Tag: Climate Change Downscaling Techniques

Abstract

The gravity recovery and climate experiment (GRACE) satellite mission, which was active between March 2002 and June 2017 and its successor, the GRACE follow-on (GRACE-FO), which has been in operation since May 2018, marked the pioneering remote sensing missions to track changes in terrestrial water storage (TWS) across time. TWS encompasses the cumulative water masses found in the Earth’s soil column, including elements like surface water, soil moisture, snow water equivalent and groundwater (GW). Over the course of the last 20 years, there has been extensive research conducted on fluctuations in the mass of different Elements of the Earth's system, such as the hydrosphere, seas, cryosphere, and solid Earth, utilizing time-varying gravity measurements from the GRACE/GRACE-FO missions. This technology can be utilised to improve monitoring results of large-scale spatial and temporal variations in the water cycle patterns. A review of recent GRACE data used for monitoring terrestrial hydrology over India is provided in this work. The primary applications of GRACE data in the context of large-scale terrestrial hydrological monitoring, such as assessing alterations in terrestrial water storage, involve: retrieving the hydrological components of GW, analysing droughts, floods, land subsidence and determining how glaciers are responding to climate change, have recently been described. India has the tenth position globally in the utilization of GRACE data. Therefore, more investigation is required to completely understand the potential of GRACE data. It was found through a review of the literature that several hydrological models have not yet been thoroughly examined with GRACE data. Furthermore, small river basins can be analysed at a fine scale with downscale GRACE data using machine learning/artificial intelligence. In the Indian context, no research has been conducted to estimate river discharge by using GRACE data.

Abstract

Tropical terrestrial ecosystems play an important role in modulating the global carbon balance. However, the complex dynamics and factors controlling tropical aboveground live biomass carbon (AGC) are not fully understood. Here, using remotely sensed observations, we find a moderate net AGC sink of 0.21 ± 0.06 PgC yr⁻¹ throughout the global tropics from 2010 to 2020. This arises from a gross loss of −1.79 PgC yr⁻¹ offset by a marked gain of 2.01 ± 0.06 PgC yr⁻¹. Fire emissions in non-forested African shrubland/savanna biomes, coupled with post-fire carbon recovery, substantially dominated the interannual variability of tropical AGC. Fire radiative power was identified as the primary determinant of the spatial variability in AGC gains, with soil moisture also playing a crucial role in shaping trends. We highlight the dominant roles of anthropogenic and hydroclimatic determinants in orchestrating tropical land carbon dynamics and advocate for land management to conserve indispensable ecosystem services worldwide.

Abstract

Due to the complex interactions between terrain and atmospheric processes, climate modeling can be challenging in regions with complex topography, such as South Asia. This research examines a critical gap in the added value of high-resolution climate simulations for daily wind speed in South Asia, focusing on the innovative terrain-modified wind speed (W_t) adjustment method. By applying distribution added value (DAV) and upper-tail PDF (95th percentile) analyses, we systematically evaluate the performance of regional climate models (RCMs) and global climate models (GCMs) pre- and post-W_t. The comparison of DAV results before and after applying the W_t method revealed the impact of the W_t adjustment on the performance of the regional climate models. In several models, such as IPSL-RCA, NorESM1-RCA, and CanESM2-RCA, the incorporation of W_t led to substantial improvement, as shown by positive DAV values. In the Upper-tail PDF analysis, improvements were more consistent, indicating that W_t adjustment generally enhanced the representation of extreme wind events. However, some models such as NorESM1-RCA and CanESM2-RCA consistently perform well before and after W_t adjustment by depicting positive DAV values. Overall, the results show that the W_t is effective in improving the representation of wind speed for most climate models. According to the DAV analysis, high-resolution models have a positive added value of 15% on average over lower-resolution models. The contribution of this study is bridging the gap between the observed wind speed patterns in South Asia and climate model outputs. As a result of this research, tailored approaches to evaluating and adjusting models are revealed, emphasizing the complexity of model behavior. In the subcontinent research domain, the results of this study provide crucial insights for climate-related decision-making, risk assessment, and infrastructure development.

Abstract

Wind forecasting is an integral part of wind energy management as a crucial instrument for predicting wind patterns in coastal areas. One common technique to predict the wind field in a specific area is the dynamical downscaling method, which is based on a physical model and requires a substantial computational cost. Instead, this study proposes a novel approach for wind downscaling based on deep learning techniques as a substitution for a dynamical downscaling method. Our methodology starts with generating a high-resolution wind dataset by dynamically downscaling global climate data using RegCM4.7. Then, we employ a feature selection technique to identify the optimal global wind data points that exhibit a strong spatial correlation with the local wind data of interest. The selected features from global climate data and the target from the high-resolution wind data are used to develop a machine learning-based model to predict wind variability in a specific location. We consider various models, namely multilayer perceptron (MLP), AdaBoost, XGBoost, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM), and conduct performance analysis to find an optimum model. The BiLSTM model has been shown to be the most optimal algorithm for wind downscaling among various machine learning models. We also evaluated the model’s performance by conducting a comparative analysis between its predictions and the observed wind data gathered from Jakarta and Meulaboh. This analysis yields significant insights into the accuracy and applicability of our methodology. Our approach reveals a strong correlation coefficient of 0.963 and a low root mean square error (RMSE) of 0.476. These results highlight the efficacy of our method in correctly downscaling wind data.

Abstract

Quantifying the vulnerability of population to multi-faceted climate change impacts on human well-being remains an urgent task. Recently, weather and climate extremes have evolved into bivariate events that heighten climate risks in unexpected ways. To investigate the potential impacts of climate extremes, this study analyzes the frequency, magnitude, and severity of observed and future compound hot-dry extremes (CHDEs) over East Africa. The CHDE events were computed from the observed precipitation and maximum temperature data of the Climatic Research Unit gridded Timeseries version five (CRU TS4.05) and outputs of climate models of Coupled Model Intercomparison Project Phase 6 (CMIP6). In addition, this study quantifies the population exposure to CHDE events based on future population density datasets under two Shared Socioeconomic Pathways (SSPs). Using the 75th/90th and 25th/10th percentile of precipitation and temperature as threshold to define severe and moderate events, the results show that the East African region experienced multiple moderate and severe CHDE events during the last twenty years. Based on a weighted multi-model ensemble, projections indicate that under the SSP5-8.5 scenario, the frequency of moderate CHDE will double, and severe CHDE will be 1.6 times that of baseline (i.e., an increase of 60%). Strong evidence of an upward trajectory is noted after 2080 for both moderate and severe CHDE. Southern parts of Tanzania and northeastern Kenya are likely to be the most affected, with all models agreeing (signal-to-noise ratio, SNR > 1), indicating a likely higher magnitude of change during the mid- and far-future. Consequentially, population exposure to these impacts is projected to increase by up to 60% for moderate and severe CHDEs in parts of southern Tanzania. Attribution analysis highlights that climate change is the primary driver of CHDE exposure under the two emission pathways. The current study underscores the urgent need to reduce CO₂ emissions to prevent exceeding global warming thresholds and to develop regional adaptation measures.

Abstract

Climate change is increasingly recognized as a critical factor driving shifts in the distribution of dominant tree species within various forest ecosystems, including evergreen, deciduous, and thorn forests. These shifts pose significant threats to biodiversity and the essential ecosystem services that forests provide. In Tamil Nadu, India, where forest ecosystems are integral to both ecological balance and local livelihoods, there is an urgent need to predict potential changes in species distributions under future climate scenarios to inform effective conservation strategies. This study addresses this need by utilizing the MaxEnt species distribution model to assess the habitat suitability of dominant tree species in these forest types. The analysis spans current conditions (baseline period 1985–2014) and future projections (2021–2050) under the SSP2-4.5 emissions scenario, leveraging bioclimatic variables at a 1 km resolution. Key climatic factors such as annual mean temperature, precipitation of the driest month, and precipitation seasonality were identified as major drivers of habitat suitability, particularly in the Eastern and Western Ghats of Tamil Nadu. Model projections suggest a potential decrease in suitable habitat area by 32% for evergreen species and 18% for deciduous species, whereas thorn forest species might experience a 71% increase in suitable area. These findings underscore the critical need for targeted conservation actions to mitigate anticipated habitat losses and bolster the resilience of these vital forest ecosystems in the face of ongoing climate change.

Abstract

Global climate models are important tools for estimating the possible future impacts of climate change and developing necessary adaptation strategies. This study assessed the suitability of global climate models for local climate projections in Hatay, Türkiye. Temperature and precipitation data from different Coupled Model Intercomparison Project Phase 6 climate models were compared with ground-based observations. For stations lacking historical data, multilayer perceptron artificial neural networks were used to generate data. These networks were trained with data from neighboring stations from 1980 to 2014. The most suitable global climate model was determined using a multi-criteria decision-making approach. As a result of the study, it was determined that the multilayer perceptron models effectively generated long-term temperature data with a normalized root mean square error of less than 0.50. Precipitation estimates, while less accurate, achieved reasonable accuracy with a normalized root mean square error of less than 0.70. The evaluation of global climate models revealed a tendency to underestimate minimum temperatures and overestimate maximum temperatures and precipitation. Specifically, the EC-EARTH3, CMCC-ESM2, and MPI-ESM1-2-HR models excelled in maximum temperature estimations; the CMCC-ESM2, GFDL-CM4, and TAIESM1 models were superior for minimum temperatures; and the EC-EARTH3, GFDL-CM4, and MPI-ESM1-2-HR models performed best for precipitation. The findings of this study will provide a framework for the assessment and selection of appropriate climate models for local regions and will help to develop targeted adaptation strategies.

Abstract

Background

Geospatial methods are common in environmental exposure assessments and increasingly integrated with health data to generate comprehensive models of environmental impacts on public health.

Objective

Our objective is to review geospatial exposure models and approaches for health data integration in environmental health applications.

Methods

We conduct a literature review and synthesis.

Results

First, we discuss key concepts and terminology for geospatial exposure data and models. Second, we provide an overview of workflows in geospatial exposure model development and health data integration. Third, we review modeling approaches, including proximity-based, statistical, and mechanistic approaches, across diverse exposure types, such as air quality, water quality, climate, and socioeconomic factors. For each model type, we provide descriptions, general equations, and example applications for environmental exposure assessment. Fourth, we discuss the approaches used to integrate geospatial exposure data and health data, such as methods to link data sources with disparate spatial and temporal scales. Fifth, we describe the landscape of open-source tools supporting these workflows.

Abstract

Ensemble characteristics of a 10-member analog ensemble (AE) system for simultaneous prediction of six surface meteorological variables are examined at six station locations in the north-west Himalaya (NWH), India for lead times, 0 h (0 h)[d0], 24 h (d1), 48 h (d2) and 72 h (d3). The maximum (MMX), minimum (MNX) and mean (ME) values of each variable in analog days are found to exhibit statistically significant positive correlations with their corresponding observations at each station location for d0 through d3. The MEs of the variables are found to reproduce statistics (temporal mean, temporal standard deviation), empirical distributions of the observations on the variables reasonably well, and the MEs of the variables exhibit reasonable values of the RMSEs for d0 through d3. The observations on each variable and multiple variables simultaneously fall within their ranges (MMXs, MNXs) in ensemble members for maximum number of days for all lead times. The AE system is found to exhibit high spatial and temporal consistency in its predictive characteristics at six station locations in the NWH. Despite our short length data, these results are very interesting and suggest practical utility of the AE system for simultaneous prediction of variables at local scale utilizing local scale surface meteorological observations. Similar studies on various other types of ensemble systems can help to assess their practical utility for various forecasting applications.