26. June 2013, 12 IMSC, Jeju. Korea
The color of precipitation
Bunde, A., U. Büntgen, J. Ludescher,
J. Luterbacher, and H. von Storch
Is there memory in precipitation?, Nature climate change 3: 174-175, 2013
Motivation
• The time span of systematic meteorological measurements at
the global scale is, mainly restricted to the 20th century. Preinstrumental information on precipitation variability therefore
mainly derives from proxy-based reconstructions and output
from climate model simulations, with both lines of
independent evidence ideally covering the past millennium.
• Here we address, if these sources reflect a consistent picture
of historical precipitation variability – in fact, they do not.
• We compare tree ring-based precipitation reconstructions
from North America, Central Europe and High Asia with forced
millennial simulations and instrumental station
measurements from the same geographic areas.
Proxy data
Tree ring based precipitation reconstructions from Central Europe [Büntgen et
al., 2011], the southern Colorado Plateau [Salzer et al., 2005] and the high
mountains of North Pakistan[Treydte, 2006)].
- An April-June reconstruction for Central Europe covering nearly the past
2500 years derived from a total of 7284 precipitation sensitive oak treering width series from Northeast France, Northeast Germany and
Southeast Germany.
- A October-July reconstruction for North America back to AD 570 derived
from three lower forest border tree-ring chronologies from the southern
Colorado Plateau, composed of pines and Douglas fir.
- An annual reconstruction of annual precipitation back to AD 828 for High
Asia derived from four annually resolved oxygen isotope ratio (d180)
chronologies from juniper tree-ring cellulose in the Karakorum mountains
of northern Pakistan.
Millennial climate simulations
Output of an ECHAM 6/CMIP5 -millennium simulations, exposed to estimated
external forcing used for the simulation, and extended over the period 8002000 A.D.
To compare with the reconstructions, we selected in the model only those
time spans which have been used in the reconstructions.
• Variations in total solar irradiance imply a change between the Late
Maunder Minimum and today of about 0.1.
• Volcanic forcing according to the reconstructions of atmospheric optical
depth due to volcanic aerosols derived from acidity in polar ice cores by
Crowley et al. (2008).
• Vegetation cover was prescribed according to [Pongratz et al., 2009.].
We also used the earlier ECHO-G model. Since these data resulted in the
same conclusions as for the ECHAM6-model, no ECHO-G results are shown.
Observational Local data
• Instrumental daily data in Central Europe (Potsdam, Germany), Colorado
(Gunnison) and Central Asia (Irkutsk). For High Asia, daily precipitation
data ranging over 100y were not available.
• For eliminating the short-term persistence due to weather events we
averaged the observational data over 1 month.
• For eliminating the mean seasonal cycle, we subtracted the seasonal
means.
Monthly means
of precipitation,
(a-c) the proxy
derived
estimates
(d-f)
the model data,
(g-i) the
instrumental
data.
(j-l) the
synthetic data
To quantify the temporal rhythm of each precipitation record, we first consider
the persistence lengths l that are defined by the number of successive years in
each record during which precipitation is either below or above the long-term
median (dry or wet period).
Colour of precipitation.
(a) Histograms of persistence lengths for tree ring-based precipitation
reconstructions from Central Europe (396BC-604AC and 1000-2000), North
America (1000-1988), and High Asia (1000-1998).
(b) Histograms for the ECHAM6 precipitation output (850-1850) for the same
areas as in (a).
(c) Histograms for synthetic long-term persistent data with Hurst exponents 0.8
and 0.9, as well as for white noise (H=0.5), for data of comparable length
(L=1000).
(d) Histograms of local monthly precipitation measurements (Potsdam,
Germany, 1893-2000), Gunnison, Colorado (1893-1994), and Irkutsk, Central
Asia, 1882-1994).
The scales are in years for model and reconstructed data, and in months for the
instrumental data.
Histograms of
persistence
lengths
(a) Derived from
proxy data
(b) From climate
model
(c) Synthetic
long memory
series
(d) Derived from
local data
CE=Central
Europe
The difference Delta Pn between
the (conditional) average
precipitation Pn after n
consecutive wet or dry years
(resp. months) and the mean
precipitation P, in units of the
standard deviation of each
record, for
(e) the reconstructions,
(f) the model data,
(g) the synthetic data, and
(h) the instrumental data.
The colour code is the same as
in (a-d).
Deviations from the mean
precipitation after wet and dry
periods of specific lengths.
Probability density
function PQ(r) of the
return intervals
between events that
exceed the threshold
Q. The threshold is
characterized by its
return period RQ.
Thus, we find …
• Precipitation records extending cross hundredths of years
derived from proxy data exhibit long memory, while
• Precipitation from local observations extending about 100
years, or so, exhibit short term memory (white)
• Precipitation from millennial climate model output exhibit
also short term memory (white).
Possible explanations
• Regional and area averaged precipitation have
different memories. - Likely not the case, as
studies with averaged local observations indicate.
• 100 years o data in the observed records are too
short. – Maybe.
• Climate model output is unrealistic. – Maybe.
• Proxy data have archived not precipitation but
other quantities, in particular soil moisture. – For
me the most plausible explanation.
The color of precipitation
Our analysis indicates that precipitation could be
WHITE, if we consider the conclusion drawn form
local observations and from climate model output
reliable
PINK, if we believe in the evidence provided by
proxy data.
• a) DFA2 fluctuation functions G(s)=s for the
shuffled paleo data from (top to down) (i)
Central Europe (398 BC - 602 AD), (ii) Central
Europe (1000 - 2000), (iii) Colorado (10001988), (iv) North Pakistan (1000-1998). b)
Same
• as a) but for the corresponding WT2
fluctuation function F(s).