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Evaluation of Quasi-biweekly Oscillation Prediction in the Asian Summer Monsoon Regions by BCC S2S Model
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摘要: 利用1994-2013年ERA-Interim及NCEP/NCAR再分析数据，对国家气候中心（BCC）次季节到季节尺度模式（S2S）1994-2013年的回报试验数据进行亚洲季风区准双周振荡（QBWO）预报能力评估，并诊断模式预报误差来源。结果表明：BCC S2S模式对QBWO的预报能力随着预报提前时间的增长而降低，9 d后预报技巧明显减弱，其周期、传播特征和强度出现误差；在提前9 d预报中，印度洋地区QBWO对流-环流系统结构松散，信号偏弱，对流向东传播，这与印度洋平均态的预报误差有关，夏季对流平均态低层水汽场在西太平洋和阿拉伯海较强，而东印度洋、孟加拉湾一带偏弱；西北太平洋地区QBWO具有向西北传播的特征，但强度偏弱，可能原因是预报低估了QBWO对流西北侧低层涡度的超前信号，经涡度方程诊断发现，地转涡度平流正贡献微弱，相对涡度平流在对流西北侧引发负涡度，从而减弱了对流西北侧由低层正涡度引发的有利条件。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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图 1 1994—2013年夏季(5—10月)季节内尺度MV-EOF的OLR(填色)和850 hPa风场(矢量)的第3和第4模态分布
(a)再分析数据第3模态，(b)模式提前1 d预报第3模态，(c)模式提前6 d预报第3模态，(d)模式提前11 d预报第3模态，(e)模式提前16 d预报第3模态，(f)再分析数据第4模态，(g)模式提前1 d预报第4模态，(h)模式提前6 d预报第4模态，(i)模式提前11 d预报第4模态，(j)模式提前16 d预报第4模态
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
图 2 BCC S2S模式预报QBWO的双变量时间相关系数(a)MV-EOF，(b)投影法
Fig. 2 Forecast skills of BCC S2S model measured by multi-variate anomaly correlation coefficient of QBWO (a)MV-EOF, (b)projections
图 3 QBWO第3、第4模态对应时间系数的超前-滞后相关系数
Fig. 3 Lead-lag correlation coefficients between time series of the third mode and the fourth mode associated with QBWO
图 4 MV-EOF第4模态对应的时间系数功率谱分布(虚线为红噪音检验)
(a)再分析数据，(b)模式提前9 d预报
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
图 5 再分析数据和模式提前9 d预报QBWO生命周期8位相合成OLR(填色)和850 hPa风场(矢量)
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
图 6 1994—2013年夏季(5—10月)气候平均分布(a)再分析数据OLR，(b)模式提前9 d预报OLR，(c)再分析数据700~1000 hPa比湿，(d)模式提前9 d预报700~1000 hPa比湿
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
图 7 1994—2013年夏季(5—10月)10~30 d OLR(填色)和850 hPa涡度(等值线，单位：10-6 s-1，由0.5×10-6 s-1起始，每条线间隔为0.5×10-6 s-1)与MV-EOF第4模态对应时间系数超前回归
(黄色框为同期对流西北侧涡度最大值中心)
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
图 8 再分析数据对流西北侧涡度大值中心的涡度方程收支诊断
Fig. 8 Diagnostic results of vorticity equation averaged over the positive vorticity center to the northwest of convection
图 9 1994—2013年夏季(5—10月)10~30 d 850 hPa的涡度方程收支项(等值线，单位：10-12 s-2)和OLR(填色)与MV-EOF第4模态对应时间系数的同期回归
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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