Climate Data Decision Tree

Overview

This page provides guidance for selecting the appropriate type of climate data for a given application.

Summary

Station observations (in situ): Best for local truth at point locations (e.g., a substation, a met mast, a river gauge) and for direct validation of any gridded dataset.

Reanalysis: Best for spatially complete, temporally consistent, multivariate historical fields.

Global climate model (GCM) projections: Best for characterizing large‑scale climate change signals. Not designed to reproduce the exact timing of historical weather.

Regional climate model (RCM) projections: Best when regional processes/topography/coasts matter (extreme precipitation, wind regimes, snow processes) and when decision scales require finer spatial detail than a GCM. Not designed to reproduce the exact timing of historical weather.

Bias‑adjusted climate projections (bias-adjusted GCM/RCM): Best when an application is bias‑sensitive and one needs locally plausible distributions for impact models. Not designed to reproduce the exact timing of historical weather.

“Rule of thumb” for choosing data

• If you need observed historical frequency/intensity at a site (e.g., design‑value validation), start with station data. If trends/extremes are important, make sure to use homogenized station data.

• If you need spatially complete, multivariate, hourly‑to‑daily historical forcing for system models (e.g. wind/solar generation, demand models spanning a service territory), begin with reanalysis, then validate and apply corrections if necessary.

• If you need future climate change information, use RCMs if the decision depends on regional detail (e.g. complex terrain, coastal winds, convective precipitation, snow), otherwise use GCMs.

• Use bias-adjusted datasets when the impact model is demonstrably bias‑sensitive (e.g., degree‑day thresholds, heat‑stress exceedances, precipitation intensity‑duration metrics).

Decision Tree

The figure below provides a high-level guide for selecting climate datasets based on whether the application is focused on historical conditions or future climate change, the need for spatial completeness, physical consistency, local detail, and bias sensitivity.

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flowchart TD

    A([Define decision context]) --> B{Need future climate change?}

    %% Historical / baseline branch
    B -- No --> C{Need spatially complete gridded fields?}
    C -- No --> D{Do you have a reliable observation station nearby?}
    
    D -- Yes --> DD[Station observational data] 
    D -- No --> F[Reanalysis data]
    
    C -- Yes --> E{Need multivariate physical consistency?}
  
    E -- Yes --> F[Reanalysis data]
    E -- No --> T{Need complex variables?}

    T -- Yes --> F[Reanalysis data]
    T -- No --> U{In a region with a dense observational network?}

    U -- No --> F[Reanalysis data]
    U -- Yes --> V[Spatially interpolated observations]

    %% Future / planning branch
    B -- Yes --> K{Is the application bias- or threshold-sensitive?}

    K -- No --> H{Is regional detail important?}
    K -- Yes --> M{Does the application rely on more than one variable?}

    H -- No --> I[GCM projections ensemble]
    H -- Yes --> J[RCM projections ensemble]

    M -- No --> N[Bias-adjusted projections]
    M -- Yes --> O{Is the physical consistency between the variables important?}

    O -- No --> N[Bias-adjusted projections]
    O -- Yes --> S[Multivariate bias-adjusted projections]

    %% Reanalysis bias check
    %% F --> P{Bias too large for decision metric?}
    %% P -- No --> Q[Proceed with reanalysis]
    %% P -- Yes --> R[Apply targeted bias correction]

    %% Styling
    classDef start fill:#f5f5f5,stroke:#333,stroke-width:1.5px;
    classDef obs fill:#dceeff,stroke:#4a78a8,stroke-width:1px;
    classDef rean fill:#e3f7df,stroke:#5a8f5a,stroke-width:1px;
    classDef proj fill:#ffe9cc,stroke:#c48a3a,stroke-width:1px;
    classDef adjust fill:#f3e5ff,stroke:#8a63b8,stroke-width:1px;
    classDef decision fill:#ffffff,stroke:#555,stroke-width:1.5px;

    class A start;
    class B,C,E,H,K,M,O,U,T decision;
    class D,V obs;
    class F rean;
    class I,J proj;
    class N,S adjust;

Climate Projection Data Decision Tree

A first AI generated, still stupid draft!

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flowchart TD

    A["Start: What type of question?"]
    
    A --> B{"Process understanding or applied impact study?"}
    
    %% PROCESS BRANCH
    B -->|Process understanding| C{"Spatial scale needed?"}
    C -->|Continental / large-scale| D{"Need to quantify internal variability?"}
    C -->|Regional / local detail| E{"Need strong physical consistency?"}
    
    D -->|Yes| F["CanLEAD"]
    D -->|No| G["CMIP6"]
    
    E -->|Yes| H["CORDEX-NA"]
    E -->|No| I{"Willing to bias-correct yourself?"}
    
    I -->|Yes| H
    I -->|No| J{"Geographic domain?"}
    
    J -->|Canada| K["CanDCS-M6"]
    J -->|North America| L["ESPO-G6"]
    
    %% IMPACT BRANCH
    B -->|Applied impact / thresholds| M{"Do absolute values and thresholds matter?"}
    
    M -->|No (relative change ok)| C
    M -->|Yes| N{"Geographic domain?"}
    
    N -->|Canada| K
    N -->|North America| L
    N -->|Global| O["NEX-GDDP-CMIP6"]
    
    %% UNCERTAINTY REFINEMENT
    K --> P{"Need larger model spread?"}
    L --> P
    O --> P
    
    P -->|Yes| Q["Consider CMIP6 or CORDEX alongside"]
    P -->|No| R["Use selected dataset"]
    
    G --> S{"Need regional refinement later?"}
    S -->|Yes| H
    S -->|No| R
    
    F --> R
    H --> R