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Books on the topic 'Large-scale atmospheric circulation'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Charney, Jule G. Dynamics of large-scale atmospheric and oceanic processes: Selected papers of Jule Gregory Charney. Hampton, VA: A. Deepak Pub., 2002.

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2

Agumba, Francis Obita. Fluctuations of long rains in Kenya in relation to large-scale circulations. Nairobi: Republic of Kenya, Meteorological Dept., Institute for Meteorological Training and Research, 1985.

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3

Läuter, Matthias. Grossäumige Zirkulationsstrukturen in einem nichtlinearen adaptiven atmosphärenmodell =: Large-scale circulation structures in a nonlinear adaptive model of the atmosphere. Bremerhaven: Alfred-Wegener-Institut für Polar- und Meeresforschung, 2005.

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4

Weisheimer, Antje. Niederfrequente Variabilität grossräumiger atmosphärischer Zirkulationsstrukturen in spektralen Modellen niederer Ordnung =: Ultra-low-frequency variability of large scale atmospheric circulation patterns in spectral low-order models. Bremerhaven: Alfred-Wegener-Institut für Polar- und Meeresforschung, 2000.

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5

Workshop on Cloud Processes and Cloud Feedbacks in Large-scale Models (1999 Reading, Berkshire, United Kingdom). Workshop on Cloud Processes and Cloud Feedbacks in Large-scale Models, European Centre for Medium-range Weather Forecasts, Reading, Berkshire, United Kingdom, 9-13 November 1999. Geneva, Switzerland: Joint Planning Staff for WCRP, World Meteorological Organization, 2000.

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6

NATO, Advanced Study Institute on Large-Scale Transport Processes in Oceans and Atmosphere (1985 Les Houches Haute-Savoie France). Large-scale transport processes in oceans and atmosphere. Dordrecht: D. Reidel Pub. Co., 1986.

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7

Coupling large-scale hydrological and atmospheric models. Wallingford: IAHS, 1995.

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8

Coupling large-scale hydrological and atmospheric models. Wallingford, Oxfordshire, UK: International Association of Hydrological Sciences in cooperation with the Ruhr University Bochum, 1995.

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9

Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-scale Circulation. Cambridge University Press, 2006.

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10

Vallis, Geoffrey K. Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-Scale Circulation. Cambridge University Press, 2017.

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11

Vallis, Geoffrey K. Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-Scale Circulation. Cambridge University Press, 2007.

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12

Vallis, Geoffrey K. Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-Scale Circulation. Cambridge University Press, 2017.

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13

Vallis, Geoffrey K. Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-Scale Circulation. Cambridge University Press, 2017.

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14

Vallis, Geoffrey K. Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-Scale Circulation. Cambridge University Press, 2006.

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15

Vallis, Geoffrey K. Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-Scale Circulation. Cambridge University Press, 2012.

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16

A Mathematical Theory of Large-scale Atmosphere/ocean Flow. Imperial College Press, 2006.

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17

Schultz, G. A., M. Hornbogen, P. Viterbo, and J. Noilhan. Special Publication: Coupling Large-scale Hydrological and Atmospheric Models (Special Publication). IAHS Press, 1995.

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18

Silva, Sirilath J. De. Statistical relationships between the mesoscale organization of convection, precipitation and the large-scale wind fields during the GATE. 1990.

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19

L, Miller Timothy, and George C. Marshall Space Flight Center., eds. Current scientific issues in large scale atmospheric dynamics: Proceedings of a workshop held at the NASA George C. Marshall Space Flight Center, Marshall Space Flight Center, Alabama, June 20-21, 1985. Washington, D.C: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1986.

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20

Nonlinear Physical Oceanography: A Dynamical Systems Approach to the Large Scale Ocean Circulation and El Nino (Atmospheric and Oceanographic Sciences Library). Springer, 2000.

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21

Tibaldi, Stefano, and Franco Molteni. Atmospheric Blocking in Observation and Models. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.611.

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The atmospheric circulation in the mid-latitudes of both hemispheres is usually dominated by westerly winds and by planetary-scale and shorter-scale synoptic waves, moving mostly from west to east. A remarkable and frequent exception to this “usual” behavior is atmospheric blocking. Blocking occurs when the usual zonal flow is hindered by the establishment of a large-amplitude, quasi-stationary, high-pressure meridional circulation structure which “blocks” the flow of the westerlies and the progression of the atmospheric waves and disturbances embedded in them. Such blocking structures can have lifetimes varying from a few days to several weeks in the most extreme cases. Their presence can strongly affect the weather of large portions of the mid-latitudes, leading to the establishment of anomalous meteorological conditions. These can take the form of strong precipitation episodes or persistent anticyclonic regimes, leading in turn to floods, extreme cold spells, heat waves, or short-lived droughts. Even air quality can be strongly influenced by the establishment of atmospheric blocking, with episodes of high concentrations of low-level ozone in summer and of particulate matter and other air pollutants in winter, particularly in highly populated urban areas.Atmospheric blocking has the tendency to occur more often in winter and in certain longitudinal quadrants, notably the Euro-Atlantic and the Pacific sectors of the Northern Hemisphere. In the Southern Hemisphere, blocking episodes are generally less frequent, and the longitudinal localization is less pronounced than in the Northern Hemisphere.Blocking has aroused the interest of atmospheric scientists since the middle of the last century, with the pioneering observational works of Berggren, Bolin, Rossby, and Rex, and has become the subject of innumerable observational and theoretical studies. The purpose of such studies was originally to find a commonly accepted structural and phenomenological definition of atmospheric blocking. The investigations went on to study blocking climatology in terms of the geographical distribution of its frequency of occurrence and the associated seasonal and inter-annual variability. Well into the second half of the 20th century, a large number of theoretical dynamic works on blocking formation and maintenance started appearing in the literature. Such theoretical studies explored a wide range of possible dynamic mechanisms, including large-amplitude planetary-scale wave dynamics, including Rossby wave breaking, multiple equilibria circulation regimes, large-scale forcing of anticyclones by synoptic-scale eddies, finite-amplitude non-linear instability theory, and influence of sea surface temperature anomalies, to name but a few. However, to date no unique theoretical model of atmospheric blocking has been formulated that can account for all of its observational characteristics.When numerical, global short- and medium-range weather predictions started being produced operationally, and with the establishment, in the late 1970s and early 1980s, of the European Centre for Medium-Range Weather Forecasts, it quickly became of relevance to assess the capability of numerical models to predict blocking with the correct space-time characteristics (e.g., location, time of onset, life span, and decay). Early studies showed that models had difficulties in correctly representing blocking as well as in connection with their large systematic (mean) errors.Despite enormous improvements in the ability of numerical models to represent atmospheric dynamics, blocking remains a challenge for global weather prediction and climate simulation models. Such modeling deficiencies have negative consequences not only for our ability to represent the observed climate but also for the possibility of producing high-quality seasonal-to-decadal predictions. For such predictions, representing the correct space-time statistics of blocking occurrence is, especially for certain geographical areas, extremely important.

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22

(Editor), J. Willebrand, and D.L.T. Anderson (Editor), eds. Large-Scale Transport Processes in Oceans and Atmosphere (NATO Science Series C:). Springer, 1986.

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23

Bouchet, Freddy, Tapio Schneider, Antoine Venaille, and Christophe Salomon, eds. Fundamental Aspects of Turbulent Flows in Climate Dynamics. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198855217.001.0001.

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This book collects the text of the lectures given at the Les Houches Summer School on “Fundamental aspects of turbulent flows in climate dynamics”, held in August 2017. Leading scientists in the fields of climate dynamics, atmosphere and ocean dynamics, geophysical fluid dynamics, physics and non-linear sciences present their views on this fast growing and interdisciplinary field of research, by venturing upon fundamental problems of atmospheric convection, clouds, large-scale circulation, and predictability. Climate is controlled by turbulent flows. Turbulent motions are responsible for the bulk of the transport of energy, momentum, and water vapor in the atmosphere, which determine the distribution of temperature, winds, and precipitation on Earth. Clouds, weather systems, and boundary layers in the oceans and atmosphere are manifestations of turbulence in the climate system. Because turbulence remains as the great unsolved problem of classical physics, we do not have a complete physical theory of climate. The aim of this summer school was to survey what is known about how turbulent flows control climate, what role they may play in climate change, and to outline where progress in this important area can be expected, given today’s computational and observational capabilities. This book reviews the state-of-the-art developments in this field and provides an essential background to future studies. All chapters are written from a pedagogical perspective, making the book accessible to masters and PhD students and all researchers wishing to enter this field. It is complemented by online video of several lectures and seminars recorded during the summer school.

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24

Kucharski, Fred, and Muhammad Adnan Abid. Interannual Variability of the Indian Monsoon and Its Link to ENSO. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.615.

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The interannual variability of Indian summer monsoon is probably one of the most intensively studied phenomena in the research area of climate variability. This is because even relatively small variations of about 10% to 20% from the mean rainfall may have dramatic consequences for regional agricultural production. Forecasting such variations months in advance could help agricultural planning substantially. Unfortunately, a perfect forecast of Indian monsoon variations, like any other regional climate variations, is impossible in a long-term prediction (that is, more than 2 weeks or so in advance). The reason is that part of the atmospheric variations influencing the monsoon have an inherent predictability limit of about 2 weeks. Therefore, such predictions will always be probabilistic, and only likelihoods of droughts, excessive rains, or normal conditions may be provided. However, even such probabilistic information may still be useful for agricultural planning. In research regarding interannual Indian monsoon rainfall variations, the main focus is therefore to identify the remaining predictable component and to estimate what fraction of the total variation this component accounts for. It turns out that slowly varying (with respect to atmospheric intrinsic variability) sea-surface temperatures (SSTs) provide the dominant part of the predictable component of Indian monsoon variability. Of the predictable part arising from SSTs, it is the El Niño Southern Oscillation (ENSO) that provides the main part. This is not to say that other forcings may be neglected. Other forcings that have been identified are, for example, SST patterns in the Indian Ocean, Atlantic Ocean, and parts of the Pacific Ocean different from the traditional ENSO region, and springtime snow depth in the Himalayas, as well as aerosols. These other forcings may interact constructively or destructively with the ENSO impact and thus enhance or reduce the ENSO-induced predictable signal. This may result in decade-long changes in the connection between ENSO and the Indian monsoon. The physical mechanism for the connection between ENSO and the Indian monsoon may be understood as large-scale adjustment of atmospheric heatings and circulations to the ENSO-induced SST variations. These adjustments modify the Walker circulation and connect the rising/sinking motion in the central-eastern Pacific during a warm/cold ENSO event with sinking/rising motion in the Indian region, leading to reduced/increased rainfall.

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25

Busuioc, Aristita, and Alexandru Dumitrescu. Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.770.

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This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art global climate models, in terms of spatial resolution and their performance in simulation of climate characteristics, they are still skillful only in reproducing large-scale feature of climate variability, such as global mean temperature or various circulation patterns (e.g., the North Atlantic Oscillation). However, these models are not able to provide reliable information on local climate characteristics (mean temperature, total precipitation), especially on extreme weather and climate events. The main reason for this failure is the influence of local geographical features on the local climate, as well as other factors related to surrounding large-scale conditions, the influence of which cannot be correctly taken into consideration by the current dynamical global models.Impact models, such as hydrological and crop models, need high resolution information on various climate parameters on the scale of a river basin or a farm, scales that are not available from the usual global climate models. Downscaling techniques produce regional climate information on finer scale, from global climate change scenarios, based on the assumption that there is a systematic link between the large-scale and local climate. Two types of downscaling approaches are known: a) dynamical downscaling is based on regional climate models nested in a global climate model; and b) statistical downscaling is based on developing statistical relationships between large-scale atmospheric variables (predictors), available from global climate models, and observed local-scale variables of interest (predictands).Various types of empirical-statistical downscaling approaches can be placed approximately in linear and nonlinear groupings. The empirical-statistical downscaling techniques focus more on details related to the nonlinear models—their validation, strengths, and weaknesses—in comparison to linear models or the mixed models combining the linear and nonlinear approaches. Stochastic models can be applied to daily and sub-daily precipitation in Romania, with a comparison to dynamical downscaling. Conditional stochastic models are generally specific for daily or sub-daily precipitation as predictand.A complex validation of the nonlinear statistical downscaling models, selection of the large-scale predictors, model ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more global climate models as drivers, including their ability to simulate the input in downscaling models. Comparison between future statistical downscaled climate signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the regional climate models, gives a measure of the reliability of downscaled regional climate changes.

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