A New Climate Nowcasting Tool Based on Paleoclimatic Data

• Main Authors: Costas Varotsos, Yuri Mazei, Elena Novenko, Andrey N. Tsyganov, Alexander Olchev, Tatiana Pampura, Natalia Mazei, Yulia Fatynina, Damir Saldaev, Maria Efstathiou
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Sustainability
• Subjects: nowcasting; paleoclimate; Climate Moisture Index; Aridity Index; Gutenberg–Richter law
• Online Access: https://www.mdpi.com/2071-1050/12/14/5546

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.