ARC Centre of Excellence for Climate System Science

ABSTRACT The impacts of extreme precipitation events (EPEs) on society are strongly influenced by their spatial footprint, yet the spatial scales of such events remain underexplored. Here we present the first continent‐wide analysis of the spatial scales of daily EPEs in Australia. We estimate the characteristic spatial scale of EPEs seasonally across the Australian continent using daily station observations and semivariograms. A semivariogram is a spatial statistical function that measures how spatial autocorrelation in precipitation decays with distance. Consistent with global analyses of satellite data, EPEs generally have larger spatial scales at higher latitudes. However, our analysis reveals complex seasonal and geographical dependencies that highlight the role of topography and meteorological regimes. We also analyse EPE spatial scales under different phases of the El Niño–Southern Oscillation (ENSO). In SON and DJF, southeastern Australia exhibits larger spatial scales during La Niña, although no uniform pattern is observed across the continent. Long‐term changes were analysed using 2070 stations with continuous operation between 1960 and 2023. Southwestern Australia shows a notable reduction in median EPE length scale in most seasons, while eastern regions exhibit a decrease in MAM and an increase in SON. Together, these findings provide a new climatological reference for the spatial scale of EPEs in Australia. These results also highlight the need to better understand the physical factors controlling the spatial scale of precipitation extremes in current and future climates.

Abstract. The stable isotopic composition of precipitation (δ2HP, δ18OP; “water isotopes”) is a powerful tool for tracking water through the atmosphere, as well as fingerprinting land-surface water masses and identifying water cycle biases in isotope-enabled climate models. Water isotopes also underpin our understanding of multi-decadal to multi-centennial water cycle variability via their retrieval from palaeoclimate archives. Water isotopes thereby increase our understanding of past and present – and hence future – water cycle variability. Understanding the drivers of spatial and temporal water isotope variability is a critical first step in applying these tracers for a better understanding of the water cycle. However, water isotope observations are sparse in both space and time. Here we develop and apply a machine learning (random forest) approach to predict spatially continuous monthly δ2HP and δ18OP across the Australian continent at 0.25° resolution from 1962–2023. We train the random forest models on monthly δ2HP (n=5199) and δ18OP (n=5217) observations from 60 sites across Australia. We also predict the deuterium excess of precipitation (dxsP, defined as δ2HP-8×δ18OP). Out-of-sample δ2HP and δ18OP prediction skill is high both geographically and temporally. Skill is slightly lower for the secondary parameter dxsP, likely reflecting the larger reliance of spatio-temporal dxsP variability on moisture source conditions. The random forest models accurately capture both the seasonal cycle of precipitation isotopic variability and long-term annual-mean precipitation isotopic variability across the continent, and outperform estimates from an isotope-enabled atmosphere general circulation model over an equivalent time period. We show that spatio-temporal variability in precipitation amount, precipitation intensity, and surface temperature are particularly important for monthly δ2HP and δ18OP variations across the continent, with local surface pressure also important for dxsP. Drivers of site-level δ2HP, δ18OP, and dxsP are more varied. Overall, the new random forest modelled dataset reveals clear spatial and temporal variability in δ2HP, δ18OP, and dxsP across the Australian continent over the past decades – providing a robust foundation for hydrology, ecology, and palaeoclimate research, as well as an accessible framework for predicting water isotope values in other locations.

<strong class="journal-contentHeaderColor">Abstract.</strong> A comparison of simulated and observed overturning transports and related properties across the Overturning in the Subpolar North Atlantic Program (OSNAP) sections for the 2014&ndash;2022 period is presented, considering both depth and density space transports. The effort was motivated by the observational transport estimates at both OSNAP-West (OW) and OSNAP-East (OE) sections which show a minor role for the Labrador Sea (LS) in setting the mean and variability of the overturning in the subpolar North Atlantic. There are 9 participating groups from around the world, contributing a total of 18 ocean &ndash; sea simulations with 6 different ocean models. The simulations use a common set of interannually-varying atmospheric forcing datasets. The horizontal resolutions of the simulations range from nominal 1&deg; to eddy-resolving resolutions of 0.1&deg;&ndash;0.05&deg;. While there are many differences between the simulations and observations as well as among the individual simulations in terms of transport properties, the simulations show significantly larger transports at OE than at OW in general agreement with the observations. Analyzing overturning circulations in both depth and density space together provides a more complete picture of the overturning properties and features. This analysis also reveals that, in both the simulations and observations, northward and southward flows substantially cancel each other, producing much smaller residual (total) transports. Such cancellations tend to be much more prominent in depth space than in density space. In general, the observed transport features are captured better at OE than OW. The simulations generally show larger (smaller) transports with positive (negative) temperature and salinity biases in the upper ocean near the OSNAP sections, but with no such relationship with density biases. In high-resolution simulations, the transport profiles agree better with the observations in general, but challenges remain in some other metrics considered in our analysis. When transports are calculated using a density referenced to 2000-m depth, rather than the ocean surface, the relative contributions of transports at OW increase modestly.

Abstract Synoptic weather typing is a common technique for categorizing regional circulation patterns over a region. Defined and commonly used sets of synoptic weather types (SWTs) have the potential to simplify the analysis and interpretation of the regional circulation on the weather time and spatial scales and create a common language between the often segregated weather and climate research communities. However, circulation classifications are potentially complicated over Australia owing to its position in both the tropics and extratropics. Here we present a set of 30 SWTs over Australia by k means clustering the 850 hPa wind field in the ERA5 data set. We show that the Australian SWTs represent recognizable weather patterns over the continent throughout the year. Importantly, the SWTs capture features in the extratropics together with characteristics associated with the tropical monsoon. An initial analysis of the relationship of the SWTs with the modes of climate variability, high impact weather and renewable energy related variables showing the utility of the SWTs in a wide range of applications across disciplines within earth system sciences and beyond.

Abstract. The equilibrium climate sensitivity (ECS) – the equilibrium global mean temperature response to a doubling of atmospheric CO2 – is a high-profile metric for quantifying the Earth system's response to human-induced climate change. A widely applied approach to estimating the ECS is the “Gregory method” (Gregory et al., 2004), which uses an ordinary least squares (OLS) regression between the net radiative flux, N, and surface air temperature anomalies, ΔT, from a 150 year experiment in which atmospheric CO2 concentrations are quadrupled. The ECS is determined by extrapolating the linear fit to N=0, i.e. the ΔT-intercept, indicating the point at which the system is back in equilibrium. This method has been used to compare ECS estimates across the CMIP5 and CMIP6 ensembles and will likely be a key diagnostic for CMIP7. Despite its widespread application, there is little consistency or transparency between studies in how the climate model data is processed prior to the regression, leading to potential discrepancies in ECS estimates. We identify 32 alternative data processing pathways, varying by differences in global mean weighting, net radiative flux variable, anomaly calculation method, and linear regression fit. Using 44 CMIP6 models, we systematically assess the impact of these choices on ECS estimates and calculate uncertainty ranges using two bootstrap approaches. While the inter-model ECS range is insensitive to the data processing pathway, individual outlier models exhibit notable differences. Approximating a model's native grid cell area (if irregular) with cosine of the latitude can decrease the ECS by 11 %, the choice of N-variable can change the ECS by 6 %, and some anomaly calculation methods can introduce spurious temporal correlations in the processed data. Beyond data processing choices, we also evaluate an alternative linear regression method – total least squares (TLS) – which has a more statistically robust basis than OLS. However, for consistency with previous literature, and given TLS may reduce the ECS compared to OLS (by up to 24 %), thereby making a known bias in the Gregory method worse, we do not feel there is sufficient clarity to recommend a transition to TLS in all cases. To improve reproducibility and comparability in future studies, we recommend a standardised Gregory method: weighting the global mean by cell area, using the top of the atmosphere (as opposed to the top of model) N-variable, and calculating anomalies by first applying a rolling average to the preindustrial control timeseries then subtracting from the raw CO2 quadrupling experiment. This approach accounts for model drift while reducing noise in the data to best meet the pre-conditions of the linear regression. While CMIP6 results of the multi-model mean ECS appear insensitive to these processing choices, similar assumptions may not hold for CMIP7, underscoring the need for standardised data preparation in future climate sensitivity assessments.

Life is ubiquitous throughout the ocean, with species abundance and richness often greatest below the surface. As a result, ocean extremes throughout the water column may impact resident marine organisms and ecosystems. However, ocean extremes, such as marine heatwaves, have been commonly described based on surface observations. Given the importance of subsurface ocean processes, such as nutrient recycling, (de)oxygenation, and carbon transport, there has been an increasing focus on subsurface marine heatwaves (MHWs). Subsurface MHWs are prolonged warm ocean temperature extremes, and have a diversity of vertical structures linked with different driving mechanisms. Warming may be confined to the surface mixed layer; it may extend much deeper, potentially affecting the entire water column; it may appear only below the surface, with no surface signature, or it may be isolated near to or connected with the seafloor. Based on existing literature and a new analysis of subsurface MHW structure, we propose a comprehensive naming convention, differentiating between mixed layer, deep, thermocline, submerged and benthic marine heatwaves. Most surface-confined MHWs are associated with surface heat fluxes or shallow ocean advection or mixing. Conversely, many subsurface events are likely related to the vertical or horizontal displacement of temperature gradients/fronts, deep advection, and/or subduction of warm waters below the mixed layer. Different MHW vertical structures also have varying impacts on ocean biogeochemistry. However, due to the sparsity of physical, biogeochemical and biological observations, as well as the complexity of identifying and describing subsurface MHWs, there is limited understanding of the impact of subsurface MHW extremes. The nomenclature proposed in this paper seeks to provide a common language for understanding subsurface MHWs, thus enabling inter-disciplinary studies to quantify their impact. • Proposed a classification scheme for subsurface marine heatwaves. • Reviewed emerging studies on subsurface ocean temperature extremes. • Identified unique drivers differing from surface heatwave mechanisms. • Explored biological impacts and observational challenges of subsurface events.

Abstract Vertical profiles of temperature T and salinity S in the ocean commonly exhibit fine-scale variability in the form of interleaving, with cold and fresh layers in between warm and salty. These features are widespread in the Southern Ocean, particularly in the Polar Front Zone of the Antarctic Circumpolar Current (ACC) and in energetic regions downstream of complex topography. Interleaving is a signature of cross-frontal exchange—with potentially significant contributions to poleward heat transport, mixing, and water mass transformation in the frontal regions. With typical time scales on the order of days and vertical scales of O (10) m in the upper 1000 m of the ACC, interleaving features are not captured in global climate models and their generation mechanism remains unclear. Motivated by in situ observations of thermohaline interleaving in the Southern Ocean, we use a Lagrangian particle-tracking approach to demonstrate the generation of interleaving features by isopycnal stirring and chaotic advection. Particles are advected backward-in-time along-isopycnal surfaces, using realistic gradient wind velocities from SatGEM-2—a full-depth time-evolving representation of Southern Ocean T – S and velocity fields, constructed from observations. With the addition of small-scale perturbations to the velocity field, accounting for unresolved motions, interleaving features develop on time scales of &lt;10 days in the energetic meander regions of the ACC—highlighting the importance of small-scale processes in reproducing the observed variability. While characteristics of the large-scale thermohaline and velocity field are important, we show how transient features of the flow field cause particle dispersion and mediate cross-frontal exchange processes within the energetic meanders.

<strong class="journal-contentHeaderColor">Abstract.</strong> Antarctic sea-ice cover reached historically low levels in 2023, consistent with the simulated decrease in sea-ice extent in response to anthropogenic warming. Antarctic sea ice is closely linked to multiple components of the Earth system, thus its demise could precipitate widespread, cascading changes across the cryosphere, atmosphere, and ocean. However, the nature and strength of these interconnections are poorly understood, and they are often inadequately represented in models. In this review paper we use modern observations, models and paleoclimate archives covering the last glacial cycle to gain insights into how reductions in sea ice may affect other components of the Earth system. We review how Antarctic sea ice interacts with ocean and atmosphere circulation, ice sheets and ice shelves, marine productivity, and the carbon cycle over the last glacial cycle, for which we have the most robust sea-ice reconstructions. The review finds strong evidence from theory and models for impacts of Antarctic sea ice on the Earth system. Paleo-proxy reconstructions provide examples where changes in sea ice co-occur with changes in the carbon cycle, marine productivity, and ocean circulation. However, challenges remain in isolating the impact of sea ice in a highly interconnected system.

Abstract The Precipitation Severity Index (PSI) is used to examine the occurrence of and mechanisms behind widespread persistent rainfall events in Sumatra. Analysis of cloud properties from Himawari‐8 is combined with data from IMERG and ERA5 to explore new results about changes in cloud properties relating to heavy rainfall and intraseasonal variability. This study brings a fresh angle to the challenge of understanding the Madden‐Julian Oscillation (MJO) modulation of cloud and precipitation over Sumatra by examining changes in the total cloud area and, more importantly, changes in the cold cloud tops that indicate the deepest convection. Although widespread cold clouds are likely regardless of MJO phase, the enhanced prevalence of cold tops requires the support of the large‐scale convective envelope. These results provide insight about how the MJO influences the nature as well as the amount of convection. Heavy rainfall is shown to make a larger percentage contribution to the total rainfall during the MJO‐enhanced convection phases (2‐3‐4) over Sumatra. This contribution is suggested to be related to a higher prevalence of cold cloud core areas in phases 2 and 4. In phase 3, there is a remarkable increase in the percentage contribution of heavy rainfall in the cold cloud area but a relatively lower prevalence of the coldest cloud core area, suggesting large‐scale environmental support for heavy rainfall but not necessarily the most intense convection. These results exemplify the role of mesoscale processes and cloud microphysics in modulating the MJO‐scale modulation of rainfall.