ALBERT

Description: Chemistry plays an indispensable role in investigations of the atmosphere; however, many climate models either ignore or greatly simplify atmospheric chemistry, limiting both their accuracy and their scope. We present the development and evaluation of the online global atmospheric chemical model BCC-GEOS-Chem v1.0, coupling the GEOS-Chem chemical transport model (CTM) as an atmospheric chemistry component in the Beijing Climate Center atmospheric general circulation model (BCC-AGCM). The GEOS-Chem atmospheric chemistry component includes detailed tropospheric HOx–NOx–volatile organic compounds–ozone–bromine–aerosol chemistry and online dry and wet deposition schemes. We then demonstrate the new capabilities of BCC-GEOS-Chem v1.0 relative to the base BCC-AGCM model through a 3-year (2012–2014) simulation with anthropogenic emissions from the Community Emissions Data System (CEDS) used in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The model captures well the spatial distributions and seasonal variations in tropospheric ozone, with seasonal mean biases of 0.4–2.2 ppbv at 700–400 hPa compared to satellite observations and within 10 ppbv at the surface to 500 hPa compared to global ozonesonde observations. The model has larger high-ozone biases over the tropics which we attribute to an overestimate of ozone chemical production. It underestimates ozone in the upper troposphere which is likely due either to the use of a simplified stratospheric ozone scheme or to biases in estimated stratosphere–troposphere exchange dynamics. The model diagnoses the global tropospheric ozone burden, OH concentration, and methane chemical lifetime to be 336 Tg, 1.16×106 molecule cm−3, and 8.3 years, respectively, which is consistent with recent multimodel assessments. The spatiotemporal distributions of NO2, CO, SO2, CH2O, and aerosol optical depth are generally in agreement with satellite observations. The development of BCC-GEOS-Chem v1.0 represents an important step for the development of fully coupled earth system models (ESMs) in China.

Description: Seismoelectric measurements are conducted with a synthetic porous rock sample to model an ocean exploration. Two kinds of seismoelectric coupling signals, that is, the interfacial EM wave signal and the coseismic electric signal, have been recorded by the electrodes buried inside a rock sample instead of those located in the fluid or in the solid region near the interface as performed in previous works. These seismoelectric signals are clearly observed and identified with a high signal-to-noise ratio. The characteristics of the measured interfacial EM wave and coseismic electric signals are analysed with the experimental data. We also simulate the seismoelectric conversion fields and make a comparison between the measured and simulated seismoelectric signals. The result shows that the simulated and measured signals match well for both the interfacial EM wave and the coseismic electric fields accompanying the fast P wave. Our results also show that the amplitudes of seismoelectric signals are in the order of tens to hundreds of microvolts with our experimental system. This confirms that the seismoelectric signals are measurable in the interior of the rocks with current measurement techniques, suggesting the seismoelectric measurement to be a potential method for studying characteristics of the material beneath the seafloor.

Description: We report studies of the relationships between the total bolometric luminosity (Lbol or LTIR) and the molecular line luminosities of J = 1 − 0 transitions of H13CN, H13CO+, HCN, and HCO+ with data obtained from ACA observations in the ‘ATOMS’ survey of 146 active Galactic star-forming regions. The correlations between Lbol and molecular line luminosities $L^{prime }_{ m mol}$ of the four transitions all appear to be approximately linear. Line emission of isotopologues shows as large scatters in Lbol–$L^{prime }_{ m mol}$ relations as their main line emission. The log(Lbol/$L^{prime }_{ m mol}$) for different molecular line tracers have similar distributions. The Lbol-to-$L^{prime }_{ m mol}$ ratios do not change with galactocentric distances (RGC) and clump masses (Mclump). The molecular line luminosity ratios (HCN-to-HCO+, H13CN-to-H13CO+, HCN-to-H13CN, and HCO+-to-H13CO+) all appear constant against Lbol, dust temperature (Td), Mclump, and RGC. Our studies suggest that both the main lines and isotopologue lines are good tracers of the total masses of dense gas in Galactic molecular clumps. The large optical depths of main lines do not affect the interpretation of the slopes in star formation relations. We find that the mean star formation efficiency (SFE) of massive Galactic clumps in the ‘ATOMS’ survey is reasonably consistent with other measures of the SFE for dense gas, even those using very different tracers or examining very different spatial scales.

Description: The variations in oceanic and atmospheric modes on various timescales play important roles in generating global and regional climate variability. Many efforts have been devoted to identifying the relationships between the variations in climate modes and regional climate variability, but these have rarely explored the interconnections among these climate modes. Here we use climate indices to represent the variations in major climate modes and examine the harmonic relationship among the driving forces of climate modes using slow feature analysis (SFA) and wavelet analysis. We find that all of the significant peak periods of driving-force signals in the climate indices can be represented as harmonics of four base periods: 2.32, 3.90, 6.55, and 11.02 years. We infer that the period of 2.32 years is associated with the signal of the quasi-biennial oscillation (QBO). The periods of 3.90 and 6.55 years are linked to the intrinsic variability of the El Niño–Southern Oscillation (ENSO), and the period of 11.02 years arises from the sunspot cycle. Results suggest that the base periods and their harmonic oscillations related to QBO, ENSO, and solar activities act as key connections among the climatic modes with synchronous behaviors, highlighting the important roles of these three oscillations in the variability of the Earth's climate. Highlights. i. The harmonic relationship among the driving forces of climate modes was investigated by using slow feature analysis and wavelet analysis.ii. All of the significant peak periods of driving-force signals in climate indices can be represented as the harmonics of four base periods.iii. The four base periods related to QBO, ENSO, and solar activities act as the key linkages among different climatic modes with synchronous behaviors.

Description: Top-down emission estimates provide valuable up-to-date information on pollution sources; however, the computational effort and spatial resolution of satellite products involved with developing these emissions often require them to be estimated at resolutions that are much coarser than is necessary for regional air quality forecasting. This work thus introduces several approaches to downscaling coarse-resolution (2∘×2.5∘) posterior SO2 and NOx emissions for improving air quality assessment and forecasts over China in October 2013. As in Part 1 of this study, these 2∘×2.5∘ posterior SO2 and NOx emission inventories are obtained from GEOS-Chem adjoint modeling with the constraints of OMPS SO2 and NO2 products retrieved at 50 km×50 km at nadir and ∼190km×50km at the edge of ground track. The prior emission inventory (MIX) and the posterior GEOS-Chem simulations of surface SO2 and NO2 concentrations at coarse resolution underestimate observed hot spots, which is called the coarse-grid smearing (CGS) effect. To mitigate the CGS effect, four methods are developed: (a) downscale 2∘×2.5∘ GEOS-Chem surface SO2 and NO2 concentrations to the resolution of 0.25∘×0.3125∘ through a dynamic downscaling concentration (MIX-DDC) approach, which assumes that the 0.25∘×0.3125∘ simulation using the prior MIX emissions has the correct spatial distribution of SO2 and NO2 concentrations but a systematic bias; (b) downscale surface NO2 simulations at 2∘×2.5∘ to 0.05∘×0.05∘ according to the spatial distribution of Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NL) observations (e.g., NL-DC approach) based on correlation between VIIRS NL intensity with TROPOspheric Monitoring Instrument (TROPOMI) NO2 observations; (c) downscale posterior emissions (DE) of SO2 and NOx to 0.25∘×0.3125∘ with the assumption that the prior fine-resolution MIX inventory has the correct spatial distribution (e.g., MIX-DE approach); and (d) downscale posterior NOx emissions using VIIRS NL observations (e.g., NL-DE approach). Numerical experiments reveal that (a) using the MIX-DDC approach, posterior SO2 and NO2 simulations improve on the corresponding MIX prior simulations with normalized centered root mean square error (NCRMSE) decreases of 63.7 % and 30.2 %, respectively; (b) the posterior NO2 simulation has an NCRMSE that is 17.9 % smaller than the prior when they are both downscaled through NL-DC, and NL-DC is able to better mitigate the CGS effect than MIX-DDC; (c) the simulation at 0.25∘×0.3125∘ using the MIX-DE approach has NCRMSEs that are 58.8 % and 14.7 % smaller than the prior 0.25∘×0.3125∘ MIX simulation for surface SO2 and NO2 concentrations, respectively, but the RMSE from the MIX-DE posterior simulation is slightly larger than that from the MIX-DDC posterior simulation for both SO2 and NO2; (d) the NL-DE posterior NO2 simulation also improves on the prior MIX simulation at 0.25∘×0.3125∘, but it is worse than the MIX-DE posterior simulation; (e) in terms of evaluating the downscaled SO2 and NO2 simulations simultaneously, using the posterior SO2 and NOx emissions from joint inverse modeling of both species is better than only using one (SO2 or NOx) emission from corresponding single-species inverse modeling and is similar to using the posterior emissions of SO2 and NOx emission inventories respectively from single-species inverse modeling. Forecasts of surface concentrations for November 2013 using the posterior emissions obtained by applying the posterior MIX-DE emissions for October 2013 with the monthly variation information derived from the prior MIX emission inventory show that (a) the improvements of forecasting surface SO2 concentrations through MIX-DE and MIX-DDC are comparable; (b) for the NO2 forecast, MIX-DE shows larger improvement than NL-DE and MIX-DDC; (c) NL-DC is able to better decrease the CGS effect than MIX-DE but shows larger NCRMSE; (d) the forecast of surface O3 concentrations is improved by MIX-DE downscaled posterior NOx emissions. Overall, for practical forecasting of air quality, it is recommended to use satellite-based observation already available from the last month to jointly constrain SO2 and NO2 emissions at coarser resolution and then downscale these posterior emissions at finer spatial resolution suitable for regional air quality modeling for the present month.

Description: SO2 and NO2 observations from the Ozone Mapping and Profiler Suite (OMPS) sensor are used for the first time in conjunction with the GEOS-Chem adjoint model to optimize both SO2 and NOx emission estimates over China for October 2013. Separate and joint (simultaneous) optimizations of SO2 and NO2 emissions are both conducted and compared. Posterior emissions, compared to the prior, yield improvements in simulating columnar SO2 and NO2, in comparison to measurements from the Ozone Monitoring Instrument (OMI) and OMPS. The posterior SO2 and NOx emissions from separate inversions are 748 Gg S and 672 Gg N, which are 36 % and 6 % smaller than prior MIX emissions (valid for 2010), respectively. In spite of the large reduction of SO2 emissions over the North China Plain, the simulated sulfate–nitrate–ammonium aerosol optical depth (AOD) only decrease slightly, which can be attributed to (a) nitrate rather than sulfate as the dominant contributor to AOD and (b) replacement of ammonium sulfate with ammonium nitrate as SO2 emissions are reduced. For joint inversions, both data quality control and the weight given to SO2 relative to NO2 observations can affect the spatial distributions of the posterior emissions. When the latter is properly balanced, the posterior emissions from assimilating OMPS SO2 and NO2 jointly yield a difference of −3 % to 15 % with respect to the separate assimilations for total anthropogenic SO2 emissions and ±2 % for total anthropogenic NOx emissions; but the differences can be up to 100 % for SO2 and 40 % for NO2 in some grid cells. Improvements on SO2 and NO2 simulations from the joint inversions are overall consistent with those from separate inversions. Moreover, the joint assimilations save ∼ 50 % of the computational time compared to assimilating SO2 and NO2 separately in a sequential manner of computation. The sensitivity analysis shows that a perturbation of NH3 to 50 % (20 %) of the prior emission inventory can (a) have a negligible impact on the separate SO2 inversion but can lead to a decrease in posterior SO2 emissions over China by −2.4 % (−7.0 %) in total and up to −9.0 % (−27.7 %) in some grid cells in the joint inversion with NO2 and (b) yield posterior NOx emission decreases over China by −0.7 % (−2.8 %) for the separate NO2 inversion and by −2.7 % (−5.3 %) in total and up to −15.2 % (−29.4 %) in some grid cells for the joint inversion. The large reduction of SO2 between 2010 and 2013, however, only leads to ∼ 10 % decrease in AOD regionally; reducing surface aerosol concentration requires the reduction of emissions of NH3 as well.

Description: The ATOMS, standing for ALMA Three-millimeter Observations of Massive Star-forming regions, survey has observed 146 active star-forming regions with ALMA band 3, aiming to systematically investigate the spatial distribution of various dense gas tracers in a large sample of Galactic massive clumps, to study the roles of stellar feedback in star formation, and to characterize filamentary structures inside massive clumps. In this work, the observations, data analysis, and example science of the ATOMS survey are presented, using a case study for the G9.62+0.19 complex. Toward this source, some transitions, commonly assumed to trace dense gas, including CS J = 2−1, HCO+J = 1−0, and HCN J = 1−0, are found to show extended gas emission in low-density regions within the clump; less than 25 per cent of their emission is from dense cores. SO, CH3OH, H13CN, and HC3N show similar morphologies in their spatial distributions and reveal well the dense cores. Widespread narrow SiO emission is present (over ∼1 pc), which may be caused by slow shocks from large–scale colliding flows or H ii regions. Stellar feedback from an expanding H ii region has greatly reshaped the natal clump, significantly changed the spatial distribution of gas, and may also account for the sequential high-mass star formation in the G9.62+0.19 complex. The ATOMS survey data can be jointly analysed with other survey data, e.g. MALT90, Orion B, EMPIRE, ALMA_IMF, and ALMAGAL, to deepen our understandings of ‘dense gas’ star formation scaling relations and massive protocluster formation.

Description: Enhancer RNA (eRNA) is a type of long non-coding RNA transcribed from DNA enhancer regions. Despite critical roles of eRNA in gene regulation, the expression landscape of eRNAs in normal human tissue remains unexplored. Using numerous samples from the Genotype-Tissue Expression project, we characterized 45 411 detectable eRNAs and identified tens of thousands of associations between eRNAs and traits, including gender, race, and age. We constructed a co-expression network to identify millions of putative eRNA regulators and target genes across different tissues. We further constructed a user-friendly data portal, Human enhancer RNA Atlas (HeRA, https://hanlab.uth.edu/HeRA/). In HeRA, users can search, browse, and download the eRNA expression profile, trait-related eRNAs, and eRNA co-expression network by searching the eRNA ID, gene symbol, and genomic region in one or multiple tissues. HeRA is the first data portal to characterize eRNAs from 9577 samples across 54 human tissues and facilitates functional and mechanistic investigations of eRNAs.

Description: Discovering driver pathways is an essential step to uncover the molecular mechanism underlying cancer and to explore precise treatments for cancer patients. However, due to the difficulties of mapping genes to pathways and the limited knowledge about pathway interactions, most previous work focus on identifying individual pathways. In practice, two (or even more) pathways interplay and often cooperatively trigger cancer. In this study, we proposed a new approach called CDPathway to discover cooperative driver pathways. First, CDPathway introduces a driver impact quantification function to quantify the driver weight of each gene. CDPathway assumes that genes with larger weights contribute more to the occurrence of the target disease and identifies them as candidate driver genes. Next, it constructs a heterogeneous network composed of genes, miRNAs and pathways nodes based on the known intra(inter)-relations between them and assigns the quantified driver weights to gene–pathway and gene–miRNA relational edges. To transfer driver impacts of genes to pathway interaction pairs, CDPathway collaboratively factorizes the weighted adjacency matrices of the heterogeneous network to explore the latent relations between genes, miRNAs and pathways. After this, it reconstructs the pathway interaction network and identifies the pathway pairs with maximal interactive and driver weights as cooperative driver pathways. Experimental results on the breast, uterine corpus endometrial carcinoma and ovarian cancer data from The Cancer Genome Atlas show that CDPathway can effectively identify candidate driver genes [area under the receiver operating characteristic curve (AUROC) of $geq $0.9] and reconstruct the pathway interaction network (AUROC of〉0.9), and it uncovers much more known (potential) driver genes than other competitive methods. In addition, CDPathway identifies 150% more driver pathways and 60% more potential cooperative driver pathways than the competing methods. The code of CDPathway is available at http://mlda.swu.edu.cn/codes.php?name=CDPathway.