International audienceProcesses that describe the distribution of vegetation and ecosystem succession after disturbance are an important component of dynamic global vegetation models (DGVMs). The vegetation dynamics module (ORC-VD) within the process-based ecosystem model ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) has not been updated and evaluated since many years and is known to produce unrealistic results. This study presents a new parameterization of ORC-VD for mid-to high-latitude regions in the Northern Hemisphere, including processes that influence the existence, mortality and competition between tree functional types. A new set of metrics is also proposed to quantify the performance of ORC-VD, using up to five different data sets of satellite land cover, forest biomass from remote sensing and inventories, a data-driven estimate of gross primary productivity (GPP) and two gridded data sets of soil organic carbon content. The scoring of ORC-VD derived from these metrics integrates uncertainties in the observational data sets. This multi-data set evaluation framework is a generic method that could be applied to the evaluation of other DGVM models. The results of the original ORC-VD published in 2005 for mid-to high-latitudes and of the new parameterization are evaluated against the above-described data sets. Significant improvements were found in the model-ing of the distribution of tree functional types north of 40 • N. Three additional sensitivity runs were carried out to separate the impact of different processes or drivers on simulated vegetation distribution, including soil freezing which limits net primary production through soil moisture availability in the root zone, elevated CO 2 concentration since 1850, and the effects of frequency and severity of extreme cold events during the spin-up phase of the model

Abstract. To improve the simulation of vegetation-climate feedbacks in the high latitudes, three new circumpolar Plant Functional Types (PFTs) were added in the ORCHIDEE land surface model, namely non-vascular plants (NVPs) representing bryophytes and lichens, arctic shrubs, and arctic C3 grasses. Non-vascular plants are assigned no stomatal conductance, very shallow roots, and can desiccate during dry episodes and become active again during wet periods, which gives them a larger phenological plasticity compared to grasses and shrubs. Shrubs have a specific carbon allocation scheme, and differ from trees by their larger survival rates in winter, due to protection by snow. Arctic C3 grasses have the same equations than in the original ORCHIDEE version, but different parameter values, optimized from in-situ observations of biomass and NPP in Siberia. In situ observations of living biomass and productivity from Siberia were used to calibrate the parameters of the new PFTs using a Bayesian optimization procedure. With the new PFTs, we obtain a lower Net Primary Productivity (NPP) by 31 % (from 55° N), as well as a lower roughness length (−41 %), transpiration (+33 %) and a higher winter albedo (by 3.6 %) due to a larger snow cover. A simulation of the water balance and runoff and drainage in the high northern latitudes using the new PFTs results in an increase of fresh water discharge in the Arctic ocean by 11 % (+140 km−3 y−1), owing to less evapotranspiration. Future developments should focus on the competition between these three PFTs and boreal trees PFTs, in order to simulate their area changes in response to climate change, and the effect of carbon-nitrogen interactions.

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Parameterizations of plant competition processes involving shrubs, mosses, grasses, and trees were introduced with the recently implemented shrubs and mosses plant functional types in the ORCHIDEE dynamic global vegetation model in order to improve the representation of high latitude vegetation dynamics. Competition is based on light capture for growth, net primary productivity, and survival to cold-induced mortality during winter. Trees are assumed to outcompete shrubs and grasses for light, and shrubs outcompete grasses. Shrubs are modeled to have a higher survival than trees to extremely cold winters because of thermic protection by snow. The fractional coverage of each plant type is based on their respective net primary productivity and winter mortality of trees and shrubs. Gridded simulations were carried out for the historical period and the 21st century following the RCP4.5 and 8.5 scenarios. We evaluate the simulated present-day vegetation with an observation-based distribution map and literature data of boreal shrubs. The simulation produces a realistic present-day boreal vegetation distribution, with shrubs, mosses north of trees and grasses. Nevertheless, the model underestimated local shrub expansion compared to observations from selected sites in the Arctic during the last 30 years suggesting missing processes (nutrients and microscale effects). The RCP4.5 and RCP8.5 projections show a substantial decrease of bare soil, an increase in tree and moss cover and an increase of shrub net primary productivity. Finally, the impact of new vegetation types and associated processes is discussed in the context of climate feedbacks.Plain Language Summary Changes in the northern vegetation exert feedbacks on climate through surface energy and greenhouse gas fluxes. For example, increased vegetation cover can lead to warming due to stronger absorption of shortwave radiation (through decreased albedo). In this study we developed a new version of the ORCHIDEE dynamic vegetation model, allowing us to simulate the dynamical cover of mosses and shrubs, two important types of northern vegetation, alongside with grasses and trees. The prevalence of the different forms of vegetation is ruled by light capture during the growing season, mortality during the cold conditions, and competition for space. The new model is tested for present-day land cover and used for future climate projections. We simulated a realistic vegetation map for historical simulations and a substantial decrease of bare soil with shifts of vegetation in future simulations. However, the model underestimated local shrub expansion compared to observations.