Quantifies the degree of coupling between soil moisture, and surface latent and sensible heat flux using a statistical method. One great advantage is that this metric largely resembles results found from phase 1 of the GLACE, but does not require multiple carefully constructed simulations (See Figure 1 from Dirmeyer 2011 compared to Figure 1 from Koster et al. 2004). This method typically requires daily data, but similar signals can be retrieved using monthly data. In general daily data with a long enough time record to return stable linear slopes is recommended.
Quantifies that atmospheric background state by the variable θBM and hBCL. Higher θBM and hBCL translate to drier, more stable atmospheric background state with respect to convection. Practically these variables put the local surface forcing within the context of the background state. See Figure 7 from Part I, and Figure 1 from Part 2<
A similar method can be applied to surface fluxes and boundary layer height and the lifting condensation level to return the atmospheric segment of coupling. Combining these two segments, the terrestrial and atmospheric segments, yields the two-legged coupling index that gives a measure of land-atmosphere coupling from a statistical perspective and guided by physical relationships. These coupling indices can be readily calculated from models and most reanalysis datasets. See Figure 1 from Dirmeyer et al. 2014
Needs surface fluxes (either sensible or latent heat flux depending on what coupling is being investigated) and soil moisture. These can be daily averages or monthly averages so long as the number of data points are large enough to produce stable statistical relationships.
Can work with the atmospheric leg of land-atmosphere coupling process chain as well so long as atmospheric variables are used (e.g. boundary layer height and/or lifted condensation level).
Prototype Subroutine Call
*** Note: This subroutine simply calculates the standard deviation of soil moisture and multiplies it by the slope of flux-soilm and currently does not perform any significance testing of the relationship. This is currently left to the user, but there are plans to include a significance mask.
How to Calculate
Dirmeyer, P. A. (2011), The terrestrial segment of soil moisture–climate coupling, Geophys. Res. Lett., 38, L16702, doi:10.1029/2011GL048268  Detailed Evaluation
Dirmeyer, P. A., Z. Wang, M. J. Mbuh, and H. E. Norton (2014), Intensified land surface control on boundary layer growth in a changing climate, Geophys. Res. Lett., 41, 1290–1294, doi:10.1002/2013GL058826