Abstract
Atmospheric pollutants and environmental indicators are often used to reconstruct historic atmospheric pollution from peat, as it accumulates over time by decomposing plant material, thus recording a history of air pollution. In the present study, three key parameters related to the peat bogs’ surface wetness dynamics in European Russia during the Holocene were investigated using modern statistical analysis. These parameters are: (i) the water table depth (WTD) in relation to the surface, which is reconstructed based on the community structure of the subfossil testate amoeba assemblages; (ii) the peat humification estimated as absorption of alkaline extract that directly reflects moisture at which the peat was formed; (iii) the Climate Moisture Index (CMI) and the Aridity Index derived from pollen-based reconstructions of the mean annual temperature and precipitation and classifying moisture conditions as the ratio between available annual precipitation and potential land surface evapotranspiration. All these parameters provide useful information about the paleoclimate (atmospheric moisture component) dynamics. High values of WTD and peat humification appear to comply with Gutenberg–Richter law. It is noteworthy that this law also seems to reproduce the high values of the modeled climate moisture and aridity indices. The validity of this new result is checked by replacing “conventional time” with “natural time”. On this basis, a new nowcasting tool is developed to more accurately estimate the average waiting time for the extreme values of these climate parameters. This will help to understand climate variability better to address emerging development needs and priorities by implementing empirical studies of the interactions between climatic effects, mitigation, adaptation, and sustainable growth.
Highlights
A better understanding of the climate–land surface interaction in the past and present is a very important issue, especially for predicting its future changes
To describe the surface wetness, we used in our study three key parameters: (i) the water table depth (WTD) in relation to the surface, which was reconstructed based on the community structure of the subfossil testate amoeba assemblages, using the transfer function developed by Tsyganov et al [20]; (ii) the peat humification estimated as absorption of alkaline extract that directly reflects the moisture in which the peat was formed [21]; (iii) the Climate Moisture Index (CMI) suggested by Willmott and Feddema [22] and the Aridity Index obtained using pollen-based reconstructions of the mean annual temperature and precipitation which allows the classification of moisture conditions as the ratio between available annual precipitation and potential land surface evapotranspiration [15,23]
The date calibration was given by the IntCal13 calibration curve of the northern hemisphere, which is defined by tree-ring measurements from 0 to 13,900 cal Before Present (BP) and was supplemented by the addition of the Lake Suigetsu macrofossil data
Summary
A better understanding of the climate–land surface interaction in the past and present is a very important issue, especially for predicting its future changes. Mathematical models can be very useful in deriving the key components of the energy, water, and carbon budgets of the ground surface and to predict the spatial and temporal variability of surface evapotranspiration, surface moisture and runoff, carbon uptake and release, etc. Over the past few decades, many process-based and statistical models of different levels of complexity have been developed and implemented in various climatological, hydrological, and ecological studies [3,4]. While process-based models use mathematical representations of physical and biological processes on the land surface–atmosphere interface, statistical models apply mathematically-formalized ways to derive reality using statistical methods. Recent progress in the modeling of paleo ecological processes has been made using advanced mathematical tools and sophisticated models of statistical physics developed based on long-term biological data [5,6,7]. Natural time and multifractal analyses are core concepts for studies aimed at building nowcasting models [8,9,10]
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