Global climate change: Effects of future temperatures on emergency department visits for mental disorders in Beijing, China.

Rising temperatures can increase the risk of mental disorders. As climate change intensifies, the future disease burden due to mental disorders may be underestimated. Using data on the number of daily emergency department visits for mental disorders at 30 hospitals in Beijing, China during 2016-2018, the relationship between daily mean temperature and such visits was assessed using a quasi-Poisson model integrated with a distributed lag nonlinear model. Emergency department visits for mental disorders attributed to temperature changes were projected using 26 general circulation models under four climate change scenarios. Stratification analyses were then conducted by disease subtype, sex, and age. The results indicate that the temperature-related health burden from mental disorders was projected to increase consistently throughout the 21st century, mainly driven by high temperatures. The future temperature-related health burden was higher for patients with mental disorders due to the use of psychoactive substances and schizophrenia as well as for women and those aged <65 years. These findings enhance our knowledge of how climate change could affect mental well-being and can be used to advance and refine targeted approaches to mitigating and adapting to climate change with a view on addressing mental disorders.

The Response of Non-Point Source Pollution to Climate Change in an Orchard-Dominant Coastal Watershed.

China is the largest global orchard distribution area, where high fertilization rates, complex terrain, and uncertainties associated with future climate change present challenges in managing non-point source pollution (NPSP) in orchard-dominant growing areas (ODGA). Given the complex processes of climate, hydrology, and soil nutrient loss, this study utilized an enhanced Soil and Water Assessment Tool model (SWAT-CO2) to investigate the impact of future climate on NPSP in ODGA in a coastal basin of North China. Our investigation focused on climate-induced variations in hydrology, nitrogen (N), and phosphorus (P) losses in soil, considering three Coupled Model Intercomparison Project phase 6 (CMIP6) climate scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Continuous changes in CO2 levels significantly influenced evapotranspiration (ET) and water yield in ODGA. Influenced by sandy soils, nitrate leaching through percolation was the principal pathway for N loss in the ODGA. Surface runoff was identified as the primary pathway for P loss. Compared to the reference period (1971-2000), under three future climate scenarios, the increase in precipitation of ODGA ranged from 15% to 28%, while the growth rates of P loss and surface runoff were the most significant, both exceeding 120%. Orchards in the northwest basin proved susceptible to nitrate leaching, while others were more sensitive to N and P losses via surface runoff. Implementing targeted strategies, such as augmenting organic fertilizer usage and constructing terraced fields, based on ODGA's response characteristics to future climate, could effectively improve the basin's environment.

Optimizing biochar application for enhanced cotton and sugar beet production in Xinjiang: a comprehensive study.

BACKGROUND: Optimizing biochar application is vital for enhancing crop production and ensuring sustainable agricultural production. A 3-year field experiment was established to explore the effects of varying the biochar application rate (BAR) on crop growth, quality, productivity and yields. BAR was set at 0, 10, 50 and 100 t ha-1 in 2018; 0, 10, 25, 50 and 100 t ha-1 in 2019; and 0, 10, 25 and 30 t ha-1 in 2020. Crop quality and growth status and production were evaluated using the dynamic technique for order preference by similarity to ideal solution with the entropy weighted method (DTOPSIS-EW), principal component analysis (PCA), membership function analysis (MFA), gray relation analysis (GRA) and the fuzzy Borda combination evaluation method. RESULTS: Low-dose BAR (≤ 25 t ha-1 for cotton; ≤ 50 t ha-1 for sugar beet) effectively increased biomass, plant height, leaf area index (LAI), water and fertility (N, P and K) productivities, and yield. Biochar application increased the salt absorption and sugar content in sugar beet, with the most notable increases being 116.45% and 20.35%, respectively. Conversely, BAR had no significant effect on cotton fiber quality. The GRA method was the most appropriate for assessing crop growth and quality. The most indicative parameters for reflecting cotton and sugarbeet growth and quality status were biomass and LAI. The 10 t ha-1 BAR consistently produced the highest scores and was the most economically viable option, as evaluated by DTOPSIS-EW. CONCLUSION: The optimal biochar application strategy for improving cotton and sugar beet cultivation in Xinjiang, China, is 10 t ha-1 biochar applied continuously. © 2024 Society of Chemical Industry.

Future climate impacts on forest growth and implications for carbon sequestration through reforestation in southeast Australia.

Reforestation is identified as one of the key nature-based solutions to deliver carbon dioxide removal, which will be required to achieve the net zero ambition of the Paris Agreement. However, the potential for sequestration through reforestation is uncertain because climate change is expected to affect the drivers of forest growth. This study used the process-based 3-PG model to investigate the effects of climate change on development of above-ground biomass (AGB), as an indicator of forest growth, in regenerating native forests in southeast Australia. We investigated how changing climate affects AGB, by combining historical data and future climate projections based on 25 global climate models (GCMs) for the Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways. We found that the ensemble means of 25 GCMs indicated an increase in temperature with large variations in projected rainfall. When these changes were applied in 3-PG, we found an increase in the simulated AGB by as much as 25% under a moderate emission scenario. This estimate rose to 51% under a high emission scenario by the end of the 21st century across nine selected sites in southeast Australia. However, when CO2 response was excluded, we found a large decrease in AGB at the nine sites. Our modelling results showed that the modelled response to elevated atmospheric CO2 (the CO2 fertilization effect) was largely responsible for the simulated increase of AGB (%). We found that the estimates of future changes in the AGB were subject to uncertainties originating from climate projections, future emission scenarios, and the assumed response to CO2 fertilization. Such modelling simulation improves understanding of possible climate change impacts on forest growth and the inherent uncertainties in estimating mitigation potential through reforestation, with implications for climate policy in Australia.

Projecting environmental suitable areas for malaria transmission in China under climate change scenarios.

INTRODUCTION: The proportion of imported malaria cases in China has increased over recent years, and has presented challenges for the malaria elimination program in China. However, little is known about the geographic distribution and environmental suitability for malaria transmission under projected climate change scenarios. METHODS: Using the MaxEnt model based on malaria presence-only records, we produced environmental suitability maps and examined the relative contribution of topographic, demographic, and environmental risk factors for P. vivax and P. falciparum malaria in China. RESULTS: The MaxEnt model estimated that environmental suitability areas (ESAs) for malaria cover the central, south, southwest, east and northern regions, with a slightly wider range of ESAs extending to the northeast region for P. falciparum. There was spatial agreement between the location of imported cases and area environmentally suitable for malaria transmission. The ESAs of P. vivax and P. falciparum are projected to increase in some parts of southwest, south, central, north and northeast regions in the 2030s, 2050s, and 2080s, by a greater amount for P. falciparum under the RCP8.5 scenario. Temperature and NDVI values were the most influential in defining the ESAs for P. vivax, and temperature and precipitation the most influential for P. falciparum malaria. CONCLUSION: This study estimated that the ESA for malaria transmission in China will increase with climate change and highlights the potential establishment of further local transmission. This model should be used to support malaria control by targeting areas where interventions on malaria transmission need to be enhanced.

Climate change adaptation options in rainfed upland cropping systems in the wet tropics: A case study of smallholder farms in North-West Cambodia.

While climate change is confirmed to have serious impacts on agricultural production in many regions worldwide, researchers have proposed various measures that farmers can apply to cope with and adapt to those changes. However, it is often the case that not every adaptation measure would be practical and adoptable in a specific region. Farmers may have their own ways of managing and adapting to climate change that need to be taken into account when considering interventions. This study aimed to engage with farmers to: (1) better understand small-holder knowledge, attitudes and practices in relation to perceived or expected climate change; and (2) document cropping practices, climate change perceptions, constraints to crop production, and coping and adaptation options with existing climate variability and expected climate change. This study was conducted in 2015 in Sala Krau village near Pailin (12°52'N, 102°45'E) and Samlout (12°39'N, 102°36'E) of North-West Cambodia. The methods used were a combination of focus group discussions and one-on-one interviews where 132 farming households were randomly selected. We found that farmers were conscious of changes in climate over recent years, and had a good understanding of likely future changes. While farmers are aware of some practices that can be modified to minimize risk and cope with anticipated changes, they are reluctant to apply them. Furthermore; there are no government agricultural extension services provided at the village level and farmers have relied on each other and other actors in the value chain network for information to support their decision-making. There is a lack of knowledge of the principles of conservation agriculture that urgently require agricultural extension services in the region to build farmer ability to better cope and adapt to climate change.

[Applications of near infrared reflectance spectroscopy technique (NIRS) to soil attributes research].

Soil is a much complicated substance, because animals, plants and microbes live together, organic and inorganic exist together. So soil contains a large amount of information. The traditional method in laboratory is a time-consuming effort. But the technology of near infrared reflectance spectroscopy (NIRS) has been widely used in many areas, owing to its rapidness, high efficiency, no pollution and low cost, NIRS has become the most important method to detect the composition of soil. This paper mainly introduce some traditional methods in laboratory, the basic processes of soil detection by NIRS, some algorithms for data preprocessing and modeling. Besides, the present paper illustrates the latest research progress and the development of portable near infrared instruments of the soil. According to this paper, the authors also hope to promote the application conditions of NIRS in the grassland ecology research in China, and accelerate the modernization of research measures in this area.

Modelling changes in vegetation productivity and carbon balance under future climate scenarios in southeastern Australia.

Australia, characterized by extensive and heterogeneous terrestrial ecosystems, plays a critical role in the global carbon cycle and in efforts to mitigate climate change. Prior research has quantified vegetation productivity and carbon balance within the Australian context over preceding decades. Nonetheless, the responses of vegetation and carbon dynamics to the evolving phenomena of climate change and escalating concentrations of atmospheric carbon dioxide remain ambiguous within the Australian landscape. Here, we used LPJ-GUESS model to assess the impacts of climate change on Gross Primary Productivity (GPP) and Net Biome Productivity (NBP) of carbon for the state of New South Wales (NSW) in southeastern Australia. LPJ-GUESS simulations were driven by an ensemble of 27 global climate models under different emission scenarios. We investigated the change of GPP for different vegetation types and whether NSW ecosystems will be a net sink or source of carbon under climate change. We found that LPJ-GUESS successfully simulated GPP for the period 2003-2021, demonstrating a comparative performance with GPP derived from upscaled eddy covariance fluxes (R2 = 0.58, nRMSE = 14.2 %). The simulated NBP showed a larger interannual variation compared with flux data and other inversion products but could capture the timing of rainfall-driven carbon sink and source variations in 2015-2020. GPP would increase by 10.3-19.5 % under a medium emission scenario and 19.7-46.8 % under a high emission scenario. The mean probability of NSW acting as a carbon sink in the future showed a small decrease with a large uncertainty with >8 of the 27 climate models indicating an increased potential for carbon sink. These findings emphasize the significance of emission scenarios in shaping future carbon dynamics but also highlight considerable uncertainties stemming from different climate projections. Our study represents a baseline for understanding natural ecosystem dynamics and their key role in governing land carbon uptake and storage in Australia.

Assessment of the sustainability of groundwater utilization and crop production under optimized irrigation strategies in the North China Plain under future climate change.

Over-exploitation of groundwater due to intensive irrigation and anticipated climate change pose severe threats to the water and food security worldwide, particularly in the North China Plain (NCP). Limited irrigation has been recognized as an effective way to improve crop water productivity and slow the rapid decline of groundwater levels. Whether optimized limited irrigation strategies could achieve a balance between groundwater pumping and grain production in the NCP under future climate change deserves further study. In this study, an improved Soil and Water Assessment Tool (SWAT) model was used to simulate climate change impacts on shallow groundwater levels and crop production under limited irrigation strategies to suggest optimal irrigation management practices under future climate conditions in the NCP. The simulations of eleven limited irrigation strategies for winter wheat with targeted irrigations at different growth stages and with irrigated or rainfed summer maize were compared with future business-as-usual management. Climate change impacts showed that mean wheat (maize) yield under adequate irrigation was expected to increase by 13.2% (4.9%) during the middle time period (2041-2070) and by 11.2% (4.6%) during the late time period (2071-2100) under three SSPs compared to the historical period (1971-2000). Mean decline rate of shallow groundwater level slowed by approximately 1 m a-1 during the entire future period (2041-2100) under three SSPs with a greater reduction for SSP5-8.5. The average contribution rate of future climate toward the balance of shallow groundwater pumping and replenishment was 62.9%. Based on the simulated crop yields and decline rate of shallow groundwater level under the future climate, the most appropriate limited irrigation was achieved by applying irrigation during the jointing stage of wheat with rainfed maize, which could achieve the groundwater recovery and sustainable food production.

Assessing impacts of global climate change on water and food security in the black soil region of Northeast China using an improved SWAT-CO2 model.

Future climate change may have substantial impacts on both water resources and food security in China's black soil region. The Liao River Basin (LRB; 220,000 km2) is representative of the main black soil area, making it ideal for studying climate change effects on black soil. In this study, the Soil and Water Assessment Tool (SWAT) model was first initialized for the LRB. Actual evapotranspiration (ETa) values calculated using the Surface Energy Balance System (SEBS) model and city-level corn (Zea mays L.) yields were then used to calibrate the SWAT model. Finally, the SWAT model was modified to accept dynamic CO2 input and output crop transpiration, soil evaporation, and canopy interception separately to explore the impacts of future climate change on ET related variables and crop water productivity (CWP) in the LRB. Simulation scenario design included 22 General Circulation Models (GCMs) and 4 Shared Socioeconomic Pathways (SSPs) scenarios from the latest Coupled Model Intercomparison Project 6 (CMIP6) for two 30-year periods of 2041-2070 and 2071-2100. The predicted results showed a significant (P < 0.05) increase in air temperatures and precipitation in the LRB. In contrast, solar radiation decreased significantly and was most reduced for the SSP3-7.0 scenario. Reference evapotranspiration (ETo), ETa, and soil evaporation significantly increased in future scenarios, while canopy interception and crop transpiration showed significant reductions, particularly under the 2071-2100 SSP5-8.5 scenario. Overall, corn yield elevated considerably (P < 0.05) with the largest increase for the SSP5-8.5 scenario during 2071-2100. However, the SSP3-7.0 scenario indicated a significant decline in yield. Future changes in CWP were similar to those for corn yield, with significant increases in the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. These findings suggested future climate change may have a positive impact on corn production in the black soil region of the LRB.

Projecting the excess mortality due to heatwave and its characteristics under climate change, population and adaptation scenarios.

BACKGROUND: Heatwaves have significant adverse effects on human health. The frequency, duration, and intensity of heatwaves are projected to increase dramatically, in the context of global warming. However, there are few comprehensive assessments of the health impact of heatwaves considering different definitions, and their characteristics under climate change scenarios. OBJECTIVE: We aimed to compare future excess mortality related to heatwaves among different definitions under climate change, population, and adaptation scenarios in China and further explore the mortality burden associated with heatwave characteristics. METHODS: Daily data during 2010-2019 were collected in Guangzhou, China. We adopted nine common heatwave definitions and applied quasi-Poisson models to estimate the effects of heatwaves and their characteristics' impact on mortality. We then projected the excess mortality associated with heatwaves and their characteristics concerning climate change, population, and adaptation scenarios. RESULTS: The relative risks of the nine common heatwave definitions ranged from 1.05 (95% CI: 1.01, 1.10) to 1.24 (95% CI: 1.13, 1.35). Heatwave-related excess mortality will consistently increase in the future decades considering multiple heatwave definitions, with more rapidly increasing rates under the Shared Socioeconomic Path5-8.5 and non-adaptability scenarios. Regarding heatwave characteristics, the intensity is the main factor involved in the threat of heatwaves. The increasing trend of characteristic-related mortality burden is similar to that of heatwaves, and the mortality burden caused by the duration of the heatwaves was the largest among all characteristics. CONCLUSIONS: This study provides a comprehensive picture of the impact of heatwaves and their characteristics on public health under various climate change scenarios, population changes, and adaptive assumptions. The results may provide important public health implications for policymakers in planning climate change adaptation and mitigation policies, and implementing specific plans.

China can be self-sufficient in maize production by 2030 with optimal crop management.

Population growth and economic development in China has increased the demand for food and animal feed, raising questions regarding China's future maize production self-sufficiency. Here, we address this challenge by combining data-driven projections with a machine learning method on data from 402 stations, with data from 87 field experiments across China. Current maize yield would be roughly doubled with the implementation of optimal planting density and management. In the 2030 s, we estimate a 52% yield improvement through dense planting and soil improvement under a high-end climate forcing Shared Socio-Economic Pathway (SSP585), compared with a historical climate trend. Based on our results, yield gains from soil improvement outweigh the adverse effects of climate change. This implies that China can be self-sufficient in maize by using current cropping areas. Our results challenge the view of yield stagnation in most global areas and provide an example of how food security can be achieved with optimal crop-soil management under future climate change scenarios.

Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates.

Extreme weather events threaten food security, yet global assessments of impacts caused by crop waterlogging are rare. Here we first develop a paradigm that distils common stress patterns across environments, genotypes and climate horizons. Second, we embed improved process-based understanding into a farming systems model to discern changes in global crop waterlogging under future climates. Third, we develop avenues for adapting cropping systems to waterlogging contextualised by environment. We find that yield penalties caused by waterlogging increase from 3-11% historically to 10-20% by 2080, with penalties reflecting a trade-off between the duration of waterlogging and the timing of waterlogging relative to crop stage. We document greater potential for waterlogging-tolerant genotypes in environments with longer temperate growing seasons (e.g., UK, France, Russia, China), compared with environments with higher annualised ratios of evapotranspiration to precipitation (e.g., Australia). Under future climates, altering sowing time and adoption of waterlogging-tolerant genotypes reduces yield penalties by 18%, while earlier sowing of winter genotypes alleviates waterlogging by 8%. We highlight the serendipitous outcome wherein waterlogging stress patterns under present conditions are likely to be similar to those in the future, suggesting that adaptations for future climates could be designed using stress patterns realised today.

Simulation of Wheat Response to Future Climate Change Based on Coupled Model Inter-Comparison Project Phase 6 Multi-Model Ensemble Projections in the North China Plain.

Global climate change results in more extreme temperature events, which poses a serious threat to wheat production in the North China Plain (NCP). Assessing the potential impact of temperature extremes on crop growth and yield is an important prerequisite for exploring crop adaptation measures to deal with changing climate. In this study, we evaluated the effects of heat and frost stress during wheat sensitive period on grain yield at four representative sites over the NCP using Agricultural Production System Simulator (APSIM)-wheat model driven by the climate projections from 20 Global Climate Models (GCMs) in the Coupled Model Inter-comparison Project phase 6 (CMIP6) during two future periods of 2031-2060 (2040S) and 2071-2100 (2080S) under societal development pathway (SSP) 245 and SSP585 scenarios. We found that extreme temperature stress had significantly negative impacts on wheat yield. However, increased rainfall and the elevated atmospheric CO2 concentration could partly compensate for the yield loss caused by extreme temperature events. Under future climate scenarios, the risk of exposure to heat stress around flowering had no great change but frost risk in spring increased slightly mainly due to warming climate accelerating wheat development and advancing the flowering time to a cooler period of growing season. Wheat yield loss caused by heat and frost stress increased by -0.6 to 4.2 and 1.9-12.8% under SSP585_2080S, respectively. We also found that late sowing and selecting cultivars with a long vegetative growth phase (VGP) could significantly compensate for the negative impact of extreme temperature on wheat yields in the south of NCP. However, selecting heat resistant cultivars in the north NCP and both heat and frost resistant cultivars in the central NCP may be a more effective way to alleviate the negative effect of extreme temperature on wheat yields. Our findings showed that not only heat risk should be concerned under climate warming, but also frost risk should not be ignored.

Deficit Irrigation at Pre-Anthesis Can Balance Wheat Yield and Water Use Efficiency under Future Climate Change in North China Plain.

BACKGROUND: Deficit irrigation (DI) is a feasible strategy to enhance crop WUE and also has significant compensation effects on yield. Previous studies have found that DI has great potential to maintain crop production as full irrigation (FI) does. Therefore, adopting DI to improve crop production and safeguard groundwater resources is of great importance in water scarce regions, e.g., the North China Plain (NCP). Under the background of global warming, it is worth investigating whether DI continues to play such a key role under future climate scenarios. METHODS: We studied the response of winter wheat yield and WUE to different DI levels at pre-anthesis under two Shared Socioeconomic Pathways (SSPs) scenarios (SSP245 and SSP585) using the Agricultural Production Systems Simulator (APSIM) model driven by 21 general circulation models (GCMs) from the Coupled Model Inter-Comparison Project phase 6 (CMIP6). Additionally, we explored the effects of different nitrogen (N) fertilizer application rates on DI. RESULTS: We found that simulated wheat yield would increase by 3.5-45.0%, with WUE increasing by 8.8-46.4% across all treatments under future climate change. Moderate deficit irrigation (DI3, ≤0.4 PAWC at the sowing to flowering stage) under the N3 (150 kg N ha-1) condition was identified as the optimum irrigation schedule for the study site under future climate change. However, compensation effects of DI3 on yield and WUE became weak in the future, which was mainly due to increased growing season rainfall projected by GCMs. In addition, we found that N fertilizer application could mitigate the effect of DI3. CONCLUSIONS: We highlight that in water scarce regions of NCP, DI remains an effective strategy to maintain higher yield and enhance water use under future climate scenarios. Results strongly suggest that moderate deficit irrigation under a 150 kg N ha-1 condition could mitigate the contradiction between production and water consumption and ensure food safety in the NCP.

Climate Change Impact on Yield and Water Use of Rice-Wheat Rotation System in the Huang-Huai-Hai Plain, China.

Global climate change has had a significant impact on crop production and agricultural water use. Investigating different future climate scenarios and their possible impacts on crop production and water consumption is critical for proposing effective responses to climate change. In this study, based on daily downscaled climate data from 22 Global Climate Models (GCMs) provided by Coupled Model Intercomparison Project Phase 6 (CMIP6), we applied the well-validated Agricultural Production Systems sIMulator (APSIM) to simulate crop phenology, yield, and water use of the rice-wheat rotation at four representative stations (including Hefei and Shouxian stations in Anhui province and Kunshan and Xuzhou stations in Jiangsu province) across the Huang-Huai-Hai Plain, China during the 2041-2070 period (2050s) under four Shared Socioeconomic Pathways (i.e., SSP126, SSP245, SSP370, and SSP585). The results showed a significant increase in annual mean temperature (Temp) and solar radiation (Rad), and annual total precipitation (Prec) at four investigated stations, except Rad under SSP370. Climate change mainly leads to a consistent advance in wheat phenology, but inconsistent trends in rice phenology across four stations. Moreover, the reproductive growth period (RGP) of wheat was prolonged while that of rice was shorted at three of four stations. Both rice and wheat yields were negatively correlated with Temp, but positively correlated with Rad, Prec, and CO2 concentration ([CO2]). However, crop ET was positively correlated with Rad, but negatively correlated with [CO2], as elevated [CO2] decreased stomatal conductance. Moreover, the water use efficiency (WUE) of rice and wheat was negatively correlated with Temp, but positively correlated with [CO2]. Overall, our study indicated that the change in Temp, Rad, Prec, and [CO2] have different impacts on different crops and at different stations. Therefore, in the impact assessment for climate change, it is necessary to explore and analyze different crops in different regions. Additionally, our study helps to improve understanding of the impacts of climate change on crop production and water consumption and provides data support for the sustainable development of agriculture.

Optimal Strategy on Radiation Estimation for Calculating Universal Thermal Climate Index in Tourism Cities of China.

The Universal Thermal Climate Index (UTCI) is believed to be a very powerful tool for providing information on human thermal perception in the domain of public health, but the solar radiation as an input variable is difficult to access. Thus, this study aimed to explore the optimal strategy on estimation of solar radiation to increase the accuracy in UTCI calculation, and to identify the spatial and temporal variation in UTCI over China. With daily meteorological data collected in 35 tourism cities in China from 1961 to 2020, two sunshine-based Angstrom and Ogelman models, and two temperature-based Bristow and Hargreaves models, together with neural network and support vector machine-learning methods, were tested against radiation measurements. The results indicated that temperature-based models performed the worst with the lowest NSE and highest RMSE. The machine-learning methods performed better in calibration, but the predictive ability decreased significantly in validation due to big data requirements. In contrast, the sunshine-based Angstrom model performed best with high NSE (Nash-Sutcliffe Efficiency) of 0.84 and low RMSE (Root Mean Square Error) of 35.4 J/m2 s in validation, which resulted in a small RMSE of about 1.2 °C in UTCI calculation. Thus, Angstrom model was selected as the optimal strategy on radiation estimation for UTCI calculation over China. The spatial distribution of UTCI showed that days under no thermal stress were high in tourism cities in central China within a range from 135 to 225 days, while the largest values occurred in Kunming and Lijiang in southwest China. In addition, days under no thermal stress during a year have decreased in most tourism cities of China, which could be attributed to the asymmetric changes in significant decrease in frost days and slightly increase in hot days. However, days under no thermal stress in summer time have indeed decreased, accompanying with increasing days under strong stress, especially in the developed regions such as Yangze River Delta and Zhujiang River Delta. Based on the study, we conclude that UTCI can successfully depict the overall spatial distribution and temporal change of the thermal environments in the tourism cities over China, and can be recommend as an efficient index in the operational services for assessing and predicting thermal perception for public health. However, extreme cold and heat stress in the tourism cities of China were not revealed by UTCI due to mismatch of the daily UTCI with category at hourly scale, which makes it an urgent task to redefine category at daily scale in the next research work.

Increasing dominance of Indian Ocean variability impacts Australian wheat yields.

The relationships between crop productivity and climate variability drivers are often assumed to be stationary over time. However, this may not be true in a warming climate. Here we use a crop model and a machine learning algorithm to demonstrate the changing impacts of climate drivers on wheat productivity in Australia. We find that, from the end of the nineteenth century to the 1980s, wheat productivity was mainly subject to the impacts of the El Niño Southern Oscillation. Since the 1990s, the impacts from the El Niño Southern Oscillation have been decreasing, but those from the Indian Ocean Dipole have been increasing. The warming climate has brought more occurrences of positive Indian Ocean Dipole events, resulting in severe yield reductions in recent decades. Our findings highlight the need to adapt seasonal forecasting to the changing impacts of climate variability to inform the management of climate-induced yield losses.

Crop traits enabling yield gains under more frequent extreme climatic events.

Climate change (CC) in central China will change seasonal patterns of agricultural production through increasingly frequent extreme climatic events (ECEs). Breeding climate-resilient wheat (Triticum aestivum L.) genotypes may mitigate adverse effects of ECEs on crop productivity. To reveal crop traits conducive to long-term yield improvement in the target population of environments, we created 8,192 virtual genotypes with contrasting but realistic ranges of phenology, productivity and waterlogging tolerance. Using these virtual genotypes, we conducted a genotype (G) by environment (E) by management (M) factorial analysis (G×E×M) using locations distributed across the entire cereal cropping zone in mid-China. The G×E×M invoked locally-specific sowing dates under future climates that were premised on shared socioeconomic pathways SSP5-8.5, with a time horizon centred on 2080. Across the simulated adaptation landscape, productivity was primarily driven by yield components and phenology (average grain yield increase of 6-69% across sites with optimal combinations of these traits). When incident solar radiation was not limiting carbon assimilation, ideotypes with higher grain yields were characterised by earlier flowering, higher radiation-use efficiency and larger maximum kernel size. At sites with limited solar radiation, crops required longer growing periods to realise genetic yield potential, although higher radiation-use efficiency and larger maximum kernel size were again prospective traits enabling higher rates of yield gains. By 2080, extreme waterlogging stress in some regions of mid-China will impact substantially on productivity, with yield penalties of up to 1,010 kg ha-1. Ideotypes with optimal G×M could mitigate yield penalty caused by waterlogging by up to 15% under future climates. These results help distil promising crop trait by best management practice combinations that enable higher yields and robust adaptation to future climates and more frequent extreme climatic events, including flash flooding and soil waterlogging.

Building social resilience in North Korea can mitigate the impacts of climate change on food security.

Adaptation based on social resilience is proposed as an effective measure to mitigate hunger and avoid food shocks caused by climate change. But these have not been investigated comprehensively in climate-sensitive regions. North Korea (NK) and its neighbours, South Korea and China, represent three economic levels that provide us with examples for examining climatic risk and quantifying the contribution of social resilience to rice production. Here our data-driven estimates show that climatic factors determined rice biomass changes in NK from 2000 to 2017, and climate extremes triggered reductions in production in 2000 and 2007. If no action is taken, NK will face a higher climatic risk (with continuous high-temperature heatwaves and precipitation extremes) by the 2080s under a high-emissions scenario, when rice biomass and production are expected to decrease by 20.2% and 14.4%, respectively, thereby potentially increasing hunger in NK. Social resilience (agricultural inputs and population development for South Korea; resource use for China) mitigated climate shocks in the past 20 years (2000-2019), even transforming adverse effects into benefits. However, this effect was not significant in NK. Moreover, the contribution of social resilience to food production in the undeveloped region (15.2%) was far below the contribution observed in the developed and developing regions (83.0% and 86.1%, respectively). These findings highlight the importance of social resilience to mitigate the adverse effects of climate change on food security and human hunger and provide necessary quantitative information.