Impacts of Global Warming on Ecology and Meteorology and the Related Physical Mechanisms, Evaluation and Prediction

Anomalous subsidence over the western part of the western Pacific subtropical high (WPSH) caused record-breaking precipitation anomalies over the Yangtze-Huaihe River catchment in early summer 2020 (June–July 2020). The meridional circulation (MC) made a positive contribution to this anomalous subsidence, while the zonal circulation (ZC) made a negative contribution. The quantitative contributions of the MC and ZC to this anomalous subsidence were approximately 110% and –10% in June, 130% and –30% in July, and 120% and –20% for the mean of June and July, respectively, suggesting that the MC played a dominant role in the anomalous subsidence of the western part of the WPSH. The anomalous MC, with a rising branch located at the Maritime Continent and a descending branch located over South China, was forced by the warming of the northern tropical Indian Ocean and the rapidly developed La Niña event, which further resulted in the intensification and southwestward expansion of the WPSH and thus in heavy rainfall over the Yangtze River region.

India, one of the most disaster-prone countries in the world, has suffered severe economic losses as well as life losses as per the World Focus report1. Over 80% of its land and more than 50 million of its people are affected by weather disasters. Disaster mitigation necessitates reliable future predictions, which need a focused climate change research. From the climate change perspective, the summer monsoon, the main lifeline of India, is predicted to change very adversely. The duration of the rainy season is going to shrink and pre-monsoon drying can also occur. These future changes can impact the increase of vector-borne diseases such as malaria, dengue, and others. In another recent study, 29 world experts from various institutions found that the largest exposure to disasters like tropical cyclones, river floods, droughts, heatwaves, is over India. improved, and skillful prediction, we suggest a three-staged cumulative method, namely, K is for observational analysis, U is for knowledge & understanding, and M is for modelling & prediction. In this brief note, we report our perspective of imminent weather disasters to India namely, monsoons and tropical cyclones, and how the weather and climate forecasting can be improved, leading to a better climate change adaptation.

Interannual variation of snowfall over the Tibetan Plateau (TP) in early winter (November-December) and its related atmospheric attribution are clarified. Meanwhile, the influence of tropical sea surface temperatures (SSTs) on TP snowfall is investigated by diagnostic analysis and Community Atmosphere Model (CAM5) simulations.

Quantitatively projecting the impact of future climate change on the socio-economy and exploring its internal mechanism are of great practical significance to adapt to climate change and prevent climate risks. Based on the economy-climate (C-D-C) model, this paper introduces a yield impact of climate change (YICC) model that can quantitatively project the climate change impact. The model is based on the YICC as its core concept and uses the impact ratio of climate change (IRCC) indicator to assess the response of the economic system to climate change over a long period of time. The YICC is defined as the difference between the economic output under changing climate condition and that under assumed invariant climate condition. The IRCC not only reflects the sensitivity of economic output to climate change but also reveals the mechanism of the nonlinear interaction between climate change and non-climatic factors on the socio-economic system. Using the main grain-producing areas in China as a case study, we use the prediction data of the ensemble average of 5 GCMs in CMIP6 to project the possible impact of climate change on production in the next 15 to 30 years under three future scenarios. The results indicate that the long-term climate change in the future will have a restraining effect on production in North region and enhance production in South region. From 2021 to 2035, climate change will reduce production by 0.60% to 2.09% in North region, and increase production by 1.80% to 9.01% in South region under three future scenarios. From 2021 to 2050, compared with the climate change impact in 2021~2035, the negative impact of climate change on production in North region will weaken with the increase in emission concentration, and the positive impact on production in South region will enhance with the emission concentration. Among them, climate change will reduce grain output in North region by 0.52%~1.99%, and increase grain output in South region by 1.35%~9.56% under the three future scenarios. The combination of economic results and climate change research is expected to provide scientific support for further revealing the economic mechanism of climate change impacts.

Wind speed is an important meteorological condition affecting the urban environment. Thus, analyzing the typical characteristics of the wind speed diurnal variation is helpful for forecasting pollutant diffusion. Based on the K-means clustering method, the diurnal variation characteristics of the wind speed in Beijing during 2008–2017 are studied, and the spatio-temporal characteristics of the wind speed diurnal variations are analyzed. The results show that there are mainly 5–7 clusters of typical characteristics of the wind speed diurnal variation at different stations in Beijing, and the number of clusters near the city is smaller than that in the suburbs. The typical number of the wind speed diurnal variation during 2013–2015 is smaller than that in other periods, which means the anomalous clusters of the diurnal variation are reduced. Besides, the numbers of different clusters in different years are often switched. Especially, the switch between clusters 5 and 6 and the switch between clusters 6 and 7 are frequent. Based on the second cluster analysis of the clustering results at Beijing station, we find 12 clusters of the diurnal variation, including 9 clusters of “large in the daytime, while small at night”, 2 clusters of “monotonous”, and 1 cluster of “strong wind”. Furthermore, the low-speed clusters of wind mainly locate in the city with a significant increasing trend, while the high-speed clusters and the monotonous clusters of wind locate in the suburbs with a decreasing trend.