Classification of Regional Climate Variability in the State of California

2009 ◽  
Vol 48 (8) ◽  
pp. 1527-1541 ◽  
Author(s):  
John T. Abatzoglou ◽  
Kelly T. Redmond ◽  
Laura M. Edwards
Keyword(s):  
Climate Variability ◽  
Large Scale ◽  
Regional Climate ◽  
Principal Component ◽  
The State ◽  
Monthly Precipitation ◽  
Regional Patterns ◽  
Novel Approach ◽  
Circulation Patterns

Abstract A novel approach is presented to objectively identify regional patterns of climate variability within the state of California using principal component analysis on monthly precipitation and temperature data from a network of 195 climate stations statewide and an ancillary gridded database. The confluence of large-scale circulation patterns and the complex geography of the state result in 11 regional modes of climate variability within the state. A comparison between the station and gridded analyses reveals that finescale spatial resolution is needed to adequately capture regional modes in complex orographic and coastal settings. Objectively identified regions can be employed not only in tracking regional climate signatures, but also in improving the understanding of mechanisms behind regional climate variability and climate change. The analysis has been incorporated into an operational tool called the California Climate Tracker.

scholarly journals Trends of climate change at the mid-low Nazas-Aguanaval inland basin based on a geographical approach

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Omag Cano Villegas ◽  
Gisela Muro-Pérez ◽  
Enrique Jurado ◽  
Joel Flores ◽  
José Gamaliel Castañeda-Gaytan ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Chihuahuan Desert ◽  
Swat Model ◽  
Regional Climate ◽  
Management Strategies ◽  
Monthly Precipitation ◽  
Sampling Point ◽  
Regional Patterns ◽  
Mean Values

An integrative geographical climatology is presented to objectively identify regional patterns of climate variability within the mid-low Nazas-Aguanaval basin within the States of Durango and Coahuila in Central Northern Mexico, using decadal mean values for maximum and minimum temperature, as well as monthly precipitation during the seven periods from 1951-2020. The historical data was acquired from 26 field meteorological stations and 44 grid points from the SWAT model. Furthermore, the data was categorized by means of geographical features of altitude, longitude and latitude in three groups each. A combination of meteorological vulnerability from all the categories for each sampling point was then estimated for each locality. From the overall analysis, western sites resulted as the most vulnerable to climatic changes, while eastern and central (latitude) displayed the lowest variability occurrence. By means of downscaling the meteorological variation, it is possible to improve the understanding of mechanisms relying on regional climate variability and climate change. This evaluation can be further incorporated to management strategies for different stakeholders in arid and semi-arid lands, particularly within the Chihuahuan Desert.

Estimating the regional climate signal in a late Pleistocene and early Holocene lake-sediment δ18O record from Vermont, USA.

2016 ◽  
Vol 86 (1) ◽  
pp. 67-78 ◽  
Author(s):  
Maximilian Benedict Mandl ◽  
Bryan Nolan Shuman ◽  
Jeremiah Marsicek ◽  
Laurie Grigg
Keyword(s):  
Oxygen Isotope ◽  
Large Scale ◽  
Regional Climate ◽  
Principal Component ◽  
Early Holocene ◽  
Laurentide Ice Sheet ◽  
Oceanic Circulation ◽  
Climate Signal ◽  
Local Character ◽  
Climate Reconstructions

AbstractWe present a new oxygen isotope (δ18O) record from carbonate-rich lake sediments from central Vermont. The record from Twin Ponds spans from 13.5 cal ka BP (1950 AD) to present, but contains a 6 ka long hiatus starting shortly after 7.5 cal ka BP. We compare the record for ca. 13.5–7.5 cal ka BP with published δ18O data from the region after using a Bayesian approach to produce many possible chronologies for each site. Principal component analysis then identified chronologically-robust, multi-site oxygen isotope signals, including negative values during the Younger Dryas, but no significant deviations from the early Holocene mean of the regional records. However, differences among sites indicate significant trends that likely relate to interacting changes in the regional gradients of seasonal temperatures and precipitation as well as moisture sources, moisture pathways, and aridity that were controlled by large-scale climatic controls such as insolation, the progressive decline of the Laurentide Ice Sheet, and changes in oceanic circulation. Centennial shifts punctuate these trends at ca. 9.3 and 8.2 cal ka BP, and reveal that the local character of these short-lived features requires a detailed understanding of lake hydrology and regional isotopic gradients to yield reliable information for regional climate reconstructions.

Finding Dynamical Modes of Atmospheric Variability Using Conservation Properties

2020 ◽  
Author(s):  
Dominic Jones ◽  
John Methven ◽  
Tom Frame ◽  
Paul Berrisford
Keyword(s):  
Large Scale ◽  
Potential Temperature ◽  
Normal Modes ◽  
Phase Speed ◽  
Principal Component ◽  
Wave Activity ◽  
Seasonal Effects ◽  
Monthly Precipitation ◽  
Wave Patterns ◽  
Background State

<p>It is evident that persistent large-scale weather phenomena are an important factor in extreme seasonal climate; this has been especially true in boreal summers over the last two decades. Large, relatively slowly changing modes of variability on the mid-latitude jet are key to understanding high impact weather events. High monthly precipitation totals in the summer, for example, are linked to stationary Rossby wave patterns; stationary winter jet patterns can direct North Atlantic cyclones towards the UK and Europe. These wave patterns are often diagnosed but without a link to their phase speeds or dynamics.</p><p>To examine these slow modes we define an atmospheric background state as a function of isentropic and materially conserved co-ordinates (potential temperature and PV), resulting in a slowly changing, zonally symmetric background state. We then extract patterns of variability from the set of perturbations by employing an alternative Empirical Orthogonal Function (EOF) technique which utilizes a conserved wave activity as a weighted covariance. This results in statistical (EOF) patterns which possess an intrinsic dynamical phase speed and frequency, which are predicted from the conservation properties pseudomomentum and pseudoenergy. These statistical modes are a recombination of the dynamical normal modes in a system with quasi-linear dynamics.</p><p>We examine long runs with relaxation to unstable background jets but without orography, diurnal or seasonal effects, where large amplitude wave activity emerges. These simplified situations are used to test whether or not the predicted phase speeds from theory (given the structures found) matches with the observed phase speeds deduced from the principal component time series of the ENMs. Our hypothesis is that slow wave motion is explained by the structure and conservation properties of the modes. We are able to explore the dependence on the structures by varying the background state.</p>

Barotropic and Baroclinic Annular Variability in the Southern Hemisphere

2014 ◽  
Vol 71 (4) ◽  
pp. 1480-1493 ◽  
Author(s):  
David W. J. Thompson ◽  
Jonathan D. Woodworth
Keyword(s):  
Kinetic Energy ◽  
Climate Variability ◽  
Southern Hemisphere ◽  
Large Scale ◽  
Principal Component ◽  
Southern Annular Mode ◽  
Eddy Kinetic Energy ◽  
Propagating Waves ◽  
Vertically Propagating ◽  
High Degree

Abstract The leading patterns of large-scale climate variability in the Southern Hemisphere are examined in the context of extratropical kinetic energy. It is argued that variability in the Southern Hemisphere extratropical flow can be viewed in the context of two distinct and largely independent structures, both of which exhibit a high degree of annularity: 1) a barotropic structure that dominates the variance in the zonal-mean kinetic energy and 2) a baroclinic structure that dominates the variance in the eddy kinetic energy. The former structure corresponds to the southern annular mode (SAM) and has been extensively examined in the literature. The latter structure emerges as the leading principal component time series of eddy kinetic energy and has received seemingly little attention in previous work. The two structures play very different roles in cycling energy through the extratropical troposphere. The SAM is associated primarily with variability in the meridional propagation of wave activity, has a surprisingly weak signature in the eddy fluxes of heat, and can be modeled as Gaussian red noise with an e-folding time scale of approximately 10 days. The baroclinic annular structure is associated primarily with variations in the amplitude of vertically propagating waves, has a very weak signature in the wave fluxes of momentum, and exhibits marked quasi periodicity on time scales of approximately 25–30 days. Implications for large-scale climate variability are discussed.

scholarly journals Progress in understanding of Indian Ocean circulation, variability, air-sea exchange and impacts on biogeochemistry

2021 ◽  
Author(s):  
Helen E. Phillips ◽  
Amit Tandon ◽  
Ryo Furue ◽  
Raleigh Hood ◽  
Caroline Ummenhofer ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Climate Variability ◽  
Ocean Circulation ◽  
Large Scale ◽  
New Technologies ◽  
Heat Content ◽  
Deep Ocean ◽  
Small Scale ◽  
Circulation Patterns ◽  
The Indian Ocean

Abstract. Over the past decade, our understanding of the Indian Ocean has advanced through concerted efforts toward measuring the ocean circulation and its water properties, detecting changes in water masses, and linking physical processes to ecologically important variables. New circulation pathways and mechanisms have been discovered, which control atmospheric and oceanic mean state and variability. This review brings together new understanding of the ocean-atmosphere system in the Indian Ocean since the last comprehensive review, describing the Indian Ocean circulation patterns, air-sea interactions and climate variability. The second International Indian Ocean Expedition (IIOE-2) and related efforts have motivated the application of new technologies to deliver higher-resolution observations and models of Indian Ocean processes. As a result we are discovering the importance of small scale processes in setting the large-scale gradients and circulation, interactions between physical and biogeochemical processes, interactions between boundary currents and the interior, and between the surface and the deep ocean. In the last decade we have seen rapid warming of the Indian Ocean overlaid with extremes in the form of marine heatwaves. These events have motivated studies that have delivered new insight into the variability in ocean heat content and exchanges in the Indian Ocean, and climate variability on interannual to decadal timescales.This synthesis paper reviews the advances in these areas in the last decade.

A Methodological Approach to Assess the Co-Behavior of Climate Processes over Southern Africa

2019 ◽  
Vol 32 (9) ◽  
pp. 2483-2495 ◽  
Author(s):  
Kwesi A. Quagraine ◽  
Bruce Hewitson ◽  
Christopher Jack ◽  
Izidine Pinto ◽  
Christopher Lennard
Keyword(s):  
Southern Africa ◽  
Large Scale ◽  
Southern Oscillation ◽  
Methodological Approach ◽  
Principal Component ◽  
Self Organizing Maps ◽  
Circulation Patterns ◽  
Climate Forcings ◽  
The Impact ◽  
Precipitation And Temperature

Abstract The study develops an approach to assess co-behavior of climate processes. The regional response of precipitation and temperature patterns over southern Africa to the combined roles (co-behavior) of El Niño–Southern Oscillation (ENSO), Antarctic Oscillation (AAO), and intertropical convergence zone (ITCZ) is evaluated. Self-organizing maps (SOMs) classify circulation patterns over the subcontinent, and principal component analysis (PCA) is used to identify related patterns across the data. The tropical rain belt index (TRBI), a measure of the ITCZ, is generally in phase with the AAO but mostly out of phase with ENSO. The phases of AAO may enhance or suppress ENSO impact on the location and distribution of regional precipitation and temperature over the region. This understanding of the co-behavior of large-scale processes is important to assess the impact these processes collectively have on precipitation and temperature, especially under future climate forcings.

scholarly journals Identification of dominant sources of sea level pressure for precipitation forecasting over Wales

2012 ◽  
Vol 15 (3) ◽  
pp. 1002-1021 ◽  
Author(s):  
Azadeh Ahmadi ◽  
Dawei Han
Keyword(s):  
General Circulation ◽  
Large Scale ◽  
Circulation Model ◽  
Principal Component ◽  
Support Vector ◽  
Monthly Precipitation ◽  
Climate Variables ◽  
Forward Selection ◽  
Precipitation Forecasting ◽  
Variable Combination

Downscaling methods are utilized to assess the effects of large scale atmospheric circulation on local hydrological variables such as precipitation and runoff. In this paper, a methodology of statistical downscaling using a support vector machine (SVM) approach is presented to simulate and predict the precipitation using general circulation model (GCM) data. Due to the complexity and issues related to finding a relationship between the large scale climatic parameters and local precipitation, the climate variables (predictors) affecting monthly precipitation variations over Wales are identified using a combination of the methods including the principal component analysis (PCA), fuzzy clustering, backward selection, forward selection, and Gamma test (GT). The effectiveness of those tools is illustrated through their implementations in the case study. It has been found that although the GT itself fails to identify the best input variable combination, it provides useful and narrowed-down options for further exploration. The best input variable combination is achieved by the GT and forward selection method. This approach can be a useful way for assessing the impacts of climate variables on precipitation forecasting.

Evaluating the Efficiency of Radial Basis Function Classifier with Different Feature Selection for Identifying Dementia

2019 ◽  
Vol 16 (2) ◽  
pp. 627-632 ◽  
Author(s):  
S. Valarmathy ◽  
R. Ramani
Keyword(s):  
Feature Selection ◽  
Radial Basis Function ◽  
Basis Function ◽  
Large Scale ◽  
Performance Metrics ◽  
Principal Component ◽  
Specificity And Sensitivity ◽  
Radial Basis ◽  
Selection For

The Magnetic Resonance Imaging (MRI) based classification process for the classification of dementia is presented in this work. The classifier's performance may be enhanced by means of improving the extracted features that are inputted into its classifier. These MRI images are all duly segmented by making use of the wavelet. For choosing a subset that has optimal features, it may become inflexible and all issues relating to the feature selection will be shown as the NonDeterministic Polynomial (NP)-hard. The work further deals with techniques of optimization that are used in the case of feature selection for picking an optimal feature set. The Principal Component Analysis (PCA) will find an application of a large scale in signal processing. The noise estimation and the source separation are all possible. For this, the Radial Basis Function (RBF) and its classifier have been optimized to this structure by making use of the Genetic Algorithm (GA)-Artificial Immune System (AIS) algorithm. Such an optimized classifier of the RBF will classify a feature set that is provided by the GA, the AIS and the GA-AIS algorithm of feature selection. A classifier will be evaluated on the basis of its performance metrics. All classifiers will be evaluated keeping the accuracy, specificity, and sensitivity in making use of an optimized set of features. The results of the experiment have clearly demonstrated the feature selection and its effectiveness to improve the accuracy of the classification of all the images.

Empirical Downscaling of High-Resolution Regional Precipitation from Large-Scale Reanalysis Fields

2012 ◽  
Vol 51 (1) ◽  
pp. 100-114 ◽  
Author(s):  
Robert E. Nicholas ◽  
David S. Battisti
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Jiangxi Province ◽  
Monthly Precipitation ◽  
Upper Colorado River Basin ◽  
Precipitation Anomalies ◽  
Empirical Downscaling ◽  
High Skill

AbstractThis study describes an EOF-based technique for statistical downscaling of high-spatial-resolution monthly-mean precipitation from large-scale reanalysis circulation fields. The method is demonstrated and evaluated for four widely separated locations: the southeastern United States, the upper Colorado River basin, China’s Jiangxi Province, and central Europe. For each location, the EOF-based downscaling models successfully reproduce the observed annual cycle while eliminating the biases seen in NCEP–NCAR reanalysis precipitation. They also frequently reproduce the monthly precipitation anomalies with greater fidelity than is seen in the precipitation field derived directly from reanalysis, and they outperform a suite of regional climate models over the two U.S. locations. With the relatively high skill achieved over a range of climate regimes, this technique may be a viable alternative to numerical downscaling of monthly-mean precipitation for many locations.

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