GMST
Ledi Hoxha
26 February 2019
Load the necessary packages:
library(ncdf4);
library(ncdf4.helpers);
library(maps);
library(RColorBrewer)
library(lattice)
library(latticeExtra)Set working directory:
setwd("C:\\Users\\Ledi\\Documents\\FunR")- read HadCRUT data: available from: # https://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/gridded_fields/HadCRUT.4.6.0.0.median_netcdf.zip
nc <- nc_open(file.path(getwd(),"HadCRUT.4.6.0.0.median.nc")); # open netcdf file
tobs <- ncvar_get(nc,"temperature_anomaly"); # load temperature anomaly
ts <- ncvar_get(nc,"time"); # load the time
lat <- ncvar_get(nc,"latitude") # load the latitude
lon <- ncvar_get(nc,"longitude") # load the longitudeGet the units:
tunits<-ncatt_get(nc,"time",attname="units")tunits = “days since 1850-1-1 00:00:00”
tustr<-strsplit(tunits$value, " ")Check the data:
date<-as.character(as.Date(ts,origin=unlist(tustr)[3]))Flag missing data:
tobs[t<-90] = NA
tobs[t>490] = NALoad global mean surface temperature data from giss temp: https://data.giss.nasa.gov/gistemp/
gmst = read.csv(file.path(getwd(),'global_temps_monthly.csv'),skip = 1,header=TRUE)Annual avarage:
gmst <- data.frame(Year = gmst$Year, t = rowMeans(gmst[, 2:13], na.rm = T))Visualisation of global mean temperature data:
plot(gmst,type="l",col="red",
main="GMST",xlab="Years",ylab="T Anomaly")
legend("topleft",
legend=c("GIS TEMP"),
col= c("red"), lty=1,lwd=1,bty="n")
- Create annually averaged gridded data from HadCRUT. 3a. visualize the data.
Pick out an index:
year_index <- which(date=="1901-06-16")
mapmat= tobs[,,year_index];
int=seq(-6,6,length.out = 11);colors <- rev(brewer.pal(10, "RdYlBu"))
rgb.palette <- colorRampPalette(colors)
filled.contour(lon, lat, mapmat, color.palette=rgb.palette, levels=int,
plot.title=title(main=paste0('HadCRUT Anom. in ', date[year_index]),
xlab="Latitude",ylab="Longitude", cex.lab=1.5),
plot.axes={axis(1, cex.axis=1.5);
axis(2, cex.axis=1.5);
map('world', add=TRUE);
grid()},
key.title=title(main="[oC]"),
key.axes={axis(4, cex.axis=1.5)})
Repeat the same for a different date:
year_index2 <- which(date=="2017-08-16")
mapmat= tobs[,,year_index2];
int=seq(-6,6,length.out = 11)
colors <- rev(brewer.pal(10, "RdYlBu"))
rgb.palette <- colorRampPalette(colors)
filled.contour(lon, lat, mapmat, color.palette=rgb.palette, levels=int,
plot.title=title(main=paste0('HadCRUT Anom. in ', date[year_index2]),
xlab="Latitude",ylab="Longitude", cex.lab=1.5),
plot.axes={axis(1, cex.axis=1.5);
axis(2, cex.axis=1.5);
map('world', add=TRUE);
grid()},
key.title=title(main="[oC]"),
key.axes={axis(4, cex.axis=1.5)})
Pre-allocate a blank array:
allyears <- format(as.Date(date, format="%Y-%m-%d"),"%Y")
years <- unique(allyears)tann <- array(NA,c(dim(tobs)[1:2],length(years)))
for (i in 1:length(years)){
tann[,,i]<-apply(tobs[,,allyears==years[i]],c(1,2),mean,na.rm=TRUE)
}Creating a global mean surface temperature timeseries. 4a. Ignorant averaging.
Dimensions of tann are long x lat x year.
To get a zonal average want to average so that the output is lat x year.
To get a meridional avarage, want output to be lon x year.
merid_means<-apply(tann,c(1,3),mean,na.rm=TRUE)
mean_merid_means <- apply(merid_means,2,mean,na.rm=TRUE)Calculate the mean of the zonal means:
zonal_means<-apply(tann,c(2,3),mean,na.rm=TRUE)
mean_zonal_means <- apply(zonal_means,2,mean,na.rm=TRUE)Plot the GIS GMST, the mean of the meridional means, and mean of zonal means on one plot:
plot(gmst,type="l",col="red",
main="GMST",xlab="Years",ylab="T Anomaly")
lines(years, mean_merid_means, type="l", col="blue")
lines(years, mean_zonal_means, type="l", col="green")
legend("topleft",
legend=c("GIS TEMP", "MERID MEAN", "ZONAL MEAN"),
col= c("red", "blue", "green"), lty=1,lwd=1,bty="n")
Plot the zonal and meridional means as differences relative to the GIS temp:
Note: GIS runs from 1880 to 2017; hadcrut from 1850 to 2017year1880<- which(years==1880)
year1850 <- which(years==1850)
year1880## [1] 31years[year1880:168]## [1] "1880" "1881" "1882" "1883" "1884" "1885" "1886" "1887" "1888" "1889"
## [11] "1890" "1891" "1892" "1893" "1894" "1895" "1896" "1897" "1898" "1899"
## [21] "1900" "1901" "1902" "1903" "1904" "1905" "1906" "1907" "1908" "1909"
## [31] "1910" "1911" "1912" "1913" "1914" "1915" "1916" "1917" "1918" "1919"
## [41] "1920" "1921" "1922" "1923" "1924" "1925" "1926" "1927" "1928" "1929"
## [51] "1930" "1931" "1932" "1933" "1934" "1935" "1936" "1937" "1938" "1939"
## [61] "1940" "1941" "1942" "1943" "1944" "1945" "1946" "1947" "1948" "1949"
## [71] "1950" "1951" "1952" "1953" "1954" "1955" "1956" "1957" "1958" "1959"
## [81] "1960" "1961" "1962" "1963" "1964" "1965" "1966" "1967" "1968" "1969"
## [91] "1970" "1971" "1972" "1973" "1974" "1975" "1976" "1977" "1978" "1979"
## [101] "1980" "1981" "1982" "1983" "1984" "1985" "1986" "1987" "1988" "1989"
## [111] "1990" "1991" "1992" "1993" "1994" "1995" "1996" "1997" "1998" "1999"
## [121] "2000" "2001" "2002" "2003" "2004" "2005" "2006" "2007" "2008" "2009"
## [131] "2010" "2011" "2012" "2013" "2014" "2015" "2016" "2017"merid_means[year1880:168]## [1] 0.015859008 -0.113658805 0.045033487 -0.191485671 -0.314136781
## [6] -0.367997795 -0.843229599 -0.619653930 -0.762922705 0.053306513
## [11] -0.432949510 0.061192007 -0.509140567 -0.281407002 -0.003170180
## [16] -0.158106353 -0.359635223 -0.574381120 -0.226700453 0.247145196
## [21] -0.094131382 -0.476865010 -0.292555586 0.165821457 0.014278571
## [26] -0.152581270 -0.274938188 -0.202398700 -0.286091771 -0.492952027
## [31] -0.310045857 -0.145487936 0.285891225 -0.148617491 0.094751841
## [36] 0.315241910 0.041411316 0.154607928 0.638557178 -0.123477879
## [41] -0.630748417 -1.133595619 -0.561039813 -0.733743883 -0.515140734
## [46] -0.720536169 -0.345388525 -0.095280324 -0.057890887 -0.589819980
## [51] -0.251933534 0.237454440 -0.259872660 0.323816282 -0.632795245
## [56] -0.436226184 -0.150800257 -0.020030238 0.066819035 -0.047359703
## [61] -0.449192484 -0.441801499 -0.197559208 -0.379473193 -0.222055690
## [66] -0.542989723 -0.328647702 -0.230537231 -0.294841128 0.052173599
## [71] -0.186742047 -0.185113704 0.132129410 0.188139430 -0.034646626
## [76] -0.183710160 -0.378200167 -0.441020733 -0.842646120 -0.299258332
## [81] 0.211268913 0.059353772 0.044771322 0.422436999 0.130065574
## [86] -0.070905015 0.245927978 -0.008862256 -0.776718610 -0.477192240
## [91] -0.332012746 -1.039243659 -0.252975312 -0.228197258 -0.326782186
## [96] 0.086638802 -0.376009146 -0.560748763 -0.785938399 -0.651250665
## [101] -0.174491655 -0.330403016 0.053996742 -0.144795445 -0.139717361
## [106] 0.212022866 -0.052821385 0.149384317 -0.543264228 -0.183300039
## [111] 0.104401269 -0.314170341 -0.050764014 -0.935934230 -0.916268319
## [116] -0.223459736 0.066285172 -0.735457022 -1.267891344 -0.461576599
## [121] -0.360228888 0.049003725 -0.212539162 -0.347084387 -0.537711834
## [126] -0.368655648 -0.037284551 -0.137087448 -0.522239484 -0.527300150
## [131] -0.414555058 -0.502935970 -0.639767353 -0.580369833 -0.427173339
## [136] -0.603951462 -0.352515968 -0.658661335zonal_means[1880:168]## [1] -0.3450987394 -0.2397433458 -0.3643823012 -0.3870130916
## [5] -0.4364269972 NaN NaN NaN
## [9] NaN 0.4531621933 -1.5041078497 -1.3550889338
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## [657] NaN NaN -4.8986997604 -1.0760575706
## [661] 0.0767467414 -0.0095972386 -0.4961185178 -0.7260606575
## [665] -0.7548704404 -0.1300780530 -0.1041685949 -0.0003689891
## [669] -0.2796107448 -0.3401338819 -0.3674515650 -0.2690443772
## [673] -0.2782714812 -0.3370194969 -0.1148845972 -0.1724340853
## [677] -0.3228777210 -0.3365596595 -0.3121870064 -0.1888467054
## [681] -0.3264387360 -0.1599803275 -0.2394712006 -0.2168262873
## [685] 0.0719892880 0.6301591786 1.0816221635 NaN
## [689] NaN NaN NaN NaN
## [693] NaN NaN NaN -1.7283845040
## [697] -1.4842349810 -0.4801740756 -0.0847744117 -0.1892948325
## [701] -0.2639375110 -0.1421378480 0.0160713408 0.0476030041
## [705] -0.1891426946 -0.2360666003 -0.2837531871 -0.3821719722
## [709] -0.2111582076 -0.3205042802 -0.2459985059 -0.1972382726
## [713] 0.0020388016 -0.0890223820 -0.1246650525 -0.0695223565
## [717] -0.1096947094 -0.0827305384 -0.3469111137 -0.0335941459
## [721] 0.1872981603 1.0225935636 NaN NaN
## [725] NaN NaN NaN NaN
## [729] NaN NaN -3.0404052019 -2.9474355185
## [733] -1.2221528071 -1.0086062132 -0.5070509254 -0.6042445335
## [737] -0.5197757631 -0.2467023987 -0.1029686732 -0.2229416082
## [741] -0.1128576351 -0.2635835928 -0.0173475028 -0.0553992514
## [745] -0.0228243315 0.1485535199 0.0772697501 -0.0896653715
## [749] -0.0012715613 0.0466123382 -0.1042601917 -0.1638441859
## [753] -0.2385019741 -0.1735061426 -0.4998663865 -0.2913999328
## [757] 0.3106452547 1.2816649745 1.0491521557 NaN
## [761] NaN NaN NaN NaN
## [765] NaN NaN -1.5237787142 -0.5332627391
## [769] -0.4461545337 -0.0954890763 0.0311445715 -0.2219021708
## [773] -0.6763959169 -0.2422084955 0.0041051866 -0.3236625802
## [777] -0.2382857932 -0.4222913639 -0.1960136097 -0.0265471998
## [781] -0.0343316001 -0.0703201896 -0.1896468889 -0.1670794738
## [785] -0.0040008857 -0.0843752470 -0.1411480166 -0.1766712113
## [789] -0.1735513030 -0.2143913056 -0.3955148503 -0.1508978017
## [793] 0.1074961745 0.6463711111 1.2381510735 NaN
## [797] NaN NaN NaN NaN
## [801] NaN NaN -0.8791440502 -0.9372991646
## [805] -0.9521046717 -0.5277023391 -0.4602091370 -0.6872119704
## [809] -0.7359427841 -0.0952266816 -0.0348320634 0.1541943328
## [813] 0.1196022826 -0.2345753508 0.1723289011 -0.0984329410
## [817] -0.2877964297 -0.5374462070 -0.5780073319 -0.2156156439
## [821] -0.0830850108 -0.0795459850 -0.2348096431 -0.0686281724
## [825] -0.1422028873 -0.0741666903 -0.3363711268 -0.2966838350
## [829] 0.3876700896 1.0882632192 NaN NaN
## [833] NaN NaN NaN NaN
## [837] NaN 1.5683877468 0.3035521772 0.5733325695
## [841] 0.1824652610 0.1960611428 0.3677433991 0.4916768531
## [845] 0.0568506273 -0.2014444193 -0.1567389790 -0.1537534772
## [849] -0.0324773779 0.1431941320 0.0506065294 0.4562882200
## [853] 0.3722249932 0.2721498783 0.5745483691 0.5782531674
## [857] 0.3889162066 0.1515370907 0.0476537808 -0.0202405669
## [861] -0.2541803777 -0.4135368193 -0.5744225375 -0.3767124967
## [865] 0.0125446095 0.9297015936 1.8666543365 NaN
## [869] NaN NaN NaN NaN
## [873] NaN -0.0912745148 -0.1079256364 -1.2797551883
## [877] -0.5867779905 -0.6476007128 -0.1937604438 -0.0246244315
## [881] 0.2609462545 0.1096372873 0.1663237158 0.2419387845
## [885] 0.1108017657 -0.0550630044 0.1420574913 0.2402083090
## [889] 0.1834077611 0.3877714323 0.5443075270 0.2229681344
## [893] 0.4862983234 0.3288135946 -0.0386678971 -0.0805150461
## [897] -0.3868565882 -0.3987046740 -0.5279348388 -0.2665277969
## [901] 0.2316072304 0.3807586696 2.2470707893 NaN
## [905] NaN NaN NaN NaN
## [909] NaN 0.5505373964 0.2473184019 -0.9370831406
## [913] -0.5476379607 -0.4703285875 -0.5062665140 -0.3294951047
## [917] -0.5921384863 0.1818373938 -0.1475242999 -0.1611982678
## [921] -0.3951095666 -0.4459152861 -0.0539506348 -0.2154501668
## [925] -0.3422089176 -0.3505422427 -0.2791644356 -0.3847217235
## [929] -0.3065119841 -0.4703441780 -0.1895821255 -0.1330679727
## [933] -0.4465059978 -0.3241401991 -0.5842848718 -0.4004994119
## [937] 0.1479468724 0.3140991356 NaN NaN
## [941] NaN NaN NaN NaN
## [945] NaN 0.8793428540 -0.9872630412 -1.1888831475
## [949] -1.0380959026 -0.9200360551 -0.4285371641 -0.5856881826
## [953] -0.8296885390 -0.3736083677 -0.0544382045 -0.2518610627
## [957] -0.1619608202 -0.4162468960 -0.6487716152 -0.3582429793
## [961] -0.2959331576 -0.2797772281 -0.2871841611 -0.2088165787
## [965] -0.1270680757 -0.0966850066 -0.2153699650 -0.0989667133
## [969] -0.2801724515 -0.2243371256 -0.3071458075 -0.3178980169
## [973] 0.1410508596 0.1975216656 2.4743952751 NaN
## [977] NaN NaN NaN NaN
## [981] NaN 0.0484191328 -1.0187584116 -0.7914351721
## [985] -0.7569139696 -0.4716285912 0.1870928871 0.2400450759
## [989] -0.4125502627 -0.0896180152 0.0294341714 -0.0976055534
## [993] -0.3743767437 -0.3892408142 -0.6463961502 -0.5332141939
## [997] -0.5385046537 -0.7012856204 -0.7644373115 -0.6563049577
## [1001] -0.4880277098 -0.3145975357 -0.4638602869 -0.2085324148
## [1005] -0.3411435929 -0.2592062058 -0.3554938062 -0.3279787036
## [1009] -0.2292472650 -0.1097095223 -0.4773904204 -1.1632208824
## [1013] NaN NaN NaN NaN
## [1017] NaN NaN -1.2033895507 -0.7013362419
## [1021] -0.7748901972 -0.1940283276 -0.0411956774 0.1189543560
## [1025] -0.2991547161 -0.1218965280 -0.0600430826 -0.0418613736
## [1029] -0.2712175562 -0.5634979264 -0.3629235925 -0.4059037523
## [1033] -0.2781727151 -0.2362607386 -0.3404232452 -0.4164264280
## [1037] -0.4521516274 -0.5102411046 -0.2872426537 -0.0678699167
## [1041] -0.2807920507 0.0252733369 -0.2657260542 -0.1210138788
## [1045] -0.5704658912 -0.1854864767 -0.2757589817 NaN
## [1049] NaN NaN NaN NaN
## [1053] NaN NaN -1.4938158592 -0.3446311195
## [1057] -0.3734266646 0.6854862599 0.4339882804 0.2884519079
## [1061] -0.1946640035 -0.2836950282 -0.2716940167 -0.3982876511
## [1065] -0.2739796527 -0.2778951207 -0.2822568607 -0.1878985884
## [1069] -0.1839094888 -0.2164571626 -0.2789742711 -0.5050892005
## [1073] -0.1609312851 -0.5720864938 -0.3544824491 -0.4023497315
## [1077] -0.2106190466 0.0284577442 0.0819650406 -0.1334445882
## [1081] -0.3016493131 -0.1155968921 NaN NaN
## [1085] NaN NaN NaN NaN
## [1089] NaN 0.0495396523 -0.9031655692 -1.4435809853
## [1093] -1.5101115001 -1.2315751077 -0.3766696409 -0.7975936049
## [1097] -0.6842539429 -0.2419434929 0.0599893776 -0.0523571508
## [1101] 0.2461811348 -0.1236276881 -0.1506895729 -0.1957940561
## [1105] -0.2142682316 -0.1648006907 -0.0243198405 -0.1216289710
## [1109] -0.1864894763 -0.1299654928 -0.2087182378 -0.3172988361
## [1113] -0.2642814532 -0.1250001393 -0.2777626391 -0.5919317614
## [1117] -0.1343136983 -0.2555113924 0.1495307088 NaN
## [1121] NaN NaN NaN NaN
## [1125] NaN NaN -1.3106479213 -0.5062396006
## [1129] 0.0906587372 -0.8049006577 -0.4371861036 -0.5610795329
## [1133] -0.8292188585 -0.0877132590 0.0483291064 -0.1614126206
## [1137] 0.1114250632 0.0700324619 0.1031404461 0.1734557525
## [1141] -0.2817180824 -0.5060052576 -0.3687975390 -0.3047445753
## [1145] -0.4414278863 -0.2828823554 -0.2528825085 -0.0575894822
## [1149] -0.0993591359 -0.0953353561 -0.3462484770 -0.3958782667
## [1153] -0.2223089352 -0.3461803889 -1.1462328434 NaN
## [1157] NaN NaN NaN NaN
## [1161] NaN NaN NaN -0.9068776307
## [1165] -0.8605639652 -0.3597302683 0.4499968316 0.2608529960
## [1169] -0.4326759160 -0.7794279806 -0.2873758228 -0.1898428059
## [1173] 0.0737389880 0.2914627909 -0.0325250792 0.0977651172
## [1177] -0.0955463597 -0.2460556294 -0.3691176576 -0.2504901529
## [1181] -0.0910853658 -0.1734802915 -0.0300481852 -0.1559555128
## [1185] -0.4032234964 -0.4000523934 -0.6844308009 -0.3599839547
## [1189] -0.0197145464 0.0583910015 0.9665241838 NaN
## [1193] NaN NaN NaN NaN
## [1197] NaN NaN NaN -0.5270235725
## [1201] -0.8785836813 -0.1855022620 0.2376685366 0.3903454304
## [1205] 0.2095216865 -0.0249156440 -0.2111031271 -0.2450159282
## [1209] 0.2621884237 -0.0114098887 -0.4620060597 -0.0142253459
## [1213] -0.0241888538 0.1243278104 0.1158332339 -0.0365010048
## [1217] 0.0089900539 0.1318766637 -0.0046345262 -0.2477004574
## [1221] -0.5008127331 -0.4188492098 -0.4347763032 -0.0060136709
## [1225] 0.7663096387 0.1902116229 NaN NaN
## [1229] NaN NaN NaN NaN
## [1233] NaN NaN NaN -1.9934543362
## [1237] -1.9935234468 -1.5447235930 -0.5687460130 -0.2757992618
## [1241] -0.1083763273 -0.4197740752 -0.0460982462 -0.3799296483
## [1245] -0.3051093704 -0.2295838945 -0.1061339626 -0.2466019318
## [1249] -0.2466924834 -0.2547374127 -0.2400443137 -0.1349956164
## [1253] -0.0007493972 -0.2319858580 -0.1976222521 -0.2707556615
## [1257] -0.4097059710 -0.3880746956 -0.2733496495 -0.5615716739
## [1261] -0.4634054754 -0.6437671557 NaN NaN
## [1265] NaN NaN NaN NaN
## [1269] NaN NaN NaN -1.1340300068
## [1273] -1.9607718540 -0.5169585925 0.1019720085 0.4402361230
## [1277] 0.1604244674 -0.0720584833 0.1309855880 -0.4570451499
## [1281] -0.2252567482 -0.2451330233 -0.0149426121 -0.4150031628
## [1285] -0.5817138613 -0.4935354280 -0.1865778030 0.0642242019
## [1289] -0.0450665763 -0.0612478498 -0.0694027212 -0.5472731068
## [1293] -0.2452525425 -0.5145726802 -0.5827391657 -0.2295049246
## [1297] NaN NaN NaN NaN
## [1301] NaN NaN NaN NaN
## [1305] NaN NaN NaN -1.4376702656
## [1309] -0.8000635517 -0.4818490104 -0.6268698782 -0.3435398084
## [1313] -0.2640188867 -0.0549439864 -0.2137861186 -0.0742578664
## [1317] -0.3384022561 -0.4418212722 -0.4352756934 -0.1263723937
## [1321] -0.1094678340 -0.0670391989 -0.1609819043 -0.1208747795
## [1325] -0.0129377710 -0.2435159311 -0.2074963231 -0.3035363051
## [1329] -0.5133163466 -0.5425597266 -0.3031005943 -0.0558047309
## [1333] -0.5438044548 -0.5689172536 NaN NaN
## [1337] NaN NaN NaN NaN
## [1341] NaN NaN NaN -0.7371397863
## [1345] -1.1408404783 -0.9132695217 -0.8076443192 -0.8347901422
## [1349] -0.3840955609 -0.2592406915 -0.3686761714 -0.2444301129
## [1353] -0.2823006949 -0.3523555128 -0.4552965629 -0.7335883858
## [1357] -0.4789207568 -0.2901644995 -0.0576545000 -0.1220529486
## [1361] -0.4703104043 -0.8418532463 -0.4085079520 -0.4283440349
## [1365] -0.3259594180 -0.4015037280 -0.2241863304 -0.1785838509
## [1369] -0.3365420878 NaN NaN NaN
## [1373] NaN NaN NaN NaN
## [1377] NaN NaN NaN 0.9132103721
## [1381] -0.1362477155 -0.0100759374 0.3921173241 0.6873830197
## [1385] 0.0185401855 -0.1697857158 -0.2459992355 -0.1907355504
## [1389] -0.5261004125 -0.7167334803 -0.8084957609 -0.7848772225
## [1393] -0.5284604251 -0.3034665040 -0.3528430662 -0.1868529911
## [1397] -0.1951619760 -0.5999654256 -0.2917385568 -0.0485569145
## [1401] -0.4443857327 -0.3421054491 -0.3484780416 -0.5887448390
## [1405] NaN NaN NaN NaN
## [1409] NaN NaN NaN NaN
## [1413] NaN NaN NaN -1.0717503981
## [1417] -1.6819962926 -1.9976240716 -1.3158666904 -0.9162262628
## [1421] -0.3083504050 -0.8696513143 -0.4507023581 -0.5682009292
## [1425] -1.1760111998 -0.6271935323 -0.6933030218 -0.6146968024
## [1429] -0.7947582003 -0.5514716143 -0.5639173860 -0.5815378440
## [1433] -0.3931746366 -0.4270877666 -0.3465009406 -0.4189466036
## [1437] -0.6053325852 -0.1578220902 -0.4135780770 -0.3617216807
## [1441] -0.3646851975 -0.6844892296 NaN NaN
## [1445] NaN NaN NaN NaN
## [1449] NaN NaN NaN 0.7178581543
## [1453] -0.7769600833 -0.4120222054 -0.4199325252 -0.6401769672
## [1457] -0.8137800937 -0.3428019882 -0.0720319796 -0.2990924638
## [1461] -0.3273250101 -0.3559638333 -0.4565566802 -0.5166207888
## [1465] -0.7099601762 -0.6009449265 -0.8776921496 -0.6823710982
## [1469] -0.5900967164 -0.6783209331 -0.6837655020 -0.7727336272
## [1473] -0.9957918958 -0.6899591042 -0.3855166498 0.1495638978
## [1477] -0.3081244698 -0.6100667696 NaN NaN
## [1481] NaN NaN NaN NaN
## [1485] NaN NaN NaN -0.1870262194
## [1489] -1.1598312362 -1.1013100719 -0.9326246764 -0.7836004906
## [1493] 0.0321017084 -0.1826241460 -0.0215647035 0.1160419744
## [1497] -0.1244494177 -0.5005653807 -0.3734788884 -0.3517766760
## [1501] -0.5281136657 -0.5262444232 -0.2123590631 -0.6403154517
## [1505] -0.4950542905 -0.4017449645 -0.2343600231 -0.2331396334
## [1509] -0.2637237129 -0.2146184280 -0.3818676139 -0.2453969878
## [1513] -0.3899149178 -0.4655768112 -0.2803618014 NaN
## [1517] NaN NaN NaN NaN
## [1521] NaN NaN NaN -0.6788679906
## [1525] -1.9004844227 0.3646014074 -0.0004649660 0.1710577708
## [1529] 0.2394242235 -0.2653415146 -0.5589653503 -0.5623889367
## [1533] -0.2372979561 -0.4298085251 -0.5400926730 -0.2857889978
## [1537] -0.2879069301 -0.2928457853 -0.2822848763 -0.1547533644
## [1541] -0.3069702881 -0.3971059797 -0.4225369926 -0.5711659700
## [1545] -0.4554637323 -0.3152969051 -0.5557816946 -0.3300089770
## [1549] -0.2738287302 0.0412561852 0.2193313241 NaN
## [1553] NaN NaN NaN NaN
## [1557] NaN NaN NaN 0.9077309452
## [1561] -0.6479053686 0.2513050548 -0.1905457203 -0.2685489621
## [1565] -0.3149380837 -0.5651876158 -0.4493468305 -0.5106069763
## [1569] 0.0101983903 -0.3713709427 -0.4772175466 -0.4305143868
## [1573] -0.5807250741 -0.4872474687 -0.4132115693 -0.5141992099
## [1577] -0.4225049251 -0.6690322021 -0.7827100041 -0.5963287829
## [1581] -0.5840734434 -0.4781891567 -0.5461050053 -0.5508005567
## [1585] -0.4706459532 0.0831120034 0.8113767803 NaN
## [1589] NaN NaN NaN NaN
## [1593] NaN NaN NaN -0.5967547523
## [1597] -0.7500189813 0.8751648066 0.3051024546 -0.0820065062
## [1601] -0.4395513235 -0.7181642943 -0.7982366828 -0.2046917889
## [1605] -0.2274474557 -0.3063638263 -0.5317480521 -0.4210252301
## [1609] -0.7521952330 -0.4300189211 -0.5884008142 -0.5424376955
## [1613] -0.3992626412 -0.5719609469 -0.4640492582 -0.5359999651
## [1617] -0.4172793498 -0.2623012968 -0.5191431293 -0.4382637000
## [1621] -0.5815191361 -0.1704192025 NaN NaN
## [1625] NaN NaN NaN NaN
## [1629] NaN NaN NaN -1.7943608395
## [1633] -0.2068463523 -0.8154581295 -0.9348559743 -0.3072229879
## [1637] -0.3894724690 -0.2481600554 -0.5177331815 -0.3427091889
## [1641] -0.0228474632 -0.2562944429 -0.3775874608 -0.4075338578
## [1645] -0.4683332520 -0.4755219238 -0.4059716759 -0.4491287146
## [1649] -0.3782515813 -0.5842528715 -0.5346400193 -0.2306816061
## [1653] -0.1759489949 -0.2167002668 -0.4232970371 -0.5302560396
## [1657] -0.2950827035 -0.1283268350 -0.4217897654 NaN
## [1661] NaN NaN NaN NaN
## [1665] NaN NaN -1.4058441222 -0.7597541934
## [1669] -1.2373706003 -0.5617681324 -1.1073867635 -0.7599229003
## [1673] 0.0390487652 0.3148417535 0.0569200498 -0.1881181503
## [1677] -0.0993041968 -0.3442139703 -0.1682184970 -0.1060696199
## [1681] -0.1454859801 -0.0336193624 -0.1152324052 -0.1502742015
## [1685] -0.2486367319 -0.4096227797 -0.2238402571 -0.3477128928
## [1689] -0.4082937208 -0.2131613229 -0.4308149433 -0.5868189208
## [1693] -0.2280274528 0.0438421087 NaN NaN
## [1697] NaN NaN NaN NaN
## [1701] NaN NaN NaN 0.1499978781
## [1705] -0.9964171643 -0.2479996480 -0.0336040158 0.1874621472
## [1709] -0.1658006927 -0.0235374650 0.2353032304 0.1639615707
## [1713] -0.0886189539merid_anom <- gmst$t - mean_merid_means[year1880:168]
zonal_anom <- gmst$t - mean_zonal_means[year1880:168]
plot(years[31:168],merid_anom,type="l",col="blue",
main="GMST",xlab="Years", ylab="T Anomaly", ylim= c(-.2,.4))
lines(years[31:168], zonal_anom, type="l", col="green")
legend("topleft",
legend=c("Mean of the Meridional Means Anomally", "Mean of Zonal Means Anomally"),
col= c("blue", "green"), lty=1,lwd=1,bty="n")