Cropping patterns for phenology stability and resource conservation under extreme climates

The increasing frequency of extreme climate events may significantly alter the species composition, structure, and functionality of ecosystems, thereby diminishing their stability and resilience. This study draws on temperature and precipitation data from 53 meteorological stations across Mongolia, covering the period from 1983 to 2016, along with MODIS normalized difference vegetation index (NDVI) data from 2001 to 2016. The climate anomaly method and the curvature method of cumulative NDVI logistic curves were employed to identify years of extreme climate events and to extract the start of the growing season (SOS) in Mongolia. Furthermore, the study assessed the impact of extreme climate events on the SOS across different vegetation types and evaluated the sensitivity of the SOS to extreme climate indices. The study results show that, compared to the multi-year average green-up period from 2001 to 2016, extreme climate events significantly impact the SOS. Extreme dryness advanced the SOS by 6.9 days, extreme wetness by 2.5 days, and extreme warmth by 13.2 days, while extreme cold delayed the SOS by 1.2 days. During extreme drought events, the sensitivity of SOS to TN90p (warm nights) was the highest; in extremely wet years, the sensitivity of SOS to TX10p (cool days) was the strongest; in extreme warm events, SOS was most sensitive to TX90p (warm days); and during extreme cold events, SOS was most sensitive to TNx (maximum night temperature). Overall, the SOS was most sensitive to extreme temperature indices during extreme climate events, with a predominantly negative sensitivity. The response and sensitivity of SOS to extreme climate events varied across different vegetation types. This is crucial for understanding the dynamic changes of ecosystems and assessing potential ecological risks.

Frequent extreme climate events have attracted considerable attention around the world. Malus sieversii in Xinjiang is the ancestor of cultivated apple, and it is mainly distributed in the Ili river valley at end of the Tianshan Mountains. Wild fruit forests have been degraded, but the cause remains unclear. In order to identify whether extreme climate events caused this degradation reanalysis data and atmospheric circulation indices were used to determine the trends and the reasons for extreme climate changes. Subsequently, we further investigated the effect of extreme climate events on wild fruit forest using characteristics of extreme climate indices and tree-ring chronology. We found increasing trends in both extreme precipitation and warm indices, and decreasing trends in cool indices. Extreme climate events were mainly associated with the Atlantic Multidecadal Oscillation (AMO). Analysis of data of wind and geopotential height field at 500 hPa showed that strengthening wind, increasing geopotential height, cyclone and anti-cyclone circulation drivers contributed to extreme climate events. In the non-degraded region, there were significant positive correlations between tree-ring chronology and both extreme precipitation and extreme warm indices (except for warm spell duration indicator). The other extreme indices (except for heavy rain days) had a large correlation range with tree-rings in a 4–8-year period. These results indicated that extreme precipitation and extreme warm indices intensified M. sieversii growth of the non-degraded region on multi-time scales. In contrast, the degraded region showed insignificant negative relationship between tree-ring chronology and both extreme precipitation and extreme warm indices [except for warm spell duration index (WSDI)], and significant negative correlations in a 4–8-year period were detected between tree-ring chronology and most of the extreme precipitation indices, including heavy rain days, very wet days, cold spell duration indicator, simple precipitation intensity index (SDII), and annual total precipitation. Under the long disturbance of inappropriate anthropic activities, extreme climate has caused the outbreak of pests and diseases resulting in the degeneration of wild fruit forest. Our study provides scientific guidance for the ecosystem conservation in wild fruit forest in China, and also across the region.

The increasing frequency of extreme climate events may significantly alter the species composition, structure, and functionality of ecosystems, thereby diminishing their stability and resilience. This study draws on temperature and precipitation data from 53 meteorological stations across Mongolia, covering the period from 1983 to 2016, along with MODIS normalized difference vegetation index (NDVI) data from 2001 to 2016. The climate anomaly method and the curvature method of cumulative NDVI logistic curves were employed to identify years of extreme climate events and to extract the start of the growing season (SOS) in Mongolia. Furthermore, the study assessed the impact of extreme climate events on the SOS across different vegetation types and evaluated the sensitivity of the SOS to extreme climate indices. The study results show that, compared to the multi-year average green-up period from 2001 to 2016, extreme climate events significantly impact the SOS. Extreme dryness advanced the SOS by 6.9 days, extreme wetness by 2.5 days, and extreme warmth by 13.2 days, while extreme cold delayed the SOS by 1.2 days. During extreme drought event, the sensitivity of SOS to TN90p (warm nights) was the highest; in extremely wet years, the sensitivity of SOS to TX10p (cool days) was the strongest; in extreme warm event, SOS was most sensitive to TX90p (warm days); and during extreme cold events, SOS was most sensitive to TNx (maximum night temperature). Overall, the SOS was most sensitive to extreme temperature indices during extreme climate events, with a predominantly negative sensitivity. The response and sensitivity of SOS to extreme climate events varied across different vegetation types. This is crucial for understanding the dynamic changes of ecosystems and assessing potential ecological risks.

The Qinling Mountains, situated in the climatic transition zone between northern and southern China, represent a critical region for climate and ecological studies due to their unique transitional characteristics and the rising frequency of extreme climate events. As net primary productivity (NPP) is a key indicator of ecosystem stability, clarifying its response to extreme climate events is essential for understanding ecological resilience in this region. In this study, daily observational data from 123 meteorological stations (1960–2023) were used to derive eight extreme temperature and precipitation indices. Combined with MODIS NPP data (2001–2023), we applied Theil–Sen slope estimation, Mann–Kendall significance testing, ridge regression, Pearson correlation analysis, and Moran’s I spatial autocorrelation to systematically investigate the spatiotemporal dynamics and driving mechanisms of NPP. The main findings are as follows: (1) From 2001 to 2023, the mean annual NPP in the Qinling region was 558.43 ± 134.27 gC·m−2·year−1, showing a significant increasing trend of 5.44 gC·m−2·year−1 (p < 0.05). (2) Extreme temperature indices exhibited significant changes, whereas among the precipitation indices, only the number of days with daily precipitation ≥ 20 mm (R20) showed a significant trend, suggesting that extreme temperatures exert a stronger influence in the region. (3) Correlation analysis indicated that temperature-related indices were generally positively correlated, precipitation-related indices displayed even stronger associations, and covariation existed among extreme precipitation events of varying intensities. Moreover, precipitation indices demonstrated relatively stable spatial distributions, while temperature indices fluctuated considerably. (4) Absolute contribution analysis further revealed that the number of days with daily minimum temperature below the 10th percentile (TN10p) contributed up to 3.53 gC·m−2·year−1 to annual NPP variation in the Henan subregion, whereas maximum rainfall over five consecutive days (Rx5day) exerted an overall negative effect on NPP (−0.77 gC·m−2·year−1). By integrating long-term meteorological observations with remote sensing products, this study quantitatively evaluates the differential impacts of extreme climate events on vegetation within a climatic transition zone, offering important implications for ecological conservation and adaptive management in the Qinling Mountains.

A new assessment of climate change impacts on food production shortfalls and water availability in Russia

Recent increases in the frequency of Extreme Climate Events (ECEs) such as heatwaves and floods have been attributed to climate change, and could have pronounced ecosystem and evolutionary impacts because they provide little opportunity for organisms to acclimate or adapt. Here we synthesize information on a series of ECEs in Australia from 2011-2017 that led to well-documented, abrupt and extensive mortality of key marine habitat-forming organisms – corals, kelps, seagrasses and mangroves – along nearly more than 45% of the continental coastline of Australia. Coral bleaching occurred across much of northern Australia due to marine heatwaves affecting different regions in 2011, 2013, 2016 and 2017, while seagrass was impacted by anomalously high rainfall events in 2011 on both east and west tropical coasts. A marine heatwave off western Australia during the 2011 La Niña extended into temperate and subtropical regions, causing widespread mortality of kelp forests and seagrass communities at their northern distribution limits. Mangrove forests experienced high mortality during the 2016 El Niño across coastal areas of northern and north-western Australia due to severe water stress driven by drought and anomalously low mean sea levels. This series of ECEs reflects a variety of different events – marine heatwaves, intense rainfall from tropical storms, and drought. Their repeated occurrence and wide extent are consistent with projections of increased frequency and intensity of ECEs, and have broad implications elsewhere because similar trends are predicted globally. The unprecedented and widespread nature of these ECE impacts has likely produced substantial ecosystem-wide repercussions. Predictions from ecosystem models suggest that the widespread mortality of habitat-forming taxa will have long-term and in some cases irreversible consequences, especially if they continue to become more frequent or severe. The abrupt ecological changes that are caused by ECEs could have greater long-term impacts than slower warming that leads to gradual reorganisation and possible evolution and adaptation. ECEs are an emerging threat to marine ecosystems, and will require better seasonal prediction and mitigation strategies.

In recent years, extreme climatic events such as heavy rainfall and droughts are common and have contributed to the loss of lives, damage of properties, destruction of the environment and socio-economic livelihood of people predominantly in many developing countries. Characterizing these events to understand their temporal and spatial evolution is of great considerable benefit to different sectors; for instance, energy, agriculture, health and water resource sectors. In this study, we use the outputs of regional climate models to characterize the temporal and spatial evolution of extreme climatic events over Tanzania. Results reveal that all regions across Tanzania are projected to experience a statistically significant increased frequency of extreme climatic events related to temperatures. However, the frequency of extreme climatic events related to rainfall is projected to increase at a non-significant level across most regions. The presented increase in extreme climatic events is likely to pose significant damage to the agriculture sector, water sector and other socio-economic livelihoods of people over many regions in Tanzania. It is therefore recommended that appropriate policies should be put in place to help different sectors and communities at large to adapt to the projected increase in extreme climatic events, especially on the projected warming of near-surface temperatures.

Radley Horton,1,a Daniel Bader,1,a Yochanan Kushnir,2 Christopher Little,3 Reginald Blake,4 and Cynthia Rosenzweig5 1Columbia University Center for Climate Systems Research, New York, NY. 2Ocean and Climate Physics Department, Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY. 3Atmospheric and Environmental Research, Lexington, MA. 4Physics Department, New York City College of Technology, CUNY, Brooklyn, NY. 5Climate Impacts Group, NASA Goddard Institute for Space Studies; Center for Climate Systems Research, Columbia University Earth Institute, New York, NY

Climate change has increased the frequency of extreme climate events, with different regions showing different sensitivities to these events. In this study, the full subset regression analysis, correlation analysis, and multiple linear regression analysis were used to analyze trends of extreme climate changes and their effects on vegetation on the Mongolian Plateau from both historical and future perspectives. The results showed significant increasing and decreasing trends in extreme warming and extreme cooling indices, respectively, over the past three decades. The extreme temperature indices and precipitation trends under three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios) were consistent with historical trends, and the rates at which temperature and precipitation increased were positively correlated with increasing radiation intensity. In comparison with historical changes, there were gradual increases in areas of regions with increasing temperature and precipitation and decreases in areas with decreasing precipitation. There was an overall increasing trend in the normalized difference vegetation index (NDVI) of the Mongolian Plateau, and the indices that had the greatest influence on the NDVI during the analysis of climate extremes were: (1) the number of days of heavy rainfall (R20); (2) the number of summer days (SU25) and; (3) high extreme daily minimum temperature (TNx). There was an increasing trend in the NDVI from 2021 to 2080, and the rate of the NDVI increase decreased with increasing radiation intensity. The rates of change in the NDVI under all three scenarios were lower than that of the historical period.

Global warming has led to an increasing frequency of extreme climate events; however, existing studies have largely focused on individual extremes or selected pairs occurring at specific locations over relatively short time frames, with limited attention to the long-term spatial synchrony of multiple extremes across different geographic regions. This study addresses this gap by examining the synchrony of eight precipitation and temperature extreme climate events across China from 1930 to 2022, using monthly-scale data. We quantify synchrony using two indicators: the proportions of land area (LAp) and population (Popp) affected by these events. Our results reveal that over the 93-year period, the annual LAp increased approximately 3.5-fold, while Popp doubled, suggesting that land exposure is expanding faster than population exposure. Although underpopulated regions northwest of the Hu Huangyong Line exhibit generally lower exposure levels, they experience a higher rate of increase in land exposure compared to densely populated areas. Extreme wet and hot events drive more than 60% of the total increase in land area exposure. We also identify a significant shift in ecosystem exposure: forest lands were the most affected before 1956, but since then, grasslands have become the dominant ecosystem exposed, especially during the warmer months. This study enhances understanding of the long-term spatial synchrony of multiple extreme climate events in China and emphasizes the critical need to integrate climatic, ecological, and social vulnerabilities into adaptation strategies to effectively manage growing risks.

Extreme climate events frequently exert serious effects on terrestrial vegetation activity. However, these effects are still uncertain in widely distributed areas with different climate zones. Transect analysis is important to understand how terrestrial vegetation responds to climate change, especially extreme climate events, by substituting space for time. In this paper, seven extreme climate indices and the Normalized Difference Vegetation Index (NDVI) are employed to examine changes in the extreme climate events and vegetation activity. To reduce the uncertainty of the NDVI, two satellite-derived NDVI datasets, including the third generation Global Inventory Monitoring and Modeling System (GIMMS-3g) NDVI dataset and the NDVI from the National Oceanic and Atmospheric Administration (NOAA) satellites on Star Web Servers (SWS), were employed to capture changes in vegetation activity. The impacts of climate extremes on vegetation activity were then assessed over the period of 1982–2012 using the North–South Transect of Eastern China (NSTEC) as a case. The results show that vegetation activity was overall strengthened from 1982 to 2012 in the NSTEC. In addition, extreme high temperature events revealed an increased trend of approximately 5.15 days per decade, while a weakened trend (not significant) was found in extreme cold temperature events. The strengthened vegetation activities could be associated with enhanced extreme high temperature events and weakened extreme cold temperature events over the past decades in most of the NSTEC. Despite this, inversed changes were also found locally between vegetation activity and extreme climate events (e.g., in the Northeast Plain). These phenomena could be associated with differences in vegetation type, human activity, as well as the combined effects of the frequency and intensity of extreme climate events. This study highlights the importance of accounting for the vital roles of extreme climate effects on vegetation activity.

The increasing frequency of extreme climate events (ECEs) is expected to significantly affect crop yields in the future, threatening regional and global food security. However, uncertainties in yield projections persist due to regional variability, model differences, and scenario assumptions. Leveraging historical agricultural disaster and meteorological data from China (1995–2014), this study employs the vulnerability curve assessment to determine the most appropriate models for assessing crop yields affected by different ECEs (drought, extreme precipitation, extreme low temperature, and extreme wind) across six regions. By integrating multi-model and multi-scenario (SSP1-2.6, SSP3-7.0, SSP5-8.5) future climate data from Coupled Model Intercomparison Project Phase 6 (CMIP6), we conducted pooled prediction of the individual and combined impacts of different ECEs on crop yields for the near-term (2020–2040) and mid-term (2041–2060). The median of multi-model prediction of crop yield reductions in China was −16.0% (range: −32.5% to −2.6%), with more severe losses in Northeast, Northwest, and North China, particularly under higher radiative forcing scenarios. Drought is the most destructive of the four types of ECEs. These results will aid decision-makers in identifying high-risk zones for crop yields affected by ECEs and provide a scientific basis for the developing targeted adaptation strategies in various regions.

Plant-pollinator interactions are essential for the functioning of terrestrial ecosystems, but are increasingly affected by global change. The risks to such mutualistic interactions from increasing temperature and more frequent extreme climatic events such as drought or advanced snow melt are assumed to depend on network specialization, species richness, local climate and associated parameters such as the amplitude of extreme events. Even though elevational gradients provide valuable model systems for climate change and are accompanied by changes in species richness, responses of plant-pollinator networks to climatic extreme events under different environmental and biotic conditions are currently unknown. Here, we show that elevational climatic gradients, species richness and experimentally simulated extreme events interactively change the structure of mutualistic networks in alpine grasslands. We found that the degree of specialization in plant-pollinator networks (H2') decreased with elevation. Nonetheless, network specialization increased after advanced snow melt at high elevations, whereas changes in network specialization after drought were most pronounced at sites with low species richness. Thus, changes in network specialization after extreme climatic events depended on climatic context and were buffered by high species richness. In our experiment, only generalized plant-pollinator networks changed in their degree of specialization after climatic extreme events. This indicates that contrary to our assumptions, network generalization may not always foster stability of mutualistic interaction networks.

The increasing intensity and frequency of extreme climate events have made improving the adaptability to extreme climate events a strategic imperative for manufacturing companies. This paper investigates whether manufacturing enterprises increase green technology innovation affected by different extreme climate events. Based on panel data of Chinese listed manufacturing enterprises, we show that extreme precipitation events can positively promote green technology innovation, yet extreme temperature events do not. Heterogeneity analyses suggest that the effect of extreme precipitation events on green technology innovation is more significant for non-state-owned enterprises, poor performance enterprises, and high R&D intensity enterprises than other enterprises. Furthermore, the facilitating effect of extreme precipitation events on green technology innovation is merely temporary.

In recent years, the eastern China and even the whole northern hemisphere have suffered from frequent extreme climate events in summer. For example, the extremely hot summer of 2018 over East Asia, the abnormal precipitation in eastern China in the midsummer of 2021, and the high summer temperature in the Northern hemisphere in 2022 August. These extreme climate events have brought severe challenges to human life, economic development and ecological environment. Revealing the physical mechanism of such events is of great significance for disaster prevention and mitigation and policy making.In fact, atmospheric circulation anomalies play an important role in regulating extreme climate events on a regional scale. However, the existing research mainly focus on the influence of the horizontal vortex circulations processes such as blocking and wave train, but the effects of local vertical circulations, especially the interaction between the local vertical and horizontal circulations, are still lacking. To explore a dynamic approach that considers the actual atmospheric circulation as a whole, Hu et al. (2017,2018a, 2018b, 2020) proposed a novel method called the three-pattern decomposition of global atmospheric circulation (3P-DGAC). Unlike the traditional two-dimensional decomposition method, which ignores the effects of the horizontal motion of low-latitudes and the vertical motion of mid-high latitudes, this method considers the effects of mid-high latitude divergent circulation and low latitude vortex circulation on the actual atmospheric circulation, which is conducive to the study of the dynamics of the actual atmospheric circulation from the global perspective. Specifically, the 3P-DGAC extends Rossby wave at mid-latitudes, Hadley circulation and Walker circulation at low latitudes to the global scale, and argues that the actual atmospheric circulation can be understood as the sum of the superposition of the horizontal vortex circulation, the meridional and zonal circulation. Thus, the 3P-DGAC provides a suitable tool for studying the dynamics of three-dimensional structure of local atmospheric circulation.Using the 3P-DGAC method, we have studied the dynamics of the extreme climate events that have occurred in recent years and revealed the corresponding physical mechanisms, the findings suggest that local vertical circulations play a non-negligible role in extreme climate events. This study is expected to provide a reliable theoretical reference for the prediction of extreme climate events.