Rainfall in Queensland: Part 4: the ability of HiGEM to simulate Queensland's rainfall variability and its drivers

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 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. 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. 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. 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. 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. 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. 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. 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

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

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

A comparison of three canopy interception models for a leafless mixed deciduous forest stand in the eastern United States

Canopy interception of incident precipitation is a critical component of the forest water balance during each of the four seasons. Models have been developed to predict precipitation interception from standard meteorological variables because of acknowledged difficulty in extrapolating direct measurements of interception loss from forest to forest. No known study has compared and validated canopy interception models for a leafless deciduous forest stand in the eastern United States. Interception measurements from an experimental plot in a leafless deciduous forest in northeastern Maryland (39°42'N, 75°5'W) for 11 rainstorms in winter and early spring 2004/05 were compared to predictions from three models. The Mulder model maintains a moist canopy between storms. The Gash model requires few input variables and is formulated for a sparse canopy. The WiMo model optimizes the canopy storage capacity for the maximum wind speed during each storm. All models showed marked underestimates and overestimates for individual storms when the measured ratio of interception to gross precipitation was far more or less, respectively, than the specified fraction of canopy cover. The models predicted the percentage of total gross precipitation (PG) intercepted to within the probable standard error (8.1%) of the measured value: the Mulder model overestimated the measured value by 0.1% of PG; the WiMo model underestimated by 0.6% of PG; and the Gash model underestimated by 1.1% of PG. The WiMo model’s advantage over the Gash model indicates that the canopy storage capacity increases logarithmically with the maximum wind speed. This study has demonstrated that dormant-season precipitation interception in a leafless deciduous forest may be satisfactorily predicted by existing canopy interception models

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

Mean-state biases and interannual variability affect perceived sensitivities of the Madden-Julian oscillation to air-sea coupling

Atmosphere–ocean feedbacks often improve the Madden–Julian oscillation (MJO) in climate models, but these improvements are balanced by mean-state biases that can degrade the MJO through changing the basic state on which the MJO operates. The Super-Parameterized Community Atmospheric Model (SPCAM3) produces perhaps the best representation of the MJO among contemporary models, which improves further in a coupled configuration (SPCCSM3) despite considerable mean-state biases in tropical sea-surface temperatures and rainfall. We implement an atmosphere–ocean-mixed-layer configuration of SPCAM3 (SPCAM3-KPP) and use a flux-correction technique to isolate the effects of coupling and mean-state biases on the MJO. When constrained to the observed ocean mean state, air–sea coupling does not substantially alter the MJO in SPCAM3, in contrast to previous studies. When constrained to the SPCCSM ocean mean state, SPCAM3-KPP fails to produce an MJO, in stark contrast to the strong MJO in SPCCSM3. Further KPP simulations demonstrate that the MJO in SPCCSM3 arises from an overly strong sensitivity to El Niño–Southern Oscillation (ENSO) events. Our results show that simulated inter-annual variability and coupled-model mean-state biases affect the perceived response of the MJO to coupling. This is particularly concerning in the context of internal variability in coupled models, as many MJO sensitivity studies in coupled models use relatively short (20–50 year) simulations that undersample interannual–decadal variability. Diagnosing the effects of coupling on the MJO requires simulations that carefully control for mean-state biases and interannual variability

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

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

Rainfall in Queensland: Part 3: empirical orthogonal teleconnection analysis of inter-annual variability in Queensland rainfall: understanding the influence of atmospheric drivers

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. 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. 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

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