JCDP

Japan

If you use any of the illustrations on this page for your research and education, please clearly indicate the source below.
JCDP (https://jcdp.jp/)

Japan Climate Atlas (1991-2020)

This page displays various types of Japan Climate Atlas based on JMA’s New Normal (1991-2020).　Due to the page size, south of 30 degrees north latitude is omitted.

The data was acquired from the Japan Meteorological Agency from its website at https://www.data.jma.go.jp/obd/stats/data/mdrr/normal/index.html

Annual Mean Temperature (1991-2020)

Annual Mean Total Precipitation (1991-2020)

January Mean Temperature(1991-2020)

January Mean Precipitation (1991-2020)

July Mean Temperature(1991-2020)

July Mean Precipitation (1991-2020)

Typhoon Tracks

Typhoon Tracks (including Tropical Cyclones)

The Generic Mapping Tools (GMT) were used to create the figure.
The best track data was obtained from the Regional Specialized Meteorological Center (RSMC), Tokyo, Japan (see below).
https://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/besttrack.html
https://www.generic-mapping-tools.org/

2001-2010

2011-2020

Monthly Typhoon Tracks (2001-2020)

Typhoon tracks (including tropical cyclones and extratropical transition of typhoons) for June – October from 2001 to 2020 are displayed. Orange (Green) lines indicate typhoon tracks in the previous (following) month.
The Generic Mapping Tools (GMT) was used to create the figure. The best track data was obtained from the Regional Specialized Meteorological Center (RSMC), Tokyo, Japan (see below).
https://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/besttrack.html
https://www.generic-mapping-tools.org/

Typhoon Tracks in June

Typhoon Tracks in July

Typhoon Tracks in August

Typhoon Tracks in September

Typhoon Tracks in October

Typhoon Tracks in the first half of the 20th century (1902-1940)

The original data is based on the typhoon tarck map of the Monthly Bulletins of Philippine Weather Bureau (Jan 1902-Aug 1940).
(Monthly Bulletins of Philippine Weather Bureau).
The map was created using the Earth Dennou Library.
https://www.gfd-dennou.org/library/dcl/
References: Kubota H and Chan J.C.L. 2009. Interdecadal variability of tropical cyclone landfall in the Philippines from 1902 to 2005. Geophysical Research Letters 36: L12802
https://doi.org/10.1029/2009GL038108

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.