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Laigang Wang
and
Kaicun Wang

Abstract

Digital elevation models (DEMs) have important meteorological, hydrological, and climatological applications. This research studies the uncertainties of six widely accepted global DEM datasets over China and their derivative parameters, including slope and aspect, in calculating the surface-received solar radiation and extracting the river networks. The authors’ results indicate that, although the absolute height values of the six DEM data are nearly identical, substantial and significant differences are introduced when estimating the surface-received solar radiation. The extracted drainage streamflows of the Pearl River basin in South China are close to the actual river networks in general but are quite different in some details that cannot be ignored. Results herein highlight that the uncertainties of DEM themselves as well as their derived parameters must be considered in analogous study.

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Xiaoyan Wang
and
Kaicun Wang

Abstract

Boundary layer height (BLH) significantly impacts near-surface air quality, and its determination is important for climate change studies. Integrated Global Radiosonde Archive data from 1973 to 2014 were used to estimate the long-term variability of the BLH based on profiles of potential temperature, relative humidity, and atmospheric refractivity. However, this study found that there was an obvious inhomogeneity in the radiosonde-derived BLH time series because of the presence of discontinuities in the raw radiosonde dataset. The penalized maximal F test and quantile-matching adjustment were used to detect the changepoints and to adjust the raw BLH series. The most significant inhomogeneity of the BLH time series was found over the United States from 1986 to 1992, which was mainly due to progress made in sonde models and processing procedures. The homogenization did not obviously change the magnitude of the daytime convective BLH (CBLH) tendency, but it improved the statistical significance of its linear trend. The trend of nighttime stable BLH (SBLH) is more dependent on the homogenization because the magnitude of SBLH is small, and SBLH is sensitive to the observational biases. The global daytime CBLH increased by about 1.6% decade−1 before and after homogenization from 1973 to 2014, and the nighttime homogenized SBLH decreased by −4.2% decade−1 compared to a decrease of −7.1% decade−1 based on the raw series. Regionally, the daytime CBLH increased by 2.8%, 0.9%, 1.6%, and 2.7% decade−1 and the nighttime SBLH decreased significantly by −2.7%, −6.9%, −7.7%, and −3.5% decade−1 over Europe, the United States, Japan, and Australia, respectively.

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Chunlüe Zhou
and
Kaicun Wang

Abstract

Daytime (0800–2000 Beijing time) and nighttime (2000–0800 Beijing time) precipitation at approximately 2100 stations in China from 1979 to 2014 was used to evaluate eight current reanalyses. Daytime, nighttime, and nighttime–daytime contrast of precipitation were examined in aspects of climatology, seasonal cycle, interannual variability, and trends. The results show that the ECMWF interim reanalysis (ERA-Interim), ERA-Interim/Land, Japanese 55-year Reanalysis (JRA-55), and NCEP Climate Forecast System Reanalysis (CFSR) can reproduce the observed spatial pattern of nighttime–daytime contrast in precipitation amount, exhibiting a positive center over the eastern Tibetan Plateau and a negative center over southeastern China. All of the reanalyses roughly reproduce seasonal variations of nighttime and daytime precipitation, but not always nighttime–daytime contrast. The reanalyses overestimate drizzle and light precipitation frequencies by greater than 31.5% and underestimate heavy precipitation frequencies by less than −30.8%. The reanalyses successfully reproduce interannual synchronizations of daytime and nighttime precipitation frequencies and amounts with an averaged correlation coefficient r of 0.66 against the observed data but overestimate their year-to-year amplitudes by approximately 64%. The trends in nighttime, daytime, and nighttime–daytime contrast of the observed precipitation amounts are mainly dominated by their frequencies (r = 0.85). Less than moderate precipitation frequency has exhibited a significant downward trend (−2.5% decade−1 during nighttime and −1.7% decade−1 during daytime) since 1979, which is roughly captured by the reanalyses. However, only JRA-55 captures the observed trend of nighttime precipitation intensity (2.4% decade−1), while the remaining reanalyses show negative trends. Overall, JRA-55 and CFSR provide the best reproductions of the observed nighttime–daytime contrast in precipitation intensity, although they have considerable room for improvement.

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Zhengtai Zhang
and
Kaicun Wang

Abstract

Surface wind speed (SWS) from meteorological observation, global atmospheric reanalysis, and geostrophic wind speed (GWS) calculated from surface pressure were used to study the stilling and recovery of SWS over China from 1960 to 2017. China experienced anemometer changes and automatic observation transitions in approximately 1969 and 2004, resulting in SWS inhomogeneity. Therefore, we divided the entire period into three sections to study the SWS trend, and found a near-zero annual trend in the SWS in China from 1960 to 1969, a significant decrease of −0.24 m s−1 decade−1 from 1970 to 2004, and a weak recovery from 2005 to 2017. By defining the 95th and 5th percentiles of daily mean wind speeds as strong and weak winds, respectively, we found that the SWS decrease was primarily caused by a strong wind decrease of −8% decade−1 from 1960 to 2017, but weak wind showed an insignificant decreasing trend of −2% decade−1. GWS decreased with a significant trend of −3% decade−1 before the 1990s; during the 1990s, GWS increased with a trend of 3% decade−1 whereas SWS continued to decrease with a trend of 10% decade−1. Consistent with SWS, GWS demonstrated a weak increase after the 2000s. After detrending, both SWS and GWS showed synchronous decadal variability, which is related to the intensity of Aleutian low pressure over the North Pacific. However, current reanalyses cannot reproduce the decadal variability and cannot capture the decreasing trend of SWS either.

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Chunlüe Zhou
and
Kaicun Wang

Abstract

Surface air temperature T a is largely determined by surface net radiation R n and its partitioning into latent (LE) and sensible heat fluxes (H). Existing model evaluations by comparison of absolute flux values are of limited help because the evaluation results are a blending of inconsistent spatial scales, inaccurate model forcing data, and imperfect parameterizations. This study further evaluates the relationships of LE and H with R n and environmental parameters, including T a , relative humidity (RH), and wind speed (WS), using ERA-Interim data at a 0.125° × 0.125° grid with observations at AmeriFlux sites from 1998 to 2012. The results demonstrate ERA-Interim can roughly reproduce the absolute values of environmental parameters, radiation, and turbulent fluxes. The model performs well in simulating the correlation of LE and H with R n , except for the notable correlation overestimation of H against R n over high-density vegetation (e.g., deciduous broadleaf forest, grassland, and cropland). The sensitivity of LE to R n in the model is similar to that observed, but that of H to R n is overestimated by 24.2%. Over the high-density vegetation, the correlation coefficient between H and T a is overestimated by over 0.2, whereas that between H and WS is underestimated by over 0.43. The sensitivity of H to T a is overestimated by 0.72 W m−2 °C−1, whereas that of H to WS in the model is underestimated by 16.15 W m−2 (m s−1)−1 over all of the sites. The model cannot accurately capture the responses of evaporative fraction [EF; EF = LE / (LE + H)] to R n and environmental parameters. This calls for major research efforts to improve the intrinsic parameterizations of turbulent fluxes, particularly over high-density vegetation.

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Kaicun Wang
and
Shunlin Liang

Abstract

A simple and accurate method to estimate regional or global latent heat of evapotranspiration (ET) from remote sensing data is essential. The authors proposed a method in an earlier study that utilized satellite-determined surface net radiation (Rn ), a vegetation index, and daytime-averaged/daily maximum air temperature (Ta ) or land surface temperature (Ts ) data. However, the influence of soil moisture (SM) on ET was not considered and is addressed in this paper by incorporating the diurnal Ts range (DTsR). ET, measured by the energy balance Bowen ratio method at eight enhanced facility sites on the southern Great Plains in the United States and by the eddy covariance method at four AmeriFlux sites during 2001–06, is used to validate the improved method. Site land cover varies from grassland, native prairie, and cropland to deciduous forest and evergreen forest. The correlation coefficient between the measured and predicted 16-day daytime-averaged ET using a combination of Rn , enhanced vegetation index (EVI), daily maximum Ts , and DTsR is about 0.92 for all the sites, the bias is −1.9 W m−2, and the root-mean-square error (RMSE) is 28.6 W m−2. The sensitivity of the revised method to input data error is small. Implemented here is the revised method to estimate global ET using diurnal Ta range (DTaR) instead of DTsR because DTsR data are not available yet, although DTaR-estimated ET is less accurate than DTsR-estimated ET. Global monthly ET is calculated from 1986 to 1995 at a spatial resolution of 1° × 1° from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II global interdisciplinary monthly dataset and is compared with the 15 land surface model simulations of the Global Soil Wetness Project-2. The results of the comparison of 118 months of global ET show that the bias is 4.5 W m−2, the RMSE is 19.8 W m−2, and the correlation coefficient is 0.82. Incorporating DTaR distinctively improves the accuracy of the estimate of global ET.

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Kaicun Wang
and
Shunlin Liang

Abstract

Changes in surface net radiation Rn control the earth’s climate, the hydrological cycle, and plant photosynthesis. However, Rn is not readily available. This study develops a method to estimate surface daytime Rn from solar shortwave radiation measurements as well as conventional meteorological observations (or satellite retrievals) including daily minimum temperature, daily temperature range, and relative humidity, and vegetation indices from satellite data. Measurements collected at 22 U.S. and 2 Tibetan Plateau, China, sites from 2000 to 2006 are used to develop and validate the method. Land cover types include desert, semidesert, croplands, grasslands, and forest. Site elevations range from 98 to 4700 m. The results show that the method estimates Rn under clear and cloudy conditions accurately over a range of land cover types, elevations, and climates without requiring local calibration. The results show that the method estimates Rn accurately. The bias varies from −7.8 to 9.7 W m−2 (±3% in relative value) for different sites, and the root-mean-square error ranges from 12.8 to 21 W m−2 (from +5% to +9% in relative value) for different sites, with an average of 16.9 W m−2 (+6% relative) for all sites. The correlation coefficient for all sites is about 0.99. The correlation coefficient between the measured and predicted annual anomaly (year average subtracted from multiyear average) in daytime Rn is as high as 0.91, demonstrating that the method accurately estimates long-term variation in Rn .

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Chunlüe Zhou
and
Kaicun Wang

Abstract

Knowledge of the evaporative fraction (EF: the ratio of latent heat flux to the sum of sensible and latent heat fluxes) and its controls is particularly important for accurate estimates of water flux, heat exchange, and ecosystem response to climatic changes. In this study, the biological and environmental controls on monthly EF were evaluated across 81 AmeriFlux sites, mainly in North America, for 2000–12. The land-cover types of these sites include forest, shrubland, grassland, and cropland, and the local climates vary from humid to arid. The results show that vegetation coverage, indicated by the normalized difference vegetation index (NDVI), has the best agreement with EF (site-averaged partial correlation coefficient ρ = 0.53; significance level p < 0.05) because of vegetation transpiration demand. The minimum air temperature is closely related to EF (site-averaged ρ = 0.51; p < 0.05) because of the inhibition of respiratory enzyme activity. Relative humidity, an indicator of surface aridity, shows a significant positive correlation with EF (site-averaged ρ = 0.46; p < 0.05). The impacts of wind speed and diurnal air temperature range on EF depend on land-cover types and are strong over grasslands and cropland. From these findings, empirical methods were established to predict monthly EF using meteorological data and NDVI. Correlation coefficients between EF estimates and observations range from 0.80 to 0.93, with root-mean-square errors varying from 0.09 to 0.12. This study demonstrates the varying controls on EF across different landscapes and enhances understanding of EF and its dynamics under changing climates.

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Chunlüe Zhou
and
Kaicun Wang

Abstract

Precipitation is expected to increase under global warming. However, large discrepancies in precipitation sensitivities to global warming among observations and models have been reported, partly owing to the large natural variability of precipitation, which accounts for over 90% of its total variance in China. Here, the authors first elucidated precipitation sensitivities to the long-term warming trend and interannual–decadal variations of surface air temperature T a over China based on daily data from approximately 2000 stations from 1961 to 2014. The results show that the number of dry, trace, and light precipitation days has stronger sensitivities to the warming trend than to the T a interannual–decadal variation, with 14.1%, −35.7%, and −14.6% K−1 versus 2.7%, −7.9%, and −3.1% K−1, respectively. Total precipitation frequency has significant sensitivities to the warming trend (−18.5% K−1) and the T a interannual–decadal variation (−3.6% K−1) over China. However, very heavy precipitation frequencies exhibit larger sensitivities to the T a interannual–decadal variation than to the long-term trend over Northwest and Northeast China and the Tibetan Plateau. A warming trend boosts precipitation intensity, especially for light precipitation (9.8% K−1). Total precipitation intensity increases significantly by 13.1% K−1 in response to the warming trend and by 3.3% K−1 in response to the T a interannual–decadal variation. Very heavy precipitation intensity also shows significant sensitivity to the interannual–decadal variation of T a (3.7% K−1), particularly in the cold season (8.0% K−1). Combining precipitation frequency and intensity, total precipitation amount has a negligible sensitivity to the warming trend, and the consequent trend in China is limited. Moderate and heavy precipitation amounts are dominated by their frequencies.

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Ning An
and
Kaicun Wang

Abstract

Clouds determine the amount of solar radiation incident to the surface. Accurately quantifying cloud fraction is of great importance but is difficult to accomplish. Satellite and surface cloud observations have different fields of view (FOVs); the lack of conformity of different FOVs may cause large discrepancies when comparing satellite- and surface-derived cloud fractions. From the viewpoint of surface-incident solar radiation, this paper compares Moderate Resolution Imaging Spectroradiometer (MODIS) level-2 cloud-fraction data with three surface cloud-fraction datasets at five Surface Radiation Network (SURFRAD) sites. The correlation coefficients between MODIS and the surface cloud fractions are in the 0.80–0.91 range and vary at different SURFRAD sites. In a number of cases, MODIS observations show a large cloud-fraction bias when compared with surface data. The variances between MODIS and the surface cloud-fraction datasets are more apparent when small convective or broken clouds exist in the FOVs. The magnitude of the discrepancy between MODIS and surface-derived cloud fractions depends on the satellite’s view zenith angle (VZA). On average, relative to surface cloud-fraction data, MODIS observes a larger cloud fraction at VZA > 40° and a smaller cloud fraction at VZA < 20°. When comparing long-term MODIS averages with surface datasets, Aqua MODIS observes a higher annual mean cloud fraction, likely because convective clouds are better developed in the afternoon when Aqua is observing.

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