MJO Simulation Diagnostics

The Madden–Julian oscillation (MJO) interacts with and influences a wide range of weather and climate phenomena (e.g., monsoons, ENSO, tropical storms, midlatitude weather), and represents an important, and as yet unexploited, source of predictability at the subseasonal time scale. Despite the important role of the MJO in climate and weather systems, current global circulation models (GCMs) exhibit considerable shortcomings in representing this phenomenon. These shortcomings have been documented in a number of multimodel comparison studies over the last decade. However, diagnosis of model performance has been challenging, and model progress has been difficult to track, because of the lack of a coherent and standardized set of MJO diagnostics. One of the chief objectives of the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group is the development of observation-based diagnostics for objectively evaluating global model simulations of the MJO in a consistent framework. Motivation for this activity is reviewed, and the intent and justification for a set of diagnostics is provided, along with specification for their calculation, and illustrations of their application. The diagnostics range from relatively simple analyses of variance and correlation to more sophisticated space–time spectral and empirical orthogonal function analyses. These diagnostic techniques are used to detect MJO signals, to construct composite life cycles, to identify associations of MJO activity with the mean state, and to describe interannual variability of the MJO.

Factors affecting biomass production and nutritive value of Calliandra calothyrsus leaf as fodder for ruminants

Calliandra calothyrsus is a tree legume native to Mexico and Central America. The species has attracted considerable attention for its capacity to produce both fuelwood and foliage for either green manure or fodder. Its high content of proanthocyanidins (condensed tannins) and associated low digestibility has, however, limited its use as a feed for ruminants, and there is also a widespread perception that wilting the leaves further reduces their nutritive value. Nevertheless, there has been increasing uptake of calliandra as fodder in certain regions, notably the Central Highlands of Kenya. The present study, conducted in Embu, Kenya, investigated effects of provenance, wilting, cutting frequency and seasonal variation both in the laboratory (in vitro digestibility, crude protein, neutral detergent fibre, extractable and bound proanthocyanidins) and in on-station animal production trials with growing lambs and lactating goats. The local Kenyan landrace of calliandra (Embu) and a closely-related Guatemalan provenance (Patulul) were found to be significantly different, and superior, to a provenance from Nicaragua (San Ramon) in most of the laboratory traits measured, as well as in animal production and feed efficiency. Cutting frequency had no important effect on quality; and although all quality traits displayed seasonal variation there was little discernible pattern to this variation. Wilting had a much less negative effect than expected, and for lambs fed calliandra as a supplement to a low quality basal feed (maize stover), wilting was actually found to give higher live-weight gain and feed efficiency. Conversely, with a high quality basal diet (Napier grass) wilting enhanced intake but not live-weight gain, so feed efficiency was greater for fresh material. The difference between fresh and wilted leaves was not great enough to justify the current widespread recommendation that calliandra should always be fed fresh.

Genetic least squares for system identification

The recursive least-squares algorithm with a forgetting factor has been extensively applied and studied for the on-line parameter estimation of linear dynamic systems. This paper explores the use of genetic algorithms to improve the performance of the recursive least-squares algorithm in the parameter estimation of time-varying systems. Simulation results show that the hybrid recursive algorithm (GARLS), combining recursive least-squares with genetic algorithms, can achieve better results than the standard recursive least-squares algorithm using only a forgetting factor.

Relationship between deadbeat and pole-placement discrete-time PID controllers

This paper considers PID control in terms of its implementation by means of an ARMA plant model. Two controller actions are considered, namely pole placement and deadbeat, both being applied via a PID structure for the adaptive real-time control of an industrial level system. As well as looking at two controller types separately, a comparison is made between the forms and it is shown how, under certain circumstances, the two forms can be seen to be identical. It is shown how the pole-placement PID form does not in fact realise an action which is equivalent to the deadbeat controller, when all closed-loop poles are chosen to be at the origin of the z-plane.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century

The boreal summer Asian monsoon has been evaluated in 25 Coupled Model Intercomparison Project-5 (CMIP5) and 22 CMIP3 GCM simulations of the late 20th Century. Diagnostics and skill metrics have been calculated to assess the time-mean, climatological annual cycle, interannual variability, and intraseasonal variability. Progress has been made in modeling these aspects of the monsoon, though there is no single model that best represents all of these aspects of the monsoon. The CMIP5 multi-model mean (MMM) is more skillful than the CMIP3 MMM for all diagnostics in terms of the skill of simulating pattern correlations with respect to observations. Additionally, for rainfall/convection the MMM outperforms the individual models for the time mean, the interannual variability of the East Asian monsoon, and intraseasonal variability. The pattern correlation of the time (pentad) of monsoon peak and withdrawal is better simulated than that of monsoon onset. The onset of the monsoon over India is typically too late in the models. The extension of the monsoon over eastern China, Korea, and Japan is underestimated, while it is overestimated over the subtropical western/central Pacific Ocean. The anti-correlation between anomalies of all-India rainfall and Niño-3.4 sea surface temperature is overly strong in CMIP3 and typically too weak in CMIP5. For both the ENSO-monsoon teleconnection and the East Asian zonal wind-rainfall teleconnection, the MMM interannual rainfall anomalies are weak compared to observations. Though simulation of intraseasonal variability remains problematic, several models show improved skill at representing the northward propagation of convection and the development of the tilted band of convection that extends from India to the equatorial west Pacific. The MMM also well represents the space-time evolution of intraseasonal outgoing longwave radiation anomalies. Caution is necessary when using GPCP and CMAP rainfall to validate (1) the time-mean rainfall, as there are systematic differences over ocean and land between these two data sets, and (2) the timing of monsoon withdrawal over India, where the smooth southward progression seen in India Meteorological Department data is better realized in CMAP data compared to GPCP data.

Antisense-induced myostatin exon skipping leads to muscle hypertrophy in mice following Octa‑guanidine morpholino oligomer treatment

Myostatin is a negative regulator of muscle mass, and several strategies are being developed to knockdown its expression to improve muscle-wasting conditions. Strategies using antimyostatin-blocking antibodies, inhibitory-binding partners, signal transduction blockers, and RNA interference system (RNAi)-based knockdown have yielded promising results and increased muscle mass in experimental animals. These approaches have, however, a number of disadvantages such as transient effects or adverse immune complications. We report here the use of antisense oligonucleotides (AOs) to manipulate myostatin pre-mRNA splicing and knockdown myostatin expression. Both 2’O-methyl phosphorothioate RNA (2’OMePS) and phosphorodiamidate morpholino oligomers (PMO) led to efficient exon skipping in vitro and in vivo and knockdown of myostatin at the transcript level. The substantial myostatin exon skipping observed after systemic injection of Vivo-PMO into normal mice led to a significant increase in soleus muscle mass as compared to the controls injected with normal saline suggesting that this approach could be feasible to ameliorate muscle-wasting pathologies.