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  • 1
    Monograph available for loan
    Monograph available for loan
    Cambridge : Cambridge University Press
    Call number: AWI A13-12-0036
    Description / Table of Contents: The modeling of the past, present, and future climates is of fundamental importance to the issue of climate change and variability. Climate change and climate modeling provides a solid foundation for science students in all disciplines for our current understanding of global warming and important natural climate variations such as El Niño, and lays out the essentials of how climate models are constructed. As issues of climate change and impacts of climate variability become increasingly important, climate scientists must reach out to science students from a range of disciplines. Climate models represent one of our primary tools for predicting and adapting to climate change. An understanding of their strengths and limitations - and of what aspects of climate science are well understood and where quantitative uncertainities arise - can be communicated very effectively to students from a broad range of the sciences. This book will provide a basis for students to make informed decisions concerning climate change, whether they go on to study atmospheric science at a higher level or not. The book has been developed over a number of years form the course that the author teaches at UCLA. It has been extensively class-tested by hundreds of students, and assumes no previous background in atmospheric science except basic calculus and physics.
    Type of Medium: Monograph available for loan
    Pages: XV, 282 Seiten , Illustrationen
    Edition: 1. published 2011, reprinted 2012
    ISBN: 9780521602433
    Language: English
    Note: Contents: Preface. - 1. Overview of climate variability and climate science. - 1.1 Climate dynamics, climate change and climate prediction. - 1.2 The chemical and physical climate system. - 1.2.1 Chemical and physical aspects of the climate system. - 1.2.2 El Niño and global warming. - 1.3 Climate models: a brief overview. - 1.4 Global change in recent history. - 1.4.1 Trace gas concentrations. - 1.4.2 A word on the ozone hole. - 1.4.3 Some history of global warming studies. - 1.4.4 Global temperatures. - 1.5 El Niño: an example of natural climate variability. - 1.5.1 Some history of El Niño studies. - 1.5.2 Observations of El Niño: the 1997-98 event. - 1.5.3 The first El Niño forecast with a coupled ocean-atmosphere model. - 1.6 Paleoclimate variability. - Notes. - 2. Basics of global climate. - 2.1 Components and phenomena in the climate system. - 2.1.1 Time and space scales. - 2.1.2 Interactions among scales and the parameterization problem. - 2.2 Basics of radiative forcing. - 2.2.1 Blackbody radiation. - 2.2.2 Solar energy input. - 2.3 Globally averaged energy budget: first glance. - 2.4 Gradients of radiative forcing and energy transports. - 2.5 Atmospheric circulation. - 2.5.1 Vertical structure. - 2.5.2 Latitude structure of the circulation. - 2.5.3 Latitude-Iongitude dependence of atmospheric climate features. - 2.6 Ocean circulation. - 2.6.1 Latitude-longitude dependence of oceanic climate features. - 2.6.2 The ocean vertical structure. - 2.6.3 The ocean thermohaline circulation. - 2.7 Land surface proeesses. - 2.8 The carbon cycle. - Notes. - 3. Physical processes in the climate system. - 3.1 Conservation of momentum. - 3.1.1 Coriolis force. - 3.1.2 Pressure gradient force. - 3.1.3 Velocity equations. - 3.1.4 Application: geostrophic wind. - 3.1.5 Pressure-height relation: hydrostatic balance. - 3.1.6 Application: pressure coordinates. - 3.2 Equation of state. - 3.2.1 Equation of state for the atmosphere: ideal gas law. - 3.2.2 Equation of state for the ocean. - 3.2.3 Application: atmospheric height-pressure-temperature relation. - 3.2.4 Application: thermal circulations. - 3.2.5 Application: sea level rise due to oceanic thermal expansion. - 3.3 Temperature equation. - 3.3.1 Ocean temperature equation. - 3.3.2 Temperature equation for air. - 3.3.3 Application: the dry adiabatic lapse rate near the surface. - 3.3.4 Application: decay of a sea surface temperature anomaly. - 3.3.5 Time derivative following the parcel. - 3.4 Continuity equation. - 3.4.1 Oceanic continuity equation. - 3.4.2 Atmospheric continuity equation. - 3.4.3 Application: coastal upwelling. - 3.4.4 Application: equatorial upwelling. - 3.4.5 Application: conservation of warm water mass in an idealized layer above the thermocline. - 3.5 Conservation of mass applied to moisture. - 3.5.1 Moisture equation for the atmosphere and surface. - 3.5.2 Sources and sinks of moisture, and latent heat. - 3.5.3 Application: surface melting on an ice sheet. - 3.5.4 Salinity equation for the ocean. - 3.6 Moist processes. - 3.6.1 Saturation. - 3.6.2 Saturation in convection; lifting condensation level. - 3.6.3 The moist adiabat and lapse rate in convective regions. - 3.6.4 Moist convection. - 3.7 Wave processes in the atmosphere and ocean. - 3.7.1 Gravity waves. - 3.7.2 Kelvin waves. - 3.7.3 Rossby waves. - 3.8 Overview. - Notes. - 4. El Niño and year-to-year climate prediction. - 4.1 Recap of El Niño basics. - 4.1.1 The Bjerknes hypothesis. - 4.2 Tropical Pacific climatology. - 4.3 ENSO mechanisms I: extreme phases. - 4.4 Pressure gradients in an idealized upper layer. - 4.4.1 Subsurface temperature anomalies in an idealized upper layer. - 4.5 Transition into the 1997-98 El Niño. - 4.5.1 Subsurface temperature measurements. - 4.5.2 Subsurface temperature anomalies during the onset of El Niño. - 4.5.3 Subsurface temperature anomalies during the transition to La Niña. - 4.6 El Niño mechanisms II: dynamics of transition phases. - 4.6.1 Equatorial jets and the Kelvin wave. - 4.6.2 The Kelvin wave speed. - 4.6.3 What sets the width of the Kelvin wave and equatorial jet?. - 4.6.4 Response of the ocean to a wind anomaly. - 4.6.5 The delayed oscillator model and the recharge oscillator model. - 4.6.6 ENSO transition mechanism in brief. - 4.7 El Niño prediction. - 4.7.1 Limits to skill in ENSO forecasts. - 4.8 El Niño remote impacts: teleconnections. - 4.9 Other interannual climate phenomena. - 4.9.1 Hurricane season forecasts. - 4.9.2 Sahel drought. - 4.9.3 North Atlantic oscillation and annular modes. - Notes. - 5. Climate models. - 5.1 Constructing a climate model. - 5.1.1 An atmospheric model. - 5.1.2 Treatment of sub-grid-scale processes. - 5.1.3 Resolution and computational cost. - 5.1.4 An ocean model and ocean-atmosphere coupling. - 5.1.5 Land surface, snow, ice and vegetation. - 5.1.6 Summary of principal climate model equations. - 5.1.7 Climate system modeling. - 5.2 Numerical representation of atmospheric and oceanic equations. - 5.2.1 Finite-difference versus spectral models. - 5.2.2 Time-stepping and numerical stability. - 5.2.3 Staggered grids and other grids. - 5.2.4 Parallel computer architecture. - 5.3 Parameterization of small-scale processes. - 5.3.1 Mixing and surface fluxes. - 5.3.2 Dry convection. - 5.3.3 Moist convection. - 5.3.4 Land surface processes and soil moisture. - 5.3.5 Sea ice and snow. - 5.4 The hierarchy of climate models. - 5.5 Climate simulations and climate drift. - 5.6 Evaluation of climate model simulations for present-day climate. - 5.6.1 Atmospheric model climatology from specified SST. - 5.6.2 Climate model simulation of climatology. - 5.6.3 Simulation of ENSO response. - Notes. - 6. The greenhouse effect and climate feedbacks. - 6.1 The greenhouse effect in Earth's current climate. - 6.1.1 Global energy balance. - 6.1.2 A global-average energy balance model with a one-layer atmosphere. - 6.1.3 Infrared emissions from a layer. - 6.1.4 The greenhouse effect: example with a completely IR-absorbing atmosphere. - 6.1.5 The greenhouse effect in a one-layer atmosphere, global-average model. - 6.1.6 Temperatures from the one-layer energy balance model. - 6.2 Global warming I: example in the global-average energy balance model. - 6.2.1 Increases in the basic greenhouse effect. - 6.2.2 Climate feedback parameter in the one-layer global-average model. - 6.3 Climate feedbacks. - 6.3.1 Climate feedback parameter. - 6.3.2 Contributions of climate feedbacks to global-average temperature response. - 6.3.3 Climate sensitivity. - 6.4 The water vapor feedback. - 6.5 Snow/ice feedback. - 6.6 Cloud feedbacks. - 6.7 Other feedbacks in the physical climate system. - 6.7.1 Stratospheric cooling. - 6.7.2 Lapse rate feedback. - 6.8 Climate response time in transient climate change. - 6.8.1 Transient climate change versus equilibrium response experiments. - 6.8.2 A doubled-CO2 equilibrium response experiment. - 6.8.3 The role of the oceans in slowing warming. - 6.8.4 Climate sensitivity in transient climate change. - Notes. - 7. Climate model scenarios for global warming. - 7.1 Greenhouse gases, aerosols and other climate forcings. - 7.1.1 Scenarios, forcings and feedbacks. - 7.1.2 Forcing by sulfate aerosols. - 7.1.3 Commonly used scenarios. - 7.2 Global-average response to greenhouse warming scenarios. - 7.3 Spatial patterns of warming for time-dependent scenarios. - 7.3.1 Comparing projections of different climate models. - 7.3.2 Multi-model ensemble averages. - 7.3.3 Polar amplification of warming. - 7.3.4 Summary of spatial patterns of the response. - 7.4 Ice, sea level, extreme events. - 7.4.1 Sea ice and snow. - 7.4.2 Land ice. - 7.4.3 Extreme events. - 7.5 Summary: the best-estimate prognosis. - 7.6 Climate change observed to date. - 7.6.1 Temperature trends and natural variability: scale dependence. - 7.6.2 Is the observed trend consistent with natural variability or anthropogenic forcing?. - 7.6.3 Sea ice, land ice, ocean heat storage and sea level rise. - 7.7 Emissions
    Location: AWI Reading room
    Branch Library: AWI Library
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  • 2
    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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  • 3
    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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  • 4
    Publication Date: 2022-05-25
    Description: Author Posting. © American Meteorological Society, 2013. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 26 (2013): 9247–9290, doi:10.1175/JCLI-D-12-00593.1.
    Description: This is the second part of a three-part paper on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) that evaluates the twentieth-century simulations of intraseasonal to multidecadal variability and teleconnections with North American climate. Overall, the multimodel ensemble does reasonably well at reproducing observed variability in several aspects, but it does less well at capturing observed teleconnections, with implications for future projections examined in part three of this paper. In terms of intraseasonal variability, almost half of the models examined can reproduce observed variability in the eastern Pacific and most models capture the midsummer drought over Central America. The multimodel mean replicates the density of traveling tropical synoptic-scale disturbances but with large spread among the models. On the other hand, the coarse resolution of the models means that tropical cyclone frequencies are underpredicted in the Atlantic and eastern North Pacific. The frequency and mean amplitude of ENSO are generally well reproduced, although teleconnections with North American climate are widely varying among models and only a few models can reproduce the east and central Pacific types of ENSO and connections with U.S. winter temperatures. The models capture the spatial pattern of Pacific decadal oscillation (PDO) variability and its influence on continental temperature and West Coast precipitation but less well for the wintertime precipitation. The spatial representation of the Atlantic multidecadal oscillation (AMO) is reasonable, but the magnitude of SST anomalies and teleconnections are poorly reproduced. Multidecadal trends such as the warming hole over the central–southeastern United States and precipitation increases are not replicated by the models, suggesting that observed changes are linked to natural variability.
    Description: The authors acknowledge the support of NOAA/Climate Program Office/Modeling, Analysis, Predictions and Projections (MAPP) program as part of the CMIP5 Task Force.
    Description: 2014-06-01
    Keywords: North America ; Regional effects ; Coupled models ; Decadal variability ; Interannual variability ; Intraseasonal variability
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: application/pdf
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  • 5
    Publication Date: 2018-04-17
    Description: A substantial fraction of precipitation is associated with mesoscale convective systems (MCSs), which are currently poorly represented in climate models. Convective parameterizations are highly sensitive to the assumptions of an entraining plume model, in which high equivalent potential temperature air from the boundary layer is modified via turbulent entrainment. Here we show, using multiinstrument evidence from the Green Ocean Amazon field campaign (2014–2015; GoAmazon2014/5), that an empirically constrained weighting for inflow of environmental air based on radar wind profiler estimates of vertical velocity and mass flux yields a strong relationship between resulting buoyancy measures and precipitation statistics. This deep-inflow weighting has no free parameter for entrainment in the conventional sense, but to a leading approximation is simply a statement of the geometry of the inflow. The structure further suggests the weighting could consistently apply even for coherent inflow structures noted in field campaign studies for MCSs over tropical oceans. For radar precipitation retrievals averaged over climate model grid scales at the GoAmazon2014/5 site, the use of deep-inflow mixing yields a sharp increase in the probability and magnitude of precipitation with increasing buoyancy. Furthermore, this applies for both mesoscale and smaller-scale convection. Results from reanalysis and satellite data show that this holds more generally: Deep-inflow mixing yields a strong precipitation–buoyancy relation across the tropics. Deep-inflow mixing may thus circumvent inadequacies of current parameterizations while helping to bridge the gap toward representing mesoscale convection in climate models.
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
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  • 6
    Publication Date: 2017-01-23
    Description: Precipitation accumulations, integrated over rainfall events, can be affected by both intensity and duration of the storm event. Thus, although precipitation intensity is widely projected to increase under global warming, a clear framework for predicting accumulation changes has been lacking, despite the importance of accumulations for societal impacts. Theory for changes in the probability density function (pdf) of precipitation accumulations is presented with an evaluation of these changes in global climate model simulations. We show that a simple set of conditions implies roughly exponential increases in the frequency of the very largest accumulations above a physical cutoff scale, increasing with event size. The pdf exhibits an approximately power-law range where probability density drops slowly with each order of magnitude size increase, up to a cutoff at large accumulations that limits the largest events experienced in current climate. The theory predicts that the cutoff scale, controlled by the interplay of moisture convergence variance and precipitation loss, tends to increase under global warming. Thus, precisely the large accumulations above the cutoff that are currently rare will exhibit increases in the warmer climate as this cutoff is extended. This indeed occurs in the full climate model, with a 3 °C end-of-century global-average warming yielding regional increases of hundreds of percent to 〉1,000% in the probability density of the largest accumulations that have historical precedents. The probabilities of unprecedented accumulations are also consistent with the extension of the cutoff.
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
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  • 7
    Publication Date: 2014-01-17
    Print ISSN: 0027-8424
    Electronic ISSN: 1091-6490
    Topics: Biology , Medicine , Natural Sciences in General
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  • 8
    Publication Date: 2018-05-01
    Print ISSN: 0022-4928
    Electronic ISSN: 1520-0469
    Topics: Geography , Geosciences , Physics
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  • 9
    Publication Date: 2018-05-01
    Print ISSN: 0022-4928
    Electronic ISSN: 1520-0469
    Topics: Geography , Geosciences , Physics
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  • 10
    Publication Date: 2016-09-23
    Description: The relationships between the onset of tropical deep convection, column water vapor (CWV), and other measures of conditional instability are analyzed with 2 yr of data from the DOE Atmospheric Radiation Measurement (ARM) Mobile Facility in Manacapuru, Brazil, as part of the Green Ocean Amazon (GOAmazon) campaign, and with 3.5 yr of CWV derived from global positioning system meteorology at a nearby site in Manaus, Brazil. Important features seen previously in observations over tropical oceans—precipitation conditionally averaged by CWV exhibiting a sharp pickup at high CWV, and the overall shape of the CWV distribution for both precipitating and nonprecipitating points—are also found for this tropical continental region. The relationship between rainfall and CWV reflects the impact of lower-free-tropospheric moisture variability on convection. Specifically, CWV over land, as over ocean, is a proxy for the effect of free-tropospheric moisture on conditional instability as indicated by entraining plume calculations from GOAmazon data. Given sufficient mixing in the lower troposphere, higher CWV generally results in greater plume buoyancies through a deep convective layer. Although sensitivity of buoyancy to other controls in the Amazon is suggested, such as boundary layer and microphysical processes, the CWV dependence is consistent with the observed precipitation onset. Overall, leading aspects of the relationship between CWV and the transition to deep convection in the Amazon have close parallels over tropical oceans. The relationship is robust to averaging on time and space scales appropriate for convective physics but is strongly smoothed for averages greater than 3 h or 2.5°.
    Print ISSN: 0022-4928
    Electronic ISSN: 1520-0469
    Topics: Geography , Geosciences , Physics
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