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

scholarly journals NAO Influence on the MJO and its Prediction Skill in the Subseasonal-to-Seasonal Prediction Models

2021 ◽  
pp. 1-45
Author(s):  
Hai Lin ◽  
Zhiyong Huang ◽  
Harry Hendon ◽  
Gilbert Brunet
Keyword(s):  
Prediction Models ◽  
Numerical Models ◽  
Research Programme ◽  
Tropical Indian Ocean ◽  
Seasonal Prediction ◽  
Forecast Skill ◽  
Phase Changes ◽  
Prediction Skill ◽  
The North ◽  
The North Atlantic Oscillation

AbstractBased on the database of the Subseasonal to Seasonal (S2S) Prediction project of the World Weather Research Programme (WWRP) / World Climate Research Programme (WCRP), the influence of the North Atlantic Oscillation (NAO) on the Madden- Julian Oscillation (MJO) and its forecast skill is investigated. It is found that most models can capture the MJO phase changes following positive and negative NAO events. About 20 days after initialized with a positive (negative) NAO, the forecast MJO appears more frequently in phase 7 (3), which corresponds to reduced (enhanced) convection in the tropical Indian Ocean and enhanced (suppressed) convection in the western Pacific. In most S2S models the MJO prediction skill is dependent on the NAO amplitude and phase in the initial condition. A strong NAO leads to a better MJO forecast skill than a weak NAO. The MJO skill tends to be higher when the forecast starts from a negative NAO than a positive NAO. These results indicates that there is a strong Northern extratropical influence on the MJO and its forecast skill. It is important for numerical models to better represent the NAO influence to improve the simulation and prediction of the MJO.

scholarly journals Altered sub-seasonal predictability of Community Atmosphere Model 5 (CAM5) in CESM 1.2.1 by the choices of dynamical core

2020 ◽  
Author(s):  
Ha-Rim Kim ◽  
Baek-Min Kim ◽  
Sang-Yoon Jun ◽  
Yong-Sang Choi
Keyword(s):  
North America ◽  
Prediction Models ◽  
Initial Conditions ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
The Arctic ◽  
Late Winter ◽  
Grid System ◽  
Early Winter ◽  
Dynamical Core

Abstract. This study investigates the prediction skill of sub-seasonal prediction models that vary based on the choice of two dynamical cores: the finite volume (FV) dynamical core on a latitude-longitude grid system and the spectral element (SE) dynamical core on a cubed-sphere grid system. Recent research showed that the SE dynamical core on a uniform grid system increases parallel scalability and removes the need for polar filters for mitigating uncertainty in climate prediction, particularly for the Arctic region. However, it still remains questionable whether the choice of dynamical cores can actually yield significant changes in prediction skill. To tackle this issue, we implemented a sub-seasonal prediction model based on the Community Atmospheric Model version 5 by incorporating the above two dynamical cores with virtually the same physics schemes. Sub-seasonal prediction skills of the SE dynamical core and FV dynamical core are verified with ERA-Interim reanalysis during the early winter (November–December) and the late winter (January–February) from 2001/2002 to 2017/2018. The prediction skills of two different dynamical cores were significantly different regardless of the similar physics scheme. In the ocean, the predictability of the SE dynamical core is similar to that of the FV dynamical core, mostly because our simulation configuration imposes the same boundary and initial conditions at the surface. Notable differences in the one-month predictability between the two cores are observed for the wintertime Arctic and mid-latitudes, particularly over North America and Eurasia continents. With a one-month lead, the SE dynamical core exhibited higher predictability over North America in late winter (r ≈ 0.45 in SE, r ≈ 0.10 in FV) whereas the FV dynamical core showed relatively higher predictability in East Asia and Eurasia in early winter (r ≈ 0.15 in SE, r ≈ 0.43 in FV). Therefore, we conclude that caution is needed when selecting the dynamical cores of sub-seasonal prediction models. Partially, these differences can be ascribed to the different manifestations of Arctic-mid-latitude linkage in the two dynamical cores; the SE dynamical core captures warmer Arctic and colder mid-latitudes relatively better than the FV dynamical core.

scholarly journals Forecast Skill of the NAO in the Subseasonal to-Seasonal Prediction Models

2021 ◽  
pp. 1-50
Author(s):  
Pei-Ning Feng ◽  
Hai Lin ◽  
Jacques Derome ◽  
Timothy M. Merlis
Keyword(s):  
Prediction Models ◽  
Model Performance ◽  
Seasonal Prediction ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Boreal Winter ◽  
Consistent Difference ◽  
Initial Condition ◽  
Quasi Biennial Oscillation ◽  
Project Model

AbstractThe prediction skill of the North Atlantic Oscillation (NAO) in boreal winter is assessed in the operational models of the WCRP/WWRP Subseasonal-to-Seasonal (S2S) prediction project. Model performance in representing the contribution of different processes to the NAO forecast skill is evaluated. The S2S models with relatively higher stratospheric vertical resolutions (high-top models) are in general more skillful in predicting the NAO than those models with relatively lower stratospheric resolutions (low-top models). Comparison of skill is made between different groups of forecasts based on initial condition characteristics: phase and amplitude of the NAO, easterly and westerly phases of the quasi-biennial oscillation (QBO), warm and cold phases of ENSO, and phase and amplitude of the Madden-Julia Oscillation (MJO). The forecasts with a strong NAO in the initial condition are more skillful than with a weak NAO. Those with negative NAO tend to have more skillful predictions than positive NAO. Comparisons of NAO skill between forecasts during easterly and westerly QBO and between warm and cold ENSO show no consistent difference for the S2S models. Forecasts with strong initial MJO tend to be more skillful in the NAO prediction than weak MJO. Among the eight phases of MJO in the initial condition, phases 3-4 and phase 7 have better NAO forecast skills compared with the other phases.The results of this study have implications for improving our understanding of sources of predictability of the NAO. The situation dependence of the NAO prediction skill is likely useful in identifying “ windows of opportunity” for subseasonal to seasonal predictions.

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.

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
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