Search Results

You are looking at 1 - 9 of 9 items for :

  • Model performance/evaluation x
  • All content x
Clear All
Lifen Jiang, Yaner Yan, Oleksandra Hararuk, Nathaniel Mikle, Jianyang Xia, Zheng Shi, Jerry Tjiputra, Tongwen Wu, and Yiqi Luo

wide range in current permafrost areas, active layer parameters, and model ability to simulate the coupling between soil and air temperatures ( Koven et al. 2013 ). Additionally, projected loss of permafrost extent in response to climate change also varied greatly between the models. Evaluating the modelsperformance and understanding the sources of uncertainties in the simulated contemporary state of the land carbon cycle are essential steps forward to improve the credibility of future climate

Full access
A. Anav, P. Friedlingstein, M. Kidston, L. Bopp, P. Ciais, P. Cox, C. Jones, M. Jung, R. Myneni, and Z. Zhu

models at the global scale or over large latitudinal bands (see below). For all other model variables, the evaluation is performed at the grid level, conserving the spatial information. However, when presenting the results, all model performances are averaged over the following domains for land variables: global (90°S–90°N), Southern Hemisphere (20°–90°S), Northern Hemisphere (20°–90°N), and the tropics (20°S–20°N). Considering the ocean carbon, according to Gruber et al. (2009) , we aggregate

Full access
Pu Shao, Xubin Zeng, Koichi Sakaguchi, Russell K. Monson, and Xiaodong Zeng

associated microorganisms ( Bond-Lamberty and Thomson 2010 ), and a more detailed Rh evaluation of these eight ESMs using in situ observations is reported in a separate study (Shao et al. 2013, manuscript submitted to Environ. Res. Lett. ). Note that the performances of CCSM4 and NorESM1-M are similar in predicting global GPP, NPP, Ra, and Rh because they share the same land model (CLM4CN). For instance, their Rg is higher than others, which is calculated as a higher fraction of the fixed carbon [ A

Full access
ChuanLi Jiang, Sarah T. Gille, Janet Sprintall, and Colm Sweeney

with in situ values is not straightforward. In addition, its estimation through the bulk algorithm described above ( Sweeney et al. 2007 ; Takahashi et al. 2009 ), using the measured p CO 2 and the other available data products, results in large variations and uncertainties. This further complicates the evaluation of the CMIP5 model performance. Hence, in the following we present only an intermodel comparison of the sea-to-air CO 2 flux from the eight CMIP5 ESMS ( Fig. 3b ) with no comparison

Full access
Pierre Friedlingstein, Malte Meinshausen, Vivek K. Arora, Chris D. Jones, Alessandro Anav, Spencer K. Liddicoat, and Reto Knutti

simulations and their evaluation against observations; sections 4 and 5 analyze the twenty-first-century changes in CO 2 , temperature, and the global carbon cycle. 2. CMIP5 experiments, emission-driven protocol, and model implementations Despite the importance of the carbon cycle and its feedback on the climate system, the CMIP5 experiment protocol was designed to allow participation of groups having a climate model without an interactive carbon cycle as well as groups having an ESM including the

Full access
Charles D. Koven, William J. Riley, and Alex Stern

( Ciais et al. 2012 ; DeConto et al. 2012 ). Here we analyze output from a set of earth system models (ESMs) ( Table 1 ) that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5) ( Taylor et al. 2009 ) to evaluate the permafrost model predictions against observations and theoretical expectations and to compare the predicted fate of permafrost under warming scenarios. Because the models participating in this exercise do not include critical process representation needed to

Full access
Vivek K. Arora, George J. Boer, Pierre Friedlingstein, Michael Eby, Chris D. Jones, James R. Christian, Gordon Bonan, Laurent Bopp, Victor Brovkin, Patricia Cadule, Tomohiro Hajima, Tatiana Ilyina, Keith Lindsay, Jerry F. Tjiputra, and Tongwen Wu

global carbon cycle in the Canadian Earth System Model (CanESM1): Preindustrial control simulation . J. Geophys. Res. , 115 , G03014 , doi:10.1029/2008JG000920 . Coleman , K. , and D. S. Jenkinson , 1999 : ROTHC-26.3: A model for the turnover of carbon in soil. IACR Rep., 43 pp [Available online at http://www.rothamsted.ac.uk/aen/carbon/mod26_3_dos.pdf .] Collins , W. J. , and Coauthors , 2011 : Development and evaluation of an Earth-system model—HadGEM2 . Geosci. Model Dev. , 4

Full access
V. Brovkin, L. Boysen, V. K. Arora, J. P. Boisier, P. Cadule, L. Chini, M. Claussen, P. Friedlingstein, V. Gayler, B. J. J. M. van den Hurk, G. C. Hurtt, C. D. Jones, E. Kato, N. de Noblet-Ducoudré, F. Pacifico, J. Pongratz, and M. Weiss

model Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg (JSBACH; Raddatz et al. 2007 ) sharing the horizontal grid of the atmospheric model. This grid setup is a low-resolution version (LR) of the model used for centennial-time-scale simulations in CMIP5. A detailed description of the model and an evaluation of the model performance regarding temperature and precipitation fields is given by Giorgetta et al. (2013) . The land surface model of MPI-ESM, JSBACH ( Raddatz et al. 2007

Full access
Jörg Schwinger, Jerry F. Tjiputra, Christoph Heinze, Laurent Bopp, James R. Christian, Marion Gehlen, Tatiana Ilyina, Chris D. Jones, David Salas-Mélia, Joachim Segschneider, Roland Séférian, and Ian Totterdell

control experiment) of 3.7–5.3 K in the fully coupled simulation ( Fig. 3a ), the response in the BGC experiments remains below 10% of these values, except for HadGEM2-ES, where it is found to be 16%. A very similar picture emerges when evaluating Δ T COU − Δ T RAD , and we find a maximum difference of 0.13 K between Δ T COU and Δ T BGC + Δ T RAD . Using a γ value of 20 Pg C K −1 , which is at the upper end of values calculated for the set of CMIP5 models ( Table 2 ), this temperature

Full access