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Crucial to plant development, ambient temperature triggers intricate mechanisms enabling adaptive responses to temperature variations. The precise coordination of chromatin modifications in shaping cell developmental fate under diverse temperatures remains elusive. Our study, integrating comprehensive transcriptome, epigenome profiling, and genetics, demonstrates that lower ambient temperature (16°C) partially restores developmental defects caused by H3K27me3 loss in prc2 mutants by specifically depositing H2A.Zub at ectopically expressed embryonic genes in Arabidopsis, such as ABA INSENSITIVE 3 (ABI3) and LEAFY COTYLEDON 1 (LEC1). This deposition leads to downregulation of these genes and compensates for H3K27me3 depletion. Polycomb-repressive complex 1 (PRC1)-catalyzed H2A.Zub and PRC2-catalyzed H3K27me3 play roles in silencing transcription of embryonic genes for post-germination development. Low-temperature-induced reduction of TOE1 protein level decelerates H2A.Z turnover at specific loci, sustaining repression of embryonic genes and alleviating requirement for PRC2-H3K27me3 at post-germination stage. Our findings offer mechanistic insights into the cooperative epigenetic layers, facilitating plant adaptation to varying environmental temperatures. 

Abstract Realistic simulation of leaf photosynthetic and respiratory processes is needed for accurate prediction of the global carbon cycle. These two processes systematically acclimate to long‐term environmental changes by adjusting photosynthetic and respiratory traits (e.g., the maximum photosynthetic capacity at 25°C (V_cmax,25) and the leaf respiration rate at 25°C (R₂₅)) following increasingly well‐understood principles. While some land surface models (LSMs) now account for thermal acclimation, they do so by assigning empirical parameterizations for individual plant functional types (PFTs). Here, we have implemented an Eco‐Evolutionary Optimality (EEO)‐based scheme to represent the universal acclimation of photosynthesis and leaf respiration to multiple environmental effects, and that therefore requires no PFT‐specific parameterizations, in a standard version of the widely used LSM, Noah MP. We evaluated model performance with plant trait data from a 5‐year experiment and extensive global field measurements, and carbon flux measurements from FLUXNET2015. We show that observedR₂₅andV_cmax,25vary substantially both temporally and spatially within the same PFT (C.V.>20%). Our EEO‐based scheme captures 62% of the temporal and 70% of the spatial variations inV_cmax,25(73% and 54% of the variations inR₂₅). The standard scheme underestimates gross primary production by 10% versus 2% for the EEO‐based scheme and generates a larger spread inr(correlation coefficient) across flux sites (0.79 ± 0.16 vs. 0.84 ± 0.1, mean ± S.D.). The standard scheme greatly overestimates canopy respiration (bias: ∼200% vs. 8% for the EEO scheme), resulting in less CO₂uptake by terrestrial ecosystems. Our approach thus simulates climate‐carbon coupling more realistically, with fewer parameters. 

Comprehending the impact of wildfire smoke on photovoltaic (PV) systems is of utmost importance in ensuring the dependability and consistency of power systems, particularly due to the growing prevalence of PV installations and the occurrence of wildfires. Nevertheless, this issue has not received extensive investigation within the current literature. A major obstacle in studying this phenomenon lies in accurately quantifying the impact of smoke. Conventional techniques such as aerosol optical depth (AOD) and PM 2.5 are inadequate for accurately assessing the influence of wildfire smoke on PV systems due to the complex interplay of smoke elevation, dynamics, and nonlinear effects on the solar spectral irradiance. To address this challenge, a new methodology is developed in this research that employs the optical properties of wildfire smoke. This approach utilizes the spectral response (SR) of PV devices to estimate the theoretical reduction in PV power output. The findings of this study enable precise measurement of the power output reduction caused by wildfire smoke for different types of PV cells. This newly devised method can be adopted for power system operation and planning to ensure the stability and reliability of power grids. Additionally, this study highlights the need to consider different PV cell technologies in regions at high risk of wildfires to minimize the power reduction caused by wildfire smoke. 

Accurate wildfire prediction in diverse and geographically dispersed areas is crucial for effective wildfire management. However, the limited availability of labeled data in data-challenged regions, along with the unique characteristics of these areas, poses challenges for training robust prediction models. This study investigates the performance of a convolutional neural network (CNN) on datasets comprising Landsat images from Canada and Alaska. Through principal component analysis (PCA), the study uncovers distinct differences in data distribution between the two regions. It is observed that the reduced data size of the Alaskan dataset, along with its distinct data distribution, leads to a decrease in the CNN's accuracy to 75% compared to an impressive 98% achieved on the Canadian dataset. To address this limitation, we propose a teacher-student model approach, transferring knowledge from a CNN trained on the larger Canadian dataset. The results demonstrate a significant accuracy improvement to 88.96% on the Alaskan dataset. Our findings highlight the effectiveness of the teacherstudent model in mitigating data scarcity challenges, enhancing wildfire prediction capabilities in regions with limited training data. This research contributes to improved wildfire monitoring and prevention strategies in challenging geographical locations.