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Michael E. Mann and Jeffrey Park

Abstract

Coherent spatiotemporal modes of climatic variability are isolated based on a multivariate frequency domain singular value decomposition (SVD) of nearly a century of monthly Northern Hemisphere sea level pressure (SLP) and surface temperature data. Insight into the underlying physical processes associated with potential climatic signals is obtained by examining the relationship between surface temperature and inferred atmospheric circulation patterns as they evolve over the a typical cycle, taking potential seasonal distinctions into account. Our analysis provides evidence for two significant independent secular variations describing a secular warming trend (and accompanying changes in circulation patterns) and a century timescale “oscillation” marked by high-amplitude variations in temperature and SLP in the North Atlantic that are similar to those observed in recent model simulations. Quasi-oscillatory interdecadal (16–18 yr timescale) variability also displays a pattern similar to those predicted in recent model experiments, with an apparent origin in the North Pacific. Weaker quasi-decadal (10–11-yr timescale), largely cold-season oscillatory behavior is more closely tied to the North Atlantic and may involve analogous mechanisms. Interannual variability is examined with an “evolutive” generalization of our procedure to captures the time-evolving frequency and amplitude characteristics of the associated climate signal. Variability exhibiting the characteristic climatic patterns of the global El Niño-Southern Oscillation (ENSO) phenomenon is described by two largely distinct frequency bands within the broader 3–7-yr ENSO band. The drifting central frequencies of these two dominant bands is suggestive of nonstationary behavior in ENSO. A quasibiennial signal exhibits a gradual trend toward increasing frequency. Prospects for improved long-range climate forecasting are discussed.

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Jeffrey Park and Michael E. Mann

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For the purpose of climate signal detection, we introduce a method for identifying significant episodes of large-scale oscillatory variability. The method is based on a multivariate wavelet algorithm that identifies coherent patterns of variation simultaneously within particular ranges of time and periodicity (or frequency) that may vary regionally in the timing and amplitude of the particular temperature oscillation. By using this methodology, an analysis is performed of the instrumental record of global temperatures spanning the past 140 years. The duration of an “episode” is chosen to correspond to 3–5 cycles at a specified oscillation period, which is useful for detecting signals associated with the global El Niño/Southern Oscillation (ENSO) phenomenon. To confirm the robustness of signals detected in the earliest, sparse data (only 111 5° longitude by 5° latitude grid points are available back to 1854), we performed multiple analyses overlapping in time, using increasingly dense subsets of the full (1570 grid point) temperature data. In every case, significant interannual episodes are centered in the 3–7 year period range corresponding to the conventional band of ENSO-related variance and describe intervals of quasi-oscillatory variability of decadal-scale duration. These episodes consist of a sequence of one or two warm and cold events with sea surface temperature fluctuations in the eastern tropical Pacific of amplitude ±0.6°–1.1°C. Each episode includes one or more historically prominent El Niño events. The signals are characterized as significant, however, by virtue of their global-scale pattern of temperature variations as well as their oscillatory pattern in time. The 1920–1940 interval of increasing global temperatures was bracketed by oscillatory episodes with unusual global patterns of expression relative to the recent ENSO episodes of the 1970s and 1980s. The episodes that preceded the 1920–1940 and 1975–present intervals of rapid warming were associated with globally averaged temperature fluctuations of T GLB > 0.4°C, the largest among those identified. In contrast, the episode that concludes the 1920–1940 temperature rise exhibits a global-mean fluctuation T GLB = 0.05°C, smallest among the observed episodes. These observations motivate speculation about the possible relationship between ENSO variability and global warming, in particular, the relationship between ENSO and the transient storage of heat in the tropical upper ocean layer, and the relationship between secular climate change and the amplitude of interannual ENSO events.

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Balaji Rajagopalan, Michael E. Mann, and Upmanu Lall

Abstract

Guided by the increasing awareness and detectability of spatiotemporally organized climatic variability at interannual and longer timescales, the authors motivate the paradigm of a climate system that exhibits excitations of quasi-oscillatory eigenmodes with characteristic timescales and large-scale spatial patterns of coherence. It is assumed that any such modes are superposed on a spatially and temporally autocorrelated stochastic noise background. Under such a paradigm, a previously described (Mann and Park) multivariate frequency-domain approach is promoted as a particularly effective means of spatiotemporal signal identification and reconstruction, and an associated forecasting methodology is introduced. This combined signal detection/forecasting scheme exhibits significantly greater skill than conventional forecasting approaches in the context of a synthetic example consistent with the adopted paradigm. The example application demonstrates statistically significant skill at 5–10-yr lead times. Applications to operational long-range climatic forecasting are motivated and discussed.

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Shaleen Jain, Upmanu Lall, and Michael E. Mann

Abstract

Historical variations in the equator-to-pole surface temperature gradient (EPG) and the ocean–land surface temperature contrast (OLC) based on spatial finite differencing of gridded historical sea surface and land air temperatures are analyzed. The two temperature gradients represent zonally symmetric and asymmetric thermal forcings of the atmosphere. The strength and position of the Hadley cell and of the westerlies is related to the EPG, while the strength of the eddies coupled to the mid/high-latitude quasigeostrophic flow is related to the OLC. Taking these two parameters as simple yet highly meaningful diagnostics of the low-frequency variability of the atmosphere and climate system, the authors revisit a number of timely issues in the area of diagnostic climate studies. Of particular interest are seasonality and its variations and evidence of warming expected from greenhouse gas increases. Investigations of possible effects of CO2-induced greenhouse warming are pursued by comparing the trends in EPG and OLC estimated from the observations and by using the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model (GCM) results for control and transient-increased CO2 simulations. Significant differences are noted between the trends in EPG and OLC for observational data and the increased CO2 GCM scenario. However, the dynamical response of both EPG and OLC during subperiods with warming and cooling is consistent with that exhibited by the GFDL GCM. In this sense, the “fingerprint” of anthropogenic forcing of the climate is not clearly evident in these basic diagnostics of large-scale climate variability.

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Oliver W. Frauenfeld, Robert E. Davis, and Michael E. Mann

Abstract

A new and distinctly interdecadal signal in the climate of the Pacific Ocean has been uncovered by examining the coupled behavior of sea surface temperatures (SSTs) and Northern Hemisphere atmospheric circulation. This interdecadal Pacific signal (IPS) of ocean–atmosphere interaction exhibits a highly statistically significant interdecadal component yet contains little to no interannual (El Niño scale) variability common to other Pacific climate anomaly patterns. The IPS thus represents the only empirically derived, distinctly interdecadal signal of Pacific Ocean SST variability that likely also represents the true interdecadal behavior of the Pacific Ocean–atmosphere system. The residual variability of the Pacific’s leading SST pattern, after removal of the IPS, is highly correlated with El Niño anomalies. This indicates that by simply including an atmospheric component, the leading mode of Pacific SST variability has been decomposed into its interdecadal and interannual patterns. Although the interdecadal signal is unrelated to interannual El Niño variability, the interdecadal ocean–atmosphere variability still seems closely linked to tropical Pacific SSTs. Because prior abrupt changes in Pacific SSTs have been related to anomalies in a variety of physical and biotic parameters throughout the Northern Hemisphere, and because of the persistence of these changes over several decades, isolation of this interdecadal signal in the Pacific Ocean–atmosphere system has potentially important and widespread implications to climate forecasting and climate impact assessment.

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Fangxing Fan, Michael E. Mann, and Caspar M. Ammann

Abstract

The Asian summer monsoon (ASM) and its variability were investigated over the past millennium through the analysis of a long-term simulation of the NCAR Climate System Model, version 1.4 (CSM 1.4) coupled model driven with estimated natural and anthropogenic radiative forcing during the period 850–1999. Analysis of the simulation results indicates that certain previously proposed mechanisms, such as warmer large-scale temperatures favoring a stronger monsoon through their effect on Eurasian snow cover, appear inconsistent with the mechanisms active in the simulation. Forced changes in tropical Pacific sea surface temperatures play an apparent role in the long-term changes in the ASM. Analyses of the simulation results suggest that the direct radiative effect of solar forcing variations on the ASM is quite weak and that dynamical responses may be far more important. Volcanic radiative forcing leads to a clearly detectable short-term reduction in the strength of the ASM. Comparisons with long-term proxy reconstructions of the ASM are attempted but are limited by the divergent behavior among different reconstructions as well as the limitations in the model’s coupled dynamics.

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Michael E. Mann, Scott Rutherford, Eugene Wahl, and Caspar Ammann

Abstract

Two widely used statistical approaches to reconstructing past climate histories from climate “proxy” data such as tree rings, corals, and ice cores are investigated using synthetic “pseudoproxy” data derived from a simulation of forced climate changes over the past 1200 yr. These experiments suggest that both statistical approaches should yield reliable reconstructions of the true climate history within estimated uncertainties, given estimates of the signal and noise attributes of actual proxy data networks.

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Liang Ning, Michael E. Mann, Robert Crane, and Thorsten Wagener

Abstract

This study uses a statistical downscaling method based on self-organizing maps (SOMs) to produce high-resolution, downscaled precipitation estimates over the state of Pennsylvania in the mid-Atlantic region of the United States. The SOMs approach derives a transfer function between large-scale mean atmospheric states and local meteorological variables (daily point precipitation values) of interest. First, the SOM was trained using seven coarsely resolved atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis dataset to model observed daily precipitation data from 17 stations across Pennsylvania for the period 1979–2005. Employing the same coarsely resolved variables from nine general circulation model (GCM) simulations taken from the historical analysis of the Coupled Model Intercomparison Project, phase 3 (CMIP3), the trained SOM was subsequently applied to simulate daily precipitation at the same 17 sites for the period 1961–2000. The SOM analysis indicates that the nine model simulations exhibit similar synoptic-scale features to the (NCEP) observations over the 1979–2007 training interval. An analysis of the sea level pressure signatures and the precipitation distribution corresponding to the trained SOM shows that it is effective in differentiating characteristic synoptic circulation patterns and associated precipitation. The downscaling approach provides a faithful reproduction of the observed probability distributions and temporal characteristics of precipitation on both daily and monthly time scales. The downscaled precipitation field shows significant improvement over the raw GCM precipitation fields with regard to observed average monthly precipitation amounts, average monthly number of rainy days, and standard deviations of monthly precipitation amounts, although certain caveats are noted.

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Scott D. Rutherford, Michael E. Mann, Eugene Wahl, and Caspar Ammann

Abstract

Smerdon et al. report two errors in the climate model grid data used in previous pseudoproxy-based climate reconstruction experiments that do not impact the main conclusions of those works. The errors did not occur in subsequent works and therefore have no impact on the results presented therein. Results presented here for the Climate System Model (CSM) using multiple pseudoproxy noise realizations show that the quantitative differences between the incorrect and corrected results are within the expected variability of the noise realizations. It should also be made clear that the climate reconstruction method used in Smerdon et al. to illustrate the nature of the errors, the Regularized Expectation Maximization method with Ridge Regression (RegEM-Ridge), is known to produce climate reconstructions with considerable variance loss and has been superseded by RegEM-TTLS (TTLS indicates truncated total least squares).

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Michael E. Mann, Scott Rutherford, Eugene Wahl, and Caspar Ammann
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