Indicators Of Climate Change Research Articles

Antarctic sea ice surface temperature bias in atmospheric reanalyses induced by the combined effects of sea ice and clouds

Sea-ice surface temperature from atmospheric reanalysis has been used as an indicator of ice melt and climate change. However, its performance in atmospheric reanalyses is not fully understood in Antarctica. Here, we quantified biases in six widely-used reanalyses using satellite observations, and found strong and persistent warm biases in most reanalyses examined. Further analysis of the biases revealed two main culprits: incorrect cloud properties, and inappropriate sea-ice representation in the reanalysis products. We found that overestimated cloud simulation can contribute more than 4 K warm bias, with ERA5 exhibiting the largest warm bias. Even in reanalysis with smaller biases, this accuracy is achieved through a compensatory relationship between relatively lower cloud fraction bias and overestimated sea ice insulation effect. A dynamic downscaling simulation shows that differences in sea-ice representation can contribute a 2.3 K warm bias. The representation of ice concentration is the primary driver of the spatial distribution of biases by modulating the coupling between sea ice and clouds, as well as surface heat conduction. The lack of a snow layer in all reanalyses examined also has an impact on biases.

Prediction of heatwave related mortality magnitude, duration and frequency with climate variability and climate change information

Given the link between climatic factors on one hand, such as climate change and low frequency climate oscillation indices, and the occurrence and magnitude of heat waves on the other hand, and given the impact of heat waves on mortality, these climatic factors could provide some predictive skill for mortality. We propose a new model, the Mortality-Duration-Frequency (MDF) relationship, to relate the intensity of an extreme summer mortality event to its duration and frequency. The MDF model takes into account the non-stationarities observed in the mortality data through covariates by integrating information concerning climate change through the time trend and climate variability through climate oscillation indices. The proposed approach was applied to all-cause mortality data from 1983 to 2018 in the metropolitan regions of Quebec and Montreal in eastern Canada. In all cases, models introducing covariates lead to a substantial improvement in the goodness-of-fit in comparison to stationary models without covariates. Climate change signal is more important than climate variability signal in explaining maximum summer mortality. However, climate indices successfully explain a part of the interannual variability in the maximum summer mortality. Overall, the best models are obtained with the time trend and the North Atlantic Oscillation (NAO) used as covariates. No country has yet integrated teleconnection information in their heat-health watch and warning systems or adaptation plans. MDF modeling has the potential to be useful to public health managers for the planning and management of health services. It allows predicting future MDF curves for adaptive management using the values of the covariates.

Declines in Brook Trout Abundance Linked to Atmospheric Warming in Maryland, USA

Salmonid fishes provide an important indicator of climate change given their reliance on cold water. We evaluated temporal changes in the density of stream-dwelling brook trout (Salvelinus fontinalis) from surveys conducted over a 36-year period (1988–2023) by the Maryland Department of Natural Resources in Eastern North America. Nonparametric trend analyses revealed decreasing densities of adult fish (age 1+) in 19 sites (27%) and increases in 5 sites (7%). In contrast, juvenile fish (age 0) densities decreased in 4 sites (6%) and increased in 10 sites (14%). Declining adult brook trout trends were related to atmospheric warming rates during the study period, and this relationship was stronger than the effects of land use change or non-native brown trout. In contrast, juvenile fish trends generally increased with elevation but were not related to air temperature trends or land use change. Our analysis reveals significant changes in several brook trout populations over recent decades and implicates warming atmospheric conditions in population declines. Our findings also suggest the importance of temperature for adult survival rather than recruitment limitation in brook trout population dynamics.

Accelerated glacier mass loss in the mid-latitude Eurasia from 2019 to 2022 revealed by ICESat-2

The dynamics of glaciers serve as one of the most important indicators of climate change. Whilst current research has primarily concentrated on long-term interannual glacier mass balance and its response to climate change, glaciers may respond more rapidly to climate change, highlighting the urgent need for intra-annual mass balance estimations. Investigating seasonal or short-term variations in glacier mass balance not only enhances our understanding of the interactions between glaciers and the climate system but also provides crucial data for water resource management and ecological protection. The ICESat-2 and NASADEM datasets were used to estimate the inter- and intra-annual glacier mass balance changes in the mid-latitude Eurasia from 2019 to 2022. Additionally, the response of glacier mass balance to regional air temperature and precipitation values was analysed using ERA5-Land data and multiple regression analysis, respectively. From 2019 to 2022, glacier mass loss in mid-latitude Eurasia reached −45.02 ± 34.21 Gt per year, contributing to a global sea-level rise of 0.12 ± 0.09 mm per year. The glacier melt rate in the study area from 2019 to 2022 was 2.33 times higher than that from 2000 to 2019. With the exception of the Western Kunlun region, which experienced a weak accumulation rate of 0.04 ± 0.35 m w.e. per year, all other areas experienced ablation states. Seasonal mass balance responds differently to temperature and precipitation variations across seasons: higher temperatures in different seasons lead to more negative mass balances, while increased winter and spring precipitation can slow down glacier melt. Air temperature dominates the glacier mass balance changes in the study area. The intense heat in 2022 raised average glacier temperatures by 1.04 °C compared to 2019–2021, resulting in a more negative mass balance and an increased ice loss of −0.34 ± 1.01 m w.e. per year (−35.07 ± 103.22 Gt per year). This analysis indicates that glacier mass balance is highly sensitive to climate change, even on a seasonal scale. Moreover, the high precision and spatiotemporal resolution ICESat-2 data can facilitate the investigation of large-scale glacier mass balance on short time scales.

Lake responses and mechanisms to El Niño on the Tibetan Plateau using deep learning-based semantic segmentation

Numerous lakes across the Tibetan Plateau (TP) serve as crucial indicators of climate change and are significantly influenced by El Niño events. Previous studies of lake response to El Niño events have focused on a limited number of lakes. Despite advances in remote sensing technology, there have been few comprehensive and large-scale studies using deep learning, and there are still gaps in understanding the response mechanisms on a larger scale. This study leverages advanced deep-learning techniques to map lake responses, offering unprecedented insights into the large-scale hydrological impacts of El Niño. Our results show that lakes shrink significantly during El Niño events on the TP. Lakes located in the central and southern parts of the TP and small lakes with areas ranging from 1 to 50 km2 (over 60 % of them) exhibited strong responses. The range of lake response to El Niño events varies with their intensity, with stronger El Niño events causing an expansion of the response range along the latitudinal direction. We propose four possible mechanisms for lake response patterns to El Niño from the perspective of lake water sources. Strong shrinkage is primarily caused by decreased precipitation and increased evaporation, with a possible contribution from reduced meltwater. Strong expansion is due to increased precipitation, more glacier and frozen soil meltwater, and reduced evaporation. For slight shrinkage and expansion patterns, the balance of meltwater may offset or even counteract the El Niño signal. The study’s results could improve predictions of extreme weather events like droughts and floods in the Third Pole region, enhance water resource management and responsiveness, and offer valuable insights for ecological monitoring and early warning systems development.

Hydroformer: Frequency Domain Enhanced Multi‐Attention Transformer for Monthly Lake Level Reconstruction With Low Data Input Requirements

AbstractLake level changes are critical indicators of hydrological balance and climate change, yet long‐term monthly lake level reconstruction is challenging with incomplete or short‐term data. Data‐driven models, while promising, struggle with nonstationary lake level changes and complex dependencies on meteorological factors, limiting their applicability. Here, we introduce the Hydroformer, a frequency domain enhanced multi‐attention Transformer model designed for monthly lake level reconstruction, utilizing reanalysis data. This model features two innovative mechanisms: (a) Frequency‐Enhanced Attention (FEA) for capturing long‐term temporal dependence, and (b) Causality‐based Cross‐dimensional Attention (CCA) to elucidate how specific meteorological factors influence lake level. Seasonal and trend patterns of catchment meteorological factors and lake level are initially identified by a time series decomposition block, then independently learned and refined within the model. Tested across 50 lakes globally, the Hydroformer excelled in reconstruction periods ranging from half to three times the training‐test length. The model exhibited good performance even when training data missing rates were below 50%, particularly in lakes with significant seasonal fluctuations. The Hydroformer demonstrated robust generalization across lakes of varying sizes, from 10.11 to 18,135 km2, with median values for R2, MAE, MSE, and RMSE at 0.813, 0.313, 0.215, and 0.4, respectively. Furthermore, the Hydroformer outperformed data‐driven models, improving MSE by 29.2% and MAE by 24.4% compared to the next best model, the FEDformer. Our method proposes a novel approach for reconstructing long‐term water level changes and managing lake resources under climate change.

Field trial of a seismoacoustic method for ice cover parameters monitoring on the Franz Josef Land archipelago

Among the physical parameters of the freezing seas ice cover, ice thickness is of key importance, and its measurement is one of the most important tasks. The increased interest in the state of the sea ice cover as an indicator of global climatic changes, as well as the growth of comprehensive development of the Arctic shelf has caused intensive development of technical and methodological bases for ice observations. Despite the great variety of approaches to ice thickness estimation, all of them are not without weaknesses. Thus, most contact methods imply direct human presence, which significantly complicates the procedure, taking into account, among other factors, the rough weather conditions of the Arctic. Remote methods depend on weather conditions and cannot always provide high spatial resolution. In this connection, it is promising to use satellite observations coupled with the results of autonomous “ground” measurements, which can be seismoacoustic data containing information on the characteristics of elastic waves propagating in the ice-covered sea, is promising. The purpose of this work is to experimentally test a new passive method for monitoring ice cover parameters along long profiles based on the analysis of natural seismoacoustic fields. The article analyzes the data of a full-scale seismoacoustic experiment with a multichannel group of geophones placed on the floating ice of Alexandra Island in the Franz Josef Land archipelago within the framework of a complex expedition of the Russian Geographical Society. The demonstrates that it is in principle possible to use flexural-gravity waves propagating in the floating ice to estimate its characteristics, both in the active mode and by analyzing the ambient noise, is demonstrated. The results of ice parameter reconstruction obtained in a nondestructive manner using seismoacoustic waves and averaged over long profiles are compared with the data of direct contact measurements. This can be further used for monitoring seasonal and multiyear variability of sea ice thickness of freezing seas, including shelf zones.

SST trends in certain areas of the Barents Sea in the winter season and mechanisms of their formation

The climate changes observed over the past few decades are most clearly manifested in the Arctic Ocean. Sea surface temperature (SST) is one of the most reliable indicators of climate change. In this paper we analyze the changes of winter SST for the western, northeastern and southeastern regions of the Barents Sea and examine the relationship of the emerging STS trends with the influence of various external factors. The working data set is represented by average monthly SST values taken from the ERA-5 reanalysis for the period 1949–2023 with a spatial resolution of 0.25×0.25° and average water temperature values on the Kola Meridian section in the 0–50 m layer. Additionally, the Arctic Oscillation (AO), Arctic Dipole (AD) and Atlantic Multidecadal Oscillation (AMO) indices were used as external factors that may affect SST variability. The time series analyzed was divided into three periods: 1949–1969, 1970–1990, 1991–2023, where the variability of the analyzed parameters was different. Thus, in the first period the trend in SST changes was negative, for the second period it was slightly negative or neutral, and for the third period it was positive. It is shown that SST in all the regions of the Barents Sea has undergone significant changes, which were most noticeable in the “warm” period of 1991–2023, when the rate of SST increasing was up to 10·10-2 °C/year in areas under the warm Atlantic water influence. The analysis of SST variability in the Barents Sea shows that the positive anomalies observed in the recent years are most likely associated with the changes in the atmospheric circulation. The Wavelet coherence analysis showed the closest agreement between the changes in the sea surface temperature and the AD index in the winter season, and with the AMO index.

Macrozoobenthos of subarctic lakes as an indicator of climate change

Changes in the macrozoobenthos of two northern lakes differing in degree of humification of water over the 50-year period that have passed since the first studies were considered. In the lake with water uncolored by humus, the benthos biomass increased, and in the humified one, it decreased. The changes that have occurred are due to climate warming, especially pronounced in winter. It was concluded that even closely located bodies of water can respond differently to climate change. Depending on the nature of the catchment area and the morphology of the lakes, either an increase in their productivity due to the supply of nutrients or a decrease due to humification can be observed.

Renewable Energy Consumption, Climate Change, and Economic Growth: A Case of Selected Countries

This research examines the nexus between renewable energy consumption, climate change, and economic growth across 88 countries, categorized by income levels (high, upper-middle, lower-middle, low, and Arab countries) from 1990-2020. Using the Panel Autoregressive Distributed Lag (PARDL) model, the study captures both short- and long-term relationships among the variables. Key findings reveal that economic growth is linked to rising temperatures, while the impact on precipitation varies across income groups. Non-renewable energy consumption has mixed effects: it mitigates climate change in developed countries but exacerbates pollution in developing nations. Conversely, renewable energy lowers average temperatures in lower-income countries, but in high-income countries, it shows positive correlations with climate change indicators. The significant error correction terms across all panels indicate a quick adjustment toward long-run equilibrium. The study underscores the need for tailored climate and energy strategies, advocating for increased renewable energy infrastructure in developing countries and improved energy efficiency in developed nations. For Arab countries, which face specific climate risks, enhanced regional cooperation on mitigation measures is recommended. This research emphasizes that addressing climate change requires strategies that consider the distinct economic and environmental contexts of different country groups.

Warm Index and Growing Degree Days as Indicators of Climate Changes for Growth of Quercus Eagilops and Pistacia Khinjuk Trees in the Dohuk Region

Warm Index and Growing Degree Days as Indicators of Climate Changes for Growth of Quercus Eagilops and Pistacia Khinjuk Trees in the Dohuk Region

Reversal of the Spatiotemporal Patterns at the End of the Growing Season of Typical Steppe Vegetation in a Semi-Arid Region by Increased Precipitation

Vegetation phenology serves as a sensitive indicator of climate change. However, the mechanism of the hydrothermal role in vegetation phenology changes is still controversial. Utilizing the data on the Fraction of Absorbed Photosynthetically Active Radiation (FPAR) from MODIS and meteorological data, the study employed the dynamic threshold method to derive the end of the growing season (EOS). The research delved into the spatiotemporal patterns of the EOS for typical steppe vegetation in the semi-arid region of Inner Mongolia spanning the period from 2003 to 2022. Furthermore, the investigation scrutinized the response of EOS to temperature and precipitation dynamics. The results showed that (1) the dynamic threshold method exhibited robust performance in the EOS of typical steppe vegetation, with an optimal threshold of 45% and a Root Mean Square Error (RMSE) of 5.5 days (r = 0.81); (2) the spatiotemporal patterns of the EOS of typical steppe vegetation in the semi-arid region experienced a noteworthy reversal from 2003 to 2022; (3) the lag effects of precipitation and temperature on the EOS were found, and the lag time scales were mainly 1 month and 2 months. The increase in precipitation in August was the key reason for the reversal of the EOS, and satisfying the precipitation was a prerequisite for the temperature to delay the EOS. The study emphasizes the important role of water availability in regulating the response of the EOS to hydrothermal factors and highlights the utility and reliability of FPAR in monitoring the EOS of typical steppe vegetation.

Climatic temperature indices for the coastal resilience observatory in Tabasco (Dos Bocas)‒LANRESC

This study focused on calculating climate change indices derived from maximum and minimum temperature variables for the Coastal Resilience Observatory in Tabasco, Dos Bocas, located in Paraíso, Tabasco, Mexico. The indices were computed using CLIMPACT2, 2007 version, and a total of 26 indices were obtained for the period from 1940 to 2022. Subsequently, significant trends were calculated using statistical tests such as Mann‒Kendall, trend-free prewhitening, bias correction applied to prewhitening, and variance correction (by two methods). Sen's slope method was employed to determine the extent of alteration in extreme climate indices. Increasing trends were found in the indices tn90p (%), tx90p (%), tr (days), txgt50p (%), wdsi (days), su (days), tmge10 (days), tmge5 (days), gddgrow (days), and gsl (days), indicating the presence of local warming and providing evidence of climate change detection. These results contribute to updating climate information in the area and serve as a proposal for replication in the other six Coastal Observatories of the National Coastal Resilience Laboratory in Mexico, as part of measures related to climate stressors in coastal areas where these observatories are located. This information is valuable for decision-makers and the general population, as it will support socio-environmental adaptation and mitigation measures in the face of climate change as part of efforts to enhance the resilience of socioecosystems in coastal areas.

Extreme snowfall variations in the Southeastern Tibetan Plateau under warming climate

Snowfall is a critical component of the Earth system and an important indicator and amplifier of climate change. Climate warming is reducing the seasonal snowpack globally, which could have catastrophic consequences for the regions in high dependence on snow for water recharge. However, the climate influences on extreme snowfall events, which significantly impact humans, are still poorly understood. This study uses the Tibetan Plateau snowpack observation dataset combined with NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) climate data to investigate the spatial and temporal characteristics of snowfall, and to explore its response mechanism to climate change and future change trends across the Lower Yarlung Zangbo River (LYZR) in Southeastern Tibetan Plateau (SETP). Annual snowfall decreased at a rate of 1.05 mm per decade (P < 0.05) during the historical period (1960–2014). Relative to 1960–2014, annual snowfall would decline of ∼38 % and ∼ 73 % by the end of the 21st century under the SSP245 and SSP585 scenarios, respectively. At the same time, a general decrease in extreme snowfall averaged the LYZR is expected for historical and future periods, but it showed notable spatial variations. The regional increase in extreme snowfall is mainly distributed along the valley of Brahmaputra-Yarlung Zangbo. In addition, precipitation accounted for 66.5 % of the snowfall variations during the historical period. Meanwhile, according to future warming, temperature would dominate snowfall variations, contributing 56.66 % to 72.92 % during 2015–2100. The projected response of mean and extreme snowfall to climate change indicates that it is a double-edged sword. To scientifically address the risks and challenges posed by snowfall changes in alpine regions, it is imperative to limit future climate warming.

Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice

Sea ice and its surface snow are crucial components of the energy cycle and mass balance between the atmosphere and ocean, serving as sensitive indicators of climate change. Observing and understanding changes in snow depth on Antarctic sea ice are essential for sea ice research and global climate change studies. This study explores the feasibility of retrieving snow depth on Antarctic sea ice using data from the Chinese marine satellite HY-2B. Using generic retrieval algorithms, snow depth on Antarctic sea ice was retrieved from HY-2B Scanning Microwave Radiometer (SMR) data, and compared with existing snow depth products derived from other microwave radiometer data. A comparison against ship-based snow depth measurements from the Chinese 35th Antarctic Scientific Expedition shows that snow depth derived from HY-2B SMR data using the Comiso03 retrieval algorithm exhibits the lowest RMSD, with a deviation of −1.9 cm compared to the Markus98 and Shen22 models. The snow depth derived using the Comiso03 model from HY-2B SMR shows agreement with the GCOM-W1 AMSR-2 snow depth product released by the National Snow and Ice Data Center (NSIDC). Differences between the two primarily occur during the sea ice ablation and in the Bellingshausen Sea, Amundsen Sea, and the southern Pacific Ocean. In 2019, the monthly average snow depth on Antarctic sea ice reached its maximum in January (36.2 cm) and decreased to its minimum in May (15.3 cm). Thicker snow cover was observed in the Weddell Sea, Ross Sea, and Bellingshausen and Amundsen seas, primarily due to the presence of multi-year ice, while thinner snow cover was found in the southern Indian Ocean and the southern Pacific Ocean. The derived snow depth product from HY-2B SMR data demonstrates high accuracy in retrieving snow depth on Antarctic sea ice, highlighting its potential as a reliable alternative for snow depth measurements. This product significantly contributes to observing and understanding changes in snow depth on Antarctic sea ice and its relationship with climate change.

Implementing life cycle thinking and climate change indicators in small and medium size enterprises

The policy for a low emission society requires that companies measure their impact from a life cycle perspective for both products and corporate reporting. This work presents new climate performance indicators to evaluate business models (BM) based on life cycle assessment (LCA) and benchmarked with statistics for Sustainable Development Goal indicator 9.4.1. The research was conducted by action case studies involving 20 small and medium sized enterprises (SMEs) from various sectors. The SMEs did not have previous experience with LCA. Therefore, a work process called “LCA á la carte” was designed to gradually increase details and effort. The results of simplified indicators on the manufacturing SMEs showed that the direct emissions to value added are lower than industry average. However, when upstream emissions and value added were included, the emission intensities are about industry average. The full life cycle climate indicators are presented for a boat production company that experiment with new BMs. Use phase electrification was found to be the most effective measure (60 % lower emission intensity), while material recycling and boat rental each reduced the emission intensity with about 30 %. However, a combination of these new BMs gives best results and could be needed to reach future benchmarks. This study suggests LCA for BM as a combination of a work process, actors' perspectives, and performance indicators. The results highlight that the scope of activities included in the performance indicators is crucial. New reporting standards such as the European Sustainable Development Standard (ESRS) should review the emission intensity indicators to ensure consistency between the scopes of activities included.

Climate Change Prediction Model using MCDM Technique based on Neutrosophic Soft Functions with Aggregate Operators

The increasing impact of climate change necessitates innovative approaches in modeling and prediction to mitigate its adverse effects. This paper introduces a novel methodology integrating Neutrosophic Soft Functions (NSFs) into climate change prediction frameworks. NSFs, a hybrid of Neutrosophic Set Theory and Soft Set Theory, provide a flexible framework for handling uncertain and imprecise information inherent in climate data. This study explores the application of NSFs in capturing the complex interplay of various climatic variables, including temperature, precipitation, humidity, and atmospheric pressure, thereby enhancing the accuracy and reliability of climate change predictions. By incorporating NSFs into existing predictive models, such as neural networks and fuzzy systems, this research demonstrates significant improvements in forecast precision, particularly in scenarios with limited or noisy data. Additionally, the paper discusses the integration of NSFs with advanced machine learning algorithms for climate pattern recognition and anomaly detection, enabling timely identification of climate change indicators and facilitating proactive measures for adaptation and mitigation. Through empirical validation using real-world climate datasets, this study underscores the efficacy of NSFs in enhancing the predictive capabilities of climate change models, thereby contributing to more informed decision-making in climate-related policies and strategies.

Spatiotemporal variation and teleconnections of extreme precipitation in the Upper Indus Basin: insights for natural hazard assessment

ABSTRACT The study investigates trends and teleconnections of extreme precipitation events in the Upper Indus Basin (UIB) within the Indian region, part of the crucial geo-ecological Hindu Kush Himalayan Mountain system. Utilizing high-resolution (10 km × 10 km) HAR data from 2002 to 2013, we analyzed 11 indices established by the Expert Team on Climate Change Detection and Indices (ETCCDI) to observe variations in extreme precipitation at the monthly, seasonal, and annual time scale. The result shows an increase in dry and wet extreme precipitation frequency and intensity, increasing monsoon precipitation and a shift of winter precipitation, increasing continuous dry days. Using the APHRODITE daily (0.25° × 0.25°) data from 1952 to 2014, continuous wavelet transform and wavelet coherence analysis indicate significant periodicities and correlations with large-scale climate anomalies such as the El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). Wavelet coherence analysis reveals that monthly extreme precipitation events significantly correlate with ENSO at a 3–5 years periodicity, while annual extremes show significant coherence with NAO at 8–10 years and over 16 years periodicities. The study highlights the impact of global climate change on regional precipitation and the need for adaptive water management policies to mitigate flood risks during the rainy season and address water scarcity in the dry season.

Evaluation of future wind climate over the Eastern Mediterranean Sea

The aim of this study is to evaluate wind climate characteristics in the Eastern Mediterranean Sea until the end of the 21st century using the wind fields simulated by the EURO-CORDEX Regional Climate Models (RCM), namely the Rossby Centre regional atmospheric model (version RCA4). Long-term averages, long-term trends, and long-term variability in wind characteristics under two Representative Concentration Pathway (RCP 4.5 and 8.5) scenarios were assessed for two future periods: the near future for the years 2021–2060 and the middle future for the years 2061–2100. Relative to the historical period, the most remarkable changes in the mean wind speeds were projected under the RCP 8.5 scenario in the middle future in the central and southern Aegean Sea (exceeding 5 % increase) and in the eastern and northern Levantine basin (up to 5 % decrease), compatible with the definition of the climate scenarios as the RCP8.5 scenario assumes a rising radiative forcing until 2100. The changes in extreme wind characteristics are more noteworthy in agreement with the indicators of climate change (i.e., an increase in the intensity and the frequency of extreme events). 100-year return period wind speed increases by about 29 % in the western Levantine basin under the RCP 8.5 scenario while the most drastic decrease in 100-year extreme wind speed was found in the central Levantine basin with a reduction of exceeding −29 % for the middle future period under the RCP 8.5 scenario. These decreasing and increasing behaviors in extreme wind speeds are expected in the study area as the Mediterranean is affected by many climate systems.

Response of alpine vegetation function to climate change in the Tibetan Plateau: A perspective from solar-induced chlorophyll fluorescence

Vegetation change in the Tibetan Plateau (TP) is a crucial indicator of climate change in alpine regions. Previous studies have reported an overall greening trend in the vegetation structure across the TP, especially in its northeastern part, in response to a warming climate. However, variations in the vegetation function and the possible drivers remain poorly understood. Considering the optimal temperature for plants in TP is usually higher than the current temperature, our hypothesis is the function and structure of alpine vegetation have changed synchronously over past few decades. To test this hypothesis, we analyzed satellite-observed solar-induced chlorophyll fluorescence (SIF) and leaf area index (LAI) in the Yellow River source (YRS) region in the northeastern TP to quantify the long-term trends in vegetation functional and structural states, respectively. The results suggest that from 1982 to 2018, SIF increased significantly in 77.71 % of the YRS area, resulting in a significant upward trend of 0.52 × 10−3 mW m−2 nm−1 sr−1 yr−1 (p < 0.001) for the regional-mean SIF. This represents a 16.1 % increase in SIF, which is close in magnitude to the increase in LAI over the same period. The synchronous changes between vegetation function and structure suggest that improved greenness corresponds to a similar level of change in carbon uptake across YRS. Additionally, we used a multiple regression approach to quantify the contribution of climatic factors to SIF changes in YRS. Our analyses show that the increases in SIF were primarily driven by rising temperatures. Spatially, temperature dominated SIF changes in most parts of YRS, except for certain dry parts in the northern and western YRS, where precipitation had a greater impact. Our results are crucial for a comprehensive understanding of climate regulations on vegetation structure and function in high-elevation regions.