The flexible global ocean-atmosphere-land system model, Grid-point Version 2: FGOALS-g2

Li, Lijuan; Lin, Pengfei; Yu, Yongqiang; Wang, Bin; Zhou, Tianjun; Liu, Li; Liu, Jiping; Bao, Qing; Xu, Shiming; Huang, Wenyu; Xia, Kun; Pu, Ye; Dong, Lijun; Shen, Shuling; Liu, Yimin; Hu, Ning; Liu, Mimi; Sun, Wenqi; Shi, Xiangjun; Zheng, Weipeng; Wu, Bo; Song, Mirong; Liu, Hailong; Zhang, Xuehong; Wu, Guoxiong; Xue, Wei; Huang, Xiaomeng; Yang, Guangwen; Song, Zhenya; Qiao, Fangli

doi:10.1007/s00376-012-2140-6

Cited by 257 publications

(102 citation statements)

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“…For simulations, we use historical and RCP4.5 runs from 44 different GCMs from the CMIP5 archive [Taylor et al, 2012;Bi et al, 2013;Xin et al, 2013;Ji et al, 2014;von Salzen et al, 2013;Meehl et al, 2012;Hurrell et al, 2012;Scoccimarro et al, 2011;Voldoire et al, 2013;Rotstayn et al, 2010;Hazeleger et al, 2010;Li et al, 2013;Delworth et al, 2006;Donner et al, 2011;Schmidt et al, 2014;Smith et al, 2010;Collins et al, 2011;Jones et al, 2011;Volodin et al, 2010;Dufresne et al, 2013;Hourdin et al, 2013;Sakamoto et al, 2012;Watanabe et al, 2010Watanabe et al, , 2011Giorgetta et al, 2013;Yukimoto et al, 2012;Bentsen et al, 2013], and from the 100 realization single-model large ensemble of the MPI-ESM [Giorgetta et al, 2013]. The large ensemble uses the model version MPI-ESM1.1 in low resolution (LR) configuration, with resolution T63 and 47 vertical levels in the atmosphere and 1.5 ∘ resolution and 40 vertical levels in the ocean.…”

Section: Datamentioning

confidence: 99%

Internal variability in simulated and observed tropical tropospheric temperature trends

Suárez-Gutiérrez

Thorne

et al. 2017

Geophysical Research Letters

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We explore the extent to which internal variability can reconcile discrepancies between observed and simulated warming in the upper tropical troposphere. We compare all extant radiosonde‐based estimates for the period 1958–2014 to simulations from the Coupled Model Intercomparison Project phase 5 multimodel ensemble and the 100 realization Max Planck Institute large ensemble. We consider annual mean temperatures and all available 30‐and 15‐year trends. Most observed trends fall within the ensemble spread for most of the record, and trends calculated over 15‐year periods show better agreement than 30‐year trends, with generally larger discrepancies for the older observational products. The simulated amplification of surface warming aloft in the troposphere is consistent with observations, and the linear correlation between surface and simultaneous tropospheric warming trends decreases with trend length. We conclude that trend differences between observations and simulations of tropical tropospheric temperatures are dominated by observational uncertainty and chaotic internal variability rather than by systematic errors in model performance.

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Section: Datamentioning

confidence: 99%

Internal variability in simulated and observed tropical tropospheric temperature trends

Suárez-Gutiérrez

Thorne

et al. 2017

Geophysical Research Letters

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“…This assumption may not hold true for proxy reconstructions, and a number of modifications to the standard technique have been made here to account for this complication. First, the control run segments were converted into ''pseudo-reconstructions'' by adding a reconstruction uncertainty, modeled as a random Normal time series for each sector/season with a mean of 0 and a [Gent et al, 2011] 501 5 3 CESM1-CAM5 [Hurrell et al, 2013] 235 3 1 CSIRO-Mk3-6 [Rotstayn et al, 2012] 500 5 5 FGOALS-g2 [Li et al, 2013] 700 3 3 HadGEM2-ES [Martin et al, 2011] 576 5 0 IPSL-CM5A-LR [Mignot and Bony, 2013] 1000 5 0 IPSL-CM5A-MR [Mignot and Bony, 2013] 300 3 2 MIROC-ESM [Watanabe et al, 2011] 630 3 3 MIROC5 [Watanabe et al, 2010] 670 5 0 NorESM1-M [Bentsen et al, 2012] (Table 1). Second, in general total least squares regression is used to estimate b since uncertainty in the response vector X can be accounted for [Allen and Stott, 2003;Hannart et al, 2014], but this requires a parametric estimate of the internal variability b-range that does not account for observational error.…”

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confidence: 99%

Century‐scale perspectives on observed and simulated Southern Ocean sea ice trends from proxy reconstructions

et al. 2016

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Since 1979 when continuous satellite observations began, Southern Ocean sea ice cover has increased, whilst global coupled climate models simulate a decrease over the same period. It is uncertain whether the observed trends are anthropogenically forced or due to internal variability, or whether the apparent discrepancy between models and observations can be explained by internal variability. The shortness of the satellite record is one source of this uncertainty, and a possible solution is to use proxy reconstructions, which extend the analysis period but at the expense of higher observational uncertainty. In this work, we evaluate the utility for change detection of 20th century Southern Ocean sea ice proxies. We find that there are reliable proxies for the East Antarctic, Amundsen, Bellingshausen and Weddell sectors in late winter, and for the Weddell Sea in late autumn. Models and reconstructions agree that sea ice extent in the East Antarctic, Amundsen and Bellingshausen Seas has decreased since the early 1970s, consistent with an anthropogenic response. However, the decrease is small compared to internal variability, and the change is not robustly detectable. We also find that optimal fingerprinting filters out much of the uncertainty in proxy reconstructions. The Ross Sea is a confounding factor, with a significant increase in sea ice since 1979 that is not captured by climate models; however, existing proxy reconstructions of this region are not yet sufficiently reliable for formal change detection.

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“…The Flexible Global Ocean-Atmosphere-Land System Model, Grid point Version 2 (F GOALS− g2), used in this study, is a climate model from the fifth phase of the Coupled Model Intercomparison Project (CMIP 5) which is composed of separate component models of the atmosphere, ocean, sea ice, and land surface (Li et al, 2013). FGOALS-g2 is one of the newest version of FGOALS; it has been developed by a community of scientists and graduate students from the Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), the Institute of Atmospheric Physics (IAP) of the Chinese Academy of Sciences, Center for Earth System Science (CESS)/Tsinghua University, and the First Institute of Oceanography (FIO)/State Oceanic Administration.…”

Section: Modeled Hydrological Excitation Of Polar Motionmentioning

confidence: 99%

Hydrological Excitations of Polar Motion Derived from Different Variables of Fgoals − g2 Climate Model

Wińska

2016

Artificial Satellites

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The hydrological contribution to decadal, inter-annual and multi-annual suppress polar motion derived from climate model as well as from GRACE (Gravity Recovery and Climate Experiment) data is discussed here for the period 2002.3-2016.0. The data set used here are Earth Orientation Parameters Combined 04 (EOP C04), Flexible Global Ocean-Atmosphere-Land System Model: Grid-point Version 2 (FGOAL-g2) and Global Land Data Assimilation System (GLDAS) climate models and GRACE CSR RL05 data for polar motion, hydrological and gravimetric excitation, respectively. Several Hydrological Angular Momentum (HAM) functions are calculated here from the selected variables: precipitation, evaporation, runoff, soil moisture, accumulated snow of the FGOALS and GLDAS climate models as well as from the global mass change fields from GRACE data provided by the International Earth Rotation and Reference System Service (IERS) Global Geophysical Fluids Center (GGFC). The contribution of different HAM excitation functions to achieve the full agreement between geodetic observations and geophysical excitation functions of polar motion is studied here.

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The flexible global ocean-atmosphere-land system model, Grid-point Version 2: FGOALS-g2

Cited by 257 publications

References 50 publications

Internal variability in simulated and observed tropical tropospheric temperature trends

Internal variability in simulated and observed tropical tropospheric temperature trends

Century‐scale perspectives on observed and simulated Southern Ocean sea ice trends from proxy reconstructions

Hydrological Excitations of Polar Motion Derived from Different Variables of Fgoals − g2 Climate Model

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