Download JMA/MRI

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

IPCC Fourth Assessment Report wikipedia, lookup

Climate change, industry and society wikipedia, lookup

Surveys of scientists' views on climate change wikipedia, lookup

Effects of global warming on humans wikipedia, lookup

Public opinion on global warming wikipedia, lookup

Scientific opinion on climate change wikipedia, lookup

Climate change feedback wikipedia, lookup

Solar radiation management wikipedia, lookup

Climate change and poverty wikipedia, lookup

Attribution of recent climate change wikipedia, lookup

Physical impacts of climate change wikipedia, lookup

Climate sensitivity wikipedia, lookup

Climate change and agriculture wikipedia, lookup

Global warming wikipedia, lookup

General circulation model wikipedia, lookup

Global warming hiatus wikipedia, lookup

Climate change in Tuvalu wikipedia, lookup

Instrumental temperature record wikipedia, lookup

Climate change adaptation wikipedia, lookup

Effects of global warming on Australia wikipedia, lookup

Atmospheric model wikipedia, lookup

Numerical weather prediction wikipedia, lookup

El Niño–Southern Oscillation wikipedia, lookup

Transcript
Tokyo Climate Conference, 6 July 2009, Tokyo
Toward Seasonal Climate Forecasting and
Climate Projections in Future
Akio KITOH
Meteorological Research Institute, Tsukuba, Japan
ENSO influences worldwide climate even out of the
tropical Pacific on seasonal to inter-annual scales.
Sea Surface Temperature
anomaly in November 1997
Accumulated Precipitation Anomaly
during Nov.1997-Apr.1998
from JMA webpage
from BAMS, 1999, 80, S1-48
JMA/MRI
ENSO is the most successfully predicted large-scale
phenomenon on seasonal to inter-annual scales
Observation
Dec1997
- Feb1998
Sea Surface
Temperature
Prediction
from
31 July 1997
by
JMA/MRI
model
4-month lead
Precipitation
Surface Air
Temperature
Atmosphere-ocean coupled models are necessary for
the seasonal prediction of ENSO and its influences
Short-term Prediction
Seasonal Prediction
Model
Model
Atmosphere – Land
Models
Atmosphere - Land – Ocean
Coupled Models
Given
Sea Surface Temperature
Coupled Ocean
Local Relationship between Sea Surface Temperature (SST)
and Rain Anomalies in Coupled models is more realistic than
in Atmospheric models
Rain -> SST
1month lead
Rain = SST
Rain <- SST
1month lag
ECHAM
Wang et al. (2005)
Next JMA Seasonal Prediction System
developed by JMA/MRI
JMA/MRI Coupled Model
• JMA/MRI Unified
Atmospheric Model
• 180km Resolution
(TL95L40)
• Ocean Model (MRI.COM)
• 1.0°by 0.3-1.0° 50layer
• 1-hour Coupling
•
Wind-stress, Heat-flux Adjustment
Ocean Initials and Data
• MOVE/MRI.COM
• Usui et al. (2006)
• 3D-VAR (T,S)
• TAO/TRITON array
• Altimeter Data
• Argo Float
JMA/MRI
Improved NINO3.4 SST Prediction Skill
(170-120W, 5S-5N)
NEW
NEW
Operational
Persistent
持続予報
Operational
OLD
持続予報
Persistent
NEW
NEW
気候値予報
Precipitation Anomaly Prediction Skill
JMA/MRI
Jan 31 => Jun-Aug (1984-2005)
Coupled Model
Atmospheric Model
Coupled model shows better skill than Atmosphere-only model
blue region : Upper tercile ROC skill is better than climatological one
ROC: Relative Operating Characteristic
Precipitation Anomaly Prediction Skill
Jan 31 => Jun-Aug
Jul 31 => Dec-Feb
Skill for boreal winter is higher than that for boreal summer
blue region : Upper tercile ROC skill is better than climatological one
ROC: Relative Operating Characteristic
JMA/MRI
JMA/MRI
Surface Air Temperature Anomaly Prediction Skill
Jan 31 => Jun-Aug
Jul 31 => Dec-Feb
Prediction skill of temperature is higher than that of precipitation
blue region : Upper tercile ROC skill is better than climatological one
ROC: Relative Operating Characteristic
JMA/MRI
South Asia Summer Monsoon Index (WYI)
(4-month lead: JJA from JAN)
CGCM
AGCM
Blue: Forecast
Red: Analysis
Blue: Forecast
Red: Analysis
ACC: 0.59
ACC: 0.35
WYI Definition:
U850–
U200 [0-20N,40-110E]
East Asia Summer Monsoon Index (DU2)
(4-month lead: JJA from JAN)
CGCM
AGCM
Blue: Forecast
Red: Analysis
Blue: Forecast
Red: Analysis
ACC: 0.58
ACC: -0.05
DU2 Definition:
U850[5-15N,90-130E] U850[22.5-32.5N,110-140E]
JMA/MRI
Multi-Model Ensemble
WMO Lead Centre for LRF MME
From LRF MME Homepage
APEC Climate Center
From APCC Homepage
European DEMETER Project
DEMETER
Multi-model ensemble skill out-performs single model
ensemble with the same member size
RPSS: Rank Probability Skill Score (Wilks 1995)
From ECMWF Web Page
Forecast quality
of DEMETER
hindcasts
Skill depends on regions,
seasons and variables
Significant skills for
precipitation in
DJF_Amazon and
JJA_Southeast Asia
JJA & DJF_East Asia
and JJA_Australia for
temperature
WCRP Position Paper on Seasonal
Prediction.
Report from the First WCRP Seasonal
Prediction Workshop (Barcelona,
Spain, 4-7 June 2007). February 2008.
WCRP Informal Report No.3/2008,
ICPO Publication No.127.
DEMETER
SINTEX-F showed the highest ENSO
prediction skill among 10 coupled GCMs
JAMSTEC
Nino3.4 index
(1982-2001)
Adapted from Jin et al. 2008, APCC CliPAS
JAMSTEC
ENSO can be predicted out to 1-year lead and
even up to 2-years ahead in some cases by
SINTEX-F
Nino3.4 SSTA prediction
(120º-170ºW, 5ºS-5ºN)
SINTEX-F Coupled Model
Components
AGCM (MPI, Germany):
ECHAM4 (T106L19)
OGCM (LODYC, France):
OPA8 (2 x 0.52, L31)
Coupler (CERFACS, France):
OASIS2
*No flux correction, no sea ice model
Luo et al. (2008)
ECMWF
Seasonal prediction for # tropical cyclones
has already started and shows some skill …
ECMWF Newsletter No. 112 – Summer 2007
JMA/MRI
Occurrence location of tropical cyclones are
well predicted in the Northwest Pacific
latitude
longitude
… as occurrence location is related with ENSO
more tropical storms
form in the SE
quadrant during the
warm phase, and in
the NW quadrant
during the cold phase,
thus ENSO prediction
is the key
Wand and Chan (2002) etc
Toward further improvement of seasonal prediction
NWP model
Typhoon prediction model
El Niño prediction model
Seasonal prediction model
Climate model
Earth system model
It is necessary to explore other
predictability sources in the
Earth system
Climate model development (IPCC AR4)
Toward further improvement of seasonal prediction
NWP model
Typhoon prediction model
El Niño prediction model
Seasonal prediction model
Climate model
Earth system model
It is necessary to explore other
predictability sources in the
Earth system
Improving atmosphere-ocean coupled
models will lead to constant
improvement of seasonal predictions
based on slow-coupled process like
ENSO.
On the other hand, high
predictability from ENSO seems to
be limited within relatively lowlatitudes.
Therefore, for more complete
seasonal prediction, we need to
explore other influential elements
that show relatively long-range
persistency or predictability in the
Earth system that consists of upper
and/or polar atmosphere, land, snow
and ice, chemical processes besides
the low-latitude troposphere.
JAPAN Winter Temperature is significantly
correlated with Arctic Oscillation besides ENSO
Xie et al. (1999)
AO
Possible
Causes
ENSO
・ Atlantic SST anomaly
・ Snow over Eurasia
・ Arctic Sea Ice Cover
・ Stratosphere, Ozone
・ Volcano Eruption
・ Global Warming
Stratospheric Harbingers of Anomalous Weather
(Troposphere-Stratosphere Interaction)
Baldwin and Dunkerton (2001)
AR4 to AR5: Need of climate change information
for adaptation studies in near future
Another emerging issue is a
projection of future changes in
weather extremes in order to
contribute to decision-makings
for the disaster prevention and
other adaptation studies under
the global warming environment.
• fill a gap between seasonal-tointerannual prediction and climate
change projections
• sufficiently high resolution
projection is needed for resolve
weather extremes
• changes in weather extremes will
become significant much earlier than
mean climate change
IPCC AR4
CMIP3 models
Projected changes
in extremes
Intensity of precipitation
events is projected to
increase.
Even in areas where mean
precipitation decreases,
precipitation intensity is
projected to increase but
there would be longer periods
between rainfall events.
“It rains less frequently, but
when it does rain, there is
more precipitation for a given
event.” (Tebaldi et al. 2006)
Extremes will have more
impact than changes in mean
climate
Number of TC Generated in Each Latitude
TC freqency
Annual global average
20%
(Observation:84)
Present =82
decrease
Future =66 (20% decrease)
Latitude
Observation
Present-day(25yr)
Future(25yr)
Radial Profile Change around TC
Precipitation
Surface Wind
Present Experiment
Future Experiment
Change rate
Radial Distance in km from Storm Center
・Large changes occur near inner-core region, 40-60% for precipitation
and 15-20% for surface wind.
・A surface wind speed increase of more than 4% can be seen up to 500
km from storm center.
Cooperation activities of the MRI group
(by Earth Simulator computed model outputs for adaptation
studies)
Cooperation under the JICA (Japan International Cooperation Agency) funds
 Adaptation studies in agriculture in Argentina: Argentina (three, 2008)
 Adaptation studies in monsoon Asia: Bangladesh, Indonesia,
Philippines, Thailand, Vietnam (one each, 2008 & 2009)
 Adaptation studies in the Yucatan: Mexico (two, 2009)
Cooperation under the World Bank funds
This collaboration started
after COP10 (2004)
 Adaptation study in Coastal Zones of Caribbean countries:
Barbados(one, 2005), Belize (one, 2005)
 Adaptation studies in Colombian coastal areas, high mountain
ecosystems: Colombia (two, 2005; 2009)
 Adaptation to Climate Impacts in the Coastal Wetlands of the Gulf of
Mexico: Mexico (two, 2006)
 Adaptation to Rapid Glacier Retreat in the Tropical Andes: Peru (one,
2006), Ecuador (one, 2006; 2009), Bolivia (one, 2006; 2009)
 Amazon Dieback: Brazil (two, 2008)
Other collaborations with India, Korea, Thailand, USA, Switzerland, …
SUMMARY
• ENSO is the major source of the predictability on seasonal to
inter-annual time-scales at the present. ENSO prediction was
much improved for the past a few decades, and can be
extended up to 1-year lead or longer.
• Probabilistic representation using initial ensembles is adapted
for seasonal prediction of precipitation and surface air
temperature because of small ratios of signal to noise. Multimodel ensemble technique contributes to improvement of
seasonal prediction skills. Seasonal prediction skills are
strongly dependent on regions, seasons and the elements to
predict as well as ENSO situations.
• In addition to steady improvement of atmosphere-ocean
coupled models, it is necessary to explore other predictability
sources in the Earth system in future.
• High resolution model is now used to project future changes in
weather extremes and tropical cyclones under the global
warming environment. Such data is useful for various
application studies, including adaptation to climate change.