A New Climate Nowcasting Tool Based on Paleoclimatic Data

Abstract

<jats:p>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.</jats:p>

Delimitation and Prioritization of Climate-Affected Areas Using the Example of a Post-Industrial City

“…A better understanding of the interaction between climate and land surface in the past and present is very important, especially for predicting its future changes. In order to effectively address changing development needs and priorities, it is necessary to gain an understanding of climate variability by using novel tools, such as the nowcasting tool, which provides an estimation of the average waiting time for extreme values of climate parameters [5]. For coastal cities, there is the modeling of tropical cyclones (IMT) using cluster algorithms that evaluate the instability of the atmosphere-ocean system [6,7].…”

“…For coastal cities, there is the modeling of tropical cyclones (IMT) using cluster algorithms that evaluate the instability of the atmosphere-ocean system [6,7]. The new tools make it possible to better understand the interplay between climate impacts, mitigation strategies, adaptation measures and sustainable progress [5].…”

A new method of nowcasting wildfires and impacts: a case study for the USA

“…This study attempts to develop a new nowcasting tool for wildfires (NTW) and implement it, using data from the past forty years on the number of wildfires and burned acres in the USA. A similar methodology has been applied by Varotsos et al (2020), where a novel nowcasting tool for climate parameters was developed.…”

A probe into the Multifractal Behaviour of Total Ozone Time Series through Detrended Fluctuation Analysis

“…However, by eliminating the longterm trend, antipersistence (persistence) for time lags more (less) than ten days was detected for the entire data record. Other literatures in this direction include Varotsos (2005) and Varotsos et al (2012Varotsos et al ( , 2013Varotsos et al ( , 2016Varotsos et al ( , 2020aVarotsos et al ( , 2020b. Varotsos et.…”