Tag: Climate Change Downscaling Techniques

Abstract

Internal variability changes in soil surface temperature and reflective radiation drove climate to change and vice versa over all time scales. This study investigates the heat flux components and the climate variables that drive the change in surface soil temperature down to 15.0 cm. We analyze the correlations of energy and radiation components to climate variables by coding principle component analysis (PCA) and finding the radiative forcing of atmosphere-energy-climate-soil continuum systems using datasets derived from ERA5 and NCEP/NCAR projections. The vectors contributing to the continuum were the shortwave, net solar radiation, and sensible flux are the main drivers. The average 72-year shortwave in the study locations was − 190.63 W/m². Because of the upwelling radiation to the atmosphere, the longwave flux did not exceed the shortwave over the study’s location. The sensible heat flux was the lowest in the northwestern highlands (approximately 32 Watt/m²) and the highest range during summer was 150–180 Watt/m² over the country. This variability in net radiation partitioning led to changes in surface warming and the responding climate. This study found that the average monthly soil surface temperature bound was 10–20 °C from November to March at all locations except for Amman and Ruwaished. The calculated soil heat storage is positive all year in the Dead Sea with an annual average of 76.53 W/m². The lowest storage heat was in Amman with an annual average of − 44.42 W/m². The anomalies of annual ERA5 reanalysis of main climate contributors extended from (− 5.46 to + 5.53 °C), (− 5.66 to + 4.36 °C), (− 1.3 to 2.87 mm/day), and around (− 25.97% to a maximum of 20.99%) for maximum and minimum near-surface air temperatures, daily precipitation, and relative humidity, respectively. The long-term mean evaporation was very low approximately − 1.85 × 10–7 mm. Mean monthly wind speed illustrates low-frequency variability by − 0.06 m/s. PCA represented the correlation coefficients of the climate variables that affected soil temperature the most: near-surface air temperature, maximum, and minimum (> 0.95). Soil–water content, precipitation, and humidity played a secondary negative role to a certain extent by regulating and slowing down the soil heat transfer − 61, − 64, and − 91%, respectively. This study enhances the understanding of energy partitioning and incorporates satellite products and climate simulations to recognize key influencing factors of energy changes and climate footprints toward soil heat flux that affect the biosphere, humans, and energy use.

Abstract

The need for renewable energy sources is recently necessitated by attaining sustainability and climate change mitigation. Accordingly, the use of renewable energy sources has been growing rapidly during the last two decades. Yet, the potentials of renewable energy sources are generally influenced by several climatic factors that either determine the source of energy such as wind speed in the case of wind power or affect the performance of system such as the reduction in solar PV power production due to temperature increase. This highlights the need for assessing climate change impacts on renewable energy sources in the future to ensure their reliability and sustainability.

This paper is intended to assess impacts of climate change on wind and solar potential energy in Egypt by the year 2065 under RCP 8.5 scenario. For this purpose, a GIS-based methodology of three main steps was applied. The results revealed that solar energy potential in Egypt is expected to be relatively less vulnerable to climate change compared to wind energy. In this respect, it was found that while wind energy potential was estimated to range ± 12%. By the year 2065 under RCP 8.5 scenario, PV module power is expected to decrease by about 1.3% on average. Such assessment can assist in developing more sustainable and flexible renewable energy policy in Egypt.

Abstract

Background

Malaria is one of the major vector-borne diseases most sensitive to climatic change in West Africa. The prevention and reduction of malaria are very difficult in Benin due to poverty, economic insatiability and the non control of environmental determinants. This study aims to develop an intelligent outbreak malaria early warning model driven by monthly time series climatic variables in the northern part of Benin.

Methods

Climate data from nine rain gauge stations and malaria incidence data from 2009 to 2021 were extracted from the National Meteorological Agency (METEO) and the Ministry of Health of Benin, respectively. Projected relative humidity and temperature were obtained from the coordinated regional downscaling experiment (CORDEX) simulations of the Rossby Centre Regional Atmospheric regional climate model (RCA4).

A structural equation model was employed to determine the effects of climatic variables on malaria incidence. We developed an intelligent malaria early warning model to predict the prevalence of malaria using machine learning by applying three machine learning algorithms, including linear regression (LiR), support vector machine (SVM), and negative binomial regression (NBiR).

Results

Two ecological factors such as factor 1 (related to average mean relative humidity, average maximum relative humidity, and average maximal temperature) and factor 2 (related to average minimal temperature) affect the incidence of malaria. Support vector machine regression is the best-performing algorithm, predicting 82% of malaria incidence in the northern part of Benin.

The projection reveals an increase in malaria incidence under RCP4.5 and RCP8.5 over the studied period.

Conclusion

These results reveal that the northern part of Benin is at high risk of malaria, and specific malaria control programs are urged to reduce the risk of malaria.

Abstract

Considering the spatial and temporal effects of atmospheric pollutants, using the geographically and temporally weighted regression and geo-intelligent random forest (GTWR-GeoiRF) model and Sentinel-5P satellite remote sensing data, combined with meteorological, emission inventory, site observation, population, elevation, and other data, the high-precision ozone concentration and its spatiotemporal distribution near the ground in China from March 2020 to February 2021 were estimated. On this basis, the pollution status, near-surface ozone concentration, and population exposure risk were analyzed. The findings demonstrate that the estimation outcomes of the GTWR-GeoiRF model have high precision, and the precision of the estimation results is higher compared with that of the non-hybrid model. The downscaling method enhances estimation results to some extent while addressing the issue of limited spatial resolution in some data. China’s near-surface ozone concentration distribution in space shows obvious regional and seasonal characteristics. The eastern region has the highest ozone concentrations and the lowest in the northeastern region, and the wintertime low is higher than the summertime high. There are significant differences in ozone population exposure risks, with the highest exposure risks being found in China’s eastern region, with population exposure risks mostly ranging from 0.8 to 5.

Abstract

Based on numerical weather prediction model Weather Research and Forecasting (WRF) and Hydrologic Modeling System (HEC-HMS), a coupling model is constructed in Taihang Piedmont basin. The WRF model parameter scheme combinations composed of microphysics, planetary boundary layers, and cumulus parameterizations suitable for the study area are optimized. In both time and space, we tested the effects of the WRF model by a multi-index evaluation system composed of relative error, root meantime square error, probability of detection, false alarm ratio, and critical success index and established this system in two stages. A multi-attribute decision-making model based on Technique for Order Preference by Similarity to an Ideal Solution and grey correlation degree is proposed to optimize each parameter scheme. Among 18 parameter scheme combinations, Mellor-Yamada-Janjic, Grell-Devinji, Purdue-Lin, Betts-Miller-Janjić, and Single-Moment6 are ideal choices according to the simulation performance in both time and space. Using the unidirectional coupling method, the rolling rainfall forecast results of the WRF model in the 24 h and 48 h forecast periods are input to HEC-HMS hydrological model to simulate three typical floods. The coupling simulation results are better than the traditional forecast method, and it prolongs the flood forecast period of the Taihang Piedmont basin.

Abstract

This study aimed to create Greenhouse Gas - Air Pollution Interactions and Synergies (GAINS)-Korea, an integrated model for evaluating climate and air quality policies in Korea, modeled after the international GAINS model. GAINS-Korea incorporates specific Korean data and enhances granularity for enabling local government-level analysis. The model includes source-receptor matrices used to simulate pollutant dispersion in Korea, generated through CAMx air quality modeling. GAINS-Korea's performance was evaluated by examining different scenarios for South Korea. The business as usual scenario projected emissions from 2010 to 2030, while the air quality scenario included policies to reduce air pollutants in line with air quality and greenhouse gas control plans. The maximum feasible reduction scenario incorporated more aggressive reduction technologies along with air quality measures. The developed model enabled the assessment of emission reduction effects by both greenhouse gas and air pollutant emission reduction policies across 17 local governments in Korea, including changes in PM_2.5 (particulate matter less than 2.5 μm) concentration and associated benefits, such as reduced premature deaths. The model also provides a range of visualization tools for comparative analysis among different scenarios, making it a valuable resource for policy planning and evaluation, and supporting decision-making processes.

Abstract

The Indo-Gangetic Plain (IGP), which is the site of India's Green Revolution, covers almost 15% of the country's landmass and is among the most extensively fertile lands across the world. The densely populated IGP region bears great importance for the socioeconomic facets of India and contributes to a major share of the GDP of the country. The present study demonstrates the regional-specific assessment of summer monsoon precipitation and associated extremes with dynamical and thermodynamical aspects over the IGP region using high-resolution regional climate models (RCMs) under the CORDEX-CORE framework. The analysis reveals that the eastern parts of the IGP receive low-to-moderate precipitation with a higher tail than the western parts, which is due to the direction of the monsoon low-level flow. The observed mean precipitation characteristics are well represented by the RCMs. Further, the research identifies extreme precipitation events over the IGP and conducts comprehensive analysis to understand their underlying mechanisms. It has been observed that extreme precipitation events are linked with the moisture transport associated with trough activity and instability, and RCMs are capable in representing the observed precipitation extremes and underlying mechanisms at localized scales. Overall, this study represents a significant step forward in understanding the evolution of spatio-temporal variability of precipitation over the IGP region, where agriculture is a major economic activity and millions of people depend on rainfed agriculture.

Abstract

The forecasting of future streamflow aids researchers and policymakers to understand how changes in climate affect hydrological systems. However, traditional computational approaches demand intensive data specifically for the basin, and it is costly. The shift towards more contemporary and data-driven approaches known as support vector regression (SVR) in hydrological modeling utilizing only the hydro-climate data from Coupled Model Intercomparison Project Phase 6 (CMIP6) provides rapid input–output data processing with accurate future projection. CMIP6 is an updated and improved Global Climate Models (GCMs) for the exploration of the specific impacts of changing streamflow patterns for improved water management in agricultural areas. The delta change factor method was used to generate climate sequences, fed into the SVR model to project streamflow from 2021 to 2080. The SVR model fitted reasonably well, demonstrated by several statistical indicators, including Kling-Gupta Efficiency (KGE), Nash–Sutcliffe Efficiency (NSE), Percent Bias (PBias), and Root Mean Squared Error (RMSE), with the training phase performance surpassing the testing phase. Future projections indicated increased rainfall during the dry season for most months, excluding April to June. The rise in precipitation was particularly pronounced during the wet season. Maximum and minimum temperature projections increased for all SSPs, with SSP5-8.5 predicted a substantial increase. The projection revealed that seasonal streamflow changes would range between  – 19.1% to – 1.2% and – 7.5% to – 3.1% in the dry and wet seasons, respectively. A considerable streamflow reduction is anticipated for all SSPs in April and May due to increased temperatures, with the most pronounced impact in the SSP5-8.5. Assessing the effects of climate variations on water resource availability is crucial for identifying effective adaptation strategies to address the anticipated rise in irrigation demands due to global warming. The projected streamflow changes due to potential climate impacts are significant for Bukit Merah Reservoir, aiding the formulation of appropriate operational strategies for irrigation releases.

Abstract

The forecasting of future streamflow aids researchers and policymakers to understand how changes in climate affect hydrological systems. However, traditional computational approaches demand intensive data specifically for the basin, and it is costly. The shift towards more contemporary and data-driven approaches known as support vector regression (SVR) in hydrological modeling utilizing only the hydro-climate data from Coupled Model Intercomparison Project Phase 6 (CMIP6) provides rapid input–output data processing with accurate future projection. CMIP6 is an updated and improved Global Climate Models (GCMs) for the exploration of the specific impacts of changing streamflow patterns for improved water management in agricultural areas. The delta change factor method was used to generate climate sequences, fed into the SVR model to project streamflow from 2021 to 2080. The SVR model fitted reasonably well, demonstrated by several statistical indicators, including Kling-Gupta Efficiency (KGE), Nash–Sutcliffe Efficiency (NSE), Percent Bias (PBias), and Root Mean Squared Error (RMSE), with the training phase performance surpassing the testing phase. Future projections indicated increased rainfall during the dry season for most months, excluding April to June. The rise in precipitation was particularly pronounced during the wet season. Maximum and minimum temperature projections increased for all SSPs, with SSP5-8.5 predicted a substantial increase. The projection revealed that seasonal streamflow changes would range between  – 19.1% to – 1.2% and – 7.5% to – 3.1% in the dry and wet seasons, respectively. A considerable streamflow reduction is anticipated for all SSPs in April and May due to increased temperatures, with the most pronounced impact in the SSP5-8.5. Assessing the effects of climate variations on water resource availability is crucial for identifying effective adaptation strategies to address the anticipated rise in irrigation demands due to global warming. The projected streamflow changes due to potential climate impacts are significant for Bukit Merah Reservoir, aiding the formulation of appropriate operational strategies for irrigation releases.

Abstract

It is crucial to investigate the characteristics of fire danger in the Beijing-Tianjin-Hebei (BTH) region to improve the accuracy of local fire danger monitoring, forecasting, and management. With the use of instrumental observation data from 173 national meteorological stations in the BTH region from 1991 to 2020, the fire weather index (FWI) is first calculated in this study, and its spatiotemporal characteristics are analyzed. The high- and low-fire danger periods based on the FWI occur in April and August, respectively, with significant decreasing and increasing trends throughout the BTH region over the past 30 years. Next, the contributions of different meteorological factors to the FWI are quantified via a detrending technique. Most regions are affected by precipitation during the high-fire danger period. Both the maximum surface air temperature (Tmax) and precipitation, however, notably contribute to the FWI trend changes during the low-fire danger period. Then, we assess the linkage with atmospheric circulation. Abundant water vapor from the Northwest Pacific and local upward motion jointly lead to increased precipitation and, as a consequence, a decreased FWI during the high-fire danger period. A lack of water vapor from the boreal zone and local downward movement could cause adiabatic subsidence and hence, amplify the temperature and FWI during the low-fire danger period. In contrast to shared socioeconomic pathway (SSP) 585, in which the FWI in the BTH region exhibits a north–south dipole during the low-fire danger period, SSP245 yields an east–west dipole during the low-fire danger period. This study reveals that there is a higher-than-expected probability of fire danger during the low-fire danger period. Therefore, it is essential to intensify research on the fire danger during the low-fire danger period to improve our ability to predict summer fire danger.