Boreal carbon loss due to poleward shift in low-carbon ecosystems

Journal name:
Nature Geoscience
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Published online

Climate change can be thought of in terms of geographical shifts in climate properties. Examples include assessments of shifts in habitat distributions1, of the movement needed to maintain constant temperature or precipitation2, and of the emergence and disappearance of climate zones3. Here I track the movement of analogue climates within climate models. From the model simulations, I define a set of vectors that link a historical reference climate for each location to the location in a changed climate whose seasonal temperature and precipitation cycles best match the reference climate. I use these vectors to calculate the change in vegetation carbon storage with climate change due to ecosystems following climate analogues. Comparing the derived carbon content change to direct carbon projections by coupled carbon–climate models reveals two regions of divergence. In the tropical forests, vector projections are fundamentally uncertain because of a lack of close climatic analogues. In the southern boreal forest, carbon losses are projected in the vector perspective because low-carbon ecosystems shift polewards. However, the majority of carbon–climate models—typically without explicit simulation of the disturbance and mortality processes behind such shifts—instead project vegetation carbon gains throughout the boreal region. Southern boreal carbon loss as a result of ecosystem shift is likely to offset carbon gains from northern boreal forest expansion.

At a glance


  1. Climate analogue velocity vectors and statistical difference of analogue climates.
    Figure 1: Climate analogue velocity vectors and statistical difference of analogue climates.

    Multi-model mean climate change and associated best fit for the forward (where climate is going to) and reverse (where climate is coming from) directions. Arrows for both vectors point in the time-forwards direction. a,c, Vectors of mean distance and direction to the best fit climate for the mid-century period (2040–2059) RCP4.5, relative to the baseline historical period (1960–1989). Vector length shows actual distance travelled by the analogue climate over the interval, and the vector colours indicate the mean climate speed. b,d, Multi-model ensemble mean error values of the best climate analogue gridcells. Poor fits (large SED values) in b,d correspond to areas of disappearing and novel climates, respectively, from ref. 3.

  2. Lagrangian and ESM terrestrial carbon responses to climate change.
    Figure 2: Lagrangian and ESM terrestrial carbon responses to climate change.

    Comparison of vegetation carbon changes in the 5 ESMs participating in the CMIP5 esmFdbk2 experiment. Left column (a,d,g,j,m): climate vectors (in reverse, ‘coming from’ direction) overlaid onto historical-period mean biomass carbon concentration from each model (kgCm−2). Middle column (b,e,h,k,n): change in vegetation carbon between the historical (1960–1989) and future (2040–2060 of RCP4.5) climates calculated as a carbon transport by climate velocity approach. Right column (c,f,i,l,o): change in vegetation carbon between the historical and future climates as calculated by the terrestrial carbon component of the ESMs. ps, Zonal mean plots show initial vegetation C (p), Lagrangian change in vegetation C (q), ESM change in vegetation C (r), and multi-model means (s).


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