Argentina vs Brazil

Argentina vs Brazil: Tropical nights

Tropical nights in 2014: 42.6 nights in Argentina and 282 nights in Brazil, in the CMIP6 climate models’ median run. In 2014 the value was higher in Brazil, by 239.4 nights, or 6.61 times that in Argentina.

nights a year · CMIP6

Argentina vs Brazil: Tropical nightsTropical nights in 2014: 42.6 nights in Argentina and 282 nights in Brazil, in the CMIP6 climate models’ median run. In 2014 the value was higher in Brazil, by 239.4 nights, or 6.61 times that in Argentina.
ArgentinaBrazilCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Brazil 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 Brazil at 317.3 nights in 2100.

Every year

nights a year · CMIP6

YearArgentinaBrazil
201442.6282
201341.8282.5
201241283.6
201141.5281.8
201040.8280.2
200941.6279.2
200841.4277.8
200739.6275.9
200639.3275.4
200540.1275.8
200440.6278.5
200338.7276.7
200240.2276.2
200139.5277.7
200039.5273.3
199938.4271
199838.8273.6
199738.5270.9
199637264.7
199535.7263.8
199434.7263.6
199334.3258.3
199233.4252.8
199136.8269.7
199036272.5
198937267.1
198836.9264.7
198736.1265.9
198636.4265.7
198536260.6
198434.7261.1
198335.2255.9
198235.7264.8
198137.3265.3
198036.3262
197937.5261.2
197835.7259.7
197735.1261.8
197635.7258.4
197534.8252.4
197435.4258.7
197334.1261
197235.5258.1
197134.7256.5
197034.9254.5
196933.4255.1
196834.6254.9
196733250.3
196634.3254.5
196532.2250.8
196430.3246.2
196332241.9
196235.2257.9
196135.6258.2
196035.6260.3
195934.5255.7
195833.3258.1
195735.7256.6
195635251.2
195534.4257.2
195433.6258
195335.8255.2
195234.4255.8
195135.4256.3
195033.9254.4
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearArgentinaBrazil
201544.433.5–53.4284257.6–299.5
201643.532.4–53.4281.5261.4–296.3
201742.834.2–52.6284.1263.2–297.8
201842.632.7–52.6283.4263.1–298.8
201943.633.4–53.1283.7262.9–302.4
202045.735.5–57.2286.3264.2–303.6
202145.634.3–56.4287.9266.9–302.3
202244.633.3–56.4289.6270.7–303.9
202344.934.9–53.9287.3270.5–303
202445.834.2–57.5291.5268.2–304.6
202546.536–58.1289.6269.1–304.8
202647.936.7–57289.7266.2–305.1
202746.434.5–56.3289.5269.1–303.7
202846.133.5–58.5291.2271.9–306
202948.336–58.8295.5276.3–309.6
203047.835.4–60.3292.2269.1–306.9
203146.935.5–58.7295.3274–308.9
20324837.9–59.7296.8280.1–310.9
203347.436.7–57.6295.4273.3–308.5
203447.837.8–58.9294.3273.5–311.8
203547.339–59295.2274.4–310.9
203648.939.2–58.4295.1278.2–307.8
20375141.2–60.9299.9283.7–311.3
203848.737.9–59.7299.6283.5–312.1
203948.839.1–58.4296.9279.9–311
204050.440.4–62.3298.5279.4–311.9
204150.740.4–61.7301.4282.2–314.1
20425037.3–61.1300.6277.8–314.7
204350.340.5–61298.2279.8–312.7
20445240–65300.3281.2–315.6
204552.441.3–65.6299.9279.7–313.1
204651.941.2–62.7302.8285.8–316.6
204751.542–64303.3285–316.4
204854.542.6–65304.4287.3–317.3
204953.942.8–64.9305.4288.4–318.5
205052.543.6–62.4303.9283.9–316.2
205155.743.3–67.4305.7287.5–317.5
205255.642.2–67305.9290.6–318.8
205355.342.6–67.6305.3285.9–317.5
205454.743–66.7306.1288.4–319.3
205555.942.3–67.2306.4290.8–319.8
205657.642.8–68.6306.7288.3–321.1
205756.842.3–68.7306.4287.6–321.1
205854.841.5–68.1309291–321.4
205957.844.9–70.3310.4290.7–322.8
206056.943.4–67.9311.6294.4–325.1
206156.542.3–70.6307.5290.3–321.2
206257.345.8–68.5309291.5–322.8
206358.244.6–70.2310.8290.6–322.8
206458.845.8–71.5309.6290.2–321.1
206558.746–71.1311.1291.6–323.1
206659.947–68.9312.2294.2–324.2
206758.944.8–71.5310.9291.5–323.3
206857.844.3–70.3311.5293–324.5
206959.445.4–70.5310.6292.2–323.6
207061.348.2–74.3312.7293–325.3
207160.148.7–73.2311.5292.1–323.9
207260.146.7–73.7313.3297.8–325.5
207360.847.6–73.5313.8298.6–327.2
207460.447.6–73.7311.4292.4–323.5
207561.347.9–73.9310.9291.5–324.5
207661.146.8–75.8314.2294.4–327.7
207760.647.1–73.1315.1295.6–326.8
207863.449.4–75.6314.9296.3–327.1
207959.948.7–73313292.8–326.8
20806348.1–75.5311.9294.7–325.4
208161.448.7–73.5313.5295.5–325.6
208261.747.7–75315296.8–327.1
208362.148.2–75.6315.7295.3–328.7
208462.950.3–76316.6298.5–327.9
20856246.9–76.4317.4299.3–329.8
208662.749.4–76.4315.8294–328
208761.847.9–74.9316.5297.9–328.5
208864.450.8–77.5315.6297.3–328.8
208962.749.3–76.3315296.6–327.9
209064.152.5–77.1316298.9–330.3
209164.451.7–78.3317298.1–330.6
209265.650.8–78.3317.6298.7–330.7
209362.749.7–76.8316.3297.5–328.6
209463.950.1–78.5316.4300.2–327.5
209562.948.8–77.9319299.8–330.7
209664.749.4–78.9317.7299.5–330.8
209764.552.4–80.1316.9297.8–329
209865.652.7–78.7318.6299.9–331.9
209965.249.3–80.2318.1300–330.2
210065.250–81.7317.3298.4–330.3

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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