Atmospheric rivers (ARs) are large, spatially coherent weather systems with high concentrations of elevated water vapor. These systems often cause severe downpours and flooding over the western coastal United States – and with the availability of more atmospheric moisture in the future under global warming we expect ARs to play an important role as potential causes of extreme precipitation changes. Therefore, we aim to investigate changes in extreme precipitation properties correlated with AR events in a warmer climate, which are large-scale meteorological patterns affecting the weather and climate of California.
We have recently developed the TECA (Toolkit for Extreme Climate Analysis) software for automatically identifying and tracking features in climate data sets. Specifically, we can now identify ARs that make landfall on the western coast of North America. Based on this detection procedure, we can investigate the impact of ARs by exploring the spatial extent of AR precipitation using climate model (CMIP5) simulations and characterize spatial patterns of dependence for future projections between AR precipitation extremes under climate change within the statistical framework. Our results show that AR events in the future RCP (Representative Concentration Pathway)8.5 scenario (2076–2100) tend to produce heavier rainfall with higher frequency and longer days than events from the historical run (1981–2005). We also find that the dependence between extreme precipitation events has a shorter spatial range, within localized areas in California, under the high future emissions scenario than under the historical run.
Atmospheric rivers (ARs) are narrow bands of elevated tropospheric water
vapor that are thousands of kilometers long and hundreds of kilometers wide.
ARs have generated recent interest because they have been proven capable of
producing extreme precipitation and flood damage over the western coastal
United States.
In order to collect statistics on AR events, we implemented a pattern
detection scheme in TECA (Toolkit for Extreme Climate Analysis)
There are relatively few studies concerning the spatial variability of
precipitation caused by the landfall of ARs and the impact of ARs on extreme
precipitation under a changing climate.
Climate extremes can be analyzed via a specific statistical theory, the
so-called extreme value theory, to quantify the distribution of extreme
values (e.g., annual maximum, annual minimum, or excesses over high
thresholds) and the probability of the rare events
In spatial extreme value analysis, multivariate extreme value theory analyzes
the dependence of extremes at multiple locations. Max-stable processes (in
the framework of stochastic processes) have been applied to model joint
distribution of spatial extremes and their dependence
The novel aspects of our study follow from the statistical analysis of extreme precipitation associated with ARs using a concept of a spatial dependence structure. Here, we quantify spatial extremal dependence to analyze extreme precipitation events caused by ARs associated with large-scale coherent weather systems, focusing on the California region. This study provides detailed characterizations of changes in AR properties and the spatial dependence of extreme rainfall under AR conditions in projections of future climate change. Our principal motivation behind this study is to better understand the characteristics of extreme precipitation in ARs in a warming scenario. As the physical mechanisms causing different types of storms differ, it can be expected that their statistical descriptions will differ as well. In Sect. 2, we introduce the framework of TECA developed at the Lawrence Berkeley National Laboratory as an AR identifier, describe our methodology for spatial extreme value analysis, and introduce our metric of spatial dependence. We focus on the characterization of spatial properties in extreme precipitation from ARs identified in ensembles of climate simulations from CMIP5. Section 3 discusses overall trends in AR events and extreme precipitation, as well as the results from the analysis of spatial dependence between precipitation extremes during AR events. Our conclusions and further discussion are presented in Sect. 4.
Total daily precipitable water (prw) field (in kg m
ARs play a prominent role in the climatology of inland precipitation over the
western United States.
The CMIP5 models, modeling groups, and ensemble member(s) used for historical (1981–2005) and RCP8.5 runs (2076–2100) in the study.
Our AR detection code
In this analysis, we consider two variables from CMIP5 models for the
historical and RCP8.5 (the most
aggressive warming scenario considered in the CMIP5) experiments: (1) daily
hus (the mass fraction of water vapor in moist air, in
kg kg
Our goal in this study is to explore how the frequency and duration of ARs change in future, warmer climates together with the resulting changes in the spatial properties of extreme precipitation events. Changes in the statistics of AR events and extreme precipitations are explored by comparing two 25-year time periods spanning 1981–2005 and 2076–2100. Here, we use the calendar year from 1 January to 31 December and treat an AR event that bridges the new year as the event in the year in which it starts. We focus on the occurrence of ARs in California and note that most AR events in California occur in winter and spring. For selected grid points in California, we take the maximum value from the time series of daily precipitation only for the dates in which ARs are detected within a given year and define this as the annual maximum AR precipitation. In order to compare AR-related extreme precipitation against overall extreme precipitation driven by all sources of events, we also compute the annual maximum precipitation by taking the maximum value from daily precipitation during the entire year at each grid point.
We use spatial extreme value analysis to quantify the relationships between extreme events occurring at different locations on different days in the same year. Dependence between extremes is affected by the geographic location. Spatial covariance structure is useful for modeling the dependence between spatially distributed variables where the correlations are defined as a function of distance. Under a spatial analysis framework, one can characterize dependence between extremes at two locations by making the modeling assumption regarding the covariance structure (the details of the covariance structure are be described later on). Spatial dependence structure in this study describes whether extremes at different sites, which may occur from distinct ARs during the whole time period, show more or less similarity when the sites are physically close to or far apart from each other.
One appropriate statistical methodology for modeling spatial extremes is the
max-stable process approach. Max-stable processes have been used to
stochastically model the joint distribution of extreme values at multiple
sites
For example,
An extremal coefficient provides numerical values in a specific interval
for the measurement of dependence between extremes, while the estimation of
variogram functions provide information on the covariance structure of spatial
processes for extremes. The extremal coefficient quantifies the degree of
spatial dependence for extremes at different locations and is based on the
multivariate extreme value theory
To summarize our methodology, we first identify those AR events, via the TECA
detection procedure, that make landfall in California in the CMIP5
simulations. We then characterize the changes in total AR days and AR
frequency by comparing the intensity of extreme precipitation in present-day
and future runs conditioned on the occurrence of ARs. We also characterize
the spatial patterns of dependence for future projections under climate
change within the framework of extreme value theory. For the application of
spatial tail dependence, we fit the Brown–Resnick max-stable processes with
a power law variogram,
We examine changes in the overall behavior of ARs using CMIP5 multi-model
ensemble simulations in a changing climate under the RCP8.5 emissions
scenario. When an AR satisfying the conditions – length and width of merged
polygons, and prw exceeding a certain threshold – is detected and it
overlaps any portion of the California region, we treat it as an individual
AR event. If the detected event lasts more than 1 day, it is counted as a
single AR event. AR days (i.e., total days of ARs in a year) are counted for
two 25-year time periods. The boxplots of AR days for each model, including
multi-model ensemble means of AR days, are shown for historical (blue) and
RCP8.5 (red) runs in Fig.
Boxplot summaries of annual values of atmospheric river days
(AR days, unit: days yr
Figure
Yearly statistics of ARs show
Boxplot summaries of annual numbers of atmospheric river events (AR
frequency, unit: events yr
We turn now to the characterization of extreme precipitation during ARs
making landfall in California. Figure
Boxplot summaries of annual maximum AR precipitation
(unit: mm day
Now we investigate changes in the spatial properties of extreme precipitation
associated with ARs in a warmer climate. To find spatial variability between
AR extreme precipitations, we summarize the ensemble means of maximum
precipitation amounts within the AR events at each grid point. Nine grid
points on the common grid are selected among all 34 grid points in California
(Fig.
Increases in maximum precipitation during AR events are consistent with a
general pattern toward a warmer climate in the region, but the amount of
increases vary spatially – showing relatively larger changes in the extreme
rainfall amounts for northern California compared to those for southern
California. Figure
Multi-model ensemble means of range and smoothing parameter estimates, standard deviations, and 95 % confidence intervals for parameters from modeling of Brown–Resnick processes. Range parameter estimates decrease under the future RCP8.5 scenario.
Ensemble means of pairwise extremal coefficients of annual maximum
AR precipitation from a focal grid point (black triangle; grid locations 23,
16, and 9 from top to bottom) to other locations in California for CMIP5
multi-models. Changes are shown over two 25-year time periods –
From fitting the Brown–Resnick max-stable process to maximum AR precipitation,
the range (
In Fig.
Lower (
The range of spatial dependence (green area) is concentrated within a smaller
localized area in California for the future under the highest emissions
scenario than for the current climate. In particular, the range of strong
dependence from a focal grid point in northern California to other points
becomes narrower than the range of dependence from the focal grid points in
central or southern California under RCP8.5. Though we arbitrarily selected
three focal locations as representative of the three regions of California,
the decreasing pattern of dependence range is true for other focal locations
as well. The blue colors in the difference plots of Fig.
Figure
Summary: Change in atmospheric river properties from multi-model
ensemble outputs under late 21st century RCP8.5 forcing compared to the
recent past (historical). There are increases in AR days, AR frequency, and
heavy rainfall associated with ARs, while spatial dependence between the
annual maximum AR precipitation decreases in the future under a warming
scenario. The 95 % confidence interval (CI) is calculated for the difference in
means (RCP8.5 run
A summary of the changes in pairwise spatial dependence from
To summarize pairwise spatial dependence visually over the entire grid, we
transform the extremal coefficients to the values between 0 (complete
independence) and 1 (complete dependence) by a simple calculation. We propose
an arbitrary value, 1.3, as a threshold of strong dependence, and count the
number of values with strong dependence (
Figure
We have studied the influence of ARs on the spatial
coherence of extreme precipitation under a changing climate. We have detected
AR events using the TECA framework and investigated changes in properties of
ARs such as total AR days, frequency, intensity of precipitation extremes
associated with ARs, and spatial dependence patterns of extreme rainfall in
multi-model ensemble means from CMIP5 simulations. A brief summary is
provided in Table
Our current analysis remains preliminary due to the limited data availability
from the CMIP5 models. More GCM simulations need to be considered to
characterize changes in AR properties and the behavior of tail dependence
across models. Furthermore, simulated extreme precipitation amounts from the
relatively coarse horizontal resolutions of the CMIP5 models are substantially
lower than in the real world
This research was supported by the Director, Office of Science, Office of Advanced Scientific Computing Research, of the US Department of Energy under contract no. DE-AC02-05CH11231. This work was funded under the CASCADE Science Focus Area funded by DOE/BER as part of their Regional and Global Climate Modeling Program. This research used resources of the National Energy Research Scientific Computing Center. Edited by: X. Zhang Reviewed by: two anonymous referees