Model–data comparison and data assimilation of mid-Holocene Arctic sea ice concentration

Klein, François; Goosse, Hugues; Mairesse, Aurélien; Vernal, Anne de

doi:10.5194/cp-10-1145-2014

Cited by 8 publications

(3 citation statements)

References 69 publications

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“…HadGEM2-ES is an updated version of the same model chosen for the Holocene anomaly fields. It is in the middle of the range of models contributing to the CMIP5 ensemble in terms of simulated temperature change across the Arctic region (Andrews et al, 2012;Klein et al, 2014). Atmospheric CO 2 concentrations for model input were taken from the RCP8.5 scenario extracted from the International Institute for Applied Systems Analysis website (IIASA; http: //tntcat.iiasa.ac.at/RcpDb/; page visited 14 June 2017).…”

Section: Climate Change Experimentsmentioning

confidence: 99%

Modelling past, present and future peatland carbon accumulation across the pan-Arctic region

2017

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Abstract. Most northern peatlands developed during the Holocene, sequestering large amounts of carbon in terrestrial ecosystems. However, recent syntheses have highlighted the gaps in our understanding of peatland carbon accumulation. Assessments of the long-term carbon accumulation rate and possible warming-driven changes in these accumulation rates can therefore benefit from process-based modelling studies. We employed an individual-based dynamic global ecosystem model with dynamic peatland and permafrost functionalities and patch-based vegetation dynamics to quantify long-term carbon accumulation rates and to assess the effects of historical and projected climate change on peatland carbon balances across the pan-Arctic region. Our results are broadly consistent with published regional and global carbon accumulation estimates. A majority of modelled peatland sites in Scandinavia, Europe, Russia and central and eastern Canada change from carbon sinks through the Holocene to potential carbon sources in the coming century. In contrast, the carbon sink capacity of modelled sites in Siberia, far eastern Russia, Alaska and western and northern Canada was predicted to increase in the coming century. The greatest changes were evident in eastern Siberia, north-western Canada and in Alaska, where peat production hampered by permafrost and low productivity due the cold climate in these regions in the past was simulated to increase greatly due to warming, a wetter climate and higher CO 2 levels by the year 2100. In contrast, our model predicts that sites that are expected to experience reduced precipitation rates and are currently permafrost free will lose more carbon in the future.

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Section: Climate Change Experimentsmentioning

confidence: 99%

Modelling past, present and future peatland carbon accumulation across the pan-Arctic region

2017

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“…HadGEM2-ES is an updated version of the same model chosen for the Holocene anomaly fields. It is in the middle-of-the-range of models contributing to the CMIP5 ensemble in terms of simulated temperature change across the Arctic region (Andrews et al, 2012;Klein et al, 2014). Model input of atmospheric CO 2 concentrations was taken from the RCP8.5 scenario, extracted from the International Institute for Applied Systems Analysis website (IIASA; http://tntcat.iiasa.ac.at/RcpDb/; page visited 2 Feb 2017).…”

Section: Climate Change Experimentsmentioning

confidence: 99%

Modelling past, present and future peatland carbon accumulation across the pan-Arctic

Chaudhary¹,

Miller²,

Smith³

2017

Preprint

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Abstract. Most northern peatlands developed during the Holocene, sequestering large amounts of carbon in terrestrial ecosystems. However, recent syntheses have highlighted the gaps in our understanding of peatland carbon accumulation. Assessments of the long-term carbon accumulation rate and possible warming driven changes in these accumulation rates can therefore benefit from process-based modelling studies. We employed an individual- and patch-based dynamic global ecosystem model with dynamic peatland and permafrost functionality and vegetation dynamics to quantify long-term carbon accumulation rates and to assess the effects of historical and projected climate change on peatland carbon balances across the pan-Arctic. Our results are broadly consistent with published regional and global carbon accumulation estimates. A majority of modelled peatland sites in Scandinavia, Europe, Russia and Central and eastern Canada change from carbon sinks through the Holocene to potential carbon sources in the coming century. In contrast, the carbon sink capacity of modelled sites in Siberia, Far East Russia, Alaska and western and northern Canada was predicted to increase in the coming century. The greatest changes were evident in eastern Siberia, northwest Canada and in Alaska, where peat production, from being hampered by permafrost and low productivity due the cold climate in these regions in the past, was simulated to increase greatly due to warming, wetter climate and greater CO2 levels by the year 2100. In contrast, our model predicts that sites that are expected to experience reduced precipitation rates and are currently permafrost free will lose more carbon in the future.

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“…Some ensemble member selection techniques select a single simulation from an ensemble that is closest to the empirical information of the climate (Goosse et al 2006;Crespin et al 2009;Goosse et al 2010;Matsikaris et al 2015). In an alternative setup (Goosse et al 2012;Annan and Hargreaves 2012;Mairesse et al 2013;Klein et al 2014), several highly likely members are copied proportionally to their likelihood. Finally, in other ensemble-based DA schemes, Kalman filters or variants of them are used (Dirren and Hakim 2005;Huntley and Hakim 2010;Pendergrass et al 2012;Bhend et al 2012;Steiger et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Assimilating continental mean temperatures to reconstruct the climate of the late pre-industrial period

Matsikaris

Jungclaus

2015

Clim Dyn

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An on-line, ensemble-based data assimilation (DA) method is performed to reconstruct the climate for 1750–1850 AD, and the performance is evaluated on large and small spatial scales. We use a low-resolution version of the Max Planck Institute for Meteorology MPI-ESM model and assimilate the PAGES 2K continental mean temperature reconstructions for the Northern Hemisphere (NH). The ensembles are generated sequentially for sub-periods based on the analysis of previous sub-periods. The assimilation has good skill for large-scale temperatures, but there is no agreement between the DA analysis and proxy-based reconstructions for small-scale temperature patterns within Europe or with reconstructions for the North Atlantic Oscillation (NAO) index. To explain the lack of added value in small spatial scales, a maximum covariance analysis (MCA) of links between NH temperature and sea level pressure is performed based on a control simulation with MPI-ESM. For annual values, winter and spring the Northern Annular Mode (NAM) is the pattern that is most closely linked to the NH continental temperatures, while for summer and autumn it is a wave-like pattern. This link is reproduced in the DA for winter, spring and annual means, providing potential for constraining the NAM/NAO phase and in turn regional temperature variability. It is shown that the lack of actual small-scale skill is likely due to the fact that the link might be too weak, as the NH continental mean temperatures are not the best predictors for large-scale circulation anomalies, or that the PAGES 2K temperatures include noise. Both factors can lead to circulation anomalies in the DA analysis that are substantially different from reality, leading to unrealistic representation of small-scale temperature variability. Moreover, we show that even if the true amplitudes of the leading MCA circulation patterns were known, there is still a large amount of unexplained local temperature variance. Based on these results, we argue that assimilating temperature reconstructions with a higher spatial resolution might improve the DA performance. © 2015 Springer-Verlag Berlin Heidelber

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Model–data comparison and data assimilation of mid-Holocene Arctic sea ice concentration

Cited by 8 publications

References 69 publications

Modelling past, present and future peatland carbon accumulation across the pan-Arctic region

Modelling past, present and future peatland carbon accumulation across the pan-Arctic region

Modelling past, present and future peatland carbon accumulation across the pan-Arctic

Assimilating continental mean temperatures to reconstruct the climate of the late pre-industrial period

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