50 research outputs found
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Rainfall in Queensland: Part 5: projected changes in Queensland rainfall under double-CO2 conditions in the HiGEM model
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.
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.
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.
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.
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.
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
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Diagnosing ocean feedbacks to the BSISO: SST-modulated surface fluxes and the moist static energy budget
The oceanic feedback to the atmospheric boreal summer intraseasonal oscillation (BSISO) is examined by diagnosing the sea surface temperature (SST) modification of surface fluxes and the moist static energy (MSE) on intraseasonal scales. SST variability affects intraseasonal surface latent heat (LH) and sensible heat (SH) fluxes, through its influence on air-sea moisture and temperature gradients (delta-q and delta-T). According to bulk formula decomposition, LH is mainly determined by wind-driven flux perturbations, while SH is more sensitive to thermodynamic flux perturbations. SST fluctuations tend to increase the thermodynamic flux perturbations over active BSISO regions, but this is largely offset by the wind-driven flux perturbations. Enhanced surface fluxes induced by intraseasonal SST anomalies are located ahead (north) of the convective center over both the Indian Ocean and western Pacific, favoring BSISO northward propagation. Analysis of budgets of column-integrated MSE () and its time rate of change (d/dt) show that SST-modulated surface fluxes can influence the development and propagation of the BSISO, respectively. LH and SH variability induced by intraseasonal SSTs maintain 1-2% of /day over the equatorial western Indian Ocean, Arabian Sea and Bay of Bengal, but damp about 1% of /day over the western North Pacific. The contribution of intraseasonal SST variability to d/dt can reach 12-20% over active BSISO regions. These results suggest that SST variability is conducive, but perhaps not essential, for the propagation of convection during the BSISO life cycle
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Atmospheric circulation patterns associated with extreme cold winters in the UK
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
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Rainfall in Queensland: Part 2: Is the inter-annual variability in Queensland rainfall due to variability in rainfall frequency, intensity or both?
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.
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.
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
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The Indian summer monsoon in MetUM-GOML2.0: effects of air–sea coupling and resolution
The fidelity of the simulated Indian summer monsoon is analysed in the UK Met Office Unified Model Global Ocean Mixed Layer configuration (MetUM-GOML2.0) in terms of its boreal summer mean state and propagation of the boreal summer intraseasonal oscillation (BSISO). The model produces substantial biases in mean June–September precipitation, especially over India, in common with other MetUM configurations. Using a correction technique to constrain the mean seasonal cycle of ocean temperature and salinity, the effects of regional air–sea coupling and atmospheric horizontal resolution are investigated. Introducing coupling in the Indian Ocean degrades the atmospheric basic state compared with prescribing the observed seasonal cycle of sea surface temperature (SST). This degradation of the mean state is attributable to small errors (±0.5°C) in mean SST. Coupling slightly improves some aspects of the simulation of northward BSISO propagation over the Indian Ocean, Bay of Bengal, and India, but degrades others. Increasing resolution from 200 to 90km grid spacing (approximate value at the Equator) improves the atmospheric mean state, but increasing resolution again to 40km offers no substantial improvement. The improvement to intraseasonal propagation at finer resolution is similar to that due to coupling
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Rainfall in Queensland: Part 1: a literature survey of key rainfall drivers in Queensland Australia: rainfall variability and change
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.
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.
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.
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.
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.
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.
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
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ASoP (v1.0): a set of methods for analyzing scales of precipitation in general circulation models
General circulation models (GCMs) have been criticized for their failure to represent the observed scales of precipitation, particularly in the tropics where simulated daily rainfall is too light, too frequent, and too persistent. Previous assessments have focused on temporally or spatially averaged precipitation, such as daily means or regional averages. These evaluations offer little actionable information for model developers, because the interactions between the resolved dynamics and parameterized physics that produce precipitation occur at the native gridscale and timestep.
We introduce a set of diagnostics (ASoP1) to compare the spatial and temporal scales of precipitation across GCMs and observations, which can be applied to data ranging from the gridscale and timestep to regional and sub-monthly averages. ASoP1 measures the spectrum of precipitation intensity, temporal variability as a function of intensity, and spatial and temporal coherence. When applied to timestep, gridscale tropical precipitation from ten GCMs, the diagnostics reveal that far from the "dreary" persistent light rainfall implied by daily mean data, most models produce a broad range of timestep intensities that span 1-100 mm/day. Models show widely varying spatial and temporal scales of timestep precipitation. Several GCMs show concerning quasi-random behavior that may influence alter the spectrum of atmospheric waves. Averaging precipitation to a common spatial (~600 km) or temporal (3-hr) resolution substantially reduces variability among models, demonstrating that averaging hides a wealth of information about intrinsic model behavior. When compared against satellite-derived analyses at these scales, all models produce features that are too large and too persistent
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A mechanism for the recently increased interdecadal variability of the Silk Road Pattern
The Silk Road Pattern (SRP) teleconnection manifests in summer over Eurasia, where it is associated with substantial temperature and precipitation anomalies. The SRP varies on interannual and decadal scales; reanalyses show an increase in its decadal variability around the mid-1970s. Understanding what drives this decadal variability is particularly important, because contemporary seasonal prediction models struggle to predict the phase of the SRP. Based on analysis of observations and multiple targeted numerical experiments, this study proposes a mechanism for decadal SRP variability. Causal Effect Network analysis confirms a positive feedback loop between the eastern portion of the SRP pattern and vertical motion over India on synoptic timescales. Anomalies over a larger region of subtropical South Asia can reinforce a background state that projects onto the positive or negative SRP through this mechanism. This effect is isolated and confirmed in targeted numerical simulations. The transition from weak to strong decadal variability in the mid-1970s is consistent with more spatially coherent interannual precipitation variability over subtropical South Asia. Furthermore, results suggest that oceanic variability does not directly force the SRP. Nevertheless, sea surface temperatures in the North Atlantic and the North Pacific may indirectly affect the SRP by modulating South Asian rainfall on decadal timescales