Dr Linden Ashcroft with colleagues from ANU
An evaluation of a state-of-the-art climate dataset to capture pre-1900 Southern Hemisphere atmospheric circulation variability and changes
Anthropogenic climate change has been most clearly observed in the midlatitude regions of the world. However, the limited number of observations for the Southern Hemisphere prior to the year 1900 has prevented the development of a long-term understanding of these changes. This is particularly important for placing contemporary changes to Australia’s climate into a long-term context.
In this project, you will examine the ability of a newly developed global climate dataset (20th Century Reanalysis version 3) to reveal insights about the atmospheric dynamics of the pre-industrial period in the Southern Hemisphere. The ability of this dataset to capture these dynamics is a long-standing and globally significant question. Such an evaluation was impossible before now, however recently recovered historical observations from across southern Australia now provide the necessary information base for a thorough and robust assessment.
This project will be an excellent opportunity if you are interested in learning more about the past changes and the drivers of Australia’s climate, as well as analysing dynamics associated with daily extremes like heatwaves and severe storms.
Find an expert: Dr Linden Ashcroft
Dr Linden Ashcroft in collaboration with colleagues at the University of Newcastle
From cyclones to snowfall: recuing the climate of eastern NSW, 1843–2021
Over the past 10 years, a collection of weather observations has been rescued across eastern New South Wales, spanning the 1840s to the 1950s. Combined with modern observations from the Bureau of Meteorology, these records provide a rare opportunity to examine 175 years of climate variability in a part of Australia that is influenced by temperate and subtropical weather features: where both snow and tropical cyclones occur.
As our climate changes, there is an urgent need to understand more about this unique region in the pre-industrial period, to identify the change in both mean and extreme weather and climate conditions.
In this project you will address this need by studying the unexplored weather records and diaries taken by farmers, scientists and colonial families. In collaboration with historians, archivists and atmospheric scientists from the University of Newcastle, you will delve into these pieces of history, conduct rigorous testing to determine their quality, and combine instrumental, documentary and reanalysis datasets to reveal the climate in this unique part of Australia from the 1840s to today.
Find an expert: Dr Linden Ashcroft
Prof. Craig Bishop
Estimation of observation error biases and error correlation
Currently, just a few percent of the hundreds of millions of observations taken by satellites are used for weather and climate forecasting. A primary reason for this is that many of these observations have errors whose biases and error correlations are poorly known. In this project, you will explore and discover new and more accurate methods of estimating observation error biases and correlations and hence enable the wealth of information in these observations to be used to create more accurate weather and climate prediction systems.
Assimilation of high-resolution satellite cloud imagery
Clouds and precipitation are primary sources of error in both weather and climate models. An extremely promising way of correcting these errors is to use advanced cloud observation assimilation techniques to define and correct these errors. Current cloud observation assimilation techniques are poorly equipped to do this because they do not account for the non-Gaussian nature of uncertainties or the strongly non-linear relationship between cloud, temperature and humidity. In this project, you will develop and test new algorithms for assimilation of high-resolution satellite cloud imagery such as that generated by the Himawari and Severi satellites.
Use of machine learning, artificial intelligence and high-resolution simulation to improve coarse resolution models
Clouds and precipitation are primary sources of error in both weather and climate models. Because climate models need to be run for hundreds of years, they are run at a much coarser resolution than the models used for short-term weather prediction. The coarseness of climate model resolution causes them to mis-represent climate critical processes like oceanic upwelling near coastlines and associated vast regions of high albedo low clouds that influence the total climate warming to increasing Green House Gas emissions. Nevertheless, such processes are well represented by high resolution coupled weather forecast models.
In this project, you will discover Machine learning methods to render coarse resolution models statistically indistinguishable from high resolution models filtered to the scale of the coarse resolution models.
Use of machine learning, artificial intelligence and data assimilation to improve parameterizations in high resolution models
Even our highest-resolution models of the atmosphere and ocean require the simplified representation of “parameterization” of poorly resolved processes such as cloud particle formation, rain, turbulent mixing. Ideally, these parameterization schemes should be tuned to maximize the ability of the high resolution model to produce trajectories that are consistent with high spatio-temporal resolution observations. However, methods for doing this are in their infancy.
In this project, you will build tools that enable the creation of Machine Learning based parameterizations that enable model trajectories that stay close to observations over much longer periods of time than those in current usage.
Assimilation of ice and chlorophyll observations into ocean models and coupled models
Forecasts and observations of ice and chlorophyll have highly asymmetric non-Gaussian uncertainty distributions and, in the case of ice, a strongly non-linear relationship with the temperature of the ocean. Current ocean data assimilation schemes do a poor job of accounting for such non-Gaussianity and non-linearity. In this project, you will discover and implement new methods for assimilating ice and chlorophyll observations and demonstrate their superiority to existing techniques.
PhD in past climate reconstruction
The last decade has seen an explosion of research showing how corals and trees in both living and fossilized forms can be used to infer averages of the temperature and precipitation experienced while they were developing. These approaches provide proxy temperature and precipitation record dating back hundreds/thousands of years at locations where there were no temperature or precipitation observations made by humans. It is likely that more and more of these proxy temperature records will be discovered in the coming years at new locations. Each year more such proxy temperature and precipitation records are discovered.
If selected, your PhD research will create new methods for finding the range of possible atmospheric and oceanic trajectories that are consistent with these observations. Your primary tool will be data assimilation methods which use climate models to optimally propagate and combine observational information that is distributed through space and time. A BSc (Hons) is a pre-requisite for the position. A major or minor in one or more of Applied Mathematics, Statistics and Physics would increase your chances of selection for the position.
Statistical corrections for improved week 3 prediction
Coupled models have known biases. Advanced statistical forecast models such as Linear Inverse Models (LIMs) have no bias when tested against historical data. Furthermore, while coupled models tend to under-persist larger scale modes associated with blocking, NAO, SAM, etc., LIMs persist such modes in accord with past behaviour. In this project, you will discover improved statistical models for predicting the errors in sub-seasonal forecast models and use them to improve week 3 predictions.
Event attribution and climate change
The climate is warming. Warmer air holds much more water vapour than colder air. Water vapour releases latent heat when it changes phase to form clouds of liquid and ice particles; consequently, warmer air provides cyclones with a greater reservoir of latent heat energy than colder air. On the other hand, the moist adiabats associated with warm surface air leave the post-convection atmosphere in a stabler state than the post-convective air associated with colder surface air.
To what extent are today’s extreme weather events made more extreme by the warmer background state?
To address this question, one would like to see how differently the precursors of today’s extreme weather events would subsequently evolve if they had been produced in the pre-industrial climate. Similarly, one would like to know how the precursors of extreme events in a pre-industrial climate would behave if they were produced in today’s climate. In this project, in collaboration with the Bureau of Meteorology, you will use advanced data assimilation tools to address this question.
Improving ocean data assimilation with Hybrid covariance models and vertical covariance localization
In the last decade. Hybrid forecast error covariance models that mix climatological covariances with ensemble-based flow dependent covariances, and vertical model space ensemble covariance localization have led to some of the biggest ever jumps in forecast skill at major weather prediction centres. In this project, working in collaboration with the Bureau of Meteorology, you will discover and understand how such changes would affect ocean forecasting accuracy.
Improving climate projections of extremes using advanced ensemble post-processing techniques on CMIPx ensembles.
On average, in the summers of 2080-2100, how many days will Tmax exceed 35 C in Hobart/Melbourne/Perth/Adelaide/Sydney?
One relatively low cost and promising approach for attempting to narrow the uncertainty in answers to these questions is by assigning weights to CMIPx ensemble members based on their performance relative to historical observations and then using this weighted ensemble to make a prediction. A plausible measure of the extent to which this approach can reduce projection error can be obtained by replacing the actual observations by pseudo-observation counterparts from just one of the CMIPx projections. One can then test the ability of sub-ensembles (that do not include the member used for generating the obs) weighted using the historical pseudo-observations to predict future pseudo-observations (e.g. from 2080-2100). In this project, you will strive to improve ensemble weighting techniques and design relevant metrics of changes in extremes.
Find an expert: Prof. Craig Bishop
Dr Josephine Brown
Holocene changes in the Australian monsoon
The Australian monsoon is the dominant circulation feature over northern Australia. This project will investigate climate model simulations of the Australian monsoon over the Holocene (the last 11,000 years) to explore how the monsoon changes in response to changes in the global climate. These new climate model simulations are part of the international PMIP4 project, and include a simulation using the Australian ACCESS-ESM1.5 climate model.
This project will develop skills in analysis of climate model output, understanding of atmospheric dynamics in the tropics and comparison of models with a range of proxy records such as pollen and speleothems.
South Pacific Convergence Zone variability in past and future climates
The South Pacific Convergence Zone (SPCZ) is a band of convective rainfall that extends across the southwest Pacific and influences the climate of many Pacific Island nations. The SPCZ varies on interannual time scales in response to El Nino-Southern Oscillation and on multi-decadal time scales in response to the Interdecadal Pacific Oscillation. This project will evaluate the interannual and decadal variability of the SPCZ in the current generation of global climate models (CMIP6). This relationship will then be investigated in past climates (mid-Holocene, 6000 years before present) and also in future climate simulations.
This project will develop skills in analysis of climate model output, as well as understanding of the climate variability of the Pacific region.
Find an expert: Dr Josephine Brown
Dr Stacey Hitchcock and Prof. Todd Lane
Organised Thunderstorms and Aviation Turbulence
Turbulence is a well-known hazard to aviation. Severe turbulence encounters can result in injuries and millions of dollars of operational costs to airlines, so significant effort is made to avoid turbulence where possible.
Thunderstorms generate turbulence within the storm, and can induce gravity waves in the clear air surrounding them. These waves are capable of propagating significant distances from the parent storm. This poses a challenge, as clear air turbulence is invisible.
This project could include analysis of observations and/or simulations of turbulence and the storms responsible.
Organised Thunderstorms and Severe Weather
The organisation of thunderstorms determines their impact – from essential rainfall for communities to damaging flash floods, hail, wind, and tornadoes.
Better understanding of the environments and the dynamics of these organised storms can help us to improve both short-term prediction of thunderstorm hazards and long-term prediction of how they might change in the future.
This project could involve the analysis of radar, surface, and upper air observations or high resolution numerical simulations to better understand the characteristics of organized thunderstorms.
Find an expert: Dr Stacey Hitchcock
Find an expert: Prof. Todd Lane
A/Prof. Malte Meinshausen
Analyzing CMIP6 climate model data
In this MSc project, a candidate who is well versed in python could analyze the newest generation of climate model data from the Sixth Coupled Model Intercomparison Project (CMIP6). We look for a candidate that is interested to analyze global, hemispheric, and large-scale regional precipitation and temperature time series (as we derived from the underlying gridded data and made available on cmip6.unimelb.edu.au).
One possible research question would be to correlate large-scale regional data against aerosol emissions, GHG forcings and temperatures (similar to what we assessed in this paper by Frieler et al., 2012) based on the new CMIP6 data. Another possible research question would be to consider the correlation between the model’s historical warming and precipitation and its future projections. Using historical observational constraints, possibly a weighting of models could be derived in terms of how well they matched historical observations.
Find an expert: A/Prof. Malte Meinshausen
Prof. Peter Rayner
Measuring and modelling the contribution of traffic to urban pollution and greenhouse emissions.
Upcoming and potential projects which may be co-supervised by CSIRO staff
- Measurements of CO and CO2 from an instrument on roof.
- Use to separate combustion from biospheric sources.
- Quantify the role of traffic in CO2 emissions.
- Study the impact of COVID on atmospheric pollution in Melbourne.
- Upscale to urban modelling (potential PhD).
- Students will gain skills in atmospheric measurement and modelling.
- Examine the relationship between urbanisation and pollution globally.
Measuring and modelling the contribution of traffic to urban pollution and greenhouse emissions
Upcoming and potential projects which may be supported by Architecture Building and Planning
- Combine a new data set on urban growth with satellite data on pollutants.
- Establish relationships between urban growth and pollutant emissions.
- Calculate the trend in health impacts of urban emissions and population increase.
- Students will gain skills in handling big data and urban science
Improved modelling of the planetary boundary layer using radon measurements
- Movement of heat, moisture, momentum and tracers through ABL a major problem for both climate and air quality models.
- Continuous measurements of Radon provide a measurement constraint.
- Data assimilation techniques can include this constraint into models.
- Students will gain skills in atmospheric modelling and general data assimilation techniques
The interplay of the El Nino Southern Oscillation, atmospheric dynamics and the growth rate of atmospheric CO2
Upcoming and potential projects which may be co-supervised by CSIRO staff
- The relative role of the northern hemisphere, tropics and southern hemisphere in removing anthropogenic CO2 from the atmosphere revealed by relative growth rates of atmospheric CO2 around the globe.
- These growth rates are also affected by atmospheric transport.
- Variations in transport are linked to El Nino episodes.
- The project will study long-term analyses of atmospheric dynamics (ERA5) and high-precision CO2 records to probe this relationship and allow improved attribution of growth rate variations to sources/sinks or transport.
- Students will gain skills in atmospheric dynamics and analysing trace gas records
Find an expert: Prof. Peter Rayner
A/Prof Robyn Schofield
Indoor air quality and airborne pathogen risk
Over the past 20 months, we have obtained an appreciation for the value of "safe air" in our buildings. This project proposes to use PM2.5 and CO2 observations to generate a picture of the health of our workspaces in terms of airborne pathogens, wildfire smoke and outgassing of building materials. Working together with colleagues from infrastructure services, music and dentistry this project would develop safe air metrics and criteria for evaluating mitigation strategies. Understanding the energy and carbon footprint costs of increased ventilation and filtration will form an important part of this project.
Find an expert: Prof. Robyn Schofield
A/Prof. Robyn Schofield and Dr Linden Ashcroft
Air quality versus heat stress: a case study of Victorian schools
Schools across Victoria have been designed to be cost and energy efficient to warm and cool: a combination of electric/gas heating and reverse cycle air conditioners and open windows have been found to be sufficient in the past. But in the last two years our outside air has become unsafe due to bushfire smoke, and our indoor air spaces have become dangerous for the transmission of respiratory viruses such as SARS-CoV-2 (the virus that causes COVID 19).
So how do we manage these competing risks of thermal comfort, heat stress, poor air quality and airborne virus transmission? And how should schools plan for the future?
In this project you will explore these questions using air quality data and temperature observations from across Victoria. You will then compare your findings with the current state health and education guidelines, to see if there is are smart ways to keep our children (and their growing lungs and brains) safe while in school.
Find an expert: Dr Linden Ashcroft
Find an expert: A/Prof. Robyn Schofield