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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]

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]

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]

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]

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]

• 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]

The ability of climate models to capture extreme precipitation events is crucially important, but most of the existing models contain significant biases for the simulation of extreme precipitation. To understand the causes of these biases, we used five different cumulus parameterization schemes in the regional Climate–Weather Research and Forecasting (CWRF) model to investigate its performance and biases in the simulation of extreme precipitation events in China. In general, the ensemble cumulus parameterization (ECP) scheme was the most skillful in reproducing the spatial distribution of the 95th percentile daily precipitation (P95) and the other four schemes either overestimated (the Kain-Fritsch Eta and Tiedtke schemes) or underestimated (the Betts-Miller-Janjic and New Simplified Arakawa-Schubert schemes) P95. Compared with the observational data, ECP scheme significantly improved the simulation of extreme precipitation in China and had the highest correlation and the smallest root-mean-square error in most areas and seasons. To clarify the underlying physical processes of P95 simulation biases, we established a regression model of extreme precipitation based on ECP scheme. This showed that P95 in North China is mainly affected by moisture convergence, planetary boundary layer height and lifting condensation level (relative importance 18–32%). In Central China, the vertical upward motion of water vapor, sensible heat flux and planetary boundary layer height (relative importance 18–30%) are main factors associated with P95. In South China, the vertical upward motion and horizontal transport of water vapor are predominant (relative importance 26–37%). In addition, the net surface energy, surface and atmospheric radiation flux, total precipitable water, convective available potential energy and cloud water path also have a high correlation with P95 (the second most important factor; relative importance 14–31%). The influence of each factor on the simulation of P95 is different when using different cumulus parameterization schemes and the interaction among the different factors determines the ability of CWRF model to simulate extreme precipitation. These results provide important references for future model evaluations and improvements. • Five cumulus schemes were compared for their effects on China P95 using CWRF. • The ensemble cumulus parameterization outperformed the other schemes in simulating P95. • The underlying physical processes and mechanisms of P95 biases were explored. [ABSTRACT FROM AUTHOR]

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

The weak and sustained tropical cyclone (TC) Rumbia (2018) and its associated heavy precipitation after landfall are simulated by the Weather Research and Forecasting model using three microphysics parameterizations (MP) and two planetary boundary layer (PBL) schemes. Key factors regulating the heavy rainfall distribution of landed TC at three stages (landfall, inland slow-moving and recurving stage) and their sensitivities to multi-physics parameterizations are examined. Results show that heavy rainfall distribution is largely regulated by the intensity of TC itself at landfall, but more affected by environmental factors at the two inland stages, including the environmental vertical wind shear, the associated upper-level wind divergence and low-level moisture convergence. Different MP and PBL schemes mainly affect the simulation of TC thermodynamic structure and key environmental factors, leading to their different forecast skills at three stages. Specifically, the Yonsei University (YSU) PBL scheme systematically outperforms the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme in simulating stronger TC and heavy rainfall intensity. With the advantageous YSU PBL scheme, the Ferrier MP scheme only shows a slight advantage in simulating the intensity of TC and heavy rainfall at landfall. However, the WRF single-moment 6-class (WSM6) and Thompson scheme produce more graupel or snow, and better simulate the key environmental factors, showing their respective advantages in simulating the heavy rainfall structure and location at the two inland stages. This implies that the use of nonlocal YSU PBL scheme and the MP schemes with sophisticated ice processes shows superiority in simulating the postlandfall heavy rainfall induced by weak TCs. • Postlandfall rainfall of weak TC is highly affected by environmental factors. • Ice species and radial distribution of diabatic heating affect TC structures and rainfall. • The MYNN PBL scheme underestimates TC intensity and heavy rainfall compared to YSU. [ABSTRACT FROM AUTHOR]