Observational constraints on the assessment of land-atmosphere coupling strength

A variety of metrics have been developed in recent years to characterize the strength of land-atmosphere interactions in regional and global climate models. Evaluation of these metrics against observations has been difficult due to limited observations and/or metric definitions requiring specific model experiments that are not replicable in the real world. In this work, we evaluate the observational data requirements necessary for characterizing a variety of land-atmosphere-related metrics. We show that far more data are needed to get a reliable estimate of a metric assessing the relationship between variables (e.g., the Triggering Feedback Strength, which assess the relationship between before-noon evaporative fraction and afternoon rainfall) than are needed to determine the mean of a directly observable variable (e.g., daily precipitation). Additionally, while unbiased noise increases the spread of a directly observable variable without changing the mean, preliminary results suggest that it degrades the strength of the connection between variables, yielding a unidirectional, negative impact on metrics assessing this connectivity.