Influence of Surface Types on the Seasonality and Inter-Model Spread of Arctic Amplification in CMIP6

A robust phenomenon termed the Arctic Amplification(AA)refers to the stronger warming taking place over the Arctic compared to the global mean.The AA can be confirmed through observations and reproduced in climate model simulations and shows significant seasonality and inter-model spread.This study focuses on the influence of surface type on the seasonality of AA and its inter-model spread by dividing the Arctic region into four surface types:ice-covered,ice-retreat,ice-free,and land.The magnitude and inter-model spread of Arctic surface warming are calculated from the difference between the abrupt-4×CO_(2)and pre-industrial experiments of 17 CMIP6 models.The change of effective thermal inertia(ETI)in response to the quadrupling of CO_(2) forcing is the leading mechanism for the seasonal energy transfer mechanism,which acts to store heat temporarily in summer and then release it in winter.The ETI change is strongest over the ice-retreat region,which is also responsible for the strongest AA among the four surface types.The lack of ETI change explains the nearly uniform warming pattern across seasons over the ice-free(ocean)region.Compared to other regions,the ice-covered region shows the maximum inter-model spread in JFM,resulting from a stronger inter-model spread in the oceanic heat storage term.However,the weaker upward surface turbulent sensible and latent heat fluxes tend to suppress the inter-model spread.The relatively small inter-model spread during summer is caused by the cancellation of the inter-model spread in ice-albedo feedback with that in the oceanic heat storage term.

Effects of PHC on Water Quality of Jiaozhou BayⅢ.Land Transfer Process

Based on investigation data of PHC content in Jiaozhou Bay,China from 1979 to 1983,the seasonal variations of PHC content and monthly changes of precipitation in Jiaozhou Bay were analyzed. The results showed that seen from the spatial and temporal distribution,the seasonal variation of PHC content in the surface water of Jiaozhou Bay was based on the flow of the rivers as well as human activity,so PHC content in the rivers depended on the flow of the rivers and human activity,and the peaks and valleys of PHC content appeared in various seasons. The seasonal variation of PHC content in the surface water of Jiaozhou Bay depended on its land transfer process. The land transfer process was composed of use of PHC by mankind,deposition of PHC in soil and on the earth＇s surface,and transportation of PHC to offshore waters of sea by rivers and surface runoff. PHC content depended on mankind during the process from being used to entering soil and on precipitation during the process of being transported from soil to ocean.

Inventory Management and Demand Forecasting Improvement of a Forecasting Model Based on Artificial Neural Networks

Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.