scholarly journals Comparison of stochastic parameterizations in the framework of a coupled ocean–atmosphere model

2018 ◽  
Vol 25 (3) ◽  
pp. 605-631 ◽  
Author(s):  
Jonathan Demaeyer ◽  
Stéphane Vannitsem
Keyword(s):  
Probability Distributions ◽  
Low Frequency ◽  
Model Errors ◽  
Low Frequency Variability ◽  
Ocean Atmosphere ◽  
Physically Based ◽  
Intermediate Order ◽  
Atmosphere Model ◽  
The Impact ◽  
New Framework

Abstract. A new framework is proposed for the evaluation of stochastic subgrid-scale parameterizations in the context of the Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM), a coupled ocean–atmosphere model of intermediate complexity. Two physically based parameterizations are investigated – the first one based on the singular perturbation of Markov operators, also known as homogenization. The second one is a recently proposed parameterization based on Ruelle's response theory. The two parameterizations are implemented in a rigorous way, assuming however that the unresolved-scale relevant statistics are Gaussian. They are extensively tested for a low-order version known to exhibit low-frequency variability (LFV), and some preliminary results are obtained for an intermediate-order version. Several different configurations of the resolved–unresolved-scale separations are then considered. Both parameterizations show remarkable performances in correcting the impact of model errors, being even able to change the modality of the probability distributions. Their respective limitations are also discussed.

scholarly journals Comparison of stochastic parameterizations in the framework of a coupled ocean-atmosphere model

2018 ◽  
Author(s):  
Jonathan Demaeyer ◽  
Stéphane Vannitsem
Keyword(s):  
Probability Distributions ◽  
Low Frequency ◽  
Markov Operator ◽  
Model Errors ◽  
Low Frequency Variability ◽  
Ocean Atmosphere ◽  
Physically Based ◽  
Intermediate Order ◽  
Atmosphere Model ◽  
The Impact

Abstract. A new framework is proposed for the evaluation of stochastic subgrid-scale parameterizations in the context of MAOOAM, a coupled ocean-atmosphere model of intermediate complexity. Two physically-based parameterizations are investigated, the first one based on the singular perturbation of Markov operator, also known as homogenization. The second one is a recently proposed parameterization based on the Ruelle's response theory. The two parameterization are implemented in a rigorous way, assuming however that the unresolved scale relevant statistics are Gaussian. They are extensively tested for a low-order version known to exhibit low-frequency variability, and some preliminary results are obtained for an intermediate-order version. Several different configurations of the resolved-unresolved scale separations are then considered. Both parameterizations show remarkable performances in correcting the impact of model errors, being even able to change the modality of the probability distributions. Their respective limitations are also discussed.

Nonlinearly induced low-frequency variability in a midlatitude coupled ocean-atmosphere model of intermediate complexity

2002 ◽  
Vol 19 (3-4) ◽  
pp. 303-320 ◽  
Author(s):  
E. van der Avoird ◽  
Dijkstra H. ◽  
Nauw J. ◽  
Schuurmans C.
Keyword(s):  
Low Frequency ◽  
Intermediate Complexity ◽  
Low Frequency Variability ◽  
Ocean Atmosphere ◽  
Atmosphere Model

scholarly journals A Modular Arbitrary-Order Ocean-Atmosphere Model: MAOOAM v1.0

2016 ◽  
Author(s):  
Lesley De Cruz ◽  
Jonathan Demaeyer ◽  
Stéphane Vannitsem
Keyword(s):  
Arbitrary Order ◽  
Low Frequency ◽  
Spectral Expansion ◽  
Truncation Level ◽  
Reduced Order ◽  
New Model ◽  
Low Frequency Variability ◽  
Model Dynamics ◽  
Ocean Atmosphere ◽  
Atmosphere Model

Abstract. This paper describes a reduced-order quasi-geostrophic coupled ocean-atmosphere model that allows for an arbitrary number of atmospheric and oceanic modes to be retained in the spectral decomposition. The modularity of this new model allows one to easily modify the model physics. Using this new model, coined "Modular Arbitrary-Order Ocean-Atmosphere Model" (MAOOAM), we analyse the dependence of the model dynamics on the truncation level of the spectral expansion, and unveil spurious behaviour that may exist at low resolution by a comparison with the higher resolution versions. In particular, we assess the robustness of the coupled low-frequency variability when the number of modes is increased. An "optimal" version is proposed for which the ocean resolution is sufficiently high while the total number of modes is small enough to allow for a tractable and extensive analysis of the dynamics.

scholarly journals The Modular Arbitrary-Order Ocean-Atmosphere Model: MAOOAM v1.0

2016 ◽  
Vol 9 (8) ◽  
pp. 2793-2808 ◽  
Author(s):  
Lesley De Cruz ◽  
Jonathan Demaeyer ◽  
Stéphane Vannitsem
Keyword(s):  
Arbitrary Order ◽  
Low Frequency ◽  
Spectral Expansion ◽  
Truncation Level ◽  
Reduced Order ◽  
New Model ◽  
Low Frequency Variability ◽  
Model Dynamics ◽  
Ocean Atmosphere ◽  
Atmosphere Model

Abstract. This paper describes a reduced-order quasi-geostrophic coupled ocean–atmosphere model that allows for an arbitrary number of atmospheric and oceanic modes to be retained in the spectral decomposition. The modularity of this new model allows one to easily modify the model physics. Using this new model, coined the "Modular Arbitrary-Order Ocean-Atmosphere Model" (MAOOAM), we analyse the dependence of the model dynamics on the truncation level of the spectral expansion, and unveil spurious behaviour that may exist at low resolution by a comparison with the higher-resolution configurations. In particular, we assess the robustness of the coupled low-frequency variability when the number of modes is increased. An "optimal" configuration is proposed for which the ocean resolution is sufficiently high, while the total number of modes is small enough to allow for a tractable and extensive analysis of the dynamics.

scholarly journals Low-frequency variability and heat transport in a low-order nonlinear coupled ocean–atmosphere model

2015 ◽  
Vol 309 ◽  
pp. 71-85 ◽  
Author(s):  
Stéphane Vannitsem ◽  
Jonathan Demaeyer ◽  
Lesley De Cruz ◽  
Michael Ghil
Keyword(s):  
Heat Transport ◽  
Low Frequency ◽  
Low Frequency Variability ◽  
Ocean Atmosphere ◽  
Atmosphere Model ◽  
Low Order

scholarly journals Shift Detection in Hydrological Regimes and Pluriannual Low-Frequency Streamflow Forecasting Using the Hidden Markov Model

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2058 ◽  
Author(s):  
Larissa Rolim ◽  
Francisco de Souza Filho
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Extreme Events ◽  
Hidden Markov ◽  
Low Frequency ◽  
Streamflow Forecasting ◽  
Hydrologic Time Series ◽  
Low Frequency Variability ◽  
The Impact

Improved water resource management relies on accurate analyses of the past dynamics of hydrological variables. The presence of low-frequency structures in hydrologic time series is an important feature. It can modify the probability of extreme events occurring in different time scales, which makes the risk associated with extreme events dynamic, changing from one decade to another. This article proposes a methodology capable of dynamically detecting and predicting low-frequency streamflow (16–32 years), which presented significance in the wavelet power spectrum. The Standardized Runoff Index (SRI), the Pruned Exact Linear Time (PELT) algorithm, the breaks for additive seasonal and trend (BFAST) method, and the hidden Markov model (HMM) were used to identify the shifts in low frequency. The HMM was also used to forecast the low frequency. As part of the results, the regime shifts detected by the BFAST approach are not entirely consistent with results from the other methods. A common shift occurs in the mid-1980s and can be attributed to the construction of the reservoir. Climate variability modulates the streamflow low-frequency variability, and anthropogenic activities and climate change can modify this modulation. The identification of shifts reveals the impact of low frequency in the streamflow time series, showing that the low-frequency variability conditions the flows of a given year.

scholarly journals Coupled Ocean–Atmosphere Response to Seasonal Modulation of Ocean Color: Impact on Interannual Climate Simulations in the Tropical Pacific

2007 ◽  
Vol 20 (2) ◽  
pp. 353-374 ◽  
Author(s):  
J. Ballabrera-Poy ◽  
R. Murtugudde ◽  
R-H. Zhang ◽  
A. J. Busalacchi
Keyword(s):  
Solar Radiation ◽  
Interannual Variability ◽  
Seasonal Cycle ◽  
General Circulation ◽  
Ocean Color ◽  
Circulation Model ◽  
Enso Events ◽  
Ocean Atmosphere ◽  
Atmosphere Model ◽  
The Impact

Abstract The ability to use remotely sensed ocean color data to parameterize biogenic heating in a coupled ocean–atmosphere model is investigated. The model used is a hybrid coupled model recently developed at the Earth System Science Interdisciplinary Center (ESSIC) by coupling an ocean general circulation model with a statistical atmosphere model for wind stress anomalies. The impact of the seasonal cycle of water turbidity on the annual mean, seasonal cycle, and interannual variability of the coupled system is investigated using three simulations differing in the parameterization of the vertical attenuation of downwelling solar radiation: (i) a control simulation using a constant 17-m attenuation depth, (ii) a simulation with the spatially varying annual mean of the satellite-derived attenuation depth, and (iii) a simulation accounting for the seasonal cycle of the attenuation depth. The results indicate that a more realistic attenuation of solar radiation slightly reduces the cold bias of the model. While a realistic attenuation of solar radiation hardly affects the annual mean and the seasonal cycle due to anomaly coupling, it significantly affects the interannual variability, especially when the seasonal cycle of the attenuation depth is used. The seasonal cycle of the attenuation depth interacts with the low-frequency equatorial dynamics to enhance warm and cold anomalies, which are further amplified via positive air–sea feedbacks. These results also indicate that interannual variability of the attenuation depths is required to capture the asymmetric biological feedbacks during cold and warm ENSO events.

Low-frequency variability of storms in the northern Black Sea and associated processes in the ocean–atmosphere system

2013 ◽  
Vol 14 (5) ◽  
pp. 1861-1871 ◽  
Author(s):  
A. Polonsky ◽  
V. Evstigneev ◽  
V. Naumova ◽  
E. Voskresenskaya
Keyword(s):  
Black Sea ◽  
Low Frequency ◽  
Atmosphere System ◽  
Low Frequency Variability ◽  
Ocean Atmosphere

scholarly journals Climate Regime Variability for Past and Present Time Slices Simulated by the Fast Ocean Atmosphere Model

2009 ◽  
Vol 22 (1) ◽  
pp. 58-70 ◽  
Author(s):  
Dörthe Handorf ◽  
Klaus Dethloff ◽  
Andrew G. Marshall ◽  
Amanda Lynch
Keyword(s):  
Climate Model ◽  
Low Frequency ◽  
Empirical Orthogonal Functions ◽  
Climate Regime ◽  
Orthogonal Functions ◽  
Medium Resolution ◽  
Ocean Atmosphere ◽  
Dimensional State Space ◽  
Atmosphere Model ◽  
Low Dimensional

Abstract This paper presents an analysis of Northern Hemisphere climate regime variability for three different time slices, simulated by the Fast Ocean Atmosphere Model (FOAM). The three time slices are composed of present-day conditions, the mid-Holocene, and the Last Glacial Maximum (LGM). Climate regimes have been determined by analyzing the structure of a spherical probability density function in a low-dimensional state space spanned by the three leading empirical orthogonal functions. This study confirms the ability of the FOAM medium-resolution climate model to reproduce low-frequency climate variability in the form of regime-like behavior. Three to four regimes have been detected for each time slice. Compared with present-day conditions, new climate regimes appeared for the LGM. For the mid-Holocene, which had slightly different boundary conditions and external forcings than the present-day simulation, the frequency of occurrence of the regimes was altered while only slight changes were found in the structure of some regimes.

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