Israel vs Thailand

Israel vs Thailand: Tropical nights

Tropical nights in 2014: 97.9 nights in Israel and 286.4 nights in Thailand, in the CMIP6 climate models’ median run. In 2014 the value was higher in Thailand, by 188.5 nights, or 2.93 times that in Israel.

nights a year · CMIP6

Israel vs Thailand: Tropical nightsTropical nights in 2014: 97.9 nights in Israel and 286.4 nights in Thailand, in the CMIP6 climate models’ median run. In 2014 the value was higher in Thailand, by 188.5 nights, or 2.93 times that in Israel.
IsraelThailandCMIP6 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 Israel at 149.4 nights and Thailand at 320.8 nights in 2100.

Every year

nights a year · CMIP6

YearIsraelThailand
201497.9286.4
201398289.1
201296.3286.6
201196.1286.4
201095.5286
200992.5286.9
200894.1284.8
200792.7283.2
200692284.3
200592.3281.7
200490.7287.5
200393.9279.3
200289.5280
200191285.5
200089.5283.4
199986.2282.5
199884.8282.7
199784.2284.8
199683.7279.8
199579.3278.7
199480.1280.8
199371.6275.6
199274.9268.8
199178.4276.7
199080.2279.2
198976.9278.4
198876.5279.6
198775.1277.9
198678.3277.5
198574276
198469.4277.9
198368.9273.9
198273.5275.7
198173.5277.8
198075.3277.9
197968.7278
197871.6276
197772.3277.3
197673.2275.6
197570.9274.4
197472.5276
197371.9280.6
197269.9273.9
197173.1273.2
197071.6276
196970.6274.8
196869.6277
196766.9270.3
196668.8274.4
196563.5275.8
196466.1271.9
196370.9274.7
196273275.4
196171.5278.5
196074.9276.2
195975274.4
195873278.7
195774.8277.4
195674.3277
195572.8274.6
195471.4275
195373.7275.4
195272275.1
195175.1277.5
195069.5276.2
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearIsraelThailand
2015105.988.4–122.7290.6272.2–305
2016104.186.9–120.1287.1269.8–306.2
2017107.892.2–121.4290273.8–302.8
201810993.8–124.2292.6275.1–308.7
201910989.1–124.5291.2272.8–306.7
2020111.594.8–123.5292.4272.2–307.8
202111097.7–128.4294.2276.3–308.5
2022111.493.6–130.2294.8277.2–311.9
2023117.297.9–130.5296.1281.9–310.2
2024114.5100.4–127.3291.7275.5–307.8
2025116.698.3–129.2294276.7–306.3
2026115.9104.9–126.2296278–309.3
2027115.699–127.1295.8276.3–311.1
2028116.3100.2–127.9297279.3–315.1
2029117103.1–129.2300.3278.7–315
2030118.6102.1–131.2297.7280.2–312.5
2031116.8104.4–132.8300.8282.8–317.1
2032121.4106.5–134.7297.2280.9–311.4
2033122.1108.9–134.7300281.9–315.8
2034119.7103.1–133.4300.8279–318.4
2035122.8105.8–138.8298.6281.5–317.8
2036120.2105.5–136.2300.4279.9–316.9
2037121.6106.3–139.4299.8280–315.1
2038126.5105.7–142.1304.8286.6–319.3
2039127.7110.5–147.1300.9281.9–317
2040124.2109.7–144.9301.6284.5–315.7
2041126108.4–141.5303.1285.1–318.8
2042128.5105.8–144.3303.8290–318.3
2043126.5111.8–144.3302285.8–317.9
2044125.9108.5–142.9304.3286.3–318.8
2045130.9117.5–148.6307.1287.7–324.2
2046131114.4–147.2305.9285.2–321
2047132.3116.1–145.4306.6289.5–321.5
2048129.9118.5–146.9309.4288.9–325.7
2049127.9108.7–143.9308.1287.2–324.1
2050133.3114.3–147307.5291–321.5
2051130.3116.7–143.9312.8294.8–326
2052130.3119.9–143.5310.9291.2–323.4
2053135120.4–149.7308.3289.5–323.3
2054134.1118.8–149.3308.8292.8–322.3
2055134.1115.2–150.3308.6290.6–325.1
2056136110.1–151.1311.3294.9–326.3
2057132.7110.9–155308.5286.7–326.4
2058135.7119.1–155.4308291.1–325.1
2059138.8118.5–156.7312.7295.8–325.3
2060136.1116.3–159.2313.1294.6–328.6
2061136119–150.7314.4289.5–331.1
2062139118.7–156311.4293.4–330.3
2063137.4120.1–151.2314.8296.9–327.3
2064139.3121–154.3312.5295.4–330
2065139.6122.3–154.3309.6293.1–329.3
2066137.3119.6–153.7312.1292.6–328
2067142.8126–157.8311.2289.6–329.4
2068141.2122.8–156.4310.8292.4–330.6
2069141.5123.7–157.6315.5296.1–329.5
2070141.8120.5–157.5314.2292.9–330.7
2071142.9124.3–161.8313.8296.4–330.8
2072144.3127–158.5314.8296–329.6
2073143.6127.3–159.2316.5299.9–332.1
2074138.8123–162.2317.2295–333.8
2075138.1120.8–154.6317.2295.3–331.4
2076143.2126.8–161.5317.4299.8–335
2077142.8120.5–161.6317.5297.3–335.9
2078143.6128.2–160.6315.9297.1–331.6
2079148.6123.7–160.2318.5300.5–332.3
2080145.5123.5–163.7319.8297.4–337.5
2081143.7128.1–161.2317.7299.5–334.9
2082145.8129.5–160.6317.9294.8–333
2083149.4126–163.1315297.8–332.9
2084148128.2–164316295.5–330
2085149127.9–162.4322.6300.8–334.7
2086148127.4–167.1319.4295.2–335.2
2087146.4123.7–165.7320.2302.2–334.7
2088147.6133.5–165.3321.4299.8–334.8
2089148.4131.4–169.1318.9296.4–338.1
2090147.6132.4–168.8317.6304–337.4
2091151.9131.7–170.6320.1299.8–336.1
2092148.2132–167.4316.3298–336.4
2093154.1135.3–172.2316.1297.7–333.7
2094153.2133.6–170.3323.4301.8–337.3
2095148.2131.4–164.1322.9302.4–338.9
2096150.5130.5–172.6319.6302.8–336.7
2097149.8134.4–172.7319.8299.1–336
2098150.9131.3–170.1320.5303.9–336
2099149.5129–166.6319.9303.1–334.4
2100149.4130.1–168.5320.8301.1–336.7

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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