Tanzania vs Thailand

Tanzania vs Thailand: Tropical nights

Tropical nights in 2014: 106.3 nights in Tanzania and 286.4 nights in Thailand, in the CMIP6 climate models’ median run. In 2014 the value was higher in Thailand, by 180.1 nights, or 2.69 times that in Tanzania.

Watch it change on the map

nights a year · CMIP6

Tanzania vs Thailand: Tropical nightsTropical nights in 2014: 106.3 nights in Tanzania and 286.4 nights in Thailand, in the CMIP6 climate models’ median run. In 2014 the value was higher in Thailand, by 180.1 nights, or 2.69 times that in Tanzania.
TanzaniaThailandCMIP6 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 Tanzania at 208 nights and Thailand at 320.8 nights in 2100.

Every year

nights a year · CMIP6

YearTanzaniaThailand
2014106.3286.4
2013105.5289.1
2012108.4286.6
2011109286.4
2010103.9286
2009101.1286.9
2008100.3284.8
200799.5283.2
2006100.4284.3
200598.7281.7
2004100.4287.5
200399.6279.3
200298.4280
2001101.5285.5
200094.9283.4
199994.2282.5
199896.8282.7
199793.5284.8
199689.6279.8
199588.5278.7
199485.4280.8
199381.2275.6
199280.1268.8
199189.6276.7
199091.1279.2
198989.1278.4
198887.6279.6
198788.5277.9
198689.4277.5
198583276
198483.1277.9
198382.1273.9
198284.8275.7
198188277.8
198085277.9
197985.5278
197882.9276
197784.1277.3
197681275.6
197580.8274.4
197482.2276
197383.6280.6
197281.4273.9
197180.7273.2
197081.8276
196979.5274.8
196879.1277
196777.5270.3
196678.1274.4
196578275.8
196473.8271.9
196373.1274.7
196281.1275.4
196182.6278.5
196083.9276.2
195981.4274.4
195882.1278.7
195780.9277.4
195680277
195581.6274.6
195481.4275
195380.6275.4
195281.4275.1
195182.1277.5
195080.7276.2
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearTanzaniaThailand
2015114.895.2–137.4290.6272.2–305
2016108.391.6–125.6287.1269.8–306.2
2017114.893.6–137.8290273.8–302.8
2018116.497.4–137.1292.6275.1–308.7
2019113.495.2–133.2291.2272.8–306.7
2020119.396.2–143.5292.4272.2–307.8
2021119.5100.9–147.1294.2276.3–308.5
2022124.4105.3–147.4294.8277.2–311.9
2023122.1102.1–147.8296.1281.9–310.2
2024126.2100–150.1291.7275.5–307.8
2025124.6100.9–154294276.7–306.3
2026126.999.1–155.6296278–309.3
2027128102.9–147.7295.8276.3–311.1
2028129.9106.4–152.1297279.3–315.1
2029134.1111–156.5300.3278.7–315
2030129.4106.4–151.1297.7280.2–312.5
2031136.4115.9–158.4300.8282.8–317.1
2032135.1114–166.8297.2280.9–311.4
2033135.2110.9–161.9300281.9–315.8
2034134.7112.5–166.3300.8279–318.4
2035140.2114.6–165298.6281.5–317.8
2036137.8113.3–165.5300.4279.9–316.9
2037142.9121.5–168.9299.8280–315.1
2038147.9124.3–169.3304.8286.6–319.3
2039144.3117.6–171.3300.9281.9–317
2040140.5117–178301.6284.5–315.7
2041149.1121.7–175.6303.1285.1–318.8
2042146.3120.5–185.3303.8290–318.3
2043144.6117–173302285.8–317.9
2044147.7121.2–175.8304.3286.3–318.8
2045150.1125.2–183.4307.1287.7–324.2
2046161.5128.6–192.2305.9285.2–321
2047160.2129.7–194.4306.6289.5–321.5
2048158.8132.7–193.1309.4288.9–325.7
2049157.6131.1–196308.1287.2–324.1
2050159.7127.8–192307.5291–321.5
2051163.2135.5–197.8312.8294.8–326
2052161.9136.2–199.1310.9291.2–323.4
2053160.6127.5–197.7308.3289.5–323.3
2054163.2131.5–200.2308.8292.8–322.3
2055167.4137–208.3308.6290.6–325.1
2056166.4130.8–209.7311.3294.9–326.3
2057168134.6–208.3308.5286.7–326.4
2058168.7134.9–210.3308291.1–325.1
2059174.7144.9–219.5312.7295.8–325.3
2060181.7138.1–219.8313.1294.6–328.6
2061171.8135.5–218.1314.4289.5–331.1
2062177.5139.9–224311.4293.4–330.3
2063181.4140.1–213.8314.8296.9–327.3
2064185.3140.1–215.2312.5295.4–330
2065183.5141.1–221.8309.6293.1–329.3
2066181.9146.3–219.3312.1292.6–328
2067178.8138.8–229.2311.2289.6–329.4
2068186144.2–228.4310.8292.4–330.6
2069183.7141.7–225.3315.5296.1–329.5
2070180.7140.4–228.2314.2292.9–330.7
2071184.9146.9–235.2313.8296.4–330.8
2072191.3155–226.1314.8296–329.6
2073191.6152.4–235.6316.5299.9–332.1
2074183.3149.7–231.7317.2295–333.8
2075183.8144.8–233317.2295.3–331.4
2076191.1150.9–233.2317.4299.8–335
2077191.3152.6–238.2317.5297.3–335.9
2078189156.6–233.8315.9297.1–331.6
2079190.5147–237.3318.5300.5–332.3
2080197.3150.7–241.2319.8297.4–337.5
2081195.6153.4–237.4317.7299.5–334.9
2082194.5157.3–237.3317.9294.8–333
2083193.2151–249.8315297.8–332.9
2084201.1152.2–246.7316295.5–330
2085204.2162.3–252.3322.6300.8–334.7
2086195156–248.7319.4295.2–335.2
2087196.7160.8–244.5320.2302.2–334.7
2088208.4154.7–246.9321.4299.8–334.8
2089201.3158.6–248.2318.9296.4–338.1
2090197.9160.7–253.6317.6304–337.4
2091210161.6–256320.1299.8–336.1
2092205.8158.3–253.6316.3298–336.4
2093207.2161.2–251.9316.1297.7–333.7
2094207.9161.3–250.6323.4301.8–337.3
2095214.2166.4–258.5322.9302.4–338.9
2096205.3159.8–257.4319.6302.8–336.7
2097206.6158.4–256.8319.8299.1–336
2098209.1161–258.3320.5303.9–336
2099208.5160.1–256319.9303.1–334.4
2100208158.6–258.6320.8301.1–336.7

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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