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Xubin Zeng

Abstract

Vegetation root distribution is one of the factors that determine the overall water holding capacity of the land surface and the relative rates of water extraction from different soil layers for vegetation transpiration. Despite its importance, significantly different root distributions are used by different land surface models. Using a comprehensive global field survey dataset, vegetation root distribution (including rooting depth) has been developed here for three of the most widely used land cover classifications [i.e., the Biosphere–Atmosphere Transfer Scheme (BATS), International Geosphere–Biosphere Program (IGBP), and version 2 of the Simple Biosphere Model (SiB2)] for direct use by any land model with any number of soil layers.

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Xubin Zeng

Abstract

The relationship of monthly precipitation P to precipitable water w and cloud-top temperature as represented by the Geostationary Operational Environmental Satellite (GOES) Precipitation Index (GPI) is obtained over tropical land, coast, and ocean:
Pa1wa2
where coefficients a 1 and a 2 are determined using one year of the Global Precipitation Climatology Project (GPCP) monthly rain gauge data and then independently tested using four other years of gauge data. This algorithm, over land, gives more accurate precipitation estimates than are obtained using the cloud-top temperature alone (i.e., GPI) and is as accurate as the state-of-the-art multisatellite algorithm (MS) from GPCP. Over coastal and oceanic regions, this algorithm has a smaller bias in precipitation estimation than GPI but has the same correlation coefficient with gauge data as GPI. Compared with MS, it has a much smaller bias but larger mean absolute deviation. Evaluation using the Pacific atoll–island gauge data also shows that this algorithm can reproduce well the observed meridional distribution of precipitation across the ITCZ and SPCZ near the date line.

This algorithm is then used to produce a five-year (January 1988–December 1992) 2.5° × 2.5° integrated dataset of precipitation and precipitable water between 40°N and 40°S for climate model evaluation. The small bias of this algorithm (particularly over ocean) also suggests that it would be a good data source for precipitation merging algorithms.

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Xubin Zeng and Aihui Wang

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While progress has been made in the treatment of turbulence below, within, and above canopy in land models, not much attention has been paid to the convergence of canopy roughness length and displacement height to bare soil values as the above-ground biomass, or the sum of leaf and stem area indices, becomes zero. Preliminary formulations have been developed to ensure this convergence for the Community Land Model version 3 (CLM3) and are found to significantly improve the wintertime simulation of sensible heat flux (SH) compared with observational data over the Cabauw site in the Netherlands. The simulation of latent heat flux (LH) is also moderately improved. For global offline CLM3 simulations, the new formulations change SH by more than 5 W m−2 over many regions, while the change of LH is less than 1 W m−2 over most of the regions.

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Xubin Zeng and Mark Decker
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William Lytle and Xubin Zeng

Abstract

Reanalysis products are widely used to study the land–atmosphere exchanges of energy, water, and carbon fluxes and have been evaluated using in situ data above or below ground. Here, measurements for several years at five flux tower sites in the United States (with a total of 315 576 h of data) are used for the coupled evaluation of both below- and aboveground processes from three global reanalysis products and six global land data assimilation products. All products show systematic errors in precipitation, snow depth, and the timing of the melting and onset of snow. Despite the biases in soil moisture, all products show significant correlations with observed daily soil moisture for the periods with unfrozen soil. While errors in 2-m air temperature are highly correlated with errors in skin temperature for all sites, the correlations between skin and soil temperature errors are weaker, particularly over the sites with seasonal snow. While net short- and longwave radiation flux errors have opposite signs across all products, the net radiation and ground heat flux errors are usually smaller in magnitude than turbulent flux errors. On the other hand, the all-product averages usually agree well with the observations on the evaporative fraction, defined as the ratio of latent heat over the sum of latent and sensible heat fluxes. This study identifies the strengths and weaknesses of these widely used products and helps understand the connection of their errors in above- versus belowground quantities.

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Kyle Davis and Xubin Zeng

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Building upon our previous seasonal hurricane prediction model, here we develop two statistical models to predict the number of major hurricanes (MHs) and accumulated cyclone energy (ACE) in the North Atlantic basin using monthly data from March to May for an early June forecast. The input data include zonal pseudo–wind stress to the 3/2 power, sea surface temperature in the North Atlantic, and, depending on the magnitude of the Atlantic multidecadal oscillation index, the multivariate ENSO index. From 1968 to 2017, these models have a mean absolute error of 0.96 storms for MHs and 30 units for ACE. When tested over an independent period from 1958 to 1967, the models show a 22% improvement for MHs and 16% for ACE over a no-skill metric based on a 5-yr running average. Both the MH and ACE results show consistent improvements over those produced by three other centers using statistical–dynamical hybrid models and a 5-yr running average prediction over the period 2000–17 for MHs (2003–17 for ACE) in a simulated real-time prediction. These improvements vary from 25% to 37% for MHs and from 15% to 37% for ACE. While most forecasting centers called for a slightly above-average hurricane season in May/June 2017, our models predicted in June 2017 a very active season, in much better agreement with observations.

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Aihui Wang and Xubin Zeng

Abstract

Land surface air temperature (SAT) is one of the most important variables in weather and climate studies, and its diurnal cycle is also needed for a variety of applications. Global long-term hourly SAT observational data, however, do not exist. While such hourly products could be obtained from global reanalyses, they are found to be unrealistic in representing the SAT diurnal cycle.

Global hourly 0.5° SAT datasets are developed here based on four reanalysis products [Modern-Era Retrospective Analysis for Research and Applications (MERRA for 1979–2009), 40-yr ECMWF Re-Analysis (ERA-40 for 1958–2001), ECMWF Interim Re-Analysis (ERA-Interim for 1979–2009), and NCEP–NCAR reanalysis for 1948–2009)] and the Climate Research Unit Time Series version 3.10 (CRU TS3.10) for 1948–2009. The three-step adjustments include the spatial downscaling to 0.5° grid cells, the temporal interpolation from 6-hourly (in ERA-40 and NCEP–NCAR reanalysis) to hourly using the MERRA hourly SAT climatology for each day (and the linear interpolation from 3-hourly in ERA-Interim to hourly), and the bias correction in both monthly-mean maximum (Tmax) and minimum (Tmin) SAT using the CRU data.

The final products have exactly the same monthly Tmax and Tmin as the CRU data, and perform well in comparison with in situ hourly measurements over six sites and with a regional daily SAT dataset over Europe. They agree with each other much better than the original reanalyses, and the spurious SAT jumps of reanalyses over some regions are also substantially eliminated. One of the uncertainties in the final products can be quantified by their differences in the true monthly mean (using 24-hourly values) and the monthly averaged diurnal cycle.

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Zhuo Wang and Xubin Zeng

Abstract

Snow albedo plays an important role in land models for weather, climate, and hydrometeorological studies, but its treatment in various land models still contains significant deficiencies. Complementary to previous studies that evaluated the snow albedo as part of an overall land model study, the snow albedo formulations as used in four major weather forecasting and climate models [European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP) “Noah” land model, National Center for Atmospheric Research (NCAR) Community Land Model (CLM3), and NCEP global model] were directly evaluated here using multiyear Boreal Ecosystem–Atmosphere Study (BOREAS) in situ data over grass and forest sites. First, four idealized cases over grass and forest sites were designed to understand better the different albedo formulations in these models. Then the BOREAS data were used to evaluate snow albedo and relevant formulations and to identify deficiencies of each model. Based on these analyses, suggestions that involve only minor changes in parameters or formulations were made to significantly reduce these deficiencies of each model. For the ECMWF land model, using the square root of snow water equivalent (SWE), rather than SWE itself, in the computation of snow fraction would significantly reduce the underestimation of albedo over grass. For the NCEP Noah land model, reducing (increasing) the critical SWE for full snow cover over short (tall) vegetation would reduce the underestimate (overestimate) of snow albedo over the grass (forest) site. For the NCAR CLM3, revising the coefficient used in the ground snow-fraction computation would substantially reduce the albedo underestimation over grass. For the albedo formulations in the NCEP global model, replacing the globally constant fresh snow albedo by the vegetation-type-dependent Moderate-Resolution Imaging Spectroradiometer (MODIS) maximum snow albedo would significantly improve the overestimation of model albedo over forest.

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Xubin Zeng and Er Lu

Abstract

Different criteria have been used in the past to define the monsoon onset and retreat over different monsoon regions and even over different parts of the same monsoon region. Here an objective criterion is proposed to define, for the first time, globally unified summer monsoon onset (or retreat) dates using a single meteorological variable (i.e., the global daily 1° × 1° normalized precipitable water data) with the threshold value being the Golden Ratio (0.618). Results are found to be consistent with those determined using long-term rainfall data over most monsoon regions. The precipitable water data have also been used to refine the definition of monsoon regions on a grid-cell-by-cell basis. The objective definitions of these basic monsoon characteristics would provide one of the necessary foundations for global monsoon research. They, along with the onset/retreat data over a 10-yr period (1988–97), would also facilitate the diagnostics and validation of global monsoon simulations.

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Michael Barlage and Xubin Zeng

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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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