| 1. |
- Lindgren, Amelie, et al.
(författare)
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Extensive loss of past permafrost carbon but a net accumulation into present-day soils
- 2018
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 560:7717, s. 219-222
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Tidskriftsartikel (refereegranskat)abstract
- Atmospheric concentrations of carbon dioxide increased between the Last Glacial Maximum (LGM, around 21,000 years ago) and the preindustrial era(1). It is thought that the evolution of this atmospheric carbon dioxide (and that of atmospheric methane) during the glacial-to-interglacial transition was influenced by organic carbon that was stored in permafrost during the LGM and then underwent decomposition and release following thaw(2,3). It has also been suggested that the rather erratic atmospheric delta C-13 and Delta C-14 signals seen during deglaciation(1.4) could partly be explained by the presence of a large terrestrial inert LGM carbon stock, despite the biosphere being less productive (and therefore storing less carbon)(5,6). Here we present an empirically derived estimate of the carbon stored in permafrost during the LGM by reconstructing the extent and carbon content of LGM biomes, peatland regions and deep sedimentary deposits. We find that the total estimated soil carbon stock for the LGM northern permafrost region is smaller than the estimated present-day storage (in both permafrost and non-permafrost soils) for the same region. A substantial decrease in the permafrost area from the LGM to the present day has been accompanied by a roughly 400-petagram increase in the total soil carbon stock. This increase in soil carbon suggests that permafrost carbon has made no net contribution to the atmospheric carbon pool since the LGM. However, our results also indicate potential postglacial reductions in the portion of the carbon stock that is trapped in permafrost, of around 1,000 petagrams, supporting earlier studies(7). We further find that carbon has shifted from being primarily stored in permafrost mineral soils and loess deposits during the LGM, to being roughly equally divided between peatlands, mineral soils and permafrost loess deposits today.
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| 2. |
- Lindgren, Amelie, et al.
(författare)
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GIS-based Maps and Area Estimates of Northern Hemisphere Permafrost Extent during the Last Glacial Maximum
- 2016
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Ingår i: Permafrost and Periglacial Processes. - : Wiley. - 1045-6740 .- 1099-1530. ; 27:1, s. 6-16
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Tidskriftsartikel (refereegranskat)abstract
- This study presents GIS-based estimates of permafrost extent in the northern circumpolar region during the Last Glacial Maximum (LGM), based on a review of previously published maps and compilations of field evidence in the form of ice-wedge pseudomorphs and relict sand wedges. We focus on field evidence localities in areas thought to have been located along the past southern border of permafrost. We present different reconstructions of permafrost extent, with areal estimates of exposed sea shelf, ice sheets and glaciers, to assess areas of minimum, likely and maximum permafrost extents. The GIS-based mapping of these empirical reconstructions allows us to estimate the likely area of northern permafrost during the LGM as 34.5 million km(2) (which includes 4.7 million km(2) of permafrost on exposed coastal sea shelves). The minimum estimate is 32.7 million km(2) and the maximum estimate is 35.3 million km(2). The extent of LGM permafrost is estimated to have been between c. 9.1 to 11.7 million km(2) larger than its current extent on land (23.6 million km(2)). However, 2.4 million km(2) of the lost land area currently remains as subsea permafrost on the submerged coastal shelves. The LGM permafrost extent in the northern circumpolar region during the LGM was therefore about 33 percent larger than at present. The net loss of northern permafrost since the LGM is due to its disappearance in large parts of Eurasia, which is not compensated for by gains in North America in areas formerly covered by the Laurentide ice sheet.
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| 3. |
- Lindgren, Amelie, et al.
(författare)
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Millennial-scale analysis of land >23 ˚N as a carbon source and sink since the Last Glacial Maximum
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The transfers of carbon between land, ocean and atmosphere, and their relation to temperature variability over glacial and interglacial cycles continue to intrigue the scientific community. Over the past four decades, many have focused on the role of the Southern Ocean to explain the atmospheric carbon dioxide (CO2) patterns seen in ice core records, but recent advances also include mentions of a possible terrestrial component. We quantify important terrestrial organic soil carbon (C) stocks north of 23˚, using palaeo-data and modeled climate to reconstruct terrestrial C dynamics from the Last Glacial Maximum until present at millennial time steps. During the deglaciation, C storage declined to reach a minimum around 10 kyr BP, a trend which then turned and led to progressively higher soil C stocks during the Holocene. Net changes in mineral soil C stocks are small, even though significant geographic shifts occurred; instead, deglacial and interglacial terrestrial C stock dynamics are dominated by losses from permafrost loess, inundation of continental shelves and gains in peatlands, processes commonly overlooked in complex Earth System Models.
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| 4. |
- Lindgren, Amelie, 1987-
(författare)
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Northern Permafrost Region Soil Carbon Dynamics since the Last Glacial Maximum : a terrestrial component in the glacial to interglacial carbon cycle
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- At the Last Glacial Maximum (LGM), after ~100,000 years of relatively cold temperatures and progressively lower atmospheric carbon dioxide (CO2) concentrations, CO2 levels reached ~180 ppm, which is less than half of what we see today in a much warmer world (~400 ppm). Although much of this increase since the LGM is due to human-induced emissions, about 100 ppm of this increase can be attributed to natural variations seen over glacial to interglacial cycles. The sources for this natural CO2 rise remain unclear despite considerable efforts to constrain its origin. This thesis attempts to describe and quantify the role of soil carbon in this context, with emphasis on the permafrost hypothesis, which states that a shift from glacial to interglacial conditions released permafrost soil carbon to the atmosphere during the deglaciation. We present empirical estimates of the change in the Northern permafrost area between the LGM and present, and the associated soil carbon stock changes. We also partition these soil carbon stock changes at millennial intervals to capture not only the size but the timing of change. We find that the soil carbon stocks north of the Tropics decreased after the LGM to reach a minimum around 10,000 years ago, after which stocks increased to more than compensate for past losses. This may present part of a solution to untangle the marine and atmospheric 13C record, where the marine records suggest that the terrestrial carbon stock has grown since the LGM, while the atmospheric record also indicates terrestrial losses. To estimate the mineral soil carbon stocks, we have relied on vegetation reconstructions. Some of these reconstructions were created with a novel data-driven machine learning approach. This method may facilitate robust vegetation reconstruction when evidence of past conditions is readily available. Results in this thesis highlight the importance of permafrost, loess deposits and peatlands when considering the soil carbon cycle over long time scales.
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| 5. |
- Lindgren, Amelie, et al.
(författare)
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Reconstructing Past Global Vegetation With Random Forest Machine Learning, Sacrificing the Dynamic Response for Robust Results
- 2021
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Ingår i: Journal of Advances in Modeling Earth Systems. - 1942-2466. ; 13:2
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Tidskriftsartikel (refereegranskat)abstract
- Vegetation is an important component in the Earth system, providing a direct link between the biosphere and atmosphere. As such, a representative vegetation pattern is needed to accurately simulate climate. We attempt to model global vegetation (biomes) with a data‐driven approach, to test if this allows us to create robust global and regional vegetation patterns. This not only provides quantitative reconstructions of past vegetation cover as a climate forcing, but also improves our understanding of past land cover‐climate interactions which have important implications for the future. By using a Random Forest (RF) machine learning tool, we train the vegetation reconstruction with available biomized pollen data of present and past conditions to produce broad‐scale vegetation patterns for the preindustrial (PI), the mid‐Holocene (MH, ∼6,000 years ago), and the Last Glacial Maximum (LGM, ∼21,000 years ago). We test the method's robustness by introducing a systematic temperature bias based on existing climate model spread and compare the result with that of LPJ‐GUESS, an individual‐based dynamic global vegetation model. The results show that the RF approach is able to produce robust patterns for periods and regions well constrained by evidence (the PI and the MH), but fails when evidence is scarce (the LGM). The apparent robustness of this method is achieved at the cost of sacrificing the ability to model dynamic vegetation response to a changing climate.
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| 6. |
- Lindgren, Amelie, et al.
(författare)
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Reconstructing past vegetation with Random Forest Machine Learning, sacrificing the dynamic response for robust results
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Vegetation is an important feature in the Earth system, providing a direct link between the biosphere and atmosphere. As such, a representative vegetation pattern is needed to accurately simulate climate. We attempt to reconstruct past and present vegetation with a data driven approach, to test if this allows us to create robust global and regional vegetation patterns. The motivation for this stems from the possibility of avoiding circular arguments when studying past time periods where vegetation is used to reconstruct climate, and climate is used to construct vegetation. By using the Random Forest machine learning tool, we train the vegetation reconstruction with available biomized pollen data of present and past conditions and are able to produce reasonable broad-scale vegetation patterns for the Pre-Industrial and the Mid-Holocene together with a few modeled climate variables. We test the methods robustness by introducing a systematic temperature bias based on existing climate model spread and compare the result with that of LPJ-GUESS, a process-based dynamic global vegetation model. Results prove that the Random Forest approach is able to produce robust patterns for periods and regions well constrained by evidence, but fails when evidence is scarce. The robustness is achieved by sacrificing a dynamic vegetation response to a changing climate.
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