Diurnal Cycle Research Articles

Investigation of the characteristics of low-level jets over North America in a convection-permitting Weather Research and Forecasting simulation

Abstract. In this study, we utilized a high-resolution (4 km) convection-permitting Weather Research and Forecasting (WRF) simulation spanning a 13-year period (2000–2013) to investigate the climatological features of low-level jets (LLJs) over North America. The 4 km simulation enabled us to represent the effects of orography and the underlying surface on the boundary layer winds better. Focusing on the continental US and the adjacent border regions of Canada and Mexico, this study not only identified several well-known large-scale LLJs, such as the southerly Great Plains LLJ and the summer northerly California coastal LLJ, but also the winter Quebec northerly LLJ which received less focus before. All these LLJs reach their peak in the nighttime in the diurnal cycle. Thus, the different thermal and dynamic mechanisms forming these three significant LLJs are investigated in this paper. Inertial oscillation theory dominates in the Great Plain LLJ, and the California coastal LLJ is formed by the baroclinic theory, whereas the Quebec LLJ is associated with both theories. Moreover, the high-resolution simulation revealed climatic characteristics of weaker and smaller-scale LLJs or low-level wind maxima in regions with complex terrains, such as the northerly LLJs in the foothill regions of the Rocky Mountains and the Appalachians during the winter. This study provides valuable insights into the climatological features of LLJs in North America, and the high-resolution simulation offers a more detailed understanding of LLJ behavior near complex terrains and other smaller-scale features.

Air-Processed Efficient Perovskite Solar Cells With Full Lifecycle Management.

Despite the outstanding power conversion efficiency of perovskite solar cells (PSCs) realized over the years, the entire lifecycle from preparation and operation to discarding of PSCs still needs to be carefully considered when it faces the upcoming large-scale production and deployment. In this study, bio-derived chitin-based polymers are employed to realize the full lifecycle regulation of air-processed PSCs by forming multiple coordinated and hydrogen bonds to stabilize the lead iodide and organic salt precursor inks, accelerating the solid-liquid reaction and crystallization of two-step deposition process, then achieving the high crystalline and oriented perovskites with less notorious charge defects in the open air. The air-prepared PSCs exhibit a decent efficiency of 25.18% with high preparation reproducibility and improved operational stability toward the harsh environment and mechanical stress stimuli. The modified PSCs display negligible fatigue behavior with keeping 92% of its initial efficiency after operating for 32 diurnal cycles (ISOS-LC-1 protocol). Meanwhile, closed-loop lead management of end-of-life PSCs including suppression of lead leakage, toxicity evaluation of broken devices, and recycling of lead iodide components are comprehensively investigated. This work sheds light on a promising avenue to realize the entire lifecycle regulation of air-processed efficient and stable PSCs.

Examining Tropical Convection Features at Storm‐Resolving Scales Over the Maritime Continent Region

AbstractGlobal Storm Resolving Models (GSRMs) provide a way to understand weather and climate events across scales for better‐informed climate impacts. In this work, we apply the recently developed and validated CAM (Community Atmosphere Model)—MPAS (Model for Prediction Across Scales) modeling framework, based on the open‐source Community Earth System Model (CESM2), to examine the tropical convection features at the storm resolving scale over the Maritime Continent region at 3 km horizontal spacing. We target two global numerical experiments during the winter season of 2018 for comparison with observation in the region. We focus on the investigation of the representations of the convective systems, precipitation statistics, and tropical cyclone behaviors. We found that regional‐refined experiments show more accurate precipitation distributions, diurnal cycles, and better agreement with observations for tropical cyclone features in terms of intensity and strength statistics. We expect the exploration of this work will further advance the development and use of the storm‐resolving model in precipitation predictions across scales.

First top-down diurnal adjustment to NOx emissions inventory in Asia informed by the Geostationary Environment Monitoring Spectrometer (GEMS) tropospheric NO2 columns

Pioneering the use of the Geostationary Environment Monitoring Spectrometer’s (GEMS) observation data in air quality modeling, we adjusted Asia’s NOx emissions inventory by leveraging the instrument’s unprecedented sampling frequency. GEMS tropospheric NO2 columns served as top-down constraints, guiding our Bayesian inversion to constrain NOx emissions in Asia during spring 2022. This enabled the model to better capture the diurnal variation in NOx emissions, such as its morning rush hour peak, particularly when more retrievals were available each day, improving the simulation accuracy to a certain extent. The GEMS-informed adjustment reduced the extent of model underestimation of surface NO2 concentrations from 17.38 to 5.58% in Korea and from 13.05 to 4.54% in China, showing about 9.40% and 5.77% greater improvements, respectively, compared to the adjustment based on the sun-synchronous low earth orbit observation proxy. Our findings highlight the potential of geostationary observation data in refining the diurnal cycle of inventoried NOx emissions, thereby more effectively improving the accuracy of air quality simulations.

Addressing challenges for operating electrochemical solar fuels technologies under variable and diurnal conditions

The outdoor operation of electrochemical solar fuels devices must contend with challenges presented by the cycles of solar irradiance, temperature, and other meteorological factors. Herein, we discuss challenges associated with these fluctuations presented over three timescales, including the effects of diurnal cycling over the course of many days, a single diurnal cycle over the course of hours, and meteorological phenomena that cause fluctuations on the order of seconds to minutes. We also highlight both reaction-independent and reaction-specific effects of variable conditions for the hydrogen evolution reaction and CO2 reduction reaction. We identify key areas of research for advancing the outdoor operation of solar fuels technology and highlight the need for metrics and benchmarks to enable the comparison of diurnal studies across systems and geographical locations.

Measurement report: Urban ammonia and amines in Houston, Texas

Abstract. Ammonia and amines play critical roles in secondary aerosol formation, especially in urban environments. However, fast measurements of ammonia and amines in the atmosphere are very scarce. We measured ammonia and amines with a chemical ionization mass spectrometer (CIMS) at the urban center in Houston, Texas, the fourth most populated urban site in the United States, during October 2022. Ammonia concentrations were on average four parts per billion by volume (ppbv), while the concentration of an individual amine ranged from several parts per trillion by volume (pptv) to hundreds of pptv. These reduced nitrogen compounds were more abundant during weekdays than on weekends and correlated with measured CO concentrations, implying they were mostly emitted from pollutant sources. Both ammonia and amines showed a distinct diurnal cycle, with higher concentrations in the warmer afternoon, indicating dominant gas-to-particle conversion processes taking place with the changing ambient temperatures. Studies have shown that dimethylamine is critical for new particle formation (NPF) in the polluted boundary layer, but currently there are no amine emission inventories in global climate models (as opposed to ammonia). Our observations made in the very polluted area of Houston, as well as a less polluted site (Kent, Ohio) from our previous study (You et al., 2014), indicate there is a consistent ratio of dimethylamine over ammonia at these two sites. Thus, our observations can provide a relatively constrained proxy of dimethylamine using 0.1 % ammonia concentrations at polluted sites in the United States to model NPF processes.

Modulation of the Arabidopsis Starch Metabolic Network by the Cytosolic Acetyl-CoA Pathway in the Context of the Diurnal Illumination Cycle

The starch metabolic network was investigated in relation to other metabolic processes by examining a mutant with altered single-gene expression of ATP citrate lyase (ACL), an enzyme responsible for generating cytosolic acetyl-CoA pool from citrate. Previous research has shown that transgenic antisense plants with reduced ACL activity accumulate abnormally enlarged starch granules. In this study, we explored the underlying molecular mechanisms linking cytosolic acetyl-CoA generation and starch metabolism under short-day photoperiods. We performed transcriptome and quantification of starch accumulation in the leaves of wild-type and antisense seedlings with reduced ACL activity. The antisense-ACLA mutant accumulated more starch than the wild type under short-day conditions. Zymogram analyses were conducted to compare the activities of starch-metabolizing enzymes with transcriptomic changes in the seedling. Differential expression between wild-type and antisense-ACLA plants was detected in genes implicated in starch and acetyl-CoA metabolism, and cell wall metabolism. These analyses revealed a strong correlation between the transcript levels of genes responsible for starch synthesis and degradation, reflecting coordinated regulation at the transcriptomic level. Furthermore, our data provide novel insights into the regulatory links between cytosolic acetyl-CoA metabolism and starch metabolic pathways.

Tidal levels significantly change bacterial community composition in a tropical estuary during the dry season

AbstractEstuaries are usually characterized by strong spatial and temporal variability in water physicochemical conditions and are often largely affected by human activities. One important source of variability is caused by tides that can swiftly alter not only physicochemical conditions but also the abundance and composition of the biota. The effect of the diurnal tidal cycle on microbial community composition during different seasons remains uncertain, although this knowledge underlies having effective monitoring programs for water quality and potential identification of health risk conditions. In this study, we assessed the bacterioplankton community composition and diversity across four tidal water levels in a tropical estuary characterized by a mixed semidiurnal tide regime (i.e., two high and two low tides of varying amplitudes) during both dry and wet seasons. The bacterial community composition varied significantly among the four tidal levels, but only during the dry season, when the influence of the seawater intrusion was largest. Bacterial indicators’ taxa identified using the Indicator Value Index were found within Cyanobacteria, Actinobacteriota, Bacteroidota, and Proteobacteria. The indicator taxon Cyanobium sp. had a prominent presence across multiple tidal levels. The main predicted phenotypes of the bacterial communities were associated with potential pathogenicity, gram-negative, and biofilm formation traits. While there were no marked predicted phenotypic differences between seasons, pathogenic and gram-negative traits were more prevalent in the dry season, while biofilm formation traits dominated in the wet season. Overall, our findings underscore the intricate relationship between river hydrodynamics and bacterial composition variability and hint a significant human impact on the water quality of the Bangpakong River.

Satellite-based estimation of monthly mean hourly 1-km urban air temperature using a diurnal temperature cycle model

Cities worldwide face escalating climate change risks, underscoring the need for spatially and temporally resolved urban air temperature (Ta) data. While satellite-derived land surface temperature (LST) data have been widely used to estimate Ta, high-resolution hourly Ta estimation in urban areas remains underexplored. Traditional methods typically rely on LST data from geostationary satellites and continuous 24-h Ta observations from weather stations. To address these limitations, we introduce a method that combines a diurnal temperature cycle (DTC) model with a random forest model to estimate monthly mean hourly urban Ta at 1-km resolution. This approach leverages a limited number of diurnal Ta observations from weather stations, MODIS LST data, and ancillary information. The core idea of the proposed method is to transform the estimation of monthly mean hourly 1-km Ta into estimating 1-km DTC model parameters, primarily daily maximum and minimum Ta values. This method capitalizes on MODIS LST's ability to estimate daily Ta extremes and requires only four diurnal Ta observations within a daily cycle to estimate monthly mean hourly 1-km Ta. Station-based five-fold cross-validation yields overall RMSE values consistently below 1.0 °C across nine cities with diverse geographic and climatic contexts. The accuracy achieved with only four diurnal Ta observations rivals that obtained using continuous 24-h Ta observations. Even with a limited training set of ten stations, the overall RMSE remains below 1.0 °C for most cities. The proposed method proves effective for both single-city and multi-city modeling and can estimate daily hourly 1-km Ta under clear-sky conditions. In conclusion, this study offers a feasible, efficient, and versatile method for accurately estimating monthly mean hourly 1-km Ta, which can be readily applied to other cities and holds potential for various applications.

Comparison of Climatology of Precipitation Diurnal Cycle in Kalimantan Deduced from Rain Gauge and IMERG Data

Abstract The diurnal cycle is repetitive pattern of changes in weather factors on land and oceans due to the Earth rotation. Kalimantan Island as the largest island in the Maritime Islands region has a complex rainfall pattern that is influenced by local factors and global circulation. The research was conducted to find out the climatological comparison of the diurnal cycle of rain in Kalimantan based on rain gauge data and Integrated Multi-SatellitE Retrievals for GPM Version 07 (IMERG V07) data for the period 2016 - 2022. Data validation using rain gauge data to validate and calibrate IMERG satellite data, improving the accuracy and reliability of satellite rainfall estimates. The diurnal cycle is evaluated through a Precipitation Amount (PA), Precipitation Frequency (PF), and Precipitation Intensity (PI) every hour. The results show that the maximum rainfall occurs in the afternoon and evening along the coast, and in the middle of the night and morning on the Kalimantan plains. Kalimantan has high rainfall intensity values at short rainfall durations, i.e. rain lasting less than 3 hours. This is expected to improve the quality of hydrometeorological disaster mitigation in Kalimantan.

FIELD EVALUATION OF THE EFFICACY OF PASSIVE RADIATIVE COOLING INFRASTRUCTURE: A CASE STUDY IN PHOENIX ARIZONA

Radiative properties of shade structures affect their surface temperatures, sensible heat fluxes and longwave and shortwave radiation exchange. In fact, structures with high solar reflectance and thermal emittance have the potential to remain below ambient air temperatures, convecting sensible heat from the air to the surface and then radiating that heat to space—a sort of radiant heat pump.We explore cooling benefits of urban surfaces with high solar reflectance and high thermal emittance radiative cooling films through a field measurement campaign in Phoenix Arizona, USA. The tested films have solar reflectance and selective thermal emittance (in wavelengths 8-13 μm) close to 95%. We applied films in both before-after and control-test experimental designs on thin metal roofs of park shade structures. We measured surface temperatures, surface heat fluxes, upward- and downward-welling longwave and shortwave radiation, and local weather conditions.Results demonstrate the ability of radiant cooling films to reduce surface temperatures on hot days below ambient air temperatures. Test surfaces with cooling films were an average of 7°C cooler than control shelter surfaces over the diurnal cycle, reducing sensible heat fluxes into the environment by up to 80%, and lowering mean radiant temperatures for pedestrians using the shelters by more than 3°C. It was also observed that the sum of the net reflected shortwave and emitted longwave radiation over the diurnal cycle can exceed the total incident longwave and shortwave radiation on the surface, demonstrating the ability of these materials to radiatively “pump” heat out of the city.

Evaluating Machine Learning–Based Probabilistic Convective Hazard Forecasts Using The HRRR: Quantifying Hazard Predictability and Sensitivity to Training Choices

Abstract The High-Resolution Rapid Refresh (HRRR) model provides hourly updating forecasts of convective-scale phenomena, which can be used to infer the potential for convective hazards (e.g., tornadoes, hail, and wind gusts), across the United States. We used deterministic 2019–20 HRRR, version 4 (HRRRv4), forecasts to train neural networks (NNs) to generate 4-hourly probabilistic convective hazard forecasts [neural network probability forecasts (NNPFs)] for HRRRv4 initializations in 2021, using storm reports as ground truth. The NNPFs were compared to the skill of a smoothed updraft helicity (UH) baseline to quantify the benefit of the NNs. NNPF skill varied by initialization time and time of day but was all superior to the UH forecast. NNPFs valid at hours between 1800 and 0000 UTC were most skillful in aggregate, significantly exceeding the baseline forecast skill. Overnight NNPFs (i.e., valid 0600–1200 UTC) were least skillful, indicating a diurnal cycle in hazard predictability that was present across all HRRRv4 initializations. We explored the sensitivity of HRRRv4 NNPF skill to NN training choices. Including an additional year of 2021 HRRRv4 forecasts for training slightly improved skill for 2022 HRRRv4 NNPFs, while reducing the training dataset size by 40% using only forecasts with storm reports was not detrimental to forecast skill. Finally, NNs trained with 2018–20 HRRRv3 forecasts led to a reduction in NNPF skill when applied to 2021 HRRRv4 forecasts. In addition to documenting practical predictability challenges with convective hazard prediction, these findings reinforce the need for a consistent model configuration for optimal results when training NNs and provide best practices when constructing a training dataset with operational convection-allowing model forecasts. Significance Statement Convective hazards, such as hail and tornadoes, are often challenging to predict. To improve hazard predictions, we used machine learning (ML) to generate forecasts of convective hazards across the United States leveraging forecasts of prior events. The ML hazard forecasts were consistently better than a non-ML approach and varied in skill based on the time of day, with nighttime forecasts being particularly challenging. ML forecasts of wind gusts and hail were more skillful than tornadoes. Different strategies of constructing the dataset of prior events led to differences in forecast performance; thus, this work provides recommendations for how to assemble these datasets and train ML models to generate improved forecasts of severe weather events.

Europa’s H2O2: Temperature Insensitivity and a Correlation with CO2

H2O2 is part of Europa’s water-ice radiolytic cycle and a potential source of oxidants to Europa’s subsurface ocean. However, factors controlling the concentration of this critical surface species remain unclear. Though laboratory experiments suggest that Europa’s H2O2 should be concentrated in the coldest, most ice-rich regions toward the poles, Keck adaptive optics observations have shown the strongest H2O2 signatures in comparatively warm, salt-bearing terrain at low latitudes. As a result, it was suggested that the local non-ice composition of these terrains—particularly hypothesized enrichments of CO2—may be a more dominant control on H2O2 than temperature or water-ice abundance. Here we use observations of Europa from the NASA Infrared Telescope Facility, Keck Observatory, and JWST to disentangle the potential effects of temperature and composition. In order to isolate the effect of temperature on Europa’s H2O2, we use the ground-based observations to assess its response to temperature changes over timescales associated with Europa’s daily eclipse and diurnal cycle. We use JWST Cycle 1 data to look for any geographic correlation between Europa’s H2O2 and CO2. Changes in Europa’s 3.5 μm H2O2 absorption band both from pre- to post-eclipse and across a local day suggest minimal effects of the local temperature on these timescales. In contrast, the JWST observations show a strong positive correlation between Europa’s H2O2 and CO2 bands, supporting the previously suggested possibility that the presence of CO2 in the ice may enhance H2O2 concentrations via electron scavenging.

Substance P and dopamine form a "push-pull" system that diurnally regulates retinal gain.

The operation of the retina, like other brain circuits, is under modulatory control. One coordinator of changes in retinal function is dopamine, a neuromodulator released in a light-dependent way to adjust vision on a diurnal cycle. Here, we demonstrate that substance P is a similarly powerful retinal modulator that interacts with the dopamine system. By imaging glutamatergic synaptic transmission in larval zebrafish, we find that substance P decreases the contrast sensitivity of ON and OFF visual channels up to 8-fold, with suppression of visual signals being strongest through the "transient" pathway responding to higher frequencies. These actions are exerted in the morning, in large part by suppressing the amplification of visual signals by dopamine, but substance P is almost completely inactive in the afternoon. Modulation of retinal gain is accompanied by changes in patterns of vesicle release at the synapses of bipolar cells: increased gain shifts coding of stimulus strength from the rate of release events to their amplitude generated by a process of multivesicular release (MVR). Together, these actions of substance P reduce the flow of visual information, measured in bits, ∼3-fold. Thus, whereas dopamine "pushes" the retina to transmit information at higher rates in the afternoon, substance P acts in antiphase to suppress dopamine signaling and "pull down" information transmission in the morning.

Long Term 5G Base Station Traffic Prediction Method Based on Spatial-Temporal Correlations

In the domain of 5G network management, accurately predicting traffic volumes at base stations remains a critical yet challenging endeavor, primarily due to the complexities inherent in the spatial and temporal data interactions. Current methods often fall short in effectively harnessing long-term trends and spatial interconnections among base stations. To bridge these gaps, this paper introduces the GCformer model, a novel approach that capitalizes on both spatial relationships and temporal patterns for multi-base station traffic prediction. Spatially, the proposed model employs graph convolutional networks to integrate diverse spatial information and construct insightful adjacency matrices that includes Euclidean distances and non-Euclidean distances (area types of base station locations and similarities in traffic flow among various stations), thereby enhancing the predictability of traffic dynamics. Temporally, the application of the Transformer's attention mechanism enables better capture of long-term relational dependencies in the temporal domain of 5G base station traffic data. Additionally, a time-variant optimization module is designed to establish diurnal cycle data for each base station's traffic, replacing the traditional positional encoding with a more nuanced model that improves the learning of historical data correlations. Empirical results from exhaustive case studies confirm the superiority of the GCformer model in forecasting traffic volumes. The GCformer exhibits a 4.01% improvement in mean squared error and a 3.37% enhancement in mean absolute error compared to the best-performing baseline model, showcasing its potential to significantly improve operational strategies in 5G networks.

Synoptic Influences on the Diurnal Cycle of Rainfall over Western Puerto Rico

Abstract The objective of this study is to understand rainfall processes over tropical islands by identifying synoptic conditions that influence the diurnal cycle of rainfall over western Puerto Rico. Summer rainfall over Puerto Rico is dominated by its afternoon peak, yet there is large variability in its behavior that remains challenging to predict. We use radiosonde and airborne data collected through the NASA Convective Processes Experiment—Aerosols and Winds (CPEX-AW) field campaign (August–September 2021) to achieve our objective, in addition to the network of surface station data over the island. We find that the background wind speed and humidity have strong influences on afternoon rainfall through different mechanisms. A stronger background wind inhibits afternoon rainfall likely by reducing land–sea thermal contrast and weakening sea-breeze convergence over the island. At the same time, an inversion layer often forms with a stronger background wind that further inhibits deep convection. When the background wind is weak and sea breezes are prominent, afternoon rainfall increases exclusively over the island, while limited rainfall appears over the surrounding ocean. However, enhanced rainfall still occurs over the island with weak sea breezes if humidity is high, accompanied by enhanced rainfall over the surrounding ocean due to the offshore movement and development of convective storms. The sources of variability in background wind and humidity are mostly independent, resulting in a wide range of synoptic conditions and associated effects on the island rainfall. This expanded understanding of the mechanisms causing variability of diurnal rainfall can lead to improved forecasts over Puerto Rico and other tropical islands.

Heterogeneity of the diurnal cycle of precipitation in the Amazon Basin

Heterogeneity of the diurnal cycle of precipitation in the Amazon Basin

Characteristics of light absorption and environmental effects of Brown carbon aerosol in Chongqing during summer and winter based on online measurement: Implications of secondary formation

Light-absorbing organic carbon (i.e., brown carbon, BrC) significantly contributes to light absorption and radiative forcing in the atmospheric particles. However, the secondary formation of BrC and optical properties of secondary BrC are poorly understood. In this study, we analyzed and evaluated the light absorption and environmental effects of BrC and secondary BrC from July 1st to 31st, 2022 (summer) and January 20th to February 20th, 2023 (winter) in Chongqing. BrC and secondary BrC light absorption were estimated via a seven-wavelength aethalometer and the statistical approach. The average values of secondary BrC light absorption (AbsBrC,sec,λ) accounted for 46.2–56.5% of AbsBrC. AbsBrC,370 and AbsBrC,sec,370 were significantly higher during winter (26.2 ± 13.2 and 9.1 ± 5.2 Mm−1 respectively) than that during summer (7.2 ± 4.1 and 5.2 ± 3.5 Mm−1 respectively) (p < 0.001), suggesting secondary formation played an essential role in BrC. A diurnal cycle of AbsBrC,sec,370 was explained by the photobleaching of light-absorbing chromophores under the oxidizing conditions in the daytime, and the formation of chromophores via aqueous reactions with NH4+ and NOx after sunset during winter. PSCF analysis showed that transport of anthropogenic emissions from the northeastern and southeastern areas of Chongqing was the important source of the secondary BrC in PP during winter. During winter, the average values of SFEBrC and SFEBrC,sec were 31.9 and 27.4 W g−1 lower than that during summer (64.7 and 44.5 W g−1), respectively. In contrast, J[NO2] values of SFEBrC and SFEBrC,sec decreased by 23.3% and 8.7% during winter higher than that during summer (19.9% and 5.6%), indicating that BrC and secondary BrC cause substantial radiative effects and atmospheric photochemistry. Overall, this study is helpful in understanding the characterization and secondary formation of BrC and accurately evaluating the environmental effects of BrC in Chongqing.

Remote sensing of temperature-dependent mosquito and viral traits predicts field surveillance-based disease risk.

Mosquito-borne diseases contribute substantially to the global burden of disease, and are strongly influenced by environmental conditions. Ongoing and rapid environmental change necessitates improved understanding of the response of mosquito-borne diseases to environmental factors like temperature, and novel approaches to mapping and monitoring risk. Recent development of trait-based mechanistic models has improved understanding of the temperature dependence of transmission, but model predictions remain challenging to validate in the field. Using West Nile virus (WNV) as a case study, we illustrate the use of a novel remote sensing-based approach to mapping temperature-dependent mosquito and viral traits at high spatial resolution and across the diurnal cycle. We validate the approach using mosquito and WNV surveillance data controlling for other key factors in the ecology of WNV, finding strong agreement between temperature-dependent traits and field-based metrics of risk. Moreover, we find that WNV infection rate in mosquitos exhibits a unimodal relationship with temperature, peaking at ~24.6-25.2°C, in the middle of the 95% credible interval of optimal temperature for transmission of WNV predicted by trait-based mechanistic models. This study represents one of the highest resolution validations of trait-based model predictions, and illustrates the utility of a novel remote sensing approach to predicting mosquito-borne disease risk.

Cloud water adjustments to aerosol perturbations are buffered by solar heating in non-precipitating marine stratocumuli

Abstract. Marine low-level clouds are key to the Earth's energy budget due to their expansive coverage over global oceans and their high reflectance of incoming solar radiation. Their responses to anthropogenic aerosol perturbations remain the largest source of uncertainty in estimating the anthropogenic radiative forcing of climate. A major challenge is the quantification of the cloud water response to aerosol perturbations. In particular, the presence of feedbacks through microphysical, dynamical, and thermodynamical pathways at various spatial and temporal scales could augment or weaken the response. Central to this problem is the temporal evolution in cloud adjustment, governed by entangled feedback mechanisms. We apply an innovative conditional Monte Carlo subsampling approach to a large ensemble of diurnal large-eddy simulation of non-precipitating marine stratocumulus to study the role of solar heating in governing the evolution in the relationship between droplet number and cloud water. We find a persistent negative trend in this relationship at night, confirming that the role of microphysically enhanced cloud-top entrainment. After sunrise, the evolution in this relationship appears buffered and converges to ∼-0.2 in the late afternoon. This buffering effect is attributed to a strong dependence of cloud-layer shortwave absorption on cloud liquid water path. These diurnal cycle characteristics further demonstrate a tight connection between cloud brightening potential and the relationship between cloud water and droplet number at sunrise, which has implications for the impact of the timing of advertent aerosol perturbations.