Nanjing University of Information Science and Technology

About: Nanjing University of Information Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Precipitation & Aerosol. The organization has 14129 authors who have published 17985 publications receiving 267578 citations. The organization is also known as: Nan Xin Da.

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

Abstract: . The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the primary future climate projections within the Coupled Model Intercomparison Project Phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models for concentration driven simulations. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century encompassing the Tier 1 experiments (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by 1.15 °C) reached at the upper end of the 5–95 % envelope of the highest scenario, SSP5-8.5. This is due to both the wider range of radiative forcing that the new scenarios cover and to higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensembles' spread, according to a set of initial condition ensemble simulations available under SSP3-7.0. The same experiments suggest a tendency for internal variability to decrease along the course of the century, a new result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP, but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades in mid-century, as represented in SSP5-3.4OS, does not affect the end outcome in terms of temperature and precipitation changes by 2100, which return to the same level as those reached by the gradually increasing SSP4-3.4. Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level show all scenarios reaching 1.5 °C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering 20–28 years from present. 2 °C of warming is reached as early as the late '30s by the ensemble mean under SSP5-8.5, but as late as the late '50s under SSP1-2.6. The highest warming level considered, 5 °C, is reached only by the ensemble mean under SSP5-8.5, and not until the mid-90s.

Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach

Abstract: Extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. In this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. This is achieved via an evolutionary method, i.e., genetic programming (GP), which evolves the motion feature descriptor on a population of primitive 3D operators (e.g., 3D-Gabor and wavelet). In this way, the scale and shift invariant features can be effectively extracted from both color and optical flow sequences. We intend to learn data adaptive descriptors for different datasets with multiple layers, which makes fully use of the knowledge to mimic the physical structure of the human visual cortex for action recognition and simultaneously reduce the GP searching space to effectively accelerate the convergence of optimal solutions. In our evolutionary architecture, the average cross-validation classification error, which is calculated by an support-vector-machine classifier on the training set, is adopted as the evaluation criterion for the GP fitness function. After the entire evolution procedure finishes, the best-so-far solution selected by GP is regarded as the (near-)optimal action descriptor obtained. The GP-evolving feature extraction method is evaluated on four popular action datasets, namely KTH, HMDB51, UCF YouTube, and Hollywood2. Experimental results show that our method significantly outperforms other types of features, either hand-designed or machine-learned.

East Asian Studies of Tropospheric Aerosols and their Impact on Regional Climate (EAST‐AIRC): An overview

Abstract: As the most populated region of the world, Asia is a major source of aerosols with potential large impact over vast downstream areas, Papers published in this special section describe the variety of aerosols observed in China and their effects and interactions with the regional climate as part of the East Asian Study of Tropospheric Aerosols and their Impact on Regional Climate (EAST-AIRC), The majority of the papers are based on analyses of observations made under three field projects, namely, the Atmospheric Radiation Measurements (ARM) Mobile Facility mission in China (AMF-China), the East Asian Study of Tropospheric Aerosols: An International Regional Experiment (EAST-AIRE), and the Atmospheric Aerosols of China and their Climate Effects (AACCE), The former two are U,S,-China collaborative projects, and the latter is a part of the China's National Basic Research program (or often referred to as "973 project"), Routine meteorological data of China are also employed in some studies, The wealth of general and speCIalized measurements lead to extensive and close-up investigations of the optical, physical, and chemical properties of anthropogenic, natural, and mixed aerosols; their sources, formation, and transport mechanisms; horizontal, vertical, and temporal variations; direct and indirect effects; and interactions with the East Asian monsoon system, Particular efforts are made to advance our understanding of the mixing and interaction between dust and anthropogenic pollutants during transport. Several modeling studies were carried out to simulate aerosol impact on radiation budget, temperature, precipitation, wind and atmospheric circulation, fog, etc, In addition, impacts of the Asian monsoon system on aerosol loading are also simulated.

Extreme Precipitation Indices over China in CMIP5 Models. Part I: Model Evaluation

Abstract: Compared to precipitation extremes calculated from a high-resolution daily observational dataset in China during 1960–2005, simulations in 31 climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) have been quantitatively assessed using skill-score metrics. Four extreme precipitation indices, including the total precipitation (PRCPTOT), maximum consecutive dry days (CDD), precipitation intensity (SDII), and fraction of total rainfall from heavy events (R95T) are analyzed. Results show that CMIP5 models still have wet biases in western and northern China. Especially in western China, the models’ median relative error is about 120% for PRCPTOT; the 25th and 75th percentile errors are of 70% and 220%, respectively. However, there are dry biases in southeastern China, where the underestimation of PRCPTOT reach 200 mm. The performance of CMIP5 models is quite different between western and eastern China. The simulations are more reliable in the east than in the west in terms of...