Enhancement of seasonal prediction of East Asian summer rainfall related to western tropical Pacific convection

Lee, Doo Young; Ahn, Joong‐Bae; Yoo, Jinho

doi:10.1007/s00382-014-2343-x

Cited by 14 publications

(14 citation statements)

References 41 publications

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“…Particularly, climate prediction over inland continental regions such as Mongolia is one of the most challenging issues for state-of-the-art meteorology due to the limitations of current observation that hinder our understanding of the interaction between land surface and atmosphere and due to the difficulties in modelling the complex and inhomogeneous distribution of the terrestrial surface in detail. Thus, in spite of their relatively low climate prediction skill over continents, state-of-the-art CGCMs are still widely used for seasonal forecasting in many meteorological research centres and laboratories, such as the National Centers for Environmental Prediction (NCEP; Toth et al, 2001), the European Centre for Medium-Range Weather Forecast (ECMWF; Anderson et al, 2007), the Asia-Pacific Economic Cooperation Climate Center (APCC; Lee et al, 2013;Lee et al, 2014) and Pusan National University (PNU; Sun and Ahn, 2014;Kim and Ahn, 2015). Thus, in spite of their relatively low climate prediction skill over continents, state-of-the-art CGCMs are still widely used for seasonal forecasting in many meteorological research centres and laboratories, such as the National Centers for Environmental Prediction (NCEP; Toth et al, 2001), the European Centre for Medium-Range Weather Forecast (ECMWF; Anderson et al, 2007), the Asia-Pacific Economic Cooperation Climate Center (APCC; Lee et al, 2013;Lee et al, 2014) and Pusan National University (PNU; Sun and Ahn, 2014;Kim and Ahn, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Meehl (1995) claimed that the coupled general circulation model (CGCM) is the ultimate tool for predicting the longterm weather and climate. Thus, in spite of their relatively low climate prediction skill over continents, state-of-the-art CGCMs are still widely used for seasonal forecasting in many meteorological research centres and laboratories, such as the National Centers for Environmental Prediction (NCEP; Toth et al, 2001), the European Centre for Medium-Range Weather Forecast (ECMWF; Anderson et al, 2007), the Asia-Pacific Economic Cooperation Climate Center (APCC; Lee et al, 2013;Lee et al, 2014) and Pusan National University (PNU; Sun and Ahn, 2014;Kim and Ahn, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network

Gerelchuluun

Ahn

2018

Intl Journal of Climatology

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The hindcast data of Pusan National University coupled general circulation model (PNU CGCM), a participant model of the Asia‐Pacific Economic Cooperation Climate Center (APCC) Multi‐Model Ensemble Climate Prediction System, and August–October sea‐surface temperature (SST) in the northern Barents–Kara Sea (BKI) and the sea‐ice extent (SIE) in the Chukchi Sea (East Siberian Sea index [ESI]) are used for predicting 20 × 20‐km‐resolution anomalous surface air temperature at 2‐m height (aT2m) over Mongolia for boreal winter. For this purpose, area‐averaged surface air temperature (TI) and sea‐level pressure (SLP) over Mongolia are defined. Then four large‐scale indices, TImdl and SHImdl obtained from PNU CGCM, and TIMLR and SHIMLR obtained from multiple linear regressions on BKI and ESI, are incorporated using the artificial neural network (ANN) method for the prediction and statistical downscaling to obtain the monthly and seasonal 20 × 20‐km‐resolution aT2m over Mongolia in winter. An additional statistical method, which uses BKI and ESI as predictors of TI and SHI together with dynamic prediction by the CGCM, is used because of the relatively low skill of seasonal predictions by most of the state‐of‐the‐art models and the multi‐model ensemble systems over high‐latitude landlocked Eurasian regions such as Mongolia. The results show that the predictabilities of monthly and seasonal 20 × 20‐km‐resolution aT2m over Mongolia in winter are improved by applying ANN to both statistical and dynamical predictions compared to utilizing only dynamic prediction. The predictability gained by the proposed method is also demonstrated by the probabilistic forecast implying that the method forecasts aT2m over Mongolia in winter reasonably well.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network

Gerelchuluun

Ahn

2018

Intl Journal of Climatology

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“…To improve rainfall prediction over EA, multi-model ensemble (MME) predictions have been conducted (Wang et al 2009, Lee et al 2015. Based on evaluation of the MME seasonal hindcasts performed by 14 climate models that participate in the Climate Prediction and its Application to Society (CliPAS) project and 6 models from the DEMETER project, Wang et al (2009) concluded that forecast of monsoon precipitation remains a major challenge and the seasonal rainfall predictions over land and during local summer have little skill.…”

Section: Introductionmentioning

confidence: 99%

“…Based on evaluation of the MME seasonal hindcasts performed by 14 climate models that participate in the Climate Prediction and its Application to Society (CliPAS) project and 6 models from the DEMETER project, Wang et al (2009) concluded that forecast of monsoon precipitation remains a major challenge and the seasonal rainfall predictions over land and during local summer have little skill. Lee et al (2015) added downscaling methods to CilPAS prediction data in order to enhance prediction skill but there is no considerable improvement.…”

Section: Introductionmentioning

confidence: 99%

Improving Seasonal Prediction of East Asian Summer Rainfall: Experiments with NESM3.0

Yang¹,

Wang²,

Li³

2018

Preprint

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It has been an outstanding challenge for global climate models to simulate and predict East Asia (EA) summer monsoon (EASM) rainfall. This study evaluates the dynamical hindcast skills with the newly developed Nanjing University of Information Science and Technology Earth System Model version 3.0 (NESM3.0). To improve the poor prediction of an earlier version of NESM3.0, we have modified convective parameterization schemes to suppress excessive deep convection and enhance insufficient shallow and stratiform clouds. The new version of NESM3.0 with modified parameterizations (MOD hereafter) yields significantly improved rainfall prediction in the northern and southern China but not over the Yangtze River Valley. The improved prediction is primarily attributed to the improvements in the predicted climatological summer mean rainfall and circulations, seasonal march of the subtropical rain belt, Nino 3.4 SST anomaly, and the rainfall anomalies associated with the development and decay of El Nino events. However, the MOD still has notable biases in the predicted leading mode of interannual variability of precipitation. The leading mode captures the dry (wet) anomalies over the South China Sea (northern EA) but misplaced precipitation anomalies over the Yangtze River Valley. The model can capture the interannual variation of the circulation indices very well, but the bias in the circulation-rainfall connection caused predicted rainfall errors. The results here suggest that over EA land regions, the skillful rainfall prediction relies on not only model’s capability in predicting better summer mean and seasonal march of rainfall and ENSO teleconnection with EASM, but also accurate prediction of the leading modes of interannual variability.

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“…Many previous studies have assessed the deterministic skills of different MME systems in historical predictions of the WNP‐EASM variability [e.g., Kang and Shukla , ; Wang et al , ; Chowdary et al , ; Lee et al , , , ; Li et al , ; Tang et al , ; Min et al , ; Ma et al , ]. The results from these studies indeed indicate that some benefit could be received from compositing single‐model ensembles (SMEs), but this benefit is sometimes cast into doubt by the fact that the MME does not always beat the best SME [ Kang and Shukla , ; Lee et al , ].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic versus deterministic skill in predicting the western North Pacific‐East Asian summer monsoon variability with multimodel ensembles

Yang

Xie

et al. 2016

JGR Atmospheres

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Based on historical forecasts of three quasi-operational multimodel ensemble (MME) systems, this study assesses the superiority of coupled MME over contributing single-model ensembles (SMEs) and over uncoupled atmospheric MME in predicting the Western North Pacific-East Asian summer monsoon variability. The probabilistic and deterministic forecast skills are measured by Brier skill score (BSS) and anomaly correlation (AC), respectively. A forecast-format-dependent MME superiority over SMEs is found. The probabilistic forecast skill of the MME is always significantly better than that of each SME, while the deterministic forecast skill of the MME can be lower than that of some SMEs. The MME superiority arises from both the model diversity and the ensemble size increase in the tropics, and primarily from the ensemble size increase in the subtropics. The BSS is composed of reliability and resolution, two attributes characterizing probabilistic forecast skill. The probabilistic skill increase of the MME is dominated by the dramatic improvement in reliability, while resolution is not always improved, similar to AC. A monotonic resolution-AC relationship is further found and qualitatively explained, whereas little relationship can be identified between reliability and AC. It is argued that the MME's success in improving the reliability arises from an effective reduction of the overconfidence in forecast distributions. Moreover, it is examined that the seasonal predictions with coupled MME are more skillful than those with the uncoupled atmospheric MME forced by persisting sea surface temperature (SST) anomalies, since the coupled MME has better predicted the SST anomaly evolution in three key regions.

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Enhancement of seasonal prediction of East Asian summer rainfall related to western tropical Pacific convection

Cited by 14 publications

References 41 publications

Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network

Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network

Improving Seasonal Prediction of East Asian Summer Rainfall: Experiments with NESM3.0

Probabilistic versus deterministic skill in predicting the western North Pacific‐East Asian summer monsoon variability with multimodel ensembles

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