Intraseasonal oscillations in 15 atmospheric general circulation models: results from an AMIP diagnostic subproject

Slingo, Julia; Sperber, Kenneth R.; Boyle, J. S.; Céron, J.-P.; Dix, Martin; Dugas, B.; Ebisuzaki, Wesley; Fyfe, John C.; Gregory, D.; Guérémy, Jean‐François; Hack, James J.; Harzallah, Ali; Inness, Peter; Kitoh, A.; Lau, William K. M.; McAvaney, B. J.; Madden, Roland A.; Matthews, Adrian J.; Palmer, Tim; Park, C.-K.; Randall, David A.; Rennó, N. O.

doi:10.1007/s003820050112

Cited by 203 publications

(268 citation statements)

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“…Over the Indian basins, the intraseasonal variability such as the Madden-Julian Oscillation (MJO) may play a strong role in the modulation of tropical storm activity, and GCMs are generally not very skilful in predicting the MJO (Slingo et al 1996). The multi-model has a particularly high skill over the western North Pacific and the South Pacific for the period 1987-2001.…”

Section: Resultsmentioning

confidence: 99%

Seasonal forecasting of tropical storm frequency using a multi‐model ensemble

Vitart

2006

Quart J Royal Meteoro Soc

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SUMMARYThe skill of seven coupled ocean-atmosphere models to predict the frequency of tropical storms from 1987 to 2001 has been assessed using a procedure for tracking model tropical storms. The tropical storm tracker takes account of the difference of atmospheric horizontal resolution between the different models. Results indicate that the models display some skill in predicting the interannual variability of tropical storms over the Atlantic, the eastern North Pacific, the western North Pacific, the Australian basin and the South Pacific. A simple multimodel forecast has been built by adding all the seven ensemble forecasts together after calibration. The skill of the simple multi-model system is overall better than the skill of any individual model. Over a specific basin, combining several models leads to better forecasts than the best individual model. This indicates that the multimodel approach could benefit the dynamical seasonal forecast of tropical storms.This conclusion is also valid for a longer time period . However, the individual models and the simple multi-model display less skill in predicting the interannual variability of tropical storms in the earlier decades.

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Section: Resultsmentioning

confidence: 99%

Seasonal forecasting of tropical storm frequency using a multi‐model ensemble

Vitart

2006

Quart J Royal Meteoro Soc

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“…So more realistic SST simulations (variability as well as mean state) in coupled climate models could enhance blocking development and maintenance. AEA98 noted that nine of their AGCMs, also analysed by Slingo et al (1996), with low tropical variability were not able to simulate blocking well.…”

Section: Discussionmentioning

confidence: 99%

Assessment of atmosphere-ocean general circulation model simulations of winter northern hemisphere atmospheric blocking

Vial

Osborn

2011

Clim Dyn

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An assessment of six coupled AtmosphereOcean General Circulation Models (AOGCMs) is undertaken in order to evaluate their ability in simulating winter atmospheric blocking highs in the northern hemisphere. The poor representation of atmospheric blocking in climate models is a long-standing problem (e.g. D'Andrea et al. in Clim Dyn 4:385-407, 1998), and despite considerable effort in model development, there is only a moderate improvement in blocking simulation. A modified version of the Tibaldi and Molteni (in Tellus A 42:343-365, 1990) blocking index is applied to daily averaged 500 hPa geopotential fields, from the ERA-40 reanalysis and as simulated by the climate models, during the winter periods from 1957 to 1999. The two preferred regions of blocking development, in the Euro-Atlantic and North Pacific, are relatively well captured by most of the models. However, the prominent error in blocking simulations consists of an underestimation of the total frequency of blocking episodes over both regions. A more detailed analysis revealed that this error was due to an insufficient number of medium spells and long-lasting episodes, and a shift in blocking lifetime distributions towards shorter blocks in the Euro-Atlantic sector. In the Pacific, results are more diverse; the models are equally likely to overestimate or underestimate the frequency at different spell lengths. Blocking spatial signatures are relatively well simulated in the Euro-Atlantic sector, while errors in the intensity and geographical location of the blocks emerge in the Pacific. The impact of models' systematic errors on blocking simulation has also been analysed. The time-mean atmospheric circulation biases affect the frequency of blocking episodes, and the maximum event duration in the Euro-Atlantic region, while they sometimes cause geographical mislocations in the Pacific sector. The analysis of the systematic error in timevariability has revealed a negative relationship between the high-frequency variability of the transient eddies in the areas affected by blocking and blocking frequency. The blocking responses to errors in the low-frequency variability are different according to the region considered; the amplitude of the low-frequency variability is positively related to the blocking frequency and persistence in the Euro-Atlantic sector, while no such consistency is observed in the Pacific.

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“…The principal aim of this section is to give a sense of how the NICAM AMIP-type simulation is at work. Although the underlying mechanisms are not clearly understood, it was shown by modeling studies that there is a systematic manner in which ISO biases and mean state biases are related (Slingo et al 1996;Kim et al 2011). A more detailed discussion on the mean state is found in Kodama et al (2015).…”

Section: Overview Of Mean State and Intraseasonal Variabilitymentioning

confidence: 99%

Tropical intraseasonal oscillation simulated in an AMIP-type experiment by NICAM

et al. 2016

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and northward propagation, during summer. The overall shape of the seasonal cycle as measured by the numbers of significant MJO and BSISO days in a month is relatively well captured. Two major biases, however, are also identified. The amplitude of the simulated ISO is weaker by a factor of ~2. Significant BSISO events sometimes appear even during winter (December-April), amounting to ~30 % of the total significant ISO days as opposed to ~2 % in the observation. The results here warrant further studies using the simulation dataset to understand not only many aspects of the dynamics and physics of the ISO but also its role in weather and climate. It is also demonstrated that the concept of the bimodal ISO representation provides a useful framework for assessing model's capability to simulate, and illuminating model's deficiencies in reproducing, the ISO. The nature and causes of the two major biases are also discussed.

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Intraseasonal oscillations in 15 atmospheric general circulation models: results from an AMIP diagnostic subproject

Cited by 203 publications

References 0 publications

Seasonal forecasting of tropical storm frequency using a multi‐model ensemble

Seasonal forecasting of tropical storm frequency using a multi‐model ensemble

Assessment of atmosphere-ocean general circulation model simulations of winter northern hemisphere atmospheric blocking

Tropical intraseasonal oscillation simulated in an AMIP-type experiment by NICAM

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