Indicators Of Climate Change Research Articles

Two-decade satellite observations reveal variability in size-fractionated phytoplankton primary production in the South China Sea

Understanding variations in marine phytoplankton primary production (PP) is crucial for assessing the response of the marine environment to climate change and for quantifying the ocean carbon cycle. However, the spatiotemporal variability of size-fractionated PP in the South China Sea (SCS) remains ambiguous. Our study investigated the monthly, seasonal, and inter-annual variability of size-fractionated PP in the SCS using satellite observations from 2002 to 2022. There were noticeable seasonal and monthly variations in size-fractionated PP, with notably high PP values appearing during the cold season. The disparities in distribution and the distinct fluctuation patterns between size-fractionated PP suggest that total PP alone is not a comprehensive indicator of marine ecosystem health. Over the past two decades in the SCS, there were more pronounced decreases in total, pico-, and nano-PP, whereas micro-PP displayed no significant trend. The most pronounced decline occurred in the northern SCS, contrasted by increases in coastal areas. These size-fractionated PP anomalies showed strong correlations with climate change indices, highlighting the impact of environmental factors on these anomalies, such as sea surface temperature, mixed layer depth, and wind speed. Our findings emphasize the importance considering size-fractionated PP to gain a more nuanced understanding of the ocean carbon cycle and the marine ecosystem's response to climate changes.

The sustainability of development pathways and climate change vulnerability in the Americas

Sustainability transformation requires societies to shift their development pathways to maintain ecological integrity and supply basic human needs over generations. We examine the sustainability of the development pathways of 351 national and sub-national geopolitical units in the Americas between 2001 and 2020. Then, we test the hypothesis that such pathways are influenced by land area, population size and topographic heterogeneity. Finally, we combine information about the sustainability of development pathways with population density and future climate change index to identify the Americas' socio-climatic hotspots. The Americas is a mosaic of geopolitical units following distinct development pathways. Most (74.3%) of the geopolitical units have, to date, followed unsustainable development pathways. Altogether, they represent 37.6% of the Americas' land area and are home to 83.3% of the Americas' population. Land area and topographic heterogeneity facilitate sustainable pathways, while population size constrains them. Climate change will affect all geopolitical units, but those with unsustainable development pathways, a high population density and high climate change index are expected to be particularly vulnerable and should be considered priorities for climate-resilient development programmes. There is a need for integrated place-based policies that strengthen ecological and socio-economic infrastructures to advance sustainable development pathways under changing climate conditions.

Being an annual plant in a water-limited Mediterranean-alpine mountain; the case of rare-endemic and threatened Arenaria nevadensis

Mountain ecosystems are proving to be particularly vulnerable to the effects of global change, with Mediterranean high mountains standing out as among the most susceptible regions worldwide. The impact of climate change on biota operates across key levels: metabolism, phenology, evolution, and spatial distribution. Monitoring species within these ecosystems is crucial to establish early warning indicators for effective mitigation and adaptation strategies. Within this context, annual plants, like the critically endangered species Arenaria nevadensis endemic to Sierra Nevada, could offer advantages as indicators due to their rapid life cycles and dependence on water sources.This study aims to investigate the impact of climatic change on this specific annual, endemic, and threatened species while assessing its potential as an indicator for shifting snow patterns affecting alpine biota. Analyzing data collected between 2005 and 2021 from four populations of Arenaria nevadensis alongside hydro-meteorological data revealed notable variations in population parameters linked to climatic fluctuations. Factors such as precipitation and snowpack significantly influenced plant size, reproductive potential, and population size, with neighboring populations showing distinct responses to climatic variations.The observed unpredictability in population trends across consecutive years underscores the climatic stochasticity inherent in Mediterranean mountains, particularly Sierra Nevada. Forecasts of decreased precipitation and increased temperatures are expected to diminish snowpack depth and duration, posing a severe threat to this critically endangered species. Conservation efforts should prioritize managing higher elevation populations and exploring new suitable habitats for restoration or assisted migration.While Arenaria nevadensis demonstrates promise as a climate change indicator owing to its short life cycle, restricted distribution, and sensitivity to climatic shifts, comprehensive understanding of its germination ecology, seed bank role, and reproductive biology remains lacking. Addressing these knowledge gaps is crucial to enhance its effectiveness as an indicator species.

Lithological features of Lake Bannoe sediments (Southern Urals) as an indicator of environmental and climate changes in the Holocene

Research subject. Bottom sediments of Lake Bannoe (Southern Urals). Aim. Identification of lithologic features of Lake Bannoe sediments, which could reflect sedimentation conditions in the Holocene. Materials and methods. The detailed complex analysis included radiocarbon dating, grain-size analysis, X-ray diffraction analysis, electron microscopy, X-ray fluorescence and isotope analysis, coercive spectrometry and pollen analysis. Results. Radiocarbon dating showed that sedimentation in Lake Bannoe began no later than ~13 thousand years ago. Combination of data from various laboratory studies unraveled four lithological zones and the corresponding stages in the sedimentation history. The grain size, allothigenic particles, carbonate minerals, organic matter and isotopic composition of carbon and oxygen are the most informative indicators. Grain size variations and the ratio between allothigenic and carbonate components reflect changes in the Lake’s depth and clastic material supply, which, in turn, is associated with humidity. Organic matter parameters (TOC, δ13Corg, C/N ratio) can be considered as indicators of climate-sensitive changes in bioproductivity of the sedimentation basin. They also reflect the ratio of exogenous and endogenous organic matter in the sedimentary environment. The isotopic composition of carbon and oxygen (δ13Ccarb, δ18Ocarb) in sedimentary carbonates is an informative indicator of lithological zones and climatic events of the Holocene due to its sensitivity to changes in biomass, temperature fluctuations, and fresh water inflow. The paramagnetic component k_para was used as an indicator of the allothigenic material input into the lake basin for the first time in this region. Conclusions. The granulometric, mineral, and chemical composition, as well as the magnetic properties of Lake Bannoe sediments reflect the history of Lake sedimentation in the Southern Urals, which agrees mainly with the climate stages of the Holocene.

Changes in glacier surface temperature across the Third Pole from 2000 to 2021

Glacier surface temperature is not only an important indicator of climate change, but is also directly related to glacier physical processes and mass balance. Glaciers over the Third Pole have shown heterogeneous but accelerated mass loss over the past two decades. However, few studies have focused on changes of glacier surface temperature in this region. In this study, we investigate this change from 2000 to 2021 using an altitudinal area-weighting method based on Landsat surface temperature products. The results show that this method can robustly derive inter-annual variability and long-term trends of subregional glacier surface temperature. Over the entire Third Pole, glacier surfaces exhibited warming at the rate of +0.17 ± 0.35 °C dec−1 (p = 0.32). The warming had a distinct seasonal pattern: warming in autumn (+0.50 ± 0.53 °C dec−1, p = 0.06) but cooling in spring (−0.22 ± 0.36 °C dec−1, p = 0.23). Correspondingly, the ablation season 0 °C isotherm altitude has risen >100 m and the glacier area with GST ≥0 °C has expanded by 10%. Moreover, the GST changes displayed large spatial heterogeneity. In the westerlies-dominated regions (e.g., Karakoram and Kun Lun), glacier surfaces showed slight warming or even cooling on average (from −0.10 to +0.06 °C dec−1), while in the monsoon-dominated regions (e.g., East Himalaya, Southeast Tibetan Plateau and Hengduan Mountains), strong warming was observed (> +0.50 °C dec−1, p < 0.05). Glacier surfaces generally showed a faster warming at north slopes than south slopes, especially in the ablation season. These results emphasize that different magnitudes of glacier surface warming are important indicators for the contrasting glacier changes over the Third Pole. The method presented here opens the way to investigate long-term surface temperature change of global mountain glaciers, which can help us further understand the mechanisms of glacier response to global climate change.

Decadal co‐variation of the aerosols over East Asia and the East Asian jet streams in the boreal winter

AbstractThe changes in the East Asian polar‐front jet (EAPJ) and the East Asian subtropical jet (EASJ) profoundly impact the weather and climate in East Asia. However, the link between aerosols and the jet streams is still unclear. Here, we investigated the decadal co‐variation of aerosols and the East Asian jet streams in the boreal winter during the period of 1980–2019. In synch with a positive change in aerosol optical depth over East Asia, 300‐hPa winds show an equatorward shift of the land branch of the EASJ, and weakened EAPJ and oceanic branch of the EASJ. This can be linked to the enhanced meridional temperature gradient along 30°–50° N but weakened in northern regions and the decreasing synoptic‐scale transient eddy kinetic energy over subtropical Pacific. Relative importance estimation of aerosols and ocean signals emphasized the contributions of aerosols in jet variations. In turn, meteorological conditions related to jet streams also contribute to variations in aerosols, the decadal co‐variations are a result of their interactions, particularly for the oceanic branch of EASJ. The findings would be helpful in providing potential indicators of climate change.

Threshold Effect of Trade on Climate Change in South Africa

The relationship between trade and climate change is not a simple linear relationship. In this paper, using the threshold regression model, we estimated the effect of trade on climate change in South Africa. The paper applied the LM test to examine the nonlinear inference approach to test whether nonlinearity existed and if the threshold model was relevant to the study. The results show that when energy use is set as the threshold variable, the relationship between trade and climate change measured as methane is U-shaped. Also, in other models of GHG as climate change indicators, the results show that the effect of trade on climate change is not dynamic. This result supports the idea that high and low trade effects may have different impacts on climate change indicators. It is, therefore, recommended that all exporters in South Africa resort to more innovative environmental mechanisms to reduce the contribution to climate. The suggestion for future studies is to consider exports of different sectors to climate change. This approach will avoid the generalization of exporting firms as the worst emitters.

Global Warming and Climate Change

On a careful observation, we find that in recent times there has been a striking change in the average temperatures in many regions. The global average surface temperature had risen since last century. The 1990s have been the warmest decade and the year 1998 has been the warmest year recorded so far and the second warmest year 2001. The tropical and coastal areas are facing cold temperatures, rainfall is either in excess or scanty, and there is an increase in the frequency and intensity of the droughts. The human activities are primarily responsible for these and the climate change due to global warming is going to alter their lives so much that the very survival of the mankind is at stake. Globalization, development, climate change and human health are closely interconnected. These are also causing conflicts among states with regard to responsibilities of causing and reducing environmental changes. Only consistent and serious efforts to mitigate them can alter the existing conditions and prevent further deterioration. In this research paper highlights the Global warming effects, indicators of climate change and worldwide efforts to Global Warming and Climate Change.

Decoding autumn phenology: Unraveling the link between observation methods and detected environmental cues.

Leaf coloring and fall mark the end of the growing season (EOS), playing essential roles in nutrient cycling, resource allocation, ecological interactions, and as climate change indicators. However, understanding future changes in autumn phenology is challenging due to the multitude of likely environmental cues and substantial variations in timing caused by different derivation methods. Yet, it remains unclear whether these two factors are independent or if methodological uncertainties influence the environmental cues determined. We derived start of growing season (SOS) and EOS at a mixed beech forest in Central Germany for the period 2000-2020 based on four different derivation methods using a unique long-term data set of in-situ data, canopy imagery, eddy covariance measurements, and satellite remote sensing data and determined their influence on a predictor analysis of leaf senescence. Both SOS and EOS exhibited substantial ranges in mean onset dates (39.5 and 28.6 days, respectively) across the different methods, although inter-annual variations and advancing SOS trends were similar across methods. Depending on the data, EOS trends were advanced or delayed, but inter-annual patterns correlated well (mean r = .46). Overall, warm, dry, and less photosynthetically productive growing seasons were more likely to be associated with a delayed EOS, while colder, wetter, and more photosynthetically productive vegetation periods resulted in an earlier EOS. In addition, contrary to recent results, no clear influence of pre-solstice vegetation activity on the timing of senescence was detected. However, most notable were the large differences in sign and strength of potential drivers both in the univariate and multivariate analyses when comparing derivation methodologies. The results suggest that an ensemble analysis of all available phenological data sources and derivation methods is needed for general statements on autumn phenology and its influencing variables and correct implementation of the senescence process in ecosystem models.

Performance of CHIRPS for estimating precipitation extremes in the Wala Basin, Jordan

Abstract The evaluation of open-source precipitation data is crucial to enable the selection of the most appropriate product for a specific research. This study aims to evaluate the capability of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) with a spatial resolution of 0.05° for estimating monthly and annual precipitation in the Wala basin, Jordan, from 1987 to 2017 using a point-to-pixel comparison approach. Eleven precipitation extreme indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), were used in this study. The findings show that CHIRPS correlated moderately with stations in monthly precipitation estimation, with the Pearson correlation coefficient values ranging from 0.50 to 0.73. However, CHIRPS had low correlations with stations in most of the extreme indices, except PRCPTOT, R10mm, and R20mm. The CHIRPS, particularly in the extreme years, overestimated low precipitation amounts and underestimated high ones. Moreover, CHIRPS underestimated the calculation of consecutive dry days, consecutive wet days, R10mm, R20mm, and R30mm, while an overestimation was found for the R95p, R99p, and Rx1day. The trend analysis and Wilcox text showed a lack of resemblance between the CHIRPS and gauges, showing a bias correction is needed before applying an extreme analysis in this region.

Impacts of improved horizontal resolutions in the simulations of mean and extreme precipitation using CMIP6 HighResMIP models over West Africa.

We conducted an analysis of 16 historical simulations from the High-Resolution Model Intercomparison Project (HighResMIP) as part of the Coupled Model Intercomparison Project (CMIP) phase 6 (CMIP6). These simulations encompass both high- and low-resolution models and aim to investigate the impact of improved horizontal resolution on mean and extreme precipitation in West Africa between 1985 and 2014. Six Expert Team on Climate Change Detection and Indices (ETCCDI) were used to charactererize extreme indices. Bias adjustment was used to detect and adjust the biases in the models. Our observations indicate that the southeastern and southwestern regions of West Africa experience the most significant precipitation, which aligns with the simulations from HighResMIP. The enhanced horizontal resolution notably influences the simulation of orographically induced rainfall in elevated areas and intensifies precipitation in various aspects. When examining the highest 1-day precipitation, our observations reveal that most of the Guinea Coast region had 1-day rainfall exceeding 100mm. However, this was overestimated and in some simulations underestimated by HighResMIP simulations. Furthermore, an increase in horizontal resolution appears to enhance the ability of high-resolution models to replicate the observed patterns of heavy precipitation (R10mm) and very heavy rainfall (R20mm) days. Spatial and temporal analysis suggests that uncertainty exists in the simulation of extreme precipitation in both high- and low-resolution simulations over West Africa. Also, bias adjustment shows a significant bias in the simulations. To address this issue, we employed a bias adjustment approach.

Effect of climate change to characteristic of extreme rainfall over Batanghari watershed

Climate change will cause extreme rainfall, both wet and dry. Extreme rainfall will cause catastrophic floods and droughts. Therefore, it is important to analyse extreme rainfall and flood events in the future. Disasters and environmental issues in the Batanghari watershed include flooding, drought, forest and land fires, sedimentation, and water quality degradation. Therefore, the Batanghari watershed is selected as a study area in this research. Research on climate change in the Batanghari watershed is crucial to understanding the trend of extreme conditions now and in the future. Characteristics of extreme rainfall will be analysed using the climate index that was developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). Results show that climate change in the Batanghari watershed is indicated by increasing 5 of 6 extreme rainfall indices: R95p, R99p, Rx1day, Rx5day, and SDII. An increase in extreme rainfall is correlated with an increase in flood events in the Batanghari watershed in similar periods. The extreme rainfall index that most influences flooding in the Batanghari watershed is PRCPTOT and R99p. Flooding is expected to occur more frequently in the future than it does now. The Batanghari Hilir Sub-watershed is more vulnerable to extreme flooding in the future than other sub-watershed.

Harmful blooms across a longitudinal gradient in central Europe during heatwave: Cyanobacteria biomass, cyanotoxins, and nutrients

Climate change has increased the frequency, duration and intensity of heatwaves in Europe. These extreme events result in alterations of physical, chemical, and biological properties of lakes that may synergistically promote cyanobacterial dominance. In our study we focused on cyanobacterial blooms in lakes distributed over a longitudinal gradient in Central Europe during one of the “top ten European heat waves” in summer 2015. 92 lakes were included in the study, located across three climatic subregions: cool northern lakes, situated in Lithuania, temperate northern lakes in Poland, and warm northern lakes in Croatia. The objective of the study was to determine if cyanobacterial biomass, predominant species, and cyanotoxin concentration differed, across the south-north gradient, as a function of water temperature, total phosphorus, and total nitrogen. Statistical significance of observed patterns was tested using the Kruskal-Wallis rank sum test and the generalized linear model. We found the lowest average epilimnion temperature, but the highest average cyanobacterial biomass in the northern, ‘cool’ lakes while the highest average temperature with the lowest average cyanobacterial biomass in the southern, ‘warm’ lakes. The concentration of cyanotoxins was also the highest in the ‘cool’ lakes. Total phosphorus and total nitrogen correlated significantly with cyanobacterial biomass, cyanotoxins concentration and biomass of some cyanobacterial species (mainly Planktothrix agardhii), regardless of the latitude. Only in the ‘cool’ lakes concentration of cyanotoxins (microcystins and anatoxin-a) correlated significantly with cyanobacterial biomass and the biomass of some dominant cyanobacterial species (P. agardhii). Our results emphasized the differences of heat weaves impact on lakes of various latitudes, with the strongest increase in toxic cyanobacterial blooms in northern ‘cool’ lakes, situated in high latitudes. On the other hand, nutrients directly enhanced blooms across all the studied latitudes of Central Europe. The cyanobacteria species dominating in blooms might be recognized as ecological indicators of climate change, especially in the north-eastern part of Europe.

Heatwave 1987: the Piraeus versus Athens case.

Heatwaves represent the main indices of climate change, while mortality is one of the established markers of their human effects. For unknown reasons populations adapt to temperature variations/challenges differently. Thus, to allow better precision and prediction, heatwave evaluations should be enriched by historical context and local data. The mortality data for 1987 were collected from the Piraeus municipality registry, whereas data for Athens were obtained from literature retrieved from PUBMED. Ambient characteristics were extracted from the Geronikolou's 1991 BSc thesis and the reports of national organizations. From the death events, the odds ratio and relative risk in Piraeus compared to the Athens were calculated. Finally, a simple neural network proposed the dominant ambient parameter of the heatwave effects in the city residents of each location. The 1987 heatwave was more lethal (seven-fold) in Athens than in Piraeus and dependent on atmospheric nitric oxide (NO) concentration (with probability 0.999). In the case of Piraeus in 1987, ozone characterized the phenomenon (with probability 0.993). The odds of dying due to a heatwave are highly dependent on lifestyle, population sensitivity to preventive measures and public health policy, while the phenomenon was mainly moderated by ozone in Piraeus in 1987, and NO in Athens irrespective of year.

Geospatial modeling of climate change indices at Mexico City using machine learning regression

PurposeClimate change is a problem that concerns all of us. Despite the information produced by organizations such as the Expert Team on Climate Change Detection and Indices and the United Nations, only a few cities have been planned taking into account the climate changes indices. This paper aims to study climatic variations, how climate conditions might change in the future and how these changes will affect the activities and living conditions in cities, specifically focusing on Mexico city.Design/methodology/approachIn this approach, two distinct machine learning regression models, k-Nearest Neighbors and Support Vector Regression, were used to predict variations in climate change indices within select urban areas of Mexico city. The calculated indices are based on maximum, minimum and average temperature data collected from the National Water Commission in Mexico and the Scientific Research Center of Ensenada. The methodology involves pre-processing temperature data to create a training data set for regression algorithms. It then computes predictions for each temperature parameter and ultimately assesses the performance of these algorithms based on precision metrics scores.FindingsThis paper combines a geospatial perspective with computational tools and machine learning algorithms. Among the two regression algorithms used, it was observed that k-Nearest Neighbors produced superior results, achieving an R2 score of 0.99, in contrast to Support Vector Regression, which yielded an R2 score of 0.74.Originality/valueThe full potential of machine learning algorithms has not been fully harnessed for predicting climate indices. This paper also identifies the strengths and weaknesses of each algorithm and how the generated estimations can then be considered in the decision-making process.

Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model’s Generalizability in Permafrost Mapping

This paper assesses trending AI foundation models, especially emerging computer vision foundation models and their performance in natural landscape feature segmentation. While the term foundation model has quickly garnered interest from the geospatial domain, its definition remains vague. Hence, this paper will first introduce AI foundation models and their defining characteristics. Built upon the tremendous success achieved by Large Language Models (LLMs) as the foundation models for language tasks, this paper discusses the challenges of building foundation models for geospatial artificial intelligence (GeoAI) vision tasks. To evaluate the performance of large AI vision models, especially Meta’s Segment Anything Model (SAM), we implemented different instance segmentation pipelines that minimize the changes to SAM to leverage its power as a foundation model. A series of prompt strategies were developed to test SAM’s performance regarding its theoretical upper bound of predictive accuracy, zero-shot performance, and domain adaptability through fine-tuning. The analysis used two permafrost feature datasets, ice-wedge polygons and retrogressive thaw slumps because (1) these landform features are more challenging to segment than man-made features due to their complicated formation mechanisms, diverse forms, and vague boundaries; (2) their presence and changes are important indicators for Arctic warming and climate change. The results show that although promising, SAM still has room for improvement to support AI-augmented terrain mapping. The spatial and domain generalizability of this finding is further validated using a more general dataset EuroCrops for agricultural field mapping. Finally, we discuss future research directions that strengthen SAM’s applicability in challenging geospatial domains.

Assessment of mean precipitation and precipitation extremes in Iran as simulated by dynamically downscaled RegCM4

This paper aims to assess the mean precipitation and precipitation extremes over Iran as simulated by the Regional Climate Model (RegCM4). A simulation spanning 20 years (1991–2010) at a horizontal resolution of 20 Km is driven by the NCEP / NCAR reanalysis. We evaluated the model by comparing simulated precipitation with observations using Bias, Root-Mean-Square Error, and Index of Agreement metrics. We examined the extreme precipitations based on a set of extreme indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) in three categories of intensity (Rx1day and SDII), duration (CDD and CWD), and frequency (R10mm and R20mm). The linear trends are calculated using the Theil–Sen estimator method, and the statistical significance (95% confidence level) is determined by using a modified Mann-Kendall (MMK) trend test. The RegCM4 model satisfactory captured the spatial distribution of precipitation and precipitation extremes, although high bias remained in small parts of Iran, including the northwest and southeast. The northwest bias is due to spring convectional precipitation and the southeast bias could be caused by precipitation from the Asian summer monsoon system, which both of them may not be well simulated by the applied Grell convective scheme. Results indicate that the model reasonably captures Rx1day, SDII, CWD, R10mm, and R20mm Indices over Iran. In good agreement with precipitation observations, the southern coast of the Caspian Sea represents the second-highest extreme precipitation, except for SDII, which is probably due to the high frequency of rainy days in this region. The highest CDD of more than 200 days is found in the arid and semi-arid regions of the southeast. In general, precipitation decreased in most regions of Iran, especially the western, southern, and interior regions. In addition, the results reveal that heavy (R10mm) and very heavy (R20mm) precipitation events have also decreased in the same regions. Results also emphasize an increase in consecutive dry days (CDD) in most parts, especially in the southeast, which deserves more attention in future research. The decreasing trend of precipitation and the increasing trend of CDD show that Iran has become drier in the 2000 s compared to the 1990 s

Life cycle assessment as a prospective tool for sustainable agriculture and food planning at a local level

Owing to the far-reaching environmental consequences of agriculture and food systems, such as their contribution to climate change, there is an urgent need to reduce their impact. International and national governments set sustainability targets and implement corresponding measures. Nevertheless, critics of the globalized system claim that a territorial administrative scale is better suited to address sustainability issues. Yet, at the sub-national level, local authorities rarely apply a systemic environmental assessment to enhance their action plans. This paper employs a territorial life cycle assessment methodology to improve local environmental agri-food planning. The objective is to identify significant direct and indirect environmental hotspots, their origins, and formulate effective mitigation strategies. The methodology is applied to the administrative department of Finistere, a strategic agricultural region in North-Western France. Multiple environmental criteria including climate change, fossil resource scarcity, toxicity, and land use are modeled. The findings reveal that the primary environmental hotspots of the studied local food system arise from indirect sources, such as livestock feed or diesel consumption. Livestock reduction and organic farming conversion emerge as the most environmentally efficient strategies, resulting in a 25% decrease in the climate change indicator. However, the overall modeled impact reduction is insufficient following national objectives and remains limited for the land use indicator. These results highlight the innovative application of life cycle assessment led at a local level, offering insights for the further advancement of systematic and prospective local agri-food assessment. Additionally, they provide guidance for local authorities to enhance the sustainability of planning strategies.

A Review on the Utilization of Herbarium Data in Phenological Research Addressing Climate Change Implications

In recent years, the profound impact of climate change on biodiversity has become increasingly evident, with significant consequences for critical life events in the natural world. It underscores the significant influence of climate variations on seasonal life events within the biotic environment of flora, such as blooming response, leaf growth, leaf senescence and fruit ripening. These phenological shifts, particularly in temperate and tropical ecosystems, serve as vital indicators of climatic changes. This review study delves into the intricate relationship of herbarium data for bridging the gap in plant phenological observation for the investigations on climatic change. This comprehensive literature review of 30 key research papers published in the past two decades, which focus on the utility of herbarium specimens as tools to document phenological changes, was critically analysed in this research study. Long-term observation of phenological data by various phenological observation networks has paved the way for advanced environmental analysis in climatic change studies. However, despite the importance of phenological data from field observation, its collection and long-term observation records, present challenges, emphasizing the need for spatiotemporal depth of phenological data. A noticeable gap in field data collection has hindered various studies reliant on plant phenological data. In response to these challenges, herbaria serve as invaluable repositories of plant specimens, boasting rich historical records spanning over a century, providing a deep spatiotemporal and taxonomic insight. Research findings indicate the fidelity of herbarium records to fill the gap in field observations, with no significant differences between the field observations and herbarium data, highlighting the reliability of herbarium data in capturing phenological information. Overcoming the challenges associated with extensive herbarium datasets, digitalization and the implementation of crowdsourcing platforms have emerged as effective strategies, granting researchers easy online access to these data. Moreover, it is evident that in Sri Lanka, there exists a research gap in utilizing herbarium data for phenological studies in climate change observation and the implementation of online accessibility for herbarium data, highlighting the need to address this gap in future research endeavours.&#x0D; &#x0D; Keywords: Climate change, Herbarium data, Biodiversity, Phenological data

Winter warm spells over Italy: Spatial–temporal variation and large‐scale atmospheric circulation

AbstractThis article analyses the winter warm spells (WWS) that occurred in central Mediterranean over the period 1993–2022, examining the daily maximum temperatures collected at eight airport sites located in the Italian Peninsula, belonging to different climate zones. According to the definition proposed in 1999 by the Expert Team on Climate Change Detection and Indices (ETCCDI), a WWS is a sequence of at least six consecutive days when daily maximum air temperature exceeds the calendar day 90th percentile centred on a 5‐day window for a base period. WWS occurring over the entire Italian territory or only over northern/central/southern Italy have been identified and related to the peculiar synoptic conditions. It was found that December is the month most prone to WWS and, on average, WWS last 9.4 days in northern Italy, 6.6 days in central Italy, and 8.5 days in southern Italy. Over the period under investigation, the Italian Peninsula experienced only one common event characterized by persistent high‐pressure systems associated with air subsidence over western Mediterranean and, therefore, with exceptional warming. Finally, it has been proven that the definition of WWS proposed by ETCCDI allows to capture synoptic scale events but, in orographically complex areas such as Italy, underestimates moderate spells, which generally might have a duration of at least 3 days. Consequently, it is important to consider the possibility of reducing the period length threshold used for the detection of WWS when orographically heterogeneous regions are studied.