Arctic climate change with a 2 ∘C global warming: Timing, climate patterns and vegetation change

The signatories to United Nations Framework Convention on Climate Change are charged with stabilizing the concentrations of greenhouse gases in the atmosphere at a level that prevents dangerous interference with the climate system. A number of nations, organizations and scientists have suggested that global mean temperature should not rise over 2 ∘C above preindustrial levels. However, even a relatively moderate target of 2 ∘C has serious implications for the Arctic, where temperatures are predicted to increase at least 1.5 to 2 times as fast as global temperatures. High latitude vegetation plays a significant role in the lives of humans and animals, and in the global energy balance and carbon budget. These ecosystems are expected to be among the most strongly impacted by climate change over the next century. To investigate the potential impact of stabilization of global temperature at 2 ∘C, we performed a study using data from six Global Climate Models (GCMs) forced by four greenhouse gas emissions scenarios, the BIOME4 biogeochemistry-biogeography model, and remote sensing data. GCM data were used to predict the timing and patterns of Arctic climate change under a global mean warming of 2 ∘C. A unified circumpolar classification recognizing five types of tundra and six forest biomes was used to develop a map of observed Arctic vegetation. BIOME4 was used to simulate the vegetation distributions over the Arctic at the present and for a range of 2 ∘C global warming scenarios. The GCMs simulations indicate that the earth will have warmed by 2 ∘C relative to preindustrial temperatures by between 2026 and 2060, by which stage the area-mean annual temperature over the Arctic (60-90∘N) will have increased by between 3.2 and 6.6 ∘C. Forest extent is predicted by BIOME4 to increase in the Arctic on the order of 3 × 106 km2 or 55% with a corresponding 42% reduction in tundra area. Tundra types generally also shift north with the largest reductions in the prostrate dwarf-shrub tundra, where nearly 60% of habitat is lost. Modeled shifts in the potential northern limit of trees reach up to 400 km from the present tree line, which may be limited by dispersion rates. Simulated physiological effects of the CO2 increase (to ca. 475 ppm) at high latitudes were small compared with the effects of the change in climate. The increase in forest area of the Arctic could sequester 600 Pg of additional carbon, though this effect is unlikely to be realized over next centur

Reconstituting human past dynamics over a landscape : pleading for the co-integration of both micro village-level modelling and macro-level ecological socio-modelling

International audienceThis communication tends to elaborate a plea for the necessity of a specific modelling methodology which does not sacrifice two modelling principles: explanation Micro and correlation Macro. Actually, three goals are assigned to modelling strategies: describe, understand and predict. One tendency in historical and spatial modelling is to develop models at a micro level in order to describe and by that way, understand the connection between local ecological contexts, acquired through local ecological data, and local social practices, acquired through archaeology. However, such a method faces difficulties for expanding its validity: It is validated by its adequacy with local data but the prediction step is unreachable and quite nothing can be said for places out where. On the other hand, building models at a far larger scale, for instance at the continent and even the world level, enhances the connection between ecology and its temporal variability. Such connections are based on well-improved theories but lower the " small causes, big effects " emergence corresponding to agent-based approaches and the related inherent variability of socio-ecological dynamics that one can notice at a lower scale: for instance, the emergence of social innovations can be simulated only as an input parameter. We then propose a plea for combining both elements for building large-scale modelling tools, which aims are to describe and provide predictions on long-term past evolutions, that include the test of explaining socio-anthropological hypotheses, i.e. the emergence and the spread of local social innovations

The role of land cover in the climate of glacial Europe

Earth system models show wide disagreement when simulating the climate of the continents at the Last Glacial Maximum (LGM). This disagreement may be related to a variety of factors, including model resolution and an incomplete representation of Earth system processes. To assess the importance of resolution and land–atmosphere feedbacks on the climate of Europe, we performed an iterative asynchronously coupled land–atmosphere modelling experiment that combined a global climate model, a regional climate model, and a dynamic vegetation model. The regional climate and land cover models were run at high (18 km) resolution over a domain covering the ice-free regions of Europe. Asynchronous coupling between the regional climate model and the vegetation model showed that the land–atmosphere coupling achieves quasi-equilibrium after four iterations. Modelled climate and land cover agree reasonably well with independent reconstructions based on pollen and other paleoenvironmental proxies. To assess the importance of land cover on the LGM climate of Europe, we performed a sensitivity simulation where we used LGM climate but present-day (PD) land cover. Using LGM climate and land cover leads to colder and drier summer conditions around the Alps and warmer and drier climate in southeastern Europe compared to LGM climate determined by PD land cover. This finding demonstrates that LGM land cover plays an important role in regulating the regional climate. Therefore, realistic glacial land cover estimates are needed to accurately simulate regional glacial climate states in areas with interplays between complex topography, large ice sheets, and diverse land cover, as observed in Europe

The pyrogeography of eastern boreal Canada from 1901 to 2012 simulated with the LPJ-LMfire model

Wildland fires are the main natural disturbance shaping forest structure and composition in eastern boreal Canada. On average, more than 700 000 ha of forest burns annually and causes as much as CAD 2.9 million worth of damage. Although we know that occurrence of fires depends upon the coincidence of favourable conditions for fire ignition, propagation, and fuel availability, the interplay between these three drivers in shaping spatiotemporal patterns of fires in eastern Canada remains to be evaluated. The goal of this study was to reconstruct the spatiotemporal patterns of fire activity during the last century in eastern Canada's boreal forest as a function of changes in lightning ignition, climate, and vegetation. We addressed this objective using the dynamic global vegetation model LPJ-LMfire, which we parametrized for four plant functional types (PFTs) that correspond to the prevalent tree genera in eastern boreal Canada (Picea, Abies, Pinus, Populus). LPJ-LMfire was run with a monthly time step from 1901 to 2012 on a 10 km2 resolution grid covering the boreal forest from Manitoba to Newfoundland. Outputs of LPJ-LMfire were analyzed in terms of fire frequency, net primary productivity (NPP), and aboveground biomass. The predictive skills of LPJ-LMfire were examined by comparing our simulations of annual burn rates and biomass with independent data sets. The simulation adequately reproduced the latitudinal gradient in fire frequency in Manitoba and the longitudinal gradient from Manitoba towards southern Ontario, as well as the temporal patterns present in independent fire histories. However, the simulation led to the underestimation and overestimation of fire frequency at both the northern and southern limits of the boreal forest in Québec. The general pattern of simulated total tree biomass also agreed well with observations, with the notable exception of overestimated biomass at the northern treeline, mainly for PFT Picea. In these northern areas, the predictive ability of LPJ-LMfire is likely being affected by the low density of weather stations, which leads to underestimation of the strength of fire- weather interactions and, therefore, vegetation consumption during extreme fire years. Agreement between the spatiotemporal patterns of fire frequency and the observed data across a vast portion of the study area confirmed that fire therein is strongly ignition limited. A drier climate coupled with an increase in lightning frequency during the second half of the 20th century notably led to an increase in fire activity. Finally, our simulations highlighted the importance of both climate and fire in vegetation: despite an overarching CO2- induced enhancement of NPP in LPJ-LMfire, forest biomass was relatively stable because of the compensatory effects of increasing fire activity