scholarly journals Observationally constrained projection of the reduced intensification of extreme climate events in Central Asia from 0.5 °C less global warming

2019 ◽  
Vol 54 (1-2) ◽  
pp. 543-560 ◽  
Author(s):  
Dongdong Peng ◽  
Tianjun Zhou ◽  
Lixia Zhang ◽  
Wenxia Zhang ◽  
Xiaolong Chen
Keyword(s):  
Global Warming ◽  
Central Asia ◽  
Coupled Model ◽  
Correction Method ◽  
Maximum Precipitation ◽  
Extreme Climate Events ◽  
Extreme Climate ◽  
Rcp 8.5 ◽  
Climate Events ◽  
The Impact

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.

scholarly journals Impact of recent climate extremes on mosquito-borne disease transmission in Kenya

2021 ◽  
Vol 15 (3) ◽  
pp. e0009182
Author(s):  
Cameron Nosrat ◽  
Jonathan Altamirano ◽  
Assaf Anyamba ◽  
Jamie M. Caldwell ◽  
Richard Damoah ◽  
...  
Keyword(s):  
Climate Change ◽  
Disease Transmission ◽  
Enzyme Linked Immunosorbent Assay ◽  
Mosquito Abundance ◽  
Targeted Interventions ◽  
Extreme Climate Events ◽  
Extreme Climate ◽  
Recent Climate ◽  
Climate Events ◽  
The Impact

Climate change and variability influence temperature and rainfall, which impact vector abundance and the dynamics of vector-borne disease transmission. Climate change is projected to increase the frequency and intensity of extreme climate events. Mosquito-borne diseases, such as dengue fever, are primarily transmitted by Aedes aegypti mosquitoes. Freshwater availability and temperature affect dengue vector populations via a variety of biological processes and thus influence the ability of mosquitoes to effectively transmit disease. However, the effect of droughts, floods, heat waves, and cold waves is not well understood. Using vector, climate, and dengue disease data collected between 2013 and 2019 in Kenya, this retrospective cohort study aims to elucidate the impact of extreme rainfall and temperature on mosquito abundance and the risk of arboviral infections. To define extreme periods of rainfall and land surface temperature (LST), we calculated monthly anomalies as deviations from long-term means (1983–2019 for rainfall, 2000–2019 for LST) across four study locations in Kenya. We classified extreme climate events as the upper and lower 10% of these calculated LST or rainfall deviations. Monthly Ae. aegypti abundance was recorded in Kenya using four trapping methods. Blood samples were also collected from children with febrile illness presenting to four field sites and tested for dengue virus using an IgG enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR). We found that mosquito eggs and adults were significantly more abundant one month following an abnormally wet month. The relationship between mosquito abundance and dengue risk follows a non-linear association. Our findings suggest that early warnings and targeted interventions during periods of abnormal rainfall and temperature, especially flooding, can potentially contribute to reductions in risk of viral transmission.

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 ◽  
...  
Keyword(s):  
Global Warming ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
Climate Forcing ◽  
Sea Ice Extent ◽  
Extreme Climate Events ◽  
Atmospheric Circulation Patterns ◽  
Extreme Climate ◽  
Substantial Progress ◽  
Climate Events

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.

Changes in Extreme Climate Events in Rice-Growing Regions Under Different Warming Scenarios in China

2021 ◽  
Vol 9 ◽  
Author(s):  
Jieming Chou ◽  
Weixing Zhao ◽  
Jiangnan Li ◽  
Yuan Xu ◽  
Fan Yang ◽  
...  
Keyword(s):  
Global Warming ◽  
Critical Time ◽  
Regional Governance ◽  
Disaster Mitigation ◽  
Central China ◽  
Time Points ◽  
Extreme Climate Events ◽  
Extreme Climate ◽  
Extreme Precipitation Events ◽  
Climate Events

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.

scholarly journals Edge Detection Reveals Abrupt and Extreme Climate Events

2020 ◽  
Vol 33 (15) ◽  
pp. 6399-6421
Author(s):  
Sebastian Bathiany ◽  
Johan Hidding ◽  
Marten Scheffer
Keyword(s):  
Coupled Model ◽  
The Arctic ◽  
Edge Detector ◽  
Extreme Climate Events ◽  
Extreme Climate ◽  
New Strategy ◽  
Intercomparison Project ◽  
Abrupt Shifts ◽  
Climate Events ◽  
Impacts Of Climate Change

AbstractThe most discernible and devastating impacts of climate change are caused by events with temporary extreme conditions (“extreme events”) or abrupt shifts to a new persistent climate state (“tipping points”). The rapidly growing amount of data from models and observations poses the challenge to reliably detect where, when, why, and how these events occur. This situation calls for data-mining approaches that can detect and diagnose events in an automatic and reproducible way. Here, we apply a new strategy to this task by generalizing the classical machine-vision problem of detecting edges in 2D images to many dimensions (including time). Our edge detector identifies abrupt or extreme climate events in spatiotemporal data, quantifies their abruptness (or extremeness), and provides diagnostics that help one to understand the causes of these shifts. We also publish a comprehensive toolset of code that is documented and free to use. We document the performance of the new edge detector by analyzing several datasets of observations and models. In particular, we apply it to all monthly 2D variables of the RCP8.5 scenario of the Coupled Model Intercomparison Project (CMIP5). More than half of all simulations show abrupt shifts of more than 4 standard deviations on a time scale of 10 years. These shifts are mostly related to the loss of sea ice and permafrost in the Arctic. Our results demonstrate that the edge detector is particularly useful to scan large datasets in an efficient way, for example multimodel or perturbed-physics ensembles. It can thus help to reveal hidden “climate surprises” and to assess the uncertainties of dangerous climate events.

scholarly journals Extreme Climate and Absence from Work: Evidence from Jamaica

2021 ◽  
Author(s):  
Nekeisha Spencer ◽  
Mikhail-Ann Urquhart
Keyword(s):  
Labor Market ◽  
Climatic Data ◽  
Work Absence ◽  
Negative Effects ◽  
Individual Level ◽  
Extreme Climate Events ◽  
Extreme Climate ◽  
Absence From Work ◽  
Climate Events ◽  
The Impact

AbstractThis study investigated the impact of extreme climate events on work absence in Jamaica. To this end, we constructed a quarterly individual level dataset on labor market and climatic data for 2004–2014. We find that while excess rainfall increases the odds of being temporarily absent from work, heat is unlikely to have an effect. The estimated outcome of excess rainfall is reasonable given the possibility of flooded roads, which can impede travel to work. This draws attention to the development of e-commuting policies to mitigate any negative effects on productivity.

scholarly journals Hurricane Driven Changes in Vegetation Structure and Ecosystem Services in Tropical Urban Yards: a Study Case in San Juan, Puerto Rico

2021 ◽  
Author(s):  
Sofía Olivero-Lora ◽  
Julissa Rojas-Sandoval ◽  
Elvia J Melendez-Ackerman ◽  
Juan Orengo-Rolon
Keyword(s):  
Puerto Rico ◽  
Ecosystem Services ◽  
Vegetation Structure ◽  
Urban Forests ◽  
San Juan ◽  
Extreme Climate Events ◽  
Extreme Climate ◽  
Residential Yards ◽  
Climate Events ◽  
The Impact

Abstract Urban forests are valuable spaces for species conservation, protection of local biodiversity and provision of ecosystem services. However, they are also vulnerable to the impact of extreme climate events like hurricanes. Understanding how urban forests are responding to hurricane disturbances is crucial to improve their design, management, and resilience. Here we analyzed pre-and post-hurricane surveys in 52 residential yards in San Juan to assess urban forests responses after Hurricanes Irma and Maria impacted Puerto Rico in 2017. We used these surveys to compare vegetation structure and composition (including species-specific mortality and damage rates) and to quantify changes in the ecosystem services provided by these yards. We found that hurricane disturbances significantly altered the structure but not the composition of yard vegetation. We detected a 27% reduction and 31% mortality of standing stems, and a significant reduction in plants health. Yard species composition was dominated by non-native species and this trend did not change with hurricane disturbance. Changes in vegetation structure translated into substantial reductions in ecosystem services. Food provision, an important service provided by a large proportion of yards before the hurricane, reported the highest reduction (41.9%) while carbon storage was the service that changed the least (9%). Our combined results emphasize the key role played by residential yards providing ecosystem services in tropical cities and call for further efforts to manage private and public urban forests in ways that may ensure their resilience to mitigate extreme climate events, provide multiple ecosystem services, and promote long-term urban sustainability.

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