Response of the Asian Summer Monsoons to a High-latitude Thermal Forcing: Mechanisms and Nonlinearities

This study investigates mechanisms and nonlinearities in the response of the Asian Summer Monsoons (ASM) to high-latitude thermal forcings of different amplitudes. Using a suite of runs carried out with an intermediate-complexity atmospheric general circulation model, we find that the imposed forcings produce a strong precipitation response over the eastern ASM but a rather weak response over the southern ASM. The forcing also causes a precipitation dipole with wet conditions over the eastern Tibetan Plateau (TP) and dry conditions over the Bay of Bengal (BoB) and southeast Asia. A moderate increase of precipitation along the southern margin of the TP is also produced. Simulations designed to isolate the causal mechanisms show that thermodynamic interactions involving the tropical surface oceans are far less important than the water-vapour feedback for the transmission of information from the high-latitudes to the ASM. Additionally, we assess the nonlinearity of the ASM precipitation response to the forcing amplitude using a novel application of the empirical orthogonal function method. The response can be decomposed in two overlapping patterns. The first pattern represents a precipitation dipole with wet conditions over the eastern TP and dry conditions over BoB, which linearly increases with forcing amplitude becoming quasi-stationary for large forcing amplitudes (i.e. amplitudes leading to Arctic temperature anomalies larger than 10 degrees C). The second pattern is associated with increased precipitation over the southeastern TP and is nonlinearly dependent on forcing, being most important for intermediate forcing amplitudes (i.e. amplitudes leading to Arctic temperature anomalies between 5 and 10 degrees C)

Breaking the Dynamics of Emotions and Fear in Conflict and Reconstruction

This paper is all about the construction of a new analytical framework to understand conflict and cooperation both at the international and at the domestic level with the aim of then finding mechanisms to reduce tensions and initiate conflict resolution schemes. The existing research literature on such analytical frameworks has so far been conducted a) mostly on standard social science disciplinary lines and has not incorporated the important work done on these questions by neuro-scientists and behavioral geneticists and b) is not really capable except in very specific instances to deal with the evolving dynamics of conflict and cooperation. Conflict over scarce resources (territory, mates, food) between members of the same species is a universal feature of evolution. Often conflict, especially armed conflict is supposed to be due to shows of force by two or more parties in order to appropriate or dominate resources. Force appears thus not to be the only decisive factor; perceived entitlement and powerful feelings of injustice thereby generated in the case of challenge, extended to group identity are also at the basis of conflict and aggression in humans. The relationship between environment and conflict, the role of emotions such as fear, and the absence of clear definition and enforcement of property rights within societies are also factors in the development of conflict. Thus we have here developed an analytically based numerical model that aims to include finding on these topics by Neuroscience and to emphasize the role of emotions in conflict and cooperation dynamics. This model has been simulated without specific reference to a particular country with the result that economic conditions drive our system since in one case sustained growth produces stability and end of combats whereas deteriorating capital growth and GDP collapse lead to increased hostile coalition participation and more fighting. However, the mere trigger of economic conditions is insufficient to explain conflict escalation, which results from increased participation in mutually hostile coalitions and greater fighting propensity where emotions such as fear and resentment play their role. Finally a detailed empirical analysis of the current Syrian conflict has been undertaken which shows the ability of the model to forecast actual historical developments. This study also indicates that worsening economic conditions are not the only triggering factors in civil conflict. Perceptions of opportunities due to a weakening of a regime's authority also play a major rol

Multidecadal changes in winter circulation-climate relationship in Europe: frequency variations, within-type modifications, and long-term trends

Using pressure fields classified by the SANDRA algorithm, this study investigates the changes in the relationship between North Atlantic/European sea level pressure (SLP) and gridded European winter (DJF) temperature and precipitation back to 1750. Important changes in the frequency of the SLP clusters are found, though none of them indicating significant long-term trends. However, for the majority of the SLP clusters a tendency toward overall warmer and partly wetter winter conditions is found, most pronounced over the last decades. This suggests important within-type variations, i.e. the temperature and precipitation fields related to a particular SLP pattern change their characteristics over time. Using a decomposition scheme we find for temperature and precipitation that within-type-related variations dominate over those due to changed frequencies of the SLP clusters: Approximately 70% (60%) of European winter temperature (precipitation) variations can be explained by within-type changes, most strongly expressed over Eastern Europe and Scandinavia. This indicates that the current European winter warming cannot be explained by changed frequencies of the SLP patterns alone, but to a larger degree by changed characteristics of the patterns themselves. Potential sources of within-type variations are discusse

A Pseudoproxy Evaluation of Bayesian Hierarchical Modeling and Canonical Correlation Analysis for Climate Field Reconstructions over Europe

A pseudoproxy comparison is presented for two statistical methods used to derive annual climate field reconstructions (CFRs) for Europe. The employed methods use the canonical correlation analysis (CCA) procedure presented by Smerdon et al. and the Bayesian hierarchical model (BHM) method adopted from Tingley and Huybers. Pseudoproxy experiments (PPEs) are constructed from modeled temperature data sampled from the 1250-yr paleo-run of the NCAR Community Climate System Model (CCSM) version 1.4 model by Ammann et al. Pseudoproxies approximate the distribution of the multiproxy network used by Mann et al. over the European region of interest. Gaussian white noise is added to the temperature data to mimic the combined signal and noise properties of real-world proxies. Results indicate that, while both methods perform well in areas with good proxy coverage, the BHM method outperforms the CCA method across the entire field and additionally returns objective error estimates

Five hundred years of gridded high-resolution precipitation reconstructions over Europe and the connection to large-scale circulation

We present seasonal precipitation reconstructions for European land areas (30°W to 40°E/30-71°N; given on a 0.5°×0.5° resolved grid) covering the period 1500-1900 together with gridded reanalysis from 1901 to 2000 (Mitchell and Jones 2005). Principal component regression techniques were applied to develop this dataset. A large variety of long instrumental precipitation series, precipitation indices based on documentary evidence and natural proxies (tree-ring chronologies, ice cores, corals and a speleothem) that are sensitive to precipitation signals were used as predictors. Transfer functions were derived over the 1901-1983 calibration period and applied to 1500-1900 in order to reconstruct the large-scale precipitation fields over Europe. The performance (quality estimation based on unresolved variance within the calibration period) of the reconstructions varies over centuries, seasons and space. Highest reconstructive skill was found for winter over central Europe and the Iberian Peninsula. Precipitation variability over the last half millennium reveals both large interannual and decadal fluctuations. Applying running correlations, we found major non-stationarities in the relation between large-scale circulation and regional precipitation. For several periods during the last 500years, we identified key atmospheric modes for southern Spain/northern Morocco and central Europe as representations of two precipitation regimes. Using scaled composite analysis, we show that precipitation extremes over central Europe and southern Spain are linked to distinct pressure patterns. Due to its high spatial and temporal resolution, this dataset allows detailed studies of regional precipitation variability for all seasons, impact studies on different time and space scales, comparisons with high-resolution climate models as well as analysis of connections with regional temperature reconstruction

Sustainable Materials: Production Methods and End-of-life Strategies

All three natural polymers of biomass and the monomer platforms derived from them present multiple avenues to develop products from specialty to bulk markets, which could serve as entry points into the industry for bio based sustainable materials. However, several roadblocks still exist in the pathway of technology development of these materials due to challenges related to cost-competitiveness, scalability, performance and sustainability. This review outlines these major technical challenges as four key checkpoints (cost-competitive, scalability, sustainability, performance) to be addressed for successful market entry of a new sustainable material

Comparison of climate field reconstruction techniques: application to Europe

This paper presents a comparison of principal component (PC) regression and regularized expectation maximization (RegEM) to reconstruct European summer and winter surface air temperature over the past millennium. Reconstruction is performed within a surrogate climate using the National Center for Atmospheric Research (NCAR) Climate System Model (CSM) 1.4 and the climate model ECHO-G 4, assuming different white and red noise scenarios to define the distortion of pseudoproxy series. We show how sensitivity tests lead to valuable "a priori” information that provides a basis for improving real world proxy reconstructions. Our results emphasize the need to carefully test and evaluate reconstruction techniques with respect to the temporal resolution and the spatial scale they are applied to. Furthermore, we demonstrate that uncertainties inherent to the predictand and predictor data have to be more rigorously taken into account. The comparison of the two statistical techniques, in the specific experimental setting presented here, indicates that more skilful results are achieved with RegEM as low frequency variability is better preserved. We further detect seasonal differences in reconstruction skill for the continental scale, as e.g. the target temperature average is more adequately reconstructed for summer than for winter. For the specific predictor network given in this paper, both techniques underestimate the target temperature variations to an increasing extent as more noise is added to the signal, albeit RegEM less than with PC regression. We conclude that climate field reconstruction techniques can be improved and need to be further optimized in future application