ncpp – needs, process components, structure of scientific climate impacts study approach, etc
TRANSCRIPT
NCPP – needs, process components, structure of
scientific climate impacts study approach, etc.
NCPP - Two main elements identified so far
NCPP
Providing future downscaledand value-added climate
information
Archiving and providing a repository of best practices
standards/guidance, tools, etc.
Digital data access
Documentation
Translationalinformation
Types of needs*, identified so far• # 1 - Supplying already existing downscaled data that fulfill the open-source
and review (quality) criteria in GIS format• For non-standard areas – watersheds, states, other
• # 2 – Re-gridding or interpolation to locations of already existing downscaled data – tools and translational information
• # 3 - Supplying sets of indices (value-added information) by sector as needed for climate change impacts studies
• For different spatial scales – states, regions, cities, watersheds, ecological regions, other
• # 4 - Downscaling variables not available in existing downscaled data set portals
– Standard available variables from existing portals – tmax, tmin, precipitation, on monthly or daily scale
• Supplying this data in GIS format• Re-gridding as necessary• Supplying application-specific indices
• # 5 – Supplying narratives relative to specific variables, and sectors and at specific spatial scales – historical and for future periods (what has happened, what is going to happen)
• # 6 – Supplying translational information relative to downscaling methodology, uncertainty of results, interpretation of results, re-gridding procedures, etc.
* the needs are not listed based on importance or priority
• This presentation focuses first on:– Details related to need # 3 - Supplying sets of
indices (value-added information) by industry as needed for climate change impacts studies
– Details related to need # 4 - Downscaling variables not available in existing downscaled data set portals
Scientific approach to climate change impacts study
• Some definitions– Index or measure - a number derived from
observations or simulations– Growing Degree Days, Heating and Cooling Degree Days,
frequency and intensity of heat waves, Cold spells, date of first fall frost and last spring frost, exceedance probability of annual precipitation, persistence of rain/dry days, etc.
– Ensemble - A group of parallel model simulations used for climate projections.
– Variation of the results across the ensemble members gives an estimate of uncertainty.
– Ensembles made with the same model but different initial conditions only characterize the uncertainty associated with internal climate variability
– Multi-model ensembles including simulations by several models also include the impact of model differences.
Assessment of the quality of the observed gridded data sets for the 1951-1999 period
Comparison of the model simulations and the observational data sets
for the historical period 1951-1999
Evaluation of the precipitation variability during 2001-2099
Starting stage – need # 3
Structure of a climate impacts study - focus on process when using readily available downscaled data
Example: Example: Historical and projected future precipitation variability Historical and projected future precipitation variability in the Colorado River Basinin the Colorado River Basin
Assessment of the homogeneity of the observed gridded data sets for the 1951-1999 period
Comparison of the model simulations and the observational data sets
for the historical period 1951-1999
Evaluation of the precipitation variability during 2001-2099;
Inclusion of translational information – Inclusion of translational information – how to interpret the results and the uncertaintyhow to interpret the results and the uncertainty
Structure of a climate impacts study - focus on process when using readily available downscaled data
Example: Historical and projected future precipitation variability Example: Historical and projected future precipitation variability
in the Colorado River Basinin the Colorado River Basin
Quality assessment ofobserved data;
Inclusion of translational Inclusion of translational informationinformation
Bias evaluation ofthe GCM downscaled data;
Inclusion of translationalInclusion of translationalinformation – how to interpretinformation – how to interpret
the biasesthe biasesResults may impact:
Set of models used in future precipitation changes analysis
(choice of best models)Interpretation of results
(choice of interpretation based only on non-biased models)
Early future period2001-2049
Downscaled by L. Brekke
et al.
Downscaled by J. Eischeid
(NOAA)
A1bA2 B1 A1b
Late future period2051-2099
Downscaled by L. Brekke
et al.
Downscaled by J. Eischeid
(NOAA)
A1bA2 A1bB1
Evaluation of the precipitation variability during 2001-2099
Mid future period2026-2074
B1
Downscaled by L. Brekke
et al.
Downscaled by J. Eischeid
(NOAA)
A1bA2 A1b
Calculated set of measures of precipitation variability
Calculate deltas (changes) for all GCM future periods versus the GCM historical period Qualitative or quantitative comparison of the future changes between periods, SRES emissions scenarios,
and downscaling methodologies
Description of Description of measures’ calculationmeasures’ calculation
Description of deltas calculations, comparison methodology, interpretation of results and uncertaintyDescription of deltas calculations, comparison methodology, interpretation of results and uncertainty
Summary - Using already downscaled data sets – for ex., Maurer et al., Hayhoe, NARCCAP, other
Validation - comparison between GCM and observed measures/indices during historical period of overlap
Observed data set 1, 2
Products: a) future changes in various measures/indices of interest for time segments or overlapping periods, with uncertainty
assessment, b) narratives, c) probabilistic distributions of measures/indices, d) other
Translational information: Interpretation guidance of results,Translational information: Interpretation guidance of results,Uncertainty around the measures/indices,Uncertainty around the measures/indices, Details of Details of
Probability distributions’ methodologyProbability distributions’ methodology
Calculation of measures/indices
Feedbackfrom users
Data set 1 – multiple GCMs, SRES emissions
scenarios
Data set N – multiple GCMs, SRES emissions
scenarios
Access to downscaled GCM and/or RCM data and to observed data:
Quality control, homogeneity,testing, if needed
Translational Translational info about info about
GCM biasesGCM biases
Examples of existing data sets for the US that can be used:
• Observed data sets – Gridded data:
• Maurer et al. 2002 – daily and monthly; ~12 km resolution
• PRISM – Precipitation Regressions on Independent Slopes Model, (Daly et al 2004, 2006) –monthly, 4 km or less resolution
– Station data• Located at NCDC or at state
climatologist offices:
– Data from COOP stations - daily, monthly
– Data from the Historical Climatology Network (homogenized) – monthly
• Downscaled data sets:– Gridded data:
• Maurer et al. 2007 CMIP3 BCSD downscaled monthly data set, approx. 12km, 1950-2099, 16 GCMs, 36 projections, 3 SRES scenarios
• Hayhoe downscaled daily data set – to be serviced by USGS – approx. 12km, tmax, tmin, precip, 1960-2099, 4GCMs, 4 SRES scenarios, CONUS and Alaska
• NARCCAP dynamically downscaled data set
• CMIP3 BCCA downscaled daily data set – same spatial domain as BCSD; same resolution; 3 time periods 1961-2000, 2045-2064, and 2080-2099; tmax, tmin, precip
• Project portals at institutions
– Station or location data• At Universities, institutions
Generalization - Using already downscaled data sets – for ex., Maurer et al., Hayhoe, NARCCAP, other
Components of the process
Validation of measures/indices tool
Products
Calculation of measures/indices
Feedbackfrom users
Access to downscaled GCM and/or RCM data
Quality control tool
Translational Translational Information Information
tooltool
Access to observed data:
Ensemble analysis tool; Uncertainty analysis tool; Probabilistic distributions tool, other
Translational information toolTranslational information tool
Later stages Structure of a climate impacts study - focus on
process when downscaling GCM and/or RCM data
The main differences are related to the beginning of the process and the access to data portals of raw GCM and/or RCM data and
the subsequent downscaling of the GCM or RCM data.
The subsequent slides focus on the downscaling procedures when using different downscaling techniques.
Observed data set 1, 2Data set 1 – multiple GCMs, SRES emissions
scenarios
Data set N – multiple GCMs, SRES emissions
scenarios
Access to downscaled GCM and/or RCM data and to observed data:START
LATER
Observed data set 1, 2Data set 1 – multiple GCMs, SRES emissions
scenarios
Data set N – multiple GCMs, SRES emissions
scenarios
Downscaling GCM and/or RCM data; Access to observed data:
Structure of a climate impacts study – focus on downscaling process (statistical or empirical –dynamical downscaling)
User identified requirements for the downscaled product – Measures/indices of interest, temporal and spatial scalesUser and provider agreed upon sources of uncertainty
Set of GCMs, SRES emissions scenarios, downscaling techniques
Access to observed data sets of the variables of interest and predictors (if needed) - quality controlled, homogeneous
Access to raw GCM data sets of the variables of interest and
of the predictors (if needed)
Develop transfer functions for a given period
Validate transfer functions on separate periodComparison indicates downscaling biases
Apply transfer functions on GCM predictor data for
control period
Validate CGM transfer functionsfor control period vs OBS data
Comparison indicates GCM + downscaling biases
Apply transfer functions on GCM future data to obtain downscaled projectionsProducts - Calculate additional indices of interest, potential changes, or
downscaled data needed for input in process models, other
Definition of “observed”, “current”,
“control” and “future” climates
for RCM simulations and
the types of comparisons that must be
performed. Note that it is not
appropriate to compare future
climate projections directly to
observations. (Winkler et al.,
2011)
Structure of a climate impacts study – focus on downscaling process (dynamical downscaling)
User identified requirements for the downscaled product – Measures of interest, temporal and spatial scales
User and provider agreed upon sources of uncertaintySet of GCMs, SRES emissions scenarios, downscaling techniques
Products:time series,
climatologies, potential changes
for future time slices, other
Create quantile map
Structure of a climate impacts study – focus on downscaling process - Example – Maurer et al. 2007 downscaling effort
(disaggregation downscaling)
Step 1
Biascorrection
Observed griddeddata
GCM 20th centurydata
Re-grid observed and GCM 20th and 21st century data to same resolution (2º)
GCM 20th centurydata
For each grid cell, create monthly cumulative distribution functions (CDFs) by variable of interest
By grid cell and for each month, adjust the 20th and 21st century GCM data by using the OBS values for given quantiles of the CDF
Step 2 SpatialDown-scaling
Compute Factor values
by grid celland time step
Interpolate2º factor
values to 1/8º
Apply factor values to original 1/8º observed data - Resulting in BSCD GCM data
END
Comparison of the model simulations and the observational data sets
for the historical period 1951-1999
Observed data sets GCM data sets
Calculated variables/measures compared to:
Maurer et al. 2002Gridded observed data
Downscaled by L. Brekke et al.
(2007)
37 model
runs
Translational Translational info about the info about the
data sets; data sets; the comparisonthe comparisonmethodology;methodology;
about theabout theinterpretation of interpretation of the biases of thethe biases of the
downscaleddownscaled data;data;
Downscaled by Jon Eischeid
(NOAA)
30model
runs
PRISM (Daly et al. 2004, 2006) Gridded
Observed data