What climate sensitivity index is most useful for projections?

Transient climate response (TCR), transient response at 140 years (T140) and equilibrium climate sensitivity (ECS) indices are intended as benchmarks for comparing the magnitude of climate response projected by climate models. It is generally assumed that TCR or T140 would explain more variability between models than ECS for temperature change over the 21st Century, since this timescale is the realm of transient climate change. Here we find that TCR explains more variability across CMIP5 than ECS for global temperature change since pre-industrial, for 50- or 100-year global trends up to the present, and for projected change under representative concentration pathways in regions of delayed warming such as the Southern Ocean. However, unexpectedly we find that ECS correlates higher than TCR for projected change from the present in the global mean and in most regions. This higher correlation doesn’t relate to aerosol forcing, and the physical cause requires further investigation

Transient response of the global mean warming rate and its spatial variation

The Earth has warmed over the past century. The warming rate (amount of warming over a given period) varies in time and space. Observations show a recent increase in global mean warming rate, which is initially maintained in model projections, but which diverges substantially in future depending on the emissions scenario followed. Scenarios that stabilize forcing lead to much lower warming rates, as the rate depends on the change in forcing, not the amount. Warming rates vary spatially across the planet, but most areas show a shift toward higher warming rates in recent decades. The areal distribution of warming rates is also changing shape to include a longer tail in recent decades. Some areas of the planet are already experiencing extreme warming rates of about 1 °C/decade. The fat tail in areal distribution of warming rates is pronounced in model runs when the forcing and global mean warming rate is increasing, and indicates a climate state more prone to regime transitions. The area-proportion of the Earth displaying warming/cooling trends is shown to be directly related to the global mean warming rate, especially for trends of length 15 years and longer. Since the global mean warming rate depends on the forcing rate, the proportion of warming/cooling trend areas in future also depends critically on the choice of future forcing scenario. Keywords: Climate variability, Climate projection, Transient response, Extreme warmin

Australian climate warming: observed change from 1850 and global temperature targets

Mean annual temperature is often used as a benchmark for monitoring climate change and as an indicator of its potential impacts. The Paris Agreement of 2015 aims to keep the global average temperature well below 2°C above pre-industrial levels, with a preferred limit of 1.5°C. Therefore, there is interest in understanding and examining regional temperature change using this framework of ‘global warming levels’, as well as through emissions pathways and time horizons. To apply the global warming level framework regionally, we need to quantify regional warming from the late 19th century to today, and to future periods where the warming levels are reached. Here we supplement reliable observations from 1910 with early historical datasets currently available back to 1860 and the latest set of global climate model simulations from CMIP5/CMIP6 to examine the past and future warming of Australia from the 1850–1900 baseline commonly used as a proxy for pre-industrial conditions. We find that Australia warmed by ~1.6°C between 1850–1900 and 2011–2020 (with uncertainty unlikely to substantially exceed ±0.3°C). This warming is a ratio of ~1.4 times the ~1.1°C global warming over that time, and in line with observed global land average warming. Projections for global warming levels are also quantified and suggest future warming of slightly less than the observed ratio to date, at ~1.0–1.3 for all future global warming levels. We also find that to reliably examine regional warming under the emissions pathway framework using the latest climate models from CMIP6, appropriate weights to the ensemble members are required. Once these weights are applied, results are similar to CMIP5

Comparison of various climate change projections of eastern Australian rainfall

The Australian eastern seaboard is a distinct climate entity from the interior of the continent, with different climatic influences on each side of the Great Dividing Range. Therefore, it is plausible that downscaling of global climate models could reveal meaningful regional detail, or ‘added value’, in the climate change signal of mean rainfall change in eastern Australia un-der future scenarios. However, because downscaling is typically done using a limited set of global climate models and downscaling methods, the results from a downscaling study may not represent the range of uncertainty in plausible projected change for a region suggested by the ensemble of host global climate models. A complete and unbiased representation of the plausible changes in the climate is essential in producing climate projections useful for future planning. As part of this aim it is important to quantify any differences in the change signal between global climate models and downscaling, and understand the cause of these differ-ences in terms of plausible added regional detail in the climate change signal, the impact of sub-sampling global climate models and the effect of the downscaling models themselves. Here we examine rainfall projections in eastern Australia under a high emissions scenario by late in the century from ensembles of global climate models, two dynamical downscaling models and one statistical downscaling model. We find no cases where all three downscaling methods show the same clear regional spatial detail in the change signal that is distinct from the host models. However, some downscaled projections suggest that the eastern seaboard could see little change in spring rainfall, in contrast to the substantial rainfall decrease inland. The change signal in the downscaled outputs is broadly similar at the large scale in the various model outputs, with a few notable exceptions. For example, the model median from dynamical downscaling projects a rainfall increase over the entirety of eastern Australia in autumn that is greater than the global models. Also, there are some instances where a downscaling method produces changes outside the range of host models over eastern Australia as a whole, thus ex-panding the projected range of uncertainty. Results are particularly uncertain for summer, where no two downscaling studies clearly agree. There are also some confounding factors from the model configuration used in downscaling, where the particular zones used for statis-tical models and the model components used in dynamical models have an influence on results and produce additional uncertainty

A Bayesian method for evaluating and discovering disease loci associations

Background: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need. Methodology/Findings: We introduce the Bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a Bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found. Conclusions/Significance: We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations. © 2011 Jiang et al

mTORC1 plays an important role in osteoblastic regulation of B-lymphopoiesis

Skeletal osteoblasts are important regulators of B-lymphopoiesis, serving as a rich source of factors such as CXCL12 and IL-7 which are crucial for B-cell development. Recent studies from our laboratory and others have shown that deletion of Rptor, a unique component of the mTORC1 nutrient-sensing complex, early in the osteoblast lineage development results in defective bone development in mice. In this study, we now demonstrate that mTORC1 signalling in pre-osteoblasts is required for normal B-lymphocyte development in mice. Targeted deletion of Rptor in osterix-expressing pre-osteoblasts (Rptor; ob; -/-; ) leads to a significant reduction in the number of B-cells in the bone marrow, peripheral blood and spleen at 4 and 12 weeks of age. Rptor; ob; -/-; mice also exhibit a significant reduction in pre-B and immature B-cells in the BM, indicative of a block in B-cell development from the pro-B to pre-B cell stage. Circulating levels of IL-7 and CXCL12 are also significantly reduced in Rptor; ob; -/-; mice. Importantly, whilst Rptor-deficient osteoblasts are unable to support HSC differentiation to B-cells in co-culture, this can be rescued by the addition of exogenous IL-7 and CXCL12. Collectively, these findings demonstrate that mTORC1 plays an important role in extrinsic osteoblastic regulation of B-cell development

Anti-stromal treatment together with chemotherapy targets multiple signalling pathways in pancreatic adenocarcinoma.

Stromal targeting for pancreatic ductal adenocarcinoma (PDAC) is rapidly becoming an attractive option, due to the lack of efficacy of standard chemotherapy and increased knowledge about PDAC stroma. We postulated that the addition of stromal therapy may enhance the anti-tumour efficacy of chemotherapy. Gemcitabine and all-trans retinoic acid (ATRA) were combined in a clinically applicable regimen, to target cancer cells and pancreatic stellate cells (PSCs) respectively, in 3D organotypic culture models and genetically engineered mice (LSL-Kras(G12D) (/+) ;LSL-Trp53(R172H) (/+) ;Pdx-1-Cre: KPC mice) representing the spectrum of PDAC. In two distinct sets of organotypic models as well as KPC mice, we demonstrate a reduction in cancer cell proliferation and invasion together with enhanced cancer cell apoptosis when ATRA is combined with gemcitabine, compared to vehicle or either agent alone. Simultaneously, PSC activity (as measured by deposition of extracellular matrix proteins such as collagen and fibronectin) and PSC invasive ability were both diminished in response to combination therapy. These effects were mediated through a range of signalling cascades (Wnt, hedgehog, retinoid, and FGF) in cancer as well as stellate cells, affecting epithelial cellular functions such as epithelial-mesenchymal transition, cellular polarity, and lumen formation. At the tissue level, this resulted in enhanced tumour necrosis, increased vascularity, and diminished hypoxia. Consequently, there was an overall reduction in tumour size. The enhanced effect of stromal co-targeting (ATRA) alongside chemotherapy (gemcitabine) appears to be mediated by dampening multiple signalling cascades in the tumour-stroma cross-talk, rather than ablating stroma or targeting a single pathway. © 2016 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.This work was supported by project grants from the Knowledge Transfer Network (Engineering and Physical Sciences Research Committee) and Pancreatic Cancer Research Fund (UK) to HMK. CF was supported by an EMBO long term fellowship and by a Marie Curie Intra8European Fellowship within the 7th European Community Framework Programme. TB and FR were supported by Cancer Research UK (grant C14303/A17197). Other grant funding includes project grants from Pancreatic Cancer Research Fund, Cancer Research UK and Barts Charity.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1002/path.472