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- Author or Editor: Kaicun Wang x
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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.
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.
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.
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.
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.
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.
Abstract
The surface and air temperature gradient (T S00 − T air) drives the development of the convective boundary layer and the occurrence of clouds and precipitation. However, its variability is still poorly understood due to the lack of high-quality observations. This study fills in this gap by investigating the diurnal to decadal variability in T S00 − T air from 2002 to 2022 based on hourly observations collected at over 100 stations of the U.S. Climate Reference Network. It is found that T S00 − T air reaches its maximum at noon with an average of 6.85°C over the contiguous United States, which decreases to 4.28°C when the soil moisture exceeds 30%. The daily minimum of T S00 − T air has an average of −2.08°C, which generally occurs in the early evening but is postponed as the cloud fraction decreases. Moreover, while existing studies have used the near-surface soil temperature, such as the 5-cm soil temperature (T S05), to calculate T S05 − T air, we find that T S00 − T air and T S05 − T air have opposite diurnal cycles, and their amplitudes differed drastically. The daily minimum of T S00 − T air has a significant decreasing trend (−0.50° ± 0.007°C decade−1) from 2002 to 2022 due to T air increasing at a higher rate than T S00 during the nighttime. The occurrence frequency of near-surface stable conditions (T S00 − T air < 0) increases significantly, and the frequency of unstable conditions (T S00 − T air > 0) decreases notably throughout the year except for winter. When it is stable, the magnitude of T S00 − T air tends to decrease while the T S00 − T air tends to increase when it is unstable, which is consistent with the drying condition caused by the precipitation deficit. This study provides the first observational evidence on how T S00 − T air responds to warming.
Significance Statement
The impact of global warming on surface-air temperature gradients is a crucial scientific issue that needs to be addressed. These gradients determine changes in cloud and precipitation, affecting water resources. However, traditional surface temperature measurements from weather stations are of high uncertainty due to direct exposure to the insolation. Satellite retrieval of surface temperature is limited by the availability of clear sky conditions, with low accuracy for temperature gradient calculations. Despite their importance, high-accuracy data of land surface temperature are still lacking. To address this issue, the U.S. Climate Reference Network (USCRN) uses infrared radiometers to continuously monitor surface temperature with high accuracy and sampling frequency. This study reports on surface-air temperature gradients at more than 100 U.S. stations, providing insight into diurnal to decadal variability over the contiguous United States. The study also highlights the significant difference between the land surface temperature–air temperature gradient and soil temperature–air temperature gradient.
Abstract
The surface and air temperature gradient (T S00 − T air) drives the development of the convective boundary layer and the occurrence of clouds and precipitation. However, its variability is still poorly understood due to the lack of high-quality observations. This study fills in this gap by investigating the diurnal to decadal variability in T S00 − T air from 2002 to 2022 based on hourly observations collected at over 100 stations of the U.S. Climate Reference Network. It is found that T S00 − T air reaches its maximum at noon with an average of 6.85°C over the contiguous United States, which decreases to 4.28°C when the soil moisture exceeds 30%. The daily minimum of T S00 − T air has an average of −2.08°C, which generally occurs in the early evening but is postponed as the cloud fraction decreases. Moreover, while existing studies have used the near-surface soil temperature, such as the 5-cm soil temperature (T S05), to calculate T S05 − T air, we find that T S00 − T air and T S05 − T air have opposite diurnal cycles, and their amplitudes differed drastically. The daily minimum of T S00 − T air has a significant decreasing trend (−0.50° ± 0.007°C decade−1) from 2002 to 2022 due to T air increasing at a higher rate than T S00 during the nighttime. The occurrence frequency of near-surface stable conditions (T S00 − T air < 0) increases significantly, and the frequency of unstable conditions (T S00 − T air > 0) decreases notably throughout the year except for winter. When it is stable, the magnitude of T S00 − T air tends to decrease while the T S00 − T air tends to increase when it is unstable, which is consistent with the drying condition caused by the precipitation deficit. This study provides the first observational evidence on how T S00 − T air responds to warming.
Significance Statement
The impact of global warming on surface-air temperature gradients is a crucial scientific issue that needs to be addressed. These gradients determine changes in cloud and precipitation, affecting water resources. However, traditional surface temperature measurements from weather stations are of high uncertainty due to direct exposure to the insolation. Satellite retrieval of surface temperature is limited by the availability of clear sky conditions, with low accuracy for temperature gradient calculations. Despite their importance, high-accuracy data of land surface temperature are still lacking. To address this issue, the U.S. Climate Reference Network (USCRN) uses infrared radiometers to continuously monitor surface temperature with high accuracy and sampling frequency. This study reports on surface-air temperature gradients at more than 100 U.S. stations, providing insight into diurnal to decadal variability over the contiguous United States. The study also highlights the significant difference between the land surface temperature–air temperature gradient and soil temperature–air temperature gradient.
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 .
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 .
Abstract
The observed surface wind speed (SWS) over China has declined in the past four decades, and recently, the trend has reversed, which is known as SWS stilling and recovery. The observed SWS is vulnerable to changes in nonclimatic factors, i.e., inhomogeneity. Unfortunately, most of the existing studies on the long-term trend of SWS were based on raw datasets without homogenization. In this study, by means of geostrophic wind speed and penalized maximal t test, we conduct a systematic homogeneity test and exploration of the homogenization impact for SWS at over 2000 stations in China from 1970 to 2017. The results show that the inhomogeneity in the observed SWS over China is detectable at 59% of national weather stations. The breakpoint years are mainly concentrated in the late 1970s, mid-1990s, and early 2000s. Overall, 18% of breakpoints are caused by station relocations, and the remaining breakpoints are likely related to anemometer replacement and measurement environment changes that occurred during the mid-1990s and early 2000s. After homogenization, the decreasing trend in SWS during 1970–2017 decreased from −0.15 to −0.05 m s−1 decade−1. The homogenized SWS recovery period advanced from the early twenty-first century to the early 1990s, which is consistent with the SWS variations, excluding the impact of urbanization around weather stations. The phase change in the Western Hemisphere warm pool (WHWP) might be one of the causes of homogenized SWS recovery.
Abstract
The observed surface wind speed (SWS) over China has declined in the past four decades, and recently, the trend has reversed, which is known as SWS stilling and recovery. The observed SWS is vulnerable to changes in nonclimatic factors, i.e., inhomogeneity. Unfortunately, most of the existing studies on the long-term trend of SWS were based on raw datasets without homogenization. In this study, by means of geostrophic wind speed and penalized maximal t test, we conduct a systematic homogeneity test and exploration of the homogenization impact for SWS at over 2000 stations in China from 1970 to 2017. The results show that the inhomogeneity in the observed SWS over China is detectable at 59% of national weather stations. The breakpoint years are mainly concentrated in the late 1970s, mid-1990s, and early 2000s. Overall, 18% of breakpoints are caused by station relocations, and the remaining breakpoints are likely related to anemometer replacement and measurement environment changes that occurred during the mid-1990s and early 2000s. After homogenization, the decreasing trend in SWS during 1970–2017 decreased from −0.15 to −0.05 m s−1 decade−1. The homogenized SWS recovery period advanced from the early twenty-first century to the early 1990s, which is consistent with the SWS variations, excluding the impact of urbanization around weather stations. The phase change in the Western Hemisphere warm pool (WHWP) might be one of the causes of homogenized SWS recovery.
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.
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.
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.
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.
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.
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.
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.
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.
