17 Search Results

Climate models exhibit significant rainfall bias in mountainous regions. One reason is the insufficient representation of orographic anisotropy in these models. In this study, we implement the orographic drag scheme with 3-D orographic anisotropy (all flow directions (AFD)) into a general circulation model and investigate its impact on Nanling rainfall simulation where models have systematic dry bias in the summer. It is shown that the AFD alleviated the Nanling mountain rainfall bias by over 60%. This is through an anomalous “lower-convergence-higher-divergence” deceleration pattern of the flow windward of the Nanling Mountains that enhanced vertical motion and low-level moisture convergence. Themore » results suggest the importance of explicit orographic anisotropy representation in rainfall simulation in mountainous regions.« less

The BiLateral Operational Storm-Scale Observation and Modeling (BLOSSOM) project was initiated to establish a long-term supersite to improve understanding of cloud physical states and processes as well as to support satellite and climate model programs over the WFF site via a bilateral approach of storm-scale observations and process modeling. This study highlights a noble systematic validation framework of the BLOSSOM ensemble cloud-process simulations through mixed-phase, light-rain, and deep-convective precipitation cases. The framework consists of creating a domain-shifted ensemble of large-scale forcing datasets, and configuring and performing cloud-process simulations with three different bulk microphysics schemes. Validation uses NASA S-band dual-POLarimetric radarmore » (NPOL) observations in the form of statistical composites and skill scores via a polarimetric radar simulator and newly developed CfRad Data tool (CfRAD). While the simulations capture the overall structures of the reflectivity composites, polarimetric signals are still poorly simulated, mainly due to a lack of representation of ice microphysics diversity in shapes, orientation distributions, and their complex mixtures. Despite the limitation, this new ensemble-based validation framework demonstrates that 1) no particular forcing or microphysics scheme outperforms the rest and 2) the skill scores of coarse- and fine-resolution ensemble simulations with different domain-shifted forcing and microphysics schemes are highly correlated with each other with no clear improvement. On the other hand, this suggests that coarse-resolution ensemble simulations are relevant for selecting the best meteorological forcing and microphysics scheme before conducting computationally demanding large eddy simulations (LESs) in support of aircraft and satellite instrument development as well as cloud-precipitation-convection parameterizations.« less

Deficiencies in convection trigger functions, used in deep convection parameterizations in General Circulation Models (GCMs), have critical impacts on climate simulations. A novel convection trigger function is developed using the machine learning (ML) classification model XGBoost. The large-scale environmental information associated with convective events is obtained from the long-term constrained variational analysis forcing data from the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites representing, respectively, continental mid-latitude and tropical convection. The ML trigger is separately trained and evaluated per site, and jointly trained and evaluated at both sites as a unified trigger.more » The performance of the ML trigger is compared with four convective trigger functions commonly used in GCMs: dilute convective available potential energy (CAPE), undilute CAPE, dilute dynamic CAPE (dCAPE), and undilute dCAPE. The ML trigger substantially outperforms the four CAPE-based triggers in terms of the F1 score metric, widely used to estimate the performance of ML methods. The site-specific ML trigger functions can achieve, respectively, 91% and 93% F1 scores at SGP and MAO. The unified trigger also has a 91% F1 score, with virtually no degradation from the site-specific training, suggesting the potential of a global ML trigger function. The ML trigger alleviates a GCM deficiency regarding the overprediction of convection occurrence, offering a promising improvement to the simulation of the diurnal cycle of precipitation. Furthermore, to overcome the black box issue of the ML methods, insights derived from the ML model are discussed, which may be leveraged to improve traditional CAPE-based triggers.« less

Abstract A reasonable representation of orographic anisotropy in earth system models is vital for improving weather and climate modeling. In this study, we implement the orographic drag scheme, including 3‐D orographic anisotropy (3D‐AFD), into the Chinese Academy of Sciences Earth System Model version 2 (CAS‐ESM 2.0). Three groups of simulations named sensitivity run, medium‐range forecast, and seasonal forecast respectively are conducted using the updated CAS‐ESM model and validated against station observation and reanalysis data. These simulations are run using the three schemes—3D‐AFD scheme, the 3D orographic anisotropy scheme for the eight‐direction (3D‐8x), and the 2D isotropic scheme (2D)—to compare theirmore » performance in CAS‐ESM 2.0. Sensitivity runs show that the 3D‐AFD provide more continuous transition of calculated drag as function of wind direction than the 3D‐8x, while the drag calculated using the 2D scheme show no variation with change of wind direction. Enhanced drag in the medium range and seasonal forecast using the updated CAS‐ESM alleviates part of the winter wind speed bias over the Tibetan Plateau (TP) and the cold bias over TP and the Siberian polar region. It is shown that the 3D‐AFD scheme alleviates more bias than that of the 3D‐8x scheme (by wind speed reduction of 1 ∼ 2 m/s and temperature of 1 ∼ 2 K) especially in the seasonal forecast. The results suggest that reasonable representation of the orographic anisotropy is important in climate modeling.« less

The effects of entrained environment air and aerosols on cloud properties remain a critical 16 yet understudied topic. This study first introduces a new entraining cloud parcel model 17 considering entrained aerosols. With entrained aerosols represented by a newly introduced 18 parameter, the impacts of entrainment rate and entrained aerosols on cloud microphysical 19 properties are investigated. The results show that the relationships between entrainment rate 20 and cloud microphysical properties are highly nonlinear when the entrained aerosols vary. With 21 the lateral entrainment-mixing, a new phenomenon is revealed that the height of maximum 22 parcel supersaturation cannot be reachedmore » when the entrainment rate is beyond a certain 23 critical value. This new critical entrainment rate is different from the critical entrainment rate 24 defined by Barahona and Nenes [2007], beyond which clouds cannot form. This finding has 25 important implications for developing parameterization of droplet activation, which is based 26 primarily on the assumption of the existence of maximum supersaturation.« less

Surface topography strongly impacts the regional atmospheric circulation dynamically and thermodynamically. Over the Great Plains in the United States, the gently tilted slope is an important factor that impacts clouds, convection and regional circulations. This study enhances an atmospheric constrained variational analysis by using a terrain-following sigma vertical coordinate and applies to the data collected at the Southern Great Plain (SGP) site of the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program. Sensitivity studies are performed to examine the impact of the terrain effects on the derived large-scale atmospheric forcing fields and the simulated shallow-cumulus clouds in large-eddy simulationmore » (LES) driven by the forcing. We found that the terrain impacts on the large-scale forcing fields are mainly at lower levels and are strongly controlled by up/downslope winds. The response of the derived forcing to the slope of the terrain is monotonic, but the response of the simulated shallow-cumulus clouds is more complex and depends on several factors. Overall, the terrain impact is small over SGP due to the small slope angle. However, the flat-surface assumption may cause larger biases in the large-scale forcing fields at locations with steeper terrain. The new sigma-coordinate algorithm, with its consideration of surface slope, should be more suitable to derive large-scale objective analysis over regions with steep terrain for application to force single-column models, cloud resolving models and LES models.« less

In this study, the prediction of wintertime extratropical cyclone activity from subseasonal to seasonal timescale in current dynamical models' reforecasts is investigated. On seasonal time scales, the North American Multi-Model Ensemble (NMME) models show skillful predictions over the eastern North Pacific, North America and the western North Atlantic with at least 5 months lead. The prediction skill is highly related to El Nino-Southern Oscillation (ENSO), as using the ENSO-related SST pattern gives rise to prediction skill with very similar spatial pattern and amplitude. On subseasonal time scales, models in the Seasonal to Sub-seasonal Prediction (S2S) dataset have skillful predictions upmore » to 4 weeks lead over regions from the eastern North Pacific to the western Atlantic, as well as northern Europe, the eastern Atlantic and East Asia. Generally, forecast skill improves with a larger ensemble size. The subseasonal prediction skill from the Pacific to the western Atlantic is related to ENSO, and that over eastern Atlantic, Europe and East Asia are associated with stratospheric polar vortex anomalies. Current models do not show much skill from the Madden-Julian Oscillation (MJO), as the MJO impact on extratropical cyclone activity is not well captured by the models. European Centre for Medium-Range Weather Forecasts (ECMWF) model has the highest single model subseasonal prediction skill. The prediction skill in the ECMWF model is higher than its estimated potential predictability, likely because the signal-to-noise ratio is too low in the model hindcasts.« less

Ten–year warm–season (May–August) observations at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are used to examine the large–scale atmospheric moisture budget and the strength of land–atmosphere coupling. Atmospheric moisture budget components, computed from the ARM variational analysis data, are compared between different wet/dry scenarios and convection regimes. Consistent with previous studies, large–scale moisture flux convergence dominates the SGP regional moisture budget on the daily time scale, but surface evaporation does play a more prominent role on late–afternoon deep convection days. Moreover, the relationship between moisture flux convergence and precipitation increases significantly from dry year to wet years.more » The land–atmosphere coupling strength is quantified for local convective events within the “Local L–A Coupling Process Chain” (Santanello et al., 2018, https://doi.org/10.1175/BAMS–D–17–0001.1). The impact of land surface on the evolution of planetary boundary layer is highly dependent on the vegetation leaf area index. A significantly larger surface sensible heat flux is found over the SGP forest region in midsummer, which is accompanied by a much higher planetary boundary layer development and a large increase in shallow cumulus cloud fraction. Here the significant relationship between afternoon precipitation and surface turbulent flux only exists in the northeast part of the ARM SGP site, which is mainly covered by grassland/pasture.« less

This study analyzes the summertime precipitation bias over the Central United States and its relationship to the simulated large-scale environment and the convection scheme in the Energy Exascale Earth System Model Atmosphere Model version 1. This relationship is mainly examined in a set of short-term hindcasts initialized with realistic large-scale conditions for the summer of 2011. Besides the uniform 1° model resolution, we adopt Regionally Refined Meshes to increase the model resolution to 0.25°over the contiguous United States. Additional five-year Atmospheric Model Intercomparison Project simulations are conducted to confirm that the results from the hindcasts are consistent with the climatemore » runs. We find that the summertime dry precipitation bias over the Great Plains and the wet bias over the Rockies cannot be reduced simultaneously by changing resolution or tuning parameters. As for the diurnal cycle, Energy Exascale Earth System Model Atmosphere Model version 1 captures the general diurnal variation of the large-scale moisture transport and the large-scale upward motion over the Central United States. However, the diurnal cycle of precipitation over the Great Plains is out of phase with the diurnal variation of the large-scale environment because the convective precipitation dominates the total precipitation and its diurnal cycle, and it does not directly respond to the local moisture convergence and the large-scale upward motion. These results reemphasize the importance of improving the coupling of the convection to the large-scale environment in reducing the summer precipitation bias over the Central United States in climate models with the resolution of ~0.25°.« less

Differences in the surface turbulent heat fluxes measured by the eddy correlation flux measurement system (ECOR) and the energy balance Bowen ratio system (EBBR) are examined using 12 years of continuous measurements collected at multiple stations in the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) surface observational network. The flux measurements are found strongly impacted by their upwind surface types, which vary with wind direction. For collocated ECOR and EBBR at the central facility, the fluxes measured by ECOR and EBBR agree much better when their upwind fetches are over the same surface type. Among all stations atmore » SGP, ECOR measures more over winter wheat fields while EBBR measures mostly over grassland. The different seasonality of growth cycles between winter wheat and grass causes systematic differences in measured fluxes between ECOR and EBBR. These differences impact the derived large-scale forcing as illustrated in a constrained variational analysis, in which the state variables have to be adjusted according to different fluxes to keep the column-integrated energy and moisture budgets in balance. This impact prevails in summertime on nonprecipitating days. A single-column model test shows that model-simulated boundary layer development is impacted by using the large-scale forcing data of different surface turbulent fluxes. It is recommended to include both ECOR and EBBR measurements to better represent the domain-mean turbulent fluxes and atmospheric budgets of energy and water vapor at the SGP.« less