Category Archives: cat1

On Munshi mush

Posted on 5 June 2016 by

(Slightly updated on 2016-06-11.) Professor Emeritus Jamal Munshi of Sonoma State University has papers recently cited in science denier circles as evidence that the conventional associations between mean global surface temperature and cumulative carbon emissions are, well, bunk, due to … Continue reading →

Posted in Bayes, Bayesian, Berkeley Earth Surface Temperature project, BEST, carbon dioxide, cat1, climate, climate change, climate data, climate education, climate models, convergent cross-mapping, dynamic linear models, ecology, ENSO, environment, Ethan Deyle, evidence, geophysics, George Sughihara, global warming, greenhouse gases, information theoretic statistics, Kalman filter, mathematics, maths, meteorology, model comparison, NOAA, oceanography, prediction, state-space models, statistics, Takens embedding theorem, Techno Utopias, the right to know, theoretical physics, time series, zero carbon | 1 Comment

HadCRUT4 and GISTEMP series filtered and estimated with simple RTS model

Posted on 18 March 2016 by

Happy Vernal Equinox! This post has been updated today with some of the equations which correspond to the models. An assessment of whether or not there was a meaningful slowdown or “hiatus” in global warming, was recently discussed by Tamino … Continue reading →

Posted in AMETSOC, anemic data, Bayesian, boosting, bridge to somewhere, cat1, changepoint detection, climate, climate change, climate data, climate disruption, climate models, complex systems, computation, data science, dynamical systems, geophysics, George Sughihara, global warming, hiatus, information theoretic statistics, machine learning, maths, meteorology, MIchael Mann, multivariate statistics, physics, prediction, Principles of Planetary Climate, rationality, reasonableness, regime shifts, sea level rise, time series | 4 Comments

“Causal feedbacks in climate change”

Posted on 20 January 2016 by

Today I was reviewing and re-reading the nonlinear time series technical literature I have, seeking ideas on how to go about using the statistical ensemble learning technique called “boosting” with them. (See the very nice book, R. E. Schapire, Y. … Continue reading →

Posted in Anthropocene, boosting, Carbon Cycle, carbon dioxide, Carbon Worshipers, cat1, climate, climate change, climate data, climate disruption, complex systems, convergent cross-mapping, denial, differential equations, diffusion processes, dynamical systems, ecology, Egbert van Nes, empirical likelihood, ensembles, environment, Ethan Deyle, Floris Takens, forecasting, fossil fuels, geophysics, George Sughihara, global warming, greenhouse gases, Hao Ye, machine learning, Maren Scheffer, mathematics, maths, meteorology, physics, rationality, reasonableness, science, state-space models, Takens embedding theorem, time series, Timothy Lenton, Victor Brovkin | 2 Comments

climate internal variability is just residual variance from modeling with a smooth curve?

Posted on 27 December 2014 by

I happened across what I consider to be an amazing slide while “reading around” the work of Deser and colleagues. It is reproduced below, taken from Dagg and Wills: (Click image to see a larger picture, and use browser ‘back’ … Continue reading →

Posted in cat1, citizen science, climate, climate education, forecasting, geophysics, mathematics, mathematics education, maths, meteorology, physics, rationality, reasonableness, science, statistics | 4 Comments

Brian Hayes on clear climate models for the curious public

Posted on 28 October 2014 by

American Scientst has a nice article by Brian Hayes recounting the basic physics of climate, and then recommending both public engagement with clear, simple climate models, at least by the curious and scientifically literate, and the development of models which … Continue reading →

Posted in astrophysics, carbon dioxide, Carl Sagan, cat1, citizen science, civilization, climate, climate education, conservation, consumption, differential equations, education, energy, environment, forecasting, geophysics, mathematics, maths, meteorology, oceanography, physics, reasonableness, risk, science, scientific publishing, statistics