Tag: Climate Change Downscaling Techniques

Abstract

Air pollution is a pressing issue in Delhi, with smog occurrences causing reduced visibility and various respiratory problems. A series of severe SMOG (smoke + fog) episodes between 2013 and 2017 with reduced visibility and exceptionally high PM_2.5 concentrations have been reported in Delhi especially around Diwali festival (October–November). The Smog of 2016 is referred as Great Smog of Delhi. This study examined remote sensing data from 2013 to 2017 to investigate smog episodes in Delhi during pre-Diwali, post-Diwali, and Diwali. Satellite-derived parameters viz absorbing aerosol index (AAI), aerosol optical depth (AOD), and ozone monitoring instrument (OMI) along with air pollution data and climatic parameters were used to analyze smog episodes. The results showed that during smog episodes, AOD, AAI and PM_2.5 concentrations exceeded permissible limits significantly at all stations across Delhi during the Diwali festival. The ground-based observations at different locations across Delhi and satellite data-derived datasets confirmed the severity of smog episodes. The findings indicate that burning of fire crackers coupled with agriculture stubble burning and subsequent transport of the smoke from North Western states through the Capital had a greater impact on deteriorating air quality in Delhi than local pollution, especially during unfavorable weather conditions associated with high humidity and weaker winds. The outcomes highlight the significance of remotely sensed information in identifying smog episodes and their severity in Delhi. It also underlines the necessity for efficient interventions to control air pollution, particularly amid festivals like Diwali.

Abstract

This research was carried out to predict daily streamflow for the Swat River Basin, Pakistan through four deep learning (DL) models: Feed Forward Artificial Neural Networks (FFANN), Seasonal Artificial Neural Networks (SANN), Time Lag Artificial Neural Networks (TLANN) and Long Short-Term Memory (LSTM) under two Shared Socioeconomic Pathways (SSPs) 585 and 245. Taylor Diagram, Random Forest, and Gradient Boosting techniques were used to select the best combination of General Circulation Models (GCMs) for Multi-Model Ensemble (MME) computation. MME was computed via the Random Forest technique for Maximum Temperature (T_max), Minimum Temperature (T_min), and precipitation for the aforementioned three techniques. The best MME for T_max, T_min, and precipitation was rendered by Compromise Programming. The DL models were trained and tested using observed precipitation and temperature as independent variables and discharge as dependent variables. The results of deep learning models were evaluated using statistical performance indicators such as root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R²). The TLANN demonstrated superior performance compared to the other models based on RMSE, MSE, MAE, and R² during training (65.25 m³/s, 4256.97 m³/s, 46.793 m³/s and 0.7978) and testing (72.06 m³/s, 5192.95 m³/s, 51.363 m³/s and 0.7443) respectively. Subsequently, TLANN was utilized to make predictions based on MME of SSP245 and SSP585 scenarios for future streamflow until the year 2100. These results can be used for planning, management, and policy-making regarding water resources projects in the study area.

Abstract

High resolution regional climate models (RCM) are necessary to capture local precipitation but are too expensive to fully explore the uncertainties associated with future projections. To resolve the large cost of RCMs, Doury et al. (2023) proposed a neural network based RCM-emulator for the near-surface temperature, at a daily and 12 km-resolution. It uses existing RCM simulations to learn the relationship between low-resolution predictors and high resolution surface variables. When trained the emulator can be applied to any low resolution simulation to produce ensembles of high resolution emulated simulations. This study assesses the suitability of applying the RCM-emulator for precipitation thanks to a novel asymmetric loss function to reproduce the entire precipitation distribution over any grid point. Under a perfect conditions framework, the resulting emulator shows striking ability to reproduce the RCM original series with an excellent spatio-temporal correlation. In particular, a very good behaviour is obtained for the two tails of the distribution, measured by the number of dry days and the 99th quantile. Moreover, it creates consistent precipitation objects even if the highest frequency details are missed. The emulator quality holds for all simulations of the same RCM, with any driving GCM, ensuring transferability of the tool to GCMs never downscaled by the RCM. A first showcase of downscaling GCM simulations showed that the RCM-emulator brings significant added-value with respect to the GCM as it produces the correct high resolution spatial structure and heavy precipitation intensity. Nevertheless, further work is needed to establish a relevant evaluation framework for GCM applications.

Abstract

This research aimed to assess changes in mean and extreme rainfall within the Kaduna River Basin (KRB), specifically examining the implications of two Representative Concentration Pathways (RCPs)—4.5 and 8.5 scenarios. Employing a quantile mapping technique, this study corrected inherent biases in four Regional Climate Models, enabling the examination of mean precipitation and six indices capturing extreme precipitation events for the 2050s. These findings were compared against a historical reference period spanning from 1981 to 2010, considering the basin's upstream and downstream segments. Results revealed an average annual rainfall reduction under scenarios 4.5 (21.39%) and 8.5 (20.51%) across the basin. This decline exhibited a more pronounced impact on monthly rainfall during the wet season (April to October) compared to the dry season (November to March). Notably, a substantial decrement in wet indices, excluding consecutive wet days (CWD), was foreseen in both seasons for the upstream and downstream areas, signalling an impending drier climate. The anticipated rise in consecutive dry days (CDD) is poised to manifest prominently downstream attributed to global warming-induced climate change brought on by increased anthropogenic emissions of greenhouse gases. These findings accentuate a heterogeneous distribution of extreme rainfall, potentially leading to water scarcity issues throughout the KRB, especially impacting upstream users. Moreover, the projections hint at an increased risk of flash floods during intense wet periods. Consequently, this study advocates the implementation of targeted disaster risk management strategies within the KRB to address these foreseeable challenges.

Abstract

The global net-zero transition needed to combat climate change may have profound effects on the energy–food–water–air quality nexus. Accomplishing the net-zero target while addressing other environmental challenges to achieve sustainable development is a policy pursuit for all. Here we develop a multi-model interconnection assessment framework to explore and quantify the co-benefits and trade-offs of climate action for environment-related sustainable development goals in China. We find that China is making progress towards many of the sustainable development goals, but still insufficiently. The net-zero transition leads to substantial sustainability improvements, particularly in energy and water systems. However, the co-benefits alone cannot ensure a sustainable energy–food–water–air quality system. Moreover, uncoordinated policies may exacerbate threats to energy security and food security as variable renewables and bioenergy expand. We urge the implementation of pragmatic measures to increase incentives for demand management, improve food system efficiency, promote advanced irrigation technology and further strengthen air pollutant control measures.

Abstract

Over centuries, European vintners have developed a profound knowledge about grapes, environment, and techniques that yield the most distinguishable wines. In many regions, this knowledge is reflected in the system of wine geographical indications (GI), but climate change is challenging this historical union. Here, we present a climate change vulnerability assessment of 1085 wine GIs across Europe and propose climate-resilient development pathways using an ensemble of biophysical and socioeconomic indicators. Results indicate that wine regions in Southern Europe are among the most vulnerable, with high levels also found in Eastern Europe. Vulnerability is influenced by the rigidity of the GI system, which restricts grape variety diversity and thus contributes to an increased sensitivity to climate change. Contextual deficiencies, such as limited socioeconomic resources, may further contribute to increased vulnerability. Building a climate-resilient wine sector will require rethinking the GI system by allowing innovation to compensate for the negative effects of climate change.

Abstract

Context

The Regional Safe and Just Operating Space (RSJOS), serving as a conceptual framework that supports environmental governance and policy formulation, has garnered growing recognition. However, the application of ecosystem services in the RSJOS framework still constitutes a knowledge gap in the realm of landscape sustainability science.

Objectives

Our objective was to discuss the role of ecosystem services within the Safe and Just Operating Space (SJOS) framework to promote regional sustainability.

Methods

We analyzed the relationship between ecosystem services and the SJOS framework, including their similarities in core concepts and research objectives, as well as how ecosystem services relate to environmental ceilings and social foundations. Based on these analyses, we discussed the potential and challenges of bridging safe space and just space using an ecosystem services approach.

Results

We found that ecosystem services have the potential to help understand the interaction between ecological ceilings and social foundations when assessing RSJOS, using ecosystem service flows to link “safe” and “just” boundaries. However, challenges in applying ecosystem services to assess RSJOS can limit the benefits of this framework.

Conclusions

The examination of RSJOS should extend beyond snapshots of the current regional state and encompass their inherent interconnections and impact mechanisms. This broader perspective can subsequently inform policy decisions. Ecosystem services play a pivotal role in addressing the challenges within the RSJOS framework.

Abstract

Context

The Regional Safe and Just Operating Space (RSJOS), serving as a conceptual framework that supports environmental governance and policy formulation, has garnered growing recognition. However, the application of ecosystem services in the RSJOS framework still constitutes a knowledge gap in the realm of landscape sustainability science.

Objectives

Our objective was to discuss the role of ecosystem services within the Safe and Just Operating Space (SJOS) framework to promote regional sustainability.

Methods

We analyzed the relationship between ecosystem services and the SJOS framework, including their similarities in core concepts and research objectives, as well as how ecosystem services relate to environmental ceilings and social foundations. Based on these analyses, we discussed the potential and challenges of bridging safe space and just space using an ecosystem services approach.

Results

We found that ecosystem services have the potential to help understand the interaction between ecological ceilings and social foundations when assessing RSJOS, using ecosystem service flows to link “safe” and “just” boundaries. However, challenges in applying ecosystem services to assess RSJOS can limit the benefits of this framework.

Conclusions

The examination of RSJOS should extend beyond snapshots of the current regional state and encompass their inherent interconnections and impact mechanisms. This broader perspective can subsequently inform policy decisions. Ecosystem services play a pivotal role in addressing the challenges within the RSJOS framework.

Abstract

This article provides an epistemological assessment of climate analogue methods, with specific reference to the use of spatial analogues in the study of the future climate of target locations. Our contention is that, due to formal and conceptual inadequacies of geometrical dissimilarity metrics and the loss of relevant information, especially when reasoning from the physical to the socio-economical level, purported inferences from climate analogues of the spatial kind we consider here prove limited in a number of ways. Indeed, we formulate five outstanding problems concerning the search for best analogues, which we call the problem of non-uniqueness of the source, problem of non-uniqueness of the target, problem of average, problem of non-causal correlations and problem of inferred properties, respectively. In the face of such problems, we then offer two positive recommendations for a fruitful application of this methodology to the assessment of impact, adaptation and vulnerability studies of climate change, especially in the context of what we may prosaically dub “twin cities”. Arguably, such recommendations help decision-makers constrain the set of plausible climate analogues by integrating local knowledge relevant to the locations of interest.

Abstract

This article provides an epistemological assessment of climate analogue methods, with specific reference to the use of spatial analogues in the study of the future climate of target locations. Our contention is that, due to formal and conceptual inadequacies of geometrical dissimilarity metrics and the loss of relevant information, especially when reasoning from the physical to the socio-economical level, purported inferences from climate analogues of the spatial kind we consider here prove limited in a number of ways. Indeed, we formulate five outstanding problems concerning the search for best analogues, which we call the problem of non-uniqueness of the source, problem of non-uniqueness of the target, problem of average, problem of non-causal correlations and problem of inferred properties, respectively. In the face of such problems, we then offer two positive recommendations for a fruitful application of this methodology to the assessment of impact, adaptation and vulnerability studies of climate change, especially in the context of what we may prosaically dub “twin cities”. Arguably, such recommendations help decision-makers constrain the set of plausible climate analogues by integrating local knowledge relevant to the locations of interest.