Assessing Evapotranspiration Estimates from the Global Soil Wetness Project Phase 2 (GSWP-2) Simulations

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).We assess the simulations of global-scale evapotranspiration from the Global Soil Wetness Project Phase 2 (GSWP-2) within a global water-budget framework. The scatter in the GSWP-2 global evapotranspiration estimates from various land surface models can constrain the global, annual water budget fluxes to within ±2.5%, and by using estimates of global precipitation, the residual ocean evaporation estimate falls within the range of other independently derived bulk estimates. However, the GSWP-2 scatter cannot entirely explain the imbalance of the annual fluxes from a modern-era, observationally-based global water budget assessment, and inconsistencies in the magnitude and timing of seasonal variations between the global water budget terms are found. Inter-model inconsistencies in evapotranspiration are largest for high latitude inter-annual variability as well as for inter-seasonal variations in the tropics, and analyses with field-scale data also highlights model disparity at estimating evapotranspiration in high latitude regions. Analyses of the sensitivity simulations that replace uncertain forcings (i.e. radiation, precipitation, and meteorological variables) indicate that global (land) evapotranspiration is slightly more sensitive to precipitation than net radiation perturbations, and the majority of the GSWP-2 models, at a global scale, fall in a marginally moisture-limited evaporative condition. Finally, the range of global evapotranspiration estimates among the models is larger than any bias caused by uncertainties in the GSWP-2 atmospheric forcing, indicating that model structure plays a more important role toward improving global land evaporation estimates (as opposed to improved atmospheric forcing).NASA Energy and Water-cycle Study (NEWS, grant #NNX06AC30A), under the NEWS Science and Integration Team activities

Coupling constant corrections in a holographic model of heavy ion collisions with nonzero baryon number density

Sufficiently energetic collisions of heavy ions result in the formation of a droplet of a strongly coupled liquid state of QCD matter known as quark-gluon plasma. By using gauge-gravity duality (holography), a model of a rapidly hydrodynamizing and thermal- izing process like this can be constructed by colliding sheets of energy density moving at the speed of light and tracking the subsequent evolution. In this work, we consider the dual gravitational description of such collisions in the most general bulk theory with a four-derivative gravitational action containing a dynamical metric and a gauge field in five dimensions. Introducing the bulk gauge field enables the analysis of collisions of sheets which carry nonzero “baryon” number density in addition to energy density. Introducing the four-derivative terms enables consideration of such collisions in a gauge theory with finite gauge coupling, working perturbatively in the inverse coupling. While the dynamics of energy and momentum in the presence of perturbative inverse-coupling corrections has been analyzed previously, here we are able to determine the effect of such finite coupling corrections on the dynamics of the density of a conserved global charge, which we take as a model for the dynamics of nonzero baryon number density. In accordance with expec- tations, as the coupling is reduced we observe that after the collisions less baryon density ends up stopped at mid-rapidity and more of it ends up moving near the lightcone

A Forward Looking Version of the MIT Emissions Prediction and Policy Analysis (EPPA) Model

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).This paper documents a forward looking multi-regional general equilibrium model developed from the latest version of the recursive-dynamic MIT Emissions Prediction and Policy Analysis (EPPA) model. The model represents full inter-temporal optimization (perfect foresight), which makes it possible to better address economic and policy issues such as borrowing and banking of GHG allowances, efficiency implications of environmental tax recycling, endogenous depletion of fossil resources, international capital flows, and optimal emissions abatement paths among others. It was designed with the flexibility to represent different aggregations of countries and regions, different horizon lengths, as well as the ability to accommodate different assumptions about the economy, in terms of economic growth, foreign trade closure, labor leisure choice, taxes on primary factors, vintaging of capital and data calibration. The forward-looking dynamic model provides a complementary tool for policy analyses, to assess the robustness of results from the recursive EPPA model, and to illustrate important differences in results that are driven by the perfect foresight behavior. We present some applications of the model that include the reference case and its comparison with the recursive EPPA version, as well as some greenhouse gas mitigation cases where we explore economic impacts with and without inter-temporal trade of permits.This research was supported by the U.S Department of Energy, U.S. Environmental Protection Agency, U.S. National Science Foundation, U.S. National Aeronautics and Space Administration, U.S. National Oceanographic and Atmospheric Administration; and the Industry and Foundation Sponsors of the MIT Joint Program on the Science and Policy of Global Change: Alstom Power (USA), American Electric Power (USA), A.P. Møller-Maersk (Denmark), Cargill (USA), Chevron Corporation (USA), CONCAWE & EUROPIA (EU), DaimlerChrysler AG (USA), Duke Energy (USA), Electric Power Research Institute (USA), Electricité de France, Enel (Italy), Eni (Italy), Exelon Power (USA), ExxonMobil Corporation (USA), Ford Motor Company (USA), General Motors (USA), Iberdrola Generacion (Spain), J-Power (Japan), Merril Lynch (USA), Murphy Oil Corporation (USA), Norway Ministry of Petroleum and Energy, Oglethorpe Power Corporation (USA), RWE Power (Germany), Schlumberger (USA),Shell Petroleum (Netherlands/UK), Southern Company (USA), StatoilHydro (Norway), Tennessee Valley Authority (USA), Tokyo Electric Power Company (Japan), Total (France), G. Unger Vetlesen Foundation (USA)

Angular analysis of the decay

The angular distribution of the flavor-changing neutral current decay B+→K+μ+μ- is studied in proton-proton collisions at a center-of-mass energy of 8 TeV. The analysis is based on data collected with the CMS detector at the LHC, corresponding to an integrated luminosity of 20.5 fb-1. The forward-backward asymmetry AFB of the dimuon system and the contribution FH from the pseudoscalar, scalar, and tensor amplitudes to the decay width are measured as a function of the dimuon mass squared. The measurements are consistent with the standard model expectations

Sensitivity of Climate Change Projections to Uncertainties in the Estimates of Observed Changes in Deep-Ocean Heat Content

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).The MIT 2D climate model is used to make probabilistic projections for changes in global mean surface temperature and for thermosteric sea level rise under a variety of forcing scenarios. The uncertainties in climate sensitivity and rate of heat uptake by the deep ocean are quantified by using the probability distributions derived from observed 20th century temperature changes. The impact on climate change projections of using the smallest and largest estimates of 20th century deep ocean warming is explored. The impact is large in the case of global mean thermosteric sea level rise. In the MIT reference ("business as usual") scenario the median rise by 2100 is 27 and 43 cm in the respective cases. The impact on increases in global mean surface air temperature is more modest, 4.9 C and 3.9 C in the two respective cases, because of the correlation between climate sensitivity and ocean heat uptake required by 20th century surface and upper air temperature changes. The results are also compared with the projections made by the IPCC AR4's multi-model ensemble for several of the SRES scenarios. The multi-model projections are more consistent with the MIT projections based on the largest estimate of ocean warming. However the range for the rate of heat uptake by the ocean suggested by the lowest estimate of ocean warming is more consistent with the range suggested by the 20th century changes in surface and upper air temperatures, combined with expert prior for climate sensitivity.This work was supported in part by the Office of Science (BER), U.S. Dept. of Energy Grant No. DE-FG02-93ER61677, NSF, and by the MIT Joint Program on the Science and Policy of Global Change

Potential Climatic Impacts and Reliability of Very Large-Scale Wind Farms

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).Meeting future world energy needs while addressing climate change requires large-scale deployment of low or zero greenhouse gas (GHG) emission technologies such as wind energy. The widespread availability of wind power has fueled legitimate interest in this renewable energy source as one of the needed technologies. For very large-scale utilization of this resource, there are however potential environmental impacts, and also problems arising from its inherent intermittency, in addition to the present need to lower unit costs. To explore some of these issues, we use a threedimensional climate model to simulate the potential climate effects associated with installation of wind-powered generators over vast areas of land or coastal ocean. Using windmills to meet 10% or more of global energy demand in 2100, could cause surface warming exceeding 1oC over land installations. In contrast, surface cooling exceeding 1oC is computed over ocean installations, but the validity of simulating the impacts of windmills by simply increasing the ocean surface drag needs further study. Significant warming or cooling remote from both the land and ocean installations, and alterations of the global distributions of rainfall and clouds also occur. These results are influenced by the competing effects of increases in roughness and decreases in wind speed on near-surface turbulent heat fluxes, the differing nature of land and ocean surface friction, and the dimensions of the installations parallel and perpendicular to the prevailing winds. These results are also dependent on the accuracy of the model used, and the realism of the methods applied to simulate windmills. Additional theory and new field observations will be required for their ultimate validation. Intermittency of wind power on daily, monthly and longer time scales as computed in these simulations and inferred from meteorological observations, poses a demand for one or more options to ensure reliability, including backup generation capacity, very long distance power transmission lines, and onsite energy storage, each with specific economic and/or technological challenges.This study received support from the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsors

Margin-based Ranking and an Equivalence between AdaBoost and RankBoost

We study boosting algorithms for learning to rank. We give a general margin-based bound for ranking based on covering numbers for the hypothesis space. Our bound suggests that algorithms that maximize the ranking margin will generalize well. We then describe a new algorithm, smooth margin ranking, that precisely converges to a maximum ranking-margin solution. The algorithm is a modification of RankBoost, analogous to “approximate coordinate ascent boosting.” Finally, we prove that AdaBoost and RankBoost are equally good for the problems of bipartite ranking and classification in terms of their asymptotic behavior on the training set. Under natural conditions, AdaBoost achieves an area under the ROC curve that is equally as good as RankBoost’s; furthermore, RankBoost, when given a specific intercept, achieves a misclassification error that is as good as AdaBoost’s. This may help to explain the empirical observations made by Cortes andMohri, and Caruana and Niculescu-Mizil, about the excellent performance of AdaBoost as a bipartite ranking algorithm, as measured by the area under the ROC curve

The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List

We are interested in supervised ranking algorithms that perform especially well near the top of the ranked list, and are only required to perform sufficiently well on the rest of the list. In this work, we provide a general form of convex objective that gives high-scoring examples more importance. This “push” near the top of the list can be chosen arbitrarily large or small, based on the preference of the user. We choose ℓp-norms to provide a specific type of push; if the user sets p larger, the objective concentrates harder on the top of the list. We derive a generalization bound based on the p-norm objective, working around the natural asymmetry of the problem. We then derive a boosting-style algorithm for the problem of ranking with a push at the top. The usefulness of the algorithm is illustrated through experiments on repository data. We prove that the minimizer of the algorithm’s objective is unique in a specific sense. Furthermore, we illustrate how our objective is related to quality measurements for information retrieval

Biofuels, Climate Policy and the European Vehicle Fleet

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).We examine the effect of biofuels mandates and climate policy on the European vehicle fleet, considering the prospects for diesel and gasoline vehicles. We use the MIT Emissions Prediction and Policy Analysis (EPPA) model, which is a general equilibrium model of the world economy. We expand this model by explicitly introducing current generation biofuels, by accounting for stock turnover of the vehicle fleets and by disaggregating gasoline and diesel cars. We find that biofuels mandates alone do not substantially change the share of diesel cars in the total fleet given the current structure of fuel taxes and tariffs in Europe that favors diesel vehicles. Jointly implemented changes in fiscal policy, however, can reverse the trend toward more diesel vehicles. We find that harmonizing fuel taxes reduces the welfare cost associated with renewable fuel policy and lowers the share of diesel vehicles in the total fleet to 21% by 2030 compared to 25% in 2010. We also find that eliminating tariffs on biofuel imports, which under the existing regime favor biodiesel and impede sugar ethanol imports, is welfare-enhancing and brings about further substantial reductions in CO2 emissions.This study received support from the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsors

Distributional Impacts of a U.S. Greenhouse Gas Policy: A General Equilibrium Analysis of Carbon Pricing

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).We develop a new model of the U.S., the U.S. Regional Energy Policy (USREP) model that is resolved for large states and regions of the U.S. and by income class and apply the model to investigate a $15 per ton CO2 equivalent price on greenhouse gas emissions. Previous estimates of distributional impacts of carbon pricing have been done outside of the model simulation and have been based on energy expenditure patterns of households in different regions and of different income levels. By estimating distributional effects within the economic model, we include the effects of changes in capital returns and wages on distribution and find that the effects are significant and work against the expenditure effects. We find the following: First, while results based only on energy expenditure have shown carbon pricing to be regressive we find the full distributional effect to be neutral or slightly progressive. This demonstrates the importance of tracing through all economic impacts and not just focusing on spending side impacts. Second, the ultimate impact of such a policy on households depends on how allowances, or the revenue raised from auctioning them, is used. Free distribution to firms would be highly regressive, benefiting higher income households and forcing lower income households to bear the full cost of the policy and what amounts to a transfer of wealth to higher income households. Lump sum distribution through equal-sized household rebates would make lower income households absolutely better off while shifting the costs to higher income households. Schemes that would cut taxes are generally slightly regressive but improve somewhat the overall efficiency of the program. Third, proposed legislation would distribute allowances to local distribution companies (electricity and natural gas distributors) and public utility commissions would then determine how the value of those allowances was used. A significant risk in such a plan is that distribution to households might be perceived as lowering utility rates That reduced the efficiency of the policy we examined by 40 percent. Finally, the states on the coasts bear little cost or can benefit because of the distribution of allowance revenue while mid-America and southern states bear the highest costs. This regional pattern reflects energy consumption and energy production difference among states. Use of allowance revenue to cut taxes generally exacerbates these regional differences because coastal states are also generally higher income states, and those with higher incomes benefit more from tax cuts.MIT Joint Program on the Science and Policy of Global Change through a combination of government, industry, and foundation funding, the MIT Energy Initiative, and additional support for this work from a coalition of industrial sponsors