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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 temperature (SST) that influence ocean-atmosphere interaction and impact on the monsoon. Compared to the southeast, 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 UK during June–July 2016. Physical and bio-geochemical observations were made using a CTD, five ocean gliders, a uCTD, a VMP, two 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◦E and 89◦E with a 10-day time series at 89◦E, 8◦N. 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 preconditioning of the atmosphere to convection.

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.

This study compares the simulation of sub-seasonal tropical variability by a set of six state-of-the-art AGCMs in two experiments in aqua-planet configuration: a zonally-symmetric experiment, and an experiment with a warm pool centered on the equator.\ud \ud In all six models, the presence of the warm pool generates zonal asymmetries in the simulated mean states in the form of a “Gill-type” response, made more complex by feedbacks between moisture, convective heating and circulation. Noticeable differences appear from one model to another. Only half the models simulate mean low-level equatorial westerlies over the warm pool area.\ud \ud The presence of the warm pool can also favor the development of large-scale variability consistent with observed Madden-Julian Oscillation (MJO) characteristics, but this happens only in half the models. Our results do not support the idea that the presence of the warm pool and/or of mean low-level equatorial westerlies are sufficient conditions for MJO-like variability to arise in the models. Comparing spectral characteristics of the simulated Convectively Coupled Equatorial Waves (CCEWs) in the aquaplanet experiments and the corresponding coupled atmosphere-ocean (i.e. CMIP) and atmosphere-only (i.e. AMIP) simulations, we also show that there is more consistency for a given model across its configurations, than for a given configuration across the six models. Overall, our results confirm that the simulation of sub-seasonal variability by given model is significantly influenced by the parameterization of sub-grid physical processes (most-likely cloud processes), both directly and through modulation of the mean state.

The Madden-Julian oscillation (MJO) is a convectively coupled 30-70 day (intraseasonal) tropical atmospheric mode that drives variations in global weather, but which is poorly simulated in most atmospheric general circulation models. Over the past two decades, field campaigns and modeling experiments have suggested that tropical atmosphere-ocean interactions may sustain or amplify the pattern of enhanced and suppressed atmospheric convection that defines the MJO, and encourage its eastward propagation through the Indian and Pacific Oceans. New observations collected during the past decade have advanced our understand of the ocean response to atmospheric MJO forcing and the resulting intraseasonal sea surface temperature (SST) fluctuations. Numerous modeling studies have revealed a considerable impact of the mean state on MJO ocean-atmosphere coupled processes, as well as the importance of resolving the diurnal cycle of atmosphere--upper-ocean interactions. New diagnostic methods provide insight to atmospheric variability and physical processes associated with the MJO, but offer limited insight on the role of ocean feedbacks. Consequently, uncertainty remains concerning the role of the ocean in MJO theory. Our understanding of how atmosphere-ocean coupled processes affect the MJO can be improved by collecting observations in poorly sampled regions of MJO activity, assessing oceanic and atmospheric drivers of surface fluxes, improving the representation of upper-ocean mixing in coupled-model simulations, designing model experiments that minimize mean-state differences, and developing diagnostic tools to evaluate the nature and role of coupled ocean-atmosphere processes over the MJO cycle.

This study examines the atmospheric circulation patterns and surface features associated with the seven coldest winters in the U.K. since 1870, using the 20th Century Reanalysis. Six of these winters are outside the scope of previous reanalysis datasets; we examine them here for the first time. All winters show a marked lack of the climatological southwesterly flow over the UK, displaying easterly and northeasterly anomalies. Six of the seven winters (all except 1890) were associated with a negative phase of the North Atlantic Oscillation; 1890 was characterised by a blocking anticyclone over and northeast of the UK.

The "Vertical structure and physical processes of the Madden-Julian oscillation (MJO)" project comprises three experiments, designed to evaluate comprehensively the heating, moistening and momentum associated with tropical convection in general circulation models (GCMs). We consider here only those GCMs that performed all experiments. Some models display relatively higher or lower MJO fidelity in both initialized hindcasts and climate simulations, while others show considerable variations in fidelity between experiments. Fidelity in hindcasts and climate simulations are not meaningfully correlated.\ud \ud The analysis of each experiment led to the development of process-oriented diagnostics, some of which distinguished between GCMs with higher or lower fidelity in that experiment. We select the most discriminating diagnostics and apply them to data from all experiments, where possible, to determine if correlations with MJO fidelity hold across scales and GCM states. While normalized gross moist stability had a small but statistically significant correlation with MJO fidelity in climate simulations, we find no link with fidelity in medium-range hindcasts. Similarly, there is no association between timestep-to-timestep rainfall variability, identified from short hindcasts, and fidelity in medium-range hindcasts or climate simulations. Two metrics that relate precipitation to free-tropospheric moisture--the relative humidity for extreme daily precipitation, and variations in the height and amplitude of moistening with rain rate--successfully distinguish between higher- and lower-fidelity GCMs in hindcasts and climate simulations. To improve the MJO, developers should focus on relationships between convection and both total moisture and its rate of change. We conclude by offering recommendations for further experiments.

Many theories for the Madden-Julian oscillation (MJO) focus on diabatic processes, particularly the evolution of vertical heating and moistening. Poor MJO performance in weather and climate models is often blamed on biases in these processes and their interactions with the large-scale circulation. We introduce one of three components of a model-evaluation project, which aims to connect MJO fidelity in models to their representations of several physical processes, focusing on diabatic heating and moistening. This component consists of 20-day hindcasts, initialised daily during two MJO events in winter 2009-10.\ud \ud The 13 models exhibit a range of skill: several have accurate forecasts to 20 days' lead, while others perform similarly to statistical models (8-11 days). Models that maintain the observed MJO amplitude accurately predict propagation, but not vice versa. We find no link between hindcast fidelity and the precipitation-moisture relationship, in contrast to other recent studies. There is also no relationship between models' performance and the evolution of their diabatic-heating profiles with rain rate. A more robust association emerges between models' fidelity and net moistening: the highest-skill models show a clear transition from low-level moistening for light rainfall to mid-level moistening at moderate rainfall and upper-level moistening for heavy rainfall. The mid-level moistening, arising from both dynamics and physics, may be most important. Accurately representing many processes may be necessary, but not sufficient for capturing the MJO, which suggests that models fail to predict the MJO for a broad range of reasons and limits the possibility of finding a panacea.

Queensland experiences considerable inter-annual and decadal rainfall variability, which impacts water-resource management, agriculture and infrastructure. To understand the mechanisms by which large-scale atmospheric and coupled air–sea processes drive these variations, empirical orthogonal teleconnection (EOT) analysis is applied to 1900–2010 seasonal Queensland rainfall. Fields from observations and the 20th Century Reanalysis are regressed onto the EOT timeseries to associate the EOTs with large-scale drivers. In winter, spring and summer the leading, state-wide EOTs\ud are highly correlated with the El Nino–Southern Oscillation (ENSO); the Inter-decadal Pacific Oscillation modulates the\ud summer ENSO teleconnection. In autumn, the leading EOT is associated with locally driven, late-season monsoon variations, while ENSO affects only tropical northern Queensland. Examining EOTs beyond the first, southeastern Queensland and the Cape York peninsula emerge as regions of coherent rainfall variability. In the southeast, rainfall anomalies respond to the strength and moisture content of onshore easterlies, controlled by Tasman Sea blocking. The summer EOT associated with onshore flow and blocking has been negative since 1970, consistent with the observed decline in rainfall along the heavily populated coast. The southeastern Queensland EOTs show considerable multi-decadal variability, which is independent of large-scale drivers. Summer rainfall in Cape York is associated with tropical-cyclone activity.

Climate drivers are more predictable than the rainfall patterns themselves and as such it is important to identify and understand the influence of climate drivers on inter-annual and decadal variations in Queensland rainfall. Identifying these climate drivers in historical records would provide a baseline against which to evaluate the climate model simulations of these drivers and Queensland rainfall. In addition understanding of the climate driver/rainfall relationship would greatly improve seasonal rainfall prediction.\ud \ud Empirical orthogonal teleconnection (EOT) analysis is applied to the 1900-2008 gridded SILO rainfall dataset to determine the climate drivers of seasonal rainfall in Queensland. EOT analysis identifies coherent spatial patterns of rainfall variability in the historical dataset, allowing the identification of climate drivers of specific regional rainfall variations. Most of the published work to date has not been able to identify the influence of each individual driver on Queensland rainfall as most rainfall seasons were significantly correlated with more than one driver. It is therefore necessary to decompose spatially coherent variations in Queensland rainfall and link these variations to individual driving atmospheric phenomena.\ud \ud To achieve this a combination of ERA-40, the new ECMWF Interim Reanalyses and other observational datasets, particularly those developed in Queensland and by the Met Office Hadley Centre, to investigate the processes and phenomena associated with the behaviour of Queensland rainfall over the last few decades were used.

This report investigates the effect of changes in the number of rainy days, the amount of rain that falls on rainy days or a combination of the two on the inter-annual variations in Queensland rainfall. The objective is to determine the association between climate drivers and the occurrence of rainfall in Queensland. Analysis of this relationship will enable focusing on the impact of those drivers that influence the key portions of the rainfall distribution in Queensland by using this information and knowledge to predict how these drivers will influence the occurrence of rainfall in a changing climate. Understanding the frequency and magnitude of these phenomena can significantly improve societal resilience.\ud \ud Knowledge of the relationship between climate drivers and occurrence of rainfall allows for a better understanding of global climate model outputs. The future analysis of key climate drivers for Queensland rainfall variability depends on which of these drivers affect the key portions of the rainfall distribution that are variable on inter-annual temporal scales. Because of limited horizontal resolution global climate models are not able to capture observed distribution of rainfall intensity particularly for intense rainfall. However, if the models are able to capture the observed frequency of rainfall in Queensland, then this information could be used to assess the inter-annual variability of rainfall amounts in these models. Focusing attention on changes in the frequency and intensity of Queensland rainfall in association with global and regional climate phenomena will help understand how these drivers will influence rainfall occurrence in future.\ud \ud The gridded SILO rainfall dataset was analysed for 1900-2008. Analysis was performed on the November-April half-year, as this period accounts for at least 80% of the annual rainfall in Queensland. Several thresholds of rainfall were used to define a "rainy day": 5 mm/day, 10 mm/day, 25 mm/day and 50 mm/day.

Queensland’s climate experiences considerable natural inter-annual and decadal variability in its rainfall. To determine how Queensland's rainfall is going to change in the coming decades as the planet warms, it is critical to establish which global and regional climate phenomena are driving this variability.\ud \ud Understanding the potential impacts of climate change is essential to inform strategies and actions to avoid or manage dangerous levels of change. The impacts of these changes are important especially for those sectors that are vulnerable to changes in rainfall, such as water-resource management and agriculture. In 2007, the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC, 2007) confirmed that there is currently substantial uncertainty in rainfall projections for the Australian region for the coming century. This reinforces the urgent need to reduce that uncertainty. Improving the current understanding of key processes and phenomena that influence Queensland rainfall on timescales from days to decades will help to address that uncertainty, as there may be greater confidence in the impacts of climate change on these phenomena than for rainfall. A reduced uncertainty in rainfall changes would support effective planning to address potential changes in the hydrological cycle at the regional and local level.\ud \ud Part of the uncertainty in future rainfall changes arises from the various competing and interacting influences on Queensland rainfall, which are associated with synoptic and climate drivers across various time scales, such as tropical cyclones, the Madden-Julian Oscillation (MJO), the El-Niño Southern Oscillation (ENSO) and the Inter-decadal Pacific Oscillation (IPO). The MJO controls the sub-seasonal variations in the summer monsoon rainfall with a period of 30-60 days. It primarily affects northern Queensland and modulates the monsoon and trade-wind circulations. Active periods of the MJO also increase the probability of tropical cyclone formation in the Coral Sea.\ud \ud On inter-annual timescales, Queensland's rainfall is heavily influenced by ENSO: wet conditions prevail in La Niña (cold equatorial Pacific Ocean temperatures) years, while El Niño (warm equatorial Pacific Ocean temperatures) promotes drought. Rainfall is sensitive to both the magnitude and position of El-Niño and La Niña events. Central Pacific ENSO events have a much stronger impact on Queensland than eastern Pacific events. The link between Queensland's rainfall and the ENSO can fluctuate from one decade to the next, but shows no long-term trend.\ud \ud The IPO describes the slowly evolving variations in Pacific Ocean temperatures, with a period of about 20-30 years. Its positive (warm) phase resembles an expanded El-Niño, while its negative (cool) phase resembles an expanded La Niña. El-Niño and La Niña events canoccur in either phase of the IPO. The IPO influences the relationship between ENSO and Queensland rainfall: warm phases show a weak connection, while cold phases display a strong connection.\ud \ud Knowledge and prediction of the influence of these drivers on regional and local rainfall will help improve the understanding of climate changes at those smaller scales. This may also result in more-accurate predictions of climate variability and change over the next 20 years - a key period for climate-change adaptation efforts.\ud \ud This report reviews existing studies of observed changes in Queensland rainfall - its mean, variability and extreme events - to determine the dominant remote synoptic and climate drivers of rainfall. The review found that although many studies have described variations in rainfall in Queensland and across Australia, only a few have explained these changes as due to variations in the impact of known remote rainfall drivers. Even fewer studies have provided plausible physical mechanisms for the variability in the influence of those drivers. Analysis has often focused on annual-or seasonal-mean rainfall, neglecting the spatial and temporal characteristics of rainfall. Little research had been undertaken on the interactions between temporal scales of variability, despite strong indications that these interactions are key to determining the character of Queensland’s rainfall.

Stakeholders and policymakers are seeking detailed information on the impacts of climate change and variability, especially on rainfall, at the local and regional levels. This information can be delivered by only those global climate models that represent the atmosphere and ocean at fine resolution, to robustly simulate the weather systems that produce rainfall. These models provide us with a better understanding of the key meteorological phenomena that affect Queensland. That knowledge can then be used to improve the simulation of these phenomena in lowerresolution\ud climate models. Implementing these improvements will provide more accurate predictions on weekly to seasonal and decadal timescales, as well as more robust predictions of the impacts of climate change on these phenomena.\ud \ud High-resolution Global Environment Model, version 1.1 (HiGEM) is a global, coupled climate model that was developed by the U.K. academic community. It is based on the U.K. Hadley Centre's HadGEM1 model, but HiGEM has considerably higher resolution: 90 km in the atmosphere and 30 km in the ocean. HiGEM has been used in this research as its increased resolution may allow the model to better represent regional climate variability and change in Queensland.\ud \ud In this research, a 150 year control simulation of HiGEM was assessed to evalute the ability of the model to simulate Queensland's rainfall and its inter-annual and decadal variability. HiGEM was also assessed for its ability to produce the observed Empirical Orthogonal Teleconnection (EOT) patterns of rainfall avariability obtained from the SILO gridded rainfall dataset.\ud \ud In the mean, HiGEM produces less rainfall over Queensland than observed, particularly in the north of the state. Most of this dry bias occurs because the model simulates a weaker Australian summer monsoon than is observed. However, HiGEM represents well the relationship between the El Niño Southern Oscillation (ENSO) and Queensland rainfall, on annual and seasonal timescales. The model even captured the observed asymmetric correlation between the ENSO and Queensland rainfall: stronger La Niña events cause stronger flood years in Queensland, but stronger El Niño events do not cause stronger droughts.\ud \ud The research found that HiGEM lacks the ability to model decadal variations in Queensland rainfall and in the teleconnection between the ENSO and rainfall. This is likely due to the model's inability to simulate the Interdecadal Pacific Oscillation (IPO), which has been identified as the key driver of these variations.\ud \ud In relation to the generation of tropical cyclones, HiGEM captures the observed regions of tropical-cyclone formation and the correct distributions of tropical cyclone tracks, but simulates too many tropical cyclones in the Southwest Pacific.\ud \ud When EOT analysis is applied to HiGEM and the results are compared with the EOT patterns computed using observed rainfall, HiGEM performs well for those EOTs related to the ENSO in summer, winter and spring. HiGEM also represents the relationship between Southeast Queensland rainfall and onshore easterly winds, including the decadal variations in the winds' strength and moisture content. Futher, HiGEM correctly simulates the observed association between the frequency of tropical cyclones and summer rainfall in Cape York.\ud \ud The success of HiGEM at reproducing many of the observed EOTs, particularly in summer, increases our confidence in the model's ability to predict the impact of climate change on Queensland's rainfall and its drivers.

This report analyses projected changes in Queensland rainfall from the HiGEM global climate model under atmospheric carbon dioxide (CO2) concentrations of approximately 690 parts per million (ppm), equivalent to the CO2 concentration in the late 21st century under a moderate (IPCC SRES (2012) A1B) emissions scenario.\ud \ud HiGEM is a high-resolution version of the U.K. Met Office model (HadGEM1). Previously reported research found that with present-day CO2, HiGEM accurately simulated many observed climate drivers of Queensland rainfall, including the El Niño Southern Oscillation; this increases our confidence in the model’s projections of rainfall change.\ud \ud The HiGEM model projects that average surface temperatures in Queensland will warm by approximately 2°C under doubled CO2, with the strongest warming in autumn and winter. Consistent with many other studies, the land warms by more than the ocean, leading to greater warming inland in Queensland and less along the coast.\ud \ud While HiGEM projects small changes to annual-total rainfall, the November–April wet season becomes compressed: 10–20 per cent more rain falls during January and February, with 10–40 per cent less in November, March and April. The Queensland wet season begins up to 10 days later—particularly along the coast—and ends up to 20 days earlier—particularly in the southwest. Precipitation falls in fewer but much more intense events.\ud \ud The HiGEM model projects that Queensland will rely more strongly upon heavy mid-summer rains for its annual precipitation. This has important consequences for agriculture and for water storage. The frequency of extreme rain days (greater than 100 millimetre accumulation) in HiGEM increases by up to 40 per cent, particularly in summer during the intensified monsoon. HiGEM also projects that the average duration of extreme rainfall will rise (by 20 per cent) as will the area covered by each event (15 per cent). The number of light rain days (1–5 millimetre) is projected to decrease by 5–10 per cent. Tropical cyclones become slightly less frequent near Queensland.\ud \ud The HiGEM model projects that many climate drivers of rainfall will remain robust in a warmer world. The correlation with ENSO declines, but there are an inadequate number of ENSO events in the future-climate simulation on which to base firm conclusions. Rainfall variations in southwest and southeast Queensland become less connected to those in the rest of the state, mostly due to an earlier end of the wet season in those regions.