## 2.32.1. Anomaly Forcing[¶](#anomaly-forcing "Permalink to this headline")
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The ‘Anomaly Forcing’ atmospheric forcing mode provides a means to drive CLM with projections of future climate conditions without the need for large, high-frequency datasets. From an existing climate simulation spanning both the historical and future time periods, a set of anomalies are created by removing a climatological seasonal cycle based on the end of the historical period from each year of the future time period of the simulation. These anomalies can then be applied to a repeating high-frequency forcing dataset of finite duration (e.g. 10 years). State and flux forcing variables are adjusted using additive and multiplicative anomalies, respectively:

(2.32.16)[¶](#equation-31-16 "Permalink to this equation")\\\[\\begin{split}\\begin{array}{lr} S^{'} = S + k\_{anomaly} & \\quad {\\rm state \\ variable} \\\\ F^{'} = f \\times k\_{anomaly} & \\quad {\\rm flux \\ variable} \\end{array}\\end{split}\\\]

where \\(S^{'}\\) is the adjusted atmospheric state variable, \\(S\\) is the state variable from the high-frequency reference atmospheric forcing dataset, and \\(k\_{anomaly}\\) is an additive anomaly. Similarly, \\(F^{'}\\) is the adjusted atmospheric flux variable, \\(F\\) is the flux variable from the high-frequency reference atmospheric forcing dataset, and \\(k\_{anomaly}\\) is a multiplicative anomaly. State variables are temperature \\(T\_{atm}\\), pressure \\(P\_{atm}\\), humidity \\(q\_{atm}\\), and wind \\(W\_{atm}\\). Flux variables are precipitation \\(P\\), atmospheric shortwave radiation \\(S\_{atm} \\, \\downarrow\\), and atmospheric longwave radiation \\(L\_{atm} \\, \\downarrow\\).
