Title: Neglected Spatiotemporal Variations of Model Biases in Ensemble-Based Climate Projections

Journal: Geophysical Research Letters

DOI: https://doi.org/10.1029/2022GL098063

Abstract: The Bayesian model averaging (BMA) method has been widely used for generating probabilistic climate projections. However, the averaging weights used in BMA can only reflect the spatially- and temporally-averaged performance of each ensemble member, without the ability to address the spatiotemporal variations of model biases. This can lead to inevitable exaggeration or understatement of the contributions of individual members to the ensemble mean, thus reducing the robustness of the resulting probabilistic projections. Here we propose a new method to help address the neglected spatiotemporal variations of model biases. Through the proposed method, the BMA weights are used as prior distributions to drive the Bayesian discriminant analysis in order to generate refined weights for individual ensemble models according to their spatially- and temporally-clustered performance. Through applying the proposed method to Canada, we demonstrate its effectiveness in generating robust probabilistic climate projections (e.g., the average R² increases from 0.82 to 0.89).

Title: Solar photovoltaics can help China fulfill a net-zero electricity system by 2050 even facing climate change risks

Journal: Resources, Conservation and Recycling

DOI: https://doi.org/10.1016/j.resconrec.2022.106596

Abstract: As China has pledged to become carbon neutral by 2060, electrifying its energy sector is no doubt one of the priority measures to support the transition towards a more sustainable and decarbonized energy system. Solar photovoltaics (PV) has been known as one of the most promising renewable technologies to facilitate the electrification of energy systems. The feasibility of utilizing PV to implement a nationwide decarbonized electricity system now becomes an urgent unanswered question, especially in the context of global climate change and rapid economic growth in China. Here, by using a GIS-based multiple-criteria decision-making approach we address this question by conducting a comprehensive feasibility analysis with consideration of various economic, technological, logistical, and climate change factors. We show that it is feasible for China to fulfill a net-zero electricity system by 2050, through the installation of 7.46 TW solar PV panels on about 1.8% of the national land area (mostly in western China) with a total capital investment of 4.55 trillion USD in the next 30 years. Besides, we show that future climate change may lead to a slight decrease (less than 5%) in solar energy potential, but this would not affect the capability of the nationwide PV system to meet the need for a fully-electrified energy system.

The following paper about the photovoltaics power generation potentials for China has been recently accepted for publication by Resources, Conservation & Recycling.

Ji, L., Y. Wu, X. Zhao, L. Sun, X. Wang, Y. Xie, J. Guo, G. Huang, and J. Pan. Photovoltaics Can Help China Fulfill A Net-Zero Electricity System by 2050. Resources, Conservation & Recycling, accepted on August 4, 2022.

More details will come soon once the paper is published.

The following paper about the neglected spatiotemporal variations of model biases in ensemble-based climate projections has been recently accepted for publication by Geophysical Research Letters.

Song, T., G. Huang, and X. Wang. Neglected Spatiotemporal Variations of Model Biases in Ensemble-Based Climate Projections. Geophysical Research Letters, accepted on August 4, 2022.

More details will come soon once the paper is published.

Title: Field evaluation of a deep learning-based smart variable-rate sprayer for targeted application of agrochemicals

Journal: Smart Agricultural Technology

DOI: https://doi.org/10.1016/j.atech.2022.100073

Abstract: The field performance of a newly developed novel smart variable-rate sprayer was evaluated. The sprayer uses convolutional neural networks (CNNs) for target detection and spot-applications of agrochemicals within potato (Solanum tuberosum L.) fields attacked by lamb’s quarters (Chenopodium album L.) and corn spurry (Spergula arvensis L.) weeds and the early blight potato disease caused by Alternaria solani Sorauer. There was a non-significant effect of treatment conditions (i.e., cloudy, partly cloudy, and sunny) on spray volume during weed and diseased plant detection experiments (p-value = 0.93 and 0.75, respectively) showing that the smart sprayer performed well during all treatment conditions. There was a significant effect of spraying application techniques on the use of spray volume (p-value ≤ 0.05) reflecting a significant saving of spraying liquid during variable-rate application (VA). On average, the sprayer reduced spray volume by 47 and 51% for weeds and diseased plant detection experiments as compared to the values of chemicals applied at constant-rate application (CA), respectively, under all treatment conditions. The analysis of water-sensitive papers (WSP) data resulted in non-significant differences between CA and VA under all field conditions. These results suggest that this sprayer has a great potential to get a suitable spot application of agrochemicals and reduce the use of plant protection products thereby ensuring farm profits and environmental stewardship.