Advances in Climate Change Research最新文献

In late November 2022, most regions in China were hit by a strong northwest-path cold wave, bringing a high-hazard extreme low-temperature event (ELTE). Several parts of Northwest China experienced extremely low temperatures and record-breaking snow depths. A stratosphere–troposphere synergetic effect was suggested to be closely related to the ELTE according to a diagnostic analysis. On the one hand, the concurrent establishment of two blockings in Europe–Northeast Atlantic and North Pacific led to a polar vortex split at the tropopause, and the Arctic Oscillation phase subsequently turned negative. An airflow with high potential vorticity (PV) was squeezed out of the Arctic. Meanwhile, a high-PV air that originated from the lower stratospheric Arctic was conveyed southwards to the western Siberian Plain along the sloping isentropic surface. This condition triggered a tropospheric response in which the East Asia trough deepened due to the intensified cyclonic circulation induced by the high-PV intrusion. On the other hand, downward propagation of stratospheric anomalies accompanied by stratospheric polar vortex displacement and split was observed in mid- and late November, respectively. Changes in stratospheric circulation contributed to enhanced blockings over Europe–Northeast Atlantic in the lower stratosphere or upper troposphere. As a result, the inverted omega-shaped circulation pattern was formed in the middle to upper troposphere, and it consisted of the intensified East Asia trough and two blockings in the upstream and downstream regions. The high-PV air upstream of the East Asia trough was advected to China, which directly led to the outbreak of the ELTE. The establishment of double blockings and the displacement or split of the stratospheric polar vortex can be efficient signals for cold-event prediction in China. This study provides novel insights into the cause of ELTEs under warming climates in the future.

At present, the main focus of research lies in examining the connection between economic complexity and carbon emissions as a whole, but there is a scarcity of quantitative investigations on the link between the above variables within specific industries. Therefore, this study introduces economic complexity as a new variable to build a panel model within the traditional Environmental Kuznets Curve framework. Based on the data of the countries along the Belt and Road from 1998 to 2018, we used the Granger causality test to examine the causal relationship between variables, and use the Fully Modified Ordinary Least Square and Dynamic Ordinary Least Square methods to estimate the coefficients of variables. The key factor linking economic complexity and carbon emissions in the logistics industry is technology innovation Economic complexity can explain and predict the changes in carbon emissions of logistics industry more reasonably, and the relationship between them in line with the environmental kuznets curve hypothesis. Only high-income countries can increase economic complexity while reducing carbon emissions of logistics industry. Based on the empirical analysis, it is suggested that upper-middle income and lower middle-income countries can formulate relevant policies and regulations, and high-income countries can improve the relevant policies and regulations to promote the reduction of carbon emissions of the logistics industry. Studying the impact of economic complexity on carbon emissions in the logistics industry can help better predict and respond to the impact of climate change on the logistics industry.

Due to their huge socio-economic impacts and complex formation causes, extreme and continuous drought events have become the focus and nodus of research in recent years. In the midsummer (July–August) of 2022, a severe drought event occurred in the whole Yangtze River Basin (YRB), China. During that period, the precipitation in the upper, middle and lower reaches of the YRB dropped over 40% less than the 1961–2021 climatic mean, which had never happened previously. Furthermore, the temperature was the highest during 1961–2022. The record-breaking magnitude of less rainfall and high temperature directly led to the continuous development of this extreme drought event. An atmospheric moisture budget analysis revealed that the YRB midsummer rainfall anomaly was dominated by the anomalous powerful vertical moisture advection, which was derived from the strongest descending motion over the whole YRB in the 2022 midsummer during 1981–2022. The western Pacific subtropical high (WPSH) during the midsummer remained stronger, more westward and lasted longer than the climatic mean. As a result, the whole YRB was controlled by a positive geopotential height centre. Further evidence revealed that the anomalous subtropical zonal flow played a crucial role in inducing the extreme descent over the YRB. Moreover, the anomalous upper-tropospheric easterly flow over the YRB in 2022 is the strongest during 1981–2022, modulating the generation of the unprecedented descent anomaly over the YRB. The likelihood that an integrated connection of severe drought in East Asia and flood in West Asia and northwestern South Asia would increase when the extremely strong easterly anomalies in the upper troposphere emerged and induced descending adiabatic flow on the eastern sides of the Tibetan Plateau. The results of this study can provide scientific insights into the predictability of extreme drought events and provide ways to improve predictions.

Compared with physical models, WRF-Solar, as an excellent numerical forecasting model, includes abundant novel cloud physical and dynamical processes, which enablesenable the high-frequency output of radiation components which are urgently needed by the solar energy industry. However, the popularisation of WRF-Solar in a wide range of applications, such as the estimation of diffuse radiation, suffers from unpredictable influences of cloud and aerosol optical property parameters. This study assessed the accuracy of the improved numerical weather prediction (WRF-Solar) model in simulating global and diffuse radiation. Aerosol optical properties at 550 nm, which were provided by a moderate resolution imaging spectroradiometer, were used as input to analyse the differences in accuracies obtained by the model with/without aerosol input. The sensitivity of WRF-Solar to aerosol and cloud optical properties and solar zenith angle (SZA) was analysed. The results show the superiority of WRF-Solar to WRF-Dudhia in terms of their root mean square error (RMSE) and mean absolute error (MAE). The coefficients of determination between WRF-Solar and WRF-Dudhia revealed no statistically significant difference, with values greater than 0.9 for the parent and nested domains. In addition, the relative RMSE (RRMSE%) reached 46.60%. The experiment on WRF-Solar and WRF-Dudhia revealed a negative bias for global radiation, but WRF-Solar attained a slightly lower RMSE and higher correlation coefficient than WRF-Dudhia. The WRF-Solar-simulated results on diffuse radiation under clear sky conditions were slightly poorer, with RMSE, RRMSE, mean percentage error and MAE of 181.93 W m⁻², 170.52%, 93.04% and 138 W m⁻², respectively. Based on Himawari-8 cloud data, statistical results on cloud optical thickness (COT) for cloudy days revealed that WRF-Solar overestimated diffuse radiation at COTs greater than 20. Moreover, when the aerosol optical depth was greater than or equal to 0.8, WRF-Solar also overestimated the diffuse radiation, with a mean difference of 58.57 W m⁻². The errors of WRF-Solar simulations in global and diffuse radiation exhibited a significant dependence on the SZA. The dispersion degree of deviation increased gradually with the decrease in the SZA. Thus, WRF-Solar serves as an improved numerical tool that can provide high temporal and high-spatial-resolution solar radiation data for the prediction of photovoltaic power. Studies should explore the improvement of cumulus parameterisation schemes to enhance the accuracy of solar radiation component estimation and prediction under cloudy conditions.

Single Alter shielded T-200BM3 weighing precipitation gauges are widely used in the measurement of all precipitation types (rainfall, snow and mixed precipitation) in unattended boreal or alpine regions, but their original datasets must be adjusted for undercatch errors caused by wind in snowy, windy and harsh environments. Therefore, previous researchers have developed many adjustment methods for all precipitation types on different time scales. However, which adjustment method is suitable for T-200BM3 weighing gauge wind-induced error adjustment in harsh alpine regions is unclear. Therefore, precipitation measurement intercomparison experiments were conducted in the Qilian Mountains from July 2018 to July 2021, and eight adjustment methods; were evaluated for wind-induced errors for daily, individual precipitation event, hourly, and half-hourly time scales. Z2004 outperformed the other adjustment methods in regard to the daily measurements of snow and mixed precipitation. Regarding individual snowfall events, M2007 reduced the absolute value of RMSE (bias) from 1.44 to 1.32 mm (0.77–0.24 mm) and could be recommended for snowfall event adjustment. K2017-1 attained a better performance than K2017-2 in regard to half-hourly snowfall and mixed sample adjustment and was more suitable for half-hourly snowfall sample adjustment. K2017-1 reduced the absolute value of bias from 0.07 to 0.00 mm for snowfall. Finally, Z2004, M2007, and K2017-1 yielded better adjustment results for the daily accumulation precipitation amount (>2 mm d⁻¹), individual snowfall events (>2 mm per event), and half-hourly accumulation snowfall or mixed samples (>1 mm 30 min⁻¹), respectively. However, further intercomparison in different climate regions is needed for trace precipitation samples.

Outward Foreign Direct Investment (OFDI) is a crucial decision in the internationalization strategy of firms and they confront diversified environmental policies and practices of host countries, when engaging in cross-border investments. However, there is a lack of research to investigate whether foreign direct investment (OFDI) is an important factor affecting the environmental commitment of firms. This study aims to empirically analyze the impact and mechanism by which firm outward foreign direct investment (OFDI) influences pro-environmental commitments, utilizing data collected from Chinese listed companies between 2010 and 2020. The results show: 1) there is a significant positive relationship between firm OFDI and pro-environmental commitments; 2) mechanistic tests show that the OFDI can promote pro-environmental commitments through two channels: public attention and technological learning capacity; 3) heterogeneity analysis reveals that the green effects of OFDI are more pronounced firms with institutional investors and state ownership, as well as in firms situated in host countries with more stringent environmental regulations; 4) further analysis reveals OFDI-induced greater green commitments generated real environmental and economic outcomes: firms invested more in environmental protection and reduced CO₂ emissions. In the meantime, they experienced lower excessive debt risk and higher innovative performance. The findings have positive implications for the promotion of firms’ pro-environmental behavior and sustainable development.

Extensive investigations on the projection of heat waves (HWs) were conducted on the basis of coarse-resolution global climate models (GCMs). However, these investigations still fail to characterise the future changes in HWs regionally over China. PRECIS dynamical downscaling with a horizontal resolution of 25 km × 25 km was employed on the basis of GCM-HadCM3 to provide reliable projections on HWs over the Chinese mainland, and six statistical downscaling methods were used for bias correction under RCP4.5 and RCP8.5 scenarios. The multi-method ensemble (MME) of the top three dynamical downscaling methods with good performance was used to project future changes. Results showed that PRECIS primarily replicated the detailed spatiotemporal pattern of HWs. However, PRECIS overestimated the HWs in the Northwest and Southeast and expanded the areas of HWs in the Northeast and Southwest. Three statistical downscaling methods (quantile mapping, CDF-t and quantile delta mapping) demonstrated good performance in improving PRECIS simulation for reproducing HWs. By contrast, parametric-based trend-preserving approaches such as scaled distribution mapping and ISI-MIP are outperformed by the three aforementioned methods in downscaling HWs, particularly in the high latitudes of China. Based on MME projections, at the end of the 21st century, the national average of the number of HW days each year, the length of the longest HW event in the year and the extreme maximum temperature in HW will increase by 3 times, 1 time and 1.3 °C, respectively, under the RCP4.5 scenario, whilst that under the RCP8.5 scenario will increase by 8 times, 3 times and 3.7 °C, respectively, relative to 1986–2005. The Northwest is regionally projected to suffer long and hot HWs, whilst the South and Southeast will experience frequent consecutive HWs. Thus, HWs projected by the combined dynamical and statistical downscaling method are highly reliable in projecting HWs over China.