Exploring the potential impacts of anthropogenic heating on urban climate during heatwaves

This study modeled the impact of anthropogenic heat (AH) on urban climates, focusing on Sydney during 2017's heightened temperatures. The motivation behind this study stems from the increasing significance of understanding urban heat dynamics as cities globally grapple with regional climate change, necessitating targeted strategies for effective climate resilience cities. By investigating how varying levels of AH influence local urban climate conditions, this study addresses a critical gap in current urban climate study, particularly in the context of Sydney, an area that has not yet been extensively explored. Utilizing the weather research and forecasting (WRF) model coupled with building effect parameterization (BEP) and building energy model (BEM), i.e., the WRF/BEP + BEM model, four AH release scenarios were analyzed. Higher AH levels, especially at 14:00 LT, exhibited significant peaks: 266.5 W m-2 for sensible heat and 35.3 W m-2 for latent heat compared to the control scenario. This increase corresponded to a notable rise in ambient temperatures by 2.1 °C, with surface temperatures surging by 8.1 °C. Wind speeds notably increased by 4.6 m s-1 during higher AH release periods, affecting city airflow patterns. Moreover, elevated AH levels amplified the convective planetary boundary layer (PBL) height by 2013.7 m at 14:00 LT, potentially impacting pollutant dispersion and atmospheric quality. Notably, heightened AH profiles intensified sea breeze circulations, particularly impacting densely populated urban areas. These findings demonstrate a direct link between AH, exacerbated local urban warming, altered boundary layer dynamics, and intensified sea breeze circulations. This study emphasizes the urgent need to comprehend and manage AH for sustainable urban development and effective climate resilience strategies in Sydney and similar urban environments. By shedding light on these relationships, this study aims to contribute to the formulation of policies that mitigate urban overheating and enhance the livability of urban areas.

Research on ozone pollution control strategies for urban agglomerations based on ozone formation sensitivity and emission source contributions

Despite recent enhancements in China's anthropogenic emission controls, ozone (O3) concentrations have continuously increased owing to its nature as a secondary pollutant and the complexities of its production and consumption processes. This study quantified the contributions of urban and sectoral cross-emission sources to O3 levels and identified the anthropogenic emission sources requiring targeted control. Moreover, O3 sensitivity tests were conducted to determine optimal reduction ratios for nitrogen oxides (NOx) and volatile organic compounds (VOCs) emissions. The results were used to recommend effective measures for controlling O3 pollution in the Central Plains urban agglomeration (CPUA). The top 35 cities and sectoral cross-emission sources accounted for 80% of the O3 concentrations in the region, indicating the need for prioritized management of these sources. To achieve reductions in O3 concentrations across all cities, it was found that a 10% reduction in total NOx emissions would require a minimum of 18% reduction in VOCs emissions. Our results indicated that the appropriate coordination of reductions in VOCs and NOx emissions reduced the maximum daily 8-h average O3 (MDA8) concentrations in CPUA by 0.14%-4.78%. Enhancing control measures for prioritized emission sources reduced MDA8 concentrations by 0.78%-7.09%. Furthermore, adjusting the production and emission hours of the industrial sector resulted in a decrease in MDA8 concentrations by 1.10%-12.62%. Overall, our findings indicate that appropriately coordinated reduction of precursor emissions can reduce O3 levels. Further efforts to mitigate O3 pollution should include optimizing the timing of emissions from the industrial sector and other major sources of VOCs emissions.

Trigger thresholds and their dynamics of vegetation production loss under different atmospheric and soil drought conditions

Many evidences have shown that both atmospheric and soil droughts can constrain vegetation growth and further threaten its ability to sequester carbon. However, the trigger thresholds of vegetation production loss under different atmospheric and soil drought conditions are still unknown. In this study, we proposed a Copula and Bayesian equations-based framework to investigate trigger thresholds of various vegetation production losses under different atmospheric and soil drought conditions. The trigger thresholds dynamics and their possible causes were also investigated. To achieve this goal, we first simulated the gross primary production, soil moisture, and vapor pressure deficit over China during 1961-2018 using an individual-based, spatially explicit dynamic global vegetation model. The main drivers of the dynamic change in trigger thresholds were then explored by Random Forest model. We found that soil drought caused greater stress on gross primary production loss than atmospheric drought, with a larger impact area and higher probability of damage. In terms of spatial distribution, the risk probability of gross primary production loss was higher in eastern China than in western China, and the drought trigger threshold was also smaller in eastern China. In addition, the trigger thresholds for atmospheric and soil drought in most regions exhibited a decreasing trend from 1961 to 2018, while the CO2 fertilization enhanced the drought tolerance of vegetation. The reduction in CO2 fertilization effect slowed down the downward trend of trigger threshold for soil drought, while the increase in temperature exacerbated the downward trend of trigger threshold for atmospheric drought. This study highlighted the larger effect of soil drought on vegetation production loss than atmospheric drought and implied that climate change can modulate the trigger threshold of vegetation production losses under drought conditions. These findings provide scientific guidance for managing the increasing risk of drought on vegetation and optimizing watershed water allocation.

Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene

Climate change and human activities alter the global freshwater cycle, causing nonstationary processes as its distribution shifting over time, yet a comprehensive understanding of these changes remains elusive. Here, we develop a remote sensing-informed terrestrial reanalysis and assess the nonstationarity of and interconnections among global water cycle components from 2003 to 2020. We highlight 20 hotspot regions where terrestrial water storage exhibits strong nonstationarity, impacting 35% of the global population and 45% of the area covered by irrigated agriculture. Emerging long-term trends dominate the most often (48.2%), followed by seasonal shifts (32.8%) and changes in extremes (19%). Notably, in mid-latitudes, this encompasses 34% of Asia and 27% of North America. The patterns of nonstationarity and their dominant types differ across other water cycle components, including precipitation, evapotranspiration, runoff, and gross primary production. These differences also manifest uniquely across hotspot regions, illustrating the intricate ways in which each component responds to climate change and human water management. Our findings emphasize the importance of considering nonstationarity when assessing water cycle information toward the development of strategies for sustainable water resource usage, enhancing resilience to extreme events, and effectively addressing other challenges associated with climate change.

Evaluation of WRF-Chem air quality forecasts during the AEROMMA and STAQS 2023 field campaigns

A real-time air quality forecasting system was developed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to provide support for flight planning activities during the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and NASA Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. The forecasting system operated on two separate domains centered on Chicago, IL, and New York City, NY, and provided 72-hour predictions of atmospheric composition, aerosols, and clouds. This study evaluates the Chicago-centered forecasting system's 1-, 2-, and 3-day ozone (O3) forecast skill for Chiwaukee Prairie, WI, a rural area downwind of Chicago that often experiences high levels of O3 pollution. Comparisons to vertical O3 profiles collected by a Tropospheric Ozone Lidar Network (TOLNet) instrument revealed that forecast skill decreases as forecast lead time increases. When compared to surface measurements, the forecasting system tended to underestimate O3 concentrations on high O3 days and overestimate on low O3 days at Chiwaukee Prairie regardless of forecast lead time. Using July 25, 2023, as a case study, analyses show that the forecasts underestimated peak O3 levels at Chiwaukee Prairie during this regionwide bad air quality day. Wind speed and direction data indicates that this underestimation can partially be attributed to lake breeze simulation errors. Surface fine particulate matter (PM2.5) measurements, Geostationary Operational Environmental Satellite-16 (GOES-16) aerosol optical depth (AOD) data, and back trajectories from the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model show that transported Canadian wildfire smoke impacted the Lake Michigan region on this day. Errors in the forecasted chemical composition and transport of the smoke plumes also contributed to underpredictions of O3 levels at Chiwaukee Prairie on July 25, 2023. The results of this work help identify improvements that can be made for future iterations of the WRF-Chem forecasting system.Implications: Air quality forecasting is an important tool that can be used to inform the public about upcoming high pollution days so that individuals may plan accordingly to limit their exposure to health-damaging air pollutants. Forecasting also helps scientists make decisions about where to make observations during air quality field campaigns. A variety of observational datasets were used to evaluate the accuracy of an air quality forecasting system that was developed for NOAA and NASA field campaigns that occurred in the summer of 2023. These evaluations inform areas of improvement for future development of this air quality forecasting system.

Assessing urban heat island mitigation potential of realistic roof greening across local climate zones: A highly-resolved weather research and forecasting model study

Given their multifold benefits, green roofs are often considered to mitigate the urban heat island (UHI) effect. Most mesoscale studies consider 100 % green roof fraction or the same green roof fraction in each urban land use category while analysing the influence of green roofs on the UHI effect, which can overestimate their impact on UHI. Consequently, the impact of green roofs evaluated in these studies may not be suitable for informing policy decisions. Furthermore, the effect of morphologies on temperature reduction due to green roofs has not been previously studied. To address this gap, in this paper, we evaluate the impact of a realistic fraction of green roofs specific to the respective local climate zones (LCZ) on the UHI effect during a heatwave in Liège, Belgium, employing a high-resolution WRF study using the BEP-BEM parameterisation with LCZ land use classification. The realistic fraction is estimated for every LCZ class based on the average percentage of flat roofs observed in each LCZ class in Liège. Accordingly, distinct realistic fractions of green roofs are assigned to each LCZ class in WRF. We run the WRF simulation for the base scenario (without green roofs), extreme scenario (100 % green roof fraction), and realistic scenario. The results indicate a limited reduction in near-surface air and surface temperature in a realistic scenario, with a nighttime increase in temperature. Additionally, in the extreme scenario, the temperature reduction largely depends on the morphology. However, in a realistic scenario, it depends on the green roof fraction. Other indicators like heat index and UHI intensity also are not reduced considerably with realistic greening. Therefore, realistic roof greening alone will not be sufficient to achieve an impact on a city-scale.

Effectiveness of inter-regional collaborative emission reduction for ozone mitigation under local-dominated and transport-affected synoptic patterns

In recent years, the concentrations of ozone and the pollution days with ozone as the primary pollutant have been increasing year by year. The sources of regional ozone mainly depend on local photochemical formation and transboundary transport. The latter is influenced by different weather circulations. How to effectively reduce the inter-regional emission to control ozone pollution under different atmospheric circulation is rarely reported. In this study, we classify the atmospheric circulation of ozone pollution days from 2014 to 2019 over Central China based on the Lamb-Jenkinson method and the global analysis data of the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) operation. The effectiveness of emission control to alleviate ozone pollution under different atmospheric circulation is simulated by the WRF-Chem model. Among the 26 types of circulation patterns, 9 types of pollution days account for 79.5% of the total pollution days and further classified into 5 types. The local types (A and C type) are characterized by low surface wind speed and stable weather conditions over Central China due to a high-pressure system or a southwest vortex low-pressure system, blocking the diffusion of pollutants. Sensitivity simulations of A-type show that this heavy pollution process is mainly contributed by local emission sources. Removing the anthropogenic emission of pollutants over Central China would reduce the ozone concentration by 39.1%. The other three circulation patterns show pollution of transport characteristics affected by easterly, northerly, or southerly winds (N-EC, EC, S-EC-type). Under the EC-type, removing anthropogenic pollutants of East China would reduce the ozone concentration by 22.7% in Central China.

Groundwater-dependent ecosystem map exposes global dryland protection needs

Groundwater is the most ubiquitous source of liquid freshwater globally, yet its role in supporting diverse ecosystems is rarely acknowledged1,2. However, the location and extent of groundwater-dependent ecosystems (GDEs) are unknown in many geographies, and protection measures are lacking1,3. Here, we map GDEs at high-resolution (roughly 30 m) and find them present on more than one-third of global drylands analysed, including important global biodiversity hotspots4. GDEs are more extensive and contiguous in landscapes dominated by pastoralism with lower rates of groundwater depletion, suggesting that many GDEs are likely to have already been lost due to water and land use practices. Nevertheless, 53% of GDEs exist within regions showing declining groundwater trends, which highlights the urgent need to protect GDEs from the threat of groundwater depletion. However, we found that only 21% of GDEs exist on protected lands or in jurisdictions with sustainable groundwater management policies, invoking a call to action to protect these vital ecosystems. Furthermore, we examine the linkage of GDEs with cultural and socio-economic factors in the Greater Sahel region, where GDEs play an essential role in supporting biodiversity and rural livelihoods, to explore other means for protection of GDEs in politically unstable regions. Our GDE map provides critical information for prioritizing and developing policies and protection mechanisms across various local, regional or international scales to safeguard these important ecosystems and the societies dependent on them.

Characterization of winter PM2.5 source contributions and impacts of meteorological conditions and anthropogenic emission changes in the Sichuan Basin, 2002-2020

In this study, the Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality-Integrated Source Apportionment Method (CMAQ-ISAM) were utilized, which were integrated with the Multiresolution Emission Inventory for China (MEIC) emission inventory, to simulate winter PM2.5 concentrations, regional transport, and changes in emission source contributions in the Sichuan basin (SCB) from 2002 to 2020, considering variations in meteorological conditions and anthropogenic emissions. The results indicated a gradual decrease in the basin's winter average PM2.5 concentration from 300 μg/m3 to 120 μg/m3, with the most significant decrease occurring after 2014, reflecting the actual impact of China's air pollution control measures. Spatially, the main pollution area shifted from Chongqing to Chengdu and the western basin. The sources of PM2.5 at the eastern and western margins of the basin have remained stable and have been dominated by local emissions for many years, while the sources of PM2.5 in the central part of the basin have evolved from a multiregional co-influenced source during the early period to a high proportion of local emissions; except for boundary condition sources, residential sources were the main PM2.5 sources in the basin (approximately 29.70 %), followed by industrial sources (approximately 14.11 %). Industrial sources exhibited higher contributions in Chengdu and Chongqing and gradually stabilized with residential sources over the years, while residential sources dominated in the eastern and western parts of the basin and exhibited a declining trend. Meteorological conditions exacerbated pollution in the whole basin from 2008 to 2014, especially in the west (21-40 μg/m3). The eastern basin and Chongqing exhibited more years with alleviated meteorological pollution, including a 40+ μg/m3 decrease in Chongqing from 2002 to 2005. Reduced anthropogenic emissions alleviated annual pollution levels, with a greater reduction (> -20 μg/m3) after 2011 due to pollution control measures.

Assessing the role of cold front passage and synoptic patterns on air pollution in the Korean Peninsula

Various numerical experiments using WRF (Weather Research & Forecasting Model) and CMAQ (Community Multiscale Air Quality Modeling System) were performed to analyze the phenomenon of rapidly high concentration PM2.5 after the passage of a cold front in an area with limited local emissions. The episode period was from January 14 to 23, 2018, and analysis was conducted by dividing it into two stages according to the characteristics of changes in PM2.5 concentrations during the period. Through the analysis of observational data during the episode period, we confirmed meteorological impacts (decrease in temperature, increase in wind speed and relative humidity) and an increase in air pollution (PM10 and PM2.5) attributed to the passage of a cold front. Using CMAQ's IPR (Integrated Process Rate) analysis, the contribution of the horizontal advection process was observed in transporting PM2.5 to Gangneung at higher altitudes, and the PM2.5 concentrations at the surface increased because the vertical advection process was influenced by the terrain. Notably, in Stage 2 (64 μg·m-3), a higher contribution of the vertical advection process compared to Stage 1 (35 μg·m-3) was observed, which is attributed to the differences in synoptic patterns following the passage of the cold front. During Stage 2, following the cold front, atmospheric stability (dominance of high-pressure system) led to air subsidence and the presence of a temperature inversion layer, creating favorable meteorological conditions for the accumulation of air pollutants. This study offers the mechanisms of air pollution over the Korean Peninsula under non-stationary meteorological conditions, particularly in relation to the passage of the cold front (low-pressure system). Notably, the influence of a cold front can vary according to the synoptic patterns that develop following its passage.

Homogenized gridded dataset for drought and hydrometeorological modeling for the continental United States

We present a novel data set for drought in the continental US (CONUS) built to enable computationally efficient spatio-temporal statistical and probabilistic models of drought. We converted drought data obtained from the widely-used US Drought Monitor (USDM) from its native geo-referenced polygon format to a 0.5 degree regular grid. We merged known environmental drivers of drought, including those obtained from the North American Land Data Assimilation System (NLDAS-2), US Geological Survey (USGS) streamflow data, and National Oceanic and Atmospheric Administration (NOAA) teleconnections data. The resulting data set permits statistical and probabilistic modeling of drought with explicit spatial and/or temporal dependence. Such models could be used to forecast drought at short-range, seasonal to sub-seasonal, and inter-annual timescales with uncertainty, extending the reach and value of the current US Drought Outlook from the National Weather Service Climate Prediction Center. This novel data product provides the first common gridded dataset that includes critical variables used to inform hydrological and meteorological drought.

California Case Study of Wildfires and Prescribed Burns: PM2.5 Emissions, Concentrations, and Implications for Human Health

Wildfires are a significant threat to human health, in part through degraded air quality. Prescribed burning can reduce wildfire severity but can also lead to an increase in air pollution. The complexities of fires and atmospheric processes lead to uncertainties when predicting the air quality impacts of fire and make it difficult to fully assess the costs and benefits of an expansion of prescribed fire. By modeling differences in emissions, surface conditions, and meteorology between wildfire and prescribed burns, we present a novel comparison of the air quality impacts of these fire types under specific scenarios. One wildfire and two prescribed burn scenarios were considered, with one prescribed burn scenario optimized for potential smoke exposure. We found that PM2.5 emissions were reduced by 52%, from 0.27 to 0.14 Tg, when fires burned under prescribed burn conditions, considerably reducing PM2.5 concentrations. Excess short-term mortality from PM2.5 exposure was 40 deaths for fires under wildfire conditions and 39 and 15 deaths for fires under the default and optimized prescribed burn scenarios, respectively. Our findings suggest prescribed burns, particularly when planned during conditions that minimize smoke exposure, could be a net benefit for the impacts of wildfires on air quality and health.

Terrain effect on atmospheric process in seasonal ozone variation over the Sichuan Basin, Southwest China

Terrain effect is challenging for understanding atmospheric environment changes under complex topography. This study targets the Sichuan Basin (SCB), a deep basin isolated by plateaus and mountains in Southwest China, by employing WRF-Chem with integrated process rates (IPR) analysis to characterize the terrain-driven seasonal variations of tropospheric ozone (O3) with atmospheric physical and chemical processes. Results show that the basin terrain exerts reversed impacts on regional air quality changes by aggravating summertime and alleviating wintertime near-surface O3 with the relative contributions oscillating seasonally between -40% and 40% in SCB. Similarly, a seasonal shift of vertical O3 structures is dominated by summertime positive and wintertime negative changes in the lower troposphere induced by basin terrain. The key contributions of atmospheric process to near-surface O3 are identified with vertical and horizontal transport, which is dominated by basin terrain with intensifying seasonal and diurnal variations. With the existence of basin, the daytime O3 productions at the near-surface layer are elevated in months of warm seasons (April and July) but inhibited in the cold seasons (October and January), presenting a seasonal transition of primary factor from meteorology to aerosol-radiation forcing on photochemical reactions. Driven by plateau-basin thermodynamic forcing, horizontal O3 transport between the SCB and eastern TP is enhanced by mountain-plains solenoid (MPS), and even nocturnal O3-rich layers contribute to the impacts of vertical exchange on near-surface O3 levels. The terrain effects of deep basin under the interaction of Asian monsoons and westerlies could jointly change atmospheric physical and chemical processes to construct the seasonal and diurnal O3 evolution patterns over the SCB region.

Diagnosing evapotranspiration responses to water deficit across biomes using deep learning

Accounting for water limitation is key to determining vegetation sensitivity to drought. Quantifying water limitation effects on evapotranspiration (ET) is challenged by the heterogeneity of vegetation types, climate zones and vertically along the rooting zone. Here, we train deep neural networks using flux measurements to study ET responses to progressing drought conditions. We determine a water stress factor (fET) that isolates ET reductions from effects of atmospheric aridity and other covarying drivers. We regress fET against the cumulative water deficit, which reveals the control of whole-column moisture availability. We find a variety of ET responses to water stress. Responses range from rapid declines of fET to 10% of its water-unlimited rate at several savannah and grassland sites, to mild fET reductions in most forests, despite substantial water deficits. Most sensitive responses are found at the most arid and warm sites. A combination of regulation of stomatal and hydraulic conductance and access to belowground water reservoirs, whether in groundwater or deep soil moisture, could explain the different behaviors observed across sites. This variety of responses is not captured by a standard land surface model, likely reflecting simplifications in its representation of belowground water storage.

Continental United States climate projections based on thermodynamic modification of historical weather

Regional climate models can be used to examine how past weather events might unfold under different climate conditions by simulating analogue versions of those events with modified thermodynamic conditions (i.e., warming signals). Here, we apply this approach by dynamically downscaling a 40-year sequence of past weather from 1980-2019 driven by atmospheric re-analysis, and then repeating this 40-year sequence a total of 8 times using a range of time-evolving thermodynamic warming signals that follow 4 80-year future warming trajectories from 2020-2099. Warming signals follow two emission scenarios (SSP585 and SSP245) and are derived from two groups of global climate models based on whether they exhibit relatively high or low climate sensitivity. The resulting dataset, which contains 25 hourly and over 200 3-hourly variables at 12 km spatial resolution, can be used to examine a plausible range of future climate conditions in direct reference to previously observed weather and enables a systematic exploration of the ways in which thermodynamic change influences the characteristics of historical extreme events.

On the local warming potential of urban rooftop photovoltaic solar panels in cities

Understanding and evaluating the implications of photovoltaic solar panels (PVSPs) deployment on urban settings, as well as the pessimistic effects of densely populated areas on PVSPs efficiency, is becoming incredibly valuable. Thus, the deployment of low-efficiency, low-cost, and widely available PVSPs may diminish total solar reflectance, raising the risks of PVSPs-based urban heating, particularly during the summertime heatwaves. This study employs and assesses physical parameterizations that account for the impact of PVSPs on Sydney's urban environment in the context of the mesoscale model weather research and forecasting (WRF). To account for the impacts of PVSPs, the parameterization presented in this paper assumes that PVSP arrays are parallel, detachable from roofs, and consist of a single layer. Results showed that increasing PVSPs can raise peak summer ambient temperatures by up to 1.4 °C and surface temperatures by up to 2.3°C at city-scale. Temperature variability was found between the city's eastern and western parts due to the presence of PVSPs. In addition, local warming effects of PVSP were observed at urban district-scale as well. The large-scale deployment of PVSPs at local district-scale of the Sydney during a typical hot day caused air temperature to rise by 1.5 °C during the daytime and decrease by 2.7 °C at nighttime. The patterns of the city's ambient temperature distribution were found to be strongly dependent on synoptic meteorological conditions and advection flow strength. The maximum increases in sensible heat flux and latent heat flux were 245.5 Wm-2 and 11.5 Wm-2, respectively. Wind speed may be raised by up to 1.2 ms-2 due to regional low effect over city domain. As a result, large-scale deployment of PVSPs promotes advective flow between the city and its environs. Modification of the PVSPs in Sydney results in an increase in planetary boundary layer (PBL) heights of up to 537.9 m above the city and may lower pollutant concentrations at ground level. The advent of sea breeze in the city's eastern parts, which reduces the temperature of the coastal zone, along with inland westerly winds, which heat the city's western zones, lessened the intensity of the urban heat island (UHI) phenomenon induced by PVSPs warming. The findings of this study can be used to help policymakers make informed decisions about the use of PVSPs systems. PVSPs with a high solar reflectance in wavelengths that do not convert solar energy to electricity can be considered as an alternative solution to reduce local warming in urban environments.

Quantifying the air quality impact of ship emissions in China's Bohai Bay

Bohai Bay, as a significant economic bay area in China, has experienced considerable ecological consequences during its rapid economic development. One of the major environmental challenges is the emission of air pollutants from ships, which has had a severe impact on regional air quality and the health of residents. To assess the influence of pollutants on the air quality around the Bohai Bay area, a Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model was established using a 9 km × 9 km high-resolution ship emission gridded inventory from 2018. The WRF-CMAQ model was employed to compare two scenarios: vessel emissions and non-vessel emissions, in order to evaluate the impact of ship emissions. By analyzing the pollutant concentrations in Bohai Bay and the degree of change in pollutant concentration in six cities under these two scenarios, significant differences were observed. Furthermore, a comparison of the hourly concentration contributions of ship emissions between port cities and inland cities within the same region revealed that inland cities were less affected by ship emissions. The main contributing factors to this disparity were identified as wind direction and wind speed.

A multiscale analysis of heatwaves and urban heat islands in the western U.S. during the summer of 2021

Extreme heat events are occurring more frequently and with greater intensity due to climate change. They result in increased heat stress to populations causing human health impacts and heat-related deaths. The urban environment can also exacerbate heat stress because of man-made materials and increased population density. Here we investigate the extreme heatwaves in the western U.S. during the summer of 2021. We show the atmospheric scale interactions and spatiotemporal dynamics that contribute to increased temperatures across the region for both urban and rural environments. In 2021, daytime maximum temperatures during heat events in eight major cities were 10-20 °C higher than the 10-year average maximum temperature. We discuss the temperature impacts associated with processes across scales: climate or long-term change, the El Niño-Southern Oscillation, synoptic high-pressure systems, mesoscale ocean/lake breezes, and urban climate (i.e., urban heat islands). Our findings demonstrate the importance of scale interactions impacting extreme heat and the need for holistic approaches in heat mitigation strategies.

Impact of coronavirus-driven reduction in aerosols on precipitation in the western United States

Among the many impacts of COVID-19, the pandemic led to improved air quality conditions in the countries under quarantine due to the shutdown of industries, drastically reduced traffic, and lockdowns. Meanwhile, the western United States, particularly the coastal areas from Washington to California, received much less precipitation than normal during early 2020. Is it possible that this reduction in precipitation was driven by the reduced aerosols due to the coronavirus? Here we show that the reduction in aerosols resulted in higher temperatures (up to ∼0.5 °C) and generally lower snow amounts but cannot explain the observed low precipitation amounts over this region. In addition to an assessment of the effects of the coronavirus-related reduction in aerosols on precipitation across the western United States, our findings also provide basic information on the potential impacts different mitigation efforts aimed at reducing anthropogenic aerosols would have on the regional climate.

Description and evaluation of a newly developed emission inventory processing system (EMIPS)

Commonly available emission inventories are often incompatible with the input requirements of atmospheric chemistry models due to temporal and spatial resolution, pollutant types, etc. We present the Emission Inventory Processing System (EMIPS) version 1, an open-source and multi-scale atmospheric emission modeling framework that prepares specific emissions inputs for atmospheric chemistry models. EMIPS is a multifunctional and user-friendly system, coded in Jython and developed on the MeteoInfo software platform. It allows users to freely combine and process emission inventories to generate model-ready emissions data. The core functions of EMIPS include preprocessing, temporal allocation, spatial allocation, chemical speciation, and vertical allocation. We detail the implementation of each function in the body of this paper, and several examples are provided for illustration. The emission outputs obtained with EMIPS have been evaluated by simulating four pollutants (PM_2.5, PM₁₀, NO₂, and O₃) concentrations in January 2017 using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and comparison of model results with the observations indicates the model can reproduce the temporal and spatial patterns of pollutants, suggesting that EMIPS is capable of supporting atmospheric chemistry modeling. We expect this work could help to improve air quality research and forecast.

Groundwater depletion in California's Central Valley accelerates during megadrought

Groundwater provides nearly half of irrigation water supply, and it enables resilience during drought, but in many regions of the world, it remains poorly, if at all managed. In heavily agricultural regions like California's Central Valley, where groundwater management is being slowly implemented over a 27-year period that began in 2015, groundwater provides two-thirds or more of irrigation water during drought, which has led to falling water tables, drying wells, subsiding land, and its long-term disappearance. Here we use nearly two decades of observations from NASA's GRACE satellite missions and show that the rate of groundwater depletion in the Central Valley has been accelerating since 2003 (1.86 km³/yr, 1961-2021; 2.41 km³/yr, 2003-2021; 8.58 km³/yr, 2019-2021), a period of megadrought in southwestern North America. Results suggest the need for expedited implementation of groundwater management in the Central Valley to ensure its availability during the increasingly intense droughts of the future.

Achieving Brazil's Deforestation Target Will Reduce Fire and Deliver Air Quality and Public Health Benefits

Climate, deforestation, and forest fires are closely coupled in the Amazon, but models of fire that include these interactions are lacking. We trained machine learning models on temperature, rainfall, deforestation, land-use, and fire data to show that spatial and temporal patterns of fire in the Amazon are strongly modified by deforestation. We find that fire count across the Brazilian Amazon increases by 0.44 percentage points for each percentage point increase in deforestation rate. We used the model to predict that the increased deforestation rate in the Brazilian Amazon from 2013 to 2020 caused a 42% increase in fire counts in 2020. We predict that if Brazil had achieved the deforestation target under the National Policy on Climate Change, there would have been 32% fewer fire counts across the Brazilian Amazon in 2020. Using a regional chemistry-climate model and exposure-response associations, we estimate that the improved air quality due to reduced smoke emission under this scenario would have resulted in 2,300 fewer deaths due to reduced exposure to fine particulate matter. Our analysis demonstrates the air quality and public health benefits that would accrue from reducing deforestation in the Brazilian Amazon.

Machine learning based estimation of field-scale daily, high resolution, multi-depth soil moisture for the Western and Midwestern United States

Background

High-resolution soil moisture estimates are critical for planning water management and assessing environmental quality. In-situ measurements alone are too costly to support the spatial and temporal resolutions needed for water management. Recent efforts have combined calibration data with machine learning algorithms to fill the gap where high resolution moisture estimates are lacking at the field scale. This study aimed to provide calibrated soil moisture models and methodology for generating gridded estimates of soil moisture at multiple depths, according to user-defined temporal periods, spatial resolution and extent.

Methods

We applied nearly one million national library soil moisture records from over 100 sites, spanning the U.S. Midwest and West, to build Quantile Random Forest (QRF) calibration models. The QRF models were built on covariates including soil moisture estimates from North American Land Data Assimilation System (NLDAS), soil properties, climate variables, digital elevation models, and remote sensing-derived indices. We also explored an alternative approach that adopted a regionalized calibration dataset for the Western U.S. The broad-scale QRF models were independently validated according to sampling depths, land cover type, and observation period. We then explored the model performance improved with local samples used for spiking. Finally, the QRF models were applied to estimate soil moisture at the field scale where evaluation was carried out to check estimated temporal and spatial patterns.

Results

The broad-scale QRF model showed moderate performance (R² = 0.53, RMSE = 0.078 m³/m³) when data points from all depth layers (up to 100 cm) were considered for an independent validation. Elevation, NLDAS-derived moisture, soil properties, and sampling depth were ranked as the most important covariates. The best model performance was observed for forest and pasture sites (R² > 0.5; RMSE < 0.09 m³/m³), followed by grassland and cropland (R² > 0.4; RMSE < 0.11 m³/m³). Model performance decreased with sampling depths and was slightly lower during the winter months. Spiking the national QRF model with local samples improved model performance by reducing the RMSE to less than 0.05 m³/m³ for grassland sites. At the field scale, model estimates illustrated more accurate temporal trends for surface than subsurface soil layers. Model estimated spatial patterns need to be further improved and validated with management data.

Conclusions

The model accuracy for top 0-20 cm soil depth (R² > 0.5, RMSE < 0.08 m³/m³) showed promise for adopting the methodology for soil moisture monitoring. The success of spiking the national model with local samples showed the need to collect multi-year high frequency (e.g., hourly) sensor-based field measurements to improve estimates of soil moisture for a longer time period. Future work should improve model performance for deeper depths with additional hydraulic properties and use of locally-selected calibration datasets.

The sensitivities of ozone and PM2.5 concentrations to the satellite-derived leaf area index over East Asia and its neighboring seas in the WRF-CMAQ modeling system

Vegetation plays an important role as both a sink of air pollutants via dry deposition and a source of biogenic VOC (BVOC) emissions which often provide the precursors of air pollutants. To identify the vegetation-driven offset between the deposition and formation of air pollutants, this study examines the responses of ozone and PM_2.5 concentrations to changes in the leaf area index (LAI) over East Asia and its neighboring seas, using up-to-date satellite-derived LAI and green vegetation fraction (GVF) products. Two LAI scenarios that examine (1) table-prescribed LAI and GVF from 1992 to 1993 AVHRR and 2001 MODIS products and (2) reprocessed 2019 MODIS LAI and 2019 VIIRS GVF products were used in WRF-CMAQ modeling to simulate ozone and PM_2.5 concentrations for June 2019. The use of up-to-date LAI and GVF products resulted in monthly mean LAI differences ranging from -56.20% to 96.81% over the study domain. The increase in LAI resulted in the differences in hourly mean ozone and PM_2.5 concentrations over inland areas ranging from 0.27 ppbV to -7.17 ppbV and 0.89 μg/m³ to -2.65 μg/m³, and the differences of those over the adjacent sea surface ranging from 0.69 ppbV to -2.86 ppbV and 3.41 μg/m³ to -7.47 μg/m³. The decreases in inland ozone and PM_2.5 concentrations were mainly the results of dry deposition accelerated by increases in LAI, which outweighed the ozone and PM_2.5 formations via BVOC-driven chemistry. Some inland regions showed further decreases in PM_2.5 concentrations due to reduced reactions of PM_2.5 precursors with hydroxyl radicals depleted by BVOCs. The reductions in sea surface ozone and PM_2.5 concentrations were accompanied by the reductions in those in upwind inland regions, which led to less ozone and PM_2.5 inflows. The results suggest the importance of the selective use of vegetation parameters for air quality modeling.

Impact of Sea Breeze on the Transport of Ship Emissions: A Comprehensive Study in the Bohai Rim Region, China

Air pollutants from ship exhaust have a negative impact on air quality in coastal areas, which can be greatly exacerbated by sea breeze circulation. However, our understanding of this issue is still limited, especially in coastal areas with a complex topography and winding coastlines, such as the Bohai Rim region in China. In order to fill this knowledge gap, the Weather Research and Forecast model coupled with the chemistry (WRF/Chem) modeling system was employed to investigate the influence of sea breeze circulation on the transport of PM2.5 emitted by ships from April to September in 2014. The major findings are as follows: (1) The concentration of PM2.5 due to ship emissions was 2.94 μg/m3 on days with a sea breeze and 2.4 times higher than on days without a sea breeze in coastal cities in the region. (2) The difference in the contribution of ship emissions during days with a sea breeze and days without a sea breeze decreases with increasing distance from the coastline but remains non-negligible up to 50 km inland. (3) The shape of the coastline, the topographic height of the land area, and the latitude have a significant impact on sea breeze circulation and thus on the transport of ship emissions. (4) The differences in the contribution of ship emissions under days with a sea breeze versus days without a sea breeze were more evident than those under onshore versus alongshore and offshore winds, indicating that sea breeze circulation can cause cyclic accumulation of pollutants and thus reinforce the impact of ship emissions on coastal air quality more than by onshore winds. It should be emphasized that during the switching from sea breeze to a non-sea breeze, the pollutants that have been transported to the land area by sea breeze have not yet been carried back to sea, resulting in the ship contribution value still not significantly reduced even if the wind is a non-sea breeze at that moment. In addition, other factors e.g., emissions, precipitation, and chemistry can also play an important role in the observed trends in this study.

Computation and Analysis of an Offshore Wind Power Forecast: Towards a Better Assessment of Offshore Wind Power Plant Aerodynamics

For the first time, the Weather Research and Forecast (WRF) model with the Wind Farm Parameterization (WFP) modeling method is utilized for a short-range wind power forecast simulation of 48 h of an offshore wind farm with 100 turbines located on the east coast of the China Yellow Sea. The effects of the horizontal multi-grid downsize method were deployed and investigated on this simulation computation. The simulation was validated with the field data from the Supervisory Control and Data Acquisition (SCADA) system, and the results showed that the horizontal mesh downsize method improved the accuracy of wind speed and then wind power forecast. Meanwhile, the wind power plant aerodynamics with turbine wake and sea–land shore effects were investigated, where the wake effects from the wind farm prolonged several miles downstream, evaluated at two wind speeds of 7 m/s and 10 m/s instances captured from the 48 h of simulation. At the same time, it was interesting to find some sea–land atmospheric effects with wind speed oscillation, especially at the higher wind speed condition. Finally, the research results show that the WRF + WFP model for the wind power forecast for production operation may not be ready at this stage; however, they show that the methodology helps to evaluate the wind power plant aerodynamics with wake effects and micrometeorology of the sea–land interconnection region. This plant aerodynamics study set the stage for a wake turbine interaction study in the future, such as one utilizing the NREL FAST.FARM tool.

Comparison of the Forecast Performance of WRF Using Noah and Noah-MP Land Surface Schemes in Central Asia Arid Region

The land surface scheme (LSS) plays a very important role in the forecast of surface meteorological elements in the Central Asia arid region. Therefore, two LSSs viz. Noah and Noah-MP were evaluated over the Central Asia arid region in January and July 2017 using the Weather Research and Forecasting (WRF) model at a 3-km horizontal grid resolution. The objective was to assess the performance of WRF LSSs by calculated the mean error (ME) and the root mean squared error (RMSE) of simulated hourly meteorological elements, such as 2-m air temperature, 10-m wind speed, soil temperature, soil moisture at 5 and 25 cm thickness, and surface soil heat flux at 5 cm thickness. The results showed that, compared to Noah, Noah-MP modeled less surface sensible heat flux in the northern Xinjiang (15~20 W∙m−2) and surface latent heat flux in most areas of Xinjiang (<10 W∙m−2) in January, and mainly generated less sensible heat flux in most areas of north Xinjiang and the mountainous regions of southern Xinjiang (≤20 W·m−2) which on the contrary, generated more surface latent heat flux in most parts of Xinjiang (15~20 W·m−2) in July. Meanwhile, the surface soil heat flux generated from Noah-MP was closer to the observations at Hongliuhe and Kelameili stations in January, the ME increased by 17.5% and reduced by 80.7%, respectively, the RMSE decreased by 44.4% and 61.7%, respectively, and closer to the observations at Xiaotang station in July, the ME and RMSE reduced by 19.1% and 20.5%, respectively. Compared to Noah, Noah-MP improved the overall simulation of soil temperature and soil moisture over the northern and eastern Xinjiang (at 10 cm thickness), the ME and RMSE of simulated soil temperature reduced by 85.0% and 13.4% in January, decreased by 78.6% and increased by 6.2% in July, respectively, and the ME and RMSE of simulated soil moisture reduced by 67.2% and 14.9% in January, reduced by 33.3% and 2.8% in July, respectively. Compared to Noah, Noah-MP’s results were lowered for the simulated 10-m wind speed and 2-m air temperature, especially the simulated 2-m air temperature over the cold climate regions of northern Xinjiang, was improved significantly, the ME and RMSE of simulated 10-m wind speed reduced by 0.8% and 4.9% in January, decreased by 6.7% and 2.8% in July, respectively, the ME and RMSE of simulated 2-m air temperature reduced by 2.8% and 1.0% in July, respectively. This study demonstrated the advantage of coupled Noah-MP over the Central Asia arid region, providing the basis for WRF/Noah-MP in future operational applications in the Central Asia arid region.

Impact of Urbanization on Meteorology and Air Quality in Chengdu, a Basin City of Southwestern China

Rapid urbanization has the potential to fundamentally perturb energy budget and alter urban air quality. While it is clear that urban meteorological parameters are sensitive to urbanization-induced changes in landscapes, a gap exists in our knowledge about how changes in land use and land cover affect the dynamics of urban air quality. Herein, we simulated a severe O3 episode (10–16 July 2017) and a highly polluted PM2.5 episode (25–30 December 2017) and assessed the changes of meteorological phenomenon and evolution of air pollutants induced by urbanization. We found that the urban expansion area (i.e., land use transition from natural to urban surfaces between 2000 and 2017, UEA) has a significant increase in nocturnal 2-m temperature (T2) with maximum values reaching 3 and 4°C in summer and winter, respectively. In contrast, UEA experienced cooling in the daytime with stronger reductions of T2 in winter than in summer. The T2 variability is primarily attributed to the intense thermal inertia and high heat capacity of the urban canopy and the shadowing effect caused by urbanization. Owing to increased surface roughness and decreased surface albedo as well as shadowing effects, the ventilation index (VI) of UEA increased up to 1,200 m2/s in winter while decreased up to 950 m2/s in summer. Changes in meteorological phenomenon alter physical and chemical processes associated with variations in PM2.5 and O3 concentrations. Urbanization leads to enhanced vertical advection process and weakened aerosol production, subsequently causing PM2.5 levels to decrease by 33.2 μg/m3 during the day and 4.6 μg/m3 at night, respectively. Meanwhile, O3 levels increased by 61.4 μg/m3 at 20:00 due to the reduction of horizontal advection induced by urbanization, while O3 concentrations changed insignificantly at other times. This work provides valuable insights into the effects of urbanization on urban meteorology and air quality over typical megacities, which support informed decision-making for urban heat and air pollution mitigation.

Impact of Inter-Annual Variation in Meteorology from 2010 to 2019 on the Inter-City Transport of PM2.5 in the Beijing–Tianjin–Hebei Region

Air pollution has become a great challenge to achieving sustainable development. Among the pollutants, aerosols significantly affect human health and play an important role in global climate change. The concentration of aerosols in the ambient air is influenced strongly by the regional transport of pollutants and their precursors and may vary considerably under different meteorological conditions in different years. This inter-annual variation in meteorology may yield conflicting results in the quantification of the contribution from regional transport of air pollutants. It creates uncertainty for local governments to develop pollution control measures to reduce the challenges to sustainable development. Previous studies on this issue are often year-specific or cover short time spans, and the inter-city transport of air pollutants in the long term is still not fully understood. Therefore, in this study, the Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality (CMAQ) model was used to assess inter-annual variations in the contribution of inter-city transport to the PM2.5 concentration in the Beijing–Tianjin–Hebei region from 2010 to 2019. To highlight the impact of inter-annual variations in meteorology, the authors used the same emission inventory and the same model configurations for the 10-year simulation. The major findings can be summarized as follows: (1) Both PM2.5 concentration and inter-city transport in the Beijing–Tianjin–Hebei (BTH) region were influenced by the inter-annual variation in meteorological conditions. (2) The simulated annual average concentrations in 13 cities in BTH are highly variable, with fluctuations ranging from 30.8% to 54.1%, and more evident variations were found in seasonal results. (3) Seven out of thirteen cities have a contribution from regional transport exceeding 50%, which are located in the eastern half of the Beijing–Tianjin–Hebei region. (4) The magnitude of the regional transport contribution varies significantly among the cities of BTH, on an annual basis, from a minimum inter-annual fluctuation of 8.9% to a maximum of 37.2%, and seasonal fluctuation is even more strongly evident. These results indicate that, when formulating pollution control strategies, inter-annual changes in meteorological conditions should not be ignored.

Impact of deep basin terrain on PM2.5 distribution and its seasonality over the Sichuan Basin, Southwest China

The terrain effect on atmospheric environment is poorly understood in particular for the polluted region with underlying complex topography. Therefore, this study targeted the Sichuan Basin (SCB), a deep basin with severe PM_2.5 pollution enclosed by the eastern Tibetan Plateau (TP), Yunnan-Guizhou Plateaus (YGP) and mountains over Southwest China, and we investigated the terrain effect on seasonal PM_2.5 distribution and the meteorological mechanism based on the WRF-Chem simulation with stuffing the basin topography. It is characterized that the three-dimensional distribution of topography-induced PM_2.5 concentrations over the SCB with the seasonal shift of regional PM_2.5 averages from approximately 30 μg m^-3 in summer to 90 μg m^-3 in winter at surface layer and from summertime 10 μg m^-3 to wintertime 30 μg m^-3 in the lower free troposphere. Such basin-forced PM_2.5 changes presented the vertically monotonical declines concentrated within the lower troposphere below 3.6 km in spring, 2.3 km in summer, 2.6 km in autumn and 4.8 km in winter. Impacts of deep basin aggravated PM_2.5 accumulation within the SCB and transport toward the surrounding plateaus contributing approximately 50-90% to PM_2.5 levels over the regions of eastern TP and northern YGP. In the SCB, atmospheric thermal structure in the lower troposphere could build a vertical convergence layer between the boundary layer and free troposphere, acting as a lid inhibiting air diffusion, which was regulated by the terrain effects on interactions of westerlies and Asian monsoons, especially the wintertime strong warm lid deteriorating air pollution in the SCB. Furthermore, warm and humid air conditions within the basin prompted sulfur oxidation ratio by +0.02 and nitrogen oxidation ratio by +0.22 effectively producing the secondary PM_2.5 in atmospheric environment.

Impact of Different Double-Moment Microphysical Schemes on Simulations of a Bow-Shaped Squall Line in East China

Four typical double-moment cloud microphysical schemes (Milbrandt, Morrison, NSSL and WDM6) in WRF (Weather Research and Forecasting) are used to investigate the impact of the different schemes on the simulation of a straight squall line evolving into a bow-shaped one in East China on 15 April 2016. Although simulations with Milbrandt, Morrison and WDM6 schemes can produce bow-shaped squall lines, only the WDM6 scheme can simulate the evolution process of the straight squall line to a bow-shaped one well. The simulation results with the NSSL scheme produce a broken straight squall line. The possible reason is that the range and intensity of cold pools and the rear inflows are different among the four schemes when the observed squall line evolves into a standard bow-shaped squall line. The mixing ratio and number concentration of rain water with the WDM6 scheme is largest in strong convective areas, and the cold pool and rear inflow are also strongest among the four schemes. Compared with the WDM6 scheme, the mixing ratio and number concentration of rain water are less with the Milbrandt and Morrison schemes. Also, the cold pool and rear inflow with the Milbrandt scheme are weaker than for the WDM6 scheme but stronger than those with the Morrison scheme. The rainwater mixing ratio is very low and no obvious cold pool and rear inflow exist with the NSSL scheme.

Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16

A new dynamical core, known as the Finite-Volume Cubed-Sphere (FV3) and developed at both NASA and NOAA, is used in NOAA's Global Forecast System (GFS) and in limited-area models for regional weather and air quality applications. NOAA has also upgraded the operational FV3GFS to version 16 (GFSv16), which includes a number of significant developmental advances to the model configuration, data assimilation, and underlying model physics, particularly for atmospheric composition to weather feedback. Concurrent with the GFSv16 upgrade, we couple the GFSv16 with the Community Multiscale Air Quality (CMAQ) model to form an advanced version of the National Air Quality Forecasting Capability (NAQFC) that will continue to protect human and ecosystem health in the US. Here we describe the development of the FV3GFSv16 coupling with a "state-of-the-science" CMAQ model version 5.3.1. The GFS-CMAQ coupling is made possible by the seminal version of the NOAA-EPA Atmosphere-Chemistry Coupler (NACC), which became a major piece of the next operational NAQFC system (i.e., NACC-CMAQ) on 20 July 2021. NACC-CMAQ has a number of scientific advancements that include satellite-based data acquisition technology to improve land cover and soil characteristics and inline wildfire smoke and dust predictions that are vital to predictions of fine particulate matter (PM_2.5) concentrations during hazardous events affecting society, ecosystems, and human health. The GFS-driven NACC-CMAQ model has significantly different meteorological and chemical predictions compared to the previous operational NAQFC, where evaluation of NACC-CMAQ shows generally improved near-surface ozone and PM_2.5 predictions and diurnal patterns, both of which are extended to a 72 h (3 d) forecast with this system.

Filling Temporal Gaps within and between GRACE and GRACE-FO Terrestrial Water Storage Records: An Innovative Approach

Temporal gaps within the Gravity Recovery and Climate Experiment (GRACE) (gap: 20 months), between GRACE and GRACE Follow-On (GRACE-FO) missions (gap: 11 months), and within GRACE-FO record (gap: 2 months) make it difficult to analyze and interpret spatiotemporal variability in GRACE- and GRACE-FO-derived terrestrial water storage (TWSGRACE) time series. In this study, an overview of data and approaches used to fill these gaps and reconstruct the TWSGRACE record at the global scale is provided. In addition, the study provides an innovative approach that integrates three machine learning techniques (deep-learning neural networks [DNN], generalized linear model [GLM], and gradient boosting machine [GBM]) and eight climatic and hydrological input variables to fill these gaps and reconstruct the TWSGRACE data record at both global grid and basin scales. For each basin and grid cell, the model performance was assessed using Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (CC), and normalized root-mean-square error (NRMSE), a leader model was selected based on the model performance, and variables that significantly control leader model outputs were defined. Results indicate that (1) the leader model reconstructed the TWSGRACE with high accuracy over both grid and local scales, particularly in wet and low anthropogenically active regions (grid scale: NSE = 0.65 ± 0.20, CC = 0.81 ± 0.13, and NSE = 0.56 ± 0.16; basin scale: NSE = 0.78 ± 0.14, CC = 0.89 ± 0.07, and NRMSE = 0.43 ± 0.14); (2) no single model was flawless in reconstructing the TWSGRACE over all grids or basins, so a combination of models is necessary; (3) basin-scale models outperform grid-scale models; (4) the DNN model outperforms both GLM and GBM at the basin scale, whereas the GBM outperforms at the grid scale; (5) among other inputs, the Global Land Data Assimilation System (GLDAS)-derived TWS controls the model performance on both basin and grid scales; and (6) the reconstructed TWSGRACE data captured extreme climatic events over the investigated basins and grid cells. The developed approach is robust, effective, and could be used to accurately reconstruct TWSGRACE for any hydrologic system across the globe.

Energy-Based Approaches in Estimating Actual Evapotranspiration Focusing on Land Surface Temperature: A Review of Methods, Concepts, and Challenges

The surface energy balance (SEB) model is a physically based approach in which aerodynamic principles and bulk transfer theory are used to estimate actual evapotranspiration. A wide range of different methods have been developed to parameterize the SEB equation; however, few studies addressed solutions to the SEB considering the land surface temperature (LST). Therefore, in the current review, a clear and comprehensive classification is provided for energy-based approaches considering the key role of LST in solving the energy budget. In this regard, three general approaches are presented using LSTs derived by climate and land surface models (LSMs), satellite-based data, and energy balance closure. In addition, this review surveys the concepts, required inputs, and assumptions of energy-based LSMs and SEB algorithms in detail. The limitations and challenges of aforementioned approaches including land surface temperature, surface energy imbalance, and calculation of surface and aerodynamic resistance network are also assessed. According to the results, since the accuracy of resulting LSTs are affected by weather conditions, surface energy closure, and use of vegetation/meteorological information, all approaches are faced with uncertainties in determining ET. In addition, for further study, an interactive evaluation of water and energy conservation laws is recommended to improve the ET estimation accuracy.

Quantifying Changes in Groundwater Storage and Response to Hydroclimatic Extremes in a Coastal Aquifer Using Remote Sensing and Ground-Based Measurements: The Texas Gulf Coast Aquifer

With the increasing vulnerability of groundwater resources, especially in coastal regions, there is a growing need to monitor changes in groundwater storage (GWS). Estimations of GWS have been conducted extensively at regional to global scales using GRACE and GRACE-FO observations. The major goal of this study was to evaluate the applicability of uninterrupted monthly GRACE-derived terrestrial water storage (TWSGRACE) records in facilitating detection of long- and short-term hydroclimatic events affecting the GWS in a coastal area. The TWSGRACE data gap was filled with reconstructed values from multi-linear regression (MLR) and artificial neural network (ANN) models and used to estimate changes in GWS in the Texas coastal region (Gulf Coast and Carrizo–Wilcox Aquifers) between 2002 and 2019. The reconstructed TWSGRACE, along with soil moisture storage (SMS) from land surface models (LSMs), and surface water storage (SWS) were used to estimate the GRACE-derived GWS (GWSGRACE), validated against the GWS estimated from groundwater level observations (GWSwell) and extreme hydroclimatic event records. The results of this study show: (1) Good agreement between the predicted TWSGRACE data gaps from the MLR and ANN models with high accuracy of predictions; (2) good agreement between the GWSGRACE and GWSwell records (CC = 0.56, p-value < 0.01) for the 2011–2019 period for which continuous GWLwell data exists, thus validating the approach and increasing confidence in using the reconstructed TWSGRACE data to monitor coastal GWS; (3) a significant decline in the coastal GWSGRACE, at a rate of 0.35 ± 0.078 km3·yr−1 (p-value < 0.01), for the 2002–2019 period; and (4) the reliable applicability of GWSGRACE records in detecting multi-year drought and wet periods with good accuracy: Two drought periods were identified between 2005–2006 and 2010–2015, with significant respective depletion rates of −8.9 ± 0.95 km3·yr−1 and −2.67 ± 0.44 km3·yr−1 and one wet period between 2007 and 2010 with a significant increasing rate of 2.6 ± 0.63 km3·yr−1. Thus, this study provides a reliable approach to examine the long- and short-term trends in GWS in response to changing climate conditions with significant implications for water management practices and improved decision-making capabilities.

WRF Rainfall Modeling Post-Processing by Adaptive Parameterization of Raindrop Size Distribution: A Case Study on the United Kingdom

Raindrop size distribution (RSD) is a key parameter in the Weather Research and Forecasting (WRF) model for rainfall estimation, with gamma distribution models commonly used to describe RSD under WRF microphysical parameterizations. The RSD model sets the shape parameter (μ) as a constant of gamma distribution in WRF double-moment bulk microphysics schemes. Here, we propose to improve the gamma RSD model with an adaptive value of μ based on the rainfall intensity and season, designed using a genetic algorithm (GA) and the linear least-squares method. The model can be described as a piecewise post-processing function that is constant when rainfall intensity is <1.5 mm/h and linear otherwise. Our numerical simulation uses the WRF driven by an ERA-interim dataset with three distinct double-moment bulk microphysical parameterizations, namely, the Morrison, WDM6, and Thompson aerosol-aware schemes for the period of 2013–2017 over the United Kingdom at a 5 km resolution. Observations were made using a disdrometer and 241 rain gauges, which were used for calibration and validation. The results show that the adaptive-μ model of the gamma distribution was more accurate than the gamma RSD model with a constant shape parameter, with the root-mean-square error decreasing by averages of 23.62%, 11.33%, and 22.21% for the Morrison, WDM6, and Thompson aerosol-aware schemes, respectively. This model improves the accuracy of WRF rainfall simulation by applying adaptive RSD parameterization and can be integrated into the simulation of WRF double-moment microphysics schemes. The physical mechanism of the RSD model remains to be determined to improve its performance in WRF bulk microphysics schemes.

The Multi-Scale Dynamics Organizing a Favorable Environment for Convective Density Currents That Redirected the Yarnell Hill Fire

The deadly shift of the Yarnell Hill, Arizona wildfire was associated with an environment exhibiting gusty wind patterns in response to organized convectively driven circulations. The observed synoptic (>2500 km) through meso-β (approximately 100 km) scale precursor environment that organized a mid-upper tropospheric cross-mountain mesoscale jet streak circulation and upslope thermally direct flow was examined. Numerical simulations and observations indicated that both circulations played a key role in focusing the upper-level divergence, ascent, downdraft potential, vertical wind shear favoring mobile convective gust fronts, and a microburst. This sequence was initiated at the synoptic scale by a cyclonic Rossby Wave Break (RWB) 72 h prior, followed by an anticyclonic RWB. These RWBs combined to produce a mid-continent baroclinic trough with two short waves ushering in cooler air with the amplifying polar jet. Cool air advection with the second trough and surface heating across the Intermountain West (IW) combined to increase the mesoscale pressure gradient, forcing a mid-upper tropospheric subsynoptic jet around the periphery of the upstream ridge over Southern Utah and Northern New Mexico. Convection was triggered by an unbalanced secondary jetlet circulation within the subsynoptic jet in association with a low-level upslope flow accompanying a mountain plains solenoidal circulation above the Mogollon Rim (MR) and downstream mountains.

Combinational Optimization of the WRF Physical Parameterization Schemes to Improve Numerical Sea Breeze Prediction Using Micro-Genetic Algorithm

This study aims to improve the performance of the Weather Research and Forecasting (WRF) model in the sea breeze circulation using the micro-Genetic Algorithm (micro-GA). We found the optimal combination of four physical parameterization schemes related to the sea breeze system, including planetary boundary layer (PBL), land surface, shortwave radiation, and longwave radiation, in the WRF model coupled with the micro-GA (WRF-μGA system). The optimization was performed with respect to surface meteorological variables (2 m temperature, 2 m relative humidity, 10 m wind speed and direction) and a vertical wind profile (wind speed and direction), simultaneously for three sea breeze cases over the northeastern coast of South Korea. The optimized set of parameterization schemes out of the WRF-μGA system includes the Mellor–Yamada–Nakanishi–Niino level-2.5 (MYNN2) for PBL, the Noah land surface model with multiple parameterization options (Noah-MP) for land surface, and the Rapid Radiative Transfer Model for GCMs (RRTMG) for both shortwave and longwave radiation. The optimized set compared with the various other sets of parameterization schemes for the sea breeze circulations showed up to 29 % for the improvement ratio in terms of the normalized RMSE considering all meteorological variables.

Strategies towards PM2.5 attainment for non-compliant cities in China: A case study

The northern part of the Yangtze River Delta (YRD) region in China suffers from high concentrations of fine particular matter (PM_2.5) during the past years yet received much less attention compared to the other parts of the YRD region. In this study, we integrated observational data, control policies and strategies, and air quality simulations to develop PM_2.5 attainment demonstration by year 2030 for the city of Bengbu, which represents a typical non-compliant city in the northern YRD region. In 2018, the annual average PM_2.5 concentration in Bengbu was 51.8 μg/m³, which was 48 % higher than the standard of 35 μg/m³ set by the National Ambient Air Quality Standards (NAAQS). Different future emission scenarios were developed for year 2025 as mid-term and year 2030 as long-term. Integrated meteorology and air quality modeling system together with monitoring data was applied to predict the air quality under the future emission scenarios. Results show that when a conservative emission reduction ratio of 40 % was assumed for surrounding regions, the annual average PM_2.5 concentration in Bengbu could meet the target value by 2030, in which case emissions of SO₂, NOx, PM_2.5, VOCs, and NH₃ need to be reduced by 70.6 %, 43.5 %, 47.2 %, 33.4 %, and 47.5 %, respectively. PM_2.5 concentration in Bengbu is not only controlled by local emission reductions but also affected by emission reductions of surrounding regions as well as contribution from long-range transport. More attentions need to be paid to the control of VOCs emissions in the near future to avoid increase of ozone concentrations while reducing PM_2.5. Our results provide scientific support for the local government to formulate future air pollution prevention and control strategies, sub-regional joint-control among surrounding cities, as well as trans-regional joint-control between the north China and the YRD region.

Improving weather forecasting by assimilation of water vapor isotopes

Stable water isotopes, which depend on meteorology and terrain, are important indicators of global water circulation. During the past 10 years, major advances have been made in general circulation models that include water isotopes, and the understanding of water isotopes has greatly progressed as a result of innovative, improved observation techniques. However, no previous studies have combined modeled and observed isotopes using data assimilation, nor have they investigated the impacts of real observations of isotopes. This is the first study to assimilate real satellite observations of isotopes using a general circulation model, then investigate the impacts on global dynamics and local phenomena. The results showed that assimilating isotope data improved not only the water isotope field but also meteorological variables such as air temperature and wind speed. Furthermore, the forecast skills of these variables were improved by a few percent, compared with a model that did not assimilate isotope observations.

Impact of Lightning Data Assimilation on Forecasts of a Leeward Slope Precipitation Event in the Western Margin of the Junggar Basin

A moderate precipitation event occurring in northern Xinjiang, a region with a continental climate with little rainfall, and in leeward slope areas influenced by topography is important but rarely studied. In this study, the performance of lightning data assimilation is evaluated in the short-term forecasting of a moderate precipitation event along the western margin of the Junggar Basin and eastern Jayer Mountain. Pseudo-water vapor observations driven by lightning data are assimilated in both single and cycling analysis experiments of the Weather Research and Forecast (WRF) three-dimensional variational (3DVAR) system. Lightning data assimilation yields a larger increment in the relative humidity in the analysis field at the observed lightning locations, and the largest increment is obtained in the cycling analysis experiment. Due to the increase in water vapor content in the analysis field, more suitable thermal and dynamic conditions for moderate precipitation are obtained on the leeward slope, and the ice-phase and raindrop particle contents increase in the forecast field. Lightning data assimilation significantly improves the short-term leeward slope moderate precipitation prediction along the western margin of the Junggar Basin and provides the best forecast skill in cycling analysis experiments.

Insight into ozone profile climatology over northeast China from aircraft measurement and numerical simulation

Tropospheric ozone is a major pollutant that can harm human health, animals and plants. With a rapid development in Northeast China, ozone pollution has become an increasingly serious environmental challenge. To study the ozone distribution and the potential sources of ozone precursors in Northeast China, we analyzed vertical ozone profiles from the In-service Aircraft for a Global Observing System (IAGOS) in 2012-2014 and provided the climatological vertical structure of tropospheric ozone over Shenyang. The tropospheric ozone generally presents high in hot months, mainly due to the combined effects of the strong solar radiation and high volatile organic compounds emission in summer. While in cold months, the ozone is low because of weak solar radiation and high nitrogen oxides emission. Besides, a low-ozone center exists within lower troposphere in August, which is mainly caused by the East Asian summer monsoon prevailing in summer. To analyze the sources of ozone, typical ozone pollution episodes were studied and the results revealed the different pathways for the enhancement of ozone pollution in Shenyang: regional transport of anthropogenic emissions from North China Plain (NCP), long-range transport from Siberian biomass burning and local photochemical production. Modeling results show that the largest contribution to the surface ozone in Northeast China is local anthropogenic emissions (exceed 90%); the regional transport of NCP anthropogenic emissions contribute more to the pollutants around 2 km, and account for more than 50% pollutants during highly ozone polluted days; through long-range transport, Siberian biomass burning in the spring also have a nonnegligible effect on the near-ground ozone in Northeast China. Overall, this study provides tropospheric ozone climatology and its source attribution in Northeast China, and highlight the great importance of regional transport of anthropogenic and biomass burning emissions in ozone pollution.

Estimation of Evapotranspiration and Its Components across China Based on a Modified Priestley–Taylor Algorithm Using Monthly Multi-Layer Soil Moisture Data

Although soil moisture (SM) is an important constraint factor of evapotranspiration (ET), the majority of the satellite-driven ET models do not include SM observations, especially the SM at different depths, since its spatial and temporal distribution is difficult to obtain. Based on monthly three-layer SM data at a 0.25° spatial resolution determined from multi-sources, we updated the original Priestley Taylor–Jet Propulsion Laboratory (PT-JPL) algorithm to the Priestley Taylor–Soil Moisture Evapotranspiration (PT-SM ET) algorithm by incorporating SM control into soil evaporation (Es) and canopy transpiration (T). Both algorithms were evaluated using 17 eddy covariance towers across different biomes of China. The PT-SM ET model shows increased R2, NSE and reduced RMSE, Bias, with more improvements occurring in water-limited regions. SM incorporation into T enhanced ET estimates by increasing R2 and NSE by 4% and 18%, respectively, and RMSE and Bias were respectively reduced by 34% and 7 mm. Moreover, we applied the two ET algorithms to the whole of China and found larger increases in T and Es in the central, northeastern, and southern regions of China when using the PT-SM algorithm compared with the original algorithm. Additionally, the estimated mean annual ET increased from the northwest to the southeast. The SM constraint resulted in higher transpiration estimate and lower evaporation estimate. Es was greatest in the northwest arid region, interception was a large fraction in some rainforests, and T was dominant in most other regions. Further improvements in the estimation of ET components at high spatial and temporal resolution are likely to lead to a better understanding of the water movement through the soil–plant–atmosphere continuum.

Assimilating FY-4A Lightning and Radar Data for Improving Short-Term Forecasts of a High-Impact Convective Event with a Dual-Resolution Hybrid 3DEnVAR Method

A dual-resolution, hybrid, three-dimensional ensemble-variational (3DEnVAR) data assimilation method combining static and ensemble background error covariances is used to assimilate radar data, and pseudo-water vapor observations to improve short-term severe weather forecasts with the Weather Research and Forecast (WRF) model. The higher-resolution deterministic forecast and the lower-resolution ensemble members have 3 and 9 km horizontal resolution, respectively. The water vapor pseudo-observations are derived from the combined use of total lightning data and cloud top height from the Fengyun-4A(FY-4A) geostationary satellite. First, a set of single-analysis experiments are conducted to provide a preliminary performance evaluation of the effectiveness of the hybrid method for assimilating multisource observations; second, a set of cycling analysis experiments are used to evaluate the forecast performance in convective-scale high-frequency analysis; finally, different hybrid coefficients are tested in both the single and cycling experiments. The single-analysis results show that the combined assimilation of radar data and water vapor pseudo-observations derived from the lightning data is able to generate reasonable vertical velocity, water vapor and hydrometeor adjustments, which help to trigger convection earlier in the forecast/analysis and reduce the spin-up time. The dual-resolution hybrid 3DEnVAR method is able to adjust the wind fields and hydrometeor variables with the assimilation of lightning data, which helps maintain the triggered convection longer and partially suppress spurious cells in the forecast compared with the three-dimensional variational (3DVAR) method. A cycling analysis that introduced a large number of observations with more frequent small adjustments is able to better resolve the observed convective events than a single-analysis approach. Different hybrid coefficients can affect the forecast results, either in the single deterministic or cycling analysis experiments. Overall, we found that a static coefficient of 0.4 and an ensemble coefficient of 0.6 yields the best forecast skill for this event.

Evaluating the Forecast Skill of a Hydrometeorological Modelling System in Greece

A hydrometeorological forecasting system has been operating at the Institute of Marine Biological Resources and Inland Waters (IMBRIW) of the Hellenic Centre for Marine Research (HCMR) since September 2015. The system consists of the Advanced Weather Research and Forecasting (WRF-ARW) model, the WRF-Hydro hydrological model, and the HEC-RAS hydraulic–hydrodynamic model. The system provides daily 120 h weather forecasts focusing on Greece (4 km horizontal resolution) and hydrological forecasts for the Spercheios and Evrotas rivers in Greece (100 m horizontal resolution), also providing flash flood inundation forecasts when needed (5 m horizontal resolution). The main aim of this study is to evaluate precipitation forecasts produced in a 4-year period (September 2015–August 2019) using measurements from meteorological stations across Greece. Water level forecasts for the Evrotas and Spercheios rivers were also evaluated using measurements from hydrological stations operated by the IMBRIW. Moreover, the forecast skill of the chained meteorological–hydrological–hydraulic operation of the system was investigated during a catastrophic flash flood in the Evrotas river. The results indicated that the system provided skillful precipitation and water level forecasts. The best evaluation results were yielded during rainy periods. They also demonstrated that timely flash flood forecasting products could benefit flood warning and emergency responses due to their efficiency and increased lead time.

Urbanization and Winter Precipitation: A Case Study Analysis of Land Surface Sensitivity

Urban modification of precipitation regimes is well documented in the urban climate literature. Studies investigating urbanization and non-convective precipitation, specifically winter precipitation, are limited. The theoretical framework here argues that the collective influence of urbanization extends beyond traditional city limits and the surrounding rural areas and can impact regional climate in non-adjacent cities. This paper utilizes the weather research and forecasting model (WRF-ARW) to simulate a cold-season synoptic system over the Northeastern United States over a variety of urban land surface scenarios. This case study centers on the potential boundary layer urban heat island effect on the lower troposphere and its ability to impact winter precipitation type at the local to regional scales. Results show a significant reduction in temperatures near the modified surface and subtle reductions over adjacent urban areas. When surface wind speeds are less than 5 ms−1, the boundary layer heat island increases air temperatures on the order of 3–4 °C at altitudes up to 925 mb. When combined with encroaching warm air near 850 mb during transitional precipitation events, the boundary layer heat island increases the thickness of the melting layer and consequently exposes falling hydrometeors to longer melting duration and phase change. Model simulations also show regional connections through remote temperature and relative humidity changes in urban areas removed from reforested areas.

Advection Fog over the Eastern Yellow Sea: WRF Simulation and Its Verification by Satellite and In Situ Observations

An observed sea fog event over the Eastern Yellow Sea on 15–16 April 2012 was reproduced in the Weather Research and Forecasting (WRF) simulation with high-resolution to investigate the roles of physical processes and synoptic-scale flows on advection fog with phase transition. First, it was verified by a satellite-based fog detection algorithm and in situ observation data. In the simulation, longwave (infrared) radiative cooling (LRC) with a downward turbulent sensible heat flux (SHF), due to the turbulence after sunset, triggered cloud formation over the surface when warm-moist air advection occurred. At night, warm air advection with continuous cooling due to longwave radiation and SHF near the surface modulated the change of the SHF from downward to upward, resulting in a drastic increase in the turbulent latent heat flux (LHF) that provided sufficient moisture at the lower atmosphere (self-moistening). This condition represents a transition from cold-sea fog to warm-sea fog. Enhanced turbulent mixing driven by a buoyancy force increased the depth of the sea fog and the marine atmospheric boundary layer (MABL) height, even at nighttime. In addition, cold air advection with a prevailing northerly wind at the top of the MABL led to a drastic increase in turbulent mixing and the MABL height and rapid growth of the height of sea fog. After sunrise, shortwave radiative warming in the fog layers offsetting the LRC near the surface weakened thermal instability, which contributed to the reduction in the MABL height, even during the daytime. In addition, dry advection of the northerly wind induced dissipation of the fog via evaporation. An additional sensitivity test of sea surface salinity showed weaker and shallower sea fog than the control due to the decrease in both the LHF and local self-moistening. Detailed findings from the simulated fog event can help to provide better guidance for fog detection using remote sensing.

Detecting Desert Locust Breeding Grounds: A Satellite-Assisted Modeling Approach

The objective of this study is to evaluate the ability of soil physical characteristics (i.e., texture and moisture conditions) to better understand the breeding conditions of desert locust (DL). Though soil moisture and texture are well-known and necessary environmental conditions for DL breeding, in this study, we highlight the ability of model-derived soil moisture estimates to contribute towards broader desert locust monitoring activities. We focus on the recent DL upsurge in East Africa from October 2019 though June 2020, utilizing known locust observations from the United Nations Food and Agriculture Organization (FAO). We compare this information to results from the current literature and combine the two datasets to create “optimal thresholds” of breeding conditions. When considering the most optimal conditions (all thresholds met), the soil texture combined with modeled soil moisture content predicted the estimated DL egg-laying period 62.5% of the time. Accounting for the data errors and uncertainties, a 3 × 3 pixel buffer increased this to 85.2%. By including soil moisture, the areas of optimal egg laying conditions decreased from 33% to less than 20% on average.

Synoptic Climatology of Lake-Effect Snow Events off the Western Great Lakes

As the mesoscale dynamics of lake-effect snow (LES) are becoming better understood, recent and ongoing research is beginning to focus on the large-scale environments conducive to LES. Synoptic-scale composites are constructed for Lake Michigan and Lake Superior LES events by employing an LES case repository for these regions within the U.S. North American Regional Reanalysis (NARR) data for each LES event were used to construct synoptic maps of dominant LES patterns for each lake. These maps were formulated using a previously implemented composite technique that blends principal component analysis with a k-means cluster analysis. A sample case from each resulting cluster was also selected and simulated using the Advanced Weather Research and Forecast model to obtain an example mesoscale depiction of the LES environment. The study revealed four synoptic setups for Lake Michigan and three for Lake Superior whose primary differences were discrepancies in a surface pressure dipole structure previously linked with Great Lakes LES. These subtle synoptic-scale differences suggested that while overall LES impacts were driven more by the mesoscale conditions for these lakes, synoptic-scale conditions still provided important insight into the character of LES forcing mechanisms, primarily the steering flow and air–lake thermodynamics.