Argentina vs Tanzania

Argentina vs Tanzania: Tropical nights

Tropical nights in 2014: 42.6 nights in Argentina and 106.3 nights in Tanzania, in the CMIP6 climate models’ median run. In 2014 the value was higher in Tanzania, by 63.7 nights, or 2.49 times that in Argentina.

nights a year · CMIP6

Argentina vs Tanzania: Tropical nightsTropical nights in 2014: 42.6 nights in Argentina and 106.3 nights in Tanzania, in the CMIP6 climate models’ median run. In 2014 the value was higher in Tanzania, by 63.7 nights, or 2.49 times that in Argentina.
ArgentinaTanzaniaCMIP6 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 Argentina at 65.2 nights and Tanzania at 208 nights in 2100.

Every year

nights a year · CMIP6

YearArgentinaTanzania
201442.6106.3
201341.8105.5
201241108.4
201141.5109
201040.8103.9
200941.6101.1
200841.4100.3
200739.699.5
200639.3100.4
200540.198.7
200440.6100.4
200338.799.6
200240.298.4
200139.5101.5
200039.594.9
199938.494.2
199838.896.8
199738.593.5
19963789.6
199535.788.5
199434.785.4
199334.381.2
199233.480.1
199136.889.6
19903691.1
19893789.1
198836.987.6
198736.188.5
198636.489.4
19853683
198434.783.1
198335.282.1
198235.784.8
198137.388
198036.385
197937.585.5
197835.782.9
197735.184.1
197635.781
197534.880.8
197435.482.2
197334.183.6
197235.581.4
197134.780.7
197034.981.8
196933.479.5
196834.679.1
19673377.5
196634.378.1
196532.278
196430.373.8
19633273.1
196235.281.1
196135.682.6
196035.683.9
195934.581.4
195833.382.1
195735.780.9
19563580
195534.481.6
195433.681.4
195335.880.6
195234.481.4
195135.482.1
195033.980.7
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearArgentinaTanzania
201544.433.5–53.4114.895.2–137.4
201643.532.4–53.4108.391.6–125.6
201742.834.2–52.6114.893.6–137.8
201842.632.7–52.6116.497.4–137.1
201943.633.4–53.1113.495.2–133.2
202045.735.5–57.2119.396.2–143.5
202145.634.3–56.4119.5100.9–147.1
202244.633.3–56.4124.4105.3–147.4
202344.934.9–53.9122.1102.1–147.8
202445.834.2–57.5126.2100–150.1
202546.536–58.1124.6100.9–154
202647.936.7–57126.999.1–155.6
202746.434.5–56.3128102.9–147.7
202846.133.5–58.5129.9106.4–152.1
202948.336–58.8134.1111–156.5
203047.835.4–60.3129.4106.4–151.1
203146.935.5–58.7136.4115.9–158.4
20324837.9–59.7135.1114–166.8
203347.436.7–57.6135.2110.9–161.9
203447.837.8–58.9134.7112.5–166.3
203547.339–59140.2114.6–165
203648.939.2–58.4137.8113.3–165.5
20375141.2–60.9142.9121.5–168.9
203848.737.9–59.7147.9124.3–169.3
203948.839.1–58.4144.3117.6–171.3
204050.440.4–62.3140.5117–178
204150.740.4–61.7149.1121.7–175.6
20425037.3–61.1146.3120.5–185.3
204350.340.5–61144.6117–173
20445240–65147.7121.2–175.8
204552.441.3–65.6150.1125.2–183.4
204651.941.2–62.7161.5128.6–192.2
204751.542–64160.2129.7–194.4
204854.542.6–65158.8132.7–193.1
204953.942.8–64.9157.6131.1–196
205052.543.6–62.4159.7127.8–192
205155.743.3–67.4163.2135.5–197.8
205255.642.2–67161.9136.2–199.1
205355.342.6–67.6160.6127.5–197.7
205454.743–66.7163.2131.5–200.2
205555.942.3–67.2167.4137–208.3
205657.642.8–68.6166.4130.8–209.7
205756.842.3–68.7168134.6–208.3
205854.841.5–68.1168.7134.9–210.3
205957.844.9–70.3174.7144.9–219.5
206056.943.4–67.9181.7138.1–219.8
206156.542.3–70.6171.8135.5–218.1
206257.345.8–68.5177.5139.9–224
206358.244.6–70.2181.4140.1–213.8
206458.845.8–71.5185.3140.1–215.2
206558.746–71.1183.5141.1–221.8
206659.947–68.9181.9146.3–219.3
206758.944.8–71.5178.8138.8–229.2
206857.844.3–70.3186144.2–228.4
206959.445.4–70.5183.7141.7–225.3
207061.348.2–74.3180.7140.4–228.2
207160.148.7–73.2184.9146.9–235.2
207260.146.7–73.7191.3155–226.1
207360.847.6–73.5191.6152.4–235.6
207460.447.6–73.7183.3149.7–231.7
207561.347.9–73.9183.8144.8–233
207661.146.8–75.8191.1150.9–233.2
207760.647.1–73.1191.3152.6–238.2
207863.449.4–75.6189156.6–233.8
207959.948.7–73190.5147–237.3
20806348.1–75.5197.3150.7–241.2
208161.448.7–73.5195.6153.4–237.4
208261.747.7–75194.5157.3–237.3
208362.148.2–75.6193.2151–249.8
208462.950.3–76201.1152.2–246.7
20856246.9–76.4204.2162.3–252.3
208662.749.4–76.4195156–248.7
208761.847.9–74.9196.7160.8–244.5
208864.450.8–77.5208.4154.7–246.9
208962.749.3–76.3201.3158.6–248.2
209064.152.5–77.1197.9160.7–253.6
209164.451.7–78.3210161.6–256
209265.650.8–78.3205.8158.3–253.6
209362.749.7–76.8207.2161.2–251.9
209463.950.1–78.5207.9161.3–250.6
209562.948.8–77.9214.2166.4–258.5
209664.749.4–78.9205.3159.8–257.4
209764.552.4–80.1206.6158.4–256.8
209865.652.7–78.7209.1161–258.3
209965.249.3–80.2208.5160.1–256
210065.250–81.7208158.6–258.6

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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