Climate Forecasting Unit CFU R common diagnostics CFU_load CFU_season CFU_clim CFU_anoCFU_anocrossvalid CFU_plotclimCFU_plotano CFU_smoothing CFU_trend CFU_animvsltime

Climate Forecasting Unit CFU R common diagnostics CFU_load Minimum set of arguments : 1) var 2) exp 3) obs (can be obs=NULL) 4) sdates You can request area-averages, longitudinal or latitudinal averages, 2d fields You can define any region by sending masks You can request a subset of leadtimes You can work on a subdomain by providing lat/lon borders

Climate Forecasting Unit CFU_season CFU R common diagnostics This function computes averages over extended season. It can be used to compute annual means for exemple.

Climate Forecasting Unit CFU R common diagnostics CFU_clim This function computes per-pair climatologies, one climatology per member or one for all the members together. If you have only one start date, your climatology should be computed as a simple annual cycle not with CFU_clim. If you don’t have observations, you don’t need the per-pair method. Your clim is clim=CFU_mean1dim(exp, 3)

Climate Forecasting Unit CFU R common diagnostics CFU_anocrossvalid This function computes anomalies using the cross- validation method, i.e. for each startdate, the climatology is computed using all the other startdates. It also uses the per-pair method.

Climate Forecasting Unit CFU R common diagnostics CFU_trend This function provides not only the linear trend but also the linearly detrended data.

Climate Forecasting Unit CFU R common diagnostics CFU_load CFU_clim CFU_anoCFU_anocrossvalid CFU_plotclimCFU_plotanoCFU_animvsltime mod = array(dim=c(nexp, nmemb, nsdates, nltimes) to mod = array(dim=c(nexp, nmemb, nsdates, nltimes, nlat, nlon) obs = array(dim=c(nobs, nmemb, nsdates, nltimes) to obs = array(dim=c(nobs, nmemb, nsdates, nltimes, nlat, nlon) Those functions work only with the common diagnostic structure.

Climate Forecasting Unit CFU R common diagnostics CFU_season CFU_smoothing CFU_trend For those functions, the input structure is free. Input matrix can have any number of dimensions and the dimension along which the trend, smoothing or season has to be computed should be specified. Default parameters : common diagnostic structure, leadtime dimensions for CFU_season/CFU_smoothing, nsdates for CFU_trend You can use them on any time series

Climate Forecasting Unit CFU R common diagnostics CFU_load CFU_season CFU_clim CFU_anoCFU_anocrossvalid CFU_plotclimCFU_plotano CFU_smoothing CFU_trend CFU_animvsltime

Climate Forecasting Unit CFU R common diagnostics CFU_anoCFU_anocrossvalid CFU_spread CFU_corr CFU_RMS CFU_trend CFU_ratioRMS CFU_ratioSDRMSCFU_RMSSS CFU_consist_trend CFU_animvsltimeCFU_plotvsltimeCFU_plotequimap

Climate Forecasting Unit CFU R common diagnostics CFU_spread CFU_corr CFU_RMS CFU_trend CFU_ratioRMS CFU_ratioSDRMSCFU_RMSSS For those functions, the input structure is free. Default : common diagnostic structure Scores are computed for each experimental dataset versus each observational dataset in your input matrix.

Climate Forecasting Unit CFU R common diagnostics CFU_anoCFU_anocrossvalid CFU_consist_trend CFU_animvsltimeCFU_plotvsltime Those functions expect the common diagnostic structure

Climate Forecasting Unit CFU R common diagnostics CFU_plotequimap For this function, (lat,lon) expected and a second matrix of flags=T/F with the same dimensions is expected for significance level It has many functionalities to make nice plots for publication. Color levels (square or smoothed), contours, dots …, continents can be filled in grey or show as black lines. Colorbar can be drawn or not…. It can be used in a multipanel after splitting the space with layout

Climate Forecasting Unit CFU R common diagnostics CFU_spread CFU_corr CFU_RMS CFU_trend CFU_ratioRMS CFU_ratioSDRMSCFU_RMSSS CFU_consist_trend Confidence intervals or significance levels or both are systematically provided.

Climate Forecasting Unit CFU R common diagnostics CFU_corr CFU_RMSCFU_ratioRMS CFU_ratioSDRMSCFU_RMSSS For those functions, there are issues about the temporal dependance of the data for confidence intervals/significance levels. For non-parametric tests, a window of dependence has to be defined, for parametric ones, a number of independant data has to be defined. Those functions currently use parametric tests with a number of independant data defined following the classical formula from Von Storch and Zwiers (2001). This might change depending on the literature. Call to CFU_eno

Climate Forecasting Unit CFU R common diagnostics CFU_spread CFU_corr CFU_RMS CFU_trend CFU_ratioRMS CFU_ratioSDRMSCFU_RMSSS CFU_consist_trend bootstrap one sided T-test Fisher transform chi2 one-sided Fisher test two-sided Fisher test one-sided Fisher test T- distribution

Climate Forecasting Unit CFU R common diagnostics CFU_anoCFU_anocrossvalid CFU_spread CFU_corr CFU_RMS CFU_trend CFU_ratioRMS CFU_ratioSDRMSCFU_RMSSS CFU_consist_trend CFU_animvsltimeCFU_plotvsltimeCFU_plotequimap

Climate Forecasting Unit CFU R common diagnostics CFU_eno CFU_mean1dim CFU_meanlistdim CFU_insertdim CFU_colorbar For those functions, the input structure is free. This function makes a colorbar if you send the levels and colors. Useful for multipanels after calling layout

Climate Forecasting Unit CFU R common diagnostics ~]$ R  source(‘/cfu/pub/scripts/R/common_diagnostics.txt’) [1] List of functions : [1] [1] CFU_load [1] CFU_season [1] CFU_clim [1] CFU_ano [1] CFU_ano_crossvalid [1] CFU_smoothing [1] CFU_plotano [1] CFU_plotclim [1] CFU_spread [1] CFU_plotvsltime [1] CFU_corr [1] CFU_RMS [1] CFU_RMSSS

Climate Forecasting Unit CFU R common diagnostics [1] CFU_ratioRMS [1] CFU_ratioSDRMS [1] CFU_trend [1] CFU_consist_trend [1] CFU_plotequimap [1] CFU_colorbar [1] CFU_animvsltime [1] CFU_eno [1] CFU_enlarge [1] CFU_insertdim [1] CFU_mean1dim [1] CFU_meanlistdim [1] CFU_inilistdims [1] [1] For more information about any function, type info_cd('function name')  info_cd(‘CFU_load’)

Climate Forecasting Unit CFU R common diagnostics [1] [1] Description [1] ~~~~~~~~~~~~~ [1] [1] Load experimental data and corresponding observed ones in 2 matrix with similar structures [1] If loading EC-Earth experiments, PUT FIRST THE EXPERIMENT ID WITH THE LARGEST NUMBER [1] OF MEMBERS & if possible, THE LARGEST NUMBER OF LEADTIMES. If not possible, fill up the nleatime argument. [1] [1] Inputs [1] ~~~~~~~~ [1] [1] - var= 'tas','prlr','tos','g500','g200','ta50','psl','hflsd','hfssd','rls','rss','rsds','uas','vas'

Climate Forecasting Unit [1] - exp=c('ecmwf','ukmo','cerfacs','ifm','DePreSysAsimDec','DePreSysNoAsimDec','DePr eSysAsimSeas','ECMWF_S3Seas','ECMWF_S4Seas','ECMWF_S4SeasQWeCI','hadcm 3dec','miroc4dec','miroc5dec','mri-cgcm3dec','cancm4dec1','cancm4dec2','cnrm- cm5dec','knmidec','mpimdec','gfdldec','cmcc- cmdec','hadcm3his','miroc4his','miroc5his','mri-cgcm3his','cancm4his','cnrm- cm5his','knmihis','i00k','b013','b014','yve2'...) [1] - obs=c('ERA40','NCEP','ERAint','GHCN','ERSST','HADISST','GPCP','GPCC','CRU','DS9 4','OAFlux','DFS4.3','NCDCglo','NCDCland','NCDCoc','GISSglo','GISSland','GISSoc','H adCRUT3glo','HadSST2oc','CRUTEM3land') [1] - sdates=c('YYYYMMDD','YYYYMMDD') [1] - lonmin, lonmax, latmin, latmax : domain border 0 <= lonmin,lonmax <= 360 [1] default : world [1] - nleadtime : optional argument needed only if the first exp does not have the largest number of leadtimes. [1] default : number of leadtimes of the first experiment. CFU R common diagnostics

Climate Forecasting Unit [1] - leadtimemin : output only the leadtimes from leadtimemin. default = 1 [1] - leadtimemax : output only the leadtimes before leadtimemax. default = nleadtime [1] - output = 'areave' / 'lon' / 'lat' / 'lonlat' [1] 1) Time series of area-averaged variables over the specified domain [1] 2) Time series of meridional averages as a function of longitudes [1] 3) Time series of zonal averages as a function of latitudes [1] 4) Time series of 2d fields [1] default : 'areave' [1] - method = 'bilinear' / 'bicubic' / 'conservative' / 'distance-weighted' [1] Method of interpolation for 'lon' / 'lat' / 'lonlat' output options [1] default : 'conservative' [1] - grid = to choose the output grid [1] possible options : rNXxNY or tTRgrid, ex: r96x72, t106grid [1] default : model grid, argument need to be filled if various exp on various grids CFU R common diagnostics

Climate Forecasting Unit [1] - maskmod=list(mask[lon,lat]) = 1/0 : kept/removed grid cell over the entire model domains [1] Warning : list() compulsory even if 1 model !!! [1] default : 1 everywhere [1] - maskobs=list(mask[lon,lat]) = 1/0 : kept/removed grid cell over the entire [1] observed domains, only necessary for 'areave' output option [1] Warning : list() compulsory even if 1 dataset !!! [1] default : 1 everywhere [1] CFU R common diagnostics

Climate Forecasting Unit [1] Outputs [1] ~~~~~~~~~ [1] [1] $mod = model outputs [1] $obs = observations [1] $lat = latitudes of the model grid [1] $lon = longitudes of the model grid [1] [1] 2 matrix with dimensions [1] c(nmod/nexp or nobs, nmemb/nparam, nsdates, nltime) if output = 'areave' [1] c(nmod/nexp or nobs, nmemb/nparam, nsdates, nltime, nlat ) if = 'lat' [1] c(nmod/nexp or nobs, nmemb/nparam, nsdates, nltime, nlon ) if = 'lon' [1] c(nmod/nexp or nobs, nmemb/nparam, nsdates, nltime, nlat, nlon) = 'lonlat' [1] [1] Author [1] ~~~~~~~~ [1] [1] CFUers March 2011 CFU R common diagnostics