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Assessment and simulation of global terrestrial latent heat flux by synthesis of CMIP5 climate models and surface eddy covariance observations

Yao, Yunjun; Liang, Shunlin; Li, Xianglan; Liu, Shaomin; Chen, Jiquan; Zhang, Xiaotong; Jia, Kun; Jiang, Bo; Xie, Xianhong and Munier, Simon, et al. (2016) In Agricultural and Forest Meteorology 223. p.151-167
Abstract

The latent heat flux (LE) between the terrestrial biosphere and atmosphere is a major driver of the global hydrological cycle. In this study, we evaluated LE simulations by 45 general circulation models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) by a comparison with eddy covariance (EC) observations from 240 globally distributed sites from 2000 to 2009. In addition, we improved global terrestrial LE estimates for different land cover types by synthesis of seven best CMIP5 models and EC observations based on a Bayesian model averaging (BMA) method. The comparison results showed substantial differences in monthly LE among all GCMs. The model CESM1-CAM5 has the best performance with the highest predictive skill and... (More)

The latent heat flux (LE) between the terrestrial biosphere and atmosphere is a major driver of the global hydrological cycle. In this study, we evaluated LE simulations by 45 general circulation models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) by a comparison with eddy covariance (EC) observations from 240 globally distributed sites from 2000 to 2009. In addition, we improved global terrestrial LE estimates for different land cover types by synthesis of seven best CMIP5 models and EC observations based on a Bayesian model averaging (BMA) method. The comparison results showed substantial differences in monthly LE among all GCMs. The model CESM1-CAM5 has the best performance with the highest predictive skill and a Taylor skill score (S) from 0.51-0.75 for different land cover types. The cross-validation results illustrate that the BMA method has improved the accuracy of the CMIP5 GCM's LE simulation with a decrease in the averaged root-mean-square error (RMSE) by more than 3 W/m2 when compared to the simple model averaging (SMA) method and individual GCMs. We found an increasing trend in the BMA-based global terrestrial LE (slope of 0.018 W/m2 yr-1, p <0.05) during the period 1970-2005. This variation may be attributed directly to the inter-annual variations in air temperature (Ta), surface incident solar radiation (Rs) and precipitation (P). However, our study highlights a large difference from previous studies in a continuous increasing trend after 1998, which may be caused by the combined effects of the variations of Rs, Ta, and P on LE for different models on these time scales. This study provides corrected-modeling evidence for an accelerated global water cycle with climate change.

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publication status
published
subject
keywords
BMA, CMIP5, GCMs, Global terrestrial LE, Taylor skill score
in
Agricultural and Forest Meteorology
volume
223
pages
17 pages
publisher
Elsevier
external identifiers
  • scopus:84963760995
  • wos:000376835000014
ISSN
0168-1923
DOI
10.1016/j.agrformet.2016.03.016
language
English
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yes
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e447e5d9-5967-4966-8229-feb8a68722b2
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2016-05-10 07:48:01
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2017-09-17 09:13:04
@article{e447e5d9-5967-4966-8229-feb8a68722b2,
  abstract     = {<p>The latent heat flux (LE) between the terrestrial biosphere and atmosphere is a major driver of the global hydrological cycle. In this study, we evaluated LE simulations by 45 general circulation models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) by a comparison with eddy covariance (EC) observations from 240 globally distributed sites from 2000 to 2009. In addition, we improved global terrestrial LE estimates for different land cover types by synthesis of seven best CMIP5 models and EC observations based on a Bayesian model averaging (BMA) method. The comparison results showed substantial differences in monthly LE among all GCMs. The model CESM1-CAM5 has the best performance with the highest predictive skill and a Taylor skill score (S) from 0.51-0.75 for different land cover types. The cross-validation results illustrate that the BMA method has improved the accuracy of the CMIP5 GCM's LE simulation with a decrease in the averaged root-mean-square error (RMSE) by more than 3 W/m<sup>2</sup> when compared to the simple model averaging (SMA) method and individual GCMs. We found an increasing trend in the BMA-based global terrestrial LE (slope of 0.018 W/m<sup>2</sup> yr<sup>-1</sup>, p &lt;0.05) during the period 1970-2005. This variation may be attributed directly to the inter-annual variations in air temperature (T<sub>a</sub>), surface incident solar radiation (R<sub>s</sub>) and precipitation (P). However, our study highlights a large difference from previous studies in a continuous increasing trend after 1998, which may be caused by the combined effects of the variations of R<sub>s</sub>, T<sub>a</sub>, and P on LE for different models on these time scales. This study provides corrected-modeling evidence for an accelerated global water cycle with climate change.</p>},
  author       = {Yao, Yunjun and Liang, Shunlin and Li, Xianglan and Liu, Shaomin and Chen, Jiquan and Zhang, Xiaotong and Jia, Kun and Jiang, Bo and Xie, Xianhong and Munier, Simon and Liu, Meng and Yu, Jian and Lindroth, Anders and Varlagin, Andrej and Raschi, Antonio and Noormets, Asko and Pio, Casimiro and Wohlfahrt, Georg and Sun, Ge and Domec, Jean Christophe and Montagnani, Leonardo and Lund, Magnus and Eddy, Moors and Blanken, Peter D. and Grünwald, Thomas and Wolf, Sebastian and Magliulo, Vincenzo},
  issn         = {0168-1923},
  keyword      = {BMA,CMIP5,GCMs,Global terrestrial LE,Taylor skill score},
  language     = {eng},
  month        = {06},
  pages        = {151--167},
  publisher    = {Elsevier},
  series       = {Agricultural and Forest Meteorology},
  title        = {Assessment and simulation of global terrestrial latent heat flux by synthesis of CMIP5 climate models and surface eddy covariance observations},
  url          = {http://dx.doi.org/10.1016/j.agrformet.2016.03.016},
  volume       = {223},
  year         = {2016},
}