Iran vs Tanzania

Iran vs Tanzania: Tropical nights

Tropical nights in 2014: 90.8 nights in Iran and 106.3 nights in Tanzania, in the CMIP6 climate models’ median run. In 2014 the value was higher in Tanzania, by 15.5 nights, or 1.17 times that in Iran.

nights a year · CMIP6

Iran vs Tanzania: Tropical nightsTropical nights in 2014: 90.8 nights in Iran and 106.3 nights in Tanzania, in the CMIP6 climate models’ median run. In 2014 the value was higher in Tanzania, by 15.5 nights, or 1.17 times that in Iran.
IranTanzaniaCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Tanzania has been higher since 1965, when it overtook Iran.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts Iran at 124.4 nights and Tanzania at 208 nights in 2100.

Every year

nights a year · CMIP6

YearIranTanzania
201490.8106.3
201390.6105.5
201290.3108.4
201189.8109
201087103.9
200987.2101.1
200888100.3
200787.999.5
200688.2100.4
200587.198.7
200486.4100.4
200385.999.6
200287.798.4
200186.6101.5
200086.594.9
199985.194.2
199883.896.8
199784.393.5
199682.489.6
199581.688.5
199480.685.4
199377.781.2
19927780.1
199181.989.6
199083.191.1
198981.789.1
198880.487.6
198779.888.5
198680.189.4
198579.783
198478.883.1
198375.582.1
198277.984.8
198180.288
198079.485
197980.285.5
197878.182.9
197777.884.1
197677.281
197576.680.8
197476.482.2
197376.983.6
197277.481.4
19717580.7
197075.381.8
196975.679.5
196876.379.1
196776.177.5
196675.578.1
196575.378
196475.373.8
196376.473.1
196277.581.1
196177.782.6
196077.683.9
195976.881.4
195878.482.1
195778.280.9
195676.280
195576.581.6
195477.681.4
195377.380.6
195278.281.4
195176.682.1
195077.780.7
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearIranTanzania
201594.482.7–106114.895.2–137.4
201694.183.1–105.3108.391.6–125.6
201793.981.7–104114.893.6–137.8
201894.183–106.3116.497.4–137.1
201995.183.4–105.8113.495.2–133.2
202095.985.5–107.5119.396.2–143.5
202197.286–107.7119.5100.9–147.1
202296.285.2–108.3124.4105.3–147.4
202398.485.7–110.6122.1102.1–147.8
20249885.1–110.5126.2100–150.1
202598.787.2–110.5124.6100.9–154
202699.186.7–111.7126.999.1–155.6
2027100.288.7–112.1128102.9–147.7
202899.686.2–111.6129.9106.4–152.1
202999.386.7–109.9134.1111–156.5
2030100.889.8–111.6129.4106.4–151.1
2031102.990.1–114.8136.4115.9–158.4
2032101.289–113.9135.1114–166.8
2033102.889.9–115.1135.2110.9–161.9
2034104.690.7–117.3134.7112.5–166.3
203510592.1–116.2140.2114.6–165
2036103.591.4–115.2137.8113.3–165.5
203710693.1–119.4142.9121.5–168.9
2038105.993.2–116.5147.9124.3–169.3
2039104.392.9–116.6144.3117.6–171.3
2040105.592.3–118.4140.5117–178
2041104.892.5–116.4149.1121.7–175.6
2042107.793.4–120.7146.3120.5–185.3
204310692.9–118.8144.6117–173
2044106.893.6–121.2147.7121.2–175.8
2045108.795.1–121.8150.1125.2–183.4
2046109.296.1–121.9161.5128.6–192.2
2047110.596.9–123.3160.2129.7–194.4
2048111.498.6–124158.8132.7–193.1
204911095.7–123.4157.6131.1–196
2050112.199.6–125.1159.7127.8–192
2051111.797.6–124.1163.2135.5–197.8
205211298.3–126161.9136.2–199.1
2053111.597.5–124160.6127.5–197.7
2054111.899–126.3163.2131.5–200.2
2055114.6101.4–128167.4137–208.3
2056113.1100.2–126.2166.4130.8–209.7
2057113.899.9–128.9168134.6–208.3
2058114.2100–127.7168.7134.9–210.3
2059113.498.9–126.3174.7144.9–219.5
2060116.3100.3–129.5181.7138.1–219.8
2061115100.5–127.6171.8135.5–218.1
2062115.799.7–131.1177.5139.9–224
2063117103.7–130.3181.4140.1–213.8
2064115.1101.7–130.3185.3140.1–215.2
2065117.3102.8–132.3183.5141.1–221.8
2066117.7103.7–132.1181.9146.3–219.3
2067119103–133.5178.8138.8–229.2
2068118.1104.3–131.4186144.2–228.4
2069118.4103.8–133.2183.7141.7–225.3
2070118.2104.9–132.9180.7140.4–228.2
2071119.3105.1–135.3184.9146.9–235.2
2072119103.2–135.1191.3155–226.1
2073120.6104.9–137.7191.6152.4–235.6
2074119.1104.4–134.5183.3149.7–231.7
2075119.7105.9–135.5183.8144.8–233
2076118.9102.6–137191.1150.9–233.2
2077120.3104.3–135.3191.3152.6–238.2
2078119.2105.3–136189156.6–233.8
2079120.1103.2–135.6190.5147–237.3
2080120.4106.1–139.5197.3150.7–241.2
2081120.5104.3–137.1195.6153.4–237.4
2082122.5107.5–137.7194.5157.3–237.3
2083122.6105.8–140.4193.2151–249.8
2084123.7107.6–139.1201.1152.2–246.7
2085121.3106.6–140.6204.2162.3–252.3
2086122.1107.9–139.8195156–248.7
2087123.7108–140.7196.7160.8–244.5
2088124106.5–142.2208.4154.7–246.9
2089124.1109–139.7201.3158.6–248.2
2090122.5105.4–141.9197.9160.7–253.6
2091124.3108–140.5210161.6–256
2092125.5110.1–140205.8158.3–253.6
2093124.5109–139.3207.2161.2–251.9
2094125108.7–142.9207.9161.3–250.6
2095125.3108.5–141214.2166.4–258.5
2096125.8109–142.4205.3159.8–257.4
2097123.6106.9–141.8206.6158.4–256.8
2098125.8109.4–143.8209.1161–258.3
2099123.1107.8–140.1208.5160.1–256
2100124.4107.9–143.7208158.6–258.6

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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