Simulated Patterns of Unforced Centennial-Scale. Climate Variability in the Tropical Pacific

Transcription

1 Simulated Patterns of Unforced Centennial-Scale Climate Variability in the Tropical Pacific Kristopher B. Karnauskas Woods Hole Oceanographic Institution Jason E. Smerdon and Richard Seager Lamont-Doherty Earth Observatory of Columbia University 1 1 Jesus Fidel González-Rouco Universidad Complutense de Madrid Submitted to Journal of Climate-EC, July 0 1 1

2 Abstract Quantifying and understanding unforced variability at the centennial time scale is critical for detecting and attributing externally forced changes. The era of modern instrumental records of key climate variables such as sea surface temperature (SST) and sea level pressure (SLP) began less than 00 years ago, requiring that this variability be characterized with paleoclimatic evidence or multi-century simulations from general circulation models (GCMs). Centennial-scale climate variability in the tropical Pacific is investigated herein using long control integrations from two global coupled GCMs. Both models produce substantial centennial-scale variability in the mean zonal SST and SLP gradients across the equatorial Pacific. The pattern of the centennial mode is dissimilar to the interannual El Niño-Southern Oscillation (ENSO) and the dominant mode of Pacific decadal variability in that the most prominent expression in temperature is found in the western equatorial thermocline, yet the global repercussions are analogous to ENSO as expressed in a wave pattern of alternating highs and lows emanating from the western tropical Pacific. Implications for ENSO modulation and forced-trend detection are discussed. 1

3 Introduction The dominant influence of the tropical Pacific on global interannual climate variability has motivated considerable interest in understanding its potential response to anthropogenic forcing, including whether the tropical Pacific could become more E -like or - like. Much depends on the answers; for example, the amount of future drying in southwestern North America and even the sign of hydrologic change in large parts of South America appear to depend on whether the zonal SST gradient in the equatorial Pacific strengthens or weakens (Seager and Vecchi 0). Additionally, past precipitation events, such as major North American droughts (Schubert et al. 00, Seager et al. 00) and the global trend over recent decades (Hoerling et al. 00, Seager and Vecchi 0, Seager and Naik 0), have been strongly influenced by changes in the equatorial Pacific zonal SST gradient that amount to no more than a fraction of a degree Centigrade. Although the actual response of the tropical Pacific to radiative forcing (of any origin) is not yet clear, paleoclimate records indicate that significant changes in the equatorial zonal SST gradient have surely occurred over thousands to millions of years (e.g., Wara et al. 00). Recent modeling work has also suggested that the zonal SST gradient in the Indo-Pacific sector may have played an important role in shaping the atmospheric circulation and hydrological regime during the Medieval Climate Anomaly at the turn of the last millennium (Graham et al. 0). It therefore is likely that changes in the gradient will play an important role in shaping the character of contemporary climate change, which further underscores the importance of understanding the magnitude of variability in the equatorial SST gradient and the associated underlying dynamics on a wide range of time scales. A growing number of studies have used instrumental data to quantify observed changes in the mean climate of the tropical Pacific including any potential response to anthropogenic

4 forcing (Cane et al. 1, Cane 00, Vecchi et al. 00, Karnauskas et al. 00, Bunge and Clarke 00, Compo and Sardeshmukh 0, Kumar et al. 0, Deser et al. 0, Tung et al. 0, Zhang et al. 0). These studies adopt various approaches for addressing the signal-tonoise problem of estimating long-term trends in 0- year time series that contain highamplitude variance at interannual time scales and longer. Nevertheless, they do not consider the possibility that naturally occurring, or unforced low-frequency variability inherent to the coupled dynamics of the tropical Pacific, but operating on centennial time scales, could also create centennial-length trends. Until very recently, only simple models or models of intermediate complexity limited in domain to the tropical Pacific basin (e.g., Zebiak and Cane 1) could be integrated over sufficiently long periods to quantify internal low-frequency variability. Control and forced simulations from fully coupled GCMs that span a millennium or more are, however, becoming more widely available and thus making possible the robust characterization of multidecadal-to-centennial variability in model simulated climates. Wittenberg (00) showed that the range of natural low-frequency variability in the period and amplitude of the El Niño-Southern Oscillation (ENSO) in a 000-year GCM control simulation precludes the attribution of changes in ENSO behavior seen in the modern instrumental record to anthropogenic or external forcing. Yeh et al. (0) used a similarly complex model to quantify low-frequency variations in the spatial pattern of ENSO, contrasting the eastern and central Pacific flavors of El Niño events. Corroborating these coupled-model studies, Li et al. (0) have recently presented proxy evidence from tree rings and Pacific corals for significant variability in the amplitude of ENSO on interdecadal time scales. Focusing on the mean state, we examine centennial-scale climate variability in the tropical Pacific, as well as associated global patterns of SST and sea level pressure (SLP), using

5 long (millennial) control integrations of two state-of-the-art global coupled GCMs. The models are briefly described in the following section, the results are presented in section, and a discussion of the implications for ENSO modulation and forced-trend detection is given in section Models Annual resolution output fields from millennial control integrations of two fully coupled global GCMs are employed in this study. In the simulations, solar radiation, atmospheric composition, and all other aspects of the external forcing are held constant. Simulated climate variability at any time scale is therefore solely the result of internal dynamics captured by the model rather than a response to any specified forcing. The Geophysical Fluid Dynamics Laboratory (GFDL) Coupled Model version.1 (CM.1; GFDL hereafter) is described by Delworth et al. (00). The control simulation used herein was run with an atmospheric resolution of latitude by longitude by vertical levels; oceanic resolution is 1 latitude (increasing to 1/ near the equator) by 1 longitude by 0 levels. The simulation spans,000 years after omitting the first 00 years as model spinup. Simulation of the tropical Pacific climate and ENSO variability by the GFDL model is assessed by Wittenberg et al. (00); despite biases in mean climate common to most coupled models such as an equatorial cold bias, the basic climatology as well as the spatial pattern and periodicity of ENSO are well reproduced. The GFDL model does not apply flux adjustment. The ECHO-G coupled climate model (Legutke and Voss 1) combines the ECHAM atmospheric model (Roeckner et al. 1) with the HOPE-G ocean model (Legutke and Maier- Reimer 1). The control simulation used herein was run at T0 (~. ) by 1 levels

6 atmospheric resolution, T (~. ; meridional resolution increasing to ~0. near the equator) by 0 levels oceanic resolution, and spans 1,000 years. The ECHO-G model applies a timeinvariant flux adjustment (heat and freshwater fluxes) to avoid climate drift. An assessment of the simulated mean climate and ENSO variability by the ECHO-G model is given by Min et al. (00) Results Following Karnauskas et al. (00), the zonal SST gradient is defined as the difference between SST averaged within the western equatorial Pacific ( E- W x S- N) and the eastern equatorial Pacific ( W-0 W x S- N). The zonal SLP gradient is computed using the same areas, but the sense of the difference is reversed such that a large positive value corresponds with a strong zonal SLP gradient. The spectral content of the full annually resolved time series of zonal SST and SLP gradients from the GFDL and ECHO-G models as well as observational estimates are shown in Fig. 1. In the ECHO-G model, both the zonal SST and SLP gradients exhibit significant variability at the 0-year period (with SST also yielding significance across 00- years). Although not significant at the % confidence level (assuming a lag-1 autoregressive noise model with persistence equal to the estimate of the model time series), both the zonal SST and SLP gradients in the GFDL model share distinctive spectral peaks at the 1-year period. In light of the spectral content of the zonal gradients in both models, a low pass (cutoff period 0 years) Butterworth filter was applied to each time series (Fig. ). In both models, the correlations between low-pass filtered (LP hereinafter) zonal SST and SLP gradients are near unity, suggesting Bjerknes (1)-like coupling (Bjerknes 1) extending to the centennial time scale.

7 Cross-correlation analysis (not shown) indicates that the zonal SST gradient leads SLP by ~1 year in the GFDL model but there is no apparent lag in the ECHO-G model. To characterize the global spatial patterns associated with the simulated centennial-scale variability in the equatorial Pacific zonal gradients, Figs. and show the unfiltered annual SST, SLP, and equatorial subsurface temperature fields composited on the LP zonal SST gradient time series (a threshold of 0. standard deviates was used but the results are insensitive to this choice between 0-1 standard deviates). The broad features of the composite fields are highly consistent between the GFDL and ECHO-G models. The difference in composite SST fields for strong minus weak LP zonal gradients are characterized by a +0. C anomaly in the western equatorial Pacific and a -0. C anomaly in the eastern equatorial Pacific, with cold anomalies also found in the southeastern Indian Ocean. In the ECHO-G model, the warm anomaly in the western equatorial Pacific is shifted ~ eastward relative to GFDL. The composite subsurface ocean temperature fields for both models are also very similar, indicating warm (cold) anomalies at ~0 m depth in the western (eastern) equatorial Pacific of +0. C (-0. C) which is equivalent to a stronger tilt in the thermocline. Also shown in Figs. and are composite SLP fields that are also in very good agreement between the GFDL and ECHO-G models. Consistent with the sense of the composite differencing (strong-weak LP zonal SST gradient), the zonal SLP gradient across the equatorial Pacific is strengthened. A wave pattern of alternating highs and lows emanating from the western tropical Pacific is seen in both model composite SLP fields extending well into the midlatitudes of both hemispheres. Consistent with the expected circulation around the strong high and low pressure anomalies in the North and South Pacific (~0 latitude), a horseshoe pattern in SST emerges which is especially coherent in the Northern Hemisphere. Such anomalous circulations

8 and SST distributions imply similar time scale precipitation variability in regions such as Oceania and the Americas Discussion At the sea surface, unforced centennial-scale variability yields overall changes in the zonal gradient of ~0. C (Figs. and ). Such changes are equivalent to trends that have been estimated over the modern instrumental era since ~ (see references in Introduction). Shown in Fig. are the time series of linear trends computed within moving 1-year windows on the unfiltered annual zonal SST gradients. In both the GFDL and ECHO-G models, significant positive and negative (strengthening and weakening gradient, respectively) trends can be found in several windows within the simulations. Trends in the GFDL model, as estimated by linear regression, can exceed 0. C per century. Secular trend magnitudes in the ECHO-G model are about half the size of those in the GFDL model, but nonetheless occasionally pass significance despite the fact that these are unforced control simulations. Comparison of the trends computed on unfiltered annual data with the LP time series (also shown in Fig. ) indicates the clear control of the centennial-scale variations on the estimated centennial trends. Diagnostic studies of the mechanisms for modeled centennial-scale unforced climate variability in the tropical Pacific must be pursued, but it is important to note here the simulated correlation between ENSO variance and the mean state (Fig. ). Although the slope varies between the two models, periods of weaker gradients are associated with periods of strong ENSO variability and vice versa. This suggests that the apparent centennial-scale variability in the mean state could simply be a statistical rectification of an asymmetric process (i.e., El Niño events are warmer than La Niña events are cold). If so, one would expect the composite patterns

9 to closely resemble a La Niña event. This is not the case; given the large amplitude SST signal in the western Pacific, the centennial pattern does not resemble ENSO. This can be contrasted with an inter-model comparison of the predicted response to anthropogenic forcing (IPCC 00), which shows no clear relationship between predicted changes in the mean state and predicted changes in ENSO variance. It should also be noted that simulated centennial variability in the tropical Pacific Ocean is no stronger than in other parts of the world ocean and is actually weaker than in many high latitude areas. The global implications of the variability, however, could be larger given the sensitivity of global atmospheric circulation to small changes in the warmest SSTs. The model-based work presented here has two clear and important implications that depend on whether the model variability has a counterpart in nature or not: 1. If nature exhibits such strong natural variability of tropical Pacific SSTs on centennial time scales, then assumptions that the observed trend over the past century to century and a half is a response to radiative forcing are tenuous. It could in fact be that the observed trend over the past century and a half is merely reflective of natural variability. If so, it could strengthen or weaken in the future as the natural variability evolves. This will combine with, and potentially interact with, any forced response with implications for tropical Pacific and global climate.. If the centennial variability in the models is spurious, it is nevertheless a component of the models and will continue to influence coupled GCM projections of future climate, as well as initialized decadal hindcasts and forecasts that are being conducted with these models. In both cases, it must be known at what stage the natural centennial variability

10 1 exists at the beginning of the forecast or projection in order to isolate the forced change from the spurious modeled natural variability. Given the above implications, our findings place a premium on efforts to develop long records of past tropical Pacific SST variability. This can only be done using proxy climate records such as corals in the equatorial Pacific, southeastern Indian Ocean, and the South China Sea or from ocean sediment cores in regions with sedimentation rates high enough to allow adequate temporal resolution. Land-based records from lakes, speleothems and trees would also be useful to ascertain the associated hydrologic signal, although care must be taken interpreting these given the potential influence of SST variability in more than just the tropical Pacific. It is a matter of importance, from the points of view of understanding long-term climate variability and change and practical application of models, to determine if high-amplitude centennial variability of tropical Pacific SSTs is an inherent property of the real climate system Acknowledgements The authors thank NOAA GFDL for providing the CM.1 climate model output and Delia Oppo for helpful comments on a draft of this manuscript. JES and RS were supported by the NOAA award Global Decadal Hydroclimate Variability and Change (NAOAR). 1

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15 Figure captions Fig. 1. Power spectra of zonal SST (left) and SLP (right) gradients (as defined in main text and in Karnauskas et al. 00) from observations (black), GFDL CM.1 (blue), and ECHO-G (red). Observed SST (SLP) data are from NOAA ERSST vb (HadSST1.1) data sets. Significance estimates (dashed lines) are based on the th percentile of,000 randomly generated time series, each having the same lag-1 autocorrelation as the time series being evaluated. The frequency axis is limited periods between and 1,000 years. Arrows indicate features discussed in main text. Fig.. Time series of low-pass filtered (0-yr cutoff) zonal SST (heavy) and SLP (thin) gradients in GFDL CM.1 (top) and ECHO-G (bottom). Fig.. GFDL model fields composited on the low-pass filtered zonal SST gradient. Composite fields were computed as the difference between the mean of all unfiltered fields concurrent with the low-pass filtered zonal SST gradient exceeding +0. standard deviations minus the mean of all unfiltered fields concurrent with the low-pass filtered zonal SST gradient falling below -0. standard deviations. Fields shown are SST ( C; upper left), SLP (hpa; upper right), and equatorial ocean temperatures down to 00 m ( C; bottom). Fig.. As in Fig., but for the ECHO-G model. Fig.. Time series of running linear trends (1 yr window) in the unfiltered zonal SST gradient (thin black) in GFDL CM.1 (left) and ECHO-G (right). Also shown for reference are the time series of low-pass filtered zonal SST gradient (gray, scaled by a factor of 1/ to facilitate comparison). Trends significant at the % confidence level are colored red. Fig.. Scatter diagram comparing normalized low-pass filtered zonal SST gradient and 0-yr running variance for the GFDL CM.1 (blue) and ECHO-G (red) models. 1

16 Figures Fig. 1. Power spectra of zonal SST (left) and SLP (right) gradients (as defined in main text and in Karnauskas et al. 00) from observations (black), GFDL CM.1 (blue), and ECHO-G (red). Observed SST (SLP) data are from NOAA ERSST vb (HadSST1.1) data sets. Significance estimates (dashed lines) are based on the th percentile of,000 randomly generated time series, each having the same lag-1 autocorrelation as the time series being evaluated. The frequency axis is limited periods between and 1,000 years. Arrows indicate features discussed in main text. 1

17 Fig.. Time series of low-pass filtered (0-yr cutoff) zonal SST (heavy) and SLP (thin) gradients in GFDL CM.1 (top) and ECHO-G (bottom). 1

18 Fig.. GFDL model fields composited on the low-pass filtered zonal SST gradient. Composite fields were computed as the difference between the mean of all unfiltered fields concurrent with the low-pass filtered zonal SST gradient exceeding +0. standard deviations minus the mean of all unfiltered fields concurrent with the low-pass filtered zonal SST gradient falling below -0. standard deviations. Fields shown are SST ( C; upper left), SLP (hpa; upper right), and equatorial ocean temperatures down to 00 m ( C; bottom). 1

19 Fig.. As in Fig., but for the ECHO-G model. 1

20 Fig.. Time series of running linear trends (1 yr window) in the unfiltered zonal SST gradient (thin black) in GFDL CM.1 (left) and ECHO-G (right). Also shown for reference are the time series of low-pass filtered zonal SST gradient (gray, scaled by a factor of 1/ to facilitate comparison). Trends significant at the % confidence level are colored red. 0

21 Fig.. Scatter diagram comparing normalized low-pass filtered zonal SST gradient and 0-yr running variance for the GFDL CM.1 (blue) and ECHO-G (red) models. 1