Alternative Climate Normals: Impacts to the Energy Industry

Argüez, Anthony; Vose, Russell S.; Dissen, Jenny

doi:10.1175/bams-d-12-00155.1

Cited by 12 publications

(5 citation statements)

References 3 publications

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“…Thanks to specialized government agencies, such as the national oceanic and Atmospheric Administration (noAA), most of this data/information is freely accessible online and in various formats (from raw data to integrated map applications where data can be manipulated). one of the particularities of the United States in this regard is the availability of tools and other resources for specific climate-related impacts and sectors: for example, the University of Arizona's new Mexico Agroclimate webtool helps to evaluate the effect of the El niño-Southern oscillation (EnSo) on agricultural indicators; 204 the national Climate Data Centre released in 2011 the new 1981-2010 U.S. climate normals for use in energy load forecasting and building design standards; 205 and the Environmental Protection Agency Climate Ready Water Utilities Toolbox guides users through a risk and adaptation assessment process. 206 Across the country, hundreds of cities and counties and close to twenty states have started a climate change adaptation planning process, which is one instrument to engage with the private sector and identify areas where resilience improvements can be made.…”

Section: United Statesmentioning

confidence: 99%

Enabling Environment for Private Sector Adaptation

Stenek¹,

Amado²,

Greenall³

2013

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The material in this publication is copyrighted. IFC encourages the dissemination of the content for educational purposes. Content from this publication may be used freely without prior permission, provided that clear attribution is given to IFC and that content is not used for commercial purposes. The findings, interpretations, views, and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the Executive Directors of the International Finance Corporation or of the International Bank for Reconstruction and Development (the World Bank) or the governments they represent. IFC and the World Bank do not guarantee the accuracy of the data in this publication and accept no responsibility for any consequences of their use. Cover photo courtesy of the World Bank Photo Library.

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Section: United Statesmentioning

confidence: 99%

Enabling Environment for Private Sector Adaptation

Stenek¹,

Amado²,

Greenall³

2013

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“…2); 2) this period roughly corresponds with the period of global satellite coverage, which should provide more complete and less heterogeneous data; and 3) 1981-2010 corresponds to the latest normals that have been released by NOAA for other geophysical variables (Arguez et al 2012; Applequist et al 2012; Durre et al 2013) and have already been used in the annual summaries from RSMC Miami (Avila and Stewart 2013; Blake and Kimberlain 2013; Diamond 2013).Since complete global coverage in IBTrACS-WMO only begins in 1990, some results in this study will be presented for1990 . Either 21 years (1990 or 30 years should provide robust interannual statistics of tropical cyclones, as it does for other geophysical variables(Arguez and Vose 2011;Arguez et al 2013). However, known interdecadal oscillations(Kossin and Vimont 2007;Aiyyer and Thorncroft 2011) preclude our ability to draw conclusions about long-term trends from these data.…”

mentioning

confidence: 99%

The Impact of Best Track Discrepancies on Global Tropical Cyclone Climatologies using IBTrACS

Schreck

Knapp

Kossin

2014

Monthly Weather Review

175

131

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Using the International Best Track Archive for Climate Stewardship (IBTrACS), the climatology of tropical cyclones is compared between two global best track datasets: 1) the World Meteorological Organization (WMO) subset of IBTrACS (IBTrACS-WMO) and 2) a combination of data from the National Hurricane Center and the Joint Typhoon Warning Center (NHC1JTWC). Comparing the climatologies between IBTrACS-WMO and NHC1JTWC highlights some of the heterogeneities inherent in these datasets for the period of global satellite coverage 1981-2010. The results demonstrate the sensitivity of these climatologies to the choice of best track dataset. Previous studies have examined best track heterogeneities in individual regions, usually the North Atlantic and west Pacific. This study puts those regional issues into their global context. The differences between NHC1JTWC and IBTrACS-WMO are greatest in the west Pacific, where the strongest storms are substantially weaker in IBTrACS-WMO. These disparities strongly affect the global measures of tropical cyclone activity because 30% of the world's tropical cyclones form in the west Pacific. Because JTWC employs similar procedures throughout most of the globe, the comparisons in this study highlight differences between WMO agencies. For example, NHC1JTWC has more 96-kt (;49 m s 21 ) storms than IBTrACS-WMO in the west Pacific but fewer in the Australian region. This discrepancy probably points to differing operational procedures between the WMO agencies in the two regions. Without better documentation of historical analysis procedures, the only way to remedy these heterogeneities will be through systematic reanalysis.

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“…The 1986-2005 time frame covers 20 years instead of 30 years, which is the standard length of climate normals. The use of 20-year-long periods can already be considered long enough to form a climatological sample (e.g., Arguez and Vose, 2011;Arguez et al, 2013). Moreover, 1986-2005 is used for the sake of consistency with the latest Assessment Report of the Intergovernmental Panel on Climate Change (e.g., Chapter 12 of IPCC, 2013).…”

Section: Validation Datamentioning

confidence: 99%

Validation of RegCM regional and HadGEM global climate models using mean and extreme climatic variables

Pieczka¹,

Bartholy²,

Pongrácz³

et al. 2019

Időjárás

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⎯ The horizontal resolution of global climate models (GCMs) is still too coarse to evaluate regional climatic differences, therefore, to analyze regional environmental changes, it is essential to downscale the GCM simulation results. One of the methods widely and most often used for this purpose is dynamical downscaling. In the present paper we examine the ability of a specific global (HadGEM2-ES) and a specific regional climate model (RegCM) to describe present climatic conditions in different geographical areas within the Med-CORDEX domain. Our main goal with this validation is to inform researchers, who are planning to complete climate change impact studies about the major characteristics of the simulation outputs, serving as important input in such studies. So we analyzed annual and seasonal mean fields, mean error fields relative to the reference measurements, and selected climate indices. On the basis of the results, dynamical downscaling generally cools the HadGEM results, which depends on the distance from the ocean, and orography. A clear improvement can be recognized in the root-meansquare error (RMSE) of temperature indices when using finer resolution. Moreover, dynamical downscaling with higher resolution often increases the precipitation in mountains. Furthermore, in order to quantify the potential added value of RegCM simulations, a complex measure was introduced to take into account both the magnitude and spatial extent of bias. The analysis shows a general improvement in the cold-related indices in Central Europe and all temperature-related climate indices in Western Europe. The influence of model resolution is usually so strong, that it results in the underestimation of precipitation indices changing into overestimation and vice versa.

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Alternative Climate Normals: Impacts to the Energy Industry

Cited by 12 publications

References 3 publications

Enabling Environment for Private Sector Adaptation

Enabling Environment for Private Sector Adaptation

The Impact of Best Track Discrepancies on Global Tropical Cyclone Climatologies using IBTrACS

Validation of RegCM regional and HadGEM global climate models using mean and extreme climatic variables

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