ArticlePDF Available

Impacts of climate change on heat waves in northern coast of Persian Gulf

Authors:








  


    

CMIPCanESMMPI-ESM-MRCSIRO-Mk--(CMCC-CESMRCP
 

    


.
  
   


RCP
 khoorani@hormozgan.ac.irEmail:


                
IPCC, 

   
   
Raisanen et al,
IPCC

              
Hoogwijk, ;Grubler,
 GCM
    
(Hoogwijk, )
     
       


Murphy, 

NOAA,


Kovats and Koppe, 
         
Ding, et al,  

Schar, et al, 

  (Mokhov, )
Kripalani and Kulkarani,   
Karl and Knigh, 


Heat wave
- Intergovernmental Panel on Climate Change
-General Circulation Model
Downscaling
Dynamical Downscaling
-Statistical Downscaling
- Russo




CMIP
RCP

            
             
Russo,

RCP RCP 
RCPRCP 
Kim et al,
           

Hayhoe et al, 


  




CMIPRCP.




-Representative Concentration Pathways









°' N
°' N
°' N
°' N

°' E
°' E
°' E
°' E





CMIPRCP
RCP 








CanESM


MPI-ESM-MR


CSIRO-Mk--



CMCC-CESM



MLP's














(RMSE)

RMSE=



N






Fujibe and et al,   
- Canadian Earth System Model
- Max-Planck-Institut für Meteorologie
- Commonwealth Scientific and Industrial Research Organization
- CMCC Carbon Earth System Model

   


󰇛 󰇜 󰇛󰇜


ijN




=(i,j,n)=T(i,j,n)-T(I,j)
 =(I,j,n) i j n




 Fujibe, and et al,


󰇛󰇜





󰇛󰇜






󰇛󰇜






󰇛󰇜






NTD

󰇛󰇜=󰇛󰇜󰇛󰇜
󰇛󰇜
󰇛󰇜
- Normalized Thermal Deviation


   NTD   
NTD






RMSE


RMSE









CanESM






MPI-ESM-MR






CSIRO-MK--







CMCC-CESM







CSIRO-MK--
CanESM




      

T







RCPCanESM



     

       



MPI-ESM-MR

     MPI-ESM-MR
      


     

CSIRO-Mk--
 





    

CMCC-CESM

CMCC-CESM

   












CanESMCSIRO-Mk--
MPI-ESM-MRCSIRO-Mk--MPI-ESM-MRCMCC-
CESM










CanESM





MPI-ESM-MR





CSIRO-Mk--





CMCC-CESM


















 











































































  

           



CMCC-CESM



Li, et al, ; Li, et al, 








   



 .


  .



-









CSIRO-Mk--





Fujibe, F, Yamazaki, N., Kobayashi, K., and Nakamigawa, H. . long-term changes of
temperature extremes and day-to-day variability in Japan, papers in Meterology and Geophysics,
. -.
Grübler, A., O'Neill, B., Riahi, K., Chirkov, V., Goujon, A., Kolp, P., ... and Slentoe, E. .
Regional, national, and spatially explicit scenarios of demographic and economic change based on
SRES. Technological Forecasting and Social Change, (). -.
Hayhoe, K., Sheridan, S., Kalkstein, L., and Greene, S. . Climate change, heat waves, and
mortality projections for Chicago. Journal of Great Lakes Research, . -.
Hoogwijk, M., Faaij, A., de Vries, B., and Turkenburg, W. . Exploration of regional and
global costsupply curves of biomass energy from short-rotation crops at abandoned cropland and
rest land under four IPCC SRES land-use scenarios. Biomass and Bioenergy, (). -.
Hoogwijk, M., Faaij, A., Eickhout, B., de Vries, B., and Turkenburg, W. . Potential of
biomass energy out to , for four IPCC SRES land-use scenarios. Biomass and Bioenergy,
(). -.
IPCC, : Summary for Policymakers. Climate Change : The Physical Science Basis.
Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.
Karl, T. R., and Knight, R. W. . The  Chicago heat wave: How likely is a recurrence?,
Bulletin of the American Meteorological Society, (). -.
Kim, D. W., Deo, R. C., Chung, J. H., and Lee, J. S. . Projection of heat wave mortality
related to climate change in Korea. Natural Hazards, (). -.
Koppe, C., Kovats, S., Jendritzky, G., and Menne, B. . Heat-waves: risks and responses.
Health and Global Environmental Change Series, no. . World Health Organizatios.
Kripalani, R. H., and Kulkarni, A. . The relationship between some large-scale atmospheric
parameters and rainfall over Southeast Asia: A comparison with features over India. Theoretical
and Applied Climatology, (). -.
Li, W., Li, L., Ting, M., and Liu, Y. . Intensification of Northern Hemisphere subtropical
highs in a warming climate. NATURE GEOSCIENCE, VOL . -.
Li, W., Li, L., Fu, R., Deng, Y., Wang, H. . Changes to the North Atlantic Subtropical High
andItsRoleintheIntensificationofSummerRainfallVariabilityintheSoutheasternUnitedStates.
Journal of Climate, (). -.
Mokhov, I. I., and Chernokulsky, A. V. . Regional model assessments of forest fire risks in
the Asian part of Russia under climate change. Geography and Natural Resources, (). -
.
Murphy, J. . An evaluation of statistical and dynamical techniques for downscaling local
climate. Journal of Climate, (). -.
NOAA (). Natural Hazard Statistics. National Oceanic and Atmospheric Administration.
Räisänen, J., Hansson, U., Ullerstig, A., Döscher, R., Graham, L. P., Jones, C., ... and Willén, U.
. European climate in the late twenty-first century: regional simulations with two driving
global models and two forcing scenarios. Climate dynamics, (). -.
Russo, S., Dosio, A., Graversen, R. G., Sillmann, J., Carrao, H., Dunbar, M. B., ... and Vogt, J. V.
. Magnitude of extreme heat waves in present climate and their projection in a warming
world. Journal of Geophysical Research: Atmospheres, (). -.
Schär, C., Vidale, P. L., Lüthi, D., Frei, C., Häberli, C., Liniger, M. A., and Appenzeller, C. .
The role of increasing temperature variability in European summer heatwaves. Nature,
(). -.


Ding, T., Qian, W., and Yan, Z. . Changes in hot days and heat waves in China during 
. International Journal of Climatology, (). -.
Article
Full-text available
The aim of this study was to investigate the effects of climate change on the climate condition of outdoor tourists in Hormozgan province, Iran, through Outdoor Tourism Climate Index (OTCI). For this purpose, the data pertaining to 7 weather stations as well as 2 global climate models (GCMs) under 2.6 and 8.5 Representative Concentration Pathways (RCP) were applied. GCMs were statistically downscaled by the change factor (CF) approach. The findings illuminated that, based on OTCI, December, January, February, and March were regarded as the optimal months for the outdoor tourism activities. Nevertheless, for the concerned months, the range of OTCI score in twenty-first century is changing from 2 to − 12 regarding the base period (1980–2010). Mostly, the changes in OTCI score is predicted to occur at March, April, May, October, and November, whereas June would record the least. Moreover, Hajiabad and Kish stations in the North and South of the under-study area would encounter the most and least changes, respectively, in the future.
Article
Full-text available
Heat waves associated with climate change are a significant future concern. Although deaths from heat disorders are a direct effect of heat wave incidences, only a few studies have addressed the causal factors between heat wave incidences and deaths from heat disorder. This study applies regression analysis to the time series data in order to deduce the causal factors that affect the number of deaths from heat disorders (NDHD) in Korea using observational dataset from 1994–2012. The duration of a heat wave and the age of the population are highly correlated with the magnitude of the NDHD. Based on this correlation we also analyze heat wave projections to the climate change scenarios produced using the Hadley Centre Global Environmental Model version 3 under the Representative Concentration Pathways (RCP 4.5 and RCP 8.5) and to the single aging population scenario till 2060. The magnitude of the NDHD is expected to elevate by approximately fivefold under the RCP4.5 and 7.2-fold under the RCP 8.5 scenarios compared to the current baseline value (≈23 people per summer). Of greater concern is that the steady death rate increase is expected to be intercepted by the more severe events in future compared to the present period. Under both RCP scenarios considered, the extreme cases are projected to eventuate around the 2050s with approximately 250 deaths. We find that in spite of the greenhouse gas policy proposed to meet reductions under the RCP 4.5 scenario; serious heat wave damage in terms of human mortality may still be unavoidable in Korea.
Article
Full-text available
An extreme heat wave occurred in Russia in the summer of 2010. It had serious impacts on humans and natural ecosystems, it was the strongest recorded globally in recent decades and exceeded in amplitude and spatial extent the previous hottest European summer in 2003. Earlier studies have not succeeded in comparing the magnitude of heat waves across continents and in time. This study introduces a new Heat Wave Magnitude Index (HWMI) that can be compared over space and time. The index is based on the analysis of daily maximum temperature, in order to classify the strongest heat waves that occurred worldwide during the three study periods 1980-1990, 1991-2001 and 2002-2012. In addition, multi-model ensemble outputs from the Intercomparison Project Phase 5 (CMIP5) are used to project future occurrence and severity of heat waves, under different Representative Concentration Pathways (RCP), adopted by the Intergovernmental Panel on Climate Change (IPCC) for its fifth Assessment Report (AR5). Results show that the percentage of global area affected by heat waves has increased in recent decades. Moreover, model predictions reveal an increase in the probability of occurrence of extreme and very extreme heat waves in the coming years: in particular, by the end of this century, under the most severe IPCC AR5 scenario, events of the same severity as that in Russia in the summer of 2010 will become the norm and are projected to occur as often as every two years for regions such as southern Europe, North America, South America, Africa and Indonesia.
Article
Full-text available
Semi-permanent high-pressure systems over the subtropical oceans, known as subtropical highs, influence atmospheric circulation, as well as global climate. For instance, subtropical highs largely determine the location of the world’s subtropical deserts, the zones of Mediterranean climate and the tracks of tropical cyclones. The intensity of two such high-pressure systems, present over the Northern Hemisphere oceans during the summer, has changed in recent years. However, whether such changes are related to climate warming remains unclear. Here, we use climate model simulations from the Intergovernmental Panel on Climate Change Fourth Assessment Report, reanalysis data from the 40-year European Centre for Medium-Range Weather Forecasts, and an idealized general circulation model, to assess future changes in the intensity of summertime subtropical highs over the Northern Hemisphere oceans. The simulations suggest that these summertime highs will intensify in the twenty-first century as a result of an increase in atmospheric greenhouse-gas concentrations. We further show that the intensification of subtropical highs is predominantly caused by an increase in thermal contrast between the land and ocean. We suggest that summertime near-surface subtropical highs could play an increasingly important role in regional climate and hydrological extremes in the future.
Article
Full-text available
This study investigates the changes of the North Atlantic subtropical high (NASH) and its impact on summer precipitation over the southeastern (SE) United States using the 850-hPa geopotential height field in the National Centers for Environmental Prediction (NCEP) reanalysis, the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), long-tertii rainfall data, and Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) model simulations during the past six decades (1948-2007). The results show that the NASH in the last 30 yr has become more intense, and its western ridge has displaced westward with an enhanced meridional movement compared to the previous 30 yr. When the NASH moved closer to the continental United States in the three most recent decades, the effect of the NASH on the interannual variation of SE U.S. precipitation is enhanced through the ridge's north south movement. The study's attribution analysis suggested that the changes of the NASH are mainly due to anthropogenic warming. In the twenty-first century with an increase of the atmospheric CO(2) concentration, the center of the NASH would be intensified and the western ridge of the NASH would shift farther westward. These changes would increase the likelihood of both strong anomalously wet and dry summers over the SE United States in the future, as suggested by the IPCC AR4 models.
Article
Full-text available
The deadly heat wave of July 1995 that affected much of the U.S. midwest, most notably Chicago, Illinois, has been put into historical perspective. The heat wave has been found to be remarkably unusual, but only partially because of the extreme high apparent temperatures (an index of the combined effect of temperature and humidity on humans), where the authors calculate a return period of the peak apparent temperature of 23 yr. Of greater significance were the very high temperatures that persisted day and night over an extended 48-h period. Analysis presented here indicates that for Chicago such an extended period of continuously high day and night apparent temperature is unprecedented in modern times. The 2-day period where the minimum apparent temperature failed to go below 31.5°C (89°F) is calculated to be an extremely rare event (probability of occurrence
Article
Full-text available
Presented are the results from analyzing the changes in the fire danger conditions in the Asian part of Russia within the context of prospective climate change in the 21 st century. It is found that with a rise in temperature, a substantial influence on the general increase of fire danger is exerted by the distribution function of precipitation. With general warming by the end of the 21 st century, the middle and subtropical latitudes will see, along with increasing fire danger risk, an extension of the fire-hazardous period, whereas the high latitudes will undergo more limited changes in the risk.
Article
Full-text available
Monthly rainfall data for 135 stations for periods varying from 25 to 125 years are utilised to investigate the rainfall climatology over the southeast Asian monsoon regime. Monthly rainfall patterns for the regions north of equator show that maximum rainfall along the west coasts occurs during the summer monsoon period, while the maximum along the east coasts is observed during the northeast monsoon period. Over the Indonesian region (south of the equator) maximum rainfall is observed west of 125 °E during northern winter and east of 125 °E during northern summer. The spatial relationships of the seasonal rainfall (June to September) with the large scale parameters – the Subtropical Ridge (STR) position over the Indian and the west Pacific regions, the Darwin Pressure Tendency (DPT) and the Northern Hemisphere Surface Temperature (NHST) – reveal that within the Asian monsoon regime, not only are there any regions which are in-phase with Indian monsoon rainfall, but there are also regions which are out-of-phase. The spatial patterns of correlation coefficients with all the parameters are similar, with in-phase relationships occurring over the Indian region, some inland regions of Thailand, central parts of Brunei and the Indonesian region lying between 120° to 140 °E. However, northwest Philippines and some southern parts of Kampuchea and Vietnam show an out-of-phase relationship. Even the first Empirical Orthogonal Function of seasonal rainfall shows similar spatial configuration, suggesting that the spatial correlation patterns depict the most dominant mode of interannual rainfall variability. The influence of STR and DPT (NHST) penetrates (does not penetrate) upto the equatorial regions. Possible dynamic causes leading to the observed correlation structure are also discussed.
Article
An assessment is made of downscaling estimates of screen temperature and precipitation observed at 976 European stations during 1983-94. A statistical downscaling technique, in which local values are inferred from observed atmospheric predictor variables, is compared against two dynamical downscaling techniques, based on the use of the screen temperature or precipitation simulated at the nearest grid point in integrations of two climate models. In one integration a global general circulation model (GCM) is constrained to reproduce the observed atmospheric circulation over the period of interest, while the second involves a high-resolution regional climate model (RCM) nested inside the GCM.The dynamical and statistical methods are compared in terms of the correlation between the estimated and observed time series of monthly anomalies. For estimates of temperature a high degree of skill is found, especially over western, central, and northern Europe; for precipitation skill is lower (average correlations ranging from 0.4 in summer to 0.7 in winter). Overall, the dynamical and statistical methods show similar levels of skill, although the statistical method is better for summertime estimates of temperature while the dynamical methods give slightly better estimates of wintertime precipitation. In general, therefore, the skill with which present-day surface climate anomalies can be derived from atmospheric observations is not improved by using the sophisticated calculations of subgrid-scale processes made in climate models rather than simple empirical relationships. It does not necessarily follow that statistical and dynamical downscaling estimates of changes in surface climate will also possess equal skill.By the above measure the two dynamical techniques possess approximately equal skill; however, they are also compared by assessing errors in the mean and variance of monthly values and errors in the simulated distributions of daily values. Such errors arise from systematic biases in the models plus the effect of unresolved local forcings. For precipitation the results show that the RCM offers clear benefits relative to the GCM: the simulated variability of both daily and monthly values, although lower than observed, is much more realistic than in the GCM because the finer grid reduces the amount of spatial smoothing implicit in the use of grid-box variables. The climatological means are also simulated better in the winter half of the year because the RCM captures some of the mesoscale detail present in observed distributions. The temperature fields contain a mesoscale orographic signal that is skillfully reproduced by the RCM; however, this is not a source of increased skill relative to the GCM since elevation biases can be largely removed using simple empirical corrections based on spatially averaged lapse rates. Nevertheless, the average skill of downscaled climatological mean temperature values is higher in the RCM in nearly all months. The additional skill arises from better resolution of local physiographical features, especially coastlines, and also from the dynamical effects of higher resolution, which generally act to reduce the large-scale systematic biases in the simulated values. Both models tend to overestimate the variability of both daily and monthly mean temperature. On average the RCM is more skillful in winter but less skillful in summer, due to excessive drying of the soil over central and southern Europe.The downscaling scores for monthly means are compared against scores obtained by using a predictor variable consisting of observations from the nearest station to the predictand station. In general the downscaling scores are significantly worse than those obtained from adjacent stations, indicating that there remains considerable scope for refining the techniques in future. In the case of dynamical downscaling progress can be made by reducing systematic errors through improvements in the representation of physical processes and increased resolution; the prospects for improving statistical downscaling will depend on the availability of the observational data needed to provide longer calibration time series and/or a wider range of predictor variables.
Article
Communicated by Marlene Evans Index words: Climate change Extreme heat Chicago Heat waves Health impacts Mortality Over the coming century, climate change is projected to increase both mean and extreme temperatures as heat waves become more frequent, intense, and long-lived. The city of Chicago has already experienced a number of severe heat waves, with a 1995 event estimated to be responsible for nearly 800 deaths. Here, future projections under SRES higher (A1FI) and lower (B1) emission scenarios are used to estimate the frequency of 1995-like heat wave events in terms of both meteorological characteristics and impacts on heat-related mortality. Before end of century, 1995-like heat waves could occur every other year on average under lower emissions and as frequently as three times per year under higher. Annual average mortality rates are projected to equal those of 1995 under lower emissions and reach twice 1995 levels under higher. An "analog city" analysis, transposing the weather conditions from the European Heat Wave of 2003 (responsible for 70,000 deaths across Europe) to the city of Chicago, estimates that if a similar heat wave were to occur over Chicago, more than ten times the annual average number of heat-related deaths could occur in just a few weeks. Climate projections indicate that an EHW-type heat wave could occur in Chicago by mid-century. Between mid-and end-of-century, there could be as many as five such events under lower, and twenty-five under higher emissions. These results highlight the importance of both preventive mitigation and responsive adaptation strategies in reducing the vulnerability of Chicago's population to climate change-induced increases in extreme heat.