Shifts in Arctic vegetation and associated feedbacks under climate change

Pearson, Richard G.; Phillips, Steven J.; Loranty, M. M.; Beck, Pieter S. A.; Damoulas, Theodoros; Knight, Sarah; Goetz, Scott J.

doi:10.1038/nclimate1858

Cited by 693 publications

(716 citation statements)

References 28 publications

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“…Many of the ecosystem types that were abundant in our study area are similar to those in the circumpolar region (Walker et al 2005), and the main drivers of change, such as thermokarst , fire (Barrett et al 2011), shrub expansion (Myers- Smith et al 2012), and lake drainage (Smith et al 2005) also are common in the Russian and Canadian north. Thus, the types of shifts in vegetation in northwest Alaska are likely to be widespread with their accompanying effects on albedo, evapotranspiration, and biomass across the broader circumpolar region and provide feedbacks to the global system (Chapin et al 2006;Euskirchen et al 2009;Rocha et al 2012;SNAP 2012;Pearson et al 2013). The release of carbon dioxide and methane from decomposition of soil carbon from thawing permafrost ) is of particular concern to the global climate system.…”

Section: Discussionmentioning

confidence: 99%

“…Pearson et al (2013) used a climate-envelop type model to project that vegetation in 48−69 % of the circumarctic region will shift to a different class by the 2050s under scenarios of climate change and restricted tree dispersal. Climate-envelop modeling by SNAP (2012) projected that ecosystem types (cliomes) in northwest Alaska would experience large shifts in forest types but didn't quantify changes by regions.…”

Section: Discussionmentioning

confidence: 99%

“…A third model adjusts the sensitivity (transition rate) of the ecosystems to temperature changes based on perceived positive and negative feedbacks associated with 23 biophysical drivers. While the assumption of linear temperature dependency is simplistic, it provides empirical projections that can be compared to other climate-envelop (Pearson et al 2013) and dynamic-ecosystem (Euskirchen et al 2009) models, which also use simplifying assumptions when reducing the complexity of ecological characteristics and drivers. The modeling uses a non-spatial approach to predict changes in areal extent, because the large number of factors and spatial complexity affecting each transition precludes spatial prediction.…”

Section: Introductionmentioning

confidence: 99%

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Projected changes in diverse ecosystems from climate warming and biophysical drivers in northwest Alaska

Jorgenson¹,

Marcot

Swanson

et al. 2015

Climatic Change

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Climate warming affects arctic and boreal ecosystems by interacting with numerous biophysical factors across heterogeneous landscapes. To assess potential effects of warming on diverse local-scale ecosystems (ecotypes) across northwest Alaska, we compiled data on historical areal changes over the last 25-50 years. Based on historical rates of change relative to time and temperature, we developed three state-transition models to project future changes in area for 60 ecotypes involving 243 potential transitions during three 30-year periods (ending 2040, 2070, 2100). The time model, assuming changes over the past 30 years continue at the same rate, projected a net change, or directional shift, of 6 % by 2100. The temperature model, using past rates of change relative to the past increase in regional mean annual air temperatures (1°C/30 year), projected a net change of 17 % in response to expected warming of 2, 4, and 6°C at the end of the three periods. A rate-adjusted temperature model, which adjusted transition rates (±50 %) based on assigned feedbacks associated with 23 biophysical drivers, estimated a net change of 13 %, with 33 ecotypes gaining and 23 ecotypes losing area. Major drivers included shrub and tree expansion, fire, succession, and thermokarst. Overall, projected A. R. DeGange Alaska Climate Science Center, U. S. Geological Survey, 4210 University Drive, Anchorage, AK 99508, USA changes will be modest over the next century even though climate warming increased transition rates up to 9 fold. The strength of this state-transition modeling is that it used a large dataset of past changes to provide a comprehensive assessment of likely future changes associated with numerous drivers affecting the full diversity of ecosystems across a broad region.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Projected changes in diverse ecosystems from climate warming and biophysical drivers in northwest Alaska

Jorgenson¹,

Marcot

Swanson

et al. 2015

Climatic Change

View full text Add to dashboard Cite

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“…With the current warming trajectory, the colonisation of shrubs could be significant (e.g. Pearson et al, 2013;Frost and Epstein, 2014), and as observed by Blok et al (2011b) it could lead to an Arctic greening (Blok et al, 2011b;Bonfils et al, 2012) with increased leaf area, decreased surface albedo in winter, and potential increase of temperatures at local and regional scales. For example, based on statistical modelling, Pearson et al (2013) show that more than half of the vegetated areas of the Arctic are likely to shift to a different physiognomic class by 2050, with a > 50 % increase in woody cover.…”

Section: Introductionmentioning

confidence: 99%

“…Altogether, highlatitude vegetation significantly affects regional and global climates and overall leads to positive climate feedbacks (e.g. Pearson et al, 2013). High-latitude vegetation must therefore be correctly represented in ESMs, in particular in the light of projected strong Arctic and sub-Arctic climate warming and related biogeographic shifts.…”

Section: Introductionmentioning

confidence: 99%

Towards a more detailed representation of high-latitude vegetation in the global land surface model ORCHIDEE (ORC-HL-VEGv1.0)

et al. 2017

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Abstract. Simulation of vegetation-climate feedbacks in high latitudes in the ORCHIDEE land surface model was improved by the addition of three new circumpolar plant functional types (PFTs), namely non-vascular plants representing bryophytes and lichens, Arctic shrubs and Arctic C 3 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 (i.e. adaptability and resilience to severe climatic constraints) 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 C 3 grasses have the same equations as in the original ORCHIDEE version, but different parameter values, optimised from in situ observations of biomass and net primary productivity (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 optimisation procedure. With the new PFTs, we obtain a lower 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 increased 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 yr −1 ), owing to less evapotranspiration. Future developments should focus on the competition between these three PFTs and boreal tree PFTs, in order to simulate their area changes in response to climate change, and the effect of carbon-nitrogen interactions.

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Rates and radiocarbon content of summer ecosystem respiration in response to long‐term deeper snow in the High Arctic of NW Greenland

Lupascu

Welker

et al. 2014

JGR Biogeosciences

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The amount and timing of snow cover control the cycling of carbon (C), water, and energy in arctic ecosystems. The implications of changing snow cover for regional C budgets, biogeochemistry, hydrology, and albedo due to climate change are rudimentary, especially for the High Arctic. In a polar semidesert of NW Greenland, we used a~10 year old snow manipulation experiment to quantify how deeper snow affects magnitude, seasonality, and 14 C content of summer C emissions. We monitored ecosystem respiration (R eco ), soil CO 2 , and their 14 C contents over three summers in vegetated and bare areas. Additional snowpack, elevated soil water content (SWC), and temperature throughout the growing season in vegetated, but not in bare, areas. Daily R eco was positively correlated to temperature, but negatively correlated to SWC; consequently, we found no effect of increased snow on daily flux. Cumulative summertime R eco was not related to annual snowfall, but to water year precipitation (winter snow plus summer rain). Experimentally increased snowpack shortened the growing season length and reduced summertime R eco up to 40%. Soil CO 2 was older under increased snow. However, we found no effect of snow depth on the R eco age because older C emissions were masked by younger CO 2 produced from the litter layer or plant respiration. In the High Arctic, anticipated changes in precipitation regime associated with warming are a key uncertainty for understanding future C cycling. In polar semideserts, water year precipitation is an important driver of summertime R eco . Permafrost C is vulnerable to changes in snowpack, with a deeper snowpack-promoting decomposition of older soil C.

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Shifts in Arctic vegetation and associated feedbacks under climate change

Cited by 693 publications

References 28 publications

Projected changes in diverse ecosystems from climate warming and biophysical drivers in northwest Alaska

Projected changes in diverse ecosystems from climate warming and biophysical drivers in northwest Alaska

Towards a more detailed representation of high-latitude vegetation in the global land surface model ORCHIDEE (ORC-HL-VEGv1.0)

Rates and radiocarbon content of summer ecosystem respiration in response to long‐term deeper snow in the High Arctic of NW Greenland

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