R code for RegEM EIV turn-key routine can be found here.

This code was featured in Schmidt et al. (2011).

Schmidt, G.A., Mann, M.E., Rutherford, S.D., A comment on “A statistical analysis of multiple temperature proxies: Are reconstructions of surface temperatures over the last 1000 years reliable?” by McShane and Wyner, Ann. Appl. Stat., 5, 65 70, 2011.

Matlab code for smoothing routine (Mann, 2004) can be found here.

An updated version yields a smoothed series based on combination of boundary constraints that minimizes MSE relative to raw time series.

Updated routines from Mann (2008) can be found here.

The Smoothing Routine is Described in:
Mann, M.E., On Smoothing Potentially Non-Stationary Climate Time Series, Geophysical Research Letters, 31, L07214, doi: 10.1029/2004GL019569, 2004.

Updated routine described in:
Mann, M.E., Smoothing of Climate Time Series Revisited, Geophys. Res. Lett., 35, L16708, doi:10.1029/2008GL034716, 2008.

This multitaper spectral analysis provides an optimally low-variance, high-resolution spectral estimate. Assumptions regarding signal (narrowband, but not strictly periodic) and noise (“red”) that are most appropriate in the context of climate studies provide a “robust” method for accurate determination of the noise component of the spectrum.

The MTM Method is Described in: Mann, M.E., Lees. J., Robust Estimation of Background Noise and Signal Detection in Climatic Time Series, Climatic Change, 33, 409-445, 1996.

The MTM Method is Used in the SSA-MTM Toolkit

MTM Fortran code:
Includes required subroutines and sample data for comparison with results shown in the above Mann & Lees paper.

An enhanced version with “evolutive” spectral analysis and spectral coherence estimation is also now available.

A separate package is available for performing complex demodulation of a time series as used in:
Mann, M.E., Park. J., Greenhouse Warming and Changes in the Seasonal Cycle of Temperature: Model Versus Observation, Geophysical Research Letters, 23, 1111-1114, 1996.

Mann, M.E., Lees. J., Robust Estimation of Background Noise and Signal Detection in Climatic Time Series, Climatic Change, 33, 409-445, 1996.

Mann, M.E., Lees. J., Robust Estimation of Background Noise and Signal Detection in Climatic Time Series, Climatic Change, 33, 409-445, 1996.

MTM-SVD is the detection of irregular spatiotemporal oscillatory signals immersed in spatially-correlated coloured noise with optimal signal detection properties. This “evolutive” approach to detecting intermittent and/or frequency-modulated spatiotemporal oscillations reconstructs spatial and temporal patterns of oscillatory climate signals

MTM-SVD codes, synthetic test dataset, and analysis results:
NOTE: an issue was brought to our attention about averaging angles in the code: mtm-svd-recon.f.; more details and a potential fix can be found here. The FORTRAN codes include:

• • “LFV” multivariate spectrum estimation
  • Spatiotemporal signal reconstruction
  • Bootstrap confidence level estimation procedure, along with required subroutines, makefiles, and the synthetic test input and output.

A MATLAB version of the code can be found HERE. MATLAB code generously provided by Marco Correa-Ramirez and Samuel Hormazabal See their paper here.
Python version of the code can be found HERE and is derived from the MATLAB code developed by Marco Correa-Ramirez and Samuel Hormazabal. Python code generously provided by Mathilde Jutras, a doctoral student at McGill University as of July 2020.

Mann, M.E., Park, J., Spatial Correlations of Interdecadal Variation in Global Surface Temperatures, Geophysical Research Letters, 20, 1055-1058, 1993.

Mann, M.E., Lall, U., Saltzman, B., Decadal-to-century scale climate variability: Insights into the Rise and Fall of the Great Salt Lake,Geophysical Research Letters, 22, 937-940, 1995.

Mann, M.E., Park, J., Bradley, R.S., Global Interdecadal and Century-Scale Climate Oscillations During the Past Five Centuries, Nature, 378, 266-270, 1995.

Mann, M.E., Park, J., Greenhouse Warming and Changes in the Seasonal Cycle of Temperature: Model Versus Observations, Geophysical Research Letters, 23, 1111-1114, 1996.

Koch, D., Mann, M.E., Spatial and Temporal Variability of 7Be Surface Concentrations, Tellus, 48B, 387-396, 1996.

Mann, M.E., Park, J., Joint Spatio-Temporal Modes of Surface Temperature and Sea Level Pressure Variability in the Northern Hemisphere During the Last Century, Journal of Climate, 9, 2137-2162, 1996.

Rajagopalan, B., Mann, M.E., and Lall, U., A Multivariate Frequency-Domain Approach to Long-Lead Climatic Forecasting, Weather and Forecasting, 13, 58-74, 1998.

Tourre, Y., Rajagopalan, B., and Kushnir, Y., Dominant patterns of climate variability in the Atlantic over the last 136 years, Journal of Climate, 12, 2285-2299, 1998.

Mann, M.E., Park, J, Oscillatory Spatiotemporal Signal Detection in Climate Studies: A Multiple-Taper Spectral Domain Approach, Advances in Geophysics, 41, 1-131, 1999. (click here for version w/ color figures)

Delworth, T.L., and Mann, M.E., Observed and Simulated Multidecadal Variability in the Northern Hemisphere, Climate Dynamics, 16, 661-676, 2000.

Mann, M.E., Bradley, R.S., Hughes, M.K., Long-term variability in the El Nino Southern Oscillation and associated teleconnections, Diaz, H.F. and Markgraf, V. (eds), El Nino and the Southern Oscillation: Multiscale Variability and its Impacts on Natural Ecosystems and Society, Cambridge University Press, Cambridge, UK, 357-412, 2000.