(1) Background: Hong Kong is one of the most densely populated cities in the world, with millions of people exposed to severe air pollution. Surface ozone, mostly produced photochemically from anthropogenic precursor gases, is harmful to both humans and vegetation. The phytotoxicity of ozone has been shown to damage plant photosynthesis, induce early leaf death, and retard growth. (2) Methods: We use genotypes of bush bean Phaseolus vulgaris with various degrees of sensitivity to ozone to investigate the impacts of ambient ozone on the morphology and development of the beans. We use ozone-induced foliar injury index and measure the flowering and fruit production to quantify the ozone stress on the plants. (3) Results: We expected that the ozone-sensitive genotype would suffer from a reduction of yield. Results, however, show that the ozone-sensitive genotype suffers higher ozone-induced foliar damage as expected but produces more pods and beans and heavier beans than the ozone-resistant genotype. (4) Conclusions: It is postulated that the high ozone sensitivity of the sensitive genotype causes stress-induced flowering, and therefore results in higher bean yield. A higher than ambient concentration of ozone is needed to negatively impact the yield production of the ozone-sensitive genotype. Meanwhile, ozone-induced foliar damage shows a graduated scale of damage pattern that can be useful for indicating ozone levels. This study demonstrates the usefulness of bioindicators to monitor the phytotoxic effects of ozone pollution in a subtropical city such as Hong Kong.

Fires occurring over the peatlands in Indonesian Borneo accompanied by droughts have posed devastating impacts on human health, livelihoods, economy and the natural environment, and their prevention requires comprehensive understanding of climate‐associated risk. Although it is widely known that the droughts are associated with El Niño events, the onset process of El Niño and thus the drought precursors and their possible changes under the future climate are not clearly understood. Here, we use a causal network approach to quantify the strength of teleconnections to droughts at a seasonal timescale shown in observations and climate models. We portray two drivers of June‐July‐August (JJA) droughts identified through literature review and causal analysis, namely Niño 3.4 sea surface temperature (SST) in JJA (El Niño Southern Oscillation [abbreviated as ENSO]) and SST anomaly over the eastern North Pacific to the east of the Hawaiian Islands (abbreviated as Pacific SST) in March‐April‐May (MAM) period. We argue that the droughts are strongly linked to ENSO variability, with drier years corresponding to El Niño conditions. The droughts can be predicted with a lead time of 3 months based on their associations with Pacific SST, with higher SST preceding drier conditions. We find that under the SSP585 scenario, the Coupled Model Intercomparison Phase 6 (CMIP6) multi‐model ensembles show significant increase in both the maximum number of consecutive dry days in the Indonesian Borneo region in JJA (p = 0.006) and its linear association with Pacific SST in MAM (p = 0.001) from year 2061 to 2100 compared with the historical baseline. Some models are showing unrealistic amounts of JJA rainfall and underestimate drought risks in Indonesian Borneo and their teleconnections, owing to the underestimation of ENSO amplitude and overestimation of local convections. Our study strengthens the possibility of early warning triggers of fires and stresses the need for taking enhanced climate risk into consideration when formulating long‐term policies to eliminate fires.

The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) version 1.0 is described here. This version of the model simulates plant ecophysiological (e.g., photosynthetic and stomatal) responses to varying meteorological conditions and concentrations of CO₂ and ground-level ozone (O₃) based on prescribed meteorological and atmospheric chemical inputs from various sources. Driven by the same meteorological data used in the GEOS-Chem chemical transport model, this allows asynchronously coupled experiments with GEOS-Chem simulations with unique coherency for investigating biosphere–atmosphere chemical interactions. TEMIR agrees well with FLUXNET site-level gross primary productivity (GPP) in terms of both the diurnal and monthly cycles (correlation coefficients R²>0.85 and R²>0.8, respectively) for most plant functional types (PFTs). Grass and shrub PFTs have larger biases due to generic model representations. The model performs best when driven by local site-level meteorology rather than reanalyzed gridded meteorology. Simulation using gridded meteorology agrees well for annual GPP in seasonality and spatial distribution with a global average of 134 Pg C yr⁻¹. Application of Monin–Obukhov similarity theory to infer canopy conditions from gridded meteorology does not improve model performance, predicting an increase of +7 % in global GPP. Present-day O₃ concentrations simulated by GEOS-Chem and an O₃ damage scheme at high sensitivity show a 2 % reduction in global GPP with prominent reductions of up to 15 % in eastern China and the eastern USA. Regional correlations are generally unchanged when O₃ is present and biases are reduced, especially for regions with high O₃ damage. An increase in atmospheric CO₂ concentration of 20 ppmv from the level in 2000 to the level in 2010 modestly decreases O₃ damage due to reduced stomatal uptake, consistent with ecophysiological understanding. Our work showcases the utility of this version of TEMIR for evaluating biogeophysical responses of vegetation to changes in atmospheric composition and meteorological conditions.

The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) version 1.0 is described here. This version of the model simulates plant ecophysiological (e.g., photosynthetic, stomatal) responses to varying meteorological conditions and concentrations of CO₂ and ground-level ozone (O₃) based on prescribed meteorological and atmospheric chemical inputs from various sources. Driven by the same meteorological data used in the GEOS-Chem chemical transport model, this allows asynchronously coupled experiments with GEOS-Chem simulations with unique coherency for investigating biosphere-atmosphere chemical interactions. TEMIR agrees well with FLUXNET site-level gross primary productivity (GPP) in terms of both the diurnal and monthly cycles (correlation coefficients R² > 0.85 and R² > 0.8, respectively) for most plant functional types (PFTs). Grass and shrub PFTs have larger biases due to generic model representations. The model performs best when driven by local site-level meteorology rather than reanalyzed gridded meteorology. Simulation using gridded meteorology agrees well for annual GPP in seasonality and spatial distribution with a global average of 134 Pg C yr^–1. Application of Monin-Obukhov similarity theory to infer canopy conditions from gridded meteorology does not improve model performance, predicting a uniform increase of +21 % for global GPP. Present-day O₃ concentrations simulated by GEOS-Chem and an O₃ damage scheme at high sensitivity show a 2 % reduction in global GPP with prominent reductions by up to 15 % in eastern China and the eastern US. Regional correlations are generally unchanged when O₃ is present, and biases are reduced especially for regions with high O₃ damages. An increase in atmospheric CO₂ concentration by 20 ppmv from year-2000 to year-2010 level modestly decreases O₃ damage due to reduced stomatal uptake, consistent with ecophysiological understanding. Our work showcases the utility of this version of TEMIR for evaluating biogeophysical responses of vegetation to changes in atmospheric composition and meteorological conditions.