JGR-NEW HEAT WAVE INDEX

JGR-NEW HEAT WAVE INDEX
JGR-NEW HEAT WAVE INDEX

JournalofGeophysicalResearch:

Atmospheres

RESEARCH ARTICLE

10.1002/2014JD022098

Key Points:

?De?nition of a new Heat Wave Magnitude Index

?Classi?cation of the magnitude of the heat waves occurred in the present ?Prediction of heat wave magnitude under climate scenarios

Correspondence to:S.Russo,

simone.russo@jrc.ec.europa.eu

Citation:

Russo,S.,A.Dosio,R.G.Graversen,J.Sillmann,H.Carrao,M.B.Dunbar,A.Singleton,P .Montagna,P .Barbola,and J.V.Vogt (2014),Magnitude of extreme heat waves in present cli-mate and their projection in a warming world,J.Geophys.Res.Atmos.,119,doi:10.1002/2014JD022098.

Received 3JUN 2014Accepted 19OCT 2014

Accepted article online 24OCT 2014

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License,which permits use and distri-bution in any medium,provided the original work is properly cited,the use is non-commercial and no mod-i?cations or adaptations are made.

Magnitude of extreme heat waves in present climate and their projection in a warming world

Simone Russo 1,2,Alessandro Dosio 1,Rune G.Graversen 3,Jana Sillmann 4,Hugo Carrao 1,

Martha B.Dunbar 1,Andrew Singleton 5,Paolo Montagna 6,Paulo Barbola 1,and Jürgen V.Vogt 1

1European Commission,Joint Research Centre,Ispra,Italy,2Institute for Environmental Protection and Research,Rome,

Italy,3Department of Physics and Technology,University of Troms?,Troms?,Norway,4Center for International Climate and Environmental Research,Oslo,Norway,5Norwegian Meteorological Institute,Oslo,Norway,6Istituto di Scienze Marine,Centro Nazionale delle Ricerche,Bologna,Italy

Abstract 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 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,multimodel ensemble outputs from the Coupled Model Intercomparison Project Phase 5are used to project future occurrence and severity of heat waves,under di?erent Representative Concentration Pathways,adopted by the Intergovernmental Panel on Climate Change for its Fifth Assessment Report (AR5).Results show that the percentage of global area a?ected 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 AR5scenario,events of the same severity as that in Russia in the summer of 2010will become the norm and are projected to occur as often as every 2years for regions such as southern Europe,North America,South America,Africa,and Indonesia.

1.Introduction

In a future warmer climate with increasing mean temperatures,heat waves will not only become more fre-quent;their duration and intensity are very likely to increase as well [Min et al.,2011;Intergovernmental Panel on Climate Change ,2012;Coumou and Rahmstorf ,2012].In the past few years several climate indices have been applied in order to quantify the heat wave duration and severity based on nighttime minima or daytime maxima [Perkins and Alexander ,2012;Frich et al.,2002;Kiktev et al.,2003;Sch?r et al.,2004;Meehl and Tebaldi ,2004;Alexander et al.,2006;Orlowsky and Seneviratne ,2011;Russo and Sterl ,2011;Sillmann et al.,2013a].However,all of these indices have been found to have a limited robustness when applied for comparing the severity of heat waves across regions and time [Otto et al.,2012;Perkins and Alexander ,2012].Most of the de?ned heat wave indices tend to be constructed with a certain impact group or sector in mind (e.g.,human health,wildlife,agriculture,bush?re-wild?re,management,transport,and electric-ity and power)and,due to their complexity,may not be transferable across regions nor between groups,nor across time periods [Perkins and Alexander ,2012].The Heat Wave Duration Index de?ned by Frich et al.[2002],which is one of the ?rst proposed indices used to detect the impact of heat waves,is found to be statistically unrobust [Kiktev et al.,2003;Alexander et al.,2006;Russo and Sterl ,2011].This is because Frich et al.[2002]used a ?xed threshold of 5?C above climatology to compute the index.This threshold is too high in many regions,such as the tropics,where the variability of daily maximum temperature is low.To overcome this problem,the Expert Team on Climate Change Detection and Indices (ETCCDI;see http://etccdi.paci?https://www.360docs.net/doc/7113029382.html,/list_27_indices.shtml)replaced this index with the Warm Spell Duration Index (WSDI)which is calculated using a percentile-based threshold.The WSDI is able to improve the spatial intercomparability of heat wave duration,but it appears to have some limitations as well.It is calculated for seasons and years individually,which means that heat waves,occurring across two di?erent seasons,are split into two [Orlowsky and Seneviratne ,2011;Intergovernmental Panel on Climate Change ,2012].

Figure1.HWMI estimation:calculation scheme example for the2010heat wave in Russia.(a)ERA-I time series of the year2010of daily maximum temperature in Russia(open circles joined by gray and black lines)and daily threshold (black line).The open circles that are not in gray represent the daily maximum temperature values of the2010Russia heat wave.They constitute14subheat waves(four of these,selected arbitrarily,are represented by red,green,cyan, and blue open circles).(b)Empirical distribution of the sum of the three highest consecutive daily maximum tempera-tures of each year within the reference period1981–2010(black points)?tted by means of the kernel density estimator (see section S2)in order to obtain the empirical cumulative distribution function(black line).(c)WSDI and HWMI calcu-lated for the Russian heat wave in2010and for three di?erent conceptual types of heat wave de?ned for comparisons (see section2.4).

In addition,the WSDI takes into account only the duration of a heat wave,meaning that two heat waves

of the same length will be considered equally severe even if one of these has higher temperature values than the other.This study presents an alternative index called Heat Wave Magnitude Index(HWMI)that can be compared across regions and time and that takes into account both magnitude and duration of the heat waves.Based on this index,heat waves in the projected future climate can be compared with those of today.

2.Materials and Methods

2.1.Heat Wave Magnitude Index

The Heat Wave Magnitude Index is de?ned as the maximum magnitude of the heat waves in a year,where heat wave is the period≥3consecutive days with maximum temperature above the daily threshold for the reference period1981–2010.The threshold is de?ned as the90th percentile of daily maxima,centered on a

Figure2.Heat Wave Magnitude Index and Warm Spell Duration Index in three periods of11years with reanalysis data:(a–c)1980–1990,(d–f)1991–2001,and (g–i)2002–2012.The WSDI values are expressed as number of subheat waves calculated as WSDI/3.

31day window.Hence,for a given day d,the threshold is the90th percentile of the set of data A d de?ned by

A d=

2010

?

y=1981

d+15

?

i=d?15

T y,i,(1)

where ?

denotes the union of sets and T y,i is the daily maximum temperature of the day i in the year y.

The minimum number of consecutive hot days required to be considered as heat wave may vary across regions:for example,Perkins and Alexander[2012],focusing on Australia,have de?ned a heat wave as

an event of at least three consecutive days above threshold,whereas Fischer and Sch?r[2010]de?ned a European heat wave as an event of at least6days duration.

Since we are focusing on a global scale,we have chosen the de?nition of3days for a heat wave.Had we de?ned a heat wave as a minimum of6days,the3day heat waves would be excluded,whereas the3day de?nition does not exclude the possibility of detecting longer heat waves.

2.2.HWMI Calculation

The HWMI computation for a speci?c year is a multiple stage process explained below.As an example,

the computations are shown for a ERA-Interim(ERA-I;see section2.5)grid point(longitude=35.625?E, latitude=56.89?N in Figure1),where the2010Russian heat wave shows its maximum value:

1.Daily threshold.By using the above de?nition,a daily threshold is calculated for the reference period 1981–2010(Figure1a,black curve).

2.Heat wave selection.For each speci?c year we select all the heat waves composed of at least three con-secutive days with daily maximum temperature above the daily threshold.For the selected grid point the length of the detected heat wave in2010is41days(Figure1a).

3.Heat wave to subheat waves.Each heat wave is then decomposed into n subheat waves,where a subheat wave is a heat wave of three consecutive days.For the selected grid point we obtain13subheat waves for a total of39days;in order to give weighting to the last2days of the heat wave,these are grouped with the consecutive day value below the threshold(Figure1a).In general,in case a heat wave has a length which is not a multiple of3days,the last days(one or two)of the heat wave are grouped into a subheat wave with consecutive days below the threshold so that the last subheat wave of the heat wave includes 3days as well.

4.Subheat wave unscaled magnitude.The sum of the three daily maximum temperatures of a subheat wave.

5.Subheat wave magnitude.The subheat wave unscaled magnitude is transformed into a probability value on a scale from0to1(see section2.3),which is de?ned as the magnitude of a subheat wave(Figure1).

6.Heat wave magnitude.The magnitude of each heat wave is de?ned as the sum of the magnitudes of the n subheat waves.

7.Heat wave magnitude index.The maximum of all heat wave magnitudes for a given year.

2.3.Estimation of Subheat Wave Magnitude

The computation of the magnitude of a subheat wave is a two-stage process:(1)An empirical(nonpara-metric)cumulative density function(ECDF)is?tted to the annual maximum of the subheat wave unscaled magnitudes within the reference period1981–2010(see Figure1b and Appendix A)and(2)the magni-tude of a subheat wave is a probability value between0and1estimated from the subheat wave unscaled magnitude using the cumulative density function obtained in the previous stage(see Figure1).

2.4.Robustness of the HWMI Versus Other Indices

In a recent study,Perkins and Alexander[2012]discussed the limitations of most of the ETCCDI extreme temperature indices with regard to the detection of heat wave events.In general,these indices take into account only one single aspect(i.e.,maximum temperature,duration,or frequency)during a de?ned period, which is not necessarily part of a heat spell.This is the case,for instance,of the TX90p index(see http:// etccdi.paci?https://www.360docs.net/doc/7113029382.html,/list_27_indices.shtml)measuring the percentage of days with daily maximum tem-perature above the90th percentile threshold(see section2).Only the WSDI,used by ETCCDI,has been speci?cally de?ned for detecting heat spells,but it considers only the duration of a heat wave:as a result, two heat waves of the same length are considered equally severe,even if all the days of one had higher (or lower)temperatures than the other.”

In order to clarify this further,we compare the WSDI and HWMI calculated for the Russian heat wave in 2010at the selected grid point(HW1,Figure1c)with those of three di?erent conceptual types of heat wave (Figure1c,HW2,HW3,and HW4)with daily temperature values de?ned as follows:HW2=HW1tempera-ture values+4?C,HW3=threshold temperature values from day190to230+0.5?C,and HW4=threshold temperature values from day169to250+0.5?C.

The heat waves HW2and HW3are the same length as the HW1,whereas the HW4length is double than that of the HW1.Therefore,the HW2and HW3are two examples of heat waves of the same duration but with

di?erent magnitudes.

If the temperature of all the days of the HW1increases by4?C,then we will have a heat wave of type HW2, with the same WSDI as HW1but greater HWMI.In contrast,an event of the type HW3is an example of a heat wave with WSDI value equal to that of HW1but lower magnitude,corresponding to a HWMI value of about 50%lower than the magnitude of HW1.Finally,the HW4heat wave represents the case of a heat wave twice the duration of the Russian heat wave but lower magnitude.

Table1.List of Record-Breaking Heat Wave Events in the Period1980–2012a

U.S.Benelux Europe Greece Russia U.S.U.S.

(1980)(1994)(2003)(2007)(2010)(2011)(2012)

NCEP-II

Grid points916151611483853

Max8.20 5.49 4.70 3.7611.71 4.17 3.75

Mean 4.65 3.26 3.30 2.46 5.50 2.72 2.71

Median 4.10 2.91 3.48 2.36 5.43 2.57 2.60

ERA-I

Grid points543747781484455

Max 5.51 4.30 6.26 4.0211.4310.44 5.56

Mean 3.46 3.01 3.53 2.58 5.37 3.51 2.96

Median 3.23 3.05 3.72 2.51 5.29 3.14 2.76

a The spatial extension is estimated by counting the grid points within a speci?c event

with HWMI equal to or greater than2.

2.5.Models and Reanalysis

Daily maximum temperature data from the ERA-Interim(ERA-I)reanalysis from European Centre for Medium-Range Weather Forecasts[Dee et al.,2011]and National Centers for Environmental

Prediction-Department of Energy Reanalysis-2(NCEP-2)[Kanamitsu et al.,2002]are applied to study heat waves in the present climate.Both data sets have a6-hourly time resolution and are available from1979 onward.The ERA-I is based on a T255resolution(~79km);however,the data are truncated to T63for comparison with the NCEP-2and model data.

For future projections,daily maximum temperatures from16model simulations were obtained from the Coupled Model Intercomparison Project Phase5(CMIP5)archive(see Appendix Table C1).The HWMI was calculated for the period2006–2100for all models under three di?erent scenarios(i.e.,Representative Concentration Pathway2.6(RCP2.6),RCP4.5,and RCP8.5),described by Taylor et al.[2012]as follows:

RCP2.6.The radiative forcing reaches a maximum near the middle of the21st century before decreasing to an eventual nominal level of2.6W m?2by the end of the century.

RCP4.5.The radiative forcing increases until the middle of the21st century before stabilizing to a level of

4.5W m?2by the end of the century.

RCP8.5.The radiative forcing increases throughout the21st century before reaching a level of about

8.5W m?2by the end of the century.

For the period between1980and2005historical model simulations were used[Taylor et al.,2012].We used the period1980–2012for a comparison with the reanalysis.

All CMIP5models and reanalysis data were interpolated onto the same T63grid(1.875×1.875?).

2.6.Uncertainties of the Ensemble Models

The spread of the ensemble models has been estimated as the annual interquartile range(IQR)of the

16HWMI global and regional medians(see Figure3).The IQR is de?ned as the di?erence between the third (Q3)and the?rst(Q1)quartiles of the set of data[Rousseeuw and Leroy,1987].In robust statistics,a value is de?ned as outlier if it is outside of the interval[Q1?1.5IQR,Q3+1.5IQR].

3.Results

In this study the heat wave magnitudes have been classi?ed according to a new scale de?ned in Appendix B for values of the HWMI with the following categories:

≤1Normal<2≤Moderate<3≤Severe<4≤Extreme<8≤Very Extreme<16≤Super Extreme

<32≤Ultra Extreme.

In the11years between2002and2012,the percentage of global area a?ected by moderate(HWMI≥2), severe(HWMI≥3),and extreme(HWMI≥4)heat waves was threefold greater than in the previous periods (1980–1990and1991–2001).This is evident from Figure2showing the geographical pattern of the

Figure3.Global and regional median time series of the HWMI relative

to1980–2100.(a)Global median:Blue and red points correspond to

the HWMI values with the NCEP-II and ERA-Interim data,respectively.

The open black circle is the maximum HWMI value of the16models within the present period1980–2012.Black,blue,and green lines denote the CMIP5multimodel ensemble median,respectively,for the RCP2.6,RCP4.5, and RCP8.5scenarios.Gray,green,and light blue shaded areas denote the annual interquartile range(see section2)of the multimodel ensemble, respectively,for the RCP2.6,RCP4.5,and RCP8.5scenarios.(b–d)As Figure3a but for the three regions a?ected by the Russia(2010),Central Europe(2003),and the U.S.(1980)heat waves.Black,blue,and green open circles in the future period denote the HWMI of the model ensemble represented only if the value is greater than or equal to the HWMI value of the2010,2003,and1980(dashed black lines).maximum HWMI,from the ERA-I

and NCEP-2data,within three peri-ods:1980–1990,1991–2001,and 2002–2012.A list of the most severe heat waves that occurred in the period1980–2012is presented in Table1.

The2010Russian heat wave shows the highest HWMI value and the largest extension with both the

ERA-interim and NCEP-2data sets.

In particular,the2010Russian heat wave shows a spatial extension nearly 3times larger and with a maximum HWMI approximately double that

of the heat wave in Europe in2003 (see Table1).Qualitatively similar conclusions were found in a recent study[Barriopedro et al.,2011]pro-viding evidence that the anomalous 2010heat wave exceeded the ampli-tude and spatial extent of the2003 European summer heat wave.Addi-tional record-breaking European heat waves occurred in1994and2007

in Germany-Benelux and Greece, respectively.According to the HWMI values calculated from reanalysis data,the regional mean and median of both these events were,however, less severe than the2003heat wave (see Table1).

Another extreme heat wave,albeit less frequently studied in the liter-ature,occurred in the U.S.in1980. According to the NCEP-2reanaly-sis,the1980U.S.heat wave can be considered the second strongest

in the period1980–2012,whereas the ERA-I data show that this event was of comparable magnitude as

the2003European heat wave(see

Table1).Two other important heat waves occurred in the U.S.in the years2011and2012with HWMI values comparable to the1994and2007events in Germany-Benelux and Greece,respectively(see Table1).

It must be noted that this comparison of heat wave strengths based on HWMI cannot be performed with the WSDI as it takes into account only the duration of a heat wave.In fact,there are many events that occurred in the last33years in di?erent regions,such as North America,Greenland,northern West Africa,and southeast Australia,of long duration resulting in WSDI values comparable to those of the2003European heat wave (Figure2,right-hand frames).However,those events show lower HWMI values than that in Europe2003; although they were of long duration,the temperature anomalies associated with these events were smaller than those of the Europe2003heat wave.

In order to evaluate whether general circulation models(GCMs)are capable of reproducing the intensity and frequency of heat waves in the present climate,as well as to quantify their changes in a future warmer

frequency (number of heat wave in 33 years)

g

b

c

h i

d e f

Figure 4.Number of extreme heat waves (HWMI ≥4)in present and future climate.(a)Historical (1980–2005)and RCP2.6scenario (2006–2012)for the multimodel CMIP5ensemble,(b)ERA-Interim,(c)NCEP-2,(d–f)median of the number of heat waves of the multimodel ensemble in the near future (2020–2052)under the RCP2.6,RCP4.5,and RCP8.5scenarios,respectively.(g–i)As Figures 4d–4f but for the future period (2068–2100).

climate,we estimated the spatial median of the HWMI of the entire globe and of three subregions,the U.S.,Europe,and Russia,composed of those grid points with HWMI values (calculated using ERA-I)equal to or greater than 2during the strongest events in 1980,2003,and 2010,respectively (see Figure 3and Table 1).GCMs HWMI values for the period 1980–2012were compared with those calculated from the ERA-I and NCEP-2reanalysis data.The global and regional yearly evolution of the HWMI model ensemble medians does not di?er signi?cantly from those estimated with the reanalysis data sets (Figure 3).The signi?cance is based on the 1%level of a Kolmogorov-Smirnov test.Moreover,the signi?cant positive HWMI global median trends estimated with the models are very similar to those estimated with reanalysis data (see Appendix Table D1).

At regional scale,however,models show limitations in simulating single extreme heat wave events.In par-ticular,for the period 1980–2012in Russia,the maximum HWMI value of the 16models (HWMI =2)di?ers signi?cantly from the regional HWMI median calculated with ERA-I and NCEP-2reanalysis (HWMI =5.43and 5.29,respectively;see Figure 3b).From a statistical point of view,the events that occurred in 2010(Russia),2003(Europe),and 1980(U.S.)can be considered outliers,rare events,or extremes [Gumbel ,1954;Rousseeuw

frequency (number of heat wave in 33 years)

g b

c

h i

d e f

Figure 5.Number of very extreme heat waves (HWMI ≥8)in present and future climate.(a)Historical (1980–2005)and RCP2.6scenario (2006–2012)for the multimodel CMIP5ensemble,(b)NCEP-2,(c)ERA-Interim,and (d–f)median of the number of heat waves of the multimodel ensemble in the near future (2020–2052)under the RCP2.6,RCP4.5,and RCP8.5scenarios,respectively.(g–i)As Figures 5d–5f but for the future period (2068–2100).

and Leroy ,1987](Figures 3b–3d and section 2.6).Similar considerations can be done for the other severe heat waves listed in Table 1,recording a maximum HWMI value of the 16models in general lower than 2.5.In a warming world with a signi?cant positive temperature trend,all the models show an increase of the HWMI global median in the near future (2020–2052)for all the RCP scenarios (Table D1).Further in the future (2068–2100),the trend is still positive under the RCP4.5and RCP8.5scenarios and slightly negative under the RCP2.6(see Table D1).

With increasing temperature,the probability of extreme events will increase and events considered extreme today may be the norm under certain future scenarios.Present rare heat waves,such as those in Europe 2003and the U.S.1980,will become the norm in around 2070under the RCP8.5climate change scenario as evident from the ensemble model median of HWMI (Figures 3c and 3d).The extreme Russian heat wave in 2010,which was detected as the strongest heat wave in the present climate,will be the norm (present value equal to the future ensemble median)at the end of the century under the RCP8.5scenario,although,it will remain rare (present value outside the interquartile range of the model ensemble)under the other two scenarios (Figure 3b).

Under the RCP8.5scenario heat waves with a magnitude equal to or greater than the magnitude of

the events in2010,2003,and1980show some probability of occurrence starting from~2050(Russia),

~2030(Europe),and~2045(U.S.)(Figures3b–3d).

The probability of occurrence of an extreme heat wave(HWMI≥4,note that in2010,the average of the HWMI in Russia was greater than5with both ERA-I and NCEP-II data sets)has been estimated by counting the number of heat waves in the following three periods of33years:1980–2012,2020–2052,and2068–2100 (Figure4).

A direct comparison between models and the reanalysis can be found in Figures4a–4c.In this?gure we show that the models are not able to represent the number and geographical pattern of heat waves as found in the reanalysis,particularly in Europe and the U.S.The models show extreme heat waves only in South America.

In the near future(2020–2052),the RCP2.6and RCP4.5scenarios show very similar results:the frequency of extreme heat waves will increase with at least one extreme heat wave in33years in the U.S.,Europe,and large parts of Africa and three extreme heat waves in northern South America,some parts of Africa,the U.S., and southern Europe(Figures4d and4e).

Under the most severe RCP8.5scenario,large areas of the U.S.,South America,Europe,and Indonesia show more than three heat waves in the period2020–2052,with one extreme heat wave every2years in northern Brazil(Figure4f).At the end of the century(2068–2100),under the RCP8.5scenario and in a very large part of the globe,an extreme heat wave will occur at least once every2years(more than15extreme events in 33years;see Figure4i).

Our results are similar to Sillmann et al.[2013b]showing that the strongest increase in heat wave dura-tion calculated with the WSDI will occur in tropical regions;however,we detect very high HWMI values also in some extratropical regions as,for example,Greenland and Canada(Figures4i and5i),which are not detected in Sillmann et al.[2013b]by using the WSDI.This is because the HWMI takes into account not only the duration of a heat wave but also the intensity estimated by weighting the temperature values of a heat wave with respect to the probability distribution of the annual maximum of the reference period(see section2.3).Under the same scenario(RCP8.5)in northern Latin America,Africa,some parts of the U.S.,and southern Europe,very extreme heat waves(HWMI>8,magnitude comparable with the maximum value of the Russian heat wave in2010)will occur with the same frequency of the extreme heat waves(HWMI>4), i.e.,once every2years(Figure5).

In contrast,at the end of the century under the RCP2.6mitigation scenario,the probability of extreme heat waves occurring is almost unchanged compared to the period2020–2052,indicating that this mitigation scenario may be e?ective in reducing the impact of climate change(Figures4d,4g,5d,and5g).

4.Conclusions

In this work we have developed the new climate index HWMI that,by taking into account both heat wave duration and intensity,enables the quanti?cation of the magnitude of heat waves across di?erent time periods and regions of the world.

We have estimated the magnitude of extreme heat waves in the present climate and their projection in a warming world by using reanalysis data and IPCC AR5model outputs,respectively.

According to the statistics of extremes[Gumbel,1954],“However big?oods get,there will always be a bigger one coming,”the same could be said for heat waves.By using the HWMI,we have shown that the extreme Russian heat wave in2010can still be considered a rare event in the future under the less severe scenarios RCP4.5and RCP2.6.This heat wave may occur under stationary climate[Dole et al.,2011],and

it is di?cult to link it to greenhouse gas-induced warming[Intergovernmental Panel on Climate Change, 2012].However,in a warmer future climate,the probability of extreme events,such as those in Russia2010, Europe2003,and the U.S.1980,will increase and events considered rare today may be considered the norm under certain future scenarios.Given the disastrous e?ects of the2003and2010events in Europe,and

the2011and2012events in the U.S.,these results show that we may be facing a serious risk of adverse impacts over larger and densely populated areas if mitigation strategies for reducing global warming are not implemented.

Appendix A:Cumulative Density Function Estimation

The empirical probability distribution function(EPDF)and the corresponding ECDF based on the annual maxima of the subheat wave unscaled magnitudes are estimated using the kernel density estimator(KDE) [Silverman,1986].The KDE is a nonparametric method for estimating EPDF and ECDF.Given independent and identically distributed annual maxima of subheat wave unscaled magnitudes,T1,T2,…,T N having the common probability density function f(T),where N=30is the number of years,the general kernel estimator for f is as follows:

?f h (T)=

1

N

N

i=1

K

(

T?T i,h

)

(A1)

T is the subheat wave unscaled magnitude,K is the kernel function,and h is the smoothing bandwidth. Comprehensive reviews of the kernel smoothing method are available in Silverman[1986].The kernel function K is chosen here to the Gaussian function

K(x,σ)=

1

σ

e?x

2

2σ2(A2)

By using the method of Sheather and Jones[1991],h is calculated.The values of the ECDF are obtained by integrating the?f h(T)as follows:

ECDF(T)=1

N

N

i=1

T

?∞

K

(

T?T i,h

)

d T(A3)

Appendix B:Heat Wave Magnitude Scale

Referring to the magnitude values of the heat waves that occurred in the last33years,heat wave magnitude is de?ned for values of the HWMI with the following categories(Figure B1):

≤1Normal<2≤Moderate<3≤Severe<4≤Extreme<8≤Very Extreme<16≤Super Extreme

<32≤Ultra Extreme.

The levels of the heat wave magnitude scale are de?ned on a T63grid using the ERA-I and NCEP-2HWMI val-ues.We have calculated at each grid point the number of heat waves with a magnitude greater than a?xed threshold in the present period1980–2012.Subsequently,we estimate the percentage of the global area that recorded(in the period between1980and2012)at least one heat wave with an HWMI value greater than a?xed threshold(see Figure B1)and de?ne the following categories:

Normal.All the grid points show at least one heat wave with HWMI≥1in33years.

Moderate.At least one heat wave with HWMI≥2is detected in70%of the global area.

Severe.At least one heat wave with HWMI≥3is detected in the25%of the global area.

Extreme.At least one heat wave with HWMI≥4is detected in5–10%of the global area.

Very extreme.At least one heat wave HWMI≥8is detected in0.1–1%of the global area.

Super extreme.HWMI≥16.

Ultra Extreme.HWMI≥32.

The last two categories are de?ned arbitrarily;in fact,the maximum HWMI value in the present is lower than12(see Table1),and the percentage of the global area with at least one heat wave with HWMI≥12

is zero.We add these two additional categories in order to detect heat waves under the most drastic IPCC AR5scenarios.

This scale can be applied to a single grid point or to the HWMI median or mean estimated in a region:for example,the Russian heat wave of2010was in median an extreme heat wave(HWMI regional median>4) with a few locations recording a very extreme magnitude(HWMI>8)(Figure2and Table1of the

main paper).

magnitude levels (HWMI value)

P e r c e n t a g e o f g l o b a l a r e a (%)

12348

1632

100

70

25

100N o r m a l

M o d e r a t e

S e v e r e

E x t r e m e

V e r y E x t r e m e

S u p e r E x t r e m e

U l t r a E x t r e m e

Figure B1.HWMI scale categories.Percentage of global area (y axis)with at least one heat wave of a ?xed HWMI value (x axis)in the period 1980–2012.The blue crosses and red open circles represent the percentage of global area with at least one heat wave of a ?xed HWMI value versus the ?xed HWMI values calculated with the NCEP-2and ERA-I data sets,respectively.

Appendix C:List of the CMIP5Used Models

The HWMI was calculated for daily maximum temperatures from 16model simulations from the Coupled Model Intercomparison Project Phase 5(CMIP5)archive (see Table C1).

Table C1.List of the AR5CMIP5Used Models Spatial Resolution Model Institution

(Longitude ×Latitude)

BCC-CSM1-1Beijing Climate Center,China 128×64(T42)Meteorological Administration,China

CanESM2Canadian Centre for Climate 128×64(T42)Modelling and Analysis,Canada CCSM4National Center for Atmospheric

288×192(T42)Research,U.S.

CNRM-CM5Centre National de Recherches 256×128(T85)Meteorogiques,Meteo France,France 128×64(T42)CSIRO-Mk3-6-0Australian Commonwealth Scienti?c and 192×96(T63)Industrial Research Organization,Australia GFDL-ESM2G Geophysical Fluid Dynamics Laboratory,U.S.144×90GFDL-ESM2M Geophysical Fluid Dynamics Laboratory,U.S.

144×90IPSL-CM5A-LR Institut Pierre-Simon Laplace,France 96×96(T42)IPSL-CM5A-MR Institut Pierre-Simon Laplace,France

144×143(T42)MIROC5

AORI (Atmosphere and Ocean Research Institute),256×128(T85)

NIES (National Institute for Environmental Studies),

JAMSTEC (Japan Agency for Marine-Earth

Science and Technology),Japan MIROC-ESM

AORI,NIES,JAMSTEC,Japan 128×64(T42)MIROC-ESM-CHEM AORI,NIES,JAMSTEC,Japan

128×64(T42)MPI-ESM-LR Max Planck Institute for Meteorology,Germany 192×96(T63)MPI-ESM-MR Max Planck Institute for Meteorology,Germany 192×96(T63)MRI-CGCM3Meteorological Research Institute,Japan 320×160(T106)NorESM1-M Norwegian Climate Centre,Norway

144×96(T42)

Table D1.Nonparametric (Kendall-Tau)Linear Trend (1%Signi?cance Level)of the Yearly Global-Averaged Median for Three Time Windows of 33Years for Each Single Model,Model Ensemble,and Reanalysis Data

1980–20122020–2052

2068–2100Historical-RCP2.6

RCP2.6RCP4.5RCP8.5RCP2.6

RCP4.5RCP8.5Model

(×10?2HWMI ×yr ?1)

(×10?2HWMI ×yr ?1)

(×10?2HWMI ×yr ?1)

BCC-CSM1-1 1.9 1.0 1.8 3.0?0.50.6 5.8CanESM2 3.1 1.7 2.1 4.60.10.615.6CCSM4 2.50.8 1.9 2.8?0.10.17.2CNRM-CM5 2.0 1.2 2.7 3.5?0.50.110.1CSIRO-Mk3-6-0 2.2 1.8 3.4 3.90.40.110.1GFDL-ESM2G 1.5 1.2 1.8 3.0?0.70.3 5.5GFDL-ESM2M 2.30.6 1.5 2.4?0.30.2 5.4IPSL-CM5A-LR 3.4 1.8 5.18.7?0.0 3.7 3.2IPSL-CM5A-MR 3.2 2.9 3.6 6.8?0.70.9 2.7MIROC5 2.3 1.6 2.3 3.1?0.0 1.48.1MIROC-ESM

1.9

2.3 2.4

3.60.20.516.0MIROC-ESM-CHEM 1.8 1.8 2.6 3.90.1 1.616.0MPI-ESM-LR 1.20.8 2.6 3.2?0.50.01

4.0MPI-ESM-MR 2.40.4 2.1 3.40.00.910.9MRI-CGCM3 1.30.2 1.3 1.8?0.20.7 4.4NorESM1-M

1.60.8 1.6

2.6?0.10.5 6.4Ensemble median 2.1 1.2 2.2

3.3?0.10.710.1Ensemble IQR

0.7

0.9

0.50.9

0.5

0.6

6.2

Reanalysis

NCEP-2 1.5------ERA-I 2.1------

Appendix D:Nonparametric Trend Values

The values of the nonparametric (Kendall-Tau)HWMI global median linear trend for three time windows of 33years estimated with the models and reanalysis data are reported in Table D1.

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巴尔扎克简介

巴尔扎克简介(1799~1850) 法国小说家.1799年5月20日生于巴黎以南的图尔城, 1850年8月18日卒于巴黎.巴尔扎克的一生,处于19世纪前半期的50年,经历了拿破仑帝国的战火纷飞的岁月,动荡不安的封建复辟王朝,以及以阴谋复辟帝制的路易□波拿巴为总统的第二共和国.他用总标题为《人间喜剧》的一系列小说,反映了剧烈的社会变革时期的法国生活. 巴尔扎克出生后不久就被送到附近的乡村去寄养. 上小学后一直到中学毕业,他始终寄住在宿舍里,没有能回 家过一段比较长的日子,享受家庭生活的温暖.离开家庭的童年生活的痛苦使他毕生难忘. 巴尔扎克的父亲原姓巴尔沙,是个精明强干的人. 他来自农村,幼年时跟当地教士学了一点文化,中年致富,在外省当过副市长,供应军粮的承包商,在巴黎经营过呢绒商业,当过巴黎驻军的军需负责人,是一个白手起家的资产者. 1816年,17岁的巴尔扎克结束中学的学业后进大学法科,并在文科旁听.18岁时,先后在诉讼代理人和公证人的办事处当见习生或书记. 从20岁开始,巴尔扎克决定从事文学创作.他在巴黎贫民区租了一间房顶上的阁楼,由父母供给极有限的一点生活费,埋头写作.他的第一部作品是五幕诗体悲剧《克伦威尔》,这是一部完全失败的作品,没有引起任何人的兴趣.接连又写了十多部小说,有的是自己所写,有的是和别人合写.这一阶段他所写的小说全用笔名发表.这些作品并没有给他带来生活所需要的物质条件,他只好暂时放弃文学.1826年借钱出版了一部普及版的《莫里哀全集》,接着又出版一部拉封丹寓言诗集, 销路不佳,亏损负债9,000法郎.后又借债经营印刷厂和铸字厂,均以赔本告终.他前后负债共达6万多法郎. 从1828年夏季开始,巴尔扎克决定重新回到文学事业上来.他写了一部以布列塔尼封建势力武装叛乱反对共和国为题材的小说《最后一个舒昂党人》(后来编入《人间喜剧》,改名《舒昂党的人们》).这是巴尔扎克所写的第一部严肃的文学作品,第一次用巴尔扎克真姓名发表.此书问世,初步奠定了作者在文学界的地位. 接着发表小说《婚姻生理学》,在读者之间引起广泛注意.1831年他的新著《驴皮记》出版,巴尔扎克立即成为法国最负盛名的作家之一. 1819到1829年,是巴尔扎克在文学事业上的探索阶段.从1829年开始,一直到1848年,是他创作《人间喜剧》的时期,也是他文学事业的全盛时期.他用超人的才智与精力,在不到20年的时间内,共创作小说91部.平均每年产生作品四,五部之多.他每日伏案一般都在10小时以上,常常连续工作18小时.有时文思如泉涌,或者为了赶写稿子,他一连几天废寝忘餐,夜以继日地劳动.根据阿尔贝·贝干教授提供的材料,巴尔扎克的杰作《高老头》(原名《高立欧老爹》)是他用三天三夜一气呵成的. 什么是《人间喜剧》的作者的创作动力人们说是因为他负债过多,需要用稿费还债.巴尔扎克年轻时经营印刷出版业确曾负债巨万.但从他成为名重一时的小说家之后,他的收入丰厚,出版商争着和他签订合同,不惜重金预约他尚未完成或尚未动笔的小说稿,早年的债务早已还清.名作家巴尔扎克生活阔绰,醉心于豪华的排场.他在巴黎同时安置了几处住宅和别墅,出门坐最富丽的马车,驾着骏马;仆役都穿制服;他也服饰华贵, 出入于名门大户的沙龙.由此可见,他的勤奋创作,绝对不是由于贫困. 巴尔扎克从年轻时开始就自信有很高的文学才能, 对文学有极大的抱负.他不舍昼夜地勤奋写作,主要因为有一股激情在内心沸腾,促使他充分发挥自己的才能, 要求在文艺界做一番伟大的事业.在他的书室里,有一座作为摆饰的小型拿破仑塑像.在塑像座盘边上,巴尔扎克亲笔写着:"他用宝剑未能完成的大业,我将用笔杆来完成." 巴尔扎克要完成的伟大事业就是《人间喜剧》这座巍峨的文学里程碑.第一次在这位小说家笔下出现《人间喜剧》这个名词是在1813年.毫无疑问,《人间喜剧》的命名是受但丁长诗《神圣喜剧》(中译《神曲》)标题的启发.1841年,巴尔扎克确定了这个庞大的创作计划.当时有四家出版商和巴尔扎克签订合同,合资承包《人间喜剧》的出版工作.1842年,巴尔扎克写了《人间喜剧·导言》,阐述他写作这部史无前例的文学巨著的宗旨.1845年巴尔扎克亲笔写的《人间喜剧总目》,一直保存到现在.根据这个《总目》,《人间喜剧》分为三大部分:《风俗研究》,《哲理研究》和《分析研究》. 《风俗研究》内容最为丰富,包括小说最多.因此这一部分又分为六个门类:1.《私人生活场景》,2.《外省生活场景》,3.《巴黎生活场景》,4.《政治生活场景》, 5.《军队生活场景》,6.《乡村生活场景》.《私人生活场景》包括32部小说,其中4部当时已有提纲,尚未起稿.已经完成的28部之中包括著名的《高老头》(1834), 《猫滚球布店》(1830),《夏倍上校》(1832)和《三十岁的女人》(1831~1834)等.《外省生活场景》包括17 部小说,其中6部尚未完成;已经发表的11部中包括《欧也妮·葛朗台》(1833),《幽谷百合》(1835)和《幻灭》(1837~1843)等.《巴黎生活场景》共有20部小说, 其中6部尚未产生;在已经发表的小说中有《金目少女》 (1834),《纽沁根银行》(1838),《塞沙·皮罗多兴衰记》(1837),

Excel函数公式完整版

EXCEL函数公式大全(完整) 函数说明 CALL调用动态链接库或代码源中的过程 EUROCONVERT用于将数字转换为欧元形式,将数字由欧元形式转换为欧元成员国货币形式,或利用欧元作为中间货币将数字由某一欧元成员国货币转化为另一欧元成员国 货币形式(三角转换关系) GETPIVOTDATA返回存储在数据透视表中的数据 REGISTER.ID返回已注册过的指定动态链接库(DLL) 或代码源的注册号 SQL.REQUEST连接到一个外部的数据源并从工作表中运行查询,然后将查询结果以数组的形式返回,无需进行宏编程 ?数学和三角函数 ?统计函数 ?文本函数 加载宏和自动化函数 多维数据集函数 函数说明 CUBEKPIMEMBER返回重要性能指标(KPI) 名称、属性和度量,并显示单元格中的名 称和属性。KPI 是一项用于监视单位业绩的可量化的指标,如每月 总利润或每季度雇员调整。 CUBEMEMBER返回多维数据集层次结构中的成员或元组。用于验证多维数据集内 是否存在成员或元组。 CUBEMEMBERPROPERTY返回多维数据集内成员属性的值。用于验证多维数据集内是否存在 某个成员名并返回此成员的指定属性。 CUBERANKEDMEMBER返回集合中的第n 个或排在一定名次的成员。用于返回集合中的一 个或多个元素,如业绩排在前几名的销售人员或前10 名学生。 CUBESET通过向服务器上的多维数据集发送集合表达式来定义一组经过计算 的成员或元组(这会创建该集合),然后将该集合返回到Microsoft Office Excel。 CUBESETCOUNT返回集合中的项数。 CUBEVALUE返回多维数据集内的汇总值。

高中语文 名著导读《高老头》巴尔扎克简介素材 新人教版必修3

巴尔扎克简介 巴尔扎克(Honore de Balzac,1799~1850),他是19世纪法国伟大的批判现实主义作家,欧洲批判现实主义文学的奠基人和杰出代表。一生创作96部长、中、短篇小说和随笔,总名为《人间喜剧》。其中代表作为《欧也妮·葛朗台》、《高老头》。100多年来,他的作品传遍了全世界,对世界文学的发展和人类进步产生了巨大的影响。马克思、恩格斯称赞他“是超群的小说家”、“现实主义大师”。 巴尔扎克出生于一个法国大革命后致富的资产阶级家庭,法科学校毕业后,拒绝家庭为他选择的受人尊敬的法律职业,而立志当文学家。为了获得独立生活和从事创作的物质保障,他曾试笔并插足商业,从事出版印刷业,但都以破产告终。这一切都为他认识社会、描写社会提供了极为珍贵的第一手材料。他不断追求和探索,对哲学、经济学、历史、自然科学、神学等领域进行了深入研究,积累了极为广博的知识。 1829年,巴尔扎克完成长篇小说《朱安党人》,这部取材于现实生活的作品为他带来巨大声誉,也为法国批判现实主义文学放下第一块基石,巴尔扎克将《朱安党人》和计划要写的一百四五十部小说总命名为《人间喜剧》,并为之写了《前言》,阐述了他的现实主义创作方法和基本原则,从理论上为法国批判现实主义文学奠定了基础。 巴尔扎克在艺术上取得巨大成就,他在小说结构方面匠心独运,小说结构多种多样,不拘一格、并善于将集中概括与精确描摹相结合,以外形反映内心本质等手法来塑造人物,他还善于以精细人微、生动逼真的环境描写再现时代风貌。恩格斯称赞巴尔扎克的《人间喜剧》写出了贵族阶级的没落衰败和资产阶级的上升发展,提供了社会各个领域无比丰富的生动细节和形象化的历史材料,“甚至在经济的细节方面(如革命以后动产和不动产的重新分配),我学到的东西也要比从当时所有职业历史学家、经济学院和统计学家那里学到的全部东西还要多”。(恩格斯:《恩格斯致玛·哈克奈斯》) 巴尔扎克以自己的创作在世界文学史上树立起不朽的丰碑。

(完整word版)excel函数的说明及其详细的解释

excel 函数的说明及其详细的解释 数据库和清单管理函数 AVERAGE返回选定数据库项的平均值 DCOUNT计算数据库中包含数字的单元格的个数 DCOUNTA计算数据库中非空单元格的个数 DGET从数据库中提取满足指定条件的单个记录 DMAX返回选定数据库项中的最大值 DMIN返回选定数据库项中的最小值 DPRODUCT乘以特定字段(此字段中的记录为数据库中满足指定条件的记录)中的值 DSTDEV根据数据库中选定项的示例估算标准偏差 DSTDEVP根据数据库中选定项的样本总体计算标准偏差 DSUM对数据库中满足条件的记录的字段列中的数字求和 DVAR根据数据库中选定项的示例估算方差 DVARP根据数据库中选定项的样本总体计算方差 GETPIVOTDATA 返回存储在数据透视表中的数据

日期和时间函数 DATE返回特定时间的系列数 DATEDIF计算两个日期之间的年、月、日数 DATEVALUE 将文本格式的日期转换为系列数 DAY 将系列数转换为月份中的日 DAYS360按每年360 天计算两个日期之间的天数 EDATE返回在开始日期之前或之后指定月数的某个日期的系列数 EOMONTH返回指定月份数之前或之后某月的最后一天的系列数 HOUR将系列数转换为小时 MINUTE将系列数转换为分钟 MONTH将系列数转换为月 NETWORKDAYS 返回两个日期之间的完整工作日数 NOW 返回当前日期和时间的系列数 SECOND将系列数转换为秒 TIME返回特定时间的系列数 TIMEVALUE将文本格式的时间转换为系列数 TODAY返回当天日期的系列数 WEEKDAY将系列数转换为星期 WORKDAY返回指定工作日数之前或之后某日期的系列数YEAR 将系列数转换为年

电子表格常用函数公式

电子表格常用函数公式 1、自动排序函数: =RANK(第1数坐标,$第1数纵坐标$横坐标:$最后数纵坐标$横坐标,升降序号1降0升) 例如:=RANK(X3,$X$3:$X$155,0) 说明:从X3 到X 155自动排序 2、多位数中间取部分连续数值: =MID(该多位数所在位置坐标,所取多位数的第一个数字的排列位数,所取数值的总个数) 例如:612730************在B4坐标位置,取中间出生年月日,共8位数 =MID(B4,7,8) =19820711 说明:B4指该数据的位置坐标,7指从第7位开始取值,8指一共取8个数字 3、若在所取的数值中间添加其他字样, 例如:612730************在B4坐标位置,取中间出生年、月、日,要求****年**月**日格式 =MID(B4,7,4)&〝年〞&MID(B4,11,2) &〝月〞& MID(B4,13,2) &〝月〞&

=1982年07月11日 说明:B4指该数据的位置坐标,7、11指开始取值的第一位数排序号,4、2指所取数值个数,引号必须是英文引号。 4、批量打印奖状。 第一步建立奖状模板:首先利用Word制作一个奖状模板并保存为“奖状.doc”,将其中班级、姓名、获奖类别先空出,确保打印输出后的格式与奖状纸相符(如图1所示)。 第二步用Excel建立获奖数据库:在Excel表格中输入获奖人以及获几等奖等相关信息并保存为“奖状数据.xls”,格式如图2所示。 第三步关联数据库与奖状:打开“奖状.doc”,依次选择视图→工具栏→邮件合并,在新出现的工具栏中选择“打开数据源”,并选择“奖状数据.xls”,打开后选择相应的工作簿,默认为sheet1,并按确定。将鼠标定位到需要插入班级的地方,单击“插入域”,在弹出的对话框中选择“班级”,并按“插入”。同样的方法完成姓名、项目、等第的插入。 第四步预览并打印:选择“查看合并数据”,然后用前后箭头就可以浏览合并数据后的效果,选择“合并到新文档”可以生成一个包含所有奖状的Word文档,这时就可以批量打印了。

Excel函数名称解释大全..

Excel函数大全 数据库和清单管理函数 DAVERAGE 返回选定数据库项的平均值 DCOUNT 计算数据库中包含数字的单元格的个数 DCOUNTA 计算数据库中非空单元格的个数 DGET 从数据库中提取满足指定条件的单个记录 DMAX 返回选定数据库项中的最大值 DMIN 返回选定数据库项中的最小值 DPRODUCT 乘以特定字段(此字段中的记录为数据库中满足指定条件的记录)中的值 DSTDEV 根据数据库中选定项的示例估算标准偏差 DSTDEVP 根据数据库中选定项的样本总体计算标准偏差 DSUM 对数据库中满足条件的记录的字段列中的数字求和 DVAR 根据数据库中选定项的示例估算方差 DVARP 根据数据库中选定项的样本总体计算方差 GETPIVOTDATA 返回存储在数据透视表中的数据 日期和时间函数 DATE 返回特定时间的系列数 DATEDIF 计算两个日期之间的年、月、日数 DATEVALUE 将文本格式的日期转换为系列数 DAY 将系列数转换为月份中的日 DAYS360 按每年 360 天计算两个日期之间的天数 EDATE 返回在开始日期之前或之后指定月数的某个日期的系列数 EOMONTH 返回指定月份数之前或之后某月的最后一天的系列数 HOUR 将系列数转换为小时 MINUTE 将系列数转换为分钟

MONTH 将系列数转换为月 NETWORKDAYS 返回两个日期之间的完整工作日数 NOW 返回当前日期和时间的系列数 SECOND 将系列数转换为秒 TIME 返回特定时间的系列数 TIMEVALUE 将文本格式的时间转换为系列数 TODAY 返回当天日期的系列数 WEEKDAY 将系列数转换为星期 WORKDAY 返回指定工作日数之前或之后某日期的系列数 YEAR 将系列数转换为年 YEARFRAC 返回代表 start_date(开始日期)和 end_date(结束日期)之间天数的以年为单位的分数 DDE 和外部函数 CALL 调用动态链接库(DLL)或代码源中的过程 REGISTER.ID 返回已注册的指定 DLL 或代码源的注册 ID SQL.REQUEST 连接外部数据源,并从工作表中运行查询,然后将结果作为数组返回,而无需进行宏编程。 有关 CALL 和 REGISTER 函数的其他信息 工程函数 BESSELI 返回经过修改的贝塞尔函数 In(x) BESSELJ 返回贝塞尔函数 Jn(x) BESSELK 返回经过修改的贝塞尔函数 Kn(x) BESSELY 返回贝塞尔函数 Yn(x) xlfctBIN2DEC BIN2DEC 将二进制数转换为十进制数 BIN2HEX 将二进制数转换为十六进制数 BIN2OCT 将二进制数转换为八进制数 COMPLEX 将实系数和虚系数转换为复数 CONVERT 将一种度量单位制中的数字转换为另一种度量单位制

巴尔扎克

巴尔扎克 1、巴尔扎克的文学史地位 巴尔扎克是法国伟大的小说家,是19世纪批判现实主义文学的主要代表。在20年间呕心沥血写作实践中,巴尔扎克在世界文学史上构筑了一座举世无双的巍峨大厦——《人间喜剧》,而他自己则成了“文学中的拿破仑”。 马克思非常推崇巴尔扎克,认为他“对现实关系具有深刻理解”。恩格斯赞誉他的作品有着“了不起的革命辩证法”,并在《致玛·哈克奈斯》一信中对他作了精辟的论述。 2、生平与创作概况 A.全称: xx·xx·巴尔扎克 B.本姓: xx C.生卒____年__月__日: 1799.5.20— 1850.8.18 D.出生地: xx E.家庭: 中产阶级之家 F.教育:1816年结束中学学业 G.主要经历:

1819-1829年,开始写作小说; 1825年起出版图书,开办印刷厂,铸造铅字,以欠债6万法郎告终; 1828-1850年,全力创作《人间喜剧》。 H.主要成就: 包括90余部长篇小说、中篇小说、短篇小说的文学巨著《人间喜剧》。 3、巴尔扎克的创作道路 1819-1829年,探索阶段;1819年,立志当作家。1820年,写作悲剧《克伦威尔》失败,开始创作神怪小说,也未获成功。此后至1828年前,屡屡经商失败,债台高筑。1828年夏起重走文学道路。1929年以真名发表历史小说《朱安党人》,初获成功,在文坛站稳脚跟。 1829-1845年,黄金时代;巴尔扎克怀着做“文学上的拿破仑”的雄心壮志,孜孜不倦地创作,建构着《人间喜剧》这座艺术大厦,接连发表了许多杰作,如《高布赛克》 (1830)、《驴皮记》 (1831)、《xx·xx》 (1833)、《xx》 (1834)、《无神论者做弥撒》 (1836)、《xx银行》 (1837)、《幻灭》(1837-1843)和《农民》 (1845)等。 1846-1850年,晚期。1848年前,巴尔扎克陆续完成了《贝姨》

EXCEL表格函数公式大全

Excel常用函数公式及技巧搜集(常用的) 【身份证信息?提取】 从身份证号码中提取出生年月日 =TEXT(MID(A1,7,6+(LEN(A1)=18)*2),"#-00-00")+0 =TEXT(MID(A1,7,6+(LEN(A1)=18)*2),"#-00-00")*1 =IF(A2<>"",TEXT((LEN(A2)=15)*19&MID(A2,7,6+(LEN(A2)=18)*2),"#-00-00")+0,) 显示格式均为yyyy-m-d。(最简单的公式,把单元格设置为日期格式) =IF(LEN(A2)=15,"19"&MID(A2,7,2)&"-"&MID(A2,9,2)&"-"&MID(A2,11,2),MID(A2,7,4)&"-"&MID(A2,11,2)&"-"&MID(A2,13,2)) 显示格式为yyyy-mm-dd。(如果要求为“1995/03/29”格式的话,将”-”换成”/”即可) =IF(D4="","",IF(LEN(D4)=15,TEXT(("19"&MID(D4,7,6)),"0000年00月00日 "),IF(LEN(D4)=18,TEXT(MID(D4,7,8),"0000年00月00日")))) 显示格式为yyyy年mm月dd日。(如果将公式中“0000年00月00日”改成“0000-00-00”,则显示格式为yyyy-mm-dd) =IF(LEN(A1:A2)=18,MID(A1:A2,7,8),"19"&MID(A1:A2,7,6)) 显示格式为yyyymmdd。 =TEXT((LEN(A1)=15)*19&MID(A1,7,6+(LEN(A1)=18)*2),"#-00-00")+0 =IF(LEN(A2)=18,MID(A2,7,4)&-MID(A2,11,2),19&MID(A2,7,2)&-MID(A2,9,2)) =MID(A1,7,4)&"年"&MID(A1,11,2)&"月"&MID(A1,13,2)&"日" =IF(A1<>"",TEXT((LEN(A1)=15)*19&MID(A1,7,6+(LEN(A1)=18)*2),"#-00-00")) 从身份证号码中提取出性别 =IF(MOD(MID(A1,15,3),2),"男","女") (最简单公式) =IF(MOD(RIGHT(LEFT(A1,17)),2),"男","女") =IF(A2<>””,IF(MOD(RIGHT(LEFT(A2,17)),2),”男”,”女”),) =IF(VALUE(LEN(ROUND(RIGHT(A1,1)/2,2)))=1,"男","女") 从身份证号码中进行年龄判断 =IF(A3<>””,DATEDIF(TEXT((LEN(A3)=15*19&MID(A3,7,6+(LEN(A3)=18*2),”#-00-00”) ,TODAY(),”Y”),) =DATEDIF(A1,TODAY(),“Y”) (以上公式会判断是否已过生日而自动增减一岁) =YEAR(NOW())-MID(E2,IF(LEN(E2)=18,9,7),2)-1900 =YEAR(TODAY())-IF(LEN(A1)=15,"19"&MID(A1,7,2),MID(A1,7,4)) =YEAR(TODAY())-VALUE(MID(B1,7,4))&"岁" =YEAR(TODAY())-IF(MID(B1,18,1)="",CONCATENATE("19",MID(B1,7,2)),MID(B1,7,4)) 按身份证号号码计算至今天年龄

Excel常用的函数计算公式大全(一看就会)

EXCEL的常用计算公式大全 一、单组数据加减乘除运算: ①单组数据求加和公式:=(A1+B1) 举例:单元格A1:B1区域依次输入了数据10和5,计算:在C1中输入 =A1+B1 后点击键盘“Enter(确定)”键后,该单元格就自动显示10与5的和15。 ②单组数据求减差公式:=(A1-B1) 举例:在C1中输入 =A1-B1 即求10与5的差值5,电脑操作方法同上; ③单组数据求乘法公式:=(A1*B1) 举例:在C1中输入 =A1*B1 即求10与5的积值50,电脑操作方法同上; ④单组数据求乘法公式:=(A1/B1) 举例:在C1中输入 =A1/B1 即求10与5的商值2,电脑操作方法同上; ⑤其它应用: 在D1中输入 =A1^3 即求5的立方(三次方); 在E1中输入 =B1^(1/3)即求10的立方根 小结:在单元格输入的含等号的运算式,Excel中称之为公式,都是数学里面的基本运算,只不过在计算机上有的运算符号发生了改变——“×”与“*”同、“÷”与“/”同、“^”与“乘方”相同,开方作为乘方的逆运算,把乘方中和指数使用成分数就成了数的开方运算。这些符号是按住电脑键盘“Shift”键同时按住键盘第二排相对应的数字符号即可显示。如果同一列的其它单元格都需利用刚才的公式计算,只需要先用鼠标左键点击一下刚才已做好公式的单元格,将鼠标移至该单元格的右下角,带出现十字符号提示时,开始按住鼠标左键不动一直沿着该单元格依次往下拉到你需要的某行同一列的单元格下即可,即可完成公司自动复制,自动计算。 二、多组数据加减乘除运算: ①多组数据求加和公式:(常用) 举例说明:=SUM(A1:A10),表示同一列纵向从A1到A10的所有数据相加; =SUM(A1:J1),表示不同列横向从A1到J1的所有第一行数据相加; ②多组数据求乘积公式:(较常用) 举例说明:=PRODUCT(A1:J1)表示不同列从A1到J1的所有第一行数据相乘; =PRODUCT(A1:A10)表示同列从A1到A10的所有的该列数据相乘; ③多组数据求相减公式:(很少用) 举例说明:=A1-SUM(A2:A10)表示同一列纵向从A1到A10的所有该列数据相减; =A1-SUM(B1:J1)表示不同列横向从A1到J1的所有第一行数据相减; ④多组数据求除商公式:(极少用) 举例说明:=A1/PRODUCT(B1:J1)表示不同列从A1到J1的所有第一行数据相除; =A1/PRODUCT(A2:A10)表示同列从A1到A10的所有的该列数据相除; 三、其它应用函数代表: ①平均函数 =AVERAGE(:);②最大值函数 =MAX (:);③最小值函数 =MIN (:); ④统计函数 =COUNTIF(:):举例:Countif ( A1:B5,”>60”) 说明:统计分数大于60分的人数,注意,条件要加双引号,在英文状态下输入。

Excel函数详解解读

Excel 函数(按字母顺序列出) 函数名称类型和说明 ABS 函数数学和三角:返回数字的绝对值 ACCRINT 函数财务:返回定期支付利息的债券的应计利息ACCRINTM 函数财务:返回在到期日支付利息的债券的应计利息ACOS 函数数学和三角:返回数字的反余弦值 ACOSH 函数数学和三角:返回数字的反双曲余弦值ACOT 函数 数学和三角:返回数字的反余切值 ACOTH 函数 数学和三角:返回数字的反双曲余切值 AGGREGATE 函数数学和三角:返回列表或数据库中的聚合 ADDRESS 函数查找和引用:以文本形式将引用值返回到工作表的单个单元格 AMORDEGRC 函数财务:使用折旧系数返回每个记帐期的折旧值AMORLINC 函数财务:返回每个记帐期的折旧值 AND 函数逻辑:如果其所有参数均为TRUE,则返回TRUE ARABIC 函数 数学和三角:将罗马数字转换为阿拉伯数字 AREAS 函数查找和引用:返回引用中涉及的区域个数 ASC 函数文本:将字符串中的全角(双字节)英文字母或片假名更改为半角(单字节)字符 ASIN 函数数学和三角:返回数字的反正弦值 ASINH 函数数学和三角:返回数字的反双曲正弦值 ATAN 函数数学和三角:返回数字的反正切值 ATAN2 函数数学和三角:返回X 和Y 坐标的反正切值ATANH 函数数学和三角:返回数字的反双曲正切值 AVEDEV 函数统计:返回数据点与它们的平均值的绝对偏差平均值AVERAGE 函数统计:返回其参数的平均值 AVERAGEA 函数统计:返回其参数的平均值,包括数字、文本和逻辑值

函数名称类型和说明 AVERAGEIF 函数统计:返回区域中满足给定条件的所有单元格的平均值(算术平均值) AVERAGEIFS 函数统计:返回满足多个条件的所有单元格的平均值(算术平均值)。 BAHTTEXT 函数文本:使用?(泰铢)货币格式将数字转换为文本 BASE 函数数学和三角:将数字转换为具备给定基数(base) 的文本表示 BESSELI 函数工程:返回修正的贝赛耳函数In(x) BESSELJ 函数工程:返回贝赛耳函数Jn(x) BESSELK 函数工程:返回修正的贝赛耳函数Kn(x) BESSELY 函数工程:返回贝赛耳函数Yn(x) BETADIST 函数 兼容性:返回beta 累积分布函数 在Excel 2007 中,这是一个统计函数。 BETA.DIST 函数 统计:返回beta 累积分布函数 BETAINV 函数 兼容性:返回指定beta 分布的累积分布函数的反函数 在Excel 2007 中,这是一个统计函数。 BETA.INV 函数 统计:返回指定beta 分布的累积分布函数的反函数BIN2DEC 函数工程:将二进制数转换为十进制数 BIN2HEX 函数工程:将二进制数转换为十六进制数 BIN2OCT 函数工程:将二进制数转换为八进制数 BINOMDIST 函数 兼容性:返回一元二项式分布的概率 在Excel 2007 中,这是一个统计函数。 BINOM.DIST 函数 统计:返回一元二项式分布的概率 BINOM.DIST.RANGE 函数 统计:使用二项式分布返回试验结果的概率

excel表格常用的函数公式

e x c e l表格常用的函数公 式 Prepared on 22 November 2020

1、如何一次性去掉诸多超链接 选中所有的超链接,按住Ctrl+c再按Enter键,就取消的所有的超链。 2、如何在每行的下面空一行 如A1列有内容,我们需要在B1、C2单元格输入1,选中周边四格 ,然后向下拉,填充序列,然后在选取定位条件,选中空值,最后点击插入行,就行了。 3、删除一列的后缀

若A1为此,在B1单元格输入=LEFT(A1,LEN (A1)-4),然后下拉填充公式。 删除前缀则相反RIGHT 4、把多个单元格串成一句 运用=CONCATENATE(“A1”,“B2”,“C2”),比如A1,B1单元格分别是8,个,我们可在C1单元格输入=CONCATENATE("我有",A1,B1,"苹果"),随即C1单元格显示我有8个苹果。 5、数据分类汇总后按需排序 在数据分类汇总后,我们选择左侧2,把数据折叠起来,然后选中你按需排序规则的那行,点击排序即可。 6、分类汇总后,只复制汇总的项 在把分类汇总后的数据折叠后(只显示分类汇总项),然后选中这些,定位——可见单元格——复制——黏贴即可。 7、【Vlookup函数】查找制定目标的相对应数值 公式:B13=VLOOKUP(A13,$B$2:$D$8,3,0) A13是所需要的值对应的属性(姓名);$B$2:$D$8是指查找的范围从B2开始一直到D8的区间范围内;3是指查找范围的第三列,即查找值所在的列;0表示精确查找或者也可填写false。 8、【sumif函数】在一定条件下求和 G2=sumif(D2:D8,”>=95”)

巴尔扎克作品经典语录大全100句

巴尔扎克作品经典语录大全100句 1、男子的才对于德而言,就和美貌之于女子差不多:能给人以希望。——巴尔扎克《莫黛斯特·米尼翁婚约》 2、苦难好比一道神奇的符箓,能加强我们的天性,使猜忌与凶恶的人愈加猜忌愈加凶恶,慈悲的人愈加慈悲。——巴尔扎克《夏倍上校》 3、真正的考验是在痛苦和幸福上。当两个人通过了这两种人生的考验,在这过程中每人的优缺点都暴露无遗,也观察了彼此的性格时,他们就可以手携手一直走到坟墓了。——巴尔扎克《莫黛斯特·米尼翁婚约》 4、在爱情方面,别有用心的虚假总比真面目可爱,就因为此,才有许多男人肯在一般手段高明的女骗子身上挥金如土。——巴尔扎克 5、人类所有的力量,只是耐心加上时间的混合。所谓强者是既有意志,又能等待时机。守财奴的生活,便是不断的运用这种力量为自我效劳。他只依赖两种情感:自尊心与利益。但利益既是自尊心的实际表现并且是真正优越的凭据,所以自尊心与利益是一物的两面,都从自私自利来的。这种人物涉及所有的情感,可以说集情感之大成,而我们个个人都跟他们一脉相通。哪有什么全无欲望的人?而没有金钱,哪个欲望够满足?——巴尔扎克《欧叶妮·葛朗台》 6、精神生活与肉体生活一样,有呼也有吸:灵魂吸收另一颗灵魂的感情来充实自己,然后以更丰富的感情送回给人家。人与人之间要没有这点美妙的关系,心就没有了生机:它缺少空气,它会受难,枯萎。——巴尔扎克《欧叶妮·葛朗台》 7、逆境不就是命运的试金石吗? ——巴尔扎克《高

老头》8、"L'amour n'est pas seulement un sentiment, il est aussi un art. 爱不光是一种感情,也是一门艺术。- Honoréde Balzac 巴尔扎克-——巴尔扎克《网络名言集》"9、感情等于才分。感受是了解的对手,正如行动是思维的抗衡。一个有天才的朋友可以通过友情、领会,和他并驾齐驱。一个常人有感情作基础,就可以比倒最伟大的艺术家。这说明女人为什么爱着一些“蠢才”。——巴尔扎克10、到处是真苦难,假欢喜。——巴尔扎克《高老头》11、哪里有穷困,哪里就有苦难。苦难,穷困,蓄势极猛,苦了,穷了,斯滥矣,大权在握,就会滥用,其理自同。——巴尔扎克《乡村医生》12、做点好事,待人要仁慈、宽厚;总之,用你的谦虚来避免厄运吧。——巴尔扎克13、苦难对于人生是一块垫脚石……对于能干的人是一笔财富,对于弱者是个万丈深渊。——巴尔扎克14、目的高尚,会使所做的事情都同样高尚。——巴尔扎克15、实笃一清如水的生活,诚实不欺的性格,不论身处哪个阶级,就算心术最坏的人,也会对之肃然起敬。——巴尔扎克16、长命也许不够好,美好的生命却够长。——巴尔扎克17、人们有多少需求,就能创造多少财富。——巴尔扎克18、艺术就是用最小的面积,惊人地集中最大量思想。——巴尔扎克19、他不断地处于与人奋斗、与天地奋斗之中,没有功夫去尽情卖弄。只有花花公子才会大肆卖弄,迫不及待地将转瞬即逝的一季庄稼收割下来,那种自尊与不管是什么东西,凡从它手下经过就要抽税的海关相差无几。——巴尔扎克20、年轻时费过力气学到的东西,即使是无聊对我们也有用。——巴尔扎克21、拐弯抹角的路成不了

Excel表格函数公式大全

E x c e l表格函数公式大全-标准化文件发布号:(9456-EUATWK-MWUB-WUNN-INNUL-DDQTY-KII

目录按顺序整理,便于打印学习 EXCEL函数大全 (3) 1.数据库和清单管理函数 (3) 2.日期和时间函数 (3) 3.DDE 和外部函数 (4) 4.工程函数 (4) Excel2003常用函数 (6) 5.ABS函数 (6) 6.AND (7) 7.AVERAGE (7) 8.CELL (8) 9.CHOOSE (8) 10.COLUMN 函数 (9) 11.CONCATENATE函数 (9) 12.COUNT (10) 13.COUNTA (10) 14.COUNTIF (10) 15.DATEDIF函数 (11) 16.DATE函数 (11) 17.DAY函数 (12) 18.DCOUNT函数 (12) 19.FIND (13) 20.FREQUENCY函数 (13) 21.IF (13) 22.INDEX (14) 23.INT (15) 24.ISERROR函数 (16) 25.ISEVEN (16) 26.ISODD (17) https://www.360docs.net/doc/7113029382.html,RGE (17) 28.LEFT或LEFTB (17) 29.LEN或LENB (18) 30.LOOKUP (18) 31.MATCH (19) 32.MAX (20) 33.MIN (21) 34.MEDIAN (21) 35.MID或MIDB (22) 36.MOD函数 (22) 37.MONTH函数 (23) 38.NOW (23) 39.OR (24) 40.RAND (24) 41.RANK函数 (25) 42.RIGHT或RIGHTB (25) 43.ROUND (26) 44.SUBTOTAL函数 (26) 45.SUM (27) 46.SUMIF (27) 47.TEXT (28) 48.TODAY (29) 49.VALUE (29) 50.VLOOKUP (30) 51.WEEKDAY函数 (31) 关于EXCEL中函数COUNT的用法 (31)

巴尔扎克——素材

巴尔扎克出生于一个法国大革命后致富的资产阶级家庭,法科学校毕业后,拒绝家庭为他 选择的受人尊敬的法律职业,而立志当文学家。为了获得独立生活和从事创作的物质保障,他曾试笔并插足商业,从事出版印刷业,但都以破产告终。这一切都为他认识社会、描写 社会提供了极为珍贵的第一手材料。他不断追求和探索,对哲学、经济学、历史、自然科学、神学等领域进行了深入研究,积累了极为广博的知识。 1822年正当巴尔扎克倍受冷遇,痛苦绝望的时刻,结识了贝尔尼夫人。贝尔尼夫人的母亲 曾是王后的侍女,对宫廷的生活,交际的秘密和妇女的命运十分熟悉。贝尔尼夫人比巴尔 扎克大22岁,具有完美的娇柔感,高雅的谈吐,沁人肺腑的同情心,以及慈祥的母爱。这一切深深吸引着巴尔扎克,使巴尔扎克感受到从小没有领略过的母爱般的温情。她对他的 一生产生了重大的影响。巴尔扎克把她称为他的母亲、朋友、家属、伴侣和顾问。[1] 1829年,巴尔扎克完成长篇小说《朱安党人》。历史小说《朱安党人》(1829)是巴尔扎 克用真名发表的 奥诺雷·德·巴尔扎克 第一部作品,描述1800年法国布列塔尼在保皇党煽动下发生的反对共和国政府的暴动。作者赋予英勇的共和国军人以应有的光彩,但也大大美化了朱安党首领孟多兰侯爵,表现出 他当时对贵族的同情。为了写这部小说,他曾细心研究有关暴动的历史文献,亲自去布列 塔尼调查山川形势和农民生活,访问暴动的目击者和参加者,还从友人柏尔里公爵夫人那 里收集许多关于朱安党人的掌故。从写神怪小说过渡到写历史小说,是巴尔扎克走向批判 现实主义的第一个重要步骤。他在《朱安党人》中描写的不是古代历史,而是属于当代社 会生活范畴的重要事件。着重反映当代社会生活,正是巴尔扎克日后所写的《人间喜剧》 的一个特点。 从1829年写《朱安党人》起,巴尔扎克的创作开始进入成熟时期,即《人间喜剧》时期(1829-1848)。在三、四十年代,他除致力于文艺创作以外,还出入巴黎上流社会的沙龙,为几种报刊撰稿,他接触的生活面非常广泛。[2] 巴尔扎克从这时期起,就在现实主义理论方面进行深入探索。他认为小说家必须面向现实 生活,使自己成为当代社会的风俗史家;又认为小说家的任务不仅在于摹写社会现象,还 须阐明产生这些现象的原因,指出人物、 影视剧中奥诺雷·德·巴尔扎克 欲念和事件背后的意义。在塑造人物的问题上,他强调特性,也强调共性;他说诗人的使 命在创造典型,使典型个性化,个性典型化;又说典型人物应该把那些多少和他类似的人 的性格特点集于一身。他还强调艺术必须为社会服务;认为艺术家不仅描写罪恶和德行, 而且要指出其中的教育意义;艺术家必须同时是道德家和政治家。 1830年4月,巴尔扎克《人间喜剧》中《私人生活场景》两卷出版了。然而,他深深地为 法国文学创作者的处境担忧。虽然法国于1791年颁布的《表演法令》和1793年颁布的

Excel表格乘法函数公式

更多课程传送门:点这里 Excel表格乘法函数公式 时间:2011-04-05 来源:Word联盟阅读:21051次评论18条 在Excel表格中,我们常常会利用Excel公式来统计一些报表或数据等,这时就少不了要用到加、减、乘、除法,在前面我们已经详细的讲解了Excel求和以及求差公式使用方法。那么我们又如何利用公式来对一些数据进行乘法计算呢?怎样快速而又方便的来算出结果呢?下面Word联盟就来教大家一步一步的使用Excel乘法公式! 我们先从简单的说起吧!首先教大家在A1*B1=C1,也就是说在第一个单元格乘以第二个单元格的积结果会显示在第三个单元格中。 1、A1*B1=C1的Excel乘法公式 ①首先,打开表格,在C1单元格中输入“=A1*B1”乘法公式。 ②输入完毕以后,我们会发现在 C1 单元格中会显示“0”,当然了,因为现在还没有输入要相乘的数据嘛,自然会显示0了。

③现在我们在“A1”和“B1”单元格中输入需要相乘的数据来进行求积,如下图,我分别在A1和B1单元格中输入10和50进行相乘,结果在C1中就会显示出来,等于“500”。 上面主要讲解了两个单元格相乘求积的方法,但是在我们平常工作中,可能会遇到更多数据相乘,下面主要说说多个单元格乘法公式运用,如:

“A1*B1*C1*D1”=E1。 2、Excel中多个单元格相乘的乘法公式 ①在E1单元格中输入乘法公式“=A1*B1*C1*D1”。 ②然后依次在A1、B1、C1、D1中输入需要相乘的数据,结果就会显示在“E1”中啦!

看看图中的结果是否正确呀!其实,这个方法和上面的差不多,只不过是多了几道数字罢了。 因为在工作中不止是乘法这么简单,偶尔也会有一些需要“加减乘除”一起运算的时候,那么当遇到这种混合运算的时候我们应当如何来实现呢?这里就要看你们小学的数学有没学好了。下面让我们一起来做一道小学时的数学题吧! 3、Excel混合运算的乘法公式,5加10减3乘2除3等于多少? 提示:加=+,减=-,乘=*,除=/。 ①首先,我们要了解这个公式怎么写,“5+10-3*2/3”这是错误的写法,正确写法应该是“(5+10-3)*2/3”。 ②好了,知道公式了,我们是不是应该马上来在Excel中的“F1”中输入“=(A1+B1-C1)*D1/E1”。 ③然后依次在A1、B1、C1、D1、E1中输入需要运算的数据。

Excel函数应用解读

目录 第1章Excel函数基础 1 1.1Excel 2003的基础知识 1 1.1.1工作簿、工作表与单元格 1 1.1.2函数与参数 3 1.2单元格的引用 3 1.2.1A1和R1C1引用样式 3 1.2.2绝对引用、相对引用和混合引用 5 1.2.3三维引用8 1.3使用公式10 1.3.1公式的基本元素10 1.3.2常见运算符10 1.3.3公式的使用12 1.3.4为公式命名14 1.4使用函数16 1.5函数的分类19 第2章数学与三角函数21 1.返回数字的绝对值:ABS21 2.返回数字的反余弦值:ACOS21 3.返回数字的反双曲余弦值:ACOSH22 4.返回数字的反正弦值:ASIN22 5.返回数字的反双曲正弦值:ASINH23 6.返回数字的反正切值:ATAN24 7.返回给定坐标值的反正切值:ATAN224 8.返回数字的反双曲正切值:ATANH25 9.按条件向上舍入:CEILING26 10.计算组合数:COMBIN27 11.计算余弦值:COS28 12.计算数字的双曲余弦值:COSH28 13.将弧度转换为度:DEGREES29

14.将数字舍入为偶数:EVEN29 15.返回e的n次幂:EXP30 16.计算数字的阶乘:FACT31 17.计算数字的双倍阶乘:FACTDOUBLE31 18.按条件向下舍入:FLOOR32 19.返回最大公约数:GCD33 20.将数字向下舍入取整:INT34 21.返回最小公倍数:LCM34 22.返回数字的自然对数:LN35 23.返回指定底数的对数:LOG35 24.返回以10为底的对数:LOG1036 25.返回数组的矩阵行列式值:MDETERM36 26.返回数组矩阵的逆矩阵:MINVERSE37 27.返回矩阵的乘积:MMULT39 28.求两数的余数:MOD39 29.返回按指定基数舍入数值:MROUND40 30.返回和的阶乘与阶乘乘积的比值:MULTINOMIAL41 31.返回舍入奇数:ODD42 32.返回数学常量:PI43 33.返回数字的乘幂:POWER43 34.返回乘积值:PRODUCT44 35.返回商的整数部分:QUOTIENT45 36.返回弧度值:RADIANS45 37.返回随机数:RAND46 38.返回指定两数之间的随机数:RANDBETWEEN46 39.转换成文本形式罗马数字:ROMAN47 40.返回按指定位数四舍五入的数字:ROUND48 41.向下舍入数字:ROUNDDOWN48 42.向上舍入数字:ROUNDUP49 43.返回幂级数之和:SERIESSUM50 44.返回数字的符号:SIGN50

外国文学史 巴尔扎克汇编

第五节巴尔扎克与《高老头》分析 一、巴尔扎克简介(1799-1850) 1、《人间喜剧》的创作:《风俗研究》《哲理研究》《分析研究》,是一部关于19世纪法国的“包罗万象的社会史”。 (1)主题上要写出“一个完整的社会”,艺术上要呈现一个“统一、独创、新鲜的整体”。 (2)主题思想:反映出资产阶级取代贵族阶级的时代潮流。 A、资产阶级的发迹史。《赛查·皮罗托盛衰记》《欧也妮·葛朗台》《纽沁根银行》 B、贵族阶级的衰亡史。《古物陈列室》 C、金钱成为社会的轴心,操纵着社会关系,这不仅反映在广义的政界、社交界等社会层面,而且反映在最亲密的家庭关系中。 (3)艺术特征: A、艺术形式的多样化和富于变化,或客观描述,或转述故事,或深入人们的情感中探讨。 B、在典型环境中塑造具有典型意义的人物形象,人物性格与物质化的社会环境水乳交融。 C、作品文本的相互交织。《人间喜剧》的一部作品中的人物形象会在多部作品中出现,人物的命运具有时间上的连续性,因而众多作品相互勾连,构成一部完整的社会生活的记录。如《高老头》中鲍赛昂夫人的凄凉结局、拉斯蒂涅的飞黄腾达。 D、写实与幻想交相辉映,突出金钱统治下的法国社会的疯狂喧嚣。《萨拉辛》

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