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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]

Terrestrial hydrology is altered by fires, particularly in snow‐dominated catchments. However, fire impacts on catchment hydrology are often neglected from land surface model (LSM) simulations. Western U.S. wildfire activity has been increasing in recent decades and is projected to continue increasing over at least the next three decades, and thus it is important to evaluate if neglecting fire impacts in operational land surface models (LSMs) is a significant error source that has a noticeable signal among other sources of uncertainty. We evaluate a widely used state‐of‐the‐art LSM (Noah‐MP) in runoff and snowpack simulations at two representative fire‐affected snow‐dominated catchments in the Pacific Northwest: Andrew's Creek in Washington and Johnson Creek in Idaho. These two catchments are selected across all western U.S. fire‐affected catchments because they are snow‐dominated and experienced more than 50% burning in a single fire event with minimal burning outside of this event, which allows analyses of distinct pre‐ and post‐fire periods. There are statistically significant shifts in model skills from pre‐to post‐fire years in simulating runoff and snowpack. At both study catchments, simulations miss enhancements in early‐spring runoff and annual runoff efficiency during post‐fire years, resulting in persistent underestimates of annual runoff anomalies throughout the 12‐year post‐fire analysis periods. Enhanced post‐fire snow accumulation and melt contributes to observed but unmodeled increases of spring runoff and annual runoff efficiency at these catchments. Informing simulations with satellite observed land cover classifications, leaf area index, and green fraction do not consistently improve the model ability to simulate hydrologic responses to fire disturbances. Plain Language Summary: Western U.S. fire activity has been increasing in recent decades and is expected to continue to increase in coming decades. Fires remove vegetation and alter soils which in turn alters terrestrial hydrology. Fire effects on hydrology are particularly significant over snowy catchments that serve as natural water towers for major western U.S. rivers. Sophisticated models often neglect or underrepresent fire effects on land surface properties, and thus are susceptible to larger errors after fires. This study compares runoff and snow simulations from a widely used land surface model (LSM) with observations to quantify fire‐induced changes in model accuracy over two snow dominated catchments in the Pacific Northwest. Simulations persistently underestimate enhanced spring runoff and annual runoff anomalies in post‐fire years. These underestimates are consistent with observed enhancements in post‐fire snow accumulation and melt, which the model mostly failed to capture. The finding that post‐fire model errors are consistent with previously published fire impacts on hydrology supports that fire is an important error source in LSM simulations that should be accounted for. Key Points: Noah‐MP underestimates annual runoff anomalies relative to observations following fires in the Pacific‐NorthwestIn post‐fire years, Noah‐MP fails to simulate deeper snowpacks that ablate faster and the associated enhanced spring runoffInforming the model with satellite‐observed vegetation characteristics (including fire effect) did not resolve post‐fire model deficiencies [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 accuracy of snow density in land surface model (LSM) simulations impacts the accuracy of simulated terrestrial water and energy budgets. However, there has been little research that has focused on enhancing snow compaction in operationally used LSMs. A baseline snow simulation with the widely used Noah‐MP LSM systematically overestimates snow depth by 55 mm even after removing daily snow water equivalent (SWE) biases. To reduce uncertainties associated with snow compaction, we enhance the most sensitive Noah‐MP snow compaction parameter—the empirical parameter for compaction due to overburden (Cbd)—such that Cbd is calculated as a function of surface air temperature as opposed to a fixed value in the baseline simulation. This enhancement improves accuracy in simulated snow compaction across the majority of western U.S. (WUS) SNOTEL test sites (biases reduced at 88% of test sites), with modest bias reductions in cooler accumulation periods (biases reduced at 70% of test sites) and substantial improvements during warmer ablation periods (biases reduced at 99% of test sites). Relatively larger improvements during warm conditions are attributable to the default Cbd value being reasonable for cold temperatures (≤−5°C). Improvements in simulated snow depth and density with observations outside of the training sites and optimization periods support that the snow compaction enhancement is transferable in space and time. Differences between enhanced and baseline gridded simulations across the total WUS support that the enhancement can have important impacts on snowpack evolution, snow albedo feedback, and snow hydrology. Plain Language Summary: Snow density, the ratio of water in the snowpack to the snow depth, is a fundamental property of snow that is important to accurately represent in snow simulations. In this research, we develop and evaluate a model enhancement designed to more accurately simulate the snow densification process in a widely used land surface model, Noah‐MP. Evaluations of the baseline snow compaction scheme, without model enhancements, reveals reasonable performance during cold conditions but substantial errors during warm conditions. We update the Noah‐MP snow compaction scheme to calculate the most sensitive snow compaction parameter as a function of surface air temperature. This enhancement provides substantially more accurate representations of snow compaction during warm conditions and provides similar performance relative to the baseline simulation during cold conditions. Improved accuracy in simulating snow densification is consistent between training sites the enhancement was optimized over and sites that were only used for validation both within and outside of the time period of optimization, supporting that improvements associated with using our model update can be expected at places and times distinct from the optimization locations and time periods. Key Points: Noah‐MP snow compaction parameterization is enhanced using SNOTEL snow observationsThe snow compaction enhancement provides substantial improvements in snow density and depth particularly during warm conditionsThe enhanced snow compaction scheme can have notable effects on snowpack evolution and snow albedo feedback [ABSTRACT FROM AUTHOR]

We enhance the Community Land Model (CLM) snow albedo modeling by implementing several new features with more realistic and physical representations of snow‐aerosol‐radiation interactions. Specifically, we incorporate the following model enhancements: (a) updating ice and aerosol optical properties with more realistic and accurate data sets, (b) adding multiple dust types, (c) adding multiple surface downward solar spectra to account for different atmospheric conditions, (d) incorporating a more accurate adding‐doubling radiative transfer solver, (e) adding nonspherical snow grain representations, (f) adding black carbon‐snow and dust‐snow internal mixing representations, and (g) adding a hyperspectral (480‐band vs. the default 5‐band) modeling capability. These model features/enhancements are included as new CLM physics/namelist options, which allows for quantification of model sensitivity to snow albedo processes and for multi‐physics model ensemble analyses for uncertainty assessment. The model updates have been included in the latest released CLM version. Sensitivity analyses reveal strong impacts of using the new adding‐doubling solver, nonspherical snow grains, aerosol‐snow internal mixing, updated aerosol optics, and different dust types. These enhanced snow albedo representations improve the CLM simulated global snowpack evolution and land surface conditions, with reduced biases in simulated snow surface albedo, snow cover, snow water equivalent, snow depth, and surface (2‐m) air temperature over many mid‐latitude mountainous regions and seasonal snowpacks but degraded performance in some northern high‐latitude regions. Plain Language Summary: Snow albedo plays a critical role in the Earth system, affecting land surface energy and water balance and also serving as an important land process that feeds back to the atmosphere. Several recent studies have identified new or improved physical representations of snow albedo processes. In this study, we leverage recent advances in snow albedo modeling to implement a number of relevant new features into the widely used Community Land Model (CLM), which is the land component of the Community Earth System Model (CESM). Specifically, we improve the ice and aerosol optical properties, the treatment of dust types and downward sunlight energy distribution at different wavelength bands, the albedo computation algorithm, and the representations of snow grain shape and how aerosol is mixed with snow grains. These model updates have been included in the latest released CLM version. Overall, the enhanced snow albedo representations improve the simulated snowpack evolution and related land surface conditions in many parts of the globe. Key Points: We enhance CLM5 snow albedo modeling by including more realistic and physical representations of snow‐aerosol‐radiation interactionsThe new adding‐doubling solver, nonspherical snow grains, aerosol‐snow internal mixing, and updated aerosol optics show strong impactsThe enhanced snow albedo representation improves the CLM simulated snowpack evolution and land surface conditions in many global regions [ABSTRACT FROM AUTHOR]

The article discusses the First International Noah-MP Annual Users' Workshop, which brought together over 200 participants from 16 countries to discuss advancements in the Noah-MP land surface model. The workshop focused on enhancing the model's capabilities, applicability, and interoperability in Earth system applications. The article highlights the various applications of the Noah-MP model, including weather prediction, climate projection, hydrology, agriculture, and urban heat island studies. It also identifies current challenges and limitations of the model, such as uncertainties in certain processes and a lack of communication and coordination within the Noah-MP community. The article concludes with recommendations for future model development and the establishment of a Noah-MP Academia Collaboratory to support community collaborations and enhance research-to-operation activities. [Extracted from the article]

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]

The Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) is a new community modeling infrastructure that enables the study of atmospheric composition and chemistry across all relevant scales. We develop a MUSICAv0 grid with Africa refinement (∼ 28 km × 28 km over Africa). We evaluate the MUSICAv0 simulation for 2017 with in situ observations and compare the model results to satellite products over Africa. A simulation from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a regional model that is widely used in Africa studies, is also included in the analyses as a reference. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Both models underestimate carbon monoxide (CO) compared to in situ observations and satellite CO column retrievals from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument. MUSICAv0 tends to overestimate ozone (O3), likely due to overestimated stratosphere-to-troposphere flux of ozone. Both models significantly underestimate fine particulate matter (PM2.5) at two surface sites in East Africa. The MUSICAv0 simulation agrees better with aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric nitrogen dioxide (NO2) column retrievals from the Ozone Monitoring Instrument (OMI) than WRF-Chem. MUSICAv0 has a consistently lower tropospheric formaldehyde (HCHO) column than OMI retrievals. Based on model–satellite discrepancies between MUSICAv0 and WRF-Chem and MOPITT CO , MODIS AOD, and OMI tropospheric NO2 , we find that future field campaign(s) and more in situ observations in the East African region (5 ∘ S–5 ∘ N, 30–45 ∘ E) could substantially improve the predictive skill of atmospheric chemistry model(s). This suggested focus region exhibits the largest model–in situ observation discrepancies, as well as targets for high population density, land cover variability, and anthropogenic pollution sources. [ABSTRACT FROM AUTHOR]

The widely used open-source community Noah with multi-parameterization options (Noah-MP) land surface model (LSM) is designed for applications ranging from uncoupled land surface hydrometeorological and ecohydrological process studies to coupled numerical weather prediction and decadal global or regional climate simulations. It has been used in many coupled community weather, climate, and hydrology models. In this study, we modernize and refactor the Noah-MP LSM by adopting modern Fortran code standards and data structures, which substantially enhance the model modularity, interoperability, and applicability. The modernized Noah-MP is released as the version 5.0 (v5.0), which has five key features: (1) enhanced modularization as a result of re-organizing model physics into individual process-level Fortran module files, (2) an enhanced data structure with new hierarchical data types and optimized variable declaration and initialization structures, (3) an enhanced code structure and calling workflow as a result of leveraging the new data structure and modularization, (4) enhanced (descriptive and self-explanatory) model variable naming standards, and (5) enhanced driver and interface structures to be coupled with the host weather, climate, and hydrology models. In addition, we create a comprehensive technical documentation of the Noah-MP v5.0 and a set of model benchmark and reference datasets. The Noah-MP v5.0 will be coupled to various weather, climate, and hydrology models in the future. Overall, the modernized Noah-MP allows a more efficient and convenient process for future model developments and applications. [ABSTRACT FROM AUTHOR]

The representation of aerosols in climate–chemistry models is important for air quality and climate change research, but it can require significant computational resources. The objective of this study was to improve the representation of aerosols in climate–chemistry models, specifically in the carbon bond mechanism, version Z (CBMZ), and modal aerosol modules with three lognormal modes (MAM3) in the WRF-CAM5 model. The study aimed to enhance the model's chemistry capabilities by incorporating biomass burning emissions, establishing a conversion mechanism between volatile organic compounds (VOCs) and secondary organic carbons (SOCs), and evaluating its performance against observational benchmarks. The results of the study demonstrated the effectiveness of the enhanced chemistry capabilities in the WRF-CAM5 model. Six simulations were conducted over the western U.S. and northeastern Pacific region, comparing the model's performance with observational benchmarks such as reanalysis, ground-based, and satellite data. The findings revealed a significant reduction in root-mean-square errors (RMSE) for surface concentrations of black carbon (BC) and organic carbon (OC). Specifically, the model exhibited a 31% reduction in RMSE for BC concentrations and a 58% reduction in RMSE for OC concentrations. These outcomes underscored the importance of accurate aerosol representation in climate–chemistry models and emphasized the potential for improving simulation accuracy and reducing errors through the incorporation of enhanced chemistry modules in such models. [ABSTRACT FROM AUTHOR]

The US Northern Great Plains and the Canadian Prairies are known as the world's breadbaskets for their large spring wheat production and exports to the world. It is essential to accurately represent spring wheat growing dynamics and final yield and improve our ability to predict food production under climate change. This study attempts to incorporate spring wheat growth dynamics into the Noah-MP crop model for a long time period (13 years) and fine spatial scale (4 km). The study focuses on three aspects: (1) developing and calibrating the spring wheat model at a point scale, (2) applying a dynamic planting and harvest date to facilitate large-scale simulations, and (3) applying a temperature stress function to assess crop responses to heat stress amid extreme heat. Model results are evaluated using field observations, satellite leaf area index (LAI), and census data from Statistics Canada and the United States Department of Agriculture (USDA). Results suggest that incorporating a dynamic planting and harvest threshold can better constrain the growing season, especially the peak timing and magnitude of wheat LAI, as well as obtain realistic yield compared to prescribing a static province/state-level map. Results also demonstrate an evident control of heat stress upon wheat yield in three Canadian Prairies Provinces, which are reasonably captured in the new temperature stress function. This study has important implications in terms of estimating crop yields, modeling the land–atmosphere interactions in agricultural areas, and predicting crop growth responses to increasing temperatures amidst climate change. [ABSTRACT FROM AUTHOR]

The Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) is a new community modeling infrastructure that enables the study of atmospheric composition and chemistry across all relevant scales. We develop a MUSICAv0 grid with Africa refinement (~28 km x 28 km over Africa). We evaluate the MUSICAv0 simulation for 2017 with in situ observations and compare the model results to satellite products over Africa. A simulation from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a regional model that is widely used in Africa studies, is also included in the analyses as a reference. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Both models underestimate carbon monoxide (CO) compared to in situ observations and satellite CO column retrievals from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument. MUSICAv0 tends to overestimate ozone (O3), likely due to overestimated stratosphere-to-troposphere flux of ozone. Both models significantly underestimate fine particulate matter (PM2.5) at two surface sites in East Africa. The MUSICAv0 simulation agrees better with aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric nitrogen dioxide (NO2) column retrievals from the Ozone Monitoring Instrument (OMI) than WRF-Chem. MUSICAv0 has a consistently lower tropospheric formaldehyde (HCHO) column than OMI retrievals. Based on model-satellite discrepancies between MUSICAv0 and WRF-Chem and MOPITT CO, MODIS AOD, and OMI tropospheric NO2, we find that future field campaign(s) and more in situ observations in an East African region (30°E - 45°E, 5°S - 5°N) could substantially improve the predictive skill of atmospheric chemistry model(s). This suggested focus region exhibits the largest model-in situ observation discrepancies, as well as targets for high population density, land cover variability, and anthropogenic pollution sources. [ABSTRACT FROM AUTHOR]

We quantify future changes in wildfire burned area and carbon emissions in the 21st century under four Shared Socioeconomic Pathways (SSPs) scenarios and two SSP5-8.5-based solar geoengineering scenarios with a target surface temperature defined by SSP2-4.5 – solar irradiance reduction (G6solar) and stratospheric sulfate aerosol injections (G6sulfur) – and explore the mechanisms that drive solar geoengineering impacts on fires. This study is based on fully coupled climate–chemistry simulations with simulated occurrence of fires (burned area and carbon emissions) using the Whole Atmosphere Community Climate Model version 6 (WACCM6) as the atmospheric component of the Community Earth System Model version 2 (CESM2). Globally, total wildfire burned area is projected to increase over the 21st century under scenarios without geoengineering and decrease under the two geoengineering scenarios. By the end of the century, the two geoengineering scenarios have lower burned area and fire carbon emissions than not only their base-climate scenario SSP5-8.5 but also the targeted-climate scenario SSP2-4.5. Geoengineering reduces wildfire occurrence by decreasing surface temperature and wind speed and increasing relative humidity and soil water, with the exception of boreal regions where geoengineering increases the occurrence of wildfires due to a decrease in relative humidity and soil water compared with the present day. This leads to a global reduction in burned area and fire carbon emissions by the end of the century relative to their base-climate scenario SSP5-8.5. However, geoengineering also yields reductions in precipitation compared with a warming climate, which offsets some of the fire reduction. Overall, the impacts of the different driving factors are larger on burned area than fire carbon emissions. In general, the stratospheric sulfate aerosol approach has a stronger fire-reducing effect than the solar irradiance reduction approach. [ABSTRACT FROM AUTHOR]

The widely-used open-source community Noah-MP land surface model (LSM) is designed for applications ranging from uncoupled land-surface and ecohydrological process studies to coupled numerical weather prediction and decadal global/regional climate simulations. It has been used in many coupled community weather/climate/hydrology models. In this study, we modernize/refactor the Noah-MP LSM by adopting modern Fortran code and data structures and standards, which substantially enhances the model modularity, interoperability, and applicability. The modernized Noah-MP is released as the version 5.0 (v5.0), which has five key features: (1) enhanced modularization and interoperability by re-organizing model physics into individual process-level Fortran module files, (2) enhanced data structure with new hierarchical data types and optimized variable declaration and initialization structures, (3) enhanced code structure and calling workflow by leveraging the new data structure and modularization, (4) enhanced (descriptive and self-explanatory) model variable naming standard, and (5) enhanced driver and interface structures to couple with host weather/climate/hydrology models. In addition, we create a comprehensive technical documentation of the Noah-MP v5.0 and a set of model benchmark and reference datasets. The Noah-MP v5.0 will be coupled to various weather/climate/hydrology models in the future. Overall, the modernized Noah-MP will allow a more efficient and convenient process for future model developments and applications. [ABSTRACT FROM AUTHOR]

Drought monitoring and forecasting systems are used in the United States (U.S.) to inform drought management decisions. Drought forecasting efforts have often been conducted and evaluated at coarse spatial resolutions (i.e., >10‐km), which miss key local drought information at higher resolutions. Addressing the importance of forecasting drought at high resolutions, this study develops statistical models to evaluate 1‐ to 3‐month lead time predictability of meteorological and agricultural summer drought across the western U.S. at a 4‐km resolution. Our high‐resolution drought predictions have statistically significant skill (p ≤ 0.05) across 70%–100% of the western U.S., varying by evaluation metric and lead time. 1‐ to 3‐month lead time drought forecasts accurately represent monitored summer drought spatial patterns during major drought events, the interannual variability of drought area from 1982 to 2020 (r = 0.84–0.93), and drought trends (r = 0.94–0.97). 71% of western U.S summer drought area interannual variability can be explained by cold‐season (November–February) climate conditions alone allowing skillful 3‐month lead time predictions. Pre‐summer drought conditions (represented by drought indices) are the most important predictors for summer drought. Thus, the statistical models developed in this study heavily rely on the autocorrelation of chosen agricultural and meteorological drought indices which estimate land surface moisture memory. Indeed, prediction skill strongly correlates with persistence of drought conditions (r ≥ 0.73). This study is intended to support future development of operational drought early warning systems that inform drought management. Plain Language Summary: Droughts are complex and devastating natural disasters with severe implications in the western U.S. for energy production, agricultural yields, and wildfires, particularly during summer months. Drought monitoring and forecasting systems are used in the U.S. to inform drought management decisions. However, existing products and analyses are typically performed at coarse spatial resolutions (>10‐km). Because summer drought is spatially variable at finer resolutions than current drought monitoring and forecasting systems, it is valuable to develop seasonal drought forecasting systems at higher spatial resolutions. In this study we present a novel statistical modeling framework to forecast summer drought conditions at a high resolution (4‐km) at 1‐ to 3‐month lead times across the western U.S. Our models provide skillful predictions of drought across most of the western U.S., but there is degraded skill in areas where drought tends to be less persistent. Future research can expand on our work by predicting drought at finer temporal resolutions while maintaining the high spatial resolution used in this study's methodology. This research is intended to support development of future operational drought early warning systems that provide high spatial resolution forecasts. Key Points: Statistical models accurately predict summer meteorological and agricultural drought at 1‐, 2‐, and 3‐month lead times at a 4‐km resolutionMost of the interannual variability in summer drought area can be explained by preceding cold‐season climate conditionsStatistical models rely heavily on the autocorrelation of drought indices in drought forecasts [ABSTRACT FROM AUTHOR]

This study contributes to the body of current knowledge about the urban effect on extreme precipitation (EP) by investigating the city–EP interaction over Lagos, Nigeria. This is a unique, first-time study that adds a "missing piece" of this information about the African continent to the comprehensive global urban precipitation "puzzle." The convection-permitting Weather Research and Forecasting (WRF) Model is employed within an ensemble simulation framework using combinations of different physical schemes and boundary/initial conditions to detect the urban signal on an extreme rainfall event that occurred on 30 May 2006. WRF simulations are verified against satellite-estimated and in situ observations, and the results from the best-performing ensemble members are used for analysis. The results show that the control simulation with urban representation generated 20%–30% more rainfall over the urban area than the nonurban sensitivity simulation, in which the city is replaced by forest. Physical mechanisms behind the differences were revealed. We found that the urbanization in Lagos reduced evapotranspiration, resulting in the increase of sensible heating (by 75 W m−2). This further enhances the urban heat-island effect (+1.5 K of air surface temperature), facilitating horizontal convergence and boosting daytime sea breeze. As a result, more moisture is transported from the southern sea area to inland areas; the moisture then converges over Lagos city, creating favorable conditions for enhancing convection and extreme-rainfall-generating processes. [ABSTRACT FROM AUTHOR]

The US Northern Great Plains and the Canadian Prairies are known as the world's breadbaskets for its large spring wheat production and exports to the world. It is essential to accurately represent spring wheat growing dynamics and final yield and improve our ability to predict food production under climate change. This study attempts to incorporate spring wheat growth dynamics into the Noah-MP crop model, for a long time period (13-year) and fine spatial scale (4-km). The study focuses on three aspects: (1) developing and calibrating the spring wheat model at point-scale, (2) applying a dynamic planting/harvest date to facilitate large-scale simulations, and (3) applying a temperature stress function to assess crop responses to heat stress amid extreme heat. Model results are evaluated using field observations, satellite leaf area index (LAI), and census data from Statistics Canada and the US Department of Agriculture (USDA). Results suggest that incorporating a dynamic planting/harvest threshold can better constrain the growing season, especially the peak timing and magnitude of wheat LAI, as well as obtain realistic yield compared to prescribing a static province/state-level map. Results also demonstrate an evident control of heat stress upon wheat yield in three Canadian Prairies Provinces, which are reasonably captured in the new temperature stress function. This study has important implications for estimating crop productions, simulating the land atmosphere interactions in croplands, and crop growth's responses to the raising temperatures amid climate change. [ABSTRACT FROM AUTHOR]

With the highest albedo of the land surface, snow plays a vital role in Earth's surface energy budget and water cycle. Snow albedo is primarily controlled by snow grain properties (e.g., size and shape) and light-absorbing particles (LAPs) such as black carbon (BC) and dust. The mixing state of LAPs in snow also has impacts on LAP-induced snow albedo reduction and surface radiative forcing (RF). However, most land surface models assume that snow grain shape is spherical and LAPs are externally mixed with the snow grains. This study improves the snow radiative transfer model in the Energy Exascale Earth System Model version 2.0 (E3SM v2.0) Land Model (ELM v2.0) by considering non-spherical snow grain shapes (i.e., spheroid, hexagonal plate, and Koch snowflake) and internal mixing of dust–snow, and it systematically evaluates the impacts on the surface energy budget and water cycle over the Tibetan Plateau (TP). A series of ELM simulations with different treatments of snow grain shape, mixing state of BC–snow and dust–snow, and sub-grid topographic effects (TOP) on solar radiation are performed. Compared with two remote sensing snow products derived from the Moderate Resolution Imaging Spectroradiometer, the control ELM simulation (ELM_Control) with the default configurations of spherical snow grain shape, internal mixing of BC–snow, external mixing of dust–snow, and without TOP as well as the ELM simulation with new model features (ELM_New) can both capture the overall snow distribution reasonably. Additionally, ELM_New overall shows smaller biases in snow cover fraction than ELM_Control in spring when snowmelt is important for water management. The estimated LAP-induced RF in ELM_New ranges from 0 to 19.3 W m -2 with the area-weighted average value of 1.5 W m -2 that is comparable to the reported values in existing studies. The Koch snowflake shape, among other non-spherical shapes, shows the largest difference from the spherical shape in spring when snow processes related to the surface energy budget and water cycle have high importance. The impacts of the mixing state of LAP in snow are smaller than the shape effects and depend on snow grain shape. Compared to external mixing, internal mixing of LAP–snow can lead to larger snow albedo reduction and snowmelt, which further affect the surface energy budget and water cycle. The individual contributions of non-spherical snow shape, mixing state of LAP–snow, and local topography impacts on the snow and surface fluxes have different signs and magnitudes, and their combined effects may be negative or positive due to complex and nonlinear interactions among the factors. Overall, the changes in net solar radiation in spring due to individual and combined effects range from -28.6 to 16.9 W m -2 and -29.7 to 12.2 W m -2 , respectively. This study advances understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offers guidance for improving snow simulations and RF estimates in Earth system models under climate change. [ABSTRACT FROM AUTHOR]

Anthropogenically forced-warming and La Niña forced-precipitation deficits caused at least a sixfold risk increase for compound extreme low precipitation and high temperature in California–Nevada from October 2020 to September 2021. [ABSTRACT FROM AUTHOR]

Mineral dust contributes up to one-half of surface aerosol loading in spring over the southwestern United States, posing an environmental challenge that threatens human health and the ecosystem. Using self-organizing map (SOM) analysis with dust deposition and flux data from WRF-Chem and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), we identify four typical dust transport patterns across the Sierra Nevada, associated with the mesoscale winds, Sierra barrier jet (SBJ), North Pacific High (NPH), and long-range cross-Pacific westerlies, respectively. We find that dust emitted from the Central Valley is persistently transported eastward, while dust from the Mojave Desert and Great Basin influences the Sierra Nevada during mesoscale transport occurring mostly in winter and early spring. Asian dust reaching the mountain range comes either from the west through straight isobars (cross-Pacific transport) or from the north in the presence of the NPH. Extensive dust depositions are found on the west slope of the mountain, contributed by Central Valley emissions and cross-Pacific remote transport. In particular, the SBJ-related transport produces deposition through landfalling atmospheric rivers, whose frequency might increase in a warming climate. [ABSTRACT FROM AUTHOR]

The Biosphere‐Atmosphere Transfer Scheme (BATS) ground snow albedo algorithm is commonly used in land‐surface models (LSM), weather forecasting and research applications. This study addresses key uncertainties in BATS simulated ground snow albedo within the Noah‐MP LSM framework through evaluation and optimization of the Noah‐MP BATS ground snow albedo formulation using 2‐band (visible and near‐infrared (NIR)) in situ albedo observations at Rocky Mountain field stations. The Noah‐MP BATS ground snow albedo scheme is extremely sensitive to its input parameters. Namely, an ensemble generated by varying BATS input parameters within potentially plausible ranges provides an average daily range (maximum ensemble member minus minimum ensemble member) of ground snow albedo exceeding 0.45 in visible and NIR bands. Parameter optimization improves agreement between simulated and in situ observed ground snow albedo in visible, NIR and broadband spectrums. Importantly, optimized parameters result in reduced biases relative to observed fresh‐snow albedo and better agreement with observed albedo decay. Our analysis across different sites supports that the optimized BATS ground snow albedo parameters are appropriate to transfer in space and time, at least within the region studied (the central‐southern Rocky Mountains). The primary error source remaining after parameter optimization is that observed fresh‐snow albedo is highly variable, particularly in the NIR spectrum, whereas BATS fresh‐snow albedo is constant, an issue which requires further investigation. This study shows significant correlations between observed fresh‐snow albedo and surface meteorological conditions (e.g., downward shortwave radiation and temperature) which can support future model development that attempts to include a time‐varying formulation for fresh‐snow albedo. Plain Language Summary: Fresh snow reflects up to 90% of incoming sunlight (albedo 0.9), but as it ages, the reflectivity declines to as much as 50% (albedo 0.5). Snow albedo has a strong influence on snowpack evolution, melt rates, and land surface energy balance. The Biosphere‐Atmosphere Transfer Schemes (BATS) snow albedo algorithm is commonly used to simulate snow albedo in climate and weather predictions. This study evaluates the BATS ground snow albedo algorithm and parameters within the widely used Noah‐MP land surface model, through comparisons with observations of snow albedo in the Rocky Mountains. The BATS snow albedo scheme is extremely sensitive to input parameters. We identify an optimal set of input parameters to calculate snow albedo using the ground observations. Additionally, applying the optimized parameters across different sites in the central‐southern Rocky Mountain region shows that optimized parameters are transferable in space and time within this region to sites with similar climate. The primary error source remaining after parameter optimization is the use of constant fresh‐snow albedo in BATS; in reality, observed fresh‐snow albedo is variable. Relationships between fresh‐snow albedo and environmental conditions could inform model development of a time‐varying formulation for fresh‐snow albedo in the future. Key Points: The Biosphere‐Atmosphere Transfer Scheme (BATS) snow albedo formulation is highly sensitive to fresh‐snow albedo and snow age input parametersParameter optimization substantially improves accuracy for BATS simulated ground snow albedo, particularly for ablation periodsOptimized parameters improve accuracy of BATS simulated snow albedo and are transferable in time and space to sites with similar climate [ABSTRACT FROM AUTHOR]

While meteorology and aerosols are identified as key drivers of snow cover (SC) variability in High Mountain Asia, complex non‐linear interactions between them are not adequately quantified. Here, we attempt to unravel these interactions through a simple relative importance (RI) analysis of meteorological and aerosol variables from ERA5/CAMS‐EAC4 reanalysis against satellite‐derived SC from Moderate Resolution Imaging Spectroradiometer across 2003–2018. Our results show a statistically significant 7% rise in the RI of aerosol‐meteorology interactions (AMI) in modulating SC during late snowmelt season (June and July), notably over low snow‐covered (LSC) regions. Sensitivity tests further reveal that the importance of meteorological interactions with individual aerosol species are more prominent than total aerosols over LSC regions. We find that the RI of AMI for LSC regions is clearly dominated by carbonaceous aerosols, on top of the expected importance of dynamic meteorology. These findings clearly highlight the need to consider AMI in hydrometeorological monitoring, modeling, and reanalyses. Plain Language Summary: Understanding the changes in snow cover (SC) over glaciers in High Mountain Asia (HMA) is important yet challenging. Despite its impact on water resources, physical processes that drive these changes are complex. In particular, large‐scale weather patterns, together with aerosol pollution hotspots in the vicinity, and its steep elevation strongly interact with each other. We use a statistical approach to assess the relevance of these interactions using geophysical data from present day reanalysis and observed SC extent from satellite products for two decades. We find that during the late snowmelt period from June to July, interactions between aerosols and meteorology are significant, specifically in low SC regions. Interactions of individual aerosol species, especially carbonaceous aerosols like black carbon are more important than total aerosol concentration. This approach in quantifying the interactions of these processes can help improve the monitoring and modeling of snow hydrology. Representing these relevant interactions in current models and reanalysis of hydrometeorology can lead to more accurate predictions of the state of snow for critical regions like HMA. Key Points: Interactions between aerosols and meteorology are significant during late snowmelt (June and July) over low snow‐covered regions in High Mountain AsiaSpecies related interactions drive the seasonal variability of the overall relative importanceCarbonaceous aerosols are more relevant than mineral dust during late snowmelt [ABSTRACT FROM AUTHOR]