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Decision‐scale relevant climate information on climate change is needed to inform policy and decision making but often involves high uncertainty. To enhance confidence in interpreting regional climate projections, it is important to understand the underlying physical processes driving the change. This study explores a methodology to investigate climate change as a function of changes in frequency of synoptic circulation. The approach examines how dynamically downscaled future climate from two regional climate models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX), driven with four general circulation models (GCMs), can give rise to surface climate changes that differ from those of the driving GCMs. The study focuses on changes in precipitation and the circulation processes driving the projected changes from the regional climate simulations. Despite uncertainty in future projections, the RCMs and GCMs both show decreases in precipitation over most of southern Africa and suggest a reduction (increase) in the frequency of circulation patterns associated with precipitation (no precipitation) over the region. However, some contradictions are seen in the centre of the domain for some ensemble members. This study shows that some of this disagreement in precipitation projections between GCMs and RCMs is due to the inconsistencies in the physical parameterizations of precipitation processes rather than inconsistences in regional‐scale circulation patterns. [ABSTRACT FROM AUTHOR]

From 2003 to 2006, the number of human immunodeficiency virus-infected people in sub-Saharan Africa able to access antiretroviral therapy (ART) has increased from 100,000 to >1 million. The World Health Organization estimates that >3.5 million patients are still in need. The challenges to more expeditious provision of ART in Africa are many. This article is an analysis of the barriers to ART scale-up that are unique to South Africa. With 5.3 million people infected and 1 million needing ART, this country carries nearly one-quarter of the treatment burden of the continent. Although South Africa is undeniably a middle-income nation, inequities born of apartheid, lack of political commitment, poverty, and cultural barriers have significantly slowed efforts to provide universal access to ART to South African citizens. [ABSTRACT FROM AUTHOR]

Observed trends in seasonal and annual total rainfall, number of rain days and daily maximum and minimum temperature were calculated for a number of stations in South Africa for the period 1960-2010. Statistically significant decreases in rainfall and the number of rain days are shown over the central and northeastern parts of the country in the autumn months and significant increases in the number of rain days around the southern Drakensberg are evident in spring and summer. Maximum temperatures have increased significantly throughout the country for all seasons and increases in minimum temperatures are shown for most of the country. A notable exception is the central interior, where minimum temperatures have decreased significantly. Regionally aggregated trends for six water management zones covering the entire country are not evident for total rainfall, but there are some significant trends for the number of rain days. Temperature in these zones has increased significantly for most seasons, with the exception of the central interior. Comparison of the observed trends with statistically downscaled global climate model simulations reveals that the models do not represent the observed rainfall changes nor the cooling trend of minimum temperature in the central interior. Although this result does not rule out the possibility of attributing observed local changes in rainfall to anthropogenically forced global change, it does have major implications for attribution studies. It also raises the question of whether an alternative statistical downscaling method or dynamical downscaling through the use of a regional climate model might better represent regional and local climatic processes and their links to global change. [ABSTRACT FROM AUTHOR]

ABSTRACT This study evaluates the capability of regional climate models ( RCMs) in simulating extreme rainfall events over Southern Africa, and in reproducing the characteristics of widespread extreme rainfall events ( WERE) in the Western Cape (South Africa). We obtained simulation datasets of nine RCMs from the Co-ordinated Regional Downscaling Experiment ( CORDEX) and compared them with observation datasets from the Global Precipitation Climatology Project ( GPCP) and Tropical Rainfall Measuring Mission ( TRMM) as well as with the reanalysis dataset ( ERAINT) that forced the simulations. A self-organising map was used to classify the WEREs into 12 nodes. The contribution of each RCM to each node was compared with the contributions of GPCP, TRMM and ERAINT. Using ERAINT dataset, we analysed the synoptic-scale atmospheric condition associated with each node. The results show that, in simulating the spatial distribution of the extreme rainfall event over Southern Africa, only four RCMs perform better than the forcing reanalysis ( ERAINT) while two RCMs perform worse than the reanalysis. All the RCMs underestimate the threshold of extreme rainfall over Western Cape, poorly simulate the inter-annual variability of the WEREs ( r ≤ 0.3), but correctly reproduce the maximum frequency of the WEREs in Autumn (March-May). The WEREs in the Western Cape may be broadly grouped into four synoptic rainfall patterns. The first pattern links WEREs with tropical rainfall activities (tropical-temperate troughs); the second pattern shows isolated WEREs; the third and fourth patterns link WEREs with rainfall activities in the mid-latitudes (frontal systems) and over the Agulhas Current, respectively. While most RCMs overestimate the frequency of the first pattern, they all underestimate the frequency of the second pattern but simulate the frequencies of the third and fourth patterns well. The results of this study should help in improving the RCM simulations over Southern Africa and in downscaling impacts of climate change on extreme rainfall events over the region. [ABSTRACT FROM AUTHOR]

The Regional Climate Model Evaluation System (RCMES) facilitates the rapid, flexible inclusion of NASA observations into climate model evaluations. RCMES provides two fundamental components. A database (RCMED) is a scalable point-oriented cloud database used to elastically store remote sensing observations and to make them available using a space time query interface. The analysis toolkit (RCMET) is a Python-based toolkit that can be delivered as a cloud virtual machine, or as an installer package deployed using Python Buildout to users in order to allow for temporal and spatial regridding, metrics calculation (RMSE, bias, PDFs, etc.) and end-user visualization. RCMET is available to users in an 'offline', lone scientist mode based on a virtual machine dynamically constructed with model outputs and observations to evaluate; or on an institution's computational cluster seated close to the observations and model outputs. We have leveraged RCMES within the content of the Coordinated Regional Downscaling Experiment (CORDEX) project, working with the University of Cape Town and other institutions to compare the model output to NASA remote sensing data; in addition we are also working with the North American Regional Climate Change Assessment Program (NARCCAP). In this paper we explain the contribution of cloud computing to RCMES's specifically describing studies of various cloud databases we evaluated for RCMED, and virtualization toolkits for RCMET, and their potential strengths in delivering user-created dynamic regional climate model evaluation virtual machines for our users. [ABSTRACT FROM AUTHOR]

Monthly-mean precipitation, mean (T), maximum (T) and minimum (T) surface air temperatures, and cloudiness from the CORDEX-Africa regional climate model (RCM) hindcast experiment are evaluated for model skill and systematic biases. All RCMs simulate basic climatological features of these variables reasonably, but systematic biases also occur across these models. All RCMs show higher fidelity in simulating precipitation for the west part of Africa than for the east part, and for the tropics than for northern Sahara. Interannual variation in the wet season rainfall is better simulated for the western Sahel than for the Ethiopian Highlands. RCM skill is higher for T and T than for T, and regionally, for the subtropics than for the tropics. RCM skill in simulating cloudiness is generally lower than for precipitation or temperatures. For all variables, multi-model ensemble (ENS) generally outperforms individual models included in ENS. An overarching conclusion in this study is that some model biases vary systematically for regions, variables, and metrics, posing difficulties in defining a single representative index to measure model fidelity, especially for constructing ENS. This is an important concern in climate change impact assessment studies because most assessment models are run for specific regions/sectors with forcing data derived from model outputs. Thus, model evaluation and ENS construction must be performed separately for regions, variables, and metrics as required by specific analysis and/or assessments. Evaluations using multiple reference datasets reveal that cross-examination, quality control, and uncertainty estimates of reference data are crucial in model evaluations. [ABSTRACT FROM AUTHOR]

Background. A potential confounding factor in the treatment of human immunodeficiency virus (HIV) infection in Africa is the frequent occurrence of opportunistic infections (OIs). OI-induced immune activation can interfere with HIV-1 clearance by increasing viral replication and target cell availability. Study design. Treatment outcomes for patients dually infected with HIV-1 and Mycobacterium tuberculosis or HIV-1 and human herpesvirus (HHV)-8 were assessed by measuring changes in viral load and CD4+ cell counts and by determining the time taken to reach undetectable HIV-1 RNA levels, assessed by means of Kaplan-Meier survival analysis. Patients with HIV-1 and Kaposi sarcoma (KS) received generic nevirapine, stavudine, and la- mivudine (3TC); patients with HIV-1 and tuberculosis (TB) received standard commercial didanosine, 3TC, and efavirenz. Results. Both cohorts exhibited a rapid, near-exponential phase I decline in viral load. Patients with TB and late-stage KS had the steepest decay kinetics. These same patients had the greatest initial increase in CD4+ cell counts. Phase II clearance was slower and more variable. The proportions of patients reaching undetectable plasma HIV-1 levels at days 7, 14, 28, 60, and 90 were, respectively, 15.8%, 30.0%, 52.6%, 78.9%, and 93.8% (Pearson's = 50.5; P< .001) for patients with TB and 0.0%, 5.0%, 22.2%, 64.7%, and 80.0% (Pearson's x² = 63.6; P<.001) for patients with KS. Conclusions. Nucleoside reverse-transcriptase inhibitor/nonnucleoside reverse -transcriptase inhibitor-based treatment regimens are highly effective in clearing rapidly replicating (phase I) virus in African patients dually infected with HIV-1 and either TB or KS. [ABSTRACT FROM AUTHOR]