recharge discharge process). As denoted by the recent Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) multimodel comparison studies, CGCMs represent various feedbacks (e.g., Guilyardi et al. 2009b). The analysis of cloud radiative feedbacks in convection/subsidence dynamical regimes in the CMIP3 models (Bony and Dufresne 2005) shows that the simulation of marine boundary layer clouds is at the heart of tropical cloud feedback uncertainties in current CGCMs. Marine boundary layer clouds occur in the CEP and therefore biases in their representation can contribute to the diversity of the ENSO response. The other possibility is that the difference in the ENSO system itself between the models. Ohba et al. (2010) investigate the simulated transition process of ENSO in the CMIP3 models and find diversity of the simulated ENSO transition system. Some of the models reproduce the features of the observed transition process of El Ni~no/La Ni~na, whereas most models fail to concurrently reproduce the process during both phases. Many of the differences between the models can be traced to the representation of deep convection, trade wind strength, and cloud feedbacks (e.g., Guilyardi et al. 2009a; Lloyd et al. 2009; Sun et al. 2009). In addition to the model biases, the starting point used for the volcanic forcing is also different between McGregor and Timmermann (2011) and this study. The SVE forcing in this study starts from summer with the peak in winter while that in McGregor and Timmermann (2011) uses random peaking. This difference could also contribute to the difference in the responses. Further detailed experiments in view of the seasonal dependence should be performed in the future. Time evolution of SST and zonal wind anomalies in response to the SVE forcing are relatively similar to other CGCM experiments conducted using the Geophysical Fluid Dynamics Laboratory Climate Model version 2.1 (Stenchikov et al. 2007) and ECHAM and the global Hamburg Ocean Primitive Equation (Lim and Yeh 2012). However, an analysis by the IPCC Fourth and Fifth Assessment Report phases 3 and 5 of the Coupled Model Intercomparison Project (e.g., Taylor et al. 2012, http://cmip-pcmdi.llnl.gov/cmip5) twentiethcentury simulation response to volcanic forcing does not provide a statistically significant response in the equatorial CEP (not shown). Numerical simulations produce a considerable range of dynamical responses to volcanic forcing (Stenchikov et al. 2006), which likely depend on diverse aspects of model formulation. The sensitivity to volcanic forcing differs considerably between models. Intermodel comparison of millennium CGCM simulation is needed in order to further discuss the threshold level of the SVE ENSO relationship. Acknowledgments. We express special thanks to Drs S. Watanabe, M. Sugiyama, and S. Emori for their helpful suggestions and discussions. This work was supported by the Program for Risk Information on ClimateChange(PRICC) frommextjapanand by the Global Environmental Research Fund (S10) of MOE Japan. REFERENCES Adams, J., M. Mann, and C. Ammann, 2003: Proxy evidence for an El Ni~no-like response to volcanic forcing. Nature, 426, 274 278, doi:10.1038/nature02101. An, S.-I., F.-F. Jin, and I.-S. Kang, 1999: The role of zonal advection feedback in phase transition and growth of ENSO in the Cane Zebiak model. J. Meteor. Soc. Japan, 77, 1151 1160. Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163 172. Bony, S., and J. L. Dufresne, 2005: Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys. Res. Lett., 32, L20806, doi:10.1029/ 2005GL023851. Cane, M. A., and R. J. Patton, 1984: A numerical model for lowfrequency equatorial dynamics. J. Phys. Oceanogr., 14, 1853 1863. Church, J. A., N. J. White, and J. M. Arblaster, 2005: Significant decadal-scale impact of volcanic eruptions on sea level and ocean heat content. Nature, 438, 74 77, doi:10.1038/ nature04237. Clement, A., R. Seager, M. A. Cane, and S. E. Zebiak, 1996: An ocean dynamical thermostat. J. Climate, 9, 2190 2196.

15 JULY 2013 O H B A E T A L. 5181 Emile-Geay, J., R. Seager, M. A. Cane, E. Cook, and G. H. Haug, 2008: Volcanoes and ENSO over the past millennium. J. Climate, 21, 3134 3148. Gleckler, P. J., K. R. Sperber, and K. AchutaRao, 2006: Annual cycle of global ocean heat content: Observed and simulated. J. Geophys. Res., 111, C06008, doi:10.1029/2005jc003223. Guemas, V., S. Corti, J. Garcıa-Serrano, F. Doblas-Reyes, M. Balmaseda, and L. Magnusson, 2013: The Indian Ocean: The region of highest skill worldwide in decadal climate prediction. J. Climate, 26, 726 739. Guilyardi, E., P. Braconnot, F. F. Jin, S. T. Kim, M. Kolasinski, T. Li, and I. Musat, 2009a: Atmosphere feedbacks during ENSO in a coupled GCM with a modified atmospheric convection scheme. J. Climate, 22, 5698 5718., A. Wittenberg, A. Fedorov, M. Collins, C. Wang, A. Capotondi, G. J. van Oldenborgh, and T. Stockdale, 2009b: Understanding El Ni~no in ocean atmosphere general circulation models: Progress and challenges. Bull. Amer. Meteor. Soc., 90, 325 340. Handler, P., 1984: Possible association of stratospheric aerosols and El Ni~no type events. Geophys. Res. Lett., 11, 1121 1124. Hasumi, H., 2006: CCSR Ocean Component Model (COCO) version 4.0. CCSR Rep. 25, 103 pp. [Available online at http:// www.ccsr.u-tokyo.ac.jp/;hasumi/coco/index.html.], and S. Emori, Eds., 2004: K-1 coupled GCM (MIROC) description. K-1 Tech. Rep., 34 pp. [Available online at http:// www.ccsr.u-tokyo.ac.jp/kyosei/hasumi/miroc/tech-repo. pdf.] Jin, F.-F., 1997: An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model. J. Atmos. Sci., 54, 811 829., and S.-I. An, 1999: Thermocline and zonal advective feedbacks within the equatorial ocean recharge oscillator model for ENSO. Geophys. Res. Lett., 26, 2989 2992. Joshi, M. M., J. M. Gregory, M. J. Webb, D. M. H. Sexton, and T. C. Johns, 2007: Mechanisms for the land/sea warming contrast exhibited by simulations of climate change. Climate Dyn., 30, 455 465, doi:10.1007/s00382-007-0306-1. Klein, S. A., B. J. Soden, and N. C. Lau, 1999: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge. J. Climate, 12, 917 932. Kug, J.-S., and I.-S. Kang, 2006: Interactive feedback between the Indian Ocean and ENSO. J. Climate, 19, 1784 1801., F.-F. Jin, and S.-I. An, 2009: Two types of El Ni~no events: Cold tongue El Ni~no and warm pool El Ni~no. J. Climate, 22, 1499 1515. Lim, H.-G., and S.-W Yeh, 2012: Volcanic effect on the tropical sea surface temperature in a millennium coupled model simulation. Geophysical Research Abstracts, Vol. 14, Abstract EGU2012-4574. [Abstract available online at http://meetingorganizer. copernicus.org/egu2012/egu2012-4574.pdf.] Lindzen, R. S., and S. Nigam, 1987: On the role of sea surface temperature gradients in forcing low-level winds and convergence in the tropics. J. Atmos. Sci., 44, 2418 2436. Lloyd, J., E. Guilyardi, H. Weller, and J. Slingo, 2009: The role of atmosphere feedbacks during ENSO in the CMIP3 models. Atmos. Sci. Lett., 10, 170 176. Mann, M., M. A. Cane, S. E. Zebiak, and A. Clement, 2005: Volcanic and solar forcing of the tropical Pacific over the past 1000 years. J. Climate, 18, 447 456. McGregor, S., and A. Timmermann, 2011: The effect of explosive tropical volcanism on ENSO. J. Climate, 24, 2178 2191.,, and O. Timm, 2010: A unified proxy for ENSO and PDO variability since 1650. Climate Past, 5, 1 17. Newman, M., S.-I. Shin, and M. A. Alexander, 2011: Natural variation in ENSO flavors. Geophys. Res. Lett., 38, L14705, doi:10.1029/2011gl047658. Nicholls, N., 1988: Low latitude volcanic eruptions and the El Ni~no- Southern Oscillation. Int. J. Climatol., 9, 91 95. Ohba, M., and H. Ueda, 2006: A role of zonal gradient of SST between the Indian Ocean and the western Pacific in localized convection around the Philippines. SOLA, 2, 176 179., and, 2007: An impact of SST anomalies in the Indian Ocean in acceleration of the El Ni~no to La Ni~na transition. J. Meteor. Soc. Japan, 85, 335 348., and, 2009: Role of nonlinear atmospheric response to SST on the asymmetric transition process of ENSO. J. Climate, 22, 177 192., and M. Watanabe, 2012: Role of the Indo-Pacific interbasin coupling in predicting asymmetric ENSO transition and duration. J. Climate, 25, 3321 3335., D. Nohara, and H. Ueda, 2010: Simulation of asymmetric ENSO transition in WCRP CMIP3 multimodel experiments. J. Climate, 23, 6051 6067. Robock, A., 2000: Volcanic eruptions and climate. Rev. Geophys., 38, 191 219., L. Oman, and G. Stenchikov, 2008: Regional climate responses to geoengineering with tropical and Arctic SO 2 injections. J. Geophys. Res., 113, D16101, doi:10.1029/ 2008JD010050. Sato, M., J. Hansen, M. McCormick, and J. Pollack, 1993: Stratospheric aerosol optical depths, 1850 1990. J. Geophys. Res., 98 (D12), 22 987 22 994. Self, S., M. R. Rampino, J. Zhao, and M. G. Katz, 1997: Volcanic aerosol perturbations and strong El Ni~no events: No general correlation. Geophys. Res. Lett., 24, 1247 1250. Shiogama, H., T. Nagashima, T. Yokohata, S. A. Crooks, and T. Nozawa, 2006: Influence of volcanic activity and changes in solar irradiance on surface air temperatures in the early twentieth century. Geophys. Res. Lett., 33, L09702, doi:10.1029/ 2005GL025622., S. Emori, T. Mochizuki, S. Yasunaka, T. Yokohata, M. Ishii, T. Nozawa, and M. Kimoto, 2010: Possible influence of volcanic activity on the decadal potential predictability of the natural variability in near-term climate predictions. Adv. Meteor., 2010, 657318, doi:10.1155/2010/657318. Stenchikov, G., K. Hamilton, R. J. Stouffer, A. Robock, V. Ramaswamy, B. Santer, and H.-F. Graf, 2006: Artic oscillation response to volcanic eruptions in the IPCC AR4 climate models. J. Geophys. Res., 111, D07107, doi:10.1029/ 2005JD006286., T. Delworth, and A. Wittenberg, 2007: Volcanic climate impacts and ENSO interactions. Eos, Trans. Amer. Geophys. Union, Vol. 88 (Spring Meeting Suppl.), Abstract A43D-09. Stothers, R., 2000: Climatic and demographic consequences of the massive volcanic eruption of 1258. Climatic Change, 45, 361 374. Sun, D.-Z., Y. Yu, and T. Zhang, 2009: Tropical water vapor and cloud feedbacks in climate models: A further assessment using coupled simulations. J. Climate, 22, 1287 1304. Sutton, R. T., B. Dong, and J. M. Gregory, 2007: Land/sea warming ratio in response to climate change: IPCC AR4 model results and comparison with observations. Geophys. Res. Lett., 34, L02701, doi:10.1029/2006gl028164.