Jordan vs Pakistan

Jordan vs Pakistan: Tropical nights

Tropical nights in 2014: 74.7 nights in Jordan and 149.9 nights in Pakistan, in the CMIP6 climate models’ median run. In 2014 the value was higher in Pakistan, by 75.3 nights, or 2.01 times that in Jordan.

nights a year · CMIP6

Jordan vs Pakistan: Tropical nightsTropical nights in 2014: 74.7 nights in Jordan and 149.9 nights in Pakistan, in the CMIP6 climate models’ median run. In 2014 the value was higher in Pakistan, by 75.3 nights, or 2.01 times that in Jordan.
JordanPakistanCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Pakistan has been higher in every year both have figures for since 1950.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts Jordan at 131.7 nights and Pakistan at 177.6 nights in 2100.

Every year

nights a year · CMIP6

YearJordanPakistan
201474.7149.9
201372.2149.1
201271.9149.9
201171.4149
201071.4148.4
200966.9147.1
200869.4147.4
200764.6147.3
200667.7146.7
200566.2146.5
200467.4147.5
200368.2146
200263148.3
200168.3145.4
200063.8144.7
199962.2146.9
199861.8144.8
199759.2143.2
199656.6141.2
199554.1141.8
199454.6142.1
199345.2140.4
199248.3139.9
199152.3142.5
199057.7142.6
198953.4142.5
198850.5140.3
198749.5140
198652140
198547.1140
198444.2138.9
198341.5136.3
198247.4138.5
198148.3141.5
198049.2139.5
197941.8139.1
197845.1139
197746.7138.6
197645.6139.2
197543136.5
197445.1138.7
197344.6137.8
197244.8137.4
197147.2136
197044.3137.7
196946.3135.9
196842.6136.6
196741.5135.2
196643136.9
196536.8138.3
196437.7135.1
196343135.7
196245137
196145.4138.6
196046.3139
195946.3138.6
195847.2137.7
195748.7140
195648.4137.8
195545.5137.5
195442.7137.8
195347.8136.9
195245.7138.1
195145136.7
195041.8139.1
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearJordanPakistan
201581.862.6–98.9152.4138.7–163.3
201679.261.3–95.2150.6139.4–161.7
201782.466.3–96.6151.3138.8–162
201884.868.8–102.1152.6139.8–161.8
201987.467.5–100.7151.8138.1–163.3
202086.866.8–102.2153.1140.3–164.5
202188.273.8–101.1154.1140.6–165.7
202288.170–103.7153.1140.2–165.7
202392.474.7–106.4155.5141.6–165.9
202490.776.3–107.1156.8143.3–168.7
202594.173.1–105.2155.7141.5–168.6
202691.678.6–106.9155.3142.2–167.9
202794.475.7–108.4156.8140.9–168.9
202892.675.9–106.3156.5143.9–167.6
202995.381.2–107.3155.9141.5–167.7
203096.381.5–111.7156.3143.3–167.6
203193.876.5–110.6157.3144.4–168.1
203210086.4–110.9157.4142.5–169.6
2033100.683.6–113.6157.7143.6–169.4
203498.882.4–112.3159.5145.6–173.4
2035101.883.2–115.2159.3146.7–170.3
203697.885.1–116.2159.1147.5–171.4
203710384.6–117.8162.2147.5–175.3
2038102.386.1–122.6161.4148.6–171.7
2039107.190.5–125.7160148.2–170.4
2040106.387.8–125.5160.9147.9–171.3
2041103.787.8–123.2160.8146.7–171.7
2042108.586.5–122.3162.3149.9–175.6
2043105.791–124.9161.1150.3–173.8
2044105.682.8–125.1163150.9–173.9
204511298.8–128163149.5–174.8
204611395.5–127.5163.5149.8–177.2
2047112.293–125.7165.2151.8–178.5
2048111.893.7–129.7166.4154.1–179.4
2049110.384.7–125.6165150.8–178.2
2050112.495.9–127.2166.9153.5–179.3
2051112.397–125.6165.6150.8–180
2052110.697.4–125.9166.7155.4–179.5
2053113.798.2–129.9166.3154.2–178.8
2054115.492.5–130.9167.5153.9–181
2055115.794.8–130.2167.8155.8–182.5
2056115.394.2–130.5166.3153.6–179.7
2057115.496.7–135.6168.3153.9–182.3
2058116.196.6–136.8167.5157–179.9
2059120.998.5–137.1170.9155.9–183.8
2060115.898.7–138.7170.3156.5–181.8
2061118.897.1–132.3168.8156.8–179.6
206212091.6–136.4171.6153.5–186.5
2063118.7101.7–135.9171.3159.5–183.8
2064121.4103.3–135.9170.2155.3–184.5
2065120.2100.4–134.9169.7156.4–185
2066120.498.5–135172.2157–186.7
2067124106.1–139.5173.2160.2–186.3
2068123.2105.3–138.1172.6157.7–186.3
2069118.8105.4–137.2171.8157.3–185.6
2070123104–140.8171.7158.9–186.7
2071123.6103.8–144.3173.4158.7–186.8
2072124.5106.3–140.9174161.5–186.9
2073126.6104.8–144.4175.6157.2–187.9
2074120.9103–139.6173.5160–186.3
2075121.7103.1–138.1173161.5–187.8
2076124.1105.4–144.2172.9158.8–186.7
2077124.2105.3–143.9175161.3–187.8
2078124.7105.6–142.8174.4160.9–189.3
2079132107–143.6172.5158.3–187.7
2080128.9106.2–144.4175.1160.7–189.2
2081125.3108.7–144.8174.9159.2–190.2
2082128107.9–145.2175.4161.8–190.6
2083130.9108–144.7176.5161.8–190.8
2084130.5110.6–145176.1160.9–190.5
2085129.1108.8–141.7176.3161.9–193.6
2086127.5108.9–147.6173.9160–189.1
2087129.8106.1–148.4176161–193.1
2088132.2114.9–149.4175.4160.5–195.2
2089128.7113.4–148.5177.2164.2–191.7
2090128.2111.9–151.3177.7163.9–192.2
2091132.8112.5–152.1177.8162.8–192.2
2092130.6108.3–152.6178.7162.9–192.9
2093135.8116.2–154.4177164–191
2094135.9116.3–150.6176.8164.1–190.7
2095133112.7–149.6178.6163.8–193.1
2096130.4108.1–151.3177.8163.7–192.5
2097134.5113.5–155.4177.3160.4–192.5
2098132.5114.9–152.3178.7162.6–195.7
2099132.1115.3–145.3176159.9–193.2
2100131.7111.2–153.1177.6162.2–193

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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