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The characteristics of gravity waves in China are investigated through an extensive analysis of high vertical resolution radiosonde observations collected over eight years across 120 stations, and are subsequently compared to those in the United States. These characteristics encompass energy density, intrinsic frequencies, horizontal and vertical wavelengths, as well as vertical and horizonal propagation directions. China and the United States, situated in mid‐latitude regions with prominent western topographical features, the Qinghai‐Tibet Plateau and Rocky Mountains respectively, demonstrate striking similarities in the generation and distribution of gravity waves. Both landmasses exhibit the strongest gravity waves during winter and the weakest during summer. And within the troposphere, the maximum energy of gravity waves is generated over and immediately downstream of the topographies. In addition, the energy level is amplified in the lower stratosphere. However, unique regional contrasts in summer are result from the differences of summer monsoon influence and the distinct western topographies. The maximum gravity wave energy in summer troposphere is observed over the north side of the Qinghai‐Tibet Plateau in China, contrasting with its location downstream of the Rockies in the United States. Plain Language Summary: Extensive studies have highlighted the global circulation effects of small‐scale atmospheric gravity waves, but our knowledge of the global distribution of gravity waves remains incomplete. High vertical resolution raw radiosonde data achieved at meteorological sounding stations present a potentially invaluable resource of deriving information on gravity waves. Previous studies have used these data achieved in China at a small subset of stations to analyze limited gravity waves parameters, which cannot provide a fully representative sample of the diverse underlying surface and weather conditions across the country and acquire complete parameters of gravity wave. Our study using the complete data set aims to contribute valuable information on the comprehensive characteristics of gravity wave activities in China, and compare these results to those derived from a similar data set in the United States. The results demonstrate many striking similarities in terms of the generation and distribution of gravity waves in two landmasses. However, unique regional contrasts in summer are result from the differences of summer monsoon influence and the distinct height and shape of their respective western topographies. These results will supplement knowledge of the global distribution of gravity waves and be beneficial to the development of the numerical weather prediction models. Key Points: This study derives characteristics of gravity wave in China using high vertical resolution radiosondeThere are many striking similarities in terms of the generation and distribution of gravity waves in China and the United StatesUnique regional contrasts in summer are result from the differences of summer monsoon influence and the distinct western topographies [ABSTRACT FROM AUTHOR]

This study explores the impact of coupling cumulus and planetary boundary layer (PBL) parameterizations on diurnal precipitation forecasting during the plum rainy season in Jiangsu Province, China, using a double grid‐nesting approach. Results show that coherent coupling of cumulus (only in the 15 km grid outer domain [O]) and PBL parameterizations leads to improved forecasting of diurnal variations in the morning, afternoon, and the evening. Increasing the frequency of the Kain‐Fritsch (KF) cumulus scheme in [O] enhances subgrid precipitation while reducing grid‐scale precipitation, resulting in a more accurate representation of daytime convective activities and a reduction in over‐forecasting of evening valley and early‐morning precipitation. Additionally, coupling a suitable PBL scheme mitigates the overpredicted afternoon peak by facilitating turbulent mixing to penetrate higher altitudes with a thicker layer, thereby reducing instability energy accumulation. A higher KF frequency in [O] retains less low tropospheric moisture, reducing moisture convergence into the 1 km grid inner domain [I] and decreasing overpredicted daytime precipitation in [I]. Various PBL schemes produce distinct vertical distributions of turbulent moisture and heat transport, impacting convection and precipitation in [I] resolved by cloud microphysics processes. The coherent coupling of these parameterizations maintains a balanced supply of convective energy and water vapor, significantly improving diurnal precipitation forecasts in [I]. Isolating these parameterizations between nested grids may undermine this improvement. Plain Language Summary: Accurate diurnal precipitation forecasting remains a challenge. This study aims to effectively couple cumulus and planetary boundary layer (PBL) parameterizations to enhance diurnal precipitation forecasts using a double grid‐nesting approach. We showed that coherent coupling of cumulus and PBL parameterizations improves forecasting of diurnal variations during the plum rainy season in Jiangsu Province, China. Increasing the frequency of the KF cumulus scheme more accurately represents daytime convective activities and reduces over‐forecasting of evening valley and early‐morning precipitation. Coupling an appropriate PBL scheme that extends turbulent mixing into deeper layers mitigates the accumulation of instability energy, thereby reducing overpredicting of the afternoon peak. A higher KF frequency in the 15 km grid outer domain [O] retains less low tropospheric moisture, reducing moisture convergence into the 1 km grid inner domain [I] and decreasing overpredicted daytime precipitation in [I]. Diverse PBL schemes result in varying vertical distributions of turbulent moisture and heat transport, affecting explicit convection and precipitation in [I]. Therefore, the cohesive integration of cumulus and PBL parameterizations across nested grids significantly enhances diurnal precipitation forecasts. Key Points: Coherent coupling between cumulus and planetary boundary layer (PBL) parameterizations improves the nested rainfall diurnal cycle forecastIncreasing cumulus parameterization frequency reduces the nested morning and outer‐domain evening valley rainfall overforecastAdjusting PBL parameterization weakens the rainfall afternoon peak and daytime total amount overprediction [ABSTRACT FROM AUTHOR]

The Journal of Geophysical Research: Atmospheres expresses gratitude to the 2854 scientists who reviewed manuscripts for the journal in 2023. Peer review is essential for maintaining the integrity and rigor of scientific research. The reviews have contributed to the improvement of paper quality, the generation of new ideas, and the advancement of young scientists' careers. The journal acknowledges the reviewers' selfless service and dedication to the scientific community and looks forward to their continued support. [Extracted from the article]

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]

The editorial board of JGR Atmospheres thanks reviewers who refereed papers in 2022. Plain Language Summary: The editorial board of JGR Atmospheres thanks reviewers who refereed papers in 2022. Key Point: The editors thank the 2022 peer reviewers [ABSTRACT FROM AUTHOR]

The Global Ensemble Forecast System version 12 (GEFSv12) has been implemented into National Centers For Environmental Prediction operations since September 2020, which was uncoupled, but increased the horizontal resolution from 34 to 25 km, increased ensemble members from 21 to 31, and extended forecasts from 16 to 35 days. It significantly improved probabilistic forecast skills in many categories, such as precipitation, tropical storms, Madden‐Julian Oscillation (MJO), etc. The improvements resulted from many aspects, including model resolution increase, dynamical core upgrade, advances in hybrid data assimilation and physical parameterizations, and more importantly, from using new stochastic schemes to improve forecast uncertainty. To further improve GEFS's sub‐seasonal forecast skill, a coupled GEFS was built up on the Unified Forecast System prototype version 5 that fully couples an atmospheric model with a land surface model, ocean model, ice model, and wave model. A set of coupled GEFS experiments were conducted to test different horizontal resolutions at approximately 50 and 25 km while adjusting the stochastic parameterization schemes for the atmosphere to better represent forecast uncertainties. The experiments were run for a 2‐year period from October 2017 to September 2019, with one initialization per week at Wednesday 00 UTC, 11 ensemble members, and were forecasted out to 35 days. The coupled GEFS significantly improves 500 hPa height anomaly correlation in week‐1, week‐2, and MJO skills compared to the current operational forecast. The forecast spread of tropical wind is greatly reduced by improved stochastic schemes and matches well with the forecast root mean square errors. The correlation of forecast error variance and ensemble variance is improved for the coupled GEFSs. Meanwhile, the spread of MJO has been greatly reduced for the coupled GEFSs to improve the MJO forecast uncertainty. Plain Language Summary: The Unified Forecast System is a new community‐based, atmosphere‐land‐ocean‐sea ice‐wave‐aerosol coupled, comprehensive Earth modeling system. A Global Ensemble Forecast System (GEFS; version 12; uncoupled) has been implemented into National Centers For Environmental Prediction operations since September 2020 with 31 members, out to 35 days to cover subseasonal prediction. A new fully coupled GEFS in this investigation has demonstrated an overall better performance around the weather, medium‐range, and weeks 3 and 4 time scales. Forecast uncertainties are quantified by adjusting the coefficients and parameters of the stochastic schemes. Key Points: The coupled Global Ensemble Forecast System has advantages over the uncoupled system for weather and subseasonal predictionsThe forecast uncertainty is quantified by different measures and diagnostic toolsModel resolution is less important for extended range than weather forecasts [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]

Key Point: The editors thank the 2021 peer reviewersThe journal received 1581 submitted manuscripts2510 scientists reviewed for the journal in 2021 [ABSTRACT FROM AUTHOR]

The impacts of urbanization on terrestrial thermal and hydrological changes should be considered for sustainable development of urban environment and economy. However, previous studies mainly focused on the impacts at short time scales, which is insufficient to provide the needed information. This study augmented the high‐resolution Conjunctive Surface‐Subsurface Process version 2 (CSSPv2) land model with an urban canopy model that incorporates domestic wastewater recharge (DWWR) and dynamical impervious surface area (ISA) to develop a new CSSPv2_Urban model and conducted 38‐year simulations over the Beijing metropolitan area at 1 km resolution to quantify the urbanization impacts. Compared with CSSPv2, CSSPv2_Urban decreases the root‐mean‐square errors in surface latent/sensible heat fluxes, near‐surface air temperature and land surface temperature by 15%–66%, increases Kling‐Gupta efficiency for streamflow by 27%–34%, and reproduces the observed long‐term trends of land surface temperature and streamflow. It also reduces the corresponding errors in the modern reanalysis products by up to 70%. The model sensitivity analyses showed that the urbanization over Beijing area contributed to 31% of the surface warming during 1979–2017 and kept the warming trend over the new development zone during 2000–2013 when global warming slowed down. Although the large fraction of ISA in the city center increased streamflow by generating more surface runoff, 39%–62% of the increasing trend of streamflow was caused by the increased DWWR. Our study highlights the importance of the urban heat island effect from a climatological perspective and suggests that the influences of urbanization on streamflow may be underestimated if ignoring DWWR. Plain Language Summary: Fast urbanization since last century has been modifying the terrestrial energy and water cycle significantly. However, quantifying urbanization's impacts on long‐term changes of terrestrial thermal and hydrological conditions at high spatial resolution, which is important for providing guidelines for urban sustainable development, is still in progress. Here, we added an urban canopy model that incorporates domestic wastewater recharge (DWWR) and dynamical impervious surface area (ISA) into the Conjunctive Surface–Subsurface Process version 2 (CSSPv2) land model and conducted high‐resolution (1 km) and long‐term (1979–2017) simulations over the Beijing metropolitan area to investigate the urbanization impacts. The newly developed CSSPv2_Urban model is superior to the CSSPv2 and modern reanalysis data sets in simulating the thermal and hydrological variables. Sensitivity analysis revealed that urbanization contributed to about 31% of the surface warming trend in Beijing and sustained the warming during 2000–2013 when the global warming slowed down. Moreover, increased ISA and DWWR during the urbanization were found to interpret the increasing streamflow trend by 30% and 51%, respectively. Thus, more attentions should be paid to the DWWR which is usually ignored in current urban models to better capture the hydrological influences of urbanization. Key Points: CSSPv2_Urban was developed with urban energy model, dynamic impervious surface area (ISA), and domestic wastewater recharge (DWWR) schemeCSSPv2_Urban 1 km high‐resolution simulation outperformed modern reanalysis for energy and water cycle over Beijing metropolitan areaSimulation suggests that ISA and DWWR contributed to 30% and 51% of the increasing streamflow trend during 1979–2017 near Beijing city center [ABSTRACT FROM AUTHOR]

Biases in land‐atmosphere interaction simulations may originate from different model components or parameterizations and are transferred to the land‐atmosphere system, which causes biases in weather and climate predictions. With a focus on East Asia, we analyze the relationships between land surface sensible and latent heat fluxes (SH and LH), evaporative fraction (EF), soil moisture, and lifting condensation level (LCL) height in ERA5 and MERRA‐2 reanalyzes and simulations from a regional climate model (CWRF) with multiple parameterizations. The results show that SH (soil moisture and EF) is generally positively (negatively) correlated with LCL height because of their mutual positive (negative) influence and the positive feedback loops. The LH‐LCL relationship is sensitive to the local climate and generally positive in humid climates and negative in the semiarid climate; however, only negative correlations can be indicative of a possible impact of LH on LCL. MERRA‐2 shows an overall stronger influence of land on LCL height than ERA5 and CWRF. Different CWRF parameterizations produce different and sometimes contrasting LH‐LCL relationships, which is strongly related to the simulated climate and can be validated. This highlights the need for a deep process‐level understanding of land‐atmosphere interactions in coupled model simulations. Plain Language Summary: Numerical weather and climate models include many complex physical processes and their descriptions are often biased to some extent. Biases in the simulation of land‐atmosphere coupling may limit the use of land surface data for weather and climate predictions. In this study, we analyze the relationships between land surface sensible and latent heat fluxes (SH and LH) and lifting condensation level (LCL) height, whose couplings are important for the land‐atmosphere system and understanding them may help improve the accuracy of land‐atmosphere interaction simulations. We find that SH is almost always positively correlated with LCL height because of their mutual positive influence and the positive feedback loops. The LH‐LCL relationship changes with the local climate and is generally positive in humid climates and negative in semiarid climates, but only negative correlations can indicate a possible impact of LH on LCL. Different descriptions of the physical processes in the CWRF model produce different and sometimes contrasting LH‐LCL relationships, which is strongly related to their simulated climate and can be validated. This illustrates the need for more process‐level understanding of land‐atmosphere interactions in coupled model simulations. Key Points: LH‐LCL correlations can be positive or negative but only negative correlations can indicate the impact of land on LCLMERRA‐2 shows an overall stronger impact of land on LCL than ERA5 and a regional climate modelLH‐LCL correlations from a model with different physics parameterizations strongly depends on the simulated climate [ABSTRACT FROM AUTHOR]

A regional Climate‐Weather Research and Forecasting (CWRF) model with a 30‐km horizontal resolution was applied to simulate the East Asian summer monsoon (EASM) and climate in China from 1980 to 2016. As compared with observations and reanalysis data, the model can reasonably reproduce the spatial distributions of the climatological mean atmospheric circulation and water vapour transport in East Asia, as well as the seasonal advance and retreat of EASM and rain bands. The correlation coefficient between the EASM circulation index in the simulation and reanalysis is.97. The model can well represent the geographic distributions of summer mean temperature and precipitation over most of China. However, model biases still exist, in particular the skill in the Yangtze–Huaihe River basin is relatively low. The simulated climatological mean temperature and precipitation deviations from observations may be related to the model's systematic circulation biases, with a thicker lower troposphere (i.e., higher temperature between 500 and 1,000 hPa) over most of China (except for the south and southwest) and a thinner one over the coastal oceans in summertime. Compared with the reanalysis, the model overestimates the land‐ocean thermal contrast, which causes the South Asian High shifted to the north and east, as well as the subtropical high and tropical convective activity located further north and persisted longer. Consequently, more water vapour transports northward, leading to more precipitation over North China–Northeast China and less precipitation over the Yangtze and Huaihe River basin. Therefore, the enhanced land‐ocean thermal contrast may be a major factor for the stronger EASM in the model, and corresponding precipitation and temperature biases in China. This analysis provides important information for further improving model performance in EASM simulation. [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]

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]

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 community Noah land surface model with multiparameterization options (Noah‐MP) provides a plethora of model configurations with varying complexity for land surface modeling. The practical application of this model requires a basic understanding of the relative abilities of its various parameterization configurations in representing spatiotemporal variability and hydrologic connectivity. We designed an ensemble of 288 experiments from multiparameterization schemes of six physical processes to assess and reduce the structural uncertainty for land surface modeling over 10 hydrologic regions in China for the period 2001–2010. The observed latent heat (LH) was well reproduced by the ensemble. Meanwhile, most experiments underestimated sensible heat (SH) throughout the year and overestimated the cold season but underestimated the warm season terrestrial water storage anomaly (TWSA). The sensitive processes and best‐performing schemes varied not only with regions but also among variables. The LH and SH were most sensitive to runoff‐groundwater (RUN), surface heat exchange coefficient (SFC), and radiation transfer (RAD). The TWSA was dominated by RUN and RAD while largely influenced by soil moisture factor for stomatal resistance (BTR) and frozen soil permeability (INF) over some limited regions. By contrast, supercooled liquid water (FRZ) had little influence on all variables. Our optimization for individual variables produced high mean Taylor skill scores that ranged from 0.95–0.99 for LH, 0.82–0.99 for SH, and 0.63–0.95 for TWSA depending on regions. The simultaneous optimization made trade‐off among the three variables, which improved TWSA performance at the cost of reducing the skill for LH and SH over a few regions. Key Points: We designed 288 experiments from multiparameterization schemes of six physical processes to assess parameterization uncertainty of Noah‐MPDominant physical processes for energy and water fluxes simulations over 10 regions in China were identified with sensitivity scoresBest scheme combinations for corresponding simulations were selected with multiple comparisons of the parameterization schemes [ABSTRACT FROM AUTHOR]

High‐resolution modeling became popular in recent years due to the availability of multisource observations, advances in understanding fine‐scale processes, and improvements in computing facilities. However, modeling of hydrological changes over mountainous regions is still a great challenge due to the sensitivity of highland water cycle to global warming, tightly coupled hydrothermal processes, and limited observations. Here we show a successful high‐resolution (3 km) land surface modeling over the Sanjiangyuan region located in the eastern Tibetan plateau, which is the headwater of three major Asian rivers. By developing a new version of a Conjunctive Surface‐Subsurface Process model named as CSSPv2, we increased Nash‐Sutcliffe efficiency by 62–130% for streamflow simulations due to the introduction of a storage‐based runoff generation scheme, reduced errors by up to 31% for soil moisture modeling after considering the effect of soil organic matter on porosity and hydraulic conductivity. Compared with ERA‐Interim and Global Land Data Assimilation System version 1.0 reanalysis products, CSSPv2 reduced errors by up to 30%, 69%, 92%, and 40% for soil moisture, soil temperature, evapotranspiration, and terrestrial water storage change, respectively, as evaluated against in situ and satellite observations. Moreover, CSSPv2 well captured the elevation‐dependent ground temperature warming trends and the decreased frozen dates during 1979–2014, and significant increasing trends (p < 0.05) in evapotranspiration and terrestrial water storage during 1982–2011 and 2003–2014 respectively, while ERA‐Interim and Global Land Data Assimilation System version 1.0 showed no trends or even negative trends. This study implies the necessity of developing high‐resolution land surface models in realistically representing hydrological changes over highland areas that are sentinels to climate change. Key Points: CSSPv2 land model was developed over headwaters with conjunctive surface‐subsurface flows, storage‐based runoff, and soil organic matterCSSPv2 outperformed global reanalysis for simulations of hydrological cycle as validated with multisource observationsCSSPv2 captured elevation‐dependent warming, increasing ET and TWS in the satellite era, while global reanalysis failed over headwaters [ABSTRACT FROM AUTHOR]

Hyperresolution land surface modeling provides an unprecedented opportunity to simulate locally relevant water and energy cycle, but lateral surface and/or subsurface flows that are essential at fine scale are often neglected by most one-dimensional land surface models (LSMs). To analyze effects of lateral flows across scales, a Conjunctive Surface-Subsurface Process model, which considers soil moisture-surface flow interaction and quasi-three-dimensional subsurface flow, is implemented over a mountainous HyperHydro test bed in southwestern USA at different resolutions. Validation over more than 70 International Soil Moisture Network stations shows that there are significant improvements in soil moisture simulations from 30 km to 4 km as finer soil property and precipitation data are used, with correlation increased by 5%-16% and error decreased by 5%. Lateral surface flow has a significant influence on surface soil moisture and ground evaporation even at coarse resolution. Effect of lateral subsurface flow on soil moisture is nontrivial at 1 km or finer resolution especially over wet areas. At 100 m resolution, topography-induced lateral subsurface flow causes drier peaks and wetter valleys, decreases latent heat by 8% at peaks, while increases it by 12% at valleys. Furthermore, influences of lateral subsurface flow on ground evaporation and vegetation transpiration are more significant during dry season due to a stronger coupling between soil moisture and evapotranspiration. Therefore, it is worthy to incorporate lateral flow processes in hyperresolution LSMs to better represent water and energy heterogeneity even with limited hyperresolution meteorological and surface data. [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]

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]

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]

This study proposes an end-to-end forecasting framework to incorporate operational seasonal climate forecasts to help farmers improve their decisions prior to the crop growth season, which are vulnerable to unanticipated drought conditions. The framework couples a crop growth model with a decision-making model for rainfed agriculture and translates probabilistic seasonal forecasts into more user-related information that can be used to support farmers' decisions on crop type and some market choices (e.g., contracts with ethanol refinery). The regional Climate-Weather Research and Forecasting model (CWRF) driven by two operational general circulation models (GCMs) is used to provide the seasonal forecasts of weather parameters. To better assess the developed framework, CWRF is also driven by observational reanalysis data, which theoretically can be considered as the best seasonal forecast. The proposed framework is applied to the Salt Creek watershed in Illinois that experienced an extreme drought event during 2012 crop growth season. The results show that the forecasts cannot capture the 2012 drought condition in Salt Creek and therefore the suggested decisions can make farmers worse off if the suggestions are adopted. Alternatively, the optimal decisions based on reanalysis-based CWRF forecasts, which can capture the 2012 drought conditions, make farmers better off by suggesting 'no-contract' with ethanol refineries. This study suggests that the conventional metric used for ex ante value assessment is not capable of providing meaningful information in the case of extreme drought. Also, it is observed that institutional interventions (e.g., crop insurance) highly influences farmers' decisions and, thereby, the assessment of forecast value. [ABSTRACT FROM AUTHOR]

Subgrid variability of subsurface moisture flux transport is strongly influenced by the local variation of topographic attributes, such as elevation, slope, and curvature. A three-dimensional volume-averaged soil moisture transport (VAST) model is developed to incorporate these effects using the volume-averaged Richards equation. The small-perturbation approach is used to decompose the equation into mean and fluctuation, which are then averaged over the model grid box. This formulation explicitly incorporates the variability of moisture flux due to subgrid variation of topographic attributes. The model is independent of scale, but the parameters need to be estimated at the model scale. It is demonstrated that the flux contribution from the subgrid variability can be comparable to that of mean flux, particularly under drier moisture conditions. This formulation can be substituted for subsurface moisture transport schemes in most existing land surface models. [ABSTRACT FROM AUTHOR]