Subseasonal Predictions of Regional Summer Monsoon Rainfall over Tropical Asian Oceans and Land

Liu, Xiangwen; Yang, Song; Li, Jianglong; Wei, Jie; Huang, Liang Ke; Gu, W

doi:10.1175/jcli-d-14-00853.1

Cited by 14 publications

(6 citation statements)

References 59 publications

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“…The failure of initialization optimization in 2010 may be caused by the obviously low predictability of the BSISO itself, in addition to the uncertainty in the interaction between model errors and initial errors. The above skill differences might be closely related to the interannual variation of BSISO amplitude (Liu et al 2015b), given that the forecasts by various models are always more skillful for BSISO events with stronger amplitude than those with weaker amplitude (Lee et al 2015;Jie et al 2017). In this study, we find that clear interannual differences are also featured by different skill degrees for the three hindcast sets, and different amplitude of skill declines from the first week to the second week in those years, altogether revealing complicated impacts of atmospheric and oceanic initializations.…”

Section: Bsiso Characteristics In the Model Simulationmentioning

confidence: 94%

Impacts of atmospheric and oceanic initial conditions on boreal summer intraseasonal oscillation forecast in the BCC model

Liu

et al. 2020

Theor Appl Climatol

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In this paper, we evaluate the capability of the Beijing Climate Center Climate System Model (BCC-CSM) in simulating and forecasting the boreal summer intraseasonal oscillation (BSISO), using its simulation and sub-seasonal to seasonal (S2S) hindcast results. Results show that the model can generally simulate the spatial structure of the BSISO, but give relatively weaker strength, shorter period, and faster transition of BSISO phases when compared with the observations. This partially limits the model’s capability in forecasting the BSISO, with a useful skill of only 9 days. Two sets of hindcast experiments with improved atmospheric and atmosphere/ocean initial conditions (referred to as EXP1 and EXP2, respectively) are conducted to improve the BSISO forecast. The BSISO forecast skill is increased by 2 days with the optimization of atmospheric initial conditions only (EXP1), and is further increased by 1 day with the optimization of both atmospheric and oceanic initial conditions (EXP2). These changes lead to a final skill of 12 days, which is comparable to the skills of most models participated in the S2S Prediction Project. In EXP1 and EXP2, the BSISO forecast skills are improved for most initial phases, especially phases 1 and 2, denoting a better description for BSISO propagation from the tropical Indian Ocean to the western North Pacific. However, the skill is considerably low and insensitive to initial conditions for initial phase 6 and target phase 3, corresponding to the BSISO convection’s active-to-break transition over the western North Pacific and BSISO convection’s break-to-active transition over the tropical Indian Ocean and Maritime Continent. This prediction barrier also exists in many forecast models of the S2S Prediction Project. Our hindcast experiments with different initial conditions indicate that the remarkable model errors over the Maritime Continent and subtropical western North Pacific may largely account for the prediction barrier.

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Section: Bsiso Characteristics In the Model Simulationmentioning

confidence: 94%

Impacts of atmospheric and oceanic initial conditions on boreal summer intraseasonal oscillation forecast in the BCC model

Liu

et al. 2020

Theor Appl Climatol

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“…At present, sub-seasonal forecast based on comprehensive ocean-land-atmosphere-ice coupled models has been an important issue in many agencies, despite many limitations it encounters, such as significant uncertainty in initial conditions, quick growth of forecast errors and limited skills in forecast of regional features (e.g., Pegion and Sardeshmukh 2011;Abhilash et al 2014;Liu et al 2013Liu et al , 2014bLiu et al , 2015b. To further promote the scientific research and operational application on sub-seasonal forecast, the World Weather Research Program (WWRP) and the World Climate Research Program (WCRP) launched a Sub-seasonal to Seasonal (S2S) Prediction Project in recent years.…”

Section: Introductionmentioning

confidence: 99%

MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

Liu

Yang

et al. 2016

Clim Dyn

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influence of exaggerated IOD variability in the model. The results imply that the upper limit of intraseasonal predictability is modulated by large-scale external forcing background state in the tropical Indian Ocean. Two additional sets of hindcast experiments with improved atmosphere and ocean initial conditions (referred to as S2S_IEXP1 and S2S_IEXP2, respectively) are carried out, and the results show that the overall MJO forecast skill is increased to 21-22 days. It is found that the optimization of initial sea surface temperature condition largely accounts for the increase of the overall MJO forecast skill, even though the improved initial atmosphere conditions also play a role. For the DYNAMO/CINDY field campaign period, the forecast skill increases to 27 days in S2S_IEXP2. Nevertheless, even with improved initialization, it is still difficult for the model to predict MJO propagation across the western hemisphere-western Indian Ocean area and across the eastern Indian Ocean-Maritime Continent area. Especially, MJO prediction is apparently limited by various interrelated deficiencies (e.g., overestimated IOD, shorter-than-observed MJO life cycle, Maritime Continent prediction barrier), due possibly to the model bias in the background moisture field over the eastern Indian Ocean and Maritime Continent. Thus, more efforts are needed to correct the deficiency in model physics in this region, in order to overcome the wellknown Maritime Continent predictability barrier.

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“…It is also skillful for predicting ENSO, including different types of El Niño (Wang et al , ; Yang and Jiang, ). With the CFS version 2, the subseasonal forecast skills and biases of the global summer monsoon and the rainfalls over tropical oceans and land have been addressed as well (Liu et al , , ). Diagnostic analysis of the biases of intraseasonal prediction of the Asian summer monsoon indicates that the intensity of the western Pacific subtropical high and the South Asian summer monsoon is underestimated (Liu et al , ).…”

Section: Introductionmentioning

confidence: 99%

Intraseasonal variability and predictability of the subtropical Asian summer rain band

Wang

Zhou

et al. 2017

Intl Journal of Climatology

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The climatological intraseasonal oscillations (CISOs) of the subtropical Asian summer rain band (SASRB) are found significantly related with the CISO in the atmospheric circulation over extratropical Eurasian continent, which is dominated by an intraseasonal North Pacific oscillation (ISNAO) pattern and its associated wave trains. The anomalous trough (ridge) of the eastward propagating wave train of the ISNAO pattern affects active (break) rainfall of SASRB and modulates the CISO of SASRB, together with the anomalous active (suppressed) convection northward propagating from the tropics and the subtropics. Moreover, the anomalous trough (ridge) around the Lake Baikal bridges the CISO in the mid‐high latitudes and that of SASRB. The NCEP Climate Forecast System version 2 (CFSv2) depicts the spatial–temporal features, principal modes, and propagation of the CISO of SASRB reasonably well. However, the mean state of precipitation and the variance of CISO are overestimated. In the model, the ISNAO pattern is too weak and its fluctuating frequency is too high. Moreover, the CFSv2 only shows marginal skills in simulating the ISNAO pattern and the propagation of its associated wave trains, and underestimates the relationship between the atmospheric circulation in the mid‐high latitudes and the CISO of SASRB. In addition, overestimation of the intensity of CISO in the tropical–subtropical and higher‐latitude atmosphere may cause overestimation of the CISO of SASRB.

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Subseasonal Predictions of Regional Summer Monsoon Rainfall over Tropical Asian Oceans and Land

Cited by 14 publications

References 59 publications

Impacts of atmospheric and oceanic initial conditions on boreal summer intraseasonal oscillation forecast in the BCC model

Impacts of atmospheric and oceanic initial conditions on boreal summer intraseasonal oscillation forecast in the BCC model

MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

Intraseasonal variability and predictability of the subtropical Asian summer rain band

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