Linking the atmospheric Pacific-South American mode with oceanic variability and predictability

While Pacific climate variability is largely understood based on El Niño-Southern Oscillation(ENSO), the North Pacific focused Pacific decadal oscillation and the basin-wide interdecadalPacific oscillation, the role of the South Pacific, including atmospheric drivers and cross-scaleinteractions, has received less attention. Using reanalysis data and model outputs, here wepropose a paradigm for South Pacific climate variability whereby the atmospheric Pacific-South American (PSA) mode acts to excite multiscale spatiotemporal responses in the upperSouth Pacific Ocean. We find the second mid-troposphere PSA pattern is fundamental tostochastically generate a mid-latitude sea surface temperature quadrupole pattern thatrepresents the optimal precursor for the predictability and evolution of both the South Pacificdecadal oscillation and ENSO several seasons in advance. We find that the PSA mode is thekey driver of oceanic variability in the South Pacific subtropics that generates a potentiallypredictable climate signal linked to the tropics

Intrinsic processes drive variability in basal melting of the Totten Glacier Ice Shelf

Over the period 2003–2008, the Totten Ice Shelf (TIS) was shown to be rapidly thinning, likely due to basal melting. However, a recent study using a longer time series found high interannual variability present in TIS surface elevation without any apparent trend. Here we show that low-frequency intrinsic ocean variability potentially accounts for a large fraction of the variability in the basal melting of TIS. Specifically, numerical ocean model simulations show that up to 44% of the modelled variability in basal melting in the 1–5 year timescale (and up to 21% in the 5–10 year timescale) is intrinsic, with a similar response to the full climate forcing. We identify the important role of intrinsic ocean variability in setting the observed interannual variation in TIS surface thickness and velocity. Our results further demonstrate the need to account for intrinsic ocean processes in the detection and attribution of change

Stochastic climate theory and modeling

Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid-scale parameterizations (SSPs) as well as for model error representation, uncertainty quantification, data assimilation, and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large-scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochastic components and non-Markovian (memory) terms. Stochastic approaches in numerical weather and climate prediction models also lead to the reduction of model biases. Hence, there is a clear need for systematic stochastic approaches in weather and climate modeling. In this review, we present evidence for stochastic effects in laboratory experiments. Then we provide an overview of stochastic climate theory from an applied mathematics perspective. We also survey the current use of stochastic methods in comprehensive weather and climate prediction models and show that stochastic parameterizations have the potential to remedy many of the current biases in these comprehensive models

The ITS1-5.8S-ITS2 Sequence Region in the Musaceae: Structure, Diversity and Use in Molecular Phylogeny

Genes coding for 45S ribosomal RNA are organized in tandem arrays of up to several thousand copies and contain 18S, 5.8S and 26S rRNA units separated by internal transcribed spacers ITS1 and ITS2. While the rRNA units are evolutionary conserved, ITS show high level of interspecific divergence and have been used frequently in genetic diversity and phylogenetic studies. In this work we report on the structure and diversity of the ITS region in 87 representatives of the family Musaceae. We provide the first detailed information on ITS sequence diversity in the genus Musa and describe the presence of more than one type of ITS sequence within individual species. Both Sanger sequencing of amplified ITS regions and whole genome 454 sequencing lead to similar phylogenetic inferences. We show that it is necessary to identify putative pseudogenic ITS sequences, which may have negative effect on phylogenetic reconstruction at lower taxonomic levels. Phylogenetic reconstruction based on ITS sequence showed that the genus Musa is divided into two distinct clades – Callimusa and Australimusa and Eumusa and Rhodochlamys. Most of the intraspecific banana hybrids analyzed contain conserved parental ITS sequences, indicating incomplete concerted evolution of rDNA loci. Independent evolution of parental rDNA in hybrids enables determination of genomic constitution of hybrids using ITS. The observation of only one type of ITS sequence in some of the presumed interspecific hybrid clones warrants further study to confirm their hybrid origin and to unravel processes leading to evolution of their genomes

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability