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  1. Abstract

    Global estimates of the land carbon sink are often based on simulations by terrestrial biosphere models (TBMs). The use of a large number of models that differ in their underlying hypotheses, structure and parameters is one way to assess the uncertainty in the historical land carbon sink. Here we show that the atmospheric forcing datasets used to drive these TBMs represent a significant source of uncertainty that is currently not systematically accounted for in land carbon cycle evaluations. We present results from three TBMs each forced with three different historical atmospheric forcing reconstructions over the period 1850–2015. We perform an analysis of variance to quantify the relative uncertainty in carbon fluxes arising from the models themselves, atmospheric forcing, and model-forcing interactions. We find that atmospheric forcing in this set of simulations plays a dominant role on uncertainties in global gross primary productivity (GPP) (75% of variability) and autotrophic respiration (90%), and a significant but reduced role on net primary productivity and heterotrophic respiration (30%). Atmospheric forcing is the dominant driver (52%) of variability for the net ecosystem exchange flux, defined as the difference between GPP and respiration (both autotrophic and heterotrophic respiration). In contrast, for wildfire-driven carbon emissions modelmore »uncertainties dominate and, as a result, model uncertainties dominate for net ecosystem productivity. At regional scales, the contribution of atmospheric forcing to uncertainty shows a very heterogeneous pattern and is smaller on average than at the global scale. We find that this difference in the relative importance of forcing uncertainty between global and regional scales is related to large differences in regional model flux estimates, which partially offset each other when integrated globally, while the flux differences driven by forcing are mainly consistent across the world and therefore add up to a larger fractional contribution to global uncertainty.

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  2. Abstract. Global water models (GWMs) simulate the terrestrial watercycle on the global scale and are used to assess the impacts of climatechange on freshwater systems. GWMs are developed within different modellingframeworks and consider different underlying hydrological processes, leadingto varied model structures. Furthermore, the equations used to describevarious processes take different forms and are generally accessible onlyfrom within the individual model codes. These factors have hindered aholistic and detailed understanding of how different models operate, yetsuch an understanding is crucial for explaining the results of modelevaluation studies, understanding inter-model differences in theirsimulations, and identifying areas for future model development. This studyprovides a comprehensive overview of how 16 state-of-the-art GWMs aredesigned. We analyse water storage compartments, water flows, and humanwater use sectors included in models that provide simulations for theInter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). Wedevelop a standard writing style for the model equations to enhance modelintercomparison, improvement, and communication. In this study, WaterGAP2used the highest number of water storage compartments, 11, and CWatM used 10compartments. Six models used six compartments, while four models (DBH,JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments.WaterGAP2 simulates five human water use sectors, while four models (CLM4.5,CLM5.0, LPJmL, and MPI-HM) simulate only watermore »for the irrigation sector. Weconclude that, even though hydrological processes are often based on similarequations for various processes, in the end these equations have beenadjusted or models have used different values for specific parameters orspecific variables. The similarities and differences found among the modelsanalysed in this study are expected to enable us to reduce the uncertaintyin multi-model ensembles, improve existing hydrological processes, andintegrate new processes.« less