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

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

The Influence on Climate Change of Differing Scenarios for Future Development Analyzed Using the MIT Integrated Global System 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/).A wide variety of scenarios for future development have played significant roles in climate policy discussions. This paper presents projections of greenhouse gas (GHG) concentrations, sea level rise due to thermal expansion and glacial melt, oceanic acidity, and global mean temperature increases computed with the MIT Integrated Global Systems Model (IGSM) using scenarios for 21st century emissions developed by three different groups: intergovernmental (represented by the Intergovernmental Panel on Climate Change), government (represented by the U.S. government Climate Change Science Program) and industry (represented by Royal Dutch Shell plc). In all these scenarios the climate system undergoes substantial changes. By 2100, the CO2 concentration ranges from 470 to 1020 ppm compared to a 2000 level of 365 ppm, the CO2-equivalent concentration of all greenhouse gases ranges from 550 to 1780 ppm in comparison to a 2000 level of 415 ppm, sea level rises by 24 to 56 cm relative to 2000 due to thermal expansion and glacial melt, oceanic acidity changes from a current pH of around 8 to a range from 7.63 to 7.91. The global mean temperature increases by 1.8 to 7.0 degrees C relative to 2000.The IGSM model used here is 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

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)

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

A semi-empirical representation of the temporal variation of total greenhouse gas levels expressed as equivalent levels of carbon dioxide

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/).In order to examine the underlying longer-term trends in greenhouse gases, that are driven for example by anthropogenic emissions or climate change, it is useful to remove the recurring effects of natural cycles and oscillations on the sources and/or sinks of those gases that have strong biological (e.g., CO2, CH4, N2O) and/or photochemical (e.g. CH4) influences on their global atmospheric cycles. We use global observations to calculate monthly estimates of greenhouse gas levels expressed as CO2 equivalents, and then fit these estimates to a semi-empirical model that includes the natural seasonal, QBO, and ENSO variations, as well as a second order polynomial expressing longer-term variations. We find that this model provides a reasonably accurate fit to the observation-based monthly data. We also show that this semiempirical model has some predictive capability; that is it can be used to provide a reasonably reliable estimate of CO2 equivalents at the current time using validated observations that lag real time by a few to several months.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

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde

Measuring Welfare Loss Caused by Air Pollution in Europe: A CGE Analysis

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/).To evaluate the socio-economic impacts of air pollution, we develop an integrated approach based on computable general equilibrium (CGE). Applying our approach to Europe shows that even there, where air quality is relatively high compared with other parts of the world, health-related damages caused by air pollution are substantial. We estimate that in 2005, air pollution in Europe caused a consumption loss of around 220 billion Euro (year 2000 prices, around 3 percent of consumption level) and a social welfare loss of around 370 billion Euro, measured as the sum of lost consumption and leisure (around 2 percent of welfare level). In addition, we estimated that a set of 2020-targeting air quality improvement policy scenarios, which are proposed in the 2005 CAFE program, would bring 18 European countries as a whole a welfare gain of 37 to 49 billion Euro (year 2000 prices) in year 2020 alone.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

Will Border Carbon Adjustments Work?

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 potential for greenhouse gas (GHG) restrictions in some nations to drive emission increases in other nations, or leakage, is a contentious issue in climate change negotiations. We evaluate the potential for border carbon adjustments (BCAs) to address leakage concerns using an economy-wide model. For 2025, we find that BCAs reduce leakage by up to two-thirds, but result in only modest reductions in global emissions and significantly reduce welfare. In contrast, BCA-equivalent leakage reductions can be achieved by very small emission charges or efficiency improvements in nations targeted by BCAs, which have negligible welfare effects. We conclude that BCAs are a costly method to reduce leakage but such policies may be effective coercion strategies. We also investigate the impact of BCAs on sectoral output and evaluate the leakage contributions of trade and changes in the price of crude oil.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

Unintended Environmental Consequences of a Global Biofuels Program

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/).Biofuels are being promoted as an important part of the global energy mix to meet the climate change challenge. The environmental costs of biofuels produced with current technologies at small scales have been studied, but little research has been done on the consequences of an aggressive global biofuels program with advanced technologies using cellulosic feedstocks. Here, with simulation modeling, we explore two scenarios for cellulosic biofuels production and find that both could contribute substantially to future global-scale energy needs, but with significant unintended environmental consequences. As the land supply is squeezed to make way for vast areas of biofuels crops, the global landscape is defined by either the clearing of large swathes of natural forest, or the intensification of agricultural operations worldwide. The greenhouse gas implications of land-use conversion differ substantially between the two scenarios, but in both, numerous biodiversity hotspots suffer from serious habitat loss. Cellulosic biofuels may yet serve as a crucial wedge in the solution to the climate change problem, but must be deployed with caution so as not to jeopardize biodiversity, compromise ecosystems services, or undermine climate policy.This study received funding from the MIT Joint Program on the Science and Policy of Global Change, which is supported by a onsortium of government, industry and foundation sponsors