Sub-seasonal prediction skill for the stratospheric meridional mass circulation variability in CFSv2

2019 ◽  
Vol 53 (1-2) ◽  
pp. 631-650 ◽  
Author(s):  
Yueyue Yu ◽  
Ming Cai ◽  
Chunhua Shi ◽  
Ruikai Yan ◽  
Jian Rao
Keyword(s):  
Seasonal Prediction ◽  
Prediction Skill ◽  
Meridional Mass Circulation ◽  
Mass Circulation

scholarly journals Lagged Ensembles, Forecast Configuration, and Seasonal Predictions

2013 ◽  
Vol 141 (10) ◽  
pp. 3477-3497 ◽  
Author(s):  
Mingyue Chen ◽  
Wanqiu Wang ◽  
Arun Kumar
Keyword(s):  
Lead Time ◽  
Geographical Location ◽  
Seasonal Prediction ◽  
Skill Score ◽  
Negative Influence ◽  
Positive Influence ◽  
Prediction Skill ◽  
Ensemble Size ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts

Abstract An analysis of lagged ensemble seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), is presented. The focus of the analysis is on the construction of lagged ensemble forecasts with increasing lead time (thus allowing use of larger ensemble sizes) and its influence on seasonal prediction skill. Predictions of seasonal means of sea surface temperature (SST), 200-hPa height (z200), precipitation, and 2-m air temperature (T2m) over land are analyzed. Measures of prediction skill include deterministic (anomaly correlation and mean square error) and probabilistic [rank probability skill score (RPSS)]. The results show that for a fixed lead time, and as one would expect, the skill of seasonal forecast improves as the ensemble size increases, while for a fixed ensemble size the forecast skill decreases as the lead time becomes longer. However, when a forecast is based on a lagged ensemble, there exists an optimal lagged ensemble time (OLET) when positive influence of increasing ensemble size and negative influence due to an increasing lead time result in a maximum in seasonal prediction skill. The OLET is shown to depend on the geographical location and variable. For precipitation and T2m, OLET is relatively longer and skill gain is larger than that for SST and tropical z200. OLET is also dependent on the skill measure with RPSS having the longest OLET. Results of this analysis will be useful in providing guidelines on the design and understanding relative merits for different configuration of seasonal prediction systems.

Seasonal-to-Decadal Predictability and Prediction of North American Climate—The Atlantic Influence

2006 ◽  
Vol 19 (23) ◽  
pp. 6005-6024 ◽  
Author(s):  
H. M. Van den Dool ◽  
Peitao Peng ◽  
Åke Johansson ◽  
Muthuvel Chelliah ◽  
Amir Shabbar ◽  
...  
Keyword(s):  
North American ◽  
Seasonal Prediction ◽  
Atmospheric Model ◽  
The United States ◽  
Seasonal Forecast ◽  
Ocean Model ◽  
Prediction Skill ◽  
Basic Material ◽  
Atlantic Sector ◽  
The Impact

Abstract The question of the impact of the Atlantic on North American (NA) seasonal prediction skill and predictability is examined. Basic material is collected from the literature, a review of seasonal forecast procedures in Canada and the United States, and some fresh calculations using the NCEP–NCAR reanalysis data. The general impression is one of low predictability (due to the Atlantic) for seasonal mean surface temperature and precipitation over NA. Predictability may be slightly better in the Caribbean and the (sub)tropical Americas, even for precipitation. The NAO is widely seen as an agent making the Atlantic influence felt in NA. While the NAO is well established in most months, its prediction skill is limited. Year-round evidence for an equatorially displaced version of the NAO (named ED_NAO) carrying a good fraction of the variance is also found. In general the predictability from the Pacific is thought to dominate over that from the Atlantic sector, which explains the minimal number of reported Atmospheric Model Intercomparison Project (AMIP) runs that explore Atlantic-only impacts. Caveats are noted as to the question of the influence of a single predictor in a nonlinear environment with many predictors. Skill of a new one-tier global coupled atmosphere–ocean model system at NCEP is reviewed; limited skill is found in midlatitudes and there is modest predictability to look forward to. There are several signs of enthusiasm in the community about using “trends” (low-frequency variations): (a) seasonal forecast tools include persistence of last 10 years’ averaged anomaly (relative to the official 30-yr climatology), (b) hurricane forecasts are based largely on recognizing a global multidecadal mode (which is similar to an Atlantic trend mode in SST), and (c) two recent papers, one empirical and one modeling, giving equal roles to the (North) Pacific and Atlantic in “explaining” variations in drought frequency over NA on a 20 yr or longer time scale during the twentieth century.

Influence of the QBO on MJO prediction skill in the subseasonal-to-seasonal prediction models

2019 ◽  
Vol 53 (3-4) ◽  
pp. 1681-1695 ◽  
Author(s):  
Yuna Lim ◽  
Seok-Woo Son ◽  
Andrew G. Marshall ◽  
Harry H. Hendon ◽  
Kyong-Hwan Seo
Keyword(s):  
Prediction Models ◽  
Seasonal Prediction ◽  
Prediction Skill

Impact of convective parameterization on the seasonal prediction skill of Indian summer monsoon

2019 ◽  
Vol 53 (9-10) ◽  
pp. 6227-6243 ◽  
Author(s):  
R. Phani Murali Krishna ◽  
Suryachandra A. Rao ◽  
Ankur Srivastava ◽  
Hari Prasad Kottu ◽  
Maheswar Pradhan ◽  
...  
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Convective Parameterization

Machine learning-based dynamical seasonal prediction of summer rainfall in China

2020 ◽  
Author(s):  
Jialin Wang ◽  
Jing Yang ◽  
Hongli Ren ◽  
Jinxiao Li ◽  
Qing Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Method ◽  
Summer Rainfall ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Gradient Boosting ◽  
Disaster Reduction ◽  
Best Fitting ◽  
Application Prospects ◽  
Dynamical Seasonal Prediction

<p>The seasonal prediction of summer rainfall is crucial for regional disaster reduction but currently has a low prediction skill. This study developed a machine learning (ML)-based dynamical (MLD) seasonal prediction method for summer rainfall in China based on suitable circulation fields from an operational dynamical prediction model CAS FGOALS-f2. Through choosing optimum hyperparameters for three ML methods to reach the best fitting and the least overfitting, gradient boosting regression trees eventually exhibit the highest prediction skill, obtaining averaged values of 0.33 in the reference training period (1981-2010) and 0.19 in eight individual years (2011-2018) of independent prediction, which significantly improves the previous dynamical prediction skill by more than 300%. Further study suggests that both reducing overfitting and using the best dynamical prediction are imperative in MLD application prospects, which warrants further investigation.</p>

scholarly journals A New Scheme for Improving the Seasonal Prediction of Summer Precipitation Anomalies

2009 ◽  
Vol 24 (2) ◽  
pp. 548-554 ◽  
Author(s):  
Huijun Wang ◽  
Ke Fan
Keyword(s):  
Summer Precipitation ◽  
Seasonal Prediction ◽  
Western Pacific ◽  
Prediction Skill ◽  
Skill Levels ◽  
Pattern Correlation ◽  
Model Predictions ◽  
Original Scheme ◽  
Precipitation Anomalies ◽  
The Western Pacific

Abstract A new scheme is developed to improve the seasonal prediction of summer precipitation in the East Asian and western Pacific region. The scheme is applied to the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) results. The new scheme is designed to consider both model predictions and observed spatial patterns of historical “analog years.” In this paper, the anomaly pattern correlation coefficient (ACC) between the prediction and the observation, as well as the root-mean-square error, is used to measure the prediction skill. For the prediction of summer precipitation in East Asia and the western Pacific (0°–40°N, 80°–130°E), the prediction skill for the six model ensemble hindcasts for the years of 1979–2001 was increased to 0.22 by using the new scheme from 0.12 for the original scheme. All models were initiated in May and were composed of nine member predictions, and all showed improvement when applying the new scheme. The skill levels of the predictions for the six models increased from 0.08, 0.08, 0.01, 0.14, −0.07, and 0.07 for the original scheme to 0.11, 0.14, 0.10, 0.22, 0.04, and 0.13, respectively, for the new scheme.

scholarly journals Improved seasonal prediction skill of rainfall for the Primera season in Central America

2017 ◽  
Vol 38 ◽  
pp. e255-e268 ◽  
Author(s):  
Eric J. Alfaro ◽  
Xandre Chourio ◽  
Ángel G. Muñoz ◽  
Simon J. Mason
Keyword(s):  
Central America ◽  
Seasonal Prediction ◽  
Prediction Skill

scholarly journals Seasonal prediction and predictability of regional Antarctic sea ice

2021 ◽  
pp. 1-68
Author(s):  
Mitchell Bushuk ◽  
Michael Winton ◽  
F. Alexander Haumann ◽  
Thomas Delworth ◽  
Feiyu Lu ◽  
...  
Keyword(s):  
Sea Ice ◽  
Heat Content ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
The Arctic ◽  
Ice Thickness ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice

AbstractCompared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen and Bellingshausen, Indian, and West Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently-developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal timescales.

scholarly journals Seasonal Prediction of Arctic Sea Ice Extent from a Coupled Dynamical Forecast System

2013 ◽  
Vol 141 (4) ◽  
pp. 1375-1394 ◽  
Author(s):  
Wanqiu Wang ◽  
Mingyue Chen ◽  
Arun Kumar
Keyword(s):  
Sea Ice ◽  
Seasonal Prediction ◽  
Arctic Sea Ice ◽  
Prediction Skill ◽  
Forecast System ◽  
Sea Ice Extent ◽  
Term Trend ◽  
Fully Coupled ◽  
Long Term Trend

Abstract While fully coupled atmosphere–ocean models have been used to study the seasonal predictability of sea ice variations within the context of models’ own variability, their capability in predicting the observed sea ice at the seasonal time scales is not well assessed. In this study, sea ice predictions from the recently developed NCEP Climate Forecast System, version 2 (CFSv2), a fully coupled atmosphere–ocean model including an interactive dynamical sea ice component, are analyzed. The focus of the analysis is the performance of CFSv2 in reproducing observed Northern Hemisphere sea ice extent (SIE). The SIE climatology, long-term trend, interannual variability, and predictability are assessed. CFSv2 contains systematic biases that are dependent more on the forecast target month than the initial month, with a positive SIE bias for the forecast for January–September and a negative SIE bias for the forecast for October–December. A large source of seasonal prediction skill is from the long-term trend, which is underestimated in the CFSv2. Prediction skill of interannual SIE anomalies is found to be primarily within the first three target months and is largest in the summer and early fall. The performance of the prediction of sea ice interannual variations varies from year to year and is found to be related to initial sea ice thickness. Potential predictability based on the forecast ensemble, its dependence on model deficiencies, and implications of the results from this study for improvements in the seasonal sea ice prediction are discussed.

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