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