Observed Tightening of Tropical Ascent in Recent Decades and Linkage to Regional Precipitation Changes

Climate models predict that the tropical ascending region should tighten under global warming, but observational quantification of the tightening rate is limited. Here we show that the observed spatial extent of the relatively moist, rainy and cloudy regions in the tropics associated with large‐scale ascent has been decreasing at a rate of −1%/decade (−5%/K) from 1979 to 2016, resulting from combined effects of interdecadal variability and anthropogenic forcings, with the former contributing more than the latter. The tightening of tropical ascent is associated with an increase in the occurrence frequency of extremely strong ascent, leading to an increase in the average precipitation rate in the top 1% of monthly rainfall in the tropics. At the margins of the convective zones such as the Southeast Amazonia region, the contraction of large‐scale ascent is related to a long‐term drying trend about −3.2%/decade in the past 38 years

A Statistical Estimation of the Occurrence of Extraterrestrial Intelligence in the Milky Way Galaxy

In the field of astrobiology, the precise location, prevalence, and age of potential extraterrestrial intelligence (ETI) have not been explicitly explored. Here, we address these inquiries using an empirical galactic simulation model to analyze the spatial–temporal variations and the prevalence of potential ETI within the Galaxy. This model estimates the occurrence of ETI, providing guidance on where to look for intelligent life in the Search for ETI (SETI) with a set of criteria, including well-established astrophysical properties of the Milky Way. Further, typically overlooked factors such as the process of abiogenesis, different evolutionary timescales, and potential self-annihilation are incorporated to explore the growth propensity of ETI. We examine three major parameters: (1) the likelihood rate of abiogenesis (λ_A); (2) evolutionary timescales (T_(evo)); and (3) probability of self-annihilation of complex life (P_(ann)). We found P_(ann) to be the most influential parameter determining the quantity and age of galactic intelligent life. Our model simulation also identified a peak location for ETI at an annular region approximately 4 kpc from the galactic center around 8 billion years (Gyrs), with complex life decreasing temporally and spatially from the peak point, asserting a high likelihood of intelligent life in the galactic inner disk. The simulated age distributions also suggest that most of the intelligent life in our galaxy are young, thus making observation or detection difficult

Impact of Cloud Ice Particle Size Uncertainty in a Climate Model and Implications for Future Satellite Missions

Ice particle size is pivotal to determining ice cloud radiative effect and precipitating rate. However, there is a lack of accurate ice particle effective radius (R_(ei)) observation on the global scale to constrain its representation in climate models. In support of future mission design, here we present a modeling assessment of the sensitivity of climate simulations to R_(ei) and quantify the impact of the proposed mission concept on reducing the uncertainty in climate sensitivity. We perturb the parameters pertaining to ice fall speed parameter and R_(ei) in radiation scheme, respectively, in National Center for Atmospheric Research CESM1 model with a slab ocean configuration. The model sensitivity experiments show that a settling velocity increase due to a larger R_(ei) results in a longwave cooling dominating over a shortwave warming, a global mean surface temperature decrease, and precipitation suppression. A similar competition between longwave and shortwave cloud forcing changes also exists when perturbing R_(ei) in the radiation scheme. Linearity generally holds for the climate response for R_(ei) related parameters. When perturbing falling snow particle size (R_(es)) in a similar way, we find much less sensitivity of climate responses. Our quadrupling CO₂ experiments with different parameter settings reveal that R_(ei) and R_(es) can account for changes in climate sensitivity significantly from +12.3% to −6.2%. By reducing the uncertainty ranges of R_(ei) and R_(es) from a factor of 2 to ±25%, a future satellite mission under design is expected to improve the climate state simulations and reduce the climate sensitivity uncertainty pertaining to ice particle size by approximately 60%. Our results highlight the importance of better observational constraints on R_(ei) by satellite missions

Radiative absorption enhancement of dust mixed with anthropogenic pollution over East Asia

The particle mixing state plays a significant yet poorly quantified role in aerosol radiative forcing, especially for the mixing of dust (mineral absorbing) and anthropogenic pollution (black carbon absorbing) over East Asia. We have investigated the absorption enhancement of mixed-type aerosols over East Asia by using the Aerosol Robotic Network observations and radiative transfer model calculations. The mixed-type aerosols exhibit significantly enhanced absorbing ability than the corresponding unmixed dust and anthropogenic aerosols, as revealed in the spectral behavior of absorbing aerosol optical depth, single scattering albedo, and imaginary refractive index. The aerosol radiative efficiencies for the dust, mixed-type, and anthropogenic aerosols are −101.0, −112.9, and −98.3 Wm⁻²τ⁻¹ at the bottom of the atmosphere (BOA); −42.3, −22.5, and −39.8 Wm⁻²τ⁻¹ at the top of the atmosphere (TOA); and 58.7, 90.3, and 58.5 Wm⁻²τ⁻¹ in the atmosphere (ATM), respectively. The BOA cooling and ATM heating efficiencies of the mixed-type aerosols are significantly higher than those of the unmixed aerosol types over the East Asia region, resulting in atmospheric stabilization. In addition, the mixed-type aerosols correspond to a lower TOA cooling efficiency, indicating that the cooling effect by the corresponding individual aerosol components is partially counteracted. We conclude that the interaction between dust and anthropogenic pollution not only represents a viable aerosol formation pathway but also results in unfavorable dispersion conditions, both exacerbating the regional air pollution in East Asia. Our results highlight the necessity to accurately account for the mixing state of aerosols in atmospheric models over East Asia in order to better understand the formation mechanism for regional air pollution and to assess its impacts on human health, weather, and climate

Impact of Cloud Ice Particle Size Uncertainty in a Climate Model and Implications for Future Satellite Missions

Ice particle size is pivotal to determining ice cloud radiative effect and precipitating rate. However, there is a lack of accurate ice particle effective radius (R_(ei)) observation on the global scale to constrain its representation in climate models. In support of future mission design, here we present a modeling assessment of the sensitivity of climate simulations to R_(ei) and quantify the impact of the proposed mission concept on reducing the uncertainty in climate sensitivity. We perturb the parameters pertaining to ice fall speed parameter and R_(ei) in radiation scheme, respectively, in National Center for Atmospheric Research CESM1 model with a slab ocean configuration. The model sensitivity experiments show that a settling velocity increase due to a larger R_(ei) results in a longwave cooling dominating over a shortwave warming, a global mean surface temperature decrease, and precipitation suppression. A similar competition between longwave and shortwave cloud forcing changes also exists when perturbing R_(ei) in the radiation scheme. Linearity generally holds for the climate response for R_(ei) related parameters. When perturbing falling snow particle size (R_(es)) in a similar way, we find much less sensitivity of climate responses. Our quadrupling CO₂ experiments with different parameter settings reveal that R_(ei) and R_(es) can account for changes in climate sensitivity significantly from +12.3% to −6.2%. By reducing the uncertainty ranges of R_(ei) and R_(es) from a factor of 2 to ±25%, a future satellite mission under design is expected to improve the climate state simulations and reduce the climate sensitivity uncertainty pertaining to ice particle size by approximately 60%. Our results highlight the importance of better observational constraints on R_(ei) by satellite missions

Air quality impact of the Northern California Camp Fire of November 2018

The Northern California Camp Fire that took place in November 2018 was one of the most damaging environmental events in California history. Here, we analyze ground-based station observations of airborne particulate matter that has a diameter <2.5 µm (PM_(2.5)) across Northern California and conduct numerical simulations of the Camp Fire using the Weather Research and Forecasting model online coupled with chemistry (WRF-Chem). Simulations are evaluated against ground-based observations of PM_(2.5), black carbon, and meteorology, as well as satellite measurements, such as Tropospheric Monitoring Instrument (TROPOMI) aerosol layer height and aerosol index. The Camp Fire led to an increase in Bay Area PM_(2.5) to over 50 µg m⁻³ for nearly 2 weeks, with localized peaks exceeding 300 µg m⁻³. Using the Visible Infrared Imaging Radiometer Suite (VIIRS) high-resolution fire detection products, the simulations reproduce the magnitude and evolution of surface PM_(2.5) concentrations, especially downwind of the wildfire. The overall spatial patterns of simulated aerosol plumes and their heights are comparable with the latest satellite products from TROPOMI. WRF-Chem sensitivity simulations are carried out to analyze uncertainties that arise from fire emissions, meteorological conditions, feedback of aerosol radiative effects on meteorology, and various physical parameterizations, including the planetary boundary layer model and the plume rise model. Downwind PM2.5 concentrations are sensitive to both flaming and smoldering emissions over the fire, so the uncertainty in the satellite-derived fire emission products can directly affect the air pollution simulations downwind. Our analysis also shows the importance of land surface and boundary layer parameterization in the fire simulation, which can result in large variations in magnitude and trend of surface PM_(2.5). Inclusion of aerosol radiative feedback moderately improves PM_(2.5) simulations, especially over the most polluted days. Results of this study can assist in the development of data assimilation systems as well as air quality forecasting of health exposures and economic impact studies

Interpretation of the Top-of-Atmosphere Energy Flux for Future Arctic Warming

With the trend of amplified warming in the Arctic, we examine the observed and modeled top-of-atmosphere (TOA) radiative responses to surface air-temperature changes over the Arctic by using TOA energy fluxes from NASA’s CERES observations and those from twelve climate models in CMIP5. Considerable inter-model spreads in the radiative responses suggest that future Arctic warming may be determined by the compensation between the radiative imbalance and poleward energy transport (mainly via transient eddy activities). The poleward energy transport tends to prevent excessive Arctic warming: the transient eddy activities are weakened because of the reduced meridional temperature gradient under polar amplification. However, the models that predict rapid Arctic warming do not realistically simulate the compensation effect. This role of energy compensation in future Arctic warming is found only when the inter-model differences in cloud radiative effects are considered. Thus, the dynamical response can act as a buffer to prevent excessive Arctic warming against the radiative response of 0.11 W m^(−2) K^(−1) as measured from satellites, which helps the Arctic climate system retain an Arctic climate sensitivity of 4.61 K. Therefore, if quantitative analyses of the observations identify contribution of atmospheric dynamics and cloud effects to radiative imbalance, the satellite-measured radiative response will be a crucial indicator of future Arctic warming