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Michael Barlage and Xubin Zeng

Abstract

Accurate modeling of surface processes requires a specification of the amount of land covered by vegetation. The National Center for Atmospheric Research Community Land Model (CLM2) does not realistically represent sparsely vegetated regions because of a lack of bare soil in the model. In this study, the existing CLM2 surface dataset is replaced by a global 1-km fractional vegetation cover dataset. This results in a doubling of global bare soil fraction in the model. It also significantly affects the fractional coverages of shrub, grass, and crop compared with only minor changes to trees. Regional changes occur most greatly in Australia, with an increase of over 0.4 in bare soil fraction. The western United States, southern South America, and southern Africa show fractional increases of more than 0.2. Simulations of CLM2 coupled with the Community Atmosphere Model (CAM2) show several regions with statistically significant decreases of up to 2 K in 2-m air temperature and up to 10 K in ground temperature, which reduces the high temperature bias in arid and semiarid regions in the model. In Australia, the vegetation changes result in an increase in net downward longwave radiation, which is balanced by an increase of latent and sensible heat fluxes and a decrease of absorbed solar radiation.

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Wen-Ying Wu, Zong-Liang Yang, and Michael Barlage

Abstract

Texas is subject to severe droughts, including the record-breaking one in 2011. To investigate the critical hydrometeorological processes during drought, we use a land surface model, Noah-MP, to simulate water availability and investigate the causes of the record drought. We conduct a series of experiments with runoff schemes, vegetation phenology, and plant rooting depth. Observation-based terrestrial water storage, evapotranspiration, runoff, and leaf area index are used to compare with results from the model. Overall, the results suggest that using different parameterizations can influence the modeled water availability, especially during drought. The drought-induced vegetation responses not only interact with water availability but also affect the ground temperature. Our evaluation shows that Noah-MP with a groundwater scheme produces a better temporal relationship in terrestrial water storage compared with observations. Leaf area index from dynamic vegetation is better simulated in wet years than dry years. Reduction of positive biases in runoff and reduction of negative biases in evapotranspiration are found in simulations with groundwater, dynamic vegetation, and deeper rooting zone depth. Multiparameterization experiments show the uncertainties of drought monitoring and provide a mechanistic understanding of disparities in dry anomalies.

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Yingsha Jiang, Fei Chen, Yanhong Gao, Michael Barlage, and Jianduo Li

Abstract

Snow cover in the Qinghai–Tibet Plateau (QTP) is a critical component in the water cycle and regional climate of East Asia. Fractional snow cover (FSC) derived from five satellite sources [the three satellites comprising the multisensor synergy of FengYun-3 (FY-3A/B/C), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Interactive Multisensor Snow and Ice Mapping System (IMS)] were intercompared over the QTP to examine uncertainties in mean snow cover and interannual variability over the last decade. A four-step cloud removal procedure was developed for MODIS and FY-3 data, which effectively reduced the cloud percentage from about 40% to 2%–3% with an error of about 2% estimated by a random sampling method. Compared to in situ snow-depth observations, the cloud-removed FY-3B data have an annual classification accuracy of about 94% for both 0.04° and 0.01° resolutions, which is higher than other datasets and is recommended for use in QTP studies. Among the five datasets analyzed, IMS has the largest snow extent (22% higher than MODIS) and the highest FSC (4.7% higher than MODIS), while the morning-overpass MODIS and FY-3A/C FSC are similar and are around 5% higher than the afternoon-overpass FY-3B FSC. Contrary to MODIS, IMS shows increasing variability in snow cover and snow duration over the last decade (2006–17). Differences in variabilities of FSC and snow duration between products are greater at 5–6 km than lower elevations, with seasonal snow-cover change showing the largest uncertainty in snowmelt date.

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Margaret A. LeMone, Bingcheng Wan, Michael Barlage, and Fei Chen

Abstract

During the 2010 Bio–Hydro–Atmosphere Interactions of Energy, Aerosols, Carbon, H2O, and Nitrogen (BEACHON) experiment in Colorado, nighttime temperatures over a site within the 2002 “Hayman” fire scar were considerably warmer than over the “Manitou” site that was located outside the fire scar. Temperature differences reached up to 7 K at the surface and extended to an average of 500 m AGL. Afternoon temperatures through the planetary boundary layer (PBL) were similar at the two locations. PBL growth during the day was more rapid at Manitou until 1300 local time, after which the two daytime PBLs had similar temperatures and depths. Observations were taken in fair weather, with weak winds. Runs of the Advanced Research version of the Weather Research and Forecasting model (ARW-WRF) coupled to the Noah-MP land surface model suggest that the fire-induced loss of surface and soil organic matter accounted for the 3–4-K warming at Hayman relative to its prefire state, more than compensating for the cooling due to the fire-induced change in vegetation from forest to grassland. Modeled surface fluxes and soil temperature and moisture changes were consistent with observational studies comparing several-year-old fire scars with adjacent unaffected forests. The remaining difference between the two sites is likely from cold-air pooling at Manitou. It was necessary to increase vertical resolution and replace terrain-following diffusion with horizontal diffusion in ARW-WRF to better capture nighttime near-surface temperature and winds. Daytime PBL growth and afternoon temperature profiles were reasonably reproduced by the basic run with postfire conditions. Winds above the surface were only fairly represented, and refinements made to capture cold pooling degraded daytime temperature profiles slightly.

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Shu-Wen Zhang, Xubin Zeng, Weidong Zhang, and Michael Barlage

Abstract

Previous studies have demonstrated that soil moisture in the top layers (e.g., within the top 1-m depth) can be retrieved by assimilating near-surface soil moisture observations into a land surface model using ensemble-based data assimilation algorithms. However, it remains a challenging issue to provide good estimates of soil moisture in the deep layers, because the error correlation between the surface and deep layers is low and hence is easily influenced by the physically limited range of soil moisture, probably resulting in a large noise-to-signal ratio. Furthermore, the temporally correlated errors between the surface and deep layers and the nonlinearity of the system make the retrieval even more difficult. To tackle these problems, a revised ensemble-based Kalman filter covariance method is proposed by constraining error covariance estimates in deep layers in two ways: 1) explicitly using the error covariance at the previous time step and 2) limiting the increase of the soil moisture error correlation with the increase of the vertical distance between the two layers. This method is then tested at three separate point locations representing different precipitation regimes. It is found that the proposed method can effectively control the abrupt changes of error covariance estimates between the surface layer and two deep layers. It significantly improves the estimates of soil moisture in the two deep layers with daily updating. For example, relative to the initial background error, after 150 daily updates, the error in the deepest layer reduces to 11.4%, 32.3%, and 27.1% at the wet, dry, and medium wetness locations, only reducing to 62.3%, 80.8%, and 47.5% with the original method, respectively. However, the improvement of deep-layer soil moisture retrieval is very slight when the updating frequency is reduced to once every three days.

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Guo Zhang, Guangsheng Zhou, Fei Chen, Michael Barlage, and Lulin Xue

Abstract

It is still a daunting challenge for land surface models (LSMs) to correctly represent surface heat exchange for water-limited desert steppe ecosystems. This study aims to improve the ability of the Noah LSM to simulate surface heat fluxes through addressing uncertainties in precipitation forcing conditions, rapidly evolving vegetation properties, soil hydraulic properties (SHPs), and key parameterization schemes. Three years (2008–10) of observed surface heat fluxes and soil temperature over a desert steppe site in Inner Mongolia, China, are used to verify model simulations. The proper seasonal distribution of precipitation, along with more realistic vegetation parameters, can improve the simulation of sensible heat flux (SH) and the seasonal variability of latent heat flux. Correctly representing the low-surface exchange coefficient is crucial for improving SH for short vegetation like this desert steppe site. Relating C zil, the coefficient in the Noah surface exchange coefficient calculation, with canopy height h improves the simulated SH and the diurnal range of soil temperature over the simulation compared with using the default constant C zil. The exponential water stress formulation proposed here for the Jarvis scheme improves the partitioning between soil evaporation and transpiration. It is found that the surface energy fluxes are very sensitive to SHPs. This study highlights the important role of the proper parameter values and appropriate parameterizations for the surface exchange coefficient and water stress function in canopy resistance in capturing the observed surface energy fluxes and soil temperature variations for this desert steppe site.

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Christine Wiedinmyer, Michael Barlage, Mukul Tewari, and Fei Chen

Abstract

Physical characteristics of forests and other ecosystems control land–atmosphere exchanges of water and energy and partly dictate local and regional meteorology. Insect infestation and resulting forest dieback can alter these characteristics and, further, modify land–atmosphere exchanges. In the past decade, insect infestation has led to large-scale forest mortality in western North America. This study uses a high-resolution mesoscale meteorological model coupled with a detailed land surface model to investigate the sensitivity of near-surface variables to insect-related forest mortality. The inclusion of this land surface disturbance in the model increased in simulated skin temperature by as much as 2.1 K. The modeled 2-m temperature increased an average of 1 K relative to the default simulations. A latent to sensible heat flux shift with a magnitude of 10%–15% of the available energy in the forested ecosystem was predicted after the inclusion of insect infestation and forest dieback. Although results were consistent across multiple model configurations, the characteristics of forests affected by insect infestations must be better constrained to more accurately predict their impacts. Despite the limited duration of the simulations (one week), these initial results suggest the importance of including large-scale forest mortality due to insect infestation in meteorological models and highlight the need for better observations of the characteristics and exchanges of these disturbed landscapes.

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Nicholas Dawson, Patrick Broxton, Xubin Zeng, Michael Leuthold, Michael Barlage, and Pat Holbrook

Abstract

Snow plays a major role in land–atmosphere interactions, but strong spatial heterogeneity in snow depth (SD) and snow water equivalent (SWE) makes it challenging to evaluate gridded snow quantities using in situ measurements. First, a new method is developed to upscale point measurements into gridded datasets that is superior to other tested methods. It is then utilized to generate daily SD and SWE datasets for water years 2012–14 using measurements from two networks (COOP and SNOTEL) in the United States. These datasets are used to evaluate daily SD and SWE initializations in NCEP global forecasting models (GFS and CFSv2, both on 0.5° × 0.5° grids) and regional models (NAM on 12 km × 12 km grids and RAP on 13 km × 13 km grids) across eight 2° × 2° boxes. Initialized SD from three models (GFS, CFSv2, and NAM) that utilize Air Force Weather Agency (AFWA) SD data for initialization is 77% below the area-averaged values, on average. RAP initializations, which cycle snow instead of using the AFWA SD, underestimate SD to a lesser degree. Compared with SD errors, SWE errors from GFS, CFSv2, and NAM are larger because of the application of unrealistically low and globally constant snow densities. Furthermore, the widely used daily gridded SD data produced by the Canadian Meteorological Centre (CMC) are also found to underestimate SD (similar to GFS, CFSv2, and NAM), but are worse than RAP. These results suggest an urgent need to improve SD and SWE initializations in these operational models.

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Anil Kumar, Fei Chen, Michael Barlage, Michael B. Ek, and Dev Niyogi

Abstract

The impact of 8-day-averaged data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor—namely, the 1-km leaf area index, absorbed photosynthetic radiation, and land-use data—is investigated for use in the Weather Research and Forecasting (WRF) model for regional weather prediction. These high-resolution, near-real-time MODIS data are hypothesized to enhance the representation of land–atmosphere interactions and to potentially improve the WRF model forecast skill for temperature, surface moisture, surface fluxes, and soil temperature. To test this hypothesis, the impact of using MODIS-based land surface data on surface energy and water budgets was assessed within the “Noah” land surface model with two different canopy-resistance schemes. An ensemble of six model experiments was conducted using the WRF model for a typical summertime episode over the U.S. southern Great Plains that occurred during the International H2O Project (IHOP_2002) field experiment. The six model experiments were statistically analyzed and showed some degree of improvement in surface latent heat flux and sensible heat flux, as well as surface temperature and moisture, after land use, leaf area index, and green vegetation fraction data were replaced by remotely sensed data. There was also an improvement in the WRF-simulated temperature and boundary layer moisture with MODIS data in comparison with the default U.S. Geological Survey land-use and leaf area index inputs. Overall, analysis suggests that recalibration and improvements to both the input data and the land model help to improve estimation of surface and soil parameters and boundary layer moisture and led to improvement in simulating convection in WRF runs. Incorporating updated land conditions provided the most notable improvements, and the mesoscale model performance could be further enhanced when improved land surface schemes become available.

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Keith M. Hines, David H. Bromwich, Le-Sheng Bai, Michael Barlage, and Andrew G. Slater

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A version of the state-of-the-art Weather Research and Forecasting model (WRF) has been developed for use in polar climates. The model known as “Polar WRF” is tested for land areas with a western Arctic grid that has 25-km resolution. This work serves as preparation for the high-resolution Arctic System Reanalysis of the years 2000–10. The model is based upon WRF version 3.0.1.1, with improvements to the Noah land surface model and snow/ice treatment. Simulations consist of a series of 48-h integrations initialized daily at 0000 UTC, with the initial 24 h taken as spinup for atmospheric hydrology and boundary layer processes. Soil temperature and moisture that have a much slower spinup than the atmosphere are cycled from 48-h output of earlier runs. Arctic conditions are simulated for a winter-to-summer seasonal cycle from 15 November 2006 to 1 August 2007. Simulation results are compared with a variety of observations from several Alaskan sites, with emphasis on the North Slope. Polar WRF simulation results show good agreement with most near-surface observations. Warm temperature biases are found for winter and summer. A sensitivity experiment with reduced soil heat conductivity, however, improves simulation of near-surface temperature, ground heat flux, and soil temperature during winter. There is a marked deficit in summer cloud cover over land with excessive incident shortwave radiation. The cloud deficit may result from anomalous vertical mixing of moisture by the turbulence parameterization. The new snow albedo parameterization for WRF 3.1.1 is successfully tested for snowmelt over the North Slope of Alaska.

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