Hans von Storch

A Spectral Nudging Technique for Dynamical Downscaling Purposes

The ‘‘spectral nudging’’ method imposes time-variable large-scale atmospheric states on a regional atmospheric model. It is based on the idea that regional-scale climate statistics are conditioned by the interplay between continental-scale atmospheric conditions and such regional features as marginal seas and mountain ranges. Following this ‘‘downscaling’’ idea, the regional model is forced to satisfy not only boundary conditions, possibly in a boundary sponge region, but also large-scale flow conditions inside the integration area. In the present paper the performance of spectral nudging in an extended climate simulation is examined. Its success in keeping the simulated state close to the driving state at larger scales, while generating smaller-scale features is demonstrated, and it is also shown that the standard boundary forcing technique in current use allows the regional model to develop internal states conflicting with the large-scale state. It is concluded that spectral nudging may be seen as a suboptimal and indirect data assimilation technique. 1. Background The state of the atmosphere cannot be observed in its entirety. Only samples of mostly point observations irregularly distributed in space are available. They are used by operational weather centers to construct, or ‘‘analyze,’’ a continuous distribution of atmospheric variables. Such analyses are our best guess of the atmospheric state and deviate from the true, unknown state to some extent. Likely, the large scales are best described, simply because they are better sampled. On the other hand, the details on scales of a few tens of kilometers and less are insufficiently sampled and subject to significant uncertainty. In the past, the analyses were prepared by hand. The major breakthrough was the systematic interpretation of observational data aided by quasi-realistic dynamical models. However, the only features that can be well reproduced by these objective analyses with quasi-realistic models are those that are well resolved by the model. For example, while the effect of the Baltic Sea may to some extent be captured, the imprint of Jutland, separating the Baltic Sea from the North Sea, may not. Thus, the missing details in analyses remains at present a major problem in weather analyses. While in days gone by the purpose of weather, or synoptic, analyses was for preparing short-term weather

Downscaling of Global Climate Change Estimates to Regional Scales: An Application to Iberian Rainfall in Wintertime

Abstract A statistical strategy to deduct regional-scale features from climate general circulation model (GCM) simulations has been designed and tested. The main idea is to interrelate the characteristic patterns of observed simultaneous variations of regional climate parameters and of large-scale atmospheric flow using the canonical correlation technique. The large-scale North Atlantic sea level pressure (SLP) is related to the regional, variable, winter (DJF) mean Iberian Peninsula rainfall. The skill of the resulting statistical model is shown by reproducing, to a good approximation, the winter mean Iberian rainfall from 1900 to present from the observed North Atlantic mean SLP distributions. It is shown that this observed relationship between these two variables is not well reproduced in the output of a general circulation model (GCM). The implications for Iberian rainfall changes as the response to increasing atmospheric greenhouse-gas concentrations simulated by two GCM experiments are examined with...

The Analog Method as a Simple Statistical Downscaling Technique: Comparison with More Complicated Methods

The derivation of local scale information from integrations of coarse-resolution general circulation models (GCM) with the help of statistical models fitted to present observations is generally referred to as statistical downscaling. In this paper a relatively simple analog method is described and applied for downscaling purposes. According to this method the large-scale circulation simulated by a GCM is associated with the local variables observed simultaneously with the most similar large-scale circulation pattern in a pool of historical observations. The similarity of the large-scale circulation patterns is defined in terms of their coordinates in the space spanned by the leading observed empirical orthogonal functions. The method can be checked by replicating the evolution of the local variables in an independent period. Its performance for monthly and daily winter rainfall in the Iberian Peninsula is compared to more complicated techniques, each belonging to one of the broad families of existing statistical downscaling techniques: a method based on canonical correlation analysis, as representative of linear methods; a method based on classification and regression trees, as representative of a weather generator based on classification methods; and a neural network, as an example of deterministic nonlinear methods. It is found in these applications that the analog method performs in general as well as the more complicated methods, and it can be applied to both normally and nonnormally distributed local variables. Furthermore, it produces the right level of variability of the local variable and preserves the spatial covariance between local variables. On the other hand linear multivariate methods offer a clearer physical interpretation that supports more strongly its validity in an altered climate. Classification and neural networks are generally more complicated methods and do not directly offer a physical interpretation.

Misuses of Statistical Analysis in Climate Research

The history of misuses of statistics is as long as the history of statistics itself. The following is a personal assessment about such misuses in our field, climate research. Some people might find my subjective essay of the matter unfair and not balanced. This might be so, but an effective drug sometimes tastes bitter.

Taking Serial Correlation into Account in Tests of the Mean

Abstract The comparison of means derived from samples of noisy data is a standard pan of climatology. When the data are not serially correlated the appropriate statistical tool for this task is usually the conventional Students t-test. However, frequently data are serially correlated in climatological applications with the result that the t test in its standard form is not applicable. The usual solution to this problem is to scale the t statistic by a factor that depends upon the equivalent sample size ne. It is shown, by means of simulations, that the revised t tea is often conservative (the actual significance level is smaller than the specified significance level) when the equivalent sample size is known. However, in most practical cases the equivalent sample size is not known. Then the test becomes liberal (the actual significance level is greater than the specified significance level). This systematic error becomes small when the true equivalent sample size is large (greater than approximately 30). ...

The atmospheric circulation and sea-surface temperature in the North-Atlantic area in winter: Their interaction and relevance for Iberian precipitation

Abstract The ocean surface-atmosphere relationships in the North Atlantic area in northern winter are empirically examined by canonical correlation analysis (CCA). This analysis is performed from two different points of view. First, the connection between atmospheric circulation anomalies, in terms of monthly mean sea level pressure (SLP) and monthly standard deviation of SLP (αSLP), and sea surface temperature (SST) anomalies of the Atlantic Ocean are directly examined. Second, the air-sea relationships are indirectly studied through their influence upon precipitation in an area likely to be influenced by the North Atlantic, the Iberian Peninsula. The canonical correlation analysis yields two pairs of patterns that describe the coherent variations of the combined SST-SLP fields; one pair of patterns for the SST-αSLP fields and one pair of patterns for the SLP-αSLP fields. All patterns are dominant in describing variance. A lag cross-correlation analysis of the time coefficients indicates that monthly mea...

Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples

An important challenge in current climate modeling is to realistically describe small-scale weather statistics, such as topographic precipitation and coastal wind patterns, or regional phenomena like polar lows. Global climate models simulate atmospheric processes with increasingly higher resolutions, but still regional climate models have a lot of advantages. They consume less computation time because of their limited simulation area and thereby allow for higher resolution both in time and space as well as for longer integration times. Regional climate models can be used for dynamical down-scaling purposes because their output data can be processed to produce higher resolved atmospheric fields, allowing the representation of small-scale processes and a more detailed description of physiographic details (such as mountain ranges, coastal zones, and details of soil properties). However, does higher resolution add value when compared to global model results? Most studies implicitly assume that dynamical down...

Detecting greenhouse-gas-induced climate change with an optimal fingerprint method

Abstract A strategy using statistically optimal fingerprints to detect anthropogenic climate change is outlined and applied to near-surface temperature trends. The components of this strategy include observations, information about natural climate variability, and a “guess pattern” representing the expected time–space pattern of anthropogenic climate change. The expected anthropogenic climate change is identified through projection of the observations onto an appropriate optimal fingerprint, yielding a scalar-detection variable. The statistically optimal fingerprint is obtained by weighting the components of the guess pattern (truncated to some small-dimensional space) toward low-noise directions. The null hypothesis that the observed climate change is part of natural climate variability is then tested. This strategy is applied to detecting a greenhouse-gas-induced climate change in the spatial pattern of near-surface temperature trends defined for time intervals of 15–30 years. The expected pattern of cl...

On the Use of “Inflation” in Statistical Downscaling

The technique of ‘‘inflating’’ in downscaling, which makes the downscaled climate variable have the right variance, is based on the assumption that all local variability can be traced back to large-scale variability. For practical situations this assumption is not valid, and inflation is an inappropriate technique. Instead, additive, randomized approaches should be adopted.

Stochastic Characterization of Regional Circulation Patterns for Climate Model Diagnosis and Estimation of Local Precipitation

Abstract Two statistical approaches for linking large-scale atmospheric circulation patterns and daily local rainfall are applied to GCM (general circulation model) climate simulations. The ultimate objective is to simulate local precipitation associated with altered climate regimes. Two regions, one in the Pacific-American sector (western region) and one in the American-Mid-Atlantic sector (eastern region), are explored. The first method is based on Classification and Regression Trees (CART) analysis. The CART method classifies observed daily sea level pressure (SLP) fields into weather types that are most strongly associated with the presence/absence of rainfall at selected index stations. After applying this method to historical SLP observations, precipitation simulations associated with GCM SLP output were validated in terms of probability of occurrence and survival time of the weather states identified by the CART analysis. Daily rainfall time series were then generated from weather classes derived b...