On the robustness of the estimates of centennial-scale variability in ...

pik.potsdam.de

On the robustness of the estimates of centennial-scale variability in ...

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Meteorologisches

Institut

Universität

Bonn




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How much do the estimates of long-term trends in heavy precipitation

depend on the data used and the methods of estimation?

What are the potential reasons for the disagreements (if any)?

To what extent can these reasons be quantified?

Project: “Large Scale Climate Changes and their Environmental Relevance”

funded by the North Rhine-Westphalia Academy of Science


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Indices – sometimes are arbitrary chosen, but easy to compute.

Limited by data record size.

PDFs – probability density functions, based on initial value

distribution (IVD) (e.g. Gamma) – may not fit well to the

data. Goodness of fit depends on the ensemble size.

EVD – extreme value distributions – e.g. GPD – not necessarily

well justified since in a general case do not depend on

the IVD properties.

95% (p95) percentiles of precipitation from the estimated Gamma distribution

for daily precipitation [Zolina et al. 2004]

occurrence of the exceedance of a given threshold, e.g. 95% (G95),

corresponding to heavy and very heavy precipitation [Groisman et al. 2005]

percentage of the seasonal total precipitation sum obtained during very wet

(>95%) days (R95) [Klein Tank and Koenen 2003]


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Mean patterns of extreme precipitation are qualitatively comparable in

different data sets, but differences between stations and NWP may

amount to 50% (Groisman et al. 1999, Booij 2002, Zolina et al. 2004).

Trend estimates do not show even qualitative agreement, exhibiting

locally different signs in NWP and station data (Zolina et al . 2004).

Trend patterns estimated using different indices qualitatively agree,

but quantitatively may show strong local differences (Zolina et al. 2005).

Spatial patterns of the trend changes are largely influenced by spatial

noise and neighboring locations may show significant trends of the

opposite sign (Frei and Schar 2001, Klein Tank and Koennen 2003,

Zolina et al. 2005, Groisman et al. 2005).

Hypothetical reasons for the disagreements observed:

Temporal inhomogeneity of sampling

Impact of the local mesoscale variability


'

Centennial (1901-2000) European stations

96 stations

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Temporal distribution of the missing values

in 96 centennial European stations

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Regularly Sampled Times

Series (RSTS) 22 stations

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Structure of gaps different

from the whole months

years

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distribution of the relative number of

missing days per season

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duration of contiguous gaps

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UnderSampled

Times Series

(USTS)

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Estimates of heavy precipitation are quite robust to the sampling

inhomogeneity

Secular trend patterns are more homogeneous in regularly sampled data

Nevertheless, sampling cannot explain the spatial noise in the estimates

of long-term trends


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locations where no sensitivity of trends to the sampling was identified;

all three indices (p95, R95, G95) computed from the homogenized time

series imply significant trends according to the chosen criteria

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1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

years


model grid cell

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stations

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convective precipitation

horizontal grid resolution

~ 0.875°- 2.5°

large scale precipitation

total

precipitation

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horizontal grid resolution 40

~ 0.875°- 2.5°

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German spatial Weather variability Service which is station not captured collection by the network

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spatial variability, captured by the network

random observational error

5454 stations

1781-2004

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, >

The estimates of heavy precipitation are quite robust to the

sampling inhomogeneity in daily records.

To ensure that sampling does not impact on precipitation

extremes, Monte-Carlo homogenization is recommended.

Trends in extreme precipitation estimated using different indices

qualitatively agree, but quantitatively may show local differences.

Estimates based on the PDF show more homogeneous patterns

(more reliable?).

Mesoscale variability strongly affects estimates of trends in

extreme precipitation and makes its difficult to compare

NWP- and station-based estimates.

High resolution station networks (e.g. BALTEX) are needed for

quantification of mesoscale precipitation variability.

High resolution regional reanalyses should be used to provide

more reliable estimates of variability in extreme precipitation.


85!>

Ensemble experiments with very high resolution regional

models for several regions of 100-200 km, for which a

perfect evaluation of the rain gauges and pluviometers

will be performed.


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