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Environmental models are common tools to simulate fluxes and states within the terrestrial energy and water cycles. Simulation results serve a wide range of purposes from infrastructure planning to weather forecasting. Regardless of the purpose, it is a common task to accurately estimate parameters within the process formulations used in environmental models for each computational unit (i.e., grid cell). The dimensionality of the parameter space is equal to the number of parameters per grid cell times the number of grid cells in distributed large-domain modelling, such as in weather forecasting. Without proper regularization of the parameter space, it is impossible to efficiently estimate parameters in this case. One regularization approach is to estimate constant parameters across the modelling domain, but this does not make use of high-resolution data that is available today (i.e., soil maps and land cover information). The Multiscale Parameter Regionalization (MPR) is a promising alternative that makes use of exactly these data sets.The multiscale parameter regionalization (MPR) translates local land surface properties into model parameters. It consists of two steps: first, it relates local geophysical data sets (e.g., soil maps) to high-resolution model parameters via transfer functions at the native resolution of the input data sets. Second, it upscales the high-resolution model parameters to the modeling scale (i.e., the scale at which the model has been applied). MPR has been introduced in the mesoscale hydrologic model (mHM, Samaniego et al. 2010, Kumar et al. 2013), but has recently been made available as a stand-alone tool.In this study, we use the stand-alone tool of MPR to apply it to the land-surface model HTESSEL. This model is the land-surface component of the ECMWF seasonal forecasting system. It uses a tiling approach to differentiate between different land cover types (e.g., high-grown vegetation and shrubs) and assumes constant parameters for each type. Soil related parameters are also considered for few soil classes for the entire domain. While this covers a large extent of subgrid variability at coarse resolutions, we find that its effect at high resolutions is reduced. In this study, we apply MPR for soil water parameters (e.g., conductivity and porosity) and vegetation parameters (e.g., stomatal conductance) that were identified as sensitive in Orth et al. (2016). We find that MPR provides more accurate fluxes over Europe across different resolutions than constant parameters in HTESSEL because it considers the subgrid variability of parameters. We also find a reduced difference between fluxes estimated at different resolutions by applying MPR (i.e., improved flux-matching).References:Samaniego L., et al. https://doi.org/10.1029/2008WR007327Kumar, R., et al. https://doi.org/10.1029/2012WR012195Orth R., et al. http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-15-0283.1 [ABSTRACT FROM AUTHOR]

High temperatures, low relative humidity, and reductions in soil moisture available to plants under intense drought conditions leads to reductions in photosynthetic activity in many plant functional types. During the summer of 2018, we see this across central and northwestern Europe in our sun-induced fluorescence (SIF) remote sensing product SIFTER [1], suggesting that the terrestrial biosphere took up less carbon from the atmosphere that year than it does under typical summer conditions. We use a variety of atmospheric drivers to generate multiyear soil moisture fields using the 5x5 km hydrological model PCR-GLOBWB, which is subsequently used along with SIF in the biosphere model SiB4 to drive simulations of gross primary production (GPP), following the method of van Schaik et al. 2018 [2]. We optimise the biosphere fluxes first using reported crop yields after Combe et al. 2017 [3] and secondly with observed atmospheric CO₂ mole fractions in the atmospheric inverse system CarbonTracker Europe [4]. We will quantify the impact of the drought on the carbon fluxes of Europe's biosphere using these optimised values, and illustrate the dominant pathways across forests, grasslands, and croplands. The severity, spatial extent, and onset- and recovery-timing of the anomaly in GPP will be compared to other recent notable drought events in EurAsia, such as that of 2003 and 2010. [1] Koren, Gerbrand, et al. "Widespread reduction in sun-induced fluorescence from the Amazon during the 2015/2016 El Niño." Philosophical Transactions of the Royal Society B: Biological Sciences 373.1760 (2018): 20170408.[2] van Schaik, Erik, et al. "Changes in surface hydrology, soil moisture and gross primary production in the Amazon during the 2015/2016 El Nino." Philosophical Transactions of the Royal Society B: Biological Sciences 373.1760 (2018): 20180084.[3] Combe, Marie, et al. "Grain yield observations constrain cropland CO2 fluxes over Europe." Journal of Geophysical Research: Biogeosciences 122.12 (2017): 3238-3259.[4] der Laan-Luijkx, Van, et al. "The CarbonTracker Data Assimilation Shell (CTDAS) v1. 0: implementation and global carbon balance 2001-2015." Geoscientific Model Development10.7 (2017): 2785-2800. [ABSTRACT FROM AUTHOR]