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

作者:Yao, YJ (Yao, Yunjun)[ 1 ] ; Liang, SL (Liang, Shunlin)[ 1 ] ; Li, XL (Li, Xianglan)[ 2 ] ; Liu, SM (Liu, Shaomin)[ 1 ] ; Chen, JQ (Chen, Jiquan)[ 3 ] ; Zhang, XT (Zhang, Xiaotong)[ 1 ] ; Jia, K (Jia, Kun)[ 1 ] ; Jiang, B (Jiang, Bo)[ 1 ] ; Xie, XH (Xie, Xianhong)[ 1 ] ; Munier, S (Munier, Simon)[ 4 ] ; Liu, M (Liu, Meng)[ 1 ] ; Yu, J (Yu, Jian)[ 1 ] ; Lindroth, A (Lindroth, Anders)[ 5 ] ; Varlagin, A (Varlagin, Andrej)[ 6 ] ; Raschi, A (Raschi, Antonio)[ 7 ] ; Noormets, A (Noormets, Asko)[ 8 ] ; Pio, C (Pio, Casimiro)[ 9 ] ; Wohlfahrt, G (Wohlfahrt, Georg)[ 10,11 ] ; Sun, G (Sun, Ge)[ 12 ] ; Domec, JC (Domec, Jean-Christophe)[ 13,14 ] ; Montagnani, L (Montagnani, Leonardo)[ 15,16 ] ; Lund, M (Lund, Magnus)[ 17 ] ; Eddy, M (Eddy, Moors)[ 18,19 ] ; Blanken, PD (Blanken, Peter D.)[ 20 ] ; Grunwald, T (Gruenwald, Thomas)[ 21 ] ; Wolf, S (Wolf, Sebastian)[ 22 ] ; Magliulo, V (Magliulo, Vincenzo)[ 23 ] 

AGRICULTURAL AND FOREST METEOROLOGY

卷: 223  页: 151-167

DOI: 10.1016/j.agrformet.2016.03.016

出版年: JUN 15 2016

摘要

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(2) 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(2) yr(-1), p < 0.05) during the period 1970-2005. This variation may be attributed directly to the inter-annual variations in air temperature (T-a), surface incident solar radiation (R-s) 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-s, T-a, 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. (C) 2016 Elsevier B.V. All rights reserved.

通讯作者地址: Yao, YJ (通讯作者)

Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China

[ 3 ] Michigan State Univ, CGCEO Geog, E Lansing, MI 48824 USA

[ 4 ] LEGOS CNES CN RS IRD UPS UMR5566, Lab Detudes & Phys & Oceanographie Spatiles, Toulouse, France

[ 5 ] Lund Univ, GeoBiosphere Sci Ctr, S-22362 Lund, Sweden

[ 6 ] ANSevertsov Inst Ecol & Evolut RAS 123103, Moscow, Russia

[ 7 ] CNR, CNR IBIMET, I-50145 Florence, Italy

[ 8 ] N Carolina State Univ, Dept Forestry & Environm Resources, Raleigh, NC 27695 USA

[ 9 ] Univ Aveiro, CESAM & Dept Ambiente Ordenamento, P-3810193 Aveiro, Portugal

[ 10 ] Univ Innsbruck, Inst Ecol, A-6020 Innsbruck, Austria

[ 11 ] European Acad Bolzano, I-39100 Bolzano, Italy

[ 12 ] United States Dept Agr Forest Serv, Southern Res Stn, Eastern Forest Environm Threat Assessment Ctr, Raleigh, NC 27606 USA

[ 13 ] UMR INRA ISPA 1391, Bordeaux Sci Agro, F-33195 Gradignan, France

[ 14 ] Duke Univ, Nicholas Sch Environm, Durham, NC 27708 USA

[ 15 ] Free Univ Bolzano, Fac Sci & Technol, I-39100 Bolzano, Italy

[ 16 ] Autonomous Prov Bolzano, Forest Serv, I-39100 Bolzano, Italy

[ 17 ] Aarhus Univ, Dept Scibiol, DK-4000 Roskilde, Denmark

[ 18 ] Alterra Wageningen UR, Climate Change & Adapt Land & Water Management, NL-6700 AA Wageningen, Netherlands

[ 19 ] Vrije Univ Amsterdam, NL-1081 HV Amsterdam, Netherlands

[ 20 ] Univ Colorado, Dept Geog, Boulder, CO 80309 USA

[ 21 ] Tech Univ Dresden, Inst Hydrol & Meteorol, Chair Meteorol, D-01062 Dresden, Germany

[ 22 ] Swiss Fed Inst Technol, Inst Agr Sci, CH-8092 Zurich, Switzerland

[ 23 ] CNR ISAFOM, Natl Res Council, I-80040 Naples, Italy