Quasi‐biennial oscillations (QBOs) in thirteen atmospheric general circulation models forced with both observed and annually repeating sea surface temperatures (SSTs) are evaluated. In most models ...the QBO period is close to, but shorter than, the observed period of 28 months. Amplitudes are within ±20% of the observed QBO amplitude at 10 hPa, but typically about half of that observed at lower altitudes (50 and 70 hPa). For almost all models, the oscillation's amplitude profile shows an overall upward shift compared to reanalysis and its meridional extent is too narrow. Asymmetry in the duration of eastward and westward phases is reasonably well captured, though not all models replicate the observed slowing of the descending westward shear. Westward phases are generally too weak, and most models have an eastward time mean wind bias throughout the depth of the QBO. The intercycle period variability is realistic and in some models is enhanced in the experiment with observed SSTs compared to the experiment with repeated annual cycle SSTs. Mean periods are also sensitive to this difference between SSTs, but only when parametrized non‐orographic gravity wave (NOGW) sources are coupled to tropospheric parameters and not prescribed with a fixed value. Overall, however, modelled QBOs are very similar whether or not the prescribed SSTs vary interannually. A portrait of the overall ensemble performance is provided by a normalized grading of QBO metrics. To simulate a QBO, all but one model used parametrized NOGWs, which provided the majority of the total wave forcing at altitudes above 70 hPa in most models. Hence the representation of NOGWs either explicitly or through parametrization is still a major uncertainty underlying QBO simulation in these present‐day experiments.
Quasi‐biennial oscillations (QBOs) in thirteen atmospheric general circulation models forced with both observed (orange) and annually repeating (grey) sea surface temperatures (SSTs) are evaluated over a range of metrics and compared against reanalysis (blue‐green). Mean periods are sensitive to this difference between SSTs, but only when parametrized non‐orographic gravity wave sources are coupled to tropospheric parameters (60LCAM5 and right there of) and not prescribed with fixed values. Overall, however, modelled QBOs are very similar whether or not the prescribed SSTs vary interannually.
A formalism is proposed to represent a broadband spectrum of Gravity Waves (GWs) via the superposition of a large ensemble of statistically independent monochromatic ones. To produce this large ...ensemble at a reasonable numerical cost, we use the fact that the life cycles of the waves needed to be parameterized in General Circulation Models (GCMs) have time scales that largely exceed the time step of the model. We can therefore launch few waves with characteristics chosen randomly at each time step, and make them having an effect on a longer time scale by applying an AR1 relation between the gravity waves drag at a given time and that at the next time step. The stochastic GW parameterization is applied to a GCM in the tropics, and its additional drag causes a realistic Quasi‐Biennial Oscillation (QBO). The more realistic wind structure also results in a better representation of the large scale equatorial waves, like the Rossby Gravity Waves (RGWs) with periods around 4–5 day.
Key Points
An improved stochastic parameterization of GWs is proposed
It can be use to help models to simulate the QBO
It also improves the models to reproduce large‐scale equatorial waves
Complete recipes spread across 15 chapters to help you overcome commonly faced issues by Python for everybody across the globe. Each recipe takes a problem-solution approach to resolve for effective ...Python.Key FeaturesDevelop expressive and effective Python programs
Best practices and common idioms through carefully explained recipes
Discover new ways to apply Python for data-focused development
Make use of Python's optional type annotationsBook DescriptionPython is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great language that can power your applications and provide great speed, safety, and scalability. It can be used for simple scripting or sophisticated web applications. By exposing Python as a series of simple recipes, this book gives you insight into specific language features in a particular context. Having a tangible context helps make the language or a given standard library feature easier to understand.
This book comes with 133 recipes on the latest version of Python 3.8. The recipes will benefit everyone, from beginners just starting out with Python to experts. You'll not only learn Python programming concepts but also how to build complex applications.
The recipes will touch upon all necessary Python concepts related to data structures, object oriented programming, functional programming, and statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively take advantage of it.
By the end of this Python book, you will be equipped with knowledge of testing, web services, configuration, and application integration tips and tricks. You will be armed with the knowledge of how to create applications with flexible logging, powerful configuration, command-line options, automated unit tests, and good documentation.What you will learnSee the intricate details of the Python syntax and how to use it to your advantage
Improve your coding with Python readability through functions
Manipulate data effectively using built-in data structures
Get acquainted with advanced programming techniques in Python
Equip yourself with functional and statistical programming features
Write proper tests to be sure a program works as advertised
Integrate application software using PythonWho this book is forThe Python book is for web developers, programmers, enterprise programmers, engineers, and big data scientists. If you are a beginner, this book will get you started. If you are experienced, it will expand your knowledge base. A basic knowledge of programming would help.
Recent work has shown that the parameters controlling parametrizations of the physical processes in climate models can be estimated from observations using filtering techniques. In this article, we ...propose an offline parameter estimation approach, without estimating the state of the climate model. It is based on the Ensemble Kalman Filter (EnKF) and an iterative estimation of the error covariance matrices and of the background state using a maximum likelihood algorithm. The technique is implemented in a subgrid‐scale orography (SSO) parametrization scheme which works in a single vertical column. First, the parameter estimation technique is evaluated using twin experiments. Then, the technique is used with synthetic observations to estimate how the parameters of the SSO scheme should change when the resolution of the input orography dataset of a general circulation model is increased. Our analysis reveals that, when the resolution of the orography dataset increases, the scheme should take into account the dynamical sheltering that can occur at low levels between mountain peaks located within the same gridbox area.
Based on the theoretical and experimental facts that gravity waves (GWs) can be spontaneously emitted during the evolution of a near‐balanced flow, a stochastic parameterization of GWs linked to ...fronts and jets is proposed. Although the spontaneous adjustment theory used predicts “exponentially” small GW fields, it is shown that it is sufficient to produce realistic GW drag at mesospheric levels. Off‐line tests using reanalyzed meteorological fields are conducted and show that the GWs emitted present a strong annual cycle following that of the sources. Also, the GW momentum fluxes in the lower stratosphere are qualitatively realistic in terms of intermittency. Online tests in a middle atmosphere general circulation model show that the scheme can potentially perform as well as highly tuned existing GW schemes.
Key Points
Spontaneous adjustment mechanism gives required GW forcing for climate models
Source‐related GW annual cycle and realistic EP flux intermittency are produced
•The thermal structure of the upper atmosphere of Venus predicted by a groundto- thermosphere 3D model for Venus is presented. The model includes the main processes contributing to the thermal ...balance of the atmosphere of Venus from 90 to 150 km, as well as a photochemical model and a non-orographic gravity waves parameterisation.•A succession of warm and cold layers is predicted: the role of radiative, photochemical and dynamical effects is described.•A comparison of model results with a selection of recent measurements shows an overall good agreement in terms of trends and order of magnitude.•Significant data-model discrepancies are also discerned and discussed. Among them, altitude layer of the predicted mesospheric local maximum (between 100–120 km) is higher then observed; thermosphere temperatures are about 40–50 K colder and up to 30 K warmer then measured at terminator and nighttime, respectively.
We present here the thermal structure of the upper atmosphere of Venus predicted by a full self-consistent Venus General Circulation Model (VGCM) developed at Laboratoire de Météorologie Dynamique (LMD) and extended up to the thermosphere of the planet. Physical and photochemical processes relevant at those altitudes, plus a non-orographic GW parameterisation, have been added. All those improvements make the LMD-VGCM the only existing ground-to-thermosphere 3D model for Venus: a unique tool to investigate the atmosphere of Venus and to support the exploration of the planet by remote sounding. The aim of this paper is to present the model reference results, to describe the role of radiative, photochemical and dynamical effects in the observed thermal structure in the upper mesosphere/lower thermosphere of the planet. The predicted thermal structure shows a succession of warm and cold layers, as recently observed. A cooling trend with increasing latitudes is found during daytime at all altitudes, while at nighttime the trend is inverse above about 110 km, with an atmosphere up to 15 K warmer towards the pole. The latitudinal variation is even smaller at the terminator, in agreement with observations. Below about 110 km, a nighttime warm layer whose intensity decreases with increasing latitudes is predicted by our GCM. A comparison of model results with a selection of recent measurements shows an overall good agreement in terms of trends and order of magnitude. Significant data-model discrepancies may be also discerned. Among them, thermospheric temperatures are about 40–50 K colder and up to 30 K warmer than measured at terminator and at nighttime, respectively. The altitude layer of the predicted mesospheric local maximum (between 100 and 120 km) is also higher than observed. Possible interpretations are discussed and several sensitivity tests performed to understand the data-model discrepancies and to propose future model improvements.
A multiwave stochastic parameterization of nonorographic gravity waves (GWs), representing GWs produced by convection and a background of GWs in the midlatitudes, is tuned and tested against momentum ...fluxes derived from long‐duration balloon flights. The tests are done offline using data sets corresponding to the Southern Ocean during the Concordiasi campaign in 2010. We also adopt the limiting constraint that the drag produced by the scheme resembles that produced by a highly tuned spectral GW parameterization, the so‐called Hines scheme. Our results show that the parameterization can reproduce the momentum flux intermittency measured during the campaign, which is relevant since it strongly impacts on the vertical distribution of the GW drag. We also show that, at the altitude of the balloon flights, the momentum flux intermittency is in good part due to the GW sources: filtering by the background winds only becomes effective at much higher altitude. These results are based on bulk formulae for the GW momentum flux that could be used to replace our background GWs by GWs produced by fronts. Finally, the GW energy spectra built out of the stochastic scheme by averaging over a large ensemble of realizations are comparable to the classical vertical spectra of GWs, used today in globally spectral schemes. This indicates that multiwave and spectral schemes can be reconciled once a stochastic approach is used.
Key Points
Importance of reproducing the observed intermittency in GW parameterizationsCharacteristics of GW intermittency arise in good part from GW sourcesReconciliation between multiwave and globally spectral GW parameterizations
By measuring the regular oscillations of the density of CO2 in the upper atmosphere (between 120 and 190 km), the mass spectrometer MAVEN/NGIMS (Atmosphere and Volatile EvolutioN/Neutral Gas Ion Mass ...Spectrometer) reveals the local impact of gravity waves. This yields precious information on the activity of gravity waves and the atmospheric conditions in which they propagate and break. The intensity of gravity waves measured by MAVEN in the upper atmosphere has been shown to be dictated by saturation processes in isothermal conditions. As a result, gravity waves activity is correlated to the evolution of the inverse of the background temperature. Previous data gathered at lower altitudes (∼95–∼150 km) during aerobraking by the accelerometers on board MGS (Mars Global Surveyor), ODY (Mars Odyssey) and MRO (Mars Reconnaissance Orbiter) are analyzed in the light of those recent findings with MAVEN. The anti-correlation between GW-induced density perturbations and background temperature is plausibly found in the ODY data acquired in the polar regions, but not in the MGS and MRO data. MRO data in polar regions exhibit a correlation between the density perturbations and the Brunt-Väisälä frequency (or, equivalently, static stability), obtained from Global Climate Modeling compiled in the Mars Climate Database. At lower altitude levels (between 100 and 120 km), although wave saturation might still be dominant, isothermal conditions are no longer verified. In this case, theory predicts that the intensity of gravity waves is no more correlated to background temperature, but to static stability. At other latitudes in the three aerobraking datasets, the GW-induced relative density perturbations are correlated with neither inverse temperature nor static stability; in this particular case, this means that the observed activity of gravity waves is not only controlled by saturation, but also by the effects of gravity-wave sources and wind filtering through critical levels. This result highlights the exceptional nature of MAVEN/NGIMS observations which combine both isothermal and saturated conditions contrary to aerobraking measurements.
•Gravity wave activity causes density perturbations in the Martian thermosphere.•MAVEN found a correlation between GW activity and inverse background temperature.•Lower-altitude aerobraking data do not show this correlation, except for Mars Odyssey.•Aerobraking data and GCMs suggest instead wave activity correlated with Static stability.•When no such correlation, a mix of saturation, critical levels and sources is suspected.
A comprehensive guide to exploring modern Python through data structures, design patterns, and effective object-oriented techniques Key Features * Build an intuitive understanding of object-oriented ...design, from introductory to mature programs * Learn the ins and outs of Python syntax, libraries, and best practices * Examine a machine-learning case study at the end of each chapter Book Description Python Object-Oriented Programming, Fourth Edition dives deep into the various aspects of OOP, Python as an OOP language, common and advanced design patterns, and hands-on data manipulation and testing of more complex OOP systems. These concepts are consolidated by open-ended exercises, as well as a real-world case study at the end of every chapter, newly written for this edition. All example code is now compatible with Python 3.9+ syntax and has been updated with type hints for ease of learning. Steven and Dusty provide a friendly, comprehensive tour of important OOP concepts, such as inheritance, composition, and polymorphism, and explain how they work together with Python's classes and data structures to facilitate good design. UML class diagrams are generously used throughout the text for you to understand class relationships. Beyond the book's focus on OOP, it features an in-depth look at Python's exception handling and how functional programming intersects with OOP. Not one, but two very powerful automated testing systems, unittest and pytest, are introduced in this book. The final chapter provides a detailed discussion of Python's concurrent programming ecosystem. By the end of the book, you will have a thorough understanding of how to think about and apply object-oriented principles using Python syntax and be able to confidently create robust and reliable programs. What you will learn * Implement objects in Python by creating classes and defining methods * Extend class functionality using inheritance * Use exceptions to handle unusual situations cleanly * Understand when to use object-oriented features, and more importantly, when not to use them * Discover several widely used design patterns and how they are implemented in Python * Uncover the simplicity of unit and integration testing and understand why they are so important * Learn to statically type check your dynamic code * Understand concurrency with asyncio and how it speeds up programs Who this book is for If you are new to object-oriented programming techniques, or if you have basic Python skills and wish to learn how and when to correctly apply OOP principles in Python, this is the book for you. Moreover, if you are an object-oriented programmer coming from other languages or seeking a leg up in the new world of Python, you will find this book a useful introduction to Python. Minimal previous experience with Python is necessary.
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