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Global temperature targets, such as the widely accepted limit of an increase above pre-industrial temperatures of two degrees Celsius, may fail to communicate the urgency of reducing carbon dioxide (CO 2 ) emissions. The translation of CO 2 emissions into regional- and impact-related climate targets could be more powerful because such targets are more directly aligned with individual national interests. We illustrate this approach using regional changes in extreme temperatures and precipitation. These scale robustly with global temperature across scenarios, and thus with cumulative CO 2 emissions. This is particularly relevant for changes in regional extreme temperatures on land, which are much greater than changes in the associated global mean. Targets for reducing atmospheric carbon dioxide are related to regional changes in climate extremes rather than to changes in global mean temperature, in order to convey their urgency better to individual countries. Regional climate mediation targets A major theme in the fifth assessment report of the Intergovernmental Panel on Climate Change was that global mean surface temperature scales linearly with cumulative emissions of greenhouse gases. Sonia Seneviratne et al . show that a similar scaling exists between cumulative emissions and regional changes in the occurrence of extremes in precipitation and temperatures. The presentation of targets regionally, rather than globally, has the advantage of conveying their urgency better to individual countries.

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.

Much of Australia has been in severe drought since at least 2017. Here we link Australian droughts to the absence of Pacific and Indian Ocean mode states that act as key drivers of drought-breaking rains. Predicting the impact of climate change on drought requires accurate modelling of these modes of variability.

• Knowledge of how water stress impacts the carbon and water cycles is a key uncertainty in terrestrial biosphere models. • We tested a new profit maximization model, where photosynthetic uptake of CO₂ is optimally traded against plant hydraulic function, as an alternative to the empirical functions commonly used in models to regulate gas exchange during periods of water stress. We conducted a multi-site evaluation of this model at the ecosystem scale, before and during major droughts in Europe. Additionally, we asked whether the maximum hydraulic conductance in the soil–plant continuum k max (a key model parameter which is not commonly measured) could be predicted from long-term site climate. • Compared with a control model with an empirical soil moisture function, the profit maximization model improved the simulation of evapotranspiration during the growing season, reducing the normalized mean square error by c. 63%, across mesic and xeric sites. We also showed that k max could be estimated from long-term climate, with improvements in the simulation of evapotranspiration at eight out of the 10 forest sites during drought. • Although the generalization of this approach is contingent upon determining k max, it presents a mechanistic trait-based alternative to regulate canopy gas exchange in global models.

For over 40 years, Plant Functional Types (PFTs) have been used to discretize the ∼400,000 species of terrestrial plants into “similar” classes. Within Earth System Models (ESMs), PFTs simplify terrestrial biosphere modeling in combination with soil information and other site characteristics. However, in flux analysis studies, PFT schemes are often implemented as the sole analytical lens to clarify complex behavior. This usage assumes that PFTs adequately enable a mapping between climate inputs and flux outputs. Here, we show that random forest models, trained using aggregated climate and flux measurements from 245 eddy‐covariance sites, cannot accurately predict PFT groupings, regardless of the nature of the PFT scheme. Similarly, PFTs provide negligible benefit when using site climate to predict site flux regimes and vice versa. While use of PFT classifications is convenient, our results suggest they do not aid analytical skill, which has important implications for future terrestrial flux studies. Plain Language Summary To understand how the land surface behaves, we often divide plants into a small number (20 or less) of ”similar” groups, such as evergreen forests, or grasslands, known as Plant Functional Types (PFTs). The idea is that landscapes with similar large‐scale characteristics will behave in the same way. In land surface models, these PFT groups determine how the simulated plants react to the climate in combination with soil information and other characteristics, yet analysis of observations often use PFT groups alone to try to explain variations in results between different experimental sites. We use machine learning to show that while PFTs might be visually compelling, they do not necessarily represent behavior groupings and might actually hide real world behavior if used for analysis. As such, we suggest that future studies instead try to look at more specific site characteristics when trying to explain analysis results. Key Points Plant Functional Types (PFTs), as often used in land flux studies, are not easily empirically associated with site climate and/or flux regimes A broad selection of alternative vegetation/land cover classifications do not offer greater predictability The disconnect between PFTs and climate/flux regimes has implications for modeling and analysis of terrestrial systems

Land surface models (LSMs) used in climate models typically represent surface hydrology as one-dimensional vertical fluxes, neglecting the lateral movement of water within and between grids. It is assumed that lateral flow of water has a negligible impact on land surface states at climate modelling resolutions of a few tens of kilometres. However, with increases in model resolution, it may be necessary to include lateral flow in LSMs as satellite observations indicate the influence of this process on ecohydrological states, particularly in water limited regions. Lateral flow has not been modelled in Australia, but there is some evidence that this process exerts a dominant influence on vegetation variability in arid and semi-arid Australia. Here we use standalone WRF-Hydro simulations to quantify the influence of overland and shallow subsurface lateral flow on surface fluxes in southeast Australia, and the impact of model resolution on the results. We perform LSM simulations at 1, 4, and 10 km resolutions, with and without lateral flow, to assess the changes in evapotranspiration. Our results show that lateral flow increases evapotranspiration near major river channels in LSM simulations at 4 and 1 km resolutions, consistent with high-resolution observations. The largest changes occur in the warm season after a wet winter, with magnitudes of 50 % or more in some areas. However, the 1 km resolution simulations also exhibit a widespread pattern of drier ridges, different from the coarser resolutions. At 10 km resolution the increases in evapotranspiration are confined to the mountainous regions. Our results suggest that it may be necessary to include lateral flow in LSMs for improved simulations of droughts and future water availability at resolutions higher than 10 km.

Dams that serve a dual purpose of water supply and flood mitigation operate to maintain a defined full supply level of water that balances the two conflicting requirements. To optimize the use of available storage space, the full supply level may be adjusted to reflect changing risks of future water shortages and future flood inflows based on known seasonal variations and current observations. The Warragamba Dam in eastern Australia is located upstream of the populated Hawkesbury-Nepean valley which has one of the largest flood exposures in the country. However, the operating protocol of the reservoir does not include provisions to reduce the full supply level of the dam for flood mitigation. Large scale climate indicators that are known to influence the hydroclimate of this region may potentially contain useful information to inform the dual use of this reservoir, but their utility for this purpose has not been studied. Here we explore whether current observations of large-scale climate along with antecedent catchment conditions can be used to estimate the probability of large inflows into the reservoir in the next 3- and 6 months, to aid flood management. We find that the predictors have a substantial influence on the probability of large inflows. The probability differences during opposite predictor phases vary by season and range from 30% to 70%. Our results indicate that considering current climate information to inform dual use of the Warragamba dam has merit.

Australia frequently experiences severe and widespread droughts, causing impacts on food security, the economy, and human health. Despite this, recent research to comprehensively understand the past trends in Australian droughts is lacking. We analyse the past changes in seasonal-scale meteorological, agricultural, and hydrological droughts – defined using the 15th percentile threshold of precipitation, soil moisture, and runoff, respectively. We complement these traditional metrics with an impact-based drought indicator built from government drought reports using machine learning. Calculating trends in time and area under drought for the various drought types, we find that although there have been widespread decreases in Australian droughts since the early 20th century, extensive regions have experienced an increase in recent decades. However, these recent changes largely remain within the range of observed variability, suggesting that they are not unprecedented in the context of the historical drought events. The drivers behind these drought trends are multi-faceted, and we show that the trends can be driven by both mean and variability changes in the underlying hydrological variable. Additionally, using explainable machine learning techniques, we unpick the key hydrometeorological variables contributing to agricultural and hydrological drought trends. The influence of these variables varies considerably between regions and seasons, with precipitation often shown to be important but rarely the main driver behind observed drought trends. This suggests the need to consider multiple drivers when assessing drought trends.

Stomatal conductance links plant water use and carbon uptake and is a critical process for the land surface component of climate models. However, stomatal conductance schemes commonly assume that all vegetation with the same photosynthetic pathway use identical plant water use strategies whereas observations indicate otherwise. Here, we implement a new stomatal scheme derived from optimal stomatal theory and constrained by a recent global synthesis of stomatal conductance measurements from 314 species, across 56 field sites. Using this new stomatal scheme, within a global climate model, subtantially increases the intensity of future heatwaves across Northern Eurasia. This indicates that our climate model has previously been under-predicting heatwave intensity. Our results have widespread implications for other climate models, many of which do not account for differences in stomatal water-use across different plant functional types and hence, are also likely under projecting heatwave intensity in the future.

Stomatal conductance schemes that optimize with respect to photosynthetic and hydraulic functions have been proposed to address biases in land‐surface model (LSM) simulations during drought. However, systematic evaluations of both optimality‐based and alternative empirical formulations for coupling carbon and water fluxes are lacking. Here, we embed 12 empirical and optimization approaches within a LSM framework. We use theoretical model experiments to explore parameter identifiability and understand how model behaviors differ in response to abiotic changes. We also evaluate the models against leaf‐level observations of gas‐exchange and hydraulic variables, from xeric to wet forest/woody species spanning a mean annual precipitation range of 361–3,286 mm yr−1. We find that models differ in how easily parameterized they are, due to: (a) poorly constrained optimality criteria (i.e., resulting in multiple solutions), (b) low influence parameters, (c) sensitivities to environmental drivers. In both the idealized experiments and compared to observations, sensitivities to variability in environmental drivers do not agree among models. Marked differences arise in sensitivities to soil moisture (soil water potential) and vapor pressure deficit. For example, stomatal closure rates at high vapor pressure deficit range between −45% and +70% of those observed. Although over half the new generation of stomatal schemes perform to a similar standard compared to observations of leaf‐gas exchange, two models do so through large biases in simulated leaf water potential (up to 11 MPa). Our results provide guidance for LSM development, by highlighting key areas in need for additional experimentation and theory, and by constraining currently viable stomatal hypotheses. Plain Language Summary Water availability is critical for plants to maintain normal function, so droughts have considerable impact on natural ecosystems. However, predicting the impact of future drought on ecosystems is hard because current global models make systematic errors in their predictions of plant responses when water is scarce. In turn, uncertainty in the modeled terrestrial water and carbon cycles remains high. Here, we evaluate a range of new modeling approaches that have the capacity to mechanistically capture plant responses to water stress. Both in theoretical experiments and comparisons to observations, we find large differences among these new modeling approaches in response to water availability and atmospheric dryness. Importantly, some approaches achieve what seems like “good” performance through compensatory mechanisms that are not supported by observations and/or through incorrect representation of plant processes. Our results provide important guidance for future model development, by highlighting areas in need of continued research, and by constraining the range of approaches presently able to reduce uncertainty in modeled plant responses and suitable for inclusion in global models. Key Points Parameter identifiability differs among stomatal conductance schemes, implying some are more suitable to global modeling than others In some schemes, seemingly good performance can result from misrepresentation of physiological processes and sensitivities to model drivers We identify a subset of hydraulics‐based stomatal optimization approaches that could improve predictive capacity in novel climate spaces