Enhanced intraseasonal rainfall variability over Northeast Australia

A gridded daily rainfall dataset prepared from observations at 3700 stations is used to analyze the intraseasonal and interannual variability of the summer monsoon rainfall over India. It is found that the major drought years are characterized by large-scale negative rainfall anomalies covering nearly all of India and persisting for the entire monsoon season. The intraseasonal variability of rainfall during a monsoon season is characterized by the occurrence of active and break phases. During the active phase, the rainfall is above normal over central India and below normal over northern India (foothills of the Himalaya) and southern India. This pattern is reversed during the break phase. It is found that the nature of the intraseasonal variability is not different during the years of major droughts or major floods. This suggests that a simple conceptual model to explain the interannual variability of the Indian monsoon rainfall should consist of a linear combination of a large-scale persistent s...

Rwanda is a landlocked country in central-eastern Africa. As a country highly dependent on rain-fed agriculture, Rwanda is vulnerable to rainfall variability. Observational data show that there are two rainy seasons in Rwanda, i.e., the long rainy season and the short rainy season. This study mainly focuses on the dominant intraseasonal rainfall mode during the long rainy season (February–May), and evaluates the forecast skill for the intraseasonal variability (ISV) over Rwanda and its surrounding regions in a state-of-the-art dynamic model. During the long rainy season, observational results reveal that the dominant intraseasonal rainfall mode in Rwanda exhibits a significant variability on the 10–25-day time scale. One-point-correlation analysis further unveils that the 10–25-day intraseasonal rainfall variability in Rwanda co-varies with that in its adjacent areas, indicating that the overall 10–25-day rainfall variability in Rwanda and its adjacent regions (8°S–3°N, 29°–37°E) should be considered collectively when studying the dominant intraseasonal rainfall variability in Rwanda. Composite results show that the development of the 10–25-day rainfall variability is associated with the anomalous westerly wind in Rwanda and its surrounding regions, which may trace back to a pair of westward-propagating equatorial Rossby waves. Based on the observational findings, an ISO_rainfall_index and an ISO_wind_index are proposed for quantitatively evaluating the forecast skill. The ECMWF model has a comparable skill in predicting the wind index and the rainfall index, with both indices showing a skill of 18 days.摘要非洲中东部地区的经济主要依靠自给农业支撑, 该地区农业经济对降水的变化尤为敏感. 本文以卢旺达为例, 观测分析指出卢旺达的次季节降雨主要集中在10–25天; 根据次季节尺度降水变率的单点相关方法, 发现卢旺达的次季节降水变率和周围区域变化一致; 进一步合成结果显示该地区次季节降水变率与异常西风有关, 这可追溯到赤道地区西传的赤道Rossby波. 最后, 本文评估了当前动力模式ECMWF对卢旺达地区(即非洲中东部)次季节降水变率的预报能力, 发现EC模式在对该区域降水和相关风场指数的预报技巧都在18天左右, 且预报技巧表现出一定的年际差异, 这可能与热带太平洋的背景海温信号有关. 该工作增进了当前对非洲中东部地区的次季节降水变率和预测水平的认知, 并且对该地区国家粮食安全和防灾减灾具有启示性意义.

Large intraseasonal rainfall variations are identified over the southern South China Sea (SSCS), tropical southeastern Indian Ocean (SEIO), and east coast of the Philippines (EPHI) in boreal winter. The present study contrasts origins and propagations and investigates interrelations of intraseasonal rainfall variations on the 10–20- and 30–60-day time scales in these regions. Different origins are identified for intraseasonal rainfall anomalies over the SSCS, SEIO, and EPHI on both time scales. On the 10–20-day time scale, strong northerly or northeasterly wind anomalies related to the East Asian winter monsoon (EAWM) play a major role in intraseasonal rainfall variations over the SSCS and EPHI. On the 30–60-day time scale, both the intraseasonal signal from the tropical Indian Ocean and the EAWM-related wind anomalies contribute to intraseasonal rainfall variations over the SSCS, whereas the EAWM-related wind anomalies have a major contribution to the intraseasonal rainfall variations over the EPHI. No relation is detected between the intraseasonal rainfall variations over the SEIO and the EAWM on both the 10–20-day and 30–60-day time scales. The anomalies associated with intraseasonal rainfall variations over the SSCS and EPHI propagate northwestward and northeastward, respectively, on the 10–20- and 30–60-day time scales. The intraseasonal rainfall anomalies display northwestward and northward propagation over the Bay of Bengal, respectively, on the 10–20- and 30–60-day time scales.

This study introduces four univariate regional indices to improve the representation of intraseasonal rainfall variability across South America throughout the year, focusing on Brazil. These indices are constructed using two distinct approaches: the linear Empirical Orthogonal Functions (EOF) method and the unsupervised machine‐learning Self‐Organizing Maps (SOM) technique. Both methods are applied to Outgoing Longwave Radiation (OLR) and precipitation‐filtered anomalies in the 30–90‐day band over the South American domain. Results demonstrate that regional indices provide valuable insights into intraseasonal South American rainfall variability, including phase and strength, compared to global indices of the Madden‐Julian Oscillation (MJO). Despite being computed using only the South American domain, the regional indices capture the tropical‐tropical MJO teleconnection through the zonal wavenumber‐1 structure. The diversity in amplitude and evolution of precipitation, primarily influenced by tropical‐extratropical teleconnections through Rossby wave trains, is more evident when using the non‐linear SOM index. The regional indices also accurately measure the impacts of intraseasonal variability on extreme precipitation events over Brazil. Case studies, such as the 2013/2014 summer drought episode, highlight this ability, when a deficient rainy season severely affected the Southeast Region of Brazil, impacting agricultural production and hydroelectric power generation. During this episode, the regional indices show agreement between drought periods and suppressed precipitation phases, while global indices indicate an inactive MJO phase. These findings underscore the effectiveness of regional indices in capturing intraseasonal variability, offering significant implications for extreme weather prediction and their impacts on South American water resources and socio‐economic activities.

While the Indian monsoon exhibits substantial variability on interannual time scales, its intraseasonal variability (ISV) is of greater magnitude and hence of critical importance for monsoon predictability. This ISV comprises a 30–50-day northward-propagating oscillation (NPISO) between active and break events of enhanced and reduced rainfall, respectively, over the subcontinent. Recent studies have implied that coupled general circulation models (CGCMs) were better able to simulate the NPISO than their atmosphere-only counterparts (AGCMs). These studies have forced their AGCMs with SSTs from coupled integrations or observations from satellite-based infrared sounders, both of which underestimate the ISV of tropical SSTs.The authors have forced the 1.25° × 0.83° Hadley Centre Atmospheric Model (HadAM3) with a daily, high-resolution, observed SST analysis from the United Kingdom National Center for Ocean Forecasting that contains greater ISV in the Indian Ocean than past products. One ensemble of simulations was forced by daily SSTs, a second with monthly means, and a third with 5-day means. The ensemble with daily SSTs displayed significantly greater variability in 30–50-day rainfall across the monsoon domain than the ensemble with monthly mean SSTs, variability similar to satellite-derived precipitation analyses. Individual ensemble members with daily SSTs contained intraseasonal events with a strength, a propagation speed, and an organization that closely matched observed events. When ensemble members with monthly mean SSTs displayed power in intraseasonal rainfall, the events were weak and disorganized, and they propagated too quickly. The ensemble with 5-day means had less intraseasonal rainfall variability than the ensemble with daily SSTs but still produced coherent NPISO-like events, indicating that SST variability at frequencies higher than 5 days contributes to but is not critical for simulations of the NPISO.It is concluded that high-frequency SST anomalies not only increased variance in intraseasonal rainfall but helped to organize and maintain coherent NPISO-like convective events. Further, the results indicate that an AGCM can respond to realistic and frequent SST forcing to generate an NPISO that closely resembles observations. These results have important implications for simulating the NPISO in AGCMs and coupled climate models, as well as for predicting tropical ISV in short- and medium-range weather forecasts.

Abstract. The simulation of intraseasonal precipitation variability over China in extended summer (May–October) is evaluated based on six climate simulations of the Met Office Unified Model. Two simulations use the Global Atmosphere 6.0 (GA6) and four the Global Coupled 2.0 (GC2) configuration. Model biases are large such that mean precipitation and intraseasonal variability reach twice their observed values, particularly in southern China. To test the impact of air–sea coupling and horizontal resolution, GA6 and GC2 at horizontal resolutions corresponding to ∼25, 60, and 135 km at 50∘ N are analyzed. Increasing the horizontal resolution and adding air–sea coupling have little effect on these biases. Pre-monsoon rainfall in the Yangtze River basin is too strong in all simulations. Simulated rainfall amounts in June are too high along the southern coast and persist in the coastal region through July, with only a weak northward progression. The observed northward propagation of the Meiyu–Baiu–Changma rainband from spring to late summer is poor in all GA6 and GC2 simulations. To assess how well the MetUM simulates spatial patterns of temporally coherent precipitation, empirical orthogonal teleconnection (EOT) analysis is applied to pentad-mean precipitation. Patterns are connected to large-scale processes by regressing atmospheric fields onto the EOT pentad time series. Most observed patterns of intraseasonal rainfall variability are found in all simulations, including the associated observed mechanisms. This suggests that GA6 and GC2 may provide useful predictions of summer intraseasonal variability despite their substantial biases in mean precipitation and overall intraseasonal variance.

The NCEP/NCAR reanalysis and precipitation data from the Australian Bureau of Meteorology are used to analyse variability in rainfall during the austral summer (December–March, DJFM) in northeast Australia (NEA). NEA rainfall shows a marked decrease over the past 50 years, mainly in the austral summer. Our analysis reveals that the summer rainfall decrease in NEA is generally an interdecadal phenomenon. The declining trend has an imprint in the tropical Australian summer monsoon (TASM). Not only does TASM have a phase‐to‐phase influence on NEA summer rainfall at the interannual scale, it is also closely linked with interdecadal variation in NEA summer rainfall. Thus, the decrease in NEA at the interdecadal scale could be attributed to corresponding variation in TASM. Moreover, the coupled linkage between TASM and NEA summer rainfall appears to be largely independent of El Niño–Southern Oscillation. One possible reason for the interdecadal weakening trend in TASM is a sustained interdecadal warming trend in sea surface temperature (SST) over the Wharton Basin (100°–130°E, 20°–5°S). When the Wharton Basin is in a cold state, anomalous westerlies occur in the lower troposphere in the TASM region, and cyclonic circulation anomalies and rising flows occur in the low and middle troposphere over NEA, which are associated with a strong TASM situation, consequently favouring enhanced rainfall over NEA; the opposite occurs in the case of a warm Wharton Basin. SST over the Wharton Basin has shown a continuous warming trend over the past 60 years, contributing to the weakening of TASM and, consequently, a decrease in NEA summer rainfall. Copyright © 2011 Royal Meteorological Society

The relationship between the Madden–Julian oscillation (MJO) and the seasonal cycle of the intraseasonal rainfall variability in the Amazon Basin (AB) are analysed using band‐pass‐filtered gauge‐based gridded rainfall data for the 1980–2009 period. Intraseasonal events (IE) have been defined and selected based on extreme values of the first principal component (PC1) time series, which comes from the empirical orthogonal function (EOF) analysis applied to the filtered rainfall data over the AB. A total of 132 IEs were identified with an average of approximately five events per year. About 25% of the total IEs in the Amazon region are not restricted to the eastwards propagating equatorially confined MJO and other mechanisms (e.g., through Rossby wave trains in the Southern Hemisphere) might play an important role. In addition, we find that the incomplete IEs (events that do not evolve through a complete life cycle) are associated with suppressed rainfall conditions over tropical South America. The development of the IEs over the AB, when compared with the different phases of the MJO index, shows a coherent relationship, where convective‐based indices are able to better account their evolution. On a global scale, the upper‐tropospheric patterns and the rainfall composites based on the PC1 time series show that the MJO is one of the main atmospheric modulator mechanisms of the intraseasonal rainfall variability over the Amazon region throughout the annual cycle. It is found that the intraseasonal variability is particularly important during the austral winter, when the percentage contribution with respect to the mean daily seasonal precipitation over some Amazon regions can reach 50%.

This study provides an empirical description of intraseasonal rainfall variability within the North American monsoon (NAM) region. Applying particular definitions to historical daily rainfall observations, it demonstrates that distinct intraseasonal rainfall modes exist and that these modes differ considerably from the monsoon core region in northwest Sonora (SON), California, to its northward extension in southeast Arizona (AZ). To characterize intraseasonal rainfall variability (ISV), separate P-mode principal component (PC) analyses were performed for SON and AZ. The results indicate that in each area, much of the ISV in rainfall can be described by three orthogonal modes. The correlations between ISV modes and total seasonal rainfall reinforce the notion of differing behaviors between the monsoon’s core and extension. For SON all three ISV modes exhibit significant correlation with seasonal rainfall, with the strongest relationship in evidence for the ISV mode, which is related to rainfall intensity. For AZ, total rainfall exhibits the strongest correlation with the ISV mode, which emphasizes season length and rainfall consistency. Examination of longer-period behavior in the ISV modes indicates that, for SON, there is a positive linear trend in intensity, but a countervailing trend toward a shorter monsoon season along with less consistent rainfall in the form of shorter wet spells. For AZ, the evidence for trend in the ISV modes is not nearly as compelling, though one of the modes appears to exhibit distinct multidecadal variability. This study also evaluates teleconnectivity between ENSO, the Pacific decadal oscillation (PDO), and the NAM’s intraseasonal rainfall variability. Results indicate that part of the intraseasonal rainfall variability in both SON and AZ is connected to ENSO while only SON exhibits a teleconnection with the long-period fluctuations of the PDO.

The middle and lower reaches of the Yangtze River basin (MLRYB) are prone to flooding because their orientation is parallel to the East Asian summer monsoon rain belt. Since the East Asian summer monsoon presents pronounced intraseasonal variability, the subseasonal prediction of summer precipitation anomalies in the MLRYB region is an imperative demand nationwide. Based on rotated empirical orthogonal function analysis, 48 stations over the MLRYB with coherent intraseasonal (10–80-day) rainfall variability are identified. Power spectrum analysis of the MLRYB rainfall index, defined as the 48-station-averaged intraseasonal rainfall anomaly, presents two dominant modes with periods of 20–30 days and 40–60 days, respectively. Therefore, the intraseasonal (10–80-day) rainfall variability is divided into 10–30-day and 30–80-day components, and their predictability sources are detected separately. Spatial-temporal projection models (STPM) are then conducted using these predictability sources. The forecast skill during the period 2003–2010 indicates that the STPM is able to capture the 30–80-day rainfall anomalies 5–30 days in advance, but unable to reproduce the 10–30-day rainfall anomalies over MLRYB. The year-to-year fluctuation in forecast skill might be related to the tropical Pacific sea surface temperature anomalies. High forecasting skill tends to appear after a strong El Niño or strong La Niña when the summer seasonal mean rainfall over the MLRYB is enhanced, whereas low skill is apparent after neutral conditions or a weak La Niña when the MLRYB summer seasonal mean rainfall is weakened. Given the feasibility of STPM, the application of this technique is recommended in the real-time operational forecasting of MLRYB rainfall anomalies during the summer flooding season.

This study investigates the impact of the interaction between large‐scale and local‐scale forcings in regulating rainfall patterns and their propagation over coastal areas of Northeast (NE) Australia using a convective‐scale regional model run for 180 days. Over the coastal areas, spatially heterogeneous rainfall patterns are evident in both radar observations and model simulations. By classifying the characteristics of three distinct rainfall groups, we found that the rainfall propagation modulates the average rainfall patterns. Modelling results suggest that the large‐scale background wind and local‐scale land–sea breeze circulations are two important factors driving rainfall propagation. Offshore rainfall propagation, which is frequently observed in coastal regions in the tropics, occurs during the days with weak easterlies near the surface and strong upper‐and mid‐level westerlies. Rainfall is triggered during the afternoon by convergence driven by the sea breeze and then propagates offshore during the nighttime with the land breeze density current and large‐scale background westerlies. In contrast, onshore rainfall propagation is observed during days with strong background easterlies from the surface to upper levels. For the No‐Propagation group, rainfall occurs during the afternoon due to the convergence of sea breezes and low‐level background westerlies, and it persists over land during the nighttime with low‐ and mid‐level easterlies. Our results also suggest that the background wind regimes associated with different phases of intraseasonal variability modulate the direction and strength of rainfall propagation, leading to different coastal rainfall patterns.

While large-scale circulation fields from atmospheric reanalyses have been widely used to study the tropical intraseasonal variability, rainfall variations from the reanalyses are less focused. Because of the sparseness of in situ observations available in the tropics and strong coupling between convection and large-scale circulation, the accuracy of tropical rainfall from the reanalyses not only measures the quality of reanalysis rainfall but is also to some extent indicative of the accuracy of the circulations fields. This study analyzes tropical intraseasonal rainfall variability in the recently completed NCEP Climate Forecast System Reanalysis (CFSR) and its comparison with the widely used NCEP/NCAR reanalysis (R1) and NCEP/DOE reanalysis (R2). The R1 produces too weak rainfall variability while the R2 generates too strong westward propagation. Compared with the R1 and R2, the CFSR produces greatly improved tropical intraseasonal rainfall variability with the dominance of eastward propagation and more realistic amplitude. An analysis of the relationship between rainfall and large-scale fields using composites based on Madden-Julian Oscillation (MJO) events shows that, in all three NCEP reanalyses, the moisture convergence leading the rainfall maximum is near the surface in the western Pacific but is above 925 hPa in the eastern Indian Ocean. However, the CFSR produces the strongest large-scale convergence and the rainfall from CFSR lags the column integrated precipitable water by 1 or 2 days while R1 and R2 rainfall tends to lead the respective precipitable water. Diabatic heating related to the MJO variability in the CFSR is analyzed and compared with that derived from large-scale fields. It is found that the amplitude of CFSR-produced total heating anomalies is smaller than that of the derived. Rainfall variability from the other two recently produced reanalyses, the ECMWF Re-Analysis Interim (ERAI), and the Modern Era Retrospective-analysis for Research and Applications (MERRA), is also analyzed. It is shown that both the ERAI and MERRA generate stronger rainfall spectra than the R1 and more realistic dominance of eastward propagating variance than R2. The intraseasonal variability in the MERRA is stronger than that in the ERAI but weaker than that in the CFSR and CMORPH.

The Bay of Bengal (BoB) receives a large amount of freshwater from rains and rivers, resulting in large upper-ocean stratification due to the freshening effect. This salinity stratification has been theorized to impact sea-surface temperature (SST) and convection on intra-seasonal time scales by affecting the ocean mixed layer and the barrier layer. This article aims to quantify the impact of salinity stratification on the sub-seasonal variability in SST and convection by using in-situ ocean observations and coupled model experiments. It is shown that monsoon intra-seasonal oscillations (MISOs) exhibit varied levels of intra-seasonal variability in SST and rainfall based on the underlying ocean conditions. The largest intra-seasonal variability in SST does not cause the largest convection variability in the north-western BoB. Instead, moderate variability in SST and rainfall associated with MISOs co-occur with deep mixed layer and thick barrier layer conditions. Realistic representation of river freshwater fluxes in a coupled ocean-atmosphere model leads to improved intra-seasonal SST and rainfall variability. Thick barrier layers in the north-western Bay attenuates the entrainment cooling of the mixed layer, and the high mixed layer heat content provides conducive oceanic conditions for the genesis of monsoon low-pressure systems (LPS), thereby affecting rainfall over India. This study has important implications for operation forecasting using coupled models.

The Indian Summer Monsoon rainfall exhibits pronounced intraseasonal variability in the Bay of Bengal (BoB). This study examines the intraseasonal rainfall variability with foci on the coupling with sea surface temperatures (SST) and its interannual modulation. The lagged composite analysis reveals that, in the northern BoB, SST warming leads the onset of intraseasonal rainfall by 5 days. Latent heat flux is reduced before the rain event but is greatly amplified during the rainfall maxima. Further analysis reveals that this intraseasonal rainfall‐SST relationship through latent heating is strengthened in negative Indian Ocean Dipole (IOD) years when the bay‐wide local SST is anomalously warm. Latent heat flux is further increased during the intraseasonal rainfall maxima leading to strengthened rainfall variability. The moisture budget analysis shows this is primarily due to stronger low‐level moisture convergence in negative IOD years. The results provide important predictive information on the monsoon rainfall and its active/break cycles.

This study investigates the impact of different specification of the underlying sea surface temperature (SST) on the prediction of intraseasonal rainfall variation associated with strong Monsoon Intraseasonal Oscillation (MISO) events in the northern Indian Ocean. A series of forecast experiments forced with observed hourly, daily, or seasonal SSTs are performed for three selected strong MISO events using the National Centers for Environmental Predictions (NCEP) atmospheric Global Forecast System (GFS). The comparison between these GFS forecasts shows that the intraseasonal SST variability is more important than its diurnal variability in the MISO prediction. The GFS experiments forced with daily SST which includes intraseasonal variability has higher prediction skill and faster speed in the northward propagation of the MISO intraseasonal rainfall anomalies than those forced with seasonal SST that do not include intraseasonal variability. No significant difference is found in the MISO prediction when GFS was forced by SST with or without SST diurnal cycle. The GFS runs forced with warmer and colder seasonal SSTs which mimic possible biases in SST prediction have comparable skill in the MISO prediction. A modified version of the NCEP Climate Forecast System coupled model (CFSm5) with 1- and 10-m vertical resolutions in the upper ocean is then used to examine their performance in the MISO prediction when all aspects of SST are actively included. The CFSm5 with 1-m vertical resolution in the upper ocean (CFSm501) shows larger amplitude of intraseasonal SST anomaly, with higher prediction skill in both intraseasonal SST and rainfall than the CFSm5 with the typical 10-m vertical resolution in the upper ocean (CFSm510) does. Compared with the uncoupled GFS, both CFSm501 and CFSm510, despite errors in predicted SSTs, have better prediction skill and more reasonable rainfall variability, which is attributed to the inclusion of active air–sea interaction. These results suggest the importance of intraseasonal variability of SST and air–sea interaction in improving the intraseasonal rainfall prediction associated with the MISO.