Contrasting the skills and biases of deterministic predictions for the two types of El Niño

2020 ◽  
Author(s):  
Fei Zheng ◽  
Jin-Yi Yu ◽  
Jiang Zhu
Keyword(s):  
Southern Oscillation ◽  
Eastern Pacific ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Coupled Models ◽  
Central Pacific ◽  
High Priority Task ◽  
Ensemble Prediction System ◽  
Priority Task ◽  
Skill Scores

<p>The tropical Pacific has experienced a new type of El Niño, which has occurred particularly frequently during the last decade and is referred to as the central Pacific (CP) El Niño. Various coupled models with different degrees of complexities have been used to make real-time El Niño predictions, but large uncertainties still exist in the forecasts. It is still not yet known how much of the uncertainty is specifically related to the new CP type of El Niño and how much is common to both this type and the conventional Eastern Pacific (EP) type of El Niño. In this study, the deterministic performance of an El Niño-Southern Oscillation (ENSO) ensemble prediction system (EPS) is examined for these two types of El Niño. Ensemble hindcasts are performed for the nine EP El Niño events and twelve CP El Niño events that have occurred since 1950. The results show that (1) the skill scores for the EP events are significantly better than those for the CP events at all lead times; (2) the systematic forecast biases come mostly from the prediction of the CP events; and (3) the systematic error is characterized by an overly warm eastern Pacific during the spring season, indicating a stronger spring prediction barrier for the CP El Niño. Further improvements of coupled atmosphere-ocean models in CP El Niño prediction should be recognized as a major challenge and high-priority task for the climate prediction community.</p>

scholarly journals ENSO predictability over the past 137 years based on a CESM ensemble prediction system

2021 ◽  
pp. 1-38
Author(s):  
Ting Liu ◽  
Xunshu Song ◽  
Youmin Tang ◽  
Zheqi Shen ◽  
Xiaoxiao Tan
Keyword(s):  
General Circulation ◽  
Southern Oscillation ◽  
Ensemble Prediction ◽  
Coupled Models ◽  
Abrupt Decrease ◽  
Ensemble Prediction System ◽  
Enso Events ◽  
Intermediate Complexity ◽  
Enso Predictability ◽  
Dispersion Component

AbstractIn this study, we conducted an ensemble retrospective prediction from 1881 to 2017 using the Community Earth System Model to evaluate El Niño–Southern Oscillation (ENSO) predictability and its variability on different timescales. To our knowledge, this is the first assessment of ENSO predictability using a long-term ensemble hindcast with a complicated coupled general circulation model (CGCM). Our results indicate that both the dispersion component (DC) and signal component (SC) contribute to the interannual variation of ENSO predictability (measured by relative entropy, RE). In detail, the SC is more important for ENSO events, whereas the DC is of comparable important for short lead times and in weak ENSO signal years. The SC dominates the seasonal variation of ENSO predictability, and an abrupt decrease in signal intensity results in the spring predictability barrier feature of ENSO. At the interdecadal scale, the SC controls the variability of ENSO predictability, while the magnitude of ENSO predictability is determined by the DC. The seasonal and interdecadal variations of ENSO predictability in the CGCM are generally consistent with results based on intermediate complexity and hybrid coupled models. However, the DC has a greater contribution in the CGCM than that in the intermediate complexity and hybrid coupled models.

Extended Prediction of North Indian Ocean Tropical Cyclones

2012 ◽  
Vol 27 (3) ◽  
pp. 757-769 ◽  
Author(s):  
James I. Belanger ◽  
Peter J. Webster ◽  
Judith A. Curry ◽  
Mark T. Jelinek
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
North Indian Ocean ◽  
Ensemble Prediction ◽  
Tropical Cyclone Genesis ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
The North

Abstract This analysis examines the predictability of several key forecasting parameters using the ECMWF Variable Ensemble Prediction System (VarEPS) for tropical cyclones (TCs) in the North Indian Ocean (NIO) including tropical cyclone genesis, pregenesis and postgenesis track and intensity projections, and regional outlooks of tropical cyclone activity for the Arabian Sea and the Bay of Bengal. Based on the evaluation period from 2007 to 2010, the VarEPS TC genesis forecasts demonstrate low false-alarm rates and moderate to high probabilities of detection for lead times of 1–7 days. In addition, VarEPS pregenesis track forecasts on average perform better than VarEPS postgenesis forecasts through 120 h and feature a total track error growth of 41 n mi day−1. VarEPS provides superior postgenesis track forecasts for lead times greater than 12 h compared to other models, including the Met Office global model (UKMET), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Global Forecasting System (GFS), and slightly lower track errors than the Joint Typhoon Warning Center. This paper concludes with a discussion of how VarEPS can provide much of this extended predictability within a probabilistic framework for the region.

The relationship between the El Niño Southern Oscillation and Australian monsoon rainfall on decadal time-scales

2021 ◽  
Author(s):  
Hanna Heidemann ◽  
Joachim Ribbe ◽  
Benjamin J. Henley ◽  
Tim Cowan ◽  
Christa Pudmenzky ◽  
...  
Keyword(s):  
Monsoon Rainfall ◽  
Decadal Variability ◽  
Southern Oscillation ◽  
Eastern Pacific ◽  
Central Pacific ◽  
Australian Monsoon ◽  
Enso Events ◽  
Pacific Decadal Variability ◽  
Prediction Systems ◽  
Different Response

<p>This research analyses the observed relationship between eastern and central Pacific El Niño Southern Oscillation (ENSO) events and Australian monsoon rainfall (AUMR) on a decadal timescale during the December to March monsoon months. To assess the decadal influence of the different flavours of ENSO on the AUMR, we focus on the phases of the Interdecadal Pacific Oscillation (IPO) over the period 1920 to 2020.  The AUMR is characterized by substantial decadal variability, which appears to be linked to the positive and negative phases of the IPO. During the past two historical negative IPO phases, significant correlations have been observed between central Pacific sea surface temperature (SST) anomalies and AUMR over both the northeast and northwest of Australia. This central Pacific SST-AUMR relationship has strengthened from the first negative IPO phase (mid-1940s to the mid-1970s) to the second (late 1990s to mid-2010s), while the eastern Pacific SST-AUMR influence has weakened. Composite rainfall anomalies over Australia reveal a different response of AUMR to central Pacific El Niño/La Niña and eastern Pacific La Niña events during positive IPO and negative IPO phases. This research clearly shows that ENSO's influence on AUMR is modulated by Pacific decadal variability, however this teleconnection, in itself, can change between similar decadal Pacific states.  Going forward, as decadal prediction systems improve and become more mainstream, the IPO phase could be used as a potential source for decadal predictability of the tendency of AUMR.  </p>

scholarly journals Merits of a 108-Member Ensemble System in ENSO and IOD Predictions

2019 ◽  
Vol 32 (3) ◽  
pp. 957-972 ◽  
Author(s):  
Takeshi Doi ◽  
Swadhin K. Behera ◽  
Toshio Yamagata
Keyword(s):  
Seasonal Prediction ◽  
Ensemble Prediction ◽  
Economic Losses ◽  
Prediction System ◽  
Ensemble Size ◽  
Ensemble Prediction System ◽  
Skill Scores ◽  
Operational Systems ◽  
The Indian Ocean ◽  
Climate Events

This paper explores merits of 100-ensemble simulations from a single dynamical seasonal prediction system by evaluating differences in skill scores between ensembles predictions with few (~10) and many (~100) ensemble members. A 100-ensemble retrospective seasonal forecast experiment for 1983–2015 is beyond current operational capability. Prediction of extremely strong ENSO and the Indian Ocean dipole (IOD) events is significantly improved in the larger ensemble. It indicates that the ensemble size of 10 members, used in some operational systems, is not adequate for the occurrence of 15% tails of extreme climate events, because only about 1 or 2 members (approximately 15% of 12) will agree with the observations. We also showed an ensemble size of about 50 members may be adequate for the extreme El Niño and positive IOD predictions at least in the present prediction system. Even if running a large-ensemble prediction system is quite costly, improved prediction of disastrous extreme events is useful for minimizing risks of possible human and economic losses.

scholarly journals The skill assessment of ENSO prediction issued by JMA ensemble prediction system and CFSv2

2021 ◽  
Vol 893 (1) ◽  
pp. 012047
Author(s):  
R Rahmat ◽  
A M Setiawan ◽  
Supari
Keyword(s):  
Southern Oscillation ◽  
Skill Score ◽  
Skill Assessment ◽  
Japan Meteorological Agency ◽  
Ensemble Prediction ◽  
In Situ Observation ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Enso Prediction ◽  
The Government

Abstract Indonesian climate is strongly affected by El Niño-Southern Oscillation (ENSO) as one of climate-driven factor. ENSO prediction during the upcoming months or year is crucial for the government in order to design the further strategic policy. Besides producing its own ENSO prediction, BMKG also regularly releases the status and ENSO prediction collected from other climate centers, such as Japan Meteorological Agency (JMA) and National Oceanic and Atmospheric Administration (NOAA). However, the skill of these products is not well known yet. The aim of this study is to conduct a simple assessment on the skill of JMA Ensemble Prediction System (EPS) and NOAA Climate Forecast System version 2 (CFSv2) ENSO prediction using World Meteorological Organization (WMO) Standard Verification System for Long Range Forecast (SVS-LRF) method. Both ENSO prediction results also compared each other using Student's t-test. The ENSO predictions data were obtained from the ENSO JMA and ENSO NCEP forecast archive files, while observed Nino 3.4 were calculated from Centennial in situ Observation-Based Estimates (COBE) Sea Surface Temperature Anomaly (SSTA). Both ENSO prediction issued by JMA and NCEP has a good skill on 1 to 3 months lead time, indicated by high correlation coefficient and positive value of Mean Square Skill Score (MSSS). However, the skill of both skills significantly reduced for May-August target month. Further careful interpretation is needed for ENSO prediction issued on this mentioned period.

Probabilistic precipitation nowcast for flash flooding purposes across Australia: verification of a new version of Short-Term Ensemble Prediction System (STEPS)

2021 ◽  
Author(s):  
Carlos Velasco-Forero ◽  
Jayaram Pudashine ◽  
Mark Curtis ◽  
Alan Seed
Keyword(s):  
Lead Time ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Prediction System ◽  
Short Term ◽  
Radar Rainfall ◽  
Flash Flooding ◽  
Ensemble Prediction System ◽  
Study Results

<div> <p>Short-term precipitation forecast plays a vital role for minimizing the adverse effects of heavy precipitation events such as flash flooding.  Radar rainfall nowcasting techniques based on statistical extrapolations are used to overcome current limitations of precipitation forecasts from numerical weather models, as they provide high spatial and temporal resolutions forecasts within minutes of the observation time. Among various algorithms, the Short-Term Ensemble Prediction System (STEPS) provides rainfall fields nowcasts in a probabilistic sense by accounting the uncertainty in the precipitation forecasts by means of ensembles, with spatial and temporal characteristic very similar to those in the observed radar rainfall fields. The Australian Bureau of Meteorology uses STEPS to generate ensembles of forecast rainfall ensembles in real-time from its extensive weather radar network. </p> </div><div> <p>In this study, results of a large probabilistic verification exercise to a new version of STEPS (hereafter named STEPS-3) are reported. An extensive dataset of more than 47000 individual 5-minute radar rainfall fields (the equivalent of more than 163 days of rain) from ten weather radars across Australia (covering tropical to mid-latitude regions) were used to generate (and verify) 96-member rainfall ensembles nowcasts with up to a 90-minute lead time. STEPS-3 was found to be more than 15-times faster in delivering results compared with previous version of STEPS and an open-source algorithm called pySTEPS. Interestingly, significant variations were observed in the quality of predictions and verification results from one radar to other, from one event to other, depending on the characteristics and location of the radar, nature of the rainfall event, accumulation threshold and lead time. For example, CRPS and RMSE of ensembles of 5-min rainfall forecasts for radars located in mid-latitude regions are better (lower) than those ones from radars located in tropical areas for all lead-times. Also, rainfall fields from S-band radars seem to produce rainfall forecasts able to successfully identify extreme rainfall events for lead times up to 10 minutes longer than those produced using C-band radar datasets for the same rain rate thresholds. Some details of the new STEPS-3 version, case studies and examples of the verification results will be presented. </p> </div>

scholarly journals Relationship between Rainfall Variability and the Predictability of Radar Rainfall Nowcasting Models

Atmosphere ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 458 ◽  
Author(s):  
Zhenzhen Liu ◽  
Qiang Dai ◽  
Lu Zhuo
Keyword(s):  
Lead Time ◽  
Rainfall Variability ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Short Term ◽  
Radar Rainfall ◽  
Ensemble Prediction System ◽  
Predictive Skill ◽  
Many Sources

Radar rainfall nowcasts are subject to many sources of uncertainty and these uncertainties change with the characteristics of a storm. The predictive skill of a radar rainfall nowcasting model can be difficult to understand as sometimes it appears to be perfect but at other times it is highly inaccurate. This hinders the decision making required for the early warning of natural hazards caused by rainfall. In this study we define radar spatial and temporal rainfall variability and relate them to the predictive skill of a nowcasting model. The short-term ensemble prediction system model is configured to predict 731 events with lead times of one, two, and three hours. The nowcasting skill is expressed in terms of six well-known indicators. The results show that the quality of radar rainfall nowcasts increases with the rainfall autocorrelation and decreases with the rainfall variability coefficient. The uncertainty of radar rainfall nowcasts also shows a positive connection with rainfall variability. In addition, the spatial variability is more important than the temporal variability. Based on these results, we recommend that the lead time for radar rainfall nowcasting models should change depending on the storm and that it should be determined according to the rainfall variability. Such measures could improve trust in the rainfall nowcast products that are used for hydrological and meteorological applications.

scholarly journals Wave Extremes in the Northeast Atlantic from Ensemble Forecasts

2013 ◽  
Vol 26 (19) ◽  
pp. 7525-7540 ◽  
Author(s):  
Øyvind Breivik ◽  
Ole Johan Aarnes ◽  
Jean-Raymond Bidlot ◽  
Ana Carrasco ◽  
Øyvind Saetra
Keyword(s):  
Ensemble Prediction ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Northeast Atlantic ◽  
The North ◽  
Time Period ◽  
The North Sea ◽  
Mean And Variance ◽  
Buoy Data

Abstract A method for estimating return values from ensembles of forecasts at advanced lead times is presented. Return values of significant wave height in the northeast Atlantic, the Norwegian Sea, and the North Sea are computed from archived +240-h forecasts of the ECMWF Ensemble Prediction System (EPS) from 1999 to 2009. Three assumptions are made: First, each forecast is representative of a 6-h interval and collectively the dataset is then comparable to a time period of 226 years. Second, the model climate matches the observed distribution, which is confirmed by comparing with buoy data. Third, the ensemble members are sufficiently uncorrelated to be considered independent realizations of the model climate. Anomaly correlations of 0.20 are found, but peak events (>P97) are entirely uncorrelated. By comparing return values from individual members with return values of subsamples of the dataset it is also found that the estimates follow the same distribution and appear unaffected by correlations in the ensemble. The annual mean and variance over the 11-yr archived period exhibit no significant departures from stationarity compared with a recent reforecast; that is, there is no spurious trend because of model upgrades. The EPS yields significantly higher return values than the 40-yr ECMWF Re-Analysis (ERA-40) and ECMWF Interim Re-Analysis (ERA-Interim) and is in good agreement with the high-resolution 10-km Norwegian Reanalyses (NORA10) hindcast, except in the lee of unresolved islands where EPS overestimates and in enclosed seas where it has low bias. Confidence intervals are half the width of those found for ERA-Interim because of the magnitude of the dataset.

scholarly journals Enhanced ENSO Prediction via Augmentation of Multimodel Ensembles with Initial Thermocline Perturbations

2020 ◽  
Vol 33 (6) ◽  
pp. 2281-2293 ◽  
Author(s):  
Terence J. O’Kane ◽  
Dougal T. Squire ◽  
Paul A. Sandery ◽  
Vassili Kitsios ◽  
Richard J. Matear ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Tropical Pacific ◽  
Sign Test ◽  
Lead Times ◽  
Multimodel Ensemble ◽  
Coupled Models ◽  
Enso Prediction ◽  
Boreal Spring ◽  
The North ◽  
Forecast Lead Time

AbstractRecent studies have shown that regardless of model configuration, skill in predicting El Niño–Southern Oscillation (ENSO), in terms of target month and forecast lead time, remains largely dependent on the temporal characteristics of the boreal spring predictability barrier. Continuing the 2019 study by O’Kane et al., we compare multiyear ensemble ENSO forecasts from the Climate Analysis Forecast Ensemble (CAFE) to ensemble forecasts from state-of-the-art dynamical coupled models in the North American Multimodel Ensemble (NMME) project. The CAFE initial perturbations are targeted such that they are specific to tropical Pacific thermocline variability. With respect to individual NMME forecasts and multimodel ensemble averages, the CAFE forecasts reveal improvements in skill when predicting ENSO at lead times greater than 6 months, in particular when predictability is most strongly limited by the boreal spring barrier. Initial forecast perturbations generated exclusively as disturbances in the equatorial Pacific thermocline are shown to improve the forecast skill at longer lead times in terms of anomaly correlation and the random walk sign test. Our results indicate that augmenting current initialization methods with initial perturbations targeting instabilities specific to the tropical Pacific thermocline may improve long-range ENSO prediction.

scholarly journals Prospects for Improving Subseasonal Predictions

2011 ◽  
Vol 139 (11) ◽  
pp. 3648-3666 ◽  
Author(s):  
Kathy Pegion ◽  
Prashant D. Sardeshmukh
Keyword(s):  
Time Lag ◽  
Thermal Inertia ◽  
Coupled Model ◽  
Longwave Radiation ◽  
Forecast Skill ◽  
Ensemble Prediction ◽  
Coupled Models ◽  
Ensemble Prediction System ◽  
Predictability Analysis ◽  
The Tropics

Abstract Extending atmospheric prediction skill beyond the predictability limit of about 10 days for daily weather rests on the hope that some time-averaged aspects of anomalous circulations remain predictable at longer forecast lead times, both because of the existence of natural low-frequency modes of atmospheric variability and coupling to the ocean with larger thermal inertia. In this paper the week-2 and week-3 forecast skill of two global coupled atmosphere–ocean models recently developed at NASA and NOAA is compared with that of much simpler linear inverse models (LIMs) based on the observed time-lag correlations of atmospheric circulation anomalies in the Northern Hemisphere and outgoing longwave radiation (OLR) anomalies in the tropics. The coupled models are found to beat the LIMs only slightly, and only if an ensemble prediction methodology is employed. To assess the potential for further skill improvement, a predictability analysis based on the relative magnitudes of forecast signal and forecast noise in the LIM framework is conducted. Estimating potential skill by such a method is argued to be superior to using the ensemble-mean and ensemble-spread information in the coupled model ensemble prediction system. The LIM-based predictability analysis yields relatively conservative estimates of the potential skill, and suggests that outside the tropics the average coupled model skill may already be close to the potential skill, although there may still be room for improvement in the tropical forecast skill.

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