Elevational transects of modern pollen samples: Site-specific temperatures as a tool for palaeoclimate reconstructions in the Alps

The Holocene ◽  
2018 ◽  
Vol 29 (2) ◽  
pp. 271-286 ◽  
Author(s):  
Giulia Furlanetto ◽  
Cesare Ravazzi ◽  
Federica Badino ◽  
Michele Brunetti ◽  
Elena Champvillair ◽  
...  
Keyword(s):  
Transfer Functions ◽  
Weighted Averaging ◽  
Taxonomic Resolution ◽  
Pollen Deposition ◽  
High Mountain ◽  
European Alps ◽  
Climate Data ◽  
Sample Site ◽  
Modern Pollen ◽  
July Temperature

The potential of quantitatively reconstructing climate from modern pollen assemblages from high mountain environments has been widely debated but seldom tested. We analysed the pollen deposition in 53 surface samples (mosses) in relation to July temperature in two elevational transects in the European Alps. Each surface-sample site was assigned climate data derived from the local-scale climate. We compared our results with a larger calibration set extracted from the European Modern Pollen Database (EMPD) and centred on the European Alps. This also was assigned local climate data. The main calibration set (234 pollen samples) had Alnus harmonized at genus level; in contrast, a second set was selected (174) that retained the taxonomic resolution of Alnus viridis, which is one of the main climate indicators in the timberline ecotone. The overall and individual pollen responses to July temperature were inferred by canonical correspondence analysis (CCA), generalized linear regression (eHOF) and weighted averaging (WA). Quantitative climate reconstructions for each sample site of the two elevational transects were obtained using transfer functions, that is, WA and WA partial least squares (WA-PLS) regressions. In each calibration set, around 30% of the pollen taxa show a relationship with July temperature through monotonic or unimodal functions. The best transfer function obtained has a good statistical performance, with a determination coefficient ( r2) of 0.74. We propose new calibration procedures formulated to include the full climate space of the modelled taxa, as well as to account for uphill pollen transport in the high mountains and for human activity.

Holocene Climatic Change in the Northern Midwest: Pollen-Derived Estimates

1984 ◽  
Vol 22 (3) ◽  
pp. 361-374 ◽  
Author(s):  
P. J. Bartlein ◽  
T. Webb ◽  
E. Fleri
Keyword(s):  
Maximum Temperature ◽  
Pollen Record ◽  
Regression Equations ◽  
Climate Data ◽  
The South ◽  
The North ◽  
Modern Pollen ◽  
Quantitative Estimates ◽  
Multiple Regression Models ◽  
July Temperature

Mapping of Holocene pollen data in the midwestern United States has revealed several broadscale vegetational changes that can be interpreted in climatic terms. These changes include (1) the early Holocene northward movement of the spruce-dominated forest and its later southward movement after 3000 yr B.P. and (2) the eastward movement of the prairie/forest border into southwestern Wisconsin by 8000 yr B.P. and its subsequent westward retreat after 6000 yr B.P. When certain basic assumptions are met, multiple regression models can be derived from modern pollen and climate data and used to transform the pollen record of these vegetational changes into quantitative estimates of temperature or precipitation. To maximize the reliability of the regression equations, we followed a sequence of procedures that minimize violations of the assumptions that underlie regression analysis. Reconstructions of precipitation during the Holocene indicated that from 9000 to 6000 yr B.P. precipitation decreased by 10 to 25% over much of the Midwest, while mean July temperature increased by 0.5° to 2.0°C. At 6000 yr B.P. precipitation was less than 80% of its modern values over parts of Wisconsin and Minnesota. After 6000 yr B.P. precipitation generally increased, while mean July temperature decreased in the north, and increased in the south. The time of the maximum temperature varies within the Midwest and is earlier in the north and later in the south.

A high mountain pollen-altitude calibration set for palaeoclimatic use in the tropical Andes

The Holocene ◽  
2006 ◽  
Vol 16 (1) ◽  
pp. 105-117 ◽  
Author(s):  
Valenti Rull
Keyword(s):  
Altitudinal Gradient ◽  
Transfer Functions ◽  
Weighted Averaging ◽  
High Mountain ◽  
Mean Square ◽  
Tropical Andes ◽  
Training Set ◽  
Inference Models ◽  
Mountain Environments ◽  
M 12

The numerical relationship between modem pollen assemblages and altitude in high mountain environments from the northern Andes is analysed, in order to found inference models that allow estimating palaeoaltitudes and palaeotemperatures from past pollen records. The calibration set (DM) consists of a 50-sample altitudinal transect between-2300 and-4600 m altitude. The overall and individual pollen responses to altitude were tested by correspondence analysis (CA), generalized linear regression (HOF) and weighted averaging (WA). Transfer functions were derived by weighted averaging partial least squares (WA-PLS) regression. Overall, altitude is the main controlling factor for the composition of pollen assemblages, as shown by the high correlation between altitude and the first CA component (r =-0.88). Individually, around 35% of the 82 pollen taxa show a significant response to altitude through monotonic or unimodal functions. The best transfer function obtained has a good statistical performance, as shown by the determination coefficient (r2tck =0.78). The prediction power, as measured by the root mean square error of prediction (RMSEP), is of 256 m (12% of the total altitudinal gradient), which is equivalent to-1.5C. These parameters fall within the performance range of the inference models developed elsewhere using pollen and other biological proxies. It is concluded that the DM training set is useful to reconstruct Pleistocene and major Holocene palaeoclimatic trends. This study demonstrates the suitability of establishing reliable transfer functions for palaeoclimatic estimation in the highest altitudes of the tropical Andes, and encourages their continued improvement.

scholarly journals Pollen-based temperature and precipitation inferences for the montane forest of Mt. Kilimanjaro during the last Glacial and the Holocene

2014 ◽  
Vol 10 (1) ◽  
pp. 195-234 ◽  
Author(s):  
L. Schüler ◽  
A. Hemp ◽  
H. Behling
Keyword(s):  
East Africa ◽  
Transfer Functions ◽  
Montane Forest ◽  
Weighted Averaging ◽  
Pollen Record ◽  
Climate Variables ◽  
The Holocene ◽  
Modern Pollen ◽  
Temperature And Precipitation ◽  
Pollen Rain

Abstract. The relationship between modern pollen-rain taxa and measured climate variables was explored along the elevational gradient of the southern slope of Mt. Kilimanjaro, Tanzania. Pollen assemblages in 28 pollen traps positioned on 14 montane forest vegetation plots were identified and their relationship with climate variables was examined using multivariate statistical methods. Canonical correspondence analysis revealed that the mean annual temperature, mean annual precipitation and minimum temperature each account for significant fractions of the variation in pollen taxa. A training set of 107 modern pollen taxa was used to derive temperature and precipitation transfer functions based on pollen subsets using weighted-averaging-partial-least-squares (WA-PLS) techniques. The transfer functions were then applied to a fossil pollen record from the montane forest of Mt. Kilimanjaro and the climate parameter estimates for the Late Glacial and the Holocene on Mt. Kilimanjaro were inferred. Our results present the first quantitatively reconstructed temperature and precipitation estimates for Mt Kilimanjaro and give highly interesting insights into the past 45 000 yr of climate dynamics in tropical East Africa. The climate reconstructions are consistent with the interpretation of pollen data in terms of vegetation and climate history of afro-montane forest in East Africa. Minimum temperatures above the frostline as well as increased precipitation turn out to be crucial for the development and expansion of montane forest during the Holocene. In contrast, consistently low minimum temperatures as well as about 25% drier climate conditions prevailed during the pre LGM, which kept the montane vegetation composition in a stable state. In prospective studies, the quantitative climate reconstruction will be improved by additional modern pollen rain data, especially from lower elevations with submontane dry forests and colline savanna vegetation in order to extend the reference climate gradient.

Is the introduction of benthic species necessary for open-water chemical reconstruction in diatom-based transfer functions?

2002 ◽  
Vol 59 (6) ◽  
pp. 938-951 ◽  
Author(s):  
Aline Philibert ◽  
Yves T Prairie
Keyword(s):  
Transfer Functions ◽  
Model Performance ◽  
Open Water ◽  
Partial Least Square ◽  
Least Square ◽  
Weighted Averaging ◽  
Benthic Species ◽  
Inference Models ◽  
Number Of Species ◽  
Chemical Conditions

Despite the overwhelming tendency in paleolimnology to use both planktonic and benthic diatoms when inferring open-water chemical conditions, it remains questionable whether all taxa are appropriate and necessary to construct useful inference models. We examined this question using a 75-lake training set from Quebec (Canada) to assess whether model performance is affected by the deletion of benthic species. Because benthic species are known to experience very different chemical conditions than their planktonic counterparts, we hypothesized that they would introduce undesirable noise in the calibration. Surprisingly, such important variables as pH, total phosphorus (TP), total nitrogen (TN), and dissolved organic carbon (DOC) were well predicted from weighted-averaging partial least square (WA-PLS) models based solely on benthic species. Similar results were obtained regardless of the depth of the lakes. Although the effective number of occurrence (N2) and the tolerance of species influenced the stability of the model residual error (jackknife), the number of species was the major factor responsible for the weaker inference models when based on planktonic diatoms alone. Indeed, when controlled for the number of species in WA-PLS models, individual planktonic diatom species showed superior predictive power over individual benthic species in inferring open-water chemical conditions.

scholarly journals Pollinator importance networks illustrate the crucial value of bees in a highly speciose plant community

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Gavin Ballantyne ◽  
Katherine C. R. Baldock ◽  
Luke Rendell ◽  
P. G. Willmer
Keyword(s):  
Plant Species ◽  
Community Level ◽  
Taxonomic Resolution ◽  
Pollen Deposition ◽  
Pollinator Effectiveness ◽  
Flower Visitors ◽  
Pollinator Importance ◽  
Visitation Frequency ◽  
Pollinator Performance ◽  
Number Of Plant Species

AbstractAccurate predictions of pollination service delivery require a comprehensive understanding of the interactions between plants and flower visitors. To improve measurements of pollinator performance underlying such predictions, we surveyed visitation frequency, pollinator effectiveness (pollen deposition ability) and pollinator importance (the product of visitation frequency and effectiveness) of flower visitors in a diverse Mediterranean flower meadow. With these data we constructed the largest pollinator importance network to date and compared it with the corresponding visitation network to estimate the specialisation of the community with greater precision. Visitation frequencies at the community level were positively correlated with the amount of pollen deposited during individual visits, though rarely correlated at lower taxonomic resolution. Bees had the highest levels of pollinator effectiveness, with Apis, Andrena, Lasioglossum and Osmiini bees being the most effective visitors to a number of plant species. Bomblyiid flies were the most effective non-bee flower visitors. Predictions of community specialisation (H2′) were higher in the pollinator importance network than the visitation network, mirroring previous studies. Our results increase confidence in existing measures of pollinator redundancy at the community level using visitation data, while also providing detailed information on interaction quality at the plant species level.

scholarly journals Evaluating Predictor Strategies for Regression-Based Downscaling with a Focus on Glacierized Mountain Environments

2017 ◽  
Vol 56 (6) ◽  
pp. 1707-1729 ◽  
Author(s):  
Marlis Hofer ◽  
Johanna Nemec ◽  
Nicolas J. Cullen ◽  
Markus Weber
Keyword(s):  
Large Scale ◽  
Global Radiation ◽  
Local Scale ◽  
Forecast Skill ◽  
Model Complexity ◽  
High Mountain ◽  
European Alps ◽  
Limited Information ◽  
Mountain Environments ◽  
Measuring Site

AbstractThis study explores the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity, and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in close proximity to mountain glaciers: 1) the Vernagtbach station in the European Alps, 2) the Artesonraju measuring site in the tropical South American Andes, and 3) the Mount Brewster measuring site in the Southern Alps of New Zealand. The large-scale dataset being evaluated is the ERA-Interim dataset. In the downscaling procedure, particular emphasis is put on developing efficient yet not overfit models from the limited information in the temporally short (typically a few years) observational records of the high mountain sites. For direct (univariate) predictors, optimum scale analysis turns out to be a powerful means to improve the forecast skill without the need to increase the downscaling model complexity. Yet the traditional (multivariate) predictor sets show generally higher skill than the direct predictors for all variables, sites, and days of the year. Only in the case of large sampling uncertainty (identified here to particularly affect observed precipitation) is the use of univariate predictor options justified. Overall, the authors find a range in forecast skill among the different predictor options applied in the literature up to 0.5 (where 0 indicates no skill, and 1 represents perfect skill). This highlights that a sophisticated predictor selection (as presented in this study) is essential in the development of realistic, local-scale scenarios by means of downscaling.

Using historical aerial imagery to assess multidecadal kinematics and elevation changes. Application to mountain permafrost in the French Alps.

2021 ◽  
Author(s):  
Diego Cusicanqui ◽  
Antoine Rabatel ◽  
Xavier Bodin ◽  
Christian Vincent ◽  
Emmanuel Thibert ◽  
...  
Keyword(s):  
Ice Core ◽  
Horizontal Displacement ◽  
Vertical Surface ◽  
Aerial Images ◽  
Surface Velocity ◽  
High Mountain ◽  
European Alps ◽  
Permafrost Degradation ◽  
Mountain Areas ◽  
Mountain Permafrost

<p>Glacial and periglacial environments are highly sensitive to climate change, even more in mountain areas where warming is faster and, as a consequence, perennial features of the cryosphere like glaciers and permafrost have been fast evolving in the last decades. In the European Alps, glaciers retreat and permafrost thawing have led to the destabilization of mountain slopes, threatening human infrastructures and inhabitants. The observation of such changes at decadal scales is often limited to sparse in situ observations.</p><p>Here, we present three study cases of mountain permafrost sites based on a multidisciplinary approach over almost seven decades. The goal is to investigate and quantify morphodynamic changes and understand the causes of these evolutions. We used stereo-photogrammetry techniques to generate orthophotos and (DEMs) from historical aerial images (available, in France since 1940s). From this, we produced diachronic comparison of DEMs to quantify vertical surface changes, as well as feature tracking techniques of multi-temporal digital orthophotos for estimating horizontal displacement rates. Locally, high-resolution datasets (i.e. LiDAR surveys, UAV acquisitions and Pléiades stereo imagery) were also exploited to improve the quality of photogrammetric products. In addition, we combine these results with geophysics (ERT and GPR) to estimate the ice content, geomorphological surveys to describe the complex environments and the relationship with climatic forcing.</p><p>The first study case is the Laurichard rock glacier, where we were able to quantify changes of emergence velocities, fluxes, and volume. Together with an acceleration of surface velocity, important surface lowering have been found over the period 1952-2019, with a striking spatiotemporal reversal of volume balance.</p><p>The second study site is the Tignes glacial and periglacial complex, where the changes of thermokarstic lakes surface were quantified. The results suggest that drainage probably affects the presence and the evolution of the largest thermorkarst. Here too, a significant ice loss was found on the central channel concomitant to an increase in surface velocities.</p><p>The third study site is the Chauvet glacial and periglacial complex where several historical outburst floods are recorded during the 20th century, likely related to the permafrost degradation, the presence of thermokarstic lakes, and an intra-glacial channel. The lateral convergence of ice flow, due to the terrain subsidence caused by the intense melting, may cause the closure of the channel with a subsequent refill of the thermokarstic depression and finally a new catastrophic event.</p><p>Our results highlight the important value of historical aerial photography for having a longer perspective on the evolution of the high mountain cryosphere, thanks to accurate quantification of pluri-annual changes of volume and surface velocity. For instance, we could evidence : (1) a speed-up of the horizontal displacements since the 1990s in comparison with the previous decades; (2) an important surface lowering related to various melting processes (ice-core, thermokarst) for the three study sites; (3) relationships between the observed evolution and the contemporaneous climate warming, with a long-term evolution controlled by the warming of the ground and short-term changes that may relate to snow or precipitation or to the activity of the glacial-periglacial landforms.</p>

Assessing the options for the operational mapping of the soil moisture content in the European Alps

2021 ◽  
Author(s):  
Felix Greifeneder ◽  
Klaus Haslinger ◽  
Georg Seyerl ◽  
Claudia Notarnicola ◽  
Massimiliano Zappa ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Temporal Dynamics ◽  
Hydrological Cycle ◽  
Balance Model ◽  
High Mountain ◽  
European Alps ◽  
Entire Area ◽  
Advantages And Disadvantages ◽  
Hourly Data

<p>Soil Moisture (SM) is one of the key observable variables of the hydrological cycle and therefore of high importance for many disciplines, from meteorology to agriculture. This contribution presents a comparison of different products for the mapping of SM. The aim was to identify the best available solution for the operational monitoring of SM as a drought indicator for the entire area of the European Alps, to be applied in the context of the Interreg Alpine Space project, the Alpine Drought Observatory.</p><p>The following datasets were considered: Soil Water Index (SWI) of the Copernicus Global Land Service [1]; ERA5 [2]; ERA5-Land [3]; UERRA MESCAN-SURFEX land-surface component [4]. All four datasets offer a different set of advantages and disadvantages related to their spatial resolution, update frequency and latency. As a reference, modelled SM time-series for 307 catchments in Switzerland were used [5]. Switzerland is well suited as a test case for the Alps, due to its different landscapes, from lowlands to high mountain.</p><p>The intercomparison was based on a correlation analysis of daily absolute SM values and the daily anomalies. Furthermore, the probability to detect certain events, such as persistent dry conditions, was evaluated for each of the SM datasets. First results showed that the temporal dynamics (both in terms of absolute values as well as anomalies) of the re-analysis datasets show a high correlation to the reference. A clear gradient, from the lowlands in the north to the high mountains in the south, with decreasing correlation is evident. The SWI data showed weak correlations to the temporal dynamics of the reference in general. Especially, during spring and the first part of the summer SM is significantly underestimated. This might be related to the influence of snowmelt, which is not taken into account in the two-layer water balance model used to model SM for deeper soil layers. Low coverage in the high mountain areas hampered a thorough comparison with the reference in these areas.</p><p>The results presented here are the foundation for selecting a suitable source for the operational mapping of SM for the Alpine Drought Observatory. The next steps will be to test the potential of MESCAN-SURFEX and ERA5-Land for the downscaling of ERA5 to take advantage of the low latency of ERA5 and the improved spatial detail of the other two datasets.  </p><p>Literature:</p><p>[1]  B. Bauer-marschallinger et al., “Sentinel-1 : Harnessing Assets and Overcoming Obstacles,” IEEE Trans. Geosci. Remote Sens., vol. 57, no. 1, pp. 520–539, 2019, doi: 10.1109/TGRS.2018.2858004.</p><p>[2]  H. Hersbach et al., “ERA5 hourly data on single levels from 1979 to present.” Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2018.</p><p>[3]  Copernicus Climate Change Service, “ERA5-Land hourly data from 2001 to present.” ECMWF, 2019, doi: 10.24381/CDS.E2161BAC.</p><p>[4]  E. Bazile, et al., “MESCAN-SURFEX Surface Analysis. Deliverable D2.8 of the UERRA Project,” 2017. Accessed: Jan. 11, 2020. [Online]. Available: http://www.uerra.eu/publications/deliverable-reports.html.</p><p>[5]  Brunner, et al.: Extremeness of recent drought events in    Switzerland: dependence on variable and return period choice, Nat. Hazards Earth Syst. Sci., 19, 2311–2323, https://doi.org/10.5194/nhess-19-2311-2019, 2019.</p>

scholarly journals Quantifying parameter uncertainty in a large-scale glacier evolution model using Bayesian inference: application to High Mountain Asia

2020 ◽  
Vol 66 (256) ◽  
pp. 175-187 ◽  
Author(s):  
David R. Rounce ◽  
Tushar Khurana ◽  
Margaret B. Short ◽  
Regine Hock ◽  
David E. Shean ◽  
...  
Keyword(s):  
Mass Balance ◽  
Parameter Uncertainty ◽  
Large Scale ◽  
Evolution Model ◽  
Model Parameters ◽  
High Mountain ◽  
Climate Data ◽  
Temperature And Precipitation ◽  
Model Parameter Uncertainty ◽  
Geodetic Mass Balance

AbstractThe response of glaciers to climate change has major implications for sea-level change and water resources around the globe. Large-scale glacier evolution models are used to project glacier runoff and mass loss, but are constrained by limited observations, which result in models being over-parameterized. Recent systematic geodetic mass-balance observations provide an opportunity to improve the calibration of glacier evolution models. In this study, we develop a calibration scheme for a glacier evolution model using a Bayesian inverse model and geodetic mass-balance observations, which enable us to quantify model parameter uncertainty. The Bayesian model is applied to each glacier in High Mountain Asia using Markov chain Monte Carlo methods. After 10,000 steps, the chains generate a sufficient number of independent samples to estimate the properties of the model parameters from the joint posterior distribution. Their spatial distribution shows a clear orographic effect indicating the resolution of climate data is too coarse to resolve temperature and precipitation at high altitudes. Given the glacier evolution model is over-parameterized, particular attention is given to identifiability and the need for future work to integrate additional observations in order to better constrain the plausible sets of model parameters.

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