Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
32,613
result(s) for
"CLIMATE FORECASTING"
Sort by:
Phenological Shifts in Lake Ice Cover Across the Northern Hemisphere: A Glimpse Into the Past, Present, and the Future of Lake Ice Phenology
Long‐term ice phenology records quantify the effects of climate change on Northern Hemisphere lakes. This study uses lake ice phenological records across a gradient of lake sizes (0.1–31,967.8 km2 in lake surface area) obtained from community science networks. We compiled in situ ice phenological records for 2,499 lakes across 15 countries for an average of 30 years. These data revealed that for the last 50 years (1971–2020), the annual mean duration of lake ice cover decreased at a rate of 9 days per decade, with a regime shift in lake ice phenology in the late 1980s. We projected that at the end of the century (2070–2099), ice duration will decrease by an average of 10 days when compared to the historical time period (1971–2000) for the shared socioeconomic pathway (SSP) 1–2.6 climate scenario (SSP126), 23 days for SSP370, and 28 days for the SSP585. Impending human development can enhance or attenuate lake ice loss, as adaptation strategies can accelerate fossil fuel use, result in conflict, or seek strategies apart from fossil fuel development. These future pathways have critical implications for the future preservation of lake ice cover. Key Points A regime shift was observed in North American lake ice phenology in the late 1980s Northern Hemisphere lakes may lose 10–28 days of ice cover by the end of the 21st century Lake ice phenology will become more unpredictable, especially under higher warming scenarios
Journal Article
Urban resilience and climate change in the MENA region
\"This book provides an overview of the geopolitical context and climate change risk profile of the Middle East and North Africa (MENA) Region. Mapping existing scientific literature and key reports on MENA climate change impacts and future projections, Charles Egbu and Nuha Eltinay establish links between the COP26 regional climate adaptation financing targets, national government investments and local case-studies. They also address gaps in Disaster Risk Reduction institutional governance in the region. The authors move beyond the existing theoretical understanding of urban resilience to investigate how it is being measured and assessed in MENA in alignment with the IPCC's climate change adaptation indicators. Finally, they explore how the vulnerabilities of the communities most in need are being measured and integrated into cities' resilience action plans and national disaster risk policies. Providing guidance and policy recommendations based on empirical research and key stakeholder engagement observations, this book will be of great interest to students, scholars and professionals who are researching and working in the areas of climate change, urban planning and environmental policy and governance\"-- Provided by publisher.
BOOK
Exploring farmers' seasonal climate forecast needs: Co-producing forecasts for food security
Seasonal climate forecasts (SCFs) are explored as an additional tool for farmers to use to act against seasonal climate fluctuations and to suppor t greater food security for themselves and their customers. In this study, we compared the SCF needs and possible emerging farming actions of commercial farmers and smallholder farmers while exploring the prospects for developing SCF tools to aid farmers. Our intent was not to produce a new SCF, but to improve the farmers' reception, understanding and uptake of existing SCFs. The results show that both farmer groups saw value in SCFs in improving their farming actions (and, by implication, improving their food security) and provided detailed information on their specific SCF needs to suppor t their decision-making, such as how to improve trust, the type of information they would like to receive, how to make SCFs more understandable, and how to make SCFs relevant for their farming actions. The needs of the two groups differed marginally, but the major barrier for smallholder farmers was SCF access as a result of a lack of smartphones and network coverage.
Journal Article
Challenges and opportunities in building community-driven adaptive capacity under climate change for smallholder farmers in the Global South
Purpose This study responded to the disproportionate impacts of climate extremes on smallholder farmers in the Global South by developing frugal innovation tools and strategies to support climate resilience and agricultural decision-making through community-academic partnerships. Design/methodology/approach Using a community-based participatory approach, academic researchers collaborated with a local development organization and stakeholders through remote focus groups and surveys to co-develop the NicaAgua mobile app. Designed for areas with limited internet connectivity and digital literacy, the app integrates real-time weather data, short- and long-term forecasts and historical climate trends, with user-friendly visuals and interpretive guidance. Findings Frugal innovation and community engagement identified six key functionalities prioritized by users: short and seasonal forecasts, early warnings, local weather station data, climate change metrics and the moon phase. The app showed moderate to high forecasting skill at local scales. Community feedback confirmed the need for accessible forecast tools tailored to local indicators, while also revealing barriers such as low digital literacy and internet access. Despite widespread smartphone ownership, older adults and women often faced challenges in app use, requiring inclusive design strategies. Originality/value This study presents a frugal, community-driven approach to localizing global climate science for vulnerable farming communities. It highlights effective strategies for designing equitable, accessible digital tools to support climate adaptation, offers lessons on fostering transboundary academic-community collaboration and contributes to building smallholder farmers’ capacity to manage climate risks in Central America.
Journal Article
Evaluating Seasonal Forecast Models for Cambodia’s Northern Tonle Sap Basin
Accurate seasonal climate forecasts are vital for regions like Cambodia's Northern Tonle Sap Basin (NTSB), where agriculture is closely tied to rainfall patterns. While most studies have focused on the TSB, the northern areas, crucial contributors to Cambodia's national food basket, have remained largely unstudied. Here, this gap is addressed by evaluating the performance of 8 state-of-the-art seasonal forecast models from the Copernicus Climate Change Service (C3S) over a 24-year hindcast period (1993–2016). The evaluation is bolstered by ground-based data from 38 agrometeorological stations. Among the models, the Ensemble, the Japan Meteorological Agency (JMA) model, and the European Centre for Medium-Range Weather Forecasts (ECMWF) model emerged as top performers, with the Ensemble particularly excelling in replicating both temporal and spatial precipitation patterns, making it invaluable for agrometeorological applications. The Ensemble demonstrates particularly strong performance in regions such as western Oddar Meanchey and eastern Preah Vihear, where biases are less than 5%. To tailor the Ensemble to the specific climatic and geographic context of the NTSB, we refined it using the Delta Change technique, and this reduced biases even further to < 1%. Our study not only contributes to improving the precision of agrometeorological advisories in a key, but under-researched region, but also sets a precedent for how regional climate forecasting can be enhanced through context-specific model evaluations and corrections. These findings provide a practical framework for supporting resilient agricultural strategies in areas vulnerable to climate change, bridging a critical gap between climate science and agricultural practice.
Journal Article
Predicting climate change using an autoregressive long short-term memory model
Climate change is a pressing global issue. Mathematical models and global climate models have traditionally been invaluable tools in understanding the Earth’s climate system, however there are several limitations. Researchers are increasingly integrating machine learning techniques into environmental science related to time-series data; however, its application in the context of climate predictions remains open. This study develops a baseline machine learning model based on an autoregressive recurrent neural network with a long short-term memory implementation to predict the climate. The data were retrieved from the ensemble-mean version of the ERA5 dataset. The model developed in this study could predict the general trends of the Earth when used to predict both the climate and weather. When predicting climate, the model could achieve reasonable accuracy for a long period, with the ability to predict seasonal patterns, which is a feature that other researchers could not achieve with the complex reanalysis data utilized in this study. This study demonstrates that machine learning models can be utilized in a climate forecasting approach as a viable alternative to mathematical models and can be utilized to supplement current work that is mostly successful in short-term predictions.
Journal Article
Indigenous weather and climate forecasting knowledge among Afar pastoralists of north eastern Ethiopia: Role in adaptation to weather and climate variability
Traditional weather and climate forecasting is used by many indigenous communities worldwide as a guide in making important decisions that enable them cope and adapt to climate change-induced extreme weather variation. In many pastoral communities in Africa, traditional weather and climate forecasting remains the most accessible and affordable source of weather and climate information. In this study, we used individual interviews and focused group discussions to systematically document indigenous weather and climate forecasting knowledge among Afar pastoralists, with the aim of making such information available, and enhance use of this knowledge in climate change adaptation and explore synergies with modern weather forecasting system. The Afar pastoralists traditionally predict weather and climate variation through the observation of diverse bio-physical entities including livestock, insects, birds, trees and wildlife. No single indicator is taken at face value; weather forecasting is undertaken in a dynamic process where information collected from different sources, including weather information from the modern weather forecasting system, is triangulated to make the safest livelihood decisions. Before any forecasting information is used, it is evaluated through three traditional institutions that collect, share and analyse the information. These institutions include (1) the Edo or range scouting where traditional rangeland scouts are sent on a mission to assess weather and other spatially and temporally variable attributes on rangelands; (2) the Dagu, a traditional secured and reputable network, where weather information is shared among users; and (3) the Adda or the traditional Afar governance system, which analyses traditional weather information before community decisions are made. This first-time systematic documentation of indigenous weather and climate forecasting knowledge among the Afar communities demonstrated the dynamic process of indigenous weather and climate knowledge production, analysis and communication. This shows the value of indigenous knowledge in contemporary pastoral communities, while highlighting synergies with the modern weather and climate knowledge system for co-production of knowledge that serves the objectives of local people.
Journal Article
Exploring farmers' seasonal climate forecast needs: Co-producing forecasts for food security
Seasonal climate forecasts (SCFs) are explored as an additional tool for farmers to use to act against seasonal climate fluctuations and to support greater food security for themselves and their customers. In this study, we compared the SCF needs and possible emerging farming actions of commercial farmers and smallholder farmers while exploring the prospects for developing SCF tools to aid farmers. Our intent was not to produce a new SCF, but to improve the farmers' reception, understanding and uptake of existing SCFs. The results show that both farmer groups saw value in SCFs in improving their farming actions (and, by implication, improving their food security) and provided detailed information on their specific SCF needs to support their decision-making, such as how to improve trust, the type of information they would like to receive, how to make SCFs more understandable, and how to make SCFs relevant for their farming actions. The needs of the two groups differed marginally, but the major barrier for smallholder farmers was SCF access as a result of a lack of smartphones and network coverage. Significance: The findings help us to understand what farmers need to know to perceive a use and make use of SCFs, and to provide guidance in bridging the gap between existing SCF products and farmers taking more informed farming actions that will increase their resilience to climate change and improve their food security. This will enable us to build seasonal climate forecasting information tools that can be easily accessed and understood by commercial and smallholder farmers alike.
Journal Article
Forecasting monthly rainfall and temperature patterns in Van Province, Türkiye, using ARIMA and SARIMA models: a long-term climate analysis
This study investigates monthly rainfall and temperature trends in Van Province, Türkiye, using ARIMA and SARIMA models, with a dataset spanning from 1955 to 2023. The ARIMA(3,1,0) model for rainfall and ARIMA(0,1,1) model for temperature were selected based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, achieving AIC scores of 788.224 and 172.077, respectively. To address seasonality, SARIMA models were also applied, with SARIMA(3,1,0)(2,1,0)[12] for rainfall and SARIMA(0,1,1)(2,1,0)[12] for temperature, yielding AIC scores of 672.061 and 163.669. Diagnostic tests, including the Ljung–Box and Jarque–Bera tests, confirmed model adequacy by indicating minimal autocorrelation and normal residual distributions. These models successfully captured seasonal and long-term patterns, offering valuable insights for regional planning in water resource management and agriculture. The study underscores the potential of ARIMA and SARIMA models for climate forecasting, with suggestions for future enhancements using hybrid approaches to improve predictions under non-linear conditions.
Journal Article