Estimating observing locations for advancing beyond the winter predictability barrier of Indian Ocean dipole event predictions

Feng, Rong; Duan, Wansuo; Mu, Mu

doi:10.1007/s00382-016-3134-3

Cited by 20 publications

(23 citation statements)

References 58 publications

(47 reference statements)

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“…As Feng et al . [] has previously described the experimental strategy on how to explore the WPB‐related initial errors, we only briefly state the results below.…”

Section: Oprs That Trigger Iod Onset and Oges In Iod Predictionsmentioning

confidence: 99%

“…Please refer to Feng et al . [] for further details. The main difference from above experiments is that 10 positive IOD were randomly selected as the reference states (i.e., reference years) to explore the WPB‐related initial errors instead of the neutral years.…”

Section: Model and Experimental Strategymentioning

confidence: 99%

“…Feng et al . [] only explored the dominant spatial patterns of the WPB‐related initial errors and did not discuss how these initial errors cause a WPB. In this section, we analyze the dominant evolution mode of these initial errors with the S‐EOF analysis based on the work of Feng et al .…”

Section: Oprs That Trigger Iod Onset and Oges In Iod Predictionsmentioning

confidence: 99%

“…In this section, we analyze the dominant evolution mode of these initial errors with the S‐EOF analysis based on the work of Feng et al . [].…”

Section: Oprs That Trigger Iod Onset and Oges In Iod Predictionsmentioning

confidence: 99%

“…Feng et al . [] demonstrated that the dominant spatial pattern of initial sea temperature errors that are most likely to cause a significant WPB for positive IOD (hereafter referred to as WPB‐related initial errors) presents a west‐east dipole pattern in the tropical Indian Ocean, especially in the subsurface ocean. In addition, the areas in which the large values of the dominant spatial pattern are located (i.e., at the thermocline depth in the eastern equatorial Indian Ocean) represent the sensitive areas of targeted observations for positive IOD.…”

Section: Introductionmentioning

confidence: 99%

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Relationship between optimal precursors for Indian Ocean Dipole events and optimally growing initial errors in its prediction

Feng

Duan

2017

J. Geophys. Res. Oceans

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Using the Geophysical Fluid Dynamics Laboratory Climate Model version 2p1, we explored the precursory disturbances that are most likely to develop into a positive Indian Ocean Dipole (IOD). The dominant spatial patterns of these precursors are defined as the optimal precursors (OPRs) of positive IOD as they are more inclined to cause a positive IOD than other superimposed initial perturbations in the experiments. Specifically, there are two types of OPRs with opposite patterns; the surface component of OPR‐1 (OPR‐2) is an indistinctive west‐east dipole pattern, with a small area of negative (positive) perturbations to the coast of Sumatra and Java. Correspondingly, there is a significant west‐east dipole pattern in the subsurface component of the OPRs, with the largest values located in the eastern equatorial Indian Ocean. The dominant mode of the time‐dependent evolutions of the precursors features rapid development of positive IOD. Furthermore, the OPRs are similar to the optimally growing initial errors (OGEs) associated with IOD predictions that have been presented in previous studies. The shortwave radiation, latent heat flux, and westward Rossby waves play an important role in the time‐dependent evolution of OGEs. Moreover, the large values of the OPRs are located in the same areas as the sensitive areas of targeted observations identified by the OGEs. This infers that intensive observations over these areas would not only reduce initial errors, improve the accuracy of initial fields and decrease the prediction errors but would also detect the precursory signals in advance, which substantially improves the forecast skill of IOD.

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“…As Feng et al . [] has previously described the experimental strategy on how to explore the WPB‐related initial errors, we only briefly state the results below.…”

Section: Oprs That Trigger Iod Onset and Oges In Iod Predictionsmentioning

confidence: 99%

Section: Model and Experimental Strategymentioning

confidence: 99%

Section: Oprs That Trigger Iod Onset and Oges In Iod Predictionsmentioning

confidence: 99%

“…In this section, we analyze the dominant evolution mode of these initial errors with the S‐EOF analysis based on the work of Feng et al . [].…”

Section: Oprs That Trigger Iod Onset and Oges In Iod Predictionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Relationship between optimal precursors for Indian Ocean Dipole events and optimally growing initial errors in its prediction

Feng

Duan

2017

J. Geophys. Res. Oceans

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Ensemble forecasting of Indian Ocean Dipole events generated by conditional nonlinear optimal perturbation method

Feng,

Duan,

et al. 2024

Intl Journal of Climatology

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In this study, we applied the conditional nonlinear optimal perturbation (CNOP) method to generate nonlinear fast‐growing initial perturbations for ensemble forecasting, aiming to assess the effectiveness of the CNOP method in improving the forecast skill of climate events. Our findings reveal a significant improvement in the forecast skill of the Indian Ocean Dipole (IOD) within the CNOP ensemble forecast, particularly at long lead times, thereby extending the skilful forecast lead times. Notably, this improvement is more prominent for strong IOD events, with skilful forecast lead times exceeding 12 months, outperforming many current state‐of‐the‐art coupled models. The high forecast skill of the CNOP method is primarily attributed to its ability to capture the uncertainties in the wind anomaly field in the eastern Indian Ocean (EIO) closely associated with IOD evolution. Consequently, CNOP ensemble members exhibit significant deviations from the control forecast, resulting in a large ensemble spread encompassing IOD evolution. Furthermore, a comparison with the climate‐relevant singular vectors (CSV) method in terms of IOD and El Niño–Southern Oscillation (ENSO) predictions reveals the superior performance of the CNOP ensemble forecast. Despite the initial perturbations for ensemble forecasting being generated aimed at improving IOD forecast skill, the CNOP method significantly improves the forecast skill of both IOD and ENSO events, with a greater improvement for ENSO. Additionally, the CNOP ensemble forecast system provides more reliable estimates of forecast uncertainties and exhibits higher reliability with increasing lead times. In conclusion, the CNOP method effectively captures the nonlinear physical processes of climate events and improve the forecast skill.

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Investigating the Initial Errors that Cause Predictability Barriers for Indian Ocean Dipole Events Using CMIP5 Model Outputs

Feng

Duan

2018

Adv. Atmos. Sci.

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Estimating observing locations for advancing beyond the winter predictability barrier of Indian Ocean dipole event predictions

Cited by 20 publications

References 58 publications

Relationship between optimal precursors for Indian Ocean Dipole events and optimally growing initial errors in its prediction

Relationship between optimal precursors for Indian Ocean Dipole events and optimally growing initial errors in its prediction

Ensemble forecasting of Indian Ocean Dipole events generated by conditional nonlinear optimal perturbation method

Investigating the Initial Errors that Cause Predictability Barriers for Indian Ocean Dipole Events Using CMIP5 Model Outputs

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