File size: 5,458 Bytes
6214201 33a2f32 6214201 10f09f3 6214201 10f09f3 6214201 10f09f3 6214201 10f09f3 6214201 10f09f3 6214201 10f09f3 33a2f32 6214201 10f09f3 6214201 10f09f3 fcb12f6 6214201 fcb12f6 10f09f3 6214201 e9aff33 6214201 2f0ca76 10f09f3 2f0ca76 6214201 2f0ca76 10f09f3 33a2f32 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | ---
language:
- en
pretty_name: "ClimX: extreme-aware climate model emulation"
tags:
- climate
- earth-system-model
- machine-learning
- emulation
- extremes
- netcdf
license: mit
task_categories:
- time-series-forecasting
- other
---
<!-- NOTE: Math formatting convention: inline math uses \\( ... \\) and math blocks use $$ ... $$. -->
# ClimX: a challenge for extreme-aware climate model emulation
ClimX is a challenge about building **fast and accurate machine learning emulators** for the NorESM2-MM Earth System Model, with evaluation focused on **climate extremes** rather than mean climate alone.
## Dataset summary
This dataset contains the **full-resolution** ClimX data in **NetCDF-4** format (targets + forcings, depending on split) with a native grid of \\(192 \times 288\\) (about \\(1^\circ\\)) resolution. It also contains the **lite-resolution** version, with a native grid of \\(12 \times 18\\) (about \\(16^\circ\\)) resolution:
- **Lite-resolution**: <1GB, \\(16\times\\) spatially coarsened, meant for rapid prototyping.
- **Full-resolution**: ~200GB, full-resolution data for large-scale training.
## What you will do (high level)
You train an emulator that predicts **daily** 2D fields for 7 surface variables:
- `tas`, `tasmax`, `tasmin`
- `pr`, `huss`, `psl`, `sfcWind`
However, the **benchmark targets are 15 extreme indices** derived from daily temperature and precipitation (ETCCDI-style indices). The daily fields are an **intermediate output** your emulator produces (useful for diagnostics and for computing the indices).
Conceptually:
$$
x_t = g(f_t, f_{t-1}, \dots, f_{t-\alpha}, x_{t-1}, x_{t-2}, \dots, x_{t-\beta})
$$
where \\(f_t\\) are forcings (greenhouse gases + aerosols) and \\(x_t\\) is the climate state.
## Dataset structure
### Spatial and temporal shape
Full-resolution daily fields:
- **Historical**: `lat: 192, lon: 288, time: 60224`
- **Projections**: `lat: 192, lon: 288, time: 31389`
### Splits and scenarios (official challenge setup)
Training uses historical + several SSP scenarios; testing is on the held-out **SSP2-4.5** scenario:
- **Train**: historical (1850–2014) + `ssp126`, `ssp370`, `ssp585` (2015–2100)
- **Test (held-out)**: `ssp245` (2015–2100)
To avoid leakage, **targets for `ssp245` are withheld** in the official evaluation; only the **forcings** are provided for that scenario. The full outputs will be released after the competition.
## Evaluation metric
The primary leaderboard metric is the region-wise **normalized Nash–Sutcliffe efficiency (nNSE)**, averaged over 15 climate extreme indices.
For each index \\(v\\), grid cell \\((i,j)\\), a validity mask \\(\mathcal{V}\\) excludes cells with negligible temporal variability. Cell-level \\(R^2\\) and nNSE are:
$$
R^2_{ij} = 1 - \frac{\mathrm{MSE}_{ij}}{\mathrm{Var}_t(gt_{ij})}, \qquad \mathrm{nNSE}_{ij} = \frac{R^2_{ij}}{2 - R^2_{ij}}
$$
For each AR6 land region \\(k\\), the area-weighted regional score is:
$$
\mathrm{nNSE}_{kv} = \frac{\sum_{(i,j)\in k \cap \mathcal{V}} \cos\phi_i \, \mathrm{nNSE}_{ij}}{\sum_{(i,j)\in k \cap \mathcal{V}} \cos\phi_i}
$$
The final score averages uniformly over valid regions and indices:
$$
S = \frac{1}{|V|} \sum_{v \in V} \frac{1}{|K_v|} \sum_{k \in K_v} \mathrm{nNSE}_{kv}
$$
\\(S=1\\) is perfect agreement, \\(S=0\\) corresponds to a mean predictor, and \\(S \to -1\\) is pathological.
## How to load the data
This dataset is distributed as **NetCDF-4** files. There are two common ways to load it.
### Option 1 (recommended): clone the ClimX code and use the helper loader
The ClimX repository already includes a helper module (`src/data/climx_hf.py`) that allows you to download the dataset from Hugging Face and open it as three lazily-loaded “virtual” xarray datasets:
```bash
git clone https://github.com/IPL-UV/ClimX.git
cd ClimX
pip install -U "huggingface-hub" xarray netcdf4 dask
```
```python
from src.data.climx_hf import download_climx_from_hf, open_climx_virtual_datasets
# Download NetCDF artifacts from HF into a local cache directory.
root = download_climx_from_hf("/path/to/hf_cache", variant="full")
# Open as three virtual datasets (lazy / dask-friendly).
ds = open_climx_virtual_datasets(root, variant="full") # or "lite"
ds.hist # historical (targets + forcings)
ds.train # projections training scenarios (targets + forcings; excludes `ssp245` scenario)
ds.test_forcings # `ssp245` scenario forcings only (no targets)
```
### Option 2: download NetCDFs and open with xarray directly
You can also download files from Hugging Face and open them with **xarray**.
Example:
```python
from huggingface_hub import hf_hub_download
import xarray as xr
path = hf_hub_download(
repo_id="isp-uv-es/ClimX",
repo_type="dataset",
filename="PATH/TO/A/FILE.nc", # replace with an actual file in this dataset repo
)
ds = xr.open_dataset(path)
print(ds)
```
## Links
- [Kaggle competition](https://www.kaggle.com/competitions/climx)
- [Full dataset (this page)](https://huggingface.co/datasets/isp-uv-es/ClimX)
- [Public code repository (challenge materials)](https://github.com/IPL-UV/ClimX)
- [Website](https://ipl-uv.github.io/ClimX/)
## License and usage
The dataset is released under **MIT**. In addition, if you are participating in the ClimX competition, please follow the competition rules (notably: restrictions on external climate training data and redistribution of competition data). |