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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. 

Abstract Accurately predicting weather and climate in cities is critical for safeguarding human health and strengthening urban resilience. Multimodel evaluations can lead to model improvements; however, there have been no major intercomparisons of urban‐focussed land surface models in over a decade. Here, in Phase 1 of the Urban‐PLUMBER project, we evaluate the ability of 30 land surface models to simulate surface energy fluxes critical to atmospheric meteorological and air quality simulations. We establish minimum and upper performance expectations for participating models using simple information‐limited models as benchmarks. Compared with the last major model intercomparison at the same site, we find broad improvement in the current cohort's predictions of short‐wave radiation, sensible and latent heat fluxes, but little or no improvement in long‐wave radiation and momentum fluxes. Models with a simple urban representation (e.g., ‘slab’ schemes) generally perform well, particularly when combined with sophisticated hydrological/vegetation models. Some mid‐complexity models (e.g., ‘canyon’ schemes) also perform well, indicating efforts to integrate vegetation and hydrology processes have paid dividends. The most complex models that resolve three‐dimensional interactions between buildings in general did not perform as well as other categories. However, these models also tended to have the simplest representations of hydrology and vegetation. Models without any urban representation (i.e., vegetation‐only land surface models) performed poorly for latent heat fluxes, and reasonably for other energy fluxes at this suburban site. Our analysis identified widespread human errors in initial submissions that substantially affected model performances. Although significant efforts are applied to correct these errors, we conclude that human factors are likely to influence results in this (or any) model intercomparison, particularly where participating scientists have varying experience and first languages. These initial results are for one suburban site, and future phases of Urban‐PLUMBER will evaluate models across 20 sites in different urban and regional climate zones. 

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.