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  • 1
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    Reply to Comment on "Cosmic rays, carbon dioxide, and climate" (2004)
    AGU (American Geophysical Union)
    In:  Eos, Transactions American Geophysical Union, 85 (48). p. 511.
    Details
    Publication Date: 2020-03-19
    Description: In our analysis [Rahmstorf et al., 2004], we arrived at two main conclusions: the data of Shaviv and Veizer [2003] do not show a significant correlation of cosmic ray flux (CRF) and climate, and the authors' estimate of climate sensitivity to CO2 based on a simple regression analysis is questionable. After careful consideration of Shaviv and Veizer's comment, we want to uphold and reaffirm these conclusions. Concerning the question of correlation, we pointed out that a correlation arose only after several adjustments to the data, including shifting one of the four CRF peaks and stretching the time scale. To calculate statistical significance, we first need to compute the number of independent data points in the CRF and temperature curves being correlated, accounting for their autocorrelation. A standard estimate [Quenouille, 1952] of the number of effective data points is urn:x-wiley:00963941:media:eost14930:eost14930-math-0001 where N is the total number of data points and r1, r2 are the autocorrelations of the two series. For the curves of Shaviv and Veizer [2003], the result is NEFF = 4.8. This is consistent with the fact that these are smooth curves with four humps, and with the fact that for CRF the position of the four peaks is determined by four spiral arm crossings or four meteorite clusters, respectively; that is, by four independent data points. The number of points that enter the calculation of statistical significance of a linear correlation is (NEFF− 2), since any curves based on only two points show perfect correlation; at least three independent points are needed for a meaningful result.
    Type: Article , NonPeerReviewed
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  • 2
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    Cosmic rays, carbon dioxide, and climate (2004)
    AGU (American Geophysical Union)
    In:  Eos, Transactions American Geophysical Union, 85 (4). pp. 38-41.
    Details
    Publication Date: 2017-02-10
    Type: Article , NonPeerReviewed
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  • 3
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    How well do regional climate models simulate the spatial dependence of precipitation? An application of pair-copula constructions (2015)
    AGU (American Geophysical Union) | Wiley
    In:  Journal of Geophysical Research: Atmospheres, 120 (7). pp. 2624-2646.
    Details
    Publication Date: 2019-04-04
    Description: We investigate how well a suite of regional climate models (RCMs) from the ENSEMBLES project represents the residual spatial dependence of daily precipitation. The study area we consider is a 200 km×200 km region in south central Norway, with RCMs driven by ERA-40 boundary conditions at a horizontal resolution of approximately 25 km×25 km. We model the residual spatial dependence with pair-copula constructions, which allows us to assess both the overall and tail dependence in precipitation, including uncertainty estimates. The selected RCMs reproduce the overall dependence rather well, though the discrepancies compared to observations are substantial. All models overestimate the overall dependence in the west-east direction. They also overestimate the upper tail dependence in the north-south direction during winter, and in the west-east direction during summer, whereas they tend to underestimate this dependence in the north-south direction in summer. Moreover, many of the climate models do not simulate the small-scale dependence patterns caused by the pronounced orography well. However, the misrepresented residual spatial dependence does not seem to affect estimates of high quantiles of extreme precipitation aggregated over a few grid boxes. The underestimation of the area-aggregated extreme precipitation is due mainly to the well-known underestimation of the univariate margins for individual grid boxes, suggesting that the correction of RCM biases in precipitation might be feasible
    Type: Article , PeerReviewed
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  • 4
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    Evidence for added value of convection-permitting models for studying changes in extreme precipitation (2015)
    AGU (American Geophysical Union) | Wiley
    In:  Journal of Geophysical Research: Atmospheres, 120 (24). pp. 12500-12513.
    Details
    Publication Date: 2019-04-04
    Description: Climate model resolution can affect both the climate change signal and present-day representation of extreme precipitation. The need to parametrize convective processes raises questions about how well the response to warming of convective precipitation extremes is captured in such models. In particular, coastal precipitation extremes can be sensitive to sea surface temperature (SST) increase. Taking a recent coastal precipitation extreme as a showcase example, we explore the added value of convection-permitting models by comparing the response of the extreme precipitation to a wide range of SST forcings in an ensemble of regional climate model simulations using parametrized and explicit convection. Compared at the same spatial scale, we find that the increased local intensities of vertical motion and precipitation in the convection-permitting simulations play a crucial role in shaping a strongly nonlinear extreme precipitation response to SST increase, which is not evident when convection is parametrized. In the convection-permitting simulations, SST increase causes precipitation intensity to increase only until a threshold is reached, beyond which further SST increase does not enhance the precipitation. This flattened response results from an improved representation of convective downdrafts and near-surface cooling, which damp the further intensification of precipitation by stabilizing the lower troposphere locally and also create cold-pools that cause subsequent convection to be triggered at sea, rather than by the coastal orography. These features are not well represented in the parametrized convection simulations, resulting in precipitation intensity having a much more linear response to increasing SSTs
    Type: Article , PeerReviewed
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  • 5
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    Epochs of phase coherence between El Niño/Southern Oscillation and Indian monsoon (2005)
    AGU (American Geophysical Union)
    In:  Geophysical Research Letters, 32 (15). L15709.
    Details
    Publication Date: 2018-03-28
    Description: We present a modern method used in nonlinear time series analysis to investigate the relation of two oscillating systems with respect to their phases, independently of their amplitudes. We study the difference of the phase dynamics between El Niño/Southern Oscillation (ENSO) and the Indian Monsoon on inter-annual time scales. We identify distinct epochs, especially two intervals of phase coherence, 1886–1908 and 1964–1980, corroborating earlier findings from a new point of view. A significance test shows that the coherence is very unlikely to be the result of stochastic fluctuations. We also detect so far unknown periods of coupling which are invisible to linear methods. These findings suggest that the decreasing correlation during the last decades might be a typical epoch of the ENSO/Monsoon system having occurred repeatedly. The high time resolution of the method enables us to present an interpretation of how volcanic radiative forcing could cause the coupling.
    Type: Article , PeerReviewed
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  • 6
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    Epochs of Phase Coherence between ENSO and Indian Monsoon (2005)
    AGU (American Geophysical Union)
    In:  Geophysical Research Letters, 32 (15). L15709.
    Details
    Publication Date: 2020-04-24
    Description: We present a modern method used in nonlinear time series analysis to investigate the relation of two oscillating systems with respect to their phases, independently of their amplitudes. We study the difference of the phase dynamics between El Niño/Southern Oscillation (ENSO) and the Indian Monsoon on inter‐annual time scales. We identify distinct epochs, especially two intervals of phase coherence, 1886–1908 and 1964–1980, corroborating earlier findings from a new point of view. A significance test shows that the coherence is very unlikely to be the result of stochastic fluctuations. We also detect so far unknown periods of coupling which are invisible to linear methods. These findings suggest that the decreasing correlation during the last decades might be a typical epoch of the ENSO/Monsoon system having occurred repeatedly. The high time resolution of the method enables us to present an interpretation of how volcanic radiative forcing could cause the coupling. us to present an interpretation of how volcanic radiative forcing could cause the coupling.
    Type: Article , PeerReviewed
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  • 7
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    Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums (2012)
    AGU (American Geophysical Union)
    In:  Geophysical Research Letters, 39 (6). L06706.
    Details
    Publication Date: 2017-06-20
    Description: Bias correcting climate models implicitly assumes stationarity of the correction function. This assumption is assessed for regional climate models in a pseudo reality for seasonal mean temperature and precipitation sums. An ensemble of regional climate models for Europe is used, all driven with the same transient boundary conditions. Although this model-dependent approach does not assess all possible bias non-stationarities, conclusions can be drawn for the real world. Generally, biases are relatively stable, and bias correction on average improves climate scenarios. For winter temperature, bias changes occur in the Alps and ice covered oceans caused by a biased forcing sensitivity of surface albedo; for summer temperature, bias changes occur due to a biased sensitivity of cloud cover and soil moisture. Precipitation correction is generally successful, but affected by internal variability in arid climates. As model sensitivities vary considerably in some regions, multi model ensembles are needed even after bias correction. Key Points: - Bias correction in general improves future climate simulations - Cloud cover, soil moisture and albedo changes may cause temperature bias changes - Precipitation biases in arid regions are affected by internal variability
    Type: Article , PeerReviewed
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  • 8
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    Validation of the present day annual cycle in heavy precipitation over the British Islands simulated by 14 RCMs (2012)
    AGU (American Geophysical Union)
    In:  Journal of Geophysical Research: Atmospheres, 117 (D18). D18107.
    Details
    Publication Date: 2018-01-19
    Description: The representation of the annual cycle of heavy daily precipitation events across the United Kingdom within 14 regional climate models (RCMs) and the European observation data set (E-OBS) over the 1961-2000 period is investigated. We model extreme precipitation as an inhomogeneous Poisson process with a non-stationary threshold and use a sinusoidal model for the location and scale parameter of the corresponding generalized extreme value distribution and a constant shape parameter. First we fit the statistical model to the UK Met Office 5 km gridded precipitation data set (UKMO). Second the statistical model is fitted to 14 reanalysis driven 25 km resolution RCMs from the ENSEMBLES project and to E-OBS. The resulting characteristics from the RCMs and from E-OBS are compared with those from UKMO. We study the peak time of the annual cycle of the monthly return levels, the relative amplitude of their annual cycle and the relative bias of their absolute values. We show that the performance of the RCMs depends strongly on the region. The RCMs show deficits in modeling the characteristics of the annual cycle, especially in modeling its relative amplitude and mainly in Eastern England. However the peak time of the annual cycle is adequately simulated by most RCMs. E-OBS exhibits considerable biases in the absolute values of all monthly return levels, but the relative amplitude and the phase of the annual cycle of heavy precipitation are well represented. Our results imply that studies which rely on the explicit annual cycle of simulated heavy precipitation should be carefully considered.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
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  • 9
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    Comparison of GCM- and RCM-simulated precipitation following stochastic postprocessing (2014)
    AGU (American Geophysical Union) | Wiley
    In:  Journal of Geophysical Research: Atmospheres, 119 (19). 11,040-11,053.
    Details
    Publication Date: 2018-02-27
    Description: In order to assess to what extent regional climate models (RCMs) yield better representations of climatic states than general circulation models (GCMs), the output of each is usually directly compared with observations. RCM output is often bias corrected, and in some cases correction methods can also be applied to GCMs. This leads to the question of whether bias-corrected RCMs perform better than bias-corrected GCMs. Here the first results from such a comparison are presented, followed by discussion of the value added by RCMs in this setup. Stochastic postprocessing, based on Model Output Statistics (MOS), is used to estimate daily precipitation at 465 stations across the United Kingdom between 1961 and 2000 using simulated precipitation from two RCMs (RACMO2 and CCLM) and, for the first time, a GCM (ECHAM5) as predictors. The large-scale weather states in each simulation are forced toward observations. The MOS method uses logistic regression to model precipitation occurrence and a Gamma distribution for the wet day distribution, and is cross validated based on Brier and quantile skill scores. A major outcome of the study is that the corrected GCM-simulated precipitation yields consistently higher validation scores than the corrected RCM-simulated precipitation. This seems to suggest that, in a setup with postprocessing, there is no clear added value by RCMs with respect to downscaling individual weather states. However, due to the different ways of controlling the atmospheric circulation in the RCM and the GCM simulations, such a strong conclusion cannot be drawn. Yet the study demonstrates how challenging it is to demonstrate the value added by RCMs in this setup.
    Type: Article , PeerReviewed
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