For the past few weeks, we've been looking into the parametric controls on change signals in datasets downscaled using GARD. @gutman asked that I put a little writeup here to document what we've looked at to date and allow for discussion on the next steps.

First, the figure that kicked off this inquiry:

Caption: Change in precipitation (2080s-1980s) for 7 different downscaling methods. The WRF-50km dataset was used to drive the four GARD methods. GARD tends to show a stronger drying trend than LOCA, BCSD, or WRF-50km, particularly when circulation variables (PR2, PR3) are used.

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Exploratory Analysis

Evaluation of Raw GARD output (no random errors, no QM)

We have looked at a series of GARD experiments using different predictor variables and transforms:

Cube Root (GARD_downscaling_production_20170929)

• PassThrough: PREC_TOT
• PR, PA, AR:
  • 1: PREC_ACC_NC, PREC_ACC_C
  • 2: PREC_ACC_NC, PREC_ACC_C, U, V, PSFC
  • 3: U, V, PSFC

Cube Root (GARD_downscaling_production_20171017)

• PR, PA, AR:
  • 0: PREC_TOT

Cube Root (GARD_downscaling_production_20171103):

• PR, PA, AR:
  • 1c: PREC_TOT
  • 2a: PREC_TOT, U, V
  • 2b: PREC_TOT, U, V, QVAPOR
  • 2c: U, V, QVAPOR

Cube Root (GARD_downscaling_production_20171116):

• PA:
  • 1d: PREC_TOT
  • 2d: PREC_TOT, U, V

Log Root (GARD_downscaling_production_20171103b):

• PR, PA, AR:
  • 1c: PREC_TOT
  • 2c: PREC_TOT, U, V

5th Root (GARD_downscaling_production_20171103c):

• PR, PA, AR:
  • 1c: PREC_TOT
  • 2c: PREC_TOT, U, V

Evaluation of GARD output after random errors

Cube Root (GARD_downscaling_production_20170929)

• PassThrough: PREC_TOT
• PR, PA, AR:
  • 1: PREC_ACC_NC, PREC_ACC_C
  • 2: PREC_ACC_NC, PREC_ACC_C, U, V, PSFC
  • 3: U, V, PSFC

Cube Root (GARD_downscaling_production_20171017)

• PR, PA, AR:
  • 0: PREC_TOT

Evaluation of GARD output after quantile mapping

Cube Root (GARD_downscaling_production_20170929)

• PassThrough: PREC_TOT
• PR, PA, AR:
  • 1: PREC_ACC_NC, PREC_ACC_C
  • 2: PREC_ACC_NC, PREC_ACC_C, U, V, PSFC
  • 3: U, V, PSFC

Cube Root (GARD_downscaling_production_20171017)

• PR, PA, AR:
  • 0: PREC_TOT

Evaluation of trends/changes in predictor variables

I looked at the following WRF-50km variables: PREC_ACC_NC, PREC_ACC_C, U, V, PSFC, T2

Single point experiments

This work is currently underway. Exploring behavior of GARD for how analog sampling is done, transforms, and different input variables.

I'll be implementing/computing a large set of metrics on the meteorlogy and hydrology datasets used in the storylines project. We'll use this issue to collect the metrics. I'll take care of the implementation.

Change Metrics

Meteorology

• Variables
  • Precipitation
  • Temperature (daily min/max/mean/range)
• Metrics
  • Time mean (all variables; monthly/seasonal/record)
  • Time wet day fraction (precipitation; monthly/seasonal/record)
  • Interannual variability (all variables; monthly/seasonal/record)
  • Time extremes, quantiles (all variables; monthly/seasonal/record)
  • Time extremes, EVD/gamma (all variables; monthly/seasonal/record)

Hydrology

• Variables
  • Streamflow (runoff/baseflow)
  • Snow water equivalent
  • Evapotranspiration
• Metrics
  • Time mean (all variables; monthly/seasonal/record)
  • Interannual variability (all variables; monthly/seasonal/record)
  • Flow duration percentiles (streamflow, daily/weekly/monthly)
  • Time extremes, quantiles (all variables; monthly/seasonal/record)
  • Time extremes, return intervals (streamflow; e.g. 10/100 year events for max/min, 7Q10)
  • Time extremes, EVD/gamma (all variables; monthly/seasonal/record)
  • Elasticity, precipitation vs. streamflow (streamflow; record)

How to suggest a metric

For most metrics, you can just follow the format above:

- [ ] METRIC (VARIABLE(S); TIME SCALE)

If the metric is particularly complicated or requires explanation, feel free to add that below.

cc @gutmann, @andywood, @julievano, @anewman89, @naddor, @flehner