| 1 | |
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| 2 | |
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| 3 | pro baseline_diff |
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| 4 | restore, 'meta.sav' |
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| 5 | meta = s |
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| 6 | restore, 'station_data.sav' |
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| 7 | data = s |
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| 8 | base = fltarr( n_elements(data)) - 999.9 |
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| 9 | diff = base |
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| 10 | for i=0,n_elements(data)-1 do begin |
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| 11 | gd0 = where( data[i].years ge 1951 and $ |
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| 12 | data[i].years le 1981 and $ |
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| 13 | data[i].years ne 0 and $ |
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| 14 | data[i].annual ne 999.9, n0 ) |
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| 15 | gd1 = where( data[i].years ge 2000 and $ |
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| 16 | data[i].years ne 0 and $ |
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| 17 | data[i].annual ne 999.9, n1 ) |
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| 18 | if n0 le 10 then continue |
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| 19 | if n1 lt 4 then continue |
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| 20 | base[i] = mean( data[i].annual[gd0] );/ float(n0) ) |
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| 21 | diff[i] = mean( data[i].annual[gd1] );/ float(n1) ) |
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| 22 | endfor |
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| 23 | stop |
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| 24 | end |
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| 25 | |
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| 26 | |
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| 27 | pro find_temp_change_magnitude |
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| 28 | restore,'data_struct.sav' |
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| 29 | trend = fltarr(n_elements(s)) |
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| 30 | line = indgen(n_elements(s[0].year)) |
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| 31 | ; for i=0,n_elements(s)-1 do begin |
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| 32 | for i=0,10 do begin |
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| 33 | gd = where( s[i].ann ne 999.90 AND s[i].year NE 0, cnt ) |
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| 34 | if cnt eq 0 then continue |
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| 35 | fit = poly_fit( line[gd], s[i].ann[gd], 1 ) |
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| 36 | ; n_years = max(s[i].year[gd]) - min(s[i].year[gd]) |
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| 37 | ; change_per_decade = (fit[1]/n_years) * 10 |
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| 38 | change_per_decade = fit[1]*10 |
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| 39 | trend[i] = change_per_decade |
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| 40 | endfor |
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| 41 | stop |
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| 42 | end |
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| 43 | |
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| 44 | |
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| 45 | pro bar |
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| 46 | ; get the min/max of all the temperatures, so we can pre-set the Y range |
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| 47 | |
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| 48 | t_all = [[s[idx].jja,s[idx].djf,s[idx].ann]] |
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| 49 | gd = where( t_all ne 999.90 AND t_all NE 0, cnt ) |
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| 50 | ;if cnt eq 0 then continue |
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| 51 | trange = minmax(t_all[gd]) |
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| 52 | |
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| 53 | plotg, s[idx].year[gd], s[idx].ann[gd], $ |
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| 54 | /yno, yrange=trange, $ |
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| 55 | xrange=[1880,2010], /xst,psym=2, xminor=2, $ |
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| 56 | xtitle='Time (Years)', ytitle='Temperature (Degrees C)', $ |
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| 57 | chars=1.2, charth=3, title=name[i] |
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| 58 | |
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| 59 | ; 1st order fit to ANN |
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| 60 | gd = where( s[idx].ann ne 999.90 AND s[idx].year NE 0 ) |
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| 61 | line = indgen(n_elements(s[idx].year)) |
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| 62 | r1 = poly_fit( line[gd], s[idx].ann[gd], 1 ) |
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| 63 | fit1 = r1[0] + r1[1]*line |
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| 64 | oplot, s[idx].year[gd], fit1[gd], color=0 |
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| 65 | |
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| 66 | ; if non-tropical, then 1st order fit to JJA and DJF |
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| 67 | if abs( lat[i] ) GE 23.26 then begin |
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| 68 | ; 1st order fit to JJA |
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| 69 | gd = where( s[idx].JJA ne 999.90 AND s[idx].year NE 0, count ) |
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| 70 | if count ne 0 then begin |
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| 71 | r1 = poly_fit( line[gd], s[idx].jja[gd], 1 ) |
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| 72 | fit1 = r1[0] + r1[1]*line |
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| 73 | color=(lat[i] ge 0)? cred: cblue |
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| 74 | oplot, s[idx].year[gd], s[idx].JJA[gd], color=color, psym=2, th=2 |
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| 75 | oplot, s[idx].year[gd], fit1[gd], color=color, thick=2 |
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| 76 | endif |
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| 77 | ; 1st order fit to DJF |
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| 78 | gd = where( s[idx].DJF ne 999.90 AND s[idx].year NE 0, count ) |
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| 79 | if count ne 0 then begin |
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| 80 | r1 = poly_fit( line[gd], s[idx].djf[gd], 1 ) |
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| 81 | fit1 = r1[0] + r1[1]*line |
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| 82 | color=(lat[i] lt 0)? cred: cblue |
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| 83 | oplot, s[idx].year[gd], s[idx].DJF[gd], color=color, psym=2, th=2 |
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| 84 | oplot, s[idx].year[gd], fit1[gd], color=color, thick=2 |
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| 85 | endif |
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| 86 | endif |
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| 87 | ; r2 = poly_fit( line[gd], ann[gd], 2 ) |
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| 88 | ; fit2 = r2[0]+r2[1]*line+r2[2]*line^2 |
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| 89 | ; oplot, year, fit2, color=254 |
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| 90 | end |
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| 91 | |
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