Economics Dictionary of ArgumentsHome
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| Climate data: Climate data refers to information collected from various sources like satellites, weather stations, and scientific instruments, capturing long-term patterns and trends in weather elements such as temperature, precipitation, humidity, wind, and atmospheric conditions. Analyzing this data aids in understanding climate change, modeling future scenarios, and formulating strategies for adaptation and mitigation. See also Climate change, Climate damages, Climate history._____________Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments. | |||
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Paul N. Edwards on Climate Data - Dictionary of Arguments
I 56 Climate data/Edwards: For long-term climate analyses - particularly climate change analyses - to be accurate, the climate data used must be homogeneous. A homogeneous climate time series is defined as one where variations are caused only by variations in weather and climate. Unfortunately, most long-term climatological time series have been affected by a number of non-climatic factors that make these data unrepresentative of the actual climate variation occurring over time. These factors include changes in: instruments, observing practices, station locations, formulae used to calculate means, and station environment.(1) Edwards: to decide whether you are seeing homogeneous data or “non-climatic factors,” you need to examine the history of the infrastructure station by station, year by year, and data point by data point, all in the context of changing standards, institutions, and communication techniques. >Infrastructure/Edwards. Since the 1950s, standardization and automation have helped to reduce the effect of “non-climatic factors” on data collection, and modeling techniques I 57 have allowed climatologists to generate relatively homogeneous data sets from heterogeneous sources.(2) But it is impossible to eliminate confounding factors completely. I 58 (…) only about ten percent of the data used by global weather prediction models originate in actual instrument readings. The remaining ninety percent are synthesized by another computer model: the analysis or “4-dimensional data assimilation” model, which creates values for all the points on a high-resolution, three-dimensional global grid. >Reanalysis/Climatology. I 356 Data globalization: (…) making data global is an ex post facto mode of standardization, dealing with deviation and inconsistency by containing the entire standardization process in a single place—a “center of calculation,” in Bruno Latour’s words.(3) >Weather forecasting/Edwards. I 381 Time/assimilation: Analysis produced through 4-D data assimilation thus represented an extremely complex model of data, far removed from the raw observations. With many millions of gridpoint values anchored to fewer than 100,000 observations, one could barely even call the analysis “based” on observations I 382 in any ordinary sense. As the data assimilation expert Andrew Lorenc put it, “assimilation is the process of finding the model representation which is most consistent with the observations.” >Weather forecasting/Edwards, >Homogenization/climatology, >Model bias/climatology. Cf. >Emission permits, >Emission reduction credits, >Emission targets, >Emissions, >Emissions trading, >Climate change, >Climate damage, >Energy policy, >Clean Energy Standards, >Climate data, >Climate history, >Climate justice, >Climate periods, >Climate targets, >Climate impact research, >Carbon price, >Carbon price coordination, >Carbon price strategies, >Carbon tax, >Carbon tax strategies. 1. T. C. Peterson et al., “Homogeneity Adjustments of In Situ Atmospheric Climate Data: A Review,” International Journal of Climatology 18 (1998): 1493– 2. D. R. Easterling et al., “On the Development and Use of Homogenized Climate Datasets,” Journal of Climate 9, no. 6 (1996): 1429–; T. Karl et al., “Long-Term Climate Monitoring by the Global Climate Observing System (GCOS),” Climatic Change 31 (1995): 135–; Peterson et al., “Homogeneity Adjustments”; R. G. Quayle et al., “Effects of Recent Thermometer Changes in the Cooperative Station Network,” Bulletin of the American Meteorological Society 72, no. 11 (1991): 1718–. 3. B. Latour. 1987. Science in Action. Cambridge: Harvard University Press 4. Lorenc A.C. (2002) Atmospheric Data Assimilation and Quality Control. In: Pinardi N., Woods J. (eds) Ocean Forecasting. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-22648-3_5._____________Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. Translations: Dictionary of Arguments The note [Concept/Author], [Author1]Vs[Author2] or [Author]Vs[term] resp. "problem:"/"solution:", "old:"/"new:" and "thesis:" is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition. |
Edwards I Paul N. Edwards A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming Cambridge 2013 |
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> Counter arguments against Edwards
> Counter arguments in relation to Climate Data
