Estimated cloud-top entrainment index explains positive low-cloud-cover feedback.

How subtropical marine low cloud cover (LCC) will respond to global warming is a major source of uncertainty in future climate change. Although the estimated inversion strength (EIS) is a good predictive index of LCC, it has a serious limitation when applied to evaluate LCC changes due to warming: The LCC decreases despite increases in EIS in future climate simulations of global climate models (GCMs). In this work, using state-of-the-art GCMs, we show that the recently proposed estimated cloud-top entrainment index (ECTEI) decreases consistently with LCC in warmer sea surface temperature (SST) climates. For the patterned SST warming predicted by coupled GCMs, ECTEI can constrain the subtropical marine LCC feedback to -0.41 ± 0.28% K-1 (90% CI), implying virtually certain positive feedback. ECTEI physically explains the heuristic model for LCC changes based on a linear combination of EIS and SST changes in previous studies in terms of cloud-top entrainment processes.

Impact of three decades of warming, increased nutrient availability, and increased cloudiness on the fluxes of greenhouse gases and biogenic volatile organic compounds in a subarctic tundra heath.

Climate change is exposing subarctic ecosystems to higher temperatures, increased nutrient availability, and increasing cloud cover. In this study, we assessed how these factors affect the fluxes of greenhouse gases (GHGs) (i.e., methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2)), and biogenic volatile organic compounds (BVOCs) in a subarctic mesic heath subjected to 34 years of climate change related manipulations of temperature, nutrient availability, and light. GHGs were sampled from static chambers and gases analyzed with gas chromatograph. BVOCs were measured using the push-pull method and gases analyzed with chromatography-mass spectrometry. The soil temperature and moisture content in the warmed and shaded plots did not differ significantly from that in the controls during GHG and BVOC measurements. Also, the enclosure temperatures during BVOC measurements in the warmed and shaded plots did not differ significantly from temperatures in the controls. Hence, this allowed for assessment of long-term effects of the climate treatment manipulations without interference of temperature and moisture differences at the time of measurements. Warming enhanced CH4 uptake and the emissions of CO2, N2O, and isoprene. Increased nutrient availability increased the emissions of CO2 and N2O but caused no significant changes in the fluxes of CH4 and BVOCs. Shading (simulating increased cloudiness) enhanced CH4 uptake but caused no significant changes in the fluxes of other gases compared to the controls. The results show that climate warming and increased cloudiness will enhance CH4 sink strength of subarctic mesic heath ecosystems, providing negative climate feedback, while climate warming and enhanced nutrient availability will provide positive climate feedback through increased emissions of CO2 and N2O. Climate warming will also indirectly, through vegetation changes, increase the amount of carbon lost as isoprene from subarctic ecosystems.

Associations of ambient temperature and total cloud cover during pregnancy with newborn vitamin D status.

OBJECTIVES: We aimed to estimate the effects of temperature and total cloud cover before birth on newborn vitamin D status. STUDY DESIGN: Prospective birth cohort. METHODS: This study included 2055 mother-newborn pairs in Wuhan, Hubei province, China. The data of temperature and total cloud cover from 30 days before birth were collected, and cord blood 25-hydroxyvitamin D [25(OH)D] were determined. Restricted cubic spline regression models, multiple linear regression models, and logistic regression models were applied to estimate the associations. RESULTS: A "J" shaped curve was observed between temperature and vitamin D status, and an inverse "J" shaped curve was observed between total cloud cover and vitamin D status. Compared to the fourth quartile (75-100th percentile, Q4) of average temperature (30 days before birth), the odds ratio (OR) for Q1 (0-25th percentile) associated with the vitamin D deficiency occurrence (<20 ng/mL) was 3.63 (95% CI, 1.54, 8.65). Compared to Q1 of the average total cloud cover (30 days before birth), the OR associated with the occurrence of vitamin D deficiency was 2.38 (95% CI, 1.63, 3.50) for the Q4. CONCLUSIONS: Low temperature and high cloud cover before delivery were significantly associated with an increased probability of vitamin D deficiency in newborns. The findings suggested that pregnancy women lacking sufficient sunlight exposure still need vitamin D supplement to overcome the potential vitamin D deficiency status.

A Combined Persistence and Physical Approach for Ultra-Short-Term Photovoltaic Power Forecasting Using Distributed Sensors.

This paper presents a novel method for forecasting the impact of cloud cover on photovoltaic (PV) fields in the nowcasting term, utilizing PV panels as sensors in a combination of physical and persistence models and integrating energy storage system control. The proposed approach entails simulating a power network consisting of a 22 kV renewable energy source and energy storage, enabling the evaluation of network behavior in comparison to the national grid. To optimize computational efficiency, the authors develop an equivalent model of the PV + energy storage module, accurately simulating system behavior while accounting for weather conditions, particularly cloud cover. Moreover, the authors introduce a control system model capable of responding effectively to network dynamics and providing comprehensive control of the energy storage system using PID controllers. Precise power forecasting is essential for maintaining power continuity, managing overall power-system ramp rates, and ensuring grid stability. The adaptability of our method to integrate with solar fencing systems serves as a testament to its innovative nature and its potential to contribute significantly to the renewable energy field. The authors also assess various scenarios against the grid to determine their impact on grid stability. The research findings indicate that the integration of energy storage and the proposed forecasting method, which combines physical and persistence models, offers a promising solution for effectively managing grid stability.

The effect of climate on melioidosis incidence in Townsville, Australia: a dry tropical region.

BACKGROUND: Townsville is in the dry tropics in Northern Australia and an endemic region for melioidosis. Melioidosis is an infectious disease caused by Burkholderia pseudomallei, a soil dwelling organism. The incidence of melioidosis is associated with high levels of rainfall and has been linked to multiple weather variables in other melioidosis endemic regions such as in Darwin. In contrast to Townsville, Darwin is in the wet-dry tropics in Northern Australia and receives 40% more rainfall. We assessed the relationship between melioidosis incidence and weather conditions in Townsville and compared the patterns to the findings in Darwin and other melioidosis endemic regions. METHOD: Performing a time series analysis from 1996 to 2020, we applied a negative binomial regression model to evaluate the link between the incidence of melioidosis in Townsville and various weather variables. Akaike's information criterion was used to assess the most parsimonious model with best predictive performance. Fourier terms and lagged deviance residuals were included to control long term seasonal trends and temporal autocorrelation. RESULTS: Humidity is the strongest predictor for melioidosis incidence in Townsville. Furthermore, the incidence of melioidosis showed a three-times rise in the Townsville region when >200 mm of rain fell within the fortnight. Prolonged rainfall had more impact than a heavy downpour on the overall melioidosis incident rate. There was no statistically significant increase in incidence with cloud cover in the multivariable model. CONCLUSION: Consistent with other reports, melioidosis incidence can be attributed to humidity and rainfall in Townsville. In contrast to Darwin, there was no strong link between melioidosis cases and cloud cover and nor single large rainfall events.

Reconstructing NDVI and land surface temperature for cloud cover pixels of Landsat-8 images for assessing vegetation health index in the Northeast region of Thailand.

Critical applications of satellite data products include monitoring vegetation dynamics and assessing vegetation health conditions. Some indicators like normalized difference vegetation index (NDVI) and land surface temperature (LST) are used to assess the status of vegetation growth and health. But one of the major problems with passive remote sensing satellite data products is cloud and shadow cover that leads to data gaps in the images. The present study proposes temporal aggregation of images over a short time span and developing short span harmonic analysis of time series (SS-HANTS) and pixel-wise multiple linear regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI datasets. The calculated root mean square (RMSE) values were ranging from 0.03 to 0.11 and 1.50 to 2.98 °C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) computed from cloud retrieved continuous NDVI and LST images at province level shows that most of the western provinces have healthy vegetation condition than other provinces in the northeast of Thailand.

Cloudiness reduces the bleaching response of coral reefs exposed to heat stress.

Climate change and warming ocean temperatures are a threat to coral reef ecosystems. Since the 1980s, there has been an increase in mass coral bleaching and associated coral mortality due to more frequent and severe thermal stress. Although most research has focused on the role of temperature, coral bleaching is a product of the interacting effects of temperature and other environmental variables such as solar radiation. High light exacerbates the effects of thermal stress on corals, whereas reductions in light can reduce sensitivity to thermal stress. Here, we use an updated global dataset of coral bleaching observations (n = 35,769) from 1985 to 2017 and satellite-derived datasets of SST and clouds to examine for the first time at a global scale the influence of cloudiness on the likelihood of bleaching from thermal stress. We find that among coral reefs exposed to severe bleaching-level heat stress (Degree Heating Weeks >8°Cˑweek), bleaching severity is inversely correlated with the interaction of heat stress and cloud fraction anomalies (p < 0.05), such that higher cloudiness implies reduced bleaching response. A Random Forest model analysis employing different set of environmental variables shows that a model employing Degree Heating Weeks and the 30-day cloud fraction anomaly most accurately predicts bleaching severity (Accuracy = 0.834; Cohen's Kappa = 0.769). Based on these results and global warm-season cloudiness patterns, we develop a 'cloudy refugia' index which identifies the central equatorial Pacific and French Polynesia as regions where cloudiness is most likely to protect corals from bleaching. Our findings suggest that incorporating cloudiness into prediction models can help delineate bleaching responses and identify reefs which may be more resilient to climate change.

Impacts of snow and cloud covers on satellite-derived PM2.5 levels.

Satellite aerosol optical depth (AOD) has been widely employed to evaluate ground fine particle (PM2.5) levels, whereas snow/cloud covers often lead to a large proportion of non-random missing AOD values. As a result, the fully covered and unbiased PM2.5 estimates will be hard to generate. Among the current approaches to deal with the data gap issue, few have considered the cloud-AOD relationship and none of them have considered the snow-AOD relationship. This study examined the impacts of snow and cloud covers on AOD and PM2.5 and made full- coverage PM2.5 predictions by considering these impacts. To estimate missing AOD values, daily gap-filling models with snow/cloud fractions and meteorological covariates were developed using the random forest algorithm. By using these models in New York State, a daily AOD data set with a 1-km resolution was generated with a complete coverage. The "out-of-bag" R2 of the gap-filling models averaged 0.93 with an interquartile range from 0.90 to 0.95. Subsequently, a random forest-based PM2.5 prediction model with the gap-filled AOD and covariates was built to predict fully covered PM2.5 estimates. A ten-fold cross-validation for the prediction model showed a good performance with an R2 of 0.82. In the gap-filling models, the snow fraction was of higher significance to the snow season compared with the rest of the year. The prediction models fitted with/without the snow fraction also suggested the discernible changes in PM2.5 patterns, further confirming the significance of this parameter. Compared with the methods without considering snow and cloud covers, our PM2.5 prediction surfaces showed more spatial details and reflected small-scale terrain-driven PM2.5 patterns. The proposed methods can be generalized to the areas with extensive snow/cloud covers and large proportions of missing satellite AOD data for predicting PM2.5 levels with high resolutions and complete coverage.

Black carbon solar absorption suppresses turbulence in the atmospheric boundary layer.

The introduction of cloud condensation nuclei and radiative heating by sunlight-absorbing aerosols can modify the thickness and coverage of low clouds, yielding significant radiative forcing of climate. The magnitude and sign of changes in cloud coverage and depth in response to changing aerosols are impacted by turbulent dynamics of the cloudy atmosphere, but integrated measurements of aerosol solar absorption and turbulent fluxes have not been reported thus far. Here we report such integrated measurements made from unmanned aerial vehicles (UAVs) during the CARDEX (Cloud Aerosol Radiative Forcing and Dynamics Experiment) investigation conducted over the northern Indian Ocean. The UAV and surface data reveal a reduction in turbulent kinetic energy in the surface mixed layer at the base of the atmosphere concurrent with an increase in absorbing black carbon aerosols. Polluted conditions coincide with a warmer and shallower surface mixed layer because of aerosol radiative heating and reduced turbulence. The polluted surface mixed layer was also observed to be more humid with higher relative humidity. Greater humidity enhances cloud development, as evidenced by polluted clouds that penetrate higher above the top of the surface mixed layer. Reduced entrainment of dry air into the surface layer from above the inversion capping the surface mixed layer, due to weaker turbulence, may contribute to higher relative humidity in the surface layer during polluted conditions. Measurements of turbulence are important for studies of aerosol effects on clouds. Moreover, reduced turbulence can exacerbate both the human health impacts of high concentrations of fine particles and conditions favorable for low-visibility fog events.

Satellite Observations of Precipitating Marine Stratocumulus Show Greater Cloud Fraction for Decoupled Clouds in Comparison to Coupled Clouds.

This study examines the relationships between marine stratocumulus clouds (MSC) coupling state with the ocean surface, their precipitation rate and fractional cloud cover (CF). This was possible by developing a novel methodology for satellite retrieval of the clouds coupling state. Decks of overcast MSC were reported in previous studies to break up often as their precipitation rate increases significantly, thus reducing CF and cloud radiative effect substantially. Here we show that decks of precipitating decoupled MSC have larger CF compared to similarly precipitating coupled MSC. The difference in CF between decoupled and coupled clouds was found to increase with precipitation rate, up to nearly doubling the CF of the heaviest precipitating decoupled MSC. This suggests that decoupling is a feature related to higher cloud radiative effect in precipitating MSC.