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Hai Lin and Zhiwei Wu

Abstract

Predicting surface air temperature (T) is a major task of North American (NA) winter seasonal prediction. It has been recognized that variations of the NA winter T’s can be associated with El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). This study presents observed evidence that variability in snow cover over the Tibetan Plateau (TP) and its adjacent areas in prior autumn (September–November) is significantly correlated with the first principal component (PC1) of the NA winter T’s, which features a meridional seesaw pattern over the NA continent. The autumn TP snow cover anomaly can persist into the following winter through a positive feedback between snow cover and the atmosphere. A positive TP snow cover anomaly may induce a negative sea level pressure and geopotential height anomaly over the eastern North Pacific, a positive geopotential height anomaly over Canada, and a negative anomaly over the southeastern United States—a structure very similar to the positive phase of the Pacific–North America (PNA) pattern. This pattern usually favors the occurrence of a warm–north, cold–south winter over the NA continent. When a negative snow cover anomaly occurs, the situation tends to be opposite. Since the autumn TP snow cover shows a weak correlation with ENSO, it provides a new predictability source for NA winter T’s.

Based on the above results, an empirical model is constructed to predict PC1 using a combination of autumn TP snow cover and other sea surface temperature anomalies related to ENSO and the NAO. Hindcasts and real forecasts are performed for the 1972–2003 and 2004–09 periods, respectively. Both show a promising prediction skill. As far as PC1 is concerned, the empirical model hindcast performs better than the ensemble mean of four dynamical models from the Canadian Meteorological Centre. Particularly, the real forecast of the empirical model exhibits a better performance in predicting the extreme phases of PC1—that is, the extremely warm winter over Canada in 2009/10—should the model include the autumn TP snow cover impacts. Since all these predictors can be readily monitored in real time, this empirical model provides a real-time forecast tool for NA winter climate.

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Po-Lin Wu, Pay-Liam Lin, and Hann-Ming Henry Juang

Abstract

Regional climate models used to resolve high-resolution local circulation by dynamically downscaling from a coarse-resolution analysis may have difficulty in keeping large-scale information the same as in the analysis. Such difficulty produces large-scale error or bias, mainly due to mathematically ill-posed lateral boundary conditions. To reduce this type of error, in this paper the authors propose a scheme called local mean bias correction (LMBC). LMBC has means in x and in y directions locally over the model domain, but not a single mean over the entire domain. In spectral space, local means are represented by the zero wavenumber in the x direction with all wavenumbers in the y direction, and the zero wavenumber in the y direction with all the wavenumbers in the x direction. The local mean perturbation can be removed to correct the local mean bias where the perturbation is defined as the difference between a regional field and a base field from the analysis. The LMBC constitutes a simple methodology to ameliorate the influence of the lateral boundary conditions (LBCs) on the integration of the regional climate model (RCM); using it in this study improved the regional climate simulation of the 1998 South China Sea (SCS) summer monsoon. Generally speaking, this scheme proved to have a well-simulated circulation during all periods of monsoon activities examined. Circulation errors during the SCS summer monsoon onset can be corrected using this LMBC scheme, though the rainfall amount was always underestimated as compared to the observation. This research demonstrated that shifting the domain north, east, west, and south as well as enlarging domain size produced results that are consistent in different cases over different years.

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Baolan Wu, Xiaopei Lin, and Lisan Yu

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The meridional shift of the Kuroshio Extension (KE) front and changes in the formation of the North Pacific Subtropical Mode Water (STMW) during 1979–2018 are reported. The surface-to-subsurface structure of the KE front averaged over 142°–165°E has shifted poleward at a rate of ~0.23° ± 0.16° decade−1. The shift was caused mainly by the poleward shift of the downstream KE front (153°–165°E, ~0.41° ± 0.29° decade−1) and barely by the upstream KE front (142°–153°E). The long-term shift trend of the KE front showed two distinct behaviors before and after 2002. Before 2002, the surface KE front moved northward with a faster rate than the subsurface. After 2002, the surface KE front showed no obvious trend, but the subsurface KE front continued to move northward. The ventilation zone of the STMW, defined by the area between the 16° and 18°C isotherms or between the 25 and 25.5 kg m−3 isopycnals, contracted and displaced northward with a shoaling of the mixed layer depth h m before 2002 when the KE front moved northward. The STMW subduction rate was reduced by 0.76 Sv (63%; 1 Sv ≡ = 106 m3 s−1) during 1979–2018, most of which occurred before 2002. Of the three components affecting the total subduction rate, the temporal induction (−∂h m/∂t) was dominant accounting for 91% of the rate reduction, while the vertical pumping (−w mb) amounted to 8% and the lateral induction (−u mb ⋅ ∇h m) was insignificant. The reduced temporal induction was attributed to both the contracted ventilation zone and the shallowed h m that were incurred by the poleward shift of KE front.

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Marc Bocquet, Carlos A. Pires, and Lin Wu

Abstract

This review discusses recent advances in geophysical data assimilation beyond Gaussian statistical modeling, in the fields of meteorology, oceanography, as well as atmospheric chemistry. The non-Gaussian features are stressed rather than the nonlinearity of the dynamical models, although both aspects are entangled. Ideas recently proposed to deal with these non-Gaussian issues, in order to improve the state or parameter estimation, are emphasized.

The general Bayesian solution to the estimation problem and the techniques to solve it are first presented, as well as the obstacles that hinder their use in high-dimensional and complex systems. Approximations to the Bayesian solution relying on Gaussian, or on second-order moment closure, have been wholly adopted in geophysical data assimilation (e.g., Kalman filters and quadratic variational solutions). Yet, nonlinear and non-Gaussian effects remain. They essentially originate in the nonlinear models and in the non-Gaussian priors. How these effects are handled within algorithms based on Gaussian assumptions is then described. Statistical tools that can diagnose them and measure deviations from Gaussianity are recalled.

The following advanced techniques that seek to handle the estimation problem beyond Gaussianity are reviewed: maximum entropy filter, Gaussian anamorphosis, non-Gaussian priors, particle filter with an ensemble Kalman filter as a proposal distribution, maximum entropy on the mean, or strictly Bayesian inferences for large linear models, etc. Several ideas are illustrated with recent or original examples that possess some features of high-dimensional systems. Many of the new approaches are well understood only in special cases and have difficulties that remain to be circumvented. Some of the suggested approaches are quite promising, and sometimes already successful for moderately large though specific geophysical applications. Hints are given as to where progress might come from.

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Zhongda Lin, Riyu Lu, and Renguang Wu

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Previous studies have found a link between north China and Indian rainfall during summer, with significantly increased rainfall in north China related to a stronger Indian summer monsoon. This link is weakened after the late 1970s, generally attributed to the reduced magnitude of interannual variability in the Indian summer rainfall. This study reveals a similar change in this rainfall link in early summer after the late 1970s. Related to a heavier Indian early summer rainfall, rainfall in north China enhances significantly before the late 1970s but not thereafter. The change in rainfall teleconnection is caused by the weakened impact on north China rainfall of a midlatitude wave train along the Asian jet in the upper troposphere. After the late 1970s, the portion of the wave train over East Asia displaces eastward, leading to an eastward shift in the associated ascending motion and, subsequently, enhanced rainfall from north China to the Yellow Sea. Moreover, the change in the midlatitude wave train is attributed to the change in the basic state over East Asia (i.e., a northward shift of the East Asian upper-tropospheric westerly jet after the late 1970s). The latter reduces stationary Rossby wavenumber and increases wavelength of the midlatitude wave train, leading to an eastward shift of the wave train over East Asia. Therefore, in this study a mechanism is proposed for the change in early summer, different from the previous mechanism for the entire summer period.

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Yonghong Yao, Hai Lin, and Qigang Wu

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The mei-yu onset over the middle to lower reaches of the Yangtze River Valley (MLYRV) varies considerably from early June to mid-July, which leads to large interannual changes in rainy-season length, total summer rainfall, and flooding potential. Previous studies have investigated the impact of El Niño–Southern Oscillation (ENSO) on the mei-yu onset. This study shows that a strong (weak) East Asian and western North Pacific (EAWNP) intraseasonal oscillation (ISO) in spring leads to an early (late) onset of the mei-yu over the MLYRV, and this ISO–mei-yu relationship is attributed to different types of ENSO in the preceding winter. A strong EAWNP ISO in spring is related to an eastern Pacific El Niño (EP El Niño) in the previous winter, and negative sea surface temperature (SST) anomalies in the eastern Indian Ocean and the South China Sea (SCS) in May, which can cause an early onset of the South China Sea summer monsoon that also favors an early mei-yu onset. In contrast, a weak EAWNP ISO in spring is associated with a central Pacific El Niño (CP El Niño) before April, but with an EP El Niño after April, and positive SST anomalies in both the eastern Indian Ocean and the SCS in May. A statistical forecast model combining the intensity of spring EAWNP ISO, CP ENSO, and EP ENSO indices shows a high prediction skill of the observed mei-yu onset date.

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Zhiwei Wu, Hai Lin, and Ted O’Brien

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Seasonal prediction of growing-season start of warm-season crops (GSSWC) is an important task for the agriculture sector to identify risks and opportunities in advance. On the basis of observational daily surface air temperature at 210 stations across Canada, this study found that the GSSWC in most Canadian areas begins during May–June and exhibits significant year-to-year variations that are dominated by two distinct leading empirical orthogonal function modes. The first mode accounts for 20.2% of the total GSSWC variances and features a monosign pattern with the maximum anomalies in central-southern Canada. It indicates that warm-season crops in most Canadian areas usually experience a consistent early or late growing-season start and those in central-southern Canada have the most pronounced interannual variations. The second mode explains 10.8% of the total variances and bears a zonal seesaw pattern in general, accompanied by prominent anomalies covering the west coast of Canada and anomalies with a reverse sign prevailing in central-eastern Canada. Therefore, a strong second-mode year represents an early GSSWC in western Canada and a late GSSWC in the rest of the regions. The predictability sources for the two distinct leading modes show considerable differences. The first mode is closely linked with the North American continental-scale snow cover anomalies and sea surface temperature anomalies (SSTAs) in the North Pacific and Indian Oceans in the prior April. For the second mode, the preceding April snow cover anomalies over western North America and SSTAs in the equatorial-eastern Pacific, North Pacific, and equatorial Indian Oceans provide precursory conditions. These snow cover anomalies and SSTAs sustain from April through May–June, influence the large-scale atmospheric circulation anomalies during the crops’ growing-start season, and contribute to the occurrence of the two leading modes of the GSSWC across Canada. On the basis of these predictors of snow cover anomalies and SSTAs in the prior April, an empirical model is established for predicting the two principal components (PCs) of the GSSWC across Canada. Hindcasting is performed for the 1972–2007 period with a leaving-nine-out cross-validation strategy and shows a statistically significant prediction skill. The correlation coefficient between the observation and the hindcast is 0.54 for PC1 and 0.48 for PC2, both exceeding the 95% confidence level. Because all of these predictors can be readily monitored in real time, this empirical model provides a new prediction tool for agrometeorological events across Canada.

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Baolan Wu, Xiaopei Lin, and Lisan Yu

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The decadal to multidecadal mixed layer variability is investigated in a region south of the Kuroshio Extension (130°E–180°, 25°–35°N), an area where the North Pacific subtropical mode water forms, during 1948–2012. By analyzing the mixed layer heat budget with different observational and reanalysis data, here we show that the decadal to multidecadal variability of the mixed layer temperature and mixed layer depth is covaried with the Atlantic multidecadal oscillation (AMO), instead of the Pacific decadal oscillation (PDO). The mixed layer temperature has strong decadal to multidecadal variability, being warm before 1970 and after 1990 (AMO positive phase) and cold during 1970–90 (AMO negative phase), and so does the mixed layer depth. The dominant process for the mixed layer temperature decadal to multidecadal variability is the Ekman advection, which is controlled by the zonal wind changes related to the AMO. The net heat flux into the ocean surface Q net acts as a damping term and it is mainly from the effect of latent heat flux and partially from sensible heat flux. While the wind as well as mixed layer temperature decadal changes related to the PDO are weak in the western Pacific Ocean. Our finding proposes the possible influence of the AMO on the northwestern Pacific Ocean mixed layer variability, and could be a potential predictor for the decadal to multidecadal climate variability in the western Pacific Ocean.

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Yonghong Yao, Hai Lin, and Qigang Wu

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Using pentad data of the Northern Hemisphere extended winter (November–March) from 1979 to 2012 derived from the daily rainfall of the National Meteorological Information Center of China, subseasonal variability of precipitation in China is analyzed. The two dominant modes of subseasonal variability are identified with an empirical orthogonal function (EOF) analysis. The first EOF mode (EOF1) is characterized by a monopole in South China, whereas the second EOF mode (EOF2) has a meridional dipole structure with opposite precipitation anomalies over the Yangtze River basin and the coastal area of South China. These two modes tend to have a phase shift to each other in both space and time, indicating that part of their variability represents a southward-propagating pattern.

The subseasonal variability is decomposed into two components: one related to the Madden–Julian oscillation (MJO) and the other independent of MJO. It is found that the MJO contributes to about 10% of the precipitation variability in South China. EOF1 is associated with MJO phase 3, corresponding to enhanced equatorial convection in the Indian Ocean and depressed convection in the western Pacific, while EOF2 is related to MJO phase 5 when the enhanced tropical convection moves to the Maritime Continent region. Subseasonal precipitation variability in China that is independent of the MJO is especially affected by processes including tropical convection variability and the “cold surge” phenomenon or the development of a Siberian high and cold-air outbreak in East Asia associated with a wave train from the North Atlantic.

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Yi-Hsuan Lin and Chun-Chieh Wu

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Remote rainfall related to tropical cyclones (TCs) can be attributed to interaction between the northeasterly monsoon and TC circulation (hereafter monsoon mode), and topographic blocking and lifting effects (hereafter topographic mode). Typhoon Khanun (2017) is a case in point affected by both modes. The objective of this study is to understand the key factors leading to uncertainty in the TC-induced remote rainfall. Ensemble simulations are conducted, with the ensemble members related to the monsoon mode classified into subtypes based on the geographic location of the precipitation maxima. The results demonstrate that frontogenesis and terrain-induced uplifting are the main mechanisms leading to the heavy precipitation in northeastern Taiwan, while the orographic lifting and the interaction between the TC circulation and the topographically blocked northeasterlies result in the heavy rainfall in southeastern Taiwan. For the topographic mode, at a larger rainfall threshold, strong relation is found between the inflow angle of the TC circulation and the cumulative frequency of the rainfall, while at a smaller rainfall threshold, rainfall cumulative frequency is related to the ensemble track directions. Sensitivity experiments with TC-related moisture reduced (MR) and the terrain of Taiwan removed (TR) show that the average of the 3-day accumulated rainfall is reduced by 40% and more than 90% over the mountainous area in MR and TR, respectively. Overall, this study highlights the fact that multiple mechanisms contribute to remote rainfall processes in Khanun, particularly the orographic forcing, thus providing better insights into the predictability of TC remote rainfall.

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