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Tuesday, October 13, 2020 | History

3 edition of Uncertainties in climate data sets found in the catalog.

Uncertainties in climate data sets

Uncertainties in climate data sets

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  • 12 Currently reading

Published by National Aeronautics and Space Administration, National Technical Information Service, distributor in [Washington, DC, Springfield, Va .
Written in English

    Subjects:
  • Climatology.,
  • Mathematical statistics.

  • Edition Notes

    StatementJames P. McGuirk.
    Series[NASA contractor report] -- NASA CR-194848., NASA contractor report -- NASA CR-194848.
    ContributionsUnited States. National Aeronautics and Space Administration.
    The Physical Object
    FormatMicroform
    Pagination1 v.
    ID Numbers
    Open LibraryOL14707623M

    To date, all model projections of future climate have included a subset of climate forcings, typically greenhouse gas emission scenarios, solar variability, and more recently, aerosol emissions. As the diverse types of radiative and nonradiative climate forcings are recognized (e.g.   In estimates of climate sensitivity obtained from global models, the need to represent clouds introduces a great deal of uncertainty. To address this issue, approaches using a high-resolution global non-hydrostatic model are promising: the model captures cloud structure by explicitly simulating meso-scale convective systems, and the results compare reasonably well Cited by: 7.

      The Global Historical Climatology Network–monthly (GHCNm) dataset is a set of monthly climate summaries from thousands of weather stations around the world. The monthly data have periods of record that vary by station with the earliest observations dating to the 18 th century. Some station records are purely historic and are no longer updated. In cases where more data are available, a more sophisticated method to calculate PET is often preferred in order to make a more complete accounting of drought variability. However, these additional variables can have large uncertainties. A gridded SPEI data set is available for based on CRU TS input data and the Penman-Monteith method.

    Federal datasets are subject to the U.S. Federal Government Data Policy. Non-federal participants (e.g., universities, organizations, and tribal, state, and local governments) maintain their own data policies. Data policies influence the usefulness of the data. Learn more about how to search for data and use this catalog. , datasets found. INSTITÜT FÜR PHYSIK DER UNIVERSITÄT POTSDAM UND POTSDAM-INSTITUT FÜR KLIMAFOLGENFORSCHUNG (PIK) UNCERTAINTIES IN CLIMATE DATA ANALYSIS PERSPECTIVES ON WORKING WITH MEASUREMENT.


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Uncertainties in climate data sets Download PDF EPUB FB2

Uncertainties in climate data sets - a challenge for Uncertainties in climate data sets book Article (PDF Available) in Bulletin (New York State Archeological Association: ) January with 97 Reads. Get this from a library. Uncertainties in climate data sets.

[James P McGuirk; United States. National Aeronautics and Space Administration.]. This paper is a systematic assessment of the errors and uncertainties contained in the time mean (–) of many different climate quantities taken from a variety of global data sets, including four popular reanalyses, the output of the climate model developed at the Geophysical Fluid Dynamics Laboratory (GFDL), and a wide range of by: Uncertainties relate to situations where it is impossible to exactly describe state of future outcomes.

In climate change adaptation, uncertainties arise from different sources, e.g. natural climate variability, future emissions, modelling, behavioural, socio-economic and technological responses and ecological dynamics. Uncertainty in Climate Predictions. Douglas Nychka,1National Center for Atmospheric Research (NCAR), Juan M.

Restrepo, University of Arizona and Claudia Tebaldi, Climate Central. Carbon dioxide (CO. 2) and other greenhouse gases, released into the atmosphere from human activities, have altered Earth’s natural Size: 2MB.

The spread amongst the five precipitation data sets in representing the spatial variability of the three climatological properties of the annual (upper panels) and summer (lower panels) precipitation over East Asia: (a, d) the mean, (b, e) the interannual variability, and.

(c, f) the trends of by: The recent IPCC AR5 includes a discussion on the sources of uncertainty in climate projections (Fig.section ), which updates previous analyses using CMIP3 (temperature, precipitation) to the latest CMIP5 dominant source of uncertainty depends on lead time, variable and spatial scale.

Global warming and climate change: Realities, uncertainties and measures Article (PDF Available) in International journal of physical sciences January with 8, ReadsAuthor: Ahzegbobor Philips Aizebeokhai.

Observational uncertainties and the large effect of internal variability on observed precipitation also precludes a more confident assessment of the causes of precipitation changes.

{,}. Analysing climate data is an exciting challenge, which is increased by non-normal distributional shape, serial dependence, uneven spacing and timescale uncertainties. This book presents bootstrap resampling as a computing-intensive method able to meet the challenge.

It shows the bootstrap to perform reliably in the most important statistical estimation techniques: regression, spectral analysis, extreme values and correlation.

This book. But Stephens didn’t even cherry-pick; he simply ignored the data. With that in mind, here are the actual uncertainties within climate science that Stephens should have written about.

In some areas of climate science, uncertainty has been nearly eliminated. The extreme values of climate data are of interest in design of marine structures and the return values of certain met-ocean parameters such as significant wave height is of particular importance.

However, there are various ways of analyzing the extremes and estimating the required return values, which introduce additional by: 4. 33 such data for climate applications, and users often need to know how large the errors and 34 uncertainties associated with the different data sets are.

This paper is a systematic assessment of 35 the errors and uncertainties contained in the time mean () of many different climate. The Olympic sport of biathlon (Figure 1) is a cross-country ski race of 20 km in which the athletes stop on four occasions to shoot cm diameter bullets from a caliber rifle at targets.

The sport requires not only great endurance, but exceptional accuracy as the athletes shoot on two occasions from the prone position (lying down) and on. Reliable estimates of uncertainty are arguably more important than the actual value being quoted. I recently came across a classic example in astronomy.

From the late s, estimates have been published for the ‘Hubble Constant’ – a measure of how fast the Universe is expanding. The uncertainties associated with the choice of statistical methods used to create globally complete SST data sets have been explored using different analysis techniques but they do not incorporate the latest understanding of measurement errors and they want for a fair benchmark against which their skill can be objectively assessed.

The gridded data are a blend of the CRUTEM4 land-surface air temperature dataset and the HadSST3 sea-surface temperature (SST) dataset. The dataset is presented as an ensemble of dataset realisations that sample the distribution of uncertainty in the global temperature record given current understanding of non-climatic factors affecting near-surface.

Reanalysis a systematic approach to produce data sets for climate monitoring and research. Reanalyses are created via an unchanging ("frozen") data assimilation scheme and model(s) which ingest all available observations every hours over the period being analyzed.

surface temperature anomaly data set of the Goddard Institute for Space Studies (GISS) [Hansen et al., ], is again a blend of land and SST data sets. The land component is presented as a gridded data set in which grid-box values are a weighted average of temperature anomalies for stations lying within km of grid-box Size: 2MB.

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We apologize for any inconvenience this may have caused. Uncertainties in Projections of the Baltic Sea Ecosystem Driven by an Ensemble of Global Climate Models Sofia Saraiva 1,2, H.

E. Markus Meier 1,3 *, Helén Andersson 1, Anders Höglund 1, Christian Dieterich 1, Matthias Gröger 1, Robinson Hordoir 1,4,5 and Kari Eilola 1Cited by: 8. Home» Presentations» Characterizing Uncertainty in Observational Data Sets for Climate Change Studies.

Characterizing Uncertainty in Observational Data Sets for Climate Change Studies. Author: Gilbert Compo. Monday, - with quantified uncertainties, for assessments of climate model simulations of the 20th century, with. The Fundamental Uncertainties of Climate Change. I agree that it would be best to concentrate on ‘reliable’ data sets, preferably following the ‘koppen’ classification so we can see if similar regions are behaving in a similar fashion.

Other regional classifications would also be worthwhile. His book ‘history and climate’ is.