Last millennium northern hemisphere summer temperatures from tree rings: Part I: The long term context

Large-scale millennial length Northern Hemisphere (NH) temperature reconstructions have been progressively improved over the last 20 years as new datasets have been developed. This paper, and its companion (Part II, Anchukaitis et al. in prep), details the latest tree-ring (TR) based NH land air temperature reconstruction from a temporal and spatial perspective. This work is the first product of a consortium called N-TREND (Northern Hemisphere Tree-Ring Network Development) which brings together dendroclimatologists to identify a collective strategy for improving large-scale summer temperature reconstructions. The new reconstruction, N-TREND2015, utilises 54 records, a significant expansion compared with previous TR studies, and yields an improved reconstruction with stronger statistical calibration metrics. N-TREND2015 is relatively insensitive to the compositing method and spatial weighting used and validation metrics indicate that the new record portrays reasonable coherence with large scale summer temperatures and is robust at all time-scales from 918 to 2004 where at least 3 TR records exist from each major continental mass. N-TREND2015 indicates a longer and warmer medieval period (∼900–1170) than portrayed by previous TR NH reconstructions and by the CMIP5 model ensemble, but with better overall agreement between records for the last 600 years. Future dendroclimatic projects should focus on developing new long records from data-sparse regions such as North America and eastern Eurasia as well as ensuring the measurement of parameters related to latewood density to complement ring-width records which can improve local based calibration substantially

Tropical–North Pacific Climate Linkages over the Past Four Centuries

Analyses of instrumental data demonstrate robust linkages between decadal-scale North Pacific and tropical Indo-Pacific climatic variability. These linkages encompass common regime shifts, including the noteworthy 1976 transition in Pacific climate. However, information on Pacific decadal variability and the tropical high-latitude climate connection is limited prior to the twentieth century. Herein tree-ring analysis is employed to extend the understanding of North Pacific climatic variability and related tropical linkages over the past four centuries. To this end, a tree-ring reconstruction of the December-May North Pacific index (NPI)-an index of the atmospheric circulation related to the Aleutian low pressure cell-is presented (1600-1983). The NPI reconstruction shows evidence for the three regime shifts seen in the instrumental NPI data, and for seven events in prior centuries. It correlates significantly with both instrumental tropical climate indices and a coral-based reconstruction of an optimal tropical Indo-Pacific climate index, supporting evidence for a tropical-North Pacific link extending as far west as the western Indian Ocean. The coral-based reconstruction (1781-1993) shows the twentieth-century regime shifts evident in the instrumental NPI and instrumental tropical Indo-Pacific climate index, and three previous shifts. Changes in the strength of correlation between the reconstructions over time, and the different identified shifts in both series prior to the twentieth century, suggest a varying tropical influence on North Pacific climate, with greater influence in the twentieth century. One likely mechanism is the low-frequency variability of the El Nino-Southern Oscillation (ENSO) and its varying impact on Indo-Pacific climate.</p

Detection of human influence on a new, validated 1500-Year temperature reconstruction

Climate records over the last millennium place the twentieth-century warming in a longer historical context. Reconstructions of millennial temperatures show a wide range of variability, raising questions about the reliability of currently available reconstruction techniques and the uniqueness of late-twentieth-century warming. A calibration method is suggested that avoids the loss of low-frequency variance. A new reconstruction using this method shows substantial variability over the last 1500 yr. This record is consistent with independent temperature change estimates from borehole geothermal records, compared over the same spatial and temporal domain. The record is also broadly consistent with other recent reconstructions that attempt to fully recover low-frequency climate variability in their central estimate. High variability in reconstructions does not hamper the detection of greenhouse gas-induced climate change, since a substantial fraction of the variance in these reconstructions from the beginning of the analysis in the late thirteenth century to the end of the records can be attributed to external forcing. Results from a detection and attribution analysis show that greenhouse warming is detectable in all analyzed high-variance reconstructions (with the possible exception of one ending in 1925), and that about a third of the warming in the first half of the twentieth century can be attributed to anthropogenic greenhouse gas emissions. The estimated magnitude of the anthropogenic signal is consistent with most of the warming in the second half of the twentieth century being anthropogenic

A Low-Cost Robust Distributed Linearly Constrained Beamformer for Wireless Acoustic Sensor Networks with Arbitrary Topology

We propose a new robust distributed linearly constrained beamformer which utilizes a set of linear equality constraints to reduce the cross power spectral density matrix to a block-diagonal form. The proposed beamformer has a convenient objective function for use in arbitrary distributed network topologies while having identical performance to a centralized implementation. Moreover, the new optimization problem is robust to relative acoustic transfer function (RATF) estimation errors and to target activity detection (TAD) errors. Two variants of the proposed beamformer are presented and evaluated in the context of multi-microphone speech enhancement in a wireless acoustic sensor network, and are compared with other state-of-the-art distributed beamformers in terms of communication costs and robustness to RATF estimation errors and TAD errors

Monsoon drought over Java, Indonesia, during the past two centuries

Monsoon droughts, which often coincide with El Nino warm events, can have profound impacts on the populations of Southeast Asia. Improved understanding and prediction of such events can be aided by high-resolution proxy climate records, but these are scarce for the tropics. Here we reconstruct the boreal autumn (October-November) Palmer Drought Severity Index (PDSI) for Java, Indonesia (1787-1988). This reconstruction is based on nine ring-width chronologies derived from living teak trees growing on the islands of Java and Sulawesi, and one coral delta O-18 series from Lombok. The PDSI reconstruction correlates significantly with El Nino-Southern Oscillation (ENSO)-related sea surface temperatures and other historical and instrumental records of tropical climate, reflecting the strong coupling between the climate of Indonesia and the large scale tropical Indo-Pacific climate system.</p

Growth rings in tropical trees : role of functional traits, environment, and phylogeny

Acknowledgments Financial support of the Centre National de la Recherche Scientifique (USR 3330), France, and from the Rufford Small Grants Foundation (UK) is acknowledged. We thank the private farmers and coffee plantation companies of Kodagu for providing permissions and logistical support for this project. We are grateful to N. Barathan for assistance with slide preparation and data entry, S. Aravajy for botanical assistance, S. Prasad and G. Orukaimoni for technical inputs, and A. Prathap, S. Shiva, B. Saravana, and P. Shiva for field assistance. The corresponding editor and three anonymous reviewers provided insightful comments that improved the manuscript.Peer reviewedPostprin

A Survey on Wireless Sensor Network Security

Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications. Due to distributed nature of these networks and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Security in sensor networks is, therefore, a particularly challenging task. This paper discusses the current state of the art in security mechanisms for WSNs. Various types of attacks are discussed and their countermeasures presented. A brief discussion on the future direction of research in WSN security is also included.Comment: 24 pages, 4 figures, 2 table

Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR)