Modeling Arctic-Boreal vegetation is a challenging but important task, since this highly dynamic ecosystem is undergoing rapid and substantial environmental change. In this work, we synthesized information on 18 dynamic vegetation models (DVMs) that can be used to project vegetation structure, composition, and function in North American Arctic-Boreal ecosystems. We reviewed the ecosystem properties and scaling assumptions these models make, reviewed their applications from the scholarly literature, and conducted a survey of expert opinion to determine which processes are important but lacking in DVMs. We then grouped the models into four categories (specific intention models, forest species models, cohort models, and carbon tracking models) using cluster analysis to highlight similarities among the models. Our application review identified 48 papers that addressed vegetation dynamics either directly (22) or indirectly (26). The expert survey results indicated a large desire for increased representation of active layer depth and permafrost in future model development. Ultimately, this paper serves as a summary of DVM development and application in Arctic-Boreal environments and can be used as a guide for potential model users, thereby prioritizing options for model development.

The pan-Arctic land surface is undergoing rapid changes in a warming climate, with near-surface permafrost projected to degrade significantly during the twenty-first century. Vegetation-related feedbacks have the potential to influence the rate of degradation of permafrost. In this study, the impact of dynamic phenology on the pan-Arctic land surface state, particularly near-surface permafrost, for the 1961-2100 period, is assessed by comparing two simulations of the Canadian Land Surface Scheme (CLASS)one with dynamic phenology, modelled using the Canadian Terrestrial Ecosystem Model (CTEM), and the other with prescribed phenology. These simulations are forced by atmospheric data from a transient climate change simulation of the 5th generation Canadian Regional Climate Model (CRCM5) for the Representative Concentration Pathway 8.5 (RCP8.5). Comparison of the CLASS coupled to CTEM simulation to available observational estimates of plant area index, spatial distribution of permafrost and active layer thickness suggests that the model captures reasonably well the overall distribution of vegetation and permafrost. It is shown that the most important impact of dynamic phenology on the land surface occurs through albedo and it is demonstrated for the first time that vegetation control on albedo during late spring and early summer has the highest potential to impact the degradation of permafrost. While both simulations show extensive near-surface permafrost degradation by the end of the twenty-first century, the strong projected response of vegetation to climate warming and increasing CO2 concentrations in the coupled simulation results in accelerated permafrost degradation in the northernmost continuous permafrost regions.

Fire is an endemic process at high latitudes, connected to a range of other land surface properties, such as land cover, biomass, and permafrost, and intimately linked to the carbon balance of the high-latitude land surface. Much of our current understanding of these links and their climate consequences is through land surface models, so it is important to ensure that for their credibility, these models should be consistent with available data. Over the vast panboreal region, a key source of information on fire is satellite data. Comparisons between satellite-based burned area data from the Global Fire Emissions Database and three dynamic vegetation models (LPJ-WM, CLM4CN, and SDGVM) indicate that all models fail to represent the observed spatial and temporal properties of the fire regime. Although the three dynamic vegetation models give comparable values of the boreal net biome production (NBP), fire emissions are found to differ by a factor 4 between the models, because of widely different estimates of burned area and because of different parameterizations of the fuel load and combustion process. Including a more realistic representation of the fire regime in the models shows that for northern high latitudes, (i) severe fire years do not coincide with source years or vice versa, (ii) the interannual variability of fire emissions does not significantly affect the interannual variability of NBP, and (iii) overall biomass values alter only slightly, but the spatial distribution of biomass exhibits changes. We also demonstrate that it is crucial to alter the current representations of fire occurrence and severity in land surface models if the links between permafrost and fire are to be captured, in particular, the dynamics of permafrost properties, such as active layer depth. This is especially important if models are to be used to predict the effects of a changing climate, because of the consequences of permafrost changes for greenhouse gas emissions, hydrology, and land cover.

[1] Large variations in the composition, structure, and function of Arctic ecosystems are determined by climatic gradients, especially of growing-season warmth, soil moisture, and snow cover. A unified circumpolar classification recognizing five types of tundra was developed. The geographic distributions of vegetation types north of 55degreesN, including the position of the forest limit and the distributions of the tundra types, could be predicted from climatology using a small set of plant functional types embedded in the biogeochemistry-biogeography model BIOME4. Several palaeoclimate simulations for the last glacial maximum (LGM) and mid-Holocene were used to explore the possibility of simulating past vegetation patterns, which are independently known based on pollen data. The broad outlines of observed changes in vegetation were captured. LGM simulations showed the major reduction of forest, the great extension of graminoid and forb tundra, and the restriction of low- and high-shrub tundra (although not all models produced sufficiently dry conditions to mimic the full observed change). Mid-Holocene simulations reproduced the contrast between northward forest extension in western and central Siberia and stability of the forest limit in Beringia. Projection of the effect of a continued exponential increase in atmospheric CO2 concentration, based on a transient ocean-atmosphere simulation including sulfate aerosol effects, suggests a potential for larger changes in Arctic ecosystems during the 21st century than have occurred between mid-Holocene and present. Simulated physiological effects of the CO2 increase (to >700 ppm) at high latitudes were slight compared with the effects of the change in climate.

The international EU-funded SIBERIA project (1998-2000) aimed at the production of an extensive forest map using spaceborne SAR data acquired by the ERS and JERS, satellites. For a large geographical region (900.000 km(2)) located in the Central Siberian Plateau, one-day ERS coherence and JERS backscatter were used to retrieve growing stock volume. A classification algorithm based on peaks in the coherence and backscatter histograms was used. Four volume classes, water and open land were considered. An independent test in 10 areas showed an accuracy above 80%. The produced forest map serves as a tool for the sustainable management of Siberian natural resources and for a better understanding of the role of boreal forests in climate change. The objective of the international EU-funded SIBERIA-II project (2002-2005) is to demonstrate the viability of full carbon accounting, including all greenhouse gasses, with a multi-sensor approach over a 2 million-km2 area in Siberia. Having recently started, a general overview of the aims and the objectives of the project is given. Using several satellite observations available and the SIBERIA database, the first step consists in the generation of several Earth Observation (EO) products (such as biomass, phenological parameters, soil moisture, snow cover etc). Together with land-cover information from local institutions, these products will be input to two dynamic vegetation models for full regional carbon accounting. To increase knowledge, additional products such as Afforestation-Reforestafion-Deforestation and fire scars maps are planned.

ALBIOC (ALbedo- BIOsphere- Carbon) is an integrated terrestrial biosphere model designed as a too] to explore the effects of climate and atmospheric CO, concentration on vegetation, land-surface characteristics and carbon storage. The model is based, although designed to be simple in structure and computationally fast, on biophysical and ecophysiological principles and simulates in a fully interactive manner the potential distribution of vegetation, terrestrial carbon storage and physical land-surface properties. Testing was extensive and focused on broad spatial patterns (5 degrees resolution) of biome distribution, and variables important for the surface energy balance and hydrological cycle (seasonal snow cover, surface albedo, runoff and evaporation) and for the global carbon cycle (seasonal canopy cover, primary production and carbon storage). Because ALBIOC simulates a range of physical and biogeochemical variables in an integrated way, it was possible to test the model against a more comprehensive range of indicators than has normally been the case for terrestrial biosphere models. The simulated vegetation distribution is as accurate as more specialised biogeography models taking into account the coarse resolution of the model. ALBIOC simulates a global NPP of 57 PgC/year, which is in the range of the values found in the literature and other model estimates. Land-surface albedo. snow depth, runoff, and FPAR showed a generally good agreement with observations within the known limits of available data sets of these variables. The model's mechanistic basis would allow extension to simulate, e.g. transient response to rapid climate change (vegetation dynamics) and carbon isotopic balances. while its computational efficiency renders it suitable for inclusion in Earth system models of intermediate complexity. (C) 2001 Elsevier Science B.V. All rights reserved.

Increases in the atmospheric concentration of carbon dioxide and associated changes in climate may exert large impacts on plant physiology and the density of vegetation cover. These may in turn provide feedbacks on climate through a modification of surface-atmosphere fluxes of energy and moisture. This paper uses asynchronously coupled models of global vegetation and climate to examine the responses of potential vegetation to different aspects of a doubled-CO2 environmental change, and compares the feedbacks on near-surface temperature arising from physiological and structural components of the vegetation response. Stomatal conductance reduces in response to the higher CO2 concentration, but rising temperatures and a redistribution of precipitation also exert significant impacts on this property as well as leading to major changes in potential vegetation structure. Overall, physiological responses act to enhance the warming near the surface, but in many areas this is offset by increases in leaf area resulting from greater precipitation and higher temperatures. Interactions with seasonal snow cover result in a positive feedback on winter warming in the boreal forest regions.