Scholarships

Observational networks at high spatial resolution and over long time periods remains a challenge in urban climate research. The emergence of low-cost Internet-of-Things sensing units presents a new approach for addressing such challenges and contributes to investigating the variability in urban microclimate with less centralized efforts. This study aims to evaluate the air temperature data crowdsourced from such sensing units, Netatmo’ citizen weather stations (CWS), used at multiple locations around Sydney area and analyze its application for monitoring the urban climate in this region. Additionally, the impact of urban form and landscaping type, determined by local climate zone (LCZ) classification done at CCRC), on the microclimate of the Sydney area can also be assessed using crowdsourced data. Overall, the objective of this study is to evaluate:

• the quality CWS data compared with the SWAQ sensing units
• assess the intra-LCZ temperature variability of air temperature to determine if significant correlations can be detected between urban characteristics and temperature.

Supervisors: Dr Negin Nazarian, Dr Melissa Hart

The Millennium drought devastated much of Southern Australia during 1996 and 2010, and altered the water and energy cycles on the land surface. These cycles play an important role in influencing the climate and determining how the drought impacts are felt on land by humans and ecosystems (Yin et al., 2014). This project will explore changes in two key energy and water variables during the Millennium drought: latent heat flux (LH) and sensible heat flux (SH). These variables help us understand the drought impacts on the land and have been measured by flux towers in different regions over Australia, together with net radiation and ground heat flux.

In this project, you will learn more about the effects of the Millennium drought on the surface energy budget, particularly on the ratio of sensible heat flux to latent heat flux (i.e., SH/LH). You will use measurements taken during the drought period (2000 – 2010) and after the drought period, from both impacted and non-impacted sites to guide analysis and conclusions. The flux measurements will also be used to evaluate the ability of gridded estimates of sensible and latent heat fluxes (e.g., FLUXCOM; Jung et al., 2018) - which are based on satellite observations rather than direct measurements - to capture this drought event.

Supervisors: Dr Sanaa Hobeichi (UNSW) and Dr Gab Abramowitz (UNSW)

Heatwaves are periods of excessively hot weather, which when severe have caused crop failures, power outages and resulted in more deaths in Australia than any other natural hazard. If global temperatures continue to rise as predicted, heatwaves will become more frequent, intense and last longer. Global climate models (GCMs) produce climate projections that are used by governments and businesses to plan for a future climate conducive to more extreme heatwaves. However, the coarse spatial resolution of GCMs cannot resolve the fine-scale drivers of regional climate, which is the scale where climate adaptation decisions are made. Regional climate models (RCMs) generate high-resolution projections by dynamically downscaling GCM outputs. Evidence of where and when RCMs provide improved information about both historical and projected heatwaves relative to driving GCMs is lacking.

This project aims to discover where and when RCMs improve (or not) on the simulation of Australian heatwaves, relative to their host GCMs. Understanding this is vital to adaptation planning for extreme weather like heatwaves. The student will work on this project with Dr Giovanni Di Virgilio (UNSW) and Dr Annette Hirsch (ANU). Experience of programming in Python or similar for data analysis is essential; familiarity with high performance computing is desirable.

Supervisors: Dr Giovanni Di Virgilio (UNSW) and Dr Annette Hirsch (UNSW)

Climate change affects the frequency and severity of certain hydrological extremes, such as the risk of flooding events or soil moisture drought. These changes in hydrological extremes are a concern for many sectors that are highly dependent on hydrological conditions, such as water resources management, infrastructure or agriculture. In order to prepare for these changes, it's crucial to gain a better understanding of the spatial and temporal pattern of climate change impacts on hydrological extremes.

The aim of this student project is to investigate the impacts of climate change on hydrological extremes, such as high runoff events, hydrological or agricultural droughts. It uses outputs of the AWRA-L hydrological model, which underpins the BoM's Australian Landscape Water Balance website. The model simulates the land surface water balance and outputs hydrological stores and fluxes, including runoff, evapotranspiration and soil moisture in three soil layers (0m–0.1m, 0.1m–1.0m, 1.0m–6.0m). As part of the Bureau's Hydrological Projections project, AWRA-L was forced with an ensemble of climate data based on:

• two scenarios for future greenhouse gas concentrations
• four general circulation models (GCM) that have been assessed to be skilful for the Australian domain
• a range of statistical and dynamical bias-correction and downscaling methods. 

Using the data, the student will:

• investigate changes in selected hydrological extreme indicators between the past and future 
• analyse uncertainties in the projections related to GCM selection, bias-correction and downscaling method, and emission scenarios.

The student will be based at the Bureau of Meteorology in Melbourne (other Bureau offices might be possible). The project would ideally suit a student with some experience in programming and data visualisation (e.g., using Python, R, Matlab). Experience in working with large datasets (e.g., on the NCI) would be preferable. The timing of the project can be arranged flexibly with the student.

Supervisors: Elisabeth Vogel (BOM), Louise Wilson (BOM), Anna Ukkola (ANU), Margot Bador (UNSW)

Hydrological impact studies analyse the effects of climate change on hydrological variables, such as changes in soil moisture, streamflow or hydrological extremes. Such studies are important, for example, for ensuring sustainable water resources management, agriculture or infrastructure development. Hydrological impact assessments are commonly based on hydrological models forced with corrected outputs of general circulation models (GCMs) that simulate future climate conditions, including temperature, precipitation, wind or solar radiation, under a range of possible scenarios for future greenhouse gas concentrations (e.g., CMIP outputs). Due to very high computing requirements of climate simulations, the model outputs are typically available at relatively coarse resolution – coarser than is needed to force hydrological models. In addition, small-scale processes that are below the climate model resolution are approximated using parameterisations, leading to potential biases in some variables or processes. To overcome these issues, bias-correction and downscaling methods have been developed to remove any systemic biases and to increase the resolution of the model output to match the spatial resolution required by the impact models.

The aim of this student project is to investigate the effect of such bias correction and downscaling methods on hydrological projections for Australia. The Bureau of Meteorology (BoM) is currently developing a National Hydrological Projections Service that will provide estimates of future climate change impacts on Australian water resources, based on four general circulation models (GCM) and a range of statistical and dynamical bias-correction and downscaling methods. The following statistical bias correction and downscaling methods have been applied to raw GCM outputs: 

• a trend-preserving quantile matching approach developed for the Intersectoral Impacts Model Intercomparison Project (ISIMIP) (Hempel, Frieler, Warszawski, Schewe, & Piontek, 2013)
• a statistical downscaling method (SDM) developed at the Bureau of Meteorology (Timbal, Fernandez & Li, 2009)
• a multi-variate bias-correction and spatial disaggregation (rBCSD) method (Mehrotra & Sharma, 2016; Nahar & Sharma, 2017), and 4) a quantile matching empirical statistical downscaling method optimised for preserving extreme events (Dowdy, 2019).

Focusing on selected hydrological indicators (e.g., the frequency and severity of heavy precipitation events, drought frequency, severity or duration) and on key catchments across Australia, the student will investigate two research questions: 

1. To which degree do bias correction and downscaling methods improve the agreement of climate model data with observations in terms of the frequency and severity of hydrological extremes (spatially and temporally) in the historical period? 
2. How do bias correction and downscaling methods affect the agreement between climate simulations for future climate change impacts on hydrological extreme indicators?

The student will be based at the Bureau of Meteorology in Melbourne (other Bureau offices might be possible). The project would ideally suit a student with some experience in programming and data visualisation (e.g., using Python, R, Matlab). Experience in working with large datasets (e.g., on the NCI) would be preferable. The timing of the project can be arranged flexibly with the student.

Supervisors: Justin Peter (BOM), Pandora Hope (BOM), Anna Ukkola (ANU), Lisa Alexander (UNSW)

Australian wheat production is highly water-limited, and wheat yields correlate strongly with precipitation amounts during the growing season. Australia is one of the top wheat producers in the world; therefore, impacts on Australian wheat production from hydrological extremes are not only felt locally, but can potentially have effects on global wheat trade.

Under certain conditions, periods of below-average rainfall may not immediately lead to negative yield impacts, as crops source water from the soil, leading to reduced or lagged impacts. Other indicators, capturing soil moisture drought, may therefore be better predictors of yield losses. One potential advantage is that, due to soil memory effects, seasonal forecasts of soil moisture can have higher skill compared to precipitation forecasts, especially during dry periods, and may therefore offer promising potential for informing seasonal forecasts of wheat yields in Australia.

The Bureau of Meteorology (BoM) is currently developing a seasonal forecasting system of hydrological variables for Australia, using the AWRA-L land surface water balance model, forced with seasonal climate forecasts of precipitation, temperature, solar radiation and wind from the ACCESS-S model. The aim of this student project is to investigate the relationships between hydrological extremes (especially soil moisture drought) and wheat production in Australia. The outcome of the project may inform the development seasonal forecasts of hydrological indicators for agricultural production in Australia.

The project is divided into two parts:

1. The first part investigates the upper limit of predictability of wheat yields using soil moisture, precipitation, temperature and solar radiation. It aims to investigate to which degree variations in wheat yield and production in Australia are explained by variations in soil moisture, temperature and solar radiation (using historical, observed data). Does using soil moisture data improve the statistical predictions compared to using precipitation data?
2. In the second part, the student may use retrospective seasonal forecasts (called hindcasts) of soil moisture, as well as climate variables, to assess the usefulness of hydrological forecasts for predicting yield losses in Australia at varying lead times.

The student will be based at the Bureau of Meteorology in Melbourne (other Bureau offices might be possible). The project would ideally suit a student with some experience in programming and data visualisation (e.g., using Python, R, Matlab). Experience in working with large datasets (e.g., on the NCI) would be preferable. The timing of the project can be arranged flexibly with the student.

Supervisors: Elisabeth Vogel (BOM), Lisa Alexander (UNSW)

The cumulative burden of air pollution on human health in Australia’s cities is not known. As climate change affects air pollution, it is anticipated that the future impacts on human health will be exacerbated. This project will explore different methods of assessing cumulative exposure to air pollution in order to make suggestions as to how we might best make policy recommendations that protect the health of all Australians, including vulnerable communities.

Supervisor: Donna Green

Drought is one of the major natural hazards affecting Australia. Continuous drought conditions can significantly affect soil moisture and, with this, the ability of soil to absorb precipitation. As such, if a drought is terminated by extreme precipitation, the risk of flash flooding is increased as less water can infiltrate into the dry soils.

This student project will determine the probability of a drought being terminated by an extreme precipitation event to assess the risk of drought-induced flooding. The student will assess observations, reanalysis data and results from a large climate model ensemble.

Requirements: Some prior programming experience (e.g., Python, MATLAB, R, etc.) or a willingness to learn.

Supervisors: Dr Nina Nadine Ridder (UNSW) and Dr Anna Ukkola (UNSW)

Many weather and climate extremes are combinations of multiple hazards, which act together and tend to exacerbate the socio-economic impact of an event. Events that are the result of multiple hazards acting together are known as Compound Events. One type of Compound Events are spatially correlated events. These events consist of weather extremes taking place at several locations simultaneously (or in one season) and are of particular importance for first responders who need to manage their resources and the dispatchment of forces.

This student project will focus on spatially correlated events in Australia that occurred in the past 30 to 40 years. Using observations over this time period, the student will:

• catalogue past compound events
• assess possible trends in their occurrence
• check for preferred spatial correlation patterns
• assess if these were accurately reproduced in reanalysis products (e.g., BARRA, ERA-5 and/or ERA-Interim).

Depending on student interest, possible hazards to assess are (but are not limited to) extreme precipitation, extreme temperatures/heatwaves, bushfires, wind storms, and/or droughts.

Requirements: Some prior programming experience (e.g., Python, MATLAB, etc.) or a willingness to learn.

Supervisors: Dr Nina Nadine Ridder and TBA

The ocean is a key component of the climate system because it can modulate the Earth's energy balance and the atmospheric CO² content. Climate models predict a slowdown of the Atlantic Ocean circulation in response to the current global warming. However, the uncertainties remain substantial and the modern instrumental record too short (a few decades) to fully capture the possible ocean circulation modes.

Longer ocean circulation records can be derived from indirect evidence (e.g., elemental or isotopic analysis of the sediments) called proxies. There is compelling evidence that under different climate states (e.g., glacial-interglacial cycles), the Atlantic Ocean has experienced significant water mass reorganisations and circulation changes. This project aims at better constraining these variations, using models that simulate proxy variations.

The project will use climate model simulations in which the circulation has been perturbed. The student will first explore and characterise the Atlantic Ocean circulation and its variability and then compare to the simulated proxies.

Requirements: Some experience of/or interest in developing skills in programming and data visualisation (e.g., ferret, python) is required. Interest in/willingness to learn about paleoclimate and/or ocean circulation is a plus. 

Supervisor: Dr Lise Missiaen

Marine heatwaves have received far less attention than their terrestrial counterparts. Yet, they can have devastating effects on marine ecosystems. While interest in these events is growing, there are still many unanswered questions. In this project, we will look at one of the two following questions: 

1. Do marine heatwaves occur preferentially in certain seasons, and if so, why? 
2. Do marine heatwaves produce a consistent response in ocean primary production?

The successful candidate will need to have good skills in data analysis and the ability to work with either Matlab or Python (or equivalent).

Supervisors: Dr Alex Sen Gupta and Dr Andrea Taschetto

Global warming is expected to increase the amount of rainfall that falls during the most extreme events. Changes in precipitation extremes are among the most impact-relevant consequences of climate change over Australia, yet global climate models struggle to simulate them. Models from phase 5 of Coupled Model Intercomparison Project (CMIP5 models) indicate a large range of future changes over Australia, including different signs of the change at the seasonal and regional scales. This is partly explained because they do not get some of the main rainfall characteristics. For instance, some models do not correctly reproduce the annual cycle of precipitation. Such important biases have significant consequences for the extremes and you will investigate these issues.

The primary goal of this project is to conduct an evaluation of the CMIP5 models for precipitation extremes over Australia. You will assess how models simulate key precipitation metrics in comparison to observations. The long-term goal will be to estimate if a minimal list of criteria can be identified for better simulation of precipitation extremes over Australia in order to improve our confidence in the future changes.

Requirements: Some prior programming and data visualisation experience (e.g., Python, MATLAB, R, etc.) or a willingness to learn.

Supervisors: Dr Margot Bador and A/Prof. Lisa Alexander

Many weather and climate extremes are combinations of multiple hazards, which act together and tend to exacerbate the socio-economic impact of an event. Events that are the result of multiple hazards acting together are known as Compound Events. One type of Compound Events are spatially correlated events. These events consist of extremes taking place at several geographic locations simultaneously (or in one season) and are of particular importance for emergency services that need to manage their resources and the dispatchment of forces.

This student project will focus on spatially correlated events in Australia that occurred in the past 30 to 40 years. Using observations over this time period, the student will:

• catalogue past compound events
• assess possible trends in their occurrence.

Depending on student interest, possible hazards to assess include extreme precipitation, extreme temperatures/heatwaves, bushfires, wind storms, and/or droughts.

Requirements: Some prior programming experience (e.g., Python, MATLAB, R, etc.) or a willingness to learn.

Supervisors: Dr Nina Ridder and Prof. Andy Pitman

Land-use modifications due to urbanization can modify the energy balance in cities; this, in turn, affects the urban thermal environment, resulting in the urban heat island (UHI) effect, whereby urban areas often experience different temperatures than surrounding rural areas. These land-use modifications vary across a city and it is important to capture this spatial variability when representing the urban environment in climate models. This project will use the World Urban Database and Access Portal Tools (WUDAPT) to classify land use of Australian metropolitan regions and surroundings. The student will critically review the WUDAPT method as compared to other land-use classification methods in urban and non-urban areas. Completed urban land-use classifications will be uploaded to the WUDAPT database and the student will be asked to provide a detailed assessment of Australian urban land use compared to cities with similar characteristics in the WUDAPT database.

Supervisors: Annette Hirsch, Melissa Hart, Jason Evans

Location: can be ANU or UNSW based

Time: available over the summer or p/t through the academic year