Argentina vs India

Argentina vs India: Tropical nights

Tropical nights in 2014: 42.6 nights in Argentina and 215.5 nights in India, in the CMIP6 climate models’ median run. In 2014 the value was higher in India, by 172.9 nights, or 5.05 times that in Argentina.

Watch it change on the map

nights a year · CMIP6

Argentina vs India: Tropical nightsTropical nights in 2014: 42.6 nights in Argentina and 215.5 nights in India, in the CMIP6 climate models’ median run. In 2014 the value was higher in India, by 172.9 nights, or 5.05 times that in Argentina.
ArgentinaIndiaCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • India 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 India at 250.4 nights in 2100.

Every year

nights a year · CMIP6

YearArgentinaIndia
201442.6215.5
201341.8216
201241217.4
201141.5215.5
201040.8215.2
200941.6213.7
200841.4213.7
200739.6213.3
200639.3212.5
200540.1212.9
200440.6214.9
200338.7212.1
200240.2213
200139.5212.2
200039.5210.8
199938.4211.9
199838.8210.3
199738.5209.5
199637207
199535.7208.8
199434.7207.1
199334.3206
199233.4203.5
199136.8208
199036209.9
198937208
198836.9207.3
198736.1207.2
198636.4207.2
198536205.9
198434.7206.7
198335.2203.3
198235.7205.7
198137.3208.4
198036.3205.3
197937.5205.5
197835.7204.1
197735.1205.9
197635.7204.6
197534.8202.6
197435.4205.3
197334.1205.6
197235.5203.8
197134.7203.6
197034.9202.6
196933.4203.6
196834.6201.7
196733201.6
196634.3203.9
196532.2202.4
196430.3201.2
196332201.8
196235.2203.8
196135.6206.3
196035.6206
195934.5206.1
195833.3204
195735.7206.5
195635204
195534.4203.2
195433.6206
195335.8204
195234.4204.4
195135.4204.3
195033.9206.3
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearArgentinaIndia
201544.433.5–53.4216.9202.1–228.4
201643.532.4–53.4215202.5–227.6
201742.834.2–52.6216.2202.1–228.5
201842.632.7–52.6218.3204.5–231.4
201943.633.4–53.1217.4202.3–230.4
202045.735.5–57.2217.6203.1–232
202145.634.3–56.4219.7204.5–233.2
202244.633.3–56.4220.5205.8–233
202344.934.9–53.9220.4206.4–233.7
202445.834.2–57.5222.6208.1–236.3
202546.536–58.1219.7202.7–236.2
202647.936.7–57220.2204.2–236.8
202746.434.5–56.3223.6207.1–239
202846.133.5–58.5222.1205.1–235.7
202948.336–58.8225.4210–240.3
203047.835.4–60.3222.7207.3–235.6
203146.935.5–58.7223.8207.1–237.8
20324837.9–59.7225.9210–240.5
203347.436.7–57.6223.8209.3–235.4
203447.837.8–58.9225.8210–242.7
203547.339–59224.9210.6–240.9
203648.939.2–58.4224.2210.4–240.3
20375141.2–60.9228.3213.6–242.6
203848.737.9–59.7228.1212.6–241.4
203948.839.1–58.4227.4211.9–242.8
204050.440.4–62.3227212.2–242
204150.740.4–61.7227.1210.7–240.5
20425037.3–61.1229.1214.8–244.2
204350.340.5–61228.9214.5–244
20445240–65229.7214.7–244.2
204552.441.3–65.6231.2216.6–246.1
204651.941.2–62.7232.6216.2–248.6
204751.542–64232.4215.5–250
204854.542.6–65235.6218.5–254.3
204953.942.8–64.9235.3218.3–249.9
205052.543.6–62.4233.5216.6–253.4
205155.743.3–67.4234.9218.6–249.9
205255.642.2–67234219.4–249.3
205355.342.6–67.6234.9219.5–252.5
205454.743–66.7237.2219.4–253.1
205555.942.3–67.2237.2220.7–256.6
205657.642.8–68.6238.8221.2–254.9
205756.842.3–68.7235.7218.5–253.8
205854.841.5–68.1238.8222.8–254.1
205957.844.9–70.3241.1224.9–256.2
206056.943.4–67.9240.9225.6–255.6
206156.542.3–70.6237.9220.6–253.7
206257.345.8–68.5239.9221.2–259.7
206358.244.6–70.2239.9222.3–256.1
206458.845.8–71.5240223.8–256.3
206558.746–71.1240.9222.9–257.3
206659.947–68.9241.5224.1–258.6
206758.944.8–71.5241.6224.8–257.9
206857.844.3–70.3242.3224.6–262.1
206959.445.4–70.5242.8225.4–259.1
207061.348.2–74.3240.1225.3–257.1
207160.148.7–73.2241.8223.7–260.2
207260.146.7–73.7242.6225.4–259.2
207360.847.6–73.5243.8228–259.3
207460.447.6–73.7241.6226.9–258.4
207561.347.9–73.9243.5226.4–258.2
207661.146.8–75.8243.2224.6–259.9
207760.647.1–73.1243.7226.6–264.6
207863.449.4–75.6246.7230.1–261.5
207959.948.7–73244.9226.4–261.3
20806348.1–75.5246.7227–263.7
208161.448.7–73.5247.5230.4–264.8
208261.747.7–75244.6227.9–262.3
208362.148.2–75.6247.5227.8–265.1
208462.950.3–76247.3228.3–266.8
20856246.9–76.4246.5227.5–267.1
208662.749.4–76.4245.6227.7–266.5
208761.847.9–74.9245.5226.7–266.2
208864.450.8–77.5247229.4–266.9
208962.749.3–76.3248.1230–266.3
209064.152.5–77.1249228.9–266.4
209164.451.7–78.3248.9230–266.5
209265.650.8–78.3249.8232.3–267.8
209362.749.7–76.8248.1229–266.5
209463.950.1–78.5249.1230.6–268.4
209562.948.8–77.9251233.2–270
209664.749.4–78.9247.8231.4–269.3
209764.552.4–80.1251.1231.4–268
209865.652.7–78.7252.3230.8–268.3
209965.249.3–80.2248.7230.1–268.3
210065.250–81.7250.4232.9–266.7

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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