Index
- Alaska Climate Divisions FAQs
- Anomalies vs. Temperature
- Arctic Sea Ice Measurements
- Billion-Dollar Disasters: Calculating the Costs
- Binomial Filter
- Climate Division Dataset Transition
- Climate Extremes Index
- CLIMAT Messages
- Climatological Rankings
- Coral Reef Bleaching
- Dead Fuel Moisture
- Definition of Drought
- Drought Indicators
- Drought in the Colorado River Basin
- Drought vs. Aridity
- El Niño: A Historical Perspective
- Explanation of the 500 mb Flow
- Future Drought
- Global Precipitation Percentile Maps
- Global Regions Definitions
- Global Temperature Anomaly Percentile Maps
- Global Temperature Uncertainty
- Groundwater Drought Indicators
- Hawaiʻi Climate Divisions FAQs
- LOESS
- Measuring Drought
- Monthly Releases
- Monthly Report RSS Feed
- National Data Flow
- nClimDiv Maximum and Minimum Temperatures
- Palmer Drought Index
- Potential Evapotranspiration
- Reforestation of Bastrop Lost Pines
- Regional Climate Centers
- Regional Snowfall Index (RSI)
- Satellite-Based Drought Indicators
- Soil Moisture Water Balance Models
- Southern Hemisphere Snow Cover Extent
- Standardized Precipitation Index
- Streamflow Drought Indicators
- Subtropical Highs
- Tornado Count
- U.S. Climate Divisions
- U.S. Climate Normals
- U.S. Drought Monitor Scale
- USHCN Version 2.5 Transition
- Water Supply vs. Water Demand
Anomalies vs. Temperature
In climate studies, temperature anomalies are often more useful than absolute temperatures. A temperature anomaly is the difference between the observed temperature and a baseline average temperature. This baseline is usually calculated by averaging 30 or more years of temperature data for a specific location.
A positive anomaly means the observed temperature was warmer than the baseline, while a negative anomaly means the observed temperature was cooler than the baseline. Unlike absolute temperatures, which can be greatly affected by factors like elevation or whether a station is in an urban or rural area, anomalies help minimize the impact of those differences. For example, a mountain top and a nearby valley might both experience a cooler-than-average summer month, but their absolute temperatures will still be quite different.
Using anomalies also helps reduce issues when stations are added, removed, or have gaps in their data. The diagram shows absolute temperatures (lines) for five nearby stations, along with the anomalies for 2008 (symbols). Notice how the anomalies all fall within a small range when compared to the wider spread of absolute temperatures. If one station—say, the coolest station at Mt. Mitchell—were removed, the average absolute temperature would shift noticeably warmer. However, because its anomaly is similar to the neighboring stations, the average anomaly would change very little.