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  • 1
    Monograph available for loan
    Monograph available for loan
    Statistical methods in the atmospheric sciences (2011)
    San Diego [u.a.] : Academic press
    Details Availability
    Call number: IASS 13.0069
    Type of Medium: Monograph available for loan
    Pages: XIX, 676 S. , graph. Darst.
    Edition: 3. ed.
    ISBN: 9780123850225
    Series Statement: International geophysics series 100
    Branch Library: RIFS Library
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  • 2
    Monograph available for loan
    Monograph available for loan
    Statistical methods in the atmospheric sciences : an introduction (1995)
    San Diego [u.a.] : Acad. Press
    Associated volumes
    Details Availability
    Call number: 5/M 00.0182 ; M 02.0084 ; AWI S2-98-0072 ; PIK N 456-02-0374
    In: International geophysics series
    Type of Medium: Monograph available for loan
    Pages: XI, 467 S.
    ISBN: 0127519653
    Series Statement: International geophysics series 59
    Classification:
    Meteorology and Climatology
    Language: English
    Location: Reading room
    Location: Upper compact magazine
    Location: Upper compact magazine
    Location: A 18 - must be ordered
    Branch Library: GFZ Library
    Branch Library: GFZ Library
    Branch Library: AWI Library
    Branch Library: PIK Library
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  • 3
    Electronic Resource
    Electronic Resource
    Estimating the consequences of CO2-induced climatic change on north american grain agriculture using general circulation model information (1988)
    Springer
    Climatic change 13 (1988), S. 19-42 
    Details
    ISSN: 1573-1480
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Physics
    Notes: Abstract A procedure to estimate the potential climatic effects of a doubling of atmospheric carbon dioxide concentration on agricultural production is illustrated. The method combines use of atmospheric general circulation models (GCMs) and process-oriented crop models. Wheat and corn (maize) yields in three important North American grain cropping regions are treated. Combined use of these two types of models can provide insights into the impacts of climate changes at the level of plant physiology, and potential means by which agricultural production practices may adapt to these changes. Specific agronomic predictions are found to depend critically on the details of the projected climate change. Uncertainties in the specification of the doubled-CO2 climate by the GCM, particularly with respect to precipitation, dictate that agricultural predictions derived from them at this time must be regarded only as illustrative of the impact assessment method.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00140160
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    Paper (German National Licenses)
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  • 4
    Electronic Resource
    Electronic Resource
    High-frequency climatic variability and crop yields (1996)
    Springer
    Climatic change 32 (1996), S. 231-235 
    Details
    ISSN: 1573-1480
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Physics
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00142463
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  • 5
    Electronic Resource
    Electronic Resource
    Impact of temperature and precipitation variability on crop model predictions (1996)
    Springer
    Climatic change 32 (1996), S. 293-311 
    Details
    ISSN: 1573-1480
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Physics
    Notes: Abstract Future climate changes, as well as differences in climates from one location to another, may involve changes in climatic variability as well as changes in means. In this study, a synthetic weather generator is used to systematically change the within-year variability of temperature and precipitation (and therefore also the interannual variability), without altering long-term mean values. For precipitation, both the magnitude and the qualitative nature of the variability are manipulated. The synthetic daily weather series serve as input to four crop simulation models. Crop growth is simulated for two locations and three soil types. Results indicate that average predicted yield decreases with increasing temperature variability where growing-season temperatures are below the optimum specified in the crop model for photosynethsis or biomass accumulation. However, increasing within-year variability of temperature has little impact on year-to-year variability of yield. The influence of changed precipitation variability on yield was mediated by the nature of the soil. The response on a droughtier soil was greatest when precipitation amounts were altered while keeping occurrence patterns unchanged. When increasing variability of precipitation was achieved through fewer but larger rain events, average yield on a soil with a large plant-available water capacity was more affected. This second difference is attributed to the manner in which plant water uptake is simulated. Failure to account for within-season changes in temperature and precipitation variability may cause serious errors in predicting crop-yield responses to future climate change when air temperatures deviate from crop optima and when soil water is likely to be depleted at depth.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00142466
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  • 6
    Electronic Resource
    Electronic Resource
    Adapting stochastic weather generation algorithms for climate change studies (1992)
    Springer
    Climatic change 22 (1992), S. 67-84 
    Details
    ISSN: 1573-1480
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Physics
    Notes: Abstract While large-scale climate models (GCMs) are in principle the most appropriate tools for predicting climate changes, at present little confidence can be placed in the details of their projections. Use of tools such as crop simulation models for investigation of potential impacts of climatic change requires daily data pertaining to small spatial scales, not the monthly-averaged and large-scale information typically available from the GCMs. A method is presented to adapt stochastic weather generation models, describing daily weather variations in the present-day climate at particular locations, to generate synthetic daily time series consistent with assumed future climates. These assumed climates are specified in terms of the commonly available monthly means and variances of temperature and precipitation, including time-dependent (so-called ‘transient’) climate changes. Unlike the usual practice of applying assumed changes in mean values to historically observed data, simulation of meteorological time series also exhibiting changes in variability is possible. Considerable freedom in climate change ‘scenario’ construction is allowed. The results are suitable for investigating agricultural and other impacts of a variety of hypothetical climate changes specified in terms of monthly-averaged statistics.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00143344
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  • 7
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    Spring Onset Predictability in the North American Multimodel Ensemble (2018)
    American Geophysical Union
    Details
    Publication Date: 2018-06-12
    Description: The predictability of spring onset is assessed using an index of its interannual variability (the “extended spring index” or SI-x) and output from the North American Multimodel Ensemble reforecast experiment. The input data to compute SI-x were treated with a daily joint bias correction approach, and the SI-x outputs computed from the North American Multimodel Ensemble were postprocessed using an ensemble model output statistic approach—nonhomogeneous Gaussian regression. This ensemble model output statistic approach was used to quantify the effects of training period length and ensemble size on forecast skill. The lead time for predicting the timing of spring onset is found to be from 10 to 60 days, with the higher end of this range located along a narrow band between 35°N to 45°N in the eastern United States. Using continuous rank probability scores and skill score (SS) thresholds, this study demonstrates that ranges of positive predictability of SI-x fall into two categories: 10–40 and 40–60 days. Using higher skill thresholds (SS equal to 0.1 and 0.2), predictability is confined to a lower range with values around 10–30 days. The postprocessing work using joint bias correction improves the predictive skill for SI-x relative to the untreated input data set. Using nonhomogeneous Gaussian regression, a positive change in the SS is noted in regions where the skill with joint bias correction shows evidence of improvement. These findings suggest that the start of spring might be predictable on intraseasonal time horizons, which in turn could be useful for farmers, growers, and stakeholders making decisions on these time scales. ©2018. The Authors.
    Print ISSN: 2169-897X
    Electronic ISSN: 2169-8996
    Topics: Geosciences , Physics
    Published by American Geophysical Union
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  • 8
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    Modified “Rule N” Procedure for Principal Component (EOF) Truncation (2016)
    American Meteorological Society
    Details
    Publication Date: 2016-04-12
    Description: Principal component analysis (PCA), also known as empirical orthogonal function (EOF) analysis, is widely used for compression of high-dimensional datasets in such applications as climate diagnostics and seasonal forecasting. A critical question when using this method is the number of modes, representing meaningful signal, to retain. The resampling-based “Rule N” method attempts to address the question of PCA truncation in a statistically principled manner. However, it is only valid for the leading (largest) eigenvalue, because it fails to condition the hypothesis tests for subsequent (smaller) eigenvalues on the results of previous tests. This paper draws on several relatively recent statistical results to construct a hypothesis-test-based truncation rule that accounts at each stage for the magnitudes of the larger eigenvalues. The performance of the method is demonstrated in an artificial data setting and illustrated with a real-data example.
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 9
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    Regularized Dawid–Sebastiani score for multivariate ensemble forecasts (2020)
    Wiley
    Details
    Publication Date: 2020-05-03
    Print ISSN: 0035-9009
    Electronic ISSN: 1477-870X
    Topics: Geography , Physics
    Published by Wiley
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  • 10
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    A Farm‐Level Analysis of Economic and Agronomic Impacts of Gradual Climate Warming (1993)
    Oxford University Press
    Details
    Publication Date: 1993-05-01
    Print ISSN: 0002-9092
    Electronic ISSN: 1467-8276
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition , Economics
    Published by Oxford University Press
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