Iran vs Thailand

Iran vs Thailand: Tropical nights

Tropical nights in 2014: 90.8 nights in Iran and 286.4 nights in Thailand, in the CMIP6 climate models’ median run. In 2014 the value was higher in Thailand, by 195.6 nights, or 3.15 times that in Iran.

nights a year · CMIP6

Iran vs Thailand: Tropical nightsTropical nights in 2014: 90.8 nights in Iran and 286.4 nights in Thailand, in the CMIP6 climate models’ median run. In 2014 the value was higher in Thailand, by 195.6 nights, or 3.15 times that in Iran.
IranThailandCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Thailand 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 Thailand at 320.8 nights in 2100.

Every year

nights a year · CMIP6

YearIranThailand
201490.8286.4
201390.6289.1
201290.3286.6
201189.8286.4
201087286
200987.2286.9
200888284.8
200787.9283.2
200688.2284.3
200587.1281.7
200486.4287.5
200385.9279.3
200287.7280
200186.6285.5
200086.5283.4
199985.1282.5
199883.8282.7
199784.3284.8
199682.4279.8
199581.6278.7
199480.6280.8
199377.7275.6
199277268.8
199181.9276.7
199083.1279.2
198981.7278.4
198880.4279.6
198779.8277.9
198680.1277.5
198579.7276
198478.8277.9
198375.5273.9
198277.9275.7
198180.2277.8
198079.4277.9
197980.2278
197878.1276
197777.8277.3
197677.2275.6
197576.6274.4
197476.4276
197376.9280.6
197277.4273.9
197175273.2
197075.3276
196975.6274.8
196876.3277
196776.1270.3
196675.5274.4
196575.3275.8
196475.3271.9
196376.4274.7
196277.5275.4
196177.7278.5
196077.6276.2
195976.8274.4
195878.4278.7
195778.2277.4
195676.2277
195576.5274.6
195477.6275
195377.3275.4
195278.2275.1
195176.6277.5
195077.7276.2
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearIranThailand
201594.482.7–106290.6272.2–305
201694.183.1–105.3287.1269.8–306.2
201793.981.7–104290273.8–302.8
201894.183–106.3292.6275.1–308.7
201995.183.4–105.8291.2272.8–306.7
202095.985.5–107.5292.4272.2–307.8
202197.286–107.7294.2276.3–308.5
202296.285.2–108.3294.8277.2–311.9
202398.485.7–110.6296.1281.9–310.2
20249885.1–110.5291.7275.5–307.8
202598.787.2–110.5294276.7–306.3
202699.186.7–111.7296278–309.3
2027100.288.7–112.1295.8276.3–311.1
202899.686.2–111.6297279.3–315.1
202999.386.7–109.9300.3278.7–315
2030100.889.8–111.6297.7280.2–312.5
2031102.990.1–114.8300.8282.8–317.1
2032101.289–113.9297.2280.9–311.4
2033102.889.9–115.1300281.9–315.8
2034104.690.7–117.3300.8279–318.4
203510592.1–116.2298.6281.5–317.8
2036103.591.4–115.2300.4279.9–316.9
203710693.1–119.4299.8280–315.1
2038105.993.2–116.5304.8286.6–319.3
2039104.392.9–116.6300.9281.9–317
2040105.592.3–118.4301.6284.5–315.7
2041104.892.5–116.4303.1285.1–318.8
2042107.793.4–120.7303.8290–318.3
204310692.9–118.8302285.8–317.9
2044106.893.6–121.2304.3286.3–318.8
2045108.795.1–121.8307.1287.7–324.2
2046109.296.1–121.9305.9285.2–321
2047110.596.9–123.3306.6289.5–321.5
2048111.498.6–124309.4288.9–325.7
204911095.7–123.4308.1287.2–324.1
2050112.199.6–125.1307.5291–321.5
2051111.797.6–124.1312.8294.8–326
205211298.3–126310.9291.2–323.4
2053111.597.5–124308.3289.5–323.3
2054111.899–126.3308.8292.8–322.3
2055114.6101.4–128308.6290.6–325.1
2056113.1100.2–126.2311.3294.9–326.3
2057113.899.9–128.9308.5286.7–326.4
2058114.2100–127.7308291.1–325.1
2059113.498.9–126.3312.7295.8–325.3
2060116.3100.3–129.5313.1294.6–328.6
2061115100.5–127.6314.4289.5–331.1
2062115.799.7–131.1311.4293.4–330.3
2063117103.7–130.3314.8296.9–327.3
2064115.1101.7–130.3312.5295.4–330
2065117.3102.8–132.3309.6293.1–329.3
2066117.7103.7–132.1312.1292.6–328
2067119103–133.5311.2289.6–329.4
2068118.1104.3–131.4310.8292.4–330.6
2069118.4103.8–133.2315.5296.1–329.5
2070118.2104.9–132.9314.2292.9–330.7
2071119.3105.1–135.3313.8296.4–330.8
2072119103.2–135.1314.8296–329.6
2073120.6104.9–137.7316.5299.9–332.1
2074119.1104.4–134.5317.2295–333.8
2075119.7105.9–135.5317.2295.3–331.4
2076118.9102.6–137317.4299.8–335
2077120.3104.3–135.3317.5297.3–335.9
2078119.2105.3–136315.9297.1–331.6
2079120.1103.2–135.6318.5300.5–332.3
2080120.4106.1–139.5319.8297.4–337.5
2081120.5104.3–137.1317.7299.5–334.9
2082122.5107.5–137.7317.9294.8–333
2083122.6105.8–140.4315297.8–332.9
2084123.7107.6–139.1316295.5–330
2085121.3106.6–140.6322.6300.8–334.7
2086122.1107.9–139.8319.4295.2–335.2
2087123.7108–140.7320.2302.2–334.7
2088124106.5–142.2321.4299.8–334.8
2089124.1109–139.7318.9296.4–338.1
2090122.5105.4–141.9317.6304–337.4
2091124.3108–140.5320.1299.8–336.1
2092125.5110.1–140316.3298–336.4
2093124.5109–139.3316.1297.7–333.7
2094125108.7–142.9323.4301.8–337.3
2095125.3108.5–141322.9302.4–338.9
2096125.8109–142.4319.6302.8–336.7
2097123.6106.9–141.8319.8299.1–336
2098125.8109.4–143.8320.5303.9–336
2099123.1107.8–140.1319.9303.1–334.4
2100124.4107.9–143.7320.8301.1–336.7

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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