Acute intermittent hypoxia (AIH) elicits plasticity in multiple respiratory motor pools and is a promising therapeutic approach to restore breathing ability in individuals with neuromuscular injury/disease. In anesthetized rats, diurnal cycle affects the magnitude and mechanism of moderate AIH (arterial partial pressure of oxygen [Formula: see text] ∼40-50 mmHg) induced phrenic motor plasticity in an AIH protocol-specific manner. However, diurnal cycle and AIH protocol effects on ventilatory long-term facilitation (vLTF) in unanesthetized rats have not been reported. Measurements of vLTF assess the collective physiological impact of plasticity in all respiratory motor pools and control for the effects of anesthesia, paralysis, and vagotomy common in phrenic LTF studies. Using 2 AIH protocols consisting of 15, 1-min (15x1; [Formula: see text] = 0.09) or 3, 5-min (3x5; [Formula: see text] = 0.105) moderate hypoxic episodes, we tested the hypothesis that diurnal cycle regulates AIH-induced vLTF in unanesthetized male Sprague-Dawley rats. Minute ventilation (V̇e), tidal volume (VT), breathing frequency (fR), and metabolic CO2 production (V̇co2) were assessed via whole-body plethysmography during mid-rest (light) and mid-active (dark) phases. Since V̇e and VT correlate strongly with V̇co2 across the rest/active cycle, and AIH decreases V̇co2, we normalized V̇e and VT to V̇co2 (V̇e/V̇co2 and VT/V̇co2) to account for changes in ventilation linked to metabolism. We found that 15x1 AIH elicits greater vLTF in mid-rest, whereas 3x5 AIH is more effective in mid-active phase, indicating that diurnal cycle regulates respiratory motor plasticity in an AIH protocol-specific manner in unanesthetized rats. Diurnal cycle is an important consideration as we translate knowledge based on nocturnal rodents to diurnal humans with neuromuscular injury/disease.NEW & NOTEWORTHY Although acute intermittent hypoxia (AIH) elicits phrenic motor plasticity in a manner influenced by diurnal cycle and the specific AIH protocol used, it is unknown how diurnal cycle and AIH protocol regulate AIH-induced ventilatory long-term facilitation (vLTF) in unanesthetized rodents. We report that shorter hypoxic episodes elicit greater vLTF in mid-rest phase, whereas longer episodes are favorable in mid-active phase. Thus, diurnal cycle and AIH protocol must be considered in studies of respiratory motor plasticity.

ABSTRACT Global temperature rises and frequent heat waves increasingly threaten rice production by destabilising canopy and root‐zone microclimates. Natural or conventional airflow management techniques often fail to provide precise, repeatable thermal regulation. In this study, we directly compared rotors airflow‐engineered microclimates with ambient airflows across diurnal cycles (9:00 a.m., 12:00 p.m., 3:00 p.m.) and rice growth stages (heading, panicle, flowering). A rotor‐based Wind Wall (WW) system was deployed to simulate rotors‐induced airflow under controlled conditions, and computational fluid dynamics (CFD) validated rotor array performance, turbulence distribution, and wind field uniformity. Relative to ambient airflow, rotors airflow reduced canopy‐top temperature variance by up to 48%, maintained peak midday temperature gradients at 0.11°C, and stabilised afternoon gradients to 0.06°C. Mid‐canopy wind speed under rotors airflow decreased from 1.817 m s −1 to 0.446 m s −1 (−75.4%) at noon but rebounded to 0.843 m s −1 (+89%) by 3:00 p.m., improving midday canopy stability. Turbulence intensity remained moderate (0.355–0.390), enhancing canopy aeration and gas exchange, while wind shear across plant layers stabilised between −0.12 and 3.91 s −1 . Physiologically, rotors airflow‐engineered microclimates improved photosynthetic efficiency by 18%, reduced midday root‐zone temperature variability by 33%, and decreased water loss by 14% compared with ambient conditions. Grain yield at flowering reached 43.2 g plant −1 , a 91% increase over restricted airflow and 23% higher than ambient airflow, with the harvest index rising to 37.24% (+14.8%). Across all growth stages and times of day, rotors‐induced airflow consistently mitigated thermal stress, stabilised microclimate conditions, and enhanced nutrient uptake, resulting in more uniform grain filling and superior yield performance. These findings highlight UAV‐based microclimate engineering as a precise and scalable strategy for controlling plant thermal and aerodynamic environments, offering a viable approach to climate change adaptation in rice production systems.

Precision irrigation is essential for sustainable agriculture under increasing water scarcity. This study compared regression and ensemble learning algorithms for forecasting irrigation requirements in sweet potato, a crop characterized by high variability in water demand. An Internet of Things (IoT)-based prototype was deployed to collect real-time data on soil moisture, temperature, humidity, light intensity, and atmospheric pressure over 42 hours and 50 minutes (August 4-5, 2025), encompassing two complete diurnal cycles at 10-minute intervals and yielding 243 temporal observations. Following preprocessing and feature engineering with lag-based temporal features, the final dataset comprised 240 samples (192 training, 48 testing) using chronological time-based splitting to prevent data leakage. Five algorithms, Support Vector Regression (SVR), AdaBoost, Extreme Gradient Boosting (XGBoost), Random Forest Regressor (RFR), and CatBoost, were evaluated under default and hyperparameter-tuned configurations using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) as evaluation metrics. Tuned Random Forest achieved superior performance (R² = 0.9802, RMSE = 9.58, MAE = 6.08), followed by default Random Forest (R² = 0.9786) and default CatBoost (R² = 0.9687). XGBoost demonstrated strong performance (R² = 0.9670 tuned) but exhibited overfitting tendencies with near-perfect training scores. SVR improved substantially after tuning (R² = 0.328 to 0.797), although it remained inferior to ensemble methods. Overall, ensemble methods, particularly XGBoost and Random Forest, demonstrated superior efficacy for sweet potato irrigation forecasting. These findings underscore the potential of IoT-integrated machine learning to enhance water-use efficiency and support sustainable smart farming practices.

The planetary boundary layer (PBL) exerts strong control on heat, moisture, and momentum exchange, yet its representation over the steep mountains and deep valleys of Liangshan remains poorly understood. This study evaluates six Weather Research and Forecasting (WRF) PBL schemes (ACM2, BL, MYJ, MYNN2.5, QNSE, and YSU) using multi-source observations from radiosondes, surface stations, and wind profiling radar during clear-sky dry-season cases in spring and winter. The schemes exhibit substantial differences in governing turbulent mixing and stratification. For the specific cases studied, QNSE best reproduces 2 m temperature in both seasons by realistically capturing nocturnal stability and large diurnal ranges, while non-local schemes overestimate nighttime temperatures due to excessive mixing. MYNN2.5 performs robustly for boundary layer growth in spring, and BL aligns most closely with radar-derived PBL height (PBLH). Vertical profile comparisons show that QNSE and MYJ better represent the lower–middle level thermodynamic structure, whereas all schemes underestimate extreme near-surface winds, reflecting unresolved terrain-induced variability. PBLH simulations reproduce diurnal cycles but differ in amplitude, with QNSE occasionally producing unrealistic spikes. Overall, no scheme performs optimally for all variables. However, QNSE and MYNN2.5 show the most balanced performance across seasons. These findings provide guidance for selecting PBL schemes for high-resolution modeling and fire–weather applications over complex terrain.

Estimating biomass burning emissions remains challenging due to both the substantial spatiotemporal variability of fires and the inherent uncertainties associated with the limited overpass frequency of polar-orbiting satellites. Integrating geostationary (5–10 min, 2 km) and polar-orbiting (twice daily, 375 m) satellite observations provides a detailed characterization of active fire diurnal cycles. However, the conventional unimodal Gaussian approximation (Pol/Geo-Uni), commonly used in fire emission models, fails to accurately reproduce the diurnal patterns. This study systematically analyzed the seasonal and diurnal variation in different types of active fires (AFs) and fire radiative power (FRP) across North America (GOES-16, ABI) and East Asia (Himawari-8, AHI). In North America, forest and savanna fires exhibited high FRP and a pronounced bimodal diurnal cycle lasting 4–8 h, whereas the corresponding fire types in East Asia exhibited a shorter, unimodal pattern of 2–4 h. Agricultural fires in East Asia were predominantly small in scale with low FRP, and frequently occurred at night. We used a modified Gaussian function to estimate dry matter burned (DMB), quantitating regional emission impacts for different fire types. The fused product (VIIRS/ABI-Bi) yielded amounts of DMB in North America that was 1.22 and 1.24 times higher than that from VIIRS/ABI-Uni and GFASv1.2, respectively. In East Asia, VIIRS/AHI-Bi DMB exceeded those from VIIRS/AHI-Uni and GFASv1.2 by 1.08 and 0.94 times, with agricultural fire estimates during the fire season being 1.18–1.62 times higher. This increase was notably pronounced in eastern China, where VIIRS/AHI-Bi DMB reached 1.76 to 9.77 times higher than estimates from VIIRS/AHI-Uni, GFED5, GFED4.1s, and GFASv1.2. Overall, integrating high spatiotemporal resolution satellite fire products with regionally diurnal models can substantially improve emission estimates, particularly for frequent, small-scale fire events.

Fine particulate matter (PM1 diameter <1 μm) strongly affects air quality and health, with sulfate, nitrate, and ammonium (SNA) as major components across East Asia. Because NH3 is a key precursor of SNA formation, accurate NH3 emission data are essential for reliable SNA simulations. This study improves NH3 emission inventories in East Asia by integrating the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the Cross-Track Infrared Sounder (CrIS), with extensive evaluation against ground-based and aircraft observations from the Korea-United States Air Quality (KORUS-AQ) campaign. Iterative linear inversion of NH3 emissions using WRF-Chem markedly enhances simulations of nitrate and ammonium, in agreement with both the aircraft and surface measurements. The absolute biases of nitrate and ammonium were reduced from 89.7 and 50.1% to 13.6 and 0.6%, respectively, compared to the aircraft observations over Seoul. However, uncertainties in nocturnal NH3 emissions remain potential sources of nighttime biases in nitrate and ammonium. Overall, the results indicate that nitrate aerosol in most of East Asia is sensitive to NH3 emission changes. To advance our understanding of SNA formation and support effective aerosol mitigation policies, improved characterization of the diurnal cycle of NH3 emissions through ground-based monitoring and high-resolution geostationary satellite observations is urgently needed.

Sea Surface Skin Temperature (SSTskin) derived from satellites and its diurnal variation are crucial for climate research, yet conventional ocean models, which primarily solve for the foundation or bulk SST, are not designed to simulate the very thin skin layer temperature (SSTskin). Consequently, specialized parameterizations or coupled model components are often required to obtain SSTskin. This study aimed to capture SSTskin diurnal warming events and evaluate the performance of the improved one-dimensional mixed-layer model (PWP: Price-Weller-Pinkel) in simulating SSTskin. Using high-frequency Himawari-8 satellite observations, a typical diurnal warming event was detected in the coastal waters off northwestern Australia, with the maximum SSTskin diurnal variation reaching 3 °C. The reliability of Himawari-8 data was validated using iQuam in situ observations, showing a mean bias of −0.28 °C. The improved PWP model (incorporating an SSTskin parameterization scheme), forced by ERA5 datasets, was used to simulate SSTskin and its diurnal variation at 90 (0.25° × 0.25°) grid points. Results indicated that the PWP model reproduced the diurnal variation cycle consistently with observations, accurately matched regions with significant warming, and achieved a mean bias of −0.37 °C. However, in low-wind-speed areas (&lt;1 m/s), abnormal SSTskin overestimation (&gt;3 °C) occurred due to rapid thinning of the mixed layer and the absence of horizontal diffusion in this one-dimensional model. The improved PWP model, with its relatively stable SSTskin parameterization scheme, provides a computationally efficient tool for studying vertical processes in the upper ocean. Future work should evaluate vertical mixing schemes under low wind speed conditions to enhance the capability of numerical models to simulate SSTskin.

Mitotic division is the process of cell growth and division that allows a plant to develop from a single cell into a mature plant. This study presents several aspects regarding mitotic activity in some pea genotypes cultivated on chernozem soil at SCDA Caracal. The mitotic index can fluctuate throughout the day, often showing peaks at certain times due to daily rhythms in cell division activity. The mitotic activity in pea root meristems was variable, and indicate values ranging from 4.6% to 8.9%, depending on the genotype and time. Thus, the most intense mitotic activity was recorded around 8 am, while the lowest value was recorded at 2 pm. The increased mitotic activity in the morning indicates that cell division is most active during this period, potentially in response to light or other diurnal cycles. The lowest rate of cell division at 2 pm suggests a period of relative dormancy for the meristematic cells. The observed pattern is typical for plants, as many biological processes, including cell division, are regulated by a circadian rhythm. Factors like light and temperature fluctuations can also play a role in regulating this rhythm. The mitotic index can be used to assess the growth potential of different pea genotypes.

NO, NO2, and O3 were measured for 1 year at a suburban site in the southeast Mediterranean. NO preserved no seasonality, but significant seasonal variations were obtained for NO2 and O3. These pollutants exhibited inverse trends with higher NO2 levels measured during wintertime, whilst higher O3 levels were measured during summertime. Photochemistry was the primary reason for the opposing variations in both pollutants, although O3 levels were frequently increased due to O3-rich plumes travelling from northeast Europe, highlighting the impact of regional contributions in the measured concentrations. Nevertheless, anthropogenic sources were identified and contributed to both NO and NO2. Diurnal variations analysis showed that NO increased usually in the early morning and was linked with primary emissions from traffic. NO2 increased simultaneously with NO in the early morning, and besides primary vehicle emissions, it was associated with secondary formation from the emitted NO. Moreover, a significant contribution from domestic heating emissions on NO2 was identified in the late evening during wintertime. Overall, a relative burden of weekdays was associated with NO (morning rush hours) and NO2 (morning rush hours, evening), whereas weekends were burdened by O3 due to the weekend effect. Comparison with European Union air quality standards showed that NO2 was considerably lower than the limit values, but a significant number of exceedances were identified for O3, especially during the warmer months. This finding suggested the relative burden of the study site from O3. In conclusion, NO at the study site was influenced by primary traffic emissions, whereas NO2 had both primary and secondary contributions, and together with photochemistry, both pollutants governed O3 diurnal and seasonal cycles.

This study presents the results of a seven-year investigation (2018–2024) into the migration patterns of thrushes (family Turdidae) under the conditions of the polar day’s termination at the «Varlam Island Station» in the Pasvik Nature Reserve (Murmansk Region). Birds were captured from July to September using ornithological nets, deployed either during morning hours or, in 2024, around the clock with the aid of acoustic lures to attract migrating individuals. We analyzed species occurrence in captures and the relationship between migration intensity and time in pre-dawn and post-dawn periods. A total of 618 birds representing seven species were captured and ringed during the study. Data analysis revealed that migratory activity of thrushes peaked in late August to early September. Chi-square values confirmed the statistical significance of these peaks: 351.8 (p &lt; 0.001) for the Bluethroat (Luscinia svecica), 41.0 (p &lt; 0.001) for the Common Redstart (Phoenicurus phoenicurus), and 36.4 (p &lt; 0.001) for the Redwing (Turdus iliacus). In contrast, the Song Thrush (Turdus philomelos) showed no statistically significant peak (χ² = 14.6, p = 0.12). The study demonstrated that Bluethroats and Redstarts exhibited a propensity for post-dawn migration, supported by correlation coefficients of Ks = 0.85 (p &lt; 0.05) and Ks = 0.79 (p &lt; 0.05), respectively. This suggests these species are less dependent on the dark phase of the night, enabling migration throughout the diurnal cycle. Conversely, Song Thrushes and Redwings adhered more strictly to nocturnal migration, though their circadian rhythms appeared disrupted under the short-night conditions of the study period. This likely necessitates adaptation to alternative navigation mechanisms, potentially independent of celestial compass systems.

The limiting factors of tree recovery from drought, particularly the coordination between carbon sources and sinks, remain poorly understood. In this study, juvenile Douglas fir (Pseudotsuga menziesii) were exposed to 28 days of mild or severe drought, followed by 35 days of recovery. We continuously monitored CO₂ and H₂O fluxes in shoots and roots to derive gas exchange and carbon accumulation, while measuring basal area to estimate stem growth and sapwood development. To identify underlying mechanisms of drought recovery, we periodically measured nonstructural carbohydrates (NSC), midday water potential (Ψmd), and foliar abscisic acid (ABA). We found no evidence that ABA or Ψmd limited gas exchange recovery, with stomatal conductance recovery instead related to drought-induced reductions in sapwood development. While carbon accumulation ultimately recovered to control levels following mild stress, severe stress caused persistent impairments, ultimately reducing carbon accumulation by 51%, with stem growth similarly affected. We found no evidence of growth being limited by NSC, which remained abundant. However, we suggest that drought-induced limitations to stem development govern this pattern. This became clear when considering the diurnal growth cycle, where daytime growth was largely absent in trees after exposure to severe drought despite accounting for up to 30% of total growth in control trees. Daytime growth appeared to depend on sufficient sapwood area, which likely buffered xylem tension to support growth conditions. Our findings suggest drought-induced reductions of stem hydraulic development constrain the recovery of gas exchange and growth. Further, altered diurnal growth patterns may explain prolonged productivity declines in forests following drought.

Abstract This study examines the genesis of atmospheric bores near sunset and their role in initiating and maintaining late‐afternoon mesoscale convective systems (MCSs) over the Southern North China Plain. A key finding is that 20% of documented bore occur during the sunset period. These bores form in the local environment preconditioned by the convective outflows from late afternoon MCSs, or by sea breezes that peak at this time. These bores aid in initiating and maintaining afternoon convection, subsequently favoring the development of nocturnal MCSs and associated bores. While bore research often concentrates on their impact on nocturnal MCSs, this study highlights that accurately predicting the diurnal cycle of MCSs requires properly representing bores. Thus, it helps address the long‐standing challenge of simulating the diurnal cycle of convective rainfall in weather and climate models.

Abstract. Radiocarbon (14CO2) observations are the benchmark for quantifying fossil fuel CO2 (ffCO2) in the atmosphere, but continuous 14CO2 measurements are not yet available. Continuous estimates of ffCO2 can be made by observing continuously measurable proxies that are co-emitted during fossil fuel combustion. This paper investigates the potential and challenges of using in situ NOx observations in urban areas to quantitatively estimate hourly ffCO2 enhancements, in the example of the ICOS pilot station in Heidelberg, Germany. The short atmospheric lifetime of NOx limits the use of the observed signal to a local area. Thus, a local NOx and ffCO2 background was approximated using the Stochastic Time-Inverted Lagrangian Transport (STILT) model and bottom-up emission estimates from the Netherlands Organisation for Applied Scientific Research (TNO). Using 14CO2 data from 185 hourly integrated flask samples between 2020–2021, mean ratios of local excess NOx (ΔNOx) to local excess ffCO2 (ΔffCO2) of 1.40 ppb ppm−1 for winter and 2.12 ppb ppm−1 for summer were calculated. These ratios were applied to the ΔNOx time series to construct continuous ΔffCO2 estimates. The uncertainty of the ΔNOx-based ΔffCO2 record was estimated at 3.94 ppm. Comparisons with 14CO2-based and ΔCO-based ΔffCO2 estimates showed good agreement, while still demonstrating distinct behaviour for individual events. ΔNOx shows considerable potential as ΔffCO2 proxy and as useful addition to ΔCO-based estimates, as both proxies have different footprints due to their lifetimes. A key challenge remains in reliably determining the seasonal and diurnal cycle of average ΔNOx to ΔffCO2 ratios.

Abstract Until recently, advection profiles in the planetary boundary layer have been difficult to quantify from observations. As a result, not much is known about its basic characteristics like magnitude and diurnal cycles, or how these differ as a function of large-scale environments. To provide insight into advective characteristics, a Green’s Theorem-based method for calculating profiles of advection in the lowest 3 km from an array of ground-based thermodynamic and kinematic profiling instruments has been applied to two years of observations from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observing site. Since advection largely depends on synoptic scale forcing, a self-organizing map (SOM) was used to classify days based on mean sea level pressure analyses; from that, the diurnal evolution of vertical profiles of advection for different synoptic environments was quantified. The overall mean magnitude of the potential temperature advection is approximately ± 3 K hr −1 while the moisture advection is approximately ±1.5 g kg −1 hr −1 , with substantial variability in the sign and magnitude of the advective tendencies at different heights and throughout the diurnal cycle. Advection magnitude is strongly connected to the strength of synoptic-scale forcing, which varies depending on the time of year. Variability in advection is larger for potential temperature than it is for moisture, and strongly-forced environments like mid-latitude cyclones feature greater variability in the advection magnitude than weakly forced or quiescent environments.

Accurate prediction of future climate change hinges upon the ability of Earth system models (ESMs) to simulate clouds and their radiative effects. Even if an ESM can simulate the correct clouds, a systematic error in the amount of sunlight reflected by clouds (and, thus, cloud radiative effect) can exist if the clouds are simulated at the incorrect time of day. In this work, we develop an analytical model connecting diurnal cloud biases to emergent mean state radiative biases. With the use of satellite observations, we demonstrate that there are errors in the time of day that clouds are occurring in ESMs that would cause bias in shortwave cloud radiative effect (SWCRE) that is greater than 45% of the total SWCRE bias, but such errors in the cloud diurnal cycle are masked by other compensating errors, indicating that these ESMs are getting the right answer for the wrong reasons.

In the paper, we examine the atmospheric part of the global electric circuit. When studying large-scale currents in the atmosphere flowing from the ionosphere to the ground, the ionosphere and Earth’s surface can be considered as ideal conductors with high accuracy. These currents are determined by the ground-ionosphere voltage and the spatial distribution of conductivity in the atmosphere. We employ a one-dimensional model of atmospheric electric fields and currents in which currents are assumed to be nearly vertical. Then it is possible to reduce the spatial distribution of conductivity to longitude and latitude distribution of conductivity of atmospheric columns. By integrating the conductivity over the entire Earth surface, we obtain the total conductivity of the atmosphere. Inside clouds, air conductivity decreases due to the ion attachment to water drops. Using available data on decrease in local conductivity within individual clouds, we analyze the effect of cloud density in latitude, longitude, and height on geographical distribution of conductivity and total conductivity of the atmosphere. By the example of 2009, it is shown that cloudiness reduces the total conductivity of the atmosphere by 20 %. Its variations during the day and year are so small that the model fair-weather electric field varies only by 2 % due to cloudiness. Judging by the results obtained, the influence of clouds on atmospheric conductivity does not explain the diurnal and seasonal cycles of the fair-weather electric field strength (Carnegie diagram).

In the paper, we examine the atmospheric part of the global electric circuit. When studying large-scale currents in the atmosphere flowing from the ionosphere to the ground, the ionosphere and Earth’s surface can be considered as ideal conductors with high accuracy. These currents are determined by the ground-ionosphere voltage and the spatial distribution of conductivity in the atmosphere. We employ a one-dimensional model of atmospheric electric fields and currents in which currents are assumed to be nearly vertical. Then it is possible to reduce the spatial distribution of conductivity to longitude and latitude distribution of conductivity of atmospheric columns. By integrating the conductivity over the entire Earth surface, we obtain the total conductivity of the atmosphere. Inside clouds, air conductivity decreases due to the ion attachment to water drops. Using available data on decrease in local conductivity within individual clouds, we analyze the effect of cloud density in latitude, longitude, and height on geographical distribution of conductivity and total conductivity of the atmosphere. By the example of 2009, it is shown that cloudiness reduces the total conductivity of the atmosphere by 20 %. Its variations during the day and year are so small that the model fair-weather electric field varies only by 2 % due to cloudiness. Judging by the results obtained, the influence of clouds on atmospheric conductivity does not explain the diurnal and seasonal cycles of the fair-weather electric field strength (Carnegie diagram).

Abstract We present lidar (LIght Detection And Ranging) and particle measurements from a mixed hardwood forest in northern Michigan over a 74‐day period in April–July 2016, capturing the peak spring tree pollen emissions season. Mini Micropulse Lidar (MPL) observations of the normalized ratio backscatter and depolarization ratios show pollen plumes emanating from the forest on 9 days during the pine and oak pollination time periods, with highly depolarizing particles within the boundary layer up to about 500 m–1.6 km depending on the meteorological conditions. We calculate the total particle backscatter and aerosol optical depth (AOD) on the 9 pollen plume days and find that during pollen plume events, the pollen contributes approximately 50% to the total AOD, with daily contributions ranging from 25% to 97%. Single particle measurements from passive samplers at three heights in and above the forest canopy (1.5, 15, and 30 m) observe pine and oak pollen grains, with biological aerosol particles comprising a large component of supermicron aerosols. Together, these results provide time‐resolved observational evidence of the diurnal cycle of pollen and their correlation with meteorological conditions, as well as the contribution of pollen to the total AOD, indicating its potential impact on climate.

Abstract The diurnal cycle of convection in the Maritime Continent (MC) has been hypothesized to act as a barrier to the eastward propagation of the Madden‐Julian oscillation (MJO). To test this hypothesis, we use a regional model with realistic MJO to simulate an event from the boreal spring of 2013 that weakened and stalled over the MC. Two simulations are conducted: one that includes the diurnal cycle of insolation (CTL), and another without it (NO_DC). The MJO in the simulations was identified and tracked using a large‐scale precipitation tracking method that distinguishes propagation and non‐propagation unlike the usual Real‐time Multivariate MJO method. In the NO_DC simulation, the absence of diurnal heating reduces land precipitation, allowing more continuous eastward MJO propagation. An analysis of moist static energy budget reveals that MJO maintenance in NO_DC is due to increased longwave heating and reduced advection, whereas the persistent MJO propagation in NO_DC is due to increased advection and reduced longwave heating and surface latent heat flux. These processes, however, may vary across different parts of the MC, emphasizing the complexity of MJO propagation across the MC.

Chagas disease, caused by the parasite Trypanosoma cruzi, is a major neglected tropical disease affecting 6–7 million people worldwide. Rhodnius prolixus, one of the most important vectors of Chagas disease in Latin America, is known to be highly sensitive to environmental temperature, which influences both its biology and parasite development. However, few studies have investigated how this vector behaviorally modulates the effects of temperature by shifting their thermopreference, particularly in response to infection. We investigated how T. cruzi infection of R. prolixus fourth-instar nymphs influences thermopreference along a temperature gradient, while examining differences across times of day and time since blood feeding. Additionally, parasite load and infection maintenance were compared between free-moving nymphs and nymphs kept at a constant 26°C. Infected nymphs exhibited a preference for temperatures approximately 1°C cooler than uninfected controls. This cold-seeking behavior emerged around 15 days post-infection and persisted until shortly after molting. Importantly, infected insects allowed to thermoregulate showed significantly lower intestinal parasite loads and a higher rate of infection clearance compared to those kept at a constant 26°C. A diurnal cycle in temperature preference was also observed, with higher preferred temperatures shortly after the beginning of the photophase, followed by a gradual decline over the day and night. These results suggest the existence of an infection-induced behavioral anapyrexia response in R. prolixus that limits T. cruzi development. This potential form of adaptive thermoregulation has important implications for the ecology of Chagas disease transmission and the development of behaviorally informed vector control strategies.