Theoretical and Applied Climatology最新文献

El Niño-Southern Oscillation (ENSO) and the antisymmetric combination mode (C-mode) have a significant impact on the seasonal precipitation in southern China (SC) during the boreal winter and spring seasons. Accurate seasonal precipitation prediction in SC is closely related to the effective foresight during the development of ENSO and C-mode events. While the prediction of ENSO events has been successful, accurately predicting C-mode events remains challenging. In this study, a machine-learning forecast model by employing the Long Short-Term Memory (LSTM) neural network is proposed to produce skillful predictions of C-mode events for lead time up to six months. During the test period from 2007 to 2021, the model, incorporating additional information about subsequent ENSO forecast obtained from the dynamical ENSO prediction model, achieves significantly better performance compared to models that only consider historical data. Based on the reliable prediction results of the C-mode event, the multiple regression used ENSO mode and C-mode index are constructed to effectively predict winter (spring) seasonal precipitation at a lead of five (four) months, respectively in SC. This statistical prediction model also demonstrates the ability to capture the extreme precipitation patterns associated with strong ENSO events.

The current trend in tropical cyclone (TC) intensity in terms of lifetime maximum intensity (LMI), power dissipation index (PDI), and potential intensity (PI) was examined to detect the impact of climate change on TC intensity. The PI was further evaluated using an ensemble of ten global circulation models that participated in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and compared with the present climate (2001–2020). The Mann–Kendall (MK) test showed no significant trend for the LMI and PDI, however, the PI showed a significant trend with an MK test value of 2.43 and a Sen’s slope value of 0.04. No statistically significant changes in the PI were observed in both the near-future (2041–2060) and far-future (2081–2100) climates compared to the present climate. The PI associated thermodynamic factors were also examined. Thermodynamic efficiency was found to be unchanged in the future climates. Two competing processes were identified that influenced the PI in future climates. Firstly, increased sea surface temperature created a favorable environment for higher PI. Secondly, increased surface moisture and dry static stability resulted in decreased upward mass flux. These two contrasting processes resulted in a net zero effect of a warmer climate on the PI of future TCs.

Afforestation is an important human activity that changes land use, and would affect regional climate. However, the impact of afforestation on the regional climate in arid and semi-arid regions remains a subject of controversy. By ensemble sensitivity simulations using Advanced Research Weather Research and Forecasting (WRF) model, this study investigates the influence of vegetation category change within a 100 km width along the middle and upper reaches of the Yellow River (YR) on regional climate. Results suggest that afforestation would reduce summer surface temperature by -0.05 ~ -0.8℃ and increase summer convective precipitation by 3 ~ 45 mm in the afforestation regions, instead, summer non-convective precipitation would increase (3 ~ 35 mm) in the upper reaches of the YR. The increased non-convective precipitation in the upper reaches of the YR could be attributed to the increased integral water vapor convergence, cloud hydrometeor, and decreased cloud top temperature. In the afforested region, the increased convective precipitation could be related to the enhanced thermodynamic and water vapor conditions. These findings emphasize that even on regional scale in semi-arid and arid regions, greening can lead to evident regional climate impacts, these also provide insight for policymakers in formulating sustainable afforestation strategies.