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RASM simulations: Quality Control for Community Based Sea Ice Model Development
<p>RASM simulations: Quality Control for Community Based Sea Ice Model Development<br> -----------------------------------------------------------------------------------</p> <p>Data from the Regional Arctic System Model (RASM) simulations by Andrew F. Roberts at Naval Postgraduate School, described in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>It was produced using RASM with CICE sea ice model version 5.2 with the namelist:</p> <p>&setup_nml<br> bfbflag = .false. <br> diagfreq = 24 <br> hist_avg = .true. <br> histfreq = 'h','x','x','x','x'<br> histfreq_n = 1,0,0,0,0<br> ice_ic = '/work4/projects/apcraig_dir/RACM/inputdata/ccsm4_init/R1008Gcaaa01a_loop2/1979-09-01/R1008Gcaaa01a_loop2.cice.r.1979-09-01-00000.nc'<br> latpnt = 90.0,-65.0 <br> lcdf64 = .true. <br> lonpnt = 0.0,-45.0 <br> ndtd = 1 <br> pointer_file = './rpointer.ice'<br> print_global = .true. <br> print_points = .false. <br> restart_ext = .false. <br> restart_format = 'pio'<br> year_init = 1 <br> /<br> &grid_nml<br> grid_file = '/work4/projects/apcraig_dir/RACM/inputdata/ice/cice/ar9v3_20101118.grid'<br> grid_format = 'bin'<br> grid_type = 'regional'<br> gridcpl_file = '/work4/projects/apcraig_dir/RACM/inputdata/ice/cice/domain.ocn.ar9v4.100920.nc'<br> kcatbound = 0 <br> kmt_file = '/work4/projects/apcraig_dir/RACM/inputdata/ice/cice/ar9v3_20110106.kmt'<br> /<br> &tracer_nml<br> restart_aero = .false. <br> restart_age = .false. <br> restart_fy = .false. <br> restart_lvl = .false. <br> restart_pond_cesm = .false. <br> restart_pond_lvl = .false. <br> restart_pond_topo = .false. <br> tr_aero = .false.<br> tr_fy = .true. <br> tr_iage = .true. <br> tr_lvl = .true. <br> tr_pond_cesm = .false. <br> tr_pond_lvl = .true. <br> tr_pond_topo = .false. <br> /<br> &thermo_nml<br> a_rapid_mode = 0.5e-03 <br> aspect_rapid_mode = 1.0 <br> conduct = 'bubbly'<br> dsdt_slow_mode = -1.5e-07 <br> kitd = 1 <br> ktherm = 2 <br> phi_c_slow_mode = 0.05 <br> phi_i_mushy = 0.85 <br> rac_rapid_mode = 10 <br> /<br> &dynamics_nml<br> advection = 'remap'<br> cf = 21.3<br> kdyn = 2 (EAP) or kdyn = 2 (EVP)<br> krdg_partic = 1<br> krdg_redist = 1<br> kstrength = 1 <br> mu_rdg = 3.0<br> ndte = 600 <br> revised_evp = .false. <br> /<br> &shortwave_nml<br> ahmax = 0.3 <br> albedo_type = 'default'<br> albicei = 0.45<br> albicev = 0.75<br> albsnowi = 0.73<br> albsnowv = 0.98<br> dt_mlt = 1.00 <br> kalg = 0.0 <br> r_ice = 0.0 <br> r_pnd = 0.0 <br> r_snw = 0.50 <br> rsnw_mlt = 1000. <br> shortwave = 'dEdd'<br> /<br> &ponds_nml<br> dpscale = 1.0e-3 <br> frzpnd = 'hlid'<br> hp1 = 0.01 <br> hs0 = 0.03 <br> hs1 = 0.03 <br> pndaspect = 0.8 <br> rfracmax = 0.85 <br> rfracmin = 0.15 <br> /<br> &forcing_nml<br> fbot_xfer_type = 'constant'<br> formdrag = .false.<br> highfreq = .true.<br> l_mpond_fresh = .false.<br> natmiter = 25 <br> tfrz_option = 'mushy'<br> /<br> &domain_nml<br> distribution_type = 'roundrobin'<br> ew_boundary_type = 'cyclic'<br> maskhalo_bound = .true.<br> maskhalo_dyn = .true.<br> maskhalo_remap = .true.<br> ns_boundary_type = 'open'<br> processor_shape = 'null'<br> /<br> &zbgc_nml<br> bgc_data_dir = 'unknown_bgc_data_dir'<br> bgc_flux_type = 'Jin2006'<br> nit_data_type = 'unknown'<br> phi_snow = 0.5 <br> restart_bgc = .false. <br> restart_hbrine = .false. <br> restore_bgc = .false. <br> sil_data_type = 'unknown'<br> skl_bgc = .false. <br> tr_bgc_am_sk = .false. <br> tr_bgc_c_sk = .false. <br> tr_bgc_chl_sk = .false. <br> tr_bgc_dms_sk = .false. <br> tr_bgc_dmspd_sk = .false. <br> tr_bgc_dmspp_sk = .false. <br> tr_bgc_sil_sk = .false. <br> tr_brine = .false. <br> /<br> &icefields_bgc_nml<br> f_aero = 'mxxxx'<br> f_aeron = 'xxxxx'<br> f_bgc_am_ml = 'xxxxx'<br> f_bgc_am_sk = 'xxxxx'<br> f_bgc_c = 'xxxxx'<br> f_bgc_c_sk = 'xxxxx'<br> f_bgc_chl = 'xxxxx'<br> f_bgc_chl_sk = 'xxxxx'<br> f_bgc_dms = 'xxxxx'<br> f_bgc_dms_ml = 'xxxxx'<br> f_bgc_dms_sk = 'xxxxx'<br> f_bgc_dmsp_ml = 'xxxxx'<br> f_bgc_dmspd = 'xxxxx'<br> f_bgc_dmspd_sk = 'xxxxx'<br> f_bgc_dmspp = 'xxxxx'<br> f_bgc_dmspp_sk = 'xxxxx'<br> f_bgc_n = 'xxxxx'<br> f_bgc_n_sk = 'xxxxx'<br> f_bgc_nh = 'xxxxx'<br> f_bgc_nit_ml = 'xxxxx'<br> f_bgc_nit_sk = 'xxxxx'<br> f_bgc_no = 'xxxxx'<br> f_bgc_s = 'xxxxx'<br> f_bgc_sil = 'xxxxx'<br> f_bgc_sil_ml = 'xxxxx'<br> f_bgc_sil_sk = 'xxxxx'<br> f_bphi = 'xxxxx'<br> f_btin = 'xxxxx'<br> f_faero_atm = 'mxxxx'<br> f_faero_ocn = 'mxxxx'<br> f_fbri = 'xxxxx'<br> f_fn = 'xxxxx'<br> f_fn_ai = 'xxxxx'<br> f_fnh = 'xxxxx'<br> f_fnh_ai = 'xxxxx'<br> f_fno = 'xxxxx'<br> f_fno_ai = 'xxxxx'<br> f_fsil = 'xxxxx'<br> f_fsil_ai = 'xxxxx'<br> f_grownet = 'xxxxx'<br> f_hbri = 'xxxxx'<br> f_ppnet = 'xxxxx'<br> /<br> &icefields_drag_nml<br> f_cdn_atm = 'hxxxx'<br> f_cdn_ocn = 'hxxxx'<br> f_drag = 'hxxxx'<br> /<br> &icefields_mechred_nml<br> f_alvl = 'hxxxx'<br> f_aparticn = 'xxxxx'<br> f_araftn = 'xxxxx'<br> f_ardg = 'hxxxx'<br> f_ardgn = 'hxxxx'<br> f_aredistn = 'xxxxx'<br> f_dardg1dt = 'xxxxx'<br> f_dardg1ndt = 'xxxxx'<br> f_dardg2dt = 'xxxxx'<br> f_dardg2ndt = 'xxxxx'<br> f_dvirdgdt = 'xxxxx'<br> f_dvirdgndt = 'xxxxx'<br> f_krdgn = 'xxxxx'<br> f_opening = 'hxxxx'<br> f_vlvl = 'hxxxx'<br> f_vraftn = 'xxxxx'<br> f_vrdg = 'hxxxx'<br> f_vrdgn = 'hxxxx'<br> f_vredistn = 'xxxxx'<br> /<br> &icefields_pond_nml<br> f_apeff = 'hxxxx'<br> f_apeff_ai = 'hxxxx'<br> f_apeffn = 'hxxxx'<br> f_apond = 'hxxxx'<br> f_apond_ai = 'hxxxx'<br> f_apondn = 'hxxxx'<br> f_hpond = 'hxxxx'<br> f_hpond_ai = 'hxxxx'<br> f_hpondn = 'hxxxx'<br> f_ipond = 'hxxxx'<br> f_ipond_ai = 'hxxxx'<br> /<br> &icefields_nml<br> f_a11 = 'hxxxx'<br> f_a12 = 'hxxxx'<br> f_aice = 'hxxxx'<br> f_aicen = 'hxxxx'<br> f_aisnap = 'hxxxx'<br> f_albice = 'hxxxx'<br> f_albpnd = 'hxxxx'<br> f_albsni = 'hxxxx'<br> f_albsno = 'hxxxx'<br> f_alidf = 'hxxxx'<br> f_alidr = 'hxxxx'<br> f_alvdf = 'hxxxx'<br> f_alvdr = 'hxxxx'<br> f_angle = .true.<br> f_anglet = .true.<br> f_blkmask = .true.<br> f_bounds = .false.<br> f_congel = 'hxxxx'<br> f_coszen = 'hxxxx'<br> f_daidtd = 'hxxxx'<br> f_daidtt = 'hxxxx'<br> f_divu = 'hxxxx'<br> f_dsnow = 'hxxxx'<br> f_dvidtd = 'hxxxx'<br> f_dvidtt = 'hxxxx'<br> f_dxt = .false.<br> f_dxu = .false.<br> f_dyt = .false.<br> f_dyu = .false.<br> f_e11 = 'xxxxx'<br> f_e12 = 'xxxxx'<br> f_e22 = 'xxxxx'<br> f_evap = 'hxxxx'<br> f_evap_ai = 'hxxxx'<br> f_fcondtop_ai = 'hxxxx'<br> f_fcondtopn_ai = 'hxxxx'<br> f_fhocn = 'hxxxx'<br> f_fhocn_ai = 'hxxxx'<br> f_flat = 'hxxxx'<br> f_flat_ai = 'hxxxx'<br> f_flatn_ai = 'hxxxx'<br> f_flwdn = 'hxxxx'<br> f_flwup = 'hxxxx'<br> f_flwup_ai = 'hxxxx'<br> f_fmeltt_ai = 'hxxxx'<br> f_fmelttn_ai = 'hxxxx'<br> f_frazil = 'hxxxx'<br> f_fresh = 'hxxxx'<br> f_fresh_ai = 'hxxxx'<br> f_frz_onset = 'hxxxx'<br> f_frzmlt = 'hxxxx'<br> f_fsalt = 'hxxxx'<br> f_fsalt_ai = 'hxxxx'<br> f_fsens = 'hxxxx'<br> f_fsens_ai = 'hxxxx'<br> f_fsensn_ai = 'hxxxx'<br> f_fsurf_ai = 'hxxxx'<br> f_fsurfn_ai = 'hxxxx'<br> f_fswabs = 'hxxxx'<br> f_fswabs_ai = 'hxxxx'<br> f_fswdn = 'hxxxx'<br> f_fswfac = 'hxxxx'<br> f_fswint_ai = 'xxxxx'<br> f_fswthru = 'hxxxx'<br> f_fswthru_ai = 'hxxxx'<br> f_fy = 'hxxxx'<br> f_hi = 'hxxxx'<br> f_hisnap = 'xxxxx'<br> f_hs = 'hxxxx'<br> f_hte = .false.<br> f_htn = .false.<br> f_iage = 'hxxxx'<br> f_icepresent = 'hxxxx'<br> f_keffn_top = 'xxxxx'<br> f_meltb = 'hxxxx'<br> f_meltl = 'hxxxx'<br> f_melts = 'hxxxx'<br> f_meltt = 'hxxxx'<br> f_mlt_onset = 'hxxxx'<br> f_ncat = .true.<br> f_qref = 'hxxxx'<br> f_rain = 'hxxxx'<br> f_rain_ai = 'hxxxx'<br> f_s11 = 'hxxxx'<br> f_s12 = 'hxxxx'<br> f_s22 = 'hxxxx'<br> f_shear = 'hxxxx'<br> f_sice = 'hxxxx'<br> f_sig1 = 'hxxxx'<br> f_sig2 = 'hxxxx'<br> f_sinz = 'xxxxx'<br> f_snoice = 'hxxxx'<br> f_snow = 'hxxxx'<br> f_snow_ai = 'hxxxx'<br> f_sss = 'hxxxx'<br> f_sst = 'hxxxx'<br> f_strairx = 'hxxxx'<br> f_strairy = 'hxxxx'<br> f_strcorx = 'hxxxx'<br> f_strcory = 'hxxxx'<br> f_strength = 'hxxxx'<br> f_strintx = 'hxxxx'<br> f_strinty = 'hxxxx'<br> f_strocnx = 'hxxxx'<br> f_strocny = 'hxxxx'<br> f_strtltx = 'hxxxx'<br> f_strtlty = 'hxxxx'<br> f_tair = 'hxxxx'<br> f_tarea = .true.<br> f_tinz = 'xxxxx'<br> f_tmask = .true.<br> f_tref = 'hxxxx'<br> f_trsig = 'hxxxx'<br> f_tsfc = 'hxxxx'<br> f_tsnz = 'xxxxx'<br> f_uarea = .true.<br> f_uatm = 'hxxxx'<br> f_uocn = 'hxxxx'<br> f_uvel = 'hxxxx'<br> f_vatm = 'hxxxx'<br> f_vgrdb = .true.<br> f_vgrdi = .true.<br> f_vgrds = .true.<br> f_vicen = 'hxxxx'<br> f_vocn = 'hxxxx'<br> f_vsnon = 'hxxxx'<br> f_vvel = 'hxxxx'<br> f_yieldstress11 = 'hxxxx'<br> f_yieldstress12 = 'hxxxx'<br> f_yieldstress22 = 'hxxxx'<br> /<br> &ice_prescribed_nml<br> prescribed_ice = .false.<br> /</p>
GOFS simulations: Quality Control for Community Based Sea Ice Model Development
<p>GOFS simulations: Quality Control for Community Based Sea Ice Model Development<br> -----------------------------------------------------------------------------------</p> <p>Data from the Global Ocean Forecast System (GOFS 3.1) from the Naval Research Laboratory, curated by Richard Allard, and described in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>It was produced using GOFS with CICE sea ice model version 4.0 with the namelist:</p> <p>&setup_nml<br> days_per_year= 366<br> , year_init = 1901<br> , istep0 = 0<br> , dt = 225.0<br> , npt = -1 <br> , ndyn_dt = 1<br> , inserthr = 0<br> , runtype = 'continue'<br> , ice_ic = 'default'<br> , restart = .true.<br> , restart_dir = './'<br> , restart_file = 'cice.restart'<br> , pointer_file = './cice.restart_file'<br> , histfreq = 'h'<br> , histfreq_n = 12<br> , dumpfreq = '5'<br> , dumpfreq_n = 1<br> , diagfreq = 8<br> , diag_type = 'stdout'<br> , diag_file = 'ice.diag.d'<br> , print_global = .true.<br> , print_points = .true.<br> , latpnt(1) = 72.011<br> , lonpnt(1) = -149.987<br> , latpnt(2) = 90.<br> , lonpnt(2) = 0.<br> , dbug = .false.<br> , incond_dir = <br> , incond_file = './cice'<br> /<br> &grid_nml<br> grid_format = 'bin'<br> , grid_type = 'hycom'<br> , grid_file = 'regional.cice.r'<br> , kmt_file = 'kmt'<br> , kcatbound = 0 <br> /<br> &domain_nml<br> nprocs = 1800<br> , processor_shape = 'slenderX4'<br> , distribution_type = 'cartesian'<br> , distribution_wght = 'latitude'<br> , ew_boundary_type = 'cyclic'<br> , ns_boundary_type = 'tripole'<br> /<br> &tracer_nml<br> tr_iage = .false.<br> , restart_age = .false.<br> , tr_pond = .false.<br> , restart_pond = .false.<br> /<br> &ice_nml<br> kitd = 1<br> , kdyn = 1<br> , ndte = 120<br> , kstrength = 1<br> , krdg_partic = 1<br> , krdg_redist = 1<br> , advection = 'remap'<br> , heat_capacity = .true.<br> , shortwave = 'default'<br> , albedo_type = 'default'<br> , albicev = 0.78<br> , albicei = 0.36<br> , albsnowv = 0.98<br> , albsnowi = 0.70 <br> , R_ice = 0.0<br> , R_pnd = 0.0<br> , R_snw = 0.0<br> , atmbndy = 'default'<br> , fyear_init = 1911 <br> , ycycle = 1 <br> , atm_data_format = 'bin'<br> , atm_data_type= 'cfsr'<br> , atm_data_dir = './cice.'<br> , calc_strair = .true.<br> , calc_Tsfc = .true.<br> , precip_units = 'mm_per_month'<br> , Tfrzpt = 'linear_S'<br> , update_ocn_f = .false.<br> , oceanmixed_ice = .true.<br> , ocn_data_format = 'bin'<br> , sss_data_type= 'cfsr'<br> , sst_data_type= 'cfsr'<br> , ocn_data_dir = 'COUPLED'<br> , oceanmixed_file= 'pop_frc.gx1v3.051202.nc'<br> , restore_sst = .true.<br> , insert_ssmi = .true.<br> , insert_sih = .false.<br> , trestore = 1 <br> , atm_netrad = .false.<br> , albnetrad = 0.00<br> /<br> &icefields_nml<br> f_tmask = .false.<br> , f_tarea = .false.<br> , f_uarea = .false.<br> , f_dxt = .false.<br> , f_dyt = .false.<br> , f_dxu = .false.<br> , f_dyu = .false.<br> , f_HTN = .false.<br> , f_HTE = .false.<br> , f_ANGLE = .false.<br> , f_ANGLET = .false.<br> , f_bounds = .false.<br> , f_hi = .true.<br> , f_hs = .true. <br> , f_Tsfc = .true. <br> , f_aice = .true. <br> , f_uvel = .true. <br> , f_vvel = .true. <br> , f_fswdn = .true. <br> , f_flwdn = .true.<br> , f_snow = .true. <br> , f_snow_ai = .true. <br> , f_rain = .false. <br> , f_rain_ai = .true. <br> , f_sst = .true. <br> , f_sss = .true. <br> , f_uocn = .true. <br> , f_vocn = .true. <br> , f_frzmlt = .true.<br> , f_fswabs = .true. <br> , f_fswabs_ai = .true. <br> , f_albsni = .true. <br> , f_alvdr = .false.<br> , f_alidr = .false.<br> , f_flat = .false. <br> , f_flat_ai = .true. <br> , f_fsens = .false. <br> , f_fsens_ai = .true. <br> , f_flwup = .false. <br> , f_flwup_ai = .true. <br> , f_evap = .false. <br> , f_evap_ai = .true. <br> , f_Tair = .true. <br> , f_Tref = .false. <br> , f_Qref = .false.<br> , f_congel = .true. <br> , f_frazil = .true. <br> , f_snoice = .true. <br> , f_meltt = .true.<br> , f_meltb = .true.<br> , f_meltl = .true.<br> , f_fresh = .false.<br> , f_fresh_ai = .true.<br> , f_fsalt = .false.<br> , f_fsalt_ai = .true.<br> , f_fhocn = .false. <br> , f_fhocn_ai = .true. <br> , f_fswthru = .false. <br> , f_fswthru_ai= .true. <br> , f_fsurf_ai = .false.<br> , f_fcondtop_ai= .false.<br> , f_fmeltt_ai = .false. <br> , f_strairx = .true. <br> , f_strairy = .true. <br> , f_strtltx = .false. <br> , f_strtlty = .false. <br> , f_strcorx = .true. <br> , f_strcory = .true. <br> , f_strocnx = .true. <br> , f_strocny = .true. <br> , f_strintx = .false. <br> , f_strinty = .false.<br> , f_strength = .true.<br> , f_divu = .true.<br> , f_shear = .false.<br> , f_sig1 = .false. <br> , f_sig2 = .false. <br> , f_dvidtt = .false. <br> , f_dvidtd = .false. <br> , f_daidtt = .false.<br> , f_daidtd = .false. <br> , f_mlt_onset = .false.<br> , f_frz_onset = .false.<br> , f_dardg1dt = .false.<br> , f_dardg2dt = .false.<br> , f_dvirdgdt = .false.<br> , f_opening = .true.<br> , f_hisnap = .false.<br> , f_aisnap = .false.<br> , f_trsig = .false.<br> , f_icepresent= .false.<br> , f_iage = .false.<br> , f_aicen = .false.<br> , f_vicen = .false.<br> , f_fsurfn_ai = .false.<br> , f_fcondtopn_ai = .false.<br> , f_fmelttn_ai = .false.<br> , f_flatn_ai = .false.<br> /</p> <p> </p> <p> </p>
ECCC simulations: Quality Control for Community Based Sea Ice Model Development
<p>ECCC simulations: Quality Control for Community Based Sea Ice Model Development<br> -----------------------------------------------------------------------------------</p> <p>Data from the Environment and Climate Change Canada (ECCC) simulation by Jean-François Lemieux at Environment and Climate Change Canada, described in the manuscript:</p> <p>Roberts, Hunke, Allard, Bailey, Craig, Lemieux and Turner (2018): Quality Control for Community Based Sea Ice Model Development.</p> <p>It was produced using ECCC with CICE sea ice model version 4.0 with the namelist:</p> <p>&setup_nml<br> days_per_year = 365<br> , year_init = 2001100100<br> , istep0 = 0<br> , dt = 600<br> , npt = 2880<br> , ndyn_dt = 1<br> , runtype = 'continue'<br> , ice_ic = 'default'<br> , restart = .true.<br> , restart_dir = 'not_used_at_cmc'<br> , restart_file = 'not_used_at_cmc'<br> , pointer_file = './ice.restart_file'<br> , dumpfreq = 's'<br> , dumpfreq_n = 2880<br> , diagfreq = 144<br> , diag_type = 'file'<br> , diag_file = 'ice_stdout'<br> , print_global = .false.<br> , print_points = .false.<br> , latpnt(1) = 90.<br> , lonpnt(1) = 0.<br> , latpnt(2) = -65.<br> , lonpnt(2) = -45.<br> , dbug = .false.<br> , histfreq = 's' <br> , histfreq_n = 144<br> , hist_avg = .false.<br> , history_dir = './history/'<br> , history_file = 'iceh'<br> , history_format = 'nc'<br> , write_ic = .false.<br> , incond_dir = './history/'<br> , incond_file = 'iceh_ic'<br> /</p> <p>&grid_nml<br> grid_format = 'cmc'<br> , grid_type = 'cmc'<br> , grid_file = 'not_used_at_cmc'<br> , kmt_file = 'not_used_at_cmc'<br> , kcatbound = 2<br> /</p> <p>&domain_nml<br> nprocs = 72<br> , processor_shape = 'square-pop'<br> , distribution_type = 'cartesian'<br> , distribution_wght = 'block'<br> , ew_boundary_type = 'closed'<br> , ns_boundary_type = 'closed'<br> /</p> <p>&tracer_nml<br> tr_iage = .false.<br> , restart_age = .false.<br> , tr_pond = .false.<br> , restart_pond = .false.<br> /</p> <p>&ice_nml<br> kitd = 1<br> , kdyn = 1<br> , ndte = 900<br> , kstrength = 0<br> , Pstar = 27.5e3<br> , floediam = 300.0<br> , hfrazilmin = 0.05 <br> , iceruf = 0.0004<br> , krdg_partic = 1<br> , krdg_redist = 1<br> , advection = 'remap'<br> , heat_capacity = .true.<br> , shortwave = 'default'<br> , albedo_type = 'default'<br> , albicev = 0.85<br> , albicei = 0.40<br> , albsnowv = 0.98<br> , albsnowi = 0.70 <br> , R_ice = 0.<br> , R_pnd = 0.<br> , R_snw = 0.<br> , atmbndy = 'default'<br> , fyear_init = 1000<br> , ycycle = 1<br> , atm_data_format = 'not_used_at_cmc'<br> , atm_data_type = 'cmc'<br> , atm_data_dir = 'not_used_at_cmc'<br> , calc_strair = .true.<br> , calc_Tsfc = .true.<br> , precip_units = 'mks'<br> , Tfrzpt = 'nonlin'<br> , update_ocn_f = .true.<br> , oceanmixed_ice = .false.<br> , oceanmixed_frc = .false.<br> , ocn_data_format = 'not_used_at_cmc'<br> , sss_data_type = 'cmcint'<br> , sst_data_type = 'cmcint'<br> , ocn_data_dir = 'not_used_at_cmc'<br> , oceanmixed_file = 'not_used_at_cmc'<br> , restore_sst = .false.<br> , trestore = 0<br> , dirassm_ice = .false.<br> , restore_ice = .false.<br> , restore_nul = .false.<br> , l_thermo = .true.<br> , l_dynmcs = .true.<br> , z0io = 0.0165<br> , l_dragio_r = .false.<br> , l_basalstress = .true.<br> , k1 = 8.0<br> , k2 = 15.0<br> , u0 = 5e-5<br> , CC = 20.0<br> , Ktens = 0.05<br> , e_ratio = 1.4<br> /</p> <p>&namcmc_nml<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! CMC grid parameters (cmc_ln_gemgrid = T ==> grtyp=Z, else grtyp=X)<br> ! The buffer zone is not output <br> ! cmc_ln_bdyopn=T ==> open boundaries including buffer zone<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> cmc_ln_gemgrid = .false.<br> , cmc_ln_nemogrid = .true.<br> , cmc_nbuff_zone = 0<br> , cmc_ln_bdyopn = .false.<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! CMC atmospheric forcing parameters<br> cmc_dt_frcd = -9<br> , cmc_dt_prec = -9<br> , cmc_ln_intrp = .true.<br> , cmc_dateo = '20011001.000000' <br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! Input varables ! Long name (doc only) ! nomvar ! Constant (C) or Field (F) !<br> ! ...! Value (if C) ! Binary (B) or Integer (I) (if F) !<br> ! ...! ip1_1 ! ip1_2 ! scaling ! offset !<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! Grid ...<br> cmc_ulat = 'u-Lat(deg)','ULAT','F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_ulon = 'u-Lon(deg)','ULON','F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_tlat = 't-Lat(deg)','TLAT','F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_tlon = 't-Lon(deg)','TLON','F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_kmt = 'Mask (0-1)','KMT', 'F',-999.0,'I',-1 ,-1 , 1., 0.<br> cmc_hte = 'HTE (m) ','HTE' ,'F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_htn = 'HTN (m) ','HTN' ,'F',-999.0,'B',-1 ,-1 , 1., 0.<br> ! Ice-ocean state ...<br> cmc_ln_gslcat = .false.<br> cmc_ln_wmocat = .false.<br> cmc_ln_avgcat = .true.<br> cmc_ln_itdspr = .true.<br> cmc_gl = 'GL de CMC ','GL' ,'F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_i8 = 'I8 de CMC ','I8' ,'F',-999.0,'B',-1 ,-1 , 1., 0.<br> cmc_uice0 = 'U ice Vel.','null','C', 0.0,' ',-999999999,-999999999, -999., -999.<br> cmc_vice0 = 'V ice Vel.','null','C', 0.0,' ',-999999999,-999999999, -999., -999.<br> ! Ocean forcing ...<br> cmc_ln_frcsss = .false.<br> cmc_ln_frcsst = .false.<br> cmc_ln_frcuvo = .false.<br> ! Atm forcing ... (for ice I. C. internal temp override by restart)<br> cmc_tair = 'Air temp ','TT', 'C', 273.15,' ',999999999,999999999, 999., 999.<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! Mixed layer parameters<br> cmc_frzeps = 0.0005<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> ! Output parameters (cmc_ofmt='F' or 'H', Forecast or Hindcast style)<br> cmc_ofmt = 'H'<br> cmc_npako = -32<br> cmc_ln_stdrsti = .true.<br> cmc_ln_stdrsto = .true.<br> cmc_npakri = -64<br> cmc_npakro = -64<br> cmc_etiket = 'CREG_HCST'<br> !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!<br> /</p> <p>&icefields_nml<br> f_tmask = .false.<br> , f_tarea = .false.<br> , f_uarea = .false.<br> , f_dxt = .false.<br> , f_dyt = .false.<br> , f_dxu = .false.<br> , f_dyu = .false.<br> , f_HTN = .false.<br> , f_HTE = .false.<br> , f_ANGLE = .false.<br> , f_ANGLET = .false.<br> , f_bounds = .false.<br> , f_hi = .true.<br> , f_hs = .false. <br> , f_Tsfc = .false. <br> , f_aice = .true. <br> , f_uvel = .false. <br> , f_vvel = .false. <br> , f_fswdn = .false. <br> , f_flwdn = .false.<br> , f_snow = .false. <br> , f_snow_ai = .false. <br> , f_rain = .false. <br> , f_rain_ai = .false. <br> , f_sst = .false. <br> , f_sss = .false. <br> , f_uocn = .false. <br> , f_vocn = .false. <br> , f_frzmlt = .false.<br> , f_fswfac = .false.<br> , f_fswabs = .false. <br> , f_fswabs_ai = .false. <br> , f_albsni = .false. <br> , f_alvdr = .false.<br> , f_alidr = .false.<br> , f_albice = .false.<br> , f_albsno = .false.<br> , f_albpnd = .false.<br> , f_coszen = .false.<br> , f_flat = .false. <br> , f_flat_ai = .false. <br> , f_fsens = .false. <br> , f_fsens_ai = .false. <br> , f_flwup = .false. <br> , f_flwup_ai = .false. <br> , f_evap = .false. <br> , f_evap_ai = .false. <br> , f_Tair = .false. <br> , f_Tref = .false. <br> , f_Qref = .false.<br> , f_congel = .false. <br> , f_frazil = .false. <br> , f_snoice = .false. <br> , f_meltt = .false.<br> , f_meltb = .false.<br> , f_meltl = .false.<br> , f_fresh = .false.<br> , f_fresh_ai = .false.<br> , f_fsalt = .false.<br> , f_fsalt_ai = .false.<br> , f_fhocn = .false. <br> , f_fhocn_ai = .false. <br> , f_fswthru = .false. <br> , f_fswthru_ai = .false. <br> , f_fsurf_ai = .false.<br> , f_fcondtop_ai = .false.<br> , f_fmeltt_ai = .false. <br> , f_strairx = .false. <br> , f_strairy = .false. <br> , f_strtltx = .false. <br> , f_strtlty = .false. <br> , f_strcorx = .false. <br> , f_strcory = .false. <br> , f_strocnx = .false. <br> , f_strocny = .false. <br> , f_strintx = .false. <br> , f_strinty = .false.<br> , f_strength = .false.<br> , f_divu = .false.<br> , f_shear = .false.<br> , f_sig1 = .false. <br> , f_sig2 = .false. <br> , f_sigI = .false. <br> , f_dvidtt = .false. <br> , f_dvidtd = .false. <br> , f_daidtt = .false.<br> , f_daidtd = .false. <br> , f_mlt_onset = .false.<br> , f_frz_onset = .false.<br> , f_dardg1dt = .false.<br> , f_dardg2dt = .false.<br> , f_dvirdgdt = .false.<br> , f_opening = .false.<br> , f_hisnap = .false.<br> , f_aisnap = .false.<br> , f_trsig = .false.<br> , f_icepresent = .false.<br> , f_iage = .false.<br> , f_aicen = .false.<br> , f_vicen = .false.<br> , f_fsurfn_ai = .false.<br> , f_fcondtopn_ai = .false.<br> , f_fmelttn_ai = .false.<br> , f_flatn_ai = .false.<br> , f_apondn = .false.<br> , f_tau_bu = .false.<br> , f_tau_bv = .false.<br> /</p> <p> </p>
Simulated tracer-gas distribution in a multiscale model of the human lung during multiple-breath nitrogene washout
<p><em><strong>Content</strong></em></p> <p><strong>baseline:</strong></p> <ul> <li>inletFlow (ASCII format data for flow rate at the mouth in m^3/s, sampling frequency 1kHz)</li> <li>primary_results (ASCII format data table with four colums: time in seconds, N2 concentration (normalized), <em>empty </em>-1, pleural pressure in Pascal)</li> </ul> <p><strong>compliance modification (local):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> <li>duct: unstructured VTK mesh data of several scalar quantities (airway dimensino, pressure, N2 concentration, flow velocity) witin the airway network. Sampling frequency 50Hz (separat vtk-file for each timestep). <em>Inspect for instance with the VisIt (Lawrence Livermore National Laboratory) free visualization software.</em></li> <li>lobule: unstructured VTK mesh data of several scalar quantities (airway dimensino, pressure, N2 concentration, flow velocity) within the trumpet lobules.</li> </ul> <p><strong>compliance modification (regional):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> <li>duct: (same format as described above)</li> <li>lobule: (same format as described above)</li> </ul> <p><strong>size modification (regional):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> </ul> <p><strong>resistance modification (local):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> </ul> <p><strong>healthy controls (local):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> </ul>
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJ-GUESS winter wheat simulations
This data set contains output data from simulations with the model LPJ-GUESS for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above groun biomass, plant day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simlations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJ-GUESS spring wheat simulations
This data set contains output data from simulations with the model LPJ-GUESS for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above groun biomass, plant day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simlations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from GEPIC rice simulations
This data set contains output data from simulations with the model GEPIC for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from GEPIC spring wheat simulations
This data set contains output data from simulations with the model GEPIC for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from GEPIC maize simulations
This data set contains output data from simulations with the model GEPIC for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from pDSSAT winter wheat simulations
This data set contains output data from simulations with the model pDSSAT for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from pDSSAT soybean simulations
This data set contains output data from simulations with the model pDSSAT for soybean as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from EPIC-TAMU maize simulations
This data set contains output data from simulations with the model EPIC-TAMU for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from EPIC-TAMU rice simulations
This data set contains output data from simulations with the model EPIC-TAMU for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from GEPIC soybean simulations
This data set contains output data from simulations with the model GEPIC for soybean as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from EPIC-TAMU winter wheat simulations
This data set contains output data from simulations with the model EPIC-TAMU for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from ORCHIDEE-crop maize simulations
This data set contains output data from simulations with the model ORCHIDEE-crop for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from ORCHIDEE-crop rice simulations
This data set contains output data from simulations with the model ORCHIDEE-crop for rice as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, anthesis day . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from PEPIC maize simulations
This data set contains output data from simulations with the model PEPIC for maize as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from PEPIC spring wheat simulations
This data set contains output data from simulations with the model PEPIC for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from EPIC-TAMU spring wheat simulations
This data set contains output data from simulations with the model EPIC-TAMU for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA (Ruane et al. 2015) data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A0= 'none', A1='regain original growing season').
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.