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Most climate models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) still suffer pronounced warm and dry summer biases in the central United States (CUS), even in high-resolution simulations. We found that the cloud base definition in the cumulus parameterization was the dominant factor determining the spread of the biases among models and those defining cloud base at the lifting condensation level (LCL) performed the best. To identify the underlying mechanisms, we developed a physically based analytical bias model (ABM) to capture the key feedback processes of land–atmosphere coupling. The ABM has significant explanatory power, capturing 80% variance of temperature and precipitation biases among all models. Our ABM analysis via counterfactual experiments indicated that the biases are attributed mostly by surface downwelling longwave radiation errors and second by surface net shortwave radiation errors, with the former 2–5 times larger. The effective radiative forcing from these two errors as weighted by their relative contributions induces runaway temperature and precipitation feedbacks, which collaborate to cause CUS summer warm and dry biases. The LCL cumulus reduces the biases through two key mechanisms: it produces more clouds and less precipitable water, which reduce radiative energy input for both surface heating and evapotranspiration to cause a cooler and wetter soil; it produces more rainfall and wetter soil conditions, which suppress the positive evapotranspiration–precipitation feedback to damp the warm and dry bias coupling. Most models using non-LCL schemes underestimate both precipitation and cloud amounts, which amplify the positive feedback to cause significant biases. [ABSTRACT FROM AUTHOR]

Most climate models still suffer large warm and dry summer biases in the central United States (CUS). As a solution, we improved cumulus parameterization to represent 1) the lifting effect of small-scale rising motions associated with Great Plains low-level jets and midtropospheric perturbations by defining the cloud base at the level of condensation, 2) the constraint of the cumulus entrainment rate depending on the boundary layer depth, and 3) the temperature-dependent cloud-to-rainwater conversion rate. These improvements acted to (i) trigger mesoscale convective systems in unfavorable environmental conditions to enhance total rainfall amount, (ii) lower cloud base and increase cloud depth to increase low-level clouds and reduce surface shortwave radiation, (iii) suppress penetrative cumuli from shallow boundary layers to remedy the overestimation of precipitation frequency, and (iv) increase water detrainment to form sufficient cirrus clouds and balanced outgoing longwave radiation. Much of these effects were nonlocal and nonlinear, where more frequent but weaker convective rainfall led to stronger (and sometimes more frequent) large-scale precipitation remotely. Together, they produced consistently heavier precipitation and colder temperature with a realistic atmospheric energy balance, essentially eliminating the CUS warm and dry biases through robust physical mechanisms. [ABSTRACT FROM AUTHOR]

This study examines the effects of five cumulus closure assumptions on simulations of summer precipitation in the continental U.S. by utilizing an ensemble cumulus parameterization (ECP) that incorporates multiple alternate closure schemes into a single cloud model formulation. Results demonstrate that closure algorithms significantly affect the summer mean, daily frequency and intensity, and diurnal variation of precipitation, with strong regional dependence. Overall, the vertical velocity (W) closure produces the smallest summer mean biases, while the moisture convergence (MC) closure most realistically reproduces daily variability. Both closures have advantages over others in simulating U.S. daily rainfall frequency distribution, though both slightly overestimate intense rain events. The MC closure is superior at capturing summer rainfall amount, daily variability, and heavy rainfall frequency over the Central U.S., but systematically produces wet biases over the North American Monsoon (NAM) region and Southeast U.S., which can be reduced by using the W closure. The instability tendency (TD) and the total instability adjustment (KF) closures are better at capturing observed diurnal signals over the Central U.S. and the NAM, respectively. The results reasonably explain the systematic behaviors of several major cumulus parameterizations. A preliminary experiment combining two optimal closures (averaged moisture convergence and vertical velocity) in the ECP scheme significantly reduced the wet (dry) biases over the Southeast U.S. in the summer of 1993 (2003), and greatly improved daily rainfall correlations over the NAM. Further improved model simulation skills may be achieved in the future if optimal closures and their appropriate weights can be derived at different time scales based on specific climate regimes. [ABSTRACT FROM AUTHOR]

This study evaluates the effects of major cumulus parameterization closures on summer precipitation simulations over the U.S. Atlantic Coasts and Gulf of Mexico. A series of mesoscale regional climate model simulations using an Ensemble Cumulus Parameterization (ECP) that incorporates multiple alternate closure schemes into a single cloud model formulation are conducted and compared to determine the systematic errors and relative performances of individual and combined closures in capturing precipitation spatiotemporal variations. The results show that closure algorithms largely affect precipitation's geographic distribution, frequency and intensity, and diurnal cycle. The quasi-equilibrium and total instability adjustment closures simulate widespread wet biases, while the instability tendency closure produces systematic dry biases. Two closure algorithms based on the average vertical velocity at the cloud base and column moisture convergence complementarily reproduce the observed precipitation pattern and amount, and capture the frequency of heavy rainfall events better than other closures. In contrast, the instability tendency closures are better at capturing the diurnal phase but yield much larger deficits in amount. Therefore, cloud base vertical velocity and moisture convergence may be the primary factors controlling precipitation seasonal mean and daily variation, while the instability tendency may play a critical role in regulating the diurnal cycle phase. [ABSTRACT FROM AUTHOR]

A consistent modeling framework with nested global and regional chemical transport models (CTMs) is used to separate and quantitatively assess the relative contributions to projections of future U.S. ozone pollution from the effects of emissions changes, climate change, long-range transport (LRT) of pollutants, and differences in modeling design. After incorporating dynamic lateral boundary conditions (LBCs) from a global CTM, a regional CTM's representation of present-day U.S. ozone pollution is notably improved, especially relative to results from the regional CTM with fixed LBCs or from the global CTM alone. This nested system of global and regional CTMs projects substantial surface ozone trends for the 2050's: 6–10 ppb decreases under the ‘clean’ A1B scenario and ∼15 ppb increases under the ‘dirty’ A1Fi scenario. Among the total trends of future ozone, regional emissions changes dominate, contributing negative 25–60% in A1B and positive 30–45% in A1Fi. Comparatively, climate change contributes positive 10–30%, while LRT effects through changing chemical LBCs account for positive 15–20% in both scenarios, suggesting introducing dynamic LBCs could influence projections of the U.S. future ozone pollution with a magnitude comparable to effects of climate change alone. The contribution to future ozone projections due to differences in modeling design, including model formulations, emissions treatments, and other factors between the global and the nested regional CTMs, is regionally dependent, ranging from negative 20% to positive 25%. It is shown that the model discrepancies for present-day simulations between global and regional CTMs can propagate into future U.S. ozone projections systematically but nonlinearly, especially in California and the Southeast. Therefore in addition to representations of emissions change and climate change, accurate treatment of LBCs for the regional CTM is essential for projecting the future U.S. ozone pollution. [ABSTRACT FROM AUTHOR]

This study investigates the refined simulation skill that results when the regional Climate extension of the Weather Research and Forecasting (CWRF) model is nested in the ECMWF Hamburg version 4.5 (ECHAM) atmospheric general circulation model over the United States during 1980-2009, where observed sea surface temperatures are used in both models. Over the contiguous US, for each of the four seasons from winter to fall, CWRF reduces the root mean square error of the ECHAM seasonal mean surface air temperature simulation by 0.19, 0.82, 2.02 and 1.85 °C, and increases the equitable threat score of seasonal mean precipitation by 0.18, 0.11, 0.09 and 0.12. CWRF also simulates much more realistically daily precipitation frequency and heavy precipitation events, typically over the Central Great Plains, Cascade Mountains and Gulf Coast States. These CWRF skill enhancements are attributed to the increased spatial resolution and physics refinements in representing orographic, terrestrial hydrology, convection, and cloud-aerosol-radiation effects and their interactions. Empirical orthogonal function analysis of seasonal mean precipitation and surface air temperature interannual variability shows that, in general, CWRF substantially improves the spatial distribution of both quantities, while temporal evolution (i.e. interannual variability) of the first 3 primary patterns is highly correlated with that of the driving ECHAM (except for summer precipitation), and they both have low temporal correlations against observations. During winter, when large-scale forcing dominates, both models also have similar responses to strong ENSO signals where they successfully capture observed precipitation composite anomalies but substantially fail to reproduce surface air temperature anomalies. When driven by the ECMWF Reanalysis Interim, CWRF produces a very realistic interannual evolution of large-scale precipitation and surface air temperature patterns where the temporal correlations with observations are significant. These results indicate that CWRF can greatly improve mesoscale regional climate structures but it cannot change interannual variations of the large-scale patterns, which are determined by the driving lateral boundary conditions. [ABSTRACT FROM AUTHOR]

This study comprehensively evaluates the effects of twelve cumulus parameterization (CUP) schemes on simulations of 1993 and 2008 Central US summer floods using the regional climate-weather research and forecasting model. The CUP schemes have distinct skills in predicting the summer mean pattern, daily rainfall frequency and precipitation diurnal cycle. Most CUP schemes largely underestimate the magnitude of Central US floods, but three schemes including the ensemble cumulus parameterization (ECP), the Grell-3 ensemble cumulus parameterization (G3) and Zhang-McFarlane-Liang cumulus parameterization (ZML) show clear advantages over others in reproducing both floods location and amount. In particular, the ECP scheme with the moisture convergence closure over land and cloud-base vertical velocity closure over oceans not only reduces the wet biases in the G3 and ZML schemes along the US coastal oceans, but also accurately reproduces the Central US daily precipitation variation and frequency distribution. The Grell (GR) scheme shows superiority in reproducing the Central US nocturnal rainfall maxima, but others generally fail. This advantage of GR scheme is primarily due to its closure assumption in which the convection is determined by the tendency of large-scale instability. Future study will attempt to incorporate the large-scale tendency assumption as a trigger function in the ECP scheme to improve its prediction of Central US rainfall diurnal cycle. [ABSTRACT FROM AUTHOR]

Observations reveal that, in summer, westward extension of the Bermuda high enhances the Great Plains low-level jet (LLJ) that transports more moisture northward, causing precipitation increases in the Midwest and decreases in the Gulf States. Meanwhile, more warm air advection from the Gulf of Mexico to the southern Great Plains and stronger clear-sky radiative heating under high pressures over the Southeast result in warmer surface temperatures across the Gulf states. The enhanced LLJ transport of cleaner marine air from the Gulf reduces surface ozone across the southern Great Plains-Midwest. In contrast, larger transport of more polluted air from the Midwest to New England and more frequent air stagnation under high pressures in the Southeast increase ozone over most of the eastern coastal states. This Bermuda high-induced ozone change reversal between the southern Great Plains-Midwest and eastern coastal states, with a magnitude of 6 and 13.5 ppb, respectively, in summer-mean maximum daily 8-h average, exhibits strong decadal variations that should be considered in the U.S. air quality dynamic management. The observed Bermuda high signatures over the Gulf states can be well captured by regional climate and air quality models. Notable model deficiencies exist over the northern Great Plains-Midwest that are more remote to the Bermuda high and LLJ control. The regional models largely reduce these deficiencies from general circulation models (GCMs). Only 7 out of 51 GCMs can represent all key regional signatures of the Bermuda high, while none can simulate its strong association with planetary sea surface temperature anomalies. The result indicates a great challenge for GCMs to predict Bermuda high variability and change. [ABSTRACT FROM AUTHOR]

Background: Future climate change may cause air quality degradation via climate-induced changes in meteorology, atmospheric chemistry, and emissions into the air. Few studies have explicitly modeled the potential relationships between climate change, air quality, and human health, and fewer still have investigated the sensitivity of estimates to the underlying modeling choices. Objectives: Our goal was to assess the sensitivity of estimated ozone-related human health impacts of climate change to key modeling choices. Methods: Our analysis included seven modeling systems in which a climate change model is linked to an air quality model, five population projections, and multiple concentration-response functions. Using the U.S. Environmental Protection Agency's (EPA's) Environmental Benefits Mapping and Analysis Program (BenMAP), we estimated future ozone (O3)-related health effects in the United States attributable to simulated climate change between the years 2000 and approximately 2050, given each combination of modeling choices. Health effects and concentration-response functions were chosen to match those used in the U.S. EPA's 2008 Regulatory Impact Analysis of the National Ambient Air Quality Standards for O3. Results: Different combinations of methodological choices produced a range of estimates of national O3-related mortality from roughly 600 deaths avoided as a result of climate change to 2,500 deaths attributable to climate change (although the large majority produced increases in mortality). The choice of the climate change and the air quality model reflected the greatest source of uncertainty, with the other modeling choices having lesser but still substantial effects. Conclusions: Our results highlight the need to use an ensemble approach, instead of relying on any one set of modeling choices, to assess the potential risks associated with O3-related human health effects resulting from climate change. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive evaluation on a Conjunctive Surface-Subsurface Process Model (CSSP) in predicting soil temperature-moisture distributions, terrestrial hydrology variations, and land-atmosphere exchanges against various in situ measurements and synthetic observations at regional-local scales over the contiguous United States. The CSSP, rooted in the Common Land Model (CoLM) with a few updates from the Community Land Model version 3.5 (CLM3.5), incorporates significant advances in representing hydrology processes with realistic surface (soil and vegetation) characteristics. These include dynamic surface albedo based on satellite retrievals, subgrid soil moisture variability of topographic controls, surface-subsurface flow interactions, and bedrock constraint on water table depths. As compared with the AmeriFlux tower measurements, the CSSP and CLM3.5 reduce surface sensible and latent heat flux errors from CoLM by 10 W m−2 on average, and have much higher correlations with observations for daily latent heat variations. The CSSP outperforms the CLM3.5 over the crop, grass, and shrub sites in depicting the latent heat annual cycles. While retaining the improvement for soil moisture in deep layers, the CSSP shows further advantage over the CLM3.5 in representing seasonal and interannual variations in root zones. The CSSP reduces soil temperature errors from the CLM3.5 (CoLM) by 0.2 (0.7) K at 0.1 m and 0.3 (0.6) K at 1 m; more realistically captures seasonal-interannual extreme runoff and streamflow over most regions and snow depth anomalies in high latitude (45°-52°N); and alleviates climatological water table depth systematic bias (absolute error) by about 1.2 (0.4) m. Clearly, the CSSP performance is overall superior to both the CoLM and CLM3.5. The remaining CSSP deficiencies and future refinements are also discussed. [ABSTRACT FROM AUTHOR]

Regional climate model (RCM) simulations, driven by low and high climate-sensitivity coupled general circulation models (CGCMs) under various future emissions scenarios, were compared to projected changes in heat wave characteristics. The RCM downscaling reduces the CGCM biases in heat wave threshold temperature by a factor of 2, suggesting a higher credibility in the future projections. All of the RCM simulations suggest that there is a high probability of heat waves of unprecedented severity by the end of the twenty-first century if a high emissions path is followed. In particular, the annual 3-day heat wave temperature increases generally by 3°–8°C; the number of heat wave days increases by 30–60 day yr−1 over much of the western and southern United States with slightly smaller increases elsewhere; the variance spectra for intermediate, 3–7 days (prolonged, 7–14 days), temperature extremes increase (decrease) in the central (western) United States. If a lower emissions path is followed, then the outcomes range from quite small changes to substantial increases. In all cases, the mean temperature climatological shift is the dominant change in heat wave characteristics, suggesting that adaptation and acclimatization could reduce effects. [ABSTRACT FROM AUTHOR]

This paper utilizes the best available quality data from multiple sources to develop consistent surface boundary conditions (SBCs) for mesoscale regional climate model (RCM) applications. The primary SBCs include 1) fields of soil characteristic (bedrock depth, and sand and clay fraction profiles), which for the first time have been consistently introduced to define 3D soil properties; 2) fields of vegetation characteristic fields (landcover category, and static fractional vegetation cover and varying leaf-plusstem- area indices) to represent spatial and temporal variations of vegetation with improved data coherence and physical realism; and 3) daily sea surface temperature variations based on the most appropriate data currently available or other value-added alternatives. For each field, multiple data sources are compared to quantify uncertainties for selecting the best one or merged to create a consistent and complete spatial and temporal coverage. The SBCs so developed can be readily incorporated into any RCM suitable for U.S. climate and hydrology modeling studies, while the data processing and validation procedures can be more generally applied to construct SBCs for any specific domain over the globe. [ABSTRACT FROM AUTHOR]

Abstract: A global climate chemistry model CAM-Chem is driven by the meteorology output from community climate system model version 3 (CCSM3) to investigate the relative contributions of changes in local anthropogenic emissions (LE) versus changes in remote anthropogenic emissions (RE) to global surface ozone air quality in 2050. On major ozone pollution regions (Europe, the United States, Asia), the effects are examined following three distinct pathways, A1FI, A1B and B1, from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) to address the uncertainty in projections of future climate and emissions. We find that projected changes in anthropogenic emissions under the A1FI scenario lead to an increase of 5–14 ppb in summertime daily maximum 8-h (DM8H) ozone concentration over U.S. by 2050, of which 48% is contributed by LE changes and 52% is contributed by RE changes. For Europe, the change in local emissions contributes 62% of the surface ozone increase in 2050 summer, while 38% of the increase is attributed to remote emission change. For Asia, changes in LE dominate the 2050 surface ozone increase with a magnitude of 10–30 ppb on summertime DM8H ozone concentration. However, under the A1B and B1 scenarios, contributions from LE changes are much larger than that from RE changes over all three regions except Asia under the B1 scenario, in which the RE changes contribute 31% of total change. The results indicate that for the United States and Europe, pollution control is a local issue under global low emission situations, while it becomes an international issue when fossil fuel use is rapidly increasing. Due to the weak Euro-Asia transport, local emission increase seems to be the main force for Asia''s ozone air quality change under all cases except the low emission scenario B1. Therefore, the strategies for regional air quality control need to be based on global emission situation. [Copyright &y& Elsevier]

Simulation of summertime U.S. surface ozone diurnal cycle is influenced by the model representation of planetary boundary layer (PBL) mixing, spatial resolution, and precursor emissions. These factors are investigated here for five major regions (Northeast, Midwest, Southeast, California, and Southwest) by using the Model for Ozone And Related chemical Tracers version 2.4 (MOZART-2.4), with important modifications, to conduct sensitivity experiments for summer 1999 with three PBL mixing schemes, two horizontal resolutions and two emissions datasets. Among these factors, the PBL mixing is dominant. The default non-local scheme well reproduces the observed ozone diurnal variation, where the timing for the afternoon maximum and the morning minimum is within 1h of the observed; biases for the minimum are less than 5ppb except over the Southeast; and the ozone maximum–minimum contrast (OMMC) is within 10ppb of observations except for the overprediction by 18.9ppb over the Northeast. In contrast, the local scheme significantly overestimates the OMMC by 10–34ppb over all regions as ozone and precursors are trapped too close to the ground. On the other hand, the full-mixing assumption underestimates the OMMC by 0–25ppb, except over the Northeast, as the nighttime ozone decline is greatly underpredicted. As compared to PBL mixing, the effects of horizontal resolutions and precursor emissions being used are smaller but non-negligible. Overall, with the non-local mixing scheme, relatively high horizontal resolution (∼1.1°) and updated emissions data, the modified MOZART is capable of simulating the main features of the observed ozone diurnal cycle. [Copyright &y& Elsevier]

• A unified agricultural modeling framework is implemented in DLEM v4.0. • Simulated results agree well with site-scale LAI, biomass and yield measurements. • Regional simulations can well reproduce spatial-temporal patterns of crop production. • DLEM v4.0 can be used to support agricultural climate adaptation and mitigation. Agricultural decision-making by different interest groups (e.g., farmers, development agents and policy makers) usually takes place on different scales (e.g., plot, landscape and country). Currently, tools to assist decision-making are either dedicated to small-scale management guidance or large-scale assessment, which ignore the cross-scale linkages and interactions and thus may not provide robust and consistent guidance and assessment. Here, we developed an advanced agricultural modeling framework by integrating the strengths of conventional crop models in representing crop growth processes and management practices into a terrestrial biosphere model (TBM), the Dynamic Land Ecosystem Model (DLEM), to meet the cross-scale application needs (e.g., adaptation and mitigation). Specifically, dynamic crop growth processes, including crop-specific phenological development, carbon allocation, yield formation, biological nitrogen fixation processes, and management practices such as tillage, cover cropping and genetic improvements, were explicitly represented in DLEM. The new model was evaluated against site-scale observations and the results showed that the model performed generally well, with an average normalized root mean square error of 19.91% for leaf area index and 17.46% for aboveground biomass at the seasonal scale and 14.42% for annual yield. Then the model was applied to simulate corn, soybean, and winter wheat productions in the conterminous United States from 1960 to 2018. The spatial patterns of simulated crop productions were consistent with ground survey data. Our model also captured both the long-term trends and interannual variations of the total national productions of the three crops. This study demonstrates the significance of fusing conventional crop modeling techniques into TBMs to establish a unified modeling framework, which holds the potential to address climate impacts, adaptation and mitigation across varied spatiotemporal scales. [ABSTRACT FROM AUTHOR]

Background: Increasing number of studies have linked air pollution exposure with renal function decline and disease. However, there is a lack of data on its impact among end-stage kidney disease (ESKD) patients and its potential modifying effect from extreme heat events (EHE)., Methods: Fresenius Kidney Care records from 28 selected northeastern US counties were used to pool daily all-cause mortality (ACM) and all-cause hospital admissions (ACHA) counts. County-level daily ambient PM 2.5 and ozone (O 3 ) were estimated using a high-resolution spatiotemporal coupled climate-air quality model and matched to ESKD patients based on ZIP codes of treatment sites. We used time-stratified case-crossover analyses to characterize acute exposures using individual and cumulative lag exposures for up to 3 days (Lag 0-3) by using a distributed lag nonlinear model framework. We used a nested model comparison hypothesis test to evaluate for interaction effects between air pollutants and EHE and stratification analyses to estimate effect measures modified by EHE days., Results: From 2001 to 2016, the sample population consisted of 43,338 ESKD patients. We recorded 5217 deaths and 78,433 hospital admissions. A 10-unit increase in PM 2.5 concentration was associated with a 5% increase in ACM (rate ratio [RR Lag0 - 3 : 1.02, 95% CI: 1.01-1.03) after adjusting for extreme heat exposures. Mortality models suggest evidence of interaction and effect measure modification, though not always simultaneously. ACM risk increased up to 8% when daily ozone concentrations exceeded National Ambient Air Quality Standards established by the United States, but the increases in risk were considerably higher during EHE days across lag periods.3 (RR Lag0 : 1.02, 95% CI: 1.01-1.03) after adjusting for extreme heat exposures. Mortality models suggest evidence of interaction and effect measure modification, though not always simultaneously. ACM risk increased up to 8% when daily ozone concentrations exceeded National Ambient Air Quality Standards established by the United States, but the increases in risk were considerably higher during EHE days across lag periods., Conclusion: Our findings suggest interdependent effects of EHE and air pollution among ESKD patients for all-cause mortality risks. National level assessments are needed to consider the ESKD population as a sensitive population and inform treatment protocols during extreme heat and degraded pollution episodes., Competing Interests: Declaration of competing interest Dr. Raimann reported being an employee of the Renal Research Institute (a wholly owned subsidiary of Fresenius Medical Care [FMC]) and owning stock in FMC. Dr. Kotanko reported receiving honoraria from UpToDate, being an employee of the Renal Research Institute, and owning stock in FMC. Dr. Maddux reported owning stock in and being employed by FMC. No other disclosures were reported., (Copyright © 2021. Published by Elsevier B.V.)