Challenges in predicting and simulating summer rainfall in the eastern China

Ping, Liang; Hu, Zeng‐Zhen; Liu, Yunyun; Yuan, Xing; Li, Xiaofan; Jiang, Xingwen

doi:10.1007/s00382-018-4256-6

Cited by 40 publications

(36 citation statements)

References 56 publications

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“…In East Asia (0 -50 N, 100 -160 E), the overall skill is low and shows obvious regional dependence, with the relatively high skill areas over tropics and western North Pacific, and low skill areas over eastern China, the Korean Peninsula, and Japan. That is consistent with the fact of low predictability for summer rainfall over the mid-high latitude land demonstrated in previous work (e.g., Liang et al, 2009Liang et al, , 2019Gao et al, 2011). In MME (Figure 1e), relatively higher skill is in the tropics and subtropical western North Pacific (WNP), as well as in the region between the Yangtze and Yellow River valleys in eastern China (between 30 and 38 N), while negative TCC is seen in South China and North China.…”

Section: Prediction Skillssupporting

confidence: 93%

“…Among these four models, ECMWF_SYS4 is better than other models, while NCEP_CFS2 is the worst one for simulating SLP, u850 and v850, particularly for LM3 and LM4. The MME result is better than or close to the best model (Yuan and Wood, ; Becker et al, ; Liu et al, ; Liang et al, ). For some models and variables, the no sole linear decline of the skill with increase of lead time may be due to the impact of small sample size and interruption of internal dynamical processes driven variability (Deser et al, ).…”

Section: Prediction Skills and Sources Of Predictabilitymentioning

confidence: 99%

“…The small correlation over the East Asian land is also consistent with Wu et al . (), implying low predictability (Liang et al, , ; Gao et al, , ).…”

Section: Prediction Skills and Sources Of Predictabilitymentioning

confidence: 99%

“…Theses analyses suggest that ENSO and NIO SST anomalies are major contributors to the predictability of interannual variability of EASM. Nevertheless, in addition to SST, East Asian summer climate is also affected by other factors, such as land surface processes, stratosphere processes (National Research Council, ; Xiao and Duan, ; Zhao et al, ), as well as atmospheric internal dynamical processes (Kosaka et al, ; He et al 2016; Liang et al, ), and unpredictable components of the climate system. Therefore, we further discuss the model biases and their possible impact on prediction skill of EASM in the following section.…”

Section: Prediction Skills and Sources Of Predictabilitymentioning

confidence: 99%

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Predictability of East Asian summer monsoon in seasonal climate forecast models

Liu

Ding

2019

Intl Journal of Climatology

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The prediction skill and source of the predictability of the East Asian summer monsoon (EASM) system are examined in this work based on four state‐of‐the‐art seasonal climate forecast models including BCC_CSM1.1, ECMWF_SYS4, NCEP_CFS2 and TCC_CPS2. The prediction of the climatology and interannual EASM pattern and the impact on the prediction are further investigated. It is noted that the four models have some skill in predicting summer rainfall in the East Asia, however, the skill is low on average and also largely regional dependence. The interannual variation of EASM measured by monsoon circulation index is well reproduced, implying that the broad‐scale feature/pattern of EASM has higher predictability than the detailed spatial variation of EASM rainfall. The possible sources of predictability of the interannual variability of EASM are associated with the El Niño‐Southern Oscillation (ENSO) and the north Indian Ocean (NIO) sea surface temperature (SST) anomalies. The correlation pattern of rainfall with the NIO SST is characterized by a tripole pattern from south to north of East Asia, which is different from the correlation distribution of the southern‐northern dipole with ENSO, suggesting that NIO SST may exert influence on the EASM independently. The major biases in climatology of EASM in the models are the northward shift of the western Pacific subtropical high (WPSH) and weak monsoonal southerly over the coast of East Asia, which leads to the prediction bias of the Meiyu/Baiu/Changma (MBC) rainfall belt. The prediction of the interannual EASM pattern presents two deficiencies: too weak rainfall variability and northward shift of the dipole rainfall pattern (opposite variation between MBC and the northwestern Pacific), that may be caused by the biases of WPSH in the models.

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Section: Prediction Skillssupporting

confidence: 93%

Section: Prediction Skills and Sources Of Predictabilitymentioning

confidence: 99%

“…The small correlation over the East Asian land is also consistent with Wu et al . (), implying low predictability (Liang et al, , ; Gao et al, , ).…”

Section: Prediction Skills and Sources Of Predictabilitymentioning

confidence: 99%

Section: Prediction Skills and Sources Of Predictabilitymentioning

confidence: 99%

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Predictability of East Asian summer monsoon in seasonal climate forecast models

Liu

Ding

2019

Intl Journal of Climatology

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“…Therefore, the spread shown in Figure 12 may be a consequence of the common primary feature of climate variability over the mid-high latitude lands, and even in the mid-high latitude oceans (Davis, 1976;Hu et al, 2011;, which are dominated by atmospheric internal variability and somewhat constrained by external and/or remote forcings, such as SST (Kosaka et al, 2012;He et al, 2016). In fact, model defaults and errors in ICs (or reanalyses) may also affect the prediction skill to some extent (Kumar and Hu, 2012;Zhu et al, 2013;Liang et al, 2018).…”

Section: Attributions Of Predictability Sourcesmentioning

confidence: 99%

Seasonal predictability of primary East Asian summer circulation patterns by three operational climate prediction models

Zhou

Ren

et al. 2019

Quart J Royal Meteoro Soc

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Seasonal predictability of the Primary East‐Asian Summer Circulation Patterns (PEASCPs), including the Western Pacific Subtropical High (WPSH), South Asian High (SAH), anomalous Philippine Sea AntiCyclone (PSAC) and East Asian Summer Monsoon (EASM), are investigated by using the hindcasts from the three operational climate prediction models, including BCC_CSM1.1(m), NCEP CFSv2 and ECMWF System 4. We show that prediction skills of the indices for representing these PEASCPs are sensitive to the initial calendar month of model prediction, and the ensemble mean of the three models provides relatively higher and more stable skills than forecasts from an individual model. In general, the indices of intensity and area have high prediction skills while the position indices have relatively low skills. Specifically, the skills of the WPSH intensity, area, SAH centre intensity, PSAC and EASM are higher, while the skills of the WPSH western boundary and SAH centre latitude are lower, and the skills of WPSH ridge line and SAH centre longitude are the lowest. Further analysis shows that the El Niño/Southern Oscillation (ENSO) has a large contribution to these prediction skills, and these patterns of atmospheric circulation anomalies in response to ENSO can be well captured by models, which is the major predictability source of the skills.

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Monthly precipitation prediction at regional scale using deep convolutional neural networks

Ni,

Wang,

Singh

et al. 2023

Hydrological Processes

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Variations in monthly precipitation are associated with climate extremes having significant socio‐economic and eco‐environmental impacts. Knowledge of monthly precipitation information is therefore valuable for policy making. Extensive research has been conducted on dynamic prediction using state‐of‐the‐art coupled climate models. However, the skilful prediction of monthly precipitation with dynamical models remains a challenge. With the development of machine learning tools, statistical predictions show comparable performance with dynamic models, but they are limited to at‐site monthly prediction, due to the lack of ability for processing spatially connected geophysical data. To improve monthly precipitation forecasting and provide regional forecasts, we propose a model termed UNet‐RegPre based on convolutional neural network and U‐net architecture. The model shows comparable prediction skills with recurrent state‐of‐the‐art dynamic forecasts (CFSv2), and has the capability to capture spatiotemporal patterns of precipitation and reproduce the main process of representative droughts. The key precursors for rainfall development identified by UNet‐RegPre shows that the constructed model can detect the climatic connection between rainfall in eastern China and summer monsoon, showing the potential to advance hydrometeorological understanding with a deep learning‐based model. These results suggest that the proposed model can be a potential tool for precipitation prediction.

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Challenges in predicting and simulating summer rainfall in the eastern China

Cited by 40 publications

References 56 publications

Predictability of East Asian summer monsoon in seasonal climate forecast models

Predictability of East Asian summer monsoon in seasonal climate forecast models

Seasonal predictability of primary East Asian summer circulation patterns by three operational climate prediction models

Monthly precipitation prediction at regional scale using deep convolutional neural networks

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