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Chris E. Forest

Researcher at Pennsylvania State University

Publications -  92
Citations -  6592

Chris E. Forest is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Climate change & Climate model. The author has an hindex of 35, co-authored 91 publications receiving 6012 citations. Previous affiliations of Chris E. Forest include University of North Carolina at Chapel Hill & University of Arizona.

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Book ChapterDOI

Evaluation of climate models

TL;DR: In this article, an overview of model capabilities as assessed in this chapter, including improvements, or lack thereof, relative to models assessed in the AR4, is presented, along with an assessment of recent work connecting model performance to the detection and attribution of climate change as well as to future projections.
Journal ArticleDOI

Quantifying Uncertainties in Climate System Properties with the Use of Recent Climate Observations

TL;DR: The joint probability density distributions for three key uncertain properties of the climate system are derived, using an optimal fingerprinting approach to compare simulations of an intermediate complexity climate model with three distinct diagnostics of recent climate observations.
Journal ArticleDOI

Uncertainty Analysis of Climate Change and Policy Response

TL;DR: In this paper, the authors apply an earth systems model to describe the uncertainty in climate projections under two different policy scenarios, and find that in the absence of greenhouse gas emissions restrictions, there is a one in forty chance that global mean surface temperature change will exceed 4.9 °C by the year 2100.
Journal ArticleDOI

Probabilistic forecast for twenty-first-century climate based on uncertainties in emissions (without policy) and climate parameters.

TL;DR: The Massachusetts Institute of Technology (MIT) Integrated Global System Model is used to make probabilistic projections of climate change from 1861 to 2100 as mentioned in this paper, and substantial improvements have been made to the model, and improved estimates of the probability distributions of uncertain input parameters have become available.
Journal ArticleDOI

Paleobotanical evidence of Eocene and Oligocene paleoaltitudes in midlatitude western North America

TL;DR: In this article, a multivariate statistical approach that includes nonlinear relationships between characters and environmental parameters was used to relate leaf physiognomy of fossil samples to modern vegetation of known environmental parameters, including moist enthalpy of the atmosphere.