Downscaling and Projection of Winter Extreme Daily Precipitation over North America

2008 ◽  
Vol 21 (5) ◽  
pp. 923-937 ◽  
Author(s):  
Jiafeng Wang ◽  
Xuebin Zhang
Keyword(s):  
Climate Change ◽  
United States ◽  
North America ◽  
Statistical Downscaling ◽  
Daily Precipitation ◽  
Generalized Extreme Value Distribution ◽  
Specific Humidity ◽  
Climate Modelling ◽  
Central United States ◽  
Distribution Parameters

Abstract Large-scale atmospheric variables have been statistically downscaled to derive winter (December–March) maximum daily precipitation at stations over North America using the generalized extreme value distribution (GEV). Here, the leading principal components of the sea level pressure field and local specific humidity are covariates of the distribution parameters. The GEV parameters are estimated using data from 1949 to 1999 and the r-largest method. This statistical downscaling procedure is found to yield skill over the southern and northern West Coast, central United States, and areas of western and eastern Canada when tested with independent data. The projected changes in covariates or predictors are obtained from transient climate change simulations conducted with the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled General Circulation Model, version 3.1 (CGCM3.1) forced by the Intergovernmental Panel on Climate Change (IPCC) A2 forcing scenario. They are then used to derive the GEV distribution parameters for the period 2050–99. The projected frequency of the current 20-yr return maximum daily precipitation for that period suggests that extreme precipitation risk will increase heavily over the south and central United States but decrease over the Canadian prairies. The difference between the statistical downscaling results and those estimated using GCM simulation is also discussed.

scholarly journals Aeroallergens and Climate Change in Tulsa, Oklahoma: Long-Term Trends in the South Central United States

2021 ◽  
Vol 2 ◽  
Author(s):  
Estelle Levetin
Keyword(s):  
Climate Change ◽  
United States ◽  
Pollen Season ◽  
Maximum Temperature ◽  
The South ◽  
South Central ◽  
Pollen Types ◽  
Central United States ◽  
Total Pollen

Climate change is having a significant effect on many allergenic plants resulting in increased pollen production and shifts in plant phenology. Although these effects have been well-studied in some areas of the world, few studies have focused on long-term changes in allergenic pollen in the South Central United States. This study examined airborne pollen, temperature, and precipitation in Tulsa, Oklahoma over 25 to 34 years. Pollen was monitored with a Hirst-type spore trap on the roof of a building at the University of Tulsa and meteorology data were obtained from the National Weather Service. Changes in total pollen intensity were examined along with detailed analyses of the eight most abundant pollen types in the Tulsa atmosphere. In addition to pollen intensity, changes in pollen season start date, end date, peak date and season duration were also analyzed. Results show a trend to increasing temperatures with a significant increase in annual maximum temperature. There was a non-significant trend toward increasing total pollen and a significant increase in tree pollen over time. Several individual taxa showed significant increases in pollen intensity over the study period including spring Cupressaceae and Quercus pollen, while Ambrosia pollen showed a significant decrease. Data from the current study also indicated that the pollen season started earlier for spring pollinating trees and Poaceae. Significant correlations with preseason temperature may explain the earlier pollen season start dates along with a trend toward increasing March temperatures. More research is needed to understand the global impact of climate change on allergenic species, especially from other regions that have not been studied.

Zoogeography and taxonomy of Dirofilaria scapiceps (Leidy, 1886) and D. uniformis Price, 1957 (Nematoda: Filarioidea) of lagomorphs in North America

1983 ◽  
Vol 61 (5) ◽  
pp. 1011-1022 ◽  
Author(s):  
Cheryl M. Bartlett
Keyword(s):  
United States ◽  
North America ◽  
Host Record ◽  
Sylvilagus Floridanus ◽  
Eastern United States ◽  
New Host ◽  
New Host Record ◽  
South Central ◽  
Central United States ◽  
Subcutaneous Tissues

Dirofilaria scapiceps (Leidy, 1886) was found in 62% of 404 Lepus americanus, 27% of 89 Sylvilagus floridanus, 13% of 31 Orytolagus cuniculus (domestic), 4% of 26 L. capensis, and none of 15 L. timidus, 2 L. californicus, and 50 L. townsendii collected in various regions of North America. Dirofilaria scapiceps in L. capensis is a new host record. The two species of Dirofilaria, D. scapiceps and D. uniformis Price, 1957, known from lagomorphs are redescribed. Dirofilaria scapiceps occurs predominantly in connective tissue surrounding tendons in the ankle region and rarely in intermuscular fascia near the knee joint of the hind leg; D. uniformis occurs in subcutaneous tissues of the trunk. Both D. scapiceps and D. uniformis are known only from lagomorphs in North America, D. scapiceps from L. americanus, L. capensis, S. floridanus, S. palustris and O. cuniculus and D. uniformis from S. floridanus, S. palustris and O. cuniculus. Dirofilaria scapiceps is present in lagomorphs in Alaska, Canada, eastern United States and Wyoming whereas D. uniformis is known only from lagomorphs in southeastern and south central United States. Dirofilaria uniformis may have evolved, through paedomorphosis, from D. scapiceps.

scholarly journals A New Look at the Variance of Summertime Temperatures over Land

2020 ◽  
Vol 33 (13) ◽  
pp. 5465-5477 ◽  
Author(s):  
Lucas R. Vargas Zeppetello ◽  
David S. Battisti ◽  
Marcia B. Baker
Keyword(s):  
Climate Change ◽  
United States ◽  
Heat Flux ◽  
Surface Temperature ◽  
Shortwave Radiation ◽  
Climatic Warming ◽  
Temperature Variance ◽  
Central United States ◽  
Very High

AbstractThe increasing frequency of very high summertime temperatures has motivated growing interest in the processes determining the probability distribution of surface temperature over land. Here, we show that on monthly time scales, temperature anomalies can be modeled as linear responses to fluctuations in shortwave radiation and precipitation. Our model contains only three adjustable parameters, and, surprisingly, these can be taken as constant across the globe, notwithstanding large spatial variability in topography, vegetation, and hydrological processes. Using observations of shortwave radiation and precipitation from 2000 to 2017, the model accurately reproduces the observed pattern of temperature variance throughout the Northern Hemisphere midlatitudes. In addition, the variance in latent heat flux estimated by the model agrees well with the few long-term records that are available in the central United States. As an application of the model, we investigate the changes in the variance of monthly averaged surface temperature that might be expected due to anthropogenic climate change. We find that a climatic warming of 4°C causes a 10% increase in temperature variance in parts of North America.

Evaluation of Statistical Downscaling Methods for Simulating Daily Precipitation Distribution, Frequency, and Temporal Sequence

2021 ◽  
Vol 64 (3) ◽  
pp. 771-784
Author(s):  
Xunchang Zhang ◽  
Mingxi Shen ◽  
Jie Chen ◽  
Joel W. Homan ◽  
Phillip R. Busteed
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Climate Changes ◽  
Daily Precipitation ◽  
Weather Generator ◽  
Temporal Sequence ◽  
Precipitation Distribution ◽  
Climate Conditions ◽  
Weather Stations ◽  
Distribution Frequency

HighlightsNine statistical downscaling methods from three downscaling categories were evaluated.Weather generator-based methods had advantages in simulating non-stationary precipitation.Differences in downscaling performance were smaller within each category than between categories.The performance of each downscaling method varied with climate conditions.Abstract. Spatial discrepancy between global climate model (GCM) projections and the climate data input required by hydrological models is a major limitation for assessing the impact of climate change on soil erosion and crop production at local scales. Statistical downscaling techniques are widely used to correct biases of GCM projections. The objective of this study was to evaluate the ability of nine statistical downscaling methods from three available statistical downscaling categories to simulate daily precipitation distribution, frequency, and temporal sequence at four Oklahoma weather stations representing arid to humid climate regions. The three downscaling categories included perfect prognosis (PP), model output statistics (MOS), and stochastic weather generator (SWG). To minimize the effect of GCM projection error on downscaling quality, the National Centers for Environmental Prediction (NCEP) Reanalysis 1 data at a 2.5° grid spacing (treated as observed grid data) were downscaled to the four weather stations (representing arid, semi-arid, sub humid, and humid regions) using the nine downscaling methods. The station observations were divided into calibration and validation periods in a way that maximized the differences in annual precipitation means between the two periods for assessing the ability of each method in downscaling non-stationary climate changes. All methods were ranked with three metrics (Euclidean distance, sum of absolute relative error, and absolute error) for their ability in simulating precipitation amounts at daily, monthly, yearly, and annual maximum scales. After eliminating the poorest two performers in simulating precipitation mean, distribution, frequency, and temporal sequence, the top four remaining methods in ascending order were Distribution-based Bias Correction (DBC), Generator for Point Climate Change (GPCC), SYNthetic weather generaTOR (SYNTOR), and LOCal Intensity scaling (LOCI). DBC and LOCI are bias-correction methods, and GPCC and SYNTOR are generator-based methods. The differences in performances among the downscaling methods were smaller within each downscaling category than between the categories. The performance of each method varied with the climate conditions of each station. Overall results indicated that the SWG methods had certain advantages in simulating daily precipitation distribution, frequency, and temporal sequence for non-stationary climate changes. Keywords: Climate change, Climate downscaling, Downscaling method evaluation, Statistical downscaling.

scholarly journals Coupling a Markov Chain and Support Vector Machine for At-Site Downscaling of Daily Precipitation

2017 ◽  
Vol 18 (9) ◽  
pp. 2385-2406 ◽  
Author(s):  
Yu-Kun Hou ◽  
Hua Chen ◽  
Chong-Yu Xu ◽  
Jie Chen ◽  
Sheng-Lian Guo
Keyword(s):  
Climate Change ◽  
Markov Chain ◽  
Statistical Downscaling ◽  
Daily Precipitation ◽  
Global Climate Models ◽  
Support Vector ◽  
Statistical Downscaling Method ◽  
Calibration And Validation ◽  
Downscaling Method ◽  
Calibration Methods

Abstract Statistical downscaling is useful for managing scale and resolution problems in outputs from global climate models (GCMs) for climate change impact studies. To improve downscaling of precipitation occurrence, this study proposes a revised regression-based statistical downscaling method that couples a support vector classifier (SVC) and first-order two-state Markov chain to generate the occurrence and a support vector regression (SVR) to simulate the amount. The proposed method is compared to the Statistical Downscaling Model (SDSM) for reproducing the temporal and quantitative distribution of observed precipitation using 10 meteorological indicators. Two types of calibration and validation methods were compared. The first method used sequential split sampling of calibration and validation periods, while the second used odd years for calibration and even years for validation. The proposed coupled approach outperformed the other methods in downscaling daily precipitation in all study periods using both calibration methods. Using odd years for calibration and even years for validation can reduce the influence of possible climate change–induced nonstationary data series. The study shows that it is necessary to combine different types of precipitation state classifiers with a method of regression or distribution to improve the performance of traditional statistical downscaling. These methods were applied to simulate future precipitation change from 2031 to 2100 with the CMIP5 climate variables. The results indicated increasing tendencies in both mean and maximum future precipitation predicted using all the downscaling methods evaluated. However, the proposed method is an at-site statistical downscaling method, and therefore this method will need to be modified for extension into a multisite domain.

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