ISEFlow Model Versions

ISEFlow pretrained weights are versioned independently from the ise-py package. Weights are hosted on HuggingFace Hub at pvankatwyk/ISEFlow and downloaded automatically on first use. Local bundled fallback weights are used when HuggingFace is unavailable (air-gapped HPC, offline development).

Use the version argument when loading a pretrained model:

from ise.models import ISEFlow_AIS, ISEFlow_GrIS

model = ISEFlow_AIS(version="v1.1.0")   # latest
model = ISEFlow_AIS(version="v1.0.0")   # legacy AIS-only weights

Current recommended version: v1.1.0

—

Version history

v1.1.0 — AIS + GrIS (current)

Released:

2026-05-08

Ice sheets:

AIS (18 sectors), GrIS (6 drainage basins)

Classes:

ISEFlow_AIS(version="v1.1.0"), ISEFlow_GrIS(version="v1.1.0")

What changed from v1.0.0

  • ``mrro_anomaly`` removed from AIS inputs. The melt-runoff anomaly variable was dropped from the AIS feature set after ablation analysis showed it contributed noise rather than signal. Code using v1.0.0-style inputs that passes mrro_anomaly must remove it before calling predict() with v1.1.0 weights.

  • Scalers saved alongside weights per ice sheet and version so that predict(..., output_scaler=True) works correctly for both AIS and GrIS.

  • NormalizingFlow architecture upgraded. The context encoder was changed from a single nn.Linear(input_size, output_size * 2) layer to a 2-layer MLP (Linear → ReLU → Linear), and flow_hidden_features is now a tunable hyperparameter rather than being hard-coded to output_size * 2.

  • ``get_latent`` changed from deterministic to stochastic. v1.0.0 pushed a fixed zero vector through the forward transform to produce latents; v1.1.0 draws samples from the conditional base distribution instead. Both approaches are preserved — NormalizingFlow.load() auto-detects the version from saved metadata and sets legacy_v1_0_0=True for old weights so inference reproduces the original latents.

AIS inputs (v1.1.0)

year, sector,
pr_anomaly, evspsbl_anomaly, smb_anomaly, ts_anomaly,
ocean_thermal_forcing, ocean_salinity, ocean_temperature,
ice_shelf_fracture, ocean_sensitivity, ocean_forcing_type, standard_melt_type,
[ISM model characteristics]

GrIS inputs (v1.1.0)

year, sector,
aSMB, aST, ocean_thermal_forcing, basin_runoff,
ice_shelf_fracture, ocean_sensitivity, standard_ocean_forcing,
[ISM model characteristics]

—

v1.0.0 — AIS + GrIS (legacy)

Released:

2026-05-07

Ice sheets:

AIS (18 sectors), GrIS (6 drainage basins)

Classes:

ISEFlow_AIS(version="v1.0.0"), ISEFlow_GrIS(version="v1.0.0")

The original pretrained release. Maintained for reproducibility of results from the ISEFlow manuscript.

AIS inputs (v1.0.0)

Identical to v1.1.0 plus mrro_anomaly (melt-runoff anomaly):

year, sector,
pr_anomaly, evspsbl_anomaly, smb_anomaly, ts_anomaly, mrro_anomaly,
ocean_thermal_forcing, ocean_salinity, ocean_temperature,
ice_shelf_fracture, ocean_sensitivity, ocean_forcing_type, standard_melt_type,
[ISM model characteristics]

Note

ISEFlow_AIS(version="v1.0.0") and ISEFlow_GrIS(version="v1.0.0") are still loadable as of package v1.1.0. The NormalizingFlow loader auto-detects the older architecture from saved metadata. ISEFlow_GrIS_DE/NF_v1_0_0 emit a DeprecationWarning on instantiation.

—

Migrating between versions

v1.0.0 → v1.1.0 (AIS)

Remove mrro_anomaly from your ISEFlowAISInputs constructor call and switch the version argument:

# v1.0.0 style — no longer valid for v1.1.0
inputs = ISEFlowAISInputs(
    ...,
    mrro_anomaly=mrro,   # remove this line
)
model = ISEFlow_AIS(version="v1.0.0")

# v1.1.0 style
inputs = ISEFlowAISInputs(
    year=year,
    sector=sector,
    pr_anomaly=pr,
    evspsbl_anomaly=evspsbl,
    smb_anomaly=smb,
    ts_anomaly=ts,
    ocean_thermal_forcing=otf,
    ocean_salinity=sal,
    ocean_temperature=temp,
    model_configs="AWI_PISM1",
    ice_shelf_fracture=False,
    ocean_sensitivity="medium",
    ocean_forcing_type="standard",
    standard_melt_type="local",
)
model = ISEFlow_AIS(version="v1.1.0")

Using absolute forcings instead of anomalies

If you have raw absolute climate values rather than pre-computed anomalies, use the from_absolute_forcings() classmethod. Pass aogcm for a bundled CMIP model or custom_climatology for a new model not in the bundled set:

inputs = ISEFlowAISInputs.from_absolute_forcings(
    year=year,
    sector=sector,
    pr=pr_abs,
    evspsbl=evspsbl_abs,
    smb=smb_abs,
    ts=ts_abs,
    ocean_thermal_forcing=otf,
    ocean_salinity=sal,
    ocean_temperature=temp,
    aogcm="noresm1-m_rcp85",   # or custom_climatology={...}
    model_configs="AWI_PISM1",
    ice_shelf_fracture=False,
    ocean_sensitivity="medium",
    ocean_forcing_type="standard",
    standard_melt_type="local",
)

Note

from_raw_values() is a deprecated alias for from_absolute_forcings() and will be removed in a future release.

—

Weight resolution

The path resolution order is:

  1. HuggingFace Hub (pvankatwyk/ISEFlow) — downloaded on first use and cached locally.

  2. Bundled local weights under ise/models/pretrained/ISEFlow/ — used automatically when HuggingFace is unreachable.

Use get_model_dir(version, ice_sheet) (from ise.models.pretrained) to resolve the path programmatically. Do not use the deprecated _path constants.

from ise.models.pretrained import get_model_dir

path = get_model_dir("v1.1.0", "AIS")