Abstract The response of Earth’s climate system to greenhouse gas (GHG) forcing appears largest in the Arctic, both in observations and models. However, in multimodel evaluations (CMIP3 through CMIP6), most models appear to underestimate the amount of warming. Here, we examine the role of turbulent parameterizations of the stable boundary layer (SBL) in impacting the magnitude of warming and the spread in model responses. Turbulent parameterization of the SBL has long been a challenge in models. Forms used in fine vertical grid research boundary layer models often do not perform well in coarser-grid operational or climate models. Global climate model (GCM) vertical grids often deteriorate to a few layers in the SBL and 500 m or more in deeper stable layers (SLs). This grid spacing may not capture well the correct strength/depth of the Arctic inversion, the entrainment warming as the SBL is destabilized by GHG forcing or the correct energy budgets. It seems likely that the large spread in GCM Arctic simulations is in part due to the differences and misapplications of these grid-dependent parameterizations. In this investigation, we propose a new approach to the SBL parameterization problem by explicitly incorporating the grid vertical spacing in the parameterization. This will be carried out by analytically recovering a stability correction function that depends on the grid spacing. Initial tests of an analytically recovered correction function indicate that the correction function provides longer-tailed stability functions for coarse-grid models. Applying this correction to a simplified Arctic profile made coarser-grid models agree better with fine-scale results. Significance Statement This article addresses how small-scale turbulence may impact large-scale climate change response in coarse-grid climate models. The impact of climate change in models depends on the vertical grid spacing. The present investigation proposes and tests a model correction that makes model results less grid dependent. It may help bring climate and small-scale boundary layer modeling communities together.

Lightning is one of the primary natural sources of nitric oxide (NO), and the influence of lightning-induced NO (LNO) emission on air quality has been investigated in the past few decades. In the current study an LNO emissions model, which derives LNO emission estimates from satellite-observed lightning optical energy, is introduced. The estimated LNO emission is employed in an air quality modeling system to investigate the potential influence of LNO on tropospheric ozone. Results show that lightning produced 0.174 Tg N of nitrogen oxides (NOx = NO + NO2) over the contiguous US (CONUS) domain between June and September 2019, which accounts for 11.4 % of the total NOx emission. In August 2019, LNO emission increased ozone concentration within the troposphere by an average of 1 %–2 % (or 0.3–1.5 ppbv), depending on the altitude; the enhancement is maximum at ∼ 4 km above ground level and minimum near the surface. The southeastern US has the most significant ground-level ozone increase, with up to 1 ppbv (or 2 % of the mean observed value) difference for the maximum daily 8 h average (MDA8) ozone. These numbers are near the lower bound of the uncertainty range given in previous studies. The decreasing trend in anthropogenic NOx emissions over the past 2 decades increases the relative contribution of LNO emissions to total NOx emissions, suggesting that the LNO production rate used in this study may need to be increased. Corrections for the sensor flash detection efficiency may also be helpful. Moreover, the episodic impact of LNO on tropospheric ozone can be considerable. Performing backward trajectory analyses revealed two main reasons for significant ozone increases: long-distance chemical transport and lightning activity in the upwind direction shortly before the event.

The US corn area footprint has changed significantly since the 20th century, declining in the southeastern states while exhibiting an increase or stable variations in the Midwest. As harvested acreage directly impacts the total corn production, understanding the influencing factors is crucial. This study assesses the role of potential drivers on the contrasting trajectories of harvested corn acreage between midwestern and southeastern US. Profit-acreage analysis reveals that antecedent profits/losses have a statistically significant influence on corn acreage changes, with southeastern US, which experienced more loss-making years, also experiencing more frequent reductions in corn acreage. The high number of loss-making years in the Southeast is primarily attributed to the region’s low corn yield, influenced by climate and other agro-environmental factors. Using a panel regression model, we find that the loss-making years in the Southeast could have reduced to fewer than 26 out of the considered 45 years, or almost similar to the average in the Midwest, by just increasing the irrigated corn area to 50%, a realistic irrigated corn area fraction already achieved in several Georgia counties. This underscores the potential for early policy interventions like irrigation facilitation to sustain and expand cropped acreage. However, we also find that this would only be economically feasible with incentives for both the installation and sustained operation of irrigation infrastructure.

The tendency of climate models to overstate warming in the tropical troposphere has long been noted. Here we examine individual runs from 38 newly released Coupled Model Intercomparison Project Version 6 (CMIP6) models and show that the warm bias is now observable globally as well. We compare CMIP6 runs against observational series drawn from satellites, weather balloons, and reanalysis products. We focus on the 1979–2014 interval, the maximum span for which all observational products are available and for which models were run using historically observed forcings. For lower-troposphere and midtroposphere layers both globally and in the tropics, all 38 models overpredict warming in every target observational analog, in most cases significantly so, and the average differences between models and observations are statistically significant. We present evidence that consistency with observed warming would require lower model Equilibrium Climate Sensitivity (ECS) values.

Table S1: Ground-level MDA8 ozone statistics over the model domain and geographic regions for June 2019.Bold numbers indicate better performance for each case.Region Case Record OBS [ppb] MOD [ppb] MB [ppb] NMB [%] RMSE [ppb] NME [%] R Domain CNTRL 46.1 47.5 1.4 3.0 8.5 14.2 0.72 LGTNO 46.1 47.8 1.7 3.6 8.5 14.2 0.73 NE CNTRL 43.9 47.8 3.9 8.9 8.3 14.8 0.72 LGTNO 43.9 48.1 4.2 9.5 8.4 14.9 0.73 SE CNTRL 41.4 44.2 2.8 6.8 8.1 15.7 0.79 LGTNO 41.4 44.7 3.3 8.0 8.3 16.0 0.79 UM CNTRL 46.1 45.7 -0.4 -0.8 7.5 12.8 0.

Clouds play an important role in the Earth's climate system since they can affect various physical and chemical processes within the atmosphere. Misplacement of clouds is a major source of error in the numerical weather prediction (NWP) models, and it also impacts the accuracy of air quality simulations since the meteorology and air quality are directly coupled. In this study, a cloud assimilation technique was utilized to improve cloud placement within the Weather Research and Forecasting (WRF) model by assimilating Geostationary Operational Environmental Satellite (GOES)-derived cloud products. Meteorological outputs from the WRF model were then used as inputs for the Community Multiscale Air Quality (CMAQ) model. The impact of cloud assimilation on air quality was tested over the June-September 2016 period. The results indicated that, by modifying model clouds, cloud assimilation corrected surface solar radiation and photochemical reaction rates, altered light sensitive biogenic emissions, adjusted horizontal transport and vertical mixing, and finally improved the prediction of surface ozone concentration. Cloud assimilation improved daytime surface ozone prediction over most of the U.S. domain, with exceptions in California. On average, cloud assimilation improved the prediction of daytime peak ozone and reduced bias by 47% (similar to 1.5 ppb). The largest improvement was seen over the southeast U.S. region (similar to 2.6 ppb reduction in daytime peak ozone), where convective clouds are more frequent and transient and biogenic volatile organic compound (VOC) emissions are more intense than elsewhere.

Development of clouds in space and time within numerical meteorological models as observed in nature is essential for producing an accurate representation of the physical atmosphere for input into air quality models. In this study, a new technique was developed to assimilate Geostationary Operational Environmental Satellite (GOES)-derived cloud fields into the Weather Research and Forecasting (WRF) meteorological model to improve the placement of clouds in space and time within the model. The simulations were performed on 36-, 12-, and 4-km grid-size domains covering the contiguous United States, the south-southeastern United States, and eastern Texas, respectively. The technique was tested over the month of August 2006. The results indicate that the assimilation technique significantly improves the agreement between the model-predicted and GOES-derived cloud fields. The daily average percentage increase in the cloud agreement was determined to be 14.02%, 11.29%, and 4.96% for the 36-, 12-, and 4-km domains, respectively. This was accomplished without degrading the model performance with respect to surface wind speed, temperature, and mixing ratio, which are important parameters for air quality applications; in some cases these variables were even slightly improved. The assimilation technique also produced improvements in the model-predicted precipitation and predicted downwelling shortwave radiation reaching the surface.

Incident solar radiation at Earth's surface, also called surface insolation, plays an important role in the Earth system as it affects surface energy balance, weather, climate, water supply, biochemical emissions, photochemical reactions, etc. The University of Alabama in Huntsville (UAH) and the NASA Short-term Prediction Research and Transition Center (SPoRT) have been generating and archiving several products, including insolation, from the Geostationary Operational Environmental Satellite (GOES) Imager for over a decade. The NASA/UAH insolation product has been used in studies to improve air quality simulations, biogenic emission estimates, correcting surface energy balance, and for cloud assimilation, but has not been thoroughly evaluated. In this study, the NASA/UAH insolation product is compared to surface pyranometer measurements from the Surface Radiation Budget Network (SURFRAD) and the U.S. Climate Reference Network (USCRN) for a 12-month period from March 2013 to February 2014. The insolation product has normalized bias values within 6% of the mean observation, a root-mean-square error between 6% and 16%, and correlation coefficients greater than 0.96 for hourly insolation estimates. It also shows better performance without the presence of clouds. However, erroneous estimates may be produced for persistent snow-covered surfaces. Further, this study attempts to demonstrate the use of such a satellite-based insolation product for model evaluation. The NASA/UAH insolation product is compared to the downward shortwave radiation from the Rapid Refresh, version 1 (RAPv1), and successfully captures the overestimation tendency in surface energy input as mentioned in previous studies. Finally, future plans for improving the retrieval algorithm and developing a GOES-16 insolation product are discussed.

The highest correlative relations for air pollution levels are often with meteorological variables such as temperature and wind speed. Today, sophisticated gridded high-resolution meteorological models are used to produce meteorological fields that drive chemical transport models for air quality management. Errors in specification of the physical atmosphere such as temperature, clouds and winds can affect the air quality predictions. Additionally, the efficiency and efficacy of emission control strategies can be compromised by errors in the meteorological fields. In this paper, the role of meteorology in air quality behavior, primarily from the viewpoint of regional ozone modeling as carried out in the U.S., is reviewed. Particular attention is given to physics and new techniques for improving meteorological model performance. Uncertainties in model turbulent mixing in the nighttime boundary layer, where large model differences exist, are examined. The role of spatial mesoscale features such as topography and land/water systems in models are discussed. The nocturnal low-level jet, a mesoscale temporal and spatial feature, and its impact on air quality are examined. Traditional air quality concerns have focused on synoptic conditions at the center of high-pressure systems. However, high ozone levels have also been associated with stationary fronts. The ability of models to capture mesoscale structure and yet retain synoptic structure and its timing is challenging. Data assimilation and its ability to improve model performance are examined. Particular attention is given to vertical nudging strategies that can affect formation of the nocturnal low-level jets. Finally, clouds can have a major impact on air quality since insolation impacts temperature, biogenic emissions and photolysis rates and extremes in stability. Traditional techniques, which attempt to insert cloud water where there is not dynamical support, can lead to additional errors. New dynamical approaches for improving model cloud performance are discussed.Implications: This article shows that there has been a considerable improvement in meteorological models used for air quality simulations. In particular, improvement in the tools for incorporating both traditional observations and new satellite data for retrospective studies has been beneficial to air quality community. However, while this trend is continuing, many challenges remain. As an example, due to having many options available in configuring a model simulation, there is a need to evaluate and recommend sets of options that provide important performance measures.

High levels of ozone have been observed along the shores of Lake Michigan for the last 40 years. Models continue to struggle in their ability to replicate ozone behavior in the region. In the retrospective way in which models are used in air quality regulation development, nudging or four-dimensional data assimilation (FDDA) of the large-scale environment is important for constraining model forecast errors. Here, paths for incorporating large-scale meteorological conditions but retaining model mesoscale structure are evaluated. For the July 2011 case studied here, iterative FDDA strategies did not improve mesoscale performance in the Great Lakes region in terms of diurnal trends or monthly averaged statistics, with overestimations of nighttime wind speed remaining as an issue. Two vertical nudging strategies were evaluated for their effects on the development of nocturnal low-level jets (LLJ) and their impacts on air quality simulations. Nudging only above the planetary boundary layer, which has been a standard option in many air quality simulations, significantly dampened the amplitude of LLJ relative to nudging only above a height of 2 km. While the LLJ was preserved with nudging only above 2 km, there was some deterioration in wind performance when compared with profiler networks above the jet between 500 m and 2 km. In examining the impact of nudging strategies on air quality performance of the Community Multiscale Air Quality model, it was found that performance was improved for the case of nudging above 2 km. This result may reflect the importance of the LLJ in transport or perhaps a change in mixing in the models.

The Great Lakes Region of the US continues experiencing exceedances of the ozone (O-3) standards, despite years of emissions controls. In part, this is due to interactions between emissions from surrounding large cities (e.g., Chicago) and meteorology, which is heavily influenced by the presence of the Great Lakes. These complex meteorology-emissions interactions pose a challenge to fully capture O-3 dynamics, particularly near shores of the lakes, where high O-3 levels are often experienced. In a simulation with the Community Multiscale Air Quality (CMAQ) model, using inputs as typically constructed, the model tends to be biased high. A literature review indicated that NOx emissions from mobile sources, possibly overestimated in the 2011 National Emission Inventory (NEI), or the version of the Carbon Bond chemical mechanism used in CMAQ could be responsible for high biases of O-3. As such, a series of sensitivity tests was conducted to identify potential causes for this bias, including emissions biases (e.g., biogenic VOCs, anthropogenic NOx), chemical mechanism choice, and O-3 dry deposition to fresh water, for high O-3 periods in July 2011. The base model/emissions configuration used the following: Carbon Bond mechanism CB05, biogenic emissions using Biogenic Emission Inventory System (BETS), and anthropogenic emissions from the 2011 National Emissions Inventory (NEI). Meteorological inputs were developed using the Weather Research and Forecasting (WRF) model (version 3.8.1). Simulated daily maximum 8-h average O-3 without and with a cutoff of 60 ppb (referred to as MDA8 O-3 and elevated MDA8 O-3 hereafter, respectively) were evaluated against measurements. The evaluation showed a high bias in MDA8 O-3 across the domain, particularly at coastal sites (by similar to 6 ppb), while elevated MDA8 O-3 (i.e., greater than 60 ppb) was biased low, with exceptions centered along the shore of Lake Michigan. Using the CB6 chemical mechanism or 50% reduction of NOx emissions from on-road mobile sources led to substantial domain-wide decreases in O-3 from the base case, and the model performance improved, particularly along the Lake Michigan shoreline and for the western domain. However, elevated MDA8 O-3 was more biased against measurements, compared to the model performance in the base case, except at a few sites along the shoreline. Using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) instead of BEIS to estimate biogenic emissions, or increasing dry deposition of O-3 to fresh water by a factor of ten (which is unrealistic), had minor impacts on simulated O-3 over land. But, combining MEGAN with CB6 resulted in improved elevated MDA8 O-3 simulation along the western coast of Lake Michigan. Finally, using CB6 combined with a 30% reduction of on-road mobile NOx emissions and MEGAN led to the best performance. Two companion papers investigate how meteorological modeling can be improved. Together, the recommended modeling system could serve as a starting point for future O-3 modeling in the region.

High mixing ratios of ozone along the shores of Lake Michigan have been a recurring theme over the last 40 years. Models continue to have difficulty in replicating ozone behavior in the region. Although emissions and chemistry may play a role in model performance, the complex meteorological setting of the relatively cold lake in the summer ozone season and the ability of the physical model to replicate this environment may contribute to air quality modeling errors. In this paper, several aspects of the physical atmosphere that may affect air quality, along with potential paths to improve the physical simulations, are broadly examined. The first topic is the consistent overwater overprediction of ozone. Although overwater measurements are scarce, special boat and ferry ozone measurements over the last 15 years have indicated consistent overprediction by models. The roles of model mixing and lake surface temperatures are examined in terms of changing stability over the lake. From an analysis of a 2009 case, it is tentatively concluded that excessive mixing in the meteorological model may lead to an underestimate of mixing in offline chemical models when different boundary layer mixing schemes are used. This is because the stable boundary layer shear, which is removed by mixing in the meteorological model, can no longer produce mixing when mixing is rediagnosed in the offline chemistry model. Second, air temperature has an important role in directly affecting chemistry and emissions. Land–water temperature contrasts are critical to lake and land breezes, which have an impact on mixing and transport. Here, satellite-derived skin temperatures are employed as a path to improve model temperature performance. It is concluded that land surface schemes that adjust moisture based on surface energetics are important in reducing temperature errors.

This study examines the influence of insolation and cloud retrieval products from the Geostationary Operational Environmental Satellite (GOES) system on biogenic emission estimates and ozone simulations in Texas. Compared to surface pyranometer observations, satellite‐retrieved insolation and photosynthetically active radiation (PAR) values tend to systematically correct the overestimation of downwelling shortwave radiation in the Weather Research and Forecasting (WRF) model. The correlation coefficient increases from 0.93 to 0.97, and the normalized mean error decreases from 36% to 21%. The isoprene and monoterpene emissions estimated by the Model of Emissions of Gases and Aerosols from Nature are on average 20% and 5% less, respectively, when PAR from the direct satellite retrieval is used rather than the control WRF run. The reduction in biogenic emission rates using satellite PAR reduced the predicted maximum daily 8 h ozone concentration by up to 5.3 ppbV over the Dallas‐Fort Worth (DFW) region on some days. However, episode average ozone response is less sensitive, with a 0.6 ppbV decrease near DFW and 0.3 ppbV increase over East Texas. The systematic overestimation of isoprene concentrations in a WRF control case is partially corrected by using satellite PAR, which observes more clouds than are simulated by WRF. Further, assimilation of GOES‐derived cloud fields in WRF improved CAMx model performance for ground‐level ozone over Texas. Additionally, it was found that using satellite PAR improved the model's ability to replicate the spatial pattern of satellite‐derived formaldehyde columns and aircraft‐observed vertical profiles of isoprene.