Comprehensive User Guide

Introduction

This dashboard provides interactive access to climate projections and observational data for Santa Barbara County. You can explore location-specific time series, compare different climate models and scenarios, analyze spatial patterns, and draw custom regions for aggregated analysis.

Page-by-Page Guide

📍 Gridbox Map Page

Selecting a Gridbox

There are two ways to select a gridbox:

• Click any grid cell directly on the map (the selected cell will be highlighted with a red border)
• Use the 'Select Gridbox' dropdown menu to choose from 762 available locations

Progressive Dropdown Sequence

Once a gridbox is selected, use the progressive dropdowns to specify your data:

1. Data Type: Choose 'Projection' for future climate projections (1950-2100) or 'Observed' for historical data (1950-2013 from Livneh dataset)
2. Model: For projections, select from 15 CMIP6 global climate models (e.g., ACCESS-CM2, CESM2-LENS, GFDL-ESM4). For observed data, this is automatically set to 'Observed'
3. Variable: Select the climate variable (temperature, precipitation, humidity, radiation, wind speed)
4. Scenario: Choose emission scenario (SSP2-4.5, SSP3-7.0, or SSP5-8.5). Note: Not all models have all scenarios available
5. Frequency: Choose data aggregation: Daily (raw daily values), Monthly (monthly averages/totals), or Annual (yearly averages/totals)

Understanding the Time Series Plot

• X-axis shows years from 1950 to 2100 (or 1950-2013 for observational data)
• Y-axis shows values in converted units (°F for temperature, inches or inches/day for precipitation)
• Hover over any point to see the exact value and year
• A trend line is automatically calculated and displayed
• The plot title shows the selected gridbox coordinates (latitude, longitude)

Additional Traces Mode

Compare multiple datasets on the same plot:

1. Click 'Enable Additional Traces' button to activate multi-trace mode
2. Use the additional progressive dropdowns to select more data combinations
3. Each trace appears in a different color with its own legend entry
4. Click legend entries to toggle traces on/off
5. Double-click a legend entry to isolate that trace
6. Up to 6 additional traces can be added for comprehensive comparison

Interactive Shapefile Drawing Tool (Loop Traces)

Analyze climate data across custom regions:

1. Click the 'Draw Polygon' button to activate drawing mode
2. Click multiple points on the map to define your region of interest
3. Double-click to close the polygon (or click near the first point)
4. The dashboard automatically identifies all gridboxes within your polygon
5. Climate data is extracted for each enclosed gridbox and averaged
6. A new trace appears on the plot showing the regional average
7. Name your custom region using the 'Loop Name' input field
8. Draw multiple regions to compare different areas of Santa Barbara County

How it works: The tool uses point-in-polygon algorithms to determine which gridboxes fall within your drawn shape. For each gridbox, it extracts the full time series, then computes the spatial average across all enclosed gridboxes. This provides a representative climate signal for your custom region.

Mean Absolute Difference (MAD) Analysis

Compare how different climate models agree or disagree:

• MAD analysis appears automatically when comparing models
• Shows the average absolute difference between two model time series
• Lower MAD values indicate better model agreement
• Useful for assessing model uncertainty and identifying reliable projections

🗺️ Variable Maps Page

What Variable Maps Show

Variable maps display the projected change in climate variables across Santa Barbara County. Each gridbox shows the difference between a future time period and a historical baseline (1981-2010), allowing you to see spatial patterns of climate change.

Selecting Map Data

1. Data Type: Choose 'Projection' for model data or 'Observed' for historical trends
2. Model: Select which climate model to display (for projection data only)
3. Variable: Choose the climate variable to map
4. Scenario: Select the emission scenario (only scenarios available for your chosen model/variable combination will appear)

Time Range Selection

• Use the 'Start Year' and 'End Year' sliders to define your analysis period
• End Year automatically adjusts to be at least one year after Start Year
• The map shows the average change during this period compared to 1981-2010 baseline

Change Type Options

• Absolute Change: Shows the raw difference in units (e.g., +3.5°F warming, +2 inches more rain)
• Percentage Change: Shows the relative change (e.g., +15% more precipitation). Most useful for precipitation and humidity variables

Understanding the Color Scale

• For temperature and most variables: Red indicates increase, blue indicates decrease
• For precipitation: Blue indicates increase (more rain), red indicates decrease (less rain)
• Darker colors indicate larger magnitude changes
• Hover over any gridbox to see the exact change value and location

How Variable Maps Are Created

Data Processing Steps:

1. The dashboard loads climate data for all 762 gridboxes covering Santa Barbara County
2. For each gridbox, daily data is aggregated to annual values (averages for temperature, totals for precipitation)
3. A baseline climatology is calculated by averaging values from 1981-2010
4. For your selected time period, annual values are averaged
5. The change is calculated: Future Average - Historical Baseline
6. For percentage change: ((Future - Baseline) / Baseline) × 100
7. All gridboxes are plotted on the map with colors representing the magnitude of change

Data Calculations & Unit Conversions

Temperature Conversions

• Projection Data: Original units are Kelvin. Converted to Fahrenheit: (K - 273.15) × 9/5 + 32
• Observational Data: Original units are Celsius. Converted to Fahrenheit: C × 9/5 + 32
• All temperature plots and maps display values in °F for user convenience

Precipitation Conversions

• Projection Data: Original units are kg m⁻² s⁻¹ (mass flux). Converted to inches/day: × 86400 / 25.4
• Observational Data: Original units are mm. Converted to inches: / 25.4
• For monthly/annual aggregations, daily values are summed to give total inches
• Daily precipitation plots show inches/day; monthly and annual show total inches

Time Aggregations

• Daily: Raw daily values from the dataset, no aggregation performed
• Monthly: Daily values averaged (temperature) or summed (precipitation) for each month
• Annual: Daily values averaged (temperature) or summed (precipitation) for each year
• For spatial maps, only annual aggregation is used to capture long-term trends

Trend Calculations

Trend lines on time series plots are calculated using linear regression:

• The slope represents the rate of change per year
• For temperature: slope in °F/year shows warming or cooling rate
• For precipitation: slope in inches/year shows increasing or decreasing precipitation
• R² values (when displayed) indicate how well the trend fits the data

Tips and Best Practices

• Compare multiple models to assess uncertainty—different models may show different magnitudes of change
• Use observational data (1950-2013) to validate model performance during the historical period
• For precipitation, annual or monthly aggregation provides more reliable trends than daily data
• SSP2-4.5 represents moderate emissions, SSP3-7.0 represents medium-high, SSP5-8.5 represents very high emissions
• The interactive shapefile drawing tool is ideal for analyzing specific watersheds, cities, or regions of interest
• Download plot images using the camera icon in the top-right of any plot
• When comparing models, pay attention to both the mean change and the spread across models

Troubleshooting

No data appears after selection

• Some model/scenario combinations may not be available. Try a different scenario or model
• Ensure you've completed all required dropdown selections
• Check that your selected time range is valid (Start Year < End Year)

Map takes a long time to load

• Variable maps process data for all 762 gridboxes, which can take 15-30 seconds
• Wait for the loading indicator to complete before adjusting settings
• Avoid rapid changes to the time sliders; wait for previous calculations to finish

Plot appears cluttered with many traces

• Click legend entries to temporarily hide traces
• Double-click a single legend entry to isolate that trace
• Consider using separate analysis sessions for different comparison groups

Comprehensive Climate Data Glossary

This glossary defines key terms, datasets, models, and methods used throughout the dashboard.

Data Sources & Datasets

Cal-Adapt

Cal-Adapt encompasses a range of research and development efforts designed to provide access to California climate data. Each component of Cal-Adapt serves a specific purpose within the broader mission. As Cal-Adapt evolves and expands, it aims to support California's Climate Change Assessments and offer a more comprehensive and powerful solution for technical and data-intensive needs.

LOCA2-Hybrid

The most recent hybrid-statistical downscaling product developed for California's Fifth Climate Change Assessment. LOCA2 statistically downscales CMIP6 global climate model outputs by calibrating them against regional observations, providing more accurate results at the local scale. This dashboard uses LOCA2-Hybrid downscaled data for all projection scenarios.

Livneh Dataset

Gridded observational dataset providing daily temperature and precipitation data from 1950-2013. This historical reconstruction combines weather station observations with gridded interpolation to provide spatially continuous observational data for model validation.

Reanalysis Datasets (Historical Reconstruction)

Reconstructions of the historical weather record that combine model data with historical observational data. Reanalysis products synthesize disparate sources of observational data and use an atmospheric model to produce a spatiotemporally continuous, self-consistent dataset, avoiding the gaps and data collection inconsistencies in weather station data. Like a climate model, reanalysis products have a complete set of atmospheric and surface weather variables on a full spatial grid.

NetCDF

Network Common Data Form—a set of interfaces for array-oriented scientific data. NetCDF files are the standard format for climate model output and gridded observational datasets.

Climate Models & Methods

Global Climate Model (GCM)

A mathematical model that represents the physical processes in the atmosphere, ocean, cryosphere, and land surface. GCMs are used to simulate the climate system and predict future climate conditions based on different scenarios of greenhouse gas emissions and other factors. GCMs produce physically consistent simulations of climate over historical and future periods.

CMIP6 (Coupled Model Intercomparison Project, Phase 6)

A coordinated international effort to produce climate model output with consistent standards in areas such as variable naming and experimental design. This consistency allows different models to be compared to each other more easily. CMIP6 is the latest generation of global climate models (ca. 2020), used in the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6), and in California's Fifth Climate Change Assessment. This dashboard includes 15 CMIP6 models.

Historical Runs from Global Climate Models

Simulations that use the historical record of greenhouse gas concentrations as inputs. Using combined simulations of the atmosphere, ocean, and land surface, GCMs produce a physically consistent timeline of plausible climate conditions over the historical period. Unlike reanalysis data, these simulations do not reproduce specific events from the historical record, but instead represent the general conditions during that time period.

Downscaled Simulations

Regional simulations over the Western United States and California, produced by downscaling Global Climate Models from CMIP6. These downscaled simulations were created in support of California's Fifth Climate Change Assessment.

Statistical Downscaling

A method to generate fine scale spatial resolution data outputs from coarser scale global climate models using statistical relationships between the coarse global climate model output and observed climatological conditions at fine scale spatial resolutions. LOCA2-Hybrid uses statistical downscaling to produce high-resolution climate projections.

Dynamical Downscaling

A method to generate fine scale spatial resolution data from coarse scale global climate models using a dynamical regional weather model. The regional model uses the global climate model output as the inputs to generate additional projections. The Analytics Engine hosts dynamically downscaled model output created using the Weather Research and Forecasting (WRF) model.

Climate Sensitivity

A measurement that describes the equilibrium change in global mean surface temperature resulting from a doubling of atmospheric carbon dioxide (CO₂) concentrations compared to pre-industrial levels (1850-1900). Different climate models have different climate sensitivities, contributing to projection uncertainty.

Climatology

The long-term average or baseline conditions for a given climate variable (such as temperature or precipitation) over a specified historical period. These values are typically calculated from multi-year datasets and are used as a reference point to assess changes, anomalies, or trends in future climate projections. Best scientific practice is to use a 30-year period to ensure the reference period captures a range of variability. This dashboard uses 1981-2010 as the baseline climatology.

Simulations & Uncertainty

Simulation

A computational process used to model and analyze the behavior of complex systems by representing them through mathematical models. Simulations involve running climate models on different parameters to predict future climate conditions based on various scenarios. In the context of this dashboard, a simulation is a specific ensemble member from a GCM.

Ensemble Member

A single simulation or GCM run within a larger set of simulations (referred to as an ensemble) that are generated to assess the uncertainty and variability in climate model projections. Each ensemble member is typically produced by slightly varying the initial conditions, model parameters, or by using different models altogether. Using multiple ensemble members helps account for the natural variability of the climate system and the uncertainties inherent within model predictions.

Model Run

Refers to data from different initial-condition ensemble runs of a GCM. Climate models are often run multiple times with slightly different initial conditions (ensemble members), and each of these runs is termed as a 'model run'. For instance, the dashboard contains LOCA2-Hybrid data for different model runs. ACCESS-CM2 r1i1p1f1 refers to one model run while ACCESS-CM2 r2i1p1f1 is another model run.

Model Uncertainty

Uncertainty in global climate model output that arises from design differences between models. Global climate models are developed by different research institutions and differ in how they represent the global climate system. In some approaches, it may be appropriate to average responses from different models to obtain a consensus model estimate (referred to as a multi-model mean). Alternatively, it may be informative to look at the spread of a particular response across multiple models.

Scenario Uncertainty

Uncertainty that arises from not knowing how people, policies, the economy, and technology will evolve in the future to address the issue of climate change. Projecting levels of future emissions requires inherent assumptions about economic production, land use, technological advancements, and energy use that create differences in the timing and strength of climate response between scenarios.

Scenarios & Projections

Shared Socioeconomic Pathways (SSPs)

Inputs into the latest generation of IPCC reports which describe potential pathways the world could take in terms of features such as political, economic, and other societal dynamics and choices which impact greenhouse gas emissions, anthropogenic aerosol generation, and land use changes. SSPs are an update to the "Representative Concentration Pathways" (RCPs) used in an earlier CMIP phase. This dashboard includes three SSP scenarios:

• SSP2-4.5: Intermediate emissions scenario representing moderate climate policy
• SSP3-7.0: Medium-high emissions scenario with limited climate policy
• SSP5-8.5: Very high emissions scenario representing fossil fuel-intensive development

Projection

A potential future evolution of a quantity or set of quantities, often associated with climate variables such as temperature or sea level. Projections are typically based on simulations produced by climate models, assuming specific scenarios of greenhouse gas emissions, land use, and other factors. Unlike predictions, projections do not imply certainty but rather illustrate a range of possible outcomes based on different assumptions and conditions. Projections are used to explore the potential impacts of climate change under various future pathways.

Climate Variables & Units

Temperature Variables

• tasmin: Daily minimum near-surface air temperature (2m above ground). Projection data in Kelvin, converted to °F for display
• tasmax: Daily maximum near-surface air temperature (2m above ground). Projection data in Kelvin, converted to °F for display
• obs_tmin / obs_tmax: Observed minimum/maximum temperature from Livneh dataset. Original units in Celsius, converted to °F

Precipitation Variables

• pr: Precipitation rate (projection data). Original units: kg m⁻² s⁻¹. Converted to inches/day for daily data, total inches for monthly/annual
• obs_pr: Observed precipitation from Livneh dataset. Original units: mm. Converted to inches (÷ 25.4)

Humidity Variables

• hursmax / hursmin: Maximum and minimum near-surface relative humidity (%)
• huss: Near-surface specific humidity (kg/kg) - the mass ratio of water vapor to total air mass

Other Variables

• rsds: Surface Downwelling Shortwave Radiation (W/m²) - solar energy reaching Earth's surface
• wspeed: Near-surface wind speed (m/s) - can be converted to mph by multiplying by 2.237

Analysis Methods

Mean Absolute Difference (MAD)

A statistical measure comparing the differences between two datasets, commonly used to assess model agreement. MAD is calculated as the average of the absolute differences between corresponding values in two time series. Lower MAD values indicate better agreement between models or between a model and observations. This dashboard displays MAD when comparing different models or scenarios.

Anomaly Calculations

The difference between an observed or projected value and a baseline climatology. Anomalies are useful for identifying extreme events and long-term trends. Calculated as: Anomaly = Observed Value - Climatological Mean. Positive anomalies indicate above-normal conditions, negative anomalies indicate below-normal conditions.

Trend Analysis

Linear regression applied to time series data to identify long-term changes. The slope of the trend line represents the rate of change per year (e.g., °F/year for temperature, inches/year for precipitation). R² values indicate how well the linear trend explains the data variability (values closer to 1 indicate stronger trends).

P-values and Statistical Significance

Statistical measures used to determine whether observed trends or differences are statistically significant (unlikely to occur by chance). P-values less than 0.05 are typically considered statistically significant, meaning there is less than 5% probability the trend occurred by random chance. Future implementations may include significance testing for spatial patterns.

Time Aggregation Options

Daily Data

Raw daily values from the climate model or observational dataset. No temporal aggregation is performed. Best for studying short-term variability and extreme events.

Monthly Aggregation

Daily values aggregated to monthly resolution. For temperature variables, monthly values represent the average of all daily values in that month. For precipitation, monthly values represent the sum of all daily precipitation in that month. Monthly aggregation reduces noise while preserving seasonal patterns.

Annual Aggregation

Daily values aggregated to annual resolution. For temperature variables, annual values represent the average of all daily values in that year. For precipitation, annual values represent the total annual precipitation. Annual aggregation is best for identifying long-term trends and is the only aggregation used for Variable Maps.

About the Santa Barbara Climate Dashboard

Project Overview

Created with support from the Bren Environmental Leadership (BEL) Program, this interactive Shiny for Python dashboard helps users explore, compare, and understand climate projections for Santa Barbara County. It uses the LOCA2-Hybrid dataset—the most recent hybrid-statistical downscaling product developed for California's Fifth Climate Change Assessment. LOCA2 statistically downscales CMIP6 global climate model (GCM) outputs by calibrating them against regional observations, providing more accurate results at the local scale.

The dashboard transforms complex climate data into clear, interactive visuals tailored for scientists, students, community members, and decision-makers. It features location-specific climate visualizations, integrated spatial analysis tools, and is fully open-source, with all code available on GitHub to support transparency, reproducibility, and collaboration. The project promotes open data science by providing transparent, up-to-date climate information and supports public education on Santa Barbara's future climate risks.

Through the Santa Barbara Climate Dashboard, users can compare model outputs, examine how historical projections align with observed data, and create interactive maps that show projected changes across different regions and scenarios in the county.

Meet the Santa Barbara Climate Dashboard Team

Climate Data Lab

This dashboard is part of the broader Climate Data Lab initiative at UC Santa Barbara. The Climate Data Lab develops tools, resources, and educational materials to make climate data more accessible to researchers, educators, and the public.

Learn more at: climate-datalab.org

Current Dashboard Features

Interactive Capabilities

• Interactive Shapefile Drawing Tool: Draw custom polygons on the Gridbox Map to analyze climate data across user-defined regions. The tool automatically identifies enclosed gridboxes and computes spatial averages.
• Variable Spatial Maps: View projected climate changes across Santa Barbara County with interactive choropleth maps showing gridbox-level changes for any time period and scenario.
• Multi-Trace Comparison: Compare up to 6 additional datasets simultaneously on a single plot. Mix and match different models, scenarios, variables, and frequencies.
• Mean Absolute Difference (MAD) Analysis: Automatically calculated when comparing models to quantify agreement and assess uncertainty.

Data Processing & Analysis

• 762 gridboxes covering Santa Barbara County at high spatial resolution
• 15 CMIP6 global climate models with multiple emission scenarios
• Observational data validation (Livneh 1950-2013)
• Automatic unit conversions for user convenience (°F, inches)
• Multiple temporal aggregations (daily, monthly, annual)
• Trend analysis with linear regression
• Climate anomaly calculations relative to 1981-2010 baseline

Technical Details

Data Coverage

Acknowledgments

This project was developed with support from the Bren Environmental Leadership (BEL) Program at UC Santa Barbara. Climate data provided by Cal-Adapt and California's Fifth Climate Change Assessment. Observational data from the Livneh gridded dataset. We thank the climate modeling groups participating in CMIP6 for making their model output publicly available.

Climate Variable Spatial Maps

Explore spatial patterns of climate change across the region. All variables show annual averages/totals.