Multi-Scale Entropy Analysis as a Method for Time-Series Analysis of Climate Data

Evidence is mounting that the temporal dynamics of the climate system are changing at the same time as the average global temperature is increasing due to multiple climate forcings. A large number of extreme weather events such as prolonged cold spells, heatwaves, droughts and floods have been recorded around the world in the past 10 years. Such changes in the temporal scaling behaviour of climate time-series data can be difficult to detect. While there are easy and direct ways of analysing climate data by calculating the means and variances for different levels of temporal aggregation, these methods can miss more subtle changes in their dynamics. This paper describes multi-scale entropy (MSE) analysis as a tool to study climate time-series data and to identify temporal scales of variability and their change over time in climate time-series. MSE estimates the sample entropy of the time-series after coarse-graining at different temporal scales. An application of MSE to Central European, variance-adjusted, mean monthly air temperature anomalies (CRUTEM4v) is provided. The results show that the temporal scales of the current climate (1960–2014) are different from the long-term average (1850–1960). For temporal scale factors longer than 12 months, the sample entropy increased markedly compared to the long-term record. Such an increase can be explained by systems theory with greater complexity in the regional temperature data. From 1961 the patterns of monthly air temperatures are less regular at time-scales greater than 12 months than in the earlier time period. This finding suggests that, at these inter-annual time scales, the temperature variability has become less predictable than in the past. It is possible that climate system feedbacks are expressed in altered temporal scales of the European temperature time-series data. A comparison with the variance and Shannon entropy shows that MSE analysis can provide additional information on the statistical properties of climate time-series data that can go undetected using traditional method

Streambed scour and fill in low‐order dryland channels

Reproduced with permission of the publisher. ©2005. American Geophysical UnionDistributions of scour and fill depths recorded in three low‐order sand bed dryland rivers were compared with the Weibull, gamma, exponential, and lognormal probability density functions to determine which model best describes the reach‐scale variability in scour and fill. Goodness of fit tests confirm that the majority of scour distributions conform to the one‐parameter exponential model at the 95% significance level. The positive relationship between exponential model parameters and flow strength provides a means to estimate streambed scour depths, at least to a first approximation, in comparable streams. In contrast, the majority of the fill distributions do not conform to the exponential model even though depths of scour and fill are broadly similar. The disparities between the distributions of scour and fill raise questions about notions of channel equilibrium and about the role of scour and fill in effecting channel change

Final report on project SP1210: Lowland peatland systems in England and Wales – evaluating greenhouse gas fluxes and carbon balances

Lowland peatlands represent one of the most carbon-rich ecosystems in the UK. As a result of widespread habitat modification and drainage to support agriculture and peat extraction, they have been converted from natural carbon sinks into major carbon sources, and are now amongst the largest sources of greenhouse gas (GHG) emissions from the UK land-use sector. Despite this, they have previously received relatively little policy attention, and measures to reduce GHG emissions either through re-wetting and restoration or improved management of agricultural land remain at a relatively early stage. In part, this has stemmed from a lack of reliable measurements on the carbon and GHG balance of UK lowland peatlands. This project aimed to address this evidence gap via an unprecedented programme of consistent, multi year field measurements at a total of 15 lowland peatland sites in England and Wales, ranging from conservation managed ‘near-natural’ ecosystems to intensively managed agricultural and extraction sites. The use of standardised measurement and data analysis protocols allowed the magnitude of GHG emissions and removals by peatlands to be quantified across this heterogeneous data set, and for controlling factors to be identified. The network of seven flux towers established during the project is believed to be unique on peatlands globally, and has provided new insights into the processes the control GHG fluxes in lowland peatlands. The work undertaken is intended to support the future development and implementation of agricultural management and restoration measures aimed at reducing the contribution of these important ecosystems to UK GHG emissions

Ground-Based Optical Measurements at European Flux Sites: A Review of Methods, Instruments and Current Controversies

This paper reviews the currently available optical sensors, their limitations and opportunities for deployment at Eddy Covariance (EC) sites in Europe. This review is based on the results obtained from an online survey designed and disseminated by the Co-cooperation in Science and Technology (COST) Action ESO903—“Spectral Sampling Tools for Vegetation Biophysical Parameters and Flux Measurements in Europe” that provided a complete view on spectral sampling activities carried out within the different research teams in European countries. The results have highlighted that a wide variety of optical sensors are in use at flux sites across Europe, and responses further demonstrated that users were not always fully aware of the key issues underpinning repeatability and the reproducibility of their spectral measurements. The key findings of this survey point towards the need for greater awareness of the need for standardisation and development of a common protocol of optical sampling at the European EC sites

Software for the frontiers of quantum chemistry:An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design

Soil respiration in a fire scar chronosequence of Canadian boreal jack pine forest

This research investigates soil respiration (Rs) in a boreal jack pine (Pinus banksiana Lamb.) fire scar chronosequence at Sharpsand Creek, Ontario, Canada. During two field campaigns in 2006 and 2007, Rs was measured in a chronosequence of fire scars in the range 0 to 59 years since fire. Mean Rs adjusted for soil temperature (Ts) and soil moisture (Ms) (Rs T,M) ranged from 0.56 μmol CO2/m2/s (32 years post fire) to 8.18 μmol CO2/m2/s (58 years post fire). Coefficient of variation (CV) of Rs adjusted for Ts and Ms ranged from 20% (16 years post fire) to 56% (58 years post fire). Across the field site, there was a significant exponential relationship between Rs adjusted for soil organic carbon (Cs) and Ts (P = 1.24*10-06; Q10 = 2.21) but no effect of Ms on Rs adjusted for Cs and Ts for the range 0.21 to 0.77 volumetric Ms (P = 0.702). Rs T,M significantly (P = 0.030) decreased after burning mature forest, though no significant (P > 0.1) difference could be detected between recently burned and unburned young forest. Rs was measured in recently burned boreal jack pine fire scar age categories that differed in their burn history and there was a significant difference in Rs T,M between previously 32 v 16 year old (P = 0.000) and previously 32 v 59 year old (P = 0.044) scars. There was a strong significant exponential increase in S R T,M with time since fire (r2 = 0.999; P = 0.006) for the chronosequence 0, 16 and 59 years post fire, and for all these age categories, Rs T,M was significantly different from one another (P < 0.05). The Joint UK Land Environment Simulator (JULES) was used to model vegetation re-growth over successional time at Sharpsand Creek, though it appeared to perform poorly in simulating leaf area index and canopy height. JULES probably over estimated heterotrophic Rs at Sharpsand Creek when Ts corrected simulated values were compared with measured Rs T,M. The results of this study contribute to a better quantitative understanding of Rs in boreal jack pine fire scars and will facilitate improvements in C cycle modelling. Further work is needed in quantifying autotrophic and heterotrophic contributions to soil respiration in jack pine systems, monitoring soil respiration for extended time periods after fire and improving the ability of JULES to simulate successional vegetation re-growth.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Simulation of the biospheric contribution to the seasonal cycle of atmospheric CO2 by a physiologically based global biosphere model

Simulation of the biospheric contribution to the seasonal cycle of atmospheric CO2 by a physiologically based global biosphere mode

A prognostic phenology scheme for global terrestrial carbon cycle models

Prognostic and mechanistic schemes for the determination of plant phenological stages from environmental conditions and the estimation of net primary production (NPP) are presented. The new schemes account for different biomes and are included in a global model of carbon cycling in the terrestrial biosphere. The capability of such a model to simulate the seasonal cycle of atmospheric CO2 is explored. The model is forced by mean monthly climate variables (temperature, precipitation and light) and the mean annual CO2-concentration. It predicts the atmosphere{biosphere CO2 exchange fluxes, leaf area index (LAI), and the times of budburst and leaf abcission. The predicted variables offer means of validation against data of the observed annual cycle of atmospheric CO2- concentration and observations of LAI derived from satellite data. Estimated annual NPP of forests appears realistic, however NPP of grass dominated biomes is greatly underestimated. This seems to be related to the fact that belowground biomass is not explicitly considered in the model. The results of a simulation of the seasonal cycle of of [sic] atmospheric CO2-concentration using a three dimensional atmospheric transport model are in satisfying agreement with the observations

Assessing the climate sensitivity of the global terrestrial carbon cycle model SILVAN

The growth rate of the atmospheric CO2 concentration exhibits interannual anomalous variations of 1–2 ppmV yr-1 which reflect the response of the global carbon fluxes to large scale climate fluctuations. The climate sensitivity of global carbon cycle models can be explored by the simulation of these variations. Here we test the climate sensitivity of the global terrestrial carbon cycle model SILVAN 2.3 using this approach. The model has a horizontal resolution of 0:5o, a 6–day time step and considers potential vegetation only. Important features are a model–generated water balance and physiological approaches to determine net primary productivity (NPP) and phenology. In the three sensitivity experiments SILVAN 2.3 was forced in addition to the monthly climatologies by: (A) observed temperature anomalies 1854–1993, (B) observed precipitation anomalies 1900–1993, and (C) observed anomalous temperature and precipitation as well as the atmospheric CO2 concentration increase 1765–1993. Simulated and observed anomalous CO2 fluxes into the atmosphere 1958–1993 are well correlated. The largest fraction of the modelled anomalous CO2 fluxes results from the temperature sensitivity of the physiological NPP model; the effect of the precipitation variations is relatively small. The simulated heterotrophic respiration is more sensitive to precipitation than to temperature. We discuss the extent to which the model response results additively from the anomalous CO2 fluxes generated by the temperature or precipitation anomalies only

Phenological rules for the leaf out date in temperate and boreal Biomes determined from NDVI

Phenological rules for the leaf out date in temperate and boreal Biomes determined from NDV