xai-cons

name

xai-cons

description

Perform observational constraint (Emergent Constraint, EC) analysis for climate research. Use historical observations to constrain future climate projections from CMIP6 multi-model ensembles, reducing prediction uncertainty. Includes inter-model regression analysis, EC relationship establishment, physical mechanism diagnostics (residual analysis, teleconnection pathways, Walker circulation, lead-lag correlation, SVD), uncertainty quantification (variance reduction, confidence intervals), and reliability assessment (binning analysis, random EC comparison). Use when conducting observational constraint analysis, CMIP multi-model evaluation, reducing prediction uncertainty, validating inter-model relationships, or climate teleconnection research. Applicable to any climate variable pairs (e.g., SST-TAS, precipitation-circulation).

XAI-Cons: Observational Constraint Analysis

Overview

This skill specializes in Observational Constraint (Emergent Constraint, EC) analysis - a powerful method that leverages inter-model differences and historical observations to reduce uncertainty in future climate projections.

Core Principle: If a significant statistical relationship exists between a historical variable (x_hist) and a future prediction variable (y_future) across multiple climate models, then observations of x_hist can "constrain" predictions of y_future, thereby narrowing the uncertainty range.

Mathematical Expression:

y_future = β × x_historical + ε

Where:

• x_historical: Historical simulation variable (e.g., 1980-2014 South Atlantic SST)
• y_future: Future prediction target (e.g., 2041-2060 East Asia temperature)
• β: Regression slope (EC sensitivity)
• ε: Residual

Three Core Steps:

1. Establish Emergent Relationship - Significant inter-model correlation (p<0.05)
2. Physical Mechanism Validation - Verify physical linkage through diagnostics
3. Constraint Quality Assessment - Evaluate using RRV, RMSE, CRPS, etc.

For detailed methodology → See references/methods.md

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When to Use This Skill

Automatic Trigger Keywords:

Method-related:

• "observational constraint", "emergent constraint", "EC analysis"
• "inter-model regression", "reduce uncertainty", "constrain prediction"
• "CMIP6 evaluation", "multi-model analysis"

Research content:

• "South Atlantic", "East Asia", "teleconnection"
• "SST-TAS relationship", "sea surface temperature"
• "Walker circulation", "atmospheric wave train"
• "residual analysis", "remove global signal"

Analysis tasks:

• "evaluate EC reliability", "calculate variance reduction"
• "lead-lag correlation", "SVD covariance analysis", "binning analysis"

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Core Functionality

1. EC Relationship Establishment

Tasks:

• Load CMIP6 multi-model historical and future data
• Calculate regional averages (e.g., South Atlantic SST, East Asia TAS)
• Perform inter-model linear regression
• Statistical tests (R², correlation r, p-value)
• Plot scatter + regression line + observational constraint point

Key Outputs:

• Regression coefficient β (EC sensitivity)
• R² (explained variance)
• Correlation coefficient r and p-value
• Constrained prediction value and uncertainty range

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2. Reliability Assessment ⭐

Tasks:

• Use binning analysis to evaluate EC significance
• Compare with random EC to exclude spurious correlations
• Calculate credibility for different correlation strengths
• Generate prior/posterior distribution comparison

Why Important: Not all statistically significant correlations are reliable ECs! Need to assess:

1. Is correlation coefficient strong enough (typically r>0.3)?
2. Better than random correlations?
3. Does constraint actually reduce uncertainty (positive variance reduction)?

Reference Code:

• scripts/examples/src/binning_inference.py ⭐ Core method
• scripts/examples/src/plot_random_EC.py - Random EC comparison
• scripts/examples/src/plot_PDF_ECS.py - Probability distributions

Key Outputs:

• 66% and 90% confidence intervals
• Prior/posterior distributions
• Variance reduction percentage
• Comparison with random ECs

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3. Physical Mechanism Diagnostics

3a. Residual Analysis

Purpose: Answer "Why this region?"

Tasks:

• Remove linear influence of global warming signal
• Calculate residual: residual = SA_SST - (β·Global_SST + intercept)
• Verify residual uncorrelated with global SST (r≈0)
• Analyze spatial correlation between residual and global temperature field
• Identify unique teleconnection patterns and wave train pathways

Key Outputs:

• Spatial correlation maps
• Wave train pathway identification
• Percentage of significantly correlated regions

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3b. Mediation Analysis

Example: Walker circulation

Tasks:

• Calculate mediator variable (e.g., Walker circulation index)
• Analyze mediation effects:
  • Path a: predictor → mediator
  • Path b: mediator → response
  • Path c': partial correlation controlling for mediator
• Calculate mediation percentage

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3c. Spatiotemporal Diagnostics

Lead-lag Correlation:

• Calculate correlation at different time lags (±5 years)
• Determine causal temporal relationship

SVD Covariance Analysis:

• Identify coupled spatial modes
• Confirm large-scale patterns, not point-to-point artifacts

Spatial Regression:

• Regression at each global grid point
• Spatial distribution of regression coefficients
• Significance testing

Composite Analysis:

• High SST vs Low SST groups (top/bottom 1/3 of models)
• Temperature difference fields
• t-test significance

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4. Uncertainty Quantification

Tasks:

• Calculate prior (unconstrained) uncertainty
• Calculate posterior (constrained) uncertainty
• Variance reduction: VR = 1 - (σ_posterior / σ_prior)²
• Confidence interval calculation

Key Metrics:

• Variance reduction percentage (should be positive)
• 66% confidence interval width
• 90% confidence interval width
• Reliability rating

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Quick Start Examples

Example 1: Basic EC Analysis

User says:

"Perform observational constraint analysis for South Atlantic SST
and East Asia temperature using CMIP6 data,
historical period 1980-2014, future period 2041-2060"

Skill executes:

1. Reference code in scripts/examples/
2. Generate scripts adapted to your data
3. Perform inter-model regression
4. Calculate statistics
5. Plot EC scatter diagram
6. Apply observational constraint

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Example 2: Reliability Assessment

User says:

"My EC relationship is r=0.39, R²=0.15, p=0.006.
Use binning method to evaluate if this EC is reliable"

Skill executes:

1. Apply logic from binning_inference.py
2. Generate binning analysis code for your data
3. Calculate credibility at different correlation strengths
4. Compare your r=0.39 with distribution
5. Provide reliability assessment

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Example 3: Physical Mechanism Diagnostics

User says:

"Analyze South Atlantic SST's unique teleconnection.
After removing global warming signal,
check if still correlated with East Asia"

Skill executes:

1. Calculate South Atlantic SST residual (remove global SST influence)
2. Verify residual uncorrelated with global SST
3. Calculate spatial correlation between residual and global temperature
4. Identify significantly correlated regions
5. Search for wave train pathways
6. Generate comprehensive analysis figure

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Available Resources

Core Tools (scripts/examples/src/)

binning_inference.py ⭐⭐⭐

• Gold standard for EC reliability assessment
• Applicable to any EC relationship
• Directly usable (adjust data paths)

tools.py

• Statistical analysis utilities
• Plotting tools
• Histograms, modal analysis, etc.

plot_PDF_ECS.py

• Probability density function visualization
• Prior/posterior distribution comparison

plot_random_EC.py

• Random EC generation and comparison
• Significance assessment

Literature Examples

Bonan et al. (2025) Nature Geoscience

• EC analysis of ocean overturning circulation
• Physical constraint methodology

Kornhuber et al. (2024) PNAS

• Complete EC analysis case study
• Full workflow from data to figures

Pakistan Case Study

• Multi-level diagnostic analysis
• EOF, MCA, composite analysis
• Publication-ready figure standards

Documentation:

• scripts/examples/README.md - Detailed example code documentation
• references/methods.md - Comprehensive methodology and theory
• references/workflow.md - Step-by-step analysis workflow (if available)
• references/best_practices.md - Best practices and FAQ (if available)

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Key Statistical Thresholds

EC Reliability Assessment Standards:

Metric	Excellent	Good	Acceptable	Weak
Correlation r	>0.6	0.4-0.6	0.3-0.4	<0.3
R²	>0.36	0.16-0.36	0.09-0.16	<0.09
p-value	<0.01	0.01-0.03	0.03-0.05	>0.05
Variance Reduction	>50%	30-50%	10-30%	<10% or negative

Note:

• These are empirical thresholds, not absolute standards
• Must combine with binning analysis for comprehensive judgment
• Physical mechanism support is crucial

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Best Practices

✅ Recommended:

1. Always assess reliability

   • Don't rely solely on p-values
   • Must perform binning analysis
   • Check if variance reduction is positive

2. Seek physical mechanisms

   • EC relationship must have physical explanation
   • Pure statistical correlation insufficient for publication
   • Use multiple diagnostic methods for cross-validation

3. Sensitivity testing

   • Different time periods
   • Different region definitions
   • Different observational datasets

❌ Avoid These Pitfalls:

1. Cherry-picking - Testing many variable pairs and only reporting significant ones
2. Ignoring uncertainty - Constrained range may still be large
3. Over-interpreting weak correlations - r<0.3 rarely reliable
4. Ignoring model dependence - Outlier models may drive correlation

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Technical Stack

Required Python Packages:

• numpy - Numerical computation
• scipy - Statistical analysis
• matplotlib - Plotting
• xarray - Multi-dimensional data (NetCDF)
• netCDF4 - NetCDF file I/O

Recommended Packages:

• pandas - Tabular data
• cartopy - Map plotting
• seaborn - Advanced visualization
• statsmodels - Advanced statistics

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Output Deliverables

Figures:

Main Figures:

1. EC scatter plot (regression line + observational constraint)
2. Spatial regression pattern map
3. Physical mechanism composite figure (4-6 panels)

Supplementary Figures:

1. Binning analysis plot
2. Residual teleconnection map
3. Lead-lag correlation curve
4. SVD covariance pattern
5. Walker circulation mediation effects
6. Composite analysis

Data:

• Regression statistics (β, R², r, p)
• Pre/post constraint predictions and uncertainties
• Variance reduction percentage
• Binning analysis statistics
• Physical diagnostic metrics

Text:

• Methods section draft
• Results section draft
• Supplementary materials description
• Reviewer response templates

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Related Resources

Internal:

• scripts/examples/README.md - Example code documentation
• scripts/examples/src/ - Core utility functions
• scripts/examples/*.ipynb - Literature implementation examples
• scripts/examples/Pakistan/ - Complete case study

External References:

• Hall et al. (2019). "Progressing emergent constraints on future climate change". Nature Climate Change.
• Brient, F. (2020). "Evaluating the robustness of emergent constraints".
• Cox et al. (2018). "Emergent constraint on ECS from global temperature variability". Nature.

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Last Updated: 2025-10-27 Version: 1.0 Maintainer: Climate-AI Research Team Based On: South Atlantic - East Asia Teleconnection Research