scholarly journals Quantifying the influence of global warming on unprecedented extreme climate events

2017 ◽  
Vol 114 (19) ◽  
pp. 4881-4886 ◽  
Author(s):  
Noah S. Diffenbaugh ◽  
Deepti Singh ◽  
Justin S. Mankin ◽  
Daniel E. Horton ◽  
Daniel L. Swain ◽  
...  

Efforts to understand the influence of historical global warming on individual extreme climate events have increased over the past decade. However, despite substantial progress, events that are unprecedented in the local observational record remain a persistent challenge. Leveraging observations and a large climate model ensemble, we quantify uncertainty in the influence of global warming on the severity and probability of the historically hottest month, hottest day, driest year, and wettest 5-d period for different areas of the globe. We find that historical warming has increased the severity and probability of the hottest month and hottest day of the year at >80% of the available observational area. Our framework also suggests that the historical climate forcing has increased the probability of the driest year and wettest 5-d period at 57% and 41% of the observed area, respectively, although we note important caveats. For the most protracted hot and dry events, the strongest and most widespread contributions of anthropogenic climate forcing occur in the tropics, including increases in probability of at least a factor of 4 for the hottest month and at least a factor of 2 for the driest year. We also demonstrate the ability of our framework to systematically evaluate the role of dynamic and thermodynamic factors such as atmospheric circulation patterns and atmospheric water vapor, and find extremely high statistical confidence that anthropogenic forcing increased the probability of record-low Arctic sea ice extent.

2019 ◽  
Vol 54 (1-2) ◽  
pp. 543-560 ◽  
Author(s):  
Dongdong Peng ◽  
Tianjun Zhou ◽  
Lixia Zhang ◽  
Wenxia Zhang ◽  
Xiaolong Chen

Abstract Arid Central Asia is highly vulnerable to extreme climate events. Information on potential future changes in extreme climate events in Central Asia is limited. In this study, the performances of models from the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulating climatological extremes in Central Asia are first evaluated, and a bias correction method is employed to constrain future projections. The responses of extreme climate events over Central Asia to future warming and, in particular, the impact of 1.5 and 2 °C global warming scenarios are then assessed based on the observationally constrained projections. During the twenty-first century, coldest night (TNn), coldest day (TXn), warmest night (TNx), warmest day (TXx), 1-day maximum precipitation (RX1 day), 5-day maximum precipitation (RX5 day), and precipitation intensity (SDII) in Central Asia would robustly increase at best estimated rates of 1.93 °C, 1.71 °C, 1.18 °C, 1.25 °C, 6.30%, 5.71%, and 4.99% per degree of global warming, respectively, under Representative Concentration Pathway (RCP) 8.5. Compared with the 2 °C warming scenario, limiting global warming to 1.5 °C could reduce the intensification (relative to 1986–2005) of TNn, TNx, TXn, TXx, RX1 day, RX5 day, and SDII by 33%, 24%, 32%, 29%, 39%, 42%, and 53% from the best estimates under RCP8.5, respectively. The avoided intensification of TNn, TNx, TXn and TXx (RX1 day and SDII) would be larger (smaller) under RCP4.5. This suggests that a low warming target is necessary for avoiding the dangerous risk of extremes in this arid region.


2021 ◽  
Vol 9 ◽  
Author(s):  
Jieming Chou ◽  
Weixing Zhao ◽  
Jiangnan Li ◽  
Yuan Xu ◽  
Fan Yang ◽  
...  

Scientific prediction of critical time points of the global temperature increases and assessment of the associated changes in extreme climate events can provide essential guidance for agricultural production, regional governance, and disaster mitigation. Using daily temperature and precipitation model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), the time points of the temperature that will increase by 1.5 and 2.0°C were assessed under three different scenarios (SSP126, SSP245, and SSP585). To characterize the change of extreme climate events in the rice-growing regions in China, six indices were designed, and a time slice method was used. An analysis from an ensemble of CMIP6 models showed that under SSP245, the global mean temperature will rise by 1.5°C/2.0°C by approximately 2030/2049. A global warming of 2.0°C does not occur under SSP126. The time for a 1.5°C/2.0°C warming all becomes earlier under SSP585. Under 1.5°C of global warming, the number of warm days (TX90p), rice heat damage index (Ha), consecutive dry days (CDD), 5-day maximum precipitation (Rx5day), and number of annual total extreme precipitation events (R99pTOT) will clearly increase, while the number of cold damage (Cd) events will decrease. All the indices show a strong variability regionally. For example, the CDD increased significantly in the Central China and South China rice-growing regions. The monthly maximum consecutive 5-day precipitation increased by as much as 6.8 mm in the Southwest China rice-growing region.


Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 282
Author(s):  
Erik Jeppesen ◽  
Donald Pierson ◽  
Eleanor Jennings

The Earth is facing a major change in climate due to ongoing global warming [...]


Sign in / Sign up

Export Citation Format

Share Document