Diagnosing errors in a land surface model (CABLE) in the time and frequency domains

We present an approach for diagnosing errors in land surface models in the time and frequency domains. The approach is applied to the Community Atmosphere-Biosphere-Land Exchange (CABLE). We compare modeled and observed fluxes of net ecosystem carbon exchange (NEE), latent heat (LE) and sensible heat (H) at two different forested flux station sites. Using wavelet analysis, we identify the frequencies where model errors are relatively large, and then analyze the sensitivities of model errors at those frequencies to selected model parameters, yielding significant improvements in several measures of model performance. At Harvard Forest, the predictions by CABLE using the modified parameter values reduce model errors at daily to yearly timescales for all three fluxes, but not at interannual timescales for NEE. At the Tumbarumba site, predictions by CABLE with modified parameter values reduce the variance of model errors of all three fluxes from daily to interannual timescales, but do not improve the agreement with the observed mean diurnal or daily time series for H. We conclude that analyzing both mean and variance of model errors at a range of time/frequency scales is useful for identifying and reducing errors of a land surface model.