Inter‐Model Spread of North Tropical Atlantic Trans‐Basin Effect Substantially Biases Tropical Pacific Sea Surface Temperature Multiyear Prediction

Yang, Jun‐Chao; Lv, Zhen; Richter, Ingo; Zhang, Yu; Lin, Xiaopei

doi:10.1029/2022gl098620

Cited by 3 publications

(5 citation statements)

References 60 publications

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“…(2022) used SST pacemaker experiments and LIM to underscore the AMV forcing on North Pacific SST decadal prediction. However, some model dependencies were also found in these prediction studies (Yang et al., 2021, 2022).…”

Section: Introductionmentioning

confidence: 69%

“…However, isolating AMV effects in such experiments is difficult. Previous LIM studies developed a methodology to quantify trans‐basin effects on SST variability and prediction (Alexander et al., 2022; Kido et al., 2022; Newman, 2007; Yang et al., 2022; L. Zhang et al., 2021; Zhao et al., 2021), which helps us to isolate AMV forcing in fully coupled experiments. Here, by employing LIM, we use multi‐model fully coupled experiments from the Coupled Model Intercomparison Project Phase 6 (CMIP6) as well as observations to study AMV strength effects on Pacific SST variability and decadal prediction.…”

Section: Introductionmentioning

confidence: 99%

“…(2015) and Yang et al. (2022) found that AMV‐related SST variability can improve tropical Pacific SST multi‐year prediction by using an initialized model experiment and linear inverse model (LIM), respectively. Lv et al.…”

Section: Introductionmentioning

confidence: 99%

“…However, isolating AMV effects in such experiments is difficult. Previous LIM studies developed a methodology to quantify trans-basin effects on SST variability and prediction (Alexander et al, 2022;Kido et al, 2022;Newman, 2007;Yang et al, 2022;L. Zhang et al, 2021;Zhao et al, 2021), which helps us to isolate AMV forcing in fully coupled experiments.…”

mentioning

confidence: 99%

“…Yang et al (2021) suggested that the AMV is a precursor of the IPV in observations using a model-based estimation to isolate internal AMV. Chikamoto et al (2015) and Yang et al (2022) found that AMV-related SST variability can improve tropical Pacific SST multi-year prediction by using an initialized model experiment and linear inverse model (LIM), respectively. Lv et al (2022) used SST pacemaker experiments and LIM to underscore the AMV forcing on North Pacific SST decadal prediction.…”

mentioning

confidence: 99%

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Model‐Dependent Atlantic Multidecadal Variability Modulations on North Pacific Sea Surface Temperature Variability and Decadal Prediction

Lv,

Yang,

Zhang

et al. 2023

Geophysical Research Letters

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Previous studies suggested that Atlantic Multidecadal Variability (AMV) modulations on pan‐Pacific sea surface temperature (SST) variability and prediction are model‐dependent. These results were mainly based on SST forcing experiments in which AMV‐related Atlantic SST anomalies were prescribed. However, the AMV itself is also model‐dependent, but its influences on the Pacific remain unclear. Here, we use multi‐model fully coupled experiments from the Coupled Model Intercomparison Project Phase 6 (CMIP6), along with observations, to study the model‐dependent AMV trans‐basin effects. We found that AMV strength is a key factor: Stronger (Weaker) model AMV than observations overestimates (underestimates) SST response and decadal prediction skills, mainly in the North Pacific. The reason is that stronger positive phased AMV, for example, leads to higher sea level pressure anomalies over the North Pacific, which lifts sea surface height and deepens thermocline to warm SST. Our study highlights the necessity to improve simulations of AMV strength.

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Section: Introductionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Model‐Dependent Atlantic Multidecadal Variability Modulations on North Pacific Sea Surface Temperature Variability and Decadal Prediction

Lv,

Yang,

Zhang

et al. 2023

Geophysical Research Letters

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Interdecadal tropical Pacific–Atlantic interaction simulated in CMIP6 models

Deng,

Huang,

Zhou

et al. 2024

Clim Dyn

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Potential Driving Systems Associated with Extreme Rainfall across East Africa during October to December (OND) Season 2019

Ingeri,

Wen,

Sebaziga

et al. 2024

GEP

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The East African (EA) region highly experiences intra-seasonal and interannual variation in rainfall amounts. This study investigates the driving factors for anomalous rainfall events observed during the season of October-November-December (OND) 2019 over the region. The study utilized daily rainfall data from Climate Hazards Group InfraRed Precipitation with Station Data Version 2 (CHIRPSv2) and the driving systems data. Statistical spatiotemporal analysis, correlation, and composite techniques were performed to investigate the teleconnection between OND 2019 seasonal rainfall and global synoptic climate systems. The findings showed that the OND 2019 experienced seasonal rainfall that was twice or greater than its seasonal climatology and varied with location. Further, the OND 2019 rainfall showed a positive correlation with the Indian Ocean Dipole (IOD) (0.81), Nino 3 (0.51), Nino 3.4 (0.47), Nino 4 (0.40), Pacific Decadal Oscillation (PDO) (0.22), and North Tropical Atlantic (NTA) (0.02), while El Nino-Southern Oscillation (ENSO) showed a negative correlation (−0.30). The region was dominated by southeasterly warming and humid winds that originated from the Indian Ocean, while the geopotential height, vertical velocity, and vorticity anomalies were closely related to the anomalous rainfall characteristics. The study deduced that the IOD was the major synoptic system that influenced maximum rainfall during the peak season of OND 2019. This study therefore provided insights on the diagnosis study of OND 2019 anomalous rainfall and its attribu-

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Inter‐Model Spread of North Tropical Atlantic Trans‐Basin Effect Substantially Biases Tropical Pacific Sea Surface Temperature Multiyear Prediction

Abstract: The tropical Pacific (TP) shows strong variability of sea surface temperature (SST) associated with El

Cited by 3 publications

References 60 publications

Model‐Dependent Atlantic Multidecadal Variability Modulations on North Pacific Sea Surface Temperature Variability and Decadal Prediction

Model‐Dependent Atlantic Multidecadal Variability Modulations on North Pacific Sea Surface Temperature Variability and Decadal Prediction

Interdecadal tropical Pacific–Atlantic interaction simulated in CMIP6 models

Potential Driving Systems Associated with Extreme Rainfall across East Africa during October to December (OND) Season 2019

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