Preferred structures in large-scale circulation and the effect of doubling greenhouse gas concentration in HadCM3

Preferred structures in the surface pressure variability are investigated in and compared between two 100-year simulations of the Hadley Centre climate model HadCM3. In the first (control) simulation, the model is forced with pre-industrial carbon dioxide concentration (1×CO2) and in the second simulation the model is forced with doubled CO2 concentration (2×CO2). Daily winter (December-January-February) surface pressures over the Northern Hemisphere are analysed. The identification of preferred patterns is addressed using multivariate mixture models. For the control simulation, two significant flow regimes are obtained at 5% and 2.5% significance levels within the state space spanned by the leading two principal components. They show a high pressure centre over the North Pacific/Aleutian Islands associated with a low pressure centre over the North Atlantic, and its reverse. For the 2×CO2 simulation, no such behaviour is obtained. At higher-dimensional state space, flow patterns are obtained from both simulations. They are found to be significant at the 1% level for the control simulation and at the 2.5% level for the 2×CO2 simulation. Hence under CO2 doubling, regime behaviour in the large-scale wave dynamics weakens. Doubling greenhouse gas concentration affects both the frequency of occurrence of regimes and also the pattern structures. The less frequent regime becomes amplified and the more frequent regime weakens. The largest change is observed over the Pacific where a significant deepening of the Aleutian low is obtained under CO2 doubling

20th century intraseasonal Asian monsoon dynamics viewed from Isomap

The Asian summer monsoon is a high dimensional and highly nonlinear phenomenon involving considerable moisture transport towards land from the ocean, and is critical for the whole region. We have used daily ECMWF reanalysis (ERA-40) sea-level pressure (SLP) anomalies to the seasonal cycle, over the region 50-145°E, 20°S-35°N to study the nonlinearity of the Asian monsoon using Isomap. We have focused on the two-dimensional embedding of the SLP anomalies for ease of interpretation. Unlike the unimodality obtained from tests performed in empirical orthogonal function space, the probability density function, within the two-dimensional Isomap space, turns out to be bimodal. But a clustering procedure applied to the SLP data reveals support for three clusters, which are identified using a three-component bivariate Gaussian mixture model. The modes are found to appear similar to active and break phases of the monsoon over South Asia in addition to a third phase, which shows active conditions over the Western North Pacific. Using the low-level wind field anomalies the active phase over South Asia is found to be characterised by a strengthening and an eastward extension of the Somali jet whereas during the break phase the Somali jet is weakened near southern India, while the monsoon trough in northern India also weakens. Interpretation is aided using the APHRODITE gridded land precipitation product for monsoon Asia. The effect of large-scale seasonal mean monsoon and lower boundary forcing, in the form of ENSO, is also investigated and discussed. The outcome here is that ENSO is shown to perturb the intraseasonal regimes, in agreement with conceptual ideas

Isomap nonlinear dimensionality reduction and bimodality of Asian monsoon convection

It is known that the empirical orthogonal function method is unable to detect possible nonlinear structure in climate data. Here, isometric feature mapping (Isomap), as a tool for nonlinear dimensionality reduction, is applied to 1958–2001 ERA-40 sea-level pressure anomalies to study nonlinearity of the Asian summer monsoon intraseasonal variability. Using the leading two Isomap time series, the probability density function is shown to be bimodal. A two-dimensional bivariate Gaussian mixture model is then applied to identify the monsoon phases, the obtained regimes representing enhanced and suppressed phases, respectively. The relationship with the large-scale seasonal mean monsoon indicates that the frequency of monsoon regime occurrence is significantly perturbed in agreement with conceptual ideas, with preference for enhanced convection on intraseasonal time scales during large-scale strong monsoons. Trend analysis suggests a shift in concentration of monsoon convection, with less emphasis on South Asia and more on the East China Sea

Archetypal Analysis: Mining Weather and Climate Extremes

Conventional analysis methods in weather and climate science (e.g., EOF analysis) exhibit a number of drawbacks including scaling and mixing. These methods focus mostly on the bulk of the probability distribution of the system in state space and overlook its tail. This paper explores a different method, the archetypal analysis (AA), which focuses precisely on the extremes. AA seeks to approximate the convex hull of the data in state space by finding “corners” that represent “pure” types or archetypes through computing mixture weight matrices. The method is quite new in climate science, although it has been around for about two decades in pattern recognition. It encompasses, in particular, the virtues of EOFs and clustering. The method is presented along with a new manifold-based optimization algorithm that optimizes for the weights simultaneously, unlike the conventional multistep algorithm based on the alternating constrained least squares. The paper discusses the numerical solution and then applies it to the monthly sea surface temperature (SST) from HadISST and to the Asian summer monsoon (ASM) using sea level pressure (SLP) from ERA-40 over the Asian monsoon region. The application to SST reveals, in particular, three archetypes, namely, El Niño, La Niña, and a third pattern representing the western boundary currents. The latter archetype shows a particular trend in the last few decades. The application to the ASM SLP anomalies yields archetypes that are consistent with the ASM regimes found in the literature. Merits and weaknesses of the method along with possible future development are also discussed

Predictability and Non-Gaussian Characteristics of the North Atlantic Oscillation

AbstractThe North Atlantic Oscillation (NAO) is the dominant mode of climate variability over the North Atlantic basin and has a significant impact on seasonal climate and surface weather conditions. It is the result of complex and nonlinear interactions between many spatiotemporal scales. Here, the authors study the statistical properties of two time series of the daily NAO index. Previous NAO modeling attempts only considered Gaussian noise, which can be inconsistent with the system complexity. Here, it is found that an autoregressive model with non-Gaussian noise provides a better fit to the time series. This result holds also when considering time series for the four seasons separately. The usefulness of the proposed model is evaluated by means of an investigation of its forecast skill

Wastewater reuse in agriculture in the outskirts of the city Batna (Algeria)

The study is based on a survey of farmers. The data collected allow us to understand the reasons for the reuse of wastewater. This resource can be an important element in irrigation water management strategy. The possibilities of wastewater reuse in agriculture are significant, as is the case in the Batna region. In this context, the presence of texts establishing the modality of wastewater reuse, are a prerequisite for promotion of wastewater reuse projects. Policymakers are faced with the need to exploit the increase in volumes to meet greater demand. To do this, the integrated management should be considered now as a public / private partnership model and as the best approach for development and efficient and sustainable management.Keywords: Farmers, Irrigation, Management, Public Policy, Text

Analysis of the phenotypic variability of twenty f3 biparental populations of bread wheat (Triticum aestivum L.) evaluated under semi-arid environment

This research was conducted to screen and analyse the variability within twenty F3 populations of bread wheat (Triticum aestivum L.) generated by Line x Tester mating design. The results indicated the presence of sufficient variability within and between F3 populations for the eight measured variables, which represent plant phenology, physiology, yield and yield components. Different populations have been identified to improve the measured variables separately. The number of spikes appeared to be the most important determinant of grain yield. PCA and cluster analyses indicated that the Acsad1069/El Wifak and Acsad1135/Hidhab, with a relatively high grain yield,  aboveground biomass and 1000 grains weight, are the best F3 populations to improve the productivity. However, Acsad899/Rmada and Acsad1135/Rmada populations were earlier and had a low number of spikes. These populations had also favorable genes for heat tolerance.Keywords: Triticum aestivum L.; Variability; Selection; Tolerance; Yield

Bispectral analysis of nonlinear interaction, predictability and stochastic modelling with application to ENSO

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Correlation between Traits and Path Analysis Coefficient for Grain Yield and Other Quantitative Traits in Bread Wheat under Semi Arid Conditions

Current research was conducted out at the Field Crop Institute-Agricultural Experimental Station of Setif (Algeria) during 2010/11 and 2011/12 crop seasons. The objectives were to determine traits affecting grain yield in 29 bread wheat genotypes and to establish the nature of relation between grain yield and yield components by partitioning the correlation coefficients between grain yield and its components into direct and indirect effects by using simple correlation, stepwise regression and path analysis. The obtained results indicated that grain yield was positively correlated to biological yield, straw yield and number of spike per plant. The results of step by step regression showed that traits including biological yield and harvest index had justified approximately 99. 7% of grain yield variations. In the path coefficient analysis, biological yield and harvest index should be considered as the main yield components because these traits showed a positive direct effects towards increasing grain yield with the values of + 1.051 and + 0.364, respectively. Depending on the findings of this study, biological yield and harvest index may be used an effective selection criterion to improve genetic yield potential of bread wheat genotypes