Bayesian neural network modeling of tree-ring temperature variability record from the Western Himalayas

Tiwari, R. K.; Maiti, Saumen

doi:10.5194/npg-18-515-2011

Cited by 14 publications

(2 citation statements)

References 35 publications

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“…According to , non-climatic variables that potentially hinder reconstructions of millennial-scale climate trends from tree-ring widths involve, inter alia, post-glacial soil development and the rise of atmospheric carbon dioxide since the early Holocene. In addition, there are unsettled statistical issues related to tree-ring-based estimates of past climate variability (Cook et al 1995;Esper et al 2005;Tiwari and Maiti 2011;Yang et al 2011Yang et al , 2012.…”

Section: Discussionmentioning

confidence: 99%

Fusing pollen-stratigraphic and dendroclimatic proxy data to reconstruct summer temperature variability during the past 7.5 ka in subarctic Fennoscandia

et al. 2012

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A new palaeoclimatic reconstruction of midsummer (July) temperatures for the last 7.5 ka in northern Fennoscandia is presented. It is based on two botanical proxies: spectra of fossil pollen and tree rings of Scots pine logs recovered from lacustrine sediments in the Arctic tree-line region. A newly developed method of proxy fusion is used to integrate the proxy-specific reconstructions of past summer temperature variability based on the pollen-stratigraphic and dendroclimatic data. The rationale behind the method is that the two proxies are likely to be connected to climate variability in a timescale-dependent fashion and, accordingly, the new reconstruction makes use of the low-and highfrequencies from pollen-stratigraphic and tree-ring data, respectively. The most prominent features of the new reconstruction are: (1) the long-term decline of temperatures by 2.0°C over the past 7.5 ka, (2) the midHolocene warmth culminating between 5 and 4 ka as a deviation from the cooling trend, (3) the Little Ice Age cool phase between 0.7 and 0.1 ka, and (4) the subsequent warming during the past century. These periods are superimposed on year-to-year variations in climate as dated to calendar-year accuracy by dendrochronology. Within the modern period, the years 1934 and 1937 are among the warmest, and the years 1903 and 1910 are among the coldest summers in the context of the past 7.5 ka. On average, the reconstructed Holocene climate was approximately 0.85°C warmer than the twentieth century.

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Section: Discussionmentioning

confidence: 99%

Fusing pollen-stratigraphic and dendroclimatic proxy data to reconstruct summer temperature variability during the past 7.5 ka in subarctic Fennoscandia

et al. 2012

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“…As such, ANNs have been applied to a range of research problems from predicting stock markets 52 to biomedical sciences 51 and monitoring water quality 53 to name a few. Specifically in environmental research, ANNs have successfully been applied in a range of dendroclimatic 54–57 , geomorphological 58,59 and aeolian studies 60–63 .…”

Section: Landscape Reactivation In Dryland Systemsmentioning

confidence: 99%

Using artificial neural networks to predict future dryland responses to human and climate disturbances

Buckland

Bailey

Thomas

2019

Sci Rep

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Land degradation and sediment remobilisation in dryland environments is considered to be a significant global environmental problem. Given the potential for currently stabilised dune systems to reactivate under climate change and increased anthropogenic pressures, identifying the role of external disturbances in driving geomorphic response is vitally important. We developed a novel approach, using artificial neural networks (ANNs) applied to time series of historical reactivation-deposition events from the Nebraska Sandhills, to determine the relationship between historic periods of sand deposition in semi-arid grasslands and external climatic conditions, land use pressures and wildfire occurrence. We show that both vegetation growth and sediment re-deposition episodes can be accurately estimated. Sensitivity testing of individual factors shows that localised forcings (overgrazing and wildfire) have a statistically significant impact when the climate is held at present-day conditions. However, the dominant effect is climate-induced drought. Our approach has great potential for estimating future landscape sensitivity to climate and land use scenarios across a wide range of potentially fragile dryland environments.

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Integrated Geoelectrical and Hydrochemical Investigation of Shallow Aquifers in Konkan Coastal Area, Maharashtra, India: Advanced Artificial Neural Networks Based Simulation Approach

Maiti

Gupta

2020

Springer Geophysics

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Bayesian neural network modeling of tree-ring temperature variability record from the Western Himalayas

Cited by 14 publications

References 35 publications

Fusing pollen-stratigraphic and dendroclimatic proxy data to reconstruct summer temperature variability during the past 7.5 ka in subarctic Fennoscandia

Fusing pollen-stratigraphic and dendroclimatic proxy data to reconstruct summer temperature variability during the past 7.5 ka in subarctic Fennoscandia

Using artificial neural networks to predict future dryland responses to human and climate disturbances

Integrated Geoelectrical and Hydrochemical Investigation of Shallow Aquifers in Konkan Coastal Area, Maharashtra, India: Advanced Artificial Neural Networks Based Simulation Approach

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