scholarly journals Projecting heat-related excess mortality under climate change scenarios in China

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jun Yang ◽  
Maigeng Zhou ◽  
Zhoupeng Ren ◽  
Mengmeng Li ◽  
Boguang Wang ◽  
...  

AbstractRecent studies have reported a variety of health consequences of climate change. However, the vulnerability of individuals and cities to climate change remains to be evaluated. We project the excess cause-, age-, region-, and education-specific mortality attributable to future high temperatures in 161 Chinese districts/counties using 28 global climate models (GCMs) under two representative concentration pathways (RCPs). To assess the influence of population ageing on the projection of future heat-related mortality, we further project the age-specific effect estimates under five shared socioeconomic pathways (SSPs). Heat-related excess mortality is projected to increase from 1.9% (95% eCI: 0.2–3.3%) in the 2010s to 2.4% (0.4–4.1%) in the 2030 s and 5.5% (0.5–9.9%) in the 2090 s under RCP8.5, with corresponding relative changes of 0.5% (0.0–1.2%) and 3.6% (−0.5–7.5%). The projected slopes are steeper in southern, eastern, central and northern China. People with cardiorespiratory diseases, females, the elderly and those with low educational attainment could be more affected. Population ageing amplifies future heat-related excess deaths 2.3- to 5.8-fold under different SSPs, particularly for the northeast region. Our findings can help guide public health responses to ameliorate the risk of climate change.

2020 ◽  
Author(s):  
James Murphy

<p>The challenge of combining initialised and uninitialised decadal projections</p><p>James Murphy, Robin Clark, Nick Dunstone, Glen Harris, Leon Hermanson and Doug Smith</p><p>During the past 10 years or so, exploratory work in initialised decadal climate prediction, using global climate models started from recent analyses of observations, has grown into a coordinated international programme that contributes to IPCC assessments. At the same time, countries have continued to develop and update their national climate change scenarios.  These typically cover the full 21<sup>st</sup> century, including the initial decade that overlaps with the latest initialised forecasts. To date, however, national scenarios continue to be based exclusively on long-term (uninitialised) climate change simulations, with initialised information regarded as a separate stream of information.</p><p>We will use early results from the latest UK national scenarios (UKCP), and the latest CMIP6 initialised predictions, to illustrate the potential and challenges associated with the notion of combining both streams of information. This involves assessing the effects of initialisation on predictability and uncertainty (as indicated, for example, by the skill of ensemble-mean forecasts and the spread amongst constituent ensemble members). Here, a particular challenge involves interpretation of the “signal-to-noise” problem, in which ensemble-mean skill can sometimes be found which is larger than would be expected on the basis of the ensemble spread. In addition to initialisation, we will also emphasise the importance of understanding how the assessment of climate risks depends on other features of prediction system design, including the sampling of model uncertainties and the simulation of internal climate variability.</p>


Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1803
Author(s):  
Inmaculada C. Jiménez-Navarro ◽  
Patricia Jimeno-Sáez ◽  
Adrián López-Ballesteros ◽  
Julio Pérez-Sánchez ◽  
Javier Senent-Aparicio

Precipitation and temperature around the world are expected to be altered by climate change. This will cause regional alterations to the hydrological cycle. For proper water management, anticipating these changes is necessary. In this study, the basin of Lake Erken (Sweden) was simulated with the recently released software SWAT+ to study such alterations in a short (2026–2050), medium (2051–2075) and long (2076–2100) period, under two different climate change scenarios (SSP2-45 and SSP5-85). Seven global climate models from the latest projections of future climates that are available (CIMP 6) were compared and ensembled. A bias-correction of the models’ data was performed with five different methods to select the most appropriate one. Results showed that the temperature is expected to increase in the future from 2 to 4 °C, and precipitation from 6% to 20%, depending on the scenario. As a result, water discharge would also increase by about 18% in the best-case scenario and by 50% in the worst-case scenario, and the surface runoff would increase between 5% and 30%. The floods and torrential precipitations would also increase in the basin. This trend could lead to soil impoverishment and reduced water availability in the basin, which could damage the watershed’s forests. In addition, rising temperatures would result in a 65% reduction in the snow water equivalent at best and 92% at worst.


Author(s):  
Jayne F. Knott ◽  
Jo E. Sias ◽  
Eshan V. Dave ◽  
Jennifer M. Jacobs

Pavements are vulnerable to reduced life with climate-change-induced temperature rise. Greenhouse gas emissions have caused an increase in global temperatures since the mid-20th century and the warming is projected to accelerate. Many studies have characterized this risk with a top-down approach in which climate-change scenarios are chosen and applied to predict pavement-life reduction. This approach is useful in identifying possible pavement futures but may miss short-term or seasonal pavement-response trends that are essential for adaptation planning. A bottom-up approach focuses on a pavement’s response to incremental temperature change resulting in a more complete understanding of temperature-induced pavement damage. In this study, a hybrid bottom-up/top-down approach was used to quantify the impact of changing pavement seasons and temperatures on pavement life with incremental temperature rise from 0 to 5°C at a site in coastal New Hampshire. Changes in season length, seasonal average temperatures, and temperature-dependent resilient modulus were used in layered-elastic analysis to simulate the pavement’s response to temperature rise. Projected temperature rise from downscaled global climate models was then superimposed on the results to determine the timing of the effects. The winter pavement season is projected to end by mid-century, replaced by a lengthening fall season. Seasonal pavement damage, currently dominated by the late spring and summer seasons, is projected to be distributed more evenly throughout the year as temperatures rise. A 7% to 32% increase in the asphalt-layer thickness is recommended to protect the base and subgrade with rising temperatures from early century to late-mid-century.


2021 ◽  
Author(s):  
Yusuke Satoh ◽  
Hideo Shiogama ◽  
Naota Hanasaki ◽  
Yadu Pokhrel ◽  
Julien Boulange ◽  
...  

<p>Droughts are anticipated to intensify or become more frequent in many parts of the world due to climate change. However, the issue of drought definition, namely the diversity of drought definition, makes it difficult to compare drought projections and hampers overviewing future changes in drought. This issue is widely known and underscored in recent reports of the Intergovernmental Panel on Climate Change, but the relative importance of the issue and its spatial distribution have never been quantitatively evaluated compared to other sources of uncertainty.</p><p>Here, using a multi-scenario and multi-model dataset with combinations of three climate change scenarios, four global climate models and seven global water models, we evaluated changes in the frequency of three categories of drought (meteorological, agricultural, and hydrological droughts) by a consistent standardized approach with four different temporal scales of accumulation periods to show how differences among the drought definitions could result in critical uncertainties. For simplicity, this study focuses on one drought index per drought category. Firstly we investigated the disagreement in the sign of changes between definitions, and then we decomposed the overall uncertainty to estimate the relative importance of each source of uncertainty. By a multifactorial ANOVA, uncertainty was decomposed into four main factors, namely drought definitions, climate change scenarios, global climate models and global water impact models, and their interactions.</p><p>Our results highlight specific regions where the sign of change disagrees between drought definitions. Importantly, changes in drought frequency in such regions tended to be statistically insignificant with low ensemble member agreement. Drought definition attributed to18% of the main factor uncertainty at the global scale, and the definition was the dominant uncertainty source over 11% of the global land area. The contribution of difference in the drought category showed a higher contribution to overall uncertainty than the difference in scales. The contribution of scenario uncertainty was the least among the main factors in general, though it is a dominant factor in the far-future in a couple of hotspot regions such as the Mediterranean region. Overall, model uncertainties were the primary source of uncertainty, and the definition issue was less important over large areas. However, definition uncertainty was the primal uncertainty source with significant changes in particular regions, such as parts of high-latitude areas in the northern hemisphere. One needs to pay attention to these regions in overviewing future drought change. Nonetheless, what this study quantified is the relative importance of uncertainty stemming from drought definition that should be avoidable or reducible if one treats drought specifically. Our results indicate that we can reduce uncertainty in drought projections to some extent and get a clearer picture by clarifying hydrological processes or sectors of interest.</p>


2021 ◽  
pp. 403-417
Author(s):  
Amit Dubey ◽  
Deepak Swami ◽  
Nitin Joshi

ncrease in the water scarcity and the related rise in demand of water coupled with the threating events of climate change, ultimately witnessed drought in the recent years to occur frequently. Therefore, Drought hydrology is drawing most of the attention. Drought which is a natural hazard can be best characterized by various hydrological and climatological parameters. In order to model drought, researchers have applied various concepts starting from simplistic model to the complex ones. The suitability of different modelling approaches and their negative and positive traits are very essential to comprehend. This paper is an attempt to review various methodologies utilized in modelling of drought such as forecasting of drought, drought modelling based on probability, Global Climate Models (GCM) under climate change scenarios. It is obtained from the present study that the past three decades have witnessed a very significant improvement in the drought modelling studies. For the larger time window of drought forecasting, hybrid models which incorporates large scale climate indices are promisingly suitable. Drought characterization based on copula models for multivariate drought characterization seems to have an edge over the others. At the end some conclusive remarks are made as far as the future drought modelling and research is concerned.


2016 ◽  
Vol 8 (2) ◽  
pp. 30 ◽  
Author(s):  
Micah J. Hewer ◽  
William A. Gough

Weather and climate have been widely recognised as having an important influence on tourism and recreational activities. However, the nature of these relationships varies depending on the type, timing and location of these activities. Climate change is expected to have considerable and diverse impacts on recreation and tourism. Nonetheless, the potential impact of climate change on zoo visitation has yet to be assessed in a scientific manner. This case study begins by establishing the baseline conditions and statistical relationship between weather and zoo visitation in Toronto, Canada. Regression analysis, relying on historical weather and visitation data, measured at the daily time scale, formed the basis for this analysis. Climate change projections relied on output produced by Global Climate Models (GCMs) for the Intergovernmental Panel on Climate Change’s 2013 Fifth Assessment Report, ranked and selected using the herein defined Selective Ensemble Approach. This seasonal GCM output was then used to inform daily, local, climate change scenarios, generated using Statistical Down-Scaling Model Version 5.2. A series of seasonal models were then used to assess the impact of projected climate change on zoo visitation. While accounting for the negative effects of precipitation and extreme heat, the models suggested that annual visitation to the zoo will likely increase over the course of the 21st century due to projected climate change: from +8% in the 2020s to +18% by the 2080s, for the least change scenario; and from +8% in the 2020s to +34% in the 2080s, for the greatest change scenario. The majority of the positive impact of projected climate change on zoo visitation in Toronto will likely occur in the shoulder season (spring and fall); with only moderate increases in the off season (winter) and potentially negative impacts associated with the peak season (summer), especially if warming exceeds 3.5 °C.


Author(s):  
Hudaverdi Gurkan ◽  
Vakhtang Shelia ◽  
Nilgun Bayraktar ◽  
Y. Ersoy Yildirim ◽  
Nebi Yesilekin ◽  
...  

Abstract The impact of climate change on agricultural productivity is difficult to assess. However, determining the possible effects of climate change is an absolute necessity for planning by decision-makers. The aim of the study was the evaluation of the CSM-CROPGRO-Sunflower model of DSSAT4.7 and the assessment of impact of climate change on sunflower yield under future climate projections. For this purpose, a 2-year sunflower field experiment was conducted under semi-arid conditions in the Konya province of Turkey. Rainfed and irrigated treatments were used for model analysis. For the assessment of impact of climate change, three global climate models and two representative concentration pathways, i.e. 4.5 and 8.5 were selected. The evaluation of the model showed that the model was able to simulate yield reasonably well, with normalized root mean square error of 1.3% for the irrigated treatment and 17.7% for the rainfed treatment, a d-index of 0.98 and a modelling efficiency of 0.93 for the overall model performance. For the climate change scenarios, the model predicted that yield will decrease in a range of 2.9–39.6% under rainfed conditions and will increase in a range of 7.4–38.5% under irrigated conditions. Results suggest that temperature increases due to climate change will cause a shortening of plant growth cycles. Projection results also confirmed that increasing temperatures due to climate change will cause an increase in sunflower water requirements in the future. Thus, the results reveal the necessity to apply adequate water management strategies for adaptation to climate change for sunflower production.


2019 ◽  
Vol 41 (3) ◽  
pp. 42-47
Author(s):  
Rebecca K. Zarger ◽  
Gina Larsen ◽  
Alexis Winter ◽  
Libby Carnahan ◽  
Ramona Madhosingh-Hector ◽  
...  

Abstract Our project investigates public perceptions of climate change risk and vulnerability in the Tampa Bay, Florida, region, specifically focused on how climate change is likely to impact water infrastructure in the area. As part of the project, our research team of anthropologists and environmentally-focused state extension agents collaboratively developed public workshops to promote more dialogue on local climate change impacts. The anthropologists developed localized climate change scenarios based on global climate models, Florida-centric models, and input from key informants. Extension agents brought expertise in climate and sustainability science and facilitating educational programming and dialogue. We documented residents' concerns and views on climate change, how local scenarios are received by the public, and how scenarios can be communicated to the public through narrative and visual formats. We consider the roles of anthropologist-extension agent partnerships in creating new spaces for dialogue on climate change futures.


2012 ◽  
Vol 16 (9) ◽  
pp. 3341-3349 ◽  
Author(s):  
R. S. Crosbie ◽  
D. W. Pollock ◽  
F. S. Mpelasoka ◽  
O. V. Barron ◽  
S. P. Charles ◽  
...  

Abstract. The Köppen-Geiger climate classification has been used for over a century to delineate climate types across the globe. As it was developed to mimic the distribution of vegetation, it may provide a useful surrogate for making projections of the future distribution of vegetation, and hence resultant hydrological implications, under climate change scenarios. This paper developed projections of the Köppen-Geiger climate types covering the Australian continent for a 2030 and 2050 climate relative to a 1990 historical baseline climate using 17 Global Climate Models (GCMs) and five global warming scenarios. At the highest level of classification for a +2.4 °C future climate (the upper limit projected for 2050) relative to the historical baseline, it was projected that the area of the continent covered by – tropical climate types would increase from 8.8% to 9.1%; – arid climate types would increase from 76.5% to 81.7%; – temperate climate types would decrease from 14.7% to 9.2%; – cold climate types would decrease from 0.016% to 0.001%. Previous climate change impact studies on water resources in Australia have assumed a static vegetation distribution. If the change in projected climate types is used as a surrogate for a change in vegetation, then the major transition in climate from temperate to arid in parts of Australia under a drier future climate could cause indirect effects on water resources. A transition from annual cropping to perennial grassland would have a compounding effect on the projected reduction in recharge. In contrast, a transition from forest to grassland would have a mitigating effect on the projected reduction in runoff.


Sign in / Sign up

Export Citation Format

Share Document