Academic and political debates over long-run climate policy often invoke "stabilization" of atmospheric concentrations of greenhouse gases (GHGs), but only rarely are non-CO2 greenhouse gases addressed explicitly. Even though the majority of short-term climate policies propose trading between gases on a global warming potential (GWP) basis, discussions of whether CO2 concentrations should be 450, 550, 650, or perhaps as much as 750 ppm leave unstated whether there should be no additional forcing from other GHGs beyond current levels or whether separate concentration targets should be established for each GHG. Here, we use an integrated modeling framework to examine multi-gas stabilization in terms of temperature, economic costs, carbon uptake, and other important consequences. We show that there are significant differences in both costs and climate impacts between different "GWP equivalent" policies and demonstrate the importance of non-CO2 GHG reduction on timescales of up to several centuries. © 2004 Elsevier

The goal of stabilizing atmospheric CO₂ concentrations has, in recent years, emerged as an important theme in international forums and negotiations directed at the issue of climate change. In this paper, we frame the stabilization problem in terms of three dimensions, labeled ‘technical,’ ‘political,’ and ‘economic.’ To illustrate this conceptual scheme, we utilize the MIT Emissions Prediction and Policy Analysis model to explore an illustrative set of stabilization policies, each of which presumes substantive participation by OECD nations, but varies the level and timing of emissions controls by the rest of the world. The analysis suggests that international agreements for policy coordination may prove elusive, despite potential aggregate benefits for cooperation.

© 1999 Elsevier Science

The purpose of this study is to develop a strategy for investment in power generation technologies in the future given the uncertainties in climate policy and fuel prices. First, such studies are commonly conducted using deterministic methods which assume a given likelihood of the carbon and gas price levels. In this study a probabilistic approach is used to address these uncertainties. Secondly, capacity expansion models conventionally apply average estimates to predict the amount of power that each generator will produce based on the technology chosen. I propose an alternate method which determines the actual generation hour-by-hour of a generator. Using this method, I also capture the variability of wind generation across the year.

To accomplish this goal, I used the Electric Reliability Council of Texas (ERCOT) as a case study. I investigated the effect of different scenarios of generation technology investments projected over a period of twenty years. I conducted two sets of analyses; first assuming that Carbon Capture and Storage (CCS) technologies will be available after 2020, then assuming that they will not. Using a dispatch model, I simulated the hours of a load duration curve for 2020 and 2030. In the first period 2010-2020, I assumed the price of carbon to either be $0 or $50/ton CO2. In the second period, I take the carbon price to be at either a low of $25/ton of CO2 or a high of $100/ton of CO2. The price of natural gas used was either a high of $15/MMBtu or a low of $3MMBtu in both periods. Using a Monte Carlo, I sample the wind generation based on the season and the time of dat. The system costs with the new investment scenarios were then evaluated in a decision tree to establish the socially optimal solution.

I find that the optimal strategy to be taken today depends on the availability of CCS technologies in 2030. Assuming that there is CCS in 2030, the more dominant strategy would be to build natural gas generators today. If we assume that there is no CCS in 2030, the strategy would depend on the probabilities of the levels of gas and carbon prices in 2020.

The MIT Integrated Global Systems Model (IGSM) version 2.3 is an intermediate complexity model that couples a zonally-averaged statistical dynamical atmospheric model with a full 3D ocean GCM and, therefore, simulates feedbacks associated with changes in ocean circulation. A fundamental feature of the IGSM2.3 is the ability to modify its climate sensitivity (through cloud adjustment), net aerosol forcing and ocean heat uptake rate (via the diapycnal diffusion coefficient). As such, the IGSM2.3 provides an efficient tool for generating probabilistic distribution functions of climate parameters (climate sensitivity, aerosol forcing and ocean heat uptake rate) using optimal fingerprint diagnostics. Probabilistic distributions of sea surface temperature (SST) and sea ice cover (SIC) changes for the 21st century can then be obtained using Latin-Hypercube sampling of climate parameters under various emissions scenarios. The emissions scenarios used in this study are based on the MIT Emissions Predictions and Policy Analysis (EPPA) model and include a no policy case where emissions of long-lived GHGs are uncertain, and a range of stabilization scenarios from stringent policy to milder policy.

In order to investigate future regional climate impacts, the MIT IGSM2.3 is coupled to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model version 3 (CAM3). For linkages between the IGSM2.3 and CAM3, the 3-D atmospheric model is driven by the IGSM2.3 SST anomalies with a climatological annual cycle taken from an observed dataset, instead of the full IGSM2.3 SSTs, to provide a better SST annual cycle and more realistic features between the ocean and atmospheric components. This approach yields a consistent regional distribution and climate change over the 20th century as compared to observational datasets. For each emissions scenario, an ensemble member of the IGSM2.3 SST/SIC probabilistic distribution drives CAM3 to span the multi-dimensional space of uncertainty in climate parameters. For consistency, for each set of IGSM2.3/CAM3 runs, the trace gas concentrations calculated by the atmospheric chemistry component of the IGSM2.3 is used to force CAM3. The cloud adjustment scheme used in the IGSM2.3 was implemented in CAM3, which allows modifying its climate sensitivity to match that of the IGSM2.3 setup that generates the SST field used to drive CAM3.

With this approach, regional climate impacts can be assessed under various emissions scenarios based on probability distributions of climate parameters. In this paper, preliminary results from these ensemble simulations are presented. A particular focus is placed on the distribution of extreme events. For example, the frequency, duration and intensity of extreme events such as heat waves, floods and droughts, precipitation and storm activities can be investigated, as well as other dynamical features such as jet stream modulation.