The 3–4-Week MJO Prediction Skill in a GFDL Coupled Model

2015 ◽  
Vol 28 (13) ◽  
pp. 5351-5364 ◽  
Author(s):  
Baoqiang Xiang ◽  
Ming Zhao ◽  
Xianan Jiang ◽  
Shian-Jiann Lin ◽  
Tim Li ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Strong Dependence ◽  
Intraseasonal Variability ◽  
Coupled Model ◽  
Prediction Skill ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Madden Julian Oscillation ◽  
Geophysical Fluid Dynamics ◽  
Potential Predictability ◽  
Anomaly Correlation Coefficient

Abstract Based on a new version of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled model, the Madden–Julian oscillation (MJO) prediction skill in boreal wintertime (November–April) is evaluated by analyzing 11 years (2003–13) of hindcast experiments. The initial conditions are obtained by applying a simple nudging technique toward observations. Using the real-time multivariate MJO (RMM) index as a predictand, it is demonstrated that the MJO prediction skill can reach out to 27 days before the anomaly correlation coefficient (ACC) decreases to 0.5. The MJO forecast skill also shows relatively larger contrasts between target strong and weak cases (32 versus 7 days) than between initially strong and weak cases (29 versus 24 days). Meanwhile, a strong dependence on target phases is found, as opposed to relative skill independence from different initial phases. The MJO prediction skill is also shown to be about 29 days during the Dynamics of the MJO/Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011 (DYNAMO/CINDY) field campaign period. This model’s potential predictability, the upper bound of prediction skill, extends out to 42 days, revealing a considerable unutilized predictability and a great potential for improving current MJO prediction.

Eastern Pacific Intraseasonal Variability: A Predictability Perspective

2014 ◽  
Vol 27 (23) ◽  
pp. 8869-8883 ◽  
Author(s):  
J. M. Neena ◽  
Xianan Jiang ◽  
Duane Waliser ◽  
June-Yi Lee ◽  
Bin Wang
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Initial Conditions ◽  
Intraseasonal Variability ◽  
Coupled Model ◽  
Eastern Pacific ◽  
Prediction Skill ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Weather And Climate

Abstract The eastern Pacific (EPAC) warm pool is a region of strong intraseasonal variability (ISV) during boreal summer. While the EPAC ISV is known to have large-scale impacts that shape the weather and climate in the region (e.g., tropical cyclones and local monsoon), simulating the EPAC ISV is still a great challenge for present-day global weather and climate models. In the present study, the predictive skill and predictability of the EPAC ISV are explored in eight coupled model hindcasts from the Intraseasonal Variability Hindcast Experiment (ISVHE). Relative to the prediction skill for the boreal winter Madden–Julian oscillation (MJO) in the ISVHE (~15–25 days), the skill for the EPAC ISV is considerably lower in most models, with an average skill around 10 days. On the other hand, while the MJO exhibits a predictability of 35–45 days, the predictability estimate for the EPAC ISV is 20–30 days. The prediction skill was found to be higher when the hindcasts were initialized from the convective phase of the EPAC ISV as opposed to the subsidence phase. Higher prediction skill was also found to be associated with active MJO initial conditions over the western Pacific (evident in four out of eight models), signaling the importance of exploring the dynamic link between the MJO and the EPAC ISV. The results illustrate the possibility and need for improving dynamical prediction systems to facilitate more accurate and longer-lead predictions of the EPAC ISV and associated weather and short-term climate variability.

scholarly journals S2S Prediction in GFDL SPEAR: MJO Diversity and Teleconnections

2021 ◽  
pp. 1-46
Author(s):  
Baoqiang Xiang ◽  
Lucas Harris ◽  
Thomas L. Delworth ◽  
Bin Wang ◽  
Guosen Chen ◽  
...  
Keyword(s):  
Coupled Model ◽  
Prediction Skill ◽  
Maritime Continent ◽  
Potential Predictability ◽  
Quasi Biennial Oscillation ◽  
The North ◽  
Anomaly Correlation Coefficient ◽  
Different Types ◽  
Biennial Oscillation ◽  
The North Pacific

AbstractA subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL SPEAR global coupled model. Based on 20-year hindcast results (2000-2019), the boreal wintertime (November-April) Madden-Julian Oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (15 days). The slow-propagating MJO detours southward when traversing the maritime continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases.The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.

scholarly journals PNA Predictability at Various Time Scales

2013 ◽  
Vol 26 (22) ◽  
pp. 9090-9114 ◽  
Author(s):  
Waqar Younas ◽  
Youmin Tang
Keyword(s):  
Time Scales ◽  
Initial Conditions ◽  
Prediction Skill ◽  
Multiple Model ◽  
Coupled Models ◽  
Potential Predictability ◽  
Effective Measure ◽  
Dispersion Component ◽  
Significant Difference ◽  
Better Than

Abstract In this study, the predictability of the Pacific–North American (PNA) pattern is evaluated on time scales from days to months using state-of-the-art dynamical multiple-model ensembles including the Canadian Historical Forecast Project (HFP2) ensemble, the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) ensemble, and the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES). Some interesting findings in this study include (i) multiple-model ensemble (MME) skill was better than most of the individual models; (ii) both actual prediction skill and potential predictability increased as the averaging time scale increased from days to months; (iii) there is no significant difference in actual skill between coupled and uncoupled models, in contrast with the potential predictability where coupled models performed better than uncoupled models; (iv) relative entropy (REA) is an effective measure in characterizing the potential predictability of individual prediction, whereas the mutual information (MI) is a reliable indicator of overall prediction skill; and (v) compared with conventional potential predictability measures of the signal-to-noise ratio, the MI-based measures characterized more potential predictability when the ensemble spread varied over initial conditions. Further analysis found that the signal component dominated the dispersion component in REA for PNA potential predictability from days to seasons. Also, the PNA predictability is highly related to the signal of the tropical sea surface temperature (SST), and SST–PNA correlation patterns resemble the typical ENSO structure, suggesting that ENSO is the main source of PNA seasonal predictability. The predictable component analysis (PrCA) of atmospheric variability further confirmed the above conclusion; that is, PNA is one of the most predictable patterns in the climate variability over the Northern Hemisphere, which originates mainly from the ENSO forcing.

Assessing Predictive Potential Associated with the MJO during the Boreal Winter

2020 ◽  
Vol 148 (12) ◽  
pp. 4957-4969
Author(s):  
Arun Kumar ◽  
Jieshun Zhu ◽  
Wanqiu Wang
Keyword(s):  
Surface Temperature ◽  
Observational Data ◽  
Prediction Skill ◽  
Meteorological Variables ◽  
Boreal Winter ◽  
Madden Julian Oscillation ◽  
Potential Predictability ◽  
Upper Limit ◽  
Prediction Systems ◽  
A Chain

AbstractIn this paper, the question of potential predictability in meteorological variables associated with skillful prediction of the Madden–Julian oscillation (MJO) during boreal winter is analyzed. The analysis is motivated by the fact that dynamical prediction systems are now capable of predicting MJO up to 30 days or earlier (measured in terms of anomaly correlation for RMM indices). Translating recent gains in MJO prediction skill and relating them back to potential for predicting meteorological variables—for example, precipitation and surface temperature—is not straightforward because of a chain of steps that go into the computation and evaluation of RMM indices. This paper assesses potential predictability in meteorological variables that could be attributed to skillful prediction of the MJO. The analysis is based on the observational data alone and assesses the upper limit of MJO-associated predictability that could be achieved.

scholarly journals The Role of Stochastic Forcing in Ensemble Forecasts of the 1997/98 El Niño

2009 ◽  
Vol 22 (10) ◽  
pp. 2526-2540 ◽  
Author(s):  
Li Shi ◽  
Oscar Alves ◽  
Harry H. Hendon ◽  
Guomin Wang ◽  
David Anderson
Keyword(s):  
El Niño ◽  
Initial Conditions ◽  
El Nino ◽  
Intraseasonal Variability ◽  
Coupled Model ◽  
Ensemble Spread ◽  
Coupled Models ◽  
Central Pacific ◽  
Ensemble Forecasts ◽  
The Impact

Abstract The impact of stochastic intraseasonal variability on the onset of the 1997/98 El Niño was examined using a large ensemble of forecasts starting on 1 December 1996, produced using the Australian Bureau of Meteorology Predictive Ocean Atmosphere Model for Australia (POAMA) seasonal forecast coupled model. This coupled model has a reasonable simulation of El Niño and the Madden–Julian oscillation, so it provides an ideal framework for investigating the interaction between the MJO and El Niño. The experiment was designed so that the ensemble spread was simply a result of internal stochastic variability that is generated during the forecast. For the initial conditions used here, all forecasts led to warm El Niño–type conditions with the amplitude of the warming varying from 0.5° to 2.7°C in the Niño-3.4 region. All forecasts developed an MJO event during the first 4 months, indicating that perhaps the background state favored MJO development. However, the details of the MJOs that developed during December 1996–March 1997 had a significant impact on the subsequent strength of the El Niño event. In particular, the forecasts with the initial MJOs that extended farther into the central Pacific, on average, led to a stronger El Niño, with the westerly winds in the western Pacific associated with the MJO leading the development of SST and thermocline anomalies in the central and eastern Pacific. These results imply a limit to the accuracy with which the strength of El Niño can be predicted because the details of individual MJO events matter. To represent realistic uncertainty, coupled models should be able to represent the MJO, including its propagation into the central Pacific so that forecasts produce sufficient ensemble spread.

scholarly journals Potential Predictability during a Madden–Julian Oscillation Event

2017 ◽  
Vol 30 (14) ◽  
pp. 5345-5360 ◽  
Author(s):  
Charles Jones ◽  
Jimy Dudhia
Keyword(s):  
Initial Conditions ◽  
Spatial Scales ◽  
Forecast Error ◽  
Winter Season ◽  
Wrf Model ◽  
Forecast Errors ◽  
Madden Julian Oscillation ◽  
Potential Predictability ◽  
The Tropics ◽  
Mean Square Errors

The Madden–Julian oscillation (MJO) is an important source of predictability. The boreal 2004/05 winter is used as a case study to conduct predictability experiments with the Weather Research and Forecasting (WRF) Model. That winter season was characterized by an MJO event, weak El Niño, strong North Atlantic Oscillation, and extremely wet conditions over the contiguous United States (CONUS). The issues investigated are as follows: 1) growth of forecast errors in the tropics relative to the extratropics, 2) propagation of forecast errors from the tropics to the extratropics, 3) forecast error growth on spatial scales associated with MJO and non-MJO variability, and 4) the relative importance of MJO and non-MJO tropical variability on predictability of precipitation over CONUS. Root-mean-square errors in forecasts of normalized eddy kinetic energy (NEKE) (200 hPa) show that errors in initial conditions in the tropics grow faster than in the extratropics. Potential predictability extends out to about 4 days in the tropics and 9 days in the extratropics. Forecast errors in the tropics quickly propagate to the extratropics, as demonstrated by experiments in which initial conditions are only perturbed in the tropics. Forecast errors in NEKE (200 hPa) on scales related to the MJO grow slower than in non-MJO variability over localized areas in the tropics and short lead times. Potential predictability of precipitation extends to 1–5 days over most of CONUS but to longer leads (7–12 days) over regions with orographic precipitation in California. Errors in initial conditions on small scales relative to the MJO quickly grow, propagate to the extratropics, and degrade forecast skill of precipitation.

scholarly journals Bred Vector and ENSO Predictability in a Hybrid Coupled Model during the Period 1881–2000

2011 ◽  
Vol 24 (1) ◽  
pp. 298-314 ◽  
Author(s):  
Youmin Tang ◽  
Ziwang Deng
Keyword(s):  
Relative Entropy ◽  
Southern Oscillation ◽  
Heat Content ◽  
Coupled Model ◽  
Prediction Skill ◽  
Good Prediction ◽  
Initial State ◽  
Potential Predictability ◽  
Average Structure ◽  
Enso Predictability

Abstract In this study, a breeding analysis was conducted for a hybrid coupled El Niño–Southern Oscillation (ENSO) model that assimilated a historic dataset of sea surface temperature (SST) for the 120 yr between 1881 and 2000. Meanwhile, retrospective ENSO forecasts were performed for the same period. For a given initial state, 15 bred vectors (BVs) of both SST and upper-ocean heat content (HC) were derived. It was found that the average structure of the 15 BVs was insensitive to the initial states and independent of season and ENSO phase. The average structure of the BVs shared many features already seen in both the final patterns of leading singular vectors and the ENSO BVs of other models. However, individual BV patterns were quite different from case to case. The BV rate (the average cumulative growth rate of BVs) varied seasonally, and the maximum value appeared at the time when the model ran through the boreal spring and summer. It was also sensitive to the strength of the ENSO signal (i.e., the stronger ENSO signal, the smaller the BV rate). Furthermore, ENSO predictability was explored using BV analysis. Emphasis was placed on the relationship between BVs, which are able to characterize potential predictability without requiring observations, and actual prediction skills, which make use of real observations. The results showed that the relative entropy, defined using breeding vectors, was a good measure of potential predictability. Large relative entropy often leads to a good prediction skill; however, when the relative entropy was small, the prediction skill seemed much more variable. At decadal/interdecadal scales, the variations in prediction skills correlated with relative entropy.

Predictability of the Madden–Julian Oscillation in the Intraseasonal Variability Hindcast Experiment (ISVHE)*

2014 ◽  
Vol 27 (12) ◽  
pp. 4531-4543 ◽  
Author(s):  
J. M. Neena ◽  
June Yi Lee ◽  
Duane Waliser ◽  
Bin Wang ◽  
Xianan Jiang
Keyword(s):  
Dynamic Models ◽  
Intraseasonal Variability ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Future Research ◽  
Boreal Winter ◽  
Madden Julian Oscillation ◽  
Maritime Continent ◽  
Coupled Models ◽  
Ensemble Mean

Abstract The Madden–Julian oscillation (MJO) represents a primary source of predictability on the intraseasonal time scales and its influence extends from seasonal variations to weather and extreme events. While the last decade has witnessed marked improvement in dynamical MJO prediction, an updated estimate of MJO predictability from a contemporary suite of dynamic models, in conjunction with an estimate of their corresponding prediction skill, is crucial for guiding future research and development priorities. In this study, the predictability of the boreal winter MJO is revisited based on the Intraseasonal Variability Hindcast Experiment (ISVHE), a set of dedicated extended-range hindcasts from eight different coupled models. Two estimates of MJO predictability are made, based on single-member and ensemble-mean hindcasts, giving values of 20–30 days and 35–45 days, respectively. Exploring the dependence of predictability on the phase of MJO during hindcast initiation reveals a slightly higher predictability for hindcasts initiated from MJO phases 2, 3, 6, or 7 in three of the models with higher prediction skill. The estimated predictability of MJO initiated in phases 2 and 3 (i.e., convection in Indian Ocean with subsequent propagation across Maritime Continent) being equal to or higher than other MJO phases implies that the so-called Maritime Continent prediction barrier may not actually be an intrinsic predictability limitation. For most of the models, the skill for single-member (ensemble mean) hindcasts is less than the estimated predictability limit by about 5–10 days (15–25 days), implying that significantly more skillful MJO forecasts can be afforded through further improvements of dynamical models and ensemble prediction systems (EPS).

scholarly journals Contributions of weakly coupled data assimilation-based land initialization to interannual predictability of summer climate over Europe

2021 ◽  
pp. 1-55
Author(s):  
Pengfei Shi ◽  
Bin Wang ◽  
Yujun He ◽  
Hui Lu ◽  
Kun Yang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Prediction Skill ◽  
Potential Predictability ◽  
Climate System Model ◽  
Climate Predictability ◽  
Summer Climate ◽  
Weakly Coupled ◽  
Coupled Data Assimilation

AbstractLand surface is a potential source of climate predictability over the Northern Hemisphere mid-latitudes but has received less attention than sea surface temperature in this regard. This study quantified the degree to which realistic land initialization contributes to interannual climate predictability over Europe based on a coupled climate system model named FGOALS-g2. The potential predictability provided by the initialization, which incorporates the soil moisture and soil temperature of a land surface reanalysis product into the coupled model with a DRP-4DVar-based weakly coupled data assimilation (WCDA) system, was analyzed first. The effective predictability (i.e., prediction skill) of the hindcasts by FGOALS-g2 with realistic and well-balanced initial conditions from the initialization were then evaluated. Results show an enhanced interannual prediction skill for summer surface air temperature and precipitation in the hindcast over Europe, demonstrating the potential benefit from realistic land initialization. This study highlights the significant contributions of land surface to interannual predictability of summer climate over Europe.

Sign in / Sign up

Export Citation Format

Share Document