PUMA toolbox Philip Mote OCCRI.net Oregon State University Philip - - PowerPoint PPT Presentation

PUMA toolbox

Philip Mote

OCCRI.net Oregon State University

Philip Mote

CMIP3 CMIP5 NARCCAP regCPDN

• bservations

reanalysis PRISM delta BCSD CA MACA etc. WRF BCCA

nationality model BCCR CCSM3 CGCM (T47) CGCM (T63) CNRM CSIRO ECHAM5 ECHO-G FGOALS GFDL-CM2.0 GFDL-CM2.1 GISS-AOM GISS-ER HADCM3 HADGEM1 INMCM IPSL MIROC MIROC-hires PCM

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PNW changes

Mote and Salathé 2010 - graphics by Jeremy Littell

Mean Median

Model used in IPCC Fourth Assessment

“Worst” “Best”

Climate variable

Gleckler, Taylor, and Doutriaux, Journal of Geophysical Research (2008)

Mean precipitation

Jan Mar May Jul Sep Nov Jan 5 10 15 20 25 cm

gfdl_cm2_1 giss_aom giss_er hadcm hadgem1 inmcm3_0 ipsl_cm4 miroc3_2_hi miroc_3.2 pcm1 multi-model avg

NCEP

20th century seasonal cycle

Mote and Salathé 2010

10

2080s PNW summer precipitation change

0.0 0.2 0.4 0.6 0.8 1.0 Skill factor

• 60
• 40
• 20

20 40 % change

Mote, Brekke, Dufgy, Maurer 2011

NARCCAP

narccap.ucar.edu

NARCCAP

climateprediction.net (CPDN)

Simulated July Tmax Observed (PRISM) July Tmax

20C3M

Meeting the needs of fire management Needs

• 1. Full meteorology: TMAX/TMIN,

ppt, winds, RH, solar

• 2. Daily timescales

Methods

• 1. Combines attributes of CA and

BCSD methods similar to BCCA (Maurer and Hidalgo, 2010)

• 2. Accounts for disappearing analogs,

new variables

• 3. Compared to daily BCSD method

TEMP, RH, PPT, 10-m winds 8km resolution

Late 20th Mid 21st

Multivariate Adaptive Constructed Analogs (MACA)

Abatzoglou and Brown, submitted

MAC advice

Considerations in design of climate scenarios Which variables and at what time/space res. how to characterize uncertainty model evaluation? Other considerations: paleo, time-to-detection,

MAC advice

Dynamical vs statistical always some statistical (remove biases, get large ensemble); dynamical for physical relationships, extremes; evaluate approaches according to needs at hand