scholarly journals Ocean–Atmosphere Coupling on Different Spatiotemporal Scales: A Mechanism for Intraseasonal Instabilities

2009 ◽  
Vol 66 (6) ◽  
pp. 1834-1844 ◽  
Author(s):  
Lei Zhou ◽  
Raghu Murtugudde
Keyword(s):  
Analytical Solutions ◽  
Large Scale ◽  
Spatial Scales ◽  
Coupled Model ◽  
Governing Equations ◽  
Resonance Mechanism ◽  
Ocean Atmosphere ◽  
Spatiotemporal Scales ◽  
Temporal And Spatial ◽  
Reasonable Choice

Abstract The possibility of interactions between oceanic and atmospheric oscillations with different temporal and spatial scales is examined with analytical solutions to idealized linear governing equations. With a reasonable choice for relevant parameters, the mesoscale oceanic features and the large-scale atmospheric oscillations can interact with each other and lead to unstable waves in the intraseasonal band in the specific coupled model presented in this study. This mechanism is different from the resonance mechanism, which requires similar temporal or spatial scales in the two media. Instead, this mechanism indicates that even in the cases in which the temporal and spatial scales are different but the dispersion relations (i.e., functions of frequency and wavenumber) of the oceanic and atmospheric oscillations are proximal, instabilities can still be generated due to the ocean–atmosphere coupling.

scholarly journals Evaluation of a unique approach to high-resolution climate modeling using the Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1

2019 ◽  
Vol 12 (8) ◽  
pp. 3725-3743 ◽  
Author(s):  
Allison C. Michaelis ◽  
Gary M. Lackmann ◽  
Walter A. Robinson
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Sea Ice ◽  
Northern Hemisphere ◽  
General Circulation ◽  
Large Scale ◽  
Southern Oscillation ◽  
Spatial Scales ◽  
Coupled Model ◽  
General Circulation Models

Abstract. We present multi-seasonal simulations representative of present-day and future environments using the global Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select 10 simulation years with varying phases of El Niño–Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analyzed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations. The present-day simulations provide a reasonable reproduction of large-scale atmospheric features in the Northern Hemisphere such as the wintertime midlatitude storm tracks, upper-tropospheric jets, and maritime sea-level pressure features as well as annual precipitation patterns across the tropics. The simulations also adequately represent tropical cyclone (TC) characteristics such as strength, spatial distribution, and seasonal cycles for most Northern Hemisphere basins. These results demonstrate the applicability of these model simulations for future studies examining climate change effects on various Northern Hemisphere phenomena, and, more generally, the utility of MPAS-A for studying climate change at spatial scales generally unachievable in GCMs.

scholarly journals Understanding the Distribution of Multimodel Ensembles

2020 ◽  
Vol 33 (21) ◽  
pp. 9447-9465
Author(s):  
Bo Christiansen
Keyword(s):  
Statistical Model ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Model Errors ◽  
High Dimensions ◽  
Ensemble Mean ◽  
Temporal And Spatial ◽  
Multimodel Ensembles ◽  
Annual Means

AbstractWhen analyzing multimodel climate ensembles it is often assumed that the ensemble is either truth centered or that models and observations are drawn from the same distribution. Here we analyze CMIP5 ensembles focusing on three measures that separate the two interpretations: the error of the ensemble mean relative to the error of individual models, the decay of the ensemble mean error for increasing ensemble size, and the correlations of the model errors. The measures are analyzed using a simple statistical model that includes the two interpretations as different limits and for which analytical results for the three measures can be obtained in high dimensions. We find that the simple statistical model describes the behavior of the three measures in the CMIP5 ensembles remarkably well. Except for the large-scale means we find that the indistinguishable interpretation is a better assumption than the truth centered interpretation. Furthermore, the indistinguishable interpretation becomes an increasingly better assumption when the errors are based on smaller temporal and spatial scales. Building on this, we present a simple conceptual mechanism for the indistinguishable interpretation based on the assumption that the climate models are calibrated on large-scale features such as, e.g., annual means or global averages.

Merged Statistical Analyses of Historical Monthly Precipitation Anomalies Beginning 1900

2010 ◽  
Vol 23 (21) ◽  
pp. 5755-5770 ◽  
Author(s):  
Thomas M. Smith ◽  
Phillip A. Arkin ◽  
Mathew R. P. Sapiano ◽  
Ching-Yee Chang
Keyword(s):  
Twentieth Century ◽  
Time Scales ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Phase Relationship ◽  
Coupled Model ◽  
Data Availability ◽  
Monthly Precipitation ◽  
Interannual Variations

Abstract A monthly reconstruction of precipitation beginning in 1900 is presented. The reconstruction resolves interannual and longer time scales and spatial scales larger than 5° over both land and oceans. Because of different land and ocean data availability, the reconstruction combines two separate historical reconstructions. One analyzes interannual variations directly by fitting gauge-based anomalies to large-scale spatial modes. This direct reconstruction is used for land anomalies and interannual oceanic anomalies. The other analyzes annual and longer variations indirectly from correlations with analyzed sea surface temperature and sea level pressure. This indirect reconstruction is used for oceanic variations with time scales longer than interannual. In addition, a method of estimating reconstruction errors is also presented. Over land the reconstruction is a filtered representation of the gauge data with data gaps filled. Over oceans the reconstruction gives an estimate of the atmospheric response to changing temperature and pressure, combined with interannual variations. The reconstruction makes it possible to evaluate global precipitation variations for periods much longer than the satellite period, which begins in 1979. Evaluations show some large-scale similarities with coupled model precipitation variations over the twentieth century, including an increasing tendency over the century. The reconstructed land and sea trends tend to be out of phase at low latitudes, similar to the out-of-phase relationship for interannual variations. This reconstruction may be used for climate monitoring, for statistical climate studies of the twentieth century, and for helping to evaluate dynamic climate models. In the future the possibility of improving the reconstruction will be explored by further improving the analysis methods and including additional data.

scholarly journals An asymptotic model for the propagation of oceanic internal tides through quasi-geostrophic flow

2017 ◽  
Vol 828 ◽  
pp. 779-811 ◽  
Author(s):  
G. L. Wagner ◽  
G. Ferrando ◽  
W. R. Young
Keyword(s):  
Wave Equation ◽  
Wave Field ◽  
Large Scale ◽  
Spatial Scales ◽  
Coupled Model ◽  
Boussinesq Equations ◽  
Internal Tides ◽  
Numerical Comparison ◽  
Asymptotic Model ◽  
Geostrophic Flow

We derive a time-averaged ‘hydrostatic wave equation’ from the hydrostatic Boussinesq equations that describes the propagation of inertia–gravity internal waves through quasi-geostrophic flow. The derivation uses a multiple-scale asymptotic method to isolate wave field evolution over intervals much longer than a wave period, assumes the wave field has a well-defined non-inertial frequency such as that of the mid-latitude semi-diurnal lunar tide, assumes that the wave field and quasi-geostrophic flow have comparable spatial scales and neglects nonlinear wave–wave dynamics. As a result the hydrostatic wave equation is a reduced model applicable to the propagation of large-scale internal tides through the inhomogeneous and moving ocean. A numerical comparison with the linearized and hydrostatic Boussinesq equations demonstrates the validity of the hydrostatic wave equation model and illustrates how the model fails when the quasi-geostrophic flow is too strong and the wave frequency is too close to inertial. The hydrostatic wave equation provides a first step toward a coupled model for energy transfer between oceanic internal tides and quasi-geostrophic eddies and currents.

scholarly journals A Review of Quantifying pCO2 in Inland Waters with a Global Perspective: Challenges and Prospects of Implementing Remote Sensing Technology

2021 ◽  
Vol 13 (23) ◽  
pp. 4916
Author(s):  
Zhidan Wen ◽  
Yingxin Shang ◽  
Lili Lyu ◽  
Sijia Li ◽  
Hui Tao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Biological Activities ◽  
Spatial Scales ◽  
Measurement Data ◽  
Global Perspective ◽  
Published Data ◽  
Inland Waters ◽  
Rivers And Streams ◽  
Temporal And Spatial

The traditional field-based measurements of carbon dioxide (pCO2) for inland waters are a snapshot of the conditions on a particular site, which might not adequately represent the pCO2 variation of the entire lake. However, these field measurements can be used in the pCO2 remote sensing modeling and verification. By focusing on inland waters (including lakes, reservoirs, rivers, and streams), this paper reviews the temporal and spatial variability of pCO2 based on published data. The results indicate the significant daily and seasonal variations in pCO2 in lakes. Rivers and streams contain higher pCO2 than lakes and reservoirs in the same climatic zone, and tropical waters typically exhibit higher pCO2 than temperate, boreal, and arctic waters. Due to the temporal and spatial variations of pCO2, it can differ in different inland water types in the same space-time. The estimation of CO2 fluxes in global inland waters showed large uncertainties with a range of 1.40–3.28 Pg C y−1. This paper also reviews existing remote sensing models/algorithms used for estimating pCO2 in sea and coastal waters and presents some perspectives and challenges of pCO2 estimation in inland waters using remote sensing for future studies. To overcome the uncertainties of pCO2 and CO2 emissions from inland waters at the global scale, more reliable and universal pCO2 remote sensing models/algorithms will be needed for mapping the long-term and large-scale pCO2 variations for inland waters. The development of inverse models based on dissolved biogeochemical processes and the machine learning algorithm based on measurement data might be more applicable over longer periods and across larger spatial scales. In addition, it should be noted that the remote sensing-retrieved pCO2/the CO2 concentration values are the instantaneous values at the satellite transit time. A major technical challenge is in the methodology to transform the retrieved pCO2 values on time scales from instant to days/months, which will need further investigations. Understanding the interrelated control and influence processes closely related to pCO2 in the inland waters (including the biological activities, physical mixing, a thermodynamic process, and the air–water gas exchange) is the key to achieving remote sensing models/algorithms of pCO2 in inland waters. This review should be useful for a general understanding of the role of inland waters in the global carbon cycle.

Cycle-Averaged Approximation of Heat Transfer Associated With Rayleigh-Plesset Gas Bubble Dynamics

2015 ◽  
Author(s):  
Kyle P. Schmitt ◽  
Abby M. Pellman ◽  
John C. Minichiello
Keyword(s):  
Pressure Wave ◽  
Bubble Dynamics ◽  
Spatial Scales ◽  
Mechanical Energy ◽  
Gas Bubbles ◽  
Governing Equations ◽  
Dissipative Effects ◽  
Complete Set ◽  
Temporal And Spatial ◽  
Bubble Oscillations

The presence of entrained gas bubbles in a bubbly media leads to both dispersive and dissipative effects on a pressure wave traveling through the system. The complete set of equations used to model this process involves the combination of macroscopic pressure propagation and Rayleigh-Plesset oscillations of individual gas bubbles. This results in disparate temporal and spatial scales that are difficult to solve numerically inside of a CFD framework. This paper presents a simplification to the set of governing equations that specifically eliminates the need to model individual bubble oscillations by using a cycle-averaged approximation. Results generated with the simplified model are verified against equivalent results considering the full set of governing equations. The approximation is shown to capture the behavior of interest — e.g., the variation in gas phase volume that alters the bulk modulus of the bubbly media or the net transfer of mechanical energy to heat — without the additional effort required to model rapid dynamics that do not contribute substantially to the pressure wave decay.

scholarly journals Novel Imaging Modalities Shedding Light on Plant Biology: Start Small and Grow Big

2020 ◽  
Vol 71 (1) ◽  
pp. 789-816 ◽  
Author(s):  
Natalie M. Clark ◽  
Lisa Van den Broeck ◽  
Marjorie Guichard ◽  
Adam Stager ◽  
Herbert G. Tanner ◽  
...  
Keyword(s):  
Plant Growth ◽  
Large Scale ◽  
Spatial Scales ◽  
Quantitative Information ◽  
Imaging Modalities ◽  
Data Sets ◽  
Large Scale Data ◽  
Temporal And Spatial ◽  
Multiple Samples ◽  
Near Future

The acquisition of quantitative information on plant development across a range of temporal and spatial scales is essential to understand the mechanisms of plant growth. Recent years have shown the emergence of imaging methodologies that enable the capture and analysis of plant growth, from the dynamics of molecules within cells to the measurement of morphometricand physiological traits in field-grown plants. In some instances, these imaging methods can be parallelized across multiple samples to increase throughput. When high throughput is combined with high temporal and spatial resolution, the resulting image-derived data sets could be combined with molecular large-scale data sets to enable unprecedented systems-level computational modeling. Such image-driven functional genomics studies may be expected to appear at an accelerating rate in the near future given the early success of the foundational efforts reviewed here. We present new imaging modalities and review how they have enabled a better understanding of plant growth from the microscopic to the macroscopic scale.

Temporal and Spatial Scales in Phytoplankton Ecology. Mechanisms, Methods, Models, and Management

1980 ◽  
Vol 37 (5) ◽  
pp. 877-900 ◽  
Author(s):  
Graham P. Harris
Keyword(s):  
Large Scale ◽  
Spatial Scales ◽  
Low Frequency ◽  
Small Scale ◽  
Frequency Variation ◽  
Phytoplankton Ecology ◽  
Variable Environments ◽  
Phytoplankton Communities ◽  
Nonsteady State ◽  
Temporal And Spatial

This article is essentially a review of the temporal and spatial scales of variability in both marine and freshwater planktonic environments and the algal responses to those scales. I assert that there are problems with our present understanding of these scales and the use of inappropriate assumptions concerning the occurrence of steady-state conditions. In a nonsteady-state environment the concepts of limiting nutrients must be changed, and the extrapolation from culture to field conditions is fraught with problems. In this paper I review the evidence for the existence and importance of small-scale, high frequency and large-scale, low frequency variation in the planktonic environment and show that such variation fundamentally affects our understanding of existing processes. Methodology and models must also reflect the true scales of variability which exist. I show that there are, at present, problems with our understanding of planktonic processes which greatly affect our ability to manage water quality. New concepts and models are urgently needed. Finally I propose a new model of community structure and process in variable environments which accounts for the correct 'algal' scales of perturbation and response and allows certain predictions to be made. It is possible to reconcile certain problems and controversies in the literature by the use of such a model. An enhanced ability to manage planktonic systems should result from an improved understanding of the true scales of variability which exist.Key words: lakes, oceans, phytoplankton, communities, nutrients, models, management, eutrophication, fluctuations, scales

scholarly journals Primary motor and sensory cortical areas communicate via spatiotemporally coordinated networks at multiple frequencies

2016 ◽  
Vol 113 (18) ◽  
pp. 5083-5088 ◽  
Author(s):  
Fritzie I. Arce-McShane ◽  
Callum F. Ross ◽  
Kazutaka Takahashi ◽  
Barry J. Sessle ◽  
Nicholas G. Hatsopoulos
Keyword(s):  
Large Scale ◽  
Sensorimotor Integration ◽  
Spatial Scales ◽  
Sensory Information ◽  
Coherence Time ◽  
Force Generation ◽  
Functional Networks ◽  
Cortical Areas ◽  
Multiple Frequencies ◽  
Temporal And Spatial

Skilled movements rely on sensory information to shape optimal motor responses, for which the sensory and motor cortical areas are critical. How these areas interact to mediate sensorimotor integration is largely unknown. Here, we measure intercortical coherence between the orofacial motor (MIo) and somatosensory (SIo) areas of cortex as monkeys learn to generate tongue-protrusive force. We report that coherence between MIo and SIo is reciprocal and that neuroplastic changes in coherence gradually emerge over a few days. These functional networks of coherent spiking and local field potentials exhibit frequency-specific spatiotemporal properties. During force generation, theta coherence (2–6 Hz) is prominent and exhibited by numerous paired signals; before or after force generation, coherence is evident in alpha (6–13 Hz), beta (15–30 Hz), and gamma (30–50 Hz) bands, but the functional networks are smaller and weaker. Unlike coherence in the higher frequency bands, the distribution of the phase at peak theta coherence is bimodal with peaks near 0° and ±180°, suggesting that communication between somatosensory and motor areas is coordinated temporally by the phase of theta coherence. Time-sensitive sensorimotor integration and plasticity may rely on coherence of local and large-scale functional networks for cortical processes to operate at multiple temporal and spatial scales.

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