Argentina vs Vietnam

Argentina vs Vietnam: Tropical nights

Tropical nights in 2014: 42.6 nights in Argentina and 242.6 nights in Vietnam, in the CMIP6 climate models’ median run. In 2014 the value was higher in Vietnam, by 200 nights, or 5.69 times that in Argentina.

nights a year · CMIP6

Argentina vs Vietnam: Tropical nightsTropical nights in 2014: 42.6 nights in Argentina and 242.6 nights in Vietnam, in the CMIP6 climate models’ median run. In 2014 the value was higher in Vietnam, by 200 nights, or 5.69 times that in Argentina.
ArgentinaVietnamCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Vietnam 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 Vietnam at 282.7 nights in 2100.

Every year

nights a year · CMIP6

YearArgentinaVietnam
201442.6242.6
201341.8243.6
201241242.4
201141.5241.9
201040.8240.9
200941.6240
200841.4238.7
200739.6238
200639.3239.1
200540.1238.3
200440.6241.8
200338.7235.9
200240.2236.3
200139.5241.3
200039.5237
199938.4236
199838.8236.8
199738.5237.1
199637231.4
199535.7229.9
199434.7231.2
199334.3225.3
199233.4221.9
199136.8229.8
199036233.1
198937229.2
198836.9231.7
198736.1230.3
198636.4230.9
198536228
198434.7230.2
198335.2224.9
198235.7227.5
198137.3229.7
198036.3228.1
197937.5228.9
197835.7227.5
197735.1229
197635.7225.8
197534.8225.8
197435.4225.7
197334.1230.3
197235.5225.6
197134.7225.5
197034.9226.1
196933.4227
196834.6227.1
196733222.9
196634.3226.8
196532.2225.4
196430.3222.6
196332224.3
196235.2227.8
196135.6229.4
196035.6228.5
195934.5226.2
195833.3230.9
195735.7228.4
195635228.7
195534.4227.5
195433.6226.3
195335.8228.2
195234.4228.5
195135.4229.2
195033.9228.9
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearArgentinaVietnam
201544.433.5–53.4245.8228.2–261.9
201643.532.4–53.4243.7226.1–259.4
201742.834.2–52.6247229.6–260.7
201842.632.7–52.6248.3228.8–263.3
201943.633.4–53.1248.1229.2–262.8
202045.735.5–57.2248.8229.6–262.9
202145.634.3–56.4251.1233.1–266.7
202244.633.3–56.4252.4234.3–266.8
202344.934.9–53.9253.4235.4–266.6
202445.834.2–57.5251.3236.7–264.6
202546.536–58.1252.4234.1–266.2
202647.936.7–57252.8232.9–268.5
202746.434.5–56.3253.2237–268.8
202846.133.5–58.5254.3237.7–272
202948.336–58.8258.1238.5–273.7
203047.835.4–60.3256.4240.9–270.1
203146.935.5–58.7255.9241.2–270.9
20324837.9–59.7257.4239.1–269.9
203347.436.7–57.6258.3241.7–273.2
203447.837.8–58.9258.8240.7–275.8
203547.339–59258.6242.5–275.4
203648.939.2–58.4258.3239.1–273.8
20375141.2–60.9258.7242.4–272.4
203848.737.9–59.7263.4247.6–277.3
203948.839.1–58.4260.2243.7–275.9
204050.440.4–62.3260.1242.8–274.8
204150.740.4–61.7263.1245.9–279
20425037.3–61.1263.6246.5–279
204350.340.5–61261.4245.5–279.6
20445240–65263.2245.2–277.6
204552.441.3–65.6265.5249.6–281.4
204651.941.2–62.7264.9246.8–278.1
204751.542–64266.9250.4–282.9
204854.542.6–65268250.5–283.8
204953.942.8–64.9267.8251.2–284.8
205052.543.6–62.4268251.2–283.5
205155.743.3–67.4270.9254.3–285.4
205255.642.2–67268.3253.3–282.8
205355.342.6–67.6268.5250.1–283.2
205454.743–66.7269.1250.8–283.6
205555.942.3–67.2270249.8–284.2
205657.642.8–68.6271.7254.8–290.1
205756.842.3–68.7270253–285.7
205854.841.5–68.1270.7252.9–286.5
205957.844.9–70.3273257.6–286.6
206056.943.4–67.9275.7255.6–290.6
206156.542.3–70.6273.5254.6–289.5
206257.345.8–68.5271.4253.1–290.2
206358.244.6–70.2272.9252.7–288.1
206458.845.8–71.5273.8254.4–288.1
206558.746–71.1272255.8–286.3
206659.947–68.9271.8254.4–287.3
206758.944.8–71.5270.8252.1–287.6
206857.844.3–70.3275.6256.9–292.4
206959.445.4–70.5276.4258.5–292
207061.348.2–74.3274.6257.8–292.8
207160.148.7–73.2276.2255.2–294.1
207260.146.7–73.7277256.8–291.4
207360.847.6–73.5278.5262.7–293.4
207460.447.6–73.7277.6259.2–295.8
207561.347.9–73.9276.4258.7–294.4
207661.146.8–75.8279.2258.5–296.6
207760.647.1–73.1280264.9–294.7
207863.449.4–75.6278.4258.6–295.5
207959.948.7–73279.3263.4–293.6
20806348.1–75.5280261.4–296.5
208161.448.7–73.5280.4261.2–297.9
208261.747.7–75280.9258.9–296
208362.148.2–75.6278.4261.6–295
208462.950.3–76279257.4–293.8
20856246.9–76.4281.3265–297.8
208662.749.4–76.4282.3260.2–297.5
208761.847.9–74.9281.9261.3–300.7
208864.450.8–77.5283.1265.7–301.5
208962.749.3–76.3280.8261.2–301.6
209064.152.5–77.1280.5264.5–297.6
209164.451.7–78.3283.3264.3–298.9
209265.650.8–78.3281.1261.5–299.4
209362.749.7–76.8280.5263.4–296.1
209463.950.1–78.5283264.5–300
209562.948.8–77.9285.5264.5–302.9
209664.749.4–78.9281.8264.8–299.8
209764.552.4–80.1281.5263.3–299
209865.652.7–78.7282.5263.9–301.5
209965.249.3–80.2282.7265.1–298
210065.250–81.7282.7261–298.2

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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