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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]

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]

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]

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]

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]