Abstract

The northward-propagating intraseasonal (30–40 day) oscillation (NPISO) between active and break monsoon phases exerts a critical control on summer-season rainfall totals over India. Advances in diagnosing these events and comprehending the physical mechanisms behind them may hold the potential for improving their predictability. While previous studies have attempted to extract active and break events from reanalysis data to elucidate a composite life cycle, those studies have relied on first isolating the intraseasonal variability in the record (e.g., through bandpass filtering, removing harmonics, or empirical orthogonal function analysis). Additionally, the underlying physical processes that previous studies have proposed have varied, both among themselves and with studies using general circulation models.

A simple index is defined for diagnosing NPISO events in observations and reanalysis, based on lag correlations between outgoing longwave radiation (OLR) over India and over the equatorial Indian Ocean. This index is the first to use unfiltered OLR observations and so does not specifically isolate intraseasonal periods. A composite NPISO life cycle based on this index is similar to previous composites in OLR and surface winds, demonstrating that the dominance of the intraseasonal variability in the monsoon climate system eliminates the need for more complex methods (e.g., time filtering or EOF analysis) to identify the NPISO. This study is also among the first to examine the NPISO using a long-period record of high-resolution sea surface temperatures (SSTs) from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager. Application of this index to those SSTs demonstrates that SST anomalies exist in near quadrature with convection, as suggested by recent coupled model studies. Analysis of the phase relationships between atmospheric fields and SSTs indicates that the atmosphere likely forced the SST anomalies. The results of this lag-correlation analysis suggest that the oscillation serves as its own most reliable—and perhaps only—predictor, and that signals preceding an NPISO event appear first over the Indian subcontinent, not the equatorial Indian Ocean where the events originate.

Abstract

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 southwest monsoon delivers over 70% of India’s annual rainfall and is crucial to the success of agriculture across much of South Asia. Monsoon precipitation is known to be sensitive to sea surface temperature (SST) in the Bay of Bengal (BoB). Here, we use a configuration of the Unified Model of the Met Office coupled to an ocean mixed layer model to investigate the role of upper-ocean features in the BoB on southwest monsoon precipitation. We focus on the pronounced zonal and meridional SST gradients characteristic of the BoB; the zonal gradient in particular has an as-yet unknown effect on monsoon rainfall. We find that the zonal SST gradient is responsible for a 50% decrease in rainfall over the southern BoB (approximately 5 mm day⁻¹), and a 50% increase in rainfall over Bangladesh and northern India (approximately 1 mm day⁻¹). This increase is remotely forced by a strengthening of the monsoon Hadley circulation. The meridional SST gradient acts to decrease precipitation over the BoB itself, similarly to the zonal SST gradient, but does not have comparable effects over land. The impacts of barrier layers and high-salinity subsurface water are also investigated, but neither has significant effects on monsoon precipitation in this model; the influence of barrier layers on precipitation is felt in the months after the southwest monsoon. Models should accurately represent oceanic processes that directly influence BoB SST, such as the BoB cold pool, in order to faithfully represent monsoon rainfall.

ABSTRACT

This study investigates the moisture sources that supply East Asian (EA) precipitation and their interannual variability. Moisture sources are tracked using the Water Accounting Model-2layers (WAM-2layers), based on the Eulerian framework. WAM-2layers is applied to five subregions over EA, driven by the ERA-Interim reanalysis from 1979 to 2015. Due to differences in regional atmospheric circulation and in hydrological and topographic features, the mean moisture sources vary among EA subregions. The tropical oceanic source dominates southeastern EA, while the extratropical continental source dominates other EA subregions. The moisture sources experience large seasonal variations, due to the seasonal cycle of the EA monsoon, the freeze–thaw cycle of the Eurasian continent, and local moisture recycling over the Tibetan Plateau. The interannual variability of moisture sources is linked to interannual modes of the coupled ocean–atmosphere system. The negative phase of the North Atlantic Oscillation increases moisture transport to northwestern EA in winter by driving a southward shift in the midlatitude westerly jet over the Mediterranean Sea, the Black Sea, and the Caspian Sea. Atmospheric moisture lifetime is also reduced due to the enhanced westerlies. In summers following El Niños, an anticyclonic anomaly over the western North Pacific increases moisture supplied from the South China Sea to the southeastern EA and shortens the traveling distance. A stronger Somali Jet in summer increases moisture to the Tibetan Plateau and therefore increases precipitation over the eastern Tibetan Plateau. The methods and findings in this study can be used to evaluate hydrological features in climate simulations.

Abstract

The coastal region of East Asia (EA) is one of the regions with the most frequent impacts from tropical cyclones (TCs). In this study, rainfall and moisture transports related to TCs are measured over EA, and the contribution of TCs to the regional water budget is compared with other contributors, especially the mean circulation of the EA summer monsoon (EASM). Based on ERA-Interim reanalysis (1979–2012), the trajectories of TCs are identified using an objective feature tracking method. Over 60% of TCs occur from July to October (JASO). During JASO, TC rainfall contributes 10%–30% of the monthly total rainfall over the coastal region of EA; this contribution is highest over the south/southeast coast of China in September. TCs make a larger contribution to daily extreme rainfall (above the 95th percentile): 50%–60% over the EA coast and as high as 70% over Taiwan Island. Compared with the mean EASM, TCs transport less moisture over EA. However, as the peak of the mean seasonal cycle of TCs lags two months behind that of the EASM, the moisture transported by TCs is an important source for the water budget over the EA region when the EASM withdraws. This moisture transport is largely performed by westward-moving TCs. These results improve understanding of the water cycle of EA and provide a useful test bed for evaluating and improving seasonal forecasts and coupled climate models.

Abstract

Previous studies documented that a distinct southward shift of central Pacific low-level wind anomalies occurring during the ENSO decaying phase is caused by an interaction between the western Pacific annual cycle and El Niño–Southern Oscillation (ENSO) variability. The present study finds that the meridional movement of the central Pacific wind anomalies appears only during traditional eastern Pacific El Niño (EP El Niño) events rather than in central Pacific El Niño (CP El Niño) events in which sea surface temperature (SST) anomalies are confined to the central Pacific. The zonal structure of ENSO-related SST anomalies therefore has an important effect on meridional asymmetry in the associated atmospheric response and its modulation by the annual cycle. In contrast to EP El Niño events, the SST anomalies of CP El Niño events extend farther west toward the warm pool region with its climatological warm SSTs. In the warm pool region, relatively small SST anomalies are thus able to excite convection anomalies on both sides of the equator, even with a meridionally asymmetric SST background state. Therefore, almost meridionally symmetric precipitation and wind anomalies are observed over the central Pacific during the decaying phase of CP El Niño events. The SST anomaly pattern of La Niña events is similar to CP El Niño events with a reversed sign. Accordingly, no distinct southward displacement of the atmospheric response occurs over the central Pacific during the La Niña decaying phase. These results have important implications for ENSO climate impacts over East Asia, since the anomalous low-level anticyclone over the western North Pacific is an integral part of the annual cycle–modulated ENSO response.

Abstract

Leading patterns of observed monthly extreme rainfall variability in Australia are examined using an empirical orthogonal teleconnection (EOT) method. Extreme rainfall variability is more closely related to mean rainfall variability during austral summer than in winter. The leading EOT patterns of extreme rainfall explain less variance in Australia-wide extreme rainfall than is the case for mean rainfall EOTs. The authors illustrate that, as with mean rainfall, the El Niño–Southern Oscillation (ENSO) has the strongest association with warm-season extreme rainfall variability, while in the cool season the primary drivers are atmospheric blocking and the subtropical ridge. The Indian Ocean dipole and southern annular mode also have significant relationships with patterns of variability during austral winter and spring. Leading patterns of summer extreme rainfall variability have predictability several months ahead from Pacific sea surface temperatures (SSTs) and as much as a year in advance from Indian Ocean SSTs. Predictability from the Pacific is greater for wetter-than-average summer months than for months that are drier than average, whereas for the Indian Ocean the relationship has greater linearity. Several cool-season EOTs are associated with midlatitude synoptic-scale patterns along the south and east coasts. These patterns have common atmospheric signatures denoting moist onshore flow and strong cyclonic anomalies often to the north of a blocking anticyclone. Tropical cyclone activity is observed to have significant relationships with some warm-season EOTs. This analysis shows that extreme rainfall variability in Australia can be related to remote drivers and local synoptic-scale patterns throughout the year.

Abstract

Skillful and reliable predictions of week-to-week rainfall variations in South America, two to three weeks ahead, are essential to protect lives, livelihoods, and ecosystems. We evaluate forecast performance for weekly rainfall in extended austral summer (November–March) in four contemporary subseasonal systems, including a new Brazilian model, at 1–5-week leads for 1999–2010. We measure performance by the correlation coefficient (in time) between predicted and observed rainfall; we measure skill by the Brier skill score for rainfall terciles against a climatological reference forecast. We assess unconditional performance (i.e., regardless of initial condition) and conditional performance based on the initial phase of the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO). All models display substantial mean rainfall biases, including dry biases in Amazonia and wet biases near the Andes, which are established by week 1 and vary little thereafter. Unconditional performance extends to week 2 in all regions except for Amazonia and the Andes, but to week 3 only over northern, northeastern, and southeastern South America. Skill for upper- and lower-tercile rainfall extends only to week 1. Conditional performance is not systematically or significantly higher than unconditional performance; ENSO and MJO events provide limited “windows of opportunity” for improved S2S predictions that are region and model dependent. Conditional performance may be degraded by errors in predicted ENSO and MJO teleconnections to regional rainfall, even at short lead times.

Abstract

Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.

Abstract

The Bay of Bengal (BoB) plays a fundamental role in controlling the weather systems that make up the South Asian summer monsoon system. In particular, the southern BoB has cooler sea surface temperatures (SST) that influence ocean–atmosphere interaction and impact the monsoon. Compared to the southeastern BoB, the southwestern BoB is cooler, more saline, receives much less rain, and is influenced by the summer monsoon current (SMC). To examine the impact of these features on the monsoon, the BoB Boundary Layer Experiment (BoBBLE) was jointly undertaken by India and the United Kingdom during June–July 2016. Physical and biogeochemical observations were made using a conductivity–temperature–depth (CTD) profiler, five ocean gliders, an Oceanscience Underway CTD (uCTD), a vertical microstructure profiler (VMP), two acoustic Doppler current profilers (ADCPs), Argo floats, drifting buoys, meteorological sensors, and upper-air radiosonde balloons. The observations were made along a zonal section at 8°N between 85.3° and 89°E with a 10-day time series at 8°N, 89°E. This paper presents the new observed features of the southern BoB from the BoBBLE field program, supported by satellite data. Key results from the BoBBLE field campaign show the Sri Lanka dome and the SMC in different stages of their seasonal evolution and two freshening events during which salinity decreased in the upper layer, leading to the formation of thick barrier layers. BoBBLE observations were taken during a suppressed phase of the intraseasonal oscillation; they captured in detail the warming of the ocean mixed layer and the preconditioning of the atmosphere to convection.