Global Climate Variability Research Articles

Quantifying the Role of Ocean Dynamics in SST Variability across GCMs and Observations

Abstract Midlatitude SSTs forced by mesoscale oceanic processes can affect the large-scale atmosphere, pointing to the ocean’s crucial role outside the tropics. Previous studies have shown oceanic mesoscale processes’ effect on global and regional climate variability. This study quantifies the local contribution of ocean dynamics to mixed-layer temperature across the globe by directly estimating the ocean heat flux divergence resolved by state-of-the-art ocean reanalysis, eddy-resolving, and eddy-parameterized versions of two U.S. national climate models and indirectly from air–sea flux satellite-based estimates. Our results show that the eddy-resolving climate simulations resolve mixed-layer temperature variances that are larger and closer to those inferred from observations than both their eddy-parameterized counterparts and ECCO over much of the extratropics. The observations and the eddy-resolving models indicate a more significant role of ocean dynamics in the mixed-layer temperature variability than the surface fluxes over most extratropics compared to their eddy-parameterized versions. A frequency domain analysis shows that the better-resolved ocean mesoscale and thermal gradients enhance the variance over a time scale from 2 months to 30 years. Results show agreement in the ocean’s contribution among satellite-based estimates, ocean reanalysis products, and ocean eddy-resolving simulations. At the same time, differences emerge for ECCO and the eddy-parameterized models, suggesting that surface fluxes account for a larger fraction of the mixed-layer temperature variability in most of the extratropics.

Socio-Economic Implications of Snow Cover Area in the Upper Indus Basin

The Indus River, one of the principal waterways in Asia, specifically the Indian Subcontinent, originates from the Himalayan Region and Qinghai-Tibet Plateau. Understanding snow cover dynamics is one of the most vital components for effective hydrological resource management and assessing the impacts of local, regional, and global climatic variations. Variations in the radiation of snow microstructures and snowpacks have profound socio-economic and environmental consequences, affecting agricultural productivity, reservoir supply for factories, environmental sustainability, governance of land resources, and overall community resilience. Fluctuations in snowmelt patterns can lead to water shortages or surplus, influencing food security and economic stability in a country largely dependent on agriculture. Utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, this study assesses the implications of Snow Cover Area (SCA) on water security and livelihood of agricultural-related sectors. A comparative analysis of snow cover reveals a shifting trend in the accumulation period, with notable anomalies affecting the agricultural output, which affects the household income related to the agricultural sector. This research underscores the necessity of adapting water management strategies to align with evolving snow cover dynamics to mitigate potential socio-economic risks.

Long-Term Changes in Deep Ocean Currents between Ulleung Island and Dok Island in the East Sea from 1996 to 2023

This study presents a comprehensive analysis of long-term deep ocean currents between Ulleung Island and Dok Island in the East Sea, based on data observed from the East Sea Current Measurement 1 (EC1) mooring station spanning from 1996 to 2023. The deep current at the Ulleung Interplain Gap, a trough situated between Ulleung Island and Dok Island, originates from the Japan Basin, a region where deep water formation occurs within the East Sea. The southward flow and velocity of this deep water are reflective of both regional and global climate dynamics. Understanding these dynamics is crucial for assessing environmental impact, both locally in Korea and globally. Significant trends and anomalies were identified in the zonal and meridional velocities within the 1,800 and 2,500-meter depth range. Data from 2012 to 2014 required corrections due to unit inconsistencies, which, after adjustment, revealed a deceleration of the southward bottom current, with speeds decreasing from 1.49 cm s<sup>-1</sup> in November 1996 to 1.14 cm s<sup>-1</sup> in April 2023. These changes may be linked to global climate variations and bottom water formation in the Japan Basin, influencing regional circulation patterns. The findings highlight the importance of sustained ocean monitoring and enhanced data management to ensure the accuracy and utility of long-term oceanographic datasets.

A 485-million-year history of Earth’s surface temperature

A long-term record of global mean surface temperature (GMST) provides critical insight into the dynamical limits of Earth’s climate and the complex feedbacks between temperature and the broader Earth system. Here, we present PhanDA, a reconstruction of GMST over the past 485 million years, generated by statistically integrating proxy data with climate model simulations. PhanDA exhibits a large range of GMST, spanning 11° to 36°C. Partitioning the reconstruction into climate states indicates that more time was spent in warmer rather than colder climates and reveals consistent latitudinal temperature gradients within each state. There is a strong correlation between atmospheric carbon dioxide (CO 2 ) concentrations and GMST, identifying CO 2 as the dominant control on variations in Phanerozoic global climate and suggesting an apparent Earth system sensitivity of ~8°C.

Rice yield prediction through integration of biophysical parameters with SAR and optical remote sensing data using machine learning models

In an era marked by growing global population and climate variability, ensuring food security has become a paramount concern. Rice, being a staple crop for billions of people, requires accurate and timely yield prediction to ensure global food security. This study was undertaken across two rice crop seasons in the Udham Singh Nagar district of Uttarakhand state to predict rice yield at 45, 60 and 90 days after transplanting (DAT) through machine learning (ML) models, utilizing a combination of optical and Synthetic Aperture Radar (SAR) data in conjunction with crop biophysical parameters. Results revealed that the ML models were able to provide relatively accurate early yield estimates. For summer rice, eXtreme gradient boosting (XGB) was the best-performing model at all three stages (45, 60, and 90 DAT), while for kharif rice, the best-performing models at 45, 60, and 90 DAT were XGB, Neural network (NNET), and Cubist, respectively. The combined ranking of ML models showed that prediction accuracy improved as the prediction date approaches harvest, and the best prediction of yield was observed at 90 DAT for both summer and kharif rice. Overall rankings indicate that for summer rice, the top three models were XGB, NNET, and Support vector regression, while for kharif rice, these were Cubist, NNET, and Random Forest, respectively. The findings of this study offer valuable insights into the potential of the combined use of remote sensing and biophysical parameters using ML models, which enhances food security planning and resource management by enabling more informed decision-making by stakeholders such as farmers, policy planners as well as researchers.

Evaluating the impact of phyto-hormones on the morpho-biochemical traits of soybean through seed treatment and foliar application

Global climate variations, mainly extreme temperatures reduce crop production and contributed to global food insecurity. Plant growth regulators (PGRs) improve physiological efficiency, photosynthetic capacity, and assimilate partitioning in field crops. However, their influence on soybean (Glycine max L.) yield and seed quality is under-researched. This study investigates the synergistic effects of auxin (2,4-D) and gibberellin (GA3) at 60 and 90 mg L−1 concentrations. This study investigates the impact of phytohormones, specifically gibberellic acid (GA3) and 2,4-dichlorophenoxyacetic acid (2,4-D), on the morphological and biochemical traits of soybean (Glycine max L.) through seed treatment and foliar application. These Growth regulators were applied through seed treatment at the time of sowing and foliar application at the time of flowering initiation stage. The experiment was designed using a RCBD arrangement and each block replicated thrice time. Seed treatment with 2,4-D at 90 mg/L showed that maximum plant height (116.80 cm), first node height (8.04 cm), No. of branches per plant (18.67), root length (18.12 cm) stem diameter (0.05 cm), no. of pods per plant (32.60), seed yield (1532.30 kg ha−1), biological yield (4109.30 ha−1), and harvest index (39.36 %). The GA3 (seed treatment) at conc. of 90 and 60 mg L−1 resulted in a significantly increased no. of nodule fresh weight, nodules per plant, and dry weight respectively. But the no. of seeds per pod did not showed significant results throughout the study. However, the foliar applied of 2,4-D at 90 mg L−1 resulted in significantly increased leaf area index (LAI) compared to the seed treatment. PGRs had a significant influence on quality traits. Thus, the application notably improved the potential of soybean and successful oilseed crop. Therefore, it is showed that using PGRs are extremely helpful for attaining higher growth, yield production, and improved quality of soybean.

Examining relations between sea-level anomalies in the Niño 3.4 region and terrestrial hydroclimatic conditions in China

As a key driver of global climate variability, El Niño–Southern Oscillation (ENSO) significantly impacts the regional hydrological cycle. Similar to the recognized ENSO monitoring index (i.e., sea surface temperature anomaly), a potential advancement in the usage of sea level anomaly (SLA) in the equatorial Pacific is expected to enhance our knowledge of ENSO and its influence on regional terrestrial hydroclimatic conditions (THCs). In this study, we employed wavelet coherence and multi-channel singular spectral analysis to investigate the ability of satellite altimetry-based SLA, and its two major components (i.e., steric SLA (SSL) and non-steric SLA (NSSL)) averaged over Niño 3.4 region, to identify ENSO-related signals. Subsequently, we explored their spatial association with THCs at interannual scales in China using a cross-correlation method. We found that the time series of sea surface temperature anomaly index (SSTaI), SLA index (SLAI), and SSL index (SSLI) displayed high consistency. SSTaI was found to present higher correlations to SLAI (0.91) and SSLI (0.88), compared to NSSL index (NSSLI; 0.40). Furthermore, the high (low) wavelet coherence and in-phase (orthogonal) relationship between SSTaI and SLAI/SSLI (NSSLI) was also revealed. This indicated that SLAI and SSLI may be good complementary indices to SSTaI in observing ENSO-related signals. Similar to the temporal association of SSTaI and SLAI/SSLI, their spatial correlation pattern with THCs also displayed consistency. The time lag characterizing the relationship between SSTaI/SLAI/SSLI and THCs was also consistent, despite some regional differences. In summary, our results indicated that the combined analyses of the time lag determined between SSTaI/SLAI/SSLI and THCs can eventually complement the time lag analysis based solely on SSTaI. This, in turn, provides a more comprehensive understanding of ENSO’s influence on THCs in China.

Precipitation changes alter plant dominant species and functional groups by changing soil salinity in a coastal salt marsh

Specific mechanisms of precipitation change due to global climate variability on plant communities in coastal salt marsh ecosystems remain unknown. Hence, a field manipulative precipitation experiment was established in 2014 and 5 years of field surveys of vegetation from 2017 to 2021 to explore the effects of precipitation changes on plant community composition. The results showed that changes in plant community composition were driven by dominant species, and that the dominance of key species changed significantly with precipitation gradient and time, and that these changes ultimately altered plant community traits (i.e., community density, height, and species richness). Community height increased but community density decreased with more precipitation averaged five years. Furthermore, changes in precipitation altered dominant species composition and functional groups mainly by influencing soil salinity. Salinity stress caused by decreased precipitation shifted species composition from a dominance of taller perennials and grasses to dwarf annuals and forbs, while the species richness decreased. Conversely, soil desalination caused by increased precipitation increased species richness, especially increasing in the dominance of grasses and perennials. Specifically, Apocynaceae became dominance from rare while Amaranthaceae decreased in response to increased precipitation, but Poaceae was always in a position of dominance. Meanwhile, the dominance of grasses and perennials has the cumulative effect of years and their proportion increased under the increased 60% of ambient precipitation throughout the years. However, the annual forb Suaeda glauca was gradually losing its dominance or even becoming extinct over years. Our study highlights that the differences in plant salinity tolerance are key to the effects of precipitation changes on plant communities in coastal salt marsh. These findings aim to provide a theoretical basis for predicting vegetation dynamics and developing ecological management strategies to adapt to future precipitation changes.

Forecasting Meteorological Drought Conditions in South Korea Using a Data-Driven Model with Lagged Global Climate Variability

Drought prediction is crucial for early risk assessment, preventing negative impacts and the timely implementation of mitigation measures for sustainable water management. This study investigated the relationship between climate variations in three seas and the prediction of December meteorological droughts in South Korea, using the Standardized Precipitation Evapotranspiration Index (SPEI). Climate indices with multiple time lags were integrated into multiple linear regression (MLR) and Random Forest (RF) models and evaluated using Pearson’s correlation coefficients (PCCs) and the Root Mean Square Error (RMSE). The results indicated that the MLR model outperformed RF model in the western inland region with a PCC of 0.52 for predicting SPEI-2. On the other hand, the RF model effectively predicted drought states of ‘moderate drought’ or worse (SPEI < −1) nationwide, achieving an average hit rate of 47.17% and Heidke skill score (HSS) of 0.56, particularly excelling in coastal areas. Nino 3.4 turned out to be the most influential factor for short-period extreme droughts (SPEI-2) with a three-month lag, contributed by the Pacific, Atlantic, and Indian Oceans. For periods of four months or longer, climate variations had a lower predictive value. However, integrating autocorrelation functions to account for the previous month’s drought status improved the accuracy. A HYBRID model, which blends linear and nonlinear approaches, further enhanced reliability, making the proposed model more applicable for drought forecasting in neighboring countries and valuable for South Korea’s drought monitoring system to support sustainable water management.

Interannual Fluctuations and Their Low-Frequency Modulation of Summertime Heavy Daily Rainfall Potential in Western Japan

Heavy rainfall under the conditions of the changing climate has recently garnered considerable attention. The statistics on heavy daily rainfall offer vital information for assessing present and future extreme events and for clarifying the impacts of global climate variability and change, working to form a favorable background. By analyzing a set of large-ensemble simulations using a global atmospheric model, this study demonstrated that two different physical processes in global climate variability control the interannual fluctuations in the 99th- and 90th-percentile values of summertime daily rainfall (i.e., the potential amounts) on Kyushu Island in western Japan. The 90th-percentile values were closely related to large-scale horizontal moisture transport anomalies due to changes in the subtropical high in the northwestern Pacific, which was usually accompanied by basin-scale warming in the Indian Ocean subsequent to the wintertime El Niño events. The contributions of the sea surface temperatures over the northern Indian Ocean and the eastern tropical Pacific Ocean showed low-frequency modulations, mainly due to the influences of the global warming tendency and the interdecadal variability in the climate system, respectively. In contrast, tropical cyclone activity played a major role in changing the 99th-percentile value. The potentials of both the tropical cyclone intensity and the existence density fluctuated, largely owing to the summertime sea surface temperature over the tropical Pacific, which can be modulated by the El Niño diversity on interdecadal timescales.

Assessing the impacts of El Niño events on the Ecuadorian territory: a computational analysis of climatologies, anomalies, and deprivation index.

Abstract This study conducts a comprehensive computational analysis to assess the climatological and socio-economic impacts of El Niño events on Ecuador, employing a detailed examination of climatologies, anomalies, and deprivation indices from 1980 to 2023. We identify significant climatic shifts and implications on regional vulnerabilities by utilizing the Essential Climate Variables dataset, alongside the Oceanic Niño Index (ONI) and the Niño 1+2 index for sea surface temperature anomalies. Additionally, the Global Gridded Relative Deprivation Index (GRDI) is integrated to evaluate socio-economic impacts, highlighting areas of heightened deprivation. Notably, regions such as Chimborazo, Cotopaxi, Azuay, Tungurahua, and Bolívar emerge as significantly vulnerable, with pronounced susceptibility to temperature changes and precipitation anomalies. Several statistical methods, including Pearson Correlation, Spearman Rank Correlation, and Kendall’s Tau are employed to establish robust causal relationships between El Niño indices and climatic anomalies. The application of Convergent Cross Mapping (CCM) provides deeper insights, particularly demonstrating causality from ONI to air temperature anomalies and from the Niño 1+2 index to precipitation anomalies. The findings of this study may suggest targeted strategies for local adaptation and resilience, contributing to a broader understanding of global climate variability.

A time of emergence (TOE) analysis on the impact and uncertainty of global warming on Korean peak summers

In recent years, South Korea has experienced a notable escalation in the intensity and frequency of summer heat. To quantitatively gauge the impact of global warming on Korean summers, this study employs the Time of Emergence (TOE) method, assessing when the effects of global warming surpass natural climate variability. Determining a precise regional TOE is challenging due to disparities between modeled climates and observations. For peak summer seasons (July and August), TOE estimates range from the 2010s to the early 2030s in Shared Socioeconomic Pathways (SSP) 5-8.5, suggesting an imminent or already reached TOE. However, for the same scenario, different methodologies and datasets project the TOE to the late 21st century, suggesting the existence of uncertainty in the TOE. One reason for this uncertainty is the discrepancies identified between climate models and observations, which suggest that climate models could delay the TOE beyond the present time. Furthermore, from 1959 to 2014, global warming accounts for less than 10% of the observed temperature. Despite this, the strengthening of global warming signals is confirmed, leading to the expectation of more extreme events than those seen in the 2018 heat wave. This raises questions about current estimates of TOE and emphasizes the need for robust climate modeling to inform effective climate action.

Changing effects of external forcing on Atlantic–Pacific interactions

Abstract. Recent studies have highlighted the increasingly dominant role of external forcing in driving Atlantic and Pacific Ocean variability during the second half of the 20th century. This paper provides insights into the underlying mechanisms driving interactions between modes of variability over the two basins. We define a set of possible drivers of these interactions and apply causal discovery to reanalysis data, two ensembles of pacemaker simulations where sea surface temperatures in either the tropical Pacific or the North Atlantic are nudged to observations, and a pre-industrial control run. We also utilize large-ensemble means of historical simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to quantify the effect of external forcing and improve the understanding of its impact. A causal analysis of the historical time series between 1950 and 2014 identifies a regime switch in the interactions between major modes of Atlantic and Pacific climate variability in both reanalysis and pacemaker simulations. A sliding window causal analysis reveals a decaying El Niño–Southern Oscillation (ENSO) effect on the Atlantic as the North Atlantic fluctuates towards an anomalously warm state. The causal networks also demonstrate that external forcing contributed to strengthening the Atlantic's negative-sign effect on ENSO since the mid-1980s, where warming tropical Atlantic sea surface temperatures induce a La Niña-like cooling in the equatorial Pacific during the following season through an intensification of the Pacific Walker circulation. The strengthening of this effect is not detected when the historical external forcing signal is removed in the Pacific pacemaker ensemble. The analysis of the pre-industrial control run supports the notion that the Atlantic and Pacific modes of natural climate variability exert contrasting impacts on each other even in the absence of anthropogenic forcing. The interactions are shown to be modulated by the (multi)decadal states of temperature anomalies of both basins with stronger connections when these states are “out of phase”. We show that causal discovery can detect previously documented connections and provides important potential for a deeper understanding of the mechanisms driving changes in regional and global climate variability.

Atmospheric oxygenation as a potential trigger for climate cooling

Secular changes in atmospheric CO2 and consequent global climate variations, are commonly attributed to global outgassing and the efficiency of silicate weathering, which may have been linked to mountain formation, land/arc distribution, and plant colonization through geological time. Although oxidative weathering has been shown to exert a significant role in the propagation of weathering fronts through the oxidation of Fe-bearing minerals, the influence of atmospheric O2 concentration (pO2) on silicate weathering, CO2 consumption, and global climate has not been thoroughly evaluated. This study presents a numerical model aimed at estimating the effects of pO2 on the climate, considering the influence of pO2 on the regolith thickness and thus weathering duration of granitic domains. Our model simulations reveal that an increase in weathering efficiency, through deeper penetration of the oxidative weathering front in the granitic regolith, would independently introduce a steady-state climate cooling of up to ∼8 °C, in step with one-order of magnitude rise in pO2. This temperature change may have repeatedly initiated the runaway ice-albedo feedback, leading to global glacial events (e.g., Neoproterozoic Snowball Earth). Increasing granitic weathering efficiency caused by a substantial pO2 increase may also have contributed to the development of icehouse climate during the Phanerozoic.

Bivalve tissues as a recorder of multidecadal global anthropogenic and climate‐mediated change in coastal areas

AbstractRecent rapid changes in climate and environmental conditions have significantly impacted coastal ecosystem functioning. However, the complex interplay between global and local effects makes it challenging to pinpoint the primary drivers. In a multi‐ecosystem study, we analyzed pluri‐decadal trends of bivalve‐δ13C as recorder of global environmental changes. These trends were correlated with large‐scale natural and anthropogenic climate proxies to identify whether coastal biota responded to global effects. Our findings revealed decreasing bivalve‐δ13C trends in all sea regions, mainly linked with increased temperature and atmospheric‐CO2 concentrations, the later generating a decrease in atmospheric‐CO2 δ13C values (Suess effect) because of fossil‐fuel burning. After removing the Suess effect from bivalve‐δ13C trends, ongoing global climate variability continues to affect most ecosystems, possibly intensified by combined, interacting regional or local effects. These results highlight the need to consider large‐scale effects to fully understand ecosystem and food web responses to the multiple effects of global change.

Strong Oceanic Forcing on Decadal Surface Temperature Variability Over Global Ocean

AbstractSea surface temperature (SST) variability on decadal timescales has been associated with global and regional climate variability and impacts. The mechanisms that drive decadal SST variability, however, remain highly uncertain. Many previous studies have examined the role of atmospheric variability in driving decadal SST variations. Here we assess the strength of oceanic forcing in driving decadal SST variability in observations and state‐of‐the‐art climate models by analyzing the relationship between surface heat flux and SST. We find a largely similar pattern of decadal oceanic forcing across all ocean basins, characterized by oceanic forcing about twice the strength of the atmospheric forcing in the mid‐ and high latitude regions, but comparable or weaker than the atmospheric forcing in the subtropics. The decadal oceanic forcing is hypothesized to be associated with the wind‐driven oceanic circulation, which is common across all ocean basins.

Oceanic Pollution and Its Influence on Global Climate Variability in India

Purpose: The aim of the study was to analyze oceanic pollution and its influence on global climate variability. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Oceanic pollution, including plastics, oil spills, heavy metals, and chemical contaminants, significantly impacts global climate variability. These pollutants harm marine ecosystems, disrupt food webs, and contribute to climate change. Plastic debris poses a severe threat to marine life, while oil spills devastate coastal environments. Heavy metals and chemical contaminants bio accumulate in marine organisms, posing risks to human health and altering biogeochemical processes essential for climate regulation. Unique Contribution to Theory, Practice and Policy: Ocean-atmosphere interaction theory, pollutants as climate forcers theory & feedback loop theory of oceanic pollution may be used to anchor future studies on oceanic pollution and its influence on global climate variability. Implement practical measures to reduce oceanic pollution, including stricter regulations on waste disposal, improved waste management infrastructure, and the promotion of sustainable consumption practices. Foster collaboration among nations to address oceanic pollution on a global scale.

METEO-DLNet: Quantitative Precipitation Nowcasting Net Based on Meteorological Features and Deep Learning

Precipitation prediction plays a crucial role in people’s daily lives, work, and social development. Especially in the context of global climate variability, where extreme precipitation causes significant losses to the property of people worldwide, it is urgently necessary to use deep learning algorithms based on radar echo extrapolation for short-term precipitation forecasting. However, there are inadequately addressed issues with radar echo extrapolation methods based on deep learning, particularly when considering the inherent meteorological characteristics of precipitation on spatial and temporal scales. Additionally, traditional forecasting methods face challenges in handling local images that deviate from the overall trend. To address these problems, we propose the METEO-DLNet short-term precipitation prediction network based on meteorological features and deep learning. Experimental results demonstrate that the Meteo-LSTM of METEO-DLNet, utilizing spatial attention and differential attention, adequately learns the influence of meteorological features on spatial and temporal scales. The fusion mechanism, combining self-attention and gating mechanisms, resolves the divergence between local images and the overall trend. Quantitative and qualitative experiments show that METEO-DLNet outperforms current mainstream deep learning precipitation prediction models in natural spatiotemporal sequence problems.

Structure, modulation and impacts of southeastern Africa’s diurnal convection

This study explores the structure, modulation, and impacts of the mean diurnal climate across southeast Africa using high-resolution hourly reanalysis and satellite rainfall. This research was motivated by upward trends in diurnal temperature range and vegetation index over the Drakensberg escarpment. Statistical analysis identified a land-sea rainfall dipole pattern associated with diurnal airflows. Point-to-field regressions (Oct–Mar 2000–2023) found that sea breeze-driven convection is underpinned by ‘col’ airflow between adjacent sub-tropical low-pressure cells (30°S, 20°E/22°S, 37°E) and a weak mid-latitude anticyclone (40°S, 10°E). Diurnal residual and case study analysis demonstrate an ~ 6-m/s sea breeze penetrating ~ 200 km inland during the day, as part of an over-turning zonal circulation. Land > sea rainfall during summer is initiated by temperature gradients ∂T/∂x ~ 5 °C at 29°S, 31°E and convergence in the afternoon ∂U/∂x ~ − 4 × 10−5 s−1. Further inland (29°S, 26°E) a 1007-hPa thermal low and counter-clockwise rotating airflows interact with the incoming sea breeze. Together these contribute to evening rainfall ~ 30 mm over the Drakensberg escarpment. The drivers of diurnal rainfall show little response to global climate variability, and help sustain water resources during multi-year dry spells over southeastern Africa.

Impacts of Climate Change and Human Activity on Lakes around the Depression of Great Lakes in Mongolia

The western region of Mongolia is characterized by an arid climate and a fragile ecological environment. It is a sensitive zone in response to global climate change and one of the major sources of dust globally. This region is home to numerous lakes, and their dynamic changes not only reflect global climate variations but also have implications for the global ecological environment quality. In this study, Landsat images were used as the data source, and Google Earth Engine (GEE) was employed to extract lakes with an area larger than 1 km2 from 1992 to 2021. The spatiotemporal characteristics of lake water area (LWA) changes were analyzed, and a structural equation model was applied to attribute the lake changes. The results indicate an overall trend of increasing lake area followed by a decrease in the study area. Specifically, lakes in the provinces of Khovd and Gobi-Altai exhibited a decreasing trend followed by an increasing trend, while lakes in the provinces of Uvs and Zavkhan showed an increasing trend followed by a decreasing trend. Three typical types of lakes, namely, alpine lakes, throughflow lakes, and terminal lakes, all exhibited a trend of increasing area followed by a decrease. The analysis of driving forces behind lake area changes reveals that climate change and human activities primarily exert indirect influences on the lake area changes in each province. Specifically, climate change and human activities lead to changes in soil moisture, which have a significant explanatory power for lake area changes. Regarding the typical types of lakes, climate change serves as the primary driving force for alpine lakes, while human activities are the main driving forces for throughflow lakes and terminal lakes.