We present a new statistical method to optimally link local weather extremes to large-scale atmospheric circulation structures. The method is illustrated using July-August daily mean temperature at 2m height (T2m) time-series over the Netherlands and 500 hPa geopotential height (Z500) time-series over the Euroatlantic region of the ECMWF reanalysis dataset (ERA40). The method identifies patterns in the Z500 time-series that optimally describe, in a precise mathematical sense, the relationship with local warm extremes in the Netherlands. Two patterns are identified; the most important one corresponds to a blocking high pressure system leading to subsidence and calm, dry and sunny conditions over the Netherlands. The second one corresponds to a rare, easterly flow regime bringing warm, dry air into the region. The patterns are robust; they are also identified in shorter subsamples of the total dataset. The method is generally applicable and might prove useful in evaluating the performance of climate models in simulating local weather extremes.
Deep Dive into Extreme Associated Functions: Optimally Linking Local Extremes to Large-scale Atmospheric Circulation Structures.
We present a new statistical method to optimally link local weather extremes to large-scale atmospheric circulation structures. The method is illustrated using July-August daily mean temperature at 2m height (T2m) time-series over the Netherlands and 500 hPa geopotential height (Z500) time-series over the Euroatlantic region of the ECMWF reanalysis dataset (ERA40). The method identifies patterns in the Z500 time-series that optimally describe, in a precise mathematical sense, the relationship with local warm extremes in the Netherlands. Two patterns are identified; the most important one corresponds to a blocking high pressure system leading to subsidence and calm, dry and sunny conditions over the Netherlands. The second one corresponds to a rare, easterly flow regime bringing warm, dry air into the region. The patterns are robust; they are also identified in shorter subsamples of the total dataset. The method is generally applicable and might prove useful in evaluating the performanc
Weather extremes such as extreme wind speeds, extreme precipitation or extreme warm or cold conditions are experienced locally. They are usually connected to circulation structures of much larger scale in the atmosphere. For example, if we restrict ourselves to the Netherlands, a well-known circulation structure that often leads to extreme hot summer days is a high pressure system that blocks the inflow of cooler maritime air masses. Moreover, the subsidence of air in its interior leads to clear skies and an abundance of sunshine that leads to high temperatures. If the blocking high persists and depletes the soil moisture due to lack of precipitation and increased evaporation, temperatures tend to soar, as it did in Correspondence to: D. Panja (dpanja@science.uva.nl) the European summer of 2003Schär et al. (2004)). Speculations about a positive feedback of dry soil on the persistence of the blocking high can also be found in the literature Ferranti and Viterbo (2006).
In order for climate models to correctly simulate the probability of extreme hot summer days, a crucial ingredient is the correct simulation of the probability of the occurrence of blocking. This is a well-known difficult feature of the atmospheric circulation to simulate realistically Pelly and Hoskins (2003). The verification of models w.r.t. this aspect is, in practice, difficult as well, since idealized model experiments suggest a high degree of internal variability of blocking frequencies even on decadal timescales Liu and Opsteegh (1995).
In a world with increasing concentrations of greenhouse gases, not only the temperature increases, also the large-scale circulation adjusts to achieve a new (thermo)dynamical balance. Models disagree on the magnitude and even the direction of this change locally van Ulden and van Oldenborgh (2006). For instance, a change in the probability of European blocking conditions in summer immediately impacts the future probability of European heat waves. This makes probability estimates of future European heat waves very uncertain. To address the questions concerning the probability of future extreme weather events, and the evaluation of climate model simulations in this respect, it is necessary to have a descriptive method that links local weather extremes to largescale circulation features. To the best of our knowledge, an optimal method to do so does not exist in the literature.
We identified two approaches in the literature to link local weather extremes to large-scale circulation features. In the first one, the circulation anomalies are classified first, the connection with local extremes is analyzed in second instance. The “Grosswetterlagen” developed by synoptic meteorologists for instance is one such classification Kysely (2002). All kinds of clustering algorithms are another example Plaut and Simonnet (2001); Cassou et al. (2005). In ERA-40 data (1958ERA-40 data ( -2000)). Left figure: first EOF; right figure: second EOF. Relative importances are 12.57% and 11.79% respectively. The patterns have been multiplied by one standard deviation of the corresponding amplitude time-series (in meters). our opinion, this approach is not optimal since in the definition of the patterns, information about the extreme is not taken into account.
In the second approach, a measure of the local extreme does enter the definition of the large-scale circulation patterns. For instance, only atmospheric states are considered for which the local extreme occurs. Next a simple averaging operator is applied [“composite method” as in Schaeffer et al. (2005)]or a clustering analysis is performed Sanchez-Gomez and Terray (2005) . The composite method falls short since it finds by definition only one typical circulation anomaly and from synoptic experience we know that often different kind of circulation anomalies lead to a similar local weather extreme. The clustering analysis is debatable since the data record is often too short to identify clusters with enough statistical confidence Hsu and Zwiers (2001).
The purpose of this paper is to report a new optimal method to relate local weather extremes to characteristic circulation patterns. This method objectively identifies, in a robust manner, the different circulation patterns that favor the occurrence of local weather extremes. The method is inspired by the Optimal Autocorrelation Functions of Selten et al. (1999). It is based on considering linear combinations of the dominant Empirical Orthogonal Functions that maximize a suitable statistical quantity. We illustrate our method by analyzing the statistical relation between extreme high daily mean temperatures at two meter height (T2m) in July and August in the Netherlands and the structure of the large-scale circulation as measured by the 500 hPa geopotential height field (Z500).
This paper is divided into five sections. Section 2 is focused on the data, where we explain the method to obtain the daily Z500 and T2m anomalies i
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