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Russia's Natural Gas Export Potential up to 2050

Recent increases in natural gas reserve estimates and advances in shale gas technology make natural gas a fuel with good prospects to serve a bridge to a low-carbon world. Russia is an important energy supplier as it holds the world largest natural gas reserves and it is the world’s largest exporter of natural gas. Energy was one of the driving forces of Russia’s recent economic recovery from the economic collapse of 1990s. These prospects have changed drastically with a global recession and the collapse of oil and gas prices from their peaks of 2008. An additional factor is an ongoing surge in a liquefied natural gas (LNG) capacity and a development of Central Asia’s and the Middle East gas supplies that can compete with Russian gas in its traditional (European) and potential (Asian) markets. To study the long-term prospects for Russian natural gas, we employ the MIT Emissions Prediction and Policy Analysis (EPPA) model, a computable general equilibrium model of the world economy. While we consider the updated reserve estimates for all world regions, in this paper we focus on the results for Russian natural gas trade. The role of natural gas is explored in the context of several policy assumptions: with no greenhouse gas mitigation policy and scenarios of emissions targets in developed countries. Scenarios where Europe takes on an even more restrictive target of 80 percent reduction of greenhouse gas emissions relative to 2005 by 2050 and reduces its nuclearbased generation are also considered. Asian markets become increasingly important for natural gas exports and several scenarios about their potential development are considered. We found that over the next 20-40 years natural gas can still play a substantial role in Russian exports and there are substantial reserves to support a development of the gas-oriented energy system both in Russia and in its current and potential gas importers. In the Reference scenario, exports of natural gas grow from Russia’s current 7 Tcf to 10-12 Tcf in 2030 and 15-18 Tcf in 2050. Alternative scenarios provide a wider range of projections, with a share of Russian gas exports shipped to Asian markets rising to 30 percent by 2030 and more than 50 percent in 2050. Patterns of international gas trade show increased flows to the Asian region from the Middle East, Central Asia, Australia and Russia. Europe’s reliance on LNG imports increases, while it still maintains sizable imports from Russia.

Quantification of Impact of Climate Uncertainty on Regional Air Quality

Uncertainties in calculated impacts of climate forecasts on future regional air quality are investigated using downscaled MM5 meteorological fields from the NASA GISS and MIT IGSM global models and the CMAQ model in 2050 in the continental US. Differences between three future scenarios: high-extreme, low-extreme and base case, are used for quantifying effects of climate uncertainty on regional air quality. GISS, with the IPCC A1B scenario, is used for the base case simulations. IGSM results, in the form of probabilistic distributions, are used to perturb the base case climate to provide the high- and low-extreme scenarios. Impacts of the extreme climate scenarios on concentrations of summertime fourth-highest daily maximum 8-h average ozone are predicted to be up to 10 ppbV (about one-seventh of the current US ozone standard of 75 ppbV) in urban areas of the Northeast, Midwest and Texas due to impacts of meteorological changes, especially temperature and humidity, on the photochemistry of tropospheric ozone formation and increases in biogenic VOC emissions, though the differences in average peak ozone concentrations are about 1–2 ppbV on a regional basis. Differences between the extreme and base scenarios in annualized PM2.5 levels are very location dependent and predicted to range between −1.0 and +1.5 μg m−3. Future annualized PM2.5 is less sensitive to the extreme climate scenarios than summertime peak ozone since precipitation scavenging is only slightly affected by the extreme climate scenarios examined. Relative abundances of biogenic VOC and anthropogenic NOx lead to the areas that are most responsive to climate change. Overall, planned controls for decreasing regional ozone and PM2.5 levels will continue to be effective in the future under the extreme climate scenarios. However, the impact of climate uncertainties may be substantial in some urban areas and should be included in assessing future regional air quality and emission control requirements.

Quantifying greenhouse gas emissions from atmospheric measurements: a critical reality check for climate legislation

Emissions reduction legislation relies upon ‘bottom-up’ accounting of industrial and biogenic greenhouse-gas (GHG) emissions at their sources. Yet, even for relatively well constrained industrial GHGs, global emissions based on ‘top-down’ methods that use atmospheric measurements often agree poorly with the reported bottom-up emissions. For emissions reduction legislation to be effective, it is essential that these discrepancies be resolved. Because emissions are regulated nationally or regionally, not globally, topdown estimates must also be determined at these scales. High-frequency atmospheric GHG measurements at well-chosen station locations record ‘pollution events’ above the background values that result from regional emissions. By combining such measurements with inverse methods and atmospheric transport and chemistry models, it is possible to map and quantify regional emissions. Even with the sparse current network of measurement stations and current inverse-modelling techniques, it is possible to rival the accuracies of regional ‘bottom-up’ emission estimates for some GHGs. But meeting the verification goals of emissions reduction legislation will require major increases in the density and types of atmospheric observations, as well as expanded inversemodelling capabilities. The cost of this effort would be minor when compared with current investments in carbon-equivalent trading, and would reduce the volatility of that market and increase investment in emissions reduction.

© 2011 The Royal Society

Quantifying greenhouse gas emissions from atmospheric measurements: a critical reality check for climate legislation

Emissions reduction legislation relies upon ‘bottom-up’ accounting of industrial and biogenic greenhouse-gas (GHG) emissions at their sources. Yet, even for relatively well-constrained industrial GHGs, global emissions based on ‘top-down’ methods that use atmospheric measurements often agree poorly with the reported bottom-up emissions. For emissions reduction legislation to be effective, it is essential that these discrepancies be resolved. Because emissions are regulated nationally or regionally, not globally, top-down estimates must also be determined at these scales. High-frequency atmospheric GHG measurements at well-chosen station locations record ‘pollution events’ above the background values that result from regional emissions. By combining such measurements with inverse methods and atmospheric transport and chemistry models, it is possible to map and quantify regional emissions. Even with the sparse current network of measurement stations and current inverse-modelling techniques, it is possible to rival the accuracies of regional ‘bottom-up’ emission estimates for some GHGs. But meeting the verification goals of emissions reduction legislation will require major increases in the density and types of atmospheric observations, as well as expanded inverse-modelling capabilities. The cost of this effort would be minor when compared with current investments in carbon-equivalent trading, and would reduce the volatility of that market and increase investment in emissions reduction.< br />
© 2011 The Royal Society

Quantifying the Cost Uncertainty of Climate Stabilization Policies

Climate change researchers are often asked to evaluate potential economic effects of climate stabilization policies. Policy costs are particularly important because policymakers use a cost/benefit framework to analyze policy options. Many different models have been developed to estimate economic costs and to inform cost/benefit decisions. This thesis examines what impact modelers' assumptions have on a model's results. Specifically, MIT's Emissions Prediction and Policy Analysis (EPPA) model is examined to understand how uncertainty in input parameters affect economic predictions of long-term climate stabilization policies. Eleven different categories of parameters were varied in a Monte Carlo simulation to understand their effect on two different climate stabilization policies. The Monte Carlo simulation results show that the structure of stabilization policy regulations has regional economic welfare effects. Carbon permits allocated by a tax-based emissions path favored energy importers with developed economies (e.g., the US and the EU). Countries with energy-intensive economies (e.g., China) will likely have negative welfare changes because of strict carbon policy constraints. Oil exporters (e.g., the Middle East) will also be negatively impacted because of terms of trade fluxes. These insights have implications for stabilization policy design. The uncertainty surrounding economic projections expose some countries to larger economic risks. Policies could be designed to share risks by implementing different permit allocation methods. Direct payments are another means to compensate countries disproportionately disadvantaged by a stabilization policy.

Quantifying the Likelihood of Regional Climate Change: A Hybridized Approach

The growing need for risk-based assessments of impacts and adaptation to climate change calls for increased capability in climate projections: the quantification of the likelihood of regional outcomes and the representation of their uncertainty. Herein, we present a technique that extends the latitudinal projections of the 2-D atmospheric model of the 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 4th 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 we demonstrate this approach 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 of the transformation coefficients is observationally based. To estimate how these coefficients may alter with climate, we characterize the climate models’ spatial responses, relative to their zonal mean, from transient increases in trace-gas concentrations and then normalize these responses against their corresponding transient global temperature responses. This procedure allows for the construction of meta-ensembles of regional climate outcomes, combining the ensembles of the MIT IGSM—which produce global and latitudinal climate projections, with uncertainty, under different global climate policy scenarios—with regionally resolved patterns from the archived IPCC climate-model projections. This approach also provides a hybridization of the climate-model longitudinal projections with the global and latitudinal patterns projected by the IGSM, and can be applied to any given state or flux variable that has the sufficient observational and model-based information.

Quantifying the Uncertainty in Climate Predictions

Uncertainties in projections of future climate change caused by an increase in greenhouse gas concentrations have been a subject of intensive study in recent years. However, in most cases, uncertainties in parameters and characteristics of models used to obtain those projections, such as climate sensitivity or radiative forcing, are described only by ranges of possible values. The resulting uncertainties in variables describing climate change, such as surface warming or sea level rise, are therefore also given just by ranges of possible values. However, for assessing the possible impact of climate change, it would be more useful to have probability distributions for these variables. There are two significant difficulties in obtaining such distributions. First, it is necessary to know probability distributions for the above mentioned uncertain parameters and model characteristics. Second, existing climate and economics models are computationally too expensive for traditional methods of uncertainty propagation such as Monte Carlo simulation.
        We demonstrate a method for calculating probability distributions for surface air temperature change and sea level rise that result from uncertainties in climate sensitivity and the rate of heat uptake by the deep ocean. These distributions are obtained by applying the Deterministic Equivalent Modeling Method to the MIT climate model. This method provides an effective way of deriving an approximation for the model and allows the propagation of uncertainty. The range and probability distribution of climate sensitivity are based on expert assessments of parameters, while those for the rate of heat uptake are based on the results of simulations with coupled atmosphere-ocean GCMs. As an example of propagating correlated uncertainties, we also show the results of calculations in which the uncertainty in projected increases in forcing is also taken into account. The probability distribution for forcing, associated with an increase in atmospheric CO2 concentrations is calculated based on the distributions for anthropogenic CO2 emissions and the rate of oceanic carbon uptake. The probability distribution for emissions has been calculated in an independent study, while the rate of ocean carbon uptake is assumed to be related to that of heat.

Quantifying Uncertainties in Climate System Properties using Recent Climate Observations

We apply the optimal fingerprint detection algorithm to three independent diagnostics of the recent climate record and derive joint probability density distributions for three uncertain properties of the climate system. The three properties are climate sensitivity, the rate of heat uptake by the deep ocean, and the strength of the net aerosol forcing. Knowing the probability distribution for these properties is essential for quantifying uncertainty in projections of climate change. We briefly describe each diagnostic and indicate its role in constraining these properties. Based on the marginal probability distributions, the 5 to 95% confidence intervals are 1.4 to 7.7K for climate sensitivity and 0.30 to 0.95 W/m2 for the net aerosol forcing using uniform priors; and 1.3 to 4.2K and 0.26 to 0.88 W/m2 using an expert prior for climate sensitivity. The oceanic heat uptake is not so well constrained. The uncertainty in the net aerosol forcing in either case is much less than the uncertainty range usually quoted for the indirect aerosol forcing alone.

Quantifying uncertainties in climate system properties with the use of recent climate observations

We derive joint probability density distributions for three key uncertain properties of the climate system, using an optimal fingerprinting approach to compare simulations of an intermediate complexity climate model with three distinct diagnostics of recent climate observations. On the basis of the marginal probability distributions, the 5 to 95% confidence intervals are 1.4 to 7.7 kelvin for climate sensitivity and -0.30 to -0.95 watt per square meter for the net aerosol forcing. The oceanic heat uptake is not well constrained, but ocean temperature observations do help to constrain climate sensitivity. The uncertainty in the net aerosol forcing is much smaller than the uncertainty range for the indirect aerosol forcing alone given in the Intergovernmental Panel on Climate Change Third Assessment Report.

© 2002 American Association for the Advancement of Science

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