Tag: Climate Change Downscaling Techniques

Abstract

Malaria is one of the major health threats in Africa, and the risk of transmission is projected to be exacerbated by global warming. Rwanda experienced an 11-fold increase in malaria incidence from 2011 to 2015 despite extensive funding and implementation of control measures. Here, we focus on Rwanda as a case study and simulate monthly malaria incidence between 2010 and 2015, employing an ensemble learning method. Next, we project future malaria prevalence using shared socio-economic pathways (SSP2-4.5 and SSP5-8.5). We find that the projected increases in temperature and precipitation may shift malaria transmission risk to the highlands of western and northern Rwanda. These two regions that currently experience low malaria transmission. The seasonal effects of malaria incidence may be less apparent from January to June, and the peak season for malaria transmission in the highlands could occur one month earlier. Our findings highlight the impacts of climate change on malaria epidemics in Rwanda, which have implications for other world regions.

Abstract

Changing ocean properties threaten coastal communities and ecosystems worldwide. This study projected possible future changes in major sea surface attributes, including sea surface height (SSH), sea surface salinity (SSS), and sea surface temperature (SST) across the Southeast Asian Seas (SEAS) for shared socioeconomic pathways (SSPs) using Global Climate Models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6). We employed Quantile Mapping (QM) to bias correct model outputs at the resolution of 0.25° considering the Ocean Reanalysis System 5 (ORAS5) data as the reference. The study identified four GCMs (CMCC-CM2-SR5, EC-Earth3, EC-Earth3-Veg, and NorESM2-MM) reliable for replicating observed sea surface characteristics. The bias-corrected top-performing GCMs revealed an increase in SSH ranging from 6 to 8 cm across most regions of SEAS during 1975–2014. The multimodel ensemble mean (MMEs) of the selected GCMs projected a 5–40 cm rise in sea level in most SEAS regions, with the most significant increases in the southern Gulf of Thailand and northern Peninsular Malaysia. Salinity and temperature projections show regional variations, with some areas seeing increases in salinity, and others may experience declines in the near future, with a significant decrease in far-future projections. The most concerning finding is the projected rise in sea surface temperature (up to 4.2 °C) in the Gulf of Thailand and the Strait of Malacca, potentially impacting marine ecosystems and fisheries. These findings highlight the urgency of robust adaptation plans to safeguard coastal communities and ecosystems from the multifaceted challenges of a warming climate.

Abstract

Sea-ice surface temperature from atmospheric reanalysis has been used as an indicator of ice melt and climate change. However, its performance in atmospheric reanalyses is not fully understood in Antarctica. Here, we quantified biases in six widely-used reanalyses using satellite observations, and found strong and persistent warm biases in most reanalyses examined. Further analysis of the biases revealed two main culprits: incorrect cloud properties, and inappropriate sea-ice representation in the reanalysis products. We found that overestimated cloud simulation can contribute more than 4 K warm bias, with ERA5 exhibiting the largest warm bias. Even in reanalysis with smaller biases, this accuracy is achieved through a compensatory relationship between relatively lower cloud fraction bias and overestimated sea ice insulation effect. A dynamic downscaling simulation shows that differences in sea-ice representation can contribute a 2.3 K warm bias. The representation of ice concentration is the primary driver of the spatial distribution of biases by modulating the coupling between sea ice and clouds, as well as surface heat conduction. The lack of a snow layer in all reanalyses examined also has an impact on biases.

Abstract

Purpose of Review

Climate change has profoundly impacted the Earth's atmospheric system and altered the terrestrial water cycle, reshaping the spatiotemporal patterns of hydrological extremes, including floods and droughts. This review aims to summarize recent advancements in understanding the response of hydrological extremes to climate change in both past and future.

Recent Findings

Historical floods driven by heavy rainfall are increasing, while those dominated by snow processes are decreasing, resulting in non-significant changes on a global average. Previously overestimated droughts, due to inaccuracies in hydrological modules within offline diagnostic metrics, have been corrected by advanced modeling results, also revealing minimal historical changes on a global scale. Earth system simulations project concurrent increases in both floods and droughts under future climate change scenarios.

Summary

Climate change influences hydrological extremes across various scales, with diverse spatial distributions and underlying mechanisms. Decision-makers should integrate multi-source information to enhance the monitoring and adaptation of hydrological extremes, particularly focusing on abrupt drought-flood alternations.

Abstract

Purpose of Review

Climate change has profoundly impacted the Earth's atmospheric system and altered the terrestrial water cycle, reshaping the spatiotemporal patterns of hydrological extremes, including floods and droughts. This review aims to summarize recent advancements in understanding the response of hydrological extremes to climate change in both past and future.

Recent Findings

Historical floods driven by heavy rainfall are increasing, while those dominated by snow processes are decreasing, resulting in non-significant changes on a global average. Previously overestimated droughts, due to inaccuracies in hydrological modules within offline diagnostic metrics, have been corrected by advanced modeling results, also revealing minimal historical changes on a global scale. Earth system simulations project concurrent increases in both floods and droughts under future climate change scenarios.

Summary

Climate change influences hydrological extremes across various scales, with diverse spatial distributions and underlying mechanisms. Decision-makers should integrate multi-source information to enhance the monitoring and adaptation of hydrological extremes, particularly focusing on abrupt drought-flood alternations.

Abstract

Understanding the variation of soil physical properties in relation to land use and elevation is essential for modeling soil-landscape relationships and sustainable land management. Hence, this study investigates the spatio-temporal variability of soil physical properties in a lower Himalayan watershed, where agriculture, forest, and grasslands are dominant. Samples from 104 sites in a 422 km² watershed were collected using a gridded sampling scheme (2 km × 2 km resolution) over 57 weeks. Spatial patterns were analyzed using the Kriging technique, and Spearman rank correlation was employed to identify landform-dependent correlations between soil properties and elevation. The interdependence of the properties was detected using principal component analysis (PCA), while the random forest (RF) approach explored the factors influencing electrical conductivity (EC), organic content (OC), soil temperature (ST), and soil moisture (SM). The results revealed that forest landforms have higher coarser fractions (40%) compared to other landforms, while grasslands have higher soil fines (66%). A positive correlation was observed for elevation with sand content (0.15*), organic content (0.42*), and specific gravity (0.03), while a negative correlation was observed for silt (0.10), clay (0.21*), bulk density (0.52*), electrical conductivity (0.41*), soil moisture (0.28*), and temperature (0.31*). Elevation, soil texture, and specific gravity were identified as critical controls for EC, OC, ST, and SM, emphasizing the importance of soil properties, especially elevation and texture, in shaping spatial distributions. These findings contribute to creating a high-resolution regional inventory for effective land use management, adaptation to climate change, and improved livelihood, specifically for mountain people.

Abstract

Understanding the variation of soil physical properties in relation to land use and elevation is essential for modeling soil-landscape relationships and sustainable land management. Hence, this study investigates the spatio-temporal variability of soil physical properties in a lower Himalayan watershed, where agriculture, forest, and grasslands are dominant. Samples from 104 sites in a 422 km² watershed were collected using a gridded sampling scheme (2 km × 2 km resolution) over 57 weeks. Spatial patterns were analyzed using the Kriging technique, and Spearman rank correlation was employed to identify landform-dependent correlations between soil properties and elevation. The interdependence of the properties was detected using principal component analysis (PCA), while the random forest (RF) approach explored the factors influencing electrical conductivity (EC), organic content (OC), soil temperature (ST), and soil moisture (SM). The results revealed that forest landforms have higher coarser fractions (40%) compared to other landforms, while grasslands have higher soil fines (66%). A positive correlation was observed for elevation with sand content (0.15*), organic content (0.42*), and specific gravity (0.03), while a negative correlation was observed for silt (0.10), clay (0.21*), bulk density (0.52*), electrical conductivity (0.41*), soil moisture (0.28*), and temperature (0.31*). Elevation, soil texture, and specific gravity were identified as critical controls for EC, OC, ST, and SM, emphasizing the importance of soil properties, especially elevation and texture, in shaping spatial distributions. These findings contribute to creating a high-resolution regional inventory for effective land use management, adaptation to climate change, and improved livelihood, specifically for mountain people.

Abstract

Sea-ice surface temperature from atmospheric reanalysis has been used as an indicator of ice melt and climate change. However, its performance in atmospheric reanalyses is not fully understood in Antarctica. Here, we quantified biases in six widely-used reanalyses using satellite observations, and found strong and persistent warm biases in most reanalyses examined. Further analysis of the biases revealed two main culprits: incorrect cloud properties, and inappropriate sea-ice representation in the reanalysis products. We found that overestimated cloud simulation can contribute more than 4 K warm bias, with ERA5 exhibiting the largest warm bias. Even in reanalysis with smaller biases, this accuracy is achieved through a compensatory relationship between relatively lower cloud fraction bias and overestimated sea ice insulation effect. A dynamic downscaling simulation shows that differences in sea-ice representation can contribute a 2.3 K warm bias. The representation of ice concentration is the primary driver of the spatial distribution of biases by modulating the coupling between sea ice and clouds, as well as surface heat conduction. The lack of a snow layer in all reanalyses examined also has an impact on biases.