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Mineral dust aerosols impact Earth’s energy budget through interactions with radiation, clouds, atmospheric chemistry, the cryosphere and biogeochemistry. In this Review, we summarize these interactions and assess the resulting impacts of dust, and of changes in dust, on global climate and climate change. The total effect of dust interactions on Earth’s global energy budget—the dust effective radiative effect—is -0.2 ± 0.5 Wm-2 (90% confidence interval), suggesting that dust net cools the climate. Global dust mass loading has increased 55 ± 30% since pre-industrial times, driven largely by increases in dust from Asia and North Africa, leading to changes in the Earth’s energy budget. Indeed, this increase in dust has produced a global mean effective radiative forcing of -0.07 ± 0.18 Wm-2, somewhat counteracting greenhouse warming. Current climate models and climate assessments do not represent the historical increase in dust and thus omit the resulting radiative forcing, biasing climate change projections and assessments of climate sensitivity. Climate model simulations of future changes in dust diverge widely and are very uncertain. Further work is thus needed to constrain the radiative effects of dust on climate and to improve the representation of dust in climate models.

There are myriad connections between air pollution and terrestrial ecosystems, but for many of the associated important elements and processes there is limited process understanding and hence predictive ability. Building [...]

We investigate regional sources contributing to CO during the Korea United States Air Quality (KORUS-AQ) campaign conducted over Korea (1 May to 10 June 2016) using 17 tagged CO simulations from the Community Atmosphere Model with chemistry (CAM-chem). The simulations use three spatial resolutions, three anthropogenic emission inventories, two meteorological fields, and nine emission scenarios. These simulations are evaluated against measurements from the DC-8 aircraft and Measurements Of Pollution In The Troposphere (MOPITT). Results show that simulations using bottom-up emissions are consistently lower (bias: −34 to −39%) and poorer performing (Taylor skill: 0.38–0.61) than simulations using alternative anthropogenic emissions (bias: −6 to −33%; Taylor skill: 0.48–0.86), particularly for enhanced Asian CO and volatile organic compound (VOC) emission scenarios, suggesting underestimation in modeled CO background and emissions in the region. The ranges of source contributions to modeled CO along DC-8 aircraft from Korea and southern (90°E to 123°E, 20°N to 29°N), middle (90°E to 123°E, 29°N to 38.5°N), and northern (90°E to 131.5°E, 38.5°N to 45°N) East Asia (EA) are 6–13%, ~5%, 16–28%, and 9–18%, respectively. CO emissions from middle and northern EA can reach Korea via transport within the boundary layer, whereas those from southern EA are transported to Korea mainly through the free troposphere. Emission contributions from middle EA dominate during continental outflow events (29–51%), while Korean emissions play an overall more important role for ground sites (up to 25–49%) and plumes within the boundary layer (up to 25–44%) in Korea. Finally, comparisons with four other source contribution approaches (FLEXPART 9.1 back trajectory calculations driven by Weather Research and Forecasting (WRF) WRF inert tracer, China signature VOCs, and CO to CO2 enhancement ratios) show general consistency with CAM-chem.

Accurate estimation of flow discharge is crucial for hydrological modelling, water resources planning, and flood prediction. This study examines seven common runoff schemes within the widely-used Noah-MP land surface model and evaluates their performance, using ERA5-Land runoff data as a benchmark for assessing runoff and in-situ streamflow observations for evaluating discharge across the globe. Then, to assess the sensitivity of global river discharge to runoff, we simulate the discharge, using the CaMa-Flood model, across various climatic regions. The results indicated significant variability in the accuracy of the runoff schemes, with model experiments that use TOPMODEL-based runoff schemes, which are based on topography, underestimates runoff across many regions, particularly in the Northern Hemisphere, while experiments using the other runoff schemes (Schaake, BATS, VIC, and XAJ) showed improved performance. Dynamic VIC consistently overestimated runoff globally. Seasonal analysis reveals substantial regional and seasonal variability. ERA5-Land and several Noah-MP schemes successfully replicated general discharge patterns of in-situ observations, with ERA5-Land and Noah-MP Schaake-scheme simulations closely aligning with observed data. The Noah-MP simulations demonstrated robust versatility across various land covers, soil types, basin sizes, and topographies, indicating its broad applicability. Despite overall good performance, significant biases in high-flow extremes highlight the need for continued model improvement or calibration. This study underscores the importance of improving land and hydrological models for accurate water resource management and climate adaptation strategies. [ABSTRACT FROM AUTHOR]

Researchers have recently focused on the interplay of the urban heat island (UHI) effect and heat waves (HWs). However, the synergies of these two phenomena remains inconclusive at present. To address this gap, this study investigated UHIs and HWs synergies during the last 30 years in the Tokyo metropolitan area, through a unique and novel approach named Land‐Surface‐Physics‐Based Downscaling (LSP‐DS). LSP‐DS integrates the widely used Noah‐Multiparameterization (Noah‐MP) land‐surface model coupled with urban canopy‐process physics, aiming to conduct high‐resolution, long‐term urban‐specific simulations with much less computational resources. Our comprehensive analysis combining observation data and numerous LSP‐DS simulations confirms exacerbated UHIs during HWs. Specifically, HWs amplify the temperature differences between urban and rural environments, which is quantified by UHI intensity (UHII). During HWs, UHII increased more at night in inland areas and more during daytime in coastal areas. HWs present especially a heightened threat to coastal regions where daytime UHII increased by approximately 1°C during HWs. The Bowen ratio can explain the increase in the daytime UHII, and the daytime accumulated storage heat increase during HWs can explain the increase in nighttime UHII. Based on future projections of the increasing frequency of high temperatures, our findings highlight the impending heat‐related health challenges faced by urban residents. Plain Language Summary: In the past 30 years in Tokyo, we studied how heat waves influence the urban heat island (UHI) effect using a unique approach called Land‐Surface‐Physics‐Based Downscaling. This method helps simulate urban‐specific conditions in high resolution over a long period using fewer computational resources. Our analysis, combining observations and simulations, confirms that heat waves worsen UHI effects. During heat waves, temperature differences between urban and rural areas (UHI intensity (UHII)) increase more at night in inland regions and more during the day in coastal areas. Coastal areas, especially, face increased daytime UHII by around 1°C during heat waves. Less vegetation in urban areas explains the increase of UHII in the daytime during heat waves, and the higher heat capacity of urban land surface explains the nighttime increase of UHII during heat waves. As heat waves become more frequent and urban populations grow, our findings emphasize the health challenges urban residents may face due to extreme heat. Key Points: Development of Land‐Surface‐Physics‐Based Downscaling approach for urban climate predictionReveals the Added Heat Load (AHL), that is, the enhancement of the urban heat island effect during heat waves (HWs) in the Tokyo regionPositive daytime AHL due to higher increase of urban Bowen ratio during HWs; positive nighttime AHL due to urban's higher heat capacity [ABSTRACT FROM AUTHOR]

The interactions between aerosols and ice clouds represent one of the largest uncertainties in global radiative forcing from pre-industrial time to the present. In particular, the impact of aerosols on ice crystal effective radius (R ei), which is a key parameter determining ice clouds' net radiative effect, is highly uncertain due to limited and conflicting observational evidence. Here we investigate the effects of aerosols on R ei under different meteorological conditions using 9-year satellite observations. We find that the responses of R ei to aerosol loadings are modulated by water vapor amount in conjunction with several other meteorological parameters. While there is a significant negative correlation between R ei and aerosol loading in moist conditions, consistent with the "Twomey effect" for liquid clouds, a strong positive correlation between the two occurs in dry conditions. Simulations based on a cloud parcel model suggest that water vapor modulates the relative importance of different ice nucleation modes, leading to the opposite aerosol impacts between moist and dry conditions. When ice clouds are decomposed into those generated from deep convection and formed in situ, the water vapor modulation remains in effect for both ice cloud types, although the sensitivities of R ei to aerosols differ noticeably between them due to distinct formation mechanisms. The water vapor modulation can largely explain the difference in the responses of R ei to aerosol loadings in various seasons. A proper representation of the water vapor modulation is essential for an accurate estimate of aerosol-cloud radiative forcing produced by ice clouds.

Aerosol-cloud interactions (aerosol indirect effects) play an important role in regional meteorological variations, which could further induce feedback on regional air quality. While the impact of aerosol-cloud interactions on meteorology and climate has been extensively studied, their feedback on air quality remains unclear. Using a fully coupled meteorology-chemistry model, we find that increased aerosol loading due to anthropogenic activities in China substantially increases column cloud droplet number concentration and liquid water path (LWP), which further leads to a reduction in the downward shortwave radiation at surface, surface air temperature and planetary boundary layer (PBL) height. The shallower PBL and accelerated cloud chemistry due to larger LWP in turn enhance the concentrations of particulate matter with diameter less than 2.5 μm (PM2.5) by up to 33.2 μg m−3 (25.1%) and 11.0 μg m−3 (12.5%) in January and July, respectively. Such a positive feedback amplifies the changes in PM2.5 concentrations, indicating an additional air quality benefit under effective pollution control policies but a penalty for a region with a deterioration in PM2.5 pollution. Additionally, we show that the cloud processing of aerosols, including wet scavenging and cloud chemistry, could also have substantial effects on PM2.5 concentrations.

We present a 14-year 12-km hourly air quality dataset created by assimilating satellite observations of aerosol optical depth (AOD) and carbon monoxide (CO) in an air quality model to fill gaps in the contiguous United States (CONUS) air quality monitoring network and help air quality managers understand long-term changes in county level air quality. Specifically, we assimilate the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and the Measurement of Pollution in the Troposphere (MOPITT) CO observations in the Community Multiscale Air Quality Model (CMAQ) every day from 01 Jan 2005 to 31 Dec 2018 to produce this dataset. The Weather Research and Forecasting (WRF) model simulated meteorological fields are used to drive CMAQ offline and to generate meteorology dependent anthropogenic emissions. Both the weather and air quality (surface fine particulate matter (PM2.5) and ozone) simulations are subjected to a comprehensive evaluation against multi-platform observations to establish the credibility of our dataset and characterize its uncertainties. We show that our dataset captures regional hourly, seasonal, and interannual variability in meteorology very well across the CONUS. The correlation coefficient between the observed and simulated surface ozone and PM2.5 concentrations for different Environmental Protection Agency (EPA) defined regions across CONUS are 0.77–0.91 and 0.49–0.79, respectively. The mean bias and root mean squared error for modeled ozone are 3.7–6.8 ppbv and 7–9 ppbv, respectively, while the corresponding values for PM2.5 are -0.9–5.6 µg/m3 and 3.0–8.3 µg/m3, respectively. We estimate that annual CONUS averaged maximum daily 8-hour average (MDA8) ozone and PM2.5 trends are -0.30 ppb/year and -0.24 μg/m3/year, respectively. Wintertime MDA8 ozone shows an increasing but statistically insignificant trend at several sites. We also found a decreasing trend in the 95th percentile of MDA8 ozone but an increasing trend in the 5th percentile. Most of the sites in the Pacific Northwest show an increasing but statistically insignificant trend during summer. An ArcGIS air quality dashboard has been developed to enable easy visualization and interpretation of county level air quality measures and trends by stakeholders, and a Python-based Streamlit application has been developed to allow the download of the air quality data in simplified text and graphic formats. [ABSTRACT FROM AUTHOR]

The accurate calibration of parameters in atmospheric and Earth system models is crucial for improving their performance, but remains a challenge due to their inherent complexity, which is reflected in input-output relationships often characterized by multiple interactions between the parameters and thus hindering the use of simple sensitivity analysis methods. This paper introduces the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a new tool designed with the aim of providing a simple and flexible framework to estimate the sensitivity and importance of parameters in complex numerical weather prediction models. This tool leverages the strengths of multiple regression-based and probabilistic machine learning methods including LASSO, Support Vector Machine, Classification and Decision Trees, Random Forest, Extreme Gradient Boosting, Gaussian Process Regression, and Bayesian Ridge Regression. These regression algorithms are used to construct computationally inexpensive surrogate models to effectively predict model outputs from input parameters, thereby significantly reducing the computational burden of running high-fidelity models for sensitivity analysis. Moreover, the multi-method approach allows for a comparative analysis of the results. Through a detailed case study with the Weather Research and Forecasting (WRF) model coupled with the Noah-MP land surface model, ML-AMPSIT is demonstrated to efficiently predict the behavior of Noah-MP model parameters with a relatively small number of model runs, by simulating a sea breeze circulation over an idealized flat domain. This paper points out how ML-AMPSIT can be an efficient tool for performing sensitivity and importance analysis also for complex models, guiding the user through the different steps and allowing for a simplification and automatization of the process. [ABSTRACT FROM AUTHOR]

The past decade has witnessed remarkable economic development, marked by rapid industrialization and urbanization across Asian regions. This surge in economic activity has led to significant emissions, resulting in alarming levels of air pollution. Our study comprehensively assessed the spatial and temporal trends of key pollutants, namely nitrogen dioxide (NO2), sulfur dioxide (SO2), and aerosol (using aerosol optical depth (AOD) at 550 nm as an indicator), from 2011 to 2021. The data sources utilized include OMI onboard the Aura satellite for NO2 and SO2, as well as MODIS onboard Terra and Aqua satellites for AOD. The results from spatial and temporal trend analyses of the three parameters show that there is a clear declining trend over China and Republic of Korea (e.g., NO2 is declining with an overall rate of −7.8 × 1012 molecules/cm2/year over China) due to the strict implementation of air pollution control policies. However, it is essential to note that both countries still grapple with substantial pollution levels, with proportions exceeding 0.5, indicating that air quality is improving but has not yet reached a safe threshold. In contrast, South Asian regions, including Bangladesh, Pakistan, and India, are experiencing an increasing trend (e.g., NO2 is increasing with an overall rate of 1.2 × 1012 molecules/cm2/year in Bangladesh), primarily due to the lack of rigorous air pollution control policies. The average emissions of NO2 and SO2 were remarkably higher in winter than in summer. Notably, the identified hotspots are statistically significant and predominantly coincide with densely populated areas, such as the North China Plain (NCP). Furthermore, this study underscores the pivotal role of sector-wise emissions in air quality monitoring and improvement. Different cities are primarily influenced by emissions from specific sectors, emphasizing the need for targeted pollution control measures. The findings presented in this research contribute valuable insights to the air quality monitoring and improvement efforts in East and South Asian regions. [ABSTRACT FROM AUTHOR]