Distributed Solar: The Democratizaton of Energy
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climate change
 David Appell's early climate science
 Climate model projections versus observations
 The beach boondoggle
 Social Cost of Carbon
 Climate Change Denying Organizations
 Climate Change: A health emergency … New England Journal of Medicine
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 An open letter to Steve Levitt
 Jacobson WWS literature index
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Category Archives: Ethan Deyle
Liang, information flows, causation, and convergent crossmapping
Someone recommended the work of Liang recently in connection with causation and attribution studies, and their application to CO2 and climate change. Liang’s work is related to information flows and transfer entropies. As far as I know, the definitive work … Continue reading →
Posted in Akaike Information Criterion, American Association for the Advancement of Science, Anthropocene, attribution, carbon dioxide, climate, climate change, climate disruption, complex systems, convergent crossmapping, ecology, Egbert van Nes, Ethan Deyle, Floris Takens, George Sughihara, global warming, Hao Ye, Hyper Anthropocene, information theoretic statistics, Lenny Smith, modelfree forecasting, nonlinear systems, physics, statistics, Takens embedding theorem, theoretical physics, Timothy Lenton, Victor Brovkin

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Just because the data lies sometimes doesn’t mean it’s okay to censor it
Or, there’s no such thing as an outlier … Eli put up a post titled “The Data Lies. The Crisis in Observational Science and the Virtue of Strong Theory” at his lagomorph blog. Think of it: Data lying. Obviously this … Continue reading →
Posted in Akaike Information Criterion, American Association for the Advancement of Science, American Meteorological Association, American Statistical Association, AMETSOC, Anthropocene, Bayes, Bayesian, climate, climate change, climate models, data science, dynamical systems, ecology, Eli Rabett, environment, Ethan Deyle, George Sughihara, Hao Ye, Hyper Anthropocene, information theoretic statistics, IPCC, Kalman filter, kriging, Lenny Smith, maximum likelihood, model comparison, modelfree forecasting, physics, quantitative ecology, random walk processes, random walks, science, smart data, statespace models, statistics, Takens embedding theorem, the right to know, Timothy Lenton, Victor Brovkin

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“Stochastic Parameterization: Towards a new view of weather and climate models”
Judith Berner, Ulrich Achatz, Lauriane Batté, Lisa Bengtsson, Alvaro De La Cámara, Hannah M. Christensen, Matteo Colangeli, Danielle R. B. Coleman, Daan Crommelin, Stamen I. Dolaptchiev, Christian L.E. Franzke, Petra Friederichs, Peter Imkeller, Heikki Järvinen, Stephan Juricke, Vassili Kitsios, François … Continue reading →
Posted in biology, climate models, complex systems, convergent crossmapping, data science, dynamical systems, ecology, Ethan Deyle, Floris Takens, George Sughihara, Hao Ye, likelihoodfree, Lorenz, mathematics, meteorological models, modelfree forecasting, physics, population biology, population dynamics, quantitative biology, quantitative ecology, Scripps Institution of Oceanography, statespace models, statistical dependence, statistics, stochastic algorithms, stochastic search, stochastics, Takens embedding theorem, time series, Victor Brovkin

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On Munshi mush
(Slightly updated on 20160611.) 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 crossmapping, 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, statespace models, statistics, Takens embedding theorem, Techno Utopias, the right to know, theoretical physics, time series, zero carbon

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“Causal feedbacks in climate change”
Today I was reviewing and rereading 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 crossmapping, 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, statespace models, Takens embedding theorem, time series, Timothy Lenton, Victor Brovkin

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