India vs Tanzania

India vs Tanzania: Tropical nights

Tropical nights in 2014: 215.5 nights in India and 106.3 nights in Tanzania, in the CMIP6 climate models’ median run. In 2014 the value was higher in India, by 109.2 nights, or 2.03 times that in Tanzania.

Watch it change on the map

nights a year · CMIP6

India vs Tanzania: Tropical nightsTropical nights in 2014: 215.5 nights in India and 106.3 nights in Tanzania, in the CMIP6 climate models’ median run. In 2014 the value was higher in India, by 109.2 nights, or 2.03 times that in Tanzania.
IndiaTanzaniaCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • India has been higher in every year both have figures for since 1950.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts India at 250.4 nights and Tanzania at 208 nights in 2100.

Every year

nights a year · CMIP6

YearIndiaTanzania
2014215.5106.3
2013216105.5
2012217.4108.4
2011215.5109
2010215.2103.9
2009213.7101.1
2008213.7100.3
2007213.399.5
2006212.5100.4
2005212.998.7
2004214.9100.4
2003212.199.6
200221398.4
2001212.2101.5
2000210.894.9
1999211.994.2
1998210.396.8
1997209.593.5
199620789.6
1995208.888.5
1994207.185.4
199320681.2
1992203.580.1
199120889.6
1990209.991.1
198920889.1
1988207.387.6
1987207.288.5
1986207.289.4
1985205.983
1984206.783.1
1983203.382.1
1982205.784.8
1981208.488
1980205.385
1979205.585.5
1978204.182.9
1977205.984.1
1976204.681
1975202.680.8
1974205.382.2
1973205.683.6
1972203.881.4
1971203.680.7
1970202.681.8
1969203.679.5
1968201.779.1
1967201.677.5
1966203.978.1
1965202.478
1964201.273.8
1963201.873.1
1962203.881.1
1961206.382.6
196020683.9
1959206.181.4
195820482.1
1957206.580.9
195620480
1955203.281.6
195420681.4
195320480.6
1952204.481.4
1951204.382.1
1950206.380.7
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearIndiaTanzania
2015216.9202.1–228.4114.895.2–137.4
2016215202.5–227.6108.391.6–125.6
2017216.2202.1–228.5114.893.6–137.8
2018218.3204.5–231.4116.497.4–137.1
2019217.4202.3–230.4113.495.2–133.2
2020217.6203.1–232119.396.2–143.5
2021219.7204.5–233.2119.5100.9–147.1
2022220.5205.8–233124.4105.3–147.4
2023220.4206.4–233.7122.1102.1–147.8
2024222.6208.1–236.3126.2100–150.1
2025219.7202.7–236.2124.6100.9–154
2026220.2204.2–236.8126.999.1–155.6
2027223.6207.1–239128102.9–147.7
2028222.1205.1–235.7129.9106.4–152.1
2029225.4210–240.3134.1111–156.5
2030222.7207.3–235.6129.4106.4–151.1
2031223.8207.1–237.8136.4115.9–158.4
2032225.9210–240.5135.1114–166.8
2033223.8209.3–235.4135.2110.9–161.9
2034225.8210–242.7134.7112.5–166.3
2035224.9210.6–240.9140.2114.6–165
2036224.2210.4–240.3137.8113.3–165.5
2037228.3213.6–242.6142.9121.5–168.9
2038228.1212.6–241.4147.9124.3–169.3
2039227.4211.9–242.8144.3117.6–171.3
2040227212.2–242140.5117–178
2041227.1210.7–240.5149.1121.7–175.6
2042229.1214.8–244.2146.3120.5–185.3
2043228.9214.5–244144.6117–173
2044229.7214.7–244.2147.7121.2–175.8
2045231.2216.6–246.1150.1125.2–183.4
2046232.6216.2–248.6161.5128.6–192.2
2047232.4215.5–250160.2129.7–194.4
2048235.6218.5–254.3158.8132.7–193.1
2049235.3218.3–249.9157.6131.1–196
2050233.5216.6–253.4159.7127.8–192
2051234.9218.6–249.9163.2135.5–197.8
2052234219.4–249.3161.9136.2–199.1
2053234.9219.5–252.5160.6127.5–197.7
2054237.2219.4–253.1163.2131.5–200.2
2055237.2220.7–256.6167.4137–208.3
2056238.8221.2–254.9166.4130.8–209.7
2057235.7218.5–253.8168134.6–208.3
2058238.8222.8–254.1168.7134.9–210.3
2059241.1224.9–256.2174.7144.9–219.5
2060240.9225.6–255.6181.7138.1–219.8
2061237.9220.6–253.7171.8135.5–218.1
2062239.9221.2–259.7177.5139.9–224
2063239.9222.3–256.1181.4140.1–213.8
2064240223.8–256.3185.3140.1–215.2
2065240.9222.9–257.3183.5141.1–221.8
2066241.5224.1–258.6181.9146.3–219.3
2067241.6224.8–257.9178.8138.8–229.2
2068242.3224.6–262.1186144.2–228.4
2069242.8225.4–259.1183.7141.7–225.3
2070240.1225.3–257.1180.7140.4–228.2
2071241.8223.7–260.2184.9146.9–235.2
2072242.6225.4–259.2191.3155–226.1
2073243.8228–259.3191.6152.4–235.6
2074241.6226.9–258.4183.3149.7–231.7
2075243.5226.4–258.2183.8144.8–233
2076243.2224.6–259.9191.1150.9–233.2
2077243.7226.6–264.6191.3152.6–238.2
2078246.7230.1–261.5189156.6–233.8
2079244.9226.4–261.3190.5147–237.3
2080246.7227–263.7197.3150.7–241.2
2081247.5230.4–264.8195.6153.4–237.4
2082244.6227.9–262.3194.5157.3–237.3
2083247.5227.8–265.1193.2151–249.8
2084247.3228.3–266.8201.1152.2–246.7
2085246.5227.5–267.1204.2162.3–252.3
2086245.6227.7–266.5195156–248.7
2087245.5226.7–266.2196.7160.8–244.5
2088247229.4–266.9208.4154.7–246.9
2089248.1230–266.3201.3158.6–248.2
2090249228.9–266.4197.9160.7–253.6
2091248.9230–266.5210161.6–256
2092249.8232.3–267.8205.8158.3–253.6
2093248.1229–266.5207.2161.2–251.9
2094249.1230.6–268.4207.9161.3–250.6
2095251233.2–270214.2166.4–258.5
2096247.8231.4–269.3205.3159.8–257.4
2097251.1231.4–268206.6158.4–256.8
2098252.3230.8–268.3209.1161–258.3
2099248.7230.1–268.3208.5160.1–256
2100250.4232.9–266.7208158.6–258.6

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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