The seasonal cycle in coupled ocean-atmosphere general circulation models

2000 ◽  
Vol 16 (10-11) ◽  
pp. 775-787 ◽  
Author(s):  
C. Covey ◽  
A. Abe-Ouchi ◽  
G. J. Boer ◽  
B. A. Boville ◽  
U. Cubasch ◽  
...  
Keyword(s):  
Seasonal Cycle ◽  
General Circulation ◽  
General Circulation Models ◽  
Ocean Atmosphere

scholarly journals The Seasonal Cycle over the Tropical Pacific in Coupled Ocean–Atmosphere General Circulation Models

1995 ◽  
Vol 123 (9) ◽  
pp. 2825-2838 ◽  
Author(s):  
C.R. Mechoso ◽  
A.W. Robertson ◽  
N. Barth ◽  
M.K. Davey ◽  
P. Delecluse ◽  
...  
Keyword(s):  
Seasonal Cycle ◽  
General Circulation ◽  
General Circulation Models ◽  
Tropical Pacific ◽  
Ocean Atmosphere ◽  
The Tropical Pacific

Global coupled ocean-atmosphere general circulation models in LASG/IAP

2004 ◽  
Vol 21 (3) ◽  
pp. 444-455 ◽  
Author(s):  
Yu Yongqiang ◽  
Zhang Xuehong ◽  
Guo Yufu
Keyword(s):  
General Circulation ◽  
General Circulation Models ◽  
Ocean Atmosphere

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.

scholarly journals Description and Evaluation of the specified-dynamics experiment in the Chemistry-Climate Model Initiative

2020 ◽  
Vol 20 (6) ◽  
pp. 3809-3840 ◽  
Author(s):  
Clara Orbe ◽  
David A. Plummer ◽  
Darryn W. Waugh ◽  
Huang Yang ◽  
Patrick Jöckel ◽  
...  
Keyword(s):  
Seasonal Cycle ◽  
General Circulation ◽  
Large Scale ◽  
Climate Model ◽  
Phase 1 ◽  
General Circulation Models ◽  
Atmospheric Composition ◽  
Cycle Amplitude ◽  
Free Running ◽  
Dynamics Simulations

Abstract. We provide an overview of the REF-C1SD specified-dynamics experiment that was conducted as part of phase 1 of the Chemistry-Climate Model Initiative (CCMI). The REF-C1SD experiment, which consisted of mainly nudged general circulation models (GCMs) constrained with (re)analysis fields, was designed to examine the influence of the large-scale circulation on past trends in atmospheric composition. The REF-C1SD simulations were produced across various model frameworks and are evaluated in terms of how well they represent different measures of the dynamical and transport circulations. In the troposphere there are large (∼40 %) differences in the climatological mean distributions, seasonal cycle amplitude, and trends of the meridional and vertical winds. In the stratosphere there are similarly large (∼50 %) differences in the magnitude, trends and seasonal cycle amplitude of the transformed Eulerian mean circulation and among various chemical and idealized tracers. At the same time, interannual variations in nearly all quantities are very well represented, compared to the underlying reanalyses. We show that the differences in magnitude, trends and seasonal cycle are not related to the use of different reanalysis products; rather, we show they are associated with how the simulations were implemented, by which we refer both to how the large-scale flow was prescribed and to biases in the underlying free-running models. In most cases these differences are shown to be as large or even larger than the differences exhibited by free-running simulations produced using the exact same models, which are also shown to be more dynamically consistent. Overall, our results suggest that care must be taken when using specified-dynamics simulations to examine the influence of large-scale dynamics on composition.

scholarly journals Understanding El Niño in Ocean–Atmosphere General Circulation Models: Progress and Challenges

2009 ◽  
Vol 90 (3) ◽  
pp. 325-340 ◽  
Author(s):  
Eric Guilyardi ◽  
Andrew Wittenberg ◽  
Alexey Fedorov ◽  
Mat Collins ◽  
Chunzai Wang ◽  
...  
Keyword(s):  
El Niño ◽  
General Circulation ◽  
El Nino ◽  
General Circulation Models ◽  
Ocean Atmosphere

scholarly journals Modeling Seasonal Sudden Stratospheric Warming Climatology Based on Polar Vortex Statistics

2017 ◽  
Vol 30 (24) ◽  
pp. 10101-10116 ◽  
Author(s):  
Matthew F. Horan ◽  
Thomas Reichler
Keyword(s):  
Statistical Model ◽  
Seasonal Cycle ◽  
General Circulation ◽  
Climate Models ◽  
Polar Vortex ◽  
General Circulation Models ◽  
Late Winter ◽  
Sudden Stratospheric Warming ◽  
Main Factors ◽  
Observational Record

This study investigates the climatological frequency distribution of sudden stratospheric warmings (SSWs). General circulation models (GCMs) tend to produce SSW maxima later in winter than observations, which has been considered as a model deficiency. However, the observed record is short, calling into question the representativeness of the observational record. To study the seasonality of SSWs and the factors behind it, the authors use observations, a long control simulation with a stratosphere resolving GCM, and also a simple statistical model that is based on the climatological seasonal cycle of the polar vortex winds. From the combined analysis, the authors conclude that the late-winter SSW maximum seen in most climate models is realistic and that observations would also have a late-winter SSW maximum if more data were available. The authors identify the seasonally varying strengths of the polar vortex and stratospheric wave driving as the two main factors behind the seasonal SSW distribution. The statistical model also indicates that there exists a continuum of weak polar vortex states and that SSWs simply form the tail of normally distributed stratospheric winds.

scholarly journals Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss ?

2021 ◽  
pp. 1-54
Author(s):  
Y. Peings ◽  
Z. Labe ◽  
G. Magnusdottir
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Internal Variability ◽  
General Circulation Models ◽  
Arctic Sea Ice ◽  
Model Assessment ◽  
Atmospheric Response ◽  
Arctic Sea ◽  
Ocean Atmosphere ◽  
Arctic Sea Ice Loss

AbstractThis study presents results from the Polar Amplification Multimodel Intercomparison Project (PAMIP) single-year time-slice experiments that aim to isolate the atmospheric response to Arctic sea ice loss at global warming levels of +2°C. Using two General Circulation Models (GCMs), the ensemble size is increased up to 300 ensemble members, beyond the recommended 100 members. After partitioning the response in groups of 100-ensemble members, the reproducibility of the results is evaluated, with a focus on the response of the mid-latitude jet streams in the North Atlantic and North Pacific. Both atmosphere-only and coupled ocean-atmosphere PAMIP experiments are analyzed. Substantial differences in the mid-latitude response are found among the different experiment subsets, suggesting that 100-member ensembles are still significantly influenced by internal variability, which can mislead conclusions. Despite an overall stronger response, the coupled ocean-atmosphere runs exhibit greater spread due to additional ENSO-related internal variability when the ocean is interactive. The lack of consistency in the response is true for anomalies that are statistically significant according to Student’s-t and False Discovery Rate tests. This is problematic for the multi-model assessment of the response, as some of the spread may be attributed to different model sensitivities while it is due to internal variability. We propose a method to overcome this consistency issue, that allows for more robust conclusions when only 100 ensemble members are used.

scholarly journals Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground

2019 ◽  
Vol 12 (7) ◽  
pp. 2797-2809 ◽  
Author(s):  
Sebastian Scher ◽  
Gabriele Messori
Keyword(s):  
Neural Networks ◽  
Seasonal Cycle ◽  
General Circulation ◽  
Climate Model ◽  
Weather Forecasting ◽  
General Circulation Models ◽  
Lead Times ◽  
Climate Forecasting ◽  
Weather And Climate ◽  
The Neural Networks

Abstract. Recently, there has been growing interest in the possibility of using neural networks for both weather forecasting and the generation of climate datasets. We use a bottom–up approach for assessing whether it should, in principle, be possible to do this. We use the relatively simple general circulation models (GCMs) PUMA and PLASIM as a simplified reality on which we train deep neural networks, which we then use for predicting the model weather at lead times of a few days. We specifically assess how the complexity of the climate model affects the neural network's forecast skill and how dependent the skill is on the length of the provided training period. Additionally, we show that using the neural networks to reproduce the climate of general circulation models including a seasonal cycle remains challenging – in contrast to earlier promising results on a model without seasonal cycle.

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