Slowing down of North Pacific climate variability and its implications for abrupt ecosystem change

This is the final version of the article. Available from the publisher via the DOI in this record.Marine ecosystems are sensitive to stochastic environmental variability, with higher-amplitude, lower-frequency--i.e., "redder"--variability posing a greater threat of triggering large ecosystem changes. Here we show that fluctuations in the Pacific Decadal Oscillation (PDO) index have slowed down markedly over the observational record (1900-present), as indicated by a robust increase in autocorrelation. This "reddening" of the spectrum of climate variability is also found in regionally averaged North Pacific sea surface temperatures (SSTs), and can be at least partly explained by observed deepening of the ocean mixed layer. The progressive reddening of North Pacific climate variability has important implications for marine ecosystems. Ecosystem variables that respond linearly to climate forcing will have become prone to much larger variations over the observational record, whereas ecosystem variables that respond nonlinearly to climate forcing will have become prone to more frequent "regime shifts." Thus, slowing down of North Pacific climate variability can help explain the large magnitude and potentially the quick succession of well-known abrupt changes in North Pacific ecosystems in 1977 and 1989. When looking ahead, despite model limitations in simulating mixed layer depth (MLD) in the North Pacific, global warming is robustly expected to decrease MLD. This could potentially reverse the observed trend of slowing down of North Pacific climate variability and its effects on marine ecosystems.National Centers for Environmental Prediction (NCEP) Reanalysis Derived data were provided by the National Oceanic and Atmospheric Administration/Ocean and Atmospheric Research/Earth System Research Laboratory Physical Sciences Division (NOAA/OAR/ERSL PSD), Boulder, CO. The PDO index was provided by the Joint Institute for the Study of the Atmosphere and Ocean (JISAO), University of Washington/NOAA. HadISST and HadSST3 data were provided by the Met Office, Exeter, United Kingdom. ERSST data were provided by the NOAA. MLD data were provided by S. A. Grodsky, University of Maryland. C.A.B. and T.M.L. were supported by the Research on Changes of Variability and Environmental Risk (RECoVER), funded by EPSRC (EP/M008495/1). C.A.B. was also supported by a PhD Studentship funded by the University of Exeter, United Kingdom. T.M.L.’s contribution was also supported by a Royal Society Wolfson Research Merit Award and EU FP7/2007-2013 under Grant Agreement 603864 (HELIX)

Early warning signals of Atlantic Meridional Overturning Circulation collapse in a fully coupled climate model.

Journal ArticleResearch Support, Non-U.S. Gov'tCopyright © 2014 Macmillan Publishers Limited. All Rights Reserved.The Atlantic Meridional Overturning Circulation (AMOC) exhibits two stable states in models of varying complexity. Shifts between alternative AMOC states are thought to have played a role in past abrupt climate changes, but the proximity of the climate system to a threshold for future AMOC collapse is unknown. Generic early warning signals of critical slowing down before AMOC collapse have been found in climate models of low and intermediate complexity. Here we show that early warning signals of AMOC collapse are present in a fully coupled atmosphere-ocean general circulation model, subject to a freshwater hosing experiment. The statistical significance of signals of increasing lag-1 autocorrelation and variance vary with latitude. They give up to 250 years warning before AMOC collapse, after ~550 years of monitoring. Future work is needed to clarify suggested dynamical mechanisms driving critical slowing down as the AMOC collapse is approached.NERCUniversity of ExeterEuropean Union Seventh Framework programme FP7/2007-201

Abrupt changes in Great Britain vegetation carbon projected under climate change

This is the final version. Available on open access from Wiley via the DOI in this recordPast abrupt ‘regime shifts’ have been observed in a range of ecosystems due to various forcing factors. Large-scale abrupt shifts are projected for some terrestrial ecosystems under climate change, particularly in tropical and high-latitude regions. However, there is very little high-resolution modelling of smaller-scale future projected abrupt shifts in ecosystems, and relatively less focus on the potential for abrupt shifts in temperate terrestrial ecosystems. Here, we show that numerous climate-driven abrupt shifts in vegetation carbon are projected in a high-resolution model of Great Britain's land surface driven by two different climate change scenarios. In each scenario, the effects of climate and CO2 combined are isolated from the effects of climate change alone. We use a new algorithm to detect and classify abrupt shifts in model time series, assessing the sign and strength of the non-linear responses. The abrupt ecosystem changes projected are non-linear responses to climate change, not simply driven by abrupt shifts in climate. Depending on the scenario, 374–1,144 grid cells of 1.5 km × 1.5 km each, comprising 0.5%–1.5% of Great Britain's land area show abrupt shifts in vegetation carbon. We find that abrupt ecosystem shifts associated with increases (rather than decreases) in vegetation carbon, show the greatest potential for early warning signals (rising autocorrelation and variance beforehand). In one scenario, 89% of abrupt increases in vegetation carbon show increasing autocorrelation and variance beforehand. Across the scenarios, 81% of abrupt increases in vegetation carbon have increasing autocorrelation and 74% increasing variance beforehand, whereas for decreases in vegetation carbon these figures are 56% and 47% respectively. Our results should not be taken as specific spatial or temporal predictions of abrupt ecosystem change. However, they serve to illustrate that numerous abrupt shifts in temperate terrestrial ecosystems could occur in a changing climate, with some early warning signals detectable beforehand.Natural Environment Research Council (NERC)Leverhulme Trus

Exploring uncertainty of Amazon dieback in a perturbed parameter Earth system ensemble

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordThe future of the Amazon rainforest is unknown due to uncertainties in projected climate change and the response of the forest to this change (forest resiliency). Here, we explore the effect of some uncertainties in climate and land surface processes on the future of the forest, using a perturbed physics ensemble of HadCM3C. This is the first time Amazon forest changes are presented using an ensemble exploring both land vegetation processes and physical climate feedbacks in a fully coupled modelling framework. Under three different emissions scenarios, we measure the change in the forest coverage by the end of the 21st century (the transient response) and make a novel adaptation to a previously used method known as "dry-season resilience" to predict the long-term committed response of the forest, should the state of the climate remain constant past 2100. Our analysis of this ensemble suggests that there will be a high chance of greater forest loss on longer timescales than is realized by 2100, especially for mid-range and low emissions scenarios. In both the transient and predicted committed responses, there is an increasing uncertainty in the outcome of the forest as the strength of the emissions scenarios increases. It is important to note however, that very few of the simulations produce future forest loss of the magnitude previously shown under the standard model configuration. We find that low optimum temperatures for photosynthesis and a high minimum leaf area index needed for the forest to compete for space appear to be precursors for dieback. We then decompose the uncertainty into that associated with future climate change and that associated with forest resiliency, finding that it is important to reduce the uncertainty in both of these if we are to better determine the Amazon's outcome.Chris Boulton was supported by a PhD studentship provided by the University of Exeter. The contributions to this work from Ben Booth and Peter Good were supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101)

Using social media to detect and locate wildfires

This is the final version of the article. Available from AAAI via the URL in this record.Methods for detecting and tracking natural hazards continue to increase in coverage, resolution and reliability. However, information on the social impacts of natural hazards is often lacking. Here we test the feasibility of using social media data (Twitter and Instagram) to detect and map an important class of natural hazard: wildfires. We analyse social media posts associated with wildfires over several time periods and compare them with wildfire occurrence data derived from satellite-based remote sensing data and on-the-ground observations. For the whole of the contiguous United States, we find significant temporal correlations between wildfire-related social media activity and wildfire occurrence, but also that there is substantial variation in the strength of this relationship at smaller spatial scales (states and counties). We then explore the utility of social media for location of wildfire events, finding good evidence to support further development of such methods. We conclude by discussing several challenges and opportunities for application of this novel data resource to provide information on impacts of natural hazards.The authors were supported by a Research on Changes of Variability and Environmental Risk (RECoVER) grant funded by EPSRC (EP/M008495/1)

FALCON: a software package for analysis of nestedness in bipartite networks

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.Nestedness is a statistical measure used to interpret bipartite interaction data in several ecological and evolutionary contexts, e.g. biogeography (species-site relationships) and species interactions (plant-pollinator and host-parasite networks). Multiple methods have been used to evaluate nestedness, which differ in how the metrics for nestedness are determined. Furthermore, several different null models have been used to calculate statistical significance of nestedness scores. The profusion of measures and null models, many of which give conflicting results, is problematic for comparison of nestedness across different studies. We developed the FALCON software package to allow easy and efficient comparison of nestedness scores and statistical significances for a given input network, using a selection of the more popular measures and null models from the current literature. FALCON currently includes six measures and five null models for nestedness in binary networks, and two measures and four null models for nestedness in weighted networks. The FALCON software is designed to be efficient and easy to use. FALCON code is offered in three languages (R, MATLAB, Octave) and is designed to be modular and extensible, enabling users to easily expand its functionality by adding further measures and null models. FALCON provides a robust methodology for comparing the strength and significance of nestedness in a given bipartite network using multiple measures and null models. It includes an “adaptive ensemble” method to reduce undersampling of the null distribution when calculating statistical significance. It can work with binary or weighted input networks. FALCON is a response to the proliferation of different nestedness measures and associated null models in the literature. It allows easy and efficient calculation of nestedness scores and statistical significances using different methods, enabling comparison of results from different studies and thereby supporting theoretical study of the causes and implications of nestedness in different biological contexts

A new method for detecting abrupt shifts in time series

This is the published version [version 1; peer review: 2 approved with reservations]. Available on open access from 1000Research via the DOI in this record. Abrupt shifts in time series are a topic of growing interest in a number of research areas. They can be caused by a range of different underlying dynamics, for example, via a mathematical bifurcation, or potentially as the result of an auto-correlated stochastic process (i.e. ‘red’ noise). Here we present a method that detects abrupt shifts by searching for gradient changes that occur over a short space of time. It can be automated, allowing many time series to be analysed by the user at once, such as from high spatial resolution data. Our method detects abrupt shifts regardless of their origin (which it cannot deduce). We present a comparison with the method of abrupt shift detection from the changepoint R package, which is based on changes in mean over the time series. Our method performs better on data with an underlying trend where comparisons of means may fail.Natural Environment Research Council (NERC

Utilization of multifrequency permittivity measurements in addition to biomass monitoring

Heinrich C, Beckmann T, Büntemeyer H, Noll T. Utilization of multifrequency permittivity measurements in addition to biomass monitoring. BMC Proceedings. 2011;5(Suppl 8)

Utilization of multifrequency permittivity measurements in addition to biomass monitoring

Heinrich C, Beckmann T, Büntemeyer H, Noll T. Utilization of multifrequency permittivity measurements in addition to biomass monitoring. BMC Proceedings. 2011;5(Suppl 8)