Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The sub-continent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite-derived rainfall data from the Microwave Infrared Rainfall Algorithm (MIRA). This dataset covers the period from 1993 to 2002 and the whole of southern Africa at a spatial resolution of 0.1° longitude/latitude. This paper concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of present-day rainfall variability over southern Africa and is not intended to discuss possible future changes in climate as these have been documented elsewhere. Simulations of current climate from the UKMeteorological Office Hadley Centre’s climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. Secondly, the ability of the model to reproduce daily rainfall extremes is assessed, again by a comparison with extremes from the MIRA dataset. The results suggest that the model reproduces the number and spatial distribution of rainfall extremes with some accuracy, but that mean rainfall and rainfall variability is underestimated (over-estimated) over wet (dry) regions of southern Africa.

Idealized SST anomaly regional climate model experiments: a note of caution

To date, a number of studies have focused on the influence of sea surface temperature (SST) on global and regional rainfall variability, with the majority of these focusing on certain ocean basins e.g. the Pacific, North Atlantic and Indian Ocean. In contrast, relatively less work has been done on the influence of the central South Atlantic, particularly in relation to rainfall over southern Africa. Previous work by the authors, using reanalysis data and general circulation model (GCM) experiments, has suggested that cold SST anomalies in the central southern Atlantic Ocean are linked to an increase in rainfall extremes across southern Africa. In this paper we present results from idealised regional climate model (RCM) experiments forced with both positive and negative SST anomalies in the southern Atlantic Ocean. These experiments reveal an unexpected response of rainfall over southern Africa. In particular it was found that SST anomalies of opposite sign can cause similar rainfall responses in the model experiments, with isolated increases in rainfall over central southern Africa as well as a large region of drying over the Mozambique Channel. The purpose of this paper is to highlight this finding and explore explanations for the behaviour of the climate model. It is suggested that the observed changes in rainfall might result from the redistribution of energy (associated with upper level changes to Rossby waves) or, of more concern, model error, and therefore the paper concludes that the results of idealised regional climate models forced with SST anomalies should be viewed cautiously.

Influence of end-capping on the self-assembly of model amyloid peptide fragments

The influence of charge and aromatic stacking interactions on the self-assembly of a series of four model amyloid peptides has been examined. The four model peptides are based on the KLVFF motif from the amyloid Beta peptide, ABeta(16-20) extended at the N terminus with two Beta-alanine residues. We have studied NH2-BetaABetaAKLVFF-COOH (FF), NH2-BetaABetaAKLVFCOOH (F), CH3CONH-BetaABetaAKLVFF-CONH2 (CapF), and CH3CONH-BetaABetaAKLVFFCONH2 (CapFF). The former two are uncapped (net charge plus 2) and differ by one hydrophobic phenylalanine residue; the latter two are the analogous capped peptides (net charge plus 1). The self-assembly characteristics of these peptides are remarkably different and strongly dependent on concentration. NMR shows a shift from carboxylate to carboxylic acid forms upon increasing concentration. Saturation transfer measurements of solvent molecules indicate selective involvement of phenylalanine residues in driving the self-assembly process of CapFF due presumably to the effect of aromatic stacking interactions. FTIR spectroscopy reveals beta-sheet features for the two peptides containing two phenylalanine residues but not the single phenylalanine residue, pointing again to the driving force for self-assembly. Circular dichroism (CD) in dilute solution reveals the polyproline II conformation, except for F which is disordered. We discuss the relationship of this observation to the significant pH shift observed for this peptide when compared the calculated value. Atomic force microscopy and cryogenic-TEM reveals the formation of twisted fibrils for CapFF, as previously also observed for FF. The influence of salt on the self-assembly of the model beta-sheet forming capped peptide CapFF was investigated by FTIR. Cryo-TEM reveals that the extent of twisting decreases with increased salt concentration, leading to the formation of flat ribbon structures. These results highlight the important role of aggregation-induced pKa shifts in the self-assembly of model beta-sheet peptides.

A comparison of extreme European daily precipitation simulated by a global and a regional climate model for present and future climates

The intensity and distribution of daily precipitation is predicted to change under scenarios of increased greenhouse gases (GHGs). In this paper, we analyse the ability of HadCM2, a general circulation model (GCM), and a high-resolution regional climate model (RCM), both developed at the Met Office's Hadley Centre, to simulate extreme daily precipitation by reference to observations. A detailed analysis of daily precipitation is made at two UK grid boxes, where probabilities of reaching daily thresholds in the GCM and RCM are compared with observations. We find that the RCM generally overpredicts probabilities of extreme daily precipitation but that, when the GCM and RCM simulated values are scaled to have the same mean as the observations, the RCM captures the upper-tail distribution more realistically. To compare regional changes in daily precipitation in the GHG-forced period 2080-2100 in the GCM and the RCM, we develop two methods. The first considers the fractional changes in probability of local daily precipitation reaching or exceeding a fixed 15 mm threshold in the anomaly climate compared with the control. The second method uses the upper one-percentile of the control at each point as the threshold. Agreement between the models is better in both seasons with the latter method, which we suggest may be more useful when considering larger scale spatial changes. On average, the probability of precipitation exceeding the 1% threshold increases by a factor of 2.5 (GCM and RCM) in winter and by I .7 (GCM) or 1.3 (RCM) in summer.