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Zhongfeng Xu and Zong-Liang Yang

Abstract

An improved dynamical downscaling method (IDD) with general circulation model (GCM) bias corrections is developed and assessed over North America. A set of regional climate simulations is performed with the Weather Research and Forecasting Model (WRF) version 3.3 embedded in the National Center for Atmospheric Research's (NCAR's) Community Atmosphere Model (CAM). The GCM climatological means and the amplitudes of interannual variations are adjusted based on the National Centers for Environmental Prediction (NCEP)–NCAR global reanalysis products (NNRP) before using them to drive WRF. In this study, the WRF downscaling experiments are identical except the initial and lateral boundary conditions derived from the NNRP, original GCM output, and bias-corrected GCM output, respectively. The analysis finds that the IDD greatly improves the downscaled climate in both climatological means and extreme events relative to the traditional dynamical downscaling approach (TDD). The errors of downscaled climatological mean air temperature, geopotential height, wind vector, moisture, and precipitation are greatly reduced when the GCM bias corrections are applied. In the meantime, IDD also improves the downscaled extreme events characterized by the reduced errors in 2-yr return levels of surface air temperature and precipitation. In comparison with TDD, IDD is also able to produce a more realistic probability distribution in summer daily maximum temperature over the central U.S.–Canada region as well as in summer and winter daily precipitation over the middle and eastern United States.

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Guo-Yue Niu and Zong-Liang Yang

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The presence of ice in soil dramatically alters soil hydrologic and thermal properties. Despite this important role, many recent studies show that explicitly including the hydrologic effects of soil ice in land surface models degrades the simulation of runoff in cold regions. This paper addresses this dilemma by employing the Community Land Model version 2.0 (CLM2.0) developed at the National Center for Atmospheric Research (NCAR) and a simple TOPMODEL-based runoff scheme (SIMTOP). CLM2.0/SIMTOP explicitly computes soil ice content and its modifications to soil hydrologic and thermal properties. However, the frozen soil scheme has a tendency to produce a completely frozen soil (100% ice content) whenever the soil temperature is below 0°C. The frozen ground prevents infiltration of snowmelt or rainfall, thereby resulting in earlier- and higher-than-observed springtime runoff. This paper presents modifications to the above-mentioned frozen soil scheme that produce more accurate magnitude and seasonality of runoff and soil water storage. These modifications include 1) allowing liquid water to coexist with ice in the soil over a wide range of temperatures below 0°C by using the freezing-point depression equation, 2) computing the vertical water fluxes by introducing the concept of a fractional permeable area, which partitions the model grid into an impermeable part (no vertical water flow) and a permeable part, and 3) using the total soil moisture (liquid water and ice) to calculate the soil matric potential and hydraulic conductivity. The performance of CLM2.0/SIMTOP with these changes has been tested using observed data in cold-region river basins of various spatial scales. Compared to the CLM2.0/SIMTOP frozen soil scheme, the modified scheme produces monthly runoff that compares more favorably with that estimated by the University of New Hampshire–Global Runoff Data Center and a terrestrial water storage change that is in closer agreement with that measured by the Gravity Recovery and Climate Experiment (GRACE) satellites.

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Marla R. Knebl Lowrey and Zong-Liang Yang

Abstract

A regional-scale weather model is used to determine the potential for flood forecasting based on model-predicted rainfall. Extreme precipitation and flooding events are a significant concern in central Texas, due to both the high occurrence and severity of flooding in the area. However, many current regional prediction models do not provide sufficient accuracy at the watershed scale necessary for flood mitigation efforts. The Weather Research and Forecasting (WRF) model, created with the purpose of improving upon the current fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), is specifically designed for regional grid spacings of 1–10 km. Previous research by the authors resulted in the development of a regional-scale prediction system over the San Antonio River basin, using a geographic information system (GIS) database, a hydrologic model, and a hydraulic model. Observed precipitation drives the prediction system; the authors hypothesize that the WRF model has the potential to predict flooding, at a lead time of several days, with a level of accuracy near that of observed precipitation. Causes of model error are also investigated, to determine the relative errors caused by model physics, initialization interval, buffer zone and domain size, and small-amplitude random errors. Results show that the Betts–Miller–Janjić cumulus and Lin et al. microphysics schemes, 48-h initialization interval, and two-domain configuration covering minimal ocean and having a parent-to-nest area ratio of greater than 10 best simulates a recent (July 2002) large storm event over the San Antonio River basin. This particular storm was selected because it produced extreme rainfall volumes and intensities, and also because its meteorological characteristics are typical of central Texas storm events. Location errors in rainfall are most significant because of their typically nonlinear patterns (increasing location error does not linearly modify streamflow output). Errors in intensity and timing show a more predictable (linear) watershed response that may be useful in the estimation of streamflow ranges for flood forecasting.

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David J. Gochis, W. James Shuttleworth, and Zong-Liang Yang

Abstract

This paper describes the second part of a study to document the sensitivity of the modeled regional moisture flux patterns and hydrometeorological response of the North American monsoon system (NAMS) to convective parameterization. Use of the convective parameterization schemes of Betts–Miller–Janjic, Kain–Fritsch, and Grell was investigated during the initial phase of the 1999 NAMS using version 3.4 of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) running in a pseudoclimate mode. Substantial differences in both the stationary and transient components of the moisture flux fields were found between the simulations, resulting in differences in moisture convergence patterns, precipitation, and surface evapotranspiration. Basin-average calculations of hydrologic variables indicate that, in most of the basins for which calculations were made, the magnitude of the evaporation-minus-precipitation moisture source/sink differs substantially between simulations and, in some cases, even the sign of the source/sink changed. There are substantial differences in rainfall–runoff processes because the basin-average rainfall intensities, proportion of rainfall from convective origin, and the runoff coefficients differ between simulations. The results indicate that, in regions of sustained, deep convection, the selection of the subgrid convective parameterization in a high-resolution atmospheric model can potentially have a hydrometeorological impact in regional analyses, which is at least as important as the effect of land surface forcing.

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Weijing Chen, Chunlin Huang, Zong-Liang Yang, and Ying Zhang

Abstract

Data assimilation provides a practical way to improve the accuracy of soil moisture simulation by integrating a land surface model and satellite data. This study establishes a multisource remote sensing data assimilation framework by incorporating a simultaneous state and parameter estimation method to acquire an accurate estimation of the soil moisture over the Tibetan Plateau. The brightness temperature of the Advanced Microwave Scanning Radiometer 2 (AMSR2) is directly assimilated into the coupled system of the Common Land Model (CoLM) and a microwave radiative transfer model (RTM) to improve the soil moisture simulation. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature product and the Beijing Normal University (BNU) leaf area index product are employed to not only improve the estimation of temperature and vegetation variables from the CoLM, but they also provide more accurate background information for the RTM during the brightness temperature assimilation. In situ measurements from the Naqu network are used to evaluate the results. The model simulation showed an obvious underestimation of soil moisture and overestimation of soil temperature, which was alleviated by the assimilation experiments, particularly in the shallow soil layers. The estimated parameters also showed advantages in the soil moisture simulation when compared with the default parameters. The assimilation experiment presents promising results in the combination of model and multisource remote sensing data for estimating soil moisture over the complex mountainous region in Tibet.

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Marian Martin, Robert E. Dickinson, and Zong-Liang Yang

Abstract

Tiny openings on the surfaces of leaves, stomata, control the flux of CO2, water vapor, and other gases between the atmosphere and the earth’s vegetated surface. An increase in atmospheric CO2 could have an effect on stomatal openings, causing indirect changes in many surface hydroclimatogical variables that could be comparable in magnitude to the direct radiative effects. Increased atmospheric CO2 is expected to increase water use efficiency in many plant types because of the closure of the stomatal openings on the leaf surface. The present study assesses this stomatal effect by doubling the stomatal resistance in two land surface schemes, the Biosphere–Atmosphere Transfer Scheme and the Land Surface Model, which are coupled to the National Center for Atmospheric Research’s Community Climate Model version 3 atmospheric general circulation model, and by evaluating the resulting hydrometeorological responses, particularly for the western United States.

Because the simulated reduction of stomatal openings restricts evapotranspiration, latent heat fluxes are reduced, causing global average annual and seasonal decreases in precipitation as well as increases in sensible heat flux, surface temperatures, runoff, and root-zone soil water. Global seasonal decreases in latent heat flux of up to 7% occur, corresponding to surface temperature increases of up to 0.5°C and precipitation decreases of up to 3%. Regional responses vary.

A focus of this study was to examine how these changes affect runoff and stream flow in the southwestern United States. Contrary to a previous empirical study of this effect, which showed an 87% mean increase in Arizona basin stream flow, this coupled land surface–atmospheric model shows no significant changes in any of the variables examined for this region.

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Omer L. Sen, W. James Shuttleworth, and Zong-Liang Yang

Abstract

Over the last decade, improved understanding of plant physiological processes has generated a significant change in the way stomatal functioning is described in advanced land surface schemes. New versions of two advanced and widely used land surface schemes, the Biosphere–Atmosphere Transfer Scheme (BATS) and the Simple Biosphere Model (SiB), reflect this change in understanding, although these two models make different assumptions regarding the response of stomata to atmospheric humidity deficit. The goal of this study was to evaluate the new, second version of BATS, here called BATS2, using Amazon field data from the Anglo–Brazilian Amazonian Climate Observational Study (ABRACOS) project, with an emphasis on comparison with the original version of BATS and the new, second version of SiB (SiB2). Evaluation of SiB2 using a 3-yr time series of ABRACOS data revealed that there is an unrealistic simulation of the yearly cycle in soil moisture status, with a resulting poor simulation of evaporation. Improved long-term simulation by SiB2 requires specification of a deeper rooting depth, and this requirement is general for all three models. In general, the original version of BATS with a revised root distribution and rooting depth gave good agreement with observations of the surface energy balance but occasionally showed excessive sensitivity to large atmospheric vapor pressure deficit. Evaluation of BATS2 revealed that changes are required in the parameters that determine stomatal behavior in the model for realistic simulation of transpiration, time-averaged respiration, and net carbon dioxide (CO2) uptake. When initiated with default values for carbon stores, BATS2 takes several hundred years to reach an equilibrium carbon balance. Aspects of the model’s representation of instantaneous carbon allocation and respiration processes indicate that BATS2 cannot be expected to provide a realistic simulation of hourly variations in CO2 exchanges. In general, all three models have weaknesses when describing the field data with default values of model parameters. If a few model parameters are modified in a plausible way, however, all three models can be made to give a good time-averaged simulation of measured exchanges. There is little evidence of sensitivity to the different forms assumed for the stomatal response to atmospheric humidity deficit, although this study suggests that assuming that leaf stress is related linearly to relative humidity is marginally preferred.

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Long Zhao, Zong-Liang Yang, and Timothy J. Hoar

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Very few frameworks exist that estimate global-scale soil moisture through microwave land data assimilation (DA). Toward this goal, such a framework has been developed by linking the Community Land Model, version 4 (CLM4), and a microwave radiative transfer model (RTM) with the Data Assimilation Research Testbed (DART). The deterministic ensemble adjustment Kalman filter (EAKF) within DART is utilized to estimate global multilayer soil moisture by assimilating brightness temperature observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). A 40-member ensemble of Community Atmosphere Model, version 4.0 (CAM4.0), reanalysis is adopted to drive CLM4 simulations. Space-specific, time-invariant microwave parameters are precalibrated to minimize uncertainties in RTM. Besides, various methods are designed to upscale AMSR-E observations for computational efficiency and time shift CAM4.0 forcing to facilitate global daily assimilations. A series of experiments are conducted to quantify the DA sensitivity to microwave parameters, choice of assimilated observations, and different CLM4 updating schemes. Evaluation results indicate that the newly established CLM4–RTM–DART framework improves the open-loop CLM4-simulated soil moisture. Precalibrated microwave parameters, rather than their default values, can ensure a more robust global-scale performance. In addition, updating near-surface soil moisture is capable of improving soil moisture in deeper layers (0–30 cm), while simultaneously updating multilayer soil moisture fails to obtain intended improvements. Future work is needed to address the systematic bias in CLM4 that cannot be fully covered through the ensemble spread in CAM4.0 reanalysis.

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David J. Gochis, W. James Shuttleworth, and Zong-Liang Yang

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This paper documents the sensitivity of the modeled evolution of the North American monsoon system (NAMS) to convective parameterization in terms of thermodynamic and circulation characteristics, stability profiles, and precipitation. The convective parameterization schemes (CPSs) of Betts–Miller–Janjic, Kain–Fritsch, and Grell were tested using version 3.4 of the PSU–NCAR fifth-generation Mesoscale Model (MM5) running in a pseudoclimate mode. Model results for the initial phase of the 1999 NAM are compared with surface climate station observations and seven radiosonde sites in Mexico and the southwestern United States. The results show substantial differences in modeled precipitation, surface climate, and atmospheric stability occuring between the different model simulations, which are attributable to the representation of convection in the model. Moreover, large intersimulation differences in the low-level circulation fields are found. While none of the CPSs tested gave perfect simulation of observations everywhere in the model domain, the Kain–Fritsch scheme generally gave significantly superior estimates of surface and upper air verification error statistics.

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Qinjian Jin, Zong-Liang Yang, and Jiangfeng Wei

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The seasonal responses of the Indian summer monsoon (ISM) to dust aerosols in local (the Thar Desert) and remote (the Middle East and western China) regions are studied using the WRF Model coupled with online chemistry (WRF-Chem). Ensemble experiments are designed by perturbing model physical and chemical schemes to examine the uncertainties of model parameterizations. Model results show that the dust-induced increase in ISM total rainfall can be attributed to the remote dust in the Middle East, while the contributions from local and remote dust are very limited. Convective rainfall shows a spatially more homogeneous increase than stratiform rainfall, whose responses follow the topography. The magnitude of dust-induced increase in rainfall is comparable to that caused by anthropogenic aerosols. The Middle East dust aerosols tend to enhance the southwesterly monsoon flow, which can transport more water vapor to southern and northern India, while the anthropogenic aerosols tend to enhance the southeasterly monsoon flow, resulting in more water vapor and rainfall over northern India. Both dust and anthropogenic aerosol-induced rainfall responses can be attributed to their heating effect in the mid-to-upper troposphere, which enhances monsoon circulations. The heating effect of dust over the Iranian Plateau seems to play a bigger role than that over the Tibetan Plateau, while the heating of anthropogenic aerosols over the Tibetan Plateau is more important. Moreover, dust aerosols can decrease rainfall over the Arabian Sea through their indirect effect. This study addresses the relative roles of dust and anthropogenic aerosols in altering the ISM rainfall and provides insights into aerosol–ISM interactions.

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