Warning: count(): Parameter must be an array or an object that implements Countable in /home/customer/www/coupling-metrics.com/public_html/wp-includes/post-template.php on line 293

Terrestrial Coupling Index

Determines the magnitude of coupling between soil moisture and surface fluxes using statistical techniques. Specifically, the terrestrial coupling index uses the daily slope of the soil moisture-surface flux relationship as weighted by the standard deviation in soil moisture to determine the degree to which soil moisture changes drive surface flux variability.

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

Required Input Data:

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).

As this is a statistical technique all general caveats related to significance apply. This metric is guided by knowledge of that soil moisture may exerts some impact on surface fluxes, but note that this interpretation may not be correct when applied to monthly or seasonally averaged soil moisture and surface flux values. Additionally, correlations between soil moisture and surface fluxes are seasonally dependent so the terrestrial coupling parameter should be applied most broadly at the seasonal scale to avoid aliasing relationships that may emerge when applied to the annual averages.

Prototype Subroutine Call

subroutine terra_coupling ( dim2, ntim, soilm, flux, tcp, missing )

Required Input


*** 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

Relevant Citations

Method Description 

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