Prediction of northern summer low-frequency circulation using a high-order vector auto-regressive model

Wang, Lei; Ting, Mingfang; Chapman, David; Lee, Dong Eun; Henderson, Naomi; Yuan, Xiaojun

doi:10.1007/s00382-015-2607-0

Cited by 8 publications

(4 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main mode of tropopause temperature variations is related to changes in equatorial planetary waves including tropical Rossby and Kelvin waves in response to ENSO 23,39,86 . ENSO influences the deep convection that further affects vertical velocity near the tropopause 87 . Xie et al 50 showed that the tropopause temperature responses to the canonical ENSO are stronger than those to ENSO Modoki.…”

Section: Discussionmentioning

confidence: 99%

Analysis of factors influencing tropical lower stratospheric water vapor during 1980–2017

Xie

Sun

et al. 2020

npj Clim Atmos Sci

View full text Add to dashboard Cite

Tropical cold point tropopause temperature (CPT) anomalies determine lower stratospheric water vapor (LSWV) variations, leading to a high correlation between variations in tropical average CPT and changes in tropical average LSWV. However, this high correlation is only found in winter and spring. This work revisits the factors controlling LSWV variations using observations and simulations over the past~40 years. It is found that the first and second empirical orthogonal function (EOF) modes of tropical CPT variations together explain the tropical average LSWV changes much better than the tropical average CPT variations. The high correlation between the first and second EOF modes of tropical CPT variations and tropical average LSWV changes holds in all four seasons. A further analysis shows that the first and second EOF modes of tropical CPT variations are related to canonical El Niño-Southern Oscillation (ENSO) activity and sea surface temperature (SST) variations in the central Pacific Ocean, respectively. ENSO Modoki is also an important factor that affects LSWV variations by influencing the vertical velocity at the tropopause. The quasi-biennial oscillation (QBO) affects the CPT, and is the third process modulating the LSWV changes. The simulations also support the results.

show abstract

Section: Discussionmentioning

confidence: 99%

Analysis of factors influencing tropical lower stratospheric water vapor during 1980–2017

Xie

Sun

et al. 2020

npj Clim Atmos Sci

View full text Add to dashboard Cite

show abstract

“…Current seasonal climate prediction schemes rarely take into account the TC effect on accumulated rainfall for water availability projections, mainly because TC trajectories are hard to predict . TC trajectories are primarily determined by the beta effect (Adem, 1956) and the dominant large-scale circulations (Hill and Lackmann, 2009), which could be adequately simulated by current climate models (e.g., Trenberth et al, 1998;Wang et al, 2015). Some schemes only predict the number of TCs and the potential intensity distribution during a season.…”

Section: Introductionmentioning

confidence: 99%

The Role of Tropical Cyclones in Precipitation Over the Tropical and Subtropical North America

Domínguez

Magaña

2018

Front. Earth Sci.

View full text Add to dashboard Cite

Tropical cyclones (TCs) are essential elements of the hydrological cycle in tropical and subtropical regions. In the present study, the contribution of TCs to seasonal precipitation around the tropical and subtropical North America is examined. When TC activity over the tropical eastern Pacific (TEP) or the Intra Americas Seas (IAS) is below (abovenormal), regional precipitation may be below (above-normal). However, it is not only the number of TCs what may change seasonal precipitation, but the trajectory of the systems. TCs induce intense precipitation over continental regions if they are close enough to shorelines, for instance, if the TC center is located, on average, less than 500 km-distant from the coast. However, if TCs are more remote than this threshold distance, the chances of rain over continental regions decrease, particularly in arid and semi-arid regions. In addition, a distant TC may induce subsidence or produce moisture divergence that inhibits, at least for a few days, convective activity farther away than the threshold distance. An analysis of interannual variability in the TCs that produce precipitation over the tropical and subtropical North America shows that some regions in northern Mexico, which mostly depend on this effect to undergo wet years, may experience seasonal negative anomalies in precipitation if TCs trajectories are remote. Therefore, TCs (activity and trajectories) are important modulators of climate variability on various time scales, either by producing intense rainfall or by inhibiting convection at distant regions from their trajectory. The impact of such variations on water availability in northern Mexico may be relevant, since water availability in dams recovers under the effects of TC rainfall. Seasonal precipitation forecasts or climate change scenarios for these regions should take into account the effect of TCs, if regional adaptation strategies are implemented.

show abstract

“…These studies not only helped us to better understand the feedbacks between the atmospheric circulation and Arctic SIC but also showed the potential predictability of sea ice at the intraseasonal time scale. The usefulness of the VAR model has also been shown in other regions of the earth's climate system, for example, in predicting tropical Pacific and Atlantic sea surface temperature anomalies, as well as the Northern Hemisphere summer atmospheric low-frequency variability (e.g., Chapman et al 2015;Lee et al 2015;Wang et al 2015). Similar statistical models, such as the Markov model (e.g., Chen and Yuan 2004), have been used extensively in the seasonal forecast of Antarctic and Arctic sea ice.…”

Section: Introductionmentioning

confidence: 99%

Predicting Summer Arctic Sea Ice Concentration Intraseasonal Variability Using a Vector Autoregressive Model*

et al. 2016

Self Cite

View full text Add to dashboard Cite

Recent Arctic sea ice changes have important societal and economic impacts and may lead to adverse effects on the Arctic ecosystem, weather, and climate. Understanding the predictability of Arctic sea ice melting is thus an important task. A vector autoregressive (VAR) model is evaluated for predicting the summertime (May–September) daily Arctic sea ice concentration on the intraseasonal time scale, using only the daily sea ice data and without direct information of the atmosphere and ocean. The intraseasonal forecast skill of Arctic sea ice is assessed using the 1979–2012 satellite data. The cross-validated forecast skill of the VAR model is found to be superior to both the anomaly persistence and damped anomaly persistence at lead times of ~20–60 days, especially over northern Eurasian marginal seas and the Beaufort Sea. The daily forecast of ice concentration also leads to predictions of ice-free dates and September mean sea ice extent. In addition to capturing the general seasonal melt of sea ice, the model is also able to capture the interannual variability of the melting, from partial melt of the marginal sea ice in the beginning of the period to almost a complete melt in the later years. While the detailed mechanism leading to the high predictability of intraseasonal sea ice concentration needs to be further examined, the study reveals for the first time that Arctic sea ice can be predicted statistically with reasonable skill at the intraseasonal time scales given the small signal-to-noise ratio of daily data.

show abstract

Prediction of northern summer low-frequency circulation using a high-order vector auto-regressive model

Cited by 8 publications

References 40 publications

Analysis of factors influencing tropical lower stratospheric water vapor during 1980–2017

Analysis of factors influencing tropical lower stratospheric water vapor during 1980–2017

The Role of Tropical Cyclones in Precipitation Over the Tropical and Subtropical North America

Predicting Summer Arctic Sea Ice Concentration Intraseasonal Variability Using a Vector Autoregressive Model*

Contact Info

Product

Resources

About