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 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 Seasonal prediction of the boreal winter stratosphere

2021 ◽  
Author(s):  
Alice Portal ◽  
Paolo Ruggieri ◽  
Froila M. Palmeiro ◽  
Javier García-Serrano ◽  
Daniela I. V. Domeisen ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Polar Vortex ◽  
Seasonal Prediction ◽  
Wave Activity ◽  
Boreal Winter ◽  
Quasi Biennial Oscillation ◽  
Stratospheric Polar Vortex ◽  
Eddy Heat Flux ◽  
Prediction Systems ◽  
Model Database

AbstractThe predictability of the Northern Hemisphere stratosphere and its underlying dynamics are investigated in five state-of-the-art seasonal prediction systems from the Copernicus Climate Change Service (C3S) multi-model database. Special attention is devoted to the connection between the stratospheric polar vortex (SPV) and lower-stratosphere wave activity (LSWA). We find that in winter (December to February) dynamical forecasts initialised on the first of November are considerably more skilful than empirical forecasts based on October anomalies. Moreover, the coupling of the SPV with mid-latitude LSWA (i.e., meridional eddy heat flux) is generally well reproduced by the forecast systems, allowing for the identification of a robust link between the predictability of wave activity above the tropopause and the SPV skill. Our results highlight the importance of November-to-February LSWA, in particular in the Eurasian sector, for forecasts of the winter stratosphere. Finally, the role of potential sources of seasonal stratospheric predictability is considered: we find that the C3S multi-model overestimates the stratospheric response to El Niño–Southern Oscillation (ENSO) and underestimates the influence of the Quasi–Biennial Oscillation (QBO).

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 Representation of Boreal Winter MJO and Its Teleconnection in a Dynamical Ensemble Seasonal Prediction System

2018 ◽  
Vol 31 (21) ◽  
pp. 8803-8818 ◽  
Author(s):  
Hyerim Kim ◽  
Myong-In Lee ◽  
Daehyun Kim ◽  
Hyun-Suk Kang ◽  
Yu-Kyung Hyun
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Seasonal Prediction ◽  
La Niña ◽  
Prediction Skill ◽  
Boreal Winter ◽  
Teleconnection Patterns ◽  
La Nina ◽  
Forecast Lead Time

This study examines the representation of the Madden–Julian oscillation (MJO) and its teleconnection in boreal winter in the Global Seasonal Forecast System, version 5 (GloSea5), using 20 years (1991–2010) of hindcast data. The sensitivity of the performance to the polarity of El Niño–Southern Oscillation (ENSO) is also investigated. The real-time multivariate MJO index of Wheeler and Hendon is used to assess MJO prediction skill while intraseasonal 200-hPa streamfunction anomalies are used to evaluate the MJO teleconnection. GloSea5 exhibits significant MJO prediction skill up to 25 days of forecast lead time. MJO prediction skill in GloSea5 also depends on initial MJO phases, with relatively enhanced (degraded) performance when the initial MJO phase is 2 or 3 (8 or 1) during the first 2 weeks of the hindcast period. GloSea5 depicts the observed MJO teleconnection patterns in the extratropics realistically up to 2 weeks albeit weaker than the observed. The ENSO-associated basic-state changes in the tropics and in the midlatitudes are reasonably represented in GloSea5. MJO prediction skill during the first 2 weeks of the hindcast is slightly higher in neutral and La Niña years than in El Niño years, especially in the upper-level zonal wind anomalies. Presumably because of the better representation of MJO-related tropical heating anomalies, the Northern Hemispheric MJO teleconnection patterns in neutral and La Niña years are considerably better than those in El Niño years.

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 Understanding Skill of Seasonal Mean Precipitation Prediction over California during Boreal Winter and Role of Predictability Limits

2020 ◽  
Vol 33 (14) ◽  
pp. 6141-6163
Author(s):  
Arun Kumar ◽  
Mingyue Chen
Keyword(s):  
Role Model ◽  
Systematic Approach ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Boreal Winter ◽  
Southeast United States ◽  
The North ◽  
Precipitation Prediction ◽  
Prediction Systems

AbstractUsing extensive hindcasts from seasonal prediction systems participating in the North American Multi-Model Ensemble (NMME), possible causes for low skill in predicting seasonal mean precipitation over California during December–February (DJF) are investigated. The analysis focuses on investigating two possibilities for low prediction skill: role model biases or inherent predictability limits. The motivation for the analysis was the seasonal prediction during DJF 2015/16 that called for enhanced probability for above normal precipitation over southern California (which was consistent with expected conditions during an extreme El Niño) while the observed precipitation was below normal. Based on various analysis approaches and using hindcast datasets from multiple seasonal prediction systems, we build up the evidence that low skill in predicting seasonal mean precipitation over California is likely to be due to inherent predictability associated with a low signal-to-noise (SNR) regime. For the same set of seasonal prediction systems, the precipitation variability over California is contrasted with that over the southeast United States where prediction skill, as well as the SNR, is higher. The discussion also notes that building a knowledge base that goes beyond the well-known response to ENSO (based on the linear regression or composite techniques) has proven to be difficult and a systematic approach to reaching resolution to some of the overarching questions is required, and toward that end, a pathway is suggested.

scholarly journals Subseasonal Forecast Skill over the Northern Polar Region in Boreal Winter

2020 ◽  
Vol 33 (5) ◽  
pp. 1935-1951 ◽  
Author(s):  
Hai Lin
Keyword(s):  
Polar Region ◽  
Polar Vortex ◽  
Arctic Region ◽  
Seasonal Prediction ◽  
Forecast Skill ◽  
The Arctic ◽  
Boreal Winter ◽  
Bering Strait ◽  
Stratospheric Polar Vortex ◽  
The Arctic Oscillation

AbstractPentad (5-day averaged) forecast skill over the Arctic region in boreal winter is evaluated for the subseasonal to seasonal prediction (S2S) systems from three operational centers: the European Centre for Medium-Range Weather Forecasts (ECMWF), the U.S. National Centers for Environmental Prediction (NCEP), and Environment and Climate Change Canada (ECCC). The results indicate that for a lead time longer than about 10 days the forecast skill of 2-m air temperature and 500-hPa geopotential height in the Arctic area is low compared to the tropical and midlatitude regions. The three S2S systems have comparable forecast skill in the northern polar region. Relatively high skill is observed in the Arctic sector north of the Bering Strait in pentads 4–6. Possible sources of S2S predictability in the polar region are explored. The polar forecast skill is found to be dependent on the phase of the Arctic Oscillation (AO) in the initial condition; that is, forecasts initialized with the negative AO are more skillful than those starting from the positive AO. This is likely due to the influence of the stratospheric polar vortex. The tropical MJO is found to also influence the prediction skill in the polar region. Forecasts starting from MJO phases 6–7, which correspond to suppressed convection in the equatorial eastern Indian Ocean and enhanced convection in the tropical western Pacific, tend to be more skillful than those initialized from other MJO phases. To improve the polar prediction on the subseasonal time scale, it is important to have a well-represented stratosphere and tropical MJO and their associated teleconnections in the model.

Roles of Remote and Local Forcings in the Variation and Prediction of Regional Maritime Continent Rainfall in Wet and Dry Seasons

2016 ◽  
Vol 29 (24) ◽  
pp. 8871-8879 ◽  
Author(s):  
Tuantuan Zhang ◽  
Song Yang ◽  
Xingwen Jiang ◽  
Bohua Huang
Keyword(s):  
Late Summer ◽  
Seasonal Prediction ◽  
Early Spring ◽  
Dry Season ◽  
Prediction Skill ◽  
Boreal Winter ◽  
Wet Season ◽  
Maritime Continent ◽  
Rainfall Prediction ◽  
Relationship Of

Abstract Seasonal prediction of extratropical climate (e.g., the East Asian climate) is partly dependent upon the prediction skill for rainfall over the Maritime Continent (MC). A previous study by the authors found that the NCEP Climate Forecast System, version 2 (CFSv2), had difference in skill between predicting rainfall over the western MC (WMC) and the eastern MC (EMC), especially in the wet season. In this study, the potential mechanisms for this phenomenon are examined. It is shown that observationally in the wet season (from boreal winter to early spring) the EMC rainfall is closely linked to both ENSO and local sea surface temperature (SST) anomalies, whereas the WMC rainfall is only moderately correlated with ENSO. The model hindcast unrealistically predicts the relationship of the WMC rainfall with local SST and ENSO (even opposite to the observed feature), which contributes to lower prediction skill for the WMC rainfall. In the dry season (from boreal late summer to fall), the rainfall over the entire MC is significantly influenced by both ENSO and local SST in observations and this feature is well captured by the CFSv2. Therefore, the hindcasts do not show apparently different skill in rainfall prediction for EMC and WMC in the dry season. The possible roles of atmospheric internal processes are also discussed.

scholarly journals Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e044500
Author(s):  
Yauhen Statsenko ◽  
Fatmah Al Zahmi ◽  
Tetiana Habuza ◽  
Klaus Neidl-Van Gorkom ◽  
Nazar Zaki
Keyword(s):  
Lymphocyte Count ◽  
Prediction Models ◽  
Model Performance ◽  
Area Under The Curve ◽  
Predictive Biomarkers ◽  
Supervised Machine Learning ◽  
Study Cohort ◽  
Threshold Values ◽  
Laboratory Findings ◽  
Study Results

BackgroundDespite the necessity, there is no reliable biomarker to predict disease severity and prognosis of patients with COVID-19. The currently published prediction models are not fully applicable to clinical use.ObjectivesTo identify predictive biomarkers of COVID-19 severity and to justify their threshold values for the stratification of the risk of deterioration that would require transferring to the intensive care unit (ICU).MethodsThe study cohort (560 subjects) included all consecutive patients admitted to Dubai Mediclinic Parkview Hospital from February to May 2020 with COVID-19 confirmed by the PCR. The challenge of finding the cut-off thresholds was the unbalanced dataset (eg, the disproportion in the number of 72 patients admitted to ICU vs 488 non-severe cases). Therefore, we customised supervised machine learning (ML) algorithm in terms of threshold value used to predict worsening.ResultsWith the default thresholds returned by the ML estimator, the performance of the models was low. It was improved by setting the cut-off level to the 25th percentile for lymphocyte count and the 75th percentile for other features. The study justified the following threshold values of the laboratory tests done on admission: lymphocyte count <2.59×109/L, and the upper levels for total bilirubin 11.9 μmol/L, alanine aminotransferase 43 U/L, aspartate aminotransferase 32 U/L, D-dimer 0.7 mg/L, activated partial thromboplastin time (aPTT) 39.9 s, creatine kinase 247 U/L, C reactive protein (CRP) 14.3 mg/L, lactate dehydrogenase 246 U/L, troponin 0.037 ng/mL, ferritin 498 ng/mL and fibrinogen 446 mg/dL.ConclusionThe performance of the neural network trained with top valuable tests (aPTT, CRP and fibrinogen) is admissible (area under the curve (AUC) 0.86; 95% CI 0.486 to 0.884; p<0.001) and comparable with the model trained with all the tests (AUC 0.90; 95% CI 0.812 to 0.902; p<0.001). Free online tool at https://med-predict.com illustrates the study results.

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