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Near-term ecological forecasting for climate change action
A substantial increase in predictive capacity is needed to anticipate and mitigate the widespread change in ecosystems and their services in the face of climate and biodiversity crises. In this era of accelerating change, we cannot rely on historical patterns or focus primarily on long-term projections that extend decades into the future. In this Perspective, we discuss the potential of near-term (daily to decadal) iterative ecological forecasting to improve decision-making on actionable time frames. We summarize the current status of ecological forecasting and focus on how to scale up, build on lessons from weather forecasting, and take advantage of recent technological advances. We also highlight the need to focus on equity, workforce development, and broad cross-disciplinary and non-academic partnerships.In this Perspective, the authors discuss the current status of ecological forecasting research, its role in helping to address the climate and biodiversity crises facing society and potential future directions, with a central focus on how to scale up ecological forecasting capabilities.
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
The potential value of seasonal drought forecasts in the context of climate change: A case study of the African elephant conservation sector
This study investigates meteorological drought in sub‐Saharan Africa within the context of elephant conservation. Prolonged drought significantly impacts elephants, leading to increased mortality rates and heightened human–elephant conflicts. We assess both the anticipated 21st century changes in impact‐relevant meteorological drought metrics and the efficacy of existing forecasting systems in predicting such droughts on seasonal time scales. The climate change element of our study uses the 6th Coupled Model Intercomparison Project (CMIP6) ensemble to evaluate projected change in 3‐month Standardized Precipitation Index (SPI3). We then carry out a quantitative assessment of seasonal forecast skill, utilizing 110 years of precipitation hindcasts generated by the European Centre for Medium Range Forecasting (ECMWF) system. Our findings indicate that persistent drought is projected to become more frequent over the 21st century in southern Africa, where the majority of elephants reside. Analysis of seasonal hindcasts indicates that, while the forecasts have greater skill than climatology, they remain highly uncertain. Previous work suggests that it may be possible to reduce this uncertainty by contextualizing forecasts within specific climate regimes. However, even with improved forecast skill, effective action hinges on the alignment of forecasts with the practical needs of conservation practitioners. Over the next decades, a co‐production approach will be critical for leveraging seasonal forecasts for climate change adaptation within the conservation sector. Comparison between present day (left plot) and future occurrence of persistent drought (middle plot) in regions of Africa inhabited by elephants (shown as dotted grid points) suggests that the southern regions, where most elephants live, are projected to get drier. Seasonal forecasts have some skill (right plot), but the predictions will be uncertain. Further development, utilising a co‐production approach may enable us to realise the potential of seasonal forecasts to facilitate early action in a more drought‐prone future.
Journal Article
Sensitivity of the polar boundary layer to transient phenomena
Numerical weather prediction and climate models encounter challenges in accurately representing flow regimes in the stably stratified atmospheric boundary layer and the transitions between them, leading to an inadequate depiction of regime occupation statistics. As a consequence, existing models exhibit significant biases in near-surface temperatures at high latitudes. To explore inherent uncertainties in modeling regime transitions, the response of the near-surface temperature inversion to transient small-scale phenomena is analyzed based on a stochastic modeling approach. A sensitivity analysis is conducted by augmenting a conceptual model for near-surface temperature inversions with randomizations that account for different types of model uncertainty. The stochastic conceptual model serves as a tool to systematically investigate which types of unsteady flow features may trigger abrupt transitions in the mean boundary layer state. The findings show that the incorporation of enhanced mixing, a common practice in numerical weather prediction models, blurs the two regime characteristic of the stably stratified atmospheric boundary layer. Simulating intermittent turbulence is shown to provide a potential workaround for this issue. Including key uncertainty in models could lead to a better statistical representation of the regimes in long-term climate simulation. This would help to improve our understanding and the forecasting of climate change in high-latitude regions.
Journal Article
Global near real-time 500 m 10 d FPAR dataset from MODIS and VIIRS for operational agricultural monitoring and crop yield forecasting
Climate change and extreme weather events pose challenges to food security, emphasizing the need for reliable and timely monitoring of crop and rangeland conditions. For this purpose, long-term consistent Earth Observation datasets on vegetation conditions are typically used in early warning and crop yield forecast systems. However, the near-real-time (NRT) production of high quality datasets and the need to guarantee long-term records present various challenges. To address these, we present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications. Our dataset combines MODIS-FPAR (Collection 6.1) and VIIRS-FPAR (Collection 2) data, ensuring continuity from 2000 to well beyond 2030. We applied a robust filtering approach based on the Whittaker smoother to produce reliable FPAR estimates in NRT, accounting for sparse and irregular spaced observations due to cloud cover. The dataset is composed of two 10 d filtered timeseries: (1) MODIS-FPAR for 2000 to 2023, being the reference dataset, and (2) intercalibrated VIIRS-FPAR for 2018 onward. While several methods can effectively smooth and gap-fill FPAR data (i.e., using observations before and after the estimation date), our method is designed for optimal filtering in NRT (i.e., using only prior observations). Our approach yields six successive estimates of the same FPAR data point with increasing quality: an inital estimate immediately after the 10 d reference period, four subsequent estimates every 10 d using new observations, and a final consolidated estimate 90 d later. The implemented filtering ingests the available FPAR observations and their original quality assessment (QA) layers. To avoid unrealistic extrapolation when observations are sparse, we impose constraints, season and location specific, to FPAR estimates. We then intercalibrated the VIIRS-FPAR with the MODIS-FPAR filtered timeseries, using a mean difference correction approach, to ensure consistency between both series. This paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers. The NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://doi.org/10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50 (Seguini et al., 2025).
Journal Article
Forecasting CO2 Emissions in India: A Time Series Analysis Using ARIMA
This study evaluates the capability of the ARIMA (Auto Regressive Integrated Moving Average) to predict CO2 emissions in India using data from 1990 to 2023, addressing a critical need for accurate forecasting amid various economic and environmental uncertainties. It is observed that ARIMA yields high accuracy with respect to the prediction, and hence, it is reliable for environmental forecasting. These predictions give policymakers evidence-based information to aid in implementing sustainable climate policies within India. To ensure reliable predictions, the study methodology utilizes the Box–Jenkins approach, which encompasses model identification, estimation, and diagnostic checking. The initial step in the study is the Augmented Dickey–Fuller (ADF) test, which assesses data stationarity as a prerequisite for precise time series forecasting. Model selection is guided by the Akaike Information Criterion (AIC), which balances prediction accuracy with model complexity. The efficiency of the ARIMA model is assessed by comparing the actual observed values to the predicted CO2 emissions and the results demonstrate ARIMA’s effectiveness in forecasting India’s CO2 emissions, validated by statistical measures that confirm the model’s robustness. The value of the present study lies in its focused assessment of the relevance of the ARIMA model to the specific environmental and economic context of India, with actionable insight for policymakers. This study enhances prior research by incorporating a focused approach to data-driven policy formulation that increases climate resilience. The establishment of a reliable model for the forecasting of CO2 will aspire to support informed decision making in environmental policy and help India move forward toward sustainable climate goals. This study only serves to highlight the applicability of ARIMA in terms of environment-based forecasting and permits further emphasis on how much this method can be a useful data-based tool in climate planning.
Journal Article
Climatic zoning of yerba mate and climate change projections: a CMIP6 approach
Yerba mate (Ilex paraguariensis) is renowned for its nutritional and pharmaceutical attributes. A staple in South American (SA) culture, it serves as the foundation for several traditional beverages. Significantly, the pharmaceutical domain has secured numerous patents associated with this plant's distinctive properties. This research delves into the climatic influence on yerba mate by leveraging the CMIP6 model projections to assess potential shifts brought about by climate change. Given its economic and socio-cultural significance, comprehending how climate change might sway yerba mate's production and distribution is pivotal. The CMIP6 model offers insights into future conditions, pinpointing areas that are either conducive or adverse for yerba mate cultivation. Our findings will be instrumental in crafting adaptive and mitigative strategies, thereby directing sustainable production planning for yerba mate. The core objective of this study was to highlight zones optimal for Ilex paraguariensis cultivation across its major producers: Brazil, Argentina, Paraguay, and Uruguay, under CMIP6's climate change forecasts. Our investigation encompassed major producing zones spanning the North, Northeast, Midwest, Southeast, and South of Brazil, along with the aforementioned countries. A conducive environment for this crop's growth features air temperatures between 21 to 25 °C and a minimum precipitation of 1200 mm per cycle. We sourced the current climate data from the WorldClim version 2 platform. Meanwhile, projections for future climatic parameters were derived from WorldClim 2.1, utilizing the IPSL-CM6A-LR model with a refined 30-s spatial resolution. We took into account four distinct socio-economic pathways over varying timelines: 2021–2040, 2041–2060, 2061–2081, and 2081–2100. Geographic information system data aided in the spatial interpolation across Brazil, applying the Kriging technique. The outcomes revealed a majority of the examined areas as non-conducive for yerba mate cultivation, with a scanty 12.25% (1.5 million km2) deemed favorable. Predominantly, these propitious regions lie in southern Brazil and Uruguay, the present-day primary producers of yerba mate. Alarming was the discovery that forthcoming climatic scenarios predominantly forecast detrimental shifts, characterized by escalating average air temperatures and diminishing rainfall. These trends portend a decline in suitable cultivation regions for yerba mate.
Journal Article
New Information on Subsea Frozen Ground in the Laptev and East Siberian Seas Based on Seismic Data
Abstract—As a result of five-year studies of approximately 1.3 mln sq km of the East Siberian Arctic Shelf, a boundary between areas with predominant distribution of frozen and thawed ground (Southern and Northern zones) was revealed, new information was obtained on the state of subsea frozen ground, cardinally different from all previous data. For the first time, it was established that in most part of the East Siberian shelf (57.6%, 737 thousand sq km), frozen ground completely degraded, which also reduced the area of possible gas hydrate existence. Frozen ground degraded most intensively on the shelf of the East Siberian Sea (76.9%, 665 thousand sq km). The results were obtained based on seismic records of refracted waves for 176 seismic lines of JSC MAGE and JSC RosGeo with total length of more than 34 thousand km. The correctness of the results is approved by data of drilling of a number of wells, including those of PJSC NK Rosneft. The results have great significance for the forecast of climate changes and increasing efficiency of hydrocarbon resources exploration and development.
Journal Article
A Systematic Review on Human Thermal Comfort and Methodologies for Evaluating Urban Morphology in Outdoor Spaces
The exponential growth of urban populations and city infrastructure globally presents distinct patterns, impacting climate change forecasts and urban climates. This study conducts a systematic review of the literature focusing on human thermal comfort (HTC) in outdoor urban environments. The findings indicate a significant surge in studies exploring HTC in open urban spaces in recent decades. While historically centered on Northern Hemisphere cities, there is a recent shift, with discussions extending to various metropolitan contexts in the Southern Hemisphere. Commonly employed urban categorization systems include Sky View Factor (SVF), Height × Width (H/W) ratio, and the emerging Local Climate Zones (LCZs), facilitating the characterization of urban areas and their usage. Various thermal indices, like Physiological Equivalent Temperature (PET), Predicted Mean Vote (PMV), Universal Thermal Climate Index (UTCI), and Standard Effective Temperature (SET), are frequently utilized in evaluating external HTC in metropolitan areas. These indices have undergone validation in the literature, establishing their reliability and applicability.
Journal Article