Interannual Climate Variability Research Articles

El Niño southern oscillation, weather patterns, and bacillary dysentery in the Yangtze River Basin, China.

Increasingly intense weather anomalies associated with interannual climate variability patterns, like El Niño-southern oscillation (ENSO), could exacerbate the occurrence and transmission of infectious diseases. However,research in China remains limited in understanding the impacts and intermediate weather changes of ENSO on bacillary dysentery (BD). This study aimed to reveal the relationship between ENSO, weather conditions, and theincidence of BD, and to identify the potential meteorological pathways moderated by ENSO inthe ENSO-BD connections. BD disease data and meteorological data, as well as ENSO index, from 2005 to 2020 were obtained for 95 cities in the Yangtze River Basin. We first established the associations between ENSO events and BD, ENSO and weather, as well as weather and BDs using two-stage statistical models. Then, we applied a causal mediation analysis to identify the specific meteorological changes in the ENSO-BD relationship. In the Yangtze River Basin, both El Niño (IRR: 1.06, 95%CI: 1.04 ~ 1.08) and La Niña (IRR: 1.03, 95%CI: 1.02 ~ 1.05) events were found to increase the risk of BD. Variations of ENSO index were associated with changes in local weather conditions. Both the increases in regional temperatures and rainfall were associated with a higher risk of BD. In the casual mediation analyses, we identified that higher temperatures and excessive rainfall associated with La Niña and El Niño events mediatedthe ENSO's effect on BD, with mediation proportions of 38.58% and 34.97%, respectively. Long-term climate variability, like ENSO, can affect regional weather conditions and lead to an increased risk of BD. We identified the mediating weather patterns in the relationship between ENSO and BD, which could improve targeted health interventions and establish an advanced early warning system in response to the BD epidemic.

Impacts of El Niño-Southern Oscillation on multi-scale morphodynamics of an embayed beach in southern China

Increased coastal erosion and extreme oceanographic forcing driven by El Niño-Southern Oscillation (ENSO) along the Pacific coast are increasingly receiving concerns. Despite considerable attention, the specific impact of these phenomena on Chinese shorelines, particularly along the South China beaches, remains inadequately understood. To address this gap, this study conducted high-frequency surveys on the Qing'an Bay beach in South China and employ a profile fitting model to refined waterline extraction from monthly satellite images. The goal was to investigate the relationship between high-frequency shoreline changes and interannual climate variability (such as ENSO). The findings indicate that the coastal evolution is intricately linked to a combination of strong wave events, summer storms, winter cold waves, and the amplifying effects of ENSO. During La Niña phase, the sea level in the western Pacific experiences a rise, with associated storms proving particularly destructive to the beach environment, leading to violent oscillations in the coastline. Specifically, in the strong La Niña phases, significant wave heights markedly surpass neutral conditions, precipitating intensified sediment movement. However, it is imperative to recognize that shoreline changes alone do not fully characterize coastal erosion. Assessment must also encompass alterations in beach volume. By calculating beach volume per unit width that accounts for the influence of ENSO variability on the beach, the seasonal characteristics of the beach showed sediment losses in summer and autumn, and gains in winter and spring, which was more obvious during strong La Niña. Additionally, conceptual model analysis reveals that during the La Niña phase, the bay is fully exposed, whereas during the El Niño phase, significant headland sheltering is observed.

Advances in giant clam (Tridacnidae spp.) sclerochronology and sclerochemistry

Understanding past environmental change through high-resolution palaeoenvironmental proxy reconstructions provides crucial baselines for understanding global environmental changes, particularly during the Quaternary Period. Such data are crucial for testing and refining climate models to enable better adaptations to current and future climate changes. Giant clams (Tridacnidae spp.), the largest bivalve mollusc in the Indo-Pacific region, possess distinctive features such as long lifespan, rapid growth, high-resolution growth patterns, wide distribution habitat, and stable shell geochemical composition. These attributes make giant clams a promising and dependable novel palaeoenvironmental archive, offering numerous available geochemical proxies. In this review, we examined preview studies on giant clams conducted for sclerochronology research, spanning publications from 1986 to 2023. The previous studies have proven that isotopic and trace elements proxies of the giant clam, including δ18O, δ13C, Sr/Ca, Mg/Ca, Ba/Ca, and Fe/Ca, can faithfully reconstruct palaeoclimate records including sea surface temperature, dissolved inorganic carbon, insolation, extreme weather events and primary productivity at ultra-high resolution. These proxies have been successfully employed for palaeoclimatic and palaeoenvironmental reconstructions from the Miocene to the present across a range of locations from South Japan to Northern Australia to the Red Sea. These studies have illuminated changes in interannual climate cycles, climate variation, and human migrations over thousands to millions of years. They have generated quantitative records that are valuable for testing and refining numerical climate models, understanding the relationship between past climate cycles and future climate variability, and providing insights into human-environment interactions over time. In the end, the review also addresses the challenges and provided recommendations for future research, highlighting persisting gaps in understanding the influence of microstructure on the shell, age dating of fossil samples, uncertainty in proxies, the need for tank experiments, and access to ultra-high resolution palaeoenvironmental records.

Evaluating constraints on offshore wind farm installation across the Taiwan Strait by exploring the influence of El Niño-Southern Oscillation on weather window assessment

The transition to renewable energy sources, such as offshore wind farms, is essential in mitigating climate change. Taiwan has set ambitious targets to harness wind energy from the Taiwan Strait, but offshore wind farm installations are highly dependent on weather conditions, particularly wind speeds. This study examines the relationship between the El Niño-Southern Oscillation (ENSO) and offshore wind farm installation by assessing weather windows—periods with wind speeds below 12 meters per second at a height of 100 meters for at least 12 hours. Our analysis shows that during La Niña years, the number of feasible weather windows decreases by up to 40%, particularly between October and June, compared to neutral and El Niño years. This decrease can be as high as fourfold in December, significantly impacting installation schedules. Seasonal variations are also notable, with wind speeds exceeding 12 m/s in winter 66.4% of the time, compared to 29.4% in spring, making spring and summer the most favorable periods for installation. However, even during these favorable seasons, La Niña years can bring higher wind speeds, necessitating careful planning. These results underscore the importance of integrating ENSO forecasts into project planning to avoid installation delays and optimize installation timelines. By leveraging seasonal and interannual climate variability predictions, decision-makers can improve the resilience of offshore wind farm projects and ensure efficient energy transition strategies.

Patterns of foliar fungal diseases and the effects on aboveground biomass in alpine meadow under simulated climate change

1.Global warming and changes in precipitation patterns significantly affect plant fungal diseases, especially plant foliar fungal diseases. Research surrounding the effects of climate change on plants are mostly concentrated on aboveground vegetation and belowground soil microorganisms, with limited attention given to foliar fungal disease. Additionally, little is known whether climate change experiments can simulate the impact of variations on foliar diseases within real natural communities.2.We conducted a long-term experiment to investigate the effect of warming and altered precipitation on plant disease in an alpine grassland on the north-eastern Tibetan Plateau. Our study tracked foliar disease across 26 species of plants including non-legume forbs, legumes, grasses and sedges from 2019 to 2021. Each species was included in a two-factor full factorial experiment manipulating both temperature and precipitation.3.In our study the non-legume forb functional group carried the primary pathogen load, sedges maintained a near zero pathogen load across all investigated scenarios. Warming significantly elevated the pathogen load in legumes and non-legume forbs, while it reduced the pathogen load of grasses. Conversely, decreased precipitation significantly increased the pathogen load of non-legume forbs and increased the pathogen load of grasses. Notably, we found that pathogen load was primarily influenced by the direct effects of warming and precipitation, rather than indirectly through changes in plant community structure. Meanwhile, interannual climate variability exhibited a strong correlation with plant community pathogen loads. Areas experiencing increased temperature and precipitation showed lower fungal disease pressure, whereas regions with elevated temperatures combined with decreased precipitation faced a heightened risk of disease in alpine meadows on the Qinghai-Tibet Plateau.4.Our findings reveal that warming and decreased precipitation led to a reduction in above-ground biomass associated with increased the pathogen load of legumes. This underscores the importance of pathogen effects in both climate change prediction and ecological management.

Persistently active El Niño–Southern Oscillation since the Mesozoic

The El Niño-Southern Oscillation (ENSO), originating in the central and eastern equatorial Pacific, is a defining mode of interannual climate variability with profound impact on global climate and ecosystems. However, an understanding of how the ENSO might have evolved over geological timescales is still lacking, despite a well-accepted recognition that such an understanding has direct implications for constraining human-induced future ENSO changes. Here, using climate simulations, we show that ENSO has been a leading mode of tropical sea surface temperature (SST) variability in the past 250 My but with substantial variations in amplitude across geological periods. We show this result by performing and analyzing a series of coupled time-slice climate simulations forced by paleogeography, atmospheric CO2 concentrations, and solar radiation for the past 250 My, in 10-My intervals. The variations in ENSO amplitude across geological periods are little related to mean equatorial zonal SST gradient or global mean surface temperature of the respective periods but are primarily determined by interperiod difference in the background thermocline depth, according to a linear stability analysis. In addition, variations in atmospheric noise serve as an independent contributing factor to ENSO variations across intergeological periods. The two factors together explain about 76% of the interperiod variations in ENSO amplitude over the past 250 My. Our findings support the importance of changing ocean vertical thermal structure and atmospheric noise in influencing projected future ENSO change and its uncertainty.

Soil nitrous oxide emissions from wheat-based rotations with different types of pulse crops

Production of agricultural crops with a low greenhouse gas (GHG) footprint is essential to mitigate the adverse effects of climate change. The inclusion of pulse crops in cereal-based rotations can enhance environmental quality by providing biologically fixed N and thereby reducing the amount of synthetic N fertilizer required for the crop rotation. The inclusion of pulse crop has the potential to reduce N2O emissions from the agricultural system in both the legume phase and the subsequent wheat phase of the rotation. However, long-term studies are necessary to thoroughly investigate N2O emissions from rotations with pulse crops, particularly in the semiarid region where pulse crops are frequently grown. In the present study, we evaluated cumulative N2O emissions and emission intensity during the rotation cycle. The assessment was conducted over 4 years, during two complete 2-yr cycles of an established rotation (years 9–12), under the climatological conditions of 2018–2021. Four rotations including wheat-wheat, pea-wheat, lentil-wheat, and chickpea-wheat were selected from a trial in Swift Current, Saskatchewan (semiarid prairies/Brown Chernozem). Our experiment was subjected to below normal precipitation, with interannual variations in climate and the last 2 years (2020–21) were drier than the first two years (2018–2019). Under such climate, PW and LW demonstrated to be environmentally sustainable, always exporting the highest N in grains (133 kg N ha−1 averaged across PW and LW and cycles) and consistently achieving the lowest N2O intensity (2.8 g N2O-N per kg exported N averaged across PW and LW and cycles). Continuous wheat presented inconsistent results, with a significant reduction in exported N from years 9–10 to 11–12 (the driest cycle). Because WW also promoted the highest cumulative N2O emissions, N2O intensity over the 2-yr was always the highest for WW. The CW consistently promoted the lowest N exports and was not resilient to dry soil conditions, with 23% lower exported N in years 11–12 than in years 9–10. Hence including pulse crops with pea or lentil in the rotation reduced N2O emissions and enhanced wheat yield resiliency.

Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation (NVDI) with MODIS NDVI

Abstract. Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia, which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing global Advanced Very High Resolution Radiometer (AVHRR) normalised-difference vegetation index (NDVI) products for their suitability for long-term vegetation monitoring in Australia. Comparisons with the MODIS NDVI highlight significant deficiencies, particularly over densely vegetated regions. Moreover, all the assessed products failed to adequately reproduce the interannual variability in the pre-MODIS era as indicated by Landsat NDVI anomalies. To address these limitations, we propose a new approach to calibrating and harmonising NOAA's Climate Data Record of AVHRR NDVI to the MODIS MCD43A4 NDVI for Australia using a gradient-boosting decision tree ensemble method. Two versions of the datasets are developed, one incorporating climate data in the predictors (“AusENDVI-clim”: Australian Empirical NDVI-climate) and another that is independent of climate data (“AusENDVI-noclim”). These datasets, spanning 1982–2013 at a spatial resolution of 0.05° and with a monthly time step, exhibit strong correlations (r2=0.89–0.94) and low mean errors compared with MODIS MCD43A4 NDVI (mean absolute error (MAE) = 0.014–0.028, RMSE = 0.021–0.046), accurately reproducing seasonal cycles over densely vegetated regions. Furthermore, they closely replicate the interannual variability in vegetation condition in the pre-MODIS era. A reliable method for gap-filling the AusENDVI record is also developed that leverages climate, atmospheric CO2 concentration, and woody-cover fraction predictors. The resulting synthetic NDVI dataset shows excellent agreement with the MODIS MCD43A4 NDVI and the recalibrated AVHRR NDVI time series (r2=0.82–0.95, MAE = 0.016–0.029, RMSE = 0.039–0.041). Finally, we provide a complete 41-year dataset where the gap-filled AusENDVI-clim from January 1982 to February 2000 is joined with the MODIS MCD43A4 NDVI from March 2000 to December 2022. Analysing 40-year per-pixel trends in Australia's annual maximum NDVI revealed increasing values, and shifts in the timing, of the annual peak NDVI across most of the continent, underscoring the dataset's potential to address crucial questions regarding the changing vegetation phenology and its drivers. The AusENDVI dataset can be used for studying Australia's changing vegetation dynamics and downstream impacts on the terrestrial carbon and water cycles, and it provides a reliable foundation for further research into the drivers of vegetation change. AusENDVI is open access and available at https://doi.org/10.5281/zenodo.10802703 (Burton et al., 2024).

Investigating the role of groundwater in Ecosystem Water Use Efficiency in India considering irrigation, climate and land use

Terrestrial ecosystems (TEs) play a crucial role in carbon sequestration and climate regulation. While interactions between surface water and ecosystems are well-studied, groundwater-ecosystem relationships remain poorly understood, particularly in groundwater-dependent regions like India. This study investigates the relationship between water table depth (WTD) and ecosystem productivity across India, considering the variation in irrigation practices, land use and climate types, from 2000 to 2021. We employ Ecosystem Water Use Efficiency (WUEe), the rate of carbon uptake per unit of water consumed, to examine these interactions at different spatial scales. Our findings reveal a strong link between WUEe and WTD. Shallower WTD regions, such as the lower Himalayas and Northeast India with forests and dominated by a wet/humid subtropical climate, exhibit higher WUEe (1.5 – 3.5 g C/kg H2O). Whereas deeper WTD regions like northwest India, characterized by shrublands and an arid climate, display lower WUEe (< 1 g C/kg H2O). This suggests vegetation in arid/semi-arid regions shows higher sensitivity to water availability compared to wetter areas. This is also evident by a declining WUEe trend and increasing elasticity of WUEe () to interannual climatic variability with increasing WTD in these regions. Furthermore, the study identifies potential unsustainable groundwater use for irrigation in areas like the Trans Gangetic plains. Irrigation has a strong correlation with evapotranspiration (ET) (r = 0.4 - 0.6) in deep WTD zones, but no correlation with WUEe. This implies that intense and unsustainable irrigation might disrupt the natural water use strategies of vegetation. This research, by improving understanding of these interactions, aims to contribute to the sustainable management of India’s groundwater resources.

Grasslands support more diverse and resilient earthworm communities to climate change than croplands in Central Europe

Diversity and community composition of earthworms, key drivers of ecosystem functions, are increasingly threatened by global change, including climate and land-use change. However, empirical evidence for interactions of these concurrent drivers in affecting earthworm communities is scarce. Here, we investigated the effects of an experimentally imposed climate change scenario, including warming and altered precipitation patterns, and land use with two croplands (both conventional farming and organic farming characterize a three-year crop rotation) and two grasslands (intensively-used meadow and extensively-used meadow) on earthworm communities across different seasons and years in a field experiment. Compared with grasslands, earthworms in croplands have lower species richness (-26 %), abundance (-80 %), and biomass (-73 %), particularly the abundance of juveniles (-83 %) and Aporrectodea rosea (-76 %) as well as the biomass of juveniles (-84 %), A. rosea (-72 %), Octolasion cyaneum (-47 %), and Lumbricus terrestris (-83 %). Due to extreme droughts in Central Europe from 2018 to 2020, earthworm abundance and biomass were low across land-use types, but in grassland they increased (abundance: +80 %; biomass: +85 %) in 2021 presumably due to increased moisture conditions. Main effects of experimental climate change and intensified management practices as well as interaction of experimental climate change and land use on abundance and biomass of earthworms were non-significant. Notably, experimental climate change and land use interactively altered earthworm community composition, with the most pronounced difference between ambient and future climate in croplands than in grasslands. This indicates that earthworm community composition more sensitively reflects changes in environmental conditions than earthworm abundance and biomass, but the latter two negatively responded to prolonged drought conditions. Our results indicate that grasslands have a higher resilience of earthworm populations to buffer adverse environmental conditions than croplands. Overall, this study provides a comprehensive overview of the response of earthworms to inter-annual climatic variability and experimental climate change under different land-use types.

Impacts of forest canopy heterogeneity on plot-scale hydrometeorological variables - Insights from an experiment in the humid boreal forest with the Canadian Land Surface Scheme

High latitude regions, including the circumpolar boreal biome, are experiencing important changes in the availability of usable surface water because of climate change. In this context, an adequate representation of the land-atmosphere interaction is critical to ensure optimal management of current and future water resources, forest management, and climate prediction. However, the task is particularly intricate in high-latitude boreal forest, as land surface model faces several challenges due to the unique environmental conditions and ecological characteristics. The objective of this study is to quantify the impact of forest landscape heterogeneity, specifically stand leaf-area index (LAI), soil texture, and drainage regime, on surface water and energy balance in a small boreal high-latitude sub-catchment. To this end, hydrometeorological conditions at seventeen 20×20 m plots in a 1-km2 boreal forest sub-basin are simulated using the Canadian Land Surface Scheme (CLASS), a land surface model, at the point scale. The subplot-scale soil texture, drainage regime, and vegetation characteristics and type are based as closely as possible on field measurements and observations for the 17 plots. The model-driven experiment comprises two sets of simulations using CLASS, each employing the same model setup and run for the 17 experimental plots. The main set employs meteorological forcing from a local micrometeorological tower within the sub-basin to investigate the plot-to-plot variability of albedo, energy fluxes, and soil state variables. A second set of simulations is conducted using meteorological forcing from the ERA5-Land reanalysis, which spans from 1986 to 2022. This data provides a longer time series, enabling a more accurate representation of the interannual climatic variability in the sub-basin. The results of the main and secondary sets of CLASS simulations are used to assess the plot-to-plot and temporal variability of several key hydrometeorological variables by calculating a monthly spread. In brief, the following conclusions and broader implications can be drawn from the findings: i) The simulated total annual evapotranspiration remains relatively uniform between plots despite notable variation in its partitioning from plot to plot. ii) In the presence of a full snowpack, the albedo exhibits substantial heterogeneity at the subplot scale, linked to the canopy's LAI. iii) Local soil properties, drainage regime, and vegetation structure and type exhibit substantial influence on the plot-to-plot variability in soil water content. iv) When parameterized with localized observations and measurements, CLASS can represent and be responsive to the complex dynamics of energy and water fluxes at the plot scale within the heterogeneous surface of boreal forests.

Retreat of the Bay of Bengal Summer Monsoon

AbstractThe Bay of Bengal summer monsoon (BOBSM) is the most prominent branch of the Asian summer monsoon system, which exhibits complex interannual variability. While previous studies have focused on the onset conditions of the BOBSM, less attention has been paid to the retreat of the BOBSM. In this study, we propose an index to measure BOBSM retreat, based on the mean zonal wind field at 850 hPa during the summer‐to‐winter monsoon transitions. By analyzing the climatic characteristics and interannual variability of the BOBSM retreat using this index, we find that BOBSM retreat exhibits significant interannual variability, which is closely related to the occurrence of Indian Ocean Dipole (IOD) events. Statistically, when a positive IOD event takes place in the boreal autumn season, the retreat of the summer monsoon occurs earlier correspondingly. Conversely, the retreat is delayed when a negative IOD event occurs.

Climatic Predictors of Long‐Distance Migratory Birds Breeding Productivity Across Europe

ABSTRACTAimOngoing climate changes represent a major determinant of demographic processes in many organisms worldwide. Birds, and especially long‐distance migrants, are particularly sensitive to such changes. To better understand these impacts on long‐distance migrants' breeding productivity, we tested three hypotheses focused on (i) the shape of the relationships with different climate variables, including previously rarely tested quadratic responses, and on regional differences in these relationships predicted by (ii) mean climatic conditions and (iii) by the rate of climate change in respective regions ranging from Spain to Finland.LocationEurope.Time Period2004–2021.Major Taxa StudiedLong‐distance migratory passerine birds.MethodsWe calculated breeding productivity from constant effort ringing sites from 11 European countries covering 34° of latitude, and extracted temperature‐ and precipitation‐related climate variables from E‐OBS and NASA MODIS datasets. To test our hypotheses, we fitted GLMM and Bayesian meta‐analytic models.ResultsWe revealed hump‐shaped responses of productivity to temperature, growing degree‐days, green‐up onset date, and precipitation anomaly, and negative responses to intense and prolonged rains across the regions. The effects of March temperature and April growing degree‐days were more negative in cold than in warm regions, except for the region with the highest accumulated heat, whereas increasing June precipitation anomalies were associated with higher productivity in both dry and wet regions. Productivity responses to climate were unrelated to the rate of climate warming.Main ConclusionsThe influence of climate on bird productivity proved to be frequently nonlinear, as expected by ecological theory. The rate of climate change is less important than regional interannual variability in climate (which is predicted to increase), but this may change with the progression of climate change in the future. Productivity declines in long‐distance migratory songbirds are particularly expected if out‐of‐norm water excess increases in frequency or strength.

Multi‐cohort survival of northern red oak seedlings at a northern hardwood forest transition

AbstractGlobal change has created less stable forest systems and given urgency to understanding limitations to the establishment of tree seedlings beyond current range boundaries. We quantified trends in 13 years of annual northern red oak (QURU) seedling survival data for 1733 marked individuals at a local species distribution boundary within the northern hardwood forest in New Hampshire, USA. Over the study period, the median distance of seedlings into the valley did not change, although there was a net gain of 89 plots (5 m2) occupied. For a subset of seedlings that were marked in their year of birth (N = 937), we examined relationships among terrain, vegetation community, and initial individual seedling traits, and evaluated their effects on time to seedling mortality using a parametric accelerated failure time model. The year of seedling germination had the largest effect on survival with increasing mortality rates for seedlings from more recent cohorts. Seedlings had longer survival times where oak seedling densities were lower, shrub cover was higher, and when the acorn remained attached. Additionally, survival time was increased in higher elevation plots, which were also located further into the valley. Interannual seedling survival (N = 1580) was strongly impacted by seedling condition in the previous year, particularly leaf number and amount of leaf damage. Most seedling deaths occurred over winter, and seedlings failed to break bud the following spring. Interannual variation in seasonal climate, particularly deep, heavy snowpack in 2019 followed by drought conditions in 2020, coincided with recent elevated mortality. Overall, the median survival time of 3–4 years and the rapid turnover of the oak seedling population currently limit ability for expansion, although the net gain of occupied plots and increase in survival at higher elevation plots with lower QURU densities present some mechanisms that could promote expansion if the current suboptimal understory conditions shift to favor QURU.

Land use modulates resistance of grasslands against future climate and inter-annual climate variability in a large field experiment.

Climate and land-use change are key drivers of global change. Full-factorial field experiments in which both drivers are manipulated are essential to understand and predict their potentially interactive effects on the structure and functioning of grassland ecosystems. Here, we present 8 years of data on grassland dynamics from the Global Change Experimental Facility in Central Germany. On large experimental plots, temperature and seasonal patterns of precipitation are manipulated by superimposing regional climate model projections onto background climate variability. Climate manipulation is factorially crossed with agricultural land-use scenarios, including intensively used meadows and extensively used (i.e., low-intensity) meadows and pastures. Inter-annual variation of background climate during our study years was high, including three of the driest years on record for our region. The effects of this temporal variability far exceeded the effects of the experimentally imposed climate change on plant species diversity and productivity, especially in the intensively used grasslands sown with only a few grass cultivars. These changes in productivity and diversity in response to alterations in climate were due to immigrant species replacing the target forage cultivars. This shift from forage cultivars to immigrant species may impose additional economic costs in terms of a decreasing forage value and the need for more frequent management measures. In contrast, the extensively used grasslands showed weaker responses to both experimentally manipulated future climate and inter-annual climate variability, suggesting that these diverse grasslands are more resistant to climate change than intensively used, species-poor grasslands. We therefore conclude that a lower management intensity of agricultural grasslands, associated with a higher plant diversity, can stabilize primary productivity under climate change.

Different climate conditions drive variations in gross primary productivity and woody biomass accumulation in a temperate and a boreal conifer forest in Canada

Understanding the complex relationships between climate, forest carbon (C) uptake and biomass growth has become a research priority, crucial for assessing the climate change impacts on forest C sequestration. Such associations are expected to vary across biomes, due to different climate constraints on tree physiology. However, our understanding of the seasonal dynamics of long-term C sequestration and how climate influences them in different forest biomes is still limited.We investigated a boreal Pinus banksiana forest (Old Jack Pine, OJP) and a temperate Pinus strobus plantation (Turkey Point, TP39) in Canada. We aimed to assess the link between C uptake and above-ground woody biomass growth, and the effects of climate inter-annual variability on them. We used daily records of climate and gross primary productivity (GPP, period 1999–2019 at OJP and 2003–2018 at TP39), and investigated xylem biomass proxies at cell (cell wall area) and tree-ring level (ring wall area) from 1970 to 2019 at OJP, and from 1970 to 2018 at TP39.In both forests, the direct link between C sink and C source was revealed by the common inter-annual variability of GPP and xylem biomass. GPP and xylem biomass were mostly influenced by spring and late summer temperature at OJP. However, at both sites, summer drought negatively influenced GPP, and biomass growth especially in recent decades. Analysis of dry and cold years evidenced short-term negative effects of low temperature on GPP and xylem biomass in OJP, and of drought in TP39.Long-term intra-annual analyses are crucial to assess the variable effects of climate on forest C cycle. Warm spring and autumn can benefit both GPP and biomass growth, but even in the boreal forest, summer drought has negative impacts. Increasing dry spells, especially in the temperate site, could reduce future forest capacity to uptake C and fix it in wood biomass.

Assessing Falling Number Stability Increases the Genomic Prediction Ability of Pre-Harvest Sprouting Resistance in Common Winter Wheat.

Pre-harvest sprouting (PHS) resistance is a complex trait, and many genes influencing the germination process of winter wheat have already been described. In the light of interannual climate variation, breeding for PHS resistance will remain mandatory for wheat breeders. Several tests and traits are used to assess PHS resistance, i.e., sprouting scores, germination index, and falling number (FN), but the variation of these traits is highly dependent on the weather conditions during field trials. Here, we present a method to assess falling number stability (FNS) employing an after-ripening period and the wetting of the kernels to improve trait variation and thus trait heritability. Different genome-based prediction scenarios within and across two subsequent seasons based on overall 400 breeding lines were applied to assess the predictive abilities of the different traits. Based on FNS, the genome-based prediction of the breeding values of wheat breeding material showed higher correlations across seasons (r=0.505-0.548) compared to those obtained for other traits for PHS assessment (r=0.216-0.501). By weighting PHS-associated quantitative trait loci (QTL) in the prediction model, the average predictive abilities for FNS increased from 0.585 to 0.648 within the season 2014/2015 and from 0.649 to 0.714 within the season 2015/2016. We found that markers in the Phs-A1 region on chromosome 4A had the highest effect on the predictive abilities for FNS, confirming the influence of this QTL in wheat breeding material, whereas the dwarfing genes Rht-B1 and Rht-D1 and the wheat-rye translocated chromosome T1RS.1BL exhibited effects, which are well-known, on FN per se exclusively.

Influence of the sea surface temperature of equatorial Pacific Ocean on the tropical cyclones activity

Introduction. Today, the study of climate is one of the most important tasks of modern science. Particular attention is paid to issues of climate variability on different time scales. The El Niño – Southern Oscillation (ENSO) is the leading signal of interannual climate variability that influences the weather and climate of the globe. It has been revealed that during the onset of its different phases, weather and climate anomalies (up to extreme ones) are observed not only over the tropical zone of the Pacific Ocean, but also in extratropical latitudes, which are transmitted through teleconnections in the ocean-atmosphere system. Numerous studies have shown that ENSO modulates tropical cyclone activity in the northwest Pacific Ocean. The purpose of the study is to determine the relationship between sea surface temperature (SST) changes in the equatorial Pacific Ocean and the frequency of tropical cyclones of varying intensity in the northwestern Pacific Ocean under current climatic conditions. Materials and methods of research. The work uses data of the number of cases and intensity of tropical cyclones (TCs) for the period 1991-2020, and series of indices ONI and SOI. Using these data, statistics on the frequency of TCs in the study area were calculated and Pearson correlation coefficients were calculated. The scientific novelty lies in obtaining quantitative characteristics of the relationship between SST and the number of TCs of varying intensity in modern climatic conditions. The practical significance of the work lies in the fact that TCs are associated with the occurrence of severe weather conditions, which, when a TC reaches the densely populated coast of Southeast Asia, can pretend to be significant losses. Therefore, information that can be used in forecasting TCs and their trajectories is very high importance. Research results. Analysis of the frequency of TCs of different intensity in the northwestern Pacific Ocean during the 1991-2020 showed that there was a decrease in the number of tropical depressions and typhoons. However, there has been a slight increase in the occurrence of tropical storms. The results of the analysis of the influence of SST variability in the equatorial Pacific Ocean on the frequency of TCs, showed that there is a statistically significant relationship only with the frequency of typhoons. The establishment of positive SST in the center of the equatorial Pacific Ocean, associated with the El Niño episode, is associated with an increase in typhoons in the northwestern Pacific Ocean.

Aridity-dependent shifts in biodiversity-stability relationships but not in underlying mechanisms.

Climate change will affect the way biodiversity influences the stability of plant communities. Although biodiversity, associated species asynchrony, and species stability could enhance community stability, the understanding of potential nonlinear shifts in the biodiversity-stability relationship across a wide range of aridity (measured as the aridity index, the precipitation/potential evapotranspiration ratio) gradients and the underlying mechanisms remain limited. Using an 8-year dataset from 687 sites in Mongolia, which included 5496 records of vegetation and productivity, we found that the temporal stability of plant communities decreased more rapidly in more arid areas than in less arid areas. The result suggests that future aridification across terrestrial ecosystems may adversely affect community stability. Additionally, we identified nonlinear shifts in the effects of species richness and species synchrony on temporal community stability along the aridity gradient. Species synchrony was a primary driver of community stability, which was consistently negatively affected by species richness while being positively affected by the synchrony between C3 and C4 species across the aridity gradient. These results highlight the crucial role of C4 species in stabilizing communities through differential responses to interannual climate variations between C3 and C4 species. Notably, species richness and the synchrony between C3 and C4 species independently regulated species synchrony, ultimately affecting community stability. We propose that maintaining plant communities with a high diversity of C3 and C4 species will be key to enhancing community stability across Mongolian grasslands. Moreover, species synchrony, species stability, species richness and the synchrony between C3 and C4 species across the aridity gradient consistently mediated the impacts of aridity on community stability. Hence, strategies aimed at promoting the maintenance of biological diversity and composition will help ecosystems adapt to climate change or mitigate its adverse effects on ecosystem stability.

Evaluation of the real-time El Niño forecasts by the climate network approach between 2011 and present

El Niño episodes are part of the El Niño-Southern Oscillation (ENSO), which is the strongest driver of interannual climate variability, and can trigger extreme weather events and disasters in various parts of the globe. Previously we have described a network approach that allows to forecast El Niño events about 1 year ahead. Here we evaluate the real-time forecasts of this approach between 2011 and 2022. We find that the approach correctly predicted (in 2013 and 2017) the onset of both El Niño periods (2014-2016 and 2018-2019) and generated only 1 false alarm in 2019. In June 2022, the approach correctly forecasted the onset of an El Niño event in 2023. For determining the p-value of the 12 real-time forecasts, we consider 2 null hypotheses: (a) random guessing where we assume that El Niño onsets occur randomly, and (b) correlated guessing where we assume that in the year an El Niño ends, no new El Niño will start. We find pa≅0.005\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p_a\\cong 0.005$$\\end{document} and pb≅0.015\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p_b\\cong 0.015$$\\end{document}, this way rejecting both the null hypotheses that the same quality of the forecast can be obtained by chance. We also discuss how the network algorithm can be further improved by systematically reducing the number of false alarms. For 2024, the method indicates the absence of a new El Niño event.