Tag: Climate Change Downscaling Techniques

Abstract

The study evaluated the impact of climate change on incoming solar radiation (RSDS) in West Africa by comparing observed data from the CMSAF solar products (SARAH and CLARA-A1) for the period 1983–2019 with simulated data from the AFR-CORDEX models (RegCM-4.7 and CCCma-canRCM4) for the historical period (1983–2004) and various RCP emission scenarios (2.6, 4.5, 8.5) for 2005–2099. The values of the RCP in parentheses signify the level of increasing radiative forcings due to varying emission controls. Assessment metrics like correlation coefficient (R), Taylor Skill Score (TSS), and root mean square errors (RMSE) were employed for comparative analysis on annual and seasonal timescales. The analyses revealed annual mean RSDS intensities of 256.22 for SARAH, 238.53 for CLARA-A1, 270.81 for Historical, 270.26 for RCP 2.6, 255.90 for RCP 4.5, and 271.93 for the RCP 8.5 scenarios in watts per square metres. The TSS analyses showed average agreement values between observed CMSAF and simulated AFR-CORDEX solar radiation with values of 0.8450 and 0.8575 with historical, 0.8750 and 0.8600 with RCP 2.6, 0.9025 and 0.8550 with RCP 4.5, and 0.8675 and 0.8525 with RCP 8.5 scenarios for SARAH and CLARA-A1 respectively. All the metrics showed better agreement with SARAH than CLARA-A1, likely due to the associated cloud influence on CLARA-A1. Notably, the CORDEX-CCCma-canRCM4 model under RCP 4.5 demonstrated the highest accuracy, with an average correlation of 0.82 and a mean TSS of 0.90 against the SARAH reference dataset. The results suggest the AFR-CORDEX model, particularly the CCCma-canRCM4 for RCP 4.5 scenario, could reliably predict solar radiation and inform climate change impacts on solar energy potential in West Africa under moderate emission conditions.

Abstract

Accurately interpreting past climate variability, especially distinguishing between forced and unforced changes, is challenging. Proxy data confirm the occurrence of large volcanic eruptions, but linking temperature patterns to specific events or origins is elusive. We present a method combining historical climate records with a machine learning model trained on climate simulations of various volcanic magnitudes and locations. This approach identifies volcanic events based solely on post-eruption temperature patterns. Validations with historical simulations and reanalysis products confirm the identification of significant volcanic events. Explainable artificial intelligence methods point to specific fingerprints in the temperature record that reveal key regions for classification and point to possible physical mechanisms behind climate disruption for major events. We detect unexpected climatic effects from smaller events and identify a northern extratropical footprint for the unidentified 1809 event. This provides an additional line of evidence for past volcanoes and refines our understanding of volcanic impacts on climate.

Abstract

Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality. This interdisciplinary effort requires bringing together machine learning and climate scientists, while also leveraging the private sector, to accelerate progress towards actionable climate science.

Abstract

Climate change has significantly impacted the1 water resources and conservation area of the Yellow River basin. The Upper Yellow River basin (UYR), referring to the area above Lanzhou station on the Yellow River is the focus of this study, the runoff changes in the UYR would greatly impact the water resources in China. Most existing studies rely on a single hydrological model (HM) to evaluate runoff changes instead of multiple models and criteria. In terms of the UYR, outputs of the previous Coupled Model International Comparison Project (CMIP) are used as drivers of HMs. In this study, the weighted results of three HMs were evaluated using multiple criteria to investigate the projected changes in discharge in the UYR using the Shared Socioeconomic Pathways (SSPs) from CMIP6. The research’s key findings include the following. 1) Annual discharge in the UYR is expected to increase by 15.2%–64.4% at the end of the 21st century under the 7 SSPs. In the long-term (2081–2100), the summer and autumn discharge will increase by 18.9%–56.6% and 11.8%–70%, respectively. 2) The risk of flooding in the UYR is likely to increase in the three future periods (2021–2040, 2041–2060, 2081–2100) under all 7 SSPs. Furthermore, the drought risk will decrease under most scenarios in all three future periods. The verified HMs and the latest SSPs are applied in this study to provide basin-scale climate impact projections for the UYR to support water resource management.

Abstract

Climate internal variability plays a crucial role in the hydroclimate system, and this study quantifies its predictability on streamflow in California using historical observations, climate simulations, and various machine learning (ML) models. Here we demonstrate that while 5% of the year-to-year variability in seasonal peak streamflow can be attributed to the well-known climate variability indices, the explained variance surpasses 30% when higher-order empirical orthogonal functions of these indices are retained in the analysis. Notably, the results highlight the significant influence of the 5^th empirical mode of the Pacific North American pattern and of the Pacific Decadal Oscillation in shaping the streamflow variability, which is consistent across all the tested ML models. A deeper investigation reveals a clear and monotonic quasi-linear response of streamflow to these dominant patterns, emphasizing the substantial role played by higher-order internal modes of variability in shaping regional hydroclimate systems, which contributes to bridging the gap between the well-known variability domains and local climate systems.

Abstract

Addressing the urgency of climate change necessitates a coordinated and inclusive effort from all relevant stakeholders. Critical to this effort is the modelling, analysis, control and integration of technological innovations within the electric energy system, which plays a major role in scaling up climate change solutions. This Perspective presents a set of research challenges and opportunities in the area of electric power systems that would be crucial in accelerating gigaton-level decarbonization. Furthermore, it highlights institutional challenges associated with developing market mechanisms and regulatory architectures, ensuring that incentives are aligned for stakeholders to effectively implement the technological solutions on a large scale.

Abstract

Purpose

Due to climate change, the severity and length of heat waves are increasing, and this trend is likely to continue while mitigation efforts are insufficient. These climatic events cause overheating inside buildings, which increases mortality. Adaptation measures reduce overheating but induce environmental impacts, including on human health. This study aims to integrate the overheating-related effects on human health in building LCA to provide a design aid combining mitigation and adaptation.

Methods

In a novel approach, an existing building LCA tool is utilised to evaluate life cycle impacts, including damage to human health expressed in DALYs. The overheating risk is then evaluated using an existing dynamic thermal simulation (DTS) tool and prospective climatic data. Overheating is expressed as a degree-hour (DH) indicator, which integrates both the severity (temperature degrees over a comfort threshold) and the duration (hours). By assuming proportionality between DALYs and DH × area in a first step, the 2003 heat wave mortality data, 2003 climatic data, and a simplified model of the national residential building stock were used to identify a characterisation factor, which can then be used to evaluate DALYs corresponding to any building using DH obtained by thermal simulation.

Results

The proposed overheating model not only allows to derive a characterisation factor for overheating to be used in building LCA but also provides practical insights. The first estimation of the characterisation factor is 1.35E-8DALY. DH-1.m-2. The method was tested in a case study corresponding to a social housing apartment building in France built in 1969 without insulation. The thickness of insulation implemented in the renovation works was varied. For this specific case study, the contribution of overheating is significant, ranging from 1.1E-5DALY.m-2.y-1 to 2.2E-5DALY.m-2.y-1, comparable to the contribution of heating. DTS and LCA results found an optimal thickness, minimising the human health indicator in DALYs. This underscores the potential of active cooling to reduce human health impacts, especially if it consumes electricity produced by a photovoltaic system integrated in the building.

Conclusion

Combining DTS and LCA makes it possible to evaluate damage indicators on human health, including building life cycles (e.g., material and energy) and overheating-related impacts. An application on a case study shows this method’s feasibility and gives a first order of magnitude of overheating health impacts induced by buildings. A more sophisticated model could replace the assumed proportionality between DALYs and DH.

Abstract

Surface Air Temperature (SAT) predictions, typically generated by Global Climate Models (GCMs), carry uncertainties, particularly across different greenhouse gas emission scenarios. Machine Learning (ML) techniques can be employed to forecast long-term temperature variations, although this is a challenging endeavour with few drawbacks, such as the influence of scenarios involving greenhouse gas emissions. Therefore, the present study utilized multiple ML approaches such as Artificial Neural Networks (ANN), multiple linear regression, support vector machine and random forest, along with various daily predicted results of GCMs from Coupled Model Intercomparison Project Phase 6 as predictors and the “India Meteorological Department’s” Maximum SAT (MSAT) as the predictand, to predict daily MSAT in the months of March, April and May (MAM) over Andhra Pradesh (AP) for the period 1981–2022. The results show that ANN outperforms other ML techniques in predicting daily MSAT, with a root mean square error of 1.41, an index of agreement of 0.89 and a correlation coefficient of 0.81. The spatial distribution of hot and heat wave days indicates that the Multiple Model Mean (MMM) underestimates these occurrences, with a minimum bias of 9 and 6 days, respectively. In contrast, the ANN model exhibits much smaller biases, with a maximum underestimation of 3 hot and 2 heat wave days. These findings demonstrate that MMM does not capture the maximum temperatures well, resulting in poor predictability. Further, future temperature projections were analysed from 2023 to 2050, which display a gradual increase in mean MSAT during MAM over AP. This research demonstrates the potential of ML techniques to enhance temperature forecasting accuracy, offering valuable insights for climate modeling and adaptation. The results are crucial for stakeholders in agriculture, health, energy, water resources, socio-economic planning, and urban development, aiding in informed decision-making and improving resilience to climate change impacts.

Abstract

Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths (T), requires root zone depth optimization (T_opt) and is limited in use due to its low spatial resolution. To estimate RZSM at 100-m resolution, we integrate the depth specificity of SWI and employed random forest (RF) downscaling. Topographic synthetic aperture radar (SAR) and optical datasets were utilized to develop three RF models (RF1: SAR, RF2: optical, RF3: SAR + optical). At the DEMMIN experimental site in northeastern Germany, T_opt (in days) varies from 20 to 60 for depths of 10 to 30 cm, increasing to 100 for 40–60 cm. RF3 outperformed other models with 1 km test data. Following residual correction, all high-resolution predictions exhibited strong spatial accuracy (R ≥ 0.94). Both products (1 km and 100 m) agreed well with observed RZSM during summer but overestimated in winter. Mean R between observed RZSM and 1 km (100 m; RF1, RF2, and RF3) SWI ranges from 0.74 (0.67, 0.76, and 0.68) to 0.90 (0.88, 0.81, and 0.82), with the lowest and highest R achieved at 10 cm and 30 cm depths, respectively. The average RMSE using 1 km (100 m; RF1, RF2, and RF3) SWI increased from 2.20 Vol.% (2.28, 2.28, and 2.35) at 30 cm to 3.40 Vol.% (3.50, 3.70, and 3.60) at 60 cm. These negligible accuracy differences underpin the potential of the proposed method to estimate RZSM for precise local applications, e.g., irrigation management.

Abstract

In an era marked by rapid global changes, the reinforcement and modernization of plant health surveillance systems have become imperative. Sixty-five scientists present here a research agenda for an enhanced and modernized plant health surveillance to anticipate and mitigate disease and pest emergence. Our approach integrates a wide range of scientific fields (from life, social, physical and engineering sciences) and identifies the key knowledge gaps, focusing on anticipation, risk assessment, early detection, and multi-actor collaboration. The research directions we propose are organized around four complementary thematic axes. The first axis is the anticipation of pest emergence, encompassing innovative forecasting, adaptive potential, and the effects of climatic and cropping system changes. The second axis addresses the use of versatile broad-spectrum surveillance tools, including molecular or imaging diagnostics supported by artificial intelligence, and monitoring generic matrices such as air and water. The third axis focuses on surveillance of known pests from new perspectives, i.e., using novel approaches to detect known species but also anticipating and detecting, within a species, the populations or genotypes that pose a higher risk. The fourth axis advocates the management of plant health as a commons through the establishment of multi-actor and cooperative surveillance systems for long-term data-driven alert systems and information dissemination. We stress the importance of integrating data and information from multiple sources through open science databases and metadata, alongside developing methods for interpolating and extrapolating incomplete data. Finally, we advocate an Integrated Health Surveillance approach in the One Health context, favoring tailored and versatile solutions to plant health problems and recognizing the interconnected risks to the health of plants, humans, animals and the environment, including food insecurity, pesticide residues, environmental pollution and alterations of ecosystem services.