Argentina vs Indonesia

Argentina vs Indonesia: Tropical nights

Tropical nights in 2014: 42.6 nights in Argentina and 306.5 nights in Indonesia, in the CMIP6 climate models’ median run. In 2014 the value was higher in Indonesia, by 263.9 nights, or 7.19 times that in Argentina.

nights a year · CMIP6

Argentina vs Indonesia: Tropical nightsTropical nights in 2014: 42.6 nights in Argentina and 306.5 nights in Indonesia, in the CMIP6 climate models’ median run. In 2014 the value was higher in Indonesia, by 263.9 nights, or 7.19 times that in Argentina.
ArgentinaIndonesiaCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Indonesia 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 Indonesia at 334.7 nights in 2100.

Every year

nights a year · CMIP6

YearArgentinaIndonesia
201442.6306.5
201341.8306.4
201241306.3
201141.5304.6
201040.8305.6
200941.6303.9
200841.4303.4
200739.6302.8
200639.3302.8
200540.1303.1
200440.6303.8
200338.7301.9
200240.2301.5
200139.5302.5
200039.5300.9
199938.4300.4
199838.8299.1
199738.5297.9
199637296.9
199535.7296.7
199434.7294.6
199334.3291.6
199233.4288.5
199136.8297.5
199036298.6
198937296
198836.9296.5
198736.1295.6
198636.4295.8
198536292.8
198434.7293.4
198335.2290.1
198235.7294.4
198137.3294.8
198036.3294.5
197937.5294.1
197835.7292.4
197735.1293.4
197635.7290.8
197534.8290.7
197435.4291.5
197334.1293
197235.5290.7
197134.7291.2
197034.9288.9
196933.4290.4
196834.6289.1
196733288.4
196634.3290.5
196532.2288.1
196430.3286.2
196332286.4
196235.2291.2
196135.6292.9
196035.6292.8
195934.5291.9
195833.3293.3
195735.7290.8
195635291.5
195534.4292.4
195433.6290
195335.8291.4
195234.4291.7
195135.4291.7
195033.9291.8
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearArgentinaIndonesia
201544.433.5–53.4308295.7–315.1
201643.532.4–53.4307.5296.9–315.6
201742.834.2–52.6308.4295.5–315.6
201842.632.7–52.6308.9296.4–316.6
201943.633.4–53.1309.9297.8–316.7
202045.735.5–57.2310.9299–318.6
202145.634.3–56.4310.4298.7–316.4
202244.633.3–56.4311.4300–319.6
202344.934.9–53.9311.4300.1–320.6
202445.834.2–57.5313.2302–321.2
202546.536–58.1312300.2–321.9
202647.936.7–57312.6299.9–322.8
202746.434.5–56.3312.7301–321.6
202846.133.5–58.5313.8302.4–321.9
202948.336–58.8315.5303.9–324.1
203047.835.4–60.3314.2303.1–322.6
203146.935.5–58.7314.4301.6–321.9
20324837.9–59.7316.9304.8–325.8
203347.436.7–57.6316.6305.4–323.3
203447.837.8–58.9315.4303.9–323.9
203547.339–59316.3305.6–327.9
203648.939.2–58.4317.7305.2–326.2
20375141.2–60.9318.1306.6–325.2
203848.737.9–59.7319.3307.6–326.7
203948.839.1–58.4319.4307.7–326.7
204050.440.4–62.3318.5306.5–329
204150.740.4–61.7320.2308.3–328
20425037.3–61.1320308.7–329.2
204350.340.5–61318.6306.6–327.8
20445240–65321309–329.6
204552.441.3–65.6320.8308.6–329.8
204651.941.2–62.7322.8308.8–331
204751.542–64323.2308.8–333
204854.542.6–65322.3310.5–332.3
204953.942.8–64.9323.8311.6–334.1
205052.543.6–62.4323.3310.3–333.5
205155.743.3–67.4323.6311.2–334.4
205255.642.2–67325.1311.7–334.7
205355.342.6–67.6323.9310.9–333.8
205454.743–66.7324.4311.9–335.4
205555.942.3–67.2325.3312.6–335
205657.642.8–68.6325.9312.4–337.2
205756.842.3–68.7326.9311.9–335.2
205854.841.5–68.1326.4313–336.3
205957.844.9–70.3326.4313.9–337.8
206056.943.4–67.9329.5314.9–338.2
206156.542.3–70.6327.9313.6–336.9
206257.345.8–68.5327.6312.4–339.1
206358.244.6–70.2327.6313.9–339.4
206458.845.8–71.5328.8314.4–338.6
206558.746–71.1327.6314.3–337.9
206659.947–68.9328.6314.7–338.5
206758.944.8–71.5328.8314.3–338
206857.844.3–70.3329.8314.4–339.9
206959.445.4–70.5329.5315.5–339.3
207061.348.2–74.3328.6315.1–338.8
207160.148.7–73.2331.4315.5–340.4
207260.146.7–73.7330.3315.9–339.4
207360.847.6–73.5331.4317.7–341
207460.447.6–73.7329.9316.8–341.1
207561.347.9–73.9330.5315.2–340.2
207661.146.8–75.8331.5316.2–341.2
207760.647.1–73.1332315.9–341.8
207863.449.4–75.6331.4318.2–340.9
207959.948.7–73331.5316.6–341.5
20806348.1–75.5332.4317.4–343.2
208161.448.7–73.5332.3318.4–341.8
208261.747.7–75332.8317.8–340.4
208362.148.2–75.6333.1318.7–342.6
208462.950.3–76333317.8–342.7
20856246.9–76.4332.6318.1–342.7
208662.749.4–76.4333.1318.9–342.3
208761.847.9–74.9332.6318–343.2
208864.450.8–77.5334.5319.5–343.1
208962.749.3–76.3333.6319–342.6
209064.152.5–77.1333.3319.6–343.3
209164.451.7–78.3334.5318.2–343.9
209265.650.8–78.3335.1319.7–343.6
209362.749.7–76.8333.9320–344.9
209463.950.1–78.5335.6320.4–342.8
209562.948.8–77.9335.4319.7–343
209664.749.4–78.9334.3319.4–346.1
209764.552.4–80.1334.2319.5–343.3
209865.652.7–78.7334.9319.4–343.4
209965.249.3–80.2334.4319.4–344.6
210065.250–81.7334.7318.7–343.3

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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