Vol.29, NO.4, 2018
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Article Navigation >  Journal of Applied Meteorological Science  > 2018  >  29(4): 436-448
He Zheng, Hsu Pang, Gao Yingxia. Evaluation of quasi-biweekly oscillation prediction in the Asian summer monsoon regions by BCC S2S model. J Appl Meteor Sci, 2018, 29(4): 436-448. DOI:  10.11898/1001-7313.20180405.
Citation: He Zheng, Hsu Pang, Gao Yingxia. Evaluation of quasi-biweekly oscillation prediction in the Asian summer monsoon regions by BCC S2S model. J Appl Meteor Sci, 2018, 29(4): 436-448. DOI:  10.11898/1001-7313.20180405.
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Evaluation of Quasi-biweekly Oscillation Prediction in the Asian Summer Monsoon Regions by BCC S2S Model

DOI: 10.11898/1001-7313.20180405

  • Key Laboratory of Meteorological Disaster of Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044

  • Received Date: 2018-03-09
  • Rev Recd Date: 2018-05-17
  • Publish Date: 2018-07-31
    Abstract
  • The quasi-biweekly oscillation (QBWO) plays an important role in global weather and climate change. It's a very important source of sub-seasonal to seasonal (S2S) predictability. Using the sub-seasonal to seasonal forecast model of Beijing Climate Center (BCC S2S), the boreal summer QBWO is simulated, the forecast skill is discussed, and the model bias is analyzed. QBWO can be obtained from the third and fourth modes of multivariate empirical orthogonal function (MV-EOF) analysis on daily anomalies of outgoing longwave radiation (OLR) and zonal wind at 850 hPa (U850) in the Asian monsoon region. According to reanalysis data, QBWO shows a northeast-southwest-tilted convection-circulation structure, propagating north/northwestward from the equatorial western Pacific and Indian Ocean. The forecast skill of BCC S2S on QBWO decreases as the forecast lead time increases, and biases become very significant in the period, propagation characteristics and strength when the lead time comes to 9 days. BCC S2S reveals a higher forecast skill of QBWO structure and propagation over the western North Pacific, while it significantly underestimated convection signal of QBWO over the tropical Indian Ocean. The convection-circulation wave structure of QBWO in 9-day lead time prediction over the Indian Ocean is loose and appears over the Arabian Sea (instead of over the tropical eastern Indian Ocean and Bay of Bengal where the reanalyzed QBWO is active). It suggests that the unrealistic Indian Ocean QBWO is related to biases of model mean state. The simulated low-level moisture and convection during boreal summer are enhanced over the western Pacific and the Arabian Sea. However, the model underestimates the abundant moisture and vigorous convection over the eastern Indian Ocean and Bay of Bengal. BCC S2S captures the structure and propagation of QBWO over the western North Pacific, but slightly underestimates the strength of QBWO wave train. This underestimation of QBWO convection might be attributable to the relatively weaker vorticity to the northwest of QBWO convection. By diagnosing the vorticity equation, it's found that although the model well simulates positive contributions of geostrophic vorticity advection and convergence effects to the northwest of convection, these contributions are still underestimated. Moreover, the simulated relative vorticity advection shows an opposite effect to reanalysis data in the 9-day lead time prediction, weakening the favorable environment of QBWO development associated with positive vorticity to the northwestern part of convection.
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  • Fig. 1  The third mode and the fourth mode of intraseasonal OLR(the shaded) with 850 hPa wind(the vector) during boreal summer(May-October) in 1994-2013 based on MV-EOF

    (a)the third mode of reanalysis, (b)the third mode of 1 d lead time prediction, (c)the third mode of 6 d lead time prediction, (d)the third mode of 11 d lead time prediction, (e)the third mode of 16 d lead time prediction, (f)the fourth mode of reanalysis, (g)the fourth mode of 1 d lead time prediction, (h)the fourth mode of 6 d lead time prediction, (i)the fourth mode of 11 d lead time prediction, (j)the fourth mode of 16 d lead time prediction

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    Fig. 2  Forecast skills of BCC S2S model measured by multi-variate anomaly correlation coefficient of QBWO (a)MV-EOF, (b)projections

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    Fig. 3  Lead-lag correlation coefficients between time series of the third mode and the fourth mode associated with QBWO

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    Fig. 4  Power spectra of time series of the fourth mode associated with QBWO

    (the dashed line is Markov red noise spectrum) (a)the reanalysis, (b)9 d lead time prediction

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    Fig. 5  8-phase composited anomalous OLR(the shaded) and 850 hPa wind(the vector) of QBWO life cycle by reanalysis and 9 d lead time prediction

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    Fig. 6  Climatological mean during boreal summer(May-October) of 1994-2013 (a)OLR of reanalysis, (b)OLR of 9 d lead time prediction, (c)700-1000 hPa specific humidity of reanalysis, (d)700-1000 hPa specific humidity of 9 d lead time prediction

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    Fig. 7  Lead regression coefficients of 10-30 d filtered OLR(the shaded, unit:W·m-2) and 850 hPa vorticity(the contour, unit:10-6 s-1, starting from 0.5×10-6 s-1 with an interval of 0.5×10-6 s-1) to time series of the fourth mode(the yellow box marks the positive vorticity center to the northwest of convection of lag 0 d) during boreal summer(May-Oct) of 1994-2013

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    Fig. 8  Diagnostic results of vorticity equation averaged over the positive vorticity center to the northwest of convection

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    Fig. 9  Regression coefficients of 10-30 d filtered 850 hPa vorticity equation budget terms (the contour, unit:10-12 s-2) and OLR(the shaded) to time series of the fourth mode during boread summer(May-Oct) of 1994-2013

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    • Received : 2018-03-09
    • Accepted : 2018-05-17
    • Published : 2018-07-31
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