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Zhang, Minghua

Publications and source records attributed to Zhang, Minghua.

At least 37 records · Page 2

Subseasonal to Seasonal Prediction of Wintertime Northern Hemisphere Extratropical Cyclone Activity by S2S and NMME Models

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 up 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.

54 ENVIRONMENTAL SCIENCES↗

Description of the ARM Large-Scale Forcing Data from the Constrained Variational Analysis (VARANAL) Version 2

This technical report represents an update of the previous technical report written by Zhang et al. (2001a) (available at http://www.arm.gov/publications/tech_reports/arm-tr-005.pdf), which described the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility constrained variational analysis (VARANAL) that is used to develop the large-scale forcing data for driving single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LES). The VARANAL algorithm was originally developed by Zhang and Lin (1997) and Zhang et al. (2001b) at Stony Brook University and was migrated to the Lawrence Livermore National Laboratory (LLNL) as the ARM operational objective analysis system in May 1999. Since then, the algorithm has been evolved with time along with the availability of new observations and techniques to meet various modeling needs. Major updates include: (1) The method used to develop multi-year continuous forcing data (Xie et al. 2004), (2) The incorporation of eddy correlation flux measurement system (ECOR) turbulent fluxes into the analysis (Tang et al. 2019), and (3) Improvements to the workflow (e.g., implementing part of the code into the ARM Data Integrator [ADI]) to increase efficiency. The ARM large-scale forcing data have been widely used for SCM/CRM/LES to understand and improve physical processes in models. Zhang et al. (2016) has provided a comprehensive review of the SCM concept, early efforts to derive forcing data for SCM studies, efforts of the ARM constrained variational analysis, and previous SCM studies using ARM cases. This technical report focuses on the constrained variational analysis algorithm and the introduction of the ARM VARANAL products.

58 GEOSCIENCES↗

Regional Moisture Budget and Land-Atmosphere Coupling Over the U.S. Southern Great Plains Inferred From the ARM Long-Term Observations

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. 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.

58 GEOSCIENCES↗

IPEAT+: A Built-In Optimization and Automatic Calibration Tool of SWAT+

For almost 30 years, the Soil and Water Assessment Tool (SWAT) has been successfully implemented to address issues around various scientific subjects in the world. On the other hand, it has been reaching to the limit of potential flexibility in further development by the current structure. The new generation SWAT, dubbed SWAT+, was released recently with entirely new coding features. SWAT+ is designed to have far more advanced functions and capacities to handle challenging watershed modeling tasks for hydrologic and water quality processes. However, it is still inevitable to conduct model calibration before the SWAT+ model is applied to engineering projects and research programs. The primary goal of this study is to develop an open-source, easy-to-operate automatic calibration tool for SWAT+, dubbed IPEAT+ (Integrated Parameter Estimation and Uncertainty Analysis Tool Plus). There are four major advantages: (i) Open-source code to general users; (ii) compiled and integrated directly with SWAT+ source code as a single executable; (iii) supported by the SWAT developer group; and, (iv) built with efficient optimization technique. The coupling work between IPEAT+ and SWAT+ is fairly simple, which can be conducted by users with minor efforts. IPEAT+ will be regularly updated with the latest SWAT+ revision. If users would like to integrate IPEAT+ with various versions of SWAT+, only few lines in the SWAT+ source code are required to be updated. IPEAT+ is the first automatic calibration tool integrated with SWAT+ source code. Users can take advantage of the tool to pursue more cutting-edge and forward-thinking scientific questions.

54 ENVIRONMENTAL SCIENCES↗

Description of the ARM Large-Scale Forcing Data from the Constrained Variational Analysis (VARANAL) Version 2

This technical report represents an update of the previous technical report written by Zhang et al., which described the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility constrained variational analysis (VARANAL) that is used to develop the large-scale forcing data for driving single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LES). The VARANAL algorithm was originally developed by Zhang and Lin and Zhang et al. at Stony Brook University and was migrated to the Lawrence Livermore National Laboratory (LLNL) as the ARM operational objective analysis system in May 1999. Since then, the algorithm has been evolved with time along with the availability of new observations and techniques to meet various modeling needs. Major updates include: 1. The method used to develop multi-year continuous forcing data, 2. The incorporation of eddy correlation flux measurement system (ECOR) turbulent fluxes into the analysis, and 3. Improvements to the workflow (e.g., implementing part of the code into the ARM Data Integrator [ADI]) to increase efficiency. The ARM large-scale forcing data have been widely used for SCM/CRM/LES to understand and improve physical processes in models. Zhang et al. has provided a comprehensive review of the SCM concept, early efforts to derive forcing data for SCM studies, efforts of the ARM constrained variational analysis, and previous SCM studies using ARM cases. This technical report focuses on the constrained variational analysis algorithm and the introduction of the ARM VARANAL products. We also extended the VARANAL algorithm into a three-dimensional constrained variational analysis (3DCVA) and designed an ensemble framework to address the forcing uncertainty. The 3D large-scale forcing data are released as another datastream named “varanal3d”. Please refer to the ARM varanal3d technical report for more information.

54 ENVIRONMENTAL SCIENCES↗

CMDV-RRM: Representation of Clouds and Convection across scales in E3SM

In order to address mission-relevant questions, DOE’s Energy Exascale Earth System Model (E3SM) is pushing the limits of horizontal resolution among global Earth System Models (ESMs). The atmospheric resolution in the recently released E3SMv1 is 110 km in the standard configuration, and 25 km in the high-resolution configuration. Furthermore, E3SM is fairly unique among global ESMs with a capability for multiresolution modeling using refined grids in most of its components (atmosphere, land, ocean, sea-ice). This Regionally Refined Model (RRM) capability is critical for future E3SM simulation campaigns that have a strong regional focus to meet DOE's needs in support of energy-sector decisions. RRM grids are also particularly well suited for model evaluation and process-level studies taking advantage of the vast amount of ASR site data collected over the past several decades. The overarching goal of this project was to understand and improve the behavior of clouds and convection in E3SM across these scales using RRM. Key deliverables that resulted from the project are summarized below, with more details provided in the subsequent sections of the report.

54 ENVIRONMENTAL SCIENCES↗

Improved Diurnal Cycle of Precipitation in E3SM With a Revised Convective Triggering Function

Abstract We revise the convective triggering function in Department of Energy's Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1) by introducing a dynamic constraint on the initiation of convection that emulates the collective dynamical effects to prevent convection from being triggered too frequently and allowing air parcels to launch above the boundary layer to capture nocturnal elevated convection. The former is referred to as the dynamic Convective Available Potential Energy (dCAPE) trigger and the latter as the Unrestricted Launch Level (ULL) trigger. Compared to the original trigger in EAMv1 that initiates convection whenever CAPE is larger than a threshold, the revised trigger substantially improves the simulated diurnal cycle of precipitation over both midlatitude and tropical lands. The nocturnal peak of precipitation and the eastward propagation of convection downstream of the Rockies and over the adjacent Great Plains are much better captured than those in the default model. The overall impact on mean precipitation is minor with some notable improvements over the Indo‐Western Pacific, subtropical Pacific and Atlantic, and South America. In general, the dCAPE trigger helps to better capture late afternoon rainfall peak, while ULL is key to capturing nocturnal elevated convection and the eastward propagation of convection. The dCAPE trigger also primarily contributes to the considerable reduction of convective precipitation over subtropical regions and the frequency of light‐to‐moderate precipitation occurrence. However, no clear improvement is seen in intense convection and the amplitude of diurnal precipitation.

54 ENVIRONMENTAL SCIENCES↗

Prediction of Tropical Cyclone Genesis from Mesoscale Convective Systems using Machine Learning

Tropical cyclone (TC) genesis is a problem of great significance in climate and weather research. Although various environmental conditions necessary for TC genesis have been recognized for a long time, prediction of TC genesis remains a challenge due to complex and stochastic processes involved during TC genesis. Different from traditional statistical and dynamical modeling of TC genesis, in this research, a machine learning framework is developed to determine whether a mesoscale convective system (MCS) would evolve into a tropical cyclone. The machine learning models 1) are built upon a number of essential environmental predictors associated with MCS/TC, 2) predict whether MCSs can become TCs at different lead times, 3) provide information about the relative importance of each predictor, which can be conducive to discovering new aspects of TC genesis. The results indicate that the machine learning classifier, AdaBoost, is able to achieve a 97.2% F1-score accuracy in predicting TC genesis over the entire tropics at a 6-hour lead time using a comprehensive set of environmental predictors. A robust performance can still be attained when the lead time is extended to 12 hours, 24 hours, and 48 hours, and when this machine learning classifier is separately applied to the North Atlantic Ocean and the Western North Pacific Ocean. In contrast, the conventional approach based on genesis potential index can only have no more than 80% F1-score accuracy. Moreover, the machine learning classifier suggests that the low-level vorticity and genesis potential index are the most important predictors to TC genesis, which is consistent with previous discoveries.

54 ENVIRONMENTAL SCIENCES↗

Adaptive Management Using Remote Sensing and Ecosystem Modeling in Response to Climate Variability and Invasive Aquatic Plants for the California Sacramento-San Joaquin Delta Water Resource

The California Sacramento-San Joaquin River Delta is the hub for California's water supply and supports important ecosystem services, agriculture, and communities in Northern to Southern California. Expansion of invasive aquatic plants in the Delta coupled with impacts of changing climate and long-term drought is detrimental to the San Francisco Bay/California Delta complex. NASA Ames Research Center and the USDA-ARS partnered with the State of California to develop science-based, adaptive-management strategies for invasive aquatic plant in the Sacramento-San Joaquin Delta. Specific mapping tools developed utilizing satellite and airborne platforms provide regular assessments of population dynamics on a landscape scale and support both strategic planning and operational decision making for resource managers. San Joaquin and Sacramento River watersheds water quality input to the Delta is modeled using the Soil-Water Assessment Tool (SWAT) and a modified SWAT tool has been customized to account for unique landscape and management of agricultural water supply and drainage within the Delta. Environmental response models for growth of invasive aquatic weeds are being parameterized and coupled with spatial distribution/biomass density mapping and water quality to study ecosystem response to climate and aquatic plant management practices. On the water validation and operational utilization of these tools by management agencies and how they are improving decision making, management effectiveness and efficiency will be discussed. The project combines science, operations, and economics related to integrated management scenarios for aquatic weeds to help land and water resource managers make science-informed decisions regarding management and outcomes.

Adaptive↗

Watershed-Scale Modeling of Land-Use and Altered Environment Impacts on Aquatic Weed Growth in the Delta

The California Sacramento-San Joaquin River Delta is the hub for California's water supply, conveying water from Northern to Southern California agriculture and communities while supporting important ecosystem services, agriculture, and communities in the Delta. Changes in climate, long-term drought, and water quality have all been suspected as playing role in the dramatic expansion of invasive aquatic plants and their impact on ecosystems of the San Francisco Bay / California Delta complex. NASA Ames Research Center, USDA-Agricultural Research Service, the State of California, UC Davis, and local governments have partnered under a USDA sponsored project (DRAAWP) to develop science-based, adaptive-management strategies for invasive aquatic plants in Sacramento-San Joaquin Delta. Critical to developing management strategies is to understand how the Delta is affected by both the magnitude of fluctuations in land-use and climate / drought induced altered environments and how the plants respond to these altered environments. We utilize the Soil Water Assessment Tool (SWAT), a watershed-scale model developed to quantify the impact of land management practices in large and complex watersheds on water quality, as the backbone for a customized Delta model - Delta-SWAT. The model uses land-use, soils, elevation, and hydrologic routing to characterize pesticide and nutrient transport from the Sacramento and San Joaquin rivers watersheds and loading into the Delta. Land-use within the Delta, as well as water extraction to supply those functions, and the resulting return of water to Delta waterways are included in Delta-SWAT. Hydrologic transport within the Delta has required significant attention to address the lack of elevation driven transport processes. Delta-SWAT water quality trend estimates are compared with water quality monitoring conducted throughout the Delta. Aquatic plant response to water quality and other environmental factors is carried out using a customized model component. Plant response to the range of water quality factors, response times, and altered temperature and light regimes of the Delta have required gap-filling studies to provide model parameters. Delta-SWAT provides a tool for evaluating temporal and spatial effects of land-use and altered environments in the Delta and contributing watersheds on aquatic weed growth. Using Delta-SWAT for simulation modeling allows evaluation of historic and current conditions as well as consideration potential climate change and management practice outcomes. Delta-SWAT adds to the scientific understanding of dynamics in the Delta and enhances development of science-informed, management strategies and practices.

Ecosystem↗

RACORO Continental Boundary Layer Cloud Investigations: 1. Case Study Development and Ensemble Large-Scale Forcings

Observation-based modeling case studies of continental boundary layer clouds have been developed to study cloudy boundary layers, aerosol influences upon them, and their representation in cloud- and global-scale models. Three 60 h case study periods span the temporal evolution of cumulus, stratiform, and drizzling boundary layer cloud systems, representing mixed and transitional states rather than idealized or canonical cases. Based on in situ measurements from the Routine AAF (Atmospheric Radiation Measurement (ARM) Aerial Facility) CLOWD (Clouds with Low Optical Water Depth) Optical Radiative Observations (RACORO) field campaign and remote sensing observations, the cases are designed with a modular configuration to simplify use in large-eddy simulations (LES) and single-column models. Aircraft measurements of aerosol number size distribution are fit to lognormal functions for concise representation in models. Values of the aerosol hygroscopicity parameter, kappa, are derived from observations to be approximately 0.10, which are lower than the 0.3 typical over continents and suggestive of a large aerosol organic fraction. Ensemble large-scale forcing data sets are derived from the ARM variational analysis, European Centre for Medium-Range Weather Forecasts, and a multiscale data assimilation system. The forcings are assessed through comparison of measured bulk atmospheric and cloud properties to those computed in "trial" large-eddy simulations, where more efficient run times are enabled through modest reductions in grid resolution and domain size compared to the full-sized LES grid. Simulations capture many of the general features observed, but the state-of-the-art forcings were limited at representing details of cloud onset, and tight gradients and high-resolution transients of importance. Methods for improving the initial conditions and forcings are discussed. The cases developed are available to the general modeling community for studying continental boundary clouds.

Boundary layers↗

RACORO Continental Boundary Layer Cloud Investigations: 3. Separation of Parameterization Biases in Single-Column Model CAM5 Simulations of Shallow Cumulus

Climatically important low-level clouds are commonly misrepresented in climate models. The FAst-physics System TEstbed and Research (FASTER) Project has constructed case studies from the Atmospheric Radiation Measurement Climate Research Facility's Southern Great Plain site during the RACORO aircraft campaign to facilitate research on model representation of boundary-layer clouds. This paper focuses on using the single-column Community Atmosphere Model version 5 (SCAM5) simulations of a multi-day continental shallow cumulus case to identify specific parameterization causes of low-cloud biases. Consistent model biases among the simulations driven by a set of alternative forcings suggest that uncertainty in the forcing plays only a relatively minor role. In-depth analysis reveals that the model's shallow cumulus convection scheme tends to significantly under-produce clouds during the times when shallow cumuli exist in the observations, while the deep convective and stratiform cloud schemes significantly over-produce low-level clouds throughout the day. The links between model biases and the underlying assumptions of the shallow cumulus scheme are further diagnosed with the aid of large-eddy simulations and aircraft measurements, and by suppressing the triggering of the deep convection scheme. It is found that the weak boundary layer turbulence simulated is directly responsible for the weak cumulus activity and the simulated boundary layer stratiform clouds. Increased vertical and temporal resolutions are shown to lead to stronger boundary layer turbulence and reduction of low-cloud biases.

parameterization↗

CGILS: Results from the First Phase of an International Project to Understand the Physical Mechanisms of Low Cloud Feedbacks in Single Column Models

1] CGILS-the CFMIP-GASS Intercomparison of Large Eddy Models (LESs) and single column models (SCMs)-investigates the mechanisms of cloud feedback in SCMs and LESs under idealized climate change perturbation. This paper describes the CGILS results from 15 SCMs and 8 LES models. Three cloud regimes over the subtropical oceans are studied: shallow cumulus, cumulus under stratocumulus, and well-mixed coastal stratus/stratocumulus. In the stratocumulus and coastal stratus regimes, SCMs without activated shallow convection generally simulated negative cloud feedbacks, while models with active shallow convection generally simulated positive cloud feedbacks. In the shallow cumulus alone regime, this relationship is less clear, likely due to the changes in cloud depth, lateral mixing, and precipitation or a combination of them. The majority of LES models simulated negative cloud feedback in the well-mixed coastal stratus/stratocumulus regime, and positive feedback in the shallow cumulus and stratocumulus regime. A general framework is provided to interpret SCM results: in a warmer climate, the moistening rate of the cloudy layer associated with the surface-based turbulence parameterization is enhanced; together with weaker large-scale subsidence, it causes negative cloud feedback. In contrast, in the warmer climate, the drying rate associated with the shallow convection scheme is enhanced. This causes positive cloud feedback. These mechanisms are summarized as the "NESTS" negative cloud feedback and the "SCOPE" positive cloud feedback (Negative feedback from Surface Turbulence under weaker Subsidence-Shallow Convection PositivE feedback) with the net cloud feedback depending on how the two opposing effects counteract each other. The LES results are consistent with these interpretations

Single Column Models↗

ARM/GCSS/SPARC TWP-ICE CRM Intercomparison Study

Specifications are provided for running a cloud-resolving model (CRM) and submitting results in a standardized format for inclusion in a n intercomparison study and archiving for public access. The simulated case study is based on measurements obtained during the 2006 Tropical Warm Pool - International Cloud Experiment (TWP-ICE) led by the U. S. department of Energy Atmospheric Radiation Measurement (ARM) program. The modeling intercomparison study is based on objectives developed in concert with the Stratospheric Processes And their Role in Climate (SPARC) program and the GEWEX cloud system study (GCSS) program. The Global Energy and Water Cycle Experiment (GEWEX) is a core project of the World Climate Research PRogramme (WCRP).

Fridlind, Ann↗

Diagnosing the Ice Crystal Enhancement Factor in the Tropics

Recent modeling studies have revealed that ice crystal number concentration is one of the dominant factors in the effect of clouds on radiation. Since the ice crystal enhancement factor and ice nuclei concentration determine the concentration, they are both important in quantifying the contribution of increased ice nuclei to global warming. In this study, long-term cloud-resolving model (CRM) simulations are compared with field observations to estimate the ice crystal enhancement factor in tropical and midlatitudinal clouds, respectively. It is found that the factor in tropical clouds is ~10 3-104 times larger than that of mid-latitudinal ones, which makes physical sense because entrainment and detrainment in the Tropics are much stronger than in middle latitudes. The effect of entrainment/detrainment on the enhancement factor, especially in tropical clouds, suggests that cloud microphysical parameterizations should be coupled with subgrid turbulence parameterizations within CRMs to obtain a more accurate depiction of cloud-radiative forcing.

Zeng, Xiping↗

A Contribution by Ice Nuclei to Global Warming

Ice nuclei (IN) significantly affect clouds via supercooled droplets, that in turn modulate atmospheric radiation and thus climate change. Since the IN effect is relatively strong in stratiform clouds but weak in convective ones, the overall effect depends on the ratio of stratiform to convective cloud amount. In this paper, 10 years of TRMM (Tropical Rainfall Measuring Mission) satellite data are analyzed to confirm that stratiform precipitation fraction increases with increasing latitude, which implies that the IN effect is stronger at higher latitudes. To quantitatively evaluate the IN effect versus latitude, large-scale forcing data from ten field campaigns are used to drive a CRM (cloud-resolving model) to generate longterm cloud simulations. As revealed in the simulations, the increase in the net downward radiative flux at the TOA (top of the atmosphere) from doubling the current IN concentrations is larger at higher latitude, which is attributed to the meridional tendency in the stratiform precipitation fraction. Surface warming from doubling the IN concentrations, based on the radiative balance of the globe, is compared with that from anthropogenic COZ . It is found that the former effect is stronger than the latter in middle and high latitudes but not in the Tropics. With regard to the impact of IN on global warming, there are two factors to consider: the radiative effect from increasing the IN concentration and the increase in IN concentration itself. The former relies on cloud ensembles and thus varies mainly with latitude. In contrast, the latter relies on IN sources (e.g., the land surface distribution) and thus varies not only with latitude but also longitude. Global desertification and industrialization provide clues on the geographic variation of the increase in IN concentration since pre-industrial times. Thus, their effect on global warming can be inferred and then be compared with observations. A general match in geographic and seasonal variations between the inferred and observed warming suggests that IN may have contributed positively to global warming over the past decades, especially in middle and high latitudes.

Zeng, Xiping↗

On the Sensitivity of Atmospheric Ensembles to Cloud Microphysics in Long-Term Cloud-Resolving Model Simulations

Month-long large-scale forcing data from two field campaigns are used to drive a cloud-resolving model (CRM) and produce ensemble simulations of clouds and precipitation. Observational data are then used to evaluate the model results. To improve the model results, a new parameterization of the Bergeron process is proposed that incorporates the number concentration of ice nuclei (IN). Numerical simulations reveal that atmospheric ensembles are sensitive to IN concentration and ice crystal multiplication. Two- (2D) and three-dimensional (3D) simulations are carried out to address the sensitivity of atmospheric ensembles to model dimensionality. It is found that the ensembles with high IN concentration are more sensitive to dimensionality than those with low IN concentration. Both the analytic solutions of linear dry models and the CRM output show that there are more convective cores with stronger updrafts in 3D simulations than in 2D, which explains the differing sensitivity of the ensembles to dimensionality at different IN concentrations.

Zeng, Xiping↗