Douglas Maraun

Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user

Precipitation downscaling improves the coarse resolution and poor representation of precipitation in global climate models and helps end users to assess the likely hydrological impacts of climate change. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user (in particular, impact modeler) needs and to discuss downscaling capabilities and gaps. End users need a reliable representation of precipitation intensities and temporal and spatial variability, as well as physical consistency, independent of region and season. In addition to presenting dynamical downscaling, we review perfect prognosis statistical downscaling, model output statistics, and weather generators, focusing on recent developments to improve the representation of space-time variability. Furthermore, evaluation techniques to assess downscaling skill are presented. Downscaling adds considerable value to projections from global climate models. Remaining gaps are uncertainties arising from sparse data; representation of extreme summer precipitation, subdaily precipitation, and full precipitation fields on fine scales; capturing changes in small-scale processes and their feedback on large scales; and errors inherited from the driving global climate model.

Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums

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. nKey Points: n- Bias correction in general improves future climate simulations n- Cloud cover, soil moisture and albedo changes may cause temperature bias changes n- Precipitation biases in arid regions are affected by internal variability

Epochs of phase coherence between El Niño/Southern Oscillation and Indian monsoon

Received 13 April 2005; revised 4 July 2005; accepted 8 July 2005; published 10 August 2005. (1) 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 Nino/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. Citation: Maraun, D., and J. Kurths (2005), Epochs of phase coherence between El Nino/Southern Oscillation and Indian monsoon, Geophys. Res. Lett., 32, L15709,

Cosmic rays, carbon dioxide, and climate

Several recent papers have applied correlation analysis to climate-related time series in the hope of finding evidence for causal relationships. For a critical discussion of correlations between solar variability, cosmic rays, and cloud cover, see Laut [2003]. n nA prominent new example is a paper by Shaviv and Veizer [2003], which claims that fluctuations in cosmic ray flux reaching the Earth can explain 66% of the temperature variance over the past 520 m.y.,and that the sensitivity of climate to a doubling of CO2 is less than previously estimated.

When will trends in European mean and heavy daily precipitation emerge

A multi-model ensemble of regional climate projections for Europe is employed to investigate how the time of emergence (TOE) for seasonal sums and maxima of daily precipitation depends on spatial scale. The TOE is redefined for emergence from internal variability only; the spread of the TOE due to imperfect climate model formulation is used as a measure of uncertainty in the TOE itself. Thereby, the TOE becomes a fundamentally limiting timescale and translates into a minimum spatial scale on which robust conclusions can be drawn about precipitation trends. Thus, minimum temporal and spatial scales for adaptation planning are also given. In northern Europe, positive winter trends in mean and heavy precipitation, and in southwestern and southeastern Europe, summer trends in mean precipitation already emerge within the next few decades. However, across wide areas, especially for heavy summer precipitation, the local trend emerges only late in the 21st century or later. For precipitation averaged to larger scales, the trend, in general, emerges earlier.

How Hard is the Euro Area Core? An Evaluation of Growth Cycles Using Wavelet Analysis

Using recent advances in time-varying spectral methods, this research analyses the growth cycles of the core of the euro area in terms of frequency content and phasing of cycles.The methodology uses the continuous wavelet transform (CWT) and also Hilbert wavelet pairs in the setting of a non-decimated discrete wavelet transform in order to analyse bivariate time series in terms of conventional frequency domain measures from spectral analysis.The findings are that coherence and phasing between the three core members of the euro area (France, Germany and Italy) have increased since the launch of the euro. Key words: time-varying spectral analysis, coherence, phase, business cycles, EMU, growth cycles, Hilbert transform, wavelet analysis JEL classification numbers: C19, C63, C65, E32, E39, E58, F40

The influence of synoptic airflow on UK daily precipitation extremes. Part II: regional climate model and E-OBS data validation

We investigate how well the variability of extreme daily precipitation events across the United Kingdom is represented in a set of regional climate models and the E-OBS gridded data set. Instead of simply evaluating the climatologies of extreme precipitation measures, we develop an approach to validate the representation of physical mechanisms controlling extreme precipitation variability. In part I of this study we applied a statistical model to investigate the influence of the synoptic scale atmospheric circulation on extreme precipitation using observational rain gauge data. More specifically, airflow strength, direction and vorticity are used as predictors for the parameters of the generalised extreme value (GEV) distribution of local precipitation extremes. Here we employ this statistical model for our validation study. In a first step, the statistical model is calibrated against a gridded precipitation data set provided by the UK Met Office. In a second step, the same statistical model is calibrated against 14 ERA40 driven 25xa0km resolution RCMs from the ENSEMBLES project and the E-OBS gridded data set. Validation indices describing relevant physical mechanisms are derived from the statistical models for observations and RCMs and are compared using pattern standard deviation, pattern correlation and centered pattern root mean squared error as validation measures. The results for the different RCMs and E-OBS are visualised using Taylor diagrams. We show that the RCMs adequately simulate moderately extreme precipitation and the influence of airflow strength and vorticity on precipitation extremes, but show deficits in representing the influence of airflow direction. Also very rare extremes are misrepresented, but this result is afflicted with a high uncertainty. E-OBS shows considerable biases, in particular in regions of sparse data. The proposed approach might be used to validate other physical relationships in regional as well as global climate models.

Changes in the annual cycle of heavy precipitation across the British Isles within the 21st century

We investigate future changes in the annual cycle of heavy daily precipitation events across the nBritish Isles in the periods 2021–2060 and 2061–2100, relative to present day climate. Twelve ncombinations of regional and global climate models forced with the A1B scenario are used. nThe annual cycle is modelled as an inhomogeneous Poisson process with sinusoidal models nfor location and scale parameters of the generalized extreme value distribution. Although the npeak times of the annual cycle vary considerably between projections for the 2061–2100 nperiod, a robust shift towards later peak times is found for the south-east, while in the nnorth-west there is evidence for a shift towards earlier peak times. In the remaining parts of the nBritish Isles no changes in the peak times are projected. For 2021–2060 this signal is weak. nThe annual cycle’s relative amplitude shows no robust signal, where differences in projected nchanges are dominated by global climate model differences. The relative contribution of nanthropogenic forcing and internal climate variability to changes in the relative amplitude ncannot be identified with the available ensemble. The results might be relevant for the ndevelopment of adequate risk-reduction strategies, for insurance companies and for the nmanagement and planning of water resources

Reply [to “Cosmic rays, carbon dioxide, and climate”]

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 Veizers comment, we want to uphold and reaffirm these conclusions. n nConcerning 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 n nNEFFxa0≅xa0N1+2∑k=1Nr1(k)r2(k) n nwhere 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.