Transcript Document

VARIABILITY IN LAND-SURFACE PRECIPITATION
ESTIMATES OVER 100-PLUS YEARS,
WITH EMPHASIS ON MOUNTAINOUS REGIONS
PROPOSED RESEARCH PRESENTATION
Elsa Nickl
Department of Geography
University of Delaware
http://climate.geog.udel.edu/~mountain
April 27, 2009
MOTIVATION:
To produce more reliable fields of land-surface precipitation
o MS Thesis (Teleconnections and Climate in the Peruvian Andes)
Central Peruvian Andes
MOTIVATION:
To improve our understanding of the spatial and temporal variability of land-surface
precipitation
o
To enhance our evaluations of climate-model estimates of hydro-climatic variables
University of Delaware, Willmott and Matsuura dataset
Spatial mean of land surface precipitation for 1900-2006
period
Availability of Gridded Land-Surface Precipitation Datasets
(based on in situ observations)
• There is a growing and partially unmet 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
available for the period 1901-2006 at 0.5o resolution:
•University of Delaware archive (Udel or Willmott and Matsuura)
•Global Precipitation Climate Center dataset (GPCC)
•Climate Research Unit dataset (CRU)
IMPORTANT PROBLEMS:
• Low spatial density of raingages in regions having complex terrain
(e.g. mountainous regions)
• Commonly used precipitation interpolation methods generally don’t
take into account topographic features
INTERPOLATION METHODS
Udel archive (Matsuura and Willmott)
1900-2006 Gridded Monthly Time Series
Climatologically Aided Interpolation method
• High-resolution climatology, interpolated to a gridded field (i) using Shepard’s algorithm
(spherical adaptation)
•Monthly precipitation differences at each station (j)
•Station differences are interpolated to a gridded field (i) using Shepard’s algorithm
• Each gridded difference is added back onto the corresponding climatology
ni
ni
j 1
j 1
Pˆi  Pi   wij Pj /  wij
INTERPOLATION METHODS
Global Precipitation Climatology Project (GPCC, 1901-2006)
SPHEREMAP interpolation tool (developed by Willmott 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
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
OBJECTIVES
To explore the spatial and temporal variability of land-surface
precipitation using three current high-resolution gridded datasets.
To develop a new approach for estimating monthly land-surface
precipitation fields from raingage station records
To re-explore the spatial and temporal variability of land-surface
precipitation using my re-estimated land-surface precipitation fields
To use my re-estimated precipitation fields to help increase the skill
of statistical forecasts based on teleconnection analysis.
DATA
o Gridded monthly land-surface precipitation (1901-2006) at 0.5o resolution
from: Udel, GPCC and CRU datasets
o US monthly land-surface precipitation (2001-2005) from the National
Climatic Data Center (NCDC)
o Central Peruvian Andes monthly land-surface precipitation (1965-2000)
from ELECTROPERU and IGP-Peru.
o US Digital Elevation information at 2.5 minutes resolution (derived from
EROS Data Center 3 arc sec)
o Global monthly raingage observations from NCDC and GSOD.
o Central Peruvian Andes Digital Elevation information at 0.5 minute
resolution from GTOPO30.
FIRST PART
SPATIAL AND TEMPORAL VARIABILITY OF LAND SURFACE
PRECIPITATION OVER 100-PLUS YEARS
RESEARCH PLAN
1.
Evaluation of land-surface precipitation change based on existing datasets
(Udel, GPCC and CRU)
o
o
o
o
Geographic percentiles and spatial means of land-surface precipitation
Trends in annual and seasonal change
Application of change-point regression to help identify when major
changes occurred
Analysis of spatial and temporal variability taking into account the
change-point year or years
2. Analysis of the spatial and temporal variability of land-surface precipitation
using “re-estimated” land surface precipitation fields (Second Part)
TEMPORAL VARIABILITY OF LAND-SURFACE PRECIPITATION
•Similar trends until the end of 1970s (except GPCC)
• During the early 1980s, two datasets (CRU and GPCC) show a decline with a “recovery”
during the early 1990s. The Udel dataset remains negative until 2006.
Udel
SPATIAL VARIABILITY OF LAND SURFACE
PRECIPITATION (1901-1976)
• Slight increases over many areas. 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
CHANGE-POINT REGRESSION
Change-point regression (Draper and Smith, 1981): 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.
3000
Long: -74.75 Lat: -11.75
Precipitation (mm)
2500
2000
-0.3 mm/10 year
1500
0.1 mm/10 year
1000
500
0
1
3500
11
21
31
41
51
61
71
81
Long: -49.75 Lat: -5.75
3000
91
01
-5.4 mm/10 year
Precipitation (mm)
2500
2000
-1.4 mm/10 year
1500
1000
500
0
1
11
21
31
41
51
61
71
81
91
01
SECOND PART
ESTIMATION OF NEW LAND-SURFACE PRECIPITATION FIELDS
RESEARCH PLAN
1.
2.
3.
4.
5.
Select areas for testing interpolation methods
Explore relationships between the spatial distributions of precipitation
and topography
Estimate “orographic” scale
Quantify relationships between the spatial patterns of precipitation and
topographic characteristics
Interpolate and evaluate
The Parameter-Regression Interpolation Model (Daly)
Principal aspects taken into account in PRISM model:
1.
Relationship between precipitation and elevation:
•
•
Precipitation increases with elevation, with a maximum in mountain crests
Relationship between precipitation and elevation can be described by a linear function
2. Spatial scale of orographic precipitation (orographic elevation)
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•
•
•
Mismatch in scale when using actual elevation of stations
“Orographic” elevation estimation in order to avoid this mismatch
The orographic scale depends on the scale of the prevailing storm type
5 min-DEM appears to approximate the scale of orographic effects explained by available
data
3. Spatial patterns of orographic precipitation (facets)
•
•
PRISM divides the mountainous areas into “facets “
Each “facet” is a contiguous area of constant slope orientation
Some recent updates (Daly, 2008): Change in the regression slope through a weighting,
based on: coastal proximity, two-layer atmosphere and effective terrain height
Areas to test interpolation method:
Western US
Central Peruvian Andes
Winter (DJF)
Summer (JJA)
Elevation and seasonal precipitation (with more than 200mm) in the Western US
Exploration of the relationships between monthly precipitation and the
spatial arrangements of topographic patterns:
Winter (DJF)
“Special” scatterplots: To explore
relationships between spatial arrangements of
elevation, slope, slope orientation and
precipitation
Western US, 2.5 min resolution:
Winter:
No apparent relationship
High precipitation values at elevations <1km
Summer:
Most precipitation is convective
Summer(JJA)
Central Peruvian Andes, 0.5 min resolution:
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)
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
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
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
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)
Pˆj 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
dz dx
dz dy
Potential exposure of station “i” to orography
EiP  f ( zi  zˆ)
We can estimate an interpolation bias for each station (when topographic influences
Are not taken into account):
ΔPi  Pi  Pˆi  f [ zi ,(dz dx),(dz dy), EiP ]
Then we can estimate
And finally:
ΔPj
Pˆj  Pˆj  ΔPj
THIRD PART
TELECONNECTION ANALYSIS
RESEARCH PLAN
1. Correlations between the “thermal content” of SST and re-estimated
land-surface precipitation fields taking into account change-points
2. Statistically-based estimation of land-surface precipitation anomalies
over the Peruvian Andes
SPATIAL DOMAIN OF SST ANOMALIES AND CLIMATE IN
THE PERUVIAN ANDES
Monthly “thermal content” of SST anomalies
 (grid-cell area * anomaly)
Tropical Pacific
For warm anomalies: > 1°C , > 0.5 ° C, >0°C
For cold anomalies:
South Atlantic
in km²C°
< 1°C , < 0.5 °C, <0°C
PRECIPITATION CHANGE AND TELECONNECTIONS
(taking into account change-point method)
1965-1975
300
250
200
Precip
(mm)
150
100
50
0
65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03
Precipitation (DJF) in the Central Peruvian Andes
http://climate.geog.udel.edu/~mountain
1976-2000