Uncertainty in the Representation of Orography in Weather and Climate Models and Implications for Parameterized Drag

The representation of orographic drag remains a major source of uncertainty for numerical weather prediction (NWP) and climate models. Its accuracy depends on contributions from both the model grid‐scale orography (GSO) and the subgrid‐scale orography (SSO). Different models use different source orography datasets and different methodologies to derive these orography fields. This study presents the first comparison of orography fields across several operational global NWP models. It also investigates the sensitivity of an orographic drag parameterisation to the inter‐model spread in SSO fields and the resulting implications for representing the northern hemisphere winter circulation in a NWP model. The inter‐model spread in both the GSO and the SSO fields is found to be considerable. This is due to differences in the underlying source dataset employed and in the manner in which this dataset is processed (in particular how it is smoothed and interpolated) to generate the model fields. The sensitivity of parameterised orographic drag to the inter‐model variability in SSO fields is shown to be considerable and dominated by the influence of two SSO fields: the standard deviation and the mean gradient of the SSO. NWP model sensitivity experiments demonstrate that the inter‐model spread in these fields is of first‐order importance to the inter‐model spread in parameterised surface stress, and to current known systematic model biases. The revealed importance of the SSO fields supports careful reconsideration of how these fields are generated, guiding future development of orographic drag parameterisations and re‐evaluation of the resolved impacts of orography on the flow

Abrupt reversal in emissions and atmospheric abundance of HCFC-133a (CF3CH2Cl)

Hydrochlorofluorocarbon HCFC-133a (CF3CH2Cl) is an anthropogenic compound whose consumption for emissive use is restricted under the Montreal Protocol. A recent study showed rapidly increasing atmospheric abundances and emissions. We report that, following this rise, the at- mospheric abundance and emissions have declined sharply in the past three years. We find a Northern Hemisphere HCFC-133a increase from 0.13 ppt (dry air mole fraction in parts-per-trillion) in 2000 to 0.50 ppt in 2012–mid-2013 followed by an abrupt reversal to 0.44 ppt by early 2015. Global emissions derived from these observations peaked at 3.1 kt in 2011, followed by a rapid decline of 0.5 kt yr−2 to 1.5 kt yr−1 in 2014. Sporadic HCFC-133a pollution events are detected in Europe from our high-resolution HCFC-133a records at three European stations, and in Asia from sam- ples collected in Taiwan. European emissions are estimated to be <0.1 kt yr−1 although emission hotspots were identi- fied in France

Evidence for strong, widespread chlorine radical chemistry associated with pollution outflow from continental Asia

The chlorine radical is a potent atmospheric oxidant, capable of perturbing tropospheric oxidative cycles normally controlled by the hydroxyl radical. Significantly faster reaction rates allow chlorine radicals to expedite oxidation of hydrocarbons, including methane, and in polluted environments, to enhance ozone production. Here we present evidence, from the CARIBIC airborne dataset, for extensive chlorine radical chemistry associated with Asian pollution outflow, from airborne observations made over the Malaysian Peninsula in winter. This region is known for persistent convection that regularly delivers surface air to higher altitudes and serves as a major transport pathway into the stratosphere. Oxidant ratios inferred from hydrocarbon relationships show that chlorine radicals were regionally more important than hydroxyl radicals for alkane oxidation and were also important for methane and alkene oxidation (>10%). Our observations reveal pollution-related chlorine chemistry that is both widespread and recurrent, and has implications for tropospheric oxidizing capacity, stratospheric composition and ozone chemistry

Estimating the Number of Paediatric Fevers Associated with Malaria Infection Presenting to Africa's Public Health Sector in 2007

Peter Gething and colleagues compute the number of fevers likely to present to public health facilities in Africa and the estimated number of these fevers likely to be infected with Plasmodium falciparum malaria parasites

Differential analysis for high density tiling microarray data

<p>Abstract</p> <p>Background</p> <p>High density oligonucleotide tiling arrays are an effective and powerful platform for conducting unbiased genome-wide studies. The <it>ab initio </it>probe selection method employed in tiling arrays is unbiased, and thus ensures consistent sampling across coding and non-coding regions of the genome. These arrays are being increasingly used to study the associated processes of transcription, transcription factor binding, chromatin structure and their association. Studies of differential expression and/or regulation provide critical insight into the mechanics of transcription and regulation that occurs during the developmental program of a cell. The time-course experiment, which comprises an <it>in-vivo </it>system and the proposed analyses, is used to determine if annotated and un-annotated portions of genome manifest coordinated differential response to the induced developmental program.</p> <p>Results</p> <p>We have proposed a novel approach, based on a piece-wise function – to analyze genome-wide differential response. This enables segmentation of the response based on protein-coding and non-coding regions; for genes the methodology also partitions differential response with a 5' versus 3' versus intra-genic bias.</p> <p>Conclusion</p> <p>The algorithm built upon the framework of Significance Analysis of Microarrays, uses a generalized logic to define regions/patterns of coordinated differential change. By not adhering to the gene-centric paradigm, discordant differential expression patterns between exons and introns have been identified at a FDR of less than 12 percent. A co-localization of differential binding between RNA Polymerase II and tetra-acetylated histone has been quantified at a p-value < 0.003; it is most significant at the 5' end of genes, at a p-value < 10^-13. The prototype R code has been made available as supplementary material [see Additional file <supplr sid="S1">1</supplr>].</p> <suppl id="S1"> <title> <p>Additional file 1</p> </title> <text> <p>gsam_prototypercode.zip. File archive comprising of prototype R code for gSAM implementation including readme and examples.</p> </text> <file name="1471-2105-8-359-S1.zip"> <p>Click here for file</p> </file> </suppl

The Iceland Greenland Seas Project

A coordinated atmosphere-ocean research project, centered on a rare wintertime field campaign to the Iceland and Greenland Seas, seeks to determine the location and causes of dense water formation by cold-air outbreaks. The Iceland Greenland Seas Project (IGP) is a coordinated atmosphere-ocean research program investigating climate processes in the source region of the densest waters of the Atlantic Meridional Overturning Circulation. During February and March 2018, a field campaign was executed over the Iceland and southern Greenland Seas that utilized a range of observing platforms to investigate critical processes in the region – including a research vessel, a research aircraft, moorings, sea gliders, floats and a meteorological buoy. A remarkable feature of the field campaign was the highly-coordinated deployment of the observing platforms, whereby the research vessel and aircraft tracks were planned in concert to allow simultaneous sampling of the atmosphere, the ocean and their interactions. This joint planning was supported by tailor-made convection-permitting weather forecasts and novel diagnostics from an ensemble prediction system. The scientific aims of the IGP are to characterize the atmospheric forcing and the ocean response of coupled processes; in particular, cold-air outbreaks in the vicinity of the marginal-ice zone and their triggering of oceanic heat loss, and the role of freshwater in the generation of dense water masses. The campaign observed the lifecycle of a long-lasting cold-air outbreak over the Iceland Sea and the development of a cold-air outbreak over the Greenland Sea. Repeated profiling revealed the immediate impact on the ocean, while a comprehensive hydrographic survey provided a rare picture of these subpolar seas in winter. A joint atmosphere-ocean approach is also being used in the analysis phase, with coupled observational analysis and coordinated numerical modelling activities underway

The Annotation, Mapping, Expression and Network (AMEN) suite of tools for molecular systems biology

<p>Abstract</p> <p>Background</p> <p>High-throughput genome biological experiments yield large and multifaceted datasets that require flexible and user-friendly analysis tools to facilitate their interpretation by life scientists. Many solutions currently exist, but they are often limited to specific steps in the complex process of data management and analysis and some require extensive informatics skills to be installed and run efficiently.</p> <p>Results</p> <p>We developed the Annotation, Mapping, Expression and Network (AMEN) software as a stand-alone, unified suite of tools that enables biological and medical researchers with basic bioinformatics training to manage and explore genome annotation, chromosomal mapping, protein-protein interaction, expression profiling and proteomics data. The current version provides modules for (i) uploading and pre-processing data from microarray expression profiling experiments, (ii) detecting groups of significantly co-expressed genes, and (iii) searching for enrichment of functional annotations within those groups. Moreover, the user interface is designed to simultaneously visualize several types of data such as protein-protein interaction networks in conjunction with expression profiles and cellular co-localization patterns. We have successfully applied the program to interpret expression profiling data from budding yeast, rodents and human.</p> <p>Conclusion</p> <p>AMEN is an innovative solution for molecular systems biological data analysis freely available under the GNU license. The program is available via a website at the Sourceforge portal which includes a user guide with concrete examples, links to external databases and helpful comments to implement additional functionalities. We emphasize that AMEN will continue to be developed and maintained by our laboratory because it has proven to be extremely useful for our genome biological research program.</p

Empirical comparison of cross-platform normalization methods for gene expression data

<p>Abstract</p> <p>Background</p> <p>Simultaneous measurement of gene expression on a genomic scale can be accomplished using microarray technology or by sequencing based methods. Researchers who perform high throughput gene expression assays often deposit their data in public databases, but heterogeneity of measurement platforms leads to challenges for the combination and comparison of data sets. Researchers wishing to perform cross platform normalization face two major obstacles. First, a choice must be made about which method or methods to employ. Nine are currently available, and no rigorous comparison exists. Second, software for the selected method must be obtained and incorporated into a data analysis workflow.</p> <p>Results</p> <p>Using two publicly available cross-platform testing data sets, cross-platform normalization methods are compared based on inter-platform concordance and on the consistency of gene lists obtained with transformed data. Scatter and ROC-like plots are produced and new statistics based on those plots are introduced to measure the effectiveness of each method. Bootstrapping is employed to obtain distributions for those statistics. The consistency of platform effects across studies is explored theoretically and with respect to the testing data sets.</p> <p>Conclusions</p> <p>Our comparisons indicate that four methods, DWD, EB, GQ, and XPN, are generally effective, while the remaining methods do not adequately correct for platform effects. Of the four successful methods, XPN generally shows the highest inter-platform concordance when treatment groups are equally sized, while DWD is most robust to differently sized treatment groups and consistently shows the smallest loss in gene detection. We provide an R package, CONOR, capable of performing the nine cross-platform normalization methods considered. The package can be downloaded at <url>http://alborz.sdsu.edu/conor</url> and is available from CRAN.</p