CAS FGOALS-f3-L Model Datasets for CMIP6 Historical Atmospheric Model Intercomparison Project Simulation
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摘要
The outputs of the Chinese Academy of Sciences(CAS) Flexible Global Ocean–Atmosphere–Land System(FGOALS-f3-L) model for the baseline experiment of the Atmospheric Model Intercomparison Project simulation in the Diagnostic,Evaluation and Characterization of Klima common experiments of phase 6 of the Coupled Model Intercomparison Project(CMIP6) are described in this paper. The CAS FGOALS-f3-L model, experiment settings, and outputs are all given. In total,there are three ensemble experiments over the period 1979–2014, which are performed with different initial states. The model outputs contain a total of 37 variables and include the required three-hourly mean, six-hourly transient, daily and monthly mean datasets. The baseline performances of the model are validated at different time scales. The preliminary evaluation suggests that the CAS FGOALS-f3-L model can capture the basic patterns of atmospheric circulation and precipitation well, including the propagation of the Madden–Julian Oscillation, activities of tropical cyclones, and the characterization of extreme precipitation. These datasets contribute to the benchmark of current model behaviors for the desired continuity of CMIP.
The outputs of the Chinese Academy of Sciences(CAS) Flexible Global Ocean–Atmosphere–Land System(FGOALS-f3-L) model for the baseline experiment of the Atmospheric Model Intercomparison Project simulation in the Diagnostic,Evaluation and Characterization of Klima common experiments of phase 6 of the Coupled Model Intercomparison Project(CMIP6) are described in this paper. The CAS FGOALS-f3-L model, experiment settings, and outputs are all given. In total,there are three ensemble experiments over the period 1979–2014, which are performed with different initial states. The model outputs contain a total of 37 variables and include the required three-hourly mean, six-hourly transient, daily and monthly mean datasets. The baseline performances of the model are validated at different time scales. The preliminary evaluation suggests that the CAS FGOALS-f3-L model can capture the basic patterns of atmospheric circulation and precipitation well, including the propagation of the Madden–Julian Oscillation, activities of tropical cyclones, and the characterization of extreme precipitation. These datasets contribute to the benchmark of current model behaviors for the desired continuity of CMIP.
The outputs of the Chinese Academy of Sciences(CAS) Flexible Global Ocean–Atmosphere–Land System(FGOALS-f3-L) model for the baseline experiment of the Atmospheric Model Intercomparison Project simulation in the Diagnostic,Evaluation and Characterization of Klima common experiments of phase 6 of the Coupled Model Intercomparison Project(CMIP6) are described in this paper. The CAS FGOALS-f3-L model, experiment settings, and outputs are all given. In total,there are three ensemble experiments over the period 1979–2014, which are performed with different initial states. The model outputs contain a total of 37 variables and include the required three-hourly mean, six-hourly transient, daily and monthly mean datasets. The baseline performances of the model are validated at different time scales. The preliminary evaluation suggests that the CAS FGOALS-f3-L model can capture the basic patterns of atmospheric circulation and precipitation well, including the propagation of the Madden–Julian Oscillation, activities of tropical cyclones, and the characterization of extreme precipitation. These datasets contribute to the benchmark of current model behaviors for the desired continuity of CMIP.
引文
Accadia,C.,S.Mariani,M.Casaioli,A.Lavagnini,and A.Speranza,2003:Sensitivity of precipitation forecast skill scores to bilinear interpolation and a simple nearest-neighbor average method on high-resolution verification grids.Wea.Forecasting,18,918-932,https://doi.org/10.1175/1520-0434(2003)018<0918:SOPFSS>2.0.CO;2.
Adler,R.F.,and Coauthors,2003:The version-2 global precipitation climatology project(GPCP)monthly precipitation analysis(1979-present).Journal of Hydrometeorology,4,1147-1167,https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.
Bao,Q.,G.X.Wu,Y.M.Liu,J.Yang,Z.Z.Wang,and T.J.Zhou,2010:An introduction to the coupled model FGOALS1.1-s and its performance in East Asia.Adv.Atmos.Sci.,27(5),1131-1142,https://doi.org/10.1007/s00376-010-9177-1.
Bao,Q.,and Coauthors,2013:The flexible global oceanatmosphere-land system model,spectral version 2:FGOALS-s2.Adv.Atmos.Sci.,30(3),561-576,https://doi.org/10.1007/s00376-012-2113-9.
Bao,Q.,X.F.Wu,J.X.Li,L.Wang,B.He,X.C.Wang,Y.M.Liu,and G.X.Wu,2019:Outlook for El Ni?o and the Indian Ocean Dipole in autumn-winter 2018-2019.Chinese Science Bulletin,64,73-78,https://doi.org/10.1360/N972018-00913.(in Chinese)
Bretherton,C.S.,and S.Park,2009:A new moist turbulence parameterization in the community atmosphere model.J.Climate,22(12),3422-3448,https://doi.org/10.1175/2008jcli2556.1.
Clough,S.A.,M.W.Shephard,E.J.Mlawer,J.S.Delamere,M.J.Iacono,K.Cady-Pereira,S.Boukabara,and P.D.Brown,2005:Atmospheric radiative transfer modeling:Asummary of the AER codes.Journal of Quantitative Spectroscopy and Radiative Transfer,91(2),233-244,https://doi.org/10.1016/j.jqsrt.2004.05.058.
Dee,D.P.,and Coauthors,2011:The ERA-interim reanalysis:Configuration and performance of the data assimilation system.Quart.J.Roy.Meteor.Soc.,137,553-597,https://doi.org/10.1002/qj.828.
Edwards,J.M.,and A.Slingo,1996:Studies with a flexible new radiation code.I:Choosing a configuration for a large-scale model.Quart.J.Roy.Meteor.Soc.,122,689-719,https://doi.org/10.1002/qj.49712253107.
Eyring,V.,S.Bony,G.A.Meehl,C.A.Senior,B.Stevens,R.J.Stouffer,and K.E.Taylor,2016:Overview of the coupled model intercomparison project phase 6(CMIP6)experimental design and organization.Geoscientific Model Development,9,1937-1958,https://doi.org/10.5194/gmd-9-1937-2016.
Gates,W.L.,1992:AMIP:The atmospheric model intercomparison project.Bull.Amer.Meteor.Soc.,73,1962-1970,https://doi.org/10.1175/1520-0477(1992)073<1962:ATAMIP>2.0.CO;2.
Gates,W.L.,and Coauthors,1999:An overview of the results of the atmospheric model intercomparison project(AMIP I).Bull.Amer.Meteor.Soc.,80,29-56.https://doi.org/10.1175/1520-0477(1999)080<0029:AOOTRO>2.0.CO;2.
Harris,L.M.,and S.-J.Lin,2014:Global-to-regional nested grid climate simulations in the GFDL high resolution atmospheric model.J.Climate,27(13),4890-4910,https://doi.org/10.1175/JCLI-D-13-00596.1.
He,S.C.,J.Yang,Q.Bao,L.Wang,and B.Wang,2019:Fidelity of the observational/reanalysis datasets and global climate models in representation of extreme precipitation in East China.J.Climate,32(1),195-212,https://doi.org/10.1175/JCLI-D-18-0104.1.
Holtslag,A.A.M.,and B.A.Boville,1993:Local versus nonlocal boundary-layer diffusion in a global climate model.J.Climate,6,1825-1842,https://doi.org/10.1175/1520-0442(1993)006<1825:LVNBLD>2.0.CO;2.
Huffman,G.J.,R.F.Adler,M.M.Morrissey,D.T.Bolvin,S.Curtis,R.Joyce,B.Mc Gavock,and J.Susskind,2001:Global precipitation at one-degree daily resolution from multisatellite observations.Journal of Hydrometeorology,2,36-50,https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2.
Huffman,G.J.,and Coauthors,2007:The TRMM multisatellite precipitation analysis(TMPA):Quasi-global,multiyear,combined-sensor precipitation estimates at fine scales.Journal of Hydrometeorology,8,38-55,https://doi.org/10.1175/JHM560.1.
Hunke,E.C.,and W.H.Lipscomb,2010:CICE:The Los Alamos Sea ice model documentation and software user’s manual version 4.1.Tech.Rep.LA-CC-06-012,675 pp.
Hurtt,G.C.,and Coauthors,2011:Harmonization of land-use scenarios for the period 1500-2100:600 years of global gridded annual land-use transitions,wood harvest,and resulting secondary lands.Climatic change,109(1-2),117,https://doi.org/10.1007/s10584-011-0153-2.
Jiang,X.,and Coauthors,2015:Vertical structure and physical processes of the Madden-Julian oscillation:Exploring key model physics in climate simulations.J.Geophys.Res.,120,4718-4748,https://doi.org/10.1002/2014JD022375.
Knapp,K.R.,M.C.Kruk,D.H.Levinson,H.J.Diamond,and C.J.Neumann,2010:The international best track archive for climate stewardship(IBTr ACS):Unifying tropical cyclone data.Bull.Amer.Meteor.Soc.,91(3),363-376,https://doi.org/10.1175/2009BAMS2755.1.
Lamarque,J.-F.,and Coauthors,2012:CAM-chem:Description and evaluation of interactive atmospheric chemistry in the community earth system model.Geoscientific Model Development,5(2),369-411,https://doi.org/10.5194/gmd-5-369-2012.
Li,J.X.,Q.Bao,Y.M.Liu,G.X.Wu,L.Wang,B.He,X.C.Wang,and J.D.Li,2019:Evaluation of FAMIL2 in simulating the climatology and seasonal-to-interannual variability of tropical cyclone characteristics.Journal of Advances in Modeling Earth Systems,https://doi.org/10.1029/2018MS001506.
Lin,S.-J.,2004:A“vertically Lagrangian”finite-volume dynamical core for global models.Mon.Wea.Rev.,132(10),2293-2307,https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2.
Lin,Y.-L.,R.D.Farley,and H.D.Orville,1983:Bulk parameterization of the snow field in a cloud model.J.Climate Appl.Meteor.,22(6),1065-1092,https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.
Liu,H.L.,P.F.Lin,Y.Q.Yu,and X.H.Zhang,2012:The baseline evaluation of LASG/IAP climate system ocean model(LI-COM)version 2.Acta Meteorologica Sinica,26(3),318-329,https://doi.org/10.1007/s13351-012-0305-y.
Matthes,K.,and Coauthors,2017:Solar forcing for CMIP6(v3.2).Geoscientific Model Development,10(6),2247-2302,https://doi.org/10.5194/gmd-10-2247-2017.
Meinshausen,M.,and Coauthors,2017:Historical greenhouse gas concentrations for climate modelling(CMIP6).Geoscientific Model Development,10,2057-2116,https://doi.org/10.5194/gmd-10-2057-2017.
Nordeng,T.E.,1994:Extended versions of the convective parameterization scheme at ECMWF and their impact on the mean and transient activity of the model in the tropics.ECMWFTechnical Memo.206,41 pp.
Oleson,K.W.,and Coauthors,2010:Technical description of version 4.0 of the community land model(CLM).NCAR/TN-478+STR,173 pp,https://doi.org/10.5065/D6FB50WZ.
Palmer,T.N.,G.J.Shutts,and R.Swinbank,1986:Alleviation of a systematic westerly bias in general circulation and numerical weather prediction models through an orographic gravity wave drag parametrization.Quart.J.Roy.Meteor.Soc.,112(474),1001-1039,https://doi.org/10.1002/qj.49711247406.
Putman,W.M.,and S.-J.Lin,2007:Finite-volume transport on various cubed-sphere grids.J.Comput.Phys.,227(1),55-78,https://doi.org/10.1016/j.jcp.2007.07.022.
Simpson,R.H.,and H.Saffir,1974:The hurricane disasterpotential scale.Weatherwise,27(4),169-186,https://doi.org/10.1080/00431672.1974.9931702.
Sun,Z.A.,and L.Rikus,1999:Improved application of exponential sum fitting transmissions to inhomogeneous atmosphere.J.Geophys.Res.,104,6291-6303,https://doi.org/10.1029/1998JD200095.
Tiedtke,M.,1989:A comprehensive mass flux scheme for cumulus parameterization in large-scale models.Mon.Wea.Rev.,117,1779-1800,https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.
Waliser,D.,and Coauthors,2009:MJO simulation diagnostics.J.Climate,22,3006-3030,https://doi.org/10.1175/2008JCLI2731.1.
Wang,X.C.,and M.H.Zhang,2014:Vertical velocity in shallow convection for different plume types.Journal of Advances in Modeling Earth Systems,6(2),478-489,https://doi.org/10.1002/2014MS000318.
Wu,G.X.,H.Liu,Y.C.Zhao,and W.P.Li,1996:A ninelayer atmospheric general circulation model and its performance.Adv.Atmos.Sci.,13(1),1-18,https://doi.org/10.1007/bf02657024.
Xiang,B.Q.,and Coauthors,2015:Beyond weather time-scale prediction for hurricane sandy and super typhoon Haiyan in a global climate model.Mon.Wea.Rev.,143(2),524-535,https://doi.org/10.1175/MWR-D-14-00227.1.
Xu,K.M.,and D.A.Randall,1996:A semiempirical cloudiness parameterization for use in climate models.J.Atmos.Sci.,53(21),3084-3102,https://doi.org/10.1175/1520-0469(1996)053<3084:ASCPFU>2.0.CO;2.
Yang,J.,Q.Bao,X.C.Wang,and T.J.Zhou,2012:The tropical intraseasonal oscillation in SAMIL coupled and uncoupled general circulation models.Adv.Atmos.Sci.,29(3),529-543,https://doi.org/10.1007/s00376-011-1087-3.
Zhou,L.J.,and Coauthors,2015:Global energy and water balance:Characteristics from finite-volume Atmospheric Model of the IAP/LASG(FAMIL1).Journal of Advances in Modeling Earth Systems,7(1),1-20,https://doi.org/10.1002/2014ms000349.
Adler,R.F.,and Coauthors,2003:The version-2 global precipitation climatology project(GPCP)monthly precipitation analysis(1979-present).Journal of Hydrometeorology,4,1147-1167,https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.
Bao,Q.,G.X.Wu,Y.M.Liu,J.Yang,Z.Z.Wang,and T.J.Zhou,2010:An introduction to the coupled model FGOALS1.1-s and its performance in East Asia.Adv.Atmos.Sci.,27(5),1131-1142,https://doi.org/10.1007/s00376-010-9177-1.
Bao,Q.,and Coauthors,2013:The flexible global oceanatmosphere-land system model,spectral version 2:FGOALS-s2.Adv.Atmos.Sci.,30(3),561-576,https://doi.org/10.1007/s00376-012-2113-9.
Bao,Q.,X.F.Wu,J.X.Li,L.Wang,B.He,X.C.Wang,Y.M.Liu,and G.X.Wu,2019:Outlook for El Ni?o and the Indian Ocean Dipole in autumn-winter 2018-2019.Chinese Science Bulletin,64,73-78,https://doi.org/10.1360/N972018-00913.(in Chinese)
Bretherton,C.S.,and S.Park,2009:A new moist turbulence parameterization in the community atmosphere model.J.Climate,22(12),3422-3448,https://doi.org/10.1175/2008jcli2556.1.
Clough,S.A.,M.W.Shephard,E.J.Mlawer,J.S.Delamere,M.J.Iacono,K.Cady-Pereira,S.Boukabara,and P.D.Brown,2005:Atmospheric radiative transfer modeling:Asummary of the AER codes.Journal of Quantitative Spectroscopy and Radiative Transfer,91(2),233-244,https://doi.org/10.1016/j.jqsrt.2004.05.058.
Dee,D.P.,and Coauthors,2011:The ERA-interim reanalysis:Configuration and performance of the data assimilation system.Quart.J.Roy.Meteor.Soc.,137,553-597,https://doi.org/10.1002/qj.828.
Edwards,J.M.,and A.Slingo,1996:Studies with a flexible new radiation code.I:Choosing a configuration for a large-scale model.Quart.J.Roy.Meteor.Soc.,122,689-719,https://doi.org/10.1002/qj.49712253107.
Eyring,V.,S.Bony,G.A.Meehl,C.A.Senior,B.Stevens,R.J.Stouffer,and K.E.Taylor,2016:Overview of the coupled model intercomparison project phase 6(CMIP6)experimental design and organization.Geoscientific Model Development,9,1937-1958,https://doi.org/10.5194/gmd-9-1937-2016.
Gates,W.L.,1992:AMIP:The atmospheric model intercomparison project.Bull.Amer.Meteor.Soc.,73,1962-1970,https://doi.org/10.1175/1520-0477(1992)073<1962:ATAMIP>2.0.CO;2.
Gates,W.L.,and Coauthors,1999:An overview of the results of the atmospheric model intercomparison project(AMIP I).Bull.Amer.Meteor.Soc.,80,29-56.https://doi.org/10.1175/1520-0477(1999)080<0029:AOOTRO>2.0.CO;2.
Harris,L.M.,and S.-J.Lin,2014:Global-to-regional nested grid climate simulations in the GFDL high resolution atmospheric model.J.Climate,27(13),4890-4910,https://doi.org/10.1175/JCLI-D-13-00596.1.
He,S.C.,J.Yang,Q.Bao,L.Wang,and B.Wang,2019:Fidelity of the observational/reanalysis datasets and global climate models in representation of extreme precipitation in East China.J.Climate,32(1),195-212,https://doi.org/10.1175/JCLI-D-18-0104.1.
Holtslag,A.A.M.,and B.A.Boville,1993:Local versus nonlocal boundary-layer diffusion in a global climate model.J.Climate,6,1825-1842,https://doi.org/10.1175/1520-0442(1993)006<1825:LVNBLD>2.0.CO;2.
Huffman,G.J.,R.F.Adler,M.M.Morrissey,D.T.Bolvin,S.Curtis,R.Joyce,B.Mc Gavock,and J.Susskind,2001:Global precipitation at one-degree daily resolution from multisatellite observations.Journal of Hydrometeorology,2,36-50,https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2.
Huffman,G.J.,and Coauthors,2007:The TRMM multisatellite precipitation analysis(TMPA):Quasi-global,multiyear,combined-sensor precipitation estimates at fine scales.Journal of Hydrometeorology,8,38-55,https://doi.org/10.1175/JHM560.1.
Hunke,E.C.,and W.H.Lipscomb,2010:CICE:The Los Alamos Sea ice model documentation and software user’s manual version 4.1.Tech.Rep.LA-CC-06-012,675 pp.
Hurtt,G.C.,and Coauthors,2011:Harmonization of land-use scenarios for the period 1500-2100:600 years of global gridded annual land-use transitions,wood harvest,and resulting secondary lands.Climatic change,109(1-2),117,https://doi.org/10.1007/s10584-011-0153-2.
Jiang,X.,and Coauthors,2015:Vertical structure and physical processes of the Madden-Julian oscillation:Exploring key model physics in climate simulations.J.Geophys.Res.,120,4718-4748,https://doi.org/10.1002/2014JD022375.
Knapp,K.R.,M.C.Kruk,D.H.Levinson,H.J.Diamond,and C.J.Neumann,2010:The international best track archive for climate stewardship(IBTr ACS):Unifying tropical cyclone data.Bull.Amer.Meteor.Soc.,91(3),363-376,https://doi.org/10.1175/2009BAMS2755.1.
Lamarque,J.-F.,and Coauthors,2012:CAM-chem:Description and evaluation of interactive atmospheric chemistry in the community earth system model.Geoscientific Model Development,5(2),369-411,https://doi.org/10.5194/gmd-5-369-2012.
Li,J.X.,Q.Bao,Y.M.Liu,G.X.Wu,L.Wang,B.He,X.C.Wang,and J.D.Li,2019:Evaluation of FAMIL2 in simulating the climatology and seasonal-to-interannual variability of tropical cyclone characteristics.Journal of Advances in Modeling Earth Systems,https://doi.org/10.1029/2018MS001506.
Lin,S.-J.,2004:A“vertically Lagrangian”finite-volume dynamical core for global models.Mon.Wea.Rev.,132(10),2293-2307,https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2.
Lin,Y.-L.,R.D.Farley,and H.D.Orville,1983:Bulk parameterization of the snow field in a cloud model.J.Climate Appl.Meteor.,22(6),1065-1092,https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.
Liu,H.L.,P.F.Lin,Y.Q.Yu,and X.H.Zhang,2012:The baseline evaluation of LASG/IAP climate system ocean model(LI-COM)version 2.Acta Meteorologica Sinica,26(3),318-329,https://doi.org/10.1007/s13351-012-0305-y.
Matthes,K.,and Coauthors,2017:Solar forcing for CMIP6(v3.2).Geoscientific Model Development,10(6),2247-2302,https://doi.org/10.5194/gmd-10-2247-2017.
Meinshausen,M.,and Coauthors,2017:Historical greenhouse gas concentrations for climate modelling(CMIP6).Geoscientific Model Development,10,2057-2116,https://doi.org/10.5194/gmd-10-2057-2017.
Nordeng,T.E.,1994:Extended versions of the convective parameterization scheme at ECMWF and their impact on the mean and transient activity of the model in the tropics.ECMWFTechnical Memo.206,41 pp.
Oleson,K.W.,and Coauthors,2010:Technical description of version 4.0 of the community land model(CLM).NCAR/TN-478+STR,173 pp,https://doi.org/10.5065/D6FB50WZ.
Palmer,T.N.,G.J.Shutts,and R.Swinbank,1986:Alleviation of a systematic westerly bias in general circulation and numerical weather prediction models through an orographic gravity wave drag parametrization.Quart.J.Roy.Meteor.Soc.,112(474),1001-1039,https://doi.org/10.1002/qj.49711247406.
Putman,W.M.,and S.-J.Lin,2007:Finite-volume transport on various cubed-sphere grids.J.Comput.Phys.,227(1),55-78,https://doi.org/10.1016/j.jcp.2007.07.022.
Simpson,R.H.,and H.Saffir,1974:The hurricane disasterpotential scale.Weatherwise,27(4),169-186,https://doi.org/10.1080/00431672.1974.9931702.
Sun,Z.A.,and L.Rikus,1999:Improved application of exponential sum fitting transmissions to inhomogeneous atmosphere.J.Geophys.Res.,104,6291-6303,https://doi.org/10.1029/1998JD200095.
Tiedtke,M.,1989:A comprehensive mass flux scheme for cumulus parameterization in large-scale models.Mon.Wea.Rev.,117,1779-1800,https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.
Waliser,D.,and Coauthors,2009:MJO simulation diagnostics.J.Climate,22,3006-3030,https://doi.org/10.1175/2008JCLI2731.1.
Wang,X.C.,and M.H.Zhang,2014:Vertical velocity in shallow convection for different plume types.Journal of Advances in Modeling Earth Systems,6(2),478-489,https://doi.org/10.1002/2014MS000318.
Wu,G.X.,H.Liu,Y.C.Zhao,and W.P.Li,1996:A ninelayer atmospheric general circulation model and its performance.Adv.Atmos.Sci.,13(1),1-18,https://doi.org/10.1007/bf02657024.
Xiang,B.Q.,and Coauthors,2015:Beyond weather time-scale prediction for hurricane sandy and super typhoon Haiyan in a global climate model.Mon.Wea.Rev.,143(2),524-535,https://doi.org/10.1175/MWR-D-14-00227.1.
Xu,K.M.,and D.A.Randall,1996:A semiempirical cloudiness parameterization for use in climate models.J.Atmos.Sci.,53(21),3084-3102,https://doi.org/10.1175/1520-0469(1996)053<3084:ASCPFU>2.0.CO;2.
Yang,J.,Q.Bao,X.C.Wang,and T.J.Zhou,2012:The tropical intraseasonal oscillation in SAMIL coupled and uncoupled general circulation models.Adv.Atmos.Sci.,29(3),529-543,https://doi.org/10.1007/s00376-011-1087-3.
Zhou,L.J.,and Coauthors,2015:Global energy and water balance:Characteristics from finite-volume Atmospheric Model of the IAP/LASG(FAMIL1).Journal of Advances in Modeling Earth Systems,7(1),1-20,https://doi.org/10.1002/2014ms000349.