Evaluation of the Surface Climatology over the Conterminous United States in the North American Regional Climate Change Assessment Program Hindcast Experiment Using a Regional Climate Model Evaluation System

Kim, Jinwon; Waliser, Duane E.; Mattmann, Chris A.; Mearns, Linda O.; Goodale, C. E.; Hart, Andrew F.; Crichton, D. J.; McGinnis, Seth; Lee, Huikyo; Loikith, Paul C.; Boustani, Maziyar

doi:10.1175/jcli-d-12-00452.1

Cited by 35 publications

(34 citation statements)

References 36 publications

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“…While it is important to be aware of these systematic biases in a model, difficulty with the parameterisation of convection in a model is a somewhat expected result, consistent with previous studies (e.g. Kim et al 2013b). Conversely, systematic biases in extreme rainfall associated with the parameterisation of large-scale rainfall are less well known.…”

Section: Discussionsupporting

confidence: 78%

Evaluation of extreme rainfall and temperature over North America in CanRCM4 and CRCM5

Whan

Zwiers

2015

Clim Dyn

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Section: Discussionsupporting

confidence: 78%

Evaluation of extreme rainfall and temperature over North America in CanRCM4 and CRCM5

Whan

Zwiers

2015

Clim Dyn

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“…In addition, the distance between the point (0 • , 1.0) and a data point in this diagram corresponds to the centered root mean square error (RMSE). This diagram has become one of the most widely used methodologies in climate studies for presenting the evaluations of multiple models and/or variables or intercomparison of multiple data sets (IPCC, 2001;Taylor, 2001;Duffy et al, 2006;Gleckler et al, 2008;Kim et al, 2013Kim et al, , 2015. The signal-to-noise ratio (SNR) for the properties shown in Fig.…”

Section: Methodology and Datamentioning

confidence: 99%

“…These recent fine-scale data sets allow us to better examine the regional precipitation and temperature climatology and to perform more reliable evaluations of today's high-resolution climate simulations, especially over the regions of complex terrain, that are important for climate-change impact assessments and climate model evaluations (Kim et al, 2013). These new data sets also introduce uncertainties in calculating regional climate characteristics because of the differences amongst them.…”

Section: Introductionmentioning

confidence: 99%

Uncertainties in calculating precipitation climatology in East Asia

Kim

Park

2016

Hydrol. Earth Syst. Sci.

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Abstract. This study examines the uncertainty in calculating the fundamental climatological characteristics of precipitation in the East Asia region from multiple fine-resolution gridded analysis data sets based on in situ rain gauge observations and data assimilations. Five observation-based gridded precipitation data sets are used to derive the long-term means, standard deviations in lieu of interannual variability and linear trends over the 28-year period from 1980 to 2007. Both the annual and summer (June-July-August) mean precipitation is examined. The agreement amongst these precipitation data sets is examined using two metrics including the signal-to-noise ratio (SNR) defined as the ratio between long-term means and the corresponding standard deviations, and Taylor diagrams, which allow examinations of the pattern correlation, the standard deviation, and the centered root mean square error. It is found that the five gauge-based precipitation analysis data sets agree well in the long-term mean and interannual variability in most of the East Asia region including eastern China, Manchuria, South Korea, and Japan, which are densely populated and have fairly highdensity observation networks. The regions of large interdata-set variations include Tibetan Plateau, Mongolia, northern Indo-China, and North Korea. The regions of large uncertainties are typically lightly populated and are characterized by severe terrain and/or extremely high elevations. Unlike the long-term mean and interannual variability, agreement between data sets in the linear trend is weak, both for the annual and summer mean values. In most of the East Asia region, the SNR for the linear trend is below 0.5: the interdata-set variability exceeds the multi-data ensemble mean.The uncertainty in the spatial distribution of long-term means among these data sets occurs both in the spatial pattern and variability, but the uncertainty for the interannual variability and time trend is much larger in the variability than in the pattern correlation. Thus, care must be taken in using long-term trends calculated from gridded precipitation analysis data for climate studies over the East Asia region.

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“…The equal weighting was employed because the accuracy of individual data sets could not be determined objectively as submitted by Kim and Park [16]. Uncertainties in the annual and seasonal climatological properties (mean, inter-annual variability and the trend) during the 40-year period were examined in terms of inter-dataset variability using signal-to-noise ratio (SNR), correlation and normalised standard deviation in relation to the reference data.…”

Section: Methods and Data Analysismentioning

confidence: 99%

“…2.5°) horizontal resolutions [15]. These recent fine-scale datasets allow us to better examine the regional precipitation and temperature climatology and to perform more reliable evaluations of today's highresolution climate simulations, especially over the regions of complex terrain, that are important for climate-change impact assessments and climate model evaluations [16]. …”

mentioning

confidence: 99%

Uncertainties in the Estimation of Global Observational Network Datasets of Precipitation over West Africa

Oj¹,

Oe²,

La³

et al. 2017

J Climatol Weather Forecasting

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This study assessed the uncertainty in estimating long-term mean precipitation, its inter-annual variability, and linear trend of three network observation datasets over West Africa. A reference data, defined as a multi-dataset ensemble of precipitation observations of the Climate Research Unit (CRU) of the University of East Anglia, the Global Precipitation Climatology Centre (GPCC) and the University of Delaware (UDEL), all at horizontal resolutions of 0.5° by 0.5° were obtained and used in this study. Uncertainties in these climatological parameters of precipitation at both annual and seasonal time scales were examined in terms of inter-dataset variability using signalto-noise ratio (SNR), correlation, root-mean-square errors and the normalised standard deviation. Results showed that the mean, inter-annual variability and trends climatology varied for different datasets. The three datasets had good agreement (SNR>5) in terms of the annual mean precipitation and its inter-annual variability in most parts of West Africa. However, the agreement between the datasets was poor in the very dry Sahel parts of northern Niger, Mali, and Mauritania (SNR ≤ 1) due to very little precipitation and possibility of relatively low station density in these regions of complex terrain. In terms of correlation (0.89 ≤ r ≤ 0.98), and normalised standard deviation, NSD (0.8 ≤ NSD ≤ 1.7), the uncertainties in the spatial variations in linear trend were larger than mean precipitation and their inter-annual variability for both annual and seasonal scales. The long-term annual precipitation trend in the region is highly uncertain except in a few small areas. observational errors are much more problematic, because their effects become relatively more pronounced as greater numbers of observations are aggregated. In this case, the author believed that averaging observations together from many different instruments/sources would tend to reduce the contribution of systematic observational errors to the uncertainty of the average.A number of researchers and institutions have developed observation-based gridded analysis datasets of global or regional coverage with fine spatial resolutions [8][9][10][11][12][13][14]. These network of observation datasets provide precipitation and/or surface air temperatures over extended periods of multiple decades at spatial resolutions of 0.5° or finer. This is, of course, a substantial improvement over previous generation data sets that are typically at much coarser (e.g. 2.5°) horizontal resolutions [15]. These recent fine-scale datasets allow us to better examine the regional precipitation and temperature climatology and to perform more reliable evaluations of today's highresolution climate simulations, especially over the regions of complex terrain, that are important for climate-change impact assessments and climate model evaluations [16].

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Evaluation of the Surface Climatology over the Conterminous United States in the North American Regional Climate Change Assessment Program Hindcast Experiment Using a Regional Climate Model Evaluation System

Cited by 35 publications

References 36 publications

Evaluation of extreme rainfall and temperature over North America in CanRCM4 and CRCM5

Evaluation of extreme rainfall and temperature over North America in CanRCM4 and CRCM5

Uncertainties in calculating precipitation climatology in East Asia

Uncertainties in the Estimation of Global Observational Network Datasets of Precipitation over West Africa

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