Argentina vs Guatemala

Argentina vs Guatemala: Tropical nights

Tropical nights in 2014: 42.6 nights in Argentina and 171.5 nights in Guatemala, in the CMIP6 climate models’ median run. In 2014 the value was higher in Guatemala, by 128.9 nights, or 4.02 times that in Argentina.

nights a year · CMIP6

Argentina vs Guatemala: Tropical nightsTropical nights in 2014: 42.6 nights in Argentina and 171.5 nights in Guatemala, in the CMIP6 climate models’ median run. In 2014 the value was higher in Guatemala, by 128.9 nights, or 4.02 times that in Argentina.
ArgentinaGuatemalaCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Guatemala 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 Guatemala at 230.9 nights in 2100.

Every year

nights a year · CMIP6

YearArgentinaGuatemala
201442.6171.5
201341.8175.4
201241171.8
201141.5173.8
201040.8171.4
200941.6169.7
200841.4167.8
200739.6167.6
200639.3167.2
200540.1169.2
200440.6167.4
200338.7165.8
200240.2165.7
200139.5165.3
200039.5163.6
199938.4162
199838.8162.1
199738.5160.7
199637157.9
199535.7153.5
199434.7155
199334.3153.5
199233.4151.7
199136.8158.2
199036161.9
198937157.5
198836.9154.2
198736.1154.7
198636.4153.1
198536150.7
198434.7150.3
198335.2148.1
198235.7151.3
198137.3156.7
198036.3152.3
197937.5153
197835.7149.5
197735.1150.6
197635.7151.6
197534.8147.4
197435.4150.2
197334.1149.6
197235.5148.2
197134.7145.5
197034.9145.4
196933.4146.8
196834.6146.7
196733142.7
196634.3145.9
196532.2143.3
196430.3143
196332144.6
196235.2149.5
196135.6149.8
196035.6151.4
195934.5147.4
195833.3150.5
195735.7151.1
195635148.2
195534.4152.5
195433.6150.4
195335.8149.3
195234.4150.3
195135.4151.8
195033.9148.6
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearArgentinaGuatemala
201544.433.5–53.4177158.4–194.6
201643.532.4–53.4178.5161.5–195.5
201742.834.2–52.6182.8166.6–194.9
201842.632.7–52.6180.2165–196.4
201943.633.4–53.1181.7159.3–199.5
202045.735.5–57.2184.1161.8–198.6
202145.634.3–56.4184.4168.1–201.1
202244.633.3–56.4184.6165.7–198.4
202344.934.9–53.9186.3171.1–207
202445.834.2–57.5187.1169.9–207.3
202546.536–58.1187.4169.7–205.6
202647.936.7–57190.9167.9–211
202746.434.5–56.3188.4171.9–210.9
202846.133.5–58.5191.3175.8–208.5
202948.336–58.8192.7175.6–208.8
203047.835.4–60.3191173.3–207.9
203146.935.5–58.7193.6175.3–211.4
20324837.9–59.7196.4178.7–214.8
203347.436.7–57.6193.3174.6–208.5
203447.837.8–58.9193.5176.2–214.4
203547.339–59193.4177.7–212.8
203648.939.2–58.4194.4180.5–212.8
20375141.2–60.9199.1180–216.2
203848.737.9–59.7202.6184.8–216.7
203948.839.1–58.4199181.6–217.6
204050.440.4–62.3200.3182.9–217.9
204150.740.4–61.7200.7182.1–218.2
20425037.3–61.1201.6180.3–222.1
204350.340.5–61201.7182.3–226.4
20445240–65200.8184.9–222.3
204552.441.3–65.6203.6188.7–225.1
204651.941.2–62.7204.5183.3–227.5
204751.542–64207.5190.3–227.5
204854.542.6–65208.2188.9–229.4
204953.942.8–64.9210.1188.7–227.4
205052.543.6–62.4211.4188–230.7
205155.743.3–67.4208.1192.7–229.2
205255.642.2–67207.7193–227.9
205355.342.6–67.6209.8186.4–233.2
205454.743–66.7209.5194.7–233.2
205555.942.3–67.2212.4196.2–231.9
205657.642.8–68.6210.9193.3–233.1
205756.842.3–68.7213.3193.7–232.4
205854.841.5–68.1211.7192.3–238.1
205957.844.9–70.3214.5190.4–237.4
206056.943.4–67.9217.4196.4–237.3
206156.542.3–70.6213.9193.7–237.1
206257.345.8–68.5216.3194–237.3
206358.244.6–70.2217.2197–240.8
206458.845.8–71.5214.5196.1–236.1
206558.746–71.1217.3195.2–238.2
206659.947–68.9217.9195.2–240
206758.944.8–71.5216.4194.1–241.1
206857.844.3–70.3221200.2–243.4
206959.445.4–70.5219.9200.5–241.9
207061.348.2–74.3219.6199.9–242.6
207160.148.7–73.2222.5203.4–243
207260.146.7–73.7225.6200.4–247.6
207360.847.6–73.5224.4199.9–248.3
207460.447.6–73.7222.5202.2–245.9
207561.347.9–73.9220.2198.7–242.6
207661.146.8–75.8222.9195.4–247.7
207760.647.1–73.1225.2198.3–245.9
207863.449.4–75.6225.4197.8–245.9
207959.948.7–73223.4199.3–248.2
20806348.1–75.5223.4201–250.7
208161.448.7–73.5224202.5–246.6
208261.747.7–75224.8201.3–245.9
208362.148.2–75.6225202.3–250.5
208462.950.3–76227.9204.9–249.9
20856246.9–76.4227.7199.2–251.3
208662.749.4–76.4224.9202.3–249.6
208761.847.9–74.9227.1197.8–249.7
208864.450.8–77.5230.6207.4–255.5
208962.749.3–76.3229.7207.4–251.4
209064.152.5–77.1228.2210–253.4
209164.451.7–78.3231.4206.6–255.4
209265.650.8–78.3230.1207.4–253.4
209362.749.7–76.8226.7206–255.5
209463.950.1–78.5227.3204.4–251.7
209562.948.8–77.9231.1207.7–255.5
209664.749.4–78.9229.9206.3–255.5
209764.552.4–80.1226.7208.6–249.5
209865.652.7–78.7231.1202.9–259.1
209965.249.3–80.2230.2203.1–256.5
210065.250–81.7230.9208.9–255.1

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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