Is large-scale terrestrial hydrological cycling well represented in Earth System Models?

This paper addresses how large-scale terrestrial water cycling is represented in the land surface schemes of Earth System Models (ESMs). Good representation is essential, for example in regional planning for climate change adaptation and in preparation for hydro-climatic extremes that have recently set records world-wide in devastating consequences for societies and deaths of thousands of people. ESMs provide simulations and projections for the climate system and its interactions with the terrestrial hydrological cycle, and are widely used to study and prepare for associated impacts of climate change. However, the reliability of ESMs is unclear with regard to their representation of large-scale terrestrial hydrology and its changes and interactions between its key variables‎. Despite being crucial for model realism, analysis of co-variations among terrestrial hydrology variables is still largely missing in ESM performance evaluations. To bridge this research gap, we have studied and identified large-scale co-variation patterns between soil moisture (SM) and the main freshwater fluxes of runoff (R), precipitation (P), and evapotranspiration (ET) from observational data and across 6405 hydrological catchments in different parts and climates of the world. Furthermore, we have compared the identified observation-based relationships with those emerging from ESMs and reanalysis products. Our results show that the most strongly correlated freshwater variables based on observational data are also the most misrepresented hydrological patterns in ESMs and reanalysis simulations. In particular, we find SM and R to have the generally strongest large-scale correlations according to the observation-based data, across the numerous studied catchments with widely different hydroclimatic characteristics. Compared to the SM-R correlation signals, the observation-based correlations are overall weaker for the commonly expected closer dependencies of: R on P; ET on P; SM on P; and ET on SM. Nevertheless, this strongest SM-R correlation and the P-R correlation are the most misrepresented hydrological patterns in reanalysis products and ESMs. Our results also show that ESM outputs can perform relatively well in simulating individual hydrological variables, while exhibiting essential inconsistencies in simulated co-variations between variables. Such investigations of large-scale terrestrial hydrology representation by ESMs can enhance our understanding of fundamental ESM biases and uncertainties while providing important insights for systematic ESM improvement with regard to the large-scale hydrological cycling over the world’s continents and regional land areas.