Tag: Climate Change Downscaling Techniques

Abstract

In order to evaluate the performance of the Regional Climate Model version 4.7 (RegCM4.7) and understand the impact of land surface schemes in simulating precipitation and temperature over Iran, two thirty-year simulations were conducted using the Biosphere-Atmosphere Transfer Scheme (BATS) and the Community Land Model version 4.5 (CLM4.5). The boundary and initial conditions data of the MPI-ESM1.2-HR Earth system model were downscaled from an initial resolution of 100 × 100 km to 30 × 30 km. Both schemes were assessed against ECMWF Reanalysis v5 (ERA5) data, with temperature prediction using the BATS scheme generally reducing bias, except in spring. The CLM4.5 model exhibited a high correlation with ERA5 data, particularly in winter. Evaluation using Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency, and Kling-Gupta efficiency indices favored the CLM4.5 model in spring and winter. However, the annual temperature correlation coefficient between the two schemes showed minimal difference. In order to enhance precipitation simulation, the common linear scaling bias correction method was modified. Precipitation simulation demonstrated improved accuracy with Modified Linear Scaling (MLS) bias correction method, with the BATS scheme showing reduced bias and lower error rates. While the Kling-Gupta and Nash-Sutcliffe indices slightly favored the BATS scheme, the difference was marginal. Conversely, the Normalized RMSE (NRMSE) index favored RegCM-CLM4.5 in spring and winter. The values of the correlation coefficient and the relative standard deviation resulting from the two land surface schemes (models) had negligible differences with each other. Overall, Taylor diagram analysis suggested similar performance of both schemes at these scales.

Abstract

Communicating climate change projections to diverse stakeholders and addressing their concerns is crucial for fostering effective climate adaptation. This paper explores the use of storyline projections as an intermediate technology that bridges the gap between climate science and local knowledge in the Pilcomayo basin. Through fieldwork and interviews with different stakeholders, key environmental concerns influenced by climate change were identified. Traditional approaches to produce regional climate information based on projections often lack relevance to local communities and fail to address their concerns explicitly. By means of storylines approach to evaluate climate projections and by differentiating between upper and middle-lower basin regions and focusing on dry (winter) and rainy (summer) seasons, three qualitatively different storylines of plausible precipitation and temperature changes were identified and related to the main potential risks. By integrating these climate results with local knowledge, a summary of the social and environmental impacts related to each storyline was produced, resulting in three narrated plausible scenarios for future environmental change. The analysis revealed that climate change significantly influences existing issues and activities in the region. Projected trends indicate a shift towards warmer and drier conditions, with uncertainties mainly surrounding summer rainfall, which impacts the probability of increased flooding and river course changes, two of the most concerning issues in the region. These findings serve as a foundation for problem-specific investigations and contribute to informed decision-making for regional climate adaptation. Finally, we highlight the importance of considering local concerns when developing climate change projections and adaptation strategies.

Abstract

The Himalayan region is characterized by its heterogeneous topography and diverse land use/land cover types that significantly influence the weather and climatic patterns in the Indian sub-continent. Predicting future precipitation is crucial for understanding and mitigating the impacts of climate change on water resources, land degradation including soil erosion by water as well as sustainability of the natural resources. The study aimed to downscale future precipitation with Shared Socioeconomic Pathway (SSP) scenarios using machine learning methods in the Tehri Dam catchment area, located in the North-Western Himalayas, India. The study compared the performance of multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) models for statistical downscaling. During the training and testing phases, RF and ANN demonstrated reasonably satisfactory results in comparison to MLR. In general, models performed best on a monthly time scale compared to daily and yearly scales where RF model performed quite well. Therefore, the RF model was used to generate future climate scenarios for the near (2015–2040), mid (2041–2070), and far (2071–2100) future periods under the shared socioeconomic pathway (SSP) scenarios. An increasing trend in precipitation was observed across the area (grids), with varying magnitudes. The SSP1-2.6 scenario was projected the least change, ranging from 1.4 to 3.3%, while the SSP2-4.5 scenario indicated an average increase of 3.7 to 14.0%. The highest emission scenario (SSP5-8.5) predicted an increase of 8.4 to 27.5% in precipitation during the twenty-first century. In general, the increase in precipitation was higher in the far future compared to the mid and near future period. This projected increase in the precipitation may have the serious implications on food security, hydrological behaviour, land degradation, and accelerated sedimentation in the Himalayan region.

Abstract

The Himalayan region is characterized by its heterogeneous topography and diverse land use/land cover types that significantly influence the weather and climatic patterns in the Indian sub-continent. Predicting future precipitation is crucial for understanding and mitigating the impacts of climate change on water resources, land degradation including soil erosion by water as well as sustainability of the natural resources. The study aimed to downscale future precipitation with Shared Socioeconomic Pathway (SSP) scenarios using machine learning methods in the Tehri Dam catchment area, located in the North-Western Himalayas, India. The study compared the performance of multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) models for statistical downscaling. During the training and testing phases, RF and ANN demonstrated reasonably satisfactory results in comparison to MLR. In general, models performed best on a monthly time scale compared to daily and yearly scales where RF model performed quite well. Therefore, the RF model was used to generate future climate scenarios for the near (2015–2040), mid (2041–2070), and far (2071–2100) future periods under the shared socioeconomic pathway (SSP) scenarios. An increasing trend in precipitation was observed across the area (grids), with varying magnitudes. The SSP1-2.6 scenario was projected the least change, ranging from 1.4 to 3.3%, while the SSP2-4.5 scenario indicated an average increase of 3.7 to 14.0%. The highest emission scenario (SSP5-8.5) predicted an increase of 8.4 to 27.5% in precipitation during the twenty-first century. In general, the increase in precipitation was higher in the far future compared to the mid and near future period. This projected increase in the precipitation may have the serious implications on food security, hydrological behaviour, land degradation, and accelerated sedimentation in the Himalayan region.

Abstract

Accurate forecasting of the temporal distribution pattern of sub-daily precipitation is of paramount importance for effective flood control design and early warning systems. This study focuses on improving the accuracy of such forecasts by employing post-processing techniques. The European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation product over Iran was adopted along with three post-processing methods including Quantile Mapping (QM), Support Vector Machine (SVM), and Random Forest (RF). The accuracy of the forecasts for various precipitation temporal characteristics, including the start, duration, and end of precipitation events were evaluated. The RF method proved to be the most effective in improving forecast accuracy, especially in regions with higher precipitation rates. Additionally, RF corrected the first quartile of precipitation forecasts across all precipitation regions, significantly enhancing forecast accuracy in regions 3 and 5 of Iran. As for the temporal distribution pattern, post-processing methods improved the accuracy of the forecasts across all regions. The QM method performed better in terms of distributing precipitation amounts among quartiles. Moreover, all post-processing methods showed a high degree of similarity between observed and forecasted temporal distribution patterns. The deterministic evaluation showed that RF outperforms other methods in enhancing the accuracy of most precipitation quartiles, particularly that of the third quartile. The SVM and QM methods showed mixed performances, improving accuracy in some quartiles but performing adversely in others. Overall, this research highlighted the importance of data post-processing in enhancing the accuracy of precipitation forecasts and their temporal distribution patterns. The RF method proved to be the most effective post-processing technique. These findings have significant implications for flood forecasting and management in regions prone to extreme precipitation events.

Abstract

The Weather Research & Forecasting Model (WRF, Version 4.4) was applied to simulate meteorological conditions in the city of Quito, Ecuador, located in a tropical Andean landscape. These simulations included the urban canopy into WRF, using the Building Environment Parameterization (BEP) scheme combined with Local Climate Zones (LCZ) land use classification; the innermost domain had a horizontal resolution of 2 km. The simulation results showed that using LCZ + BEP options improved the representation of wind speed and planetary boundary layer height (PBLH), in comparison with WRF counter fact simulations which did not use BEP. For temperature and relative humidity, implementation of LCZ did not improve WRF simulations with respect to those counter fact simulations. This may be ascribed to the use of the default LCZ thermophysical parameters, suggesting the need for gathering local built environment features. The best WRF configuration found for wind speed was the one that combined BEP scheme, LCZ land use and the Yonsei University (YSU) PBL model with topographic option activated; this happened for dry and wet seasons and for the unique meteorological conditions in December. Regarding PBLH modeling, the best configurations were YSU-BEP-LCZ (December), MYJ-BEP-LCZ (April, wet season) and YSU (August, dry season). The findings showed the major influence of urban canopy — described by LCZ — on wind circulation and PBLH simulated within the city at high horizontal resolution (2 km). This effect should be considered when modeling atmospheric pollutant dispersion, choosing urban development strategies, and analyzing prospective climate change scenarios, among other goals.

Abstract

The study investigated the use of a Hardware-in-the-Loop (HiL) technique applied in model ice experiments to enable the analysis of offshore structures with low natural frequencies under dynamic ice loading. Traditional approaches were limited by facility capacities and ineffective downscaling of the geometry of the offshore structures. The goal of the present study was to overcome these challenges and to enhance the understanding and explore the applicability of a hybrid testing technique in model ice experiments. To achieve the objective, 204 Hardware-in-the-Loop simulations in model Ice (HiLI) were analyzed. Results showed robust behavior and good performance of the HiLI due to minimal variation in measured delay, normalized root mean square error, and peak tracking error and low magnitudes of such parameters despite alterations in factors such as the choice of the numerical structural model, physical prototype, measurement system, and ice type. Notably, the performance of the HiLI was affected when testing with warm model ice or scaling for harsh ice conditions, attributed to a reduced signal-to-noise ratio and instability of the system, respectively. Experimental identification of the critical delay, along with the application of an analytical stability criterion, revealed that the instability observed, was likely induced by reducing the structural stiffness of the numerical structural model to fulfil the scaling requirements when testing for harsh ice conditions. Additionally, the study showed improved HiLI performance when the physical prototype was in contact with the model ice. This observation was further analyzed and is assumed to be caused by the coupling between the ice and physical prototype, causing a coupled and thus increased eigenfrequency of the physical prototype-ice system.

Abstract

When a hydrological drought occurs due to a decrease in water storage, there is no choice but to supply limited water. Because this has a devastating impact on the community, it is necessary to identify causes and make predictions for emergency planning. The state of change in dam inflow can be used to confirm hydrological drought conditions using the Standardized Runoff Index (SRI), and meteorological drought and climate variability are used to identify causal relationships. Multiple Linear Regression (MLR) and Generalized Additive Model (GAM) models are developed to predict accumulated hydrological drought for 6, 12, and 24 months in the Boryeong Dam basin, and the Nash-Sutcliffe model efficiency coefficient (NSE) exceeded 0.4, satisfying the suitability criteria. The estimation ability is highest when predicting a 12-month annual drought, and reliability can be further increased by reflecting some climate fluctuations in a non-linear form. The droughts of 6 month and 24 month cumulative scales are significantly influenced by the Western Hemisphere Warm Pool (WHWP) extending from the eastern North Pacific to the North Atlantic and by the Nino 3.4 region in the tropical Pacific. Furthermore, it is anticipated that the drought conditions of the inflow volume to the Boryeong Dam will worsen with increasing sea surface temperatures in both regions.

Abstract

Data from the EURO-CORDEX ensemble of regional climate model simulations and the CORDEX-Adjust dataset were evaluated over the European Alps using multiple gridded observational datasets. Biases, which are here defined as the difference between models and observations, were assessed as a function of the elevation for different climate indices that span average and extreme conditions. Moreover, we assessed the impact of different observational datasets on the evaluation, including E-OBS, APGD, and high-resolution national datasets. Furthermore, we assessed the bi-variate dependency of temperature and precipitation biases, their temporal evolution, and the impact of different bias adjustment methods and bias adjustment reference datasets. Biases in seasonal temperature, seasonal precipitation, and wet-day frequency were found to increase with elevation. Differences in temporal trends between RCMs and observations caused a temporal dependency of biases, which could be removed by detrending both observations and RCMs. The choice of the reference observation datasets used for bias adjustment turned out to be more relevant than the choice of the bias adjustment method itself. Consequently, climate change assessments in mountain regions need to pay particular attention to the choice of observational dataset and, furthermore, to the elevation dependence of biases and the increasing observational uncertainty with elevation in order to provide robust information on future climate.

Abstract

Selecting proper parameterization scheme combinations for a particular application is of great interest to Weather Research and Forecasting (WRF) model users. The goal of this research is to create an objective method for identifying a set of scheme combinations to form a Multi-Physics Ensemble (MPE) suitable for short-term precipitation forecasting over Odisha, India’s east coast state. In this study, five member ensembles for Cloud Microphysics (CMP) and Land Surface Model (LSM, conventional ensemble) are created, as well as an ensemble of the top five performing members (optimized ensemble) for 13 Monsoon Depressions (MD) and 8 Deep Depression (DD) cases. There are a total of 30 combinations (5 PBL * 5 CMP, 5 LSM with best PBL and CMP, and one with ISRO Land Use Land Cover data). WRF 4.1 is used to carry out simulations, which are initialized with ERA5 reanalysis data and have a 72-h lead time. Rainfall verification skill scores indicate that ensemble members perform significantly better than any deterministic model. Rainfall characteristics such as location, intensity, and time of occurrence are well predicted in ensemble members as measured by a higher correlation coefficient and a lower RMSE. Neighbourhood ensemble probability also demonstrates that ensemble members have a higher chance of detecting heavy to very heavy rainfall events with more spatial accuracy. The study also concludes that choice of parameterization also affects large-scale dynamical parameters (temperature, humidity, wind, hydrometeors) and thus associated rainfall. Ensemble members exhibited less bias in the composite analysis of large-scale parameters. Furthermore, a composite analysis of moisture budget components revealed that the convergence term is the most important component of moisture accumulation, resulting in rainfall during the monsoon low-pressure system. These findings indicate that the proposed method is an effective method for reducing bias in rainfall forecasts.