scholarly journals An Atmospheric Signal Lowering the Spring Predictability Barrier in Statistical ENSO Forecasts

2021 ◽  
Vol 48 (6) ◽  
Author(s):  
Dmitry Mukhin ◽  
Andrey Gavrilov ◽  
Aleksei Seleznev ◽  
Maria Buyanova
Keyword(s):  
Spring Predictability Barrier

scholarly journals Atlantic Niño/Niña Prediction Skills in NMME Models

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 803
Author(s):  
Ran Wang ◽  
Lin Chen ◽  
Tim Li ◽  
Jing-Jia Luo
Keyword(s):  
Sea Surface ◽  
Prediction Skill ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Equatorial Atlantic ◽  
Multimodel Ensemble ◽  
Spring Predictability Barrier ◽  
Anomaly Correlation Coefficient ◽  
Earth’S Climate ◽  
Atlantic Niño

The Atlantic Niño/Niña, one of the dominant interannual variability in the equatorial Atlantic, exerts prominent influence on the Earth’s climate, but its prediction skill shown previously was unsatisfactory and limited to two to three months. By diagnosing the recently released North American Multimodel Ensemble (NMME) models, we find that the Atlantic Niño/Niña prediction skills are improved, with the multi-model ensemble (MME) reaching five months. The prediction skills are season-dependent. Specifically, they show a marked dip in boreal spring, suggesting that the Atlantic Niño/Niña prediction suffers a “spring predictability barrier” like ENSO. The prediction skill is higher for Atlantic Niña than for Atlantic Niño, and better in the developing phase than in the decaying phase. The amplitude bias of the Atlantic Niño/Niña is primarily attributed to the amplitude bias in the annual cycle of the equatorial sea surface temperature (SST). The anomaly correlation coefficient scores of the Atlantic Niño/Niña, to a large extent, depend on the prediction skill of the Niño3.4 index in the preceding boreal winter, implying that the precedent ENSO may greatly affect the development of Atlantic Niño/Niña in the following boreal summer.

Can intra-seasonal wind stress forcing strongly affect spring predictability barrier for ENSO in Zebiak–Cane model?

2018 ◽  
Vol 68 (10) ◽  
pp. 1273-1284 ◽  
Author(s):  
Yue-hua Peng ◽  
Chong-wei Zheng ◽  
Tao Lian ◽  
Jie Xiang
Keyword(s):  
Wind Stress ◽  
Spring Predictability Barrier ◽  
Cane Model ◽  
Seasonal Wind

scholarly journals Assessment of the Sea Surface Temperature Predictability Based on Multimodel Hindcasts

2019 ◽  
Vol 34 (6) ◽  
pp. 1965-1977 ◽  
Author(s):  
Shouwen Zhang ◽  
Hua Jiang ◽  
Hui Wang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Forecast Accuracy ◽  
Skill Score ◽  
Point Of View ◽  
Sea Surface ◽  
Lead Times ◽  
Spring Predictability Barrier ◽  
Multimodel Ensembles ◽  
The Tropics

Abstract Based on historical forecasts of four individual forecasting systems, we conducted multimodel ensembles (MME) to predict the sea surface temperature anomaly (SSTA) variability and assessed these methods from a deterministic and probabilistic point of view. To investigate the advantages and drawbacks of different deterministic MME methods, we used simple averaged MME with equal weighs (SCM) and the stepwise pattern projection method (SPPM). We measured the probabilistic forecast accuracy by Brier skill score (BSS) combined with its two components: reliability (Brel) and resolution (Bres). The results indicated that SCM showed a high predictability in the tropical Pacific Ocean, with a correlation exceeding 0.8 with a 6-month lead time. In general, the SCM outperformed the SPPM in the tropics, while the SPPM tend to show some positive effect on the correction when at long lead times. Corrections occurred for the spring predictability barrier of ENSO, in particular for improvements when the correlation was low or the RMSE was large using the SCM method. These qualitative results are not susceptible to the selection of the hindcast periods, it is as a rule rather by chance of these individual systems. Performance of our probabilistic MME was better than the Climate Forecast System version2 (CFSv2) forecasts in forecasting COLD, NEUTRAL, and WARM SSTA categories for most regions, mainly due to the contribution of Brel, indicating more adequate ensemble construction strategies of the MME system superior to the CFSv2.

A Consolidated CLIPER Model for Improved August–September ENSO Prediction Skill

10.1175/813.1 ◽  
2004 ◽  
Vol 19 (6) ◽  
pp. 1089-1105 ◽  
Author(s):  
Benjamin Lloyd-Hughes ◽  
Mark A. Saunders ◽  
Paul Rockett
Keyword(s):  
Prediction Model ◽  
Seasonal Prediction ◽  
Boreal Summer ◽  
Northwest Pacific ◽  
Tropical Precipitation ◽  
Spring Predictability Barrier ◽  
Forecast Models ◽  
Hurricane Activity ◽  
Initial Results ◽  
Enso Indices

Abstract A prime challenge for ENSO seasonal forecast models is to predict boreal summer ENSO conditions at lead. August–September ENSO has a strong influence on Atlantic hurricane activity, Northwest Pacific typhoon activity, and tropical precipitation. However, summer ENSO skill is low due to the spring predictability barrier between March and May. A “consolidated” ENSO–climatology and persistence (CLIPER) seasonal prediction model is presented to address this issue with promising initial results. Consolidated CLIPER comprises the ensemble of 18 model variants of the statistical ENSO–CLIPER prediction model. Assessing August–September ENSO skill using deterministic and probabilistic skill measures applied to cross-validated hindcasts from 1952 to 2002, and using deterministic skill measures applied to replicated real-time forecasts from 1900 to 1950, shows that the consolidated CLIPER model consistently outperforms the standard CLIPER model at leads from 2 to 6 months for all the main ENSO indices (3, 3.4, and 4). The consolidated CLIPER August–September 1952–2002 hindcast skill is also positive to 97.5% confidence at leads out to 4 months (early April) for all ENSO indices. Optimization of the consolidated CLIPER model may lead to further skill improvements.

scholarly journals The ENSO Transition Probabilities

2017 ◽  
Vol 30 (13) ◽  
pp. 4951-4964 ◽  
Author(s):  
G. Conti ◽  
A. Navarra ◽  
J. Tribbia
Keyword(s):  
General Circulation ◽  
Transition Probabilities ◽  
Transition Probability ◽  
General Circulation Models ◽  
Spring Predictability Barrier ◽  
Predictability Index ◽  
Sea Surface Temperature Anomalies ◽  
Transition Probability Matrices ◽  
Probability Matrices ◽  
Basic Prediction

ENSO is investigated here by considering it as a transition from different states. Transition probability matrices can be defined to describe the evolution of ENSO in this way. Sea surface temperature anomalies are classified into four categories, or states, and the probability to move from one state to another has been calculated for both observations and a simulation from a GCM. This could be useful for understanding and diagnosing general circulation models elucidating the mechanisms that govern ENSO in models. Furthermore, these matrices have been used to define a predictability index of ENSO based on the entropy concept introduced by Shannon. The index correctly identifies the emergence of the spring predictability barrier and the seasonal variations of the transition probabilities. The transition probability matrices could also be used to formulate a basic prediction model for ENSO that was tested here on a case study.

Random Walks through Climate Networks: Sea Ice Prediction with Bayesian Inference

2020 ◽  
Author(s):  
William Gregory ◽  
Michel Tsamados ◽  
Julienne Stroeve ◽  
Peter Sollich
Keyword(s):  
Bayesian Inference ◽  
Sea Ice ◽  
Time Series Data ◽  
Gaussian Process Regression ◽  
Forecast Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Series Data ◽  
Sea Ice Concentration ◽  
Spring Predictability Barrier

<p><span>Spatial predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. However, with sea ice variability likely to increase under continued anthropogenic warming, increasingly complex tools are required in order to make accurate forecasts. In this study, predictions of both Arctic and Antarctic summer sea ice extents are made using a complex network statistical approach. This method exploits statistical relationships within geo-spatial time series data in order to construct regions of spatio-temporal homogeneity -- nodes, and subsequently derive teleconnection links between them. The nodes and links of the networks here are generated from monthly sea ice concentration fields in June(November), July(December) and August(January) for Arctic(Antarctic) forecasts, hence lead times extend from 1 to 3 months. Network information is then utilised within a linear Gaussian Process Regression forecast model; a Bayesian inference technique. Network teleconnection weights are used to generate priors over functions in the form of a random walk covariance kernel; the hyperparameters of which are determined by the empirical Bayesian approach of type-II maximum likelihood. We also show predictions of all other months in order to ascertain the presence of a spring predictability barrier in observational data, for both hemispheres.</span></p>

Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier

2020 ◽  
Author(s):  
Jun Meng ◽  
Jingfang Fan ◽  
Josef Ludescher ◽  
Ankit Agarwala ◽  
Xiaosong Chen ◽  
...  
Keyword(s):  
Prediction Models ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Statistical Prediction ◽  
Analysis Tool ◽  
Strong Positive Correlation ◽  
Near Surface ◽  
Spring Predictability Barrier ◽  
Prediction Horizon ◽  
The Individual

<p>The El Niño Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. An early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the “spring predictability barrier” (SPB) remains a great challenge for long (over 6-month) lead-time forecasting. To overcome this barrier, here we develop an analysis tool, the System Sample Entropy (SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Niño 3.4 region. When applying this tool to several near surface air temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Niño and the previous calendar year’s SysSampEn (complexity). We show that this correlation allows to forecast the magnitude of an El Niño with a prediction horizon of 1 year and high accuracy (i.e., Root Mean Square Error = 0.23°C for the average of the individual datasets forecasts). For the 2018 El Niño event, our method forecasts a weak El Niño with a magnitude of 1.11±0.23°C.  Our framework presented here not only facilitates a long–term forecasting of the El Niño magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.</p>

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