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 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

To improve our understanding of the spatial and temporal variability of land-surface precipitation oTo 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 period MOTIVATION:

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. 0.5 o ) and temporal (e.g. monthly, daily) resolution gridded datasets Currently there are three land-surface monthly precipitation datasets available for the period at 0.5 o 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) 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

INTERPOLATION METHODS Global Precipitation Climatology Project (GPCC, ) 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 ( ) 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 ( ) at 0.5 o resolution from: Udel, GPCC and CRU datasets o US monthly land-surface precipitation ( ) from the National Climatic Data Center (NCDC) o Central Peruvian Andes monthly land-surface precipitation ( ) 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) oGeographic percentiles and spatial means of land-surface precipitation oTrends in annual and seasonal change oApplication of change-point regression to help identify when major changes occurred oAnalysis 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.

SPATIAL VARIABILITY OF LAND SURFACE PRECIPITATION ( ) 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 Udel GPCC CRU

SPATIAL VARIABILITY OF LAND SURFACE PRECIPITATION ( ) Udel GPCC CRU 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

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. Long: Lat: mm/10 year Long: Lat: mm/10 year -1.4 mm/10 year -0.3 mm/10 year

SECOND PART ESTIMATION OF NEW LAND-SURFACE PRECIPITATION FIELDS RESEARCH PLAN 1.Select areas for testing interpolation methods 2.Explore relationships between the spatial distributions of precipitation and topography 3.Estimate “orographic” scale 4.Quantify relationships between the spatial patterns of precipitation and topographic characteristics 5.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) 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

Western US Central Peruvian Andes Areas to test interpolation method:

Winter (DJF) Summer (JJA) Elevation and seasonal precipitation (with more than 200mm) in the Western US

“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 Winter (DJF) Summer(JJA) Exploration of the relationships between monthly precipitation and the spatial arrangements of topographic patterns:

Central Peruvian Andes, 0.5 min resolution:

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

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

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

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 1.5 min 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) 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 (when topographic influences Are not taken into account): from nearby stations Pi ( Then we can estimate And finally:

RESEARCH PLAN THIRD PART TELECONNECTION ANALYSIS 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) in km²C° For warm anomalies: > 1°C, > 0.5 ° C, >0°C For cold anomalies: < 1°C, < 0.5 °C, <0°C Tropical Pacific South Atlantic

PRECIPITATION CHANGE AND TELECONNECTIONS (taking into account change-point method) Precipitation (DJF) in the Central Peruvian Andes