Israel vs Tanzania

Israel vs Tanzania: Tropical nights

Tropical nights in 2014: 97.9 nights in Israel and 106.3 nights in Tanzania, in the CMIP6 climate models’ median run. In 2014 the value was higher in Tanzania, by 8.4 nights, or 1.09 times that in Israel.

nights a year · CMIP6

Israel vs Tanzania: Tropical nightsTropical nights in 2014: 97.9 nights in Israel and 106.3 nights in Tanzania, in the CMIP6 climate models’ median run. In 2014 the value was higher in Tanzania, by 8.4 nights, or 1.09 times that in Israel.
IsraelTanzaniaCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Tanzania 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 Tanzania at 208 nights in 2100.

Every year

nights a year · CMIP6

YearIsraelTanzania
201497.9106.3
201398105.5
201296.3108.4
201196.1109
201095.5103.9
200992.5101.1
200894.1100.3
200792.799.5
200692100.4
200592.398.7
200490.7100.4
200393.999.6
200289.598.4
200191101.5
200089.594.9
199986.294.2
199884.896.8
199784.293.5
199683.789.6
199579.388.5
199480.185.4
199371.681.2
199274.980.1
199178.489.6
199080.291.1
198976.989.1
198876.587.6
198775.188.5
198678.389.4
19857483
198469.483.1
198368.982.1
198273.584.8
198173.588
198075.385
197968.785.5
197871.682.9
197772.384.1
197673.281
197570.980.8
197472.582.2
197371.983.6
197269.981.4
197173.180.7
197071.681.8
196970.679.5
196869.679.1
196766.977.5
196668.878.1
196563.578
196466.173.8
196370.973.1
19627381.1
196171.582.6
196074.983.9
19597581.4
19587382.1
195774.880.9
195674.380
195572.881.6
195471.481.4
195373.780.6
19527281.4
195175.182.1
195069.580.7
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearIsraelTanzania
2015105.988.4–122.7114.895.2–137.4
2016104.186.9–120.1108.391.6–125.6
2017107.892.2–121.4114.893.6–137.8
201810993.8–124.2116.497.4–137.1
201910989.1–124.5113.495.2–133.2
2020111.594.8–123.5119.396.2–143.5
202111097.7–128.4119.5100.9–147.1
2022111.493.6–130.2124.4105.3–147.4
2023117.297.9–130.5122.1102.1–147.8
2024114.5100.4–127.3126.2100–150.1
2025116.698.3–129.2124.6100.9–154
2026115.9104.9–126.2126.999.1–155.6
2027115.699–127.1128102.9–147.7
2028116.3100.2–127.9129.9106.4–152.1
2029117103.1–129.2134.1111–156.5
2030118.6102.1–131.2129.4106.4–151.1
2031116.8104.4–132.8136.4115.9–158.4
2032121.4106.5–134.7135.1114–166.8
2033122.1108.9–134.7135.2110.9–161.9
2034119.7103.1–133.4134.7112.5–166.3
2035122.8105.8–138.8140.2114.6–165
2036120.2105.5–136.2137.8113.3–165.5
2037121.6106.3–139.4142.9121.5–168.9
2038126.5105.7–142.1147.9124.3–169.3
2039127.7110.5–147.1144.3117.6–171.3
2040124.2109.7–144.9140.5117–178
2041126108.4–141.5149.1121.7–175.6
2042128.5105.8–144.3146.3120.5–185.3
2043126.5111.8–144.3144.6117–173
2044125.9108.5–142.9147.7121.2–175.8
2045130.9117.5–148.6150.1125.2–183.4
2046131114.4–147.2161.5128.6–192.2
2047132.3116.1–145.4160.2129.7–194.4
2048129.9118.5–146.9158.8132.7–193.1
2049127.9108.7–143.9157.6131.1–196
2050133.3114.3–147159.7127.8–192
2051130.3116.7–143.9163.2135.5–197.8
2052130.3119.9–143.5161.9136.2–199.1
2053135120.4–149.7160.6127.5–197.7
2054134.1118.8–149.3163.2131.5–200.2
2055134.1115.2–150.3167.4137–208.3
2056136110.1–151.1166.4130.8–209.7
2057132.7110.9–155168134.6–208.3
2058135.7119.1–155.4168.7134.9–210.3
2059138.8118.5–156.7174.7144.9–219.5
2060136.1116.3–159.2181.7138.1–219.8
2061136119–150.7171.8135.5–218.1
2062139118.7–156177.5139.9–224
2063137.4120.1–151.2181.4140.1–213.8
2064139.3121–154.3185.3140.1–215.2
2065139.6122.3–154.3183.5141.1–221.8
2066137.3119.6–153.7181.9146.3–219.3
2067142.8126–157.8178.8138.8–229.2
2068141.2122.8–156.4186144.2–228.4
2069141.5123.7–157.6183.7141.7–225.3
2070141.8120.5–157.5180.7140.4–228.2
2071142.9124.3–161.8184.9146.9–235.2
2072144.3127–158.5191.3155–226.1
2073143.6127.3–159.2191.6152.4–235.6
2074138.8123–162.2183.3149.7–231.7
2075138.1120.8–154.6183.8144.8–233
2076143.2126.8–161.5191.1150.9–233.2
2077142.8120.5–161.6191.3152.6–238.2
2078143.6128.2–160.6189156.6–233.8
2079148.6123.7–160.2190.5147–237.3
2080145.5123.5–163.7197.3150.7–241.2
2081143.7128.1–161.2195.6153.4–237.4
2082145.8129.5–160.6194.5157.3–237.3
2083149.4126–163.1193.2151–249.8
2084148128.2–164201.1152.2–246.7
2085149127.9–162.4204.2162.3–252.3
2086148127.4–167.1195156–248.7
2087146.4123.7–165.7196.7160.8–244.5
2088147.6133.5–165.3208.4154.7–246.9
2089148.4131.4–169.1201.3158.6–248.2
2090147.6132.4–168.8197.9160.7–253.6
2091151.9131.7–170.6210161.6–256
2092148.2132–167.4205.8158.3–253.6
2093154.1135.3–172.2207.2161.2–251.9
2094153.2133.6–170.3207.9161.3–250.6
2095148.2131.4–164.1214.2166.4–258.5
2096150.5130.5–172.6205.3159.8–257.4
2097149.8134.4–172.7206.6158.4–256.8
2098150.9131.3–170.1209.1161–258.3
2099149.5129–166.6208.5160.1–256
2100149.4130.1–168.5208158.6–258.6

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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