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Soil volumetric water content ( V W C ) is a vital parameter to understand several ecohydrological and environmental processes. Its cost-effective measurement can potentially drive various technological tools to promote data-driven sustainable agriculture through supplemental irrigation solutions, the lack of which has contributed to severe agricultural distress, particularly for smallholder farmers. The cost of commercially available V W C sensors varies over four orders of magnitude. A laboratory study characterizing and testing sensors from this wide range of cost categories, which is a prerequisite to explore their applicability for irrigation management, has not been conducted. Within this context, two low-cost capacitive sensors—SMEC300 and SM100—manufactured by Spectrum Technologies Inc. (Aurora, IL, USA), and two very low-cost resistive sensors—the Soil Hygrometer Detection Module Soil Moisture Sensor (YL100) by Electronicfans and the Generic Soil Moisture Sensor Module (YL69) by KitsGuru—were tested for performance in laboratory conditions. Each sensor was calibrated in different repacked soils, and tested to evaluate accuracy, precision and sensitivity to variations in temperature and salinity. The capacitive sensors were additionally tested for their performance in liquids of known dielectric constants, and a comparative analysis of the calibration equations developed in-house and provided by the manufacturer was carried out. The value for money of the sensors is reflected in their precision performance, i.e., the precision performance largely follows sensor costs. The other aspects of sensor performance do not necessarily follow sensor costs. The low-cost capacitive sensors were more accurate than manufacturer specifications, and could match the performance of the secondary standard sensor, after soil specific calibration. SMEC300 is accurate ( M A E , R M S E , and R A E of 2.12%, 2.88% and 0.28 respectively), precise, and performed well considering its price as well as multi-purpose sensing capabilities. The less-expensive SM100 sensor had a better accuracy ( M A E , R M S E , and R A E of 1.67%, 2.36% and 0.21 respectively) but poorer precision than the SMEC300. However, it was established as a robust, field ready, low-cost sensor due to its more consistent performance in soils (particularly the field soil) and superior performance in fluids. Both the capacitive sensors responded reasonably to variations in temperature and salinity conditions. Though the resistive sensors were less accurate and precise compared to the capacitive sensors, they performed well considering their cost category. The YL100 was more accurate ( M A E , R M S E , and R A E of 3.51%, 5.21% and 0.37 respectively) than YL69 ( M A E , R M S E , and R A E of 4.13%, 5.54%, and 0.41, respectively). However, YL69 outperformed YL100 in terms of precision, and response to temperature and salinity variations, to emerge as a more robust resistive sensor. These very low-cost sensors may be used in combination with more accurate sensors to better characterize the spatiotemporal variability of field scale soil moisture. The laboratory characterization conducted in this study is a prerequisite to estimate the effect of low- and very low-cost sensor measurements on the efficiency of soil moisture based irrigation scheduling systems.

A new compilation of the complex refractive index of liquid water is presented, spanning temperatures from 240$240$(near homogeneous freezing) to 300$300$K and wavelengths from 0.034$0.034$μm to 10 m. The real part of the refractive index is derived using the Kramers–Kronig relation, where the imaginary part is constrained by measurements reported in literature and validated through the f‐sum rule. The result reveals a significant temperature dependence, especially at wavelengths beyond the near‐infrared. Sensitivity analyses in the infrared split‐window and microwave spectral regime demonstrate substantial differences in bulk optical properties between supercooled and ambient conditions. These findings manifest the importance of accounting for temperature‐dependent refractive indices in optical radiative transfer and simulations.

Temperature-sensitive paint (TSP) can optically measure a global temperature distribution using a thermal quenching of dye molecules. The TSP measurement is often used in wind tunnel tests to measure the temperature and flow fields in the aerodynamic field. The measurement accuracy is affected by the characteristics of TSP, such as temperature sensitivity, pressure dependency, luminescent intensity, photostability, and surface condition. The characteristics depend on the formulation of TSP. This study investigates the characteristics of the TSP using dichlorotris (1,10-phenanthroline) ruthenium(II) hydrate (Ru-phen). We compare the characteristics of TSPs using different polymers, solvents, and dye concentrations. The TSPs using polyacrylic acid as a polymer shows linear calibration curves, high luminescent intensity, high photostability, and smooth surface. On the other hand, the TSPs using polymethyl methacrylate have nonlinear calibration curves, low luminescent intensity, strong photodegradation, and a rough surface.

Surface ozone (O3) has complex relationships with its precursors and is also highly sensitive to meteorological variation and climate change. In China, ground‐level ozone pollution remains a persistent air quality concern despite decreasing concentrations of other air pollutants in recent years. China's commitment to achieving carbon neutrality by 2060 is expected to result in unprecedented reductions in air pollutant emissions in the future. This study investigates the combined impacts of anthropogenic emission reductions and future climate change on the evolution of summertime surface O3 under China's carbon neutrality target and the ambitious global 2°C warming scenario. Model simulations reveal an approximately 43% decline (range 31%–49%) in summertime daily maximum 8‐hr average (MDA8) O3 in China's six heavily polluted key regions from 2020 to 2060. However, risk of rebound is also projected in some near years due to weather‐driven accelerated O3 production rate, enhanced biogenic volatile organic compound (VOC) emissions and atmospheric stagnation, partially offsetting emission reduction benefits. The substantial aerosol reductions (by over 80%) would also enhance MDA8 O3 (up to 10 ppb) from 2020 to 2060 primarily via heterogeneous reactions on aerosols. The high O3‐temperature sensitivity poses challenges to O3 mitigation in the short term, with frequent heatwaves or droughts dampening the outcomes of ongoing anthropogenic emission control. In the long term, O3‐temperature sensitivity would be reduced by nearly half thanks to continuous anthropogenic emission control, thereby gradually increasing O3 climatic resilience. Quicker and stronger emission control, especially for the anthropogenic VOCs, would significantly mitigate short‐term rebound risks. Plain Language Summary Ground‐level ozone forms when gases from vehicles, factories, and plants react in warm and sunny weather. Despite progress on mitigating other air pollutants in China, ozone pollution remains challenging to control due to non‐linear effect of its precursors, and impact of particulate matters and extreme weather. China's pledge to achieve carbon neutrality by 2060 will drastically cut human‐made pollutant emissions. Our study projects that this would reduce peak summer ozone levels by about 43% in major cities by 2060. However, in the nearer future, ozone might temporarily increase in some areas due to increased occurrence of heatwaves and stagnant weather, which speeds up chemical reaction rates, increases plants' release of gases, and traps pollutants, offsetting early pollution control gains. This strong link between heat and ozone makes short‐term control harder. Quicker, deeper cuts to gases from human activities, especially industry and vehicles, are needed to prevent this rebound. Key Points Emission cuts by pollution control and carbon neutrality policies would reduce MDA8 ozone by 31%–49% in China's key polluted regions by 2060 In the short term, ozone may rebound or decline more slowly due to climate‐driven accelerated ozone formation Ozone‐temperature sensitivity would be reduced by half from 2020 to 2060 due to continuous anthropogenic emission control

In order to investigate the thermal effect of a servo axis’ positioning error on the accuracy of machine tools, an empirical modeling method was proposed, which considers both the geometric and thermal positioning error. Through the analysis of the characteristics of the positioning error curves, the initial geometric positioning error was modeled with polynomial fitting, while the thermal positioning error was built with an empirical modeling method. Empirical modeling maps the relationship between the temperature points and thermal error directly, where the multi-collinearity among the temperature variables exists. Therefore, fuzzy clustering combined with principal component regression (PCR) is applied to the thermal error modeling. The PCR model can preserve information from raw variables and eliminate the effect of multi-collinearity on the error model to a certain degree. The advantages of this modeling method are its high-precision and strong robustness. Experiments were conducted on a three-axis machine tool. A criterion was also proposed to select the temperature-sensitivity points. The fitting accuracy of the comprehensive error modeling could reach about 89%, and the prediction accuracy could reach about 86%. The proposed modeling method was proven to be effective and accurate enough to predict the positioning error at any time during the machine tool operation.

Summary •  Near‐isogenic Brassica napus lines carrying/lacking resistance gene Rlm6 were used to investigate the effects of temperature and leaf wetness duration on phenotypic expression of Rlm6‐mediated resistance. •  Leaves were inoculated with ascospores or conidia of Leptosphaeria maculans carrying the effector gene AvrLm6. Incubation period to the onset of lesion development, number of lesions and lesion diameter were assessed. Symptomless growth of L. maculans from leaf lesions to stems was investigated using a green fluorescent protein (GFP) expressing isolate carrying AvrLm6. •  L. maculans produced large grey lesions on Darmor (lacking Rlm6) at 5–25 °C and DarmorMX (carrying Rlm6) at 25°C, but small dark spots and ‘green islands’ on DarmorMX at 5–20°C. With increasing temperature/wetness duration, numbers of lesions/spots generally increased. GFP‐expressing L. maculans grew from leaf lesions down leaf petioles to stems on DarmorMX at 25°C but not at 15°C. •  We conclude that temperature and leaf wetness duration affect the phenotypic expression of Rlm6‐mediated resistance in leaves and subsequent L. maculans spread down petioles to produce stem cankers.

The respiratory release of carbon dioxide (CO₂) from soil is a major yet poorly understood flux in the global carbon cycle. Climatic warming is hypothesized to increase rates of soil respiration, potentially fueling further increases in global temperatures. However, despite considerable scientific attention in recent decades, the overall response of soil respiration to anticipated climatic warming remains unclear. We synthesize the largest global dataset to date of soil respiration, moisture, and temperature measurements, totaling >3,800 observations representing 27 temperature manipulation studies, spanning nine biomes and over 2 decades of warming. Our analysis reveals no significant differences in the temperature sensitivity of soil respiration between control and warmed plots in all biomes, with the exception of deserts and boreal forests. Thus, our data provide limited evidence of acclimation of soil respiration to experimental warming in several major biome types, contrary to the results from multiple single-site studies. Moreover, across all nondesert biomes, respiration rates with and without experimental warming follow a Gaussian response, increasing with soil temperature up to a threshold of ∼25 °C, above which respiration rates decrease with further increases in temperature. This consistent decrease in temperature sensitivity at higher temperatures demonstrates that rising global temperatures may result in regionally variable responses in soil respiration, with colder climates being considerably more responsive to increased ambient temperatures compared with warmer regions. Our analysis adds a unique cross-biome perspective on the temperature response of soil respiration, information critical to improving our mechanistic understanding of how soil carbon dynamics change with climatic warming.

The response of soil organic carbon (SOC) decomposition to global warming is a potentially major source of uncertainty in climate prediction. However, the magnitude and direction of SOC cycle feedbacks under climate warming remain uncertain because of the knowledge gap about the global‐scale spatial pattern and temperature sensitivity (Q10) mechanism of SOC decomposition. Here, we collected data of Q10 and corresponding soil variables from 81 peer‐reviewed papers using laboratory incubation to explore how Q10 varied among different ecosystems at the global scale and whether labile and recalcitrant SOC pools had equal Q10 values. Q10 with a global average of 2.41 substantially varied among different ecosystems, ranging from the highest in cropland soils (2.76) and the lowest in wetland soils (1.84). Hump‐shaped correlations of Q10 values with the maximum at SOC = 190 g/kg and the minimum at clay = 37% were observed. However, the main influencing factors of Q10 differed among various ecosystems. Q10 values showed a clear decrease with increasing incubation temperature but no significant decrease above 25°C. In general, labile SOC was less sensitive than recalcitrant SOC to warming. Structural equation model analyses showed that total N and SOC accounted for 53% and 46%, respectively, of the variation in Q10 of labile SOC and recalcitrant SOC. This finding suggested that Q10 values of labile and recalcitrant SOC pools had different controlling factors. Our findings highlighted the importance of Q10’s variations in ecosystem types and the response of recalcitrant SOC to warming in predicting the soil C cycling and its feedback to climate change. Therefore, ecosystem type and difference in Q10 of labile and recalcitrant SOC should be considered to precisely predict the soil C dynamics under global warming. A plain language summary is available for this article. Plain Language Summary

Plant respiration constitutes a massive carbon flux to the atmosphere, and a major control on the evolution of the global carbon cycle. It therefore has the potential to modulate levels of climate change due to the human burning of fossil fuels. Neither current physiological nor terrestrial biosphere models adequately describe its short-term temperature response, and even minor differences in the shape of the response curve can significantly impact estimates of ecosystem carbon release and/or storage. Given this, it is critical to establish whether there are predictable patterns in the shape of the respiration–temperature response curve, and thus in the intrinsic temperature sensitivity of respiration across the globe. Analyzing measurements in a comprehensive database for 231 species spanning 7 biomes, we demonstrate that temperature-dependent increases in leaf respiration do not follow a commonly used exponential function. Instead, we find a decelerating function as leaves warm, reflecting a declining sensitivity to higher temperatures that is remarkably uniform across all biomes and plant functional types. Such convergence in the temperature sensitivity of leaf respiration suggests that there are universally applicable controls on the temperature response of plant energy metabolism, such that a single new function can predict the temperature dependence of leaf respiration for global vegetation. This simple function enables straightforward description of plant respiration in the land-surface components of coupled earth system models. Our cross-biome analyses shows significant implications for such fluxes in cold climates, generally projecting lower values compared with previous estimates.

As a key macrophysical property, cloud horizontal scale plays a critical role in cloud radiative effect (CRE), precipitation and convective structures. Until now, however, how cloud scales vary with surface temperature anomaly and their subsequent impacts on CRE and precipitation remains unclear. This study fills the knowledge gap utilizing active satellite observations and finds cloud‐scale temperature sensitivity is strongly dependent on cloud height. Specifically, percentage of small‐scale clouds (<50 km) significantly decreases at low altitude but increases at high altitude, implying overall rising cloud height. The variations in low‐level small‐scale clouds account for 56.1% and 42.6% of the weakened shortwave cooling and longwave warming, while large‐scale clouds (>100 km) contribute insignificantly. Notably, robust increases in precipitation intensity occur only for low‐level large‐scale clouds, with 15.2% explained by decreased cloud optical depth, while precipitation percentage increases by 0.1%–0.5% K−1 across scales. Our results provide valuable observational constraints on cloud feedbacks.