A “Timeseries Case” system

%load_ext autoreload
%autoreload 2

import os
os.chdir('/glade/u/home/fengzhu/Github/x4c/docsrc/notebooks')
import numpy as np
import x4c
print(x4c.__version__)

2024.5.2.17

dirpath = '/glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005'
case = x4c.Timeseries(dirpath)
case.vars_info

>>> case.root_dir: /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005
>>> case.path_pattern: comp/proc/tseries/month_1/casename.mdl.h_str.vn.timespan.nc
>>> case.grid_dict: {'atm': 'ne16', 'lnd': 'ne16', 'rof': 'ne16', 'ocn': 'g16', 'ice': 'g16'}
>>> case.vars_info created

{'ADRAIN': ('atm', 'cam', 'h0'),
 'ADSNOW': ('atm', 'cam', 'h0'),
 'AEROD_v': ('atm', 'cam', 'h0'),
 'ANRAIN': ('atm', 'cam', 'h0'),
 'ANSNOW': ('atm', 'cam', 'h0'),
 'AODDUST1': ('atm', 'cam', 'h0'),
 'AODDUST3': ('atm', 'cam', 'h0'),
 'AODVIS': ('atm', 'cam', 'h0'),
 'AQRAIN': ('atm', 'cam', 'h0'),
 'AQSNOW': ('atm', 'cam', 'h0'),
 'AREI': ('atm', 'cam', 'h0'),
 'AREL': ('atm', 'cam', 'h0'),
 'AWNC': ('atm', 'cam', 'h0'),
 'AWNI': ('atm', 'cam', 'h0'),
 'BURDEN1': ('atm', 'cam', 'h0'),
 'BURDEN2': ('atm', 'cam', 'h0'),
 'BURDEN3': ('atm', 'cam', 'h0'),
 'BURDENBC': ('atm', 'cam', 'h0'),
 'BURDENDUST': ('atm', 'cam', 'h0'),
 'BURDENPOM': ('atm', 'cam', 'h0'),
 'BURDENSEASALT': ('atm', 'cam', 'h0'),
 'BURDENSO4': ('atm', 'cam', 'h0'),
 'BURDENSOA': ('atm', 'cam', 'h0'),
 'CCN3': ('atm', 'cam', 'h0'),
 'CDNUMC': ('atm', 'cam', 'h0'),
 'CLDHGH': ('atm', 'cam', 'h0'),
 'CLDICE': ('atm', 'cam', 'h0'),
 'CLDLIQ': ('atm', 'cam', 'h0'),
 'CLDLOW': ('atm', 'cam', 'h0'),
 'CLDMED': ('atm', 'cam', 'h0'),
 'CLDTOT': ('atm', 'cam', 'h0'),
 'CLOUD': ('atm', 'cam', 'h0'),
 'DCQ': ('atm', 'cam', 'h0'),
 'DMS_SRF': ('atm', 'cam', 'h0'),
 'DTCOND': ('atm', 'cam', 'h0'),
 'DTV': ('atm', 'cam', 'h0'),
 'DTWR_H2O2': ('atm', 'cam', 'h0'),
 'DTWR_H2SO4': ('atm', 'cam', 'h0'),
 'DTWR_SO2': ('atm', 'cam', 'h0'),
 'FICE': ('atm', 'cam', 'h0'),
 'FLDS': ('atm', 'cam', 'h0'),
 'FLNS': ('atm', 'cam', 'h0'),
 'FLNSC': ('atm', 'cam', 'h0'),
 'FLNT': ('atm', 'cam', 'h0'),
 'FLNTC': ('atm', 'cam', 'h0'),
 'FLUT': ('atm', 'cam', 'h0'),
 'FLUTC': ('atm', 'cam', 'h0'),
 'FREQI': ('atm', 'cam', 'h0'),
 'FREQL': ('atm', 'cam', 'h0'),
 'FREQR': ('atm', 'cam', 'h0'),
 'FREQS': ('atm', 'cam', 'h0'),
 'FSDS': ('atm', 'cam', 'h0'),
 'FSDSC': ('atm', 'cam', 'h0'),
 'FSNS': ('atm', 'cam', 'h0'),
 'FSNSC': ('atm', 'cam', 'h0'),
 'FSNT': ('atm', 'cam', 'h0'),
 'FSNTC': ('atm', 'cam', 'h0'),
 'FSNTOA': ('atm', 'cam', 'h0'),
 'FSNTOAC': ('atm', 'cam', 'h0'),
 'FSUTOA': ('atm', 'cam', 'h0'),
 'H216OI': ('atm', 'cam', 'h0'),
 'H216OL': ('atm', 'cam', 'h0'),
 'H216OR': ('atm', 'cam', 'h0'),
 'H216OS': ('atm', 'cam', 'h0'),
 'H216OV': ('atm', 'cam', 'h0'),
 'H216Or': ('atm', 'cam', 'h0'),
 'H216Os': ('atm', 'cam', 'h0'),
 'H218OI': ('atm', 'cam', 'h0'),
 'H218OL': ('atm', 'cam', 'h0'),
 'H218OR': ('atm', 'cam', 'h0'),
 'H218OS': ('atm', 'cam', 'h0'),
 'H218OV': ('atm', 'cam', 'h0'),
 'H218Or': ('atm', 'cam', 'h0'),
 'H218Os': ('atm', 'cam', 'h0'),
 'H2O2_SRF': ('atm', 'cam', 'h0'),
 'H2OI': ('atm', 'cam', 'h0'),
 'H2OL': ('atm', 'cam', 'h0'),
 'H2OR': ('atm', 'cam', 'h0'),
 'H2OS': ('atm', 'cam', 'h0'),
 'H2OV': ('atm', 'cam', 'h0'),
 'H2Or': ('atm', 'cam', 'h0'),
 'H2Os': ('atm', 'cam', 'h0'),
 'H2SO4_SRF': ('atm', 'cam', 'h0'),
 'HDOI': ('atm', 'cam', 'h0'),
 'HDOL': ('atm', 'cam', 'h0'),
 'HDOR': ('atm', 'cam', 'h0'),
 'HDOS': ('atm', 'cam', 'h0'),
 'HDOV': ('atm', 'cam', 'h0'),
 'HDOr': ('atm', 'cam', 'h0'),
 'HDOs': ('atm', 'cam', 'h0'),
 'ICEFRAC': ('atm', 'cam', 'h0'),
 'ICIMR': ('atm', 'cam', 'h0'),
 'ICWMR': ('atm', 'cam', 'h0'),
 'IVT': ('atm', 'cam', 'h0'),
 'IWC': ('atm', 'cam', 'h0'),
 'LANDFRAC': ('atm', 'cam', 'h0'),
 'LHFLX': ('atm', 'cam', 'h0'),
 'LWCF': ('atm', 'cam', 'h0'),
 'NUMICE': ('atm', 'cam', 'h0'),
 'NUMLIQ': ('atm', 'cam', 'h0'),
 'OCNFRAC': ('atm', 'cam', 'h0'),
 'OMEGA': ('atm', 'cam', 'h0'),
 'OMEGAT': ('atm', 'cam', 'h0'),
 'PBLH': ('atm', 'cam', 'h0'),
 'PHIS': ('atm', 'cam', 'h0'),
 'PRECC': ('atm', 'cam', 'h0'),
 'PRECL': ('atm', 'cam', 'h0'),
 'PRECRC_H216Or': ('atm', 'cam', 'h0'),
 'PRECRC_H218Or': ('atm', 'cam', 'h0'),
 'PRECRC_H2Or': ('atm', 'cam', 'h0'),
 'PRECRC_HDOr': ('atm', 'cam', 'h0'),
 'PRECRL_H216OR': ('atm', 'cam', 'h0'),
 'PRECRL_H218OR': ('atm', 'cam', 'h0'),
 'PRECRL_H2OR': ('atm', 'cam', 'h0'),
 'PRECRL_HDOR': ('atm', 'cam', 'h0'),
 'PRECSC': ('atm', 'cam', 'h0'),
 'PRECSC_H216Os': ('atm', 'cam', 'h0'),
 'PRECSC_H218Os': ('atm', 'cam', 'h0'),
 'PRECSC_H2Os': ('atm', 'cam', 'h0'),
 'PRECSC_HDOs': ('atm', 'cam', 'h0'),
 'PRECSL': ('atm', 'cam', 'h0'),
 'PRECSL_H216OS': ('atm', 'cam', 'h0'),
 'PRECSL_H218OS': ('atm', 'cam', 'h0'),
 'PRECSL_H2OS': ('atm', 'cam', 'h0'),
 'PRECSL_HDOS': ('atm', 'cam', 'h0'),
 'PS': ('atm', 'cam', 'h0'),
 'PSL': ('atm', 'cam', 'h0'),
 'Q': ('atm', 'cam', 'h0'),
 'QFLX': ('atm', 'cam', 'h0'),
 'QRAINC': ('atm', 'cam', 'h0'),
 'QRAINS': ('atm', 'cam', 'h0'),
 'QREFHT': ('atm', 'cam', 'h0'),
 'QRL': ('atm', 'cam', 'h0'),
 'QRS': ('atm', 'cam', 'h0'),
 'QSNOWC': ('atm', 'cam', 'h0'),
 'QSNOWS': ('atm', 'cam', 'h0'),
 'RELHUM': ('atm', 'cam', 'h0'),
 'SHFLX': ('atm', 'cam', 'h0'),
 'SNOWHICE': ('atm', 'cam', 'h0'),
 'SNOWHLND': ('atm', 'cam', 'h0'),
 'SO2_SRF': ('atm', 'cam', 'h0'),
 'SOAG_SRF': ('atm', 'cam', 'h0'),
 'SOLIN': ('atm', 'cam', 'h0'),
 'SWCF': ('atm', 'cam', 'h0'),
 'T': ('atm', 'cam', 'h0'),
 'TAUGWX': ('atm', 'cam', 'h0'),
 'TAUGWY': ('atm', 'cam', 'h0'),
 'TAUX': ('atm', 'cam', 'h0'),
 'TAUY': ('atm', 'cam', 'h0'),
 'TGCLDCWP': ('atm', 'cam', 'h0'),
 'TGCLDIWP': ('atm', 'cam', 'h0'),
 'TGCLDLWP': ('atm', 'cam', 'h0'),
 'TMQ': ('atm', 'cam', 'h0'),
 'TREFHT': ('atm', 'cam', 'h0'),
 'TS': ('atm', 'cam', 'h0'),
 'TSMN': ('atm', 'cam', 'h0'),
 'TSMX': ('atm', 'cam', 'h0'),
 'U': ('atm', 'cam', 'h0'),
 'U10': ('atm', 'cam', 'h0'),
 'UU': ('atm', 'cam', 'h0'),
 'V': ('atm', 'cam', 'h0'),
 'VD01': ('atm', 'cam', 'h0'),
 'VQ': ('atm', 'cam', 'h0'),
 'VT': ('atm', 'cam', 'h0'),
 'VU': ('atm', 'cam', 'h0'),
 'VV': ('atm', 'cam', 'h0'),
 'WGUSTD': ('atm', 'cam', 'h0'),
 'WSUB': ('atm', 'cam', 'h0'),
 'Z3': ('atm', 'cam', 'h0'),
 'bc_a1_SRF': ('atm', 'cam', 'h0'),
 'dst_a1_SRF': ('atm', 'cam', 'h0'),
 'dst_a3_SRF': ('atm', 'cam', 'h0'),
 'ncl_a1_SRF': ('atm', 'cam', 'h0'),
 'ncl_a2_SRF': ('atm', 'cam', 'h0'),
 'ncl_a3_SRF': ('atm', 'cam', 'h0'),
 'num_a1_SRF': ('atm', 'cam', 'h0'),
 'num_a2_SRF': ('atm', 'cam', 'h0'),
 'num_a3_SRF': ('atm', 'cam', 'h0'),
 'pom_a1_SRF': ('atm', 'cam', 'h0'),
 'so4_a1_SRF': ('atm', 'cam', 'h0'),
 'so4_a2_SRF': ('atm', 'cam', 'h0'),
 'so4_a3_SRF': ('atm', 'cam', 'h0'),
 'soa_a1_SRF': ('atm', 'cam', 'h0'),
 'soa_a2_SRF': ('atm', 'cam', 'h0'),
 'FYarea': ('ice', 'cice', 'h'),
 'Tsfc': ('ice', 'cice', 'h'),
 'aeroiceint001': ('ice', 'cice', 'h'),
 'aeroiceint002': ('ice', 'cice', 'h'),
 'aeroiceint003': ('ice', 'cice', 'h'),
 'aeroicessl001': ('ice', 'cice', 'h'),
 'aeroicessl002': ('ice', 'cice', 'h'),
 'aeroicessl003': ('ice', 'cice', 'h'),
 'aerosnoint001': ('ice', 'cice', 'h'),
 'aerosnoint002': ('ice', 'cice', 'h'),
 'aerosnoint003': ('ice', 'cice', 'h'),
 'aerosnossl001': ('ice', 'cice', 'h'),
 'aerosnossl002': ('ice', 'cice', 'h'),
 'aerosnossl003': ('ice', 'cice', 'h'),
 'aice': ('ice', 'cice', 'h'),
 'aicen001': ('ice', 'cice', 'h'),
 'aicen002': ('ice', 'cice', 'h'),
 'aicen003': ('ice', 'cice', 'h'),
 'aicen004': ('ice', 'cice', 'h'),
 'aicen005': ('ice', 'cice', 'h'),
 'aisnap': ('ice', 'cice', 'h'),
 'apond001': ('ice', 'cice', 'h'),
 'apond002': ('ice', 'cice', 'h'),
 'apond003': ('ice', 'cice', 'h'),
 'apond004': ('ice', 'cice', 'h'),
 'apond005': ('ice', 'cice', 'h'),
 'congel': ('ice', 'cice', 'h'),
 'daidtd': ('ice', 'cice', 'h'),
 'daidtt': ('ice', 'cice', 'h'),
 'dardg1dt': ('ice', 'cice', 'h'),
 'dardg2dt': ('ice', 'cice', 'h'),
 'divu': ('ice', 'cice', 'h'),
 'dvidtd': ('ice', 'cice', 'h'),
 'dvidtt': ('ice', 'cice', 'h'),
 'dvirdgdt': ('ice', 'cice', 'h'),
 'evap': ('ice', 'cice', 'h'),
 'faero_atm001': ('ice', 'cice', 'h'),
 'faero_atm002': ('ice', 'cice', 'h'),
 'faero_atm003': ('ice', 'cice', 'h'),
 'faero_ocn001': ('ice', 'cice', 'h'),
 'faero_ocn002': ('ice', 'cice', 'h'),
 'faero_ocn003': ('ice', 'cice', 'h'),
 'fhocn': ('ice', 'cice', 'h'),
 'fhocn_ai': ('ice', 'cice', 'h'),
 'fiso_Qref001': ('ice', 'cice', 'h'),
 'fiso_Qref002': ('ice', 'cice', 'h'),
 'fiso_Qref003': ('ice', 'cice', 'h'),
 'fiso_atm001': ('ice', 'cice', 'h'),
 'fiso_atm002': ('ice', 'cice', 'h'),
 'fiso_atm003': ('ice', 'cice', 'h'),
 'fiso_evap001': ('ice', 'cice', 'h'),
 'fiso_evap002': ('ice', 'cice', 'h'),
 'fiso_evap003': ('ice', 'cice', 'h'),
 'fiso_ocn001': ('ice', 'cice', 'h'),
 'fiso_ocn002': ('ice', 'cice', 'h'),
 'fiso_ocn003': ('ice', 'cice', 'h'),
 'flat': ('ice', 'cice', 'h'),
 'flat_ai': ('ice', 'cice', 'h'),
 'flwdn': ('ice', 'cice', 'h'),
 'flwup': ('ice', 'cice', 'h'),
 'frazil': ('ice', 'cice', 'h'),
 'fresh': ('ice', 'cice', 'h'),
 'fresh_ai': ('ice', 'cice', 'h'),
 'fs': ('ice', 'cice', 'h'),
 'fsalt': ('ice', 'cice', 'h'),
 'fsalt_ai': ('ice', 'cice', 'h'),
 'fsens': ('ice', 'cice', 'h'),
 'fsens_ai': ('ice', 'cice', 'h'),
 'fswabs': ('ice', 'cice', 'h'),
 'fswdn': ('ice', 'cice', 'h'),
 'fswthru': ('ice', 'cice', 'h'),
 'fswup': ('ice', 'cice', 'h'),
 'hi': ('ice', 'cice', 'h'),
 'hisnap': ('ice', 'cice', 'h'),
 'hs': ('ice', 'cice', 'h'),
 'ice_present': ('ice', 'cice', 'h'),
 'meltb': ('ice', 'cice', 'h'),
 'meltl': ('ice', 'cice', 'h'),
 'melts': ('ice', 'cice', 'h'),
 'meltt': ('ice', 'cice', 'h'),
 'opening': ('ice', 'cice', 'h'),
 'qi': ('ice', 'cice', 'h'),
 'qs': ('ice', 'cice', 'h'),
 'rain': ('ice', 'cice', 'h'),
 'shear': ('ice', 'cice', 'h'),
 'sig1': ('ice', 'cice', 'h'),
 'sig2': ('ice', 'cice', 'h'),
 'snoice': ('ice', 'cice', 'h'),
 'snow': ('ice', 'cice', 'h'),
 'strairx': ('ice', 'cice', 'h'),
 'strairy': ('ice', 'cice', 'h'),
 'strcorx': ('ice', 'cice', 'h'),
 'strcory': ('ice', 'cice', 'h'),
 'strength': ('ice', 'cice', 'h'),
 'strintx': ('ice', 'cice', 'h'),
 'strinty': ('ice', 'cice', 'h'),
 'strocnx': ('ice', 'cice', 'h'),
 'strocny': ('ice', 'cice', 'h'),
 'transix': ('ice', 'cice', 'h'),
 'transiy': ('ice', 'cice', 'h'),
 'uvel': ('ice', 'cice', 'h'),
 'vicen001': ('ice', 'cice', 'h'),
 'vicen002': ('ice', 'cice', 'h'),
 'vicen003': ('ice', 'cice', 'h'),
 'vicen004': ('ice', 'cice', 'h'),
 'vicen005': ('ice', 'cice', 'h'),
 'vvel': ('ice', 'cice', 'h'),
 'BCDEP': ('lnd', 'clm2', 'h0'),
 'BTRAN': ('lnd', 'clm2', 'h0'),
 'BUILDHEAT': ('lnd', 'clm2', 'h0'),
 'DSTDEP': ('lnd', 'clm2', 'h0'),
 'DSTFLXT': ('lnd', 'clm2', 'h0'),
 'EFLX_DYNBAL': ('lnd', 'clm2', 'h0'),
 'EFLX_LH_TOT_R': ('lnd', 'clm2', 'h0'),
 'EFLX_LH_TOT_U': ('lnd', 'clm2', 'h0'),
 'ELAI': ('lnd', 'clm2', 'h0'),
 'ERRH2O': ('lnd', 'clm2', 'h0'),
 'ERRH2OSNO': ('lnd', 'clm2', 'h0'),
 'ERRSEB': ('lnd', 'clm2', 'h0'),
 'ERRSOI': ('lnd', 'clm2', 'h0'),
 'ERRSOL': ('lnd', 'clm2', 'h0'),
 'ESAI': ('lnd', 'clm2', 'h0'),
 'FCEV': ('lnd', 'clm2', 'h0'),
 'FCOV': ('lnd', 'clm2', 'h0'),
 'FCTR': ('lnd', 'clm2', 'h0'),
 'FGEV': ('lnd', 'clm2', 'h0'),
 'FGR': ('lnd', 'clm2', 'h0'),
 'FGR12': ('lnd', 'clm2', 'h0'),
 'FGR_R': ('lnd', 'clm2', 'h0'),
 'FGR_U': ('lnd', 'clm2', 'h0'),
 'FIRA': ('lnd', 'clm2', 'h0'),
 'FIRA_R': ('lnd', 'clm2', 'h0'),
 'FIRA_U': ('lnd', 'clm2', 'h0'),
 'FIRE': ('lnd', 'clm2', 'h0'),
 'FPSN': ('lnd', 'clm2', 'h0'),
 'FSA': ('lnd', 'clm2', 'h0'),
 'FSAT': ('lnd', 'clm2', 'h0'),
 'FSA_R': ('lnd', 'clm2', 'h0'),
 'FSA_U': ('lnd', 'clm2', 'h0'),
 'FSDSND': ('lnd', 'clm2', 'h0'),
 'FSDSNDLN': ('lnd', 'clm2', 'h0'),
 'FSDSNI': ('lnd', 'clm2', 'h0'),
 'FSDSVD': ('lnd', 'clm2', 'h0'),
 'FSDSVDLN': ('lnd', 'clm2', 'h0'),
 'FSDSVI': ('lnd', 'clm2', 'h0'),
 'FSH': ('lnd', 'clm2', 'h0'),
 'FSH_G': ('lnd', 'clm2', 'h0'),
 'FSH_NODYNLNDUSE': ('lnd', 'clm2', 'h0'),
 'FSH_R': ('lnd', 'clm2', 'h0'),
 'FSH_U': ('lnd', 'clm2', 'h0'),
 'FSH_V': ('lnd', 'clm2', 'h0'),
 'FSM': ('lnd', 'clm2', 'h0'),
 'FSM_R': ('lnd', 'clm2', 'h0'),
 'FSM_U': ('lnd', 'clm2', 'h0'),
 'FSNO': ('lnd', 'clm2', 'h0'),
 'FSOIEQ': ('lnd', 'clm2', 'h0'),
 'FSR': ('lnd', 'clm2', 'h0'),
 'FSRND': ('lnd', 'clm2', 'h0'),
 'FSRNDLN': ('lnd', 'clm2', 'h0'),
 'FSRNI': ('lnd', 'clm2', 'h0'),
 'FSRVD': ('lnd', 'clm2', 'h0'),
 'FSRVDLN': ('lnd', 'clm2', 'h0'),
 'FSRVI': ('lnd', 'clm2', 'h0'),
 'GC_HEAT1': ('lnd', 'clm2', 'h0'),
 'GC_ICE1': ('lnd', 'clm2', 'h0'),
 'GC_LIQ1': ('lnd', 'clm2', 'h0'),
 'H2OCAN': ('lnd', 'clm2', 'h0'),
 'H2OCAN_H218O': ('lnd', 'clm2', 'h0'),
 'H2OCAN_H2OTR': ('lnd', 'clm2', 'h0'),
 'H2OCAN_HDO': ('lnd', 'clm2', 'h0'),
 'H2OSNO': ('lnd', 'clm2', 'h0'),
 'H2OSNO_H218O': ('lnd', 'clm2', 'h0'),
 'H2OSNO_H2OTR': ('lnd', 'clm2', 'h0'),
 'H2OSNO_HDO': ('lnd', 'clm2', 'h0'),
 'H2OSNO_TOP': ('lnd', 'clm2', 'h0'),
 'H2OSOI': ('lnd', 'clm2', 'h0'),
 'H2OSOI_H218O': ('lnd', 'clm2', 'h0'),
 'H2OSOI_H2OTR': ('lnd', 'clm2', 'h0'),
 'H2OSOI_HDO': ('lnd', 'clm2', 'h0'),
 'HC': ('lnd', 'clm2', 'h0'),
 'HCSOI': ('lnd', 'clm2', 'h0'),
 'HEAT_FROM_AC': ('lnd', 'clm2', 'h0'),
 'LAISHA': ('lnd', 'clm2', 'h0'),
 'LAISUN': ('lnd', 'clm2', 'h0'),
 'OCDEP': ('lnd', 'clm2', 'h0'),
 'PBOT': ('lnd', 'clm2', 'h0'),
 'PCO2': ('lnd', 'clm2', 'h0'),
 'Q2M': ('lnd', 'clm2', 'h0'),
 'Q2M_H218O': ('lnd', 'clm2', 'h0'),
 'Q2M_H2OTR': ('lnd', 'clm2', 'h0'),
 'Q2M_HDO': ('lnd', 'clm2', 'h0'),
 'QBOT': ('lnd', 'clm2', 'h0'),
 'QBOT_H218O': ('lnd', 'clm2', 'h0'),
 'QBOT_H2OTR': ('lnd', 'clm2', 'h0'),
 'QBOT_HDO': ('lnd', 'clm2', 'h0'),
 'QCHARGE': ('lnd', 'clm2', 'h0'),
 'QDRAI': ('lnd', 'clm2', 'h0'),
 'QDRAI_H218O': ('lnd', 'clm2', 'h0'),
 'QDRAI_H2OTR': ('lnd', 'clm2', 'h0'),
 'QDRAI_HDO': ('lnd', 'clm2', 'h0'),
 'QDRIP': ('lnd', 'clm2', 'h0'),
 'QDRIP_H218O': ('lnd', 'clm2', 'h0'),
 'QDRIP_H2OTR': ('lnd', 'clm2', 'h0'),
 'QDRIP_HDO': ('lnd', 'clm2', 'h0'),
 'QFLX_ICE_DYNBAL': ('lnd', 'clm2', 'h0'),
 'QFLX_LIQ_DYNBAL': ('lnd', 'clm2', 'h0'),
 'QINFL': ('lnd', 'clm2', 'h0'),
 'QINFL_H218O': ('lnd', 'clm2', 'h0'),
 'QINFL_H2OTR': ('lnd', 'clm2', 'h0'),
 'QINFL_HDO': ('lnd', 'clm2', 'h0'),
 'QINTR': ('lnd', 'clm2', 'h0'),
 'QINTR_H218O': ('lnd', 'clm2', 'h0'),
 'QINTR_H2OTR': ('lnd', 'clm2', 'h0'),
 'QINTR_HDO': ('lnd', 'clm2', 'h0'),
 'QIRRIG': ('lnd', 'clm2', 'h0'),
 'QMELT_H218O': ('lnd', 'clm2', 'h0'),
 'QMELT_H2OTR': ('lnd', 'clm2', 'h0'),
 'QMELT_HDO': ('lnd', 'clm2', 'h0'),
 'QOVER': ('lnd', 'clm2', 'h0'),
 'QOVER_H218O': ('lnd', 'clm2', 'h0'),
 'QOVER_H2OTR': ('lnd', 'clm2', 'h0'),
 'QOVER_HDO': ('lnd', 'clm2', 'h0'),
 'QRGWL': ('lnd', 'clm2', 'h0'),
 'QRGWL_H218O': ('lnd', 'clm2', 'h0'),
 'QRGWL_H2OTR': ('lnd', 'clm2', 'h0'),
 'QRGWL_HDO': ('lnd', 'clm2', 'h0'),
 'QRUNOFF': ('lnd', 'clm2', 'h0'),
 'QRUNOFF_NODYNLNDUSE': ('lnd', 'clm2', 'h0'),
 'QRUNOFF_NODYNLNDUSE_H218O': ('lnd', 'clm2', 'h0'),
 'QRUNOFF_NODYNLNDUSE_H2OTR': ('lnd', 'clm2', 'h0'),
 'QRUNOFF_NODYNLNDUSE_HDO': ('lnd', 'clm2', 'h0'),
 'QRUNOFF_R': ('lnd', 'clm2', 'h0'),
 'QRUNOFF_U': ('lnd', 'clm2', 'h0'),
 'QSNOMELT': ('lnd', 'clm2', 'h0'),
 'QSNWCPICE': ('lnd', 'clm2', 'h0'),
 'QSNWCPICE_NODYNLNDUSE': ('lnd', 'clm2', 'h0'),
 'QSOIL': ('lnd', 'clm2', 'h0'),
 'QSOIL_H218O': ('lnd', 'clm2', 'h0'),
 'QSOIL_H2OTR': ('lnd', 'clm2', 'h0'),
 'QSOIL_HDO': ('lnd', 'clm2', 'h0'),
 'QVEGE': ('lnd', 'clm2', 'h0'),
 'QVEGE_H218O': ('lnd', 'clm2', 'h0'),
 'QVEGE_H2OTR': ('lnd', 'clm2', 'h0'),
 'QVEGE_HDO': ('lnd', 'clm2', 'h0'),
 'QVEGT': ('lnd', 'clm2', 'h0'),
 'QVEGT_H218O': ('lnd', 'clm2', 'h0'),
 'QVEGT_H2OTR': ('lnd', 'clm2', 'h0'),
 'QVEGT_HDO': ('lnd', 'clm2', 'h0'),
 'RAIN': ('lnd', 'clm2', 'h0'),
 'RAIN_H218O': ('lnd', 'clm2', 'h0'),
 'RAIN_H2OTR': ('lnd', 'clm2', 'h0'),
 'RAIN_HDO': ('lnd', 'clm2', 'h0'),
 'RAW1': ('lnd', 'clm2', 'h0'),
 'RAW2': ('lnd', 'clm2', 'h0'),
 'RCANV_H218O': ('lnd', 'clm2', 'h0'),
 'RCANV_H2OTR': ('lnd', 'clm2', 'h0'),
 'RCANV_HDO': ('lnd', 'clm2', 'h0'),
 'RCELLSHA_H218O': ('lnd', 'clm2', 'h0'),
 'RCELLSHA_H2OTR': ('lnd', 'clm2', 'h0'),
 'RCELLSHA_HDO': ('lnd', 'clm2', 'h0'),
 'RCELLSHA_PSN_H218O': ('lnd', 'clm2', 'h0'),
 'RCELLSHA_PSN_H2OTR': ('lnd', 'clm2', 'h0'),
 'RCELLSHA_PSN_HDO': ('lnd', 'clm2', 'h0'),
 'RCELLSUN_H218O': ('lnd', 'clm2', 'h0'),
 'RCELLSUN_H2OTR': ('lnd', 'clm2', 'h0'),
 'RCELLSUN_HDO': ('lnd', 'clm2', 'h0'),
 'RCELLSUN_PSN_H218O': ('lnd', 'clm2', 'h0'),
 'RCELLSUN_PSN_H2OTR': ('lnd', 'clm2', 'h0'),
 'RCELLSUN_PSN_HDO': ('lnd', 'clm2', 'h0'),
 'RH2M': ('lnd', 'clm2', 'h0'),
 'RH2M_R': ('lnd', 'clm2', 'h0'),
 'RH2M_U': ('lnd', 'clm2', 'h0'),
 'RLB': ('lnd', 'clm2', 'h0'),
 'RLITTER': ('lnd', 'clm2', 'h0'),
 'RLWSHA_DIAG_H218O': ('lnd', 'clm2', 'h0'),
 'RLWSHA_DIAG_H2OTR': ('lnd', 'clm2', 'h0'),
 'RLWSHA_DIAG_HDO': ('lnd', 'clm2', 'h0'),
 'RLWSHA_H218O': ('lnd', 'clm2', 'h0'),
 'RLWSHA_H2OTR': ('lnd', 'clm2', 'h0'),
 'RLWSHA_HDO': ('lnd', 'clm2', 'h0'),
 'RLWSHA_PSN_H218O': ('lnd', 'clm2', 'h0'),
 'RLWSHA_PSN_H2OTR': ('lnd', 'clm2', 'h0'),
 'RLWSHA_PSN_HDO': ('lnd', 'clm2', 'h0'),
 'RLWSHA_STDY_H218O': ('lnd', 'clm2', 'h0'),
 'RLWSHA_STDY_H2OTR': ('lnd', 'clm2', 'h0'),
 'RLWSHA_STDY_HDO': ('lnd', 'clm2', 'h0'),
 'RLWSUN_DIAG_H218O': ('lnd', 'clm2', 'h0'),
 'RLWSUN_DIAG_H2OTR': ('lnd', 'clm2', 'h0'),
 'RLWSUN_DIAG_HDO': ('lnd', 'clm2', 'h0'),
 'RLWSUN_H218O': ('lnd', 'clm2', 'h0'),
 'RLWSUN_H2OTR': ('lnd', 'clm2', 'h0'),
 'RLWSUN_HDO': ('lnd', 'clm2', 'h0'),
 'RLWSUN_PSN_H218O': ('lnd', 'clm2', 'h0'),
 'RLWSUN_PSN_H2OTR': ('lnd', 'clm2', 'h0'),
 'RLWSUN_PSN_HDO': ('lnd', 'clm2', 'h0'),
 'RLWSUN_STDY_H218O': ('lnd', 'clm2', 'h0'),
 'RLWSUN_STDY_H2OTR': ('lnd', 'clm2', 'h0'),
 'RLWSUN_STDY_HDO': ('lnd', 'clm2', 'h0'),
 'RSFC': ('lnd', 'clm2', 'h0'),
 'RSSHA': ('lnd', 'clm2', 'h0'),
 'RSSUN': ('lnd', 'clm2', 'h0'),
 'RXYLEM_H218O': ('lnd', 'clm2', 'h0'),
 'RXYLEM_H2OTR': ('lnd', 'clm2', 'h0'),
 'RXYLEM_HDO': ('lnd', 'clm2', 'h0'),
 'SABG': ('lnd', 'clm2', 'h0'),
 'SABV': ('lnd', 'clm2', 'h0'),
 'SNOBCMCL': ('lnd', 'clm2', 'h0'),
 'SNOBCMSL': ('lnd', 'clm2', 'h0'),
 'SNODSTMCL': ('lnd', 'clm2', 'h0'),
 'SNODSTMSL': ('lnd', 'clm2', 'h0'),
 'SNOOCMCL': ('lnd', 'clm2', 'h0'),
 'SNOOCMSL': ('lnd', 'clm2', 'h0'),
 'SNOW': ('lnd', 'clm2', 'h0'),
 'SNOWDP': ('lnd', 'clm2', 'h0'),
 'SNOWICE': ('lnd', 'clm2', 'h0'),
 'SNOWICE_H218O': ('lnd', 'clm2', 'h0'),
 'SNOWICE_H2OTR': ('lnd', 'clm2', 'h0'),
 'SNOWICE_HDO': ('lnd', 'clm2', 'h0'),
 'SNOWLIQ': ('lnd', 'clm2', 'h0'),
 'SNOWLIQ_H218O': ('lnd', 'clm2', 'h0'),
 'SNOWLIQ_H2OTR': ('lnd', 'clm2', 'h0'),
 'SNOWLIQ_HDO': ('lnd', 'clm2', 'h0'),
 'SNOW_H218O': ('lnd', 'clm2', 'h0'),
 'SNOW_H2OTR': ('lnd', 'clm2', 'h0'),
 'SNOW_HDO': ('lnd', 'clm2', 'h0'),
 'SNOW_SINKS': ('lnd', 'clm2', 'h0'),
 'SNOW_SOURCES': ('lnd', 'clm2', 'h0'),
 'SOILICE': ('lnd', 'clm2', 'h0'),
 'SOILLIQ': ('lnd', 'clm2', 'h0'),
 'SOILLIQICE_10CM_H218O': ('lnd', 'clm2', 'h0'),
 'SOILLIQICE_10CM_H2OTR': ('lnd', 'clm2', 'h0'),
 'SOILLIQICE_10CM_HDO': ('lnd', 'clm2', 'h0'),
 'SOILWATER_10CM': ('lnd', 'clm2', 'h0'),
 'SoilAlpha': ('lnd', 'clm2', 'h0'),
 'SoilAlpha_U': ('lnd', 'clm2', 'h0'),
 'TBOT': ('lnd', 'clm2', 'h0'),
 'TBUILD': ('lnd', 'clm2', 'h0'),
 'TG': ('lnd', 'clm2', 'h0'),
 'TG_R': ('lnd', 'clm2', 'h0'),
 'TG_U': ('lnd', 'clm2', 'h0'),
 'THBOT': ('lnd', 'clm2', 'h0'),
 'TLAI': ('lnd', 'clm2', 'h0'),
 'TLAKE': ('lnd', 'clm2', 'h0'),
 'TREFMNAV': ('lnd', 'clm2', 'h0'),
 'TREFMNAV_R': ('lnd', 'clm2', 'h0'),
 'TREFMNAV_U': ('lnd', 'clm2', 'h0'),
 'TREFMXAV': ('lnd', 'clm2', 'h0'),
 'TREFMXAV_R': ('lnd', 'clm2', 'h0'),
 'TREFMXAV_U': ('lnd', 'clm2', 'h0'),
 'TSA': ('lnd', 'clm2', 'h0'),
 'TSAI': ('lnd', 'clm2', 'h0'),
 'TSA_R': ('lnd', 'clm2', 'h0'),
 'TSA_U': ('lnd', 'clm2', 'h0'),
 'TSOI': ('lnd', 'clm2', 'h0'),
 'TSOI_10CM': ('lnd', 'clm2', 'h0'),
 'TSOI_ICE': ('lnd', 'clm2', 'h0'),
 'TV': ('lnd', 'clm2', 'h0'),
 'URBAN_AC': ('lnd', 'clm2', 'h0'),
 'URBAN_HEAT': ('lnd', 'clm2', 'h0'),
 'WA': ('lnd', 'clm2', 'h0'),
 'WASTEHEAT': ('lnd', 'clm2', 'h0'),
 'WIND': ('lnd', 'clm2', 'h0'),
 'WT': ('lnd', 'clm2', 'h0'),
 'ZBOT': ('lnd', 'clm2', 'h0'),
 'ZWT': ('lnd', 'clm2', 'h0'),
 'HMXL_2': ('ocn', 'h', 'nday1'),
 'SST': ('ocn', 'h', 'nday1'),
 'SST2': ('ocn', 'h', 'nday1'),
 'XMXL_2': ('ocn', 'h', 'nday1'),
 'ADVS': ('ocn', 'pop', 'h'),
 'ADVS_ISOP': ('ocn', 'pop', 'h'),
 'ADVS_SUBM': ('ocn', 'pop', 'h'),
 'ADVT': ('ocn', 'pop', 'h'),
 'ADVT_ISOP': ('ocn', 'pop', 'h'),
 'ADVT_SUBM': ('ocn', 'pop', 'h'),
 'BSF': ('ocn', 'pop', 'h'),
 'DIA_DEPTH': ('ocn', 'pop', 'h'),
 'EVAP_16O_F': ('ocn', 'pop', 'h'),
 'EVAP_18O_F': ('ocn', 'pop', 'h'),
 'EVAP_F': ('ocn', 'pop', 'h'),
 'EVAP_HDO_F': ('ocn', 'pop', 'h'),
 'FW': ('ocn', 'pop', 'h'),
 'FvICE_R18O': ('ocn', 'pop', 'h'),
 'FvICE_RHDO': ('ocn', 'pop', 'h'),
 'FvPER_R18O': ('ocn', 'pop', 'h'),
 'FvPER_RHDO': ('ocn', 'pop', 'h'),
 'HBLT': ('ocn', 'pop', 'h'),
 'HDIFS': ('ocn', 'pop', 'h'),
 'HDIFT': ('ocn', 'pop', 'h'),
 'HLS_SUBM': ('ocn', 'pop', 'h'),
 'HMXL': ('ocn', 'pop', 'h'),
 'HOR_DIFF': ('ocn', 'pop', 'h'),
 'IAGE': ('ocn', 'pop', 'h'),
 'IFRAC': ('ocn', 'pop', 'h'),
 'INT_DEPTH': ('ocn', 'pop', 'h'),
 'IOFF_16O_F': ('ocn', 'pop', 'h'),
 'IOFF_18O_F': ('ocn', 'pop', 'h'),
 'IOFF_F': ('ocn', 'pop', 'h'),
 'IOFF_HDO_F': ('ocn', 'pop', 'h'),
 'KAPPA_ISOP': ('ocn', 'pop', 'h'),
 'KAPPA_THIC': ('ocn', 'pop', 'h'),
 'KVMIX': ('ocn', 'pop', 'h'),
 'KVMIX_M': ('ocn', 'pop', 'h'),
 'LWDN_F': ('ocn', 'pop', 'h'),
 'LWUP_F': ('ocn', 'pop', 'h'),
 'MELTH_F': ('ocn', 'pop', 'h'),
 'MELT_16O_F': ('ocn', 'pop', 'h'),
 'MELT_18O_F': ('ocn', 'pop', 'h'),
 'MELT_F': ('ocn', 'pop', 'h'),
 'MELT_HDO_F': ('ocn', 'pop', 'h'),
 'MOC': ('ocn', 'pop', 'h'),
 'ND143': ('ocn', 'pop', 'h'),
 'ND144': ('ocn', 'pop', 'h'),
 'N_HEAT': ('ocn', 'pop', 'h'),
 'N_SALT': ('ocn', 'pop', 'h'),
 'PD': ('ocn', 'pop', 'h'),
 'PREC_16O_F': ('ocn', 'pop', 'h'),
 'PREC_18O_F': ('ocn', 'pop', 'h'),
 'PREC_F': ('ocn', 'pop', 'h'),
 'PREC_HDO_F': ('ocn', 'pop', 'h'),
 'PV': ('ocn', 'pop', 'h'),
 'QFLUX': ('ocn', 'pop', 'h'),
 'QSW_HBL': ('ocn', 'pop', 'h'),
 'QSW_HTP': ('ocn', 'pop', 'h'),
 'R18O': ('ocn', 'pop', 'h'),
 'RESID_S': ('ocn', 'pop', 'h'),
 'RESID_T': ('ocn', 'pop', 'h'),
 'RHDO': ('ocn', 'pop', 'h'),
 'RHO': ('ocn', 'pop', 'h'),
 'RHO_VINT': ('ocn', 'pop', 'h'),
 'ROFF_16O_F': ('ocn', 'pop', 'h'),
 'ROFF_18O_F': ('ocn', 'pop', 'h'),
 'ROFF_F': ('ocn', 'pop', 'h'),
 'ROFF_HDO_F': ('ocn', 'pop', 'h'),
 'SALT': ('ocn', 'pop', 'h'),
 'SALT_F': ('ocn', 'pop', 'h'),
 'SENH_F': ('ocn', 'pop', 'h'),
 'SFWF': ('ocn', 'pop', 'h'),
 'SFWF_WRST': ('ocn', 'pop', 'h'),
 'SHF': ('ocn', 'pop', 'h'),
 'SHF_QSW': ('ocn', 'pop', 'h'),
 'SNOW_F': ('ocn', 'pop', 'h'),
 'SSH': ('ocn', 'pop', 'h'),
 'SSH2': ('ocn', 'pop', 'h'),
 'SSS': ('ocn', 'pop', 'h'),
 'pid35583': ('ocn', '175101-180012', 'nc'),
 'SU': ('ocn', 'pop', 'h'),
 'SV': ('ocn', 'pop', 'h'),
 'TAUX2': ('ocn', 'pop', 'h'),
 'TAUY2': ('ocn', 'pop', 'h'),
 'TBLT': ('ocn', 'pop', 'h'),
 'TEMP': ('ocn', 'pop', 'h'),
 'TFW_S': ('ocn', 'pop', 'h'),
 'TFW_T': ('ocn', 'pop', 'h'),
 'TLT': ('ocn', 'pop', 'h'),
 'TMXL': ('ocn', 'pop', 'h'),
 'TPOWER': ('ocn', 'pop', 'h'),
 'UES': ('ocn', 'pop', 'h'),
 'UET': ('ocn', 'pop', 'h'),
 'UISOP': ('ocn', 'pop', 'h'),
 'USUBM': ('ocn', 'pop', 'h'),
 'UVEL': ('ocn', 'pop', 'h'),
 'UVEL2': ('ocn', 'pop', 'h'),
 'VDC_S': ('ocn', 'pop', 'h'),
 'VDC_T': ('ocn', 'pop', 'h'),
 'VISOP': ('ocn', 'pop', 'h'),
 'VNS': ('ocn', 'pop', 'h'),
 'VNS_ISOP': ('ocn', 'pop', 'h'),
 'VNS_SUBM': ('ocn', 'pop', 'h'),
 'VNT': ('ocn', 'pop', 'h'),
 'VNT_ISOP': ('ocn', 'pop', 'h'),
 'VNT_SUBM': ('ocn', 'pop', 'h'),
 'VSUBM': ('ocn', 'pop', 'h'),
 'VVC': ('ocn', 'pop', 'h'),
 'VVEL': ('ocn', 'pop', 'h'),
 'VVEL2': ('ocn', 'pop', 'h'),
 'WISOP': ('ocn', 'pop', 'h'),
 'WSUBM': ('ocn', 'pop', 'h'),
 'WTS': ('ocn', 'pop', 'h'),
 'WTT': ('ocn', 'pop', 'h'),
 'WVEL': ('ocn', 'pop', 'h'),
 'WVEL2': ('ocn', 'pop', 'h'),
 'XBLT': ('ocn', 'pop', 'h'),
 'XMXL': ('ocn', 'pop', 'h'),
 'dTEMP_NEG_2D': ('ocn', 'pop', 'h'),
 'dTEMP_POS_2D': ('ocn', 'pop', 'h'),
 'QCHANR_ICE': ('rof', 'mosart', 'h0'),
 'QCHANR_ICE_16O': ('rof', 'mosart', 'h0'),
 'QCHANR_ICE_18O': ('rof', 'mosart', 'h0'),
 'QCHANR_ICE_HDO': ('rof', 'mosart', 'h0'),
 'QCHANR_LIQ': ('rof', 'mosart', 'h0'),
 'QCHANR_LIQ_16O': ('rof', 'mosart', 'h0'),
 'QCHANR_LIQ_18O': ('rof', 'mosart', 'h0'),
 'QCHANR_LIQ_HDO': ('rof', 'mosart', 'h0'),
 'QCHOCNR_ICE': ('rof', 'mosart', 'h0'),
 'QCHOCNR_ICE_16O': ('rof', 'mosart', 'h0'),
 'QCHOCNR_ICE_18O': ('rof', 'mosart', 'h0'),
 'QCHOCNR_ICE_HDO': ('rof', 'mosart', 'h0'),
 'QCHOCNR_LIQ': ('rof', 'mosart', 'h0'),
 'QCHOCNR_LIQ_16O': ('rof', 'mosart', 'h0'),
 'QCHOCNR_LIQ_18O': ('rof', 'mosart', 'h0'),
 'QCHOCNR_LIQ_HDO': ('rof', 'mosart', 'h0')}

Load a variable (or multiple variables in a list)

We may specify a timespan in (start_year, end_year). It may point to a single file or multiple files.

# the case of a single file
vn = 'TEMP'
case.load(vn, timespan=(6951, 7000))
print(case.ds[vn].path)
case.ds[vn]

>>> case.ds["TEMP"] created
/glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/ocn/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.pop.h.TEMP.695101-700012.nc

<xarray.Dataset> Size: 18GB
Dimensions:               (moc_comp: 3, transport_comp: 5, transport_reg: 1,
                           z_t: 60, z_t_150m: 15, z_w: 60, z_w_top: 60,
                           z_w_bot: 60, lat_aux_grid: 91, moc_z: 61, nlat: 384,
                           nlon: 320, time: 600, d2: 2)
Coordinates:
  * z_t                   (z_t) float32 240B 500.0 1.5e+03 ... 5.375e+05
  * z_t_150m              (z_t_150m) float32 60B 500.0 1.5e+03 ... 1.45e+04
  * z_w                   (z_w) float32 240B 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
  * z_w_top               (z_w_top) float32 240B 0.0 1e+03 ... 5e+05 5.25e+05
  * z_w_bot               (z_w_bot) float32 240B 1e+03 2e+03 ... 5.5e+05
  * lat_aux_grid          (lat_aux_grid) float32 364B -90.0 -88.0 ... 88.0 90.0
  * moc_z                 (moc_z) float32 244B 0.0 1e+03 ... 5.25e+05 5.5e+05
    ULONG                 (nlat, nlon) float64 983kB ...
    ULAT                  (nlat, nlon) float64 983kB ...
    TLONG                 (nlat, nlon) float64 983kB ...
    TLAT                  (nlat, nlon) float64 983kB ...
  * time                  (time) object 5kB 6951-01-31 00:00:00 ... 7000-12-3...
Dimensions without coordinates: moc_comp, transport_comp, transport_reg, nlat,
                                nlon, d2
Data variables: (12/57)
    moc_components        (moc_comp) |S256 768B ...
    transport_components  (transport_comp) |S256 1kB ...
    transport_regions     (transport_reg) |S256 256B ...
    dz                    (z_t) float32 240B ...
    dzw                   (z_w) float32 240B ...
    KMT                   (nlat, nlon) float64 983kB ...
    ...                    ...
    nsurface_u            float64 8B ...
    time_bound            (time, d2) object 10kB ...
    TEMP                  (time, z_t, nlat, nlon) float32 18GB ...
    gw                    (nlat, nlon) float64 983kB ...
    lat                   (nlat, nlon) float64 983kB ...
    lon                   (nlat, nlon) float64 983kB ...
Attributes: (12/16)
    title:           b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2...
    history:         none
    Conventions:     CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-curr...
    contents:        Diagnostic and Prognostic Variables
    source:          CCSM POP2, the CCSM Ocean Component
    revision:        $Id: tavg.F90 56176 2013-12-20 18:35:46Z mlevy@ucar.edu $
    ...              ...
    tavg_sum:        2678399.99999999
    tavg_sum_qflux:  2678400.0
    path:            /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timese...
    vn:              TEMP
    comp:            ocn
    grid:            g16

xarray.Dataset

• Dimensions:
  • moc_comp: 3
  • transport_comp: 5
  • transport_reg: 1
  • z_t: 60
  • z_t_150m: 15
  • z_w: 60
  • z_w_top: 60
  • z_w_bot: 60
  • lat_aux_grid: 91
  • moc_z: 61
  • nlat: 384
  • nlon: 320
  • time: 600
  • d2: 2
• Coordinates: (12)
  • z_t
    (z_t)
    float32
    500.0 1.5e+03 ... 5.375e+05

    long_name :

    depth from surface to midpoint of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    500.0

    valid_max :

    537500.0

    array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,
           5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,
           1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,
           1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,
           2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,
           2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,
           4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,
           6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,
           1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,
           1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,
           3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,
           4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],
          dtype=float32)

  • z_t_150m
    (z_t_150m)
    float32
    500.0 1.5e+03 ... 1.35e+04 1.45e+04

    long_name :

    depth from surface to midpoint of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    500.0

    valid_max :

    14500.0

    array([  500.,  1500.,  2500.,  3500.,  4500.,  5500.,  6500.,  7500.,  8500.,
            9500., 10500., 11500., 12500., 13500., 14500.], dtype=float32)

  • z_w
    (z_w)
    float32
    0.0 1e+03 2e+03 ... 5e+05 5.25e+05

    long_name :

    depth from surface to top of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    0.0

    valid_max :

    525000.94

    array([     0.   ,   1000.   ,   2000.   ,   3000.   ,   4000.   ,   5000.   ,
             6000.   ,   7000.   ,   8000.   ,   9000.   ,  10000.   ,  11000.   ,
            12000.   ,  13000.   ,  14000.   ,  15000.   ,  16000.   ,  17019.682,
            18076.129,  19182.125,  20349.932,  21592.344,  22923.312,  24358.453,
            25915.58 ,  27615.26 ,  29481.47 ,  31542.373,  33831.227,  36387.473,
            39258.047,  42498.887,  46176.656,  50370.688,  55174.91 ,  60699.668,
            67072.86 ,  74439.805,  82960.695,  92804.35 , 104136.82 , 117104.016,
           131809.36 , 148290.08 , 166499.2  , 186301.44 , 207487.39 , 229803.9  ,
           252990.4  , 276809.84 , 301067.06 , 325613.84 , 350344.88 , 375189.2  ,
           400101.16 , 425052.47 , 450026.06 , 475012.   , 500004.7  , 525000.94 ],
          dtype=float32)

  • z_w_top
    (z_w_top)
    float32
    0.0 1e+03 2e+03 ... 5e+05 5.25e+05

    long_name :

    depth from surface to top of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    0.0

    valid_max :

    525000.94

    array([     0.   ,   1000.   ,   2000.   ,   3000.   ,   4000.   ,   5000.   ,
             6000.   ,   7000.   ,   8000.   ,   9000.   ,  10000.   ,  11000.   ,
            12000.   ,  13000.   ,  14000.   ,  15000.   ,  16000.   ,  17019.682,
            18076.129,  19182.125,  20349.932,  21592.344,  22923.312,  24358.453,
            25915.58 ,  27615.26 ,  29481.47 ,  31542.373,  33831.227,  36387.473,
            39258.047,  42498.887,  46176.656,  50370.688,  55174.91 ,  60699.668,
            67072.86 ,  74439.805,  82960.695,  92804.35 , 104136.82 , 117104.016,
           131809.36 , 148290.08 , 166499.2  , 186301.44 , 207487.39 , 229803.9  ,
           252990.4  , 276809.84 , 301067.06 , 325613.84 , 350344.88 , 375189.2  ,
           400101.16 , 425052.47 , 450026.06 , 475012.   , 500004.7  , 525000.94 ],
          dtype=float32)

  • z_w_bot
    (z_w_bot)
    float32
    1e+03 2e+03 ... 5.25e+05 5.5e+05

    long_name :

    depth from surface to bottom of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    1000.0

    valid_max :

    549999.06

    array([  1000.   ,   2000.   ,   3000.   ,   4000.   ,   5000.   ,   6000.   ,
             7000.   ,   8000.   ,   9000.   ,  10000.   ,  11000.   ,  12000.   ,
            13000.   ,  14000.   ,  15000.   ,  16000.   ,  17019.682,  18076.129,
            19182.125,  20349.932,  21592.344,  22923.312,  24358.453,  25915.58 ,
            27615.26 ,  29481.47 ,  31542.373,  33831.227,  36387.473,  39258.047,
            42498.887,  46176.656,  50370.688,  55174.91 ,  60699.668,  67072.86 ,
            74439.805,  82960.695,  92804.35 , 104136.82 , 117104.016, 131809.36 ,
           148290.08 , 166499.2  , 186301.44 , 207487.39 , 229803.9  , 252990.4  ,
           276809.84 , 301067.06 , 325613.84 , 350344.88 , 375189.2  , 400101.16 ,
           425052.47 , 450026.06 , 475012.   , 500004.7  , 525000.94 , 549999.06 ],
          dtype=float32)

  • lat_aux_grid
    (lat_aux_grid)
    float32
    -90.0 -88.0 -86.0 ... 88.0 90.0

    long_name :

    latitude grid for transport diagnostics

    units :

    degrees_north

    valid_min :

    -90.0

    valid_max :

    90.0

    array([-90., -88., -86., -84., -82., -80., -78., -76., -74., -72., -70., -68.,
           -66., -64., -62., -60., -58., -56., -54., -52., -50., -48., -46., -44.,
           -42., -40., -38., -36., -34., -32., -30., -28., -26., -24., -22., -20.,
           -18., -16., -14., -12., -10.,  -8.,  -6.,  -4.,  -2.,   0.,   2.,   4.,
             6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,  24.,  26.,  28.,
            30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,  48.,  50.,  52.,
            54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,  72.,  74.,  76.,
            78.,  80.,  82.,  84.,  86.,  88.,  90.], dtype=float32)

  • moc_z
    (moc_z)
    float32
    0.0 1e+03 ... 5.25e+05 5.5e+05

    long_name :

    depth from surface to top of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    0.0

    valid_max :

    549999.06

    array([     0.   ,   1000.   ,   2000.   ,   3000.   ,   4000.   ,   5000.   ,
             6000.   ,   7000.   ,   8000.   ,   9000.   ,  10000.   ,  11000.   ,
            12000.   ,  13000.   ,  14000.   ,  15000.   ,  16000.   ,  17019.682,
            18076.129,  19182.125,  20349.932,  21592.344,  22923.312,  24358.453,
            25915.58 ,  27615.26 ,  29481.47 ,  31542.373,  33831.227,  36387.473,
            39258.047,  42498.887,  46176.656,  50370.688,  55174.91 ,  60699.668,
            67072.86 ,  74439.805,  82960.695,  92804.35 , 104136.82 , 117104.016,
           131809.36 , 148290.08 , 166499.2  , 186301.44 , 207487.39 , 229803.9  ,
           252990.4  , 276809.84 , 301067.06 , 325613.84 , 350344.88 , 375189.2  ,
           400101.16 , 425052.47 , 450026.06 , 475012.   , 500004.7  , 525000.94 ,
           549999.06 ], dtype=float32)

  • ULONG
    (nlat, nlon)
    float64
    ...

    long_name :

    array of u-grid longitudes

    units :

    degrees_east

    [122880 values with dtype=float64]

  • ULAT
    (nlat, nlon)
    float64
    ...

    long_name :

    array of u-grid latitudes

    units :

    degrees_north

    [122880 values with dtype=float64]

  • TLONG
    (nlat, nlon)
    float64
    ...

    long_name :

    array of t-grid longitudes

    units :

    degrees_east

    [122880 values with dtype=float64]

  • TLAT
    (nlat, nlon)
    float64
    ...

    long_name :

    array of t-grid latitudes

    units :

    degrees_north

    [122880 values with dtype=float64]

  • time
    (time)
    object
    6951-01-31 00:00:00 ... 7000-12-...

    array([cftime.DatetimeNoLeap(6951, 1, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6951, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6951, 3, 31, 0, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(7000, 10, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 11, 30, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Data variables: (57)
  • moc_components
    (moc_comp)
    |S256
    ...

    long_name :

    MOC component names

    units :

    [3 values with dtype=|S256]

  • transport_components
    (transport_comp)
    |S256
    ...

    long_name :

    T,S transport components

    units :

    [5 values with dtype=|S256]

  • transport_regions
    (transport_reg)
    |S256
    ...

    long_name :

    regions for all transport diagnostics

    units :

    [1 values with dtype=|S256]

  • dz
    (z_t)
    float32
    ...

    long_name :

    thickness of layer k

    units :

    centimeters

    [60 values with dtype=float32]

  • dzw
    (z_w)
    float32
    ...

    long_name :

    midpoint of k to midpoint of k+1

    units :

    centimeters

    [60 values with dtype=float32]

  • KMT
    (nlat, nlon)
    float64
    ...

    long_name :

    k Index of Deepest Grid Cell on T Grid

    [122880 values with dtype=float64]

  • KMU
    (nlat, nlon)
    float64
    ...

    long_name :

    k Index of Deepest Grid Cell on U Grid

    [122880 values with dtype=float64]

  • REGION_MASK
    (nlat, nlon)
    float64
    ...

    long_name :

    basin index number (signed integers)

    [122880 values with dtype=float64]

  • UAREA
    (nlat, nlon)
    float64
    ...

    long_name :

    area of U cells

    units :

    centimeter^2

    [122880 values with dtype=float64]

  • TAREA
    (nlat, nlon)
    float64
    ...

    long_name :

    area of T cells

    units :

    centimeter^2

    [122880 values with dtype=float64]

  • HU
    (nlat, nlon)
    float64
    ...

    long_name :

    ocean depth at U points

    units :

    centimeter

    [122880 values with dtype=float64]

  • HT
    (nlat, nlon)
    float64
    ...

    long_name :

    ocean depth at T points

    units :

    centimeter

    [122880 values with dtype=float64]

  • DXU
    (nlat, nlon)
    float64
    ...

    long_name :

    x-spacing centered at U points

    units :

    centimeters

    [122880 values with dtype=float64]

  • DYU
    (nlat, nlon)
    float64
    ...

    long_name :

    y-spacing centered at U points

    units :

    centimeters

    [122880 values with dtype=float64]

  • DXT
    (nlat, nlon)
    float64
    ...

    long_name :

    x-spacing centered at T points

    units :

    centimeters

    [122880 values with dtype=float64]

  • DYT
    (nlat, nlon)
    float64
    ...

    long_name :

    y-spacing centered at T points

    units :

    centimeters

    [122880 values with dtype=float64]

  • HTN
    (nlat, nlon)
    float64
    ...

    long_name :

    cell widths on North sides of T cell

    units :

    centimeters

    [122880 values with dtype=float64]

  • HTE
    (nlat, nlon)
    float64
    ...

    long_name :

    cell widths on East sides of T cell

    units :

    centimeters

    [122880 values with dtype=float64]

  • HUS
    (nlat, nlon)
    float64
    ...

    long_name :

    cell widths on South sides of U cell

    units :

    centimeters

    [122880 values with dtype=float64]

  • HUW
    (nlat, nlon)
    float64
    ...

    long_name :

    cell widths on West sides of U cell

    units :

    centimeters

    [122880 values with dtype=float64]

  • ANGLE
    (nlat, nlon)
    float64
    ...

    long_name :

    angle grid makes with latitude line

    units :

    radians

    [122880 values with dtype=float64]

  • ANGLET
    (nlat, nlon)
    float64
    ...

    long_name :

    angle grid makes with latitude line on T grid

    units :

    radians

    [122880 values with dtype=float64]

  • days_in_norm_year
    ()
    timedelta64[ns]
    ...

    long_name :

    Calendar Length

    [1 values with dtype=timedelta64[ns]]

  • grav
    ()
    float64
    ...

    long_name :

    Acceleration Due to Gravity

    units :

    centimeter/s^2

    [1 values with dtype=float64]

  • omega
    ()
    float64
    ...

    long_name :

    Earths Angular Velocity

    units :

    1/second

    [1 values with dtype=float64]

  • radius
    ()
    float64
    ...

    long_name :

    Earths Radius

    units :

    centimeters

    [1 values with dtype=float64]

  • cp_sw
    ()
    float64
    ...

    long_name :

    Specific Heat of Sea Water

    units :

    erg/g/K

    [1 values with dtype=float64]

  • sound
    ()
    float64
    ...

    long_name :

    Speed of Sound

    units :

    centimeter/s

    [1 values with dtype=float64]

  • vonkar
    ()
    float64
    ...

    long_name :

    von Karman Constant

    [1 values with dtype=float64]

  • cp_air
    ()
    float64
    ...

    long_name :

    Heat Capacity of Air

    units :

    joule/kg/degK

    [1 values with dtype=float64]

  • rho_air
    ()
    float64
    ...

    long_name :

    Ambient Air Density

    units :

    kg/m^3

    [1 values with dtype=float64]

  • rho_sw
    ()
    float64
    ...

    long_name :

    Density of Sea Water

    units :

    gram/centimeter^3

    [1 values with dtype=float64]

  • rho_fw
    ()
    float64
    ...

    long_name :

    Density of Fresh Water

    units :

    gram/centimeter^3

    [1 values with dtype=float64]

  • stefan_boltzmann
    ()
    float64
    ...

    long_name :

    Stefan-Boltzmann Constant

    units :

    watt/m^2/degK^4

    [1 values with dtype=float64]

  • latent_heat_vapor
    ()
    float64
    ...

    long_name :

    Latent Heat of Vaporization

    units :

    J/kg

    [1 values with dtype=float64]

  • latent_heat_fusion
    ()
    float64
    ...

    long_name :

    Latent Heat of Fusion

    units :

    erg/g

    [1 values with dtype=float64]

  • ocn_ref_salinity
    ()
    float64
    ...

    long_name :

    Ocean Reference Salinity

    units :

    g/kg

    [1 values with dtype=float64]

  • sea_ice_salinity
    ()
    float64
    ...

    long_name :

    Salinity of Sea Ice

    units :

    g/kg

    [1 values with dtype=float64]

  • T0_Kelvin
    ()
    float64
    ...

    long_name :

    Zero Point for Celsius

    units :

    degK

    [1 values with dtype=float64]

  • salt_to_ppt
    ()
    float64
    ...

    long_name :

    Convert Salt in gram/gram to g/kg

    [1 values with dtype=float64]

  • ppt_to_salt
    ()
    float64
    ...

    long_name :

    Convert Salt in g/kg to gram/gram

    [1 values with dtype=float64]

  • mass_to_Sv
    ()
    float64
    ...

    long_name :

    Convert Mass Flux to Sverdrups

    [1 values with dtype=float64]

  • heat_to_PW
    ()
    float64
    ...

    long_name :

    Convert Heat Flux to Petawatts

    [1 values with dtype=float64]

  • salt_to_Svppt
    ()
    float64
    ...

    long_name :

    Convert Salt Flux to Sverdrups*g/kg

    [1 values with dtype=float64]

  • salt_to_mmday
    ()
    float64
    ...

    long_name :

    Convert Salt to Water (millimeters/day)

    [1 values with dtype=float64]

  • momentum_factor
    ()
    float64
    ...

    long_name :

    Convert Windstress to Velocity Flux

    [1 values with dtype=float64]

  • hflux_factor
    ()
    float64
    ...

    long_name :

    Convert Heat and Solar Flux to Temperature Flux

    [1 values with dtype=float64]

  • fwflux_factor
    ()
    float64
    ...

    long_name :

    Convert Net Fresh Water Flux to Salt Flux (in model units)

    [1 values with dtype=float64]

  • salinity_factor
    ()
    float64
    ...

    [1 values with dtype=float64]

  • sflux_factor
    ()
    float64
    ...

    long_name :

    Convert Salt Flux to Salt Flux (in model units)

    [1 values with dtype=float64]

  • nsurface_t
    ()
    float64
    ...

    long_name :

    Number of Ocean T Points at Surface

    [1 values with dtype=float64]

  • nsurface_u
    ()
    float64
    ...

    long_name :

    Number of Ocean U Points at Surface

    [1 values with dtype=float64]

  • time_bound
    (time, d2)
    object
    ...

    long_name :

    boundaries for time-averaging interval

    [1200 values with dtype=object]

  • TEMP
    (time, z_t, nlat, nlon)
    float32
    ...

    long_name :

    Potential Temperature

    units :

    degC

    grid_loc :

    3111

    cell_methods :

    time: mean

    [4423680000 values with dtype=float32]

  • gw
    (nlat, nlon)
    float64
    ...

    long_name :

    area of T cells

    units :

    centimeter^2

    [122880 values with dtype=float64]

  • lat
    (nlat, nlon)
    float64
    ...

    long_name :

    array of t-grid latitudes

    units :

    degrees_north

    [122880 values with dtype=float64]

  • lon
    (nlat, nlon)
    float64
    ...

    long_name :

    array of t-grid longitudes

    units :

    degrees_east

    [122880 values with dtype=float64]

• Indexes: (8)
  • z_t
    PandasIndex

    PandasIndex(Index([          500.0,          1500.0,          2500.0,          3500.0,
                    4500.0,          5500.0,          6500.0,          7500.0,
                    8500.0,          9500.0,         10500.0,         11500.0,
                   12500.0,         13500.0,         14500.0,         15500.0,
            16509.83984375, 17547.904296875, 18629.126953125,  19766.02734375,
           20971.138671875,    22257.828125,   23640.8828125,    25137.015625,
           26765.419921875, 28548.365234375,    30511.921875, 32686.798828125,
            35109.34765625,  37822.76171875,  40878.46484375,  44337.76953125,
              48273.671875,  52772.80078125,   57937.2890625,  63886.26171875,
              70756.328125,        78700.25,   87882.5234375,   98470.5859375,
             110620.421875,     124456.6875,    140049.71875,   157394.640625,
             176400.328125,   196894.421875,    218645.65625,    241397.15625,
                264900.125,    288938.46875,    313340.46875,    337979.34375,
              362767.03125,     387645.1875,     412576.8125,       437539.25,
              462519.03125,    487508.34375,     512502.8125,        537500.0],
          dtype='float32', name='z_t'))

  • z_t_150m
    PandasIndex

    PandasIndex(Index([  500.0,  1500.0,  2500.0,  3500.0,  4500.0,  5500.0,  6500.0,  7500.0,
            8500.0,  9500.0, 10500.0, 11500.0, 12500.0, 13500.0, 14500.0],
          dtype='float32', name='z_t_150m'))

  • z_w
    PandasIndex

    PandasIndex(Index([            0.0,          1000.0,          2000.0,          3000.0,
                    4000.0,          5000.0,          6000.0,          7000.0,
                    8000.0,          9000.0,         10000.0,         11000.0,
                   12000.0,         13000.0,         14000.0,         15000.0,
                   16000.0, 17019.681640625,  18076.12890625,       19182.125,
           20349.931640625,     21592.34375,      22923.3125,    24358.453125,
           25915.580078125, 27615.259765625, 29481.470703125, 31542.373046875,
             33831.2265625,  36387.47265625,    39258.046875,  42498.88671875,
               46176.65625,      50370.6875,  55174.91015625,  60699.66796875,
              67072.859375,   74439.8046875,   82960.6953125,   92804.3515625,
            104136.8203125,   117104.015625,   131809.359375,   148290.078125,
             166499.203125,     186301.4375,   207487.390625,    229803.90625,
              252990.40625,    276809.84375,     301067.0625,    325613.84375,
                350344.875,     375189.1875,    400101.15625,    425052.46875,
               450026.0625,        475012.0,     500004.6875,     525000.9375],
          dtype='float32', name='z_w'))

  • z_w_top
    PandasIndex

    PandasIndex(Index([            0.0,          1000.0,          2000.0,          3000.0,
                    4000.0,          5000.0,          6000.0,          7000.0,
                    8000.0,          9000.0,         10000.0,         11000.0,
                   12000.0,         13000.0,         14000.0,         15000.0,
                   16000.0, 17019.681640625,  18076.12890625,       19182.125,
           20349.931640625,     21592.34375,      22923.3125,    24358.453125,
           25915.580078125, 27615.259765625, 29481.470703125, 31542.373046875,
             33831.2265625,  36387.47265625,    39258.046875,  42498.88671875,
               46176.65625,      50370.6875,  55174.91015625,  60699.66796875,
              67072.859375,   74439.8046875,   82960.6953125,   92804.3515625,
            104136.8203125,   117104.015625,   131809.359375,   148290.078125,
             166499.203125,     186301.4375,   207487.390625,    229803.90625,
              252990.40625,    276809.84375,     301067.0625,    325613.84375,
                350344.875,     375189.1875,    400101.15625,    425052.46875,
               450026.0625,        475012.0,     500004.6875,     525000.9375],
          dtype='float32', name='z_w_top'))

  • z_w_bot
    PandasIndex

    PandasIndex(Index([         1000.0,          2000.0,          3000.0,          4000.0,
                    5000.0,          6000.0,          7000.0,          8000.0,
                    9000.0,         10000.0,         11000.0,         12000.0,
                   13000.0,         14000.0,         15000.0,         16000.0,
           17019.681640625,  18076.12890625,       19182.125, 20349.931640625,
               21592.34375,      22923.3125,    24358.453125, 25915.580078125,
           27615.259765625, 29481.470703125, 31542.373046875,   33831.2265625,
            36387.47265625,    39258.046875,  42498.88671875,     46176.65625,
                50370.6875,  55174.91015625,  60699.66796875,    67072.859375,
             74439.8046875,   82960.6953125,   92804.3515625,  104136.8203125,
             117104.015625,   131809.359375,   148290.078125,   166499.203125,
               186301.4375,   207487.390625,    229803.90625,    252990.40625,
              276809.84375,     301067.0625,    325613.84375,      350344.875,
               375189.1875,    400101.15625,    425052.46875,     450026.0625,
                  475012.0,     500004.6875,     525000.9375,     549999.0625],
          dtype='float32', name='z_w_bot'))

  • lat_aux_grid
    PandasIndex

    PandasIndex(Index([-90.0, -88.0, -86.0, -84.0, -82.0, -80.0, -78.0, -76.0, -74.0, -72.0,
           -70.0, -68.0, -66.0, -64.0, -62.0, -60.0, -58.0, -56.0, -54.0, -52.0,
           -50.0, -48.0, -46.0, -44.0, -42.0, -40.0, -38.0, -36.0, -34.0, -32.0,
           -30.0, -28.0, -26.0, -24.0, -22.0, -20.0, -18.0, -16.0, -14.0, -12.0,
           -10.0,  -8.0,  -6.0,  -4.0,  -2.0,   0.0,   2.0,   4.0,   6.0,   8.0,
            10.0,  12.0,  14.0,  16.0,  18.0,  20.0,  22.0,  24.0,  26.0,  28.0,
            30.0,  32.0,  34.0,  36.0,  38.0,  40.0,  42.0,  44.0,  46.0,  48.0,
            50.0,  52.0,  54.0,  56.0,  58.0,  60.0,  62.0,  64.0,  66.0,  68.0,
            70.0,  72.0,  74.0,  76.0,  78.0,  80.0,  82.0,  84.0,  86.0,  88.0,
            90.0],
          dtype='float32', name='lat_aux_grid'))

  • moc_z
    PandasIndex

    PandasIndex(Index([            0.0,          1000.0,          2000.0,          3000.0,
                    4000.0,          5000.0,          6000.0,          7000.0,
                    8000.0,          9000.0,         10000.0,         11000.0,
                   12000.0,         13000.0,         14000.0,         15000.0,
                   16000.0, 17019.681640625,  18076.12890625,       19182.125,
           20349.931640625,     21592.34375,      22923.3125,    24358.453125,
           25915.580078125, 27615.259765625, 29481.470703125, 31542.373046875,
             33831.2265625,  36387.47265625,    39258.046875,  42498.88671875,
               46176.65625,      50370.6875,  55174.91015625,  60699.66796875,
              67072.859375,   74439.8046875,   82960.6953125,   92804.3515625,
            104136.8203125,   117104.015625,   131809.359375,   148290.078125,
             166499.203125,     186301.4375,   207487.390625,    229803.90625,
              252990.40625,    276809.84375,     301067.0625,    325613.84375,
                350344.875,     375189.1875,    400101.15625,    425052.46875,
               450026.0625,        475012.0,     500004.6875,     525000.9375,
               549999.0625],
          dtype='float32', name='moc_z'))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([6951-01-31 00:00:00, 6951-02-28 00:00:00, 6951-03-31 00:00:00,
                 6951-04-30 00:00:00, 6951-05-31 00:00:00, 6951-06-30 00:00:00,
                 6951-07-31 00:00:00, 6951-08-31 00:00:00, 6951-09-30 00:00:00,
                 6951-10-31 00:00:00,
                 ...
                 7000-03-31 00:00:00, 7000-04-30 00:00:00, 7000-05-31 00:00:00,
                 7000-06-30 00:00:00, 7000-07-31 00:00:00, 7000-08-31 00:00:00,
                 7000-09-30 00:00:00, 7000-10-31 00:00:00, 7000-11-30 00:00:00,
                 7000-12-31 00:00:00],
                dtype='object', length=600, calendar='noleap', freq='ME'))

• Attributes: (16)

  title :

  b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005

  history :

  none

  Conventions :

  CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-current.htm

  contents :

  Diagnostic and Prognostic Variables

  source :

  CCSM POP2, the CCSM Ocean Component

  revision :

  $Id: tavg.F90 56176 2013-12-20 18:35:46Z mlevy@ucar.edu $

  calendar :

  All years have exactly 365 days.

  start_time :

  This dataset was created on 2024-04-12 at 02:06:24.5

  cell_methods :

  cell_methods = time: mean ==> the variable values are averaged over the time interval between the previous time coordinate and the current one. cell_methods absent ==> the variable values are at the time given by the current time coordinate.

  nsteps_total :

  101471200

  tavg_sum :

  2678399.99999999

  tavg_sum_qflux :

  2678400.0

  path :

  /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/ocn/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.pop.h.TEMP.695101-700012.nc

  vn :

  TEMP

  comp :

  ocn

  grid :

  g16

case.ds[vn][vn]

<xarray.DataArray 'TEMP' (time: 600, z_t: 60, nlat: 384, nlon: 320)> Size: 18GB
[4423680000 values with dtype=float32]
Coordinates:
  * z_t      (z_t) float32 240B 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05
    ULONG    (nlat, nlon) float64 983kB ...
    ULAT     (nlat, nlon) float64 983kB ...
    TLONG    (nlat, nlon) float64 983kB ...
    TLAT     (nlat, nlon) float64 983kB ...
  * time     (time) object 5kB 6951-01-31 00:00:00 ... 7000-12-31 00:00:00
Dimensions without coordinates: nlat, nlon
Attributes:
    long_name:     Potential Temperature
    units:         degC
    grid_loc:      3111
    cell_methods:  time: mean

xarray.DataArray

'TEMP'

• time: 600
• z_t: 60
• nlat: 384
• nlon: 320

• ...

  [4423680000 values with dtype=float32]

• Coordinates: (6)
  • z_t
    (z_t)
    float32
    500.0 1.5e+03 ... 5.375e+05

    long_name :

    depth from surface to midpoint of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    500.0

    valid_max :

    537500.0

    array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,
           5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,
           1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,
           1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,
           2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,
           2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,
           4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,
           6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,
           1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,
           1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,
           3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,
           4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],
          dtype=float32)

  • ULONG
    (nlat, nlon)
    float64
    ...

    long_name :

    array of u-grid longitudes

    units :

    degrees_east

    [122880 values with dtype=float64]

  • ULAT
    (nlat, nlon)
    float64
    ...

    long_name :

    array of u-grid latitudes

    units :

    degrees_north

    [122880 values with dtype=float64]

  • TLONG
    (nlat, nlon)
    float64
    ...

    long_name :

    array of t-grid longitudes

    units :

    degrees_east

    [122880 values with dtype=float64]

  • TLAT
    (nlat, nlon)
    float64
    ...

    long_name :

    array of t-grid latitudes

    units :

    degrees_north

    [122880 values with dtype=float64]

  • time
    (time)
    object
    6951-01-31 00:00:00 ... 7000-12-...

    array([cftime.DatetimeNoLeap(6951, 1, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6951, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6951, 3, 31, 0, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(7000, 10, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 11, 30, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (2)
  • z_t
    PandasIndex

    PandasIndex(Index([          500.0,          1500.0,          2500.0,          3500.0,
                    4500.0,          5500.0,          6500.0,          7500.0,
                    8500.0,          9500.0,         10500.0,         11500.0,
                   12500.0,         13500.0,         14500.0,         15500.0,
            16509.83984375, 17547.904296875, 18629.126953125,  19766.02734375,
           20971.138671875,    22257.828125,   23640.8828125,    25137.015625,
           26765.419921875, 28548.365234375,    30511.921875, 32686.798828125,
            35109.34765625,  37822.76171875,  40878.46484375,  44337.76953125,
              48273.671875,  52772.80078125,   57937.2890625,  63886.26171875,
              70756.328125,        78700.25,   87882.5234375,   98470.5859375,
             110620.421875,     124456.6875,    140049.71875,   157394.640625,
             176400.328125,   196894.421875,    218645.65625,    241397.15625,
                264900.125,    288938.46875,    313340.46875,    337979.34375,
              362767.03125,     387645.1875,     412576.8125,       437539.25,
              462519.03125,    487508.34375,     512502.8125,        537500.0],
          dtype='float32', name='z_t'))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([6951-01-31 00:00:00, 6951-02-28 00:00:00, 6951-03-31 00:00:00,
                 6951-04-30 00:00:00, 6951-05-31 00:00:00, 6951-06-30 00:00:00,
                 6951-07-31 00:00:00, 6951-08-31 00:00:00, 6951-09-30 00:00:00,
                 6951-10-31 00:00:00,
                 ...
                 7000-03-31 00:00:00, 7000-04-30 00:00:00, 7000-05-31 00:00:00,
                 7000-06-30 00:00:00, 7000-07-31 00:00:00, 7000-08-31 00:00:00,
                 7000-09-30 00:00:00, 7000-10-31 00:00:00, 7000-11-30 00:00:00,
                 7000-12-31 00:00:00],
                dtype='object', length=600, calendar='noleap', freq='ME'))

• Attributes: (4)

  long_name :

  Potential Temperature

  units :

  degC

  grid_loc :

  3111

  cell_methods :

  time: mean

# the case of multiple files
case.load(vn, timespan=(6901, 7000))
print(case.ds[vn].path)
case.ds[vn][vn]

>>> case.ds["TEMP"] created
['/glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/ocn/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.pop.h.TEMP.690101-695012.nc', '/glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/ocn/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.pop.h.TEMP.695101-700012.nc']

<xarray.DataArray 'TEMP' (time: 1200, z_t: 60, nlat: 384, nlon: 320)> Size: 35GB
dask.array<concatenate, shape=(1200, 60, 384, 320), dtype=float32, chunksize=(1, 30, 192, 160), chunktype=numpy.ndarray>
Coordinates:
  * z_t      (z_t) float32 240B 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05
    ULONG    (nlat, nlon) float64 983kB dask.array<chunksize=(384, 320), meta=np.ndarray>
    ULAT     (nlat, nlon) float64 983kB dask.array<chunksize=(384, 320), meta=np.ndarray>
    TLONG    (nlat, nlon) float64 983kB dask.array<chunksize=(384, 320), meta=np.ndarray>
    TLAT     (nlat, nlon) float64 983kB dask.array<chunksize=(384, 320), meta=np.ndarray>
  * time     (time) object 10kB 6901-01-31 00:00:00 ... 7000-12-31 00:00:00
Dimensions without coordinates: nlat, nlon
Attributes:
    long_name:     Potential Temperature
    units:         degC
    grid_loc:      3111
    cell_methods:  time: mean

xarray.DataArray

'TEMP'

• time: 1200
• z_t: 60
• nlat: 384
• nlon: 320

• dask.array<chunksize=(1, 30, 192, 160), meta=np.ndarray>

  Array Chunk Bytes 32.96 GiB 3.52 MiB Shape (1200, 60, 384, 320) (1, 30, 192, 160) Dask graph 9600 chunks in 5 graph layers Data type float32 numpy.ndarray

• Coordinates: (6)
  • z_t
    (z_t)
    float32
    500.0 1.5e+03 ... 5.375e+05

    long_name :

    depth from surface to midpoint of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    500.0

    valid_max :

    537500.0

    array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,
           5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,
           1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,
           1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,
           2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,
           2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,
           4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,
           6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,
           1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,
           1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,
           3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,
           4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],
          dtype=float32)

  • ULONG
    (nlat, nlon)
    float64
    dask.array<chunksize=(384, 320), meta=np.ndarray>

    long_name :

    array of u-grid longitudes

    units :

    degrees_east

    Array Chunk Bytes 0.94 MiB 0.94 MiB Shape (384, 320) (384, 320) Dask graph 1 chunks in 5 graph layers Data type float64 numpy.ndarray

  • ULAT
    (nlat, nlon)
    float64
    dask.array<chunksize=(384, 320), meta=np.ndarray>

    long_name :

    array of u-grid latitudes

    units :

    degrees_north

    Array Chunk Bytes 0.94 MiB 0.94 MiB Shape (384, 320) (384, 320) Dask graph 1 chunks in 5 graph layers Data type float64 numpy.ndarray

  • TLONG
    (nlat, nlon)
    float64
    dask.array<chunksize=(384, 320), meta=np.ndarray>

    long_name :

    array of t-grid longitudes

    units :

    degrees_east

    Array Chunk Bytes 0.94 MiB 0.94 MiB Shape (384, 320) (384, 320) Dask graph 1 chunks in 5 graph layers Data type float64 numpy.ndarray

  • TLAT
    (nlat, nlon)
    float64
    dask.array<chunksize=(384, 320), meta=np.ndarray>

    long_name :

    array of t-grid latitudes

    units :

    degrees_north

    Array Chunk Bytes 0.94 MiB 0.94 MiB Shape (384, 320) (384, 320) Dask graph 1 chunks in 5 graph layers Data type float64 numpy.ndarray

  • time
    (time)
    object
    6901-01-31 00:00:00 ... 7000-12-...

    array([cftime.DatetimeNoLeap(6901, 1, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6901, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6901, 3, 31, 0, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(7000, 10, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 11, 30, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (2)
  • z_t
    PandasIndex

    PandasIndex(Index([          500.0,          1500.0,          2500.0,          3500.0,
                    4500.0,          5500.0,          6500.0,          7500.0,
                    8500.0,          9500.0,         10500.0,         11500.0,
                   12500.0,         13500.0,         14500.0,         15500.0,
            16509.83984375, 17547.904296875, 18629.126953125,  19766.02734375,
           20971.138671875,    22257.828125,   23640.8828125,    25137.015625,
           26765.419921875, 28548.365234375,    30511.921875, 32686.798828125,
            35109.34765625,  37822.76171875,  40878.46484375,  44337.76953125,
              48273.671875,  52772.80078125,   57937.2890625,  63886.26171875,
              70756.328125,        78700.25,   87882.5234375,   98470.5859375,
             110620.421875,     124456.6875,    140049.71875,   157394.640625,
             176400.328125,   196894.421875,    218645.65625,    241397.15625,
                264900.125,    288938.46875,    313340.46875,    337979.34375,
              362767.03125,     387645.1875,     412576.8125,       437539.25,
              462519.03125,    487508.34375,     512502.8125,        537500.0],
          dtype='float32', name='z_t'))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([6901-01-31 00:00:00, 6901-02-28 00:00:00, 6901-03-31 00:00:00,
                 6901-04-30 00:00:00, 6901-05-31 00:00:00, 6901-06-30 00:00:00,
                 6901-07-31 00:00:00, 6901-08-31 00:00:00, 6901-09-30 00:00:00,
                 6901-10-31 00:00:00,
                 ...
                 7000-03-31 00:00:00, 7000-04-30 00:00:00, 7000-05-31 00:00:00,
                 7000-06-30 00:00:00, 7000-07-31 00:00:00, 7000-08-31 00:00:00,
                 7000-09-30 00:00:00, 7000-10-31 00:00:00, 7000-11-30 00:00:00,
                 7000-12-31 00:00:00],
                dtype='object', length=1200, calendar='noleap', freq='ME'))

• Attributes: (4)

  long_name :

  Potential Temperature

  units :

  degC

  grid_loc :

  3111

  cell_methods :

  time: mean

We may also load a variable with a load_idx. It equals to -1 by default, meaning the last file.

case.load(vn, load_idx=slice(-2, None))
print(case.ds[vn].path)
case.ds[vn][vn]

>>> case.ds["TEMP"] created
['/glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/ocn/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.pop.h.TEMP.690101-695012.nc', '/glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005/ocn/proc/tseries/month_1/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.pop.h.TEMP.695101-700012.nc']

<xarray.DataArray 'TEMP' (time: 1200, z_t: 60, nlat: 384, nlon: 320)> Size: 35GB
dask.array<concatenate, shape=(1200, 60, 384, 320), dtype=float32, chunksize=(1, 30, 192, 160), chunktype=numpy.ndarray>
Coordinates:
  * z_t      (z_t) float32 240B 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05
    ULONG    (nlat, nlon) float64 983kB dask.array<chunksize=(384, 320), meta=np.ndarray>
    ULAT     (nlat, nlon) float64 983kB dask.array<chunksize=(384, 320), meta=np.ndarray>
    TLONG    (nlat, nlon) float64 983kB dask.array<chunksize=(384, 320), meta=np.ndarray>
    TLAT     (nlat, nlon) float64 983kB dask.array<chunksize=(384, 320), meta=np.ndarray>
  * time     (time) object 10kB 6901-01-31 00:00:00 ... 7000-12-31 00:00:00
Dimensions without coordinates: nlat, nlon
Attributes:
    long_name:     Potential Temperature
    units:         degC
    grid_loc:      3111
    cell_methods:  time: mean

xarray.DataArray

'TEMP'

• time: 1200
• z_t: 60
• nlat: 384
• nlon: 320

• dask.array<chunksize=(1, 30, 192, 160), meta=np.ndarray>

  Array Chunk Bytes 32.96 GiB 3.52 MiB Shape (1200, 60, 384, 320) (1, 30, 192, 160) Dask graph 9600 chunks in 5 graph layers Data type float32 numpy.ndarray

• Coordinates: (6)
  • z_t
    (z_t)
    float32
    500.0 1.5e+03 ... 5.375e+05

    long_name :

    depth from surface to midpoint of layer

    units :

    centimeters

    positive :

    down

    valid_min :

    500.0

    valid_max :

    537500.0

    array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,
           5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,
           1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,
           1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,
           2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,
           2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,
           4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,
           6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,
           1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,
           1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,
           3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,
           4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],
          dtype=float32)

  • ULONG
    (nlat, nlon)
    float64
    dask.array<chunksize=(384, 320), meta=np.ndarray>

    long_name :

    array of u-grid longitudes

    units :

    degrees_east

    Array Chunk Bytes 0.94 MiB 0.94 MiB Shape (384, 320) (384, 320) Dask graph 1 chunks in 5 graph layers Data type float64 numpy.ndarray

  • ULAT
    (nlat, nlon)
    float64
    dask.array<chunksize=(384, 320), meta=np.ndarray>

    long_name :

    array of u-grid latitudes

    units :

    degrees_north

    Array Chunk Bytes 0.94 MiB 0.94 MiB Shape (384, 320) (384, 320) Dask graph 1 chunks in 5 graph layers Data type float64 numpy.ndarray

  • TLONG
    (nlat, nlon)
    float64
    dask.array<chunksize=(384, 320), meta=np.ndarray>

    long_name :

    array of t-grid longitudes

    units :

    degrees_east

    Array Chunk Bytes 0.94 MiB 0.94 MiB Shape (384, 320) (384, 320) Dask graph 1 chunks in 5 graph layers Data type float64 numpy.ndarray

  • TLAT
    (nlat, nlon)
    float64
    dask.array<chunksize=(384, 320), meta=np.ndarray>

    long_name :

    array of t-grid latitudes

    units :

    degrees_north

    Array Chunk Bytes 0.94 MiB 0.94 MiB Shape (384, 320) (384, 320) Dask graph 1 chunks in 5 graph layers Data type float64 numpy.ndarray

  • time
    (time)
    object
    6901-01-31 00:00:00 ... 7000-12-...

    array([cftime.DatetimeNoLeap(6901, 1, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6901, 2, 28, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(6901, 3, 31, 0, 0, 0, 0, has_year_zero=True), ...,
           cftime.DatetimeNoLeap(7000, 10, 31, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 11, 30, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(7000, 12, 31, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

• Indexes: (2)
  • z_t
    PandasIndex

    PandasIndex(Index([          500.0,          1500.0,          2500.0,          3500.0,
                    4500.0,          5500.0,          6500.0,          7500.0,
                    8500.0,          9500.0,         10500.0,         11500.0,
                   12500.0,         13500.0,         14500.0,         15500.0,
            16509.83984375, 17547.904296875, 18629.126953125,  19766.02734375,
           20971.138671875,    22257.828125,   23640.8828125,    25137.015625,
           26765.419921875, 28548.365234375,    30511.921875, 32686.798828125,
            35109.34765625,  37822.76171875,  40878.46484375,  44337.76953125,
              48273.671875,  52772.80078125,   57937.2890625,  63886.26171875,
              70756.328125,        78700.25,   87882.5234375,   98470.5859375,
             110620.421875,     124456.6875,    140049.71875,   157394.640625,
             176400.328125,   196894.421875,    218645.65625,    241397.15625,
                264900.125,    288938.46875,    313340.46875,    337979.34375,
              362767.03125,     387645.1875,     412576.8125,       437539.25,
              462519.03125,    487508.34375,     512502.8125,        537500.0],
          dtype='float32', name='z_t'))

  • time
    PandasIndex

    PandasIndex(CFTimeIndex([6901-01-31 00:00:00, 6901-02-28 00:00:00, 6901-03-31 00:00:00,
                 6901-04-30 00:00:00, 6901-05-31 00:00:00, 6901-06-30 00:00:00,
                 6901-07-31 00:00:00, 6901-08-31 00:00:00, 6901-09-30 00:00:00,
                 6901-10-31 00:00:00,
                 ...
                 7000-03-31 00:00:00, 7000-04-30 00:00:00, 7000-05-31 00:00:00,
                 7000-06-30 00:00:00, 7000-07-31 00:00:00, 7000-08-31 00:00:00,
                 7000-09-30 00:00:00, 7000-10-31 00:00:00, 7000-11-30 00:00:00,
                 7000-12-31 00:00:00],
                dtype='object', length=1200, calendar='noleap', freq='ME'))

• Attributes: (4)

  long_name :

  Potential Temperature

  units :

  degC

  grid_loc :

  3111

  cell_methods :

  time: mean

Check available timeseires files

vn = 'TS'
case.check_timespan(vn)

Checking dates: 100%|██████████| 140/140 [00:01<00:00, 88.05it/s]

	1	2	3	4	5	6	7	8	9	10
1	000101-005012	005101-010012	010101-015012	015101-020012	020101-025012	025101-030012	030101-035012	035101-040012	040101-045012	045101-050012
2	050101-055012	055101-060012	060101-065012	065101-070012	070101-075012	075101-080012	080101-085012	085101-090012	090101-095012	095101-100012
3	100101-105012	105101-110012	110101-115012	115101-120012	120101-125012	125101-130012	130101-135012	135101-140012	140101-145012	145101-150012
4	150101-155012	155101-160012	160101-165012	165101-170012	170101-175012	175101-180012	180101-185012	185101-190012	190101-195012	195101-200012
5	200101-205012	205101-210012	210101-215012	215101-220012	220101-225012	225101-230012	230101-235012	235101-240012	240101-245012	245101-250012
6	250101-255012	255101-260012	260101-265012	265101-270012	270101-275012	275101-280012	280101-285012	285101-290012	290101-295012	295101-300012
7	300101-305012	305101-310012	310101-315012	315101-320012	320101-325012	325101-330012	330101-335012	335101-340012	340101-345012	345101-350012
8	350101-355012	355101-360012	360101-365012	365101-370012	370101-375012	375101-380012	380101-385012	385101-390012	390101-395012	395101-400012
9	400101-405012	405101-410012	410101-415012	415101-420012	420101-425012	425101-430012	430101-435012	435101-440012	440101-445012	445101-450012
10	450101-455012	455101-460012	460101-465012	465101-470012	470101-475012	475101-480012	480101-485012	485101-490012	490101-495012	495101-500012
11	500101-505012	505101-510012	510101-515012	515101-520012	520101-525012	525101-530012	530101-535012	535101-540012	540101-545012	545101-550012
12	550101-555012	555101-560012	560101-565012	565101-570012	570101-575012	575101-580012	580101-585012	585101-590012	590101-595012	595101-600012
13	600101-605012	605101-610012	610101-615012	615101-620012	620101-625012	625101-630012	630101-635012	635101-640012	640101-645012	645101-650012
14	650101-655012	655101-660012	660101-665012	665101-670012	670101-675012	675101-680012	680101-685012	685101-690012	690101-695012	695101-700012

vn = 'TS'
case.check_timespan(vn, timespan=(1, 8000))

Checking dates: 100%|██████████| 160/160 [00:00<00:00, 12945.88it/s]

	1	2	3	4	5	6	7	8	9	10
1	000101-005012	005101-010012	010101-015012	015101-020012	020101-025012	025101-030012	030101-035012	035101-040012	040101-045012	045101-050012
2	050101-055012	055101-060012	060101-065012	065101-070012	070101-075012	075101-080012	080101-085012	085101-090012	090101-095012	095101-100012
3	100101-105012	105101-110012	110101-115012	115101-120012	120101-125012	125101-130012	130101-135012	135101-140012	140101-145012	145101-150012
4	150101-155012	155101-160012	160101-165012	165101-170012	170101-175012	175101-180012	180101-185012	185101-190012	190101-195012	195101-200012
5	200101-205012	205101-210012	210101-215012	215101-220012	220101-225012	225101-230012	230101-235012	235101-240012	240101-245012	245101-250012
6	250101-255012	255101-260012	260101-265012	265101-270012	270101-275012	275101-280012	280101-285012	285101-290012	290101-295012	295101-300012
7	300101-305012	305101-310012	310101-315012	315101-320012	320101-325012	325101-330012	330101-335012	335101-340012	340101-345012	345101-350012
8	350101-355012	355101-360012	360101-365012	365101-370012	370101-375012	375101-380012	380101-385012	385101-390012	390101-395012	395101-400012
9	400101-405012	405101-410012	410101-415012	415101-420012	420101-425012	425101-430012	430101-435012	435101-440012	440101-445012	445101-450012
10	450101-455012	455101-460012	460101-465012	465101-470012	470101-475012	475101-480012	480101-485012	485101-490012	490101-495012	495101-500012
11	500101-505012	505101-510012	510101-515012	515101-520012	520101-525012	525101-530012	530101-535012	535101-540012	540101-545012	545101-550012
12	550101-555012	555101-560012	560101-565012	565101-570012	570101-575012	575101-580012	580101-585012	585101-590012	590101-595012	595101-600012
13	600101-605012	605101-610012	610101-615012	615101-620012	620101-625012	625101-630012	630101-635012	635101-640012	640101-645012	645101-650012
14	650101-655012	655101-660012	660101-665012	665101-670012	670101-675012	675101-680012	680101-685012	685101-690012	690101-695012	695101-700012
15	700101-705012	705101-710012	710101-715012	715101-720012	720101-725012	725101-730012	730101-735012	735101-740012	740101-745012	745101-750012
16	750101-755012	755101-760012	760101-765012	765101-770012	770101-775012	775101-780012	780101-785012	785101-790012	790101-795012	795101-800012

# dirpath = '/glade/campaign/univ/ubrn0018/fengzhu/CESM_output/timeseries/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.offset.d18O.branch_old_rfiles'
# case = x4c.Timeseries(dirpath)

>>> Logs.dirpath: /glade/campaign/univ/ubrn0018/fengzhu/CESM_output/archive/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.offset.d18O.branch/logs
>>> 50 Logs.paths:
Start: ocn.log.4146414.desched1.240416-143953.gz
End: ocn.log.4343698.desched1.240502-115141.gz

# case.check_timespan('R18O')

Checking dates: 100%|██████████| 10/10 [00:00<00:00, 61.50it/s]

	1	2	3	4	5	6	7	8	9	10
1	700101-705012	705101-710012	710101-715012	715101-720012	720101-725012	725101-730012	730101-735012	735101-740012	740101-745012	745101-750012

dirpath = '/glade/campaign/univ/ubrn0018/fengzhu/CESM_output/archive/b.e13.B1850C5.ne16_g16.icesm131_d18O_fixer.Miocene.3xCO2.005.offset.d18O.branch/logs'
case = x4c.Logs(dirpath)

case.get_vars(['R18O'])

case.df_ann

	Month	R18O
Year
7001	6.5	1.000729
7002	6.5	1.000729
7003	6.5	1.000729
7004	6.5	1.000729
7005	6.5	1.000729
...	...	...
7996	6.5	1.000729
7997	6.5	1.000729
7998	6.5	1.000729
7999	6.5	1.000729
8000	6.5	1.000729

1000 rows × 2 columns