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  • 1
    Keywords: Konferenzschrift
    Type of Medium: Book
    Pages: XXI, 449 S , Ill., graph. Darst
    ISBN: 0444869360
    Series Statement: Proceedings of the International School of Physics "Enrico Fermi" 88
    DDC: 551.5
    Language: English
    Note: Parallelt.: Turbolenza e predicibilità nella fluidodinamica geofisica e la dinamica del clima
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  • 2
    Online Resource
    Online Resource
    Newark :John Wiley & Sons, Incorporated,
    Keywords: Environmental economics--Mathematical models. ; Electronic books.
    Type of Medium: Online Resource
    Pages: 1 online resource (440 pages)
    Edition: 1st ed.
    ISBN: 9781119157045
    Series Statement: Geophysical Monograph Series ; v.214
    Language: English
    Note: Intro -- Title Page -- Copyright Page -- Contents -- Contributors -- PREFACE -- ACKNOWLEDGMENTS -- Chapter 1 Introduction -- 1.1. PART I: FUNDAMENTALS AND THEORY -- 1.2. PART II: EXTREME EVENTS IN EARTH'S SPACE ENVIRONMENT -- 1.3. PART III: CLIMATE AND WEATHER EXTREMES -- 1.4. PART IV: EXTREME EVENTS IN THE SOLID EARTH -- 1.5. PART V: SOCIOECONOMIC IMPACTS OF EXTREME EVENTS -- 1.6. PART VI: PREDICTION AND PREPAREDNESS -- 1.7. SUMMARY AND CONCLUSIONS -- Part I Fundamentals and Theory -- Chapter 2 Applications of Extreme Value Theory to Environmental Data Analysis -- 2.1. INTRODUCTION: UNIVARIATE EXTREME VALUE THEORY -- 2.2. MULTIVARIATE APPROACH -- 2.3. CONCLUSION -- ACKNOWLEDGMENT -- REFERENCES -- Chapter 3 Dynamical Systems Approach to Extreme Events -- 3.1. INTRODUCTION -- 3.2. EXTREME EVENTS AND DYNAMICAL COMPLEXITY: FORMULATION -- 3.3. ONE-DIMENSIONAL MAPS IN THE INTERVAL -- 3.4. SOME FURTHER EXTREME VALUE-RELATED PROPERTIES -- 3.5. SPATIALLY EXTENDED SYSTEMS -- 3.6. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 4 Skill of Data-based Predictions versus Dynamical Models: A Case Study on Extreme Temperature Anomalies -- 4.1. INTRODUCTION -- 4.2. FORECAST CONCEPTS -- 4.3. THE DATA -- 4.4. THE FORECAST MODELS -- 4.5. PROBABILISTIC PREDICTION OF EXTREME ANOMALIES -- 4.6. FROM PROBABILISTIC TO DETERMINISTIC PREDICTIONS OF EXTREME ANOMALIES -- 4.7. DISCUSSION AND CONCLUSIONS -- REFERENCES -- Chapter 5 Detecting and Anticipating Climate Tipping Points -- 5.1. INTRODUCTION -- 5.2. TYPES OF TIPPING POINT -- 5.3. DETECTING BIFURCATIONS IN NOISY SYSTEMS -- 5.4. TIPPING POINTS IN THE ICE-CORE RECORD -- 5.5. EARLY WARNING OF BIFURCATIONS -- 5.6. EARLY WARNING OF THE END OF THE ICE AGE? -- 5.7. LIMITATIONS ON EARLY-WARNING CAPABILITY -- 5.8. DISCUSSION AND CONCLUSION -- ACKNOWLEDGMENTS -- REFERENCES. , Chapter 6 Understanding ENSO Variability and Its Extrema: A Delay Differential Equation Approach -- 6.1. INTRODUCTION AND MOTIVATION -- 6.2. MODEL AND NUMERICAL INTEGRATION METHOD -- 6.3. PHASE-LOCKING OF EXTREMA AND MULTIPLE SOLUTIONS -- 6.4. PULLBACK ATTRACTORS AND QUASI-PERIODIC ORBITS -- 6.5. SUMMARY AND DISCUSSION -- ACKNOWLEDGMENTS -- REFERENCES -- Part II Extreme Events in Earth's Space Environment -- Chapter 7 Drivers of Extreme Space Weather Events: Fast Coronal Mass Ejections -- 7.1. INTRODUCTION -- 7.2. A QUICK LOOK AT EXTREMES IN THE CME DATA -- 7.3. MORE SOPHISTICATED APPROACHES TO STUDY EXTREMES -- 7.4. ON THE TIME BETWEEN OCCURRENCES OF EXTREMES -- 7.5. THE DISTRIBUTION AND THE CLUSTERING OF FAST CMEs -- 7.6. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 8 Chicxulub Asteroid Impact: An Extreme Event at the Cretaceous/Paleogene Boundary -- 8.1. INTRODUCTION -- 8.2. EXTREME EVENTS IN THE GEOLOGICAL PAST -- 8.3. CHICXULUB IMPACT -- 8.4. CHICXULUB IMPACT AND K/Pg BOUNDARY LAYER -- 8.5. END-CRETACEOUS MASS EXTINCTION -- 8.6. DISCUSSION -- 8.7. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Part III Climate and Weather Extremes -- Chapter 9 Weather and Climatic Drivers of Extreme Flooding Events over the Midwest of the United States -- 9.1. INTRODUCTION -- 9.2. DATASETS, METHODOLOGY, AND FLOOD EVENTS -- 9.3. DAILY CIRCULATION TYPES -- 9.4. ASSOCIATIONS BETWEEN CIRCULATION TYPES AND FLOOD EVENTS -- 9.5. ASSOCIATIONS BETWEEN CIRCULATION TYPES AND ENSO OR THE MJO -- 9.6. DISCUSSION AND CONCLUDING REMARKS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 10 Analysis of the Hazards and Vulnerability of the Cancun Beach System: The Case of Hurricane Wilma -- 10.1. INTRODUCTION -- 10.2. PHYSICAL CHARACTERISTICS OF THE STUDY SITE -- 10.3. HURRICANE WILMA -- 10.4. RESULTS -- 10.5. DISCUSSION -- ANNEX HURAC MODEL -- REFERENCES. , Chapter 11 Observations and Modeling of Environmental and Human Damage Caused by the 2004 Indian Ocean Tsunami -- 11.1. INTRODUCTION -- 11.2. NUMERICAL MODEL -- 11.3. IMPACT OF THE TSUNAMI AT PAKARANG CAPE, THAILAND -- 11.4. IMPACT OF THE TSUNAMI AT BANDA ACEH CITY, INDONESIA -- 11.5. MODELED TSUNAMI INUNDATION PROCESSES AT EACH STUDIED AREA -- 11.6. INTEGRATED ANALYSES OF THE DAMAGE ATTRIBUTABLE TO THE TSUNAMI -- 11.7. CONCLUDING REMARKS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 12 Extreme Capillary Wave Events Under Parametric Excitation -- 12.1. INTRODUCTION -- 12.2. PARAMETRIC WAVE EXCITATION -- 12.3. MODULATION INSTABILITY OF CAPILLARY WAVES -- 12.4. CAPILLARY ROGUE WAVES -- 12.5. SOLITONIC NATURE OF CAPILLARY RIPPLE -- 12.6. DISCUSSION AND CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Part IV Extreme Events in the Solid Earth -- Chapter 13 A Review of Great Magnitude Earthquakes and Associated Tsunamis along the Guerrero, Mexico Pacific Coast: A Multiproxy Approach -- 13.1. INTRODUCTION -- 13.2. EARTHQUAKE AND TSUNAMI HISTORICAL DATA OF THE MEXICAN PACIFIC COAST -- 13.3. GEOLOGICAL DATA -- 13.4. HISTORICAL AND INSTRUMENTAL DATA IN SUPPORT OF GEOLOGICAL EVIDENCE OF EARTHQUAKES AND TSUNAMIS IN GUERRERO -- 13.5. DISCUSSION AND CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 14 Landslide Risk to the Population of Italy and Its Geographical and Temporal Variations -- 14.1. INTRODUCTION -- 14.2. GLOSSARY -- 14.3. RECORD OF HARMFUL LANDSLIDE EVENTS IN ITALY -- 14.4. RISK EVALUATION -- 14.5. COMPARISON TO OTHER NATURAL HAZARDS -- 14.6. CONNECTIONS BETWEEN LANDSLIDES AND OTHER HAZARDS -- 14.7. CONCLUSIONS -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 15 An Extreme Event Approach to Volcanic Hazard Assessment -- 15.1. INTRODUCTION -- 15.2. ERUPTION SIZE AND IMPACT. , 15.3. EXTREME VALUE THEORY APPROACH TO MODELING OCCURRENCES OF VERY LARGE ERUPTIONS IN GROUPS OF VOLCANOES -- 15.4. SCALING LAWS AND EXTREME VALUE METHODS: APPLICATIONS TO INDIVIDUAL AND GROUPS OF VOLCANOES -- 15.5. CONCLUDING REMARKS -- ACKNOWLEDGMENTS -- REFERENCES -- Part V Socioeconomic Impacts of Extreme Events -- Chapter 16 Economic Impact of Extreme Events: An Approach Based on Extreme Value Theory -- 16.1. INTRODUCTION -- 16.2. EXTREME VALUE THEORY -- 16.3. DAMAGE FUNCTIONS -- 16.4. ECONOMIC DAMAGE CAUSED BY HURRICANES -- 16.5. DISCUSSION -- ACKNOWLEDGMENTS -- REFERENCES -- Chapter 17 Extreme Magnitude Earthquakes and their Direct Economic Impacts: A Hybrid Approach -- 17.1. INTRODUCTION -- 17.2. HYBRID APPROACH FOR THE ESTIMATION OF DIRECT ECONOMIC IMPACTS OF EXTREME MAGNITUDE EARTHQUAKES -- 17.3. EXTREME MAGNITUDE SUBDUCTION EARTHQUAKES WORLDWIDE AND IN MEXICO -- 17.4. SYNTHETIC ACCELEROGRAMS IN MEXICO CITY FOR AN EXTREME MW 8.5 MAGNITUDE SUBDUCTION EARTHQUAKE SCENARIO WITH AN EPICENTER IN THE GUERRERO REGION -- 17.5. ESTIMATION OF THE PRELIMINARY DIRECT ECONOMIC IMPACTS ON MEXICO CITY'S ONE- TO THREE-FLOOR DWELLING STOCK DUE TO THE OCCURRENCE OF AN EXTREME MW 8.5 MAGNITUDE SUBDUCTION EARTHQUAKE SCENARIO IN THE GUERRERO REGION -- 17.6. SYNTHETIC ACCELEROGRAMS IN GUADALAJARA FOR AN EXTREME MW 8.5 MAGNITUDE SUBDUCTION EARTHQUAKE SCENARIO WITH AN EPICENTER IN THE COLIMA-JALISCO REGION -- 17.7. ESTIMATION OF THE DIRECT ECONOMIC IMPACTS IN GUADALAJARA ONE- TO THREE-FLOOR DWELLING STOCK DUE TO THE OCCURRENCE OF AN EXTREME MW 8.5 MAGNITUDE SUBDUCTION EARTHQUAKE SCENARIO IN THE COLIMA-JALISCO REGION -- 17.8. CONCLUSIONS -- ACKNOWLEDGMENTS -- Appendix 17.A. Modeling of the wave propagation of the 1985 Mw 8.01 magnitude Michoacan, Mexico earthquake. , Appendix 17.B. Modeling of the wave propagation of the 9 October 1995 Mw 8.0 magnitude Colima-Jalisco, Mexico earthquake -- Appendix 17.C. Modeling of the wave propagation of the 2008 Mw 7.9 Wenchuan, China earthquake -- Appendix 17.D. Preliminary modeling of the wave propagation of the 2011 Mw 9 Tohoku, Japan earthquake -- Appendix 17.E. Computation of the PEI for the Mw 8.5 earthquake scenario -- Appendix 17.F. Computation of the damage area for the Mw 85 earthquake scenario -- REFERENCES -- Chapter 18 Tropical Cyclones: From the Influence of Climate to Their Socioeconomic Impacts -- 18.1. INTRODUCTION -- 18.2. INTRASEASONAL TIMESCALES -- 18.3. SEASONAL TIMESCALES -- 18.4. DECADAL TIME-SCALES -- 18.5. ANTHROPOGENIC CLIMATE CHANGE -- 18.6. QUANTIFIED SOCIOECONOMIC IMPACTS -- 18.7. CONCLUSIONS -- Glossary -- REFERENCES -- Chapter 19 Impacts of Natural Disasters on a Dynamic Economy -- 19.1. INTRODUCTION -- 19.2. BUSINESS CYCLE DYNAMICS -- 19.3. NATURAL DISASTERS IN A DYNAMIC ECONOMY -- 19.4. VALIDATION WITH U.S. ECONOMIC INDICATORS -- 19.5. CONCLUDING REMARKS -- ACKNOWLEDGMENTS -- Appendix 19.A. NEDyM WITH SHOCKS -- Appendix 19.B. SINGULAR SPECTRUM ANALYSIS -- Appendix 19.C. A RANDOMLY FORCED OSCILLATOR -- REFERENCES -- Part VI Prediction and Preparedness -- Chapter 20 Extreme Tsunami Events in the Mediterranean and Its Impact on the Algerian Coasts -- 20.1. INTRODUCTION -- 20.2. TSUNAMI AND SEISMIC HAZARD IN THE MEDITERRANEAN -- 20.3. METHODOLOGY -- 20.4. RESULTS -- 20.5. DISCUSSION -- 20.6. Conclusion -- Acknowledgments -- REFERENCES -- Chapter 21 High-Tech Risks: The 2011 Tôhoku Extreme Events -- 21.1. INTRODUCTION: CONFLICTING INTERPRETATIONS -- 21.2. COMPARISON WITH OYSTER CREEK -- 21.3. THE TSUNAMI LANDSCAPE -- 21.4. TSUNAMIS AS GUIDED WAVES -- 21.5. THE HUMAN SETTING -- 21.6. STRATEGIES OF SURVIVAL: AUTONOMY AND CASCADING FAILURES. , 21.7. CONCLUSION: A COMPLEX FAILURE.
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  • 3
    Electronic Resource
    Electronic Resource
    [s.l.] : Nature Publishing Group
    Nature 358 (1992), S. 547-547 
    ISSN: 1476-4687
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Notes: [Auszug] SIR - The seasonally recurring oceanic El Nino phenomenon is associated with extreme weather conditions1. Positive phases of the tropical sea-surface temperature oscillation have been con-nected with unusually rainy weather over the southwestern United States, while the negative phases, usually ...
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Climate dynamics 11 (1995), S. 255-278 
    ISSN: 1432-0894
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Physics
    Notes: Abstract. Relative sea-level height (RSLH) data at 213 tide-gauge stations have been analyzed on a monthly and an annual basis to study interannual and interdecadal oscillations, respectively. The main tools of the study are singular spectrum analysis (SSA) and multi-channel SSA (M-SSA). Very-low-frequency variability of RSLH was filtered by SSA to estimate the linear trend at each station. Global sea-level rise, after post-glacial rebound corrections, has been found to equal 1.62±0.38 mm/y, by averaging over 175 stations which have a trend consistent with the neighboring ones. We have identified two dominant time scales of El Niño-Southern Oscillation (ENSO) variability, quasi-biennial and low-frequency, in the RSLH data at almost all stations. However, the amplitudes of both ENSO signals are higher in the equatorial Pacific and along the west coast of North America. RSLH data were interpolated along ocean coasts by latitudinal intervals of 5 or 10 degrees, depending on station density. Interannual variability was then examined by M-SSA in five regions: eastern Pacific (25° S–55° N at 10° resolution), western Pacific (35° S–45° N at 10°), equatorial Pacific (123° E–169° W, 6 stations), eastern Atlantic (30° S, 0°, and 30° N–70° N at 5°) and western Atlantic (50° S–50° N at 10°). Throughout the Pacific, we have found three dominant spatio-temporal oscillatory patterns, associated with time scales of ENSO variability; their periods are 2, 2.5–3 and 4–6 y. In the eastern Pacific, the biennial mode and the 6-y low-frequency mode propagate poleward. There is a southward propagation of low-frequency modes in the western Pacific RSLH, between 35° N and 5° S, but no clear propagation in the latitudes further south. However, equatorward propagation of the biennial signal is very clear in the Southern Hemisphere. In the equatorial Pacific, both the quasi-quadrennial and quasi-biennial modes at 10° N propagate westward. Strong and weak El Niño years are evident in the sea-level time series reconstructed from the quasi-biennial and low-frequency modes. Interannual variability with periods of 3 and 4–8 y is detected in the Atlantic RSLH data. In the eastern Atlantic region, we have found slow propagation of both modes northward and southward, away from 40–45° N. Interdecadal oscillations were studied using 81 stations with sufficiently long and continuous records. Most of these have variability at 9–13 and some at 18 y. Two significant eigenmode pairs, corresponding to periods of 11.6 and 12.8 y, are found in the eastern and western Atlantic ocean at latitudes 40° N–70° N and 10° N–50° N, respectively.
    Type of Medium: Electronic Resource
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    Climate dynamics 12 (1995), S. 101-112 
    ISSN: 1432-0894
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Physics
    Notes: Abstract Evaluation of competing El Niño/Southern Oscillation (ENSO) theories requires one to identify separate spectral peaks in equatorial wind and sea-surface temperature (SST) time series. To sharpen this identification, we examine the seasonal-to-interannual variability of these fields by the data-adaptive method of multi-channel singular spectrum analysis (M-SSA). M-SSA is applied to the equatorial band (4°N-4°S), using 1950–1990 data from the Comprehensive Ocean and Atmosphere Data Set. Two major interannual oscillations are found in the equatorial SST and surface zonal wind fields, U. The main peak is centered at about 52-months; we refer to it as the quasi-quadrennial (QQ) mode. Quasi-biennial (QB) variability is split between two modes, with periods near 28 months and 24 months. A faster, 15-month oscillation has smaller amplitude. The QQ mode dominates the variance and has the most distinct spectral peak. In time-longitude reconstructions of this mode, the SST has the form of a standing oscillation in the eastern equatorial Pacific, while the U-field is dominated by a standing oscillation pattern in the western Pacific and exhibits also slight eastward propagation in the central and western Pacific. The locations of maximum anomalies in both QB modes are similar to those of the QQ mode. Slight westward migration in SST, across the eastern and central, and eastward propagation of U, across the western and central Pacific, are found. The significant wind anomaly covers a smaller region than for the QQ. The QQ and QB modes together represent the ENSO variability well and interfere constructively during major events. The sharper definition of the QQ spectral peak and its dominance are consistent with the “devil's staircase” interaction mechanism between the annual cycle and ENSO.
    Type of Medium: Electronic Resource
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Climate dynamics 11 (1995), S. 255-278 
    ISSN: 1432-0894
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Physics
    Notes: Abstract Relative sea-level height (RSLH) data at 213 tide-gauge stations have been analyzed on a monthly and an annual basis to study interannual and interdecadal oscillations, respectively. The main tools of the study are singular spectrum analysis (SSA) and multi-channel SSA (M-SSA). Very-low-frequency variability of RSLH was filtered by SSA to estimate the linear trend at each station. Global sea-level rise, after postglacial rebound corrections, has been found to equal 1.62±0.38 mm/y, by averaging over 175 stations which have a trend consistent with the neighboring ones. We have identified two dominant time scales of El Niño-Southern Oscillation (ENSO) variability, quasi-biennial and low-frequency, in the RSLH data at almost all stations. However, the amplitudes of both ENSO signals are higher in the equatorial Pacific and along the west coast of North America. RSLH data were interpolated along ocean coasts by latitudinal intervals of 5 or 10 degrees, depending on station density. Interannual variability was then examined by M-SSA in five regions: eastern Pacific (25°S–55°N at 10° resolution), western Pacific (35°S–45°N at 10°), equatorial Pacific (123°E–169°W, 6 stations), eastern Atlantic (30°S, 0°, and 30°N–70°N at 5°) and western Atlantic (50°S–50°N at 10°). Throughout the Pacific, we have found three dominant spatio-temporal oscillatory patterns, associated with time scales of ENSO variability; their periods are 2, 2.5–3 and 4–6 y. In the eastern Pacific, the biennial mode and the 6-y low-frequency mode propagate poleward. There is a southward propagation of low-frequency modes in the western Pacific RSLH, between 35°N and 5°S, but no clear propagation in the latitudes further south. However, equatorward propagation of the biennial signal is very clear in the Southern Hemisphere. In the equatorial Pacific, both the quasi-quadrennial and quasi-biennial modes at 10°N propagate westward. Strong and weak El Niño years are evident in the sea-level time series reconstructed from the quasi-biennial and low-frequency modes. Interannual variability with periods of 3 and 4–8 y is detected in the Atlantic RSLH data. In the eastern Atlantic region, we have found slow propagation of both modes northward and southward, away from 40–45°N. Interdecadal oscillations were studied using 81 stations with sufficiently long and continuous records. Most of these have variability at 9–13 and some at 18 y. Two significant eigenmode pairs, corresponding to periods of 11.6 and 12.8 y, are found in the eastern and western Atlantic ocean at latitudes 40°N–70°N and 10°N–50°N, respectively.
    Type of Medium: Electronic Resource
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    Climate dynamics 12 (1995), S. 101-112 
    ISSN: 1432-0894
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Physics
    Notes: Abstract. Evaluation of competing El Niño/Southern Oscillation (ENSO) theories requires one to identify separate spectral peaks in equatorial wind and sea-surface temperature (SST) time series. To sharpen this identification, we examine the seasonal-to-interannual variability of these fields by the data-adaptive method of multi-channel singular spectrum analysis (M-SSA). M-SSA is applied to the equatorial band (4° N-4° S), using 1950–1990 data from the Comprehensive Ocean and Atmosphere Data Set. Two major interannual oscillations are found in the equatorial SST and surface zonal wind fields, U. The main peak is centered at about 52-months; we refer to it as the quasi-quadrennial (QQ) mode. Quasi-biennial (QB) variability is split between two modes, with periods near 28 months and 24 months. A faster, 15-month oscillation has smaller amplitude. The QQ mode dominates the variance and has the most distinct spectral peak. In time-longitude reconstructions of this mode, the SST has the form of a standing oscillation in the eastern equatorial Pacific, while the U-field is dominated by a standing oscillation pattern in the western Pacific and exhibits also slight eastward propagation in the central and western Pacific. The locations of maximum anomalies in both QB modes are similar to those of the QQ mode. Slight westward migration in SST, across the eastern and central, and eastward propagation of U, across the western and central Pacific, are found. The significant wind anomaly covers a smaller region than for the QQ. The QQ and QB modes together represent the ENSO variability well and interfere constructively during major events. The sharper definition of the QQ spectral peak and its dominance are consistent with the "devil's staircase" interaction mechanism between the annual cycle and ENSO.
    Type of Medium: Electronic Resource
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  • 8
    ISSN: 1573-1480
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Physics
    Notes: Abstract Monthly mean surface-air temperatures at 870 sites in the contiguous United States were analyzed for interannual and interdecadal variability over the time interval 1910-87. The temperatures were analyzed spatially by empirical-orthogonal-function analysis and temporally by singularspectrum analysis (SSA). The dominant modes of spatio-temporal variability are trends and nonperiodic variations with time scales longer than 15 years, decadal-scale oscillations with periods of roughly 7 and 10 years, and interannual oscillations of 2.2 and 3.3 years. Together, these modes contribute about 18% of the slower-than-annual United States temperature variance. Two leading components roughly capture the mean hemispheric temperature trend and represent a long-term warming, largest in the southwest, accompanied by cooling of the domain's southeastern quadrant. The extremes of the 2.2-year interannual oscillation characterize temperature differences between the Northeastern and Southwestern States, whereas the 3.3-year cycle is present mostly in the Western States. The 7- to 10-year oscillations are much less regular and persistent than the interannual oscillations and characterize temperature differences between the western and interior sectors of the United States. These continental- or regional-scale temperature variations may be related to climatic variations with similar periodicities, either global or centered in other regions; such variations include quasi-biennial oscillations over the tropical Pacific or North Atlantic and quasi-triennial oscillations of North Pacific sea-surface temperatures.
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  • 9
    facet.materialart.
    Unknown
    AMS (American Meteorological Society)
    In:  Bulletin of the American Meteorological Society, 95 (2). pp. 293-296.
    Publication Date: 2019-09-23
    Type: Article , PeerReviewed
    Format: text
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  • 10
    facet.materialart.
    Unknown
    Elsevier
    In:  Dynamics of Atmospheres and Oceans, 32 (3-4). pp. 419-431.
    Publication Date: 2016-11-14
    Description: The problem of error propagation is considered for spatially uncorrelated errors of the barotropic stream function in an oceanic general circulation model (OGCM). Such errors typically occur when altimetric data from satellites are assimilated into ocean models. It is shown that the error decays at first due to the dissipation of the smallest scales in the error field. The error then grows exponentially before it saturates at the value corresponding to the difference between independent realizations. A simple analytic formula for the error behavior is derived; it matches the numerical results documented for the present primitive-equation ocean model, and other models in the literature.
    Type: Article , PeerReviewed
    Format: text
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