Estimating global aerodynamic parameters in 1982–2017 using remote-sensing data and a turbulent transfer model
TL;DR: In this paper, the authors provide global estimates of these bulk aerodynamic parameters, including d, z0, rb, and rd, for the period 1982-2017 based on remote-sensed leaf area index (LAI), h, and plant functional type dependent canopy morphological characteristics.
About: This article is published in Remote Sensing of Environment.The article was published on 2021-07-01 and is currently open access. It has received 21 citations till now. The article focuses on the topics: Leaf area index & Climate model.
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TL;DR: Using time-series remote sensing products and long-term scenario simulations, the authors in this paper highlighted that crop greening can slow climate warming, which contributed to asymmetric diurnal temperature cycle changes in Northeast China.
8 citations
TL;DR: In this article , the authors used multi-source satellite measurements records and a high-resolution land-atmosphere coupled regional climate model (WRF) to investigate the land surface changes and their associated thermal and moisture impacts across three main ecosystems over the Heilong-Amur River basin from 1982 to 2018.
8 citations
TL;DR: In this article, the authors used multi-source satellite measurements records and a high-resolution land-atmosphere coupled regional climate model to investigate the land surface changes and their associated thermal and moisture impacts across three main ecosystems over the Heilong-Amur River basin (HARB) from 1982 to 2018.
8 citations
TL;DR: In this article, the authors presented a method for global estimates of 500 m daily z 0 m with a combination of machine learning techniques, wind profile equation, observations from 273 sites and MODIS remote sensing data.
Abstract: Aerodynamic roughness length ( z 0 m ) is a key parameter in the characterization of land surface turbulent heat fluxes and widely used in many surface and climate-related process models. The global products of time series of z 0 m at finer spatio-temporal resolution, however, have never been publicly available. Here we presented a practical method for global estimates of 500 m daily z 0 m with a combination of machine learning techniques, wind profile equation, observations from 273 sites and MODIS remote sensing data. Results showed that the random forest (RF) model outperformed the deep neural network (DNN) and convolutional neural network (CNN) models, and it could well reproduce the magnitude and temporal variability of daily z 0 m at almost all sites for all land cover types. In the validation of the RF-estimated daily z 0 m with the in-situ observations, the root mean square error (RMSE) varied between 0.02 m and 0.09 m, the mean absolute error (MAE) varied between 0.01 m and 0.05 m and the coefficient of determination ( R 2 ) was 1 for medium-to-high canopy shrublands, savannas and forests; for short-canopy croplands, grasslands and wetlands, the RMSE and MAE were 0.02 m and 0.01 m, respectively, and the R 2 varied between 0.94 and 1. Compared to the Climate Forecast System Version 2 (CFSv2, 0.3°/monthly) and ECMWF Reanalysis v5 (ERA5, 0.25°/monthly) products in 2019, the RF-estimated z 0 m was found to have the similar global spatial pattern but significantly larger temporal variability, and it also showed a higher and lower global mean of z 0 m over forests and non-forests, respectively. The RF-estimated z 0 m displayed a higher temporal variability but a similar global spatial pattern of this variability compared to the CFSv2, whereas the ERA5 z 0 m product exhibited almost no temporal variability except for grasslands and croplands. This study is beneficial for improving the simulation of the momentum, water and energy transfer between land and atmosphere and helping boost the development of high-resolution land surface models and Earth system models.
7 citations
TL;DR: In this paper , the authors presented a method for global estimates of 500 m daily z0m with a combination of machine learning techniques, wind profile equation, observations from 273 sites and MODIS remote sensing data.
Abstract: Aerodynamic roughness length (z0m) is a key parameter in the characterization of land surface turbulent heat fluxes and widely used in many surface and climate-related process models. The global products of time series of z0m at finer spatio-temporal resolution, however, have never been publicly available. Here we presented a practical method for global estimates of 500 m daily z0m with a combination of machine learning techniques, wind profile equation, observations from 273 sites and MODIS remote sensing data. Results showed that the random forest (RF) model outperformed the deep neural network (DNN) and convolutional neural network (CNN) models, and it could well reproduce the magnitude and temporal variability of daily z0m at almost all sites for all land cover types. In the validation of the RF-estimated daily z0m with the in-situ observations, the root mean square error (RMSE) varied between 0.02 m and 0.09 m, the mean absolute error (MAE) varied between 0.01 m and 0.05 m and the coefficient of determination (R2) was 1 for medium-to-high canopy shrublands, savannas and forests; for short-canopy croplands, grasslands and wetlands, the RMSE and MAE were 0.02 m and 0.01 m, respectively, and the R2 varied between 0.94 and 1. Compared to the Climate Forecast System Version 2 (CFSv2, 0.3°/monthly) and ECMWF Reanalysis v5 (ERA5, 0.25°/monthly) products in 2019, the RF-estimated z0m was found to have the similar global spatial pattern but significantly larger temporal variability, and it also showed a higher and lower global mean of z0m over forests and non-forests, respectively. The RF-estimated z0m displayed a higher temporal variability but a similar global spatial pattern of this variability compared to the CFSv2, whereas the ERA5 z0m product exhibited almost no temporal variability except for grasslands and croplands. This study is beneficial for improving the simulation of the momentum, water and energy transfer between land and atmosphere and helping boost the development of high-resolution land surface models and Earth system models.
7 citations
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TL;DR: Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally, and boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms.
Abstract: Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil's well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.
7,890 citations
TL;DR: It is indicated that global changes in climate have eased several critical climatic constraints to plant growth, such that net primary production increased 6% (3.4 petagrams of carbon over 18 years) globally.
Abstract: Recent climatic changes have enhanced plant growth in northern mid-latitudes and high latitudes. However, a comprehensive analysis of the impact of global climatic changes on vegetation productivity has not before been expressed in the context of variable limiting factors to plant growth. We present a global investigation of vegetation responses to climatic changes by analyzing 18 years (1982 to 1999) of both climatic data and satellite observations of vegetation activity. Our results indicate that global changes in climate have eased several critical climatic constraints to plant growth, such that net primary production increased 6% (3.4 petagrams of carbon over 18 years) globally. The largest increase was in tropical ecosystems. Amazon rain forests accounted for 42% of the global increase in net primary production, owing mainly to decreased cloud cover and the resulting increase in solar radiation.
3,126 citations
TL;DR: The second version of the NCEP Climate Forecast System (CFSv2) was made operational at the National Center for Environmental Prediction (NCEP) in 2011 as discussed by the authors.
Abstract: The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979–2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982–2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the ...
2,520 citations
TL;DR: In this paper, a simple realistic biosphere model for calculating the transfer of energy, mass and momentum between the atmosphere and the vegetated surface of the earth has been developed for use in atmospheric general circulation models.
Abstract: A simple realistic biosphere model for calculating the transfer of energy, mass and momentum between the atmosphere and the vegetated surface of the earth has been developed for use in atmospheric general circulation models. The vegetation in each terrestrial model grid is represented by an upper level, representing the perennial canopy of trees and shrubs, and a lower level, representing the annual cover of grasses and other heraceous species. The vegetation morphology and the physical and physiological properties of the vegetation layers determine such properties as: the reflection, transmission, absorption and emission of direct and diffuse radiation; the infiltration, drainage, and storage of the residual rainfall in the soil; and the control over the stomatal functioning. The model, with prescribed vegetation parameters and soil interactive soil moisture, can be used for prediction of the atmospheric circulation and precipitaion fields for short periods of up to a few weeks.
2,107 citations
TL;DR: A new global land cover database for the year 2000 (GLC2000) has been produced by an international partnership of 30 research groups coordinated by the European Commission's Joint Research Centre as discussed by the authors.
Abstract: A new global land cover database for the year 2000 (GLC2000) has been produced by an international partnership of 30 research groups coordinated by the European Commission's Joint Research Centre. The database contains two levels of land cover information—detailed, regionally optimized land cover legends for each continent and a less thematically detailed global legend that harmonizes regional legends into one consistent product. The land cover maps are all based on daily data from the VEGETATION sensor on‐board SPOT 4, though mapping of some regions involved use of data from other Earth observing sensors to resolve specific issues. Detailed legend definition, image classification and map quality assurance were carried out region by region. The global product was made through aggregation of these. The database is designed to serve users from science programmes, policy makers, environmental convention secretariats, non‐governmental organizations and development‐aid projects. The regional and global data ar...
1,605 citations