Additional risk in extreme precipitation in China from 1.5 °C to 2.0 °C global warming levels

Policy choices for China to reduce carbon emissions in coping with extreme weather: Applying a dynamic two-stage undesirable non-radial directional distance function

China is the world's largest carbon emitter and also one of many countries most affected by extreme weather. Although its government has set carbon reduction targets, the public has not established a connection between carbon reduction and coping with extreme weather. This study aims to help establish the above connection and applies a dynamic two-stage undesirable non-radial directional distance function to evaluate energy performance in the first stage while establishing CO2 emissions as a link to evaluate coping with extreme weather performance in the second stage. From empirical results, the average efficiency of 30 provinces in China in coping with extreme weather from 2011 to 2020 is only 0.484, or far lower than the energy efficiency value of 0.709. Based on the differences in performance between the two stages and the changing trends in the room for improvement of CO2 emissions, this study proposes policy options to promote the participation of the entire society in the emission reduction process.

Spatiotemporal dynamics of forest fires in the context of climate change: a review

Forest fires are sudden, destructive, hazardous, and challenging to manage and rescue, earning them a place on UNESCO's list of the world's eight major natural disasters. Currently, amid global warming, all countries worldwide have entered a period of high forest fire incidence. Due to global warming, the frequency of forest fires has accelerated, the likelihood of large fires has increased, and the spatial and temporal dynamics of forest fires have shown different trends. Therefore, the impact of climate change on the spatiotemporal dynamics of forest fires has become a hot issue in the field of forest fire research in recent years. Therefore, it is of great significance and necessity to conduct a review of the research in this area. This review delves into the interactions and impacts between climate change and the spatiotemporal dynamics of forest fires. To address this issue, scholars have mainly adopted the following research methods: first, statistical analysis methods, second, the establishment of spatiotemporal prediction models for meteorology and forest fires, and third, the coupling of climate models with forest fire risk forecasting models. The statistical analysis method relies on the analysis of historical meteorological and fire-related data to study the effects of climate change and meteorological factors on fire occurrence. Meanwhile, forest fire prediction models utilize technical tools such as remote sensing. These models synthesize historical meteorological and fire-related data, incorporating key meteorological factors such as temperature, rainfall, relative humidity, and wind. The models revealed the spatial and temporal distribution patterns of fires, identified key drivers, and explored the interactions between climate change and forest fire dynamics, culminating in the construction of predictive models. With the deepening of the study, the coupling of climate models and fire risk ranking systems became a trend in the prediction of forest fire risk trends. Moreover, as the climate warms, the increased frequency of extreme weather events like heatwaves, droughts, snow and ice storms, and El Niño-Southern Oscillation (ENSO) has accelerated forest fire occurrences and raised the risk of major fires. This review offers valuable technical insights by comprehensively analyzing the spatial and temporal characteristics of forest fires, elucidating key meteorological drivers, and exploring potential mechanisms. These insights serve as a scientific foundation for preventive measures and effective forest fire management. In the face of a changing climate, this synthesis contributes to the development of informed strategies to mitigate the escalating threat of forest fires.

Projection of climate extremes in China, an incremental exercise from CMIP5 to CMIP6

This paper presents projections of climate extremes over China under global warming of 1.5, 2, and 3 °C above pre-industrial (1861-1900), based on the latest Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. Results are compared with what produced by the precedent phase of the project, CMIP5. Model evaluation for the reference period (1985-2005) indicates that CMIP6 models outperform their predecessors in CMIP5, especially in simulating precipitation extremes. Areal averages for changes of most indices are found larger in CMIP6 than in CMIP5. The emblematic annual mean temperature, when averaged over the whole of China in CMIP6, increases by 1.49, 2.21, and 3.53 °C (relative to 1985-2005) for 1.5, 2, and 3 °C above-preindustrial global warming levels, while the counterpart in CMIP5 is 1.20, 1.93 and 3.39 °C respectively. Similarly, total precipitation increases by 5.3%, 8.6%, and 16.3% in CMIP6 and by 4.4%, 7.0% and 12.8% in CMIP5, respectively. The spatial distribution of changes for extreme indices is generally consistent in both CMIP5 and CMIP6, but with significantly higher increases in CMIP6 over Northeast and Northwest China for the hottest day temperature, and South China for the coldest night temperature. In the south bank of the Yangtze River, and most regions around 40°N, CMIP6 shows higher increases for both total precipitation and heavy precipitation. The projected difference between CMIP6 and CMIP5 is mainly attributable to the physical upgrading of climate models and largely independent from their emission scenarios.

Downscaling of Future Precipitation in China’s Beijing-Tianjin-Hebei Region Using a Weather Generator

To project local precipitation at the existing meteorological stations in China’s Beijing-Tianjin-Hebei region in the future, local daily precipitation was simulated for three periods (2006–2030, 2031–2050, and 2051–2070) under RCP 4.5 and RCP 8.5 emission scenarios. These projections were statistically downscaled using a weather generator (BCC/RCG-WG) and the output of five global climate models. Based on the downscaled daily precipitation at 174 stations, eight indices describing mean and extreme precipitation climates were calculated. Overall increasing trends in the frequency and intensity of the mean and extreme precipitation were identified for the majority of the stations studied, which is in line with the GCMs’ output. However, the downscaling approach enables more local features to be reflected, adding value to applications at the local scale. Compared with the baseline during 1961–2005, the regional average annual precipitation and its intensity are projected to increase in all three future periods under both RCP 4.5 and RCP 8.5. The projected changes in the number of days with precipitation are relatively small across the Beijing-Tianjin-Hebei region. The regional average annual number of days with precipitation would increase by 0.2~1.0% under both RCP 4.5 and RCP 8.5, except during 2031–2050 under RCP 8.5 when it would decrease by 0.7%. The regional averages of annual days with precipitation ≥25 mm and ≥40 mm, the greatest one-day and five-day precipitation in the Beijing-Tianjin-Hebei region, are projected to increase by 8~30% during all the three periods. The number of days with daily precipitation ≥40 mm was projected to increase most significantly out of the eight indices, indicating the need to consider increased flooding risk in the future. The average annual maximum number of consecutive days without precipitation in the Beijing-Tianjin-Hebei region is projected to decrease, and the drought risk in this area is expected to decrease.

Evaluation and Future Projection of Extreme Climate Events in the Yellow River Basin and Yangtze River Basin in China Using Ensembled CMIP5 Models Data

The Yellow River Basin (YLRB) and Yangtze River Basin (YZRB) are heavily populated, important grain-producing areas in China, and they are sensitive to climate change. In order to study the temporal and spatial distribution of extreme climate events in the two river basins, seven extreme temperature indices and seven extreme precipitation indices were projected for the periods of 2010-2039, 2040-2069, and 2070-2099 using data from 16 Coupled Model Intercomparison Project Phase 5 (CMIP5) models, and the delta change and reliability ensemble averaging (REA) methods were applied to obtain more robust ensemble values. First, the present evaluation indicated that the simulations satisfactorily reproduced the spatial distribution of temperature extremes, and the spatial distribution of precipitation extremes was generally suitably captured. Next, the REA values were adopted to conduct projections under different representative concentration pathway (RCP) scenarios (i.e., RCP4.5, and RCP8.5) in the 21st century. Warming extremes were projected to increase while cold events were projected to decrease, particularly on the eastern Tibetan Plateau, the Loess Plateau, and the lower reaches of the YZRB. In addition, the number of wet days (CWD) was projected to decrease in most regions of the two basins, but the highest five-day precipitation (Rx5day) and precipitation intensity (SDII) index values were projected to increase in the YZRB. The number of consecutive dry days (CDD) was projected to decrease in the northern and western regions of the two basins. Specifically, the warming trends in the two basins were correlated with altitude and atmospheric circulation patterns, and the wetting trends were related to the atmospheric water vapor content increases in summer and the strength of external radiative forcing. Notably, the magnitude of the changes in the extreme climate events was projected to increase with increasing warming targets, especially under the RCP8.5 scenario.

Impacts and socioeconomic exposures of global extreme precipitation events in 1.5 and 2.0 °C warmer climates

The rise of global mean temperature has aroused wide attention in scientific communities. To reduce the negative climate change impact, the United Union's Intergovernmental Panel on Climate Change (IPCC) set a goal to limit global warming to 1.5 °C relative to pre-industrial levels based on the previous 2.0 °C target in October 2018. To understand the necessity of more stringent emission reduction, this study investigates the impacts of additional 0.5 °C global warming from 1.5 to 2.0 °C on global extreme precipitation, and especially its socioeconomic consequences. The extreme precipitation is represented by extreme precipitation frequency (R95pF), extreme precipitation percentage (R95pT), and maximum one-day precipitation (RX1day) as indicators, calculated based on daily precipitation data extracted from 29 Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models (GCMs) under two representative concentration pathways: RCP4.5 and RCP8.5. The exposures of economy and population to extreme precipitation events are also computed and compared for two warming levels by using the Shared Socioeconomic Pathways (SSPs). The results show that most regions in the world are likely to suffer from increasing extreme precipitation hazards in a warming climate, with ascending gross domestic product (GDP) and population being exposed to extreme dangers with an additional 0.5 °C warming. R95pT and RX1day are projected to increase overwhelmingly throughout all continents, directly leading to intensified precipitation extremes and flash floods. In middle and low latitudes, the annual total wet-day precipitation (PRCPTOT) shows a rich-get-richer trend and R95pF decreases, which will reinforce the intensified trend of the magnitude of extreme precipitation. The exposures of GDP and population in regions with extensive exposure to extreme precipitation events at the 1.5 °C warming increase more remarkably with the additional 0.5 °C warming. In particular, Asia and Africa show lager sensitivity to global warming than other regions. These findings could provide information for mitigation and adaptation policymaking.

Changes in precipitation extremes in the Yangtze River Basin during 1960-2019 and the association with global warming, ENSO, and local effects

Extreme precipitation events can pose great risks to natural ecosystems and human society. Investigating past changes in the frequency, intensity, and duration of such events and understanding the possible driving factors are critical for reliable projections of future changes and for informing adaptation strategies planning. Here we analyze trends in a complete list of extreme precipitation indices (EPIs) over the Yangtze River Basin (YRB) during the period of 1960-2019. Also, we examine the possible influences of global warming, ENSO, and local effects on the spatiotemporal variability of the EPIs. Our results show that average and extreme precipitation intensities, and the frequency of extreme heavy precipitation in the YRB have significantly increased, while precipitation frequency and maximum duration of wet spells have significantly decreased. A regional difference in trend occurrence and magnitude is also observed, showing the intensity and frequency of precipitation extremes over the Middle and Lower reaches are more likely to increase and increase faster, compared with those of the Upper reach of the YRB. Furthermore, our correlation analysis shows global warming, ENSO, and local effects all are significant driving factors that control the spatiotemporal variability of precipitation extremes over the YRB. Global warming tends to enhance the frequency and intensity of precipitation extremes. The La Niña phase of ENSO often corresponds to an increase of frequency and intensity of precipitation extremes in the current year, but a decrease of frequency and intensity in the coming year. Local warming mainly exerts a reducing effect on precipitation extremes, which is likely a response to the significant decrease of relative humidity in the YRB. Our findings highlight the need for a systematic approach to examine global, regional, and local drivers of trends in precipitation extremes in the YRB, and contribute to the understanding of precipitation changes in this region.

Double increase in precipitation extremes across China in a 1.5 °C/2.0 °C warmer climate

Paris Agreement's 1.5 °C or 2.0 °C global warming targets call for human concerns on warming climate on human society and environment in general. Here we analyzed spatiotemporal patterns and related impacts of precipitation extremes on human society across China using NEX-GDDP (NASA Earth Exchange Global Daily Downscaled Projections) dataset. We found increasing trends of almost all extreme precipitation indices except consecutive dry duration (CDD). Additional 0.5 °C warmer climate from 1.5 °C to 2.0 °C global warming targets can double increase of extreme precipitation indices. Specifically, the increase of Rx5day (Max 5-day precipitation amount) is from 3.98% to 7.63%, the increase of R95pTOT (precipitation in very wet days) is from 19.41% to 34.42% and the increase of PRCPTOT (annual total wet-day precipitation) is from 3.89% to 8.23%, showing that additional 0.5 °C warmer climate can potentially increase flood risks across China. While, we also found regional differences in responses of extreme precipitation to warming climate. Extreme precipitation in the Qinghai Tibet Plateau, the Western Arid and semiarid zone and in the lower Yangtze River basin is in higher sensitivity to warming climate. Constraint of temperature increase of below 1.5 °C but not 2.0 °C will avoid 4.34% to 73.96% impacts of extreme precipitation on human society. It is particularly important for China since that more than half of territory of China is under exposure to high flood and drought disasters.

Increasing impacts from extreme precipitation on population over China with global warming

Precipitation-related extremes are among the most impact-relevant consequences of a warmer climate, particularly for China, a region vulnerable to global warming and with a large population. Understanding the impacts and risks induced by future extreme precipitation changes is critical for mitigation and adaptation planning. Here, extreme precipitation changes under different levels of global warming and their associated impacts on populations in China are investigated using multimodel climate projections from the Coupled Model Intercomparison Project Phase 5 and population projections under Shared Socio-economic Pathways. Heavy precipitation would intensify with warming across China at a rate of 6.52% (5.22%-8.57%) per degree of global warming. The longest dry spell length would increase (decrease) south (north) of ~34°N. The low warming target of the Paris Agreement could substantially reduce the extreme precipitation related impacts compared to higher warming levels. For the area weighted average changes, the intensification in wet extremes could be reduced by 3.22%, 9.42% and 16.70% over China, and the lengthening of dry spells could be reduced by 0.72%, 4.75% and 5.31% in southeastern China, respectively, if global warming is limited to 1.5 °C as compared to 2, 3 and 4 °C. The Southeastern China is the hotspot of enhanced impacts due to the dense population. The impacts on populations induced by extreme precipitation changes are dominated by climate change, while future population redistribution plays a minor role.

Heatwave Trends and the Population Exposure Over China in the 21st Century as Well as Under 1.5 °C and 2.0 °C Global Warmer Future Scenarios

Heatwaves exert negative socio-economic impacts and particularly have serious effects on public health. Based on the multi-model ensemble (MME) results of 10 downscaled high-resolution Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) model output from NASA Earth Exchange Global Daily Downscaled Projections (NASA-GDDP), the intensity (largest lasting time), frequency and total duration of heatwaves over China as well as population exposure in the 21st century and at 1.5 °C and 2.0 °C above pre-industrial levels are investigated by using the three indices, the Heat Wave Duration Index (HWDI), annual total frequency of heatwaves (N_HW) and annual total days of heatwaves (T_HW) under RCP4.5 and RCP8.5. The MME results illustrate that heatwaves are projected to become more frequent (0.40/decade and 1.26/decade for N_HW), longer-lasting (3.78 days/decade and 14.59 days/decade for T_HW) as well as more extreme (1.07 days/decade and 2.90 days/decade for HWDI under RCP4.5 and RCP8.5 respectively) over China. High latitude and high altitude regions, e.g., the Tibetan Plateau and northern China, are projected to experience a larger increase of intensity, frequency and the total time of heatwaves compared with southern China (except Central China). The total population affected by heatwaves is projected to increase significantly and will reach 1.18 billion in later part of the 21st century, and there will be more and more people expected to suffer long heatwave time (T_HW) in the 21st century. Compared with a 2.0 °C global warming climate, holding the global warming below 1.5 °C can avoid 26.9% and 29.1% of the increase of HWDI, 34.7% and 39.64% for N_TW and 35.3%–40.10% of T_HW under RCP4.5 and RCP8.5 respectively. The half-degree less of warming will not only decrease the population exposure by 53–83 million but also avoid the threat caused by longer heatwave exposure under the two scenarios. Based on the comprehensive assessment of heatwave under the two RCP scenarios, this work would help to enhance the understanding of climate change and consequent risk in China and thus could provide useful information for making climate adaptation policies.

Scalable direct N-methylation of drug-like amines using 12CO2/13CO2 by simple inorganic base catalysis

With the growing urgency of potential catastrophic climate changes due to anthropogenic CO₂ emissions, numerous efforts have been devoted to development of synthetic protocols using CO₂ as a building block in organic reactions, but the general applicability to complex drug-like substrates remains a challenge. We develop a general protocol for scalable direct N-methylation of a wide-scope drug-like amines using CO₂ and polymethylhydrosiloxane-a nontoxic, aerobically-stable hydrosilane considered as an industrial waste-via simple inorganic base catalysis. A rare application of the Sabatier principle in organic chemistry led to the discovery of cheap, nontoxic K₃PO₄ as an efficient catalyst. Preparations of a wide-scope drug-like amines with carbon-isotope label were also successfully achieved, enabling direct use of CO₂ in studies of drug absorption, distribution, metabolism and excretion.

Impacts of 1.5 °C and 2 °C global warming on winter snow depth in Central Asia

Snow depth plays an essential role in the water and energy balance of the land surface. It is of special importance in arid and semi-arid regions of Central Asia. Owing to the limited availability of field observations, the spatial and temporal variations of snow depth are still poorly known. Using the Japanese 55-year (JRA-55) and the ERA-Interim reanalysis snow depth products, we considered four global climate models (GCMs) applied in the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), examining how they represent snow depth in Central Asia during the period 1986-2005 in terms of spatial and temporal characteristics. We also investigated changes of winter (January-March) snow depth in Central Asia, at 1.5 °C and 2 °C global warming levels. Finally, the joint probabilistic behavior of winter temperature and precipitation at 1.5 °C and 2 °C global warming are investigated using the kernel density estimator (KDE). The result shows that the snow depth climatology of Central Asia is generally well simulated in both spatial pattern and temporal (inter-annual and inter-seasonal) pattern. All models approximately simulate the winter maximum and the summer minimum values of snow depth but tend to overestimate the amplitude during October-December. Only the trend in HadGEM2-ES matches fairly well to the JRA-55 reanalysis snow depth. When comparing the projections of spatial distribution of winter snow depth, distinctive spatial pattern is noted at both 1.5 °C and 2 °C global warming levels, when the snow depth is shown to increase in northeastern and to decrease in midwestern regions of Central Asia. According to the joint probability distributions of precipitation and temperature, Central Asia will tend to experience a warmer and wetter winter at both 1.5 °C and 2 °C global warming levels, which can be associated with an increase in snow depth in the northeastern regions.

Changes in Extreme Low Temperature Events over Northern China under 1.5 °C and 2.0 °C Warmer Future Scenarios

Recently, NCAR (the National Center for Atmospheric Research) released the Community Earth System Model’s low-warming simulations, which provided long-term climate data for stabilization pathways at 1.5 °C and 2.0 °C above pre-industrial levels. Based on these data, six extreme low temperature indices—TXn (coldest day), TNn (coldest night), TX10p (cool days), TN10p (cool nights), CSDI (cold spell duration indicator), and DTR (diurnal temperature range)—were calculated to assess the changes in extreme low temperature over Northern China under 1.5 °C and 2.0 °C warmer future. The results indicate that compared to the preindustrial level, the whole of China will experience 0.32–0.46 °C higher minimum surface air temperature (SAT) warming than the global average, and the winter temperature increase in Northern China will be the most pronounced over the country. In almost all the regions of Northern China, especially Northeast and Northwest China, extreme low temperature events will occur with lower intensity, frequency, and duration. Compared with the present day, the intensity of low temperature events will decrease most in Northeast China, with TXn increasing by 1.9 °C/2.0 °C and TNn increasing by 2.0 °C/2.5 °C under 1.5 °C/2.0 °C global warming, respectively. The frequency of low temperature events will decrease relatively more in North China, with TX10p decreasing by 8 days/11 days and TN10p decreasing by 7 days/9 days under 1.5 °C/2.0 °C warming. CSDI will decrease most in Northwest China, with decreases of 7 days/10 days with 1.5 °C/2.0 °C warming. DTR will decrease in the Northwest and Northeast but increase in North China, with −0.9 °C/−2.0 °C in the Northwest, −0.4 °C/−1.5 °C in the Northeast, and 1.7 °C/2.0 °C in North China in the 1.5 °C/2.0 °C warming scenarios. For temperatures lower than the 5th percentile, the PRs (probability ratios) will be 0.68 and 0.55 of that of the present day under 1.5 °C and 2.0 °C warmer futures, respectively. Global warming of 2.0 °C instead of 1.5 °C will lead to extreme low temperature events decreasing by 6–56% in regard to intensity, frequency, and duration over Northern China, and the maximal values of decrease (24–56%) will be seen in Northeast China.

Spatiotemporal variations of aridity in China during 1961-2015: decomposition and attribution

Changes in global climate intensify the hydrological cycle, directly influence precipitation, evaporation, runoff, and cause the re-distribution of water resources in time and space. The aridity index (AI), defined as the ratio of annual precipitation to annual potential evapotranspiration, is a widely used numerical indicator to quantify the degree of dryness at a given location. This study examined the effects of climate change on AI in China during 1961-2015. The results showed that the nationally averaged AI experienced a notable interdecadal transition in 1993, characterized by increasing AI (wetter) between 1961 and 1993, and decreasing AI (drier) after 1993. Overall, the decreased solar radiation (solar dimming) was the main factor affected the nationally averaged AI during 1961-1993, while the relative humidity dominated the variations of nationally averaged AI during 1993-2015. However, the roles of individual factors on the changes in AI vary in different subregions. Precipitation is one of the important contributing factors for the changes of AI in almost all subregions, except the Mid-Lower Yangtze and Huaihe basins. Solar radiation has been significantly decreased during 1961-1993 in South China, Southwest China, Mid-Lower Yangtze and Huaihe basins, and the Tibetan Plateau. Therefore, it dominated the trends of AI in these subregions. The relative humidity mainly affected the Mid-Lower Yangtze and Huaihe basins, Southwest China, and the Tibetan Plateau during 1993-2015, hence dominated the trends of AI in these subregions. The changes of temperature and wind speed, however, played a relatively weak role in the variations of AI.