Tag: Climate Change Downscaling Techniques

Abstract

The fast human climate change we are witnessing in the early twenty-first century is inextricably linked to the health and function of the biosphere. Climate change is affecting ecosystems through changes in mean conditions and variability, as well as other related changes such as increased ocean acidification and atmospheric CO₂ concentrations. It also interacts with other ecological stresses like as degradation, defaunation, and fragmentation.Ecology and climate monitoring are critical to understanding the complicated interactions between ecosystems and changing climate trends. This review paper dives into the issues of ecological and climate monitoring, emphasizing the complications caused by technical limits, data integration, scale differences, and the critical requirement for accurate and timely information. Understanding the ecological dynamics of these climatic impacts, identifying hotspots of susceptibility and resistance, and identifying management measures that may aid biosphere resilience to climate change are all necessary. At the same time, ecosystems can help with climate change mitigation and adaptation. The processes, possibilities, and constraints of such nature-based climate change solutions must be investigated and assessed. Addressing these issues is critical for developing successful policies and strategies for mitigating the effects of climate change and promoting sustainable ecosystem management. Human actions inscribe their stamp in the big narrative of our planet’s story, affecting the very substance of the global atmosphere. This transformation goes beyond chemistry, casting a spell on the physical characteristics that choreograph Earth’s brilliant dance. These qualities, like heavenly notes, create a song that echoes deep into the biosphere. We go on a journey via recorded tales of ecological transformation as they respond to the ever-shifting environment in this text. We peek into the rich fabric of change, drawing insight from interconnected observatories. Nonetheless, this growing symphony is set to unleash additional transformational stories - narratives of natural riches and rhythms that are both economically and environmentally essential. Understanding these stories is essential for navigating this developing epic. A roadmap for sustainable development necessitates the ability to comprehend these stories, a problem that resonates across the breadth of monitoring programs, particularly in the infancy of integrated sites.

Abstract

Background

In most regions and ecosystems, soils are the largest terrestrial carbon pool. Their potential vulnerability to climate and land use change, management, and other drivers, along with soils’ ability to mitigate climate change through carbon sequestration, makes them important to carbon balance and management. To date, most studies of soil carbon management have been based at either large or site-specific scales, resulting in either broad generalizations or narrow conclusions, respectively. Advancing the science and practice of soil carbon management requires scientific progress at intermediate scales. Here, we conducted the fifth in a series of ecoregional assessments of the effects of land use change and forest management on soil carbon stocks, this time addressing the Northeast U.S. We used synthesis approaches including (1) meta-analysis of published literature, (2) soil survey and (3) national forest inventory databases to examine overall effects and underlying drivers of deforestation, reforestation, and forest harvesting on soil carbon stocks. The three complementary data sources allowed us to quantify direction, magnitude, and uncertainty in trends.

Results

Our meta-analysis findings revealed regionally consistent declines in soil carbon stocks due to deforestation, whether for agriculture or urban development. Conversely, reforestation led to significant increases in soil C stocks, with variation based on specific geographic factors. Forest harvesting showed no significant effect on soil carbon stocks, regardless of place-based or practice-specific factors. Observational soil survey and national forest inventory data generally supported meta-analytic harvest trends, and provided broader context by revealing the factors that act as baseline controls on soil carbon stocks in this ecoregion of carbon-dense soils. These factors include a range of soil physical, parent material, and topographic controls, with land use and climate factors also playing a role.

Conclusions

Forest harvesting has limited potential to alter forest soil C stocks in either direction, in contrast to the significant changes driven by land use shifts. These findings underscore the importance of understanding soil C changes at intermediate scales, and the need for an all-lands approach to managing soil carbon for climate change mitigation in the Northeast U.S.

Abstract

In this study, we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model (GCM) data to drive a regional climate model (RCM) over the Asia-western North Pacific region. Three simulations were conducted with a 25-km grid spacing for the period 1980–2014. The first simulation (WRF_ERA5) was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset and served as the validation dataset. The original GCM dataset (MPI-ESM1-2-HR model) was used to drive the second simulation (WRF_GCM), while the third simulation (WRF_GCMbc) was driven by the bias-corrected GCM dataset. The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models. Results demonstrate that the WRFGCMbc significantly reduced the root-mean-square errors (RMSEs) of the climatological mean of downscaled variables, including temperature, precipitation, snow, wind, relative humidity, and planetary boundary layer height by 50%–90% compared to the WRF_GCM. Similarly, the RMSEs of interannual-to-interdecadal variances of downscaled variables were reduced by 30%–60%. Furthermore, the WRFGCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities. The leading empirical orthogonal function (EOF) shows a monopole precipitation mode in the WRFGCM. In contrast, the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China. This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

Abstract

This study explores the potential of TDX InSAR data from 2011, 2017, and 2019 for estimating and mapping canopy heights in unique forest and plantations landscape in Sabah, Malaysian Borneo. The findings offer crucial insights for sustainable forest and plantation management. The methodology encompassed the SINC forest height inversion model and two machine learning (ML) models Random Forest (RF) and Symbolic Regression (SR) augmented with diverse predictor variables and height references. Training the ML models with 70% of ICESat-2 ATL08 data and validating with the remaining 30%, we achieved an out-of-bag (OOB) RMSE of 5.4 m for RF and 5.96 m for SR. The overall validation RMSEs were 6.06 m (2011 SR), 10.36 m (2017 SR), and 7.58 m (2019 RF). For specific LULC classes, accuracies ranged from 3.92 m (2011 Mangrove RF) to 6.11 m (2017 Mangrove SR) and 4.35 m (2019 Rubber RF). Field inventory data validation in 2011 and 2019 yielded RMSEs between 4.06 m and 8.69 m, with SR as the top-performing model. Spatial distribution and canopy height classes revealed non-uniform variations in 2011, with SINC overestimating. In contrast, 2017 and 2019 showed uniform height patterns, indicating an increase in canopy heights across forest and plantation LULC, particularly in the 15–20 m range for oil palm, secondary forest, acacia mangium, and rubber. Our findings highlight the potential of InSAR-based canopy height estimation and mapping for tropical forest and plantations, which also can be applied to other areas at local scales considering the LULC landscapes dynamics.

Abstract

Multi-model Ensembles (MMEs) are widely used to reduce uncertainties associated with simulations and projections of GCM/RCM.MMEs combine the results of multiple climate models to produce a more robust and reliable prediction. By considering the range of outputs from different models, MMEs can improve the overall accuracy of climate projections. Therefore, the study focused on the use of some techniques, namely Multivariate Linear Regression (MLR), Weighted Average (WA), and some ML algorithms including Least Square Support Vector Machine (LS-SVM), Random Forest, (RF) and multivariate adaptive regression splines (MARS) to develop MMEs to simulate precipitation patterns over Iran. The regional climate models (RCMs) used in this research were extracted from the South Asia Coordinated Regional Climate Downscaling Experiments (CORDEX-SA) dataset. By comparing the individual RCMs and MMEs developed using the proposed methods, it was found that MMEs improved their capabilities compared to individual RCMs in their ability to simulate precipitation patterns. Furthermore, the study revealed that the MME developed using RF (MME-RF) exhibited more consistent performance across different spatial regions compared to other methods, especially WA technique, which displayed the lowest performance in comparison to other methods. Regarding the projections of seasonal precipitation under RCP4.5 and RCP8.5 scenarios, a potential decrease (roughly 6.5%) in precipitation in the western regions during autumn season, was observed. Whereas, the southern and southeast regions of Iran in particular showed a pronounced wetting tendency during the autumn season. According to the forecasts, the maximum percentage change (PC) of precipitation in these regions is expected to increase by 13.88%.

Abstract

Consistent drought modelling under plausible shared socioeconomic–representative concentration pathways (SSP–RCPs) are crucial for effectively managing future drought risk in agricultural environments. The Western Cape (WC) is one of South Africa’s main agro-based provinces and faces a mounting threat of water insecurity due to recurrent drought. The objective of this study was to predict meteorological drought hazard for 2021–2050 based on three CMIP6 scenarios: SSP5–8.5, SSP2–4.5 and SSP1–2.6. Precipitation simulations generated by the sixth version of Model for Interdisciplinary Research on Climate (MIROC6) under the SSP5–8.5, SSP2–4.5 and SSP1–2.6 scenarios were used from fifteen stations across the six AEZs of the WC province. The Standardised Precipitation Index (SPI) was computed at 12-month timescales. Trend analysis of precipitation datasets and the SPI-values were done at p < 0.05 using the Mann–Kendall (M–K) test. The findings revealed negative precipitation trends of − 7.6 mm/year in Ceres, while positive trends of 0.3 mm/year were observed in Malmesbury. These findings indicate an improvement from − 7.8 and − 6.4 mm/year in the same regions, respectively, compared to historical trends observed between 1980 and 2020. The results suggest that in 2042 and 2044, Bredasdorp will experience − 2 < SPI < − 1.5 under the SSP2–4.5 scenarios, while Matroosberg in 2038 under the SSP5–8.5 will experience SPI > − 2. The findings of this study will assist in the development of proactive planning and implementation of drought mitigation strategies and policies aimed at reducing water insecurity in AEZs.

Abstract

Meeting the increasing food demand in a manner that ensures both resources and environmental sustainability poses a global challenge. The conceptual framework of planetary boundaries (PBs) has emerged as a crucial reference in the study of sustainable food system, with specific focuses on key dimensions such as land use change, freshwater use, nitrogen (N) and phosphorus (P) cycles, and greenhouse gas (GHG) emissions. In this study, we summarized the threshold applications of PBs in sustainable food system research at both the global and national (China) scales. Based on these thresholds, we evaluated the sustainability conditions of the food system, considering resources such as cropland, freshwater, N and P applications, as well as environmental impacts including N and P losses, and GHG emissions. In addition, we explored the impacts of single or combined management strategies on sustainable food systems. These strategies included dietary changes, improvements in technologies and management, reductions in food loss and waste, and optimization in agricultural production distribution. Finally, we outlined future research directions in sustainable food system, including enhancing research on the interaction mechanisms among PBs elements within food systems, conducting downscaling studies of PBs elements at national and regional levels, and ensuring the rationality of policy-making for sustainable food systems in China. This study can provide a theoretical foundation and strategies guidance for sustainable food system and agricultural land use management in the future both globally and in China.

Abstract

Compound extremes, whose socioeconomic and ecological impacts are severer than that caused by each event occurring in isolation, have evolved into a hot topic in Earth Science in the past decade. In the context of climate change, many compound extremes have exhibited increasing frequency and intensity, and shown novel fashions of combinations, posing more pressing demands and tougher challenges to scientific research and disaster prevention and response. This article, via a perspective of multi-sphere interactions within the Earth System, systematically reviews the status quo, new scientific understanding, and deficiencies regarding the definition, mechanism, change, attribution, and projection of compound extremes. This study also sorts out existing challenges and outlines a potential roadmap in advancing the study on compound extremes with respect to data requirement, mechanistic diagnosis, numerical modeling, attribution and projection, risk assessment, and adaptive response. Further directions of compound extremes studies and key research topics that warrant multi-disciplinary and multisectoral coordinated efforts are also proposed. Given that climate change has reshaped the type of extremes, a transformation from the traditional single-event perspective to a compound-event perspective is needed for scientific research, disaster prevention and mitigation, and climate change adaptation, calling for bottom-up innovation in research objects, ideas, and methods. This article will add value to promoting the research on compound extremes and interdisciplinary cooperations.

Abstract

Climate change and human activity have increased fires in India. Fine particulate matter ( \(\hbox {PM}_{2.5}\) ) is released into the atmosphere by stubble burning in Punjab and Haryana and forest fires in the north-eastern and central areas of the country. Accurate short-term \(\hbox {PM}_{2.5}\) estimates are essential to protect human health and reduce acute air pollution. However, global air quality forecasting methods grapple with a persistent assumption of fire emissions. They use near-real-time fire emissions throughout the prediction cycle. Air quality forecasts are prone to inaccuracies and biases due to fire emissions’ dynamic nature. We employ spatiotemporal deep learning techniques, specifically ConvLSTM and ConvGRU, to forecast fire emission locations up to three days in advance. Through our evaluation, we find that ConvLSTM outperforms ConvGRU in terms of prediction accuracy and performance. The chosen model provides a very good correlation coefficient ( \(\approx 0.8\) ) for the 1st day forecast and a moderate value (0.5 \(-\) 0.55) for subsequent 2nd and 3rd days forecasts. The predictors NDVI, temperature, wind, surface pressure, and total cloud cover are included to our model training to improve these correlations. In Punjab-Haryana, wind input improves results. This fire burning location prediction method could improve air quality forecasting. Our deep learning model can improve forecasts by revealing the complex interactions of components and reflecting fire emissions’ dynamic nature. This research may help improve air quality forecasts in the face of rising fire events, protecting communities across the Indian subcontinent.

Abstract

In the present study, we used a bubble column photobioreactor to test carbon dioxide using Chlorella vulgaris RDS03 under laboratory conditions. The nutrient concentration of medium was optimized by Box-Behnken design through response surface methodology (RSM), and regression coefficient (R²) value of 0.999 was analyzed by analysis of variance (ANOVA). The microalga Chlorella vulgaris RDS03 was captured—98.86% of CO₂ analyzed by CO₂ utilization and biofixation kinetics, 310U mL⁻¹ of RuBisCO enzyme, 5.32 mg mL⁻¹ of biomass, 124 mg g⁻¹ of carbohydrate, 247.15 mg g⁻¹ of lipid, 4.1 mL g⁻¹ of bioethanol, and 4.9 mL g⁻¹ of biodiesel produced. The molecular weight of purified RuBisCO enzyme was analyzed as 54 kDa by 15% of SDS PAGE. The 3D homology structure of N-terminal amino acids sequence of RuBisCO was predicted with 415 amino acid residues. The biodiesel was subjected to functional group analysis using Fourier transform infrared spectroscopy (FTIR). The fifty biodiesel (FAME) compounds were identified by gas chromatograph-mass spectroscopy (GC–MS) analysis and major compounds viz., linolenic acid (C18:2), oleic acid (C18:2), stearic acid (C18:0), palmitic acids (C16:1), and myristic acid (C14:0). The produced bioethanol was confirmed using high-performance liquid chromatography (HPLC).

Graphical abstract