Evaluation of Climate Indicators€¦ · Extreme Methods (return intervals, conditional...
Transcript of Evaluation of Climate Indicators€¦ · Extreme Methods (return intervals, conditional...
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Evaluation of Climate Indicators
L. Buja (NCAR) and W. Gutowski (Iowa State U.) L. Kaatz (Denver Water), A. Abatan (Iowa State U.),
CM Ammann, BG Brown, R. Bullock, T. Fowler, E. Gilleland, J. Halley-Gotway, K. Ikeda, D. Yates (NCAR)
Sectoral Perspectives
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Standardized Model Diagnostics: CESM Example http://www2.cesm.ucar.edu
TS Trends (DJF)
TS 12-yr running Trends (monthly data)
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Weather Forecast Verification Tools offer new and expanding opportunities
Various groups of tools:
• Spatial methods (object-based, neighborhood, field deformation)
• Probabilistic Methods • Extreme Methods
(return intervals, conditional spatio-temporal fields)
• Performance diagrams (multi-diagnostic, Taylor, Roeber-event-based)
from pixels to objects
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Object-Based Evaluation of CESM-LE MODE: Method for Object-based Diagnostic Evaluation
(http://www.dtcenter.org/met/users)
• target application-specific
indices with important impact
• automatically id and merge “pixels” into aggregated features (objects)
• match objects between model run and reference (obs, other model) using fuzzy logic
• Capture full statistics of spatial objects, size, orientation, intensity, spatial biases to reference, etc
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Ensemble Spread of Precipitation Fields
Large Scale Precipitation
Convective Precipitation
PRECC PRECL
one year DJF precipitation over 30 CESM-LE members
Significant spread across ensemble
little spread
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Large Scale Precipitation via Object Attributes
Individual precipitation objects identified by MODE across 30 ensemble members
Little ensemble spread
More ensemble spread
Little ensemble agreement
Intense Precipitation (90th percentile) across 30 ensemble members
(unit: cm / day average for DJF-season)
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Global Objects of pr>500mm
Area : Obs often 2x larger than CESM Number : Obs 25% less than CESM >> CESM is more patchy than CRU-TS3.21 90th%ile intensity: ~10% higher in Obs >> CESM has less intense rainfall Observations show larger areas with more uniformly high precipitation
CRU CESM
Obj Areas Obj Counts
90th %ile intensity CRU CESM
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Mean Climate Verification with MODE
• 30 year precipitation climatology: 100mm verification • GCM in blue outlines • Observed areas in color
Attributes of the South East US Rain Objects
Area Ratio of GCM and Observation: Median GCM Precipitation: Median Observed Precipitation:
Well represented mean!
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Conditional Object Frequency Precipitation DJF - Niño 3.4 > +1
CRU TS3.21 CESM-LE
>100mm
>300mm
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Conditional Object Frequency Precipitation DJF - Niño 3.4 > -1
CRU TS3.21 CESM-LE
>100mm
>300mm
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Observations CRU TS3.21
Models CESM-LE
Field: pr(DJF)
Freq
50%
25%
75%
100%
0%
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El Niño Influence on Occurrence of >900mm DJF Rainfall
CRU TS3.21 pr (djf) CESM-LE pr (djf)
EN- EN+ EN- EN+
Freq
50%
25%
75%
100%
0%
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MODE Comparison of SPI and SPEI (36-mo averages, 1950-2012)
SPEI SPI
Object Area
Credit: A. Abatan
Object Intensity: Variability
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Next steps to further evaluate climate models • Expand location and process specific performance evaluation:
Focus diagnostics on systematic biases resulting from lack of process representation, spatial displacements, temporal distribution issues
• Need for evaluation of different characteristics of climate: Indices in addition to traditional climate fields
• Conditional analyses using spatio-temporal context of regional multivariate variability
Abitan et al.: US Precipitation 1901-2005 in CRU-gridded and CESM-LE
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Thanks!
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Improving and Strengthening the Knowledge Chain
Examples of Tools for: • Access to useful data • Efficient evaluation for applications • Approaches to better questions
and appropriate translation
Benefits: • Improved science tools • Better informed decision making • Educational opportunities
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Objectives: Relevant Information • Water Sector: Inform management and planning
decisions with relevant weather & climate information (knowledge chain: access, evaluation, translation, good practice)
• Climate Research Community: Understand weather & climate challenges, improve and translate the relevant information (understand challenges at relevant spatial and temporal scales)
• CoDesign Weather & Climate Dashboards with relevant / actionable information (transparent, tied to observations, translated for understanding and context, probabilistic, …)
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Relevant Information through a Dashboard
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Dashboard: Sorghum in Sudan Sorghum Growth Region
MAP
Climate Change Impact Summary RCP8.5 RCP4.5 RCP2.6
Climate Change Impact Potential
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Dashboard: Sorghum in Sudan Sorghum Growth Region
MAP
Climate Change Impact Summary
Future Production Index Change
Cooling Degree Days Trend in Energy Requirement
Future Growth Suitability Index
RCP2.6 RCP4.5 RCP8.5
Seasonal ENSO Teleconnections
Daily Peak Temperatures
RCP8.5 RCP4.5 RCP2.6
Climate Change Impact Potential
RCP8.5
RCP4.5
Drought Occurrence
Explanation of Inter-annual Variability
Dry Period Extremes Storage Loss
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Traditional grid-point by grid-point verification
Which simulation result is better? Fig. courtesy of E. E. Ebert
observed models with different resolution