Contents

API reference

This page provides an auto-generated summary of mesmers’ API.

Top-level functions

Train mesmer

train_gt(var, targ, esm, time, cfg[, ...])

Derive global trend (emissions + volcanoes) parameters from specified ensemble type with specified method.

train_gv(gv, targ, esm, cfg[, save_params])

Derive global variability parameters for a specified method.

train_lt(preds, targs, esm, cfg[, save_params])

Derive local trends (i.e., forced response) parameters for given ESM for given set of targets and predictors.

train_lv(preds, targs, esm, cfg[, ...])

Derive local variability (i.e., natural variabiliy) parameters.

Create emulations

create_emus_gt(params_gt, preds_gt, cfg[, ...])

Create global trend (emissions + volcanoes) emulations for specified ensemble type and method.

create_emus_gv(params_gv, preds_gv, cfg[, ...])

Create global variablity emulations for specified method.

create_emus_lt(params_lt, preds_lt, cfg[, ...])

Create local trends (i.e., forced response) emulations for given parameter set and predictors.

create_emus_lv(params_lv, preds_lv, cfg[, ...])

Create local variablity emulations.

create_emus_g(emus_gt, emus_gv, params_gt, ...)

Merge global trend and global variability emulations of the same scenarios.

create_emus_l(emus_lt, emus_lv, params_lt, ...)

Merge local trends and local variability temperature emulations of the same scenarios and targets.

make_realisations(preds_lt, params_lt, ...)

Make climate realisations based on pre-calculated MESMER parameters

Individual methods and utils

Train mesmer

train_gv_AR(params_gv, gv, max_lag, sel_crit)

Derive AR parameters of global variability under the assumption that gv does not depend on the scenario.

train_gt_ic_LOWESS(var)

Derive smooth global trend of variable from single ESM ic ensemble with LOWESS smoother.

train_gt_ic_OLSVOLC(var, gt_lowess, time, cfg)

Derive global trend (emissions + volcanoes) parameters from single ESM ic ensemble by adding volcanic spikes to LOWESS trend.

train_lv_AR1_sci(params_lv, targs, y, ...)

Derive parameters for AR(1) process with spatially-correlated innovations.

train_lv_find_localized_ecov(y, wgt_scen_eq, ...)

Find suitable localization radius for empirical covariance matrix and derive localized empirical cov matrix.

train_l_prepare_X_y_wgteq(preds, targs)

Create single array of predictors, single array of targets, and single array of weights.

Create emulations

create_emus_gv_AR(params_gv, nr_emus_v, ...)

Draw global variablity emulations from an AR process.

create_emus_OLS_each_gp_sep(params_lt, ...)

Create local trends with OLS with grid-point-specific predictors

create_emus_lv_AR1_sci(emus_lv, params_lv, ...)

Create local variablity emulations with AR(1) process with spatially-correlated innovations.

create_emus_lv_OLS(params_lv, preds_lv)

Create local variablity emulations with OLS.

IO

Load CMIP-ng data

extract_time_lon_lat_wgt3d(data)

Extract time, longitude, latitude, and 3d weights from file from ETHZ cmip-ng archive.

find_files_cmipng(gen, esm, var, scenario, ...)

Find filname in ETHZ cmip-ng archive.

load_cmipng(targ, esm, scen, cfg)

Load ESM runs from cmip-ng archive at ETHZ.

load_cmipng_file(run_path, gen, scen)

Load file in ETHZ cmip-ng archive.

load_cmipng_hfds(esm, scen, cfg)

Load ESM hfds runs from cmip-ng archive at ETHZ.

load_cmipng_tas(esm, scen, cfg)

Load ESM tas runs from cmip-ng archive at ETHZ.

preprocess_ssp534over(ds)

Preprocess datasets to manage to combine historical, ssp585, and ssp534-over into single time series.

Load constant files

gaspari_cohn(r)

smooth, exponentially decaying Gaspari-Cohn correlation function

infer_interval_breaks(x, y[, clip])

Find edges of gridcells, given their centers.

load_phi_gc(lon, lat, ls, cfg[, L_start, ...])

Loads or creates (if not available yet) distance matrix and Gaspari-Cohn correlation matrix.

load_regs_ls_wgt_lon_lat(reg_type, lon, lat)

Load constant files.

mask_percentage(regions, lon, lat)

Sample with 10 times higher resolution.

Load constant files

load_mesmer_output(name, cfg[, method_str, ...])

Load saved MESMER output (parameters or emulations).

Load observations

load_obs(targ, prod, lon, lat, cfg[, ...])

Load observations which you previously downloaded.

load_obs_tblend(prod, lon, lat, cfg, sel_ref)

Load spatially infilled tblend observations.

load_strat_aod(time, dir_obs)

Load observed global stratospheric aerosol optical depth time series.

Save mesmer bundle

save_mesmer_bundle(bundle_file, params_lt, ...)

Save all the information required to draw MESMER emulations to disk

Utils

convert_dict_to_arr(var_dict)

Convert dictionary to array.

separate_hist_future(var_c, time_c, cfg)

Separate historical and future time periods into separate keys in dictionary.

extract_land(var, reg_dict, wgt, ls[, ...])

Extract all land grid points and area weights in regions and in land-sea mask for given threshold.

extract_time_period(var, time, start, end)

Extract selected time period.