Abstract

Tropical–extratropical interactions of intraseasonal oscillations (ISOs), based on 30 years (1979–2009) of northern winter observations and theory, are compared. The phase relationships between the tropical signal of the leading theoretical ISO for a January 1979 basic state and the development of Pacific–North America (PNA)-like and North Atlantic Oscillation (NAO) teleconnection patterns are found to compare closely with those for the observed Madden–Julian oscillation (MJO). For both observations and theory positive NAO occurs 5–15 days after MJO convection [negative outgoing longwave radiation (OLR) and positive precipitation] and negative upper-troposphere velocity potential ISO anomalies are focused over the central Indian Ocean. The fluxes of wave activity, based on the upper-troposphere streamfunction of the leading theoretical mode, indicate strong tropical–extratropical interactions and have very similar structures to those obtained by H. Lin et al. based on observations of extratropical anomalies associated with MJO convection.

The second leading theoretical ISO mode for January 1979 has quite similar properties to the leading ISO mode but has a longer period of 44.5 days compared with 34.4 days and a more distinct quadrupole streamfunction structure straddling the equator. Theoretical ISO modes for other observed basic states, including January 1988 and the 30-yr average of January 1980–2009, again link the tropical ISO signal with Northern Hemisphere teleconnection patterns, particularly the NAO. The growth rates of ISO modes increase with stronger baroclinicity of the basic-state zonal winds in the main jet streams and, importantly, with increased tropical–extratropical interactions because of stronger meridional winds.

Abstract

Based on the bivariate Madden–Julian oscillation (MJO) index defined by Wheeler and Hendon and 25 yr (1979–2004) of pentad data, the association between the North Atlantic Oscillation (NAO) and the MJO on the intraseasonal time scale during the Northern Hemisphere winter season is analyzed. Time-lagged composites and probability analysis of the NAO index for different phases of the MJO reveal a statistically significant two-way connection between the NAO and the tropical convection of the MJO. A significant increase of the NAO amplitude happens about 5–15 days after the MJO-related convection anomaly reaches the tropical Indian Ocean and western Pacific region. The development of the NAO is associated with a Rossby wave train in the upstream Pacific and North American region. In the Atlantic and African sector, there is an extratropical influence on the tropical intraseasonal variability. Certain phases of the MJO are preceded by the occurrence of strong NAOs. A significant change of upper zonal wind in the tropical Atlantic is caused by a modulated transient westerly momentum flux convergence associated with the NAO.

Abstract

Ensemble integrations using a primitive-equation dry atmospheric model were performed to investigate the atmospheric transient response to tropical thermal forcings that resemble El Niño and La Niña. The response develops in the North Pacific within 1 week after the integration. The signal in the North Atlantic and Europe is established by the end of the second week. Significant asymmetry was found between the responses in El Niño and La Niña that is similar to the observations, that is, one feature is that the 550-hPa positive height response in the North Pacific of the La Niña run is located about 30° west of the negative response of the El Niño run; another feature is that the responses in the North Atlantic and Europe for the La Niña and El Niño cases have similar patterns with the same polarity. The first feature is established within 2 weeks of the integration, while the second feature develops starting from the end of the second week. Several factors contribute to this nonlinearity of the response. In the Tropics, the shape of the Rossby wave response and the zonal extent of the Kelvin wave are not symmetric between El Niño and La Niña, which seems to be associated with the dependence of the wave property on the modified zonal mean flow. This is especially important in the equatorial region to the west of the forcing, which is likely responsible for the phase shift of the major extratropical response in the North Pacific. The transient eddy activity in the extratropics feeds back to the response and helps to maintain the nonlinearity.

Abstract

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.

Abstract

The influence of the tropical Pacific sea surface temperature (SST) on the wintertime surface air temperature (SAT) in China is investigated using both the observational data and the output of coupled ocean–atmosphere numerical models during the period from 1960 to 2006. A singular value decomposition analysis (SVD) is applied between 500-hPa geopotential height (Z500) in the Northern Hemisphere and SST in the tropical Pacific Ocean to get the tropical Pacific SST-forced atmospheric patterns. The association of the SAT over China and the tropical Pacific SST is measured by calculating the temporal correlation coefficient (TCC) between the SAT and the expansion coefficient of the atmospheric component of the leading two SVD modes. Results show that the SAT over China is significantly correlated to the second SVD mode (SVD2). The SST component of SVD2 is characterized by negative tropical Pacific SST anomalies centered over the midequatorial Pacific Ocean. The atmospheric component of SVD2 (ASVD2) shares many similarities in spatial structures to the Arctic Oscillation (AO). The time variation of ASVD2, however, is found more closely correlated to the variation of SAT over China than the AO. When SVD2 is in its positive phase, the SAT over China tends to be warmer than normal. Further analysis indicates that the TCC between the SAT in China and ASVD2 is largely decreased after the long-term climate trend is removed. The time variability of the tropical Pacific SST-forced large-scale atmospheric patterns and its relationship to SAT are reasonably captured by the multimodel ensemble (MME) seasonal forecasts. An examination of the MME forecast skill indicates that ASVD2 contributes significantly to the TCC skill of MME forecasts.

Abstract

Subseasonal variability of surface air temperature (SAT) over East Asia is analyzed using the NCEP–NCAR reanalysis of 34 Northern Hemisphere extended summers. An empirical orthogonal function (EOF) analysis is performed with pentad SAT data to identify the leading modes of subseasonal SAT variability. The first (EOF1) and second (EOF2) modes, which together account for about 35% of the total variance, correspond to a monopole structure of SAT anomaly in the whole East Asian region and a dipole structure with opposite signs of variability over the north and south East Asian continent, respectively. Lead–lag regressions are calculated in order to analyze how the large-scale atmospheric circulation evolves in association with the development of the leading SAT modes. An eastward propagation of the Rossby wave from the midlatitude Atlantic Ocean is observed about three pentads before EOF1. EOF2 is influenced by both the tropical Madden–Julian oscillation (MJO) and a midlatitude wave train. These results indicate that there is potential for prediction of the dominant SAT modes on the subseasonal time scale. The subseasonal prediction of the two dominant modes is further evaluated in the operational monthly forecasting system of Environment and Climate Change Canada (ECCC). The model shows a better forecast skill than the persistence forecast. The strength of the subseasonal signal in initial conditions impacts the forecast skill. The forecasts starting with strong EOF in the initial condition are more skillful than those initialized with weak EOF. The findings in the study contribute to improving the understanding of the subseasonal variability and SAT subseasonal forecasting in East Asia.

Abstract

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.

Abstract

A primitive equation dry atmospheric model is used to perform ensemble seasonal predictions. The predictions are done for 51 winter seasons [December–January–February (DJF)] from 1948 to 1998. Ensembles of 24 forecasts are produced, with initial conditions of 1 December plus small perturbations. The model uses a forcing field that is calculated empirically from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalyses. The forcing used to forecast a given winter is the sum of its winter climatological forcing plus an anomaly. The anomalous forcing is obtained as that of the month prior to the start of the forecast (November), which is also calculated from NCEP data. The predictions are thus made without using any information about the season to be predicted.

The ensemble-mean predictions for the 51 winters are verified against the NCEP–NCAR reanalyses. Comparisons are made with the results obtained with a full GCM. It is found that the skill of the simple GCM is comparable in many ways to that of the full GCM. The skill in predicting the amplitude of the main patterns of Northern Hemisphere mean-seasonal variability, the Arctic Oscillation (AO) and the Pacific–North American (PNA) pattern is also discussed. The simple GCM has skill not only in predicting the PNA pattern during winters with strong ENSO forcing, but it also has skill in predicting the AO in winters without appreciable ENSO forcing.

Abstract

The performance of the Global Environmental Multiscale (GEM) model, the Canadian operational numerical model, in reproducing atmospheric low-frequency variability is evaluated in the context of Northern Hemisphere blocking climatology. The validation is conducted by applying a comprehensive but relatively simple blocking detection algorithm to a 20-yr (1987–2006) integration of the GEM model in climate mode. The comparison to reanalysis reveals that, although the model can reproduce Northern Hemisphere blocking climatology reasonably well, the maximum blocking frequency over the North Atlantic and western Europe is generally underestimated and its peak season is delayed from late winter to spring. This contrasts with the blocking frequency over the North Pacific, which is generally overestimated during all seasons. These misrepresentations of blocking climatology are found to be largely associated with the biases in climatological background flow. The modeled stationary waves show a seasonal delay in zonal wavenumber 1 and an eastward extension in zonal wavenumber-2 components consistent with blocking frequency biases. High-frequency eddies are, however, consistently underestimated both in the North Atlantic and Pacific, indicating that the biases in eddy fields might not be the main reason for the blocking biases in the North Pacific.

Abstract

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.