Tag: Climate Change Downscaling Techniques

Abstract

The projection of precipitation changes and the year of surpassing a 1, 2, 3, 4, and 5 °C warming above pre-industrial levels in the Middle East – West Asia (MEWA) during 2026–2100 was conducted using dynamical downscaling of the Regional Climate Modeling version 4.7 (RegCM4.7) under Shared Socio-economic Pathways (SSPs) scenarios. Two significant changes in annual precipitation were identified compared to the baseline period of 1990–2014: a decrease in the Mediterranean Basin (MB) and an increase in the Persian Gulf- the Gulf of Oman -east of the Arabian Peninsula region (POA). The above patterns were also detected during the spring of 2026–2050. However, a decrease in precipitation is anticipated around the Persian Gulf (PG) during 2076–2100. The precipitation patterns exhibit a decrease in the MB and east of it up to Iran during the summer. In contrast, there is an increase in precipitation in the POA. During autumn, precipitation increases (decreases) around the POA (MB). During the winter, there is an increase (decrease) in the precipitation of POA (from the MB to Iran). In the SSP5-8.5 scenario, a 2 °C (3 °C) warming is expected by 2050 (2068), about two (four) decades earlier than SSP2-4.5. A 4 °C (5 °C) warming is expected by 2081 (2092) in SSP5-8.5, but postponed beyond 2100 in SSP2-4.5. Out of all studied cities, Tehran is projected to experience the greatest decrease in precipitation and the highest increase in temperature. Meanwhile, Abu Dhabi is expected to encounter the greatest precipitation increase and the lowest temperature rise.

Abstract

Today, a major challenge for climate science is to overcome what is called the “usability gap” between the projections derived fromclimate models and the needs of the end-users. Regional Climate Models (RCMs) are expected to provide usable information concerning a variety of impacts and for a wide range of end-users. It is often assumed that the development of more accurate, more complex RCMs with higher spatial resolution should bring process understanding and better local projections, thus overcoming the usability gap. In this paper, I rather assume that the credibility of climate information should be pursued together with two other criteria of usability, which are salience and legitimacy. Based on the Swiss climate change scenarios, I study the attempts at meeting the needs of end-users and outline the trade-off modellers and users have to face with respect to the cascade of uncertainty. A conclusion of this paper is that the trade-off between salience and credibility sets the conditions under which RCMs can be deemed adequate for the purposes of addressing the needs of end-users and gearing the communication of the projections toward direct use and action.

Abstract

The research on the spatiotemporal changes and driving factors of ecosystems in rapidly urbanizing regions has always been a topic of widespread concern. As the fourth pole of China’s economic development, the research on the Chengdu-Chongqing region has reference significance for the urbanization process of developing countries such as India, Brazil, and South Africa.The normalized difference vegetation index (NDVI) has been widely applied in studies of plant and ecosystem changes. Based on MODIS NDVI data from 2001 to 2020 and meteorological data of the same period, this study reveals the evolution of NDVI in the Chengdu-Chongqing region from three aspects: the spatiotemporal variation characteristics of NDVI, the prediction of future trends in vegetation coverage, and the response of vegetation to climate change and human activities. During the period of plant growth, the mean NDVI achieved a value of 0.78, and the vegetation coverage rate is increasing year by year. According to the Hurst index, the future NDVI in Chengdu-Chongqing region will tend to decrease, and its trend is opposite to that of the past period of time. The Chengdu-Chongqing region vegetation positively affected by human activities is greater than those negatively affected, and in terms of vegetation degradation, the impact of human activities is greater than climate change.

Abstract

The management and improvement of air quality are global challenges aimed at protecting human health and environmental resources. For this purpose, in addition to legislative and scientific indications, numerous tools are available: measurement methods and tools for estimating and forecasting. As a collection of data presenting an emission of a pollutant (to air), emission inventories support the knowledge of sources impacting air quality by estimating atmospheric emissions within a specific (wide or limited) reference area. There are several methodological approaches for their definition, which can be classified into bottom–up or top–down methods. This paper aims to review the methodological approaches described in the literature that apply the top–down approach for the disaggregation of atmospheric emissions with high spatial and temporal resolution. The proxy variables used to apply this approach are identified, as well as the spatial and temporal resolution obtained by the authors. The results show that population density and land use are the most common parameters with respect to most of the emission sources and for numerous atmospheric pollutants. The spatial resolution of the disaggregation described in the literature varies from a few hundred metres to several kilometres, in relation to the territorial extension of the study areas. The results of the review help support the selection of the best and most popular proxy variables used to scale emissions inventories.

Abstract

The identification of agriculture and land use configurations that achieve net zero (NZ) greenhouse gas emissions is critical to inform appropriate land use and food policy, yet national NZ targets lack consistent definitions. Here, 3000 randomised scenarios projecting future agricultural production and compatible land use combinations in Ireland were screened using ten NZ definitions. When aggregating carbon dioxide, methane, and nitrous oxide emissions using various methods, 1–85% of scenarios met NZ criteria. Despite considerable variation, common actions emerged across definitions, including high rates of afforestation, organic soil re-wetting, and cattle destocking. Ambitious technical abatement of agricultural emissions moderated, but could not substitute, these actions. With abatement, 95th percentile milk output varied from 11–91% of 2021 output, but was associated with reductions of up to 98% in suckler-beef production, and a 47–387% increase in forest cover. Achieving NZ will thus require transformation of Ireland’s land sector. Lagging land use change effects require urgent action, but sustaining a just transition will require visioning of future NZ land use combinations supporting a sustainable and resilient food system, alongside an expanding circular bioeconomy. We provide new insight into the sensitivity of such visioning to NZ definitions, pointing to an urgent need for international consensus on the accounting of methane emissions in NZ targets.

Abstract

The complex topography of the Himalayan region makes it difficult to analyze its climatic variables over the region. The study has been carried out to identify the trends in climate variables and drought analysis over the Beas River basin in the western Himalayas. To understand the impact of changing climate on the Beas River basin, five downscaled global circulation models (GCMs) were used, namely BNU-ESM, Can-ESM2, CNRM CM5, MPI-ESM MR, and MPI-ESM LR. These GCMs were obtained for two representative concentration pathway (RCP) scenarios: 4.5, which represents the normal scenario, and 8.5, which represents the most extreme scenario for anticipated concentrations of carbon and greenhouse gases. The multi-model ensemble (MME) of these 5 GCMs were used to project rainfall and temperature. Further Innovative Trends Analysis (ITA) and modified Mann–Kendell (mMK) trend tests have been used for trend analysis at a 5% significance level. The drought pattern in the future timescale of the ensembled model is calculated using the Standardised Precipitation Index (SPI) for both RCPs. The ITA, Mann–Kendell, and Sen’s slope trends showed decreased precipitation under RCP 4.5 in the Manali region and showed an increasing trend for the remaining locations under both scenarios. Furthermore, SPI values showed frequent droughts under both RCPs. The study outcomes will serve as a scientific foundation for the sustainability of water resources and agricultural output in arid inland regions vulnerable to changing climate.

Abstract

Older adults are generally amongst the most vulnerable to heat and cold. While temperature-related health impacts are projected to increase with global warming, the influence of population aging on these trends remains unclear. Here we show that at 1.5 °C, 2 °C, and 3 °C of global warming, heat-related mortality in 800 locations across 50 countries/areas will increase by 0.5%, 1.0%, and 2.5%, respectively; among which 1 in 5 to 1 in 4 heat-related deaths can be attributed to population aging. Despite a projected decrease in cold-related mortality due to progressive warming alone, population aging will mostly counteract this trend, leading to a net increase in cold-related mortality by 0.1%–0.4% at 1.5–3 °C global warming. Our findings indicate that population aging constitutes a crucial driver for future heat- and cold-related deaths, with increasing mortality burden for both heat and cold due to the aging population.

Abstract

This research assesses the efficacy of thirteen bias correction methods, including traditional and machine learning-based approaches, in downscaling four chosen GCMs of Coupled Model Intercomparison Project 6 (CMIP6) in Nigeria. The 0.5° resolution gridded rainfall, maximum temperature (Tmx), and minimum temperature (Tmn) of the Climate Research Unit (CRU) for the period 1975 − 2014 was used as the reference. The Compromise Programming Index (CPI) was used to assess the performance of bias correction methods based on three statistical metrics. The optimal bias-correction technique was employed to rectify bias to project the spatiotemporal variations in rainfall, Tmx, and Tmn over Nigeria for two distinct future timeframes: the near future (2021–2059) and the distant future (2060–2099). The study's findings indicate that the Random Forest (RF) machine learning technique better corrects the bias of all three climate variables for the chosen GCMs. The CPI of RF for rainfall, Tmx, and Tmn were 0.62, 0.0, and 0.0, followed by the Power Transformation approach with CPI of 0.74, 0.36, and 0.29, respectively. The geographic distribution of rainfall and temperatures significantly improved compared to the original GCMs using RF. The mean bias-corrected projections from the multimodel ensemble of the GCMs indicated a rainfall increase in the near future, particularly in the north by 2.7–12.7%, while a reduction in the south in the far future by -3.3% to -10% for different SSPs. The temperature projections indicated a rise in the Tmx and Tm from 0.71 °C and 0.63 °C for SSP126 to 2.71 °C and 3.13 °C for SSP585. This work highlights the significance of comparing bias correction approaches to determine the most suitable approach for adjusting biases in GCM estimations for climate change research.

Abstract

This study analyzed systematic biases in surface air temperature (SAT) within Far East Asia during the boreal winter using the SNURCM and WRF regional climate models (RCMs) from the Coordinated Regional Downscaling Experiment (CORDEX)-East Asia phase II. The SAT biases were examined in relation to the East Asian winter monsoon (EAWM). The models consistently simulated lower winter temperatures over East Asia, particularly in the Manchuria (MC) region, compared to the observation, showing a positive correlation with the EAWM. This study assessed the models' ability to capture EAWM variability and revealed relationships between SAT biases and discrepancies in low-level and near-surface EAWM conditions. The findings emphasized the value of analyzing extreme monsoon years, with the RCMs exhibiting larger cold SAT biases during strong EAWM years. Systematic biases in sea-level pressure contrast and lower-level winds over the MC region were evident during years with a robust monsoon. The overestimation of low-level winds during strong EAWM years contributed to increased cold advection, affecting the MC region. These systematic errors are influenced by the internal factors of the model, such as the physics parameterization schemes, rather than large-scale circulation forced by the reanalysis data (perfect boundary condition). These results provide insights for model improvements, understanding EAWM dynamics, and call for investigation of processes in the planetary boundary layer and coupled air-sea interaction.

Abstract

Coastal urban areas like New York City (NYC) are more vulnerable to urban pluvial flooding particularly because the rapid runoff from extreme rainfall events can be further compounded by the co-occurrence of high sea-level conditions either from tide or storm surge leading to compound flooding events. Present-day urban pluvial flooding is a significant challenge for NYC and this challenge is expected to become more severe with the greater frequency and intensity of storms and sea-level rise (SLR) in the future. In this study, we advance NYC’s assessment of present and future exposure to urban pluvial flooding through simulating various storm scenarios using a citywide hydrologic and hydraulic model. This is the first citywide analysis using NYC’s drainage models focusing on rainfall-induced flooding. We showed that the city’s stormwater system is highly vulnerable to high-intensity short-duration “cloudburst” events, with the extent and volume of flooding being the largest during these events. We further showed that rainfall events coupled with higher sea-level conditions, either from SLR or storm surge, could significantly increase the volume and extent of flooding in the city. We also assessed flood exposure in terms of the number of buildings and length of roads exposed to flooding as well as the number of the affected population. This study informs NYC’s residents of their current and future flood risk and enables the development of tailored solutions to manage increasing flood risk in the city.