Intraseasonal summer rainfall variability over China in the MetUM GA6 and GC2 configurations

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.

The National Center for Atmospheric Research (NCAR) Community Climate Model, version 3.6 (CCM3) simulation of tropical intraseasonal variability in zonal winds and precipitation can be improved by implementing the microphysics of cloud with relaxed Arakawa–Schubert (McRAS) convection scheme of Sud and Walker. The default CCM3 convection scheme of Zhang and McFarlane produces intraseasonal variability in both zonal winds and precipitation that is much lower than is observed. The convection scheme of Hack produces high tropical intraseasonal zonal wind variability but no coherent convective variability at intraseasonal timescales and low wavenumbers. The McRAS convection scheme produces realistic variability in tropical intraseasonal zonal winds and improved intraseasonal variability in tropical precipitation, although the variability in precipitation is somewhat less than is observed. Intraseasonal variability in CCM3 with the McRAS scheme is highly sensitive to the parameterization of convective precipitation evaporation in unsaturated environmental air and unsaturated downdrafts. Removing these effects greatly reduces intraseasonal variability in the model. Convective evaporation processes in McRAS affect intraseasonal variability mainly through their time-mean effects and not through their variations. Convective rain evaporation and unsaturated downdrafts improve the modeled specific humidity and temperature climates of the Tropics and increase convection on the equator. Intraseasonal variability in CCM3 with McRAS is not improved by increasing the boundary layer relative humidity threshold for initiation of convection, contrary to the results of Wang and Schlesinger. In fact, intraseasonal variability is reduced for higher thresholds. The largest intraseasonal moisture variations during a model Madden–Julian oscillation life cycle occur above the boundary layer, and humidity variations within the boundary layer are small.

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.

The causes of subseasonal precipitation variability in China are investigated using observations and reanalysis data for extended winter (November–April) and summer (May–October) seasons from 1982 to 2007. For each season, the three dominant regions of coherent intraseasonal variability are identified with Empirical Orthogonal Teleconnection (EOT) analysis. While previous studies have focused on particular causes for precipitation variability or on specific regions, here a comprehensive analysis is carried out with an objective method. Furthermore, the associated rainfall anomaly timeseries are tied to specific locations in China, which facilitates their interpretation. To understand the underlying processes associated with spatially coherent patterns of rainfall variability, fields from observations and reanalysis are regressed onto EOT timeseries. The three dominant patterns in winter together explain 43% of the total space–time variance and have their origins in midlatitude disturbances that appear two pentads in advance. Winter precipitation variability along the Yangtze River is associated with wave trains originating over the Atlantic and northern Europe, while precipitation variability in southeast China is connected to the Mediterranean storm track. In summer, all patterns have a strong relationship with the Boreal Summer Intraseasonal Oscillation and are modulated by the seasonal cycle of the East Asian summer monsoon. The wet and dry phases of the regional patterns can substantially modulate the frequency of daily rainfall across China. The discovered links between weather patterns, precursors, and effects on local and remote precipitation may provide a valuable basis for hydrological risk assessments and the evaluation of numerical weather prediction models.

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.

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%.

The southern China (SC) summer rainfall exhibits prominent intraseasonal variability, which exhibits a significant increase in the early 1990s with the turning point at 1993. The SC intraseasonal rainfall events could be divided into three categories according to different propagations, including the southward-propagating (SP) events, the northwestward-propagating (NWP) events, and the northward-propagating (NP) events. This study explores the causes of the observed interdecadal increase in the intraseasonal rainfall variability over SC by comparing the SC intraseasonal rainfall events of each category between the former decadal period (P1) and the later decadal period (P2). The result indicates that such interdecadal change is due to the more frequent NP events coming from the South China Sea (SCS). Based on the moisture and vorticity budget analysis, it is revealed that the summer mean southerly wind in the middle to lower troposphere is the dominant factor of the northward propagation over the SCS, as it could induce positive meridional moisture and vorticity advection anomalies ahead of the convection. A marked interdecadal enhancement of the summer mean southerly wind over the SCS is the cause of more frequent occurrence of NP events over SC, as it provides more favorable conditions for the northward propagation. The change of the atmospheric instability over the SCS where the NP convection perturbation originates was also investigated, but no significant change was found.

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.

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 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.

Rainfall over Africa varies across timescales of a few days to several weeks due to several tropical and extratropical modes of variability. Excessive rains or prolonged drought regularly result in natural disasters and have thus a severe impact on the local economy, agriculture, spread of diseases, and entire ecosystems. The dynamical nature of the atmosphere allows the existence of planetary balanced modes, which are called Rossby waves, and smaller-scale unbalanced inertio-gravity (IG) waves. The former, which are more rotational, arise from the horizontal pressure gradient force, while for the latter gravity acts as the restoring force, making their flow pattern more divergent. The main source of variability in the extratropics stems from Rossby waves. At the equator, further types of convectively coupled equatorial waves (CCEWs) exist, namely Kelvin and mixed Rossby-gravity (MRG) waves. As the slowest intraseasonal tropical mode, the Madden–Julian Oscillation (MJO), which is related to Kelvin and Rossby waves, acts on a timescale of 30 to 90 days. Although it is primarily a planetary mode, the MJO has a specific “flavor” over the African continent. On the short intraseasonal timescale of 10 to 25 days, equatorial Rossby (ER) waves and the internal modes of the West African monsoon, the quasi-biweekly zonal dipole (QBZD) and the Sahel mode, modulate rainfall. On the synoptic timescale of a few days to a week, African easterly waves (AEWs) are a dominant mode over West Africa, whereas Kelvin waves predominantly modulate rainfall over equatorial Africa. Extratropical influences on northern and southern Africa manifest themselves in Rossby wave trains, which modulate synoptic to intraseasonal rainfall through tropical rainfall plumes, cold air surges, and upper-tropospheric dry air intrusions. Furthermore, the Saharan heat low (SHL) acts as a link between the northern hemispheric extratropics and tropics. Finally, the Indian monsoon, the Atlantic, Indian, and the Pacific Oceans can remotely affect the intraseasonal variability of African rainfall. Forecasting synoptic to intraseasonal rainfall variability is an integral part of seamless prediction between the weather and climate regimes. In the early 21st century, numerical weather prediction (NWP) systems can forecast larger intraseasonal signals such as the MJO several weeks into the future, but they still struggle to forecast shorter scale features reliably. Besides NWP, statistical models can successfully forecast intraseasonal variability of rainfall. Due to the relevance of synoptic to intraseasonal rainfall variability for African societies, early warning systems (EWSs) have been developed to mitigate impacts.