Training Seminar on Climate
Analysis using Reanalysis Data
- Exordium of this seminar -
Norihisa FUJIKAWA
Climate Prediction Division,
Japan Meteorological Agency
TCC Training Seminar
Training Seminar on Climate Analysis using Re-analysis Data
Tokyo, 1-4 Dec. 2009
Purpose of this seminar
Let’s cultivate our sensibility
for climate analysis!
At first
Climate mainly depends on geographic distribution of Continents and Oceans
JRA-25 Atlas
http://ds.data.jma.go.jp/gmd/jra
/atlas/eng/atlas-tope.htm
Surface
Temperature
Precipitation
August
January August
Tokyo Teheran Lisbon
Tokyo Teheran Lisbon
Do you know the climate in Japan ?
Climates are quite different among cities even if they lie at the same latitude.
Jan
Jan
Jan
Aug
Aug
Aug
Do you exactly know the climate in Japan ? Hythergraph(ClimoGraph)
0
50
100
150
200
250
300
350
400
450
0 10 20 30Temperature (℃)
Pre
cip
itati
on (
mm
)
Tokyo
Takada
Jan
Jan Winter Monsoon
Local topography has a large influence on the local climate.
SLP normal (Jan)
Eurasia
Central mountains of Japan Sea of Japan Pacific
North-westerly
Monsoon
(getting wet)
Cumulonimbus
Dry Monsoon
Heavy Snow
Do you exactly know the climate in Japan ?
Hythergraph(ClimoGraph)
0
50
100
150
200
250
300
350
400
450
0 10 20 30Temperature (℃)
Pre
cip
itati
on (
mm
)
Tokyo
Takada
SLP normal (Jan)
Jan
Jan Winter Monsoon
Tokyo
Takada Mountains
Local topography has a large influence on the local climate.
To understand the climate, it is
necessary to know how continental
scale geography and local topography
make influence on the climate there.
Next,
what kinds of phenomena
should we focus on ?
Tempospatial diagram of climatic phenomena
1 Min 1 Hour 1 Day 1 Week 1 Month 1Season 1 Year 10 Years
1000km
10000km
100m
1km
10km
100km
Tornado
Cumulo-
nimbus
Localized
heavy rain
Tropical
Cyclone
low (high)
pressure
Blocking
MJO Monsoon El Nino Decadal
Long Short
La
rge
S
mall
Target of Climate Monitoring
Extreme Weather
Example for multi-scale
interaction of climate system
El Nino
Probability density of a monsoon activity
Normal
year
Change the shape of distribution
Probability density of a MJO activity
Normal
year
Change the shape of distribution
Probability density of convective activity
Normal
year
Change the shape of distribution
Precipitation
Each probability
density has a tempo
spatial structure.
Statistical relationship
Time average at each grid
El Nino
Probability density of a monsoon activity
Normal
year
Change the shape of distribution
Probability density of a MJO activity
Normal
year
Change the shape of distribution
Probability density of convective activity
Normal
year
Precipitation
Each probability
density has a tempo
spatial structure.
Positive Correlation
Time average at each grid
Negative Correlation
Change the shape of distribution at each grid
El Nino
Probability density of a monsoon activity
Normal year
Change the shape of distribution
Probability density of a MJO activity
Normal
year
Change the shape of distribution
Probability density of convective activity
Normal
year
Change the shape of distribution
Precipitation Statistical relationship
Time average at each grid
Decadal
Oscillation
Global
Warming
Probability density of a ENSO activity
Normal
year
El Nino
Probability density of a monsoon activity
Normal
year
Change the shape of distribution
Probability density of a MJO activity
Normal
year
Change the shape of distribution
Probability density of convective activity
Normal
year
Change the shape of distribution
Precipitation
Time average at each grid
Each probability
density has a tempo
spatial structure.
Statistical relationship
Predictability over a season
Predictability within a season
Predictability within a month
Predictability within a few days
Program of Basic Understanding
for Climate Monitoring
• Outline of Climate System Monitoring
• Global Warming Trend and Decadal Oscillation
• Interannual Variability
• Intra-seasonal Variability
• Prediction skill of the seasonal prediction model
And then,
Let’s move onto the ITACS world!
Showing examples
analyzed with ITACS
Showing examples
analyzed with ITACS
JMA Organization
Global Environment
and Marine Department Climate Prediction Division
Climate Prediction Division
Tokyo
Climate
Centre
Administration Director
WMO
NMHSs
Programs
Global Warming Climate
monitoring
Climate diagnosing
Reanalysis ( JRA-25 )
Seasonal forecast Numerical
model developing ENSO
monitoring & forecast
RCC
GPC
GSN-MC CBS-LC