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A dipole pattern in convection between the South Atlantic convergence zone and the subtropical plains of southeastern South America characterizes summer intraseasonal variability over the region. The dipole pattern presents two main bands of temporal variability, with periods between 10 and 30 days, and 30 and 90 days; each influenced by different large-scale dynamical forcings. The dipole activity on the 30–90-day band is related to an eastward traveling wavenumber-1 structure in both OLR and circulation anomalies in the tropics, similar to that associated with the Madden–Julian oscillation. The dipole is also related to a teleconnection pattern extended along the South Pacific between Australia and South America. Conversely, the dipole activity on the 10–30-day band does not seem to be associated with tropical convection anomalies. The corresponding circulation anomalies exhibit, in the extratropics, the structure of Rossby-like wave trains, although their sources are not completely clear.

On a hemispheric scale, it is now well established that stratospheric ozone depletion has been the principal driver of externally forced atmospheric circulation changes south of the Equator in the last decades of the 20th Century. The impact of ozone depletion has been felt over the entire hemisphere, as reflected in the poleward drift of the midlatitude jet, the southward expansion of the summertime Hadley cell and accompanying precipitation trends deep into the subtropics. On a regional scale, however, surface impacts directly attributable to ozone depletion have yet to be identified. In this paper we focus on South Eastern South America (SESA), a region that has exhibited one of the largest wetting trends during the 20th Century. We study the impact of ozone depletion on SESA precipitation using output from 6 different climate models, spanning a wide range of complexity. In all cases we contrast pairs of model integrations with and without ozone depletion, but with all other forcings identically specified. This allows for unambiguous attribution of the computed precipitation trends. All 6 climate models consistently reveal that stratospheric ozone depletion results in a significant wetting of SESA over the period 1960–1999. Taken as a whole, these model results strongly suggest that the impact of ozone depletion on SESA precipitation has been as large as, and quite possibly larger than, the one caused by increasing greenhouse gases over the same period.

Using decadal prediction experiments from the WCRP/CMIP5 suite that were initialized every year from 1960-onward, we explore long-lead predictability of ENSO events. Both deterministic and probabilistic skill metrics are used to assess the ability of these decadal prediction systems to reproduce ENSO variability as represented by the NINO3.4 index (EN3.4). Several individual systems as well as the multi-model mean can predict ENSO events 3–4 years in advance, though not for every event during the hindcast period. This long-lead skill is beyond the previously documented predictability limits of initialized prediction systems. As part of the analysis, skill in reproducing the annual cycle of EN3.4, and the annual cycle of its interannual variability is examined. Most of the prediction systems reproduce the seasonal cycle of EN3.4, but are less able to capture the timing and magnitude of the variability. However, for the prediction systems used here, the fidelity of annual cycle characteristics does not appear to be related to the system’s ability to predict ENSO events. In addition, the performance of the multi-model ensemble mean is explored and compared to the multi-model mean based solely on the most skillful systems; the latter is found to yield better results for the deterministic metrics. Finally, an analysis of the near-surface temperature and precipitation teleconnections reveals that the ability of the systems to detect ENSO events far in advance could translate into predictive skill over land for several lead years, though with reduced amplitudes compared to observations.

Wind power accounts for a large portion of the European energy mix (17% of total power capacity). European power systems therefore have a significant-and growing-exposure to near-surface wind speed changes. Despite this, future changes in European wind climate remain relatively poorly studied (compared to, e.g., temperature or precipitation), and there is limited understanding of the differences shown by different general and regional circulation models (GCMs and RCMs). This study provides a step towards a process-based understanding of European wind speed changes by isolating the component associated with ‘large-scale’ atmospheric circulation changes in the CMIP5 simulations. The component associated with the large-scale atmospheric circulation is found to explain cold season windiness projections in the free troposphere over Western Europe, with the changes reflecting the poleward shift of the North Atlantic jet. However, in most GCMs the projected wind speed changes near the surface are more negative than would be expected from the large-scale circulation alone. Thus, while the spread in CMIP5 21st century near surface wind speed projections is associated with divergent projections for the large-scale atmospheric circulation, there is a remarkably good agreement concerning a relative reduction in near-surface wind speeds. This analysis suggests that projected 21st century wind speed changes over Western Europe are the result of two distinct processes. The first is associated with changes in the large-scale atmospheric circulation, while the second is likely to be more local in its connection to the near-surface boundary layer. An improved process-based understanding of both is needed for enhancing confidence in wind-power projections on multi-decadal timescales.

Sub‐seasonal forecasts are becoming more widely used in the energy sector to inform high‐impact, weather‐dependent decisions. Using pattern‐based methods (such as weather regimes) is also becoming commonplace, although until now an assessment of how pattern‐based methods perform compared with gridded model output has not been completed. We compare four methods to predict weekly‐mean anomalies of electricity demand and demand‐net‐wind across 28 European countries. At short lead times (days 0–10) grid‐point forecasts have higher skill than pattern‐based methods across multiple metrics. However, at extended lead times (day 12+) pattern‐based methods can show greater skill than grid‐point forecasts. All methods have relatively low skill at weekly‐mean national impact forecasts beyond day 12, particularly for probabilistic skill metrics. We therefore develop a method of pattern‐based conditioning, which is able to provide windows of opportunity for prediction at extended lead times: when at least 50% of the ensemble members of a forecast agree on a specific pattern, skill increases significantly. The conditioning is valuable for users interested in particular thresholds for decision‐making, as it combines the dynamical robustness in the large‐scale flow conditions from the pattern‐based methods with local information present in the grid‐point forecasts. At short lead times (days 0–10) grid‐point forecasts have higher skill than pattern‐based methods (e.g., weather regimes or targeted circulation types) across multiple metrics. However, at extended lead times (day 12+) pattern‐based methods can show greater skill. All methods have relatively low skill beyond day 12. We therefore develop a method of pattern‐based conditioning, which is able to provide windows of opportunity for prediction: when >50% of the pattern forecasts are in agreement skill increases significantly. AI ha

Cancer incidence increases with age and is a leading cause of death. Caloric restriction (CR) confers benefits on health and survival and delays cancer. However, due to CR’s stringency, dietary alternatives offering the same cancer protection have become increasingly attractive. Short cycles of a plant-based diet designed to mimic fasting (FMD) are protective against tumorigenesis without the chronic restriction of calories. Yet, it is unclear whether the fasting time, level of dietary restriction, or nutrient composition is the primary driver behind cancer protection. Using a breast cancer model in mice, we compare the potency of daily CR to that of periodic caloric cycling on FMD or an isocaloric standard laboratory chow against primary tumor growth and metastatic burden. Here, we report that daily CR provides greater protection against tumor growth and metastasis to the lung, which may be in part due to the unique immune signature observed with daily CR. Caloric restriction (CR) has been shown as an effective intervention to reduce tumorigenesis, but alternative less stringent dietary interventions have also been considered. Here, the authors show that in a murine model of breast cancer CR has a larger effect on preventing tumorigenesis and metastasis compared to periodic caloric cycling.

The seasonal cycle of the intraseasonal (IS) variability of precipitation in South America is described through the analysis of bandpass filtered outgoing longwave radiation (OLR) anomalies. The analysis is discriminated between short (10–30 days) and long (30–90 days) intraseasonal timescales. The seasonal cycle of the 30–90-day IS variability can be well described by the activity of first leading pattern (EOF1) computed separately for the wet season (October–April) and the dry season (May–September). In agreement with previous works, the EOF1 spatial distribution during the wet season is that of a dipole with centers of actions in the South Atlantic Convergence Zone (SACZ) and southeastern South America (SESA), while during the dry season, only the last center is discernible. In both seasons, the pattern is highly influenced by the activity of the Madden–Julian Oscillation (MJO). Moreover, EOF1 is related with a tropical zonal-wavenumber-1 structure superposed with coherent wave trains extended along the South Pacific during the wet season, while during the dry season the wavenumber-1 structure is not observed. The 10–30-day IS variability of OLR in South America can be well represented by the activity of the EOF1 computed through considering all seasons together, a dipole but with the stronger center located over SESA. While the convection activity at the tropical band does not seem to influence its activity, there are evidences that the atmospheric variability at subtropical-extratropical regions might have a role. Subpolar wavetrains are observed in the Pacific throughout the year and less intense during DJF, while a path of wave energy dispersion along a subtropical wavetrain also characterizes the other seasons. Further work is needed to identify the sources of the 10–30-day-IS variability in South America.

Mechanisms of resource allocation are essential for maternal and fetal survival, particularly when the availability of nutrients is limited. We investigated the responses of feto-placental development to maternal chronic protein malnutrition to test the hypothesis that maternal low protein diet produces differential growth restriction of placental and fetal tissues, and adaptive changes in the placenta that may mitigate impacts on fetal growth. C57BL/6J female mice were fed either a low-protein diet (6% protein) or control isocaloric diet (20% protein). On embryonic days E10.5, 17.5 and 18.5 tissue samples were prepared for morphometric, histological and quantitative RT-PCR analyses, which included markers of trophoblast cell subtypes. Potential endocrine adaptations were assessed by the expression of Prolactin-related hormone genes. In the low protein group, placenta weight was significantly lower at E10.5, followed by reduction of maternal weight at E17.5, while the fetuses became significantly lighter no earlier than at E18.5. Fetal head at E18.5 in the low protein group, though smaller than controls, was larger than expected for body size. The relative size and shape of the cranial vault and the flexion of the cranial base was affected by E17.5 and more severely by E18.5. The junctional zone, a placenta layer rich in endocrine and energy storing glycogen cells, was smaller in low protein placentas as well as the expression of Pcdh12, a marker of glycogen trophoblast cells. Placental hormone gene Prl3a1 was altered in response to low protein diet: expression was elevated at E17.5 when fetuses were still growing normally, but dropped sharply by E18.5 in parallel with the slowing of fetal growth. This model suggests that nutrients are preferentially allocated to sustain fetal and brain growth and suggests the placenta as a nutrient sensor in early gestation with a role in mitigating impacts of poor maternal nutrition on fetal growth.

Triple-negative breast cancer (TNBC) is a collection of biologically diverse cancers characterized by distinct transcriptional patterns, biology, and immune composition. TNBCs subtypes include two basal-like (BL1, BL2), a mesenchymal (M) and a luminal androgen receptor (LAR) subtype. Through a comprehensive analysis of mutation, copy number, transcriptomic, epigenetic, proteomic, and phospho-proteomic patterns we describe the genomic landscape of TNBC subtypes. Mesenchymal subtype tumors display high mutation loads, genomic instability, absence of immune cells, low PD-L1 expression, decreased global DNA methylation, and transcriptional repression of antigen presentation genes. We demonstrate that major histocompatibility complex I (MHC-I) is transcriptionally suppressed by H3K27me3 modifications by the polycomb repressor complex 2 (PRC2). Pharmacological inhibition of PRC2 subunits EZH2 or EED restores MHC-I expression and enhances chemotherapy efficacy in murine tumor models, providing a rationale for using PRC2 inhibitors in PD-L1 negative mesenchymal tumors. Subtype-specific differences in immune cell composition and differential genetic/pharmacological vulnerabilities suggest additional treatment strategies for TNBC. Triple negative breast cancer can be divided into additional subtypes. Here, using omics analyses, the authors show that in the mesenchymal subtype expression of MHC-1 is repressed and that this can be restored by using drugs that target subunits of the epigenetic modifier PRC2.

In the context of climate change, assessing how likely a particular change or event was caused by human influence is important for mitigation and adaptation policies. In this work we propose an extreme event attribution (EEA) methodology to analyze yearly maxima records, key indicators of climate change that spark off media attention and research in the EEA community. Although they deserve a specific statistical treatment, algorithms tailored to record analysis are lacking. This is particularly true in a non‐stationary context. This work aims at filling this methodological gap by focusing on records in transient climate simulations. We apply our methodology to study records of yearly maxima of daily precipitation issued from the numerical climate model IPSL‐CM6A‐LR. Illustrating our approach with decadal records, we detect in 2023 a clear human induced signal in half the globe, with probability mostly increasing, but decreasing in the south and north Atlantic oceans.