Legacy API reference

Legacy API reference#

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

Train mesmer#

train_gt(data, 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#

gather_gt_data(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

create_seed_dict(esms, scenarios[, ...])

define seeds for drawing emulations

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(data)

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.

get_scenario_weights(target)

derive scenario weights such that each has equal weight, i.e., 1 / number of samples (= nr_runs * nr_ts)

stack_predictors_and_targets(preds, targs)

Create single array of predictors, and single array of targets

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 data#

load_cmip_data_all_esms(esms, scenarios, ...)

Load tas and (potentially) hfds for several ESMs from cmip-ng archive at ETHZ

load_cmipng(targ, esm, scen, cfg)

Load ESM runs from cmip-ng archive at ETHZ.

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.

Load constant files#

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.

Load output#

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

Load saved MESMER output (parameters or emulations).

Load 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(data, time, start, end)

Extract selected time period.
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