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The Tibetan Plateau, as a major elevated heat source, plays a critical role in the Asian monsoon and global climate. Observational data revealed significant correlations between spring surface air temperature in the Tibetan Plateau and downstream summer precipitation interannual variations. Sensitivity experiments using the regional Climate-Weather Research and Forecasting model (CWRF) were conducted to understand the physical processes and mechanisms underlying such delayed teleconnections. A positive temperature forcing was imposed over the plateau on the surface and subsurface soil layers only at the initial conditions around May 1st, 2003. This regional forcing quickly induces positive perturbations in local air temperature and, more importantly, maintains its signal in local soil, especially deep layers, for several months. Consequently, the soil temperature serves as a charged capacitor to modulate the planetary atmospheric circulation and through Rossby wave chains to cause significant summer precipitation anomalies over broad regions. This relayed teleconnection pattern is consistent with that identified from observational data records and CWRF climate simulations during 1980–2015. Diagnostic analyses of observations and simulations suggest that the Tibetan Plateau heating significantly impacts summer East Asian monsoon climate through influencing the South Asian High and shifting the East Asian jet. [ABSTRACT FROM AUTHOR]

Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN's goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN's framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use. [ABSTRACT FROM AUTHOR]

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 employs the regional Climate-Weather Research and Forecasting model (CWRF) downscaling and its skillful multi-physics ensemble approach to enhance summer extreme precipitation prediction in the Yangtze River Basin. The CWRF simulations at 30-km driven by the ECMWF Interim reanalysis during 1980–2015 are conducted using 28 model physics configurations. The prediction skill is evaluated for four standard indices: simple daily intensity, total extreme precipitation and its fraction to total precipitation, and maximum consecutive dry days. The control CWRF configuration outperforms the driving reanalysis, which underestimates all indices despite its utilization of surface data assimilation. Notably, the downscaling skills of CWRF exhibit substantial variability across diverse physics configurations, with cumulus parameterization being most influential. In particular, the ensemble cumulus parameterization (ECP) demonstrates remarkable proficiency in capturing both spatial patterns and interannual variations. This proficiency is further magnified through ECP’s integration with Morrison or Morrison-aerosol microphysics and CCCMA, CAML or CAM radiation schemes, significantly enhancing overall skills. The ensemble average of these skill-enhanced physics configurations better reproduces observed geographic distributions and interannual anomalies for all four indices, surpassing the control CWRF’s performance. This improvement can be primarily attributed to a more accurate portrayal of the East Asian Jet and its associated regional circulation patterns by the ensemble. These findings underscore a significant opportunity for enhancing predictions of extreme precipitation by further refining the physics representation within the climate system coupling, especially among cumulus, microphysics, and radiation processes. Optimizing the multi-physics ensemble approach holds substantial promise in this endeavor. [ABSTRACT FROM AUTHOR]

Coupling 3‐D hydrodynamics with climate models is necessary but difficult for resolving multiscale interactions and has been rarely implemented in predicting Great Lakes' water level fluctuations because of issues in treating net basin supply (NBS) components and connecting channel flows. This study developed an interactive lake‐atmosphere‐hydrology modeling system by coupling the regional Climate‐Weather Research and Forecasting model (CWRF) with the 3‐D unstructured‐grid Finite Volume Coastal Ocean Model (FVCOM) in the Great Lakes region. The sensitivity of the coupled system, relative to the CWRF baseline using the 1‐D Lake, Ice, Snow and Sediment Simulator (LISSS), was evaluated in representing lake‐climate conditions during 1999–2015 against observations. As coupled with CWRF, FVCOM outperformed LISSS in simulating water surface temperature, ice cover, and vertical thermal structure at seasonal to interannual scales for all the five lakes and realistically reproduced the regional circulation patterns. In warm seasons, the improved lake conditions significantly corrected LISSS overestimates of surface air temperature together with larger‐scale circulation changes. Consequently, precipitation was generally reduced over each lake basin, mainly because of decreased surface moisture and heat fluxes along with enhanced atmospheric stability. Through the dynamic coupling, FVCOM predicts the water level fluctuations in direct response to the CWRF NBS components and connecting channel flows based on a stage‐fall‐discharge formulation. This coupled CWRF‐FVCOM reasonably captured the NBS variations and predicted the water level fluctuations for Lakes Superior and Michigan‐Huron. It represents a major advance in interacting regional climate and watershed processes to dynamically predict Great Lakes' water level seasonal‐interannual variations. Plain Language Summary: Coupling 3‐D hydrodynamics with climate models is necessary but difficult for resolving multiscale interactions and has been rarely implemented in predicting Great Lakes' water level fluctuations because of issues in treating net basin supply (NBS) components and connecting channel flows. We developed an interactive lake‐atmosphere‐hydrology modeling system by coupling the regional Climate‐Weather Research and Forecasting model (CWRF) with the 3‐D unstructured‐grid Finite Volume Coastal Ocean Model (FVCOM) and compared it with the baseline CWRF configured with 1‐D lake model to understand how 3‐D hydrodynamic processes affect the prediction of the lake and regional climate conditions. The coupled CWRF‐FVCOM system significantly improved the simulation of all the five lakes' conditions at seasonal to interannual scales and realistically reproduced the regional circulation patterns. Our analysis also explained the processes that are associated with such sensitivity. Furthermore, as a unique contribution to the literature, we constructed FVCOM to dynamically predict the water level fluctuations in direct response to the coupled CWRF NBS components and connecting channel flows based on a stage‐fall‐discharge formulation. This coupled CWRF‐FVCOM system represents a major advance in interacting regional climate and watershed processes to dynamically predict Great Lakes' water level seasonal‐interannual variations. Key Points: The coupled CWRF‐FVCOM modeling system was developed to understand and predict atmosphere‐watershed interactions over the Great Lakes RegionThe coupled system significantly improved the simulation of lakes conditions at seasonal to interannual scales at all five LakesThe coupled system dynamically predicted the water level fluctuations in direct response to the coupled CWRF NBS components [ABSTRACT FROM AUTHOR]

Regional Climate-Weather Research and Forecasting model (CWRF) simulations driven by the ECMWF-Interim reanalysis (ERI) showed that cumulus parameterization significantly impacts daily 95th percentile precipitation (P95) over the US Gulf States (GS) and Central-Midwest States (CM). This study compared interannual variations across ERI and five CWRF cumulus parameterization members based on CM and GS regional mean composites during P95 events. A structural equation model framework was used to build regressions of these variations among optimally selected fields to identify the underlying processes affecting P95. We discovered five distinct physical mechanisms, each involving unique interplays among water and energy supplies and surface and cloud forcings, with varying degrees of relative importance (%). In CM summer and CM and GS autumn, water supply (~ 60%), energy supply (~ 20%), and cloud forcing (~ − 20%) jointly determined P95. In GS spring and winter, surface forcing was predominant (84–87%), while energy and water supplies evenly accounted for the remaining impact. In CM spring, surface forcing was also predominant (85%), but was accompanied by energy supply alone. In GS summer, cloud forcing was predominant (− 84%), while water supply had the opposite impact (− 8%) to energy supply (6%). In CM winter, water supply (− 62%) also counteracted energy supply (31%) while cloud forcing played a positive role (7%). The seasonal reversal in the roles of water supply and cloud forcing occurred because the prevailing precipitation system changed from convective to stratiform processes. The choice of cumulus parameterization affected how water and energy supplies acted through surface and cloud forcings, thus determined CWRF's ability to simulate extreme precipitation. [ABSTRACT FROM AUTHOR]

Lakes differ from lands in water availability, heat capacity, albedo, and roughness, which affect local surface‐atmospheric interactions. This study modified a multilevel upper ocean model (UOM) for lake applications and evaluated its performance in Lake Taihu (China) with comprehensive measurements against three popular one‐dimensional (1‐D) lake models. These models were based on different concepts, including the self‐similarity (FLake), the wind‐driven eddy diffusion (LISSS), the k‐ε turbulence closure (SIMSTRAT), and a simplified turbulence closure (UOM). The surface flux scheme in these models was unified to exclude the discrepancies in representing air‐lake exchanges. All models in their default formulations presented obvious cold water temperature biases and largely underestimated the lake surface temperature (LST) diurnal range. For each model, these deficiencies were significantly reduced by incorporating new physics schemes or calibrated tunable parameters based on systematic sensitivity tests. The primary modifications for UOM included (1) a new scheme of decreased surface roughness lengths to better characterize the shallow lake, (2) a solar radiation penetration scheme with increased light extinction coefficient and surface absorption fraction to account for the high water turbidity, and (3) turbulent Prandtl number increased by a factor of 20 to reduce the turbulent vertical mixing. All other models were improved in these three aspects (roughness, extinction, and mixing) within their original formulations. Given these improvements, UOM showed superior performance to other models in capturing LST diurnal cycle and daily to seasonal variations, as well as summer‐autumn vertical stratification changes. The new UOM is well suited for application in shallow lakes. Plain Language Summary: Lakes, with unique characteristic differences from lands, play an important role on local‐regional weather and climate. This study improved a multilevel upper ocean model (UOM) for a shallow lake application with primary modifications on surface roughness, radiation extinction, and vertical mixing. The model was intercompared against comprehensive measurements with three other improved one‐dimensional models based on different concepts in the context of using a unified surface flux scheme to exclude the discrepancies in representing lake‐air exchanges. All models in their default formulations presented obvious cold water temperature biases and largely underestimated the lake surface temperature diurnal range. Their performances were significantly improved with the proposed modifications. In particular, the improved UOM showed superior performance to other models in capturing lake surface temperature diurnal cycle and daily to seasonal variations, as well as summer‐autumn vertical stratification changes. The new UOM is useful for inclusion in weather and climate models to represent shallow lake processes. Key Points: An upper ocean mixed layer model was improved for a shallow lake application with primary modifications on surface roughness, radiation extinction, and vertical mixingThe model was intercompared against three other representative 1‐D models based on different concepts in the context of using a unified surface flux schemeThe improved model well captured the surface temperature diurnal cycle and daily to seasonal variations of Lake Taihu [ABSTRACT FROM AUTHOR]

Climate models tend to underestimate rainfall intensity while producing more frequent light events, leading to significant bias in extreme precipitation simulation. To reduce this bias and better understand its underlying causes, we tested an ensemble of 25 physics configurations in the regional Climate-Weather Research and Forecasting model (CWRF). All configurations were driven by the ECMWF-Interim reanalysis and continuously integrated during 1980–2015 over the contiguous United States with 30-km grid spacing. Together they represent CWRF's ability to simulate characteristics of US extreme precipitation, and their spread depicts the structural uncertainty from alternate physics parameterizations. The US extreme precipitation simulation was most sensitive to cumulus parameterization among all physics configurations. The ensemble cumulus parameterization (ECP) was overall the most skilled at reproducing seasonal mean spatial patterns of daily 95th percentile precipitation (P95). Other cumulus schemes severely underestimated P95, especially over the Gulf States and the Central-Midwest States in convective prevailing seasons. CWRF with ECP outperformed the driving reanalysis, which substantially underestimated P95 despite its daily atmospheric moisture data assimilation. The CWRF improvement over ERI is much larger in warm than cold seasons. Changing alone ECP closure assumptions produced two distinct clusters of convective heating/drying effects: one altered P95 mainly by changing total precipitation intensity and another by changing rainy-day frequency. Microphysics, radiation, boundary layer, and land surface processes also impacted the result, especially under mixed synoptic and convective forcings, and some of their parameterization schemes worked with ECP to further improve P95. [ABSTRACT FROM AUTHOR]

This study investigated the multi‐grid nesting ability of a limited area model to effectively represent convections across the gray zone, the resolution around 1–10 km where both cumulus parameterization and explicit convection are problematic. It evaluated the sensitivity of Meiyu rainfall forecasts in Jiangsu, China to model configurations of grid nesting and convection treatment. These configurations consisted of grid spacings from 30, 15, 9, 5, 3 to 1 km, single or double or triple nested grids, and the traditional Kain‐Fritsch (KF) or scale‐aware Grell‐Freitas cumulus parameterization or the explicit convection in the outer domain [O]. In single nesting [O], coarse grids (>3–5 km) required parameterization to represent organized cumuli, while explicitly resolving convections in finer grids were necessary to improve forecasts. In double nesting [O] using cumulus parameterization at 30–9 km with the inner domain [I] using explicit convection at 1 km, the nesting ratio could be as large as 30 without significantly impacting [I] forecasts. This suggests a pragmatic approach to avoid the challenge in representing convections across the gray zone. Using Grell‐Freitas may improve mean [O] rainfall distributions, but this was not true for [I] forecasts due to counter errors in space and time, which were larger than using KF and at coarser grids. Triple nesting with a middle 3‐ or 5‐km grid was unnecessary and could even degrade [I] forecasts. Nesting [O] using KF to parameterize cumuli at 15 km with [I] explicitly resolving convections at 1 km achieved the best overall rainfall forecast in Jiangsu. Plain Language Summary: This study investigated the multi‐grid nesting ability of a limited area model to effectively represent convections across the gray zone, the resolution around 1–10 km where both cumulus parameterization and explicit solution are problematic. It evaluated the sensitivity of Meiyu rainfall forecasts in Jiangsu, China to model configurations of grid nesting and convection treatment. These configurations consisted of grid spacings from 30, 15, 9, 5, 3 to 1 km, single or double or triple nested grids, and the traditional or scale‐aware cumulus parameterization or the explicit convection. Parameterization in 30–9 km grids is required to represent organized cumuli, while explicitly resolving convections in cloud‐permitting grids around 1 km is necessary to improve forecasts. This coupling can be achieved through double nesting, in which the grid ratio could be as large as 30 without significantly impacting forecasts. Triple nesting with a middle grid is unnecessary and can even degrade forecasts. The result suggests a pragmatic approach to avoid the challenge in representing convections across the gray zone. Using a scale‐aware cumulus scheme may improve mean rainfall distributions in the outer coarse grid, but this may not increase forecast skill in the nesting fine grid due to counter errors in space and time. Key Points: The nesting grid ratio can be as large as 30 without significant effectAn intermediate nesting grid is undesirableA scale‐aware cumulus parameterization in a coarse grid may degrade precipitation forecast skill in the nested convection‐resolving grid [ABSTRACT FROM AUTHOR]

The performance of the regional Climate-Weather Research and Forecasting model (CWRF) for downscaling China climate characteristics is evaluated using a 1980-2015 simulation at 30 km grid spacing driven by the ECMWF Interim reanalysis (ERI). It is shown that CWRF outperforms the popular Regional Climate Modeling system (RegCM4.6) in key features including monsoon rain bands, diurnal temperature ranges, surface winds, interannual precipitation and temperature anomalies, humidity couplings, and 95th percentile daily precipitation. Even compared with ERI, which assimilates surface observations, CWRF better represents the geographic distributions of seasonal mean climate and extreme precipitation. These results indicate that CWRF may significantly enhance China climate modeling capabilities. [ABSTRACT FROM AUTHOR]

Highlights • A systematic framework is presented for parametric uncertainty quantification. • Sensitive parameters were identified with qualitative and quantitative methods. • Sensitive parameters were calibrated with adaptive surrogate-based optimization. Abstract Quantifying and reducing uncertainties in physics-based hydrological model parameters will improve model reliability for hydrological forecasting. We present an uncertainty quantification framework that combines the strengths of stepwise sensitivity analysis and adaptive surrogate-based multi-objective optimization to facilitate practical assessment and reduction of model parametric uncertainties. Framework performance was tested using the distributed hydrological model Coupled Routing and Excess Storage (CREST) for daily streamflow simulation over ten watersheds. By identifying sensitive parameters stepwisely, we reduced the number of parameters requiring calibration from twelve to seven, thus limiting the dimensionality of calibration problem. By updating surrogate models adaptively, we found the optimal sets of sensitive parameters with the surrogate-based multi-objective optimization. The calibrated CREST was able to satisfactorily simulate observed streamflow for all watersheds, improving one minus Nash-Sutcliffe efficiency (1−NSE) by 65–90% and percentage absolute relative bias (|RB|) by 60–95% compared to the default. The validation result demonstrated that the calibrated CREST was also able to reproduce observed streamflow outside the calibration period, improving 1−NSE by 40–85% and |RB| by 35–90% compared to the default. Overall, this uncertainty quantification framework is effective for assessment and reduction of model parametric uncertainties, the results of which improve model simulations and enhance understanding of model behaviors. [ABSTRACT FROM AUTHOR]

This study investigates the future U.S. PM 2.5 pollution under multiple emissions scenarios, climate states, and long-range transport (LRT) effects using the regional Community Multi-scale Air Quality (CMAQ) model integrated with a regional climate model. CMAQ with fixed chemical lateral boundary conditions (LBCs) successfully reproduces the present-day PM 2.5 pollution and its major species in rural and suburban areas, but has some discrepancies in urban areas such as the Los Angeles Basin, where detailed emissions and meteorology conditions cannot be resolved by the 30 km grid. Its performance is slightly worsened when using dynamic chemical LBCs from global chemical transport model (CTM) simulations, which provide cleaner conditions into the CMAQ lateral boundaries. Under future Intergovernmental Panel on Climate Change (IPCC) emission scenarios, CMAQ projects large PM 2.5 reductions (∼40% for A1B and ∼20% for A1Fi scenario) in the eastern United States, but slight to moderate increases (∼5% for A1B and ∼10% for A1Fi) in the western United States. The projected increases are particularly large (up to 30%) near the Mexico-U.S. border, suggesting that Mexico is a major source for future U.S. PM 2.5 pollution. The effect from climate change alone is estimated to increase PM 2.5 levels ubiquitously (∼5% for both A1B and A1Fi) over the United States, except for a small decrease in the Houston, Texas area, where anthropogenic non-methane volatile organic compounds (NMVOCs) emissions dominate. This climate penalty, however, is substantially smaller than effects of emissions change, especially in the eastern United States. Future PM 2.5 pollution is affected substantially (up to −20%) by changes in SO 2 emissions and moderately (3–5%) by changes in NO x and NH 3 emissions. The long-range transport (LRT) effects, which are estimated by comparing CMAQ simulations with fixed and dynamic LBCs, are regional dependent, causing up to 10–20% decrease over the western United States in future summertime PM 2.5 pollution. Therefore, it is important to consider the relative contributions of emissions scenarios, climate conditions, and LRT to the major PM 2.5 components in future U.S. air quality regulation. [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]

Remotely sensed hydrologic variables, in conjunction with streamflow data, have been increasingly used to conduct multivariable calibration of hydrologic model parameters. Here, we calibrated the Soil and Water Assessment Tool (SWAT) model using different combinations of streamflow and remotely sensed hydrologic variables, including Atmosphere–Land Exchange Inverse (ALEXI) Evapotranspiration (ET), Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and Soil MERGE (SMERGE) soil moisture. The results show that adding remotely sensed ET and soil moisture to the traditionally used streamflow for model calibration can impact the number and values of parameters sensitive to hydrologic modeling, but it does not necessarily improve the model performance. However, using remotely sensed ET or soil moisture data alone led to deterioration in model performance as compared with using streamflow only. In addition, we observed large discrepancies between ALEXI or MODIS ET data and the choice between these two datasets for model calibration can have significant implications for the performance of the SWAT model. The use of different combinations of streamflow, ET, and soil moisture data also resulted in noticeable differences in simulated hydrologic processes, such as runoff, percolation, and groundwater discharge. Finally, we compared the performance of SWAT and the SWAT-Carbon (SWAT-C) model under different multivariate calibration setups, and these two models exhibited pronounced differences in their performance in the validation period. Based on these results, we recommend (1) the assessment of various remotely sensed data (when multiple options available) for model calibration before choosing them for complementing the traditionally used streamflow data and (2) that different model structures be considered in the model calibration process to support robust hydrologic modeling. [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]

An uncertainty quantification framework was employed to examine the sensitivities of 24 model parameters from a newly developed Conjunctive Surface-Subsurface Process (CSSP) land surface model (LSM). The sensitivity analysis (SA) was performed over 18 representative watersheds in the contiguous United States to examine the influence of model parameters in the simulation of terrestrial hydrological processes. Two normalized metrics, relative bias (RB) and Nash-Sutcliffe efficiency (NSE), were adopted to assess the fit between simulated and observed streamflow discharge (SD) and evapotranspiration (ET) for a 14 year period. SA was conducted using a multiobjective two-stage approach, in which the first stage was a qualitative SA using the Latin Hypercube-based One-At-a-Time (LH-OAT) screening, and the second stage was a quantitative SA using the Multivariate Adaptive Regression Splines (MARS)-based Sobol' sensitivity indices. This approach combines the merits of qualitative and quantitative global SA methods, and is effective and efficient for understanding and simplifying large, complex system models. Ten of the 24 parameters were identified as important across different watersheds. The contribution of each parameter to the total response variance was then quantified by Sobol' sensitivity indices. Generally, parameter interactions contribute the most to the response variance of the CSSP, and only 5 out of 24 parameters dominate model behavior. Four photosynthetic and respiratory parameters are shown to be influential to ET, whereas reference depth for saturated hydraulic conductivity is the most influential parameter for SD in most watersheds. Parameter sensitivity patterns mainly depend on hydroclimatic regime, as well as vegetation type and soil texture. [ABSTRACT FROM AUTHOR]

A consistent set of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation parameters, including leaf and stem area index (LAI and SAI, respectively), land-cover category (LCC), fractional vegetation cover (FVC), and albedo parameterization are developed, and their impacts on North American regional climate are evaluated based on 10-yr Climate-Weather and Research Forecasting Model (CWRF) simulations. As compared with the previous Advanced Very High Resolution Radiometer (AVHRR) set, MODIS LCC increases grassland and cropland fractions in the central Great Plains and Midwest, respectively. Evergreen needleleaf forest converts to mixed forest in the Southeast, and mixed forest converts to evergreen needleleaf in Canada. FVC decreases by 0.05-0.3 over the central Great Plains but increases by 0.1-0.35 over the northern Rocky Mountains, Canada, and the U.S. Southeast. MODIS LAI is less than AVHRR by 2-6, except in the central Great Plains, eastern Rocky Mountains, and central Mexico. LCC and FVC changes over the central Great Plains reduce CWRF warm biases by 0.71°C and wet biases by 0.36 mm day−1. Large LAI reductions cause latent and sensible heat fluxes to decrease by 0.78-5.81 and 0.91-6.54 W m−2, respectively. They also lessen cold biases over the Gulf States and Southeast and wet biases over the North American monsoon region and Canada during summer. In densely vegetated regions including eastern Canada, the Ohio Valley, and the mid-Atlantic region, spring and summer precipitation decreases and temperature increases result from LAI reductions that cause positive evapotranspiration-precipitation-soil moisture feedbacks. Conversely, precipitation and temperature decreases in sparely vegetated regions, such as the Great Plains, result from FVC reductions that cause negative albedo-evapotranspiration-precipitation-soil moisture feedbacks. [ABSTRACT FROM AUTHOR]

Most current land surface models used in regional weather and climate studies capture soil-moisture transport in only the vertical direction and are therefore unable to capture the spatial variability of soil moisture and its lateral transport. They also implement simplistic surface runoff estimation from local soil water budget and ignore the role of surface flow depth on the infiltration rate, which may result in significant errors in the terrestrial hydrologic cycle. To address these issues, this study develops and describes a conjunctive surface-subsurface flow (CSSF) model that comprises a 1D diffusion wave model for surface (overland) flow fully interacted with a 3D volume-averaged soil-moisture transport model for subsurface flow. The proposed conjunctive flow model is targeted for mesoscale climate application at relatively large spatial scales and coarse computational grids as compared to the traditional coupled surface-subsurface flow scheme in a typical basin. The CSSF module is substituted for the existing 1D scheme in the common land model (CoLM) and the performance of this hydrologically enhanced version of the CoLM (CoLM+CSSF) is evaluated using a set of offline simulations for catchment-scale basins around the Ohio Valley region. The CoLM+CSSF simulations are explicitly implemented at the same resolution of the 30-km grids as the target regional climate models to avoid downscaling and upscaling exchanges between atmospheric forcings and land responses. The results show that the interaction between surface and subsurface flow significantly improves the stream discharge prediction crucial to the terrestrial water and energy budget. [ABSTRACT FROM AUTHOR]

The Cloud-Aerosol-Radiation (CAR) ensemble modeling system has recently been built to better understand cloud/aerosol/radiation processes and determine the uncertainties caused by different treatments of cloud/aerosol/radiation in climate models. The CAR system comprises a large scheme collection of cloud, aerosol, and radiation processes available in the literature, including those commonly used by the world's leading GCMs. In this study, detailed analyses of the overall accuracy and efficiency of the CAR system were performed. Despite the different observations used, the overall accuracies of the CAR ensemble means were found to be very good for both shortwave (SW) and longwave (LW) radiation calculations. Taking the percentage errors for July 2004 compared to ISCCP (International Satellite Cloud Climatology Project) data over (60°N, 60°S) as an example, even among the 448 CAR members selected here, those errors of the CAR ensemble means were only about −0.67% (−0.6 W m) and −0.82% (−2.0 W m) for SW and LW upward fluxes at the top of atmosphere, and 0.06% (0.1 W m) and −2.12% (−7.8 W m) for SW and LW downward fluxes at the surface, respectively. Furthermore, model SW frequency distributions in July 2004 covered the observational ranges entirely, with ensemble means located in the middle of the ranges. Moreover, it was found that the accuracy of radiative transfer calculations can be significantly enhanced by using certain combinations of cloud schemes for the cloud cover fraction, particle effective size, water path, and optical properties, along with better explicit treatments for unresolved cloud structures. [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]

The non-hydrostatic Weather Research and Forecasting model (WRF) was nested into NCEP's operational seasonal forecast model Climate Forecast System (CFS) to downscale seasonal prediction of winter precipitation over continental China. Using the same initial conditions, 16 ensemble downscaling forecasts configured with two alternative schemes of microphysics, cumulus, land surface and radiation in WRF were conducted at 30 km for 27-cold seasons (December-February) during 1982-2008. On average, WRF downscaling forecasts reduced wet bias of seasonal mean precipitation from CFS prediction by 25-71%, decreased errors by up to 33%, and increased equitable threat score by 0.1 for low threshold. With appropriate physical configurations, WRF could improve interannual variations over the region where CFS has correct anomaly signal. The spatial distribution of daily precipitation characteristics such as rainy frequency and extremes highlighted the sensitivity of downscaling forecasts to physical configurations, and the dominant uncertainties were introduced by land surface and radiation schemes. The differences in convective and resolved rainfall between alternative land surface and radiation schemes were consistent with differences of surface downwelling shortwave and longwave radiation through cloud-radiation feedback. Such feedback was strengthened in the land surface sensitivity experiments due to different parameterizations of surface albedo. As compared with CFS ensemble predictions with different initial conditions, the WRF ensemble downscaling forecasts with various physical schemes had larger spread, and some schemes could complement each other in different regions that provided a promising opportunity to enhance the prediction through optimization. The optimized WRF reduced error from the optimized CFS by 30% and increased pattern correlation by 0.12. Moreover, WRF physical configuration ensemble increased percentage of skillful probabilistic forecasts from CFS initial condition ensemble. [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]

Abstract: This study develops fine temporal (seasonal, day-of-week, diurnal) and vertical allocations of anthropogenic emissions for the TRACE-P inventory and evaluates their impacts on the East Asian air quality prediction using WRF-Chem simulations in July 2001 at 30-km grid spacing against available surface measurements from EANET and NEMCC. For NO2 and SO2, the diurnal and vertical redistributions of emissions play essential roles, while the day-of-week variation is less important. When all incorporated, WRF-Chem best simulates observations of surface NO2 and SO2 concentrations, while using the default emissions produces the worst result. The sensitivity is especially large over major cities and industrial areas, where surface NO2 and SO2 concentrations are reduced by respectively 3–7 and 6–12 ppbv when using the scaled emissions. The incorporation of all the three redistributions of emissions simulates surface O3 concentrations higher by 4–8 ppbv at night and 2–4 ppbv in daytime over broad areas of northern, eastern and central China. To this sensitivity, the diurnal redistribution contributes more than the other two. [Copyright &y& Elsevier]

This study aims to combine the cloud-resolving model (CRM) simulations with the Department of Energy’s Atmospheric Radiation Measurement Program (ARM) observations to provide long-term comprehensive and physically consistent data that facilitate quantifying the effects of subgrid cloud–radiation interactions and ultimately to develop physically based parameterization of these interactions in general circulation models. The CRM is applied here to simulate the midlatitude cloud systems observed at the ARM southern Great Plains (SGP) site during the 1997 intensive observation period. As in the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE), the CRM-simulated ensemble mean quantities such as cloud liquid water, cloud fraction, precipitation, and radiative fluxes are generally in line with the surface measurements, satellite, and radar retrievals. The CRM differences from the ARM estimates, when averaged over the entire period, are less than 5 W m-2 in both longwave and shortwave radiative fluxes at the top of the atmosphere and surface. Because of the different large-scale forcing and surface heat fluxes in ARM and TOGA COARE, the CRM produces different cloud distributions over the midlatitude continent and tropical ocean. However, diagnostic analyses show that the subgrid cloud variability has similar impact on the domain-averaged radiative fluxes and heating rates in ARM as in TOGA COARE. [ABSTRACT FROM AUTHOR]

Results from eight regional climate models (RCMs) participating in the Impact of Initialized Land Temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P) initiative of the Global Energy and Water Exchanges (GEWEX) are examined and compared with observations over the Tibetan Plateau (TP). The RCM common domain covers most areas of East Asia with a horizontal resolution of 20–30 km. The model simulation covers a period from April to September in each year between 1991 and 2015. This study explores the RCMs' ability for seasonal climate simulation over the TP, focusing on the summer monsoon climate as part of the LS4P initiative. An intercomparison is made among eight RCMs for precipitation, surface air temperature, mid-troposphere atmospheric circulation, moisture conditions, and surface energy fluxes. It shows that the downscaling characteristics differ significantly between two major RCM types. The RegCM4 models show positive precipitation biases over the entire TP, especially over the south and southeast TP, while the WRF models mostly show both positive and negative precipitation biases over the TP with relatively high spatial correlation between simulated and observed precipitation. The multi-model ensemble mean produces overall smaller precipitation biases than most individual RCMs, with the largest biases over the southeastern TP, and smaller surface air temperature biases over most areas of the TP, especially over the central and southwestern TP. Moreover, the ensemble mean can better reproduce the inter-annual variation of precipitation and surface air temperature than most RCMs with proper magnitude. Sensitivity analyses using RegCM4 with different physics parameterizations show that varying land and cumulus schemes may induce large precipitation differences over the TP by affecting moisture and atmospheric circulation conditions in the lower and upper troposphere, respectively. Moreover, turbulent heat and radiation fluxes differences are associated with the temperature differences between different RegCM4 models. [ABSTRACT FROM AUTHOR]

The observed lack of twentieth-century warming in the central United States (CUS), denoted here as the “warming hole,” was examined in 55 simulations driven by external historical forcings and in 19 preindustrial control (unforced) simulations from 18 coupled general circulation models (CGCMs). Twentieth-century CUS trends were positive for the great majority of simulations, but were negative, as observed, for seven simulations. Only a few simulations exhibited the observed rapid rate of warming (cooling) during 1901–40 (1940–79). Those models with multiple runs (identical forcing but different initial conditions) showed considerable intramodel variability with trends varying by up to 1.8°C century-1, suggesting that internal dynamic variability played a major role at the regional scale. The wide range of trend outcomes, particularly for those models with multiple runs, and the small number of simulations similar to observations in both the forced and unforced experiments suggest that the warming hole is not a robust response of contemporary CGCMs to the estimated external forcings. A more likely explanation based on these models is that the observed warming hole involves external forcings combined with internal dynamic variability that is much larger than typically simulated. The observed CUS temperature variations are positively correlated with North Atlantic (NA) sea surface temperatures (SSTs), and both NA SSTs and CUS temperature are negatively correlated with central equatorial Pacific (CEP) SSTs. Most models simulate rather well the connection between CUS temperature and NA SSTs. However, the teleconnections between NA and CEP SSTS and between CEP SSTs and CUS temperature are poorly simulated and the models produce substantially less NA SST variability than observed, perhaps hampering their ability to reproduce the warming hole. [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]

China's rainfall interannual predictability is generally believed to depend upon the accurate representation of its annual cycle as well as teleconnections with planetary surface anomalies, including tropical east Pacific sea surface temperature and Eurasian snow and soil moisture. A suite of general circulation model (GCM) simulations is used to ascertain the existence of these relationships. First, a comparison of thirty 1980-88 Atmospheric Model Intercomparison Project (AMIP) GCM simulations shows no clear correspondence between model skill to reproduce observed rainfall annual cycle and interannual variability. Thus, accurate representation of either component does not ensure the realistic simulation of the other. Second, diagnosis of the 1903-94 and 195097 National Center for Atmospheric Research (NCAR) Community Climate Model, version 3 (CCM3), ensemble integrations indicates the existence of teleconnections in which spring planetary surface anomalies lead China's summer rainfall variations. These teleconnections, however, are sensitive to initial conditions, which define distinct dynamic regimes during the integration period. In addition, analysis of the NCAR Climate System Model (CSM) 300-yr equilibrium simulation reveals that the teleconnections display decadal variations. These results cast doubt on the traditional physical mechanisms that explain China's rainfall teleconnections and, hence, emphasize the need to incorporate interactions between planetary surface anomalies and specific dynamic regimes. [ABSTRACT FROM AUTHOR]

A regional climate model (RCM) is being developed for U.S. Midwest applications on the basis of the newly released Pennsylvania State University–NCAR Fifth-Generation Mesoscale Model (MM5), version 3.3. This study determines the optimal RCM domain and effective data assimilation technique to accurately integrate lateral boundary conditions (LBCs) across the buffer zones. The LBCs are constructed from both the NCEP–NCAR and ECMWF reanalyses to depict forcing uncertainties. The RCM domain was chosen to correctly represent the governing physical processes while minimizing LBC errors. Sensitivity experiments are conducted for the Midwest 1993 summer flood to investigate buffer zone treatment impacts on RCM performance. The results demonstrate the superiority of the buffer zone treatment that consists of the physically based domain choice and revised assimilation technique. Given this treatment, the RCM realistically simulates both temporal variations and spatial distributions in the major flood area (MFA). This success is identified with the accurate representation of both the midlatitude upper-level jet stream and Great Plains low-level jet (LLJ). The RCM reproduces different climate regimes, where observed rainfall was identified with the periodic (5 day) passage of midlatitude cyclones in June and persistent synoptic circulations in July. The model also correctly simulates the MFA rainfall diurnal cycle (with the peak amount at 0900 UTC), which follows the LLJ cycle by approximately 3 h. On the other hand, RCM performance is substantially reduced when the southern buffer zone extends to the Tropics, where large forcing errors exist. In particular, the RCM generates a weaker LLJ and, as a consequence, a decreased amount and delayed diurnal cycle of the MFA rainfall. In addition, the MM5 default LBC data assimilation technique produces considerable model biases, whereas the revised technique improves overall RCM performance and reduces sensitivi... [ABSTRACT FROM AUTHOR]

Perennial grasses can reduce soil erosion, restore carbon stocks, and provide feedstocks for biofuels and bioproducts. Here, we show an additional benefit, amelioration of regional climate warming, and drying. Growing Miscanthus × giganteus, an example of perennial biomass crops, on US marginal land cools the Midwest Heartland summer by up to 1°C as predicted by a new coupled climate‐crop modeling system. This cooling is mainly caused by the increased duration and size of the Miscanthus × giganteus leaf canopy when compared with the existing vegetations on marginal land, resulting in larger solar reflection, more evapotranspiration, and decreased sensible heat transfer. Summer rainfall is increased through mesoscale circulation responses by 23–29 mm (14%–15%) and water vapor pressure deficit reduced by 5%–13%, lowering potential transpiration for all Midwest crops. Similar but weaker effects are simulated in the Southern Heartland. This positive feedback through the climate–crop interaction and teleconnection leads to 4%–8% more biomass production and potentially 12% higher corn and soybean yields, with greater yield stability. Growing perennials on marginal land could be a feasible solution to climate change mitigation and adaptation by strengthening food security and providing sustainable alternatives to fossil‐based products. [ABSTRACT FROM AUTHOR]

Bioenergy with carbon capture and geological storage (BECCS) is considered one of the top options for both offsetting CO2 emissions and removing atmospheric CO2. BECCS requires using limited land resources efficiently while ensuring minimal adverse impacts on the delicate food‐energy‐water nexus. Perennial C4 biomass crops are productive on marginal land under low‐input conditions avoiding conflict with food and feed crops. The eastern half of the contiguous U.S. contains a large amount of marginal land, which is not economically viable for food production and liable to wind and water erosion under annual cultivation. However, this land is suitable for geological CO2 storage and perennial crop growth. Given the climate variation across the region, three perennials are major contenders for planting. The yield potential and stability of Miscanthus, switchgrass, and energycane across the region were compared to select which would perform best under the recent (2000–2014) and future (2036–2050) climates. Miscanthus performed best in the Midwest, switchgrass in the Northeast and energycane in the Southeast. On average, Miscanthus yield decreased from present 19.1 t/ha to future 16.8 t/ha; switchgrass yield from 3.5 to 2.4 t/ha; and energycane yield increased from 14 to 15 t/ha. Future yield stability decreased in the region with higher predicted drought stress. Combined, these crops could produce 0.6–0.62 billion tonnes biomass per year for the present and future. Using the biomass for power generation with CCS would capture 703–726 million tonnes of atmospheric CO2 per year, which would offset about 11% of current total U.S. emission. Further, this biomass approximates the net primary CO2 productivity of two times the current baseline productivity of existing vegetation, suggesting a huge potential for BECCS. Beyond BECCS, C4 perennial grasses could also increase soil carbon and provide biomass for emerging industries developing replacements for non‐renewable products including plastics and building materials. [ABSTRACT FROM AUTHOR]

Agricultural soils play a dual role in regulating the Earth's climate by releasing or sequestering carbon dioxide (CO2) in soil organic carbon (SOC) and emitting non‐CO2 greenhouse gases (GHGs) such as nitrous oxide (N2O) and methane (CH4). To understand how agricultural soils can play a role in climate solutions requires a comprehensive assessment of net soil GHG balance (i.e., sum of SOC‐sequestered CO2 and non‐CO2 GHG emissions) and the underlying controls. Herein, we used a model‐data integration approach to understand and quantify how natural and anthropogenic factors have affected the magnitude and spatiotemporal variations of the net soil GHG balance in U.S. croplands during 1960–2018. Specifically, we used the dynamic land ecosystem model for regional simulations and used field observations of SOC sequestration rates and N2O and CH4 emissions to calibrate, validate, and corroborate model simulations. Results show that U.S. agricultural soils sequestered 13.2±1.16$$ 13.2\pm 1.16 $$ Tg CO2‐C year−1 in SOC (at a depth of 3.5 m) during 1960–2018 and emitted 0.39±0.02$$ 0.39\pm 0.02 $$ Tg N2O‐N year−1 and 0.21±0.01$$ 0.21\pm 0.01 $$ Tg CH4‐C year−1, respectively. Based on the GWP100 metric (global warming potential on a 100‐year time horizon), the estimated national net GHG emission rate from agricultural soils was 122.3±11.46$$ 122.3\pm 11.46 $$ Tg CO2‐eq year−1, with the largest contribution from N2O emissions. The sequestered SOC offset ~28% of the climate‐warming effects resulting from non‐CO2 GHG emissions, and this offsetting effect increased over time. Increased nitrogen fertilizer use was the dominant factor contributing to the increase in net GHG emissions during 1960–2018, explaining ~47% of total changes. In contrast, reduced cropland area, the adoption of agricultural conservation practices (e.g., reduced tillage), and rising atmospheric CO2 levels attenuated net GHG emissions from U.S. croplands. Improving management practices to mitigate N2O emissions represents the biggest opportunity for achieving net‐zero emissions in U.S. croplands. Our study highlights the importance of concurrently quantifying SOC‐sequestered CO2 and non‐CO2 GHG emissions for developing effective agricultural climate change mitigation measures. [ABSTRACT FROM AUTHOR]

Although the Soil and Water Assessment Tool (SWAT) model has been widely used to assess the environmental impacts of growing perennial grasses for bioenergy production, its utility is limited by not explicitly accounting for shoot and root biomass development. In this study, we integrated the DAYCENT model's grass growth algorithms into SWAT (SWAT–GRASS D) and further modified it by considering the impact of leaf area index (LAI) on potential biomass production (SWAT–GRASS M). Based on testing at eight sites in the US Midwest, we found that SWAT–GRASS M generally outperformed SWAT and SWAT–GRASS D in simulating switchgrass biomass yield and the seasonal development of LAI. Additionally, SWAT–GRASS M can more realistically represent root development, which is key for the allocation of accumulated biomass and nutrients between aboveground and belowground biomass pools. These improvements are critical for credible assessment of agronomic and environmental impacts of growing perennial grasses for biomass production. • We developed the SWAT-GRASS model by integrating the grass growth module from the DAYCENT model into SWAT. • We further enhanced SWAT-GRASS to better represent the effects of LAI on biomass accumulation. • The new model achieved better simulation of switchgrass yield across multiple sites. • The new model can explicitly and reasonably represent root and shoot development of switchgrass. • A new tool for comprehensive assessment of agronomic and environmental impacts of growing switchgrass. [ABSTRACT FROM AUTHOR]

The rank histogram filter (RHF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation using perfect model (identical twin) synthetic data assimilation experiments. The primary motivation is to gauge the impact on analysis quality attributable to the consideration of non-Gaussian forecast error distributions. Using the NASA Catchment land surface model, the two filters are compared at 18 globally distributed single-catchment locations for a 10-yr experiment period. It is shown that both filters yield adequate estimates of soil moisture, with the RHF having a small but significant performance advantage. Most notably, the RHF consistently increases the normalized information contribution (NIC) score of the mean absolute bias by 0.05 over that of the EnKF for surface, root-zone, and profile soil moisture. The RHF also increases the NIC score for the anomaly correlation of surface soil moisture by 0.02 over that of the EnKF (at a 5% significance level). Results additionally demonstrate that the performance of both filters is somewhat improved when the ensemble priors are adaptively inflated to offset the negative effects of systematic errors. [ABSTRACT FROM AUTHOR]

A hybrid seasonal forecasting approach was generated by the National Centers for Environmental Prediction operational Climate Forecast System (CFS) and its nesting Climate extension of Weather Research and Forecasting (CWRF) model to improve forecasting skill over the United States. Skills for the three summers of 2011-2013 were evaluated regarding location, timing, magnitude, and frequency. Higher spatial pattern correlation coefficients showed that the hybrid approach substantially improved summer mean precipitation and 2-m temperature geographical distributions compared with the results of the CFS and CWRF models. The area mean temporal correlation coefficients demonstrated that the hybrid approach also consistently improved the timing prediction skills for both variables. In general, the smaller root mean square errors indicated that the hybrid approach reduced the magnitude of the biases for both precipitation and temperature. The greatest improvements were achieved when the individual models had similar skills. The comparison with a North American multi-model ensemble further proved the feasibility of improving real-time seasonal forecast skill by using the hybrid approach, especially for heavy rain forecasting. Based on the complementary advantages of CFS the global model and CWRF the nesting regional model, the hybrid approach showed a substantial enhancement over CFS real-time forecasts during the summer. Future works are needed for further improving the quality of the hybrid approach through CWRF's optimized physics ensemble, which has been proven to be feasible and reliable. [ABSTRACT FROM AUTHOR]

The representation of transient air-sea interactions is critical to the prediction of the sea surface temperature diurnal cycle and daily variability. This study develops a multilevel upper ocean model to more realistically resolve these interactions. The model is based on the one-dimensional turbulence kinetic energy closure developed by Noh et al. [2011], and incorporates new numerical techniques and improved schemes for model physics. The primary improvements include: (1) a surface momentum flux penetration scheme to better depict velocity shear in the diurnal mixed layer; (2) a solar penetration scheme to improve the penetration of visible and near-infrared bands of solar radiation into the mixed layer ocean; (3) a scheme to resolve the cool-skin and warm-layer effects on sea skin temperature; (4) a vertical grid stretch scheme to achieve higher near-surface resolution with fewer vertical levels; (5) a trapezoidal time integration scheme for flexible time steps; (6) a relaxation term of the previous daily mean difference between observed and modeled sea surface temperature. According to the numerical experiments based on the TOGA-COARE IMET mooring buoy data and the validation by observations from the National Data Buoy Center, NOAA, the results indicate that the new upper ocean mixed layer model improves the simulation of the diurnal cycle of SST and sea skin temperature, especially in amplitude. [ABSTRACT FROM AUTHOR]

Climate changes over China from the present (1990-1999) to future (2046-2055) under the A1FI (fossil fuel intensive) and A1B (balanced) emission scenarios are projected using the Regional Climate Model version 3 (RegCM3) nests with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). For the present climate, RegCM3 downscaling corrects several major deficiencies in the driving CCSM, especially the wet and cold biases over the Sichuan Basin. As compared with CCSM, RegCM3 produces systematic higher spatial pattern correlation coefficients with observations for precipitation and surface air temperature except during winter. The projected future precipitation changes differ largely between CCSM and RegCM3, with strong regional and seasonal dependence. The RegCM3 downscaling produces larger regional precipitation trends (both decreases and increases) than the driving CCSM. Contrast to substantial trend differences projected by CCSM, RegCM3 produces similar precipitation spatial patterns under different scenarios except autumn. Surface air temperature is projected to consistently increase by both CCSM and RegCM3, with greater warming under A1FI than A1B. The result demonstrates that different scenarios can induce large uncertainties even with the same RCM-GCM nesting system. Largest temperature increases are projected in the Tibetan Plateau during winter and high-latitude areas in the northern China during summer under both scenarios. This indicates that high elevation and northern regions are more vulnerable to climate change. Notable discrepancies for precipitation and surface air temperature simulated by RegCM3 with the driving conditions of CCSM versus the model for interdisciplinary research on climate under the same A1B scenario further complicated the uncertainty issue. The geographic distributions for precipitation difference among various simulations are very similar between the present and future climate with very high spatial pattern correlation coefficients. The result suggests that the model present climate biases are systematically propagate into the future climate projections. The impacts of the model present biases on projected future trends are, however, highly nonlinear and regional specific, and thus cannot be simply removed by a linear method. A model with more realistic present climate simulations is anticipated to yield future climate projections with higher credibility. [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]

Abstract: We investigate the effects of projected global changes in climate and human-related emissions for the year 2050 relative to 2000 for trends in the potential risk of hazardous ozone pollution episodes using a global climate chemistry model, CAM-Chem, driven by meteorology output from Community Climate System Model 3. Three distinct pathways from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios A1FI, A1B and B1 are considered to address the range and uncertainty in projected climate and emission changes. The projected changes in extreme climate conditions are likely to intensify the associated extreme weather conditions that foster the risk of high ozone pollution episodes over many parts of the world. It is found that the changes in regional high surface ozone concentration by 2050 largely depends on changes in the anthropogenic emissions of ozone precursors. Our analysis under projected climate and emissions on the frequency of “hazardous ozone days” in which the peak ozone concentration exceed the limit in the summer of 2050, based on 8 and 1-h standards, show that the risk of hazardous ozone pollution episodes will likely increase in developing regions, but changes of risk in developed regions depend on scenarios. For three major pollutant regions, north America, Europe and East Asia under 8-h definition, the hazardous risk in all regions increases under the A1FI scenario with a potential rate of 39–79 days/summer by 2050, but it is likely to decrease over North America and Europe under the A1B and B1 scenarios. The risk on Europe under the A1B and B1 scenarios can be ignored, but a significant rate of 6–71 days/summer still occur on other regions. The relative variability in projected frequency of hazardous ozone days increase by using the 1-h definition, although it shows the highest risk of 17–59 days/summer under the A1FI scenario. The higher variability can be understood through statistical analysis of cumulative frequency distribution of daily peak ozone concentrations, which shows that the changes of surface ozone level are more significantly presented on the high value part. [Copyright &y& Elsevier]

The regional Climate-Weather Research and Forecasting model (CWRF) was used to downscale the NCAR Community Climate System Model V4.0 (CCSM4) projection of China precipitation changes from the present (1974–2005) to future (2019–2050) under the high emission scenario RCP8.5. The CWRF downscaling at 30-km improved CCSM4 in capturing observed key precipitation spatiotemporal characteristics, correcting rainband dislocations, seasonal-mean biases, extreme-rainfall underestimates and rainy-day overestimates. For the future, CWRF generally reduced CCSM4 projected changes in magnitude, producing still significant increases mostly in summer for mean precipitation in the Northeast, North China and Southwest and for extreme precipitation in North China, South China and the Southwest. These regional precipitation increases were direct responses to enhanced ascending motions and moisture transports from adjacent oceans as the east Asian jet shrunk westward and the Hadley circulation widened northward under global warming. The identification of such robust physical mechanisms added confidence in the CWRF downscaled regional precipitation changes. Furthermore, the CWRF downscaling corrections were systematically carried from the present into future, accounting for projection uncertainties up to 40%. Regional biases, however, could not be simply removed from projected changes because their correspondences were strongly nonlinear, highlighting CWRF's ability to project more reliable changes by reducing model structural uncertainties. [ABSTRACT FROM AUTHOR]

Past heavy precipitation events in the Chicago metropolitan area have caused significant flood-related economic and environmental damages. A key component in flood management policies and actions is determining flood magnitudes for specified return periods. This is a particularly difficult task in areas with a complex and changing climate and land-use, such as the Chicago metropolitan area. The standard design storm methodology based on the NOAA Atlas 14 and ISWS Bulletin 70 has been used in the past to estimate flood hydrographs with variable return periods in this region. In a changing climate, however, these publications may not be accurate. This study presents and illustrates a methodology for diagnostic analysis of future climate scenarios in the framework of urban flooding, and assesses the corresponding uncertainties. First, the design storms are calculated using data downscaled by a regional climate model (RCM) at 30-km spacing for the present and 2050s under the IPCC A1Fi (high) and B1 (low) emissions scenarios. Next, the corresponding flood discharges at six watersheds in suburban Chicago are estimated using a hydrologic event model. The resulting scenarios in flood frequency were first assessed through a set of diagnostic tests for precipitation timing and frequency. The study did not reveal any significant changes in the 2050s in the average timing of heavy storms, but their regularity decreased. The average timing did not exhibit any significant spatial variability throughout the region. The precipitation frequency analysis revealed distinct differences between the northern and southeastern subregions of the Chicago metropolitan area. The quantiles in the northern subregion averaged for 2-year, 5-year, and 10-year return periods exhibited a 20% and 16% increase in daily precipitation for scenarios B1 and A1Fi, respectively. The southeastern subregion, however, exhibited a decrease of 12% for scenario B1 and a minor increase of 3% for scenario A1Fi. The hydrologic effects of changing precipitation on the flood quantiles were illustrated using six small watersheds in the region. The relative increases or decreases in precipitation translated into even larger relative increases or decreases in flood peaks, due to the nonlinear nature of the rainfall-runoff process. Simulations using multiple climate models, for longer periods, finer spatio-temporal resolution, and larger areal coverage could be used to more accurately account for numerous uncertainties in the precipitation and flood projections. [ABSTRACT FROM AUTHOR]

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]

• The impacts of monsoon rainfall on food insecurity differed by earthquake exposure. • Low rainfall was linked with greater food insecurity in non-impacted areas. • More rainfall was positively linked with food insecurity in affected areas. • This is likely due to rainfall increasing landslides in earthquake-affected regions. Food insecurity is a key global health challenge that is likely to be exacerbated by climate change. Though climate change is associated with an increased frequency of extreme weather events, little is known about how multiple environmental shocks in close succession interact to impact household health and well-being. In this paper, we assess how earthquake exposure followed by monsoon rainfall anomalies affect food insecurity in Nepal. We link food security data from the 2016 Nepal Demographic and Health Survey to data on shaking intensity during the 2015 Gorkha earthquake and rainfall anomalies during the 2015 monsoon season. We then exploit spatial variation in exposure to the earthquake and monsoon rainfall anomalies to isolate their independent and compound effects. We find that earthquake exposure alone was not associated with an increased likelihood of food insecurity, likely due in part to effective food aid distribution. However, the effects of rainfall anomalies differed by severity of earthquake exposure. Among households minimally impacted by the earthquake, low rainfall was associated with increased food insecurity, likely due to lower agricultural productivity in drought conditions. Among households that experienced at least moderate shaking, greater rainfall was positively associated with food insecurity, particularly in steep, mountainous areas. In these locations, rainfall events disproportionately increased landslides, which damaged roads, disrupted distribution of food aid, and destroyed agricultural land and assets. Additional research on the social impacts of compound environmental shocks is needed to inform adaptation strategies that work to improve well-being in the face of climate change. [ABSTRACT FROM AUTHOR]