LETTER
Climate change hotspots in the CMIP5global climate
model enmble
Noah S.Diffenbaugh &Filippo Giorgi
Received:3April 2012/Accepted:1August 2012/Published online:25August 2012#The Author(s)2012.This article is published with open access
Abstract We u a statistical metric of multi-dimensional climate change to quantify the emergence of global climate change hotspots in the CMIP5climate model enmble.Our hotspot metric extends previous work through the inclusion of extreme asonal temperature and precipitation,which exert critical influence on climate change impacts.The results identify areas of the Amazon,the Sahel and tropical West Africa,Indonesia,and the Tibetan Plateau as persistent regional climate change hotspots throughout the 21st century of the RCP8.5and RCP4.5forcing pathways.In addition,areas of southern Africa,the Mediterranean,the Arctic,and Central America/western North America also emerge as prominent regional climate change hotspots in respon to intermediate and high levels of forcing.Com
parisons of different periods of the two forcing pathways suggest that the pattern of aggregate change is fairly robust to the level of global warming below approxi-mately 2°C of global warming (relative to the late-20th-century baline),but not at the higher levels of global warming that occur in the late-21st-century period of the RCP8.5pathway,with areas of southern Africa,the Mediterranean,and the Arctic exhibiting particular intensification of relative aggregate climate change in respon to high levels of forcing.Although specific impacts will clearly be shaped by the interaction of climate change with human and biological vulnerabilities,our identification of climate change hotspots can help to inform mitigation and adaptation decisions by quantifying the rate,magnitude and caus of the aggregate climate respon in different parts of the world.1Introduction
It is now established not only that human activities have been the primary cau of obrved global warming (IPCC 2007),but also that the climate system is already committed to Climatic Change (2012)114:813–822
DOI 10.1007/s10584-012-0570-x
Electronic supplementary material The online version of this article (doi:10.1007/s10584-012-0570-x)contains supplementary material,which is available to authorized urs.
N.S.Diffenbaugh (*)
Department of Environmental Earth System Science and Woods Institute for the Environment,Stanford University,473Via Ortega,Stanford,CA 94305-4216,USA
梦到生病e-mail:diffenbaugh@stanford.edu
F.Giorgi
Earth System Physics Section,Abdus Salam International Centre for Theoretical Physics,Trieste,Italy
further global warming arising from ,(Meehl et al.2005)),biogeochemical (e.g.,(Jones et al.2010)),,(Matthews and Weaver2010))and ,(Davis et al.2010))inertia.In addition,continued unconstrained increas in greenhou gas emissions are likely to cau global warming that substantially exceeds the internationally agreed-upon target(UNFCCC2009)of2°C above the pre-industrial baline (e.g.,(Matthews et al.2009;Meinshaun et al.2009)).Decisions about how best to adapt to committed warming and about what level of warming to target in order to avoid unaccept-able climate change require understanding of the pattern and magnitude of the regional and local respon to different levels of r
adiative forcing.Although in many areas of the world the impacts of climate change are likely to be determined by highly-localized physical, biological and human factors,quantifying the magnitude of integrated change across a suite of physical climate indicators can help to identify climate change“hotspots”that show the strongest and most robust aggregated respon to global-scale warming.
Giorgi(2006)quantified sub-continental-scale climate change hotspots in the late-21st-century period of Pha3of the Coupled Model Intercomparison Project(CMIP3).A weighting of changes in mean and variability of asonal temperature and precipitation revealed the Mediterranean,the northern hemisphere high-latitude regions,and Central America as the most prominent hotspots(Giorgi2006).Other aggregations of multi-dimensional climate change include integration of a-level-ri vulnerability into the Giorgi index(Diffenbaugh et al.2007a),summation of the number of asons exceeding different temperature and precipitation thresholds(Baettig et al.2007),u of statistical metrics of the distance traveled in multi-dimensional climate space(Williams et al.2007; Diffenbaugh et al.2008),and u of statistical metrics of the magnitude and/or rate of climate change experienced by particular biological categories(Loarie et al.2009;Ackerly et al.2010;Beaumont et al.2011;Sandel et al.2011).Given the availability of a new generation of global climate model simulati
ons that compri the CMIP5enmble(Taylor et al.2012),we quantify the transient emergence of global hotspot patterns using a statistical metric of aggregate multi-dimensional climate change.This metric extends the statistical approach of Diffenbaugh et al.(2008)to also include measures of extreme asonal temperature and precipitation,which are particularly important for climate change impacts. 2Methods
2.1Models
We quantify climate change hotspots in the2016–2035,2046–2065,and2080–2099periods of the CMIP5RCP8.5and RCP4.5simulations.RCP8.5and RCP4.5diverge dramatically over the21st century,reaching greenhou gas concentrations of>1370and~650ppm CO2-e(Moss et al.2010),respectively,by the year2100,along with radiative forcing of ~8.5and~4.5W/m2(Moss et al.2010),and median global warming of4.9and2.4°C above the pre-industrial baline(Rogelj et al.2012).The global warming in RCP8.5and RCP4.5most cloly match that in the A1FI and B1SRES scenarios,respectively (Rogelj et al.2012).
The suite of available simulations includes realizations from20models,including86 realizations of the baline period(1986–2005),and51realizations of the21st century in both the RCP8.5and RCP4.5pat
hways(Table S1).Following Giorgi(2006),Diffenbaugh et al.(2007a),and Diffenbaugh et al.(2008),our analysis is carried out after first interpolating the output from each model to a common1-degree geographical grid.
Further details of the CMIP5simulations are provided in the Supplemental Information (SI).
2.2Hotspot quantification
Following Diffenbaugh et al.(2008),we u the Standard Euclidean Distance (SED)to quantify the total change in multi-dimensional climate space between the prent and future periods:
精神科护理
SED total ¼菊花作用
X v
SED v !1=2ðEq :1Þfor
SED v ¼abs Δv ðÞmax abs Δv ðÞ½ ij . 2ðEq :2Þ
where abs(Δv )is the absolute value of change in climate indicator v at each grid point between the p
我懂得了珍惜时间作文500字rent and future periods,and max[abs(Δv )]ij is the maximum land-grid-point absolute value change in climate indicator v over all land grid points ij in the 2080–2099period of RCP8.,the change in each period is normalized to the maximum change in the 2080–2099period of RCP8.5).By scaling to the maximum change in the highest forcing period,our approach yields a relative metric of aggregate climate change that can be directly compared between geographic areas,forcing pathways,and time periods within a forcing pathway.
We include 7climate indicators from each of four asons (DJF,MAM,JJA,SON),yielding 28total dimensions at each grid point.The climate indicators are:absolute change in mean surface air temperature,fractional change in mean precipitation,fractional change in interannual standard deviation of de-trended surface air temperature,fractional change in interannual coefficient of variation of de-trended precipitation,frequency of occurrence of asons above the baline maximum asonal surface air temperature,frequency of occur-rence of asons below the baline minimum asonal precipitation,and frequency of occurrence of asons above the baline maximum asonal precipitation.We calculate the simulated change in each variable using the enmble mean of that variable in the baline and future periods.
Our aggregate metric only considers land grid points north of 60°S.In order to treat the change in ea
ch of the 28dimensions equally in the SED calculation,we normalize the change in each climate indicator by first calculating the absolute value of change at each land grid point and then dividing by the largest grid-point absolute-value change that occurs at any land grid point north of 60°S in the 2080–2099period of RCP8.5.We then calculate the SED at each land grid point by first squaring each of the normalized values,then summing the squared values,and then taking the square root of the sum.
Further details of the hotspot quantification are provided in the SI.
3Results and discussion
The hotspot patterns for the three future time periods of RCP8.5and RCP4.5are shown in Fig.1.The dominant global hotspot pattern emerges early in the 21st century,with
拜拜英语怎么写areas of the Amazon,the Sahel and tropical West Africa,Indonesia,and the Tibetan Plateau emerging early in the 21st century and exhibiting relatively high aggregate climate change in all three periods of both forcing pathways.In addition,areas of southern Africa,the Mediterranean,the Arctic,and Central America/western North America emerge at interme-diate and/or high levels of forcing,while areas of southern South America,Australia,the Indian Peninsula,and East Asia exhibit relatively small
–but increasing –aggregate climate change throughout the 21st century (Fig.1).
The aggregate hotspot patterns reflect the pattern and magnitude of changes in the mean,variability and extremes of asonal temperature and precipitation (Figs.2,3,S1and S2).The regions that show the strongest aggregate climate changes exhibit large values of relative change in a number of different climate indicators (Fig.2and S2).For example,in the 2080–2099period,the Amazon exhibits areas of relatively large changes in JJA mean precipitation (Figs.2and S2),DJF and SON precipitation variability (Fig.S2),DJF and MAM temperature variability (Fig.S2),and DJF,MAM,JJA and SON extreme dry asons (Fig.S2).Likewi,northeast Eurasia exhibits areas of relatively large changes in
DJF,Fig.1The relative aggregate climate change between the 1986–2005period and the 2016–2035,2046–2065and 2080–2099periods of RCP8.5and RCP4.5.The aggregate climate change is calculated using the Standard Euclidean Distance (SED)across the 28-dimensional climate space formed by 7climate indicators in each of 4asons.Prior to calculating the SED,the absolute values of change in each climate indicator are normalized to the maximum global absolute value in the 2080–2099period of RCP8.5.Only land grid points north of 60°S are ud in the normalization.The median global temperature change above the late 20th century baline is given from Rogelj et al.(2012)in the lower left corner of each panel
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MAM and SON mean temperature (Figs.2and S2),DJF and MAM mean precipitation (Figs.2and S2),and DJF,MAM,JJA and SON extreme wet asons (Fig.S2).
Comparisons of different periods of the two forcing pathways suggest that the pattern of aggregate change is fairly robust to the level of global warming below approximately 2°C of global warming (relative to the late-20th-century baline),but not at the higher levels of global warming that occur in the late-21st-century period of the RCP8.5pathway (Fig.1).For example,the tropical regions exhibit the greatest relative change throughout the RCP4.5pathway,with much of central Africa exhibiting increas in aggregate change from approximately 1.2during the 2016–2035period of RCP4.5,to ap
proximately 1.9during the 2046–2065period of RCP4.5,to approximately 2.1during the 2080–2099period of RCP4.5.In contrast,the high latitudes consistently exhibit smaller relative aggregate change than the tropics throughout the RCP4.5pathway,with broad areas of the Arctic exhibiting increas in aggregate change from approximately 0.7during the 2016–2035period of RCP4.5,to approximately 1.3during the 2046–2065period of RCP4.5,to approximatelyb级英语考试
1.6during the 2080–2099period of RCP4.5.The pattern of greatest relative aggregate change occurring over tropical regions is also en during the 2046–2065period of RCP8.5,when median global warming is larger than in the 2080–2099period of RCP4.5(Rogelj et al.2012)).However,the highest values of relative aggregate change occur much more broadly during the late-21st-century period of RCP8.5,with central Africa and Indonesia both exhibiting lower aggregate values (up to
2.5)than the Arctic (up to
梦见买甘蔗3.0),the Mediterranean (up to 2.9),the Sahel (up to 2.9),the Amazon (up to 2.8),Southern Africa (up to 2.8),and Tibet (up to 2.8).
The apparent acceleration of relative aggregate climate change over areas of the extra-tropics at high levels of global warming (Fig.1)results in part from the fact that intensification of extreme hot
ason occurrence emerges most strongly over the tropics
in Fig.2The change in December-January-February (DJF)and June-July-August (JJA)surface air temperature and precipitation between the 1986–2005period and the 2080–2099period of RCP8.5in
the CMIP5enmble
