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Climate projections from global circulation models (GCMs), part of the Coupled Model Intercomparison Project 6 (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. These biases have been identified as a major source of uncertainty in carbon cycle projections, hampering predictive capacity. In this study, we open the proverbial Pandora's box and peer under the lid of strategies to tackle climate model ensemble uncertainty. We employ a 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 reduce the climate-driven 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. Bias correction can improve model carbon outputs, but 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 simulated carbon over time compared to the target dataset, highlighting the need to account for temporal properties in correction or ensemble-averaging methods. Multivariate bias correction methods tend to reduce the uncertainty more than univariate approaches, although the overall magnitude is similar. Even after correcting the bias in the meteorological forcing dataset, the simulated vegetation distribution presents different patterns when different GCMs are used to drive LPJ-GUESS. Additionally, we found 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. This highlights that, where possible, an arithmetic ensemble average should be avoided. However, potential target datasets that would facilitate the application of machine learning approaches, i.e., that cover both the spatial and temporal domain required to derive a robust informed ensemble average, are sparse for ecosystem variables. [ABSTRACT FROM AUTHOR]

Heatwave frequency, duration and intensity has increased since the 1950s, a trend likely to continue with anthropogenic climate change. Although climate model projections are an important source of information on how heatwaves and other climate extremes may evolve with climate change it is necessary to understand whether climate models are able to adequately reproduce historical statistics of these events. We provide the first global evaluation of CMIP6 models to characterize heatwave characteristics between 1950 and 2014. Further, we also include a global heatwave evaluation of the CMIP5 models, as no study currently exists. Our results demonstrate that both ensembles underestimate mean heatwave frequency with region‐dependent biases for mean heatwave duration, intensity, and cumulative heat. Comparisons between CMIP5 and CMIP6 show that improvements in skill for the heatwave metrics evaluated here are marginal, suggesting that future improvements in simulating heatwaves may only be possible with significant advances in climate modeling capabilities. Plain Language Summary: Heatwaves are increasing in response to climate change associated with human activity, a trend that is likely to continue. In order to build confidence in projected changes in heatwaves it is necessary to first understand whether the models producing these projections can represent historical heatwave characteristics. We present the first global evaluation of the current and previous suite of climate models that inform the Intergovernmental Panel on Climate Change Assessment Reports. Our analysis considers four different heatwave characteristics that measure the frequency, duration, average intensity, and cumulative intensity over the period 1950–2014. Our results show that most climate models underestimate historical heatwave frequency with regional under‐ and overestimation of heatwave duration, average intensity and cumulative intensity. Our analyses are useful for decision makers and other users of climate model projections as they show which regions and heatwave characteristics have higher skill over the present‐day period. Key Points: First global evaluation of CMIP6 and CMIP5 models for historical heatwavesHeatwave frequency is systematically underestimated by both CMIP cyclesCumulative heat is systematically overestimated by both CMIP cycles [ABSTRACT FROM AUTHOR]

The co-occurrence of droughts and heatwaves can have significant impacts on many socioeconomic and environmental systems. Groundwater has the potential to moderate the impact of droughts and heatwaves by moistening the soil and enabling vegetation to maintain higher evaporation, thereby cooling the canopy. We use the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model, coupled to a groundwater scheme, to examine how groundwater influences ecosystems under conditions of co-occurring droughts and heatwaves. We focus specifically on south-east Australia for the period 2000–2019, when two significant droughts and multiple extreme heatwave events occurred. We found groundwater plays an important role in helping vegetation maintain transpiration, particularly in the first 1–2 years of a multi-year drought. Groundwater impedes gravity-driven drainage and moistens the root zone via capillary rise. These mechanisms reduced forest canopy temperatures by up to 5 ∘ C during individual heatwaves, particularly where the water table depth is shallow. The role of groundwater diminishes as the drought lengthens beyond 2 years and soil water reserves are depleted. Further, the lack of deep roots or stomatal closure caused by high vapour pressure deficit or high temperatures can reduce the additional transpiration induced by groundwater. The capacity of groundwater to moderate both water and heat stress on ecosystems during simultaneous droughts and heatwaves is not represented in most global climate models, suggesting that model projections may overestimate the risk of these events in the future. [ABSTRACT FROM AUTHOR]

Compound events have the potential to cause high socioeconomic and environmental losses. We examine the ability of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models to capture two bivariate compound events: the co‐occurrence of heavy rain and strong wind, and heat waves and meteorological drought. We evaluate the models over North America, Europe, Eurasia, and Australia using observations and reanalysis data set spanning 1980–2014. Some of the CMIP6 models capture the return periods of both bivariate compound events over North America, Europe, and Eurasia surprisingly well but perform less well over Australia. For heavy rain and strong wind, this poor performance was particularly clear in northern Australia which suggests limits in simulating tropical and extratropical cyclones, local convection, and mesoscale convective systems. We did not find higher model resolution improved performance in any region. Overall, our results show some CMIP6 models can be used to examine compound events, particularly over North America, Europe, and Eurasia. Plain Language Summary: Compound events, such as the co‐occurrence of heavy rain and strong wind or heat waves and drought, can have major economic, social, and environmental consequences. We therefore ask the question whether the new generation of climate models represented by the sixth phase of the Coupled Model Intercomparison Project (CMIP6) can simulate the occurrence of these important events. We found that some of the CMIP6 models do simulate these compound events surprisingly well over North America, Europe, and Eurasia. Unfortunately, they perform less well over Australia which is likely associated with the problem of simulating extratropical cyclones, local convection, and mesoscale convective systems. Our results suggest that some CMIP6 models can be used to examine these two compound events particularly over North America, Europe, and Eurasia. Key Points: Some CMIP6 models reproduce observed return periods of co‐occurring rain and wind extremes and co‐occurring heat waves and droughts wellCMIP6 models simulate these compound events over North America, Europe, or Eurasia with similar levels of skillCMIP6 models simulate these compound events over Australia with lower skill than the other regions analyzed [ABSTRACT FROM AUTHOR]

Compound events (CEs) are weather and climate events that result from multiple hazards or drivers with the potential to cause severe socio-economic impacts. Compared with isolated hazards, the multiple hazards/drivers associated with CEs can lead to higher economic losses and death tolls. Here, we provide the first analysis of multiple multivariate CEs potentially causing high-impact floods, droughts, and fires. Using observations and reanalysis data during 1980–2014, we analyse 27 hazard pairs and provide the first spatial estimates of their occurrences on the global scale. We identify hotspots of multivariate CEs including many socio-economically important regions such as North America, Russia and western Europe. We analyse the relative importance of different multivariate CEs in six continental regions to highlight CEs posing the highest risk. Our results provide initial guidance to assess the regional risk of CE events and an observationally-based dataset to aid evaluation of climate models for simulating multivariate CEs. Compound climate events such as floods and droughts together can cause severe socio-economic impacts. Here, the authors analyse global hazard pairs from 1980–2014 and find global hotspots for the occurrence of compound events. [ABSTRACT FROM AUTHOR]