HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-20-651-2016Uncertainties in calculating precipitation climatology in East AsiaKimJ.ParkS. K.spark@ewha.ac.krhttps://orcid.org/0000-0002-8538-911XJoint Institute for Regional Earth System Science and Engineering, UCLA, Los Angeles, CA, USADepartment of Atmospheric and Oceanic Sciences, UCLA, Los Angeles, CA, USADepartment of Environmental Science and Engineering, Ewha Womans University, Seoul, South KoreaDepartment of Atmospheric Science and Engineering, Ewha Womans University, Seoul, South KoreaSevere Storm Research Center, Ewha Womans University, Seoul, South KoreaCenter for Climate/Environment Change Prediction Research, Ewha Womans University, Seoul, South KoreaS. K. Park (spark@ewha.ac.kr)9February201620265165829May201510August20153January201618January2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/20/651/2016/hess-20-651-2016.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/20/651/2016/hess-20-651-2016.pdf
This study examines the uncertainty in calculating the fundamental
climatological characteristics of precipitation in the East Asia region from
multiple fine-resolution gridded analysis data sets based on in situ rain
gauge observations and data assimilations. Five observation-based gridded
precipitation data sets are used to derive the long-term means, standard
deviations in lieu of interannual variability and linear trends over the
28-year period from 1980 to 2007. Both the annual and summer
(June–July–August) mean precipitation is examined. The agreement amongst
these precipitation data sets is examined using two metrics including the
signal-to-noise ratio (SNR) defined as the ratio between long-term means and
the corresponding standard deviations, and Taylor diagrams, which allow
examinations of the pattern correlation, the standard deviation, and the
centered root mean square error. It is found that the five gauge-based
precipitation analysis data sets agree well in the long-term mean and
interannual variability in most of the East Asia region including eastern
China, Manchuria, South Korea, and Japan, which are densely populated and
have fairly high-density observation networks. The regions of large
inter-data-set variations include Tibetan Plateau, Mongolia, northern
Indo-China, and North Korea. The regions of large uncertainties are typically
lightly populated and are characterized by severe terrain and/or extremely high
elevations. Unlike the long-term mean and interannual variability, agreement
between data sets in the linear trend is weak, both for the annual and summer
mean values. In most of the East Asia region, the SNR for the linear trend is
below 0.5: the inter-data-set variability exceeds the multi-data
ensemble mean. The uncertainty in the spatial distribution of long-term means
among these data sets occurs both in the spatial pattern and variability, but
the uncertainty for the interannual variability and time trend is much larger
in the variability than in the pattern correlation. Thus, care must be taken
in using long-term trends calculated from gridded precipitation analysis data
for climate studies over the East Asia region.
Introduction
Long-term means, standard deviations in lieu of interannual
variability, and trends calculated from observed data are among the
fundamental fields in representing the characteristics of regional climates.
These climatological properties play crucial roles in defining climatological
norms, occurrence of extreme events, detection of climate change, and
projecting future climate variations and change as well as their impacts
. For example, reliability of the climate
change detection is examined by comparing the long-term means and trends
calculated from observations against those simulated in climate model
sensitivity experiments e.g.,. In addition, the
changes in key local hydrological fields such as precipitation are frequently
measured relative to their climatological means. Thus, calculating reliable
values of these properties is a critical step in climate research for
identifying regional climate characteristics, through quantification of their
changes due to external and/or internal forcings such as emissions of
anthropogenic greenhouse gases, and the impacts of such changes on regionally
important sectors.
Gridded representations of observed data on the basis of a variety of
instruments, locations, platforms, retrieval algorithms, and analysis schemes
are widely employed in climate research with various goals
. Typically, only a limited
number of such data sets were available, and most climate studies employed
a single data set which includes features needed for their analyses. Recently,
a number of researchers and institutions have introduced newly developed
observation-based gridded analysis data sets of global or regional coverage
with fine spatial resolutions
.
These newly introduced analysis data sets provide precipitation and/or surface
air temperatures over extended periods of multiple decades at spatial
resolutions of 0.5∘ or finer, which are substantial improvements
from previous generation data sets that are typically at much coarser
horizontal resolutions, for example, the 2.5∘ resolution GEWEX
Global Precipitation Climatology Project . These recent
fine-scale data sets allow us to better examine the regional precipitation and
temperature climatology and to perform more reliable evaluations of today's
high-resolution climate simulations, especially over the regions of complex
terrain, that are important for climate-change impact assessments and climate
model evaluations . These new data sets also introduce
uncertainties in calculating regional climate characteristics because of the
differences amongst them. Based on these concerns, two recent studies by
and examined uncertainty in calculating
precipitation climatology over India and its surrounding regions using
multiple precipitation analysis data sets. These two studies have revealed
independently that there exist substantial amounts of differences amongst
today's gridded precipitation data sets resulting in uncertainties in the
calculated precipitation climatology and that the uncertainty and the spread
amongst multiple data sets vary according to regions as well as seasons.
further revealed that uncertainties in the calculated
precipitation climatology defined relative to their climatological means are
generally larger in the dry regions and/or local dry seasons. These two
studies strongly suggest that uncertainty due to the differences between
various data sets needs to be examined and quantified in all climate studies
because the absolute accuracy of individual data sets cannot be quantified in
practice.
In this study, we investigate the uncertainty in calculating fundamental
properties of regional climate characteristics of precipitation over the Far
East Asian region due to the differences amongst today's fine-resolution
gridded data sets based on analyses of observed data. This study examines for
the first time the uncertainty in calculating the standard deviation, a
widely used first-order statistical moment, and linear trend against that in
calculating the average, the zero-order statistical moment. Examining the
uncertainty in assessing the key precipitation characteristics from the
current available precipitation data can help interpret future precipitation
projections. In East Asia, with huge populations and frequent hydrologic
extremes, assessing long-term variations in precipitation has been an
important concern. However, the effects of inter-data-set differences on such
assessments have not been studied so far. The uncertainty analysis for the
East Asia region in this study is also applicable to any other parts of the
world. The methodology and data are presented in Sect. 2, and results are
given in Sect. 3. Section 4 summarizes and discusses the implications of the
findings in this study.
Methodology and data
In this study, spatial variations in the long-term means, interannual
variabilities, and linear trends over the region of interest are examined in
terms of inter-data-set variability measured using signal-to-noise ratio (SNR)
and the similarity with reference data.
Five gridded precipitation data sets are used to estimate the uncertainty in
constructing regional climate characteristics over East Asia for the entire
year and for the summer season (June–July–August). Only the data sets that
cover more than 25 years are selected for analysis for reliable calculations
of the temporal variability in lieu of interannual variability and linear
trends. The period of the recent three decades examined in this study
corresponds to a period of quite steady (near monotonic) and large
increases in the global mean temperature. The analysis was limited to the
28-year period (1980∼2007) due to the length of the available data.
Examination of the precipitation trend in the period of clear warming trend
is a major scientific interest related to the link between the changes in
precipitation and temperature.
The precipitation data sets employed in this study.
Based on the selection criterion, five high-resolution gridded data sets are
selected, including the Climate Research Unit of the University of East
Anglia (CRU), University of Delaware (UDEL), Global Precipitation Climatology
Center (GPCC), the Asian Precipitation - Highly Resolved Observational Data
Integration Towards Evaluation of water resources (APHRODITE), and the Modern
Era Retrospective-analysis for Research and Applications (MERRA) land, that
are either based on rain gauge data or assimilations. These data sets and
references are summarized in Table 1. We also examined uncertainties
including the coarse-resolution Global Precipitation Climatology Project
(GPCP) data to get essentially the same conclusions that are
obtained with the original five data sets only; thus, the results including
the GPCP data are not presented here to focus on fine-resolution data
sets.
Note that there are some factors leading to differences among the data sets
– e.g., the horizontal and/or vertical resolutions, the gridding procedure,
the analysis methods. Such inter-data-set differences may be an
unavoidable source of uncertainty in this study. As seen in Table 1,
observational data are available in various resolutions and discretizations.
In fact, data sets of the same horizontal resolution can be defined in
different grid structures. The gridding procedure might also be different for
different data sets. The analysis data sets are usually based on different sets
of station (observational) data, depending on the data availability at the
time of analysis and specifics of the quality control procedures
e.g.,. Furthermore, the analysis
methodology, essentially the interpolation scheme that varies for different
analysis data sets, can contribute to the inter-data-set differences. However,
assessing the effects of different data sets and/or the analysis schemes on
the inter-data-set differences used here is beyond the scope of this study.
To alleviate the uncertainty related to the inter-data-set differences, we
have interpolated all data sets onto a common grid so that we can compare all
data sets at the same locations. The spatial interpolation procedure can
affect the characteristics of spatial variability of the interpolated data.
This can be an important concern in deriving the characteristics of
horizontal variability, e.g., spatial power spectra, but it is not expected to
have serious effects on deriving temporal variability of the interpolated
data. Because all of the properties we describe in this study are related to
the temporal variability (e.g., temporal means, standard deviations, and
trends), we expect the differences in the horizontal resolutions and
subsequent spatial interpolation have minimal impacts on the results. We have
also created a multi-data-set ensemble by simple averaging of all
observational data sets included in the analysis, using equal weights. The
equal weighting is employed because the accuracy of individual data sets
cannot be determined objectively.
Uncertainties in representing precipitation climatology due to the spread
amongst today's observational data are examined in terms of the SNR. The SNR
has been a key property in a number of climate studies in which the
uncertainties of climate signals are estimated against noises stemming from
various sources e.g.,.
In climate and weather forecast research based on ensembles of multiple model
or observation data sets, the SNR has been used to measure the reliability of
the multi-data-set ensemble mean against the spread of the data sets in the
ensemble. Within this context, the signal and noise are defined as the
associated mean and standard deviation, respectively, of multiple data sets.
The definition of “noise” can be complicated when the data reliability
varies among data sets, and the weighting factor in constructing multi-data-set
ensemble can vary for different data sets . Such complications in
calculating “noise” frequently occur in climate projections where outputs
from various models of varying performance are used to construct an ensemble
mean using the variable weighting e.g.,. Because it is
practically impossible to rank the selected observational data sets in terms
of their accuracy, the ensemble is constructed using an equal weighting.
The similarity between individual data sets and the reference data defined as
the multi-data-set ensemble is measured in terms of the pattern correlation
and the standard deviation of individual data sets relative to the reference
data sets. Measurements of these two properties are presented using Taylor
diagrams . The Taylor diagram was first introduced by
to provide a way to intuitively present two properties
simultaneously; the correlation coefficient of a data set with the reference
data are presented in the azimuth angle (the angle for perfect agreement is
zero), and the relative magnitude of the standard deviation of a data set with
respect to that of the reference data is expressed as the radial distance
(e.g., see Fig. 5a). Thus, the radial distance of 1 and the azimuthal angle
of 0∘ implies that a sample datum has the same pattern and
variability as the reference data. In addition, the distance between the
point (0∘,1.0) and a data point in this diagram corresponds to the
centered root mean square error (RMSE). This diagram has become one of the
most widely used methodologies in climate studies for presenting the
evaluations of multiple models and/or variables or intercomparison of
multiple data sets .
The climatological properties of the annual (upper panels)
and summer (lower panels) precipitation for the period 1980–2007 over East
Asia: (a, d) the mean climatology, (b, e) the standard
deviation, and (c, f) the linear trend of precipitation. These
properties are derived from the ensemble of the corresponding properties
calculated from the data sets in Table 1.
The signal-to-noise ratio (SNR) for the properties shown in Fig. 1,
calculated from the corresponding properties of the five precipitation
analysis data sets in Table 1.
ResultsRegional climatology
Figure 1 presents the three basic characteristics of the annual and summer
(June–July–August) precipitation climatology over East Asia – long-term
means, interannual variability, and trends, calculated from the ensemble mean
of the multiple data sets in Table 1. The mean annual precipitation in the
region is characterized by the wet regions in southeastern China and Japan
(Fig. 1a). Precipitation over the Korean Peninsula is characterized by maxima
in the southwestern and central regions and a rapid decrease towards the
northwestern part of the peninsula bordering with Manchuria. The driest
region covers southern Mongolia, the Gobi desert, and northern Tibetan
Plateau. Interannual variability of the annual precipitation (Fig. 1b)
also shows similar distribution as the annual means. Linear trend of the
annual precipitation varies substantially according to geography (Fig. 1c).
The most notable features include the positive trend in the driest region,
including southern Mongolia, the Gobi desert and northern Tibetan Plateau,
and the negative trend along the wet Yangtze River. Strong positive trends
are also found in much of the Korean Peninsula, the coastal region of
northern China to the west of the Shandong Peninsula, most of southern China,
and eastern Japan. Decreasing precipitation trends also occur in the region
between 45 and 50∘ N and extending from central Mongolia to
the Russian Far East. The summer rainfall climatology (Fig. 1d–f) resembles
the annual mean climatology but with larger magnitudes. This shows that the
precipitation climatology over the East Asia region is primarily determined
by the summer rainfall.
Uncertainties in precipitation climatology
The climatology presented in Fig. 1 varies for different data sets. This is
inevitable because each data set utilizes different raw data, data quality
control, and analysis methodology . Because it is practically
impossible to determine which data set is more accurate, assessing the
reliability of climatological properties calculated from various data sets as
well as the expected range of uncertainty due to the diversity of these
data sets is crucial in calculating regional climatology . In
this section, the range of uncertainty in the three precipitation
characteristics is measured in terms of the SNR and the agreement between
individual data sets and the multi-data ensemble mean in terms of the spatial
pattern correlation and the magnitude of spatial variability following the
methodology of , using the Taylor diagram.
The SNR is calculated as the ratio between the multi-data ensemble mean and
the inter-data-set variability, i.e., a measure of the magnitude of the
multi-data-set ensemble mean relative to that of the inter-data-set variations.
Thus, as SNR increases, these data sets agree more closely with each other.
There is no established threshold value of SNR to distinguish “good” from
“bad”. However, we may use some subjective guidance to interpret the SNR
values. For instance, if SNR < 1 the signal is smaller than the noise, and
it becomes a clear case that the signal is not reliable. The case with SNR
> 5 may indicate that the spread amongst the multiple data sets may be small
enough so that we can take the multi-data ensemble as the representative
value for the included data sets.
The SNRs for the annual mean precipitation (Fig. 2a) and its interannual
variability (Fig. 2b) over the 25-year period exceed 5 in most of the study
domain. Hence, the five data sets examined in this study agree well in terms
of the annual mean precipitation and its interannual variability in the East
Asia region. The regions of small SNR, i.e., showing poor agreements amongst
the selected data sets, are located in the western part of the domain, which
includes eastern Tibetan Plateau, the Gobi desert, and northern Indochina
bordering with China. It is notable that the station density is relatively
low in these regions. The SNR for the interannual variability is generally
smaller than that for the mean; thus, uncertainty in calculating the
interannual variability is larger than in calculating the mean climatology.
Unlike the annual mean and its interannual variability, the SNR for the
linear tendency of the annual precipitation (Fig. 2c) is generally below 5 in
most regions. Thus, long-term annual precipitation trend in the region is
highly uncertain except in a few small areas.
The p values in calculating the linear trend of the annual-mean
precipitation from each data set.
Same as in Fig. 3, but for the summer-mean precipitation trend.
The spread amongst the five precipitation data sets in representing
the spatial variability of the three climatological properties of the annual
(upper panels) and summer (lower panels) precipitation over East Asia:
(a, d) the mean, (b, e) the interannual variability, and
(c, f) the trends of precipitation. They are presented in terms of
their spatial pattern correlations (the azimuthal direction), the
standardized deviation, and the standard deviation of individual data sets
normalized by that of the reference data (the radial direction). The area
within the red polyline represents the range of spread amongst these
data sets.
Figures 2d–f show the SNR for the summer mean precipitation. Overall, the
reliability of the three characteristics of the summer precipitation
calculated from these five data sets is similar to that of the annual
precipitation. The SNRs for the summer precipitation climatology are somewhat
smaller than those for the annual precipitation climatology, but they still
largely exceed 5 in about the same region as for the annual precipitation.
For the interannual variability (Fig. 2b vs. Fig. 2e) and linear trend
(Fig. 2c vs. Fig. 2f), the five data sets agree more closely for the summer
mean values than for the annual mean values. It is noteworthy that the
positive tendency of the summer rainfall in southern China (Fig. 1f) is
highly reliable as all five data sets agree closely (i.e., relatively smaller
inter-data-set variations compared with the multi-data-set ensemble
mean).
To evaluate the statistical significance of trends, we have plotted the
p values from each data set in calculating the linear trend of the
annual-mean precipitation and the summer-mean precipitation (see Figs. 3 and
4, respectively). The regions of large SNR correspond to the regions of small
p values in calculating the linear trend. This suggests that some of the
uncertainty in the multi-data-set ensemble may be inherited from the
uncertainty in calculating the trend from individual data sets. Still, a
significant portion of the region of small p values shows small SNR values.
Thus, inter-data-set differences are the main cause of the uncertainty in
calculating long-term trends.
Figure 5 measures the spatial variations in the three climatological
properties represented by the five observational data sets using the Taylor
diagrams and the simple multi-data-set ensemble as the reference. In these
diagrams, the areas encompassed by the red polylines may be regarded as the
range of uncertainty see. Thus, as the area is smaller, the
uncertainty due to the differences between the examined data sets is smaller.
The spread in the azimuthal and radial direction indicates the spread in the
spatial pattern and in the magnitude of spatial variability, respectively.
Similar to Fig. 2, the uncertainties in the spatial variations of the
annual and summer mean precipitation and their interannual variability are
much less than the uncertainty in the spatial variations of the linear trend.
The distances from the reference data at the point indicated by a star (i.e.,
the reference point with both standardized deviation and correlation being
equal to 1.0) to individual data sets for the means (Fig. 5a and d) are
similar to those for their interannual variability (Fig. 5b and e),
indicating a similar level of spread amongst these data sets in representing
these two properties of the precipitation climatology in the region.
Regarding the linear trend (Figs. 5c and f), compared to the means and their
interannual variabilities, the distances between the reference point and
individual data sets are much larger. This is another indication of the larger
uncertainties in the linear trend represented by these data sets.
One interesting feature in the examination of the uncertainties in the
spatial variability in Fig. 5 is that the spreads in these data sets occur in
both the spatial pattern and the magnitude for the annual and summer mean
values; however, these data sets show more consistency in the spatial pattern
than in the variability. Figures 5b and e show that the five data sets show
similar spatial correlations with the reference data and that the predominant
spread among these data sets is in the radial direction, i.e., the magnitude
of the spatial variability. This feature is more pronounced for the linear
trend (Fig. 5c and f), which shows nearly linear distribution of the data
points in radial directions, i.e., much smaller spread in the azimuthal
direction (pattern correlations) than in the radial direction (magnitude of
variability relative to the reference data).
Summary and discussions
The uncertainties in three fundamental climatological characteristics of the
precipitation over East Asia due to the differences among available
fine-scale observation-based gridded analysis data sets have been examined
using the metrics selected for objectively measuring the spread of these
properties calculated from individual data sets. The three climatological
characteristics include the means, interannual variabilities, and linear
trends in the annual and summer mean precipitation, which are key fundamental
climatological characteristics widely used in studies for examining regional
climate characteristics and model evaluations. The spread and the magnitude of
disagreements amongst the selected data sets are measured using the
signal-to-noise ratio (SNR) and examined visually using the Taylor diagrams,
which allow simultaneous evaluations of three properties – pattern
correlation, standard deviations, and the centered mean square errors between
multiple data sets and a reference data set.
The SNR values calculated from the five selected precipitation data sets show
that the mean climatology of the annual and summer mean precipitation values
and their interannual variability are highly reliable in much of East Asia
except in southern Mongolia, the Gobi desert, and the Tibetan Plateau – the
regions of sparse population and complex terrain. Precipitation measurements
in regions of dry climate and complex terrain require high-density networks
e.g.,. Unlike the climatological mean values and interannual
variability, linear trends calculated over the 28-year period are highly
uncertain except in a few limited areas. It is striking that reliable
estimations of the temporal trend of the annual mean precipitation (Fig. 2c)
are very low compared to those for the means and the variability (Fig. 2a and
b, respectively). Reliable calculation of linear trends is only possible over
the southern China region for the summer mean precipitation. Thus extra
caution must be taken when analyzing precipitation trends over the East Asian
region.
The uncertainty characteristics also vary according to the climatological
properties. Figures 1 and 2 discussed above show that the reliability of
calculating temporal variabilities is much lower than that of time mean
values, especially for linear trends. In addition, the spatial pattern and
variability of the calculated linear trend (Fig. 5c) show much larger spread
(i.e., uncertainty) among these data sets compared to the annual means
(Fig. 5a) and interannual variability (Fig. 5b). The consistency in the
spatial pattern between individual data sets and the reference data measured
in terms of the correlation is near or over 0.95 for the temporal means and
variability whilst it barely exceeds 0.8 for the linear trend. The range of
spatial variability measured in terms of the standardized deviation (the
ratio between the standard deviation of a data sets and the reference data set)
for the linear trend is over 0.5 which is more than twice the range of the
means and the variabilities. It is also observed that uncertainties in the
spatial distribution of the annual and summer mean precipitation (Fig. 5a and d, respectively) occur in both the spatial pattern and the magnitude of
variability. For the interannual variability and linear trends, the spread in
the standardized deviation (i.e., the magnitude of variability) is much
larger than that in the spatial pattern. These may suggest that all of these
data sets are affected by some common factors in determining the
characteristics of these data sets. For example, the station data sets included
in each analysis data set may provide high consistency in the spatial
distribution pattern, but different analysis schemes may lead to a larger
spread in the magnitude of their variability because of different basis
functions employed in different interpolation schemes e.g.,. This is just a hypothesis and needs close examination in future
studies.
The uncertainty in calculating precipitation climatology in the regions
including southern Mongolia, the Gobi desert, and the Tibetan Plateau is of
a special concern. These regions can respond sensitively to climate change
because of disproportionally larger impacts of global warming on high-elevation regions and snow–ice processes e.g.,.
Because of rapid variations in the spatial precipitation distributions
according to terrain during storms, accurate measurement of precipitation in
the regions of extreme terrain requires high-density gauge networks . The
sparse population density in these regions may require higher cost to build
and maintain additional gauges to reduce the uncertainties. Remote sensing of
precipitation will play important roles in monitoring precipitation over
these regions of sparse observations in addition to the investments for
installing and maintaining additional surface observing stations.
Acknowledgements
This work is supported by the National Research Foundation of Korea grant
(no. 2009-0083527) funded by the Korean government (MSIP) and the NSF ExArch
1125798 project. The APHRODITE data were obtained from the link
http://www.chikyu.ac.jp/precip/.Edited by: D. Mazvimavi
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