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The Origin and Fate of Subantarctic Mode Water and Antarctic Intermediate Water in the Southern Ocean(2023) Li, ZhiThesisThe global ocean plays a major role in moderating atmospheric temperature rise, thereby buffering climate change. Amongst the various oceanic regions undergoing warming, the Southern Ocean is a primary heat sink in the climate system. Subantarctic Mode Water (SAMW) and Antarctic Intermediate Water (AAIW) are the dominant water masses in the upper Southern Ocean, and play a fundamental role in ocean ventilation and the uptake of heat and carbon into the ocean interior. This thesis focuses on understanding the geographic and seasonal variability in the formation of SAMW and AAIW, as well as the role of SAMW, AAIW, and other mode and intermediate waters in recent global ocean warming, using observationally based hydrography and estimates of mixing strength. Firstly, the mechanisms controlling the volumetric change of SAMW within the mixed layer and in the ocean interior are investigated separately. We find that the seasonal variability of SAMW volume in the mixed layer is governed by formation due to air-sea buoyancy fluxes (45%, lasting from July to August) and entrainment (35%), while the interior SAMW formation is controlled by subduction during August-October. The annual mean subduction estimate shows strong regional variability with hotspots of large SAMW subduction, consistent with the distribution and export pathways of SAMW over the central and eastern parts of the south Indian and Pacific Oceans. Secondly, a volume budget analysis is performed to identify the mechanisms governing the spatial and seasonal variability of AAIW. Firstly, Ekman pumping upwells the dense variety of AAIW into the mixed layer south of the Polar Front, which can be advected northward by Ekman transport into the subduction regions of lighter variety AAIW and SAMW. The subduction of light AAIW occurs mainly by lateral advection in the southeast Pacific and Drake Passage as well as eddy-induced flow between the Subantarctic and Polar Fronts. Secondly, the diapycnal transport from subducted SAMW into the AAIW layer is predominantly by mesoscale mixing near the Subantarctic Front and vertical mixing in the South Pacific, while AAIW is further replenished by transformation from Upper Circumpolar Deep Water by vertical mixing. Lastly, part of AAIW is exported out of the Southern Ocean. Our results suggest that the distribution of AAIW is set by its formation due to subduction and mixing, and its circulation eastward along the Antarctic Circumpolar Current (ACC) and northward into the subtropical gyres. Finally, the ocean absorbs >90% of anthropogenic heat in the Earth system. However, it remains unclear how this heat uptake is distributed across water masses. Here we show that ocean heat accumulation during 2010–2020 has more than doubled relative to 1990–2000. Of the total ocean heat uptake, 94% is found in global mode and intermediate water layers that have subsequently warmed and increased in volume. After factoring out volumetric changes, warming of mode and intermediate waters explains ~40% of net global ocean warming, despite occupying just ~16% of the total ocean volume. These water masses in the subtropical Pacific and Atlantic Oceans, as well as in the Southern Ocean, are responsible for a large fraction of total heat uptake, with important implications for ongoing ocean warming, sea-level rise, and climate impacts.
A Markov chain method for weighting climate model ensembles and uncertainty estimation on spatially explicit data(2023) Kulinich, MaxThesisClimate change is typically modelled using sophisticated mathematical models (climate models) of physical processes that range in temporal and spatial scales. Multi-model ensemble means of climate models show better correlation with the observations than any of the models separately. Currently, an open research question is how climate models can be combined to create an ensemble mean in an optimal way. We present a novel stochastic approach based on Markov chains to estimate model weights in order to obtain ensemble means and uncertainty estimations on spatially explicit climate data. The method was compared to existing alternatives by measuring its performance in cross-validation and model-as-truth experiments on a diverse set of public climate datasets. The Markov chain method showed improved performance over those methods when measured by a set of metrics: root mean squared error, climatological monthly root mean squared error, monthly trend bias, interannual variability, uncertainty error etc. The results of this comparative analysis should serve to motivate further studies in applications of Markov chain and other nonlinear methods that address the issues of finding optimal model weight for constructing weighted ensemble means and uncertainty estimations.
(2023) Teckentrup, LinaThesisTerrestrial ecosystems sequester about one third of anthropogenic greenhouse gas emissions every year, and strongly influence the interannual variability in the growth rate of atmospheric CO2. Ecosystems in semi-arid regions of the Southern Hemisphere have a disproportionately large impact on the year-to-year variability and trend in the net global carbon sink. In these regions, the carbon balance is linked to circulation-driven variations in both precipitation and temperature that in turn are influenced by climate modes of variability, such as the El Nino-Southern Oscillation. Typically, future carbon cycle predictions depend on terrestrial biosphere models (TBMs), and on climate predictions based on simulations by Global Circulation Models (GCMs). However, GCM simulations are associated with large biases in both the representation of climate modes of variability, and in the averages of climate variables, such as temperature and precipitation. Studies have also shown significant uncertainties in the representation of the terrestrial carbon cycle across different TBMs. This thesis explores the degree to which uncertainty in i) climate modes of variability, ii) climate simulations based on GCMs, and iii) terrestrial biosphere models represent a source of uncertainty in simulations of the terrestrial carbon cycle. The overarching goal is to achieve a constrained estimate of the future carbon cycle over Australia. This thesis first investigates whether the expression (or flavour) of El Nino (as distinct from the El Nino-La Nina cycle) affects the interannual carbon cycle variability. Using the dynamic global vegetation model LPJ-GUESS within a synthetic experimental framework, the results show that different expressions of El Nino affect interannual variability in the terrestrial carbon cycle, but the effect on longer timescales is small. This suggests that capturing the characteristics specific to the expression of El Nino may not be critical for robust simulations of the terrestrial carbon cycle on multidecadal timescales. Known as a hotspot for terrestrial carbon cycle variability, and strongly influenced by climate modes of variability, the remainder of this thesis then focuses on Australia as a testbed to study areas of uncertainty in regional carbon cycle projections. At regional scales, climate projections display large biases, which hamper predictive capacity in impact studies. Many methods exist to either remove biases in the climate forcing, or to achieve informed ensemble averages, but it is not obvious whether some methods are preferable to others. Simulations using LPJ-GUESS and climate output from the Coupled Model Intercomparison Project Phase 6 (CMIP6) show that all bias correction methods reduce the bias in simulated carbon cycle to similar degrees but can lead to different vegetation distributions in the individual simulations. Bias corrections do not influence the ensemble average, but do reduce the ensemble uncertainty significantly. Choosing an informed ensemble averaging method, such as a weighted or random forest approach, is preferential to calculating a simple arithmetic ensemble average. However, suitable target datasets for carbon cycle variables covering both the spatial and temporal scales necessary are sparse, limiting the applicability of these methods for future studies. In addition, the representation of the Australian carbon cycle in TBMs, namely those part of the TRENDY v8 ensemble, was analysed. Land-use change is the main driver for discrepancies in the simulated long-term accumulated net carbon balance across TBMs. The TBMs also have different sensitivities to atmospheric carbon dioxide (CO2) concentration, but climate drives the year-to-year variability in the net carbon sink rather than the trend. Further, differences in the timing of simulated phenology and fire dynamics, as well as simulated vegetation carbon, and apparent carbon residence time are associated with differences in simulated or prescribed vegetation cover and process representation. These results highlight the need to evaluate parameter assumptions and the key processes that drive vegetation dynamics, such as phenology, mortality, and fire, in an Australian context to reduce uncertainty across models. Since none of the TBMs investigated clearly outperforms the others, LPJ-GUESS was then taken as the model with which to constrain the Australian carbon cycle. Observed plant traits were prescribed to achieve an improved representation of the vegetation cover in LPJ-GUESS. A comparison between the model and satellite-derived datasets showed reasonable agreement for gross primary productivity and leaf area index. LPJ-GUESS further captured the woody and non-woody cover over Australia. This allowed the model to be used to explore the future terrestrial carbon cycle over Australia. Based on the above findings, this thesis then explores the future Australian terrestrial carbon cycle using the CMIP6 ensemble together with the regionally parametrised LPJ-GUESS. The uncertainty in Australia’s future carbon cycle is strongly linked to biases in the meteorological forcing, and can be significantly reduced via bias correction. However, implementing bias correction methods still leads to an unresolved uncertainty in carbon storage in the vegetation at the end of the century. Variations in carbon residence time, and model sensitivities to CO2, temperature, and precipitation are the key drivers for the discrepancy in simulated carbon stored in vegetation. Reducing this uncertainty will require improved terrestrial biosphere models, but also major improvements in the simulation of regional precipitation by global circulation models. The thesis concludes with suggestions of future work that should help to resolve the large uncertainties in the future carbon stored in vegetation over Australia.
Behavioural ecology of the greater bilby (Macrotis lagotis) and conservation tool development in a semi-wild sanctuary(2023) Cornelsen, KateThesisConservation translocations are becoming an increasingly necessary tool to stem the decline of threatened species globally. The greater bilby (Macrotis lagotis) is a nationally threatened species in Australia. While bilby translocations are expected to contribute to the species’ persistence, the scarcity of information on their behaviour and ecology prevents informed-management. By intensively studying a population of bilbies both prior to, and following reintroduction, and subsequent reinforcements to a fenced sanctuary, I aimed to (1) advance knowledge of bilby behaviour and examine behaviours potentially relevant to fitness (i.e. survival and breeding success), (2) improve ecological knowledge of bilbies within understudied (temperate) climates, and (3) use this knowledge to suggest and develop effective tools for their conservation. Chapter 1 describes the current state of research in applied conservation research, and how increased behavioural data could help address some of the current knowledge gaps for bilby conservation. In Chapter 2, I examined patterns in bilby resource selection, finding that selection changed between seasons and years due to the environmental conditions and resources available. I also found that resource requirements are likely to be behavioural-state dependent and sex-specific. In Chapter 3, I constructed social networks to examine nocturnal proximity of bilbies and concurrent burrow sharing and found that associations were non-random. Expanding on this, in Chapter 4, I found that burrow sharing was likely to help describe breeding strategies, as males strongly avoided other males, and mixed-sex dyads exhibited kin-avoidance when mate choice was more limited. In Chapter 5, I developed a test to screen personality traits in bilbies, finding links between male response to handling and relative breeding success post-release. Lastly, in Chapter 6, I described a method to collect detailed movement data on the bilby, and discussed some of the practical and animal welfare constraints for its application. My thesis provides new insights into the behavioural ecology of the bilby with potential implications for future management of the species. With further translocations necessary for long-term persistence of the bilby, this research is highly relevant to current and future management of this ecologically important species, with potential applications to other similarly at-risk species.