I linked your new per-event normalized damages to NA-SST and tried to apply Jim’s method of seperating noise from signal using the pareto principle mentioned in his paper. For your PL05 data noise cutoff turned out to be app. 5E9 US$.

Using this subset of events no trend was appearent.

Regards,
Wolfgang

****
****
R-transcript:

> imp<-read.table(“clipboard”,header=T,sep=”;”,dec=”.”) # import data (see below) from clipboard
> hursstPL05<-imp[order(imp$PL05,decreasing=T,na.last=T),-7] # rank sort REM we don’t need CL05 data here
> names(hursstPL05)
[1] “Yr” “MonthStart” “DayStart” “StormName” “BaseDamage” “PL05″ “NATLSST”
> hursstPL05$cPL05<-cumsum(hursstPL05$PL05) #calc cumulated sum of ranked PL05 normalized damages
> hursstPL05$cPL05<-hursstPL05$cPL05/hursstPL05$cPL05[nrow(hursstPL05)] #make cumulated sum of PL05 relative to total losses (which is the last row’s cPL05 value)
> hursstPL05$Rank <- 1-(1:nrow(hursstPL05)/nrow(hursstPL05)) # make rank relative REM 1-(…) is for convenience, pareto point is now close to 0.8/0.8
> plot(hursstPL05$Rank,hursstPL05$cPL05) # this looks very much like Jim’s results (keep in mind x axis has been mirrored)
> plot(hursstPL05$PL05,hursstPL05$cPL05-hursstPL05$Rank);grid() # since rank and cumsum are now both relative we look for pareto cutoff point (x axis intercept) REM from the prev plot, this is where pareto curve intercepts with line y=x, so we try to estimate y-x=0
> plot(hursstPL05$PL05,hursstPL05$cPL05-hursstPL05$Rank,xlim=c(0,1E10),ylim=c(-0.1,0.1));grid() # magnify region of interception for a better estimate
> locator() # let’s pick the interception by pointing and clicking (y=0) for convenience
$x
[1] 5334434352

$y
[1] 0.0002515137
> # this is close enough – 53E8 US$ becomes random damage cutoff
> plot(hursstPL05$NATLSST,log10(hursstPL05$PL05));grid() # now let’s plot log(PL05) over SST
> cutoffdamage<-53E8 # taken from locator above
> abline(h=log10(cutoffdamage),col=”grey”) # show cutoff damage line – sorry not many signal points there
> hursstPL05.lma<-lm(log10(PL05)~NATLSST,data=hursstPL05) # declare the overall model w/o cutoffs
> abline(hursstPL05.lma) # draw it
> summary(hursstPL05.lma) # show me the model data

Call:
lm(formula = log10(PL05) ~ NATLSST, data = hursstPL05)

Residuals:
Min 1Q Median 3Q Max
-3.5179 -0.7580 -0.0271 0.8604 2.2249

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.1588 4.3086 0.733 0.465
NATLSST 0.1954 0.1560 1.253 0.212

Residual standard error: 1.109 on 146 degrees of freedom
Multiple R-Squared: 0.01063, Adjusted R-squared: 0.003855
F-statistic: 1.569 on 1 and 146 DF, p-value: 0.2124

> hursstPL05.lmc<-lm(log10(PL05)~NATLSST,data=subset(hursstPL05,log10(PL05)>log10(53E8))) # now for the signal subset using >53E8 US$ cutoff
> abline(hursstPL05.lmc,col=”red”) # draw it
> summary(hursstPL05.lmc) # show me the model data

Call:
lm(formula = log10(PL05) ~ NATLSST, data = subset(hursstPL05,
log10(PL05) > log10(5.3e+09)))

Residuals:
Min 1Q Median 3Q Max
-0.3618 -0.2485 0.0368 0.1414 0.7611

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.33716 2.62342 3.559 0.00167 **
NATLSST 0.02847 0.09445 0.301 0.76581
—
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ‘ 1

Residual standard error: 0.2971 on 23 degrees of freedom
Multiple R-Squared: 0.003934, Adjusted R-squared: -0.03937
F-statistic: 0.09085 on 1 and 23 DF, p-value: 0.7658

****
Data sources:

Normalized damages:
http://sciencepolicy.colorado.edu/publications/special/normalized_hurricane_damages.html

Link hurricane year+name to storm start date:
http://www.climateaudit.org/data/hurricane/unisys/Track.ATL.txt

Monthly NA-SST:
http://www.cpc.noaa.gov/data/indices/sstoi.atl.indices

Data:

Yr;MonthStart;DayStart;StormName;BaseDamage;PL05;CL05;NATLSST
1950;9;1;Easy;3300000;1121198545.08702;972672385.007333;27.78
1950;10;13;King;28000000;4408121956.38413;3725252507.43757;27.69
1951;9;28;How;2000000;358069352.97984;328708795.322242;27.97
1952;8;18;Able;2800000;114732781.485231;158056794.917681;27.65
1953;8;11;Barbara;1000000;43764416.0158102;76617081.0355628;27.68
1953;9;23;Florence;200000;12022642.7780337;14316188.3703613;27.94
1954;8;25;Carol;460000000;16133671673.3372;15084813860.1831;27.25
1954;9;2;Edna;40000000;3024742035.74783;1669025614.16583;27.68
1954;10;5;Hazel;281000000;16513037016.9668;23245567610.428;27.36
1955;8;3;Connie;40000000;2321631096.31438;3809221512.46724;27.64
1955;8;7;Diane;800000000;17212609735.9547;17830947063.839;27.64
1955;9;10;Ione;88000000;3740019516.49593;6004736906.56358;28.08
1956;9;21;Flossy;25000000;577494763.566377;711117719.385467;27.63
1957;6;25;Audrey;150000000;3764307219.24643;4122645630.76675;26.61
1957;9;16;Esther;2000000;77515135.6201929;88441730.0662904;28.05
1958;9;21;Helene;11200000;509818712.22738;643883845.33683;28.11
1959;5;28;Arlene;1000000;26834452.4134174;31975141.8960604;25.83
1959;7;23;Debra;7000000;317622133.00818;287583193.32736;26.66
1959;9;20;Gracie;14000000;373551466.137319;509799371.738114;27.75
1959;6;18;NOTNAMED;2000000;140771055.072183;132753509.251097;26.34
1960;7;28;Brenda;5000000;184889923.928569;274187875.486212;27.12
1960;8;29;Donna;300000000;26817811904.9043;28920699578.6421;27.53
1960;8;29;Donna;87000000;2801842164.31962;2977933407.36724;27.53
1960;9;14;Ethel;1000000;29098891.2744484;32844899.5847996;27.87
1960;6;22;NOTNAMED;4000000;136850328.600666;139399585.618997;26.91
1961;9;3;Carla;400000000;14209129736.954;13466621200.0789;27.76
1961;9;10;Esther;6000000;259603449.376759;183786233.664288;27.76
1962;8;26;Alma;1000000;77570243.5161737;81268979.0598545;27.69
1962;9;29;Daisy;1000000;14622488.5659346;18733899.3549728;27.94
1963;9;16;Cindy;13000000;246291959.184626;242741064.476109;27.94
1964;8;5;Abby;1000000;17588326.5935661;18247717.797249;27.44
1964;8;20;Cleo;128000000;5173116167.57032;4653166630.79856;27.44
1964;8;28;Dora;250000000;7682229901.66649;6577589727.86072;27.44
1964;9;28;Hilda;125000000;2186382636.85263;2592839590.84212;27.59
1964;10;8;Isbell;10000000;634142325.701495;624127559.148902;27.57
1965;8;27;Betsy;142000000;2853986824.84002;4013938352.4303;27.16
1965;8;27;Betsy;1278000000;17856410123.1602;19030722651.0489;27.16
1965;9;24;Debbie;25000000;550857093.310467;618312616.803641;27.74
1966;6;4;Alma;10000000;243643316.823062;251656976.575758;26.9
1966;9;21;Inez;5000000;92908979.295204;130663799.98004;28.02
1967;9;5;Beulah;200000000;4016468362.23148;4047098432.03278;27.74
1968;6;1;Abby;1000000;31949630.694152;30828136.3170355;26.36
1968;6;22;Candy;3000000;32456099.4118217;36750158.6021393;26.36
1968;10;13;Gladys;7000000;592857495.099422;495114444.590688;27.72
1969;8;14;Camille;1421000000;21225180491.8357;23957867600.018;27.82
1970;7;31;Celia;454000000;5627670656.11602;5719215718.16093;26.9
1971;8;20;Doria;147000000;1334299277.62303;1307086502.41289;27.1
1971;9;5;Edith;25000000;259842605.917541;281485526.148339;27.58
1971;9;3;Fern;30000000;334044089.23212;342638045.106185;27.58
1971;9;6;Ginger;10000000;155482194.461511;214628140.653225;27.58
1972;6;14;Agnes;31000000;348605988.334489;411000085.922878;26.34
1972;6;14;Agnes;1969000000;17192005511.1422;18027963064.6195;26.34
1972;8;29;Carrie;2000000;38692840.2976145;35153384.7629863;27.17
1973;9;1;Delia;18000000;145454945.455995;151640798.813589;27.74
1974;8;29;Carmen;150000000;970296296.479568;1103059329.2921;26.98
1974;10;4;SUBTROP4;10000000;103487667.925113;96054210.188275;27.3
1975;9;13;Eloise;490000000;2791286883.13172;2834851643.30456;27.44
1976;8;6;Belle;100000000;486444597.249696;480358141.453008;27.28
1977;9;3;Babe;10000000;53776992.1291894;60998836.6104761;27.75
1978;7;30;Amelia;20000000;145903705.971742;156202197.451051;26.8
1979;7;9;Bob;20000000;55497618.0057935;66073062.6813304;27.25
1979;7;15;Claudette;400000000;1472033641.13539;1578948268.8432;27.25
1979;8;25;David;320000000;2265907697.29244;2193891625.55571;27.67
1979;8;30;Elena;10000000;35218235.6839325;39149979.5112807;27.67
1979;8;29;Frederic;2300000000;10267559525.8002;11537923782.9407;27.67
1980;7;31;Allen;300000000;1602040183.30964;1743702284.88263;27.24
1981;8;7;Dennis;25000000;171359510.015338;160414409.951321;27.61
1982;9;9;Chris;2000000;5712609.37392955;6082318.74386322;27.82
1982;6;18;SUBTROP1;10000000;37436301.3224476;34977305.1310577;26.73
1983;8;15;Alicia;2000000000;7469100008.23059;7247139031.89662;27.56
1984;9;8;Diana;65000000;285333504.681358;309924883.982795;27.6
1984;9;25;Isadore;1000000;4294911.87141379;3782009.26582964;27.6
1985;7;21;Bob;25000000;109802229.64988;108568222.658689;27.01
1985;8;12;Danny;50000000;136021923.977223;142611996.564959;27.52
1985;8;28;Elena;1250000000;3573151699.89441;3769907745.52291;27.52
1985;9;16;Gloria;900000000;2364496011.89072;2398943773.6603;27.87
1985;10;26;Juan;1500000000;3867650060.19267;4207280862.07152;27.58
1985;11;15;Kate;300000000;1016979871.43563;1088138521.05121;27.34
1986;6;23;Bonnie;2000000;4839644.88043428;4724217.96326887;26.13
1986;8;13;Charley;15000000;45187343.0571066;49674771.5405767;27.16
1987;10;9;Floyd;1000000;2371775.26466554;2649286.39799291;28.39
1987;8;9;NOTNAMED;7000000;16639735.5750958;15795937.8411339;28.09
1988;8;8;Beryl;3000000;5868431.20953051;5796936.11869893;27.73
1988;8;21;Chris;1000000;2627965.37056287;2675388.47621617;27.73
1988;9;7;Florence;2000000;4259184.7309039;4640241.44666382;28.06
1988;9;8;Gilbert;50000000;151642640.754396;160583720.535914;28.06
1988;11;17;Keith;3000000;8514551.38873669;8102891.06578335;27.35
1989;6;24;Allison;500000000;1049664034.83642;1033461438.00434;26.38
1989;7;30;Chantal;100000000;228680309.482794;218804501.895467;27.29
1989;9;10;Hugo;7000000000;15322273456.8102;17483447130.5762;27.89
1989;10;12;Jerry;70000000;170238330.092162;164551637.940327;27.89
1990;10;9;Marco;57000000;126787371.06728;120235627.094055;28.18
1991;8;16;Bob;1500000000;3044037453.10207;3066892329.81044;27.16
1992;8;16;Andrew;25500000000;55766925398.4863;52340623757.8588;27.44
1992;8;16;Andrew;1000000000;1896940231.87161;1996613736.53536;27.44
1993;6;18;Arlene;22000000;44109986.3527193;43461800.0472994;26.7
1993;8;22;Emily;35000000;82369985.0102556;77179506.289198;27.35
1994;6;30;Alberto;500000000;1005181421.78775;1017035501.35262;26.04
1994;8;14;Beryl;73000000;148740712.164718;153918448.877874;27.29
1994;11;8;Gordon;400000000;784829927.88167;783043644.190887;27.37
1995;6;3;Allison;1700000;3214014.85809597;3245587.7819801;27.05
1995;7;28;Dean;500000;952665.685336516;924870.705451224;27.49
1995;7;31;Erin;500000000;953967722.888713;953197728.498768;27.49
1995;7;31;Erin;200000000;405628332.494382;422663718.2098;27.49
1995;8;22;Jerry;26500000;53769306.4535895;49977884.2646661;28.07
1995;9;27;Opal;3000000000;6084424987.41573;6339955773.14699;28.36
1996;7;5;Bertha;270000000;491228251.162088;520502972.952672;27.15
1996;8;23;Fran;3200000000;5821964458.21734;6168924123.88352;27.74
1996;10;4;Josephine;130000000;224267747.6416;235746491.065523;28.01
1997;7;16;Danny;100000000;163560186.413961;168484613.292749;27.38
1998;8;19;Bonnie;720000000;1163745048.09035;1214916672.09466;28.35
1998;8;21;Charley;50000000;79242707.2029009;77442127.0438009;28.35
1998;8;31;Earl;79000000;125092557.753638;126232888.654257;28.35
1998;9;8;Frances;500000000;809646688.76287;783835954.874037;28.6
1998;9;15;Georges;680000000;987880134.296214;1046295571.39035;28.6
1998;9;15;Georges;1630000000;2785657266.26725;2524834055.31156;28.6
1998;10;22;Mitch;40000000;70337036.0996478;69346466.6141704;28.46
1999;8;18;Bret;60000000;76412678.0135494;93900756.7595772;27.87
1999;8;24;Dennis;157000000;246762368.324441;248150280.969978;27.87
1999;9;7;Floyd;4500000000;6714795179.51099;6787052608.93818;28.25
1999;9;19;Harvey;15000000;24308009.6960988;24022769.1984891;28.25
1999;10;12;Irene;800000000;1215699549.51022;1178217657.93973;28.15
2000;9;14;Gordon;10000000;13867299.1421151;13574119.2734164;27.91
2000;9;15;Helene;16000000;22658442.7794139;23238835.700727;27.91
2001;6;5;Allison;5000000000;6622921397.06239;6445689806.90606;26.61
2001;8;2;Barry;30000000;39667764.8001923;40210381.7100888;27.77
2001;9;11;Gabrielle;230000000;307860968.819143;306339576.949535;28.27
2002;9;5;Fay;5000000;6091464.86053774;6065422.95383364;27.93
2002;9;8;Gustav;100000;125699.708144247;126296.31724032;27.93
2002;9;12;Hanna;20000000;24750816.8454853;24893394.5158999;27.93
2002;9;14;Isidore;330000000;398215397.267126;399319939.980315;27.93
2002;9;20;Kyle;5000000;6298469.56574654;6373619.31802296;27.93
2002;9;21;Lili;860000000;1055578444.29777;1060811848.40661;27.93
2003;6;28;Bill;30000000;34960159.9520769;35278638.9583616;26.62
2003;7;7;Claudette;180000000;210951821.349526;210140801.978706;27.3
2003;9;6;Isabel;3370000000;3966169543.4725;3989771692.97868;28.63
2004;7;31;Alex;4000000;4329688.17639211;4334942.52415457;27.44
2004;8;9;Charley;15000000000;16319033805.8813;16297047080.3953;28.2
2004;8;25;Frances;8900000000;9683982516.70226;9648997103.32787;28.2
2004;8;27;Gaston;130000000;140528333.931716;141215672.29431;28.2
2004;9;2;Ivan;14200000000;15473790997.7962;15514011620.257;28.65
2004;9;13;Jeanne;6900000000;7508578395.85992;7496264391.31465;28.65
2005;7;3;Cindy;320000000;320000000;320000000;28.06
2005;7;4;Dennis;2230000000;2230000000;2230000000;28.06
2005;8;23;Katrina;81000000000;81000000000;81000000000;28.46
2005;9;6;Ophelia;1600000000;1600000000;1600000000;28.75
2005;9;18;Rita;10000000000;10000000000;10000000000;28.75
2005;10;15;Wilma;20600000000;20600000000;20600000000;28.57

It seems that; (1) there are major questions about exactly how to measure the dependent quantity (damage): and (2) there has been little discussion of how the chosen independent quantity (SST) relates to the actual physical processes that lead to damage. By the latter I mean primarily how does SST determine that the path of the storm includes structures/objects/materials that can undergo damage. Additionally, the amount of the physcial damage is primarily a function of the quality and quantity of the structures/objects/materials that are in the path. And this leads us back to (1) above.

If a paper on this subject was sent to me for review for publication in an engineering journal, and the authors did not address such fundamental issues, I would reject it.

A metric which does not connect cause and effect is not a valid metric.

Thanks!

Along those lines, regression analysis assumes that the data are measured accurately. Given the marginal size of the estimated effect — in all of the models discussed here — measurement error on the damage function could be greater than the magnitude of the effect being measured.

A final theoretical question, which I think I raised earlier but don’t recall a persuasive answer. It’s not obvious to me what we should want to measure when measuring economic damages from hurricanes. I’m uncomfortable with both insured and uninsured losses as appropriate theoretical measures; that distinction has more to do with the idiosyncracies of the market and the normal gambling that both insurers and insureds willingly and knowingly undertake. Because of that gambling, insurance coverage in year n+1 is correlated with losses in year n. I know this matters but I haven’t a clue what to do with it. We shouldn’t ignore this question because losses will depend on the proportion of assets under coverage. Finally, any jurisdiction that by regulation achieves 100% coverage experiences disastrous adverse selection and moral hazard problems. (Cf. the federal flood insurance program.)

Is the choice of insured vs. uninsured damages a debate over which lamp post to look under?

A colleague in New Jersey considering a move to the Florida coast watched the “Hardball” debate between the R and D candidates for governor, whose names I have forgotten if I ever knew them. He was particularly interested in the question how each candidate proposed to deal with the “insurance problem” there, which I gather can be shorthanded as “people on the coast don’t want to pay the full cost of living there.”

According to my colleague, the D candidate’s solution is to use state regulation to mandate lower insurance premiums and higher coverage. Such a policy would shift cost to inland residents. Because on average they are much less wealthy, this policy transfers wealth from rich to poor (i.e., a “reverse Robin Hood”). The R candidate’s solution is to federalize the coastal insurance market. Such a policy would shift cost to residents of non-hurricane vulnerable coastal counties or federal taxpayers generally. That would transfer wealth to those who live in hurricane-vulnerable areas, but it’s not clear whether it’s also a reverse Robin Hood. (The residents of Pebble Beach are not obviously economically disadvantaged compared to the residents of Palm Beach.)

Choosing between these proposed policies is not easy. Florida voters as a whole should prefer the R’s policy because it externalizes cost out of state. The problem is it’s virtually certain never to be enacted. The D’s policy is implementable at the state level, but of course it’s highly divisive within the state once its wealth transfer effects are made transparent. But either policy would distort individual behavior in ways that increase future losses from hurricanes by a much greater amount than the 10% or so of variance we are debating.

year–ASO SST–normalized damage–ln(dmg)

1950 27.19 $5,529,320,501 22.43333077
1951 27.67 $358,069,353 19.69623725
1952 27.68 $114,732,781 18.55811634
1953 27.63 $55,787,059 17.83705248
1954 27.27 $35,671,450,726 24.29761651
1955 27.62 $23,274,260,349 23.87061388
1956 27.24 $577,494,764 20.17420993
1957 27.55 $3,841,822,355 22.06921266
1958 27.91 $509,818,712 20.04956575
1959 27.20 $858,779,107 20.5710223
1960 27.51 $29,970,493,213 24.12347918
1961 27.39 $14,468,733,186 23.39525583
1962 27.66 $92,192,732 18.33939186
1963 27.52 $246,291,959 19.32202822
1964 27.32 $15,693,459,358 23.47650986
1965 27.25 $21,261,254,041 23.78015219
1966 27.63 $336,552,296 19.63426411
1967 27.26 $4,016,468,362 22.11366884
1968 27.23 $657,263,225 20.30359514
1969 27.85 $21,225,180,492 23.77845407
1970 27.34 $5,627,670,656 22.45096146
1971 27.06 $2,083,668,167 21.45739572
1972 27.15 $17,579,304,340 23.58998816
1973 27.33 $145,454,945 18.79537694
1974 26.97 $1,073,783,964 20.79445466
1975 27.10 $2,791,286,883 21.74976857
1976 27.19 $486,444,597 20.00263357
1977 27.33 $53,776,992 17.80035628
1978 27.29 $145,903,706 18.79845741
1979 27.65 $14,096,216,718 23.36917228
1980 27.76 $1,602,040,183 21.19454377
1981 27.60 $171,359,510 18.95927431
1982 27.42 $43,148,911 17.58016773
1983 27.53 $7,469,100,008 22.73404035
1984 27.11 $289,628,417 19.48410934
1985 27.47 $11,068,101,797 23.1273331
1986 27.13 $50,026,988 17.72807318
1987 27.94 $19,011,511 16.76055519
1988 27.75 $172,912,773 18.96829783
1989 27.71 $16,770,856,131 23.54290846
1990 27.73 $126,787,371 18.658022
1991 27.18 $3,044,037,453 21.83645058
1992 27.41 $57,663,865,630 24.77789657
1993 27.37 $126,479,971 18.65559452
1994 27.18 $1,938,752,062 21.38531034
1995 27.97 $7,501,957,030 22.73842976
1996 27.69 $6,537,460,457 22.60081462
1997 27.79 $163,560,186 18.91269159
1998 28.28 $6,021,601,438 22.51861908
1999 27.80 $8,277,977,785 22.83686455
2000 27.53 $36,525,742 17.41352783
2001 27.76 $6,970,450,131 22.66494564
2002 27.47 $1,491,060,293 21.12275331
2003 28.02 $4,212,081,525 22.16122279
2004 28.10 $49,130,243,738 24.61774064
2005 28.42 $107,350,000,000 25.39936036

Thanks,
Don

Thanks.

Analogies about screwdrivers and tables are great. But why not simply just share your data and methods? The paper does not provide the data, nor does your WWW site as the paper claims it does. I would have a far better chance of understanding your complaints if you simply shared the dataset. What is the problem with that?

For instance, your dataset would be important for understanding your claim of the significance of treating storms on an annual basis versus a per storm basis. I used annual values of storms with >$250M in damage and generated the _exact same results_ as you reported 1950-2005. This is not surprising, and I doubt explains anything. But I can’t know because you haven’t simply shared your data — you’ve just made assertions.

When you are ready to share your data I’ll be happy to revisit your analysis. I’ve shared my data here, and that helped you to understand what I was doing an indeed to identify a mistake. I shared my data in good faith, how about you return the favor?

Thanks!

Saying, as I have, that “a simple regression of annual loss on SST is inadequate for understanding the relationship between loss and SST” is quite different than what you are saying with “signal of SST couldn’t be seen using all historical data”. It is not appropriate to use a regression of the log of annual losses on SST as a basis for addressing this relationship. Can you tell me what 10% of the variability in log annual losses means in terms of actual losses?

I never claimed my “approach definitively resolved this question”, only that it is a better tool to address the issue of a potential relationship between losses and SST than your approach or Faust’s approach. Allow me an analogy. If the question is which tool is best for securing a leg to a table using a screw with the choices being a hammer or an awl, then the answer would be neither; it is best to use a screw driver. You can argue about the quality of the results obtained with the screw driver, but it remains the better tool to use and someone who knows how to use a screw driver is better qualified to judge the quality of the work.

You continue to compare apples with oranges. As a sensitivity test to our more complex conditional probability model, we regress the log of PER EVENT losses on preseason SST. We find a significant relationship (p=0.0086). You use different data, a shorter record, and regress the log of TOTAL ANNUAL losses on seasonal SST. As a criticism of my results, your analysis and results are only marginally relevant.

As an aside. Why have 6 different entries on a single topic?

The book is due out next year. The models and data are available with the lead author Thomas Jagger. What papers are published that show no relationship between damage and SST?

Best,
Jim