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Eastern Australia experienced record-breaking rainfall and flooding during 2022, which devastated communities and led to over AU$6 billion of damage. In this study, we conduct a multiscale analysis to understand why 2022 was unusually wet with multiple record-breaking rainfall events over the year. We assess the synoptic- and mesoscale conditions during three key periods: February–March, July, and October. These periods were chosen because they were impactful and represent three different seasons. Next, we examine the large-scale climate drivers and evaluate how unusual these events were compared to previous years. Finally, we discuss predictability and the possible impact of climate change on the drivers of heavy rainfall in eastern Australia. The exceptional rainfall was likely due to excessive slow-moving cyclones combined with ample atmospheric moisture flux. The key climate drivers (El Niño–Southern Oscillation, Indian Ocean dipole, and Southern Annular Mode) primed the synoptic conditions for frequent rain-bearing weather systems. There was limited predictability beyond the 1-week lead time for the February–March and July events, but the 1-month seasonal forecast for October indicated rainfall in eastern Australia. The complexity of multiday extreme rainfall makes it difficult to distinguish the role of climate change from natural variability. However, we highlight where future research could focus to help us understand climate risks to this region. Our results emphasize the importance of examining rainfall from multiple perspectives and considering the interactions between weather and climate scales because analyzing drivers in isolation is insufficient for explaining high-impact weather. [ABSTRACT FROM AUTHOR]

Semi‐arid ecosystems, common across the Australian continent, strongly influence the inter‐annual variability and trend in the global terrestrial net carbon sink. Here we explore the future Australian terrestrial carbon cycle using the CMIP6 ensemble, and the dynamic global vegetation model LPJ‐GUESS. Uncertainty in Australia's carbon storage in vegetation ranged between 6 and 49 PgC at the end of the century and was strongly linked to biases in the meteorological forcing. Using LPJ‐GUESS with bias‐corrected meteorological forcing reduced uncertainty in the vegetation carbon storage to between 14 and 20 PgC, with the remaining range linked to model sensitivities to rising atmospheric CO2 concentration, temperature, and precipitation variability. Reducing this uncertainty will require improved terrestrial biosphere models, but also major improvements in the simulation of regional precipitation by Global Climate Models. Plain Language Summary: Vegetation on land takes up about one‐third of greenhouse gas emissions associated with human activities and strongly influences the year‐to‐year variability in the atmospheric growth rate of CO2. Semi‐arid areas in the Southern Hemisphere, for example, in Australia, dominate the variability and trend in the net carbon sink. We use both coupled climate‐carbon cycle and uncoupled carbon cycle simulations to study future changes in the Australian carbon cycle and provide the first comprehensive regional analysis of future carbon cycle trends for Australia accounting for Global Climate Model uncertainty. Biases in the simulated climate lead to large uncertainty in the simulated carbon cycle but this uncertainty can be reduced when the biases are corrected. We identify areas of uncertainty in the process representation in the vegetation models, specifically carbon residence time, and a discrepancy in the sensitivity to three forcing variables (atmospheric CO2 concentration, temperature, and precipitation). Our study highlights that reducing precipitation biases in Global Climate Models would lead to a better understanding of future changes to the regional carbon cycle and help inform climate mitigation strategies. Key Points: A key area of future uncertainty in regional terrestrial carbon cycle projections is linked to biases in climate projectionsWe provide a comprehensive regional analysis of future carbon cycle trends for Australia accounting for uncertainty in climate projectionsApplying bias‐correction to the climate projections reduces the uncertainty in projected vegetation carbon from 6 – 49 PgC to 14–20 PgC [ABSTRACT FROM AUTHOR]

Climate projections from global circulation models (GCMs) part of the Coupled Model Intercomparison Project (CMIP6) are often employed to study the impact of future climate on ecosystems. However, especially at regional scales, climate projections display large biases in key forcing variables such as temperature and precipitation, which hamper predictive capacity. In this study we examine different methods to constrain regional projections of the carbon cycle in Australia. We employ a 5 dynamic global vegetation model (LPJ-GUESS) and force it with raw output from CMIP6 to assess the uncertainty associated with the choice of climate forcing. We then test different methods to either bias correct or calculate ensemble averages over the original forcing data to constrain the uncertainty in the regional projection of the Australian carbon cycle. We find that all bias correction methods reduce the bias of continental averages of steady-state carbon variables. Carbon pools are insensitive to the type of bias correction method applied for both individual GCMs and the arithmetic ensemble average across all corrected models. None of the bias correction methods consistently improve the change in carbon over time, highlighting the need to account for temporal properties in correction or ensemble averaging methods. Some bias correction methods reduce the ensemble uncertainty more than others. The vegetation distribution can depend on the bias correction method used. We further find that both the weighted ensemble averaging and random forest approach reduce the bias in total ecosystem carbon to almost zero, clearly outperforming the arithmetic ensemble averaging method. The random forest approach also produces the results closest to the target dataset for the change in the total carbon pool, seasonal carbon fluxes, emphasizing that machine learning approaches are promising tools for future studies. [ABSTRACT FROM AUTHOR]

The El Niño-Southern Oscillation (ENSO) influences the global climate and the variability in the terrestrial carbon cycle on interannual timescales. Two different expressions of El Niño have recently been identified: (i) Central-Pacific (CP) and (ii) Eastern-Pacific (EP). Both types of El Nino are characterised by above average sea surface temperature anomalies in the respective locations. Studies exploring the impact of these expressions of El Niño on the carbon cycle have identified changes in the amplitude of the concentration of interannual atmospheric carbon dioxide (CO2) variability, as well as different lags in terrestrial CO2 release to the atmosphere following increased tropical near surface air temperature. We employ the dynamic global vegetation model LPJ-GUESS within a synthetic experimental framework to examine the sensitivity and potential long term impacts of these two expressions of El Niño on the terrestrial carbon cycle. We manipulated the occurrence of CP and EP events in two climate reanalysis datasets during the later half of the 20th and early 21st century by replacing all EP with CP and separately all CP with EP El Niño events. We found that the different expressions of El Niño affect interannual variability in the terrestrial carbon cycle. However, the effect on longer timescales was negligible for both climate reanalysis datasets. We conclude that capturing any future trends in the relative frequency of CP and EP El Niño events may not be critical for robust simulations of the terrestrial carbon cycle. [ABSTRACT FROM AUTHOR]

Global fire-vegetation models are widely used to assess impacts of environmental change on fire regimes and the carbon cycle, and to infer relationships between climate, land use, and fire. However, differences in model structure and parameterizations, in both the vegetation and fire components of these models, could influence overall model performance, and to date there has been limited evaluation of how well different models represent various aspects of fire regimes. The Fire Model Intercomparison Project (FireMIP) is coordinating the evaluation of state-of-the-art global fire models, with the aim of improving projections of fire regime characteristic and fire impacts on ecosystems and human societies under the context of global environmental change. Here we perform a systematic evaluation of historical simulations made by nine FireMIP models in order to quantify their ability to reproduce a range of fire and vegetation benchmarks. The FireMIP models simulate a wide range in global annual total burnt area (39-536Mha), and global annual fire carbon emission (0.91-4.75PgCa-1) for modern conditions (2002-2012), but most of the range in burnt area is within observational uncertainty (345-468Mha). Benchmarking scores indicate that seven out of nine FireMIP models are able to represent the spatial pattern in burnt area. The models also reproduce the seasonality in burnt area reasonably well but struggle to simulate fire season length and are largely unable to represent inter-annual variations in burnt area. However, models that represent cropland fires see improved simulation of fire seasonality in the northern hemisphere. The three FireMIP models which explicitly simulate individual fires are able to reproduce the spatial pattern in number of fires, but fire sizes are too small in key regions and this results in an underestimation of burnt area. The correct representation of spatial and seasonal patterns in vegetation appears to correlate with a better representation of burnt area. While some FireMIP models are better at representing certain aspects of the fire regime, no model clearly outperforms all other models across the full range of variables assessed. [ABSTRACT FROM AUTHOR]

Understanding how fire regimes change over time is of major importance for understanding their future impact on the Earth system, including society. Large differences in simulated burned area between fire models show that there is substantial uncertainty associated with modelling global change impacts on fire regimes. We draw here on sensitivity simulations made by seven global dynamic vegetation models participating in the Fire Model Intercomparison Project (FireMIP) to understand how differences in models translate into differences in fire regime projections. The sensitivity experiments isolate the impact of the individual drivers of fire, which are prescribed in the simulations. Specifically these drivers are atmospheric CO2, population density, land-use change, lightning and climate. The seven models capture spatial patterns in burned area. However, they show considerable differences in the burned area trends since 1900. We analyse the trajectories of differences between the sensitivity and reference simulation to improve our understanding of what drives the global trend in burned area. Where it is possible, we link the inter-model differences to model assumptions. Overall, these analyses reveal that the strongest differences leading to diverging trajectories are related to the way anthropogenic ignitions and suppression, as well as the effects of land-use on vegetation and fire, are incorporated in individual models. This points to a need to improve our understanding and model representation of the relationship between human activities and fire to improve our abilities to model fire for global change applications. Only two models show a strong response to CO2 and the response to lightning on global scale is low for all models. The sensitivity to climate shows a spatially heterogeneous response and globally only two models show a significant trend. It was not possible to attribute the climate-induced changes in burned area to model assumptions or specific climatic parameters. However, the strong influence of climate on the inter-annual variability in burned area, shown by all the models, shows that we need to pay attention to the simulation of fire weather but also meteorological influences on biomass accumulation and fuel properties in order to better capture extremes in fire behavior. [ABSTRACT FROM AUTHOR]