Iran vs Myanmar (Burma)

Iran vs Myanmar (Burma): Tropical nights

Tropical nights in 2014: 90.8 nights in Iran and 201.9 nights in Myanmar (Burma), in the CMIP6 climate models’ median run. In 2014 the value was higher in Myanmar (Burma), by 111.1 nights, or 2.22 times that in Iran.

nights a year · CMIP6

Iran vs Myanmar (Burma): Tropical nightsTropical nights in 2014: 90.8 nights in Iran and 201.9 nights in Myanmar (Burma), in the CMIP6 climate models’ median run. In 2014 the value was higher in Myanmar (Burma), by 111.1 nights, or 2.22 times that in Iran.
IranMyanmar (Burma)CMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Myanmar (Burma) has been higher in every year both have figures for since 1950.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts Iran at 124.4 nights and Myanmar (Burma) at 244.5 nights in 2100.

Every year

nights a year · CMIP6

YearIranMyanmar (Burma)
201490.8201.9
201390.6203.1
201290.3201.1
201189.8200.9
201087202.1
200987.2199
200888198.1
200787.9196.8
200688.2198.2
200587.1196.7
200486.4200.3
200385.9195.3
200287.7196.6
200186.6199
200086.5197.2
199985.1196.4
199883.8194.7
199784.3194.4
199682.4191.4
199581.6189.9
199480.6188.8
199377.7185
199277180.5
199181.9191.7
199083.1191.5
198981.7190.3
198880.4190.7
198779.8190.8
198680.1189.7
198579.7187.4
198478.8189.1
198375.5182.3
198277.9186.7
198180.2188.4
198079.4189
197980.2189.8
197878.1186.8
197777.8187.4
197677.2185.8
197576.6183.6
197476.4185.5
197376.9187.5
197277.4184.8
197175185.2
197075.3184.9
196975.6184.3
196876.3183.7
196776.1182.3
196675.5186.2
196575.3182.9
196475.3180.6
196376.4183.1
196277.5187.3
196177.7188.7
196077.6187.1
195976.8186.5
195878.4187.1
195778.2186.2
195676.2187.9
195576.5185.9
195477.6185.7
195377.3185
195278.2186.1
195176.6187.1
195077.7186.9
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearIranMyanmar (Burma)
201594.482.7–106205.2186.4–218.9
201694.183.1–105.3202.8186.2–218.8
201793.981.7–104203.4187.3–218.3
201894.183–106.3205.5190.9–219.9
201995.183.4–105.8205.2186.7–222.2
202095.985.5–107.5208.2188.5–223.5
202197.286–107.7208.9191.2–224.1
202296.285.2–108.3209.9192.7–224.9
202398.485.7–110.6210.6195.4–226.3
20249885.1–110.5210.4193.8–226.6
202598.787.2–110.5209.9190.3–225.2
202699.186.7–111.7212.2194.3–229.2
2027100.288.7–112.1212195.6–228.6
202899.686.2–111.6213195.7–227.2
202999.386.7–109.9215.3193.9–231.9
2030100.889.8–111.6212.2194.9–228
2031102.990.1–114.8214.3197.2–230.8
2032101.289–113.9215199.3–229.6
2033102.889.9–115.1215.5198.5–230.1
2034104.690.7–117.3214.9198.4–233.5
203510592.1–116.2214.9199.2–236.8
2036103.591.4–115.2216198.7–234.5
203710693.1–119.4217.2199.6–233.5
2038105.993.2–116.5220.7204.8–235.1
2039104.392.9–116.6219.7202.4–234.3
2040105.592.3–118.4219.2202.9–235
2041104.892.5–116.4220.7203.9–237.7
2042107.793.4–120.7221.8206.1–237.2
204310692.9–118.8219.5201.2–237.1
2044106.893.6–121.2221.4201.6–239.6
2045108.795.1–121.8223.7204.8–241.5
2046109.296.1–121.9223.2203.2–240.2
2047110.596.9–123.3224.3204.8–241.1
2048111.498.6–124226.4207.7–243.8
204911095.7–123.4226.5205.6–242.8
2050112.199.6–125.1227209.3–243.5
2051111.797.6–124.1229.6212.4–246.4
205211298.3–126227.2208.6–244.2
2053111.597.5–124227.9208.7–247.7
2054111.899–126.3227.9209.6–244.8
2055114.6101.4–128229.1209.8–247.5
2056113.1100.2–126.2231.8210.4–252.1
2057113.899.9–128.9228.6208–249
2058114.2100–127.7228.9208.8–249
2059113.498.9–126.3230.7212.1–247.5
2060116.3100.3–129.5234.7211.1–251.5
2061115100.5–127.6231.9213–247.7
2062115.799.7–131.1233.6209.9–252.8
2063117103.7–130.3233215.4–250.5
2064115.1101.7–130.3233.2214.4–251.3
2065117.3102.8–132.3230.7210.4–250.7
2066117.7103.7–132.1233.5214.3–251.2
2067119103–133.5233211.5–253.3
2068118.1104.3–131.4233.1212.4–253.7
2069118.4103.8–133.2236.1214.6–253.4
2070118.2104.9–132.9235.9218.1–253.4
2071119.3105.1–135.3235.1213.5–259.6
2072119103.2–135.1234.6216.1–252.6
2073120.6104.9–137.7237.2219.3–257.2
2074119.1104.4–134.5236.4215.7–254.5
2075119.7105.9–135.5236.7214.4–255.9
2076118.9102.6–137237.7216–257
2077120.3104.3–135.3237.5219–258.7
2078119.2105.3–136238.5217.3–257.8
2079120.1103.2–135.6238.5219.2–257.2
2080120.4106.1–139.5240.5218.8–258.3
2081120.5104.3–137.1240219.7–260.9
2082122.5107.5–137.7239.4217.1–258.8
2083122.6105.8–140.4239.1220.5–259.2
2084123.7107.6–139.1241.3218.9–260.6
2085121.3106.6–140.6241.7218.8–260.7
2086122.1107.9–139.8241.7217.4–262.7
2087123.7108–140.7241.4220.5–260.9
2088124106.5–142.2241.8222.3–261.5
2089124.1109–139.7240.7221.2–262.6
2090122.5105.4–141.9241.3224.4–260.9
2091124.3108–140.5242.6220.3–262.5
2092125.5110.1–140241.9221.6–263.3
2093124.5109–139.3242.1220.2–260
2094125108.7–142.9242.5222.5–261.9
2095125.3108.5–141244.9224–264.4
2096125.8109–142.4243.9221.1–266.5
2097123.6106.9–141.8243.6219.6–264.2
2098125.8109.4–143.8243.6222.5–263.9
2099123.1107.8–140.1244.7221.8–263.5
2100124.4107.9–143.7244.5223.2–261.8

Each country on its own

Good to know

  • Nights in a year when the temperature stays above 20 °C, averaged over the country.
  • Source: World Bank Climate Change Knowledge Portal, CMIP6 multi-model median.
  • The dashed line is the CMIP6 model median (SSP2-4.5, middle of the road), a scenario rather than a forecast; the band is its models’ 10th–90th percentile.
  • Climate model values are the median of the CMIP6 models, run on past emissions to 2014 and on SSP2-4.5, a middle-of-the-road path, after: they are not measurements.

How the layers work, where their data comes from and what they can’t tell you: Countries through time

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