Multiscale Oscillations of the Global Climate System as Revealed by Wavelet Transform of Observational Data Time Series

1999 ◽  
Vol 64 (1-2) ◽  
pp. 131-142 ◽  
Author(s):  
D. M. Sonechkin ◽  
N. M. Astafyeva ◽  
N. M. Datsenko ◽  
N. N. Ivachtchenko ◽  
B. Jakubiak
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Observational Data ◽  
Global Climate ◽  
Climate System

Climate Dynamics as a Nonlinear Brownian Motion

1998 ◽  
Vol 08 (04) ◽  
pp. 799-803 ◽  
Author(s):  
D. M. Sonechkin
Keyword(s):  
Time Series ◽  
Brownian Motion ◽  
Global Climate ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Climate System ◽  
Temperature Oscillations ◽  
Almost All ◽  
Temperature Time ◽  
The Heat Balance

Based on the heat balance equation of the global climate system the well-known surface air temperature time series of the Northern and Southern hemispheres were analyzed as realizations of a fractional Brownian motion. The technique of the so-called wavelet transform was used for this purpose. The technique easily admits splitting time series of interest to statistically stationary oscillations and a trend. Such temperature oscillations were extracted which include within themselves almost all differences between both hemispheric time series. As a result of subtraction of the oscillations from the primary hemispheric series a residual trend-like component was evaluated. The latter evidences a single warming trend of the global climate system that was started from the early 20th century.

scholarly journals A 20-year record (1998–2017) of permafrost, active layer and meteorological conditions at a high Arctic permafrost research site (Bayelva, Spitsbergen)

2018 ◽  
Vol 10 (1) ◽  
pp. 355-390 ◽  
Author(s):  
Julia Boike ◽  
Inge Juszak ◽  
Stephan Lange ◽  
Sarah Chadburn ◽  
Eleanor Burke ◽  
...  
Keyword(s):  
Time Series ◽  
Energy Balance ◽  
Observational Data ◽  
Positive Feedback ◽  
Global Climate ◽  
Feedback Mechanism ◽  
The Arctic ◽  
Data Sets ◽  
Data Quality Control ◽  
Data Set

Abstract. Most permafrost is located in the Arctic, where frozen organic carbon makes it an important component of the global climate system. Despite the fact that the Arctic climate changes more rapidly than the rest of the globe, observational data density in the region is low. Permafrost thaw and carbon release to the atmosphere are a positive feedback mechanism that can exacerbate global warming. This positive feedback functions via changing land–atmosphere energy and mass exchanges. There is thus a great need to understand links between the energy balance, which can vary rapidly over hourly to annual timescales, and permafrost, which changes slowly over long time periods. This understanding thus mandates long-term observational data sets. Such a data set is available from the Bayelva site at Ny-Ålesund, Svalbard, where meteorology, energy balance components and subsurface observations have been made for the last 20 years. Additional data include a high-resolution digital elevation model (DEM) that can be used together with the snow physical information for snowpack modeling and a panchromatic image. This paper presents the data set produced so far, explains instrumentation, calibration, processing and data quality control, as well as the sources for various resulting data sets. The resulting data set is unique in the Arctic and serves as a baseline for future studies. The mean permafrost temperature is −2.8 °C, with a zero-amplitude depth at 5.5 m (2009–2017). Since the data provide observations of temporally variable parameters that mitigate energy fluxes between permafrost and atmosphere, such as snow depth and soil moisture content, they are suitable for use in integrating, calibrating and testing permafrost as a component in earth system models.The presented data are available in the Supplement for this paper (time series) and through the PANGAEA and Zenodo data portals: time series (https://doi.org/10.1594/PANGAEA.880120, https://zenodo.org/record/1139714) and HRSC-AX data products (https://doi.org/10.1594/PANGAEA.884730, https://zenodo.org/record/1145373).

From “Inconvenient Truth” to Effective Governance

2018 ◽  
Author(s):  
Richard Passarelli ◽  
David Michel ◽  
William Durch
Keyword(s):  
Climate Change ◽  
Global Climate ◽  
Climate System ◽  
Global Public Good ◽  
Environmental Groups ◽  
Quarter Century ◽  
Effective Governance ◽  
Political Response ◽  
Earth’S Climate ◽  
National Governments

The Earth’s climate system is a global public good. Maintaining it is a collective action problem. This chapter looks at a quarter-century of efforts to understand and respond to the challenges posed by global climate change and why the collective political response, until very recently, has seemed to lag so far behind our scientific knowledge of the problem. The chapter tracks the efforts of the main global, intergovernmental process for negotiating both useful and politically acceptable responses to climate change, the UN Framework Convention on Climate Change, but also highlights efforts by scientific and environmental groups and, more recently, networks of sub-national governments—especially cities—and of businesses to redefine interests so as to meet the dangers of climate system disruption.

scholarly journals Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

2021 ◽  
Vol 13 (2) ◽  
pp. 542
Author(s):  
Tarate Suryakant Bajirao ◽  
Pravendra Kumar ◽  
Manish Kumar ◽  
Ahmed Elbeltagi ◽  
Alban Kuriqi
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Suspended Sediment ◽  
River Basin ◽  
Time Series Data ◽  
Series Data ◽  
Discrete Wavelet ◽  
Original Time Series ◽  
Inference System ◽  
Original Time

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

scholarly journals Regions of intensification of extreme snowfall under future warming

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lennart Quante ◽  
Sven N. Willner ◽  
Robin Middelanis ◽  
Anders Levermann
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Observational Data ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Global Climate Models ◽  
Average Intensity ◽  
High Latitudes ◽  
Future Warming

AbstractDue to climate change the frequency and character of precipitation are changing as the hydrological cycle intensifies. With regards to snowfall, global warming has two opposing influences; increasing humidity enables intense snowfall, whereas higher temperatures decrease the likelihood of snowfall. Here we show an intensification of extreme snowfall across large areas of the Northern Hemisphere under future warming. This is robust across an ensemble of global climate models when they are bias-corrected with observational data. While mean daily snowfall decreases, both the 99th and the 99.9th percentiles of daily snowfall increase in many regions in the next decades, especially for Northern America and Asia. Additionally, the average intensity of snowfall events exceeding these percentiles as experienced historically increases in many regions. This is likely to pose a challenge to municipalities in mid to high latitudes. Overall, extreme snowfall events are likely to become an increasingly important impact of climate change in the next decades, even if they will become rarer, but not necessarily less intense, in the second half of the century.

scholarly journals Meridional changes in the South Atlantic Subtropical Gyre during Heinrich Stadials

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tainã M. L. Pinho ◽  
Cristiano M. Chiessi ◽  
Rodrigo C. Portilho-Ramos ◽  
Marília C. Campos ◽  
Stefano Crivellari ◽  
...  
Keyword(s):  
Global Climate ◽  
South Atlantic ◽  
Subtropical Gyre ◽  
Climate System ◽  
The South ◽  
Foraminiferal Species ◽  
Ocean Gyres ◽  
Long Term Monitoring ◽  
Term Monitoring

AbstractSubtropical ocean gyres play a key role in modulating the global climate system redistributing energy between low and high latitudes. A poleward displacement of the subtropical gyres has been observed over the last decades, but the lack of long-term monitoring data hinders an in-depth understanding of their dynamics. Paleoceanographic records offer the opportunity to identify meridional changes in the subtropical gyres and investigate their consequences to the climate system. Here we use the abundance of planktonic foraminiferal species Globorotalia truncatulinodes from a sediment core collected at the northernmost boundary of the South Atlantic Subtropical Gyre (SASG) together with a previously published record of the same species from the southernmost boundary of the SASG to reconstruct meridional fluctuations of the SASG over last ca. 70 kyr. Our findings indicate southward displacements of the SASG during Heinrich Stadials (HS) 6-4 and HS1, and a contraction of the SASG during HS3 and HS2. During HS6-4 and HS1, the SASG southward displacements likely boosted the transfer of heat to the Southern Ocean, ultimately strengthening deep-water upwelling and CO2 release to the atmosphere. We hypothesize that the ongoing SASG poleward displacement may further increase oceanic CO2 release.

scholarly journals Modelling the productivity of Siberian larch forests from Landsat NDVI time series in fragmented forest stands of the Mongolian forest-steppe

2021 ◽  
Vol 193 (4) ◽  
Author(s):  
Stefan Erasmi ◽  
Michael Klinge ◽  
Choimaa Dulamsuren ◽  
Florian Schneider ◽  
Markus Hauck
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Global Climate Change ◽  
Global Climate ◽  
Siberian Larch ◽  
Growing Season ◽  
Forest Productivity ◽  
Forest Steppe ◽  
Fragmented Forest ◽  
Southern Fringe

AbstractThe monitoring of the spatial and temporal dynamics of vegetation productivity is important in the context of carbon sequestration by terrestrial ecosystems from the atmosphere. The accessibility of the full archive of medium-resolution earth observation data for multiple decades dramatically improved the potential of remote sensing to support global climate change and terrestrial carbon cycle studies. We investigated a dense time series of multi-sensor Landsat Normalized Difference Vegetation Index (NDVI) data at the southern fringe of the boreal forests in the Mongolian forest-steppe with regard to the ability to capture the annual variability in radial stemwood increment and thus forest productivity. Forest productivity was assessed from dendrochronological series of Siberian larch (Larix sibirica) from 15 plots in forest patches of different ages and stand sizes. The results revealed a strong correlation between the maximum growing season NDVI of forest sites and tree ring width over an observation period of 20 years. This relationship was independent of the forest stand size and of the landscape’s forest-to-grassland ratio. We conclude from the consistent findings of our case study that the maximum growing season NDVI can be used for retrospective modelling of forest productivity over larger areas. The usefulness of grassland NDVI as a proxy for forest NDVI to monitor forest productivity in semi-arid areas could only partially be confirmed. Spatial and temporal inconsistencies between forest and grassland NDVI are a consequence of different physiological and ecological vegetation properties. Due to coarse spatial resolution of available satellite data, previous studies were not able to account for small-scaled land-cover patches like fragmented forest in the forest-steppe. Landsat satellite-time series were able to separate those effects and thus may contribute to a better understanding of the impact of global climate change on natural ecosystems.

scholarly journals A Process-Oriented Approach to Identify Evolutions of Sea Surface Temperature Anomalies with a Time-Series of a Raster Dataset

2021 ◽  
Vol 10 (8) ◽  
pp. 500
Author(s):  
Lianwei Li ◽  
Yangfeng Xu ◽  
Cunjin Xue ◽  
Yuxuan Fu ◽  
Yuanyu Zhang
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Global Climate ◽  
Sea Surface ◽  
Temperature Anomalies ◽  
Global Climate Changes ◽  
Space And Time ◽  
Novel Approach ◽  
Sea Surface Temperature Anomalies

It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describing how SSTA evolves in space and time. In light of the evolution from generation, through development, and to the dissipation of SSTA, this paper proposes a novel approach to identifying an evolution of SSTA in space and time from a time-series of a raster dataset. This method, called PoAIES, includes three key steps. Firstly, a cluster-based method is enhanced to explore spatiotemporal clusters of SSTA, and each cluster of SSTA at a time snapshot is taken as a snapshot object of SSTA. Secondly, the spatiotemporal topologies of snapshot objects of SSTA at successive time snapshots are used to link snapshot objects of SSTA into an evolution object of SSTA, which is called a process object. Here, a linking threshold is automatically determined according to the overlapped areas of the snapshot objects, and only those snapshot objects that meet the specified linking threshold are linked together into a process object. Thirdly, we use a graph-based model to represent a process object of SSTA. A node represents a snapshot object of SSTA, and an edge represents an evolution between two snapshot objects. Using a number of child nodes from an edge’s parent node and a number of parent nodes from the edge’s child node, a type of edge (an evolution relationship) is identified, which shows its development, splitting, merging, or splitting/merging. Finally, an experiment on a simulated dataset is used to demonstrate the effectiveness and the advantages of PoAIES, and a real dataset of satellite-SSTA is used to verify the rationality of PoAIES with the help of ENSO’s relevant knowledge, which may provide new references for global change research.

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