Note: Cover -- Half-title -- Series information -- Title page -- Copyright information -- Table of contents -- Preface -- Acknowledgments -- List of contributors -- Part I Diagnostics and prediction of high-impact weather -- 1 Global prediction of high-impact weather: diagnosis and performance -- 1.1 Introduction -- 1.2 Global NWP: how it is done today -- 1.3 The need for dynamical understanding: getting things right for the right reasons -- 1.4 The need for uncertainty information: assessing the degree of confidence -- 1.5 Summary -- 1.6 Opportunities for further progress -- References -- 2 Severe weather diagnosis from the perspective of generalized slantwise vorticity development -- 2.1 Introduction -- 2.2 Generalized slantwise vorticity development -- 2.2.1 Revisiting slantwise vorticity development -- 2.2.2 Diabatic vorticity development -- Impacts on vertical vorticity development of the vertical non-uniformity of diabatic heating -- Impacts on vertical vorticity development of the horizontal non-uniformity of diabatic heating -- 2.2.3 Adiabatic vorticity development -- Slantwise vorticity development on a sloping isentropic surface -- Slantwise vorticity development from a Lagrangian perspective -- Vorticity development (VD) -- Slantwise vorticity development (SVD) -- Relationship between PV2PV2 and SVD -- 2.3 Application of generalized slantwise vorticity development -- 2.3.1 Data and computational method -- Data -- Computational method -- 2.3.2 Description of the TPV in 2008 -- Track of the TPV and associated precipitation -- Large-scale circulation associated with the TPV -- 2.3.3 Diabatic vorticity development due to inhomogeneous heating -- Relative contributions to vertical vorticity development of the changes in PVePVe, PV2PV2, and thetazthetaz -- Effect of the vertical gradient of diabatic heating. , Effect of the horizontal gradient of diabatic heating -- 2.3.4 Adiabatic vorticity development due to slantwise vorticity development -- 2.4 Discussion and conclusions -- References -- 3 Probabilistic extreme event attribution -- 3.1 Introduction -- 3.2 Concepts -- 3.2.1 Weather versus climate -- 3.2.2 Risk versus probability -- 3.2.3 Metrics of attributable risk -- Risk ratio (RR) -- Fraction of attributable risk (FAR) -- Fraction of attributable increase and decrease in risk (FAIR and FADR) -- 3.2.4 Atmosphere-only modelling approaches -- Targeted probabilistic extreme event attribution -- Systematic probabilistic extreme event attribution -- 3.2.5 Coupled atmosphere-ocean modelling approaches -- 3.3 Examples: seasonal-mean extremes -- 3.3.1 Hot season -- 3.3.2 Flood -- 3.4 Current issues -- 3.4.1 Selection bias -- 3.4.2 Computational constraints -- 3.5 Summary -- References -- 4 Observed and projected changes in temperature and precipitation extremes -- 4.1 Introduction -- 4.2 Statistical characterization of extremes -- 4.2.1 Extreme value analysis -- 4.2.2 Estimation of trends -- 4.2.3 Detection and attribution -- 4.3 Temperature extremes -- 4.3.1 Observed changes -- 4.3.2 Understanding the causes -- 4.3.3 Future changes -- 4.4 Precipitation extremes -- 4.4.1 Observed changes -- 4.4.2 Understanding the causes -- 4.4.3 Future changes -- 4.5 Summary and discussion -- References -- Part II High-impact weather in mid latitudes -- 5 Rossby wave breaking: climatology, interaction with low-frequency climate variability, and links to extreme weather events -- 5.1 Introduction -- 5.2 Rossby wave breaking: definition and upper-level signature -- 5.3 Climatological occurrence of RWB and link to patterns of low-frequency variability -- 5.3.1 Climatological occurrence of RWB -- 5.3.2 RWB and patterns of low-frequency variability -- 5.4 RWB and surface weather. , 5.5 Link to high-impact weather events -- References -- 6 The influence of jet stream regime on extreme weather events -- 6.1 Introduction -- 6.2 Dynamical regimes of the large-scale circulation -- 6.3 Methods -- 6.3.1 Diagnostics of extreme events -- 6.3.2 The idealized models -- 6.4 The relation between jet stream type and the distribution and evolution of extreme weather events -- 6.4.1 Observed extreme upper level cyclonic vorticity events -- 6.4.2 Extreme events in the idealized models -- 6.4.3 The distribution of observed extreme temperature anomalies -- 6.5 Discussion -- 6.6 Acknowledgments -- References -- 7 Forecasting high-impact weather using ensemble prediction systems -- 7.1 Introduction -- 7.2 Quantifying uncertainty -- 7.2.1 An ideal ensemble prediction system -- 7.2.2 Initial condition uncertainty -- 7.2.3 Uncertainty due to model error -- 7.3 Practical ensemble prediction systems -- 7.3.1 Global EPS -- 7.3.2 Convective-scale EPS -- 7.4 Probabilistic forecast verification -- 7.4.1 Proper scoring rules -- 7.4.2 Proper score decomposition -- 7.5 Calibration and postprocessing -- 7.5.1 Postprocessing for extremes -- 7.6 Communicating uncertainty -- 7.7 Conclusion -- 7.8 Acknowledgements -- References -- 8 Storm tracks, blocking, and climate change: a review -- 8.1 Introduction -- 8.2 Climate models and a historical perspective -- 8.3 Mechanisms causing storm track change -- 8.4 Storm track projections -- 8.5 Blocking changes -- 8.6 Outlook -- References -- 9 The North Atlantic and Arctic Oscillations: climate variability, extremes, and stratosphere-troposphere interaction -- 9.1 What is the North Atlantic Oscillation and how is it related to the Arctic Oscillation? -- 9.2 The NAO as a governor of extreme weather -- 9.3 Degeneracy in the response to different drivers -- 9.4 Chaotic 'noise' or predictable signal? -- 9.5 Summary. , References -- Part III Tropical cyclones -- 10 Opportunities and challenges in dynamical and predictability studies of tropical cyclone events -- 10.1 Introduction -- 10.2 Extended-range predictions of western North Pacific tropical cyclone events -- 10.3 Extended-range predictions of Atlantic tropical cyclone events -- 10.4 Seasonal prediction of Atlantic tropical cyclone events -- 10.5 Seasonal forecasts for western North Pacific tropical cyclone events -- 10.6 Concluding remarks -- 10.7 Acknowledgments -- References -- 11 Predictability of severe weather and tropical cyclones at the mesoscales -- 11.1 Introduction -- 11.2 Mesoscale predictability of mid-latitude winter snowstorms and moist baroclinic waves -- 11.3 Mesoscale predictability of warm season severe weather events -- 11.4 Mesoscale predictability of tropical cyclones -- 11.5 Concluding remarks -- References -- 12 Dynamics, predictability, and high-impact weather associated with the extratropical transition of tropical cyclones -- 12.1 Introduction -- 12.2 Physical processes -- 12.2.1 Extratropical transition -- 12.2.2 Impacts on the mid-latitude circulation -- 12.2.3 Downstream development -- 12.3 Predictability -- 12.4 Recurving TC Oscar and extreme weather downstream over North America -- 12.4.1 Overview and life cycle -- 12.4.2 Tropical cyclone-extratropical flow interaction -- 12.4.3. Downstream flow reconfiguration -- 12.4.4 Possible role of low-frequency tropical forcing -- 12.4.5 Predictability associated with TY Oscar -- 12.5 Summary and future directions -- 12.6 Acknowledgments -- References -- 13 Secondary eyewall formation in tropical cyclones -- 13.1 Introduction -- 13.2 Environmental conditions -- 13.3 Internal mechanisms of SEF -- 13.3.1 Vortex Rossby waves -- 13.3.2 Axisymmetrization process -- 13.3.3 Beta-skirt axisymmetrization formation hypothesis. , 13.3.4 Unbalanced boundary layer dynamics near the top of the TC boundary layer -- 13.3.5 Balanced response to diabatic heating in a region of enhanced inertial stability -- 13.4 Concluding remarks -- 13.5 Acknowledgment -- References -- 14 Seasonal forecasting of floods and tropical cyclones -- 14.1 Introduction -- 14.2 Seasonal forecasting -- 14.3 POAMA and the Beijing floods of 21 July 2012 -- 14.4 May 2010 POAMA forecast -- 14.5 Tropical cyclones during the 2010/2011 rainy season -- 14.6 Cyclone Yasi -- 14.7 Downscaling -- 14.8 Summary and conclusion -- References -- Part IV Heat waves and cold-air outbreaks -- 15 European heat waves: the effect of soil moisture, vegetation, and land use -- 15.1 Introduction -- 15.2 Climatology of European heat waves -- 15.3 Dynamical processes -- 15.4 Surface hydrology -- 15.5 Soil moisture - climate feedback -- 15.6 Mesoscale effects -- 15.7 Vegetation and land-use change effects -- 15.8 Concluding remarks -- References -- 16 Western North American extreme heat, associated large-scale synoptic-dynamics, and performance by a climate model -- 16.1 Introduction -- 16.2 California heat waves: upper air large-scale meteorological patterns (LSMPs) synoptics and dynamics -- 16.3 LSMPs as a predictor of surface extreme heat -- 16.4 How well are LSMPs captured by a climate model? -- 16.5 Conclusions -- 16.6 Acknowledgments -- References -- 17 Decadal to interdecadal variations of northern China heat wave frequency: impact of the Tibetan Plateau snow cover -- 17.1 Introduction -- 17.2 Data, model, and methodology -- 17.3 The China HWF and TPSC -- 17.4 Physical mechanisms -- 17.5 Conclusion and discussion -- 17.6 Acknowledgments -- References -- 18 Global warming targets and heat wave risk -- 18.1 Introduction -- 18.2 Data -- 18.3 Results -- 18.4 Plausibility of the upper estimates. , 18.5 Role of soil drying on range of regional warming.

Note: Front Cover -- Big Data Mining for Climate Change -- Copyright -- Contents -- Preface -- 1 Big climate data -- 1.1 Big data sources -- 1.1.1 Earth observation big data -- 1.1.2 Climate simulation big data -- 1.2 Statistical and dynamical downscaling -- 1.3 Data assimilation -- 1.3.1 Cressman analysis -- 1.3.2 Optimal interpolation analysis -- 1.3.3 Three-dimensional variational analysis -- 1.3.4 Four-dimensional variational analysis -- 1.4 Cloud platforms -- 1.4.1 Cloud storage -- 1.4.2 Cloud computing -- Further reading -- 2 Feature extraction of big climate data -- 2.1 Clustering -- 2.1.1 K-means clustering -- 2.1.2 Hierarchical clustering -- 2.2 Hidden Markov model -- 2.3 Expectation maximization -- 2.4 Decision trees and random forests -- 2.5 Ridge and lasso regressions -- 2.6 Linear and quadratic discriminant analysis -- 2.6.1 Bayes classi er -- 2.6.2 Linear discriminant analysis -- 2.6.3 Quadratic discriminant analysis -- 2.7 Support vector machines -- 2.7.1 Maximal margin classi er -- 2.7.2 Support vector classi ers -- 2.7.3 Support vector machines -- 2.8 Rainfall estimation -- 2.9 Flood susceptibility -- 2.10 Crop recognition -- Further reading -- 3 Deep learning for climate patterns -- 3.1 Structure of neural networks -- 3.2 Back propagation neural networks -- 3.2.1 Activation functions -- 3.2.2 Back propagation algorithms -- 3.3 Feedforward multilayer perceptrons -- 3.4 Convolutional neural networks -- 3.5 Recurrent neural networks -- 3.5.1 Input-output recurrent model -- 3.5.2 State-space model -- 3.5.3 Recurrent multilayer perceptrons -- 3.5.4 Second-order network -- 3.6 Long short-term memory neural networks -- 3.7 Deep networks -- 3.7.1 Deep learning -- 3.7.2 Boltzmann machine -- 3.7.3 Directed logistic belief networks -- 3.7.4 Deep belief nets -- 3.8 Reinforcement learning -- 3.9 Dendroclimatic reconstructions. , 3.10 Downscaling climate variability -- 3.11 Rainfall-runoff modeling -- Further reading -- 4 Climate networks -- 4.1 Understanding climate systems as networks -- 4.2 Degree and path -- 4.3 Matrix representation of networks -- 4.4 Clustering and betweenness -- 4.5 Cut sets -- 4.6 Trees and planar networks -- 4.7 Bipartite networks -- 4.8 Centrality -- 4.8.1 Degree centrality -- 4.8.2 Closeness centrality -- 4.8.3 Betweenness centrality -- 4.9 Similarity -- 4.9.1 Cosine similarity -- 4.9.2 Pearson similarity -- 4.10 Directed networks -- 4.11 Acyclic directed networks -- 4.12 Weighted networks -- 4.12.1 Vertex strength -- 4.12.2 Weight-degree/weight-weight correlation -- 4.12.3 Weighted clustering -- 4.12.4 Shortest path -- 4.13 Random walks -- 4.14 El Niño southern oscillation -- 4.15 North Atlantic oscillation -- Further reading -- 5 Random climate networks and entropy -- 5.1 Regular networks -- 5.1.1 Fully connected networks -- 5.1.2 Regular ring-shaped networks -- 5.1.3 Star-shaped networks -- 5.2 Random networks -- 5.2.1 Giant component -- 5.2.2 Small component -- 5.3 Con guration networks -- 5.3.1 Edge probability and common neighbor -- 5.3.2 Degree distribution -- 5.3.3 Giant components -- 5.3.4 Small components -- 5.3.5 Directed random network -- 5.4 Small-world networks -- 5.4.1 Main models -- 5.4.2 Degree distribution -- 5.4.3 Clustering -- 5.4.4 Mean distance -- 5.5 Power-law degree distribution -- 5.5.1 Price's models -- 5.5.2 Barabasi-Albert models -- 5.6 Dynamics of random networks -- 5.7 Entropy and joint entropy -- 5.8 Conditional entropy and mutual information -- 5.9 Entropy rate -- 5.10 Entropy-based climate network -- 5.11 Entropy-based decision tree -- Further reading -- 6 Spectra of climate networks -- 6.1 Understanding atmospheric motions via network spectra -- 6.2 Adjacency spectra -- 6.2.1 Maximum degree -- 6.2.2 Diameter. , 6.2.3 Paths of length k -- 6.3 Laplacian spectra -- 6.3.1 Maximum degree -- 6.3.2 Connectivity -- 6.3.3 Spanning tree -- 6.3.4 Degree sequence -- 6.3.5 Diameter -- 6.4 Spectrum centrality -- 6.4.1 Eigenvector centrality -- 6.4.2 Katz centrality -- 6.4.3 Pagerank centrality -- 6.4.4 Authority and hub centralities -- 6.5 Network eigenmodes -- 6.6 Spectra of complete networks -- 6.7 Spectra of small-world networks -- 6.8 Spectra of circuit and wheel network -- 6.9 Spectral density -- 6.10 Spectrum-based partition of networks -- Further reading -- 7 Monte Carlo simulation of climate systems -- 7.1 Random sampling -- 7.1.1 Uniform distribution -- 7.1.2 Nonuniform distribution -- 7.1.3 Normal distribution -- 7.2 Variance reduction technique -- 7.2.1 Control variable method -- 7.2.2 Control vector method -- 7.3 Strati ed sampling -- 7.4 Sample paths for Brownian motion -- 7.4.1 Cholesky and Karhounen-Loève expansions -- 7.4.2 Brownian bridge -- 7.5 Quasi-Monte Carlo method -- 7.5.1 Discrepancy -- 7.5.2 Koksma-Hlawka inequality -- 7.5.3 Van der Corput sequence -- 7.5.4 Halton sequence -- 7.5.5 Faure sequence -- 7.6 Markov chain Monte Carlo -- 7.7 Gibbs sampling -- Further reading -- 8 Sparse representation of big climate data -- 8.1 Global positioning -- 8.1.1 Multidimensional scaling -- 8.1.2 Local rigid embedding -- 8.2 Embedding rules -- 8.2.1 Attractors and fractal dimension -- 8.2.2 Delay embedding -- 8.2.3 Multichannel singular spectrum analysis -- 8.2.4 Recurrence networks -- 8.3 Sparse recovery -- 8.3.1 Sparse interpolation -- 8.3.2 Sparse approximation -- 8.3.3 Greedy algorithms -- 8.4 Sparse representation of climate modeling big data -- 8.5 Compressive sampling of remote sensing big data -- 8.5.1 s-Sparse approximation -- 8.5.2 Minimal samples -- 8.5.3 Orthogonal matching pursuit -- 8.5.4 Compressive sampling matching pursuit. , 8.5.5 Iterative hard thresholding -- 8.6 Optimality -- 8.6.1 Optimization algorithm for compressive sampling -- 8.6.2 Chambolle and Pock's primal-dual algorithm -- Further reading -- 9 Big-data-driven carbon emissions reduction -- 9.1 Precision agriculture -- 9.2 Oil exploitation -- 9.3 Smart buildings -- 9.4 Smart grids -- 9.5 Smart cities -- Further reading -- 10 Big-data-driven low-carbon management -- 10.1 Large-scale data envelopment analysis -- 10.2 Natural resource management -- 10.3 Roadway network management -- 10.4 Supply chain management -- 10.5 Smart energy management -- Further reading -- 11 Big-data-driven Arctic maritime transportation -- 11.1 Trans-Arctic routes -- 11.2 Sea-ice remote-sensing big data -- 11.2.1 Arctic sea-ice concentration -- 11.2.2 Melt ponds -- 11.2.3 Arctic sea-ice extent -- 11.2.4 Arctic sea-ice thickness -- 11.2.5 Arctic sea-ice motion -- 11.2.6 Comprehensive integrated observation system -- 11.3 Sea-ice modeling big data -- 11.4 Arctic transport accessibility model -- 11.5 Economic and risk assessments of Arctic routes -- 11.6 Big-data-driven dynamic optimal trans-Arctic route system -- 11.7 Future prospects -- Further reading -- Index -- Back Cover.

Note: Cover -- Half-title -- Series information -- Title page -- Copyright information -- Contents -- Contributors -- Preface -- Objectives -- Acknowledgments -- Abbreviations -- Part I Future Earth and Planetary Issues -- 1 International Drivers to Study Climatic and Environmental Change: A Challenge to Scientific Unions -- 1.1 Climatic Change -- 1.2 International Political Drivers in Relation to Climate Change -- 1.3 International Inter-Governmental Drivers -- 1.3.1 The Work of the IPCC -- 1.3.1.1 Special Reports -- 1.4 Other International Drivers -- 1.5 The International Scientific Response -- 1.6 The Role of Scientific Unions -- 1.7 Sustainability -- 1.7.1 Quantifying Sustainability -- 1.7.2 The Role of Geodesists and Geophysicists -- 1.7.3 Neo-Cornucopianism and Malthusianism -- 1.7.3.1 Food -- 1.8 Gaps -- 1.9 Summary -- 1.9.1 Develop a New Body - Either by Merger or by Creation of a New Body -- 1.9.2 Collaborate with Like-Minded Individuals -- 1.9.3 Develop a New Program -- Internet Resources -- References -- 2 Future Earth and Expected Mega Changes -- 2.1 Introduction -- 2.2 Observed Changes in the Climate System -- 2.3 Future Climate and Expected Mega Changes -- 2.3.1 Expected Future Changes in the Earth's Environment in Some Future Years -- 2.4 What Are Mega Changes? -- 2.4.1 Increased Displacement of People -- 2.4.1.1 Widespread Drought and Undermined Food Security -- 2.4.1.2 Increased Poverty and Hunger -- 2.4.1.3 National Security Implications -- 2.4.2 Reduced Surface and Groundwater Resources, Water Crisis -- 2.4.2.1 Melting of Himalayan Glaciers -- 2.4.3 Increased Extreme Events -- 2.4.4 Irreversible Changes -- 2.4.4.1 Warmer Temperatures -- 2.4.4.2 Sea-Level Rise -- 2.4.4.3 Changing Geography -- 2.4.4.4 Ocean Acidification -- 2.4.4.5 Extinction of Species -- 2.4.4.6 Loss of Amazon Rain Forest -- 2.4.4.7 Reduced Permafrost Extent. , 2.4.4.8 Exacerbated Human Health Problem -- 2.5 Summary -- Acknowledgements -- Internet Resources -- References -- 3 Global Change, Space Weather, and Climate -- 3.1 Introduction -- 3.2 The Sun and the Heliosphere -- 3.3 The Magnetic Field of the Earth -- 3.4 Interaction between the Sun and the Earth's Magnetic Field -- 3.5 Space Weather Effects on Technology -- 3.6 The Climate System -- 3.7 Sun-Climate Relationship -- 3.8 Conclusions -- References -- 4 Climate Issues from the Planetary Perspective and Insights for the Earth -- 4.1 Introduction -- 4.2 The Terrestrial Planets -- 4.2.1 The Current Climate of Venus and Contrast with Earth -- 4.2.2 Current Climate of Mars Compared to Earth -- 4.2.3 Early Climates of Earth, Mars and Venus -- 4.2.4 Common Evolutionary Processes -- 4.3 Titan -- 4.3.1 An Earth-Like World in the Outer Solar System -- 4.3.2 Seasonal Effects and Meteorology at 10 AU -- 4.4 Space Weather in the Solar System -- 4.4.1 Planetary Space Weather Agents, Analogy and Contrast with Earth -- 4.4.2 The Significant Role of the Sun in Space Weather at the Terrestrial Planets -- 4.4.3 Space Weather Phenomena in the Outer Solar System -- 4.5 Conclusions: Implications from Planetary Studies for Future Earth Climate -- References -- Part II Future Earth and Geodetic Issues -- 5 Satellite Remote Sensing of Hydrological Change -- 5.1 Introduction - Monitoring the Water Balance -- 5.2 The Panta Rhei Scientific Decade of the International Association of Hydrological Sciences: Change in Hydrology and Society -- 5.3 The Water Balance under Human Impact -- 5.3.1 Reducing Uncertainty of Water Budget through Hydrological Modelling -- 5.3.2 Reducing Uncertainty of Water Budget through Improved Monitoring -- 5.4 Monitoring Water Balance Components by Satellite Missions -- 5.4.1 Monitoring Water Storage: The GRACE Missions. , 5.4.2 Monitoring Water Levels and Surface Discharges: The SWOT Mission -- 5.4.3 Monitoring Soil Moisture: The SMOS and SMAP Missions -- 5.4.4 Monitoring Precipitation: The TRMM and GPM Missions -- 5.4.5 Monitoring Evaporation: The Terra (CERES, MODIS) Mission -- 5.5 Concluding Remarks -- References -- 6 Geodetic Observations as a Monitor of Climate Change -- 6.1 Introduction -- 6.2 Shape of the Earth -- 6.3 The Earth's Gravity Field -- 6.3.1 Satellite Gravity -- 6.3.2 SatelliteLaser Ranging -- 6.3.3 GRACE Satellite Gravimetry -- 6.4 Earth Rotation -- 6.5 Conclusions -- References -- Part III Future Earth and the Earth's Fluid Environment -- 7 Future Earth and the Cryosphere -- 7.1 Ice and Snow on Earth -- 7.2 Past Climate and Sea Level Change Involving the Cryosphere -- 7.3 Ice, Solid Earth and Sea-Level Interactions -- 7.4 Current Changes to Ice Sheets -- 7.5 Current Changes to Glaciers -- 7.6 Projected Future Changes to the Cryospheric Contribution to Sea Level Rise -- 7.7 Techniques, Systems and Networks to Assess Cryospheric Change on Future Earth -- 7.8 Concluding Remarks -- Acknowledgements -- References -- Appendix 1 -- 8 Geographical Research and Future Earth -- 8.1 Introduction -- 8.2 Geography and Global Environmental Change -- 8.3 Human Impact on the Environment: Past, Present and 'Post-Holocene' -- 8.4 Hazards, Risks and Disasters -- 8.5 Remote Sensing and GIS -- 8.6 Steps to Sustainability? -- 8.7 The Future of Future Earth and Geography? -- References -- 9 Water Security: Integrating Lessons Learned for Water Quality, Quantity and Sustainability -- 9.1 Introduction -- 9.1.1 Defining Water Security -- 9.1.2 Case Study Template -- Arsenic Case Study -- Pesticide Use and Biodiversity Case Study -- Nitrate Case Study -- 9.2 Future Directions -- 9.3 Conclusions -- Acknowledgements -- References. , 10 Decadal Coupled Ocean-Atmosphere Interaction in North Atlantic and Global Warming Hiatus -- 10.1 Introduction -- 10.2 Data and Methodology -- 10.2.1 Data -- 10.2.2 Statistical Method -- 10.2.3 Atmospheric General Circulation Model (AGCM) -- 10.3 The Decadal-Scale Coupled Ocean-Atmosphere Mode: The NAT-NAO-AMOC-AMO (NNAA) Mode -- 10.3.1 Dominant Modes of SSTA Multidecadal Variability -- 10.3.2 The Direct Effect of the NAT on the NAO -- 10.3.3 The NAO Forcing on the AMOC and AMO -- 10.3.4 The Negative Feedback of the AMO on the NAT -- 10.3.5 A Mechanism of Multidecadal Variability in the North Atlantic -- 10.4 The Impact of the NNAA Decadal Coupled Mode on the Recent Warming Hiatus in the NHT -- 10.5 Conclusion -- Acknowledgements -- References -- 11 Sea Level Rise and Future Earth -- 11.1 Introduction -- 11.2 Observations of Recent-Past and Present-Day Sea Level Variations -- 11.2.1 Twentieth Century -- 11.2.2 Satellite Altimetry Era -- 11.3 Causes of Present-Day Sea Level Rise -- 11.3.1 Steric Sea Level -- 11.3.2 Land Ice Contribution to Sea Level -- 11.3.2.1 Glaciers -- 11.3.2.2 Ice Sheets -- 11.3.3 Land Water Storage -- 11.4 Global Mean Sea Level Budget (Altimetry Era) -- 11.5 Regional Variability in Sea Level -- 11.6 Human-Induced versus Internal Climate Variability Influence on Sea Level Change -- 11.7 Future Sea Level Changes -- 11.8 Current Challenges from a Geophysical Perspective -- 11.9 Conclusion -- References -- 12 Ocean Circulation: Knowns and Unknowns -- 12.1 Introduction -- 12.2 Abrupt Climate Changes during the Last Glacial and Related AMOC Changes -- 12.3 Deglaciation and the Evolution of the Deep Atlantic Ocean Circulation since the Last Glacial Maximum -- 12.4 Changes in Ocean Circulation Associated with Global Warming -- 12.5 Summary -- Acknowledgements -- References -- Part IV Future Earth and Regions. , 13 Asian Groundwater Perspectives on Global Change and Future Earth -- 13.1 Introduction -- 13.2 Subsurface Environmental Changes Due to Urbanization in Asian Megacities -- 13.3 Subsurface Warming Due to Global Warming and Urbanization in Asia -- 13.4 Land-Ocean Interaction and Loads from Land to the Ocean in Asia -- 13.5 Groundwater Depletion in Asia -- 13.6 Transformation toward Sustainable Water Use in Asia -- 13.7 Conclusion -- References -- 14 Africa's Broken Food Systems: Unravelling the Hidden Fortune under Climate Change -- 14.1 Background and Current Status of Affairs -- 14.2 Potentials for Agricultural Transformation -- 14.3 Solutions I - Key Players and Partnerships -- 14.4 Solutions II - Climate Action as an Opportunity to Mend Africa's Broken Food Systems -- 14.4.1 Basis to Leverage Opportunities in the Paris Deal to Actualize Agro-Industrialization -- 14.4.1.1 Fulfilling the Agenda 2030 and Paris Agreement through Sustainable Agro-Industrialization -- 14.4.2 Tapping into COP21 Opportunities -- 14.4.3 The Ecosystems Based Adaptation for Food Security Assembly (EBAFOSA)- Delivery Pathway -- 14.5 Conclusion -- Internet Resources -- References -- Part V Future Earth and Urban Environments -- 15 Nutrition, Urban Environments, and Future Earth -- 15.1 Introduction -- 15.2 Global Population Size and Projections -- 15.2.1 What Are the Drivers of These Demographic Changes? -- 15.3 Food and Nutrition Security in Urban Environments -- 15.4 Global Nutrition Situation -- 15.5 Global Nutrition Initiatives -- 15.6 What Is the Global Burden of Malnutrition? -- 15.7 Rural/Urban Differentials -- 15.8 Environmental Effects of Urbanization -- 15.9 Urbanization and Climate Change -- 15.10 Future Earth for Sustainable Development -- 15.11 Conclusions -- References -- Color Plates -- 16 Nutrition Science and Future Earth: Current Nutritional Policy Dilemmas. , 16.1 Responsibilities.