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- Assessing NARCCAP climate model effects using spatial confidence regions
Authors: Copernicus Electronic Production Support Office
Abstract: Assessing NARCCAP climate model effects using spatial confidence regions
Joshua P. French, Seth McGinnis, and Armin Schwartzman
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 67-92, https://doi.org10.5194/ascmo-3-67-2017, 2017
We assess the mean temperature effect of global and regional climate model combinations for the North American Regional Climate Change Assessment Program using varying classes of linear regression models, including possible interaction effects. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We conclusively show that accounting for multiple comparisons is important for making proper inference.
PubDate: 2017-07-14T07:40:57+02:00
- Generalised block bootstrap and its use in meteorology
Authors: Copernicus Electronic Production Support Office
Abstract: Generalised block bootstrap and its use in meteorology
László Varga and András Zempléni
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 55-66, https://doi.org10.5194/ascmo-3-55-2017, 2017
This paper proposes a new generalisation of the block bootstrap methodology, which allows for any positive real number as expected block size. We use this bootstrap for determining the p values of a homogeneity test for copulas. The methods are applied to a temperature data set - we have found some significant changes in the dependence structure between the standardised temperature values of pairs of observation points within the Carpathian Basin.
PubDate: 2017-06-14T10:28:37+02:00
- Estimating trends in the global mean temperature record
Authors: Copernicus Electronic Production Support Office
Abstract: Estimating trends in the global mean temperature record
Andrew Poppick, Elisabeth J. Moyer, and Michael L. Stein
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 33-53, https://doi.org10.5194/ascmo-3-33-2017, 2017
We show that ostensibly empirical methods of analyzing trends in the global mean temperature record, which appear to de-emphasize assumptions, can nevertheless produce misleading inferences about trends and associated uncertainty. We illustrate how a simple but physically motivated trend model can provide better-fitting and more broadly applicable results, and show the importance of adequately characterizing internal variability for estimating trend uncertainty.
PubDate: 2017-06-09T10:28:37+02:00
- A statistical framework for conditional extreme event attribution
Authors: Copernicus Electronic Production Support Office
Abstract: A statistical framework for conditional extreme event attribution
Pascal Yiou, Aglaé Jézéquel, Philippe Naveau, Frederike E. L. Otto, Robert Vautard, and Mathieu Vrac
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 17-31, https://doi.org10.5194/ascmo-3-17-2017, 2017
The attribution of classes of extreme events, such as heavy precipitation or heatwaves, relies on the estimate of small probabilities (with and without climate change). Such events are connected to the large-scale atmospheric circulation. This paper links such probabilities with properties of the atmospheric circulation by using a Bayesian decomposition. We test this decomposition on a case of extreme precipitation in the UK, in January 2014.
PubDate: 2017-04-18T10:28:37+02:00
- Reconstruction of spatio-temporal temperature from sparse historical
records using robust probabilistic principal component regression
Authors: Copernicus Electronic Production Support Office
Abstract: Reconstruction of spatio-temporal temperature from sparse historical records using robust probabilistic principal component regression
John Tipton, Mevin Hooten, and Simon Goring
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 1-16, https://doi.org10.5194/ascmo-3-1-2017, 2017
We present a statistical framework for the reconstruction of historic temperature patterns from sparse, irregular data collected from observer stations. A common statistical technique for climate reconstruction uses modern era data as a set of temperature patterns that can be used to estimate the spatial temperature patterns. We present a framework for exploration of different assumptions about the sets of patterns used in the reconstruction while providing statistically rigorous estimates.
PubDate: 2017-01-27T10:28:37+01:00
- Weak constraint four-dimensional variational data assimilation in a model
of the California Current System
Authors: Copernicus Electronic Production Support Office
Abstract: Weak constraint four-dimensional variational data assimilation in a model of the California Current System
William J. Crawford, Polly J. Smith, Ralph F. Milliff, Jerome Fiechter, Christopher K. Wikle, Christopher A. Edwards, and Andrew M. Moore
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 171-192, https://doi.org10.5194/ascmo-2-171-2016, 2016
We present a method for estimating intrinsic model error in a model of the California Current System. The estimated model error covariance matrix is used in the weak constraint formulation of the Regional Ocean Modeling System, four-dimensional variational data assimilation system, and comparison of the circulation estimates computed in this way show demonstrable improvement to those computed in the strong constraint formulation, where intrinsic model error is not taken into account.
PubDate: 2016-12-14T10:28:37+01:00
- Analysis of variability of tropical Pacific sea surface temperatures
Authors: Copernicus Electronic Production Support Office
Abstract: Analysis of variability of tropical Pacific sea surface temperatures
Georgina Davies and Noel Cressie
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 155-169, https://doi.org10.5194/ascmo-2-155-2016, 2016
Sea surface temperature (SST) is a key component of global climate models, particularly in the tropical Pacific Ocean where El Niño and La Nina events have worldwide implications. In our paper, we analyse monthly SSTs in the Niño 3.4 region and find a transformation that removes a spatial mean-variance dependence for each month. For 10 out of 12 months in the year, the transformed monthly time series gave more accurate or as accurate forecasts than those from the untransformed time series.
PubDate: 2016-11-14T10:28:37+01:00
- Evaluating NARCCAP model performance for frequencies of severe-storm
environments
Authors: Copernicus Electronic Production Support Office
Abstract: Evaluating NARCCAP model performance for frequencies of severe-storm environments
Eric Gilleland, Melissa Bukovsky, Christopher L. Williams, Seth McGinnis, Caspar M. Ammann, Barbara G. Brown, and Linda O. Mearns
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 137-153, https://doi.org10.5194/ascmo-2-137-2016, 2016
Several climate models are evaluated under current climate conditions to determine how well they are able to capture frequencies of severe-storm environments (conditions conducive for the formation of hail storms, tornadoes, etc.). They are found to underpredict the spatial extent of high-frequency areas (such as tornado alley), as well as underpredict the frequencies in the areas.
PubDate: 2016-11-04T10:28:37+01:00
- Mixture model-based atmospheric air mass classification: a
probabilistic
view of thermodynamic profiles
Authors: Copernicus Electronic Production Support Office
Abstract: Mixture model-based atmospheric air mass classification: a probabilisticview of thermodynamic profiles
Jérôme Pernin, Mathieu Vrac, Cyril Crevoisier, and Alain Chédin
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 115-136, https://doi.org10.5194/ascmo-2-115-2016, 2016
Here, we propose a classification methodology of various space-time atmospheric datasets into discrete air mass groups homogeneous in temperature and humidity through a probabilistic point of view: both the classification process and the data are probabilistic. Unlike conventional classification algorithms, this methodology provides the probability of belonging to each class as well as the corresponding uncertainty, which can be used in various applications.
PubDate: 2016-10-12T10:28:37+02:00
- A space–time statistical climate model for hurricane intensification in
the North Atlantic basin
Authors: Copernicus Electronic Production Support Office
Abstract: A space–time statistical climate model for hurricane intensification in the North Atlantic basin
Erik Fraza, James B. Elsner, and Thomas H. Jagger
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 105-114, https://doi.org10.5194/ascmo-2-105-2016, 2016
Climate influences on hurricane intensification are investigated by averaging hourly intensification rates over the period 1975–2014 in 8° by 8° latitude–longitude grid cells. The statistical effects of hurricane intensity, sea-surface temperature (SST), El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Madden–Julian Oscillation (MJO) are quantified. Intensity, SST, and NAO had a positive effect on intensification rates. The NAO effect should be further studied.
PubDate: 2016-08-02T10:28:37+02:00
- Estimating changes in temperature extremes from millennial-scale climate
simulations using generalized extreme value (GEV) distributions
Authors: Copernicus Electronic Production Support Office
PubDate: 2016-07-01T10:28:37+02:00
- A comparison of two methods for detecting abrupt changes in the variance
of climatic time series
Authors: Copernicus Electronic Production Support Office
PubDate: 2016-06-24T10:28:37+02:00
- A path towards uncertainty assignment in an operational cloud-phase
algorithm from ARM vertically pointing active sensors
Authors: Copernicus Electronic Production Support Office
Abstract: A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors
Laura D. Riihimaki, Jennifer M. Comstock, Kevin K. Anderson, Aimee Holmes, and Edward Luke
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 49-62, https://doi.org10.5194/ascmo-2-49-2016, 2016
Between atmospheric temperatures of 0 and −38 °C, clouds contain ice crystals, super-cooled liquid droplets, or a mixture of both, impacting how they influence the atmospheric energy budget and challenging our ability to simulate climate change. Better cloud-phase measurements are needed to improve simulations. We demonstrate how a Bayesian method to identify cloud phase can improve on currently used methods by including information from multiple measurements and probability estimates.
PubDate: 2016-06-10T10:28:37+02:00
- Calibrating regionally downscaled precipitation over Norway through
quantile-based approaches
Authors: Copernicus Electronic Production Support Office
PubDate: 2016-06-09T10:28:37+02:00
- Building a traceable climate model hierarchy with multi-level emulators
Authors: Copernicus Electronic Production Support Office
Abstract: Building a traceable climate model hierarchy with multi-level emulators
Giang T. Tran, Kevin I. C. Oliver, András Sóbester, David J. J. Toal, Philip B. Holden, Robert Marsh, Peter Challenor, and Neil R. Edwards
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 17-37, https://doi.org10.5194/ascmo-2-17-2016, 2016
In this work, we combine the information from a complex and a simple atmospheric model to efficiently build a statistical representation (an emulator) of the complex model and to study the relationship between them. Thanks to the improved efficiency, this process is now feasible for complex models, which are slow and costly to run. The constructed emulator provide approximations of the model output, allowing various analyses to be made without the need to run the complex model again.
PubDate: 2016-04-18T10:28:37+02:00
- Comparison of hidden and observed regime-switching autoregressive models
for (u, v)-components of wind fields in the northeastern Atlantic
Authors: Copernicus Electronic Production Support Office
Abstract: Comparison of hidden and observed regime-switching autoregressive models for (u, v)-components of wind fields in the northeastern Atlantic
Julie Bessac, Pierre Ailliot, Julien Cattiaux, and Valerie Monbet
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 1-16, https://doi.org10.5194/ascmo-2-1-2016, 2016
Several multi-site stochastic generators of zonal and meridional components of wind are proposed in this paper. Various questions are explored, such as the modeling of the regime in a multi-site context, the extraction of relevant clusterings from extra variables or from the local wind data, and the link between weather types extracted from wind data and large-scale weather regimes. We also discuss the relative advantages of hidden and observed regime-switching models.
PubDate: 2016-02-29T10:28:37+01:00
- Autoregressive spatially varying coefficients model for predicting daily
PM2.5 using VIIRS satellite AOT
Authors: Copernicus Electronic Production Support Office
Abstract: Autoregressive spatially varying coefficients model for predicting daily PM2.5 using VIIRS satellite AOT
E. M. Schliep, A. E. Gelfand, and D. M. Holland
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 59-74, https://doi.org10.5194/ascmo-1-59-2015, 2015
There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the US motivates the need for advanced statistical models to predict air quality metrics. We propose a statistical model that jointly models ground-monitoring station data and satellite-obtained data allowing for temporal and spatial misalignment, missingness, and spatially and temporally varying correlation to enhance prediction of particulate matter.
PubDate: 2015-12-16T10:28:37+01:00
- Characterization of extreme precipitation within atmospheric river events
over California
Authors: Copernicus Electronic Production Support Office
Abstract: Characterization of extreme precipitation within atmospheric river events over California
S. Jeon, Prabhat, S. Byna, J. Gu, W. D. Collins, and M. F. Wehner
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 45-57, https://doi.org10.5194/ascmo-1-45-2015, 2015
This paper investigates the influence of atmospheric rivers on spatial coherence of extreme precipitation under a changing climate. We use our TECA software developed for detecting atmospheric river events and apply statistical techniques based on extreme value theory to characterize the spatial dependence structure between precipitation extremes within the events. The results show that extreme rainfall caused by atmospheric river events is less spatially correlated under the warming scenario.
PubDate: 2015-11-17T10:28:37+01:00
- Bivariate spatial analysis of temperature and precipitation from general
circulation models and observation proxies
Authors: Copernicus Electronic Production Support Office
PubDate: 2015-05-22T10:28:37+02:00
- Joint inference of misaligned irregular time series with application to
Greenland ice core data
Authors: Copernicus Electronic Production Support Office
PubDate: 2015-03-25T10:28:37+01:00
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