Ensemble Of Model Outputs Research Articles

Multi-model ensemble projection of the global dust cycle by the end of 21st century using the Coupled Model Intercomparison Project version 6 data

Abstract. As a natural aerosol with the largest emissions on land, dust has important impacts on the atmospheric environment and climate systems. Both the emissions and transport of dust aerosols are tightly connected to meteorological conditions and as a result are confronted with strong modulations by the changing climate. Here, we project the changes in the global dust emissions and loading by the end of the 21st century, using an ensemble of model outputs from the Coupled Model Intercomparison Project version 6 (CMIP6) under four Shared Socioeconomic Pathways (SSPs). Based on the validations against site-level observations, we select 9 out of 14 models and estimate an ensemble global dust emissions of 2566 ± 1996 Tg a−1 (1 Tg = 1012 g) for the present day, in which 68 % is dry deposited and 31 % is wet deposited. Compared to 2005–2014, global dust emissions show varied responses, with a reduction of −5.6 ± 503 Tg a−1 under the SSP3–7.0 scenario but increased emissions up to 60.7 ± 542 Tg a−1 under the SSP5–8.5 scenario at 2090–2099. For all scenarios, the most significant increase in the dust emissions appears in North Africa (0.6 %–5.6 %) due to the combined effects of reduced precipitation but strengthened surface wind. In contrast, all scenarios show decreased emissions in the Taklimakan and Gobi deserts (−0.8 % to −11.9 %) because of the increased precipitation but decreased wind speed on a regional scale. The dust loading shows uniform increases over North Africa (1.6 %–13.5 %) and the downwind Atlantic, following the increased emissions but decreases over East Asia (−1.3 % to −10.5 %), and the downwind Pacific, partly due to enhanced local precipitation that promotes wet deposition. In total, global dust loading will increase by 2.0 %–12.5 % at the end of the 21st century under different climate scenarios, suggesting a likelihood of strengthened radiative and climatic perturbations by dust aerosols in a warmer climate.

Understanding climate change impacts on biome and plant distributions in the Andes: Challenges and opportunities.

AimClimate change is expected to impact mountain biodiversity by shifting species ranges and the biomes they shape. The extent and regional variation in these impacts are still poorly understood, particularly in the highly biodiverse Andes. Regional syntheses of climate change impacts on vegetation are pivotal to identify and guide research priorities. Here we review current data, knowledge and uncertainties in past, present and future climate change impacts on vegetation in the Andes. Location: Andes. Taxon: Plants.MethodsWe (i) conducted a literature review on Andean vegetation responses to past and contemporary climatic change, (ii) analysed future climate projections for different elevations and slope orientations at 19 Andean locations using an ensemble of model outputs from the Coupled Model Intercomparison Project 5, and (iii) calculated changes in the suitable climate envelope area of Andean biomes and compared these results to studies that used species distribution models.ResultsFuture climatic changes (2040–2070) are projected to be stronger at high‐elevation areas in the tropical Andes (up to 4°C under RCP 8.5), while in the temperate Andes temperature increases are projected to be up to 2°C. Under this worst‐case scenario, temperate deciduous forests and the grasslands/steppes from the Central and Southern Andes are predicted to show the greatest losses of suitable climatic space (30% and 17%–23%, respectively). The high vulnerability of these biomes contrasts with the low attention from researchers modelling Andean species distributions. Critical knowledge gaps include a lack of an Andean wide plant checklist, insufficient density of weather stations at high‐elevation areas, a lack of high‐resolution climatologies that accommodates the Andes' complex topography and climatic processes, insufficient data to model demographic and ecological processes, and low use of palaeo data for distribution modelling.Main conclusionsClimate change is likely to profoundly affect the extent and composition of Andean biomes. Temperate Andean biomes in particular are susceptible to substantial area contractions. There are, however, considerable challenges and uncertainties in modelling species and biome responses and a pressing need for a region‐wide approach to address knowledge gaps and improve understanding and monitoring of climate change impacts in these globally important biomes.

Climate-induced trends in global riverine water discharge and suspended sediment dynamics in the 21st century

Abstract Anthropogenic climate change, particularly through increased greenhouse gas (GHG) emissions, is projected to impact 21st century precipitation distribution, altering fluvial processes such as riverine water discharge and sediment dynamics, worldwide. Changes in fluvial water and sediment discharges can have profound impacts on the functioning and connectivity of earth's natural systems. In this paper, we study the natural sensitivity of water discharge and suspended sediment fluxes in large global river systems to predicted climate change in the 21st century. A global-scale hydro-geomorphic model (WBMsed) was forced with precipitation and temperature projections generated by five General Circulation Models (GCMs), each driven by four Representative Concentration Pathways (RCPs). Anthropogenic drivers were excluded from the simulations in order to isolate the signal of 21st century climate change. The results, based on an ensemble of model outputs, revealed that global river discharge and sediment dynamics are highly sensitive to anthropogenic climate change in the 21st century. Increasing global warming will lead to more extreme changes and greater rates of changes (increasing or decreasing) in both variables. Despite substantial regional heterogeneity, a global net increase is projected for both natural river discharge and sediment flux toward the end of the 21st century under all climate change scenarios. These increases are larger with increasing levels of atmospheric warming. At the end of this century, projected climate changes under RCP 2.6, 4.5, 6.0 and 8.5 scenarios, will lead to 2%, 6%, 7.5% and 11% increases respectively in mean global river discharge relative to the 1950–2005 period, while mean global suspended sediment flux will show 11%, 15%, 14% and 16.4% increases under pristine conditions. In addition to magnitudes, inter-annual variability also increases with increasing warming. Changes in sediment flux closely follow the patterns predicted for discharge, and are mostly driven by climate warming-induced spatial and temporal variation in precipitation. However, the relationship between precipitation, discharge and sediment flux was found to be non-linear both in space and time, demonstrating the utility of explicit modeling of both hydrology and geomorphology.

Convolutional neural network-based inspection of metal additive manufacturing parts

PurposePart quality inspection is playing a critical role in the metal additive manufacturing (AM) industry. It produces a part quality analysis report which can be adopted to further improve the overall part quality. However, the part quality inspection process puts heavy reliance on the engineer’s background and experience. This manual process suffers from both low efficiency and potential errors and, therefore, cannot meet the requirement of real-time detection. The purpose of this paper is to look into a deep neural network, Convolutional Neural Network (CNN), towards a robust method for online monitoring of AM parts.Design/methodology/approachThe proposed online monitoring method relies on a deep CNN that takes a real metal AM part’s images as inputs and the part quality categories as network outputs. The authors validate the efficacy of the proposed methodology by recognizing the “beautiful-weld” category from material CoCrMo top surface images. The images of “beautiful-weld” parts that show even hatch lines and appropriate overlaps indicate a good quality of an AM part.FindingsThe classification accuracy of the developed method using limited information of a small local block of an image is 82 per cent. The classification accuracy using the full image and the ensemble of model outputs is 100 per cent.Originality/valueA real-world data set of high resolution images of ASTM F75 I CoCrMo-based three-dimensional printed parts (Top surface images with magnification 63×) annotated with categories labels. Development of a CNN-based classification model for the supervised learning task of recognizing a “beautiful-weld” AM parts. The classification accuracy using the full image and the ensemble of model outputs is 100 per cent.

Sensitivity analysis of spatio-temporal models describing nitrogen transfers, transformations and losses at the landscape scale

Modelling complex systems such as agroecosystems often requires the quantification of a large number of input factors. Sensitivity analyses are useful to determine the appropriate spatial and temporal resolution of models and to reduce the number of factors to be measured or estimated accurately. Comprehensive spatial and temporal sensitivity analyses were applied to the NitroScape model, a deterministic spatially distributed model describing nitrogen transfers and transformations in rural landscapes. Simulations were led on a theoretical landscape that represented five years of intensive farm management and covering an area of 3 km2. Cluster analyses were applied to summarize the results of the sensitivity analysis on the ensemble of model outputs. The methodology we applied is useful to synthesize sensitivity analyses of models with multiple space-time input and output variables and could be ported to other models than NitroScape.