Arctic climate response to forcing from light-absorbing particles in snow and sea ice in CESM

The presence of light-absorbing aerosol particles deposited on arctic snow and sea ice influences the surface albedo, causing greater shortwave absorption, warming, and loss of snow and sea ice, lowering the albedo further. The Community Earth System Model version 1 (CESM1) now includes the radiative effects of light-absorbing particles in snow on land and sea ice and in sea ice itself. We investigate the model response to the deposition of black carbon and dust to both snow and sea ice. For these purposes we employ a slab ocean version of CESM1, using the Community Atmosphere Model version 4 (CAM4), run to equilibrium for year 2000 levels of CO<sub>2</sub> and fixed aerosol deposition. We construct experiments with and without aerosol deposition, with dust or black carbon deposition alone, and with varying quantities of black carbon and dust to approximate year 1850 and 2000 deposition fluxes. The year 2000 deposition fluxes of both dust and black carbon cause 1–2 °C of surface warming over large areas of the Arctic Ocean and sub-Arctic seas in autumn and winter and in patches of Northern land in every season. Atmospheric circulation changes are a key component of the surface-warming pattern. Arctic sea ice thins by on average about 30 cm. Simulations with year 1850 aerosol deposition are not substantially different from those with year 2000 deposition, given constant levels of CO<sub>2</sub>. The climatic impact of particulate impurities deposited over land exceeds that of particles deposited over sea ice. Even the surface warming over the sea ice and sea ice thinning depends more upon light-absorbing particles deposited over land. For CO<sub>2</sub> doubled relative to year 2000 levels, the climate impact of particulate impurities in snow and sea ice is substantially lower than for the year 2000 equilibrium simulation

Understanding the Cascade: Removing GCM Biases Improves Dynamically Downscaled Climate Projections

Polarization surrounding bias correction (BC) in creating climate projections arises from its lack of physicality. Here, we perform and analyze 18 dynamical downscaling simulations (with and without BC) to better understand the physical impacts of BC, applied before downscaling, on regional climate output across the western United States. Without BC, downscaled precipitation is systematically and unrealistically wet biased compared to a hierarchy of observationally based datasets over the 1980–2014 period due to cascading mean-state Global Climate Model (GCM) biases: (a) overly strong lower-tropospheric lapse rates (5 K/km), (b) overly cold (2 K) tropospheric temperatures, and (c) anomalous mid-tropospheric cyclonic vorticity advection. With BC, downscaled precipitation (snow) biases are virtually eliminated (halved). Identified GCM biases are common to the broader Coupled Model Intercomparison Project ensemble. Physical effects of BC on the quality of the regionalized projections, pending an evaluation of BC's distortion of the downscaled climate response, may motivate its broader application by dynamical downscalers

Targeting megakaryocytic induced fibrosis by AURKA inhibition in the myeloproliferative neoplasms

Primary myelofibrosis (PMF) is characterized by bone marrow fibrosis, myeloproliferation, extramedullary hematopoiesis, splenomegaly and leukemic progression. Moreover, the bone marrow and spleen of patients are full of atypical megakaryocytes that are postulated to contribute to fibrosis through the release of cytokines including TGF-β. Although the JAK inhibitor ruxolitinib provides symptomatic relief, it does not reduce the mutant allele burden or significantly reverse fibrosis. Here we show through pharmacologic and genetic studies that, apart from JAK2, Aurora kinase A (AURKA) is a novel therapeutic target in PMF. MLN8237, a selective AURKA inhibitor promoted polyploidization and differentiation of PMF megakaryocytes and displayed potent anti-fibrotic and anti-tumor activity in vivo. We also reveal that loss of one allele of AURKA is sufficient to ameliorate fibrosis and other PMF phenotypes in vivo. Our data suggest that megakaryocytes are drivers of fibrosis and that targeting them with AURKA inhibitors will provide therapeutic benefit in PMF

Cell-based screen for altered nuclear phenotypes reveals senescence progression in polyploid cells after Aurora kinase B inhibition.

Cellular senescence is a widespread stress response and is widely considered to be an alternative cancer therapeutic goal. Unlike apoptosis, senescence is composed of a diverse set of subphenotypes, depending on which of its associated effector programs are engaged. Here we establish a simple and sensitive cell-based prosenescence screen with detailed validation assays. We characterize the screen using a focused tool compound kinase inhibitor library. We identify a series of compounds that induce different types of senescence, including a unique phenotype associated with irregularly shaped nuclei and the progressive accumulation of G1 tetraploidy in human diploid fibroblasts. Downstream analyses show that all of the compounds that induce tetraploid senescence inhibit Aurora kinase B (AURKB). AURKB is the catalytic component of the chromosome passenger complex, which is involved in correct chromosome alignment and segregation, the spindle assembly checkpoint, and cytokinesis. Although aberrant mitosis and senescence have been linked, a specific characterization of AURKB in the context of senescence is still required. This proof-of-principle study suggests that our protocol is capable of amplifying tetraploid senescence, which can be observed in only a small population of oncogenic RAS-induced senescence, and provides additional justification for AURKB as a cancer therapeutic target.This work was supported by the University of Cambridge, Cancer Research UK, Hutchison Whampoa; Cancer Research UK grants A6691 and A9892 (M.N., N.K., C.J.T., D.C.B., C.J.C., L.S.G, and M.S.); a fellowship from the Uehara Memorial Foundation (M.S.).This is the author accepted manuscript. The final version is available from the American Society for Cell Biology via http://dx.doi.org/10.1091/mbc.E15-01-000

Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month proof-of-concept trial run designed to illustrate the utility and feasibility of the ARTMIP project

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology

Atmospheric rivers (ARs) are now widely known for their association with high‐impact weather events and long‐term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR‐related metrics based on 20+ different AR identification and tracking methods applied to Modern‐Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria‐based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all‐method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR‐related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR‐related research to consider.Fil: Rutz, Jonathan J.. National Ocean And Atmospheric Administration; Estados UnidosFil: Shields, Christine A.. National Center for Atmospheric Research; Estados UnidosFil: Lora, Juan M.. University of Yale; Estados UnidosFil: Payne, Ashley E.. University of Michigan; Estados UnidosFil: Guan, Bin. California Institute of Technology; Estados UnidosFil: Ullrich, Paul. University of California at Davis; Estados UnidosFil: O'Brien, Travis. Lawrence Berkeley National Laboratory; Estados UnidosFil: Leung, Ruby. Pacific Northwest National Laboratory; Estados UnidosFil: Ralph, F. Martin. Center For Western Weather And Water Extremes; Estados UnidosFil: Wehner, Michael. Lawrence Berkeley National Laboratory; Estados UnidosFil: Brands, Swen. Meteogalicia; EspañaFil: Collow, Allison. Universities Space Research Association; Estados UnidosFil: Goldenson, Naomi. University of California at Los Angeles; Estados UnidosFil: Gorodetskaya, Irina. Universidade de Aveiro; PortugalFil: Griffith, Helen. University of Reading; Reino UnidoFil: Kashinath, Karthik. Lawrence Bekeley National Laboratory; Estados UnidosFil: Kawzenuk, Brian. Center For Western Weather And Water Extremes; Reino UnidoFil: Krishnan, Harinarayan. Lawrence Berkeley National Laboratory; Estados UnidosFil: Kurlin, Vitaliy. University of Liverpool; Reino UnidoFil: Lavers, David. European Centre For Medium-range Weather Forecasts; Estados UnidosFil: Magnusdottir, Gudrun. University of California at Irvine; Estados UnidosFil: Mahoney, Kelly. Universidad de Lisboa; PortugalFil: Mc Clenny, Elizabeth. University of California at Davis; Estados UnidosFil: Muszynski, Grzegorz. University of Liverpool; Reino Unido. Lawrence Bekeley National Laboratory; Estados UnidosFil: Nguyen, Phu Dinh. University of California at Irvine; Estados UnidosFil: Prabhat, Mr.. Lawrence Bekeley National Laboratory; Estados UnidosFil: Qian, Yun. Pacific Northwest National Laboratory; Estados UnidosFil: Ramos, Alexandre M.. Universidade Nova de Lisboa; PortugalFil: Sarangi, Chandan. Pacific Northwest National Laboratory; Estados UnidosFil: Viale, Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentin