Local Climate Research Articles

Trend Analysis of Hydro-Meteorological Variables in the Wadi Ouahrane Basin, Algeria

In recent decades, a plethora of natural disasters, including floods, storms, heat waves, droughts, and various other weather-related events, have brought destruction worldwide. In particular, Algeria is facing several natural hydrometeorological and geological hazards. In this study, meteorological parameters (precipitation, temperature, relative humidity, wind speed, and sunshine) and runoff data were analyzed for the Wadi Ouahrane basin (northern Algeria), into which drains much of the surrounding agricultural land and is susceptible to floods. In particular, a trend analysis was performed using the Mann–Kendall (MK) test, the Sen’s slope estimator, and the Innovative Trend Analysis (ITA) method to detect possible trends in the time series over the period 1972/73–2017/2018. The results revealed significant trends in several hydro-meteorological variables. In particular, neither annual nor monthly precipitation showed a clear tendency, thus failing to indicate potential changes in the rainfall patterns. Temperature evidenced a warming trend, indicating a potential shift in the local climate, while streamflow revealed a decreasing trend, reflecting the complex interaction between precipitation and other hydrological factors.

Fundamental LCZ Mapping with ArcGIS-Based Spatial and Correlational Analyses to Study the Impacts of Infrastructural Area- Maximum Volume (A-MV) Attributes on OTC – An Urban Aw Summer Case

The aim of this study is to map the study location stretches into 100mx100m local climate zones (LCZs) on the basis of primary observations and secondary photographic data for spatial analysis of various urban infrastructural elements (grey(G)-built structures, blue(B)-water bodies and green (Gr)-natural vegetation) and impact assessment of their estimated area (EA) and maximum volume (EMV)) on the air temperature (Ta) that characterizes the outdoor thermal comfort (OTC). Climate Modification by Urban Infrastructure, Fundamental Concepts of Urban Infrastructure Mensuration Parameters, Fundamental Concepts of Urban Grey Blue Green Infrastructure (UGBGI) and Concepts Of OTC, Koppen Classification and LCZs are the linked areas. Study locations are based on socio-economic and geophysical attributes. Initial 200 pilot survey responses regarding thermal sensation votes and other subjective details were used. Preferred values of Physiological Equivalent Temperature (PET) and optimum Ta have been used. After the mapping in ArcGIS and polygonal delineation of the areas, EA values were compiled in tabular format. Based on primary measurements and secondary studies, a maximum height (hmax) value was assigned to each sub-category for EMV evaluation. Statistically, the EA and estimated perimeter (EP) values were analyzed using QQ, scatter plots, histogram and generate near table tools in ArcGIS. EA and EMV values were correlated with mean Ta values by linear regression. The R-square values generated gave a clear idea about the extent of their impacts on Ta. From the established relationships and preferred PET and optimum Ta values, optimum EA, EMV and hmax values of the broader categories(G,B,Gr) required for preferred OTC levels were finally evaluated.

Hydrometeorological response to afforestation in the UK: findings from a kilometer-scale climate model

Afforestation is of international interest for its positive benefits on carbon storage, ecology, and society, but its impacts on terrestrial and atmospheric processes are still poorly understood. This study presents the first use of a coupled land surface and convection permitting atmospheric model (CPM) to quantify hydrometeorological effects of afforestation across the United Kingdom, focusing on atmospheric processes often missing in hydrological models. Generating a scenario of 93 000 km2 (40%) additional woodland across the UK, the periods of 2042–2052 and 2062–2072 are analysed. Simulated afforestation alters seasonal and regional UK hydrometeorology. Countrywide runoff increases in all seasons (between 5.4–11 mm and 4.3–8.6% per season) due to elevated subsurface flows from greater soil moisture. Evaporation decreases in summer (−20.6 mm, −10%) but increases in winter (8.1 mm, 15%) whereas rainfall increases throughout all seasons (between 2.2–6.86 mm and 0.9%–2.2% per season). Greater winter rainfall is detected along Great Britain’s west coastline as increased surface roughness produces prolonged and heavier rainfall. In the summer, lower albedo increases potential evapotranspiration and reduces near surface specific humidity: water is locked in deeper soil layers as transpiration diminishes and the topsoil dries out. However, the magnitude of hydrometeorological change due to altered land cover is smaller than the uncertainty in local climate change projections. This work sets a precedent in illustrating the impacts of afforestation on hydrology using a high-resolution CPM and highlights the importance of coupled hydrometeorological processes when investigating land cover impacts on hydrological processes.

Multi-Scale Urban Natural Ventilation Climate Guidance: A Case Study in the Shijiazhuang Metropolitan Area

The rapid development of urbanization has caused obstructed urban natural ventilation and the contribution rate of urbanization is relatively high. Therefore, there is an urgent need for urban development planning that should respect natural ventilation and local climate to reduce negative impacts. By optimizing the urban construction layout to reduce obstruction and leave a passageway for wind to blow in, the natural ventilation environment could be improved. This paper presents a promising approach for natural ventilation planning at both the city and community scales. Based on the assessment of wind environment, heat island intensity, and ventilation potential, the results revealed that winds blowing from the western and northern mountainous area of Shijiazhuang play a natural ventilation inlet role which can provide clean air. The SSHI and SHI were mainly distributed within the Second Ring Road, which has a large proportion of the low ventilation potential level. Thus, six first-class ventilation corridors and thirteen secondary corridors were recommended, which were set to be adapted to the dominant wind direction. Subsequently, an urban climate analysis map (UCAnMap) was developed considering climate sensitivity, and planning recommendations were provided for different climate zones. The relationship between architectural spatial structure and ventilation efficiency was analyzed; the results revealed that increasing the height of the buildings will decrease the proportion of comfortable wind zones, and the overall ventilation efficiency will weaken, so the average building height of a typical block should be controlled within 45 m, which matches ventilation performance requirements. The ventilation efficiency of the block has a certain negative correlation with the building density, and as the building density decreased by more than 10%, the proportion of the comfortable wind zones could increase by 4–5%.

Future Change in the Vietnam Upwelling Under a High‐Emission Scenario

AbstractThe Vietnam upwelling is a crucial circulation feature in the South China Sea. Although previous studies have shown that various coastal upwellings around the world may intensify under global warming, future changes in the Vietnam upwelling remain unclear. To address this knowledge gap, we analyzed the long‐term trend in the Vietnam upwelling under a high‐emission scenario for the period 2006–2100, using simulation results from a global eddy‐resolving climate model. In this model, the summertime Vietnam upwelling is projected to intensify in the 21st century and is statistically significant between 12°N and 14°N. A volume flux budget analysis indicates that wind stress curl is the most important contributor to the intensification. The geostrophic flow, to some extent, may suppress the upwelling intensification. The projected increase in upwelling is shown to significantly reduce local ocean warming and freshening and thus may have vital impacts on the local climate and circulation.

Between risk perceptions and adaptation policies: centering climate justice and social innovation in heat vulnerable cities

ABSTRACT This study leverages the roles of climate justice and social innovation in the perceptions of climate risk and climate adaptation policies within the local heat-vulnerable areas of seven Korean metropolitan cities. Using survey and document-based analyses, this work examines residents’ perceptions of climate risks and evaluates local climate adaptation plans in the context of climate adaptation policies. Results demonstrate that economic and social fairness aspects and collaborative learning activities played positive roles in enhancing adaptation policies. Further, higher participation as an aspect of social innovation led to heightened climate risk perceptions. Findings imply that social innovation and climate justice features can contribute to triggering risk perceptions as an initial stage for enabling subsequent steps in support of climate adaptation policies.

Unveiling Regional Climate Patterns Through Global Subsurface Ocean Temperature Data: An AI Multi-Layer Analysis Framework

AbstractRegional-scale climate variability has far-reaching implications for both local and global climate systems, impacting factors like temperature, precipitation patterns, oceanic circulation, and the occurrence of extreme weather events. However, despite these influences, there is currently no universal methodology for the automated identification of regional-scale variability modes, including those less dominant globally, and for simultaneously exploring the influence of various ocean depth layers in characterizing these modes and diagnosing regional sea level variations. The presented innovative approach addresses these critical region-specific needs by assisting in the extraction of novel regional depth-layered variability modes and establishing their correlation with regional sea level fluctuations, employing tailored machine-learning techniques. This dual-purpose is achieved through the utilization of an optimized k-means clustering method for the automatic identification of regions with shared variability patterns across all global oceans, revealing previously unexplored regional variability modes. Additionally, guided by an EOF/PC analysis, the approach facilitates an automatic exploration of depth layers that significantly contribute to explaining sea level variability, providing insights into diverse climatic regions. Furthermore, the methodology is specifically designed for a multi-scale analysis, enabling the examination of climate variability spanning from months to several years. The results obtained through this approach have the potential to support informed decision-making regarding local climate-related changes.

Data Scaling: Implications for Climate Action and Governance in the UK

Local actors have growing prominence in climate governance but key capacities and powers remain with national policymakers. Coordination between national and local climate action is therefore of increasing importance. Underappreciated in existing academic and policy literature, coordination between actors at different scales can be affected not only by politics and institutional arrangements, but also by methods of data analysis. Exploring two datasets of GHG emissions by local area in England—one of consumption-based emissions and the other of territorial emissions—this paper shows the potential for a data scaling problem known as the modifiable areal unit problem and its possible consequences for the efficacy and equity implications of climate action. While this analysis is conceptual and does not identify specific instances of the modifiable areal unit problem or its consequences, it calls attention to methods of data analysis as possible contributors to climate governance challenges. Among other areas, future analysis is needed to explore how data scaling and other aspects of data processing and analysis may affect our understanding of non-state actors’ contribution to climate action.

The Extent to Which the Available Water Resources in Upper Egypt Can Be Affected by Climate Change

Over the past two decades, rapid climate change has severely impacted people’s lives globally, affecting their safety and sustainability. Water, a vital human resource, has been severely affected, with drought and high temperatures leading to desertification, the drying up of rivers and lakes, spontaneous fires in forests, and massive floods and torrents due to melting ice and rising sea and ocean surface water levels. The expected impacts of climate change on the Nile, Egypt’s primary water source, are significant. These impacts can vary across regions, depending on factors like local climate, socio-economic dynamics, topography, and environmental nature. Upper Egypt, characterized by arid and semi-arid regions, faces water scarcity and socio-economic development challenges. Climate change exacerbates these issues, posing significant threats to the region’s ecological sustainability and socio-economic development. Therefore, it is crucial to address these impacts to ensure the Nile’s continued vitality and sustainability. The study aims to analyze the climate change data over the past few decades, analyze its characteristics, and model its effects on Upper Egypt’s water sources. The study expected a big decrease in the water resources of the Nile. While what is currently occurring in terms of fluctuating rainfall rates between scarcity and severity contradicts the results of those studies, that is the best evidence of the need for further research and studies to obtain more reliable and consistent results with the reality that it may help decision-makers to develop scenarios to manage climate change effectively, preventing or reducing negative effects, and finding suitable alternatives. Studies predict a 10% decrease in Nile revenue at Aswan High Dam Lake by 2095, with some predicting a 30% increase. This lack of credibility underscores the need for more comprehensive studies.

The Impact of Deforestation on Local Climates and Weather Patterns

The Impact of Deforestation on Local Climates and Weather Patterns

The giving tree: impacts of deforestation on birth outcomes in Mexico

ABSTRACT Deforestation adversely impacts human health through mechanisms such as increased air pollution, shifts in disease vector distributions, and changes in local climate. Although the influence of deforestation on human health is recognized, its effects on infant health outcomes have been insufficiently studied. This paper examines the effects of deforestation on infant health by analysing birth certificate data from 2011 to 2021 across Mexican municipalities, combined with satellite-derived annual deforestation rates. We show that increased deforestation leads to decreased birth weight, higher rates of low birth weight (LBW), and reduced Apgar scores. Specifically, an increase of one standard deviation in deforestation results in a reduction of average birth weight by 19 grams, an increase in the probability of LBW by 0.43% points, and an increase in the probability of a low Apgar score by 4.7% points. These effects are consistently observed even after controlling for different definitions of forest cover, excluding data during the COVID-19 pandemic, and limiting the analysis to municipalities with at least 2% forest coverage. This study provides critical evidence that deforestation directly impairs newborn health in Mexico, highlighting a significant and underappreciated environmental cost with long-term implications for human capital.

Soil Organic Carbon Dynamics in the Long-Term Field Experiments with Contrasting Crop Rotations

Trends in soil organic carbon (SOC) were analyzed in the soils from the oldest Czech long-term field experiment, the Prague-Ruzyně Long-Term Fertilizer Experiment, conducted on Haplic Luvisol since 1955. The aim of the work was to compare the long-term dynamics of SOC in contrasting crop rotations and different fertilization regimes. The trial design includes two crop rotations (CR): simple CR with two-year rotation of sugar beet and spring wheat, and multi-crop rotation (MCR) with nine crops. Four fertilization treatments were chosen for SOC analysis: unfertilized control, only mineral fertilization (NPK), farmyard manure application (FYM), as well as FYM and NPK application. SOC content was significantly affected by both fertilization and crop rotation practices. In the simple CR, both the unfertilized control and the NPK treatment exhibited a consistent decline in SOC content over the study period, with percentages decreasing from an initial 1.33% in 1955 to 1.15% and 1.14%, respectively. Although the FYM and FYM + NPK treatments showed an increase in SOC content in the 1990s, a gradual decline was recorded in the last two decades. This decrease was not observed in MCR: positive C balances were recorded in all treatments within MCR, with the largest increase in SOC stock occurring when NPK was combined with FYM. In contrast, over the last decade, C balances have decreased in simple CR for all treatments except FYM. This trend coincides with changes in the local climate, particularly rising temperatures. The results indicate that diversified crop rotations and FYM fertilization are effective in mitigating the negative impacts of changing environmental conditions on SOC stocks.

Investigating Stagnant Air Conditions in Almaty: A WRF Modeling Approach

This study investigates stagnant atmospheric conditions in Almaty, Kazakhstan, a city nestled within a complex terrain. These conditions, characterized by weak local winds and inversion layers, trap pollutants within the city, particularly during winter. The Weather Research & Forecasting (WRF) model was employed to simulate atmospheric conditions using Local Climate Zone data. Verification of the model’s accuracy was achieved through comparisons with data from weather stations and the Landsat-9 satellite. The model successfully reproduced the observed daily temperature variations and weak winds during the testing period (13–23 January 2023). Comparisons with radiosonde data revealed good agreement for morning temperature profiles, while underestimating the complexity of the evening atmospheric structure. The analysis focused on key air quality factors, revealing cyclical patterns of ground-level and elevated inversions linked to mountain-valley circulation. The model effectively captured anabatic and katabatic flows. The study further examined the urban heat island (UHI) using a virtual rural method. The UHI exhibited daily variations in size and temperature, with heat transported by prevailing winds and katabatic flows. Statistical analysis of temperature and wind patterns under unfavorable synoptic situations revealed poor ventilation in Almaty. Data from three Januaries (2022/2023/2024) were used to create maps showing average daytime and nighttime air temperatures, wind speed, and frequency of calm winds.

Quantifying and communicating uncertain climate change hazards in participatory climate change adaptation processes

Abstract. Participatory processes for identifying local climate change adaptation measures have to be performed worldwide. As these processes require information about context-specific climate change hazards, we show in this study how to quantify climate change hazards with their uncertainties in regions all around the globe and how to best communicate the potential hazards with their uncertainties in order to identify local climate change adaptation strategies. In a participatory process on water-related adaptation in a biosphere reserve in Germany, we used the freely available output of a multi-model ensemble provided by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) initiative, which provides global coverage, to quantify the wide range of potential future changes in (ground)water resources. Our approach for quantifying the range of potential climate change hazards can be applied worldwide for local to regional study areas and also for adaptations in agriculture, forestry, fisheries, and biodiversity. We evaluated our approach to communicating uncertain local climate change hazards by means of questionnaires that the stakeholders in the participatory process and the audiences from the general public of two project result presentations answered. To support the stakeholders in participatory climate change adaptation processes, we propose the use of percentile boxes rather than boxplots for visualizing the range of potential future changes. This helps the stakeholders identify the future changes they wish to adapt to, depending on the problem (e.g., resource scarcity vs. resource excess) and their risk aversion. The general public is best informed by simple ensemble averages of potential future changes together with the model agreement on the sign of change. Using or adapting our quantification and communication approach, flexible climate change adaptation strategies can and should be developed worldwide in a participatory and transdisciplinary manner, involving stakeholders and scientists.

Elevation and local climate variation control changes in Aleppo pine growth responses to hydroclimate and drought in semi-arid Spain

Elevation and local climate variation control changes in Aleppo pine growth responses to hydroclimate and drought in semi-arid Spain

Recent multispecies tree-growth decline reveals a severe aridity change in Mediterranean Chile

Soil moisture (SM) is a crucial factor in the water cycle, sustaining ecosystems and influencing local climate patterns by regulating the energy balance between the soil and atmosphere. Due to the absence of long-term, in-situ measurements of SM, studies utilizing satellite-based data and tree-ring analysis have become valuable in assessing variations of SM at regional and multi-century scales, as well as determining its effects on tree growth. This information is particularly pertinent in biodiversity hotspots made up of semi-arid ecosystems currently threatened by climate change. In the Mediterranean Chile region (MC; 30°–37° S), an ongoing megadrought since 2010 has resulted in a significant decline in the forest throughout the area. However, the impact of SM on tree growth at a multi-species and regional level remains unexplored. We analyzed a new network of 22 tree-ring width chronologies across the MC to evaluate the main spatiotemporal tree-growth patterns of nine woody species and their correlation with SM, using PCA. We also reconstructed the SM variations over the past four centuries and assessed its connection with large-scale climate forcings. Our results indicate that the primary growth patterns (PC1) explained 27% of the total variance and displayed a significant relationship with SM between 1982–2015 (r = 0.91), accurately reflecting the current megadrought. The tree-ring SM reconstruction covers the period 1616–2018 and shows a strong decrease around the year 2007, revealing an unprecedented recent change in aridity with respect to the last four centuries. The intensity of the South Pacific subtropical anticyclone, which primarily owe their existence to the subsiding branch of the Hadley Cell, appears as the primary climatic mechanism correlated with the reconstruction and the present aridity conditions in MC. The current SM conditions align with anticipated aridity changes in MC, providing a bleak perspective of future regional climate.

Uncovering sea level patterns in Caspian Sea altimetry data

ABSTRACT This study introduces a technique to uncover concealed patterns in satellite altimetry data, reflecting sea level variations in inland water bodies. We applied the methodology to the Caspian Sea, using altimetry data from four satellite missions over 27 years (1993–2020): TOPEX/Poseidon, Jason1, Jason2, and Jason3. The approach involves two steps: estimating sea level trends and identifying breakpoints that indicate trend shifts; and leveraging time lags between breakpoints at various locations to classify water drainage areas according to their degree of influence on each period. By revealing concealed patterns in the altimetry data, this technique can provide insights to understand the impacts of global warming and local climate changes on sea level fluctuations in the Caspian Sea. We hope that our study can facilitate subsequent interdisciplinary research on the intricate interplay between climate change and hydrological processes in inland water bodies.

The effect of local climatic conditions on household consumption: a case study of South Africa

The article explores the causal effect of local climate conditions on household consumption in South Africa. The climatic conditions are represented by monthly average temperature and precipitation. The study utilises the nationally representative 2017 National Income Dynamics Study (NIDS), wave 5 data and 2017 Climate Research Unit (CRU) climate data. The parsimonious quantile regression shows that climatic conditions (precipitation, temperature, wet days, and cloud cover) impact household per capita consumption. The quadratic quantile regression model analysis shows that household per capita consumption is convex in precipitation. Below the turning point, increased precipitation is associated with decreased household per capita consumption. Above the turning point, increased precipitation is related to increased household per capita consumption. Regions that receive very low precipitation or experience extreme temperatures (very cold or very hot) require tailor-made interventions to alleviate consumption. When we control for household characteristics, the impact of climatic conditions on household per capita consumption is weak. Providing inclusive development policies and programmes can mitigate the impact of climatic conditions on household per capita consumption.

Optimal sizing of off-grid microgrid building-integrated-photovoltaic system with battery for a net zero energy residential building in different climates of Morocco

An optimal sizing of an off-grid microgrid system composed of photovoltaic (PV)/building integrated photovoltaic (BIPV)/battery energy storage installation is undergone for Net Zero Energy Residential Building blocks across six different climates of Morocco. The Particle Swarm Optimization algorithm is used to find the optimal sizing of the system, by considering the hourly spatiotemporal variations in both solar energy availability and energy demand, with the lowest Total Annualized Cost as the objective function and capacities of BIPV and battery as decision variables. The methodology adopted focuses on main load fulfillment through direct PV and BIPV power supply, backed by battery energy storage technology, to continually guarantee self-sufficiency. A key metric, the load cover factor, is introduced to quantify the ratio by which the load demand is satisfied by the solar PV and BIPV systems. The findings show that the optimal sizing of the BIPV system can help to improve the load cover factor by 0.68–2.58 %. Moreover, integrating BIPV system with PV system and Battery leads to a reduction in the Levelized Cost of Energy with approximately 8.7–20.72 %, as opposed to utilizing only the PV system and battery. Depending on the local climate, the levelized cost of energy ranges from $0.366/kWh in Ouarzazate city up to $0.664/kWh in Ifrane city. Lastly, this holistic approach aims to transform the building from its traditional role as an energy consumer to a carbon-free energy prosumer.

A data science approach to climate change risk assessment applied to pluvial flood occurrences for the United States and Canada

Abstract There is mounting pressure on (re)insurers to quantify the impacts of climate change, notably on the frequency and severity of claims due to weather events such as flooding. This is however a very challenging task for (re)insurers as it requires modeling at the scale of a portfolio and at a high enough spatial resolution to incorporate local climate change effects. In this paper, we introduce a data science approach to climate change risk assessment of pluvial flooding for insurance portfolios over Canada and the United States (US). The underlying flood occurrence model quantifies the financial impacts of short-term (12–48 h) precipitation dynamics over the present (2010–2030) and future climate (2040–2060) by leveraging statistical/machine learning and regional climate models. The flood occurrence model is designed for applications that do not require street-level precision as is often the case for scenario and trend analyses. It is applied at the full scale of Canada and the US over 10–25 km grids. Our analyses show that climate change and urbanization will typically increase losses over Canada and the US, while impacts are strongly heterogeneous from one state or province to another, or even within a territory. Portfolio applications highlight the importance for a (re)insurer to differentiate between future changes in hazard and exposure, as the latter may magnify or attenuate the impacts of climate change on losses.