We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table S1 of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. CMIP5 data processing was enabled by the CDAT analysis package. A. Dai (University at Albany) calculated the second PC of the PDSI dataset (as in Fig. 2d). This work was supported by the Climate and Environmental Sciences Division (CESD) and the Regional and Global Climate Modeling (RGCM) Program of the U.S. Department of Energy (DOE) Office of Science and was performed under the auspices of the U.S. DOE Lawrence Livermore National Laboratory (Contract DE-AC52-07NA27344). K.M. was supported by a Laboratory Directed Research and Development award (13-ERD-032). C.B. was fully supported by the DOE/OBER Early Career Research Program Award SCW1295. We thank our three reviewers for their very helpful and constructive comments, which have substantially improved our paper.
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Here the term “canonical” is used to describe “standard” or “typical” ENSO events; it does not imply that the cENSO mode is derived from canonical correlation analysis.
In the idealized experiments, this amplitude is constant, or specified in advance.
Pseudo-PCs are often used in pattern-based detection and attribution studies, in which a model-predicted anthropogenic fingerprint is searched for in observations (see, e.g., Santer et al. 2009). They also have been used to investigate the aliasing of a large-scale anthropogenic warming signal in the Pacific decadal oscillation index (Bonfils and Santer 2011) and to assess model quality in simulating the Madden–Julian oscillation (Sperber and Kim 2012). Sperber et al. (2005) have noted that use of different basis functions (e.g., leading EOFs estimated separately from observations and individual models) can hamper interpretation of intermodel and model-versus-observational differences. In contrast, projecting observational and model data onto a common basis function—as we do here—facilitates the direct comparison of modeled and observed teleconnection behavior. We are not aware of any other paper in the literature that uses pseudo-PC time series to calculate P teleconnections.
The centered statistic measures the similarity of two patterns after removal of their spatial means.
In consequence, amplification of the P response to ENSO events in the twenty-first century does not necessarily yield larger absolute values of the correlation coefficients.
The choice of 20 models is motivated by Fig. S5a, which naturally separates the models into two groups: those with rEOF1 > 0.83 and with relatively small intermodel correlation differences, and those with much lower rEOF1 values. We did not use other criteria to select the “best” models.
Relatively low temporal variance in the pseudo-PC may occur because of two factors: 1) the observed tropical Pacific variability is underestimated in the model of interest and/or 2) the simulated amplitude of tropical Pacific variability is realistic, but there are substantial spatial biases in the model SST fields, which result in poor projection of the simulated SSTR fields onto the observed cENSO pattern. The strong correlation across realizations between the amplitude of the variability of pseudo-PCs and Niño-3.4 time series (Fig. S5f) suggests that the simulated tropical Pacific SST variability is the main driver of the temporal variance in the pseudo-PCs. This supports hypothesis 1 above.
One caveat is that some component of the mean change in P could also be a result of ENSO-driven changes in the variability of P. Examples of such behavior might involve a change in the relative frequency of El Niño and La Niña events, or enhancement of the precipitation response to El Niño events (relative to La Niña events).
The 33-yr analysis time scale is determined by the length of the GPCP observational dataset (which spans the period from 1979 to 2012). The two selected analysis periods are 38 years and 80 years after the beginning of the observational period.