Argentina vs Thailand

Argentina vs Thailand: Tropical nights

Tropical nights in 2014: 42.6 nights in Argentina and 286.4 nights in Thailand, in the CMIP6 climate models’ median run. In 2014 the value was higher in Thailand, by 243.8 nights, or 6.72 times that in Argentina.

nights a year · CMIP6

Argentina vs Thailand: Tropical nightsTropical nights in 2014: 42.6 nights in Argentina and 286.4 nights in Thailand, in the CMIP6 climate models’ median run. In 2014 the value was higher in Thailand, by 243.8 nights, or 6.72 times that in Argentina.
ArgentinaThailandCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Thailand 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 Thailand at 320.8 nights in 2100.

Every year

nights a year · CMIP6

YearArgentinaThailand
201442.6286.4
201341.8289.1
201241286.6
201141.5286.4
201040.8286
200941.6286.9
200841.4284.8
200739.6283.2
200639.3284.3
200540.1281.7
200440.6287.5
200338.7279.3
200240.2280
200139.5285.5
200039.5283.4
199938.4282.5
199838.8282.7
199738.5284.8
199637279.8
199535.7278.7
199434.7280.8
199334.3275.6
199233.4268.8
199136.8276.7
199036279.2
198937278.4
198836.9279.6
198736.1277.9
198636.4277.5
198536276
198434.7277.9
198335.2273.9
198235.7275.7
198137.3277.8
198036.3277.9
197937.5278
197835.7276
197735.1277.3
197635.7275.6
197534.8274.4
197435.4276
197334.1280.6
197235.5273.9
197134.7273.2
197034.9276
196933.4274.8
196834.6277
196733270.3
196634.3274.4
196532.2275.8
196430.3271.9
196332274.7
196235.2275.4
196135.6278.5
196035.6276.2
195934.5274.4
195833.3278.7
195735.7277.4
195635277
195534.4274.6
195433.6275
195335.8275.4
195234.4275.1
195135.4277.5
195033.9276.2
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearArgentinaThailand
201544.433.5–53.4290.6272.2–305
201643.532.4–53.4287.1269.8–306.2
201742.834.2–52.6290273.8–302.8
201842.632.7–52.6292.6275.1–308.7
201943.633.4–53.1291.2272.8–306.7
202045.735.5–57.2292.4272.2–307.8
202145.634.3–56.4294.2276.3–308.5
202244.633.3–56.4294.8277.2–311.9
202344.934.9–53.9296.1281.9–310.2
202445.834.2–57.5291.7275.5–307.8
202546.536–58.1294276.7–306.3
202647.936.7–57296278–309.3
202746.434.5–56.3295.8276.3–311.1
202846.133.5–58.5297279.3–315.1
202948.336–58.8300.3278.7–315
203047.835.4–60.3297.7280.2–312.5
203146.935.5–58.7300.8282.8–317.1
20324837.9–59.7297.2280.9–311.4
203347.436.7–57.6300281.9–315.8
203447.837.8–58.9300.8279–318.4
203547.339–59298.6281.5–317.8
203648.939.2–58.4300.4279.9–316.9
20375141.2–60.9299.8280–315.1
203848.737.9–59.7304.8286.6–319.3
203948.839.1–58.4300.9281.9–317
204050.440.4–62.3301.6284.5–315.7
204150.740.4–61.7303.1285.1–318.8
20425037.3–61.1303.8290–318.3
204350.340.5–61302285.8–317.9
20445240–65304.3286.3–318.8
204552.441.3–65.6307.1287.7–324.2
204651.941.2–62.7305.9285.2–321
204751.542–64306.6289.5–321.5
204854.542.6–65309.4288.9–325.7
204953.942.8–64.9308.1287.2–324.1
205052.543.6–62.4307.5291–321.5
205155.743.3–67.4312.8294.8–326
205255.642.2–67310.9291.2–323.4
205355.342.6–67.6308.3289.5–323.3
205454.743–66.7308.8292.8–322.3
205555.942.3–67.2308.6290.6–325.1
205657.642.8–68.6311.3294.9–326.3
205756.842.3–68.7308.5286.7–326.4
205854.841.5–68.1308291.1–325.1
205957.844.9–70.3312.7295.8–325.3
206056.943.4–67.9313.1294.6–328.6
206156.542.3–70.6314.4289.5–331.1
206257.345.8–68.5311.4293.4–330.3
206358.244.6–70.2314.8296.9–327.3
206458.845.8–71.5312.5295.4–330
206558.746–71.1309.6293.1–329.3
206659.947–68.9312.1292.6–328
206758.944.8–71.5311.2289.6–329.4
206857.844.3–70.3310.8292.4–330.6
206959.445.4–70.5315.5296.1–329.5
207061.348.2–74.3314.2292.9–330.7
207160.148.7–73.2313.8296.4–330.8
207260.146.7–73.7314.8296–329.6
207360.847.6–73.5316.5299.9–332.1
207460.447.6–73.7317.2295–333.8
207561.347.9–73.9317.2295.3–331.4
207661.146.8–75.8317.4299.8–335
207760.647.1–73.1317.5297.3–335.9
207863.449.4–75.6315.9297.1–331.6
207959.948.7–73318.5300.5–332.3
20806348.1–75.5319.8297.4–337.5
208161.448.7–73.5317.7299.5–334.9
208261.747.7–75317.9294.8–333
208362.148.2–75.6315297.8–332.9
208462.950.3–76316295.5–330
20856246.9–76.4322.6300.8–334.7
208662.749.4–76.4319.4295.2–335.2
208761.847.9–74.9320.2302.2–334.7
208864.450.8–77.5321.4299.8–334.8
208962.749.3–76.3318.9296.4–338.1
209064.152.5–77.1317.6304–337.4
209164.451.7–78.3320.1299.8–336.1
209265.650.8–78.3316.3298–336.4
209362.749.7–76.8316.1297.7–333.7
209463.950.1–78.5323.4301.8–337.3
209562.948.8–77.9322.9302.4–338.9
209664.749.4–78.9319.6302.8–336.7
209764.552.4–80.1319.8299.1–336
209865.652.7–78.7320.5303.9–336
209965.249.3–80.2319.9303.1–334.4
210065.250–81.7320.8301.1–336.7

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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