UNCERTAINTY AND VARIABILITY IN LAND SURFACE
PRECIPITATION OVER 100-PLUS YEARS
Elsa Nickl and Cort Willmott
Department of Geography
University of Delaware
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 1900-2006
period
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 1901-2006 at 0.5o resolution:
•Climate Research Unit (CRU) dataset
•University of Delaware (Udel or Willmott and Matsuura) archive
•Global Precipitation Climate Center (GPCC) dataset
Low spatial density of weather stations in complex terrain regions
(e.g. mountainous regions)
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
o Gridded monthly land-surface precipitation (1901-2006) at 0.5o resolution
from: Udel archive, GPCC dataset and CRU dataset
•US monthly land-surface precipitation (2001-2005) from the National
Climatic Data Center (NCDC)
•Central Peruvian Andes land-surface precipitation climatologies (1965-2000)
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)
INTERPOLATION METHODS
Udel archive (Matsuura and Willmott)
1900-2006 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
INTERPOLATION METHODS
Climate Research Unit dataset (1901-2002)
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
INTERPOLATION METHODS
Global Precipitation Climatology Project (GPCC, 1901-2006)
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
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
SPATIAL VARIABILITY OF LAND SURFACE
PRECIPITATION (1901-1976)
Udel
• 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
Udel
SPATIAL VARIABILITY OF LAND SURFACE
PRECIPITATION (1977-2002)
• 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
GPCC
•These patterns are not present with CRU dataset to the
same extent
CRU
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 timeseries by minimizing the sum of squared
residuals of all possible change-point regressions
1965-1975
300
Precip (mm)
250
1976-2000
200
150
100
50
0
65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03
SPATIAL VARIABILITY OF CHANGE-POINT
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
NEW METHOD OF INTERPOLATION
Winter (DJF)
Summer (JJA)
Elevation and seasonal precipitation (with more than 200mm) in the Western US
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)
NEW METHOD OF INTERPOLATION
Central Peruvian Andes, 0.5 min resolution:
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)
NEW METHOD OF INTERPOLATION
Western US:
Elevation, slope, slope orientation and
precipitation during winter (DJF)
7.5 min
A slight relationship between higher winter
precipitation and SW and NE orientations at
elevations greater than 1km.
12.5 min
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
NEW METHOD OF INTERPOLATION
Central Peruvian Andes:
Elevation, slope, slope orientation and
precipitation during austral summer (DJF)
1.5 min
Localized relationship between higher
precipitation values and NE slope orientations,
especially at 2.5 min resolution
2.5 min
NEW METHOD OF INTERPOLATION
Central Peruvian Andes:
Elevation and precipitation for low and high slope values
(
NEW METHOD OF INTERPOLATION
1. Horizontal-distance and direction influences (based on modified Shepard’s
interpolator)
Pˆ j 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
•
•
zi
Orographic elevation
Longitudinal and latitudinal components of the slope of the orographic region
d z dx
•
d z dy
Potential exposure of station “i” to orography
P
Ei
We can estimate an interpolation bias for each station:
P
Δ Pi  Pi  Pˆi  f [ z i , ( d z d x ), ( d z d y ), E i ]
Then we can estimate Δ P j
And finally:
Pˆ j  Pˆ j  Δ P j
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