Intensification of very wet monsoon seasons in India under global warming

Rainfall-intense summer monsoon seasons on the Indian subcontinent that are exceeding long-term averages cause widespread floods and landslides. Here we show that the latest generation of coupled climate models robustly project an intensification of very rainfall-intense seasons (June–September). Under the shared socioeconomic pathway SSP5-8.5, very wet monsoon seasons as observed in only 5 years in the period 1965–2015 are projected to occur 8 times more often in 2050–2100 in the multi-model average. Under SSP2-4.5, these seasons become only a factor of 6 times more frequent, showing that even modest efforts to mitigate climate change can have a strong impact on the frequency of very strong rainfall seasons. Besides, we find that the increasing risk of extreme seasonal rainfall is accompanied by a shift from days with light rainfall to days with moderate or heavy rainfall. Additionally, the number of wet days is projected to increase. © 2022. The Authors

Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP6 models

The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP6 are of interest. Here, we analyze 32 models of the latest CMIP6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with a high agreement between the models independent of the SSP if global warming is the dominant forcing of the monsoon dynamics as it is in the 21st century; the multi-model mean for JJAS projects an increase of 0.33 mm d−1 and 5.3 % per kelvin of global warming. This is significantly higher than in the CMIP5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP6 simulations largely confirm the findings from CMIP5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall

Bioclimatic and Soil Moisture Monitoring Across Elevation in a Mountain Watershed: Opportunities for Research and Resource Management

Soil moisture data are critical to understanding biophysical and societal impacts of climate change. However, soil moisture data availability is limited due to sparse in situ monitoring, particularly in mountain regions. Here we present methods, specifications, and initial results from the interactive Roaring Fork Observation Network (iRON), a soil, weather, and ecological monitoring system in the Southern Rocky Mountains of Colorado. Initiated in 2012, the network is currently composed of nine stations, distributed in elevation from 1,890 to 3,680 m, that continually collect and transmit measurements of soil moisture at three depths (5, 20, and 50 cm), soil temperature (20 cm), and meteorological conditions. Time‐lapse cameras for phenological observations, snow depth sensors, and periodic co‐located vegetation surveys complement selected stations. iRON was conceived and designed with the joint purpose of supporting bioclimatic research and resource management objectives in a snow‐dominated watershed. In the short term, iRON data can be applied to assessing the impact of temperature and precipitation on seasonal soil moisture conditions and trends. As more data are collected over time, iRON will help improve understanding of climate‐driven changes to soil, vegetation, and hydrologic conditions. In presenting this network and its initial data, we hope that the network’s elevational gradient will contribute to bioclimatic mountain research, while active collaboration with partners in resource management may provide a model for science‐practice interaction in support of long‐term monitoring.Plain Language SummaryAs climate change drives shifts in temperature and precipitation, researchers and resource managers can benefit from improved monitoring of soil moisture. Understanding the relationship between soil moisture and other system components is crucial to improving water availability projections and understanding ecosystem responses to climate change. Despite their significance, in‐ground soil‐moisture measurements are often not available across multiple elevations within a single watershed. This paper presents a network in the Southern Rocky Mountains intended to help address this data gap and compliment data from other networks. The interactive Roaring Fork Observation Network consists of nine locations across an 1,800‐m change in elevation. Each station measures soil moisture at three depths, soil temperature, air temperature, humidity, and precipitation. Some stations are equipped with cameras or snow depth gauges, and for eight sites vegetation surveys are conducted. The data are available through a simple data portal. The network was established with local resource manager support, and one of its guiding purposes is to support management and restoration planning efforts. Because of the network’s ongoing monitoring across multiple elevations and habitats, interactive Roaring Fork Observation Network will provide researchers and resource managers with access to valuable information about changes in soil conditions in a changing climate.Key PointsSoil moisture is key to understanding and predicting change in hydrology and ecology amid climate variability and changeIn situ soil moisture and weather monitoring data are now available across an 1,800‐m elevation span in a mountain watershedThe network is supported and guided by resource managers and supports both research and resource management goalsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149210/1/wrcr23834_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149210/2/wrcr23834.pd

Prognostic impact of proliferative index determined by quantitative image analysis and the International Prognostic Index in patients with mantle cell lymphoma

Background: The proliferative index (PI) is a powerful prognostic factor in mantle cell lymphoma (MCL); however, its utility is hampered by interobserver variability. The mantle cell international prognostic index (MIPI) has been reported to have prognostic importance. In this study, we determined the prognostic value of the PI as determined by quantitative image analysis in MCL

Clash of Titans: A MUSE dynamical study of the extreme cluster merger SPT-CL J0307-6225

We present MUSE spectroscopy, Megacam imaging, and Chandra X-ray emission for SPT-CL J0307-6225, a z = 0.58 major merging galaxy cluster with a large BCG-SZ centroid separation and a highly disturbed X-ray morphology. The galaxy density distribution shows two main overdensities with separations of 0.144 and 0.017 arcmin to their respective BCGs. We characterize the central regions of the two colliding structures, namely 0307-6225N and 0307-6225S, finding velocity derived masses of M200, N = 2.44 ± 1.41 × 1014M⊙ and M200, S = 3.16 ± 1.88 × 1014M⊙, with a line-of-sight velocity difference of |Δv| = 342 km s-1. The total dynamically derived mass is consistent with the SZ derived mass of 7.63 h70-1 ± 1.36 × 1014M⊙. We model the merger using the Monte Carlo Merger Analysis Code, estimating a merging angle of 36+14-12 ° with respect to the plane of the sky. Comparing with simulations of a merging system with a mass ratio of 1:3, we find that the best scenario is that of an ongoing merger that began 0.96+0.31-0.18 Gyr ago. We also characterize the galaxy population using Hδand [O ii] λ3727 Å lines. We find that most of the emission-line galaxies belong to 0307-6225S, close to the X-ray peak position with a third of them corresponding to red-cluster sequence galaxies, and the rest to blue galaxies with velocities consistent with recent periods of accretion. Moreover, we suggest that 0307-6225S suffered a previous merger, evidenced through the two equally bright BCGs at the centre with a velocity difference of ∼674 km s-1

How can an understanding of plant-pollinator interactions contribute to global food security?

Pollination of crops by animals is an essential part of global food production, but evidence suggests that wild pollinator populations may be declining while a number of problems are besetting managed honey bee colonies. Animal-pollinated crops grown today, bred in an environment where pollination was less likely to limit fruit set, are often suboptimal in attracting and sustaining their pollinator populations. Research into plant-pollinator interactions is often conducted in a curiosity-driven, ecological framework, but may inform breeding and biotechnological approaches to enhance pollinator attraction and crop yield. In this article we review key topics in current plant-pollinator research that have potential roles in future crop breeding for enhanced global food security

A joint SZ-X-ray-optical analysis of the dynamical state of 288 massive galaxy clusters

We use imaging from the first three years of the Dark Energy Survey to characterize the dynamical state of 288 galaxy clusters at $0.1 \lesssim z \lesssim 0.9$ detected in the South Pole Telescope (SPT) Sunyaev-Zeldovich (SZ) effect survey (SPT-SZ). We examine spatial offsets between the position of the brightest cluster galaxy (BCG) and the center of the gas distribution as traced by the SPT-SZ centroid and by the X-ray centroid/peak position from Chandra and XMM data. We show that the radial distribution of offsets provides no evidence that SPT SZ-selected cluster samples include a higher fraction of mergers than X-ray-selected cluster samples. We use the offsets to classify the dynamical state of the clusters, selecting the 43 most disturbed clusters, with half of those at $z \gtrsim 0.5$, a region seldom explored previously. We find that Schechter function fits to the galaxy population in disturbed clusters and relaxed clusters differ at $z>0.55$ but not at lower redshifts. Disturbed clusters at $z>0.55$ have steeper faint-end slopes and brighter characteristic magnitudes. Within the same redshift range, we find that the BCGs in relaxed clusters tend to be brighter than the BCGs in disturbed samples, while in agreement in the lower redshift bin. Possible explanations includes a higher merger rate, and a more efficient dynamical friction at high redshift. The red-sequence population is less affected by the cluster dynamical state than the general galaxy population.Comment: 21 pages, 12 Figures, 4 Tables. Accepted for publication in MNRA

Explorative data analysis of MCL reveals gene expression networks implicated in survival and prognosis supported by explorative CGH analysis

<p>Abstract</p> <p>Background</p> <p>Mantle cell lymphoma (MCL) is an incurable B cell lymphoma and accounts for 6% of all non-Hodgkin's lymphomas. On the genetic level, MCL is characterized by the hallmark translocation t(11;14) that is present in most cases with few exceptions. Both gene expression and comparative genomic hybridization (CGH) data vary considerably between patients with implications for their prognosis.</p> <p>Methods</p> <p>We compare patients over and below the median of survival. Exploratory principal component analysis of gene expression data showed that the second principal component correlates well with patient survival. Explorative analysis of CGH data shows the same correlation.</p> <p>Results</p> <p>On chromosome 7 and 9 specific genes and bands are delineated which improve prognosis prediction independent of the previously described proliferation signature. We identify a compact survival predictor of seven genes for MCL patients. After extensive re-annotation using GEPAT, we established protein networks correlating with prognosis. Well known genes (CDC2, CCND1) and further proliferation markers (WEE1, CDC25, aurora kinases, BUB1, PCNA, E2F1) form a tight interaction network, but also non-proliferative genes (SOCS1, TUBA1B CEBPB) are shown to be associated with prognosis. Furthermore we show that aggressive MCL implicates a gene network shift to higher expressed genes in late cell cycle states and refine the set of non-proliferative genes implicated with bad prognosis in MCL.</p> <p>Conclusion</p> <p>The results from explorative data analysis of gene expression and CGH data are complementary to each other. Including further tests such as Wilcoxon rank test we point both to proliferative and non-proliferative gene networks implicated in inferior prognosis of MCL and identify suitable markers both in gene expression and CGH data.</p