scholarly journals Coupled models in low-frequency electromagnetic simulation LDRD Final Report 94-ERI-004

1997 ◽  
Author(s):  
D.W. Hewett ◽  
D. Bateson ◽  
M. Gibbons ◽  
M. Lambert ◽  
L. Tung ◽  
...  

2005 ◽  
Vol 18 (13) ◽  
pp. 2441-2459 ◽  
Author(s):  
J. Zavala-Garay ◽  
C. Zhang ◽  
A. M. Moore ◽  
R. Kleeman

Abstract The possibility that the tropical Pacific coupled system linearly amplifies perturbations produced by the Madden–Julian oscillation (MJO) is explored. This requires an estimate of the low-frequency tail of the MJO. Using 23 yr of NCEP–NCAR reanalyses of surface wind and Reynolds SST, we show that the spatial structure that dominates the intraseasonal band (i.e., the MJO) also dominates the low-frequency band once the anomalies directly related to ENSO have been removed. This low-frequency contribution of the intraseasonal variability is not included in most ENSO coupled models used to date. Its effect in a coupled model of intermediate complexity has, therefore, been studied. It is found that this “MJO forcing” (τMJO) can explain a large fraction of the interannual variability in an asymptotically stable version of the model. This interaction is achieved via linear dynamics. That is, it is the cumulative effect of individual events that maintains ENSOs in this model. The largest coupled wind anomalies are initiated after a sequence of several downwelling Kelvin waves of the same sign have been forced by τMJO. The cumulative effect of the forced Kelvin waves is to persist the (small) SST anomalies in the eastern Pacific just enough for the coupled ocean–atmosphere dynamics to amplify the anomalies into a mature ENSO event. Even though τMJO explains just a small fraction of the energy contained in the stress not associated with ENSO, a large fraction of the modeled ENSO variability is excited by this forcing. The characteristics that make τMJO an optimal stochastic forcing for the model are discussed. The large zonal extent is an important factor that differentiates the MJO from other sources of stochastic forcing.





2013 ◽  
Vol 26 (18) ◽  
pp. 7151-7166 ◽  
Author(s):  
Riccardo Farneti ◽  
Geoffrey K. Vallis

Abstract The variability and compensation of the meridional energy transport in the atmosphere and ocean are examined with the state-of-the-art GFDL Climate Model, version 2.1 (CM2.1), and the GFDL Intermediate Complexity Coupled Model (ICCM). On decadal time scales, a high degree of compensation between the energy transport in the atmosphere (AHT) and ocean (OHT) is found in the North Atlantic. The variability of the total or planetary heat transport (PHT) is much smaller than the variability in either AHT or OHT alone, a feature referred to as “Bjerknes compensation.” Natural decadal variability stems from the Atlantic meridional overturning circulation (AMOC), which leads OHT variability. The PHT is positively correlated with the OHT, implying that the atmosphere is compensating, but imperfectly, for variations in ocean transport. Because of the fundamental role of the AMOC in generating the decadal OHT anomalies, Bjerknes compensation is expected to be active only in coupled models with a low-frequency AMOC spectral peak. The AHT and the transport in the oceanic gyres are positively correlated because the gyre transport responds to the atmospheric winds, thereby militating against long-term variability involving the wind-driven flow. Moisture and sensible heat transports in the atmosphere are also positively correlated at decadal time scales. The authors further explore the mechanisms and degree of compensation with a simple, diffusive, two-layer energy balance model. Taken together, these results suggest that compensation can be interpreted as arising from the highly efficient nature of the meridional energy transport in the atmosphere responding to ocean variability rather than any a priori need for the top-of-atmosphere radiation budget to be fixed.



2004 ◽  
Vol 168 (1-2) ◽  
pp. 125-133 ◽  
Author(s):  
Herbert De Gersem ◽  
Kay Hameyer ◽  
Thomas Weiland


2017 ◽  
Vol 30 (4) ◽  
pp. 1461-1475 ◽  
Author(s):  
Panos J. Athanasiadis ◽  
Alessio Bellucci ◽  
Adam A. Scaife ◽  
Leon Hermanson ◽  
Stefano Materia ◽  
...  

Abstract Significant predictive skill for the mean winter North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) has been recently reported for a number of different seasonal forecasting systems. These findings are important in exploring the predictability of the natural system, but they are also important from a socioeconomic point of view, since the ability to predict the wintertime atmospheric circulation anomalies over the North Atlantic well ahead in time will have significant benefits for North American and European countries. In contrast to the tropics, for the mid latitudes the predictive skill of many forecasting systems at the seasonal time scale has been shown to be low to moderate. The recent findings are promising in this regard, suggesting that better forecasts are possible, provided that key components of the climate system are initialized realistically and the coupled models are able to simulate adequately the dominant processes and teleconnections associated with low-frequency variability. It is shown that a multisystem approach has unprecedented high predictive skill for the NAO and AO, probably largely due to increasing the ensemble size and partly due to increasing model diversity. Predicting successfully the winter mean NAO does not ensure that the respective climate anomalies are also well predicted. The NAO has a strong impact on Europe and North America, yet it only explains part of the interannual and low-frequency variability over these areas. Here it is shown with a number of different diagnostics that the high predictive skill for the NAO/AO indeed translates to more accurate predictions of temperature, surface pressure, and precipitation in the areas of influence of this teleconnection.



Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 696 ◽  
Author(s):  
Jéssica Gutiérrez ◽  
Kaoutar Zeljami ◽  
Tomás Fernández ◽  
Juan Pablo Pascual ◽  
Antonio Tazón

This paper presents and discusses the careful modeling of a Zero Biased Diode, including low-frequency noise sources, providing a global model compatible with both wire bonding and flip-chip attachment techniques. The model is intended to cover from DC up to W-band behavior, and is based on DC, capacitance versus voltage, as well as scattering and power sweep harmonics measurements. Intensive use of 3D EM (ElectroMagnetic) simulation tools, such as HFSSTM, was done to support Zero Biased Diode parasitics modeling and microstrip board modeling. Measurements are compared with simulations and discussed. The models will provide useful support for detector designs in the W-band.



2018 ◽  
Vol 31 (3) ◽  
pp. 1205-1226 ◽  
Author(s):  
Dawei Li ◽  
Rong Zhang ◽  
Thomas Knutson

Abstract In this study the mechanisms for low-frequency variability of summer Arctic sea ice are analyzed using long control simulations from three coupled models (GFDL CM2.1, GFDL CM3, and NCAR CESM). Despite different Arctic sea ice mean states, there are many robust features in the response of low-frequency summer Arctic sea ice variability to the three key predictors (Atlantic and Pacific oceanic heat transport into the Arctic and the Arctic dipole) across all three models. In all three models, an enhanced Atlantic (Pacific) heat transport into the Arctic induces summer Arctic sea ice decline and surface warming, especially over the Atlantic (Pacific) sector of the Arctic. A positive phase of the Arctic dipole induces summer Arctic sea ice decline and surface warming on the Pacific side, and opposite changes on the Atlantic side. There is robust Bjerknes compensation at low frequency, so the northward atmospheric heat transport provides a negative feedback to summer Arctic sea ice variations. The influence of the Arctic dipole on summer Arctic sea ice extent is more (less) effective in simulations with less (excessive) climatological summer sea ice in the Atlantic sector. The response of Arctic sea ice thickness to the three key predictors is stronger in models that have thicker climatological Arctic sea ice.



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