Reducing solar radiation forcing uncertainty and its impact on surface energy and water fluxes

Peng，Liqing1,2; Wei，Zhongwang3,4; Zeng，Zhenzhong5; Lin，Peirong1; Wood，Eric F.1; Sheffield，Justin6

2021-04-01

10.1175/JHM-D-20-0052.1

JOURNAL OF HYDROMETEOROLOGY   影响因子和分区

1525-755X

1525-7541

Downward shortwave radiation R determines the surface energy balance, alters evapotranspiration and hydrological conditions, and feeds back to the regional and global climate. Large-scale R estimates are usually retrieved from satellite-based top-of-atmosphere radiation and cloud parameters. These estimates are subject to biases and temporal inhomogeneity due to errors in atmospheric parameters, algorithms, and sensor changes. We found that three satellite products overestimate R by 8%-10% over Asia for 1984-2006, particularly in high latitudes. We used the model tree ensemble (MTE) machine-learning algorithm and commonly used ensemble averaging methods to integrate ground observations and satellite products. Validations based on test stations and independent networks showed that the MTE approach reduces the median relative biases from 8%-10% to 2%, which is more effective than the ensemble averaging methods. We further evaluated the impacts of uncertainty in radiation forcing on surface energy and water balances using the land surface model Noah-MP. The uncertainty of radiation data affects the prediction of sensible heat the most, and also largely affects latent heat prediction in humid regions. Holding the other variables constant, a 10% positive bias in R can lead to a 20%-60% positive bias in the monthly median sensible heat. The simulated hydrological responses to changing radiation forcing are nonlinear as a result of the interactions among evapotranspiration, snowpack, and soil moisture. Our analysis concludes that reducing uncertainty of radiation data is beneficial for predicting regional energy and water balances, which requires more high-quality ground observations and improved satellite retrieval algorithms.

Heat budgets/fluxes Hydrology In situ atmospheric observations Interpolation schemes Land surface model Machine learning Satellite observations

SCI

英语

其他

WOS:000644214500005

GEOSCIENCES

2-s2.0-85103459205

Scopus

1.Department of Civil and Environmental Engineering,Princeton University,Princeton,United States
2.Food Program,World Resources Institute,Washington,United States
3.Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies,School of Atmospheric Sciences,Sun Yat-Sen University,Guangzhou,China
4.Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),Zhuhai,China
5.School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,China
6.School of Geography and Environmental Science,University of Southampton,Southampton,United Kingdom

Peng，Liqing,Wei，Zhongwang,Zeng，Zhenzhong,et al. Reducing solar radiation forcing uncertainty and its impact on surface energy and water fluxes[J]. JOURNAL OF HYDROMETEOROLOGY,2021,22(4):813-829.

Peng，Liqing,Wei，Zhongwang,Zeng，Zhenzhong,Lin，Peirong,Wood，Eric F.,&Sheffield，Justin.(2021).Reducing solar radiation forcing uncertainty and its impact on surface energy and water fluxes.JOURNAL OF HYDROMETEOROLOGY,22(4),813-829.

Peng，Liqing,et al."Reducing solar radiation forcing uncertainty and its impact on surface energy and water fluxes".JOURNAL OF HYDROMETEOROLOGY 22.4(2021):813-829.