Our approach, activities and achievements in climate researchfrancesco/IRI_REVIEW... · 2013. 7....
Transcript of Our approach, activities and achievements in climate researchfrancesco/IRI_REVIEW... · 2013. 7....
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Our approach, activities and achievements
in climate research
Andrew W. Robertson and IRI Climate Group
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Approach
• Use-inspired climate forecasting and climate science research
• Collaborative climate and interdisciplinary research
• Participatory capacity building research
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What we mean by “Use-inspired climate forecasting and climate science
research”
➡ R&D needed to produce salient & credible climate information for better real-world management of climate related risks/climate change adaptation
(counter examples: deterministic seasonal forecasts and raw downscaled CMIP projections)
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Examples
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Operational Products:Net Assessment & ENSO Outlook
• Multi-institutional 2-tier seasonal forecasting system since late 1990s
• Currently 2 AGCMs run in house, 4 outside, plus CFSv2
• Parametric multimodel combination/calibration
• Large set of additional GCM products run each month for research & partner use
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Seasonal forecast “Net Assessments”
Net assessments were visited 52,000 times in 2012 - “Global Public Good”
Is this the extent of reliable seasonal forecast information?
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Forecasting the full distribution:“Flexible” Forecasts Maprooms
STD RAINFALL
FRE
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FORECAST SST SCENARIOS
TROP. PACIFIC: THREE
(multi-models, dynamical and statistical)
TROP. ATL and INDIAN
(2 and 3 multi-models) EXTRATROPICAL
(damped persistence)
GLOBAL ATMOSPHERIC
MODELS
COAPS(FSU)
ECHAM4.5(MPI)
CCM3.6(NCAR)
GMAO(NASA)
COLA2
GFDL
ForecastSST
Ensembles3/6 Mo. lead
PersistedSST
Ensembles3 Mo. lead
IRI DYNAMICAL CLIMATE FORECAST SYSTEM
POSTPROCESSING
MULTIMODELENSEMBLING
PERSISTED GLOBAL
SSTANOMALY
2-tiered OCEAN ATMOSPHERE
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modelweighting
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ENSO QUICK LOOK February 16, 2012 A monthly summary of the status of El Nino,La Nina and the Southern Oscillation, or “ENSO”, based on NINO3.4 index (120-170W, 5S-5N)A majority of the ENSO prediction models call for weak La Nina conditions during the February-March pe-riod, transitioning to neutral conditions during the March-May period with the most likely time of dissipationoccurring in early April.
Official Early-Feb CPC/IRI Consensus Forecast1
JFM FMA MAM AMJ MJJ JJA JAS ASO SON0
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Prob
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)ENSO state based on NINO3.4 SST Anomaly
Neutral ENSO: −0.45oC to 0.45oC
2012 2012
Mid-Feb IRI/CPC Plume-Based Forecast2
FMA MAM AMJ MJJ JJA JAS ASO SON OND0
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Time Period
ENSO state based on NINO3.4 SST AnomalyNeutral ENSO: −0.45oC to 0.45oC
2012 2012
El NiñoNeutralLa Niña
Historical Sea Surface Temperature Index
1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012−3
−2
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←NINO
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Time Period
NDJ Jan JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND-2.5
-2.0
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Nino
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DYN AVGSTAT AVGCPC CON
Dynamical Model:
Statistical Model:
NCEP CFSv2 NASA GMAO NCEP CFS JMA SCRIPPS LDEO AUS/POAMA ECMWF UKMO KMA SNU ESSIC ICM ECHAM/MOM COLA ANOM MetFRANCE COLA CCSM3 CS-IRI-MM GFDL CM2.1 CMC CANSIP
CPC MRKOV CDC LIM CPC CA CPC CCA CSU CLIPR UBC NNET FSU REGR UCLA-TCD
2011 2012
Mid-Feb 2012 Plume of Model ENSO Predictions
OBS FORECAST
IRI/CPC
Historically Speaking
El Nino and La Nina events tend to develop during the period Apr-Jun and they:- Tend to reach their maximum strength during Dec-Feb- Typically persist for 9-12 months, though occasionally persisting for up to 2 years- Typically recur every 2 to 7 years
1Official: Based on a consensus of CPC and IRI forecasters, in association with CPC/IRI ENSO Diagnostic Discussion.2Unofficial: Purely objective, based on regression, using equally weighted model predictions from the plume.
ENSO pages were visited 99,000 times
in 2012
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New IRI/CPC Joint ENSO Forecasts
IRI recently began working with CPC to issue a joint ENSO forecast.
Benefits to CPC
• Expanded forecast lead time
• Co-branding on IRI/CPC ENSO plume
• Having a partner in drafting the monthly ENSO
Diagnostic Discussion
Areas of Future Collaboration
• Developing and testing better ENSO SST
thresholds• Creating a more flexible
ENSO climatology• Investigating the utility of
other ENSO indices besides the 3-month SST average in
the NINO3.4 region
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GCM Products made at IRI
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Forecast Development+
Diagnostics & Modeling
• New US NMME and coupled model collaborations
• Forecast calibration/MME methodologies
• Verification metrics development
• Downscaling and forecast tailoring
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NMME Data Services
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Coupled GCMs in IRIDLReal-time + Hindcasts
• NMME (CFSv2, CCSM3, GFDL, NSIPP, Env-Canada)
• UKMO-GloSea5
• POAMA (Australian BoM)
• ECMWF (SST)
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120 1255
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Correlation threshold
% o
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(c) Correlation values
GCMRCM
Figure 7: Anomaly correlation coe�cient between AMJ seasonal average PAGASA rainfall
data and MOS corrected simulations of (a) GCM, (b) RCM simulated precipitation (only
positive values are plotted). Panel (c) shows the percentage of stations exceeding a given
correlation value, with the 95% one-tailed statistical significance value indicated as vertical
line.35
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(c) Correlation values
GCMRCM
Figure 5: Anomaly correlation coe�cient between AMJ seasonal average PAGASA rainfall
data and (a) GCM, (b) RCM simulated precipitation interpolated to station locations (only
positive values are plotted). Panel (c) shows the percentage of stations exceeding a given
correlation value, with the 95% one-tailed statistical significance value indicated as vertical
line.
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Dynamical (RCM, 50km) Statistical (MOS)
Downscaling: Dynamical or Statistical?
Anomaly correlation skill for April–June season
Robertson et al. (2013, MWR)GCM T42 simulations w/obs SST, 1977–2004
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Predictability of “Weather within Climate”
seasonal totalrainfall
frequency
Choose the analysis to perform: PCR or CCA
SELECTING THE ANALYSIS
y = Ax + b
JJAS rainfall correlation skill (ECHAM4-CA: made from June 1)
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1900 1910 1920 1930 1940 1950 1960 1970ï3
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1900 1910 1920 1930 1940 1950 1960 1970ï3
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4Frequency of Occurrence
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6Daily Mean Intensity
Why is frequency more predictable than intensity?
1900 1910 1920 1930 1940 1950 1960 1970ï3
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Rainfall at individual stations and station-average (NW India)
rainfall frequency mean intensity on wet days
Rainfall occurrence frequency is more spatially coherent than intensity.
Moron et al. (2007, JCL)
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Figure 15. Cross validation correlation skills in rain-season rice production of all ecosystem 0
at regional level (a) and provincial level (b). WWV plus zonal wind anomalies over the west equatorial Pacific(1980-2007) in DJF were used as predictors of MLR.
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Figure 10. Cross validation correlation skills in dry-season rice production of all ecosystem 0
at regional level(a) and provincial level (b). Seasonal precipitation anomalies in OND over 0N-25N, 110E-130E forecasted with ECHAM4.5-MOM (1981-2007) on 1st June were used as predictors of CCA. Provinces without rice production data are shaded.
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KoideJul 3, 10:03 AMFormatted: 0 inch Left Indent
Jan–Jun (Dry Season)
from prev. Jun 1
Predictability of Philippines Rice ProductionBased on CGCM hindcasts
Jul–Dec (Rainy Season)
from prev. Mar 1
ACC Skill of (a) Regional & (b) Provincial Production
1980–2007
Koide et al. (2013, JAMC)
Choose the analysis to perform: PCR or CCA
SELECTING THE ANALYSIS
y = Ax + b
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AGRHYMET Training (2012) on predictability of agroclimate and hydrological quantities
(incl. monsoon onset dates & river discharge)
SST Onset Date
Map of covariability between May SST and Rainfall Onset Dates
Choose the analysis to perform: PCR or CCA
SELECTING THE ANALYSIS
y = Ax + b
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Timescales decomposition
Greene, Goddard & Cousin (2010, EOS)
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Research: Models say that IF the subtropical North Atlantic warms up more than the global tropics, then the
Sahel could get wetter…
PastGreen: end 20th century – pre-Industrial
Blue: end – beginning 20th century
Future:Yellow: mid-21st (A1B) – end 20th
Red: end 21st – end 20th
A. Giannini
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Stochastic Simulation Framework
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Funding and research partners
• Federal science agencies – individual PI’d grants (NOAA, NASA, NSF, DOE, ONR)
• Cooperative Agreements with NOAA and USAID
• Climate service/development projects
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Linkages• CLIVAR/US-CLIVAR
– Scientific Steering Group, Co-Chair (L. Goddard)– Process Studies and Model Improvement panel (US) (A. Giannini)– Predictability, Prediction and Applications Interface panel (US)(A. Barnston)– Variability of the African Climate System (S. Mason)
• WCRP– Working Group on Regional Climate (S. Mason)– Working Group on Seasonal to Interannual Prediction (A. Robertson)– WCRP-WWRP/THORPEX Subseasonal to Seasonal Prediction Project planning group, Co-Chair (A. Robertson)
• WMO– Expert Team on Climate Services Information System (CCl), Chair (S. Mason)– Joint Working Group on Forecast Verification Research (S. Mason)
• NOAA Modeling, Analysis, Prediction & Projection (MAPP) Task Force– L. Goddard, A. Barnston, B. Lyon, S. Mason, M. Tippett,
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Red Cross - IRI example
Opportunity to use information on multiple time scales
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What’s next?
• New flexible seasonal forecast MME Net Assessment based on partnerships with climate modeling centers in US and internationally
• Pan-timescale climate forecast information from sub-seasonal to decadal
• Robust in-house climate research (forecasting, diagnostic, modeling) in collaboration with LDEO, CU, NOAA, universities, regional centers, NMHSs ...
• Tailored product development, esp. using IRIDL
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Thank You!