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# Ensemble Analysis
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Multi-model climate ensemble processing and analysis tools for climate projection assessment and uncertainty quantification. Essential for climate change impact studies and decision-making under uncertainty.
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## Capabilities
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### Ensemble Creation and Management
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Tools for creating and managing climate model ensembles.
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```python { .api }
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def create_ensemble(datasets, multiindex=True, resample_freq=None, **kwargs):
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    """
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    Create ensemble dataset from multiple climate model datasets.
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    Parameters:
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    - datasets: list or dict, climate model datasets to combine
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    - multiindex: bool, use multiindex for ensemble dimensions
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    - resample_freq: str, optional resampling frequency
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    - **kwargs: additional ensemble creation parameters
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    Returns:
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    xr.Dataset: Combined ensemble dataset
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    """
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def ensemble_mean_std_max_min(ens, weights=None, **kwargs):
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    """
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    Calculate ensemble statistics (mean, std, max, min).
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    Parameters:
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    - ens: xr.Dataset, ensemble dataset
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    - weights: xr.DataArray, optional weights for ensemble members
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    - **kwargs: additional statistical parameters
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    Returns:
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    xr.Dataset: Ensemble statistics
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    """
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def ensemble_percentiles(ens, values=[10, 25, 50, 75, 90], weights=None, **kwargs):
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    """
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    Calculate ensemble percentiles.
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    Parameters:
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    - ens: xr.Dataset, ensemble dataset
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    - values: list, percentile values to calculate (0-100)
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    - weights: xr.DataArray, optional weights for members
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    - **kwargs: additional parameters
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    Returns:
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    xr.Dataset: Ensemble percentiles
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    """
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```
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### Ensemble Agreement and Robustness
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Methods for assessing ensemble agreement and robustness of climate projections.
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```python { .api }
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def robustness_fractions(ens, test="threshold", **kwargs):
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    """
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    Calculate robustness fractions for ensemble agreement.
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    Parameters:
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    - ens: xr.Dataset, ensemble dataset
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    - test: str, robustness test ("threshold", "sign", "range")
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    - **kwargs: test-specific parameters
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    Returns:
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    xr.Dataset: Robustness fraction statistics
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    """
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def change_significance(ens, baseline_period, projection_period, **kwargs):
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    """
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    Test significance of projected changes across ensemble.
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    Parameters:
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    - ens: xr.Dataset, ensemble dataset
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    - baseline_period: slice, baseline time period
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    - projection_period: slice, projection time period  
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    - **kwargs: significance test parameters
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    Returns:
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    xr.Dataset: Change significance statistics
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    """
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```
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## Usage Examples
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```python
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import xarray as xr
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import xclim.ensembles as xce
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# Load multiple model datasets
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models = [
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    xr.open_dataset(f"model_{i}.nc") for i in range(1, 11)
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]
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# Create ensemble
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ensemble = xce.create_ensemble(models, multiindex=True)
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# Calculate ensemble statistics
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ens_stats = xce.ensemble_mean_std_max_min(ensemble)
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ens_percentiles = xce.ensemble_percentiles(ensemble, values=[10, 50, 90])
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# Assess robustness
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robustness = xce.robustness_fractions(ensemble, test="threshold")
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```