Here sea ice concentration derived from the Special Sensor Microwave Imager/Sounder and thickness derived from the Soil Moisture and Ocean Salinity and CryoSat-2 satellites are assimilated in the National Centers for Environmental Prediction Climate Forecast System using a localized error subspace transform ensemble Kalman filter (LESTKF). Three ensemble-based hindcasts are conducted to examine impacts of the assimilation on Arctic sea ice prediction, including CTL (without any assimilation), LESTKF-1 (with initial sea ice assimilation only), and LESTKF-E5 (with every 5-day sea ice assimilation). Assessment with the assimilated satellite products and independent sea ice thickness datasets shows that assimilating sea ice concentration and thickness leads to improved Arctic sea ice prediction. LESTKF-1 improves sea ice forecast initially. The initial improvement gradually diminishes after ~3-week integration for sea ice extent but remains quite steady through the integration for sea ice thickness. Large biases in both the ice extent and thickness in CTL are remarkably reduced through the hindcast in LESTKF-E5. Additional numerical experiments suggest that the hindcast with sea ice thickness assimilation dramatically reduces systematic bias in the predicted ice thickness compared with sea ice concentration assimilation only or without any assimilation, which also benefits the prediction of sea ice extent and concentration due to their covariability. Hence, the corrected state of sea ice thickness would aid in the forecast procedure. Increasing the number of ensemble members or extending the integration period to generate estimates of initial model states and uncertainties seems to have small impacts on sea ice prediction relative to LESTKF-E5.
Background Due to post-cleavage residence of the Cas9-sgRNA complex at its target, Cas9-induced DNA double-strand breaks (DSBs) have to be exposed to engage DSB repair pathways. Target interaction of Cas9-sgRNA determines its target binding affinity and modulates its post-cleavage target residence duration and exposure of Cas9-induced DSBs. This exposure, via different mechanisms, may initiate variable DNA damage responses, influencing DSB repair pathway choices and contributing to mutational heterogeneity in genome editing. However, this regulation of DSB repair pathway choices is poorly understood. Results In repair of Cas9-induced DSBs, repair pathway choices vary widely at different target sites and classical nonhomologous end joining (c-NHEJ) is not even engaged at some sites. In mouse embryonic stem cells, weakening the target interaction of Cas9-sgRNA promotes bias towards c-NHEJ and increases target dissociation and reduces target residence of Cas9-sgRNAs in vitro. As an important strategy for enhancing homology-directed repair, inactivation of c-NHEJ aggravates off-target activities of Cas9-sgRNA due to its weak interaction with off-target sites. By dislodging Cas9-sgRNA from its cleaved targets, DNA replication alters DSB end configurations and suppresses c-NHEJ in favor of other repair pathways, whereas transcription has little effect on c-NHEJ engagement. Dissociation of Cas9-sgRNA from its cleaved target by DNA replication may generate three-ended DSBs, resulting in palindromic fusion of sister chromatids, a potential source for CRISPR/Cas9-induced on-target chromosomal rearrangements. Conclusions Target residence of Cas9-sgRNA modulates DSB repair pathway choices likely through varying dissociation of Cas9-sgRNA from cleaved DNA, thus widening on-target and off-target mutational spectra in CRISPR/Cas9 genome editing.
Abstract. With semiconductor technology gradually approaching its physical and thermal limits, recent supercomputers have adopted major architectural changes to continue increasing the performance through more power-efficient heterogeneous many-core systems. Examples include Sunway TaihuLight that has four management processing elements (MPEs) and 256 computing processing elements (CPEs) inside one processor and Summit that has two central processing units (CPUs) and six graphics processing units (GPUs) inside one node. Meanwhile, current high-resolution Earth system models that desperately require more computing power generally consist of millions of lines of legacy code developed for traditional homogeneous multicore processors and cannot automatically benefit from the advancement of supercomputer hardware. As a result, refactoring and optimizing the legacy models for new architectures become key challenges along the road of taking advantage of greener and faster supercomputers, providing better support for the global climate research community and contributing to the long-lasting societal task of addressing long-term climate change. This article reports the efforts of a large group in the International Laboratory for High-Resolution Earth System Prediction (iHESP) that was established by the cooperation of Qingdao Pilot National Laboratory for Marine Science and Technology (QNLM), Texas A&M University (TAMU), and the National Center for Atmospheric Research (NCAR), with the goal of enabling highly efficient simulations of the high-resolution (25 km atmosphere and 10 km ocean) Community Earth System Model (CESM-HR) on Sunway TaihuLight. The refactoring and optimizing efforts have improved the simulation speed of CESM-HR from 1 SYPD (simulation years per day) to 3.4 SYPD (with output disabled) and supported several hundred years of pre-industrial control simulations. With further strategies on deeper refactoring and optimizing for remaining computing hotspots, as well as redesigning architecture-oriented algorithms, we expect an equivalent or even better efficiency to be gained on the new platform than traditional homogeneous CPU platforms. The refactoring and optimizing processes detailed in this paper on the Sunway system should have implications for similar efforts on other heterogeneous many-core systems such as GPU-based high-performance computing (HPC) systems.
The Soil Moisture Active Passive (SMAP) mission was designed to provide a global mapping of soil moisture (SM) measured by L-band passive and active microwave sensors. In this study, we evaluate the newly released SMAP enhanced SM products over the Tibetan Plateau by performing comparisons among SMAP standard products, in-situ observations and Community Land Model (CLM) simulations driven by high-resolution meteorological forcing. At local scales, the enhanced SMAP products, the standard products and CLM simulations all generally compare well with the in-situ observations. The SMAP products show stronger correlations (0.64-0.88) but slightly larger unbiased root mean square errors (ubRMSE,~0.06) relative to the CLM simulations (0.58-0.79 and 0.037-0.047, for correlation and ubRMSE, respectively). At the regional scale, both SMAP products show similar spatial distributions of SM on the TP (Tibetan Plateau), although, as expected, the enhanced product provides more fine details. The SMAP enhanced product is in good agreement with model simulations with respect to temporal and spatial variations in SM over most of the TP. Regions with low correlation between SMAP enhanced products and model simulations are mainly located in the northwestern TP and regions of complex topography, where meteorological stations are sparse and non-existent or elevation is highly variable. In such remote regions, CLM simulations may be problematic due to inaccurate land cover maps and/or uncertainties in meteorological forcing. The independent, high-resolution observations provided by SMAP could help to constrain the model simulation and, ultimately, improve the skill of models in these problematic regions.
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