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281 results for “community model”
A dynamic ancestral graph model and GPU-based simulation of a community based on metagenomic sampling
<p>In this paper we present an ancestral graph model of the evolution of a guild in an ecological community. The model is based on a metagenomic sampling design in that a random sample is taken at the community, as opposed the taxon, level and species are discovered by genetic sequencing. The specific implementation of the model envisions an ecological guild that was founded by colonization at some point in the past that then potentially undergoes diversification by natural selection. Within the graph, species emerge and evolve through the diversification process and their densities in the graph are dynamic and governed by both ecological drift and random genetic drift, as well as differential viability. We employ the 3% sequence divergence rule at a marker locus to identify Operational Taxonomic Units. We then explore approaches to see if there are indirect signals of the diversification process, including population genetic and ecological approaches. In terms of population genetics, we study the joint site frequency spectrum of OTUs, as well its associated statistics. In terms of ecology, we study the species (or OTU) abundance distribution. For both we observe deviations from neutrality, which indicates that there may be signals of diversifying selection in metagenomic studies under certain conditions. The model is available as a GPU-based computer program in C/C++ and using OpenCL, with the long-term goal of adding functionality iteratively to model large-scale eco-evolutionary processes for metagenomic data.</p>
Data from: Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference
<p>1. The estimation of abundance and distribution and factors governing patterns in these parameters is central to the field of ecology. The continued development of hierarchical models that best utilize available information to inform these processes is a key goal of quantitative ecologists. However, much remains to be learned about simultaneously modeling true abundance, presence, and trajectories of ecological communities.</p> <p>2. Simultaneous modeling of the population dynamics of multiple species provides an interesting mechanism to examine patterns in community processes and, as we emphasize herein, to improve species-specific estimates by leveraging detection information among species. Here we demonstrate a simple but effective approach to share information about observation parameters among species in hierarchical community abundance and occupancy models, where we use shared random effects among species to account for spatiotemporal heterogeneity in detection probability.</p> <p>3. We demonstrate the efficacy of our modeling approach using simulated abundance data, where we recover well our simulated parameters using N-mixture models. Our approach substantially increases precision in estimates of abundance compared to models that do not share detection information among species. We then expand this model, and apply it to repeated detection/non-detection data collected on six species of tits (Paridae) breeding at 119 1 km<sup>2</sup> sampling sites across a <em>P. montanus</em> hybrid zone in northern Switzerland (2004-2020). We find strong impacts of forest cover and elevation on population persistence and colonisation in all species. We also demonstrate evidence for interspecific competition on population persistence and colonization probabilities, where the presence of marsh tits reduces population persistence and colonisation probability of sympatric willow tits, potentially decreasing gene flow among willow tit subspecies.</p> <p>4. While conceptually simple, our results have important implications for the future modeling of population abundance, colonization, persistence, and trajectories in community frameworks. We suggest potential extensions of our modeling in this paper, and discuss how leveraging data from multiple species can improve model performance and sharpen ecological inference.</p>
The Community Coevolution Model with application to the study of evolutionary relationships between genes based on phylogenetic profiles
<p>Organismal traits can evolve in a coordinated way, with correlated patterns of gains and losses reflecting important evolutionary associations. Discovering these associations can reveal important information about the functional and ecological linkages among traits. Phylogenetic profiles treat individual genes as traits distributed across sets of genomes and can provide a fine-grained view of the genetic underpinnings of evolutionary processes in a set of genomes. Phylogenetic profiling has been used to identify genes that are functionally linked, and to identify common patterns of lateral gene transfer in microorganisms. However, comparative analysis of phylogenetic profiles and other trait distributions should take into account the phylogenetic relationships among the organisms under consideration.</p> <p>Here we propose the Community Coevolution Model (CCM), a new coevolutionary model to analyze the evolutionary associations among traits, with a focus on phylogenetic profiles. In the CCM, traits are considered to evolve as a community with interactions, and the transition rate for each trait depends on the current states of other traits. Surpassing other comparative methods for pairwise trait analysis, CCM has the additional advantage of being able to examine multiple traits as a community to reveal more dependency relationships. We also develop a simulation procedure to generate phylogenetic profiles with correlated evolutionary patterns that can be used as benchmark data for evaluation purposes.</p> <p>A simulation study demonstrates that CCM is more accurate than other methods including the Jaccard Index and three tree-aware methods. The parameterization of CCM makes the interpretation of the relations between genes more direct, which leads to Darwin's scenario being identified easily based on the estimated parameters. We show that CCM is more efficient and fits real data better than other methods resulting in higher likelihood scores with fewer parameters. An examination of 3786 phylogenetic profiles across a set of 659 bacterial genomes highlights linkages between genes with common functions, including many patterns that would not have been identified under a non-phylogenetic model of common distribution. We also applied the CCM to 44 proteins in the well-studied Mitochondrial Respiratory Complex I and recovered associations that mapped well onto the structural associations that exist in the complex.</p>
Trajectories of backtracked passive particles for: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea"
<p>This collection hosts the trajectories of bactracked passive particles using Ocean Parcels v2.0 (The Parcels v2.0 Lagrangian framework: new field interpolation schemes. Delandmeter, P and E van Sebille (2019), <em>Geoscientific Model Development</em>, <em>12</em>, 3571–3584) and ocean surface velocity fields from the output of: "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea". </p> <p>trajectories_2009.tar: trajectories for particles released between 2009-01-01 and 2009-12-31</p> <p>trajectories_2010.tar: trajectories for particles released between 2010-01-01 and 2010-12-31 (as shown in "CARIB12: A Regional Community Earth System Model / Modular Ocean Model 6 Configuration of the Caribbean Sea")</p> <p>trajectories_2011.tar: trajectories for particles released between 2011-01-01 and 2011-12-31</p> <p>release_sites.csv: release sites for each release date.</p> <p>ocean_parcels_backtrack_VIB_carib12.py : python script to run ocean parcels to generate the trajectories published here.</p>
Role of Troposphere-Convection-Land Coupling in the Southwestern Amazon Precipitation Bias of the Community Earth System Model version 1 (CESM1)
<p>Necessary outputs and scripts for recreating the figures for the journal article with the same title.</p>
CICE6 simulations: Quality Control for Community Based Sea Ice Model Development
<p>CICE6 simulations: Quality Control for Community Based Sea Ice Model Development<br> -----------------------------------------------------------------------------------</p> <p>Data from the CICE6 simulations by Elizabeth C. Hunke at Los Alamos National Laboratory 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 the CICE sea ice model version 6.0.0.alpha with the baseline namelist:</p> <p>&setup_nml<br> days_per_year = 365<br> use_leap_years = .false.<br> year_init = 1990<br> istep0 = 0<br> dt = 3600.0<br> npt = 43800<br> ndtd = 1<br> runtype = 'continue'<br> ice_ic = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/ic/gx1/iced_gx1_v5.nc'<br> restart = .true.<br> restart_ext = .false.<br> use_restart_time = .true.<br> restart_format = 'nc'<br> lcdf64 = .false.<br> restart_dir = './restart/'<br> restart_file = 'iced'<br> pointer_file = './ice.restart_file'<br> dumpfreq = 'm'<br> dumpfreq_n = 1<br> dump_last = .false.<br> bfbflag = .false.<br> diagfreq = 24<br> diag_type = 'stdout'<br> diag_file = 'ice_diag.d'<br> print_global = .true.<br> print_points = .true.<br> latpnt(1) = 90.<br> lonpnt(1) = 0.<br> latpnt(2) = -65.<br> lonpnt(2) = -45.<br> dbug = .false.<br> histfreq = 'm','d','x','x','x'<br> histfreq_n = 1 , 1 , 1 , 1 , 1<br> hist_avg = .true.<br> history_dir = './history/'<br> history_file = 'iceh'<br> write_ic = .true.<br> incond_dir = './history/'<br> incond_file = 'iceh_ic'<br> /</p> <p>&grid_nml<br> grid_format = 'bin'<br> grid_type = 'displaced_pole'<br> grid_file = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/grid/gx1/grid_gx1.bin'<br> kmt_file = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/grid/gx1/kmt_gx1.bin'<br> gridcpl_file = 'unknown_gridcpl_file'<br> kcatbound = 0<br> /</p> <p>&domain_nml<br> nprocs = 32<br> processor_shape = 'slenderX2'<br> distribution_type = 'cartesian'<br> distribution_wght = 'latitude'<br> ew_boundary_type = 'cyclic'<br> ns_boundary_type = 'open'<br> maskhalo_dyn = .false.<br> maskhalo_remap = .false.<br> maskhalo_bound = .false.<br> /</p> <p>&tracer_nml<br> tr_iage = .true.<br> restart_age = .false.<br> tr_FY = .true.<br> restart_FY = .false.<br> tr_lvl = .true.<br> restart_lvl = .false.<br> tr_pond_cesm = .false.<br> restart_pond_cesm = .false.<br> tr_pond_topo = .false.<br> restart_pond_topo = .false.<br> tr_pond_lvl = .true.<br> restart_pond_lvl = .false.<br> tr_aero = .false.<br> restart_aero = .false.<br> /</p> <p>&thermo_nml<br> kitd = 1<br> ktherm = 2<br> conduct = 'bubbly'<br> a_rapid_mode = 0.5e-3<br> Rac_rapid_mode = 10.0<br> aspect_rapid_mode = 1.0<br> dSdt_slow_mode = -5.0e-8<br> phi_c_slow_mode = 0.05<br> phi_i_mushy = 0.85<br> /</p> <p>&dynamics_nml<br> kdyn = 1<br> ndte = 120<br> revised_evp = .false.<br> advection = 'remap'<br> kstrength = 1<br> krdg_partic = 1<br> krdg_redist = 1<br> mu_rdg = 3<br> Cf = 17.<br> Ktens = 0.<br> e_ratio = 2.<br> basalstress = .false.<br> /</p> <p>&shortwave_nml<br> shortwave = 'dEdd'<br> albedo_type = 'default'<br> albicev = 0.78<br> albicei = 0.36<br> albsnowv = 0.98<br> albsnowi = 0.70 <br> ahmax = 0.3<br> R_ice = 0.<br> R_pnd = 0.<br> R_snw = 1.5<br> dT_mlt = 1.5<br> rsnw_mlt = 1500.<br> kalg = 0.6<br> /</p> <p>&ponds_nml<br> hp1 = 0.01<br> hs0 = 0.<br> hs1 = 0.03<br> dpscale = 1.e-3<br> frzpnd = 'hlid'<br> rfracmin = 0.15<br> rfracmax = 1.<br> pndaspect = 0.8<br> /</p> <p>&zbgc_nml<br> tr_brine = .false.<br> restart_hbrine = .false.<br> tr_zaero = .false.<br> modal_aero = .false.<br> skl_bgc = .false.<br> z_tracers = .false.<br> dEdd_algae = .false.<br> solve_zbgc = .false.<br> bgc_flux_type = 'Jin2006'<br> restore_bgc = .false.<br> restart_bgc = .false.<br> scale_bgc = .false.<br> solve_zsal = .false.<br> restart_zsal = .false.<br> bgc_data_dir = '/uknown_bgc_data_dir'<br> sil_data_type = 'default'<br> nit_data_type = 'default'<br> fe_data_type = 'default'<br> tr_bgc_Nit = .true.<br> tr_bgc_C = .true.<br> tr_bgc_chl = .false.<br> tr_bgc_Am = .true.<br> tr_bgc_Sil = .true.<br> tr_bgc_DMS = .false.<br> tr_bgc_PON = .true.<br> tr_bgc_hum = .true.<br> tr_bgc_DON = .false.<br> tr_bgc_Fe = .true. <br> grid_o = 0.006<br> grid_o_t = 0.006<br> l_sk = 0.024<br> grid_oS = 0.0<br> l_skS = 0.028<br> phi_snow = -0.3<br> initbio_frac = 0.8<br> frazil_scav = 0.8 <br> ratio_Si2N_diatoms = 1.8 <br> ratio_Si2N_sp = 0.0<br> ratio_Si2N_phaeo = 0.0<br> ratio_S2N_diatoms = 0.03 <br> ratio_S2N_sp = 0.03 <br> ratio_S2N_phaeo = 0.03<br> ratio_Fe2C_diatoms = 0.0033<br> ratio_Fe2C_sp = 0.0033<br> ratio_Fe2C_phaeo = 0.1<br> ratio_Fe2N_diatoms = 0.023 <br> ratio_Fe2N_sp = 0.023<br> ratio_Fe2N_phaeo = 0.7<br> ratio_Fe2DON = 0.023<br> ratio_Fe2DOC_s = 0.1<br> ratio_Fe2DOC_l = 0.033<br> fr_resp = 0.05<br> tau_min = 5200.0<br> tau_max = 173000.0<br> algal_vel = 0.0000000111<br> R_dFe2dust = 0.035<br> dustFe_sol = 0.005<br> chlabs_diatoms = 0.03<br> chlabs_sp = 0.01<br> chlabs_phaeo = 0.05<br> alpha2max_low_diatoms = 0.8<br> alpha2max_low_sp = 0.67<br> alpha2max_low_phaeo = 0.67<br> beta2max_diatoms = 0.018<br> beta2max_sp = 0.0025<br> beta2max_phaeo = 0.01<br> mu_max_diatoms = 1.44<br> mu_max_sp = 0.851<br> mu_max_phaeo = 0.851<br> grow_Tdep_diatoms = 0.06<br> grow_Tdep_sp = 0.06<br> grow_Tdep_phaeo = 0.06<br> fr_graze_diatoms = 0.0<br> fr_graze_sp = 0.1<br> fr_graze_phaeo = 0.1<br> mort_pre_diatoms = 0.007<br> mort_pre_sp = 0.007<br> mort_pre_phaeo = 0.007<br> mort_Tdep_diatoms = 0.03<br> mort_Tdep_sp = 0.03<br> mort_Tdep_phaeo = 0.03<br> k_exude_diatoms = 0.0<br> k_exude_sp = 0.0<br> k_exude_phaeo = 0.0<br> K_Nit_diatoms = 1.0<br> K_Nit_sp = 1.0<br> K_Nit_phaeo = 1.0<br> K_Am_diatoms = 0.3<br> K_Am_sp = 0.3<br> K_Am_phaeo = 0.3<br> K_Sil_diatoms = 4.0<br> K_Sil_sp = 0.0<br> K_Sil_phaeo = 0.0<br> K_Fe_diatoms = 1.0<br> K_Fe_sp = 0.2<br> K_Fe_phaeo = 0.1<br> f_don_protein = 0.6<br> kn_bac_protein = 0.03<br> f_don_Am_protein = 0.25<br> f_doc_s = 0.4<br> f_doc_l = 0.4<br> f_exude_s = 1.0<br> f_exude_l = 1.0<br> k_bac_s = 0.03<br> k_bac_l = 0.03<br> T_max = 0.0<br> fsal = 1.0<br> op_dep_min = 0.1<br> fr_graze_s = 0.5<br> fr_graze_e = 0.5<br> fr_mort2min = 0.5<br> fr_dFe = 0.3<br> k_nitrif = 0.0<br> t_iron_conv = 3065.0<br> max_loss = 0.9<br> max_dfe_doc1 = 0.2<br> fr_resp_s = 0.75<br> y_sk_DMS = 0.5<br> t_sk_conv = 3.0<br> t_sk_ox = 10.0<br> algaltype_diatoms = 0.0<br> algaltype_sp = 0.5<br> algaltype_phaeo = 0.5<br> nitratetype = -1.0<br> ammoniumtype = 1.0<br> silicatetype = -1.0<br> dmspptype = 0.5<br> dmspdtype = -1.0<br> humtype = 1.0<br> doctype_s = 0.5<br> doctype_l = 0.5<br> dontype_protein = 0.5<br> fedtype_1 = 0.5<br> feptype_1 = 0.5<br> zaerotype_bc1 = 1.0<br> zaerotype_bc2 = 1.0<br> zaerotype_dust1 = 1.0<br> zaerotype_dust2 = 1.0<br> zaerotype_dust3 = 1.0<br> zaerotype_dust4 = 1.0<br> ratio_C2N_diatoms = 7.0<br> ratio_C2N_sp = 7.0<br> ratio_C2N_phaeo = 7.0<br> ratio_chl2N_diatoms= 2.1<br> ratio_chl2N_sp = 1.1<br> ratio_chl2N_phaeo = 0.84<br> F_abs_chl_diatoms = 2.0<br> F_abs_chl_sp = 4.0<br> F_abs_chl_phaeo = 5.0<br> ratio_C2N_proteins = 7.0<br> /</p> <p>&forcing_nml<br> formdrag = .false.<br> atmbndy = 'default'<br> fyear_init = 1990<br> ycycle = 20<br> atm_data_format = 'bin'<br> atm_data_type = 'LYq'<br> atm_data_dir = '/usr/projects/climate/eclare/DATA/Consortium/CICE_data/forcing/gx1/COREII'<br> calc_strair = .true.<br> highfreq = .false.<br> natmiter = 5<br> calc_Tsfc = .true.<br> precip_units = 'mm_per_sec'<br> ustar_min = 0.0005<br> fbot_xfer_type = 'constant'<br> update_ocn_f = .false.<br> l_mpond_fresh = .false.<br> tfrz_option = 'mushy'<br> oceanmixed_ice = .true.<br> ocn_data_format = 'nc'<br> sss_data_type = 'ncar'<br> sst_data_type = 'ncar'<br> ocn_data_dir = '/usr/projects/climate/eclare/DATA/gx1v3/gx1v3/forcing/'<br> oceanmixed_file = 'oceanmixed_ice_depth.nc'<br> restore_sst = .false.<br> trestore = 90<br> restore_ice = .false.<br> /</p> <p>&icefields_nml<br> f_tmask = .true.<br> f_blkmask = .true.<br> f_tarea = .true.<br> f_uarea = .true.<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 = .true.<br> f_ANGLET = .true.<br> f_NCAT = .true.<br> f_VGRDi = .false.<br> f_VGRDs = .false.<br> f_VGRDb = .false.<br> f_VGRDa = .true.<br> f_bounds = .false.<br> f_aice = 'm' <br> f_hi = 'm'<br> f_hs = 'm' <br> f_Tsfc = 'm' <br> f_sice = 'm' <br> f_uvel = 'm' <br> f_vvel = 'm' <br> f_uatm = 'm' <br> f_vatm = 'm' <br> f_fswdn = 'm' <br> f_flwdn = 'm'<br> f_snowfrac = 'x'<br> f_snow = 'x' <br> f_snow_ai = 'm' <br> f_rain = 'x' <br> f_rain_ai = 'm' <br> f_sst = 'm' <br> f_sss = 'm' <br> f_uocn = 'm' <br> f_vocn = 'm' <br> f_frzmlt = 'm'<br> f_fswfac = 'm'<br> f_fswint_ai = 'm'<br> f_fswabs = 'x' <br> f_fswabs_ai = 'm' <br> f_albsni = 'm' <br> f_alvdr = 'x'<br> f_alidr = 'x'<br> f_alvdf = 'x'<br> f_alidf = 'x'<br> f_alvdr_ai = 'x'<br> f_alidr_ai = 'x'<br> f_alvdf_ai = 'x'<br> f_alidf_ai = 'x'<br> f_albice = 'x'<br> f_albsno = 'x'<br> f_albpnd = 'x'<br> f_coszen = 'x'<br> f_flat = 'x' <br> f_flat_ai = 'm' <br> f_fsens = 'x' <br> f_fsens_ai = 'm' <br> f_fswup = 'x' <br> f_flwup = 'x' <br> f_flwup_ai = 'm' <br> f_evap = 'x' <br> f_evap_ai = 'm' <br> f_Tair = 'm' <br> f_Tref = 'x' <br> f_Qref = 'x'<br> f_congel = 'm' <br> f_frazil = 'm' <br> f_snoice = 'm' <br> f_dsnow = 'x' <br> f_melts = 'm'<br> f_meltt = 'm'<br> f_meltb = 'm'<br> f_meltl = 'm'<br> f_fresh = 'x'<br> f_fresh_ai = 'm'<br> f_fsalt = 'x'<br> f_fsalt_ai = 'm'<br> f_fbot = 'm'<br> f_fhocn = 'x' <br> f_fhocn_ai = 'm' <br> f_fswthru = 'x' <br> f_fswthru_ai = 'm' <br> f_fsurf_ai = 'x'<br> f_fcondtop_ai = 'x'<br> f_fmeltt_ai = 'x' <br> f_strairx = 'm' <br> f_strairy = 'm' <br> f_strtltx = 'x' <br> f_strtlty = 'x' <br> f_strcorx = 'x' <br> f_strcory = 'x' <br> f_strocnx = 'x' <br> f_strocny = 'x' <br> f_strintx = 'x' <br> f_strinty = 'x'<br> f_taubx = 'x'<br> f_tauby = 'x'<br> f_strength = 'm'<br> f_divu = 'm'<br> f_shear = 'm'<br> f_sig1 = 'm' <br> f_sig2 = 'm' <br> f_dvidtt = 'm' <br> f_dvidtd = 'm' <br> f_daidtt = 'm'<br> f_daidtd = 'm' <br> f_dagedtt = 'm'<br> f_dagedtd = 'm' <br> f_mlt_onset = 'm'<br> f_frz_onset = 'm'<br> f_hisnap = 'd'<br> f_aisnap = 'd'<br> f_trsig = 'm'<br> f_icepresent = 'm'<br> f_iage = 'm'<br> f_FY = 'x'<br> f_aicen = 'x'<br> f_vicen = 'x'<br> f_vsnon = 'x'<br> f_snowfracn = 'x'<br> f_keffn_top = 'x'<br> f_Tinz = 'x'<br> f_Sinz = 'x'<br> f_Tsnz = 'x'<br> f_fsurfn_ai = 'x'<br> f_fcondtopn_ai = 'x'<br> f_fmelttn_ai = 'x'<br> f_flatn_ai = 'x'<br> f_fsensn_ai = 'x'<br> /</p> <p>&icefields_mechred_nml<br> f_alvl = 'm'<br> f_vlvl = 'm'<br> f_ardg = 'm'<br> f_vrdg = 'm'<br> f_dardg1dt = 'x'<br> f_dardg2dt = 'x'<br> f_dvirdgdt = 'x'<br> f_opening = 'x'<br> f_ardgn = 'x'<br> f_vrdgn = 'x'<br> f_dardg1ndt = 'x'<br> f_dardg2ndt = 'x'<br> f_dvirdgndt = 'x'<br> f_krdgn = 'x'<br> f_aparticn = 'x'<br> f_aredistn = 'x'<br> f_vredistn = 'x'<br> f_araftn = 'x'<br> f_vraftn = 'x'<br> /</p> <p>&icefields_pond_nml<br> f_apondn = 'x'<br> f_apeffn = 'x'<br> f_hpondn = 'x'<br> f_apond = 'm'<br> f_hpond = 'm'<br> f_ipond = 'm'<br> f_apeff = 'm'<br> f_apond_ai = 'm'<br> f_hpond_ai = 'm'<br> f_ipond_ai = 'm'<br> f_apeff_ai = 'm'<br> /</p> <p>&icefields_bgc_nml<br> f_faero_atm = 'x'<br> f_faero_ocn = 'x'<br> f_aero = 'x' <br> f_fbio = 'm'<br> f_fbio_ai = 'm'<br> f_zaero = 'x'<br> f_bgc_S = 'm'<br> f_bgc_N = 'm'<br> f_bgc_C = 'x'<br> f_bgc_DOC = 'm'<br> f_bgc_DIC = 'x'<br> f_bgc_chl = 'x'<br> f_bgc_Nit = 'm'<br> f_bgc_Am = 'm'<br> f_bgc_Sil = 'm'<br> f_bgc_DMSPp = 'x'<br> f_bgc_DMSPd = 'x'<br> f_bgc_DMS = 'x'<br> f_bgc_DON = 'x' <br> f_bgc_Fe = 'm' <br> f_bgc_hum = 'm' <br> f_bgc_PON = 'm'<br> f_bgc_ml = 'm'<br> f_upNO = 'm'<br> f_upNH = 'm'<br> f_bTin = 'm'<br> f_bphi = 'm' <br> f_iDi = 'm'<br> f_iki = 'm'<br> f_fbri = 'm' <br> f_hbri = 'm'<br> f_zfswin = 'm'<br> f_bionet = 'm'<br> f_biosnow = 'm'<br> f_grownet = 'm'<br> f_PPnet = 'm'<br> f_algalpeak = 'm'<br> f_zbgc_frac = 'm'<br> /</p> <p>&icefields_drag_nml<br> f_drag = 'x'<br> f_Cdn_atm = 'x'<br> f_Cdn_ocn = 'x'<br> /</p>
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>
2SPT Test Time Series: Quality Control for Community Based Sea Ice Model Development
<p>2SPT Test Time Series: Quality Control for Community Based Sea Ice Model Development</p> <p>------------------------------------------------------------------------------------</p> <p>This dataset comes from Regional Arctic System Model (RASM) simulations by Andrew F. Roberts at Naval Postgraduate School, described and used in Figure 3 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>Files for the series in Figure 3 as they appear in that paper to demonstrate the Two-Stage Paired Thickness Test:</p> <p>Figure 3a:</p> <p>R1009RBRcevp01a.cice.h.hi.1996_2000.North_Pole.nc</p> <p>R1009RBRcevp01a_2.cice.h.hi.1996_2000.North_Pole.nc</p> <p> </p> <p>Figure 3b:</p> <p>R1009RBRcevp01a.cice.h.hi.1996_2000.North_Pole.nc</p> <p>R1009RBRceap01a.cice.h.hi.1996_2000.North_Pole.nc</p> <p> </p> <p>Figure 3c:</p> <p>R1009RBRcevp01a.cice.h.hi.1996_2000.North_Bathurst_Island.nc</p> <p>R1009RBRceap01a.cice.h.hi.1996_2000.North_Bathurst_Island.nc</p> <p> </p> <p>Where the name "evp" or "eap" in the file name corresponds to the EVP and EAP simulations as described in the manuscript. These are selected timeseries from 1996 to 2000 from the RASM simasimulations described in section 2 of the above manuscript. CICE namelist settings are identical to the description for the dataset:</p> <p>Roberts, A. (2018). RASM simulations: Quality Control for Community Based Sea Ice Model Development [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1308236</p> <p> </p>
Fig. 1 in Interpreting the condition of the forest environment with use of the SCP/MIB model of carabid communities (Coleoptera: Carabidae)
Fig. 1. Correlation between values of SCP index of Carabids beetles and age of inhabiting forests.
Fig. 8 in Interpreting the condition of the forest environment with use of the SCP/MIB model of carabid communities (Coleoptera: Carabidae)
Fig. 8. The status of carabid communities living in different zones of the forest – fallow ecotone.
Dataset - Dynamic persistence of intracellular bacterial communities of uropathogenic Escherichia coli in a human bladder-chip model of urinary tract infections
<p>Dataset for manuscript posted at biorxiv: https://doi.org/10.1101/2021.01.03.42483 and in revision for eLife. Data corresponding to each main figure and its associated figure supplements are in seperate .zip folders.</p>
Multispecies modelling reveals potential for habitat restoration to re-establish boreal vertebrate community dynamics
<p>1. The restoration of habitats degraded by industrial disturbance is essential for achieving conservation objectives in disturbed landscapes. In boreal ecosystems, disturbances from seismic exploration lines and other linear features have adversely affected biodiversity, most notably leading to declines in threatened woodland caribou. Large-scale restoration of disturbed habitats is needed, yet empirical assessments of restoration effectiveness on wildlife communities remain rare.</p> <p>2. We used 73 camera trap deployments from 2015-2019 and joint species distribution models to investigate how habitat use by the larger vertebrate community (>0.2 kg) responded to variation in key seismic line characteristics (line-of-sight, width, density and mounding) following restoration treatments in a landscape disturbed by oil and gas development in northeastern Alberta.</p> <p>3. The proportion of variation explained by line characteristics was low in comparison to habitat type and season, suggesting short-term responses to restoration treatments were relatively weak. However, we predicted that lines with characteristics consistent with restored conditions would support altered community composition, with reduced use by wolf and coyote, thereby indicating that line restoration will result in reduced contact rates between caribou and these key predators.</p> <p>4. Our analysis provides a framework to assess and predict wildlife community responses to emerging restoration efforts. With the growing importance of habitat restoration for caribou and other vertebrate species, we recommend longer-term monitoring combined with landscape-scale comparisons of different restoration approaches to more fully understand and direct these critical conservation investments. Only by combining rigorous multispecies monitoring with large-scale restoration will we effectively conserve biodiversity within rapidly changing environments.</p>
Supplemental data for: Longitudinal, multi-platform metagenomics yields a high-quality genomic catalog and guides an in vitro model for cheese communities
<p><span>Microbiomes are intricately intertwined with human health, geochemical cycles, and food production. While many microbiomes of interest are highly complex and experimentally intractable, cheese rind microbiomes have proven powerful model systems for the study of microbial interactions. To provide a more comprehensive view of the genomic potential and temporal dynamics of cheese rind communities, we combine longitudinal, multi-platform metagenomics of three ripening washed-rind cheeses with whole genome sequencing of community isolates. Sequencing-based approaches revealed a highly reproducible microbial succession in each cheese, co-existence of closely related <em>Psychrobacter</em> species, and enabled the prediction of plasmid and phage diversity and their host associations. Combined with culture-based approaches, we established a genomic catalog and a paired 16-member in vitro washed rind cheese system. The combination of multi-platform metagenomic time-series data and an <em>in vitro</em> model provides a rich resource for further investigation of cheese rind microbiomes both computationally and experimentally. </span></p>
Revealing the drivers of parasite community assembly: using avian haemosporidians to model global dynamics of parasite species turnover
<p>Why do some regions share more or fewer species than others? Community assembly relies on the ability of individuals to disperse, colonize, and thrive in new regions. Therefore, many distinct factors, such as geographic distance and environmental features, can determine the odds of a species colonizing a new environment. For parasites, host community composition (i.e., resources) also plays a key role in their ability to colonize a new environment as they rely on their hosts to complete their life cycle. Thus, variation in host community composition and environmental conditions should determine parasite turnover among regions. Here, we explored the global drivers of parasite turnover using avian malaria and malaria-like (haemosporidian) parasites. We compiled global databases on avian haemosporidian lineages distributions, environmental conditions, avian species distributions and functional traits and ran generalized dissimilarity models to uncover the main drivers of parasite turnover. We demonstrated that haemosporidian parasite turnover is mainly driven by geographic distance followed by host functional traits, environmental conditions, and host distributions. The main host functional traits associated with high parasite turnover were the predominance of resident (i.e., non-migratory) species and strong territoriality while the most important climatic drivers of haemosporidian turnover were mean temperature and temperature seasonality. Overall, we establish the importance of geographic distance as a key predictor of ecological dissimilarity and show that host resources influence parasite turnover more strongly than environmental conditions. We also evidenced that parasite turnover is most pronounced among tropical and less interconnected regions (i.e., regions with mostly territorial and non-migratory hosts). Our findings provide a robust foundation for the prediction of avian pathogen spread and the emergence of infectious diseases.</p>
Input data and model implementation from: How do terrestrial wildlife communities respond to small-scale Acacia plantations embedded in harvested tropical forest?
<p class="MsoNormal"><span>To offset the declining timber supply from shifting towards more sustainable forestry practices, industrial tree plantations are expanding in tropical production forests. The conversion of natural forest to tree plantation is generally associated with loss of biodiversity and shifts toward more generalist and disturbance tolerant communities; but effects of mixed-landuse landscapes integrating natural and plantation forest remain little understood. Using camera traps, we surveyed the medium-to-large bodied terrestrial wildlife community across two mixed-land-use forest management areas in Sarawak, Malaysia Borneo which include areas dedicated for logging of natural forest and adjacent planted <em>Acacia</em> forests. We analysed data from a 25-wildlife species community using a Bayesian community occupancy model to assess species richness and species-specific occurrence responses to <em>Acacia</em> plantations at a broad scale, and to remote-sensed local habitat conditions within the different forest land-use types. All species were estimated to occur in both land-use types, but species-level percent area occupied and predicted average local species richness were slightly higher in the natural forest management areas compared to licensed planted forest. Similarly, occupancy-based species diversity profiles and defaunation indices for both a full community and only threatened and endemic species suggested the diversity and occurrence were slightly higher in the natural forest management areas. At the local scale, forest quality was the most prominent predictor of species occurrence. These associations with forest quality varied among species but were predominantly positive. Our results highlight the ability of a mixed-land-use landscape with small-scale<em> Acacia</em> plantations embedded in natural forest to retain terrestrial wildlife communities while providing an alternate source of timber. Nonetheless, there was a tendency towards reduced biodiversity in planted forests, which would likely be more pronounced in plantations that are larger or embedded in a less natural matrix.</span></p>
Designing a Synthetic Microbial Community through Genome Metabolic Modeling to enhance Plant-Microbe Interaction
<p>Supplementary data 1 - <strong>Reconstructed genome-scale metabolic networks from MAGs and Hosts</strong></p> <p>Supplementary data 2 - P<strong>lant growth-promoting traits among members of the minimal community</strong></p> <p> </p> <p>Manipulating the rhizosphere microbial community through beneficial microorganism inoculation has gained interest in improving crop productivity and stress resistance. Synthetic microbial communities, known as SynCom, mimic natural microbial compositions while reducing the number of components. However, achieving this goal requires a comprehensive understanding of natural microbial communities and a careful selection of compatible microorganisms with colonization traits, which still pose challenges. In this study, we employed an <em>in-silico</em> approach using genome metabolic modeling to design a synthetic microbial community aimed at improving the yield of important crop plants. We used a targeted approach to select a minimal community (MinCom) encompassing essential compounds for microbial metabolism and compounds relevant to plant interactions. This resulted in a reduction of the initial community size by approximately 4.5-fold. Notably, the MinCom retained crucial genes associated with essential plant growth-promoting traits, such as iron acquisition, EPS production, potassium solubilization, nitrogen fixation, GABA production, and IAA-related tryptophan metabolism. Furthermore, our selection process for the SymCom, based on a comprehensive understanding of microbe-microbe-plant interactions, yielded a set of six hub species that displayed notable taxonomic novelty, including members of the Eremiobacterota and Verrucomicrobiota phyla. Our study contributes to the growing body of research on synthetic microbial communities and their potential to enhance agricultural practices. The insights gained from our in-silico approach and the selection of hub species pave the way for further investigations into the development of tailored microbial communities that can optimize crop productivity and improve stress resilience in agricultural systems.</p>
Data from: Emergent spatial patterns can indicate upcoming regime shifts in a realistic model of coral community
<p class="western"><span>Increased stress on coastal ecosystems, such as coral reefs, seagrasses, kelp forests and other habitats can make them shift towards degraded, often algae-dominated or barren communities. This has already occurred in many places around the world, calling for new approaches to identify where such regime shifts may be triggered. Theoretical work predicts that the spatial structure of habitat-forming species should exhibit changes prior to regime shifts,</span><span><em> </em></span><span>such as an increase in spatial autocorrelation. However, extending this theory to marine systems requires theoretical models connecting field-supported ecological mechanisms to data and spatial patterns at relevant scales. To do so, we built a spatially-explicit model of sub-tropical coral communities based on experiments and long-term datasets from Rapa Nui (Easter Island, Chile), to test whether spatial indicators could signal upcoming regime shifts in coral communities. Spatial indicators anticipated degradation of coral communities following increases in frequency of bleaching events or coral mortality. However, they were generally unable to signal shifts that followed herbivore loss, a widespread and well-researched source of degradation, likely because herbivory, despite being critical for the maintenance of corals, had comparatively little effect on their self-organization. Informative trends were found both under equilibrium and non-equilibrium conditions, but were determined by the type of direct neighbor interactions between corals, which remain relatively poorly documented. These inconsistencies show that while this approach is promising, its application to marine systems will require detailed information about the type of stressor, and filling current gaps in our knowledge of interactions at play in coral communities. </span></p>
Data set for model validation in "Simulating ice segregation and thaw consolidation in permafrost environments with the CryoGrid community model"
<p>This upload contains the data set for model validation in the manuscript "Simulating ice segregation and thaw consolidation in permafrost environments with the CryoGrid community model".</p>
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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.