Mort Webster

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...

Uncertainty in emissions projections for climate models

Future global climate projections are subject to large uncertainties. Major sources of this uncertainty are projections of anthropogenic emissions. We evaluate the uncertainty in future anthropogenic emissions using a computable general equilibrium model of the world economy. Results are simulated through 2100 for carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulfur hexafluoride (SF6), sulfur dioxide (SO2), black carbon (BC) and organic carbon (OC), nitrogen oxides (NOx), carbon monoxide (CO), ammonia (NH3) and non-methane volatile organic compounds (NMVOCs). We construct mean and upper and lower 95% emissions scenarios (available from the authors at 11 � 11 latitude–longitude grid). Using the MIT Integrated Global System Model (IGSM), we find a temperature change range in 2100 of 0.9 to 4.01C, compared with the Intergovernmental Panel on Climate Change emissions scenarios that result in a range of 1.3 to 3.61C when simulated through MIT IGSM. r 2002 Elsevier Science Ltd. All rights reserved.

Impact of unit commitment constraints on generation expansion planning with renewables

Growing use of renewables pushes thermal generators against operating constraints — e.g. ramping, minimum output, and operating reserves — that are traditionally ignored in expansion planning models. We show how including such unit-commitment-derived details can significantly change energy production and optimal capacity mix. We introduce a method for efficiently combining unit commitment and generation expansion planning into a single mixed-integer optimization model. Our formulation groups generators into categories allowing integer commitment states from zero to the installed capacity. This formulation scales well, runs much faster (e.g. 5000×) than individual plant binary decisions, and makes the combined model computationally tractable for large systems (hundreds of generators) at hourly time resolutions (8760 hours) using modern solvers on a personal computer. We show that ignoring these constraints during planning can result in a sub-optimal capacity mix with significantly higher operating costs (17%) and carbon emissions (39%) and/or the inability to meet emissions targets.

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.

Linking local air pollution to global chemistry and climate

We have incorporated a reduced-form urban air chemistry model in the Massachusetts Institute of Technologys two-dimensional land and ocean resolving coupled chemistry-climate model. The computationally efficient reduced-form urban model was derived from the California Institute of Technology–Carnegie Institute of Technology (at Carnegie Mellon University) Urban Airshed Model by employing the probabilistic collocation method. To study the impact of urban air pollution on global chemistry and climate, we carried out three simulations, each including or excluding the reduced-form urban model for the time period from 1977 to 2100. All three runs use identical total emissions; however, in the two runs involving the reduced-form urban model, the emissions assigned to urban areas are allocated in different ways, depending on the scenario assumed for the future development of polluted urban areas. These two simulations are compared to the reference, which does not utilize the reduced-form urban model. We find that the incorporation of urban air chemistry processes leads to lower global tropospheric NOx, ozone, and OH concentrations, but a higher methane mole fraction, than in the reference. The tropospheric mole fraction of CO is altered either up or down depending on the projections of urban emissions. The global mean surface temperature is affected very little by implementation of the reduced-form urban model because predicted increases in CH4 are offset in part by decreases in O3, leading to only small changes in overall radiative forcing.

Global health and economic impacts of future ozone pollution

We assess the human health and economic impacts of projected 2000–2050 changes in ozone pollution using the MIT Emissions Prediction and Policy Analysis - Health Effects (EPPA-HE) model, in combination with results from the GEOS-Chem global tropospheric chemistry model of climate and chemistry effects of projected future emissions. We use EPPA-HE to assess the human health damages (including mortality and morbidity) caused by ozone pollution, and quantify their economic impacts in sixteen world regions. We compare the costs of ozone pollution under scenarios with 2000 and 2050 ozone precursor and greenhouse gas emissions (using the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario). We estimate that health costs due to global ozone pollution above pre-industrial levels by 2050 will be

A methodology for quantifying uncertainty in climate projections

580 billion (year 2000

Regional Air Quality Impacts of Increased Natural Gas Production and Use in Texas

) and that mortalities from acute exposure will exceed 2 million. We find that previous methodologies underestimate costs of air pollution by more than a third because they do not take into account the long-term, compounding effects of health costs. The economic effects of emissions changes far exceed the influence of climate alone.

An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty

Possible climate change caused by an increase ingreenhouse gas concentrations, despite having been asubject of intensive study in recent years, is stillvery uncertain. Uncertainties in projections ofdifferent climate variables are usually described onlyby the ranges of possible values. For assessing thepossible impact of climate change, it would be moreuseful to have probability distributions for thesevariables. Obtaining such distributions is usuallyvery computationally expensive and requires knowledgeof probability distributions for characteristics ofthe climate system that affect climate projections. A fewstudies of this kind have been carried out with energybalance/upwelling diffusion models. Here wedemonstrate a methodology for performing a similarstudy with a 2 dimensional (zonally averaged) climatemodel that reproduces the behavior of coupledatmosphere/ocean general circulation models morerealistically than energy balance models. Thismethodology involves application of the DeterministicEquivalent Modeling Method to derive functionalapproximations of the models probabilistic response.Monte Carlo analysis is then performed on theapproximations. An application of the methodology isdemonstrated by deriving the uncertainty in surfaceair temperature change and sea level rise due tothermal expansion of the ocean that result fromuncertainties in climate sensitivity and the rate ofheat uptake by the deep ocean for a prescribedincrease in atmospheric CO2 concentration. Wealso demonstrate propagation of correlateduncertainties through different models, by presentingresults that include uncertainty in projected carbonemissions.

Parameterization of urban subgrid scale processes in global atmospheric chemistry models

Natural gas use in electricity generation in Texas was estimated, for gas prices ranging from