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- Author or Editor: Kaicun Wang x
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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
Pan evaporation (EP), an index of atmospheric evaporative demand, has been widely reported to have weakened in the past decades. However, its interpretation remains controversial because EP observations are not globally available and observations of one of its key controls, surface incident solar radiation Rs , are even less available. Using global-distributed Rs from both direct measurements (available through the Global Energy Balance Archive) and derived from sunshine duration, the authors calculated the potential evaporation from 1982 to 2008 from approximately 1300 stations. The findings herein show that the contribution of water vapor pressure deficit (VPD) to monthly variability of EP is much larger than that of other controlling factors, of Rs , wind speed (WS), and air temperature Ta . The trend of the aerodynamic component of EP, which includes contributions of VPD, WS, and Ta , accounted for 86% of the long-term trend of EP. The aerodynamic component was then calculated from 4250 globally distributed stations and showed a negligible averaged trend from 1973 to 2008 because the reduction in WS canceled out the impact of the elevated VPD. The long-term trend of WS dominates the long-term trend of the aerodynamic component of EP at the 4250 stations. Atmospheric evaporative demand increased in most arid and semiarid areas, indicating a decrease in water availability in those areas.
Abstract
Pan evaporation (EP), an index of atmospheric evaporative demand, has been widely reported to have weakened in the past decades. However, its interpretation remains controversial because EP observations are not globally available and observations of one of its key controls, surface incident solar radiation Rs , are even less available. Using global-distributed Rs from both direct measurements (available through the Global Energy Balance Archive) and derived from sunshine duration, the authors calculated the potential evaporation from 1982 to 2008 from approximately 1300 stations. The findings herein show that the contribution of water vapor pressure deficit (VPD) to monthly variability of EP is much larger than that of other controlling factors, of Rs , wind speed (WS), and air temperature Ta . The trend of the aerodynamic component of EP, which includes contributions of VPD, WS, and Ta , accounted for 86% of the long-term trend of EP. The aerodynamic component was then calculated from 4250 globally distributed stations and showed a negligible averaged trend from 1973 to 2008 because the reduction in WS canceled out the impact of the elevated VPD. The long-term trend of WS dominates the long-term trend of the aerodynamic component of EP at the 4250 stations. Atmospheric evaporative demand increased in most arid and semiarid areas, indicating a decrease in water availability in those areas.
Abstract
Incident photosynthetically active radiation (PAR) is an important parameter for terrestrial ecosystem models. Because of its high temporal resolution, the Geostationary Operational Environmental Satellite (GOES) observations are very suited to catch the diurnal variation of PAR. In this paper, a new method is developed to derive PAR using GOES data. What makes this new method distinct from the existing method is that it does not need external knowledge of atmospheric conditions. The new method retrieves both atmospheric and surface conditions using only at-sensor radiance through interpolation of time series of observations. Validations against ground measurement are carried out at four “FLUXNET” sites. The values of RMSE of estimated and ground-measured instantaneous PAR at the four sites are 130.71, 131.44, 141.16, and 190.22 μmol m−2 s−1, respectively. At the four validation sites, the RMSE as the percentage of estimated mean PAR value are 9.52%, 13.01%, 13.92%, and 24.09%, respectively; the biases are −101.54, 16.56, 11.09, and 53.64 μmol m−2 s−1, respectively. The independence of external atmospheric information enables this method to be applicable to many situations in which external atmospheric information is not available. In addition, topographic impacts on surface PAR are examined at the 1-km resolution at which PAR is retrieved using the GOES visible band data.
Abstract
Incident photosynthetically active radiation (PAR) is an important parameter for terrestrial ecosystem models. Because of its high temporal resolution, the Geostationary Operational Environmental Satellite (GOES) observations are very suited to catch the diurnal variation of PAR. In this paper, a new method is developed to derive PAR using GOES data. What makes this new method distinct from the existing method is that it does not need external knowledge of atmospheric conditions. The new method retrieves both atmospheric and surface conditions using only at-sensor radiance through interpolation of time series of observations. Validations against ground measurement are carried out at four “FLUXNET” sites. The values of RMSE of estimated and ground-measured instantaneous PAR at the four sites are 130.71, 131.44, 141.16, and 190.22 μmol m−2 s−1, respectively. At the four validation sites, the RMSE as the percentage of estimated mean PAR value are 9.52%, 13.01%, 13.92%, and 24.09%, respectively; the biases are −101.54, 16.56, 11.09, and 53.64 μmol m−2 s−1, respectively. The independence of external atmospheric information enables this method to be applicable to many situations in which external atmospheric information is not available. In addition, topographic impacts on surface PAR are examined at the 1-km resolution at which PAR is retrieved using the GOES visible band data.
Abstract
Land surface temperature T s provides essential supplementary information to surface air temperature, the most widely used metric in global warming studies. A lack of reliable observational T s data makes assessing model simulations difficult. Here, the authors first examined the simulated T s of eight current reanalyses based on homogenized T s data collected at ~2200 weather stations from 1979 to 2003 in China. The results show that the reanalyses are skillful in simulating the interannual variance of T s in China (r = 0.95) except over the Tibetan Plateau. ERA-Interim and MERRA land versions perform better in this respect than ERA-Interim and MERRA. Observations show that the interannual variance of T s over the north China plain and south China is mostly influenced by surface incident solar radiation R s , followed by precipitation frequency, whereas the opposite is true over the northwest China, northeast China, and the Tibetan Plateau. This variable relationship is well captured by ERA-Interim, ERA-Interim land, MERRA, and JRA-55. The homogenized T s data show a warming of 0.34°C decade−1 from 1979 to 2003 in China, varying between 0.25° and 0.42°C decade−1 for the eight reanalyses. However, the reanalyses substantially underestimate the warming trend of T s over northwest China, northeast China, and the Tibetan Plateau and significantly overestimate the warming trend of T s over the north China plain and south China owing to their biases in simulating the R s and precipitation frequency trends. This study provides a diagnostic method for examining the capability of current atmospheric/land reanalysis data in regional climate change studies.
Abstract
Land surface temperature T s provides essential supplementary information to surface air temperature, the most widely used metric in global warming studies. A lack of reliable observational T s data makes assessing model simulations difficult. Here, the authors first examined the simulated T s of eight current reanalyses based on homogenized T s data collected at ~2200 weather stations from 1979 to 2003 in China. The results show that the reanalyses are skillful in simulating the interannual variance of T s in China (r = 0.95) except over the Tibetan Plateau. ERA-Interim and MERRA land versions perform better in this respect than ERA-Interim and MERRA. Observations show that the interannual variance of T s over the north China plain and south China is mostly influenced by surface incident solar radiation R s , followed by precipitation frequency, whereas the opposite is true over the northwest China, northeast China, and the Tibetan Plateau. This variable relationship is well captured by ERA-Interim, ERA-Interim land, MERRA, and JRA-55. The homogenized T s data show a warming of 0.34°C decade−1 from 1979 to 2003 in China, varying between 0.25° and 0.42°C decade−1 for the eight reanalyses. However, the reanalyses substantially underestimate the warming trend of T s over northwest China, northeast China, and the Tibetan Plateau and significantly overestimate the warming trend of T s over the north China plain and south China owing to their biases in simulating the R s and precipitation frequency trends. This study provides a diagnostic method for examining the capability of current atmospheric/land reanalysis data in regional climate change studies.
Abstract
Surface incident solar radiation (R s ) is important for providing essential information on climate change. Existing studies have shown that the R s values from current reanalyses are significantly overestimated throughout China. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the fifth generation of atmospheric reanalysis (i.e., ERA5) with a much higher spatiotemporal resolution and a major upgrade compared to its predecessor, ERA-Interim. This study is to verify whether ERA5 can improve the R s simulation using sunshine duration–derived R s values at ~2200 stations over China from 1979 to 2014 as reference data. Compared with the observed multiyear national mean, the R s overestimation is reduced from 15.88 W m−2 in ERA-Interim to 10.07 W m−2 in ERA5. From 1979 to 1993, ERA-Interim (−1.99 W m−2 decade−1; p < 0.05) and ERA5 (−2.42 W m−2 decade−1; p < 0.05) estimates of R s in China continued to decrease and the decline of the latter is closer to the observed. After 1993, they both show a strong brightening (i.e., 2.26 W m−2 decade−1 in ERA-Interim and 1.49 W m−2 decade−1 in ERA5) but observations show a nonsignificant increase by 0.30 W m−2 decade−1. Due to the improvement of total cloud cover (TCC) simulation by ERA5, its R s trend bias induced by the TCC trend bias is smaller than that in ERA-Interim. In addition, the reason why the simulation trend in ERA5 remains biased might be that ERA5 still ignores aerosol changes on interannual or decadal time scales. Therefore, subsequent reanalysis products still need to improve their simulation of clouds, water vapor, and aerosol, especially in aerosol direct and indirect effects on R s .
Abstract
Surface incident solar radiation (R s ) is important for providing essential information on climate change. Existing studies have shown that the R s values from current reanalyses are significantly overestimated throughout China. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the fifth generation of atmospheric reanalysis (i.e., ERA5) with a much higher spatiotemporal resolution and a major upgrade compared to its predecessor, ERA-Interim. This study is to verify whether ERA5 can improve the R s simulation using sunshine duration–derived R s values at ~2200 stations over China from 1979 to 2014 as reference data. Compared with the observed multiyear national mean, the R s overestimation is reduced from 15.88 W m−2 in ERA-Interim to 10.07 W m−2 in ERA5. From 1979 to 1993, ERA-Interim (−1.99 W m−2 decade−1; p < 0.05) and ERA5 (−2.42 W m−2 decade−1; p < 0.05) estimates of R s in China continued to decrease and the decline of the latter is closer to the observed. After 1993, they both show a strong brightening (i.e., 2.26 W m−2 decade−1 in ERA-Interim and 1.49 W m−2 decade−1 in ERA5) but observations show a nonsignificant increase by 0.30 W m−2 decade−1. Due to the improvement of total cloud cover (TCC) simulation by ERA5, its R s trend bias induced by the TCC trend bias is smaller than that in ERA-Interim. In addition, the reason why the simulation trend in ERA5 remains biased might be that ERA5 still ignores aerosol changes on interannual or decadal time scales. Therefore, subsequent reanalysis products still need to improve their simulation of clouds, water vapor, and aerosol, especially in aerosol direct and indirect effects on R s .
Abstract
The increasing rate of the observed daily minimum temperature T min has been much higher than that of the observed daily maximum temperature T max during the past six decades across China. In this study, the local urbanization impact on these observed asymmetric warming rates was investigated. The latest released land-cover data with a 30-m spatial resolution and annual temporal resolution from 1985 to 2017 were used to quantify the urbanization ratios around weather stations. Although urbanized areas occupied only 2.25% of the landmass in China, the percentage of stations with an urbanization ratio over 20% increased from 22.1% to 68.2% during the period 1985–2017. Significant asymmetric warming rates at urban stations were identified, which were approximately 3 times larger compared to the average asymmetry observed at all 2454 stations in China. However, this asymmetry disappeared at rural stations. These differences are mainly due to the rapid local urbanization around most meteorological stations in China since 1985, which affected the spatial representation of observations and led to the observed asymmetry warming rates. The results reported here indicate that the observed asymmetric warming rate over China from 1985 to 2017 is an observational bias due to local urbanization around most stations rather than large-scale climate change. The results also explain the phenomenon that the observed warming rate of T min remains higher than that of T max after 1990 when the surface solar radiation stops decreasing in China.
Abstract
The increasing rate of the observed daily minimum temperature T min has been much higher than that of the observed daily maximum temperature T max during the past six decades across China. In this study, the local urbanization impact on these observed asymmetric warming rates was investigated. The latest released land-cover data with a 30-m spatial resolution and annual temporal resolution from 1985 to 2017 were used to quantify the urbanization ratios around weather stations. Although urbanized areas occupied only 2.25% of the landmass in China, the percentage of stations with an urbanization ratio over 20% increased from 22.1% to 68.2% during the period 1985–2017. Significant asymmetric warming rates at urban stations were identified, which were approximately 3 times larger compared to the average asymmetry observed at all 2454 stations in China. However, this asymmetry disappeared at rural stations. These differences are mainly due to the rapid local urbanization around most meteorological stations in China since 1985, which affected the spatial representation of observations and led to the observed asymmetry warming rates. The results reported here indicate that the observed asymmetric warming rate over China from 1985 to 2017 is an observational bias due to local urbanization around most stations rather than large-scale climate change. The results also explain the phenomenon that the observed warming rate of T min remains higher than that of T max after 1990 when the surface solar radiation stops decreasing in China.
Abstract
Diurnal cycle of surface air temperature T is an important metric indicating the feedback of land–atmospheric interaction to global warming, whereas the ability of current reanalyses to reproduce its variation had not been assessed adequately. Here, we evaluate the daily maximum temperature T max, daily minimum temperature T min, and diurnal temperature range (DTR) in five reanalyses based on observations collected at 2253 weather stations over China. Our results show that the reanalyses reproduce T min very well; however, except for Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), they substantially underestimate T max and DTR by 1.21°–6.84°C over China during the period of 1980–2014. MERRA-2 overestimates T max and DTR by 0.35° and 0.81°C, which are closest with observation. The reanalyses are skillful in reproducing the interannual variability of T max and T min but relatively poor for DTR. All reanalyses underestimate the warming trend of T min by 0.13°–0.17°C (10 yr)−1 throughout China during 1980–2014, and underestimate the warming trend of T max by 0.24°–0.40°C (10 yr)−1 in northwestern China while overestimating this quantity by 0.18°–0.33°C (10 yr)−1 in southeastern China. These trend biases in T max and T min introduce a positive trend bias in DTR of 0.01°–0.26°C (10 yr)−1 within China, especially in the north China plain and southeastern China. In the five reanalyses, owing to the sensitivity discrepancies and trend biases, the surface solar radiation R s and precipitation frequency (PF) are notable deviation sources of the diurnal cycle of air temperature, which explain 31.0%–38.7% (31.9%–37.8%) and 9.8%–22.2% (7.4%–15.3%) of the trend bias in T max (DTR) over China, respectively.
Abstract
Diurnal cycle of surface air temperature T is an important metric indicating the feedback of land–atmospheric interaction to global warming, whereas the ability of current reanalyses to reproduce its variation had not been assessed adequately. Here, we evaluate the daily maximum temperature T max, daily minimum temperature T min, and diurnal temperature range (DTR) in five reanalyses based on observations collected at 2253 weather stations over China. Our results show that the reanalyses reproduce T min very well; however, except for Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), they substantially underestimate T max and DTR by 1.21°–6.84°C over China during the period of 1980–2014. MERRA-2 overestimates T max and DTR by 0.35° and 0.81°C, which are closest with observation. The reanalyses are skillful in reproducing the interannual variability of T max and T min but relatively poor for DTR. All reanalyses underestimate the warming trend of T min by 0.13°–0.17°C (10 yr)−1 throughout China during 1980–2014, and underestimate the warming trend of T max by 0.24°–0.40°C (10 yr)−1 in northwestern China while overestimating this quantity by 0.18°–0.33°C (10 yr)−1 in southeastern China. These trend biases in T max and T min introduce a positive trend bias in DTR of 0.01°–0.26°C (10 yr)−1 within China, especially in the north China plain and southeastern China. In the five reanalyses, owing to the sensitivity discrepancies and trend biases, the surface solar radiation R s and precipitation frequency (PF) are notable deviation sources of the diurnal cycle of air temperature, which explain 31.0%–38.7% (31.9%–37.8%) and 9.8%–22.2% (7.4%–15.3%) of the trend bias in T max (DTR) over China, respectively.
