JCDP

Instrumental Meteorological Data

You can download all data without password.

Please cite original publications, online resources and date accessed when using these data.

West Japan Temperature Series

WJT1821-2000[ダウンロードが見つかりません]

Parameter: temperature
Data coverage: 1825 – 2000
Citation information: Zaiki, M., Können, G.P., Tsukahara, T., Jones, P.D., Mikami, T. and Matsumoto, K., 2006: Recovery of 19th century Tokyo/Osaka meteorological data in Japan. Int. J. Climatol. 26, 399-423. DOI 10.1002/joc.1253 PDF

The west-Japan temperature (WJT) series by seasons and year for the period of 1820–2000, relative to the 1971–2000 period. Sample size indicates the number of stations used in the WJT calculation. Before 1900, WJT values were derived from the available five-station data recovered in this study, applying the variance reduction. After 1900, the values were calculated from 11 JMA observatories. Source : Int. J. Climatol. 26: 399–423 (2006)

Nagasaki

Nagasaki1819-1878[ダウンロードが見つかりません]

Parameter: temperature, pressure
Data coverage: 1819 – 1878 (T), 1826 – 1878 (P)
Citation information: Können, G.P., Zaiki, M., Baede, A.P.M., Mikami, T., Jones, P.D., and Tsukahara, T. 2003. Pre-1872 extension of the Japanese instrumental meteorological observation series back to 1819. Journal of Climate, 16: 118-131. PDF

Kobe, Osaka, Yokohama, Tokyo

Kobe1869-1888[ダウンロードが見つかりません]

Osaka1828-1871[ダウンロードが見つかりません]

Yokohama1860-1874[ダウンロードが見つかりません]

Tokyo1825-1875[ダウンロードが見つかりません]

Parameter: temperature, pressure
Data coverage: 1869-1888 (Kobe), 1828-1871 (Osaka), 1860-1874 (Yokohama), 1825-1875 (Tokyo)
Citation information: Zaiki, M., Können, G.P., Tsukahara, T., Jones, P.D., Mikami, T. and Matsumoto, K., 2006: Recovery of 19th century Tokyo/Osaka meteorological data in Japan. Int. J. Climatol. 26, 399-423. DOI 10.1002/joc.1253 PDF

Yokohama (Precipitation)

Yokohama1863-1869[ダウンロードが見つかりません]

Citation: Hirano, J., Mikami,T., Zaiki,M. and Nishina, J.(2018): Analysis of precipitation data at Yokohama Japan, from 1863 to 1869 observed by J.C. Hepburn. Journal of Geography(Chigaku Zasshi), 127, 531-541.

Topics

Anomaly maps in “Climate Atlas”

　　　　　　　　　　　　　　　　　　　　　　　
Junpei Hirano

In climatology, we use values called “anomalies” to understand the average state of climatic elements (atmospheric pressure, temperature, precipitation, sunshine duration, and solar radiation, etc.). The “normal” is defined as the average value of climatic factors during the past 30 years. Since the average value changes due to climate change such as global warming, the normal is updated every ten years. For example, for the 10-year period from 2011 to 2020, the 1981-2010 average was used as the “normal,” but it was updated in 2021, and for the 10-year period from 2021 to 2030, the 1991-2020 average will be used as the “new normal.

In weather forecasting, the term “warmer (colder) than normal” is sometimes used, but in this case, “normal” refers to the “normal” value of temperature. In other words, the “normal value” of temperature is the standard for evaluating the warmth (coldness), etc. of each year. The difference between the temperature of each year and the normal is called the ” anomaly”. In the case of temperature, a positive anomaly indicates a warmer than normal year, while a negative anomaly indicates a colder than normal year. A map of temperature anomalies shows the distribution of areas with warmer-than-normal temperatures (positive anomaly areas) and areas with colder-than-normal temperatures (negative anomaly areas).

Similar to temperature, precipitation can also be plotted based on the “normal” value. However, in the case of precipitation, anomalies are generally expressed as the ratio of precipitation to the normal (ratio of normal to precipitation). For example, if you draw a precipitation anomaly map for a year with an El Niño or La Niña event, you can see areas with more or less rainfall than normal. Global precipitation anomaly maps for summer (June-August) and winter (December-February) of El Niño events are available on the Climate Atlas “World” page. However, as the basis for the anomalies, we have used the 43-year average for the period 1979-2021, for which precipitation data are available, instead of the 30-year “normal. Green-colored areas indicate areas of higher-than-normal rainfall, while brown-colored areas indicate areas of lower-than-normal rainfall. It can be seen that in El Niño years, there is more rainfall in the eastern tropical Pacific and less in the western tropical Pacific.

Furthermore, by comparing the precipitation anomaly map with the SST anomaly map, we can see the relationship between SST anomalies and precipitation anomalies. For example, in the “Winter DJF SST anomaly map for El Niño years”, the eastern tropical Pacific is colored red, indicating that SSTs are higher than normal. In an El Niño year, the sea surface temperature in this region is high, which activates cumulus convection and leads to heavy rainfall.

Global precipitation anomaly maps for summer (June – August) and winter (December – February) La Niña years show the opposite pattern (heavy rainfall in the eastern tropical Pacific and less rainfall in the western tropical Pacific). In La Niña years, sea surface temperatures (SSTs) drop below normal in the eastern tropical Pacific (see SST anomaly distribution in La Niña years), which suppresses cumulus convection and results in less rainfall.

The JCDP “Climate Atlas” will include various climate anomaly maps in the future. We recommend that you use the anomaly maps in the JCDP Climate Atlas when explaining “extreme weather” and its spatial distribution in high school geography and geology courses and in university liberal arts courses.