Abstract
Abstract. Land-surface models (LSMs) are increasingly called upon to represent not only the exchanges of energy, water and momentum across the land–atmosphere interface (their original purpose in climate models), but also how ecosystems and water resources respond to climate, atmospheric environment, land-use and land-use change, and how these responses in turn influence land–atmosphere fluxes of carbon dioxide (CO2), trace gases and other species that affect the composition and chemistry of the atmosphere. However, the LSMs embedded in state-of-the-art climate models differ in how they represent fundamental aspects of the hydrological and carbon cycles, resulting in large inter-model differences and sometimes faulty predictions. These "third-generation" LSMs respect the close coupling of the carbon and water cycles through plants, but otherwise tend to be under-constrained, and have not taken full advantage of robust hydrological parameterizations that were independently developed in offline models. Benchmarking, combining multiple sources of atmospheric, biospheric and hydrological data, should be a required component of LSM development, but this field has been relatively poorly supported and intermittently pursued. Moreover, benchmarking alone is not sufficient to ensure that models improve. Increasing complexity may increase realism but decrease reliability and robustness, by increasing the number of poorly known model parameters. In contrast, simplifying the representation of complex processes by stochastic parameterization (the representation of unresolved processes by statistical distributions of values) has been shown to improve model reliability and realism in both atmospheric and land-surface modelling contexts. We provide examples for important processes in hydrology (the generation of runoff and flow routing in heterogeneous catchments) and biology (carbon uptake by species-diverse ecosystems). We propose that the way forward for next-generation complex LSMs will include: (a) representations of biological and hydrological processes based on the implementation of multiple internal constraints; (b) systematic application of benchmarking and data assimilation techniques to optimize parameter values and thereby test the structural adequacy of models; and (c) stochastic parameterization of unresolved variability, applied in both the hydrological and the biological domains.
Highlights
The land surface, together with the soil column underneath it, plays a key role in controlling the partitioning of available energy and water, and the land–atmosphere exchange of carbon dioxide (CO2) and the close couplingPublished by Copernicus Publications on behalf of the European Geosciences Union.I
Atmospheric modelling differs from land-surface modelling in that the equations describing weather processes are inherently chaotic, requiring ensembles of simulations to achieve probabilistic forecasts; implementing a stochastic parameterization in this context can be done by allowing ensemble members to differ in the assignment of parameter values
Because runoff is the residual of two relatively large quantities, and because there are no direct observations of evapotranspiration over large areas, streamflow data continue to have a great potential to be used to evaluate Land-surface models (LSMs)’ simulation of land–atmosphere latent heat and water vapour exchange. (This situation is evolving as improved methods for deriving evapotranspiration from remotely sensed measurements are developed: see Mueller et al, 2013.) Many LSMs fail to generate realistic temporal distributions of streamflow, limiting the potential for such data to be used to test and constrain LSMs
Summary
The land surface, together with the soil column underneath it, plays a key role in controlling the partitioning of available energy (into latent, sensible and ground heat fluxes) and water (into evapotranspiration, surface runoff, interflow, baseflow and soil moisture), and the land–atmosphere exchange of carbon dioxide (CO2) and the close coupling. Physical and hydrological processes in a land-surface model (LSM) are a prerequisite for improving the accuracy of both numerical weather forecasts and climate predictions. In emerging Earth system models, they are called upon to model land–atmosphere exchanges of biogenic greenhouse gases other than CO2; other reactive trace gases with influences on atmospheric chemistry and composition; emissions of aerosols in biomass burning and dust deflation; and emissions of volatile organic compounds as aerosol precursors. Many LSMs include representations of the slower processes of vegetation dynamics, coupled to the fast exchanges of water, energy, momentum and CO2 that are at their core (Arora, 2002). We will argue that the dominant paradigm in land-surface modelling focusses too heavily on realism at the expense of the other two R’s
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