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Michael E. Mann

Publications and source records attributed to Michael E. Mann.

3 recordsLinked to original sources
arXiv2024

Bound on forecasting skill for models of North Atlantic tropical cyclone counts

Annual North Atlantic tropical cyclone (TC) counts are frequently modeled as a Poisson process with a state-dependent rate. We provide a lower bound on the forecasting error of this class of models. Remarkably we find that this bound is already saturated by a simple linear model that explains roughly 50 percent of the annual variance using three climate indices: El Niño Southern Oscillation (ENSO), average sea surface temperature (SST) in the main development region (MDR) of the North Atlantic and the North Atlantic oscillation (NAO) atmospheric circulation index (Kozar et al 2012). As expected under the bound, increased model complexity does not help: we demonstrate that allowing for quadratic and interaction terms, or using an Elastic Net to forecast TC counts using global SST maps, produces no detectable increase in skill. We provide evidence that observed TC counts are consistent with a Poisson process, limiting possible improvements in TC modeling by relaxing the Poisson assumption.

physics.ao-ph↗
arXiv2022

Uncertainty in Climate Science: Not Cause for Inaction

Using observational data and an elementary rigorous statistical fact it is easily shown that the distribution of Earth's climate is non-stationary. Examination of records of hundreds of local Industrial Era temperature histories in the Northern Hemisphere were used to show this fact. Statistically, the mean of the ensemble has been rising during the Industrial Era. All of this confirms what climate scientists already know. The issue of predictions under uncertainties was tackled as well: a simple balance model was tuned to track an ensemble of climate records. Stochastic parametrizations were created to capture natural and anthropogenic CO2 forcings. The resulting stochastic model was then tested against historical data and then used to make future predictions. This exercise confirmed as well climate science attribution to significant global warming during the Industrial Era to anthropogenic activities. The variability of the model due to uncertainties is simply not large enough to obfuscate a clear rise in the mean temperature in the Industrial Era. Further, even if the variance of the natural CO2 contribution is greatly increased artificially (in the model), the fluctuations cannot account for the current change in the historical mean. These outcomes weaken the factual validity of the US administration, 2016-2020, claims that there are too many uncertainties in climate and climate science to make climate predictions, and further that contemporary reports of floods, extreme weather, even a rising global mean temperature are simply manifestations of a climate that always fluctuates within a nature-derived statistical distribution.

physics.ao-ph↗