This study investigates the impacts of aerosol atmospheric rivers (AARs) on extreme Particulate Matter 2.5 (PM2.5) levels (PM2.5 > 15mgm-3, as per the WHO) and on aerosol optical depth (AOD) extremes (AOD > 98th percentile) over the US and the globe, respectively, between 1997-2020. Results show that over various regions over the US, extreme PM2.5values are associated with AARs up to 70% of the time. Dust (sulfate) AARs are responsible for extreme PM2.5 levels over the southwestern (northeastern and the east coastal) US. Organic and black carbon AARs are associated with extreme PM2.5 levels over the Midwest region of the US. Globally, AARs are associated with 40-80% of the extreme AOD levels over the US, Sahel, Europe, Middle East, US, South America, East Asia, India, and South Africa. Such associations often lead to the highest or the second highest PM2.5 and AOD levels recorded over those stations between 1997-2020.

Fine particulate matter with a size less than 2.5 µm (PM2.5) is increasing due to economic growth, air pollution, and forest fires in some states in the United States. Although previous studies have attempted to retrieve the spatial and temporal behavior of PM2.5 using aerosol remote sensing and geostatistical estimation methods the coarse resolution and accuracy limit these methods. In this paper the performance of machine learning models on predicting PM2.5 is assessed with Linear Regression (LR), Decision Tree (DT), Gradient Boosting Regression (GBR), AdaBoost Regression (ABR), XG Boost (XGB), k-nearest neighbors (KNN), Long Short-Term Memory (LSTM), Random Forest (RF), and support vector machine (SVM) using PM2.5 station data from 2017-2021. To compare the accuracy of all the nine machine learning models the coefficient of determination (R2), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), root mean square error ratio (RSR), and percent bias (PBIAS) were evaluated. Among all nine models the RF and SVM models were the best for predicting PM2.5 concentrations. Comparison of the PM2.5 performance metrics displayed that the models had better predictive behavior in the western United States than that in the eastern United States.

The quest for finding extraterrestrial life has been a fascination for humanity for centuries, yet we have not yet found any concrete evidence of life beyond our planet, except for some hints of methane on Mars. It is essential to document this time of not having found any life to fully describe our perspective to other civilizations, anticipate how these events may change humanity, and appreciate the uncertainty of our knowledge about the universe.

Over 18 years of satellite data from Multi-Angle Imaging Spectroradiometer (MISR) and 14 years from Global Navigation Satellite System-radio occultation (GNSS-RO), with ERA5 reanalysis temperature profiles, are used to assess the co-variability of cloud and thermodynamic properties of the Northeast Pacific subtropical marine boundary layer. Low cloud top height (CTH) inferred from MISR and planetary boundary layer height (PBLH) inferred from GNSS-RO are well-correlated spatially for all seasons when seasonally-varying mid-latitude grids (temperature at 700 hPa < 4°C) are removed (r=0.83), or when vertical velocity at 500 hPa (ω500) indicates descent (r=0.74). The temporal correlation of PBLH and CTH is highest in the stratocumulus region (r=0.72), with the CTH versus PBLH slope close to one for heights between 0.8 km and 1.6 km of the time series. Seasonal sea-surface to 700 hPa lapse rate (LR) is spatially related with PBLH and more strongly with CTH, and ω500 modulates seasonal CTH-LR relationships. The impact of El Niño Southern Oscillation (ENSO) through teleconnections on the PBL structure is also characterized, with maximum deseasonalized temperature anomalies near or above PBL top (near the surface) during La Niña (El Niño), with CTH, PBLH, and LR anomalies largest during the strong 2015-2016 El Niño. Temperature anomalies above the PBL lead CTH’ and PBLH’ by 15 and 18 months, respectively, just under half the time scale of the periodicity of an Ocean Niño Index mode (~3.1 years), suggestive of the role of atmosphere-to-ocean exchange manifesting in a deepening PBL during warm ENSO.

Communication is an essential asset enabling humankind to forge an advanced civilization. Using approximately 31,000 languages from the Stone Age to our present digital information society, humans have connected and collaborated to accomplish remarkable feats. As the newly dawned Space Age progresses, we are attempting to communicate with intelligent species beyond our world, on distant planets and in Earth’s far future. Absent mutually understood signs, symbols, and semiotic conventions, this study, the “Message in a Bottle”, uses scientific methods to assess and design a means of communication encapsulating the story of humanity, conveying our thoughts, emotions, ingenuity, and aspirations. The message will be structured to provide a universal yet contextual understanding of modern human society, evolution of life on Earth, and challenges for the future. In assembling this space and time capsule, we aim to energize and unite current generations to celebrate and preserve humanity.

Fine particulate matter with a size less than 2.5 µm (PM2.5) is increasing due to economic growth, air pollution, and forest fires in some states in the United States. Although previous studies have attempted to retrieve the spatial and temporal behavior of PM2.5 using aerosol remote sensing and geostatistical estimation methods the coarse resolution and accuracy limit these methods. In this paper the performance of machine learning models on predicting PM2.5 is assessed with Linear Regression (LR), Decision Tree (DT), Gradient Boosting Regression (GBR), AdaBoost Regression (ABR), XG Boost (XGB), k-nearest neighbors (KNN), Long Short-Term Memory (LSTM), Random Forest (RF), and support vector machine (SVM) using PM2.5 station data from 2017-2021. To compare the accuracy of all the nine machine learning models the coefficient of determination (R2), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), root mean square error ratio (RSR), and percent bias (PBIAS) were evaluated. Among all nine models the RF and SVM models were the best for predicting PM2.5 concentrations. Comparison of the PM2.5 performance metrics displayed that the models had better predictive behavior in the western United States than that in the eastern United States.

Many regions across the globe broke their surface temperature records in recent years, further sparking concerns about the impending arrival of “tipping points” later in the 21st century. This study analyzes observed global surface temperature trends in three target latitudinal regions: the Arctic Circle, the Tropics, and the Antarctic Circle. We show that global warming is accelerating unevenly across the planet, with the Arctic warming at approximately three times the average rate of our world. We further analyzed the reliability of latitude-dependent surface temperature simulations from a suite of Coupled Model Intercomparison Project Phase 6 models and their multi-model mean. We found that GISS-E2-1-G and FGOALS-g3 were the best-performing models based on their statistical abilities to reproduce observational, latitude-dependent data. Surface temperatures were projected from ensemble simulations of the Shared Socioeconomic Pathway 2-4.5 (SSP2-4.5). We estimate when the climate will warm by 1.5, 2.0, and 2.5 ℃ relative to the preindustrial period, globally and regionally. GISS-E2-1-G projects that global surface temperature anomalies would reach 1.5, 2.0, and 2.5 ℃ in 2024 (±1.34), 2039 (±2.83), and 2057 (±5.03) respectively, while FGOALS-g3 predicts these “tipping points” would arrive in 2024 (±2.50), 2054 (±7.90), and 2087 (±10.55) respectively. Our results reaffirm a dramatic, upward trend in projected climate warming acceleration, with upward concavity in 21st century projections of the Arctic, which could lead to catastrophic consequences across the Earth. Further studies are necessary to determine the most efficient solutions to reduce global warming acceleration and maintain a low SSP, both globally and regionally.

This study derives radiatively-active hydrometeors frequencies (HFs) from CloudSat-CALIPSO satellite data to evaluate cloud fraction in present-day simulations by CMIP5 models. Most CMIP5 models do not consider precipitating and/or convective hydrometeors but CESM1-CAM5 in CMIP5 has diagnostic snow and CESM2-CAM6 in CMIP6 has prognostic precipitating ice (snow) included. However, the models do not have snow fraction available for evaluation. Since the satellite-retrieved hydrometeors include the mixtures of floating, precipitating and convective ice and liquid particles, a filtering method is applied to produce estimates of cloud-only HF (or NPCHF) from the total radiatively-active HF (THF), which is the sum of NPCHF, precipitating ice HF and convective HF. The reference HF data for model evaluation include estimates of liquid-phase NPCHF from CloudSat radar-only data (2B-CWC) and ice-phase THF from CloudSat-CALIPSO 2C-ICE combined radar/lidar data. The model evaluation results show that cloud fraction from CMIP5 multi-model mean (MMM) is significantly underestimated (up to 30 %) against the total HF estimates, mainly below the mid-troposphere over the extratropics and in the upper-troposphere over the midlatitude lands and a few tropical convective regions. The CMIP5 cloud fraction biases are reduced dramatically when compared to the cloud-only HF estimates, but the area of overestimates expands from the tropical convective regions to mid-latitudes in the lower and upper troposphere. There is no CMIP5 standard output snow fraction available for comparison against CloudSat-CALIPSO estimate. The implications of these results show that hydrometeors frequency estimates from CloudSat-CALIPSO provide a reference for GCM’s cloud fraction from stratiform and convective form.

Clouds play a significant role in the Earth’s energy balance and hydrological cycle through their effects on radiation and precipitation, and therefore are crucial for life on Earth. Earth’s NexT‐generation ICE mission (ENTICE) is proposed to measure diurnally resolved global vertical profiles of cloud ice particle size, ice water content, and in-cloud humidity and temperature using multi-frequency sub-millimeter (sub-mm) microwave radiometers and a 94 GHz cloud radar from space. The scientific objective of ENTICE is to identify the important processes by which anvil clouds evolve and interact with ambient thermodynamic conditions to advance our fundamental understanding of clouds and reduce uncertainties in cloud climate feedback. Whether such an objective could be achieved depends on the orbital sampling characteristics of the mission. In this study, ENTICE sampling statistics are simulated using five different scanning methods in a 400 km altitude precession orbit with an inclination of 65°: nadir, forward pointing, side scanning, and conical scanning for the radiometers, and nadir pointing for the radar. Using the GEOS-5 Nature Run produced at 7-km and 30-min resolution, sampling statistics with respect to cloud types and local hours with enhancement from radar are calculated for ENTICE. The wide swath of ENTICE radiometers by conical and side scanning methods ensures ample high cloud samples gathered by ENTICE over its two-year mission for different types of clouds with sufficient sampling over the diurnal cycles. Sampling differences between radar and radiometers at nadir demonstrate that the combination of radar and radiometers will allow for measurements of cloud vertical profiles. Therefore, our results show that the designed orbit sampling of ENTICE is sufficient to fulfill the mission science goals.

Changes in aerosol optical depth, both positive and negative, are observed across the globe during the 21rst Century. However, attribution of these changes to specific sources is largely uncertain as there are multiple contributing natural and anthropogenic sources that produce aerosols either directly or through secondary chemical reactions. Here we show that satellite-based changes in small-mode AOD between 2002 and 2019 observed in data from MISR can primarily be explained by changes, either directly or indirectly, in combustion emissions. We quantify combustion emissions using MOPITT total column CO observations and the adjoint of the GEOS-Chem global chemistry and transport model. The a priori fire emissions are taken from the Global Fire Emission Data base with small fires (GFED4s) but with fixed a priori for non-fire emissions. Aerosol precursor and direct emissions are updated by re-scaling them with the monthly ratio of the CO posterior to prior emissions. The correlation between modeled and observed AOD improves from a mean of 0.15 to 0.81 for the four industrial regions considered and from 0.52 to 0.75 for the four wildfire-dominant regions considered. Using these updated emissions in the GEOS-Chem global chemistry transport model, our results indicate that surface PM2.5 have declined across many regions of the globe during the 21rst century. For example, PM2.5 over China has declined by ~30% with smaller fractional declines in E. USA and Europe (from fossil emissions) and in S. America (from fires). These results highlight the importance of forest management and cleaner combustion sources in improving air-quality.