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Abstract. Elevated atmospheric CO2 concentration is expectedto increase leaf CO2 assimilation rates, thus promoting plant growthand increasing leaf area. It also decreases stomatal conductance, allowingwater savings, which have been hypothesized to drive large-scale greening,in particular in arid and semiarid climates. However, the increase in leafarea could reduce the benefits of elevated CO2 concentration through soilwater depletion. The net effect of elevated CO2 on leaf- andcanopy-level gas exchange remains uncertain. To address this question, wecompare the outcomes of a heuristic model based on the Partitioning ofEquilibrium Transpiration and Assimilation (PETA) hypothesis and three modelvariants based on stomatal optimization theory. Predicted relative changes in leaf-and canopy-level gas exchange rates are used as a metric of plant responsesto changes in atmospheric CO2 concentration. Both model approaches predictreductions in leaf-level transpiration rate due to decreased stomatalconductance under elevated CO2, but negligible (PETA) or no(optimization) changes in canopy-level transpiration due to the compensatoryeffect of increased leaf area. Leaf- and canopy-level CO2 assimilationis predicted to increase, with an amplification of the CO2fertilization effect at the canopy level due to the enhanced leaf area. Theexpected increase in vapour pressure deficit (VPD) under warmer conditions isgenerally predicted to decrease the sensitivity of gas exchange toatmospheric CO2 concentration in both models. The consistentpredictions by different models that canopy-level transpiration varieslittle under elevated CO2 due to combined stomatal conductancereduction and leaf area increase highlight the coordination ofphysiological and morphological characteristics in vegetation to maximizeresource use (here water) under altered climatic conditions. 

Temporal dynamics of urban warming have been extensively studied at the diurnal scale, but the impact of background climate on the observed seasonality of surface urban heat islands (SUHIs) remains largely unexplored. On seasonal time scales, the intensity of urban–rural surface temperature differences ($\mathrm{\Delta }{T}_s$) exhibits distinctive hysteretic cycles whose shape and looping direction vary across climatic zones. These observations highlight possible delays underlying the dynamics of the coupled urban–biosphere system. However, a general argument explaining the observed hysteretic patterns remains elusive. A coarse-grained model of SUHI coupled with a stochastic soil water balance is developed to demonstrate that the time lags between radiation forcing, air temperature, and rainfall generate a rate-dependent hysteresis, explaining the observed seasonal variations of$\mathrm{\Delta }{T}_s$. If solar radiation is in phase with water availability, summer conditions cause strong SUHI intensities due to high rural evaporative cooling. Conversely, cities in seasonally dry regions where evapotranspiration is out of phase with radiation show a summertime oasis effect controlled by background climate and vegetation properties. These seasonal patterns of warming and cooling have significant implications for heat mitigation strategies as urban green spaces can reduce$\mathrm{\Delta }{T}_s$during summertime, while potentially negative effects of albedo management during winter are mitigated by the seasonality of solar radiation.

 

Flooding impacts are on the rise globally, and concentrated in urban areas. Currently, there are no operational systems to forecast flooding at spatial resolutions that can facilitate emergency preparedness and response actions mitigating flood impacts. We present a framework for real‐time flood modeling and uncertainty quantification that combines the physics of fluid motion with advances in probabilistic methods. The framework overcomes the prohibitive computational demands of high‐fidelity modeling in real‐time by using a probabilistic learning method relying on surrogate models that are trained prior to a flood event. This shifts the overwhelming burden of computation to the trivial problem of data storage, and enables forecasting of both flood hazard and its uncertainty at scales that are vital for time‐critical decision‐making before and during extreme events. The framework has the potential to improve flood prediction and analysis and can be extended to other hazard assessments requiring intense high‐fidelity computations in real‐time.