Regional Climate Change Research Articles

Climate change regulations in subnational governments of the Southeast Asian countries: case studies from Indonesia and the Philippines

As of 2023, all Association of Southeast Asian Nations member states (AMS) have ratified the 2015 Paris Agreement and submitted their Nationally Determined Contributions (NDCs). The majority of these NDCs acknowledge the critical role of subnational governments in fulfilling their climate commitments. This research examines the scope of subnational climate regulations and policies through case studies in Indonesia and the Philippines. The findings reveal that in both countries, subnational climate regulations and policies fall short of matching the ambition of the national NDCs. The research identifies several factors contributing to this gap, including: (i) regulations being introduced superficially; (ii) slow transmission of policies from national to subnational levels; (iii) a focus on short-term projects; (iv) a lack of prioritisation of climate issues; (v) limited understanding and awareness of climate change impacts at the local level; and (vi) the absence of a clear framework for integrating other non-state actors.

Space-based long term condition monitoring of cold region pavement with PS-InSAR

The durability of cold-climate pavement is affected by ground frost heave and thaw settlement. Field monitoring of pavement elevation changes remain challenging and expansive. This study demonstrates space-based Interferometric Synthetic Aperture Radar (InSAR) technology as a potential way to monitor seasonal pavement elevation change in cold region. Traditional InSAR applications, which were designed for geoscience scales, do not achieve the needed sensitivity or spatial resolution for pavement engineering applications. The testbed used is a cold-region airport located in Qinghai Province, China. The site contains heterogeneous ground conditions in an area of 4km × 6km. Analyses were conducted using 132 ascending and 149 descending C-band InSAR images from the Sentinel-1 satellite. InSAR algorithm based on Persistent scatterers (PS-InSAR) were implemented for data analyses. Time series displacement across five distinct persistent scatterers (PSs) groups at different locations were obtained. From these, three main seasonal vertical ground deformation patterns were identified, each reflecting the diverse subsurface soil characteristics across the sites. The ground elevation changes were decomposed into baseline trend and seasonal changes. The results of seasonal ground deformations allowed to differentiate the ground soils as frost-susceptible and non-frost-susceptible, an important information for geotechnical site investigation. This helps identify areas of pavement subjected to frost heave and thaw weakening. The study demonstrates that potential of space-based InSAR technology to achieve spatial resolution and sensitivity for monitoring the performance of cold region pavement. The results allow to identify areas of pavement vulnerable to cold region climate and climate change.

Climate change projections in temperature and precipitation using cmip6 in Central Mexico

Knowing the spatiotemporal variability of temperature and precipitation, as well as their future projections, is essential to assess environmental risks and plan long-term mitigation and adaptation. In this study, the General Circulation Models (GCMs) HadGEM3-GC31-LL, MRI-ESM2-0 and ACCESS-ESM1-5 of the Coupled Model Intercomparison Project (CMIP6) have been used, under two Shared Socioeconomic Pathways (SPP2-4.5 and SSP5-8.5), to project the minimum and maximum air temperature and the mean annual precipitation in Atlacomulco Rural Development District (ARDD). 30 meteorological stations were used to perform regionalized climate change projections by downscaling using LARS-WG 7. The expected changes were evaluated for three time horizons: near horizon (2021–2040), mid horizon (2041–2060) and far horizon (2061–2080); compared to the historical period (1985–2017). Based on the results, the annual minimum temperature towards the far horizon is projected to increase by 2.0 ºC with SSP2-4.5, and 3.1 ºC with SSP5-8.5; while the annual maximum temperature towards the far horizon is projected to increase between 2.5 ºC with SSP2-4.5 and 3.8 ºC using SSP5-8.5. Precipitation shows an increase for SSP2-4.5 in the near and middle horizons with 15.7 and 42.5 mm.year−1, respectively, while the largest decrease is projected towards the middle horizon of SSP5-8.5 with − 26.3 mm.year−1. The findings of this study provide detailed information on climate change projections in the ARDD and can serve as a useful guide for the management of different ecosystems.

Data Driven-Based Machine Learning Modelling and Empirical Correlations for Predicting Snow-Covered Area in the Swat Region, Pakistan

In recent decades, global and regional climate change has emerged as a significant challenge with potential catastrophic consequences, including hurricanes, floods, sea level rise, and temperature shifts. Snow-covered area (SCA) serves as a crucial climatic parameter reflecting climate changes, yet accurately determining SCA proves to be a challenging and time-consuming task. This study aims to develop robust prediction models for SCA by employing three machine learning (ML) approaches using readily available climatic data from Swat, Pakistan, spanning two decades. The climate data encompass precipitation, daily maximum/minimum temperatures, and SCA measurements. Three ML methods—artificial neural networks (ANN), functional networks (FN), and adaptive neuro-fuzzy inference systems (ANFIS)—were employed to model SCA. Accuracy measures, including coefficient of determination (R2), average absolute percentage error (AAPE), and root mean squared error (RMSE) were utilized to evaluate model performance. All three ML models exhibited superior performance, with high R2 values and low errors. Accuracy indicators of the ANN model are better than FN and ANFIS models, yielding the highest R2 (0.956) and minimum RMSE and AAPE values (0.61 and 0.91). ANFIS demonstrated slightly better performance than FN, with RMSE, AAPE, and R2 values of 0.65, 1.1, and 0.950, respectively. FN yielded RMSE, AAPE, and R values of 1.14, 1.72, and 0.85, respectively. Additionally, two empirical correlations were derived from the optimized FN and ANN models for SCA prediction using the same input variables. This study underscores the efficacy of ML techniques in accurately and consistently predicting SCA parameters, offering valuable insights into climate change and its consequences.

Regional climatic changes and their impact on the level of avalanche hazard in East Kazakhstan

Regional climatic changes and their impact on the level of avalanche hazard in East Kazakhstan

How to develop nature-based solutions for revegetation on abandoned farmland in the Loess Plateau of China?

Adequate revegetation of abandoned farmland acts as a defence against desertification and soil loss, and can help remove carbon dioxide in the atmosphere, thereby playing an important role in regulating regional climate change. Legume, a nitrogen-fixation species, which could effectively improve vegetation coverage to control soil erosion, was widely used for revegetation. However, the dynamics of soil and plant development after legume introduction on abandoned farmland remain unclear. A 16-year in situ experiment including three treatments, natural abandonment (fallow), planting of alfalfa (Medicago sativa L.), and sweet clover (Melilotus officinalis L.) was conducted on bare farmland of the Loess Plateau in 2003-2018. The results showed that initially introduced species significantly affected the potential succession patterns in the community. Alfalfa introduction decreased plant community stability (CS) and hindered plant species establishment in early successional stages due to inter/intraspecific competition caused by high aboveground biomass (AB). Plant CS was affected by species evenness, AB, revegetation time and revegetation methods. Sweet clover facilitated succession process by rapidly improving soil conditions (organic carbon, nitrogen, and phosphorus) and quickly exiting from the community after its life span to avoid further competitive effects. During 2003-2018, the soil (water storage, organic carbon, nitrogen, and phosphorus), plant (AB, CS), and ecological related variables (plant diversity and soil carbon sequestration) contributed 60.1%, 15.7% and 20.2%, respectively, to the ecosystem health. Alfalfa planting increased ecosystem health index (EHI) in the long-term while sweet clover favours plant diversity, providing less overall EHI but recover faster than natural abandonment community. We concluded that alfalfa introduction, which provides the greatest AB, is a good option for comprehensively improving ecosystems (e.g., soil nutrient sequestration and control soil erosion) if the site in question suffers from few disturbances. Sweet clover introduction, however, is recommendable for restoring native biodiversity effectively if disturbances are frequent.

Circular Water and Wastewater Management in State College, Pennsylvania

In recent years, great attention has been paid to activities aimed at implementing a circular economy (CE) in the management of water resources around the world. One of the possibilities for the practical implementation of CE, based on the sustainable management of primary and secondary resources (waste), is the circular management of water and wastewater generated in urban wastewater treatment plants (WWTPs). This work presents examples of good practices in the implementation of CE in the college town of State College, Pennsylvania (United States of America). There are two WWTPs here – one belonging to Penn State University (Water Reclamation Facility) and another operated by the municipality (University Area Joint Authority). Both facilities implement CE goals through various initiatives dedicated to water, raw materials and energy recovery. The scope of these activities include using reclaimed water for irrigation of green areas, production of renewable energy, as well as recovery of biogenic components by processing sewage sludge in compost. This approach, where the university and municipality propose solutions in the environmental and social areas, is consistent with the idea of building social responsibility of units, which is a path to sustainability. Further actions to implement the CE model are expected to counteract ongoing climate change in various regions.

Turkey’s Climate Change Policies and Regulations

This paper examines the relationship between exchange rate pass-through (ERPT) and inflation within dynamic structure, focusing on the impact of exchange rate changes on consumer prices (CPI) in Türkiye. The theoretical and empirical analysis builds on existing frameworks, considering how central banks’ reactions to exchange rate movements play a crucial role in shaping inflationary dynamics. The study uses the ARDL model to analyse the long-term and short-term effects of exchange rate fluctuations on inflation, focusing on the interaction between the CPI and the nominal exchange rate (NEX). For these goals, we employed monthly data over the January of 2000 and August of 2024. The analysis is based on two baseline models: the first examines how changes in the exchange rate directly impact the CPI, while the second assesses the rate of change in inflation due to exchange rate variations. Stationarity of the data is ensured through unit root testing, which confirms the appropriateness of using the ARDL model. The results reveal that exchange rate movements significantly affect CPI, with a 1% increase in the exchange rate leading to a 1.1625% increase in th e CPI in the long run, as evidenced by a p-value of 0.00. Additionally, in the short term, a 1% increase in the exchange rate results in a 0.1164% rise in the CPI, indicating a persistent effect on prices. The error correction term further supports the existence of a significant relationship between the CPI and the exchange rate in both short and long-term dynamics. Based on model 2, exchange rate changes have a significant effect on inflation in the long-term relationship. In particular, it has been shown that exchange rate increases can lead to long-term increases in inflation.

Advances in Environmental Monitoring of the Caspian Sea

The paper provides a brief overview of 11 papers published in the Special Issue of Ecologica Montenegrina entitled "Advances in Environmental Monitoring of the Caspian Sea". These papers are devoted to different aspects of environmental issues of the Caspian Sea from regional climate change and dramatic sea level decline to oil pollution and algal bloom among the others. Special attention is paid to the ecologically and biologically significant marine protected areas in the Caspian Sea and the need to organize Marine Protected Areas in the region. The contributions in this special issue not only shed light on the environmental problems facing the Caspian Sea, but also emphasize the importance of interdisciplinary approaches and constant improvement of methods aimed at obtaining reliable and timely environmental data of this unique ecosystem. A combination of satellite remote sensing methods with in-situ measurements allows to improve a quality of remote sensing data via calibration and elaboration of regional algorithms. From the other side, this will improve a quality of satellite monitoring of the ecological state of whole area of the Caspian Sea which is of vital importance during a continuous decline of the sea level. The presented results further evidence the need for continuous satellite monitoring of areas subjected to ecological risks in the Caspian Sea.

Adaptation of rice to climate change in high-latitude regions of China: Will price expectation make differences?

Adaptation of rice to climate change in high-latitude regions of China: Will price expectation make differences?

The Effects of Territorial Usage on Polar Regıons and Polar Policies of Türkiye

Rising temperatures in the Arctic and Antarctic, driven by climate change, have opened new land and sea routes, posing significant threats to the biodiversity and fragile ecosystems in these regions. This article examines the impacts of climate change in the polar regions, emphasizing the importance of protecting aquatic ecosystems and addressing inter-country ownership and utilization policies. The study highlights that, beyond safeguarding economic and political rights, the demarcation of property boundaries, marine cadastre, and other utilization strategies are essential to ensuring the sustainability of aquatic ecosystems. By analyzing the Turkish Cadastre System alongside those of countries near Antarctica and prominent nations recognized for their advanced cadastral systems worldwide, 13 countries were identified for detailed examination. The findings underscore the potential benefits of establishing marine protected areas and adopting ecosystem-oriented management approaches within the framework of international arrangements, such as the Arctic Council and the Antarctic Treaty System (ATS). Furthermore, the article stresses the importance of enhancing Türkiye`s scientific research and policy efforts in the polar regions, ensuring that activities are conducted in alignment with environmental responsibility. The text emphasizes the necessity for human activities in polar regions to prioritize both ecosystem sustainability and international collaboration.

Estimation of the Net Primary Productivity of Grasslands in the Qinghai Tibet Plateau Based on a Machine Learning Model and Sensitivity Analysis to Climate Change

This study applies the Multilayer Perceptron (MLP) and Random Forest (RF) models, utilizing remote sensing and ground-based net primary productivity (NPP) data from 1992 to 2020, along with meteorological data and soil properties, to model the NPP in the alpine grassland and alpine meadow ecosystems of the Qinghai-Tibetan Plateau (TP) and assess their sensitivity to climate change. As a vital ecological barrier, the TP’s grassland ecosystems are critical for understanding the impacts of climate change. However, sensitivity analysis of the NPP in the TP grasslands has been limited, which this study aims to address by focusing on the effects of maximum temperature, solar radiation, and wind speed on the NPP. The results show that the MLP model outperforms the RF model in prediction accuracy (R2 = 0.98, RMSE = 16.24 g C·m−2·a−1, MAE = 9.04 g C·m−2·a−1). NPP responses to climate factors are diverse: linear with temperature and nonlinear with solar radiation and wind speed. Under multi-factor scenarios, the NPP in both alpine meadow and alpine grassland exhibit nonlinear trends, with a higher sensitivity to changes in all three factors than to single- or two-factor changes. Spatial distribution analysis revealed that the NPP in alpine meadows was more sensitive to climate change in the southern regions, while alpine grassland showed greater sensitivity in the central regions. This study, using machine learning models and sensitivity analysis, sheds light on the complex response of the NPP in the TP grasslands to climate change, offering valuable insights for carbon cycle research in cold ecosystems and regional climate adaptation management.

An Evaluation of Dynamical Downscaling Methods Used to Project Regional Climate Change

AbstractIn the past decade, dynamical downscaling using “pseudo‐global‐warming” (PGW) techniques has been applied frequently to project regional climate change. Such techniques generate signals by adding mean global climate model (GCM)‐simulated climate change signals in temperature, moisture, and circulation to lateral and surface boundary conditions derived from reanalysis. An alternative to PGW is to downscale GCM data directly. This technique should be advantageous, especially for simulation of extremes, since it incorporates the GCM's full spectrum of changing synoptic‐scale dynamics in the regional solution. Here, we test this assumption, by comparing simulations in Europe and Western North America. We find that for warming and changes in temperature extremes, PGW often produces similar results to direct downscaling in both regions. For mean and extreme precipitation changes, PGW generally also performs surprisingly well in many cases. Moisture budget analysis in the Western North America domain reveals why. Large fractions of the downscaled hydroclimate changes arise from mean changes in large‐scale thermodynamics and circulation, that is, increases in temperature, moisture, and winds, included in PGW by design. The one component PGW may have difficulty with is the contribution from changes in synoptic‐scale variability. When this component is large, PGW performance could be degraded. Global analysis of GCM data shows there are regions where it is large or dominant. Hence, our results provide a road map to identify, through GCM analyses, the circumstances when PGW would not be expected to accurately regionalize GCM climate signals.

METHODS AND TOOLS USED TO INVESTIGATE INTERACTION BETWEEN AEROSOLS AND CLOUDS

The interaction between aerosols and clouds is a relevant factor in global, regional and local climate change. The scientific community is trying to provide relevant theoretical and experimental benchmarks on aerosol loading, cloud formation patterns and trends. Aerosol–cloud interactions play a vital role in global climate change and are associated with one of the greatest uncertainties. In recent decades, due to its unique geographical location, the North Indian Ocean (NIO) has been gaining significant attention among scientific communities. Deep understanding of aerosols and their interaction with clouds in this region is very important both regionally and globally. The paper provides both an overview of the most used methods and tools used in experimental approaches in the field, as well as an analysis methodology regarding aerosol-cloud interaction scenarios.

Identification of Climate Change Risks to Surface and Groundwater Resources across the Central African Republic and Resilience Measures

The extensive hydrological and hydrogeological systems across the Central African Republic (CAR) provide essential urban and rural water supplies for the populations across the country. Hydrological and hydrogeological studies during the last 20 years are extremely limited, in part due to the complex security and socio-economic context. As a result, significant uncertainties exist related to water resource availability and infrastructure management. This paper examined the recent evolution of the meteorological, hydrological, and hydrogeological regimes across CAR and the potential future impacts of climate change on surface and water resource availability. This was undertaken by analyzing the available regional hydrometry data and using regional climate change projections to evaluate changes to rainfall and evapotranspiration patterns by 2050. A lack of long-term monitoring data and operational monitoring systems prevents a detailed assessment of these risks at local levels. Although the national scale findings suggest that water resources may slightly increase over the next 40 years, significant local risks remain due to intensifying, increasingly variable rainfall, and increasing demographic pressures. The lack of functional, national-scale water resource monitoring systems contributes to the fragile resilience of the populations across CAR in the face of increased climate-associated risks. Recommendations are included for measures to help characterize and manage the potential risks in the long-term including the implementation of robust water resource monitoring systems

Combining climate models and observations to predict the time remaining until regional warming thresholds are reached

Abstract The importance of climate change for driving adverse climate impacts has motivated substantial effort to understand the rate and magnitude of regional climate change in different parts of the world. However, despite decades of research, there is substantial uncertainty in the time remaining until specific regional temperature thresholds are reached, with climate models often disagreeing both on the warming that has occurred to-date, as well as the warming that might be experienced in the next few decades. Here, we adapt a recent machine learning approach to train a convolutional neural network to predict the time (and its uncertainty) until different regional warming thresholds are reached based on the current state of the climate system. In addition to predicting regional rather than global warming thresholds, we include a transfer learning step in which the climate-model-trained network is fine-tuned with limited observations, which further improves predictions of the real world. Using observed 2023 temperature anomalies to define the current climate state, our method yields a central estimate of 2040 or earlier for reaching the 1.5 °C threshold for all regions where transfer learning is possible, and a central estimate of 2040 or earlier for reaching the 2.0 °C threshold for 31 out of 34 regions. For 3.0 °C, 26 °C out of 34 regions are predicted to reach the threshold by 2060. Our results highlight the power of transfer learning as a tool to combine a suite of climate model projections with observations to produce constrained predictions of future temperatures based on the current climate.

Observed Variability and Future Projections of Urban Heatwaves in Romania

ABSTRACTThe crossroad between built‐up areas and extreme heat generates severe consequences on both socio‐ecological systems and the natural environment, and the cities are the most vulnerable. The impact is already significant in the present climate and it will be exacerbated under climate change in many regions, including Romania and especially its southern part. The heatwaves (HWs) are a major risk for our cities as they put constant pressure on population, infrastructure and services for several consecutive days. The urban HWs in 41 Romanian cities are analysed in terms of magnitude, amplitude, duration, number and frequency, as well as the variability of the yearly occurrence of the first and last HW events. The study focuses on the warm season (May–September) and refers to the observed variability (1961–2020) and future projections (2021–2050). The main findings reveal that the occurrence of HWs is not conditioned by geographical and climatic conditions and the cities in any region may experience public health risks associated with extreme heat. This requires permanent monitoring of the phenomena, including the present characteristics and estimating future variations according to different scenarios. Considering the increasing frequency and intensity of extreme heat events expected soon, there is a clear need for region‐specific adaptation, and policymakers should prioritise strategies to protect vulnerable people.

Desertification Reduces Organic Carbon Content and Nutrient Availability in Dryland Soils: Evidence From a Survey in the Amu Darya River Basin

ABSTRACTDesertification degrades drylands, undermining ecosystem services and human well‐being as a serious global issue. Assessing desertification impacts on soil organic carbon (SOC) and properties aids in preserving drylands and combating desertification. However, knowledge gaps persist regarding desertification effects on SOC and soil in Central Asia's Amu Darya River Basin. Surveys across ~1000 km in this region showed that desertification decreased SOC by ~52% versus natural vegetation. Desertification also reduced nutrient availability and altered hydrothermal conditions, pH, stoichiometry, and herbaceous richness. In particular, it decreased soil clay and silt while increasing sand content. Regional climate change and prolonged grazing are primary desertification drivers here. These pronounced impacts on soils and vegetation may threaten ecological processes and services. Our findings contribute to dryland restoration efforts and resilience against desertification.

Assessment of climate change impact on meteorological variables of Indravati River Basin using SDSM and CMIP6 models.

Climate change, one of the most pressing issues of the twenty-first century, threatens the long-term stability and short-term variability of water resources. Variations in precipitation and temperature will influence runoff and water availability, creating significant challenges as demand for potable water increases. This study addresses a critical literature gap by employing the Statistical Downscaling Model (SDSM) to downscale Global Climate Model (GCM) outputs for the Indravati River Basin, India. Maximum temperature (Tmax), minimum temperature (Tmin), and precipitation (PCP) were statistically downscaled, improving the spatial resolution of coarse GCM data. The model established strong predictor-predictand relationships, offering enhanced local-scale climate projections for the basin. This work provides critical insights into regional climate change impacts in a previously underexplored area. The study projected the meteorological variables (Tmax, Tmin, and PCP) for Chindnar, Jagdalpur, and Pathagudem stations using four GCMs, namely CanESM5, MPI-ESM1-2-HR, EC-Earth3, and NorESM2-LM for the baseline period (1990-2014). The Correlation Coefficient-values (R-values) range from 0.75 to 0.91 for maximum temperature, 0.85 to 0.96 for minimum temperature, and 0.71 to 0.83 for precipitation were achieved using SDSM. The best-performed MPI-ESM1-2-HR model was used to project maximum temperature, minimum temperature, and precipitation for 2024-2054 (2040s) and 2055-2085 (2070s) under SSP4.5 and SSP8.5 scenarios using SDSM. The downscaled results revealed significant shifts in meteorological patterns, highlighting the basin's sensitivity to different socio-economic pathways and future climate conditions. The percentage monthly, seasonal, and annual variations of Tmax, Tmin, and PCP were analysed based on each scenario and time period to suggest remedial measures for future floods and droughts.

United States pasture and rangeland conditions: 1995–2022

AbstractUSDA National Agricultural Statistics Service pasture and rangeland condition data were used to establish a novel spatiotemporal climatology of condition ratings across the conterminous United States for the May–October grazing season over the 1995–2022 study period. On average, the coverage of grazing land that provides adequate or excess feed underwent a significant reduction during a typical season. Spatially, the southwestern United States exhibited the poorest grazing land conditions on average, with over 20 years below the national mean condition rating. At the national aggregated level, conditions degraded during the 28‐year study period, and the most significant trends were observed for grazing lands considered to have poor or very poor condition coverage, which increased. Robustly increasing trends in poor and very poor condition coverage were most apparent across the western half of the United States, which is predominantly rangeland. Meanwhile, the eastern half of the United States, which is mostly pastureland, generally experienced condition improvements. Overall, continued regional climatic changes that may result in increasing temperatures, variable precipitation totals, and subsequent soil moisture declines leading to increased drought instances will continue to impose challenges for grazing land managers. Grazing land condition declines can result in increased feed supply demand and reduced grazing capacity. Should these trends continue, there will be a growing need for flexible livestock, forage, and grazing management strategies in the coming decades to adapt to climate change‐induced impacts on water‐sensitive ecosystems.