Chris E. Forest

Probabilistic Forecast for Twenty-First-Century Climate Based on Uncertainties in Emissions (Without Policy) and Climate Parameters

Abstract The Massachusetts Institute of Technology (MIT) Integrated Global System Model is used to make probabilistic projections of climate change from 1861 to 2100. Since the model’s first projections were published in 2003, substantial improvements have been made to the model, and improved estimates of the probability distributions of uncertain input parameters have become available. The new projections are considerably warmer than the 2003 projections; for example, the median surface warming in 2091–2100 is 5.1°C compared to 2.4°C in the earlier study. Many changes contribute to the stronger warming; among the more important ones are taking into account the cooling in the second half of the twentieth century due to volcanic eruptions for input parameter estimation and a more sophisticated method for projecting gross domestic product (GDP) growth, which eliminated many low-emission scenarios. However, if recently published data, suggesting stronger twentieth-century ocean warming, are used to determine...

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

Comparisons of the physiognomy of leaves from modern vegetation of known climates with that of Eocene and Oligocene fossil leaf assemblages from middle latitudes of western North America indicate paleoaltitudes comparable or higher than those at present. Using canonical correspondence analysis, a multivariate statistical approach that includes nonlinear relationships between characters and environmental parameters, we relate leaf physiognomy of fossil samples to modern vegetation of known environmental parameters, including moist enthalpy of the atmosphere, a thermodynamically conserved quantity of the atmosphere that varies with altitude. By estimating enthalpy values for both lowland leaf assemblages and coeval interior leaf assemblages, altitudes for the interior assemblages can be estimated to ±890 m. In northeastern Washington and southern British Columbia, the high Eocene altitudes indicate subsidence, or collapse, of an area undergoing crustal extension and may reflect an immediately preceding period of uplift that triggered the extension. Similarly, other areas that have undergone Cenozoic crustal extension appear to have been at least as high as they are at present. Three sites from the Rocky Mountains also indicate elevations at least as high as at present, and therefore suggest subsidence, either resulting from cooling of a hot upper mantle or erosion and isostatic compensation of surrounding terrain. High altitudes during Eocene and Oligocene time in western North America appear to have been normal, even in areas such as the Green River basin, and therefore cast doubt on the commonly inferred late Cenozoic uplift of that region.

Long-Term Climate Change Commitment and Reversibility: An EMIC Intercomparison

AbstractThis paper summarizes the results of an intercomparison project with Earth System Models of Intermediate Complexity (EMICs) undertaken in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). The focus is on long-term climate projections designed to 1) quantify the climate change commitment of different radiative forcing trajectories and 2) explore the extent to which climate change is reversible on human time scales. All commitment simulations follow the four representative concentration pathways (RCPs) and their extensions to year 2300. Most EMICs simulate substantial surface air temperature and thermosteric sea level rise commitment following stabilization of the atmospheric composition at year-2300 levels. The meridional overturning circulation (MOC) is weakened temporarily and recovers to near-preindustrial values in most models for RCPs 2.6–6.0. The MOC weakening is more persistent for RCP8.5. Elimination of anthropogenic CO2 emissions after 2300 resu...

Analysis of climate policy targets under uncertainty

Although policymaking in response to the climate change threat is essentially a challenge of risk management, most studies of the relation of emissions targets to desired climate outcomes are either deterministic or subject to a limited representation of the underlying uncertainties. Monte Carlo simulation, applied to the MIT Integrated Global System Model (an integrated economic and earth system model of intermediate complexity), is used to analyze the uncertain outcomes that flow from a set of century-scale emissions paths developed originally for a study by the U.S. Climate Change Science Program. The resulting uncertainty in temperature change and other impacts under these targets is used to illustrate three insights not obtainable from deterministic analyses: that the reduction of extreme temperature changes under emissions constraints is greater than the reduction in the median reduction; that the incremental gain from tighter constraints is not linear and depends on the target to be avoided; and that comparing median results across models can greatly understate the uncertainty in any single model.

Paleoaltimetry incorporating atmospheric physics and botanical estimates of paleoclimate

We present a method for deducing paleoaltitudes that incorporates basic physical principles of atmospheric science and inferences of paleoclimates from plant leaf physiognomy. We exploit the average west-to-east flow of the atmosphere at mid-latitudes and a thermodynamically conserved variable in the atmosphere—moist static energy (the combined internal, latent heat, and gravitational potential energy of moist air)—to develop a method that relies on a parameter that varies with height in the atmosphere in a predictable fashion. Because the surface distribution of moist static energy is constrained by atmospheric dynamics and thermodynamics, the combined internal and latent heat energies, also known as the moist enthalpy, should only vary with altitude provided we know the distribution of moist static energy. Thus, we avoid having to make assumptions about the mean annual temperature lapse rate, which varies spatially and temporally owing to unpredictable variations in atmospheric water vapor. To estimate a paleoaltitude, therefore, we require (1) a priori knowledge of the spatial distribution of moist static energy for the paleoclimate and (2) the ability to estimate paleoenthalpy for two isochronous locations: one at sea level, the other at some unknown elevation. To achieve this, we investigated the spatial distribution of moist static energy for the present-day climate of North America to estimate the deviations in moist static energy from zonal invariance. In parallel, we quantified the relation between moist enthalpy and plant leaf physiognomy of modern forests. Assuming that such deviations from zonal invariance and such relationships between physiognomy and enthalpy apply to ancient climates and fossil leaves, these investigations yield an uncertainty estimate of ±910 m in the paleoaltitude difference between two isochronous fossil assemblage locations.

Ensemble climate predictions using climate models and observational constraints

Two different approaches are described for constraining climate predictions based on observations of past climate change. The first uses large ensembles of simulations from computationally efficient models and the second uses small ensembles from state-of-the-art coupled ocean–atmosphere general circulation models. Each approach is described and the advantages of each are discussed. When compared, the two approaches are shown to give consistent ranges for future temperature changes. The consistency of these results, when obtained using independent techniques, demonstrates that past observed climate changes provide robust constraints on probable future climate changes. Such probabilistic predictions are useful for communities seeking to adapt to future change as well as providing important information for devising strategies for mitigating climate change.

Constraining uncertainties in climate models using climate change detection techniques

Predictions of 21st century climate by different atmosphere-ocean general circulation models depend on the sensitivities of the models to external radiative forcing and on their rates of heat uptake by the deep ocean. This study constrains these properties by comparing radiosonde-based observations of temperature trends in the free troposphere and lower stratosphere with corresponding simulations of a fast, flexible climate model, using objective techniques based on optimal fingerprinting. Parameter choices corresponding either to low sensitivity, or to high sensitivity combined with slow oceanic heat uptake are rejected provided the variability estimates used from the HadCM2 control run are correct. Nevertheless, the range of acceptable values is significantly wider than that usually quoted. The IPCCs range of possible sensitivities, 1.5 to 4.5 K, corresponds at best to only an 80% confidence interval. Therefore, climate change projections based on current general circulation models do not span the range of possibilities consistent with the recent climate record.

Comparing Oceanic Heat Uptake in AOGCM Transient Climate Change Experiments

The transient response of both surface air temperature and deep ocean temperature to an increasing external forcing strongly depends on climate sensitivity and the rate of the heat mixing into the deep ocean, estimates for both of which have large uncertainty. In this paper a method for estimating rates of oceanic heat uptake for coupled atmosphere‐ocean general circulation models from results of transient climate change simulations is described. For models considered in this study, the estimates vary by a factor of 2 ‰. Nevertheless, values of oceanic heat uptake for all models fall in the range implied by the climate record for the last century. It is worth noting that the range of the model values is narrower than that consistent with observations and thus does not provide a full measure of the uncertainty in the rate of oceanic heat uptake.

Quantifying the Likelihood of Regional Climate Change: A Hybridized Approach

AbstractThe growing need for risk-based assessments of impacts and adaptation to climate change calls for increased capability in climate projections: specifically, the quantification of the likelihood of regional outcomes and the representation of their uncertainty. Herein, the authors present a technique that extends the latitudinal projections of the 2D atmospheric model of the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM) by applying longitudinally resolved patterns from observations, and from climate model projections archived from exercises carried out for the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The method maps the IGSM zonal means across longitude using a set of transformation coefficients, and this approach is demonstrated in application to near-surface air temperature and precipitation, for which high-quality observational datasets and model simulations of climate change are available. The current climatology ...

Effects of initial conditions uncertainty on regional climate variability: An analysis using a low‐resolution CESM ensemble

The uncertainties surrounding the initial conditions in Earth system models can considerably influence interpretations about climate trends and variability. Here we present results from a new climate change ensemble experiment using the Community Earth System Model (CESM) to analyze the effect of internal variability on regional climate variables that are relevant for decision making. Each simulation is initialized from a unique and dynamically consistent model state sampled from a ~10,000 year fully coupled equilibrium simulation, which captures the internal unforced variability of the coupled Earth system. We find that internal variability has a sizeable contribution to the modeled ranges of temperature and precipitation. The effects increase for more localized regions. The ensemble exhibits skill in simulating key regional climate processes relevant to decision makers, such as seasonal temperature variability and extremes. The presented ensemble framework and results can provide useful resources for uncertainty quantification, integrated assessment, and climate risk management.