TY - JOUR
T1 - Sensitivity and Uncertainty of a Long-Term, High-Resolution, Global, Terrestrial Sensible Heat Flux Data Set
AU - Siemann, Amanda L.
AU - Chaney, Nathaniel
AU - Wood, Eric F.
N1 - Funding Information:
This work is supported by NOAA grant NA11OAR4310175. The data sets sup porting these conclusions are available at http://hydrology.princeton.edu/~sie- mann/data/Sensitivity_Uncertainty/ and questions can be directed to the corresponding author, Amanda L. Siemann at siemann@princeton.edu.
PY - 2018/5/27
Y1 - 2018/5/27
N2 - Sensible heat flux directly influences local and regional climate and can be estimated using remotely sensed satellite observations. Although significant efforts have been made to estimate sensitivity and uncertainty in energy flux estimates at the local and regional scales using both models and algorithms compatible with remotely sensed satellite data, few studies quantify the sensitivity or uncertainty at the global scale, enabling a global comparison among uncertainty drivers. This study uses the 10 percentile change from the mean value in the empirical cumulative distribution function for the distribution of each input data set to calculate the sensitivity of the unconstrained, terrestrial sensible heat flux to change in the input data sets and uses this sensitivity in a first-order analysis of the uncertainty in the sensible heat flux. The largest sensitivities to the Zilitinkevich empirical constant (Czil) are in the Amazon, northern Australia, and the plains of North America, while the sensitivity of the sensible heat flux to the temperature gradient is largest in dry regions of shorter vegetation. The Czil contributes most to the uncertainty of over 50–100 W/m2 in the Amazon and Indonesia, while the temperature gradient contributes most to the uncertainty elsewhere, producing an overall global average uncertainty of 24.8 W/m2. Future work should reduce the uncertainties in the temperature gradient and the Czil to reduce the uncertainty in sensible heat flux estimates.
AB - Sensible heat flux directly influences local and regional climate and can be estimated using remotely sensed satellite observations. Although significant efforts have been made to estimate sensitivity and uncertainty in energy flux estimates at the local and regional scales using both models and algorithms compatible with remotely sensed satellite data, few studies quantify the sensitivity or uncertainty at the global scale, enabling a global comparison among uncertainty drivers. This study uses the 10 percentile change from the mean value in the empirical cumulative distribution function for the distribution of each input data set to calculate the sensitivity of the unconstrained, terrestrial sensible heat flux to change in the input data sets and uses this sensitivity in a first-order analysis of the uncertainty in the sensible heat flux. The largest sensitivities to the Zilitinkevich empirical constant (Czil) are in the Amazon, northern Australia, and the plains of North America, while the sensitivity of the sensible heat flux to the temperature gradient is largest in dry regions of shorter vegetation. The Czil contributes most to the uncertainty of over 50–100 W/m2 in the Amazon and Indonesia, while the temperature gradient contributes most to the uncertainty elsewhere, producing an overall global average uncertainty of 24.8 W/m2. Future work should reduce the uncertainties in the temperature gradient and the Czil to reduce the uncertainty in sensible heat flux estimates.
KW - remotely sensed satellite data
KW - sensible heat flux
KW - sensitivity
KW - uncertainty
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U2 - 10.1029/2017JD027785
DO - 10.1029/2017JD027785
M3 - Article
AN - SCOPUS:85047638433
VL - 123
SP - 4988
EP - 5000
JO - Journal of Geophysical Research: Atmospheres
JF - Journal of Geophysical Research: Atmospheres
SN - 2169-897X
IS - 10
ER -