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  • 1
    Online Resource
    Online Resource
    The big data revolution and paleoecology
    Past Global Changes (PAGES) ; 2017
    In:  Past Global Changes Magazine Vol. 25, No. 2 ( 2017-8), p. 96-97
    Details
    In: Past Global Changes Magazine, Past Global Changes (PAGES), Vol. 25, No. 2 ( 2017-8), p. 96-97
    Type of Medium: Online Resource
    ISSN: 2411-605X , 2411-9180
    URL: Issue
    URL: Article
    DOI: 10.22498/pages.25.2
    DOI: 10.22498/pages.25.2.96
    Language: Unknown
    Publisher: Past Global Changes (PAGES)
    Publication Date: 2017
    detail.hit.zdb_id: 2779253-5
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  • 2
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    Evaluation of the realism of climate reconstruction using the Coexistence Approach with modern pollen samples from the Qinghai–Tibetan Plateau
    Elsevier BV ; 2015
    In:  Review of Palaeobotany and Palynology Vol. 219 ( 2015-08), p. 172-182
    Details
    In: Review of Palaeobotany and Palynology, Elsevier BV, Vol. 219 ( 2015-08), p. 172-182
    Type of Medium: Online Resource
    ISSN: 0034-6667
    URL: Article
    DOI: 10.1016/j.revpalbo.2015.03.011
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2015
    detail.hit.zdb_id: 1497512-9
    detail.hit.zdb_id: 209897-0
    SSG: 13
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  • 3
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    Frost and leaf‐size gradients in forests: global patterns and experimental evidence
    Wiley ; 2018
    In:  New Phytologist Vol. 219, No. 2 ( 2018-07), p. 565-573
    Details
    In: New Phytologist, Wiley, Vol. 219, No. 2 ( 2018-07), p. 565-573
    Abstract: Explanations of leaf size variation commonly focus on water availability, yet leaf size also varies with latitude and elevation in environments where water is not strongly limiting. We provide the first conclusive test of a prediction of leaf energy balance theory that may explain this pattern: large leaves are more vulnerable to night‐time chilling, because their thick boundary layers impede convective exchange with the surrounding air. Seedlings of 15 New Zealand evergreens spanning 12‐fold variation in leaf width were exposed to clear night skies, and leaf temperatures were measured with thermocouples. We then used a global dataset to assess several climate variables as predictors of leaf size in forest assemblages. Leaf minus air temperature was strongly correlated with leaf width, ranging from −0.9 to −3.2°C in the smallest‐ and largest‐leaved species, respectively. Mean annual temperature and frost‐free period were good predictors of evergreen angiosperm leaf size in forest assemblages, but no climate variable predicted deciduous leaf size. Although winter deciduousness makes large leaves possible in strongly seasonal climates, large‐leaved evergreens are largely confined to frost‐free climates because of their susceptibility to radiative cooling. Evergreen leaf size data can therefore be used to enhance vegetation models, and to infer palaeotemperatures from fossil leaf assemblages.
    Type of Medium: Online Resource
    ISSN: 0028-646X , 1469-8137
    URL: Issue
    URL: Article
    DOI: 10.1111/nph.2018.219.issue-2
    DOI: 10.1111/nph.15202
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 208885-X
    detail.hit.zdb_id: 1472194-6
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  • 4
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    A model analysis of climate and CO 2 controls on tree growth and carbon allocation in a semi-arid woodland
    Elsevier BV ; 2016
    In:  Ecological Modelling Vol. 342 ( 2016-12), p. 175-185
    Details
    In: Ecological Modelling, Elsevier BV, Vol. 342 ( 2016-12), p. 175-185
    Type of Medium: Online Resource
    ISSN: 0304-3800
    URL: Article
    DOI: 10.1016/j.ecolmodel.2016.10.005
    RVK:
    AR 10100
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2016
    detail.hit.zdb_id: 191971-4
    detail.hit.zdb_id: 2000879-X
    SSG: 12
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  • 5
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    What have we learnt from palaeoclimate simulations?
    Wiley ; 2016
    In:  Journal of Quaternary Science Vol. 31, No. 4 ( 2016-05), p. 363-385
    Details
    In: Journal of Quaternary Science, Wiley, Vol. 31, No. 4 ( 2016-05), p. 363-385
    Abstract: There has been a gradual evolution in the way that palaeoclimate modelling and palaeoenvironmental data are used together to understand how the Earth System works, from an initial and largely descriptive phase through explicit hypothesis testing to diagnosis of underlying mechanisms. Analyses of past climate states are now regarded as integral to the evaluation of climate models, and have become part of the toolkit used to assess the likely realism of future projections. Palaeoclimate assessment has demonstrated that changes in large‐scale features of climate that are governed by the energy and water balance show consistent responses to changes in forcing in different climate states, and these consistent responses are reproduced by climate models. However, state‐of‐the‐art models are still largely unable to reproduce observed changes in climate at a regional scale reliably. While palaeoclimate analyses of state‐of‐the‐art climate models suggest an urgent need for model improvement, much work is also needed on extending and improving palaeoclimate reconstructions and quantifying and reducing both numerical and interpretative uncertainties.
    Type of Medium: Online Resource
    ISSN: 0267-8179 , 1099-1417
    URL: Issue
    URL: Article
    DOI: 10.1002/jqs.v31.4
    DOI: 10.1002/jqs.2842
    Language: English
    Publisher: Wiley
    Publication Date: 2016
    detail.hit.zdb_id: 2031875-3
    SSG: 13
    SSG: 14
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  • 6
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    Allocation Mechanisms of Non-Structural Carbohydrates of Robinia pseudoacacia L. Seedlings in Response to Drought and Waterlogging
    MDPI AG ; 2018
    In:  Forests Vol. 9, No. 12 ( 2018-12-04), p. 754-
    Details
    In: Forests, MDPI AG, Vol. 9, No. 12 ( 2018-12-04), p. 754-
    Abstract: Climate change is likely to lead to an increased frequency of droughts and floods, both of which are implicated in large-scale carbon allocation and tree mortality worldwide. Non-structural carbohydrates (NSCs) play an important role in tree survival under stress, but how NSC allocation changes in response to drought or waterlogging is still unclear. We measured soluble sugars (SS) and starch in leaves, twigs, stems and roots of Robinia pseudoacacia L. seedlings that had been subjected to a gradient in soil water availability from extreme drought to waterlogged conditions for a period of 30 days. Starch concentrations decreased and SS concentrations increased in tissues of R. pseudoacacia seedlings, such that the ratio of SS to starch showed a progressive increase under both drought and waterlogging stress. The strength of the response is asymmetric, with the largest increase occurring under extreme drought. While the increase in SS concentration in response to extreme drought is the largest in roots, the increase in the ratio of SS to starch is the largest in leaves. Individual components of SS showed different responses to drought and waterlogging across tissues: glucose concentrations increased significantly with drought in all tissues but showed little response to waterlogging in twigs and stems; sucrose and fructose concentrations showed marked increases in leaves and roots in response to drought but a greater response to drought and waterlogging in stems and twigs. These changes are broadly compatible with the roles of individual SS under conditions of water stress. While it is important to consider the role of NSC in buffering trees against mortality under stress, modelling this behaviour is unlikely to be successful unless it accounts for different responses within organs and the type of stress involved.
    Type of Medium: Online Resource
    ISSN: 1999-4907
    URL: Article
    DOI: 10.3390/f9120754
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2527081-3
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  • 7
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    The PMIP4 contribution to CMIP6 – Part 1: Overview and over-arching analysis plan
    Copernicus GmbH ; 2018
    In:  Geoscientific Model Development Vol. 11, No. 3 ( 2018-03-16), p. 1033-1057
    Details
    In: Geoscientific Model Development, Copernicus GmbH, Vol. 11, No. 3 ( 2018-03-16), p. 1033-1057
    Abstract: Abstract. This paper is the first of a series of four GMD papers on the PMIP4-CMIP6 experiments. Part 2 (Otto-Bliesner et al., 2017) gives details about the two PMIP4-CMIP6 interglacial experiments, Part 3 (Jungclaus et al., 2017) about the last millennium experiment, and Part 4 (Kageyama et al., 2017) about the Last Glacial Maximum experiment. The mid-Pliocene Warm Period experiment is part of the Pliocene Model Intercomparison Project (PlioMIP) – Phase 2, detailed in Haywood et al. (2016).The goal of the Paleoclimate Modelling Intercomparison Project (PMIP) is to understand the response of the climate system to different climate forcings for documented climatic states very different from the present and historical climates. Through comparison with observations of the environmental impact of these climate changes, or with climate reconstructions based on physical, chemical, or biological records, PMIP also addresses the issue of how well state-of-the-art numerical models simulate climate change. Climate models are usually developed using the present and historical climates as references, but climate projections show that future climates will lie well outside these conditions. Palaeoclimates very different from these reference states therefore provide stringent tests for state-of-the-art models and a way to assess whether their sensitivity to forcings is compatible with palaeoclimatic evidence. Simulations of five different periods have been designed to address the objectives of the sixth phase of the Coupled Model Intercomparison Project (CMIP6): the millennium prior to the industrial epoch (CMIP6 name: past1000); the mid-Holocene, 6000 years ago (midHolocene); the Last Glacial Maximum, 21 000 years ago (lgm); the Last Interglacial, 127 000 years ago (lig127k); and the mid-Pliocene Warm Period, 3.2 million years ago (midPliocene-eoi400). These climatic periods are well documented by palaeoclimatic and palaeoenvironmental records, with climate and environmental changes relevant for the study and projection of future climate changes. This paper describes the motivation for the choice of these periods and the design of the numerical experiments and database requests, with a focus on their novel features compared to the experiments performed in previous phases of PMIP and CMIP. It also outlines the analysis plan that takes advantage of the comparisons of the results across periods and across CMIP6 in collaboration with other MIPs.
    Type of Medium: Online Resource
    ISSN: 1991-9603
    URL: Article
    DOI: 10.5194/gmd-11-1033-2018
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2018
    detail.hit.zdb_id: 2456725-5
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  • 8
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    Response of simulated burned area to historical changes in environmental and anthropogenic factors: a comparison of seven fire models
    Copernicus GmbH ; 2019
    In:  Biogeosciences Vol. 16, No. 19 ( 2019-10-09), p. 3883-3910
    Details
    In: Biogeosciences, Copernicus GmbH, Vol. 16, No. 19 ( 2019-10-09), p. 3883-3910
    Abstract: Abstract. Understanding how fire regimes change over time is of major importance for understanding their future impact on the Earth system, including society. Large differences in simulated burned area between fire models show that there is substantial uncertainty associated with modelling global change impacts on fire regimes. We draw here on sensitivity simulations made by seven global dynamic vegetation models participating in the Fire Model Intercomparison Project (FireMIP) to understand how differences in models translate into differences in fire regime projections. The sensitivity experiments isolate the impact of the individual drivers on simulated burned area, which are prescribed in the simulations. Specifically these drivers are atmospheric CO2 concentration, population density, land-use change, lightning and climate. The seven models capture spatial patterns in burned area. However, they show considerable differences in the burned area trends since 1921. We analyse the trajectories of differences between the sensitivity and reference simulation to improve our understanding of what drives the global trends in burned area. Where it is possible, we link the inter-model differences to model assumptions. Overall, these analyses reveal that the largest uncertainties in simulating global historical burned area are related to the representation of anthropogenic ignitions and suppression and effects of land use on vegetation and fire. In line with previous studies this highlights the need to improve our understanding and model representation of the relationship between human activities and fire to improve our abilities to model fire within Earth system model applications. Only two models show a strong response to atmospheric CO2 concentration. The effects of changes in atmospheric CO2 concentration on fire are complex and quantitative information of how fuel loads and how flammability changes due to this factor is missing. The response to lightning on global scale is low. The response of burned area to climate is spatially heterogeneous and has a strong inter-annual variation. Climate is therefore likely more important than the other factors for short-term variations and extremes in burned area. This study provides a basis to understand the uncertainties in global fire modelling. Both improvements in process understanding and observational constraints reduce uncertainties in modelling burned area trends.
    Type of Medium: Online Resource
    ISSN: 1726-4189
    URL: Article
    DOI: 10.5194/bg-16-3883-2019
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2019
    detail.hit.zdb_id: 2158181-2
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  • 9
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    Changes in biomass allocation buffer low CO2 effects on tree growth during the last glaciation
    Springer Science and Business Media LLC ; 2017
    In:  Scientific Reports Vol. 7, No. 1 ( 2017-02-24)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 7, No. 1 ( 2017-02-24)
    Abstract: Isotopic measurements on junipers growing in southern California during the last glacial, when the ambient atmospheric [CO 2 ] (c a ) was ~180 ppm, show the leaf-internal [CO 2 ] (c i ) was approaching the modern CO 2 compensation point for C 3 plants. Despite this, stem growth rates were similar to today. Using a coupled light-use efficiency and tree growth model, we show that it is possible to maintain a stable c i /c a ratio because both vapour pressure deficit and temperature were decreased under glacial conditions at La Brea, and these have compensating effects on the c i /c a ratio. Reduced photorespiration at lower temperatures would partly mitigate the effect of low c i on gross primary production, but maintenance of present-day radial growth also requires a ~27% reduction in the ratio of fine root mass to leaf area. Such a shift was possible due to reduced drought stress under glacial conditions at La Brea. The necessity for changes in allocation in response to changes in [CO 2 ] is consistent with increased below-ground allocation, and the apparent homoeostasis of radial growth, as c a increases today.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/srep43087
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2615211-3
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  • 10
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    Table 2: Results of cross-validation for the forest tree taxa.
    PeerJ ; 2018
    In:  PeerJ Vol. 6 ( 2018-08-22), p. e5457-
    Details
    In: PeerJ, PeerJ, Vol. 6 ( 2018-08-22), p. e5457-
    Abstract: Potential natural vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location if not impacted by human activities. PNV is useful for raising public awareness about land degradation and for estimating land potential. This paper presents results of assessing machine learning algorithms—neural networks (nnet package), random forest (ranger), gradient boosting (gbm), K-nearest neighborhood (class) and Cubist—for operational mapping of PNV. Three case studies were considered: (1) global distribution of biomes based on the BIOME 6000 data set (8,057 modern pollen-based site reconstructions), (2) distribution of forest tree taxa in Europe based on detailed occurrence records (1,546,435 ground observations), and (3) global monthly fraction of absorbed photosynthetically active radiation (FAPAR) values (30,301 randomly-sampled points). A stack of 160 global maps representing biophysical conditions over land, including atmospheric, climatic, relief, and lithologic variables, were used as explanatory variables. The overall results indicate that random forest gives the overall best performance. The highest accuracy for predicting BIOME 6000 classes (20) was estimated to be between 33% (with spatial cross-validation) and 68% (simple random sub-setting), with the most important predictors being total annual precipitation, monthly temperatures, and bioclimatic layers. Predicting forest tree species (73) resulted in mapping accuracy of 25%, with the most important predictors being monthly cloud fraction, mean annual and monthly temperatures, and elevation. Regression models for FAPAR (monthly images) gave an R-square of 90% with the most important predictors being total annual precipitation, monthly cloud fraction, CHELSA bioclimatic layers, and month of the year, respectively. Further developments of PNV mapping could include using all GBIF records to map the global distribution of plant species at different taxonomic levels. This methodology could also be extended to dynamic modeling of PNV, so that future climate scenarios can be incorporated. Global maps of biomes, FAPAR and tree species at one km spatial resolution are available for download via http://dx.doi.org/10.7910/DVN/QQHCIK .
    Type of Medium: Online Resource
    ISSN: 2167-8359
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    DOI: 10.7717/peerj.5457
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    DOI: 10.7717/peerj.5457/table-1
    DOI: 10.7717/peerj.5457/table-2
    Language: English
    Publisher: PeerJ
    Publication Date: 2018
    detail.hit.zdb_id: 2703241-3
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