1. UNCERTAINTY AND VARIABILITY IN LAND SURFACE PRECIPITATION OVER 100-PLUS YEARS
Elsa Nickl and Cort Willmott
Department of Geography
University of Delaware

2. indispensable for climate research
Understanding the spatial and temporal variability of land-surface precipitation:
indispensable for climate research
University of Delaware, Willmott and Matsuura dataset
Spatial mean of land surface precipitation for period

3. Land Surface Precipitation Fields Datasets
(based on in situ observations)
A growing demand for higher spatial (e.g. 0.5o ) and temporal (e.g. monthly, daily) resolution gridded datasets
Currently there are three land surface monthly precipitation datasets for the period at 0.5o resolution:
Climate Research Unit (CRU) dataset
University of Delaware (Udel or Willmott and Matsuura) archive
Global Precipitation Climate Center (GPCC) dataset

4. Low spatial density of weather stations in complex terrain regions (e
Low spatial density of weather stations in complex terrain regions (e.g. mountainous regions)

5. OBJECTIVES
To explore the spatial and temporal variability of land-surface precipitation using three current high resolution gridded datasets.
To propose a new approach for estimating monthly land-surface precipitation fields from rain gage station records
DATA
Gridded monthly land-surface precipitation ( ) at 0.5o resolution from: Udel archive, GPCC dataset and CRU dataset
US monthly land-surface precipitation ( ) from the National Climatic Data Center (NCDC)
Central Peruvian Andes land-surface precipitation climatologies ( ) from ELECTROPERU.
Digital Elevation information at 2.5 minutes resolution (used by PRISM, derived from EROS Data Center 3 arc sec) (US area)
Digital Elevation information at 0.5 minute resolution from GTOPO30 (Peruvian Andes area)

6. INTERPOLATION METHODS
Udel archive (Matsuura and Willmott)
Gridded Monthly Time Series
Climatologically Aided Interpolation method
High-resolution climatology
Monthly precipitation differences at each station
Station differences are interpolated to a gridded field using Shepard’s algorithm
Each gridded difference is added back onto the corresponding climatology

7. INTERPOLATION METHODS
Climate Research Unit dataset ( )
Angular Distance Weighted (ADW) interpolation
Weights 8 nearest stations from the grid point (using a Correlation Decay Distance and the directional isolation of each station)
At grid points where there is no station within CDD, interpolated anomalies
are forced to zero (as a consequence, estimated time series over some areas
are invariant for many years)
Number of years since 1901 with repetitive information

8. INTERPOLATION METHODS
Global Precipitation Climatology Project (GPCC, )
SPHEREMAP interpolation tool (developed by Wilmmott and his graduate students)
It’s an spherical adaptation of Shepard’s algorithm
Shepard’s takes into account:
Distances of the stations to the grid point (limited number of nearest stations)
Directional distribution of stations (to avoid overweighting of clustered stations)
Spatial gradients within the data field in the grid-point environment

9. TEMPORAL VARIABILITY OF LAND-SURFACE PRECIPITATION
Similar trends until the end of 1970s (except GPCC)
Early 1980s datasets show a decline with a “recovery” of 2 datasets (CRU and GPCC) in the early 1990s. Udel dataset remains negative until 2006

10. SPATIAL VARIABILITY OF LAND SURFACE PRECIPITATION (1901-1976)
Udel
SPATIAL VARIABILITY OF LAND SURFACE
PRECIPITATION ( )
Slight increases over many areas, with some very large
increases apparent in Udel and GPCC datasets, especially
over the Amazon Basin
A large but questionable decrease over the Tibetian Plateau
GPCC
CRU

11. SPATIAL VARIABILITY OF LAND SURFACE PRECIPITATION (1977-2002)
Udel
SPATIAL VARIABILITY OF LAND SURFACE
PRECIPITATION ( )
Udel and GPCC datasets show decreasing land-surface
precipitation over many regions of North America, Central
America, Central South America, equatorial Africa and the
maritime continent
These patterns are not present with CRU dataset to the
same extent
GPCC
CRU

12. PRECIPITATION CHANGE AND TELECONNECTIONS
(taking into account change-point method)
Change-point regression (Draper and Smith, 1981): to identify the years of major change.
This method determines optimal change-point in time-series by minimizing the sum of squared
residuals of all possible change-point regressions

13. SPATIAL VARIABILITY OF CHANGE-POINT

14. NEW METHOD OF INTERPOLATION
Parameter-elevation Regressions on Independent Slopes Model (PRISM) :
Linear relationship between precipitation and elevation
Estimated orographic elevation
“Facets” (contiguous areas of homogeneous slope orientation)
Exploration of the relationships between monthly precipitation and the spatial arrangements of topographic patterns:
Central Peruvian Andes
Western US

15. NEW METHOD OF INTERPOLATION
Winter (DJF)
Summer (JJA)
Elevation and seasonal precipitation (with more than 200mm) in the Western US

16. NEW METHOD OF INTERPOLATION
“Special” scatterplots: To explore relationships between spatial arrangements of elevation, slope, slope orientation and precipitation
Winter (DJF)
Western US, 2.5 min resolution:
Winter:
Not apparent relationship
High precipitation values at elevations <1km
Summer:
Most precipitation is convective
Summer(JJA)

17. NEW METHOD OF INTERPOLATION
Central Peruvian Andes, 0.5 min resolution:

18. NEW METHOD OF INTERPOLATION
Identification of the “orographic scale”
Averaging up from a high-resolution DEM to a more coarse spatial resolution
Adjustable-scale spatial ellipse (to estimate
areal extent of orographic influence)

19. NEW METHOD OF INTERPOLATION
7.5 min
Western US:
Elevation, slope, slope orientation and
precipitation during winter (DJF)
A slight relationship between higher winter
precipitation and SW and NE orientations at
elevations greater than 1km.
12.5 min

20. NEW METHOD OF INTERPOLATION
San Joaquin Valley and Sierra Nevadas:
Elevation, slope, slope orientation and
precipitation during winter (DJF)
7.5 min
A moderate relationship between higher winter
precipitation and W and SW orientations at
elevations greater than 500 meters.
12.5 min

21. NEW METHOD OF INTERPOLATION
1.5 min
Central Peruvian Andes:
Elevation, slope, slope orientation and
precipitation during austral summer (DJF)
Localized relationship between higher
precipitation values and NE slope orientations,
especially at 2.5 min resolution
2.5 min

22. NEW METHOD OF INTERPOLATION
Central Peruvian Andes:
Elevation and precipitation for low and high slope values

23. NEW METHOD OF INTERPOLATION(
NEW METHOD OF INTERPOLATION
Horizontal-distance and direction influences (based on modified Shepard’s
interpolator)
from nearby stations Pi
2. Additional topographic influences on interpolated precipitation (from elevation,
slope, slope orientation and the degree of exposure to orography
Important: the orographic scale
Orographic elevation
Longitudinal and latitudinal components of the slope of the orographic region
Potential exposure of station “i” to orography
We can estimate an interpolation bias for each station:
Then we can estimate
And finally:

24. CONCLUSIONS
The spatial and temporal variability of land-surface precipitation
over the last 100-plus years is uncertain, as is evident in the differences
between available gridded datasets
The relationships between spatial arrangements of topographic patterns
and precipitation in mountainous regions are stronger for more coarse spatial
resolutions.
A central aspect of the new interpolation method is to estimate the areal extent
of orographic influence (orographic scale)
Understanding the spatial and temporal variability of land surface precipitation
and precipitation change is useful for teleconnection analysis in Central Peruvian Andes