Temperature anomaly

Various global surface temperature datasets originally had different reference periods, but for meaningful comparison have been adjusted to have the same "0 °C" reference temperature. Without such an adjustment, the traces would be vertically offset from each other. Here, the "0 °C" value is determined based the average for 1850-1900—considered to be the "pre-industrial" temperature—and does not indicate an absolute measured temperature of "0 °C".

Temperature anomaly is the difference, positive or negative, of a temperature from a base or reference value, normally chosen as an average of temperatures over a certain reference or base period. In atmospheric sciences, the average temperature is commonly calculated over a period of at least 30 years over a homogeneous geographic region, or globally over the entire planet.

Temperatures are obtained from surface and offshore weather stations or inferred from meteorological satellite data. Temperature anomalies can be calculated based on datasets of near-surface and upper-air atmospheric temperatures or sea surface temperatures.

## Contents

* 1 Description
* 1.1 Standard deviation
* 2 Forecasting
* 3 See also
* 4 References

## Description

Temperature anomalies are a measure of temperature compared to a reference temperature, which is often calculated as an average of temperatures over a reference period, often called a base period.[1] Records of global average surface temperature are usually presented as anomalies rather than as absolute temperatures.[1]

Using reference values computed for distinct areas over the same time period establishes a baseline from which anomalies are calculated, so that normalized data is used to more accurately compare temperature patterns to what is normal.[1] For example, sub-global datasets may be for land-only, ocean-only, and hemispheric time series.[1] Anomalies provide a frame of reference that allows more meaningful comparisons between locations and more accurate calculations of temperature trends.[1]

Using different base periods does not change the shape of time series charts or affect portrayal of the trends within them.[1] For example, World Meteorological Organization (WMO) policy motivates use of a 30 year base period, whereas for conceptual simplicity a century-long base period is sometimes used to track the big-picture evolution of temperatures across the entire global surface.[1] Different meteorological organizations have used respective base periods for global mean surface temperature datasets, such as 1951–1980 (NASA GISS[2] and Berkeley Earth[3]), 1961–1990 (HadCRUT U.K.[4]), 1901–2000 (NCDC/NOAA[5]), and 1991–2020 (Japan Met[6]).

### Standard deviation

Though northern America has warmed more than its tropics, the tropics have more clearly departed from normal historical variability (coloured bands: 1σ, 2σ standard deviations).[7] The two charts have the same reference period.

Anomalies alone are not sufficient to characterize exceptionality of temperature values. The standard deviation—symbolized by a lower case sigma, σ—quantifies the degree of variation of a dataset's values (see coloured bands in chart at right). For example, a variation of +2 °C can be more significant over a region with normally stable temperatures than another of +3 °C from a region with normally large variability.[8] For this purpose, anomalies are often shown as 'standardized anomalies' namely the anomaly divided by the standard deviation.

To summarize: choice of reference period determines vertical placement of a trace on a graph, and deviation determines how much the trace is "stretched" in the vertical direction on the graph.

## Forecasting

Numerical weather prediction provides the temperature forecast for the next few days or weeks. This can be used to calculate anomalies during these forecast periods. There are two types of forecasts, deterministic and probabilistic, which will give different results.

Deterministic data are values obtained by running the forecast model with initial conditions determined by the initial conditions from data assimilation. Probabilistic data comes from predicting sets where the model (or different models) is run several times with a slight variations in the initial conditions each time.[9]

Deterministic anomalies have a standard deviation which depends only on the bias of the forecast. The deviation and the probabilistic anomalies, being calculated from several model solutions, are themselves probabilities that they will occur.

## See also

* Extreme weather
* Heat wave
* Marine heatwave

## References

1. ↑ 1.0 1.1 1.2 1.3 1.4 1.5 1.6 "Global Surface Temperature Anomalies / Background Information - FAQ". https://www.ncei.noaa.gov/access/monitoring/global-temperature-anomalies. Publication date: Precursor webpage was archived as early as December 2013.
2. ↑ "Annual Mean Temperature Change over Land and over Ocean", NASA GISS.
3. ↑ "Land and Ocean summary", Berkeley Earth.
4. ↑ "HadCRUT4 time series: ensemble medians and uncertainties > Global (NH+SH)/2" > Annual" Met Office UK.
5. ↑ "Time series / Globe / Land and Ocean", NCDC, NOAA.
6. ↑ "Annual Anomalies of Global Average Surface Temperature", Japan Met.
7. ↑ Hawkins, Ed (10 March 2020). "From the familiar to the unknown". https://www.climate-lab-book.ac.uk/2020/from-the-familiar-to-the-unknown/. (Direct link to image; Hawkins credits Berkeley Earth for data.) "The emergence of observed temperature changes over both land and ocean is clearest in tropical regions, in contrast to the regions of largest change which are in the northern extra-tropics. As an illustration, northern America has warmed more than tropical America, but the changes in the tropics are more apparent and have more clearly emerged from the range of historical variability. The year-to-year variations in the higher latitudes have made it harder to distinguish the long-term changes."
8. ↑ Environnement et Lutte contre les changements climatiques (2019). "Classification climatologique des températures" (in fr). Gouvernment of Québec. http://www.environnement.gouv.qc.ca/climat/surveillance/classification.htm.
9. ↑ Meteorological Service of Canada (2019-08-01). "Temperature Anomaly Forecast: Day 8 to 14 Outlooks". Environment and Climate Change Canada. https://weather.gc.ca/ensemble/naefs/semaine2_combinee_e.html.

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Meteorological data and variables

General|

* Adiabatic processes
* Advection
* Buoyancy
* Lapse rate
* Lightning
* Surface solar radiation
* Surface weather analysis
* Visibility
* Vorticity
* Wind
* Wind shear

Condensation|

* Cloud
* Cloud condensation nuclei (CCN)
* Fog
* Convective condensation level (CCL)
* Lifted condensation level (LCL)
* Precipitation
* Water vapor

Convection|

* Convective available potential energy (CAPE)
* Convective inhibition (CIN)
* Convective instability
* Convective momentum transport
* Conditional symmetric instability
* Convective temperature (Tc)
* Equilibrium level (EL)
* Free convective layer (FCL)
* Helicity
* K Index
* Level of free convection (LFC)
* Lifted index (LI)
* Maximum parcel level (MPL)
* Bulk Richardson number (BRN)

Temperature|

* Dew point (Td)
* Dew point depression
* Dry-bulb temperature
* Equivalent temperature (Te)
* Forest fire weather index
* Haines Index
* Heat index
* Humidex
* Humidity
* Relative humidity (RH)
* Mixing ratio
* Potential temperature (θ)
* Equivalent potential temperature (θe)
* Sea surface temperature (SST)
* Thermodynamic temperature
* Vapor pressure
* Virtual temperature
* Wet-bulb temperature
* Wet-bulb globe temperature
* Wet-bulb potential temperature
* Wind chill

Pressure|

* Atmospheric pressure
* Baroclinity
* Barotropicity
* Pressure gradient
* Pressure-gradient force (PGF)

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