719 research outputs found
Riparian plant water relations along the north fork of the Kings River, California
Plant water relations of five obligate ripar-ian species were studied along California\u27s North Fork Kings River. Diurnal stomatal conductance, transpi-ration, and xylem pressure potentials were measured throughout the 1986 growing season and in mid-season in 1987. Patterns were similar for all species although absolute values varied considerably. Maximum stomatal conductance occurred early in the day and season during favorable environmental conditions and decreased as air temperature and the vapor pressure difference between the leaf and air increased. Maximum transpiration rates occurred in mid-morning and mid-summer resulting in estimated daily water losses per unit sunlit leaf area of 163-328 mol H2O m-2. Predawn xylem pressure poten-tials remained high in 1986 when streamflows averaged 1.41 m3/s (50 cfs), however they were notably lower in 1987 at 0.7 m3/s (25 cfs)
Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for R
In spite of the interest in and appeal of convolution-based approaches for
nonstationary spatial modeling, off-the-shelf software for model fitting does
not as of yet exist. Convolution-based models are highly flexible yet
notoriously difficult to fit, even with relatively small data sets. The general
lack of pre-packaged options for model fitting makes it difficult to compare
new methodology in nonstationary modeling with other existing methods, and as a
result most new models are simply compared to stationary models. Using a
convolution-based approach, we present a new nonstationary covariance function
for spatial Gaussian process models that allows for efficient computing in two
ways: first, by representing the spatially-varying parameters via a discrete
mixture or "mixture component" model, and second, by estimating the mixture
component parameters through a local likelihood approach. In order to make
computation for a convolution-based nonstationary spatial model readily
available, this paper also presents and describes the convoSPAT package for R.
The nonstationary model is fit to both a synthetic data set and a real data
application involving annual precipitation to demonstrate the capabilities of
the package
The effect of geographic sampling on evaluation of extreme precipitation in high resolution climate models
Traditional approaches for comparing global climate models and observational
data products typically fail to account for the geographic location of the
underlying weather station data. For modern high-resolution models, this is an
oversight since there are likely grid cells where the physical output of a
climate model is compared with a statistically interpolated quantity instead of
actual measurements of the climate system. In this paper, we quantify the
impact of geographic sampling on the relative performance of high resolution
climate models' representation of precipitation extremes in Boreal winter (DJF)
over the contiguous United States (CONUS), comparing model output from five
early submissions to the HighResMIP subproject of the CMIP6 experiment. We find
that properly accounting for the geographic sampling of weather stations can
significantly change the assessment of model performance. Across the models
considered, failing to account for sampling impacts the different metrics
(extreme bias, spatial pattern correlation, and spatial variability) in
different ways (both increasing and decreasing). We argue that the geographic
sampling of weather stations should be accounted for in order to yield a more
straightforward and appropriate comparison between models and observational
data sets, particularly for high resolution models. While we focus on the CONUS
in this paper, our results have important implications for other global land
regions where the sampling problem is more severe
Quantifying the effect of interannual ocean variability on the attribution of extreme climate events to human influence
In recent years, the climate change research community has become highly
interested in describing the anthropogenic influence on extreme weather events,
commonly termed "event attribution." Limitations in the observational record
and in computational resources motivate the use of uncoupled,
atmosphere/land-only climate models with prescribed ocean conditions run over a
short period, leading up to and including an event of interest. In this
approach, large ensembles of high-resolution simulations can be generated under
factual observed conditions and counterfactual conditions that might have been
observed in the absence of human interference; these can be used to estimate
the change in probability of the given event due to anthropogenic influence.
However, using a prescribed ocean state ignores the possibility that estimates
of attributable risk might be a function of the ocean state. Thus, the
uncertainty in attributable risk is likely underestimated, implying an
over-confidence in anthropogenic influence.
In this work, we estimate the year-to-year variability in calculations of the
anthropogenic contribution to extreme weather based on large ensembles of
atmospheric model simulations. Our results both quantify the magnitude of
year-to-year variability and categorize the degree to which conclusions of
attributable risk are qualitatively affected. The methodology is illustrated by
exploring extreme temperature and precipitation events for the northwest coast
of South America and northern-central Siberia; we also provides results for
regions around the globe. While it remains preferable to perform a full
multi-year analysis, the results presented here can serve as an indication of
where and when attribution researchers should be concerned about the use of
atmosphere-only simulations
Heterocyst placement strategies to maximize growth of cyanobacterial filaments
Under conditions of limited fixed-nitrogen, some filamentous cyanobacteria
develop a regular pattern of heterocyst cells that fix nitrogen for the
remaining vegetative cells. We examine three different heterocyst placement
strategies by quantitatively modelling filament growth while varying both
external fixed-nitrogen and leakage from the filament. We find that there is an
optimum heterocyst frequency which maximizes the growth rate of the filament;
the optimum frequency decreases as the external fixed-nitrogen concentration
increases but increases as the leakage increases. In the presence of leakage,
filaments implementing a local heterocyst placement strategy grow significantly
faster than filaments implementing random heterocyst placement strategies. With
no extracellular fixed-nitrogen, consistent with recent experimental studies of
Anabaena sp. PCC 7120, the modelled heterocyst spacing distribution using our
local heterocyst placement strategy is qualitatively similar to experimentally
observed patterns. As external fixed-nitrogen is increased, the spacing
distribution for our local placement strategy retains the same shape while the
average spacing between heterocysts continuously increases.Comment: This is an author-created, un-copyedited version of an article
accepted for publication in Physical Biology. IOP Publishing Ltd is not
responsible for any errors or omissions in this version of the manuscript or
any version derived from it. The definitive publisher-authenticated version
will be available onlin
Detected changes in precipitation extremes at their native scales derived from in situ measurements
The gridding of daily accumulated precipitation -- especially extremes --
from ground-based station observations is problematic due to the fractal nature
of precipitation, and therefore estimates of long period return values and
their changes based on such gridded daily data sets are generally
underestimated. In this paper, we characterize high-resolution changes in
observed extreme precipitation from 1950 to 2017 for the contiguous United
States (CONUS) based on in situ measurements only. Our analysis utilizes
spatial statistical methods that allow us to derive gridded estimates that do
not smooth extreme daily measurements and are consistent with statistics from
the original station data while increasing the resulting signal to noise ratio.
Furthermore, we use a robust statistical technique to identify significant
pointwise changes in the climatology of extreme precipitation while carefully
controlling the rate of false positives. We present and discuss seasonal
changes in the statistics of extreme precipitation: the largest and most
spatially-coherent pointwise changes are in fall (SON), with approximately 33%
of CONUS exhibiting significant changes (in an absolute sense). Other seasons
display very few meaningful pointwise changes (in either a relative or absolute
sense), illustrating the difficulty in detecting pointwise changes in extreme
precipitation based on in situ measurements. While our main result involves
seasonal changes, we also present and discuss annual changes in the statistics
of extreme precipitation. In this paper we only seek to detect changes over
time and leave attribution of the underlying causes of these changes for future
work
Explaining the unexplainable: leveraging extremal dependence to characterize the 2021 Pacific Northwest heatwave
In late June, 2021, a devastating heatwave affected the US Pacific Northwest
and western Canada, breaking numerous all-time temperature records by large
margins and directly causing hundreds of fatalities. The observed 2021 daily
maximum temperature across much of the U.S. Pacific Northwest exceeded upper
bound estimates obtained from single-station temperature records even after
accounting for anthropogenic climate change, meaning that the event could not
have been predicted under standard univariate extreme value analysis
assumptions. In this work, we utilize a flexible spatial extremes model that
considers all stations across the Pacific Northwest domain and accounts for the
fact that many stations simultaneously experience extreme temperatures. Our
analysis incorporates the effects of anthropogenic forcing and natural climate
variability in order to better characterize time-varying changes in the
distribution of daily temperature extremes. We show that greenhouse gas
forcing, drought conditions and large-scale atmospheric modes of variability
all have significant impact on summertime maximum temperatures in this region.
Our model represents a significant improvement over corresponding
single-station analysis, and our posterior medians of the upper bounds are able
to anticipate more than 96% of the observed 2021 high station temperatures
after properly accounting for extremal dependence.Comment: 19 pages, 4 figures and 2 table
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