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- Author or Editor: Liming Zhou x
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Abstract
A frequency–wavenumber power (
Abstract
A frequency–wavenumber power (
Abstract
The impact of different surface vegetations on long-term surface temperature change is estimated by subtracting reanalysis trends in monthly surface temperature anomalies from observation trends over the last four decades. This is done using two reanalyses, namely, the 40-yr ECMWF (ERA-40) and NCEP–NCAR I (NNR), and two observation datasets, namely, Climatic Research Unit (CRU) and Global Historical Climate Network (GHCN). The basis of the observation minus reanalysis (OMR) approach is that the NNR reanalysis surface fields, and to a lesser extent the ERA-40, are insensitive to surface processes associated with different vegetation types and their changes because the NNR does not use surface observations over land, whereas ERA-40 only uses surface temperature observations indirectly, in order to initialize soil temperature and moisture. As a result, the OMR trends can provide an estimate of surface effects on the observed temperature trends missing in the reanalyses. The OMR trends obtained from observation minus NNR show a strong and coherent sensitivity to the independently estimated surface vegetation from normalized difference vegetation index (NDVI). The correlation between the OMR trend and the NDVI indicates that the OMR trend decreases with surface vegetation, with a correlation < −0.5, indicating that there is a stronger surface response to global warming in arid regions, whereas the OMR response is reduced in highly vegetated areas. The OMR trend averaged over the desert areas (0 < NDVI < 0.1) shows a much larger increase of temperature (∼0.4°C decade−1) than over tropical forest areas (NDVI > 0.4) where the OMR trend is nearly zero. Areas of intermediate vegetation (0.1 < NDVI < 0.4), which are mostly found over midlatitudes, reveal moderate OMR trends (approximately 0.1°–0.3°C decade−1). The OMR trends are also very sensitive to the seasonal vegetation change. While the OMR trends have little seasonal dependence over deserts and tropical forests, whose vegetation state remains rather constant throughout the year, the OMR trends over the midlatitudes, in particular Europe and North America, exhibit strong seasonal variation in response to the NDVI fluctuations associated with deciduous vegetation. The OMR trend rises up approximately to 0.2°–0.3°C decade−1 in winter and early spring when the vegetation cover is low, and is only 0.1°C decade−1 in summer and early autumn with high vegetation. However, the Asian inlands (Russia, northern China with Tibet, and Mongolia) do not show this strong OMR variation despite their midlatitude location, because of the relatively permanent aridity of these regions.
Abstract
The impact of different surface vegetations on long-term surface temperature change is estimated by subtracting reanalysis trends in monthly surface temperature anomalies from observation trends over the last four decades. This is done using two reanalyses, namely, the 40-yr ECMWF (ERA-40) and NCEP–NCAR I (NNR), and two observation datasets, namely, Climatic Research Unit (CRU) and Global Historical Climate Network (GHCN). The basis of the observation minus reanalysis (OMR) approach is that the NNR reanalysis surface fields, and to a lesser extent the ERA-40, are insensitive to surface processes associated with different vegetation types and their changes because the NNR does not use surface observations over land, whereas ERA-40 only uses surface temperature observations indirectly, in order to initialize soil temperature and moisture. As a result, the OMR trends can provide an estimate of surface effects on the observed temperature trends missing in the reanalyses. The OMR trends obtained from observation minus NNR show a strong and coherent sensitivity to the independently estimated surface vegetation from normalized difference vegetation index (NDVI). The correlation between the OMR trend and the NDVI indicates that the OMR trend decreases with surface vegetation, with a correlation < −0.5, indicating that there is a stronger surface response to global warming in arid regions, whereas the OMR response is reduced in highly vegetated areas. The OMR trend averaged over the desert areas (0 < NDVI < 0.1) shows a much larger increase of temperature (∼0.4°C decade−1) than over tropical forest areas (NDVI > 0.4) where the OMR trend is nearly zero. Areas of intermediate vegetation (0.1 < NDVI < 0.4), which are mostly found over midlatitudes, reveal moderate OMR trends (approximately 0.1°–0.3°C decade−1). The OMR trends are also very sensitive to the seasonal vegetation change. While the OMR trends have little seasonal dependence over deserts and tropical forests, whose vegetation state remains rather constant throughout the year, the OMR trends over the midlatitudes, in particular Europe and North America, exhibit strong seasonal variation in response to the NDVI fluctuations associated with deciduous vegetation. The OMR trend rises up approximately to 0.2°–0.3°C decade−1 in winter and early spring when the vegetation cover is low, and is only 0.1°C decade−1 in summer and early autumn with high vegetation. However, the Asian inlands (Russia, northern China with Tibet, and Mongolia) do not show this strong OMR variation despite their midlatitude location, because of the relatively permanent aridity of these regions.
Abstract
Previous studies detected significant negative correlations between the nonuniform land surface warming and the decadal weakened activities of the summer extratropical cyclones (ECs) over East Asia and the East Asian summer monsoon (EASM) after the early 1990s. Here such relationships are further examined and the possible mechanisms are explored via numerical sensitivity experiments with a regional climate model (RegCM4.5). The positive/negative sensible heat flux (SH) anomalies were added as a forcing to a key region near 50°N of East Asia in RegCM4.5 to simulate the observed ground surface temperature (GST) anomalies. The model results suggest that the nonuniform land surface warming over the Lake Baikal area (50°–60°N, 90°–120°E) can indeed cause the weakening of the extratropical cyclogenesis and affect the decadal weakening of the EASM. Warm (cold) GST forcing over the key GST region can lead to decreasing (increasing) atmospheric baroclinicity and related energy conversion of the EC activity over the key EC region (40°–50°N, 90°–120°E), resulting in an evidently weakening (enhancing) of the ECs over East Asia. Meanwhile, precipitation shows a dipole pattern with significantly suppressed (enhanced) precipitation in northern and northeastern China, and slightly enhanced (suppressed) rainfall south of 40°N of East Asia, mainly over the East China Sea. Lake Baikal and its adjacent areas are occupied by a strong anticyclonic (cyclonic) circulation while the southeast coastal areas of China have a relatively weak cyclonic (anticyclonic) circulation accompanied with an anomalous northeasterly (southwesterly) wind to the southeast of the anticyclonic circulation, which is opposite to (coincident with) the atmospheric circulation anomalies that are associated with the second mode of the EASM.
Abstract
Previous studies detected significant negative correlations between the nonuniform land surface warming and the decadal weakened activities of the summer extratropical cyclones (ECs) over East Asia and the East Asian summer monsoon (EASM) after the early 1990s. Here such relationships are further examined and the possible mechanisms are explored via numerical sensitivity experiments with a regional climate model (RegCM4.5). The positive/negative sensible heat flux (SH) anomalies were added as a forcing to a key region near 50°N of East Asia in RegCM4.5 to simulate the observed ground surface temperature (GST) anomalies. The model results suggest that the nonuniform land surface warming over the Lake Baikal area (50°–60°N, 90°–120°E) can indeed cause the weakening of the extratropical cyclogenesis and affect the decadal weakening of the EASM. Warm (cold) GST forcing over the key GST region can lead to decreasing (increasing) atmospheric baroclinicity and related energy conversion of the EC activity over the key EC region (40°–50°N, 90°–120°E), resulting in an evidently weakening (enhancing) of the ECs over East Asia. Meanwhile, precipitation shows a dipole pattern with significantly suppressed (enhanced) precipitation in northern and northeastern China, and slightly enhanced (suppressed) rainfall south of 40°N of East Asia, mainly over the East China Sea. Lake Baikal and its adjacent areas are occupied by a strong anticyclonic (cyclonic) circulation while the southeast coastal areas of China have a relatively weak cyclonic (anticyclonic) circulation accompanied with an anomalous northeasterly (southwesterly) wind to the southeast of the anticyclonic circulation, which is opposite to (coincident with) the atmospheric circulation anomalies that are associated with the second mode of the EASM.
Abstract
This paper uses the empirical orthogonal function (EOF) analysis to decompose satellite-derived nighttime land surface temperature (LST) for the period of 2003–11 into spatial patterns of different scales and thus to identify whether (i) there is a pattern of LST change associated with the development of wind farms and (ii) the warming effect over wind farms reported previously is an artifact of varied surface topography. Spatial pattern and time series analysis methods are also used to supplement and compare with the EOF results. Two equal-sized regions with similar topography in west-central Texas are chosen to represent the wind farm region (WFR) and nonwind farm region (NWFR), respectively. Results indicate that the nighttime warming effect seen in the first mode (EOF1) in WFR very likely represents the wind farm impacts due to its spatial coupling with the wind turbines, which are generally built on topographic high ground. The time series associated with the EOF1 mode in WFR also shows a persistent upward trend over wind farms from 2003 to 2011, corresponding to the increase of operating wind turbines with time. Also, the wind farm pixels show a warming effect that differs statistically significantly from their upwind high-elevation pixels and their downwind nonwind farm pixels at similar elevations, and this warming effect decreases with elevation. In contrast, NWFR shows a decrease in LST with increasing surface elevation and no warming effects over high-elevation ridges, indicating that the presence of wind farms in WFR has changed the LST–elevation relationship shown in NWFR. The elevation impacts on Moderate Resolution Imaging Spectroradiometer (MODIS) LST, if any, are much smaller and statistically insignificant than the strong and persistent signal of wind farm impacts. These results provide further observational evidence of the warming effect of wind farms reported previously.
Abstract
This paper uses the empirical orthogonal function (EOF) analysis to decompose satellite-derived nighttime land surface temperature (LST) for the period of 2003–11 into spatial patterns of different scales and thus to identify whether (i) there is a pattern of LST change associated with the development of wind farms and (ii) the warming effect over wind farms reported previously is an artifact of varied surface topography. Spatial pattern and time series analysis methods are also used to supplement and compare with the EOF results. Two equal-sized regions with similar topography in west-central Texas are chosen to represent the wind farm region (WFR) and nonwind farm region (NWFR), respectively. Results indicate that the nighttime warming effect seen in the first mode (EOF1) in WFR very likely represents the wind farm impacts due to its spatial coupling with the wind turbines, which are generally built on topographic high ground. The time series associated with the EOF1 mode in WFR also shows a persistent upward trend over wind farms from 2003 to 2011, corresponding to the increase of operating wind turbines with time. Also, the wind farm pixels show a warming effect that differs statistically significantly from their upwind high-elevation pixels and their downwind nonwind farm pixels at similar elevations, and this warming effect decreases with elevation. In contrast, NWFR shows a decrease in LST with increasing surface elevation and no warming effects over high-elevation ridges, indicating that the presence of wind farms in WFR has changed the LST–elevation relationship shown in NWFR. The elevation impacts on Moderate Resolution Imaging Spectroradiometer (MODIS) LST, if any, are much smaller and statistically insignificant than the strong and persistent signal of wind farm impacts. These results provide further observational evidence of the warming effect of wind farms reported previously.
Abstract
The process of solar radiative transfer at the land surface is important to energy, water, and carbon balance, especially for vegetated areas. Currently the most commonly used two-stream model considers the plant functional types (PFTs) within a grid to be independent of each other and their leaves to be horizontally homogeneous. This assumption is unrealistic in most cases. To consider canopy three-dimensional (3D) structural effects, a new framework of 3D canopy radiative transfer model was developed and validated by numerical simulations and shows a good agreement. A comparison with the two-stream model in the offline Community Land Model (CLM4.0) shows that an increase of canopy absorption mainly happens with sparse vegetation or with multilayer canopies with a large sun zenith angle θ sun and is due to increases of the ground and sky shadows and of the optical pathlength because of the shadow overlapping between bushes and canopy layers. A decrease of canopy absorption occurs in densely vegetated areas with small θ sun. For a one-layer canopy, these decreases are due to crown shape effects that enhance the transmission through the canopy edge. For a multilayer canopy, aside from these shape effects, transmission is also increased by the decreased ground shadow due to the shadow overlapping between layers. Ground absorption usually changes with opposite sign as that of the canopy absorption. Somewhat lower albedos are found over most vegetated areas throughout the year. The 3D model also affects the calculation of the fraction of sunlit leaves and their corresponding absorption.
Abstract
The process of solar radiative transfer at the land surface is important to energy, water, and carbon balance, especially for vegetated areas. Currently the most commonly used two-stream model considers the plant functional types (PFTs) within a grid to be independent of each other and their leaves to be horizontally homogeneous. This assumption is unrealistic in most cases. To consider canopy three-dimensional (3D) structural effects, a new framework of 3D canopy radiative transfer model was developed and validated by numerical simulations and shows a good agreement. A comparison with the two-stream model in the offline Community Land Model (CLM4.0) shows that an increase of canopy absorption mainly happens with sparse vegetation or with multilayer canopies with a large sun zenith angle θ sun and is due to increases of the ground and sky shadows and of the optical pathlength because of the shadow overlapping between bushes and canopy layers. A decrease of canopy absorption occurs in densely vegetated areas with small θ sun. For a one-layer canopy, these decreases are due to crown shape effects that enhance the transmission through the canopy edge. For a multilayer canopy, aside from these shape effects, transmission is also increased by the decreased ground shadow due to the shadow overlapping between layers. Ground absorption usually changes with opposite sign as that of the canopy absorption. Somewhat lower albedos are found over most vegetated areas throughout the year. The 3D model also affects the calculation of the fraction of sunlit leaves and their corresponding absorption.
Abstract
Turbulent mixing in the planetary boundary layer (PBL) governs the vertical exchange of heat, moisture, momentum, trace gases, and aerosols in the surface–atmosphere interface. The PBL height (PBLH) represents the maximum height of the free atmosphere that is directly influenced by Earth’s surface. This study uses a multidata synthesis approach from an ensemble of multiple global datasets of radiosonde observations, reanalysis products, and climate model simulations to examine the spatial patterns of long-term PBLH trends over land between 60°S and 60°N for the period 1979–2019. By considering both the sign and statistical significance of trends, we identify large-scale regions where the change signal is robust and consistent to increase our confidence in the obtained results. Despite differences in the magnitude and sign of PBLH trends over many areas, all datasets reveal a consensus on increasing PBLH over the enormous and very dry Sahara Desert and Arabian Peninsula (SDAP) and declining PBLH in India. At the global scale, the changes in PBLH are significantly correlated positively with the changes in surface heating and negatively with the changes in surface moisture, consistent with theory and previous findings in the literature. The rising PBLH is in good agreement with increasing sensible heat and surface temperature and decreasing relative humidity over the SDAP associated with desert amplification, while the declining PBLH resonates well with increasing relative humidity and latent heat and decreasing sensible heat and surface warming in India. The PBLH changes agree with radiosonde soundings over the SDAP but cannot be validated over India due to lack of good-quality radiosonde observations.
Abstract
Turbulent mixing in the planetary boundary layer (PBL) governs the vertical exchange of heat, moisture, momentum, trace gases, and aerosols in the surface–atmosphere interface. The PBL height (PBLH) represents the maximum height of the free atmosphere that is directly influenced by Earth’s surface. This study uses a multidata synthesis approach from an ensemble of multiple global datasets of radiosonde observations, reanalysis products, and climate model simulations to examine the spatial patterns of long-term PBLH trends over land between 60°S and 60°N for the period 1979–2019. By considering both the sign and statistical significance of trends, we identify large-scale regions where the change signal is robust and consistent to increase our confidence in the obtained results. Despite differences in the magnitude and sign of PBLH trends over many areas, all datasets reveal a consensus on increasing PBLH over the enormous and very dry Sahara Desert and Arabian Peninsula (SDAP) and declining PBLH in India. At the global scale, the changes in PBLH are significantly correlated positively with the changes in surface heating and negatively with the changes in surface moisture, consistent with theory and previous findings in the literature. The rising PBLH is in good agreement with increasing sensible heat and surface temperature and decreasing relative humidity over the SDAP associated with desert amplification, while the declining PBLH resonates well with increasing relative humidity and latent heat and decreasing sensible heat and surface warming in India. The PBLH changes agree with radiosonde soundings over the SDAP but cannot be validated over India due to lack of good-quality radiosonde observations.
Abstract
The authors establish the effect of urbanization on precipitation in the Pearl River Delta of China with data from an annual land use map (1988–96) derived from Landsat images and monthly climate data from 16 local meteorological stations. A statistical analysis of the relationship between climate and urban land use in concentric buffers around the stations indicates that there is a causal relationship from temporal and spatial patterns of urbanization to temporal and spatial patterns of precipitation during the dry season. Results suggest an urban precipitation deficit in which urbanization reduces local precipitation. This reduction may be caused by changes in surface hydrology that extend beyond the urban heat island effect and energy-related aerosol emissions.
Abstract
The authors establish the effect of urbanization on precipitation in the Pearl River Delta of China with data from an annual land use map (1988–96) derived from Landsat images and monthly climate data from 16 local meteorological stations. A statistical analysis of the relationship between climate and urban land use in concentric buffers around the stations indicates that there is a causal relationship from temporal and spatial patterns of urbanization to temporal and spatial patterns of precipitation during the dry season. Results suggest an urban precipitation deficit in which urbanization reduces local precipitation. This reduction may be caused by changes in surface hydrology that extend beyond the urban heat island effect and energy-related aerosol emissions.
Abstract
This study simulates the impacts of real-world wind farms on land surface temperature (LST) using the Weather Research and Forecasting (WRF) Model driven by realistic initial and boundary conditions. The simulated wind farm impacts are compared with the observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the first Wind Forecast Improvement Project (WFIP) field campaign. Simulations are performed over west-central Texas for the month of July throughout 7 years (2003–04 and 2010–14). Two groups of experiments are conducted: 1) direct validations of the simulated LST changes between the preturbine period (2003–04) and postturbine period (2010–14) validated against the MODIS observations; and 2) a model sensitivity test of LST to the wind turbine parameterization by examining LST differences with and without the wind turbines for the postturbine period. Overall, the WRF Model is moderately successful at reproducing the observed spatiotemporal variations of the background LST but has difficulties in reproducing such variations for the turbine-induced LST change signals at pixel levels. However, the model is still able to reproduce coherent and consistent responses of the observed LST changes at regional scales. The simulated wind farm–induced LST warming signals agree well with the satellite observations in terms of their spatial coupling with the wind farm layout. Moreover, the simulated areal mean warming signal (0.20°–0.26°C) is about a tenth of a degree smaller than that from MODIS (0.33°C). However, these results suggest that the current wind turbine parameterization tends to induce a cooling effect behind the wind farm region at nighttime, which has not been confirmed by previous field campaigns and satellite observations.
Abstract
This study simulates the impacts of real-world wind farms on land surface temperature (LST) using the Weather Research and Forecasting (WRF) Model driven by realistic initial and boundary conditions. The simulated wind farm impacts are compared with the observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the first Wind Forecast Improvement Project (WFIP) field campaign. Simulations are performed over west-central Texas for the month of July throughout 7 years (2003–04 and 2010–14). Two groups of experiments are conducted: 1) direct validations of the simulated LST changes between the preturbine period (2003–04) and postturbine period (2010–14) validated against the MODIS observations; and 2) a model sensitivity test of LST to the wind turbine parameterization by examining LST differences with and without the wind turbines for the postturbine period. Overall, the WRF Model is moderately successful at reproducing the observed spatiotemporal variations of the background LST but has difficulties in reproducing such variations for the turbine-induced LST change signals at pixel levels. However, the model is still able to reproduce coherent and consistent responses of the observed LST changes at regional scales. The simulated wind farm–induced LST warming signals agree well with the satellite observations in terms of their spatial coupling with the wind farm layout. Moreover, the simulated areal mean warming signal (0.20°–0.26°C) is about a tenth of a degree smaller than that from MODIS (0.33°C). However, these results suggest that the current wind turbine parameterization tends to induce a cooling effect behind the wind farm region at nighttime, which has not been confirmed by previous field campaigns and satellite observations.
