Factors determining the subseasonal prediction skill of summer extreme rainfall over southern China

Wu, Junting; Li, Juan; Zhu, Zhiwei; Hsu, Pang‐Chi

doi:10.1007/s00382-022-06326-w

Cited by 18 publications

(10 citation statements)

References 75 publications

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“…Two statistical metrics are applied to evaluate the performance of each CMIP6 model: (1) The pattern correlation coefficient (PCC) measures the similarities of the spatial patterns between observations and simulations, and the range of PCC value is from −1 to 1 (Li et al, 2018; Zhu & Li, 2017); (2) The domain‐averaged normalized root mean square error (NRMSE) is adopted to measure the magnitude of error of the historical simulations (Lee & Wang, 2012; Wu et al, 2022). NRMSE is the root mean square error normalized by the observed spatial standard deviation, with reference to the whole NHLMD.…”

Section: Methodsmentioning

confidence: 99%

Changes of mean and extreme precipitation and their relationship in Northern Hemisphere land monsoon domain under global warming

Jiang,

Zhu,

et al. 2023

Intl Journal of Climatology

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Reliable projections of monsoon mean and extreme precipitation are vital to water resource management, disaster mitigation as well as policy makings. Since the climate models have better capability in projecting mean precipitation, the relationship between mean and extreme precipitation is a crucial clue for reliable projections of extreme precipitation. However, selections of optimal models in reproducing historical mean/extreme precipitation and their relationship were rarely reported. Here, more credible projections of mean/extreme precipitation and their connections over Northern Hemisphere land monsoon domain (NHLMD) were explored after a systematical assessment of the models from Coupled Model Intercomparsion Project Phase 6 (CMIP6) and CMIP5. Results indicated that most models could well reproduce the climatology and the variation of mean precipitation over NHLMD, while large portions have underestimated the extreme precipitation threshold (EPTH) and overestimated the extreme precipitation days (EPDs). However, nearly all models captured the observed robust relationships between mean and extreme precipitation. The projected mean precipitation and EPDs exhibit coherent variations with increase trend over Asian‐North African monsoon, and decrease trend over North American monsoon. The significant positive correlations between mean precipitation and EPDs will continue during 21st century. Considering the relatively low skills and large uncertainty in the simulation and the projection of EPDs, the tight connection could be used to reliably project the future extreme precipitation over the NHLMD using the projection of monsoon mean precipitation via the selected models.

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Section: Methodsmentioning

confidence: 99%

Changes of mean and extreme precipitation and their relationship in Northern Hemisphere land monsoon domain under global warming

Jiang,

Zhu,

et al. 2023

Intl Journal of Climatology

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“…Considering that subseasonal prediction for EA SAT is likely modulated by the mean state such as ENSO (Martin et al 2019) and tropical intraseasonal oscillation such as the MJO (e.g., Liang and Lin 2017;Lin 2018) and BSISO (Wu et al 2022), we reexamined the robustness of the above results by removing ENSO/MJO/BSISOassociated summers (Table S3 in the supplementary materials lists the new samples of each model after the elimination of ENSO/MJO/BSISO-associated summers).…”

Section: Dependence Of Subseasonal Prediction For Ea Sat On the Eiso-...mentioning

confidence: 99%

“…Subseasonal prediction in boreal summer over EA is challenging owing to complex interactions between tropical monsoon variability and mid-high-latitude circulation systems (Liang and Lin 2017). Previous studies proved that subseasonal prediction sources over EA include preferable phases of the MJO (Lin 2018) and BSISO (Wu et al 2022), the ENSO state (Martin et al 2019), snowpack (Orsolini et al 2013;Li et al 2020), land surface conditions (Zeng and Yuan 2018;Xie et al 2019;Xue et al 2021) and stratospheric signals (Yu et al 2021). Conventional perspective considers the extratropical atmospheric perturbation as noise for prediction (Vimont et al 2001;Zhang et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Extratropical intraseasonal signals along the subtropical westerly jet as a window of opportunity for subseasonal prediction over East Asia in boreal summer

Zhu

Yang

2022

Preprint

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Previous studies suggest that boreal summer intraseasonal variations along the subtropical westerly jet (SJ), featuring quasibiweekly periodicity, frequently modulate downstream subseasonal variations over East Asia (EA). Based on subseasonal hindcasts from six dynamical models, this study discovered that the leading two-three-week prediction skills for surface air temperature (SAT) are improved significantly in summer when the SJ has strengthened intraseasonal signals, which are best demonstrated over the eastern Tibetan Plateau, Southwest Basin, and North China. The reasons are that the enhanced quasi-biweekly wave and the associated energy dispersion along the SJ cause more regular quasi-biweekly periodic variations of downstream SAT, which potentially increase regional predictability. This study suggests not only that intraseasonal variations along the SJ could provide a window of opportunity for achieving better subseasonal prediction over EA, but also that intraseasonal waves along the SJ are crucial for improving EA subseasonal prediction.

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“…Subseasonal prediction in boreal summer over EA is challenging owing to complex interactions between tropical monsoon variability and mid-high-latitude circulation systems (Liang and Lin 2018). Previous studies demonstrated that subseasonal prediction sources over EA include preferable phases of the MJO (Lin 2018) and BSISO (Wu et al 2022), the ENSO state (Martin et al 2019), snowpack (Orsolini et al 2013;Li et al 2020), land surface conditions (Zeng and Yuan 2018;Xie et al 2019;Xue et al 2021) and stratospheric signals (Yu et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Extratropical intraseasonal signals along the subtropical westerly jet as a window of opportunity for subseasonal prediction over East Asia in boreal summer

Yang

Zhu

2023

Preprint

View full text Add to dashboard Cite

Previous studies suggest that boreal summer intraseasonal variations along the subtropical westerly jet (SJ), featuring quasi-biweekly periodicity, frequently modulate downstream subseasonal variations over East Asia (EA). Based on subseasonal hindcasts from six dynamical models, this study first discovered that the leading two–three-week prediction skills for surface air temperature (SAT) are improved significantly in summer with the strengthened intraseasonal oscillation along the SJ, which are best demonstrated over the eastern Tibetan Plateau, Southwest Basin, and North China. The reasons are that the enhanced quasi-biweekly wave and its energy dispersion along the SJ cause more regular quasi-biweekly periodic variations of downstream SAT, which potentially increase regional predictability. This study suggests that the strengthened intraseasonal periodic signals along the SJ would enhance the subseasonal predictability in downstream regions, which could provide a window of opportunity for achieving better subseasonal prediction.

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Factors determining the subseasonal prediction skill of summer extreme rainfall over southern China

Cited by 18 publications

References 75 publications

Changes of mean and extreme precipitation and their relationship in Northern Hemisphere land monsoon domain under global warming

Changes of mean and extreme precipitation and their relationship in Northern Hemisphere land monsoon domain under global warming

Extratropical intraseasonal signals along the subtropical westerly jet as a window of opportunity for subseasonal prediction over East Asia in boreal summer

Extratropical intraseasonal signals along the subtropical westerly jet as a window of opportunity for subseasonal prediction over East Asia in boreal summer

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