Tag: Climate Change Downscaling Techniques

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.

Abstract

Global climate models (GCMs) and Earth system models (ESMs) exhibit biases, with resolutions too coarse to capture local variability for fine-scale, reliable drought and climate impact assessment. However, conventional bias correction approaches may cause implausible climate change signals due to unrealistic representations of spatial and intervariable dependences. While purely data-driven deep learning has achieved significant progress in improving climate and earth system simulations and predictions, they cannot reliably learn the circumstances (e.g., extremes) that are largely unseen in historical climate but likely becoming more frequent in the future climate (i.e., climate non-stationarity). This study shows an integrated trend-preserving deep learning approach that can address the spatial and intervariable dependences and climate non-stationarity issues for downscaling and bias correcting GCMs/ESMs. Here we combine the super-resolution deep residual network (SRDRN) with the trend-preserving quantile delta mapping (QDM) to downscale and bias correct six primary climate variables at once (including daily precipitation, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed) from five state-of-the-art GCMs/ESMs in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that the SRDRN-QDM approach greatly reduced GCMs/ESMs biases in spatial and intervariable dependences while significantly better-reducing biases in extremes compared to deep learning. The estimated drought based on the six bias-corrected and downscaled variables captured the observed drought intensity and frequency, which outperformed state-of-the-art multivariate bias correction approaches, demonstrating its capability for correcting GCMs/ESMs biases in spatial and multivariable dependences and extremes.

Abstract

The continued threat from climate change and human impacts on water resources demands high-resolution and continuous hydrological data accessibility for predicting trends and availability. This study proposes a novel threefold downscaling method based on machine learning (ML) which integrates: data normalization; interaction of hydrometeorological variables; and the application of a time series split for cross-validation that produces a high spatial resolution groundwater storage anomaly (GWSA) dataset from the Gravity Recovery and Climate Experiment (GRACE) and its successor mission, GRACE Follow-On (GRACE-FO). In the study, the relationship between the terrestrial water storage anomaly (TWSA) from GRACE and other land surface and hydrometeorological variables (e.g., vegetation coverage, land surface temperature, precipitation, and in situ groundwater level data) is leveraged to downscale the GWSA. The predicted downscaled GWSA datasets were tested using monthly in situ groundwater level observations, and the results showed that the model satisfactorily reproduced the spatial and temporal variations in the GWSA in the study area, with Nash-Sutcliffe efficiency (NSE) correlation coefficient values of 0.8674 (random forest) and 0.7909 (XGBoost), respectively. Evapotranspiration was the most influential predictor variable in the random forest model, whereas it was rainfall in the XGBoost model. In particular, the random forest model excelled in aligning closely with the observed groundwater storage patterns, as evidenced by its high positive correlations and lower error metrics (Mean Absolute Error (MAE) of 54.78 mm; R-squared (R²) of 0.8674). The downscaled 5 km GWSA data (based on random forest) showed a decreasing trend in storage associated with variability in the rainfall pattern. An increase in drought severity during El Niño lengthened the full recovery time of groundwater based on historical storage trends. Furthermore, the time lag between the occurrence of precipitation and recharge was likely controlled by the drought intensity and the spatial recharge characteristics of the aquifer. Projected increases in drought severity could further increase groundwater recovery times in response to droughts in a changing climate, resetting storage to a new tipping condition. Therefore, climate change adaptation strategies must recognise that less groundwater will be available to supplement the surface water supply during droughts.

Abstract

This paper evaluates the NASA Earth Exchange Global Daily Downscaled Projections’ (NEX-GDDP) CMIP6 models’ performance in simulating extreme climate indices across China and its eight subregions for the period 2081–2100 under SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios. The models effectively reproduce the spatial patterns of extreme high temperatures, especially in northern China. They show enhanced capabilities in accurately simulating the maximum daily maximum temperature (TXx) and the number of high temperature days (T35). They improve the cold bias of the TXx index in Northwest China and warm bias in South China. In terms of precipitation, the models demonstrate strong performance, evidenced by significant spatial correlations in total wet day precipitation (PTOT) simulations. They reduce the biases of PTOT and simple daily intensity (SDII) compared to CMIP6 models. Regionally, they enhance PTOT accuracy along southern coasts and in Yunnan, better captures very heavy precipitation days (R20) in the Southwest region, max 5-day precipitation (RX5D) in North China and Southwest region, and SDII in the Northeast region and Yunnan. Under SSP5-8.5 scenario, significant impacts include increased TXx in Northwest China, more heatwave days in Southwest China, and more T35 in South China. Extreme precipitation will become more frequent in South and East China, with the greatest intensity increases in Southwest China (SWC1). North China will see fewest consecutive dry days (CDD) indices, while consecutive wet days (CWD) will prominently rise in SWC1.

Abstract

Atmospheric aerosols partly compensate for the warming due to greenhouse gases by perturbing the radiation balance of the Earth–Atmosphere system. In this study, the impacts of aerosols on surface temperature are examined over the Indo-Gangetic Plain (IGP) and Himalayan Tibetan Plateau (HTP), where diverse aerosol and climatic conditions prevail. Both regions have significant impacts on the regional climate and hydrological cycles in South Asia. The IGP experiences high aerosol loading throughout the year and is expected to affect surface temperature significantly. In contrast, the HTP exhibits relatively pristine or lower aerosol loading, whose effects on surface temperature are highly uncertain due to snow albedo feedback. Climate model simulations are used to decompose the surface temperature changes due to aerosol forcing to its radiative and non-radiative components over the IGP and HTP. The shortwave cooling due to aerosols is mostly compensated by the decrease in sensible heat over the IGP. On the other hand, HTP experiences surface cooling due to the direct effects and surface warming due to aerosol-induced snow-darkening effects (deposition of absorbing aerosols on snow). The net effect of aerosols on shortwave radiation is further redistributed into non-radiative heat fluxes. This study provides a better understanding of aerosol-induced surface temperature change and its partitioning into radiative and non-radiative components.