The PRISM Approach to Mapping Climate in Complex Regions Christopher Daly Director Spatial Climate Analysis Service Oregon State University Corvallis, Oregon

Spatial Climate Analysis Service Mission Service Provide innovative, state-of-the science spatial climate products and services to clients worldwide Research Maintain scientific research and development programs that provide the basis for products and services Education Advance “geospatial climatology” as an emerging discipline

SCAS and PRISM are Unique SCAS is the only center in the world dedicated solely to the mapping of climate PRISM climate mapping technology has been continuously developed, and repeatedly peer-reviewed, since 1991 PRISM climate maps are the “gold standard” by which others are evaluated SCAS has become a leader in climate mapping products and technology worldwide

Oregon Annual Precipitation

Rationale - Observations are rarely sufficient to directly represent the spatial patterns of climate - Human-expert mapping methods often produce the best products, but are slow, inconsistent, and non-repeatable - Purely statistical mapping methods are fast and repeatable, but rarely provide the best accuracy, detail, and realism Therefore… - The best method may be a statistical approach that is automated, but developed, guided and evaluated with expert knowledge

- Knowledge acquisition capability – Elicit expert information - Knowledge base – Store of knowledge - Inference Engine – Infer solutions from stored knowledge - User interface – Interaction and explanation - Independent verification – Knowledge refinement Knowledge-Based System KBS

-Generates gridded estimates of climatic parameters -Moving-window regression of climate vs. elevation for each grid cell -Uses nearby station observations - Spatial climate knowledge base weights stations in the regression function by their climatological similarity to the target grid cell PRISM Parameter-elevation Regressions on Independent Slopes Model

Oregon Annual Precipitation Interface

PRISM Knowledge Base - Elevation Influence on Climate

Oregon Annual Precipitation Mean January Precipitation, Sierra Nevada, CA, USA

Oregon Annual Precipitation Mean August Max Temperature, Sierra Nevada, CA, USA

Mean November Precipitation, Puerto Rico

Mean June Maximum Temperature, Puerto Rico

Mean February Precipitation, European Alps

Oregon Annual Precipitation Mean September Max Temperature, Qin Ling Mountains, China

PRISM Moving-Window Regression Function Mean April Precipitation, Qin Ling Mountains, China Weighted linear regression

Governing Equation Moving-window regression of climate vs elevation y =  1 x +  0 Y = predicted climate element x = DEM elevation at the target cell  0 = y-intercept  1 = slope x,y pairs - elevation and climate observations from nearby climate stations

Station Weighting Combined weight of a station is: W = f {W d, W z, W c, W f, W p, W l, W t, W e } - Distance - Elevation - Clustering - Topographic Facet (orientation) - Coastal Proximity - Vertical Layer (inversion) - Topographic Index (cold air pooling) - Effective Terrain Height (orographic profile)

-Terrain-Induced Climate Transitions (topographic facets, moisture index) PRISM Knowledge Base - Elevation Influence on Climate

Rain Shadow: Mean Annual Precipitation Oregon Cascades Portland Eugene Sisters Redmond Bend Mt. Hood Mt. Jefferson Three Sisters N 350 mm/yr 2200 mm/yr 2500 mm/yr Dominant PRISM KBS Components Elevation Terrain orientation Terrain steepness Moisture Regime

Mean Annual Precipitation, Cascade Mtns, OR, USA

Olympic Peninsula, Washington, USA Flow Direction

Topographic Facets  = 4 km  = 60 km

Oregon Annual Precipitation Full Model 3452 mm 3442 mm 4042 mm Max ~ 7900 mm Max ~ 6800 mm Mean Annual Precipitation,

Facet Weighting Disabled Max ~ 4800 mm 3452 mm 3442 mm 4042 mm Mean Annual Precipitation,

Oregon Annual Precipitation Elevation = 0 Max ~ 3300 mm 3452 mm 3442 mm 4042 mm Mean Annual Precipitation,

Oregon Annual Precipitation Full Model 3452 mm 3442 mm 4042 mm Max ~ 7900 mm Max ~ 6800 mm Mean Annual Precipitation,

-Terrain-Induced Climate Transitions (topographic facets, moisture index) PRISM Knowledge Base - Elevation Influence on Climate - Coastal Effects

Coastal Effects: July Maximum Temperature Central California Coast – 1 km Monterey San Francisco San Jose Santa Cruz Hollister Salinas Stockton Sacramento Pacific Ocean Fremont N Preferred Trajectories Dominant PRISM KBS Components Elevation Coastal Proximity Inversion Layer 34 ° 20 ° 27 ° Oakland

Mean July Maximum Temperature, Central California, USA Coastal Proximity Weighting OFFCoastal Proximity Weighting ON

-Terrain-Induced Climate Transitions (topographic facets, moisture index) PRISM Knowledge Base - Elevation Influence on Climate - Coastal Effects - Two-Layer Atmosphere and Topographic Index

TMAX-Elevation Plot for January TMIN-Elevation Plot for January January Temperature, HJ Andrews Forest, Oregon, USA Layer 1 Layer 2

Mean Annual Precipitation, Hawaii

United S tates Potential Winter Inversion

Western US Topographic Index

Central Colorado Terrain and Topographic Index Terrain Topographic Index Gunnison

January Minimum Temperature Central Colorado Gunnison Valley Bottom Elev = 2316 m Below Inversion Lapse = 5.3°C/km T = -16.2°C

January Minimum Temperature Central Colorado Gunnison Mid-Slope Elev = 2921 m Above Inversion Lapse = 6.9°C/km T = -12.7°C

January Minimum Temperature Central Colorado Gunnison Ridge Top Elev = 3779 m Above Inversion Lapse = 6.0°C/km T = -17.9°C

Inversions – January Minimum Temperature Central Colorado Dominant PRISM KBS Components Elevation Topographic Index Inversion Layer Gunnison Lake City Crested Butte Taylor Park Res. -18 ° C -13 ° -18 ° N

PRISM Mean January Minimum Temperature, 800-m “Banana Belt” Cold air drainage Snake Plain

Inversions – July Minimum Temperature Northwestern California Ukiah CloverdaleLakeport Willits Clear Lake Pacific Ocean Lake Pilsbury. N Dominant PRISM KBS Components Elevation Inversion Layer Topographic Index Coastal Proximity 12 ° 17 ° 9°9° 16 ° 10 ° 17 °

-Terrain-Induced Climate Transitions (topographic facets, moisture index) PRISM Knowledge Base - Elevation Influence on Climate - Coastal Effects - Two-Layer Atmosphere and Topographic Index - Orographic Effectiveness of Terrain (Profile)

United States Effective Terrain United S tates Orographically Effective Terrain

Oregon Annual Precipitation

- Terrain-Induced Climate Transitions (topographic facets, moisture index) PRISM Knowledge Base - Elevation Influence on Climate - Coastal Effects - Two-Layer Atmosphere and Topographic Index - Orographic Effectiveness of Terrain (Profile) - Persistence of climatic patterns (climatologically- aided interpolation)

Oregon Annual Precipitation Leveraging Information Content of High-Quality Climatologies to Create New Maps with Fewer Data and Less Effort Climatology used in place of DEM as PRISM predictor grid

PRISM Regression of “Climate vs Climate” or “Weather vs Climate” 20 July 2000 Tmax vs Mean July Tmax

Upcoming Products - Updated mean monthly P, Tmax, Tmin maps for the US at 800-m resolution (USDA-NRCS, NPS, USFS) - Spatial-Probabilistic QC system for SNOTEL observations - Targeted climatologies for NWS River Forecast Centers (NWS Western Region) - Extended monthly time series maps of P, Tmax, Tmin, Tdew for climate monitoring

Future Directions - Engage in collaborative projects to develop the use of PRISM and PRISM climatologies for downscaling numerical weather prediction models - Continue to develop technology to move to smaller time steps and towards real time operation - Explore using remotely-sensed data to improve PRISM accuracy in under-sampled areas (and vice-versa) - Continue to develop PRISM’s Spatial Climate Knowledge Base