The Summer North Atlantic Oscillation in Cmip3 Models and Related Uncertainties in Projected Summer Drying in Europe

Abstract

This paper discusses uncertainties in model projections of summer drying in the Euro-Mediterranean region related to errors and uncertainties in the simulation of the summer NAO. The SNAO is the leading mode of summer SLP variability in the North Atlantic/European sector and modulates precipitation not only in the vicinity of the SLP dipole (northwest Europe) but also in the Mediterranean region. An analysis of CMIP3 models is conducted to determine the extent to which models reproduce the signature of the SNAO and its impact on precipitation and to assess the role of the SNAO in the projected precipitation reductions. Most models correctly simulate the spatial pattern of the SNAO and the dry anomalies in northwest Europe that accompany the positive phase. The models also capture the concurrent wet conditions in the Mediterranean, but the amplitude of this signal is too weak, especially in the east. This error is related to the poor simulation of the upper-level circulation response to a positive SNAO, namely the observed trough over the Balkans that creates potential instability and favors precipitation. The SNAO is generally projected to trend upwards in CMIP3 models, leading to a consistent signal of precipitation reduction in NW Europe, but the intensity of the trend varies greatly across models, resulting in large uncertainties in the magnitude of the projected drying. In the Mediterranean, because the simulated influence of the SNAO is too weak, no precipitation increase occurs even in the presence of a strong SNAO trend, reducing confidence in these projections. 1) Introduction Credible projections of future climate change on a regional scale require validation of the results via comparison with observations and assessment of consistency with theoretical arguments. For precipitation, however, there is little theoretical basis for expecting a change of a particular sign in a given region, except for the simple (yet fundamental) argument that the pattern of moisture flux convergence should amplify in response to warming temperatures, simply as a consequence of the Clausius-Clapeyron equation under constant relative humidity [Held and Soden, 2006]. This “wet gets wetter, dry gets dryer” mechanism should lead to decreased precipitation in subtropical areas, particularly ocean basins, where the largest moisture export occurs. Yet, in climate model simulations of the 21 century, the most pronounced and robust precipitation changes in the northern subtropics occur over land, in the Mediterranean region, where the models almost unanimously project substantial reductions in precipitation, particularly in summer, when the drying extends to northwest Europe [Fig.1; see also van Ulden and van Oldenborgh, 2006; Meehl et al., 2007; Giorgi and Coppola, 2007; Scheff and Frierson, 2012]. Furthermore, a posteriori arguments invoking the poleward expansion of the Hadley cell observed in CMIP3 models – and the concomitant expansion of the subtropical dry zone (Lu et al., 2007) – do not seem particularly relevant to the Mediterranean region in summer. Indeed, not only is the signature of the Hadley circulation confined to the eastern Mediterranean, but most of the subsidence that prevails in that region appears to be primarily driven by the Asian monsoon [Rodwell and Hoskins, 1996; Ziv et al., 2004]. Comparison of model projections with recent observed trends of summer precipitation, as a means of increasing our confidence in these projections, is also non-conclusive as the trends are weak and not statistically significant (for either the full record or the last 60 years) [Bladé et al., 2011; van Haren et al., 2012]. The model agreement regarding the sign of future precipitation changes may be due to common, well-represented processes, such as soil-moisture feedbacks [Rowell and Jones, 2006], but has not been investigated in depth and could plausibly be due, instead, to systematic biases in the models. One avenue to rule out this possibility and to validate the precipitation projections would be to explore whether the large-scale circulations that modulate precipitation in the EuroMediterranean region are correctly represented in these models – although these mechanisms themselves have not been extensively studied. Recently, the summer manifestation of the North Atlantic Oscillation, or SNAO, has been identified as a major driver of precipitation (and also temperature) variability in large parts of Europe and the Mediterranean region [Mariotti and Arkin, 2007; Folland et al., 2009; Chronis et al., 2011; Bladé et al., 2011]. Compared to its winter counterpart, the SNAO is weaker, more spatially confined and its southern lobe is displaced northeastward into the UK and southern Scandinavia, so that strong anticyclonic conditions prevail in these regions when the SNAO is in the positive phase. As a result, the SNAO directly and strongly influences precipitation in this sector, where dry conditions are experienced during positive SNAO summers. Somewhat surprisingly given its northern location, a positive SNAO also significantly enhances rainfall in the Mediterranean region, particularly the Balkans and Italy, where it accounts for between 20 and 35% of the interannual variance. Bladé et al. [2011; hereafter B2011] have argued that this influence occurs via a downstream hemispheric upper-level circulation that develops in association with the SNAO and is characterized by a well-defined trough centered over the Balkans during the positive SNAO phase. The trough entails mid-tropospheric cooling and increased potential instability and thus leads to enhanced rainfall in the Mediterranean region. B2011 also investigated the realism of the SNAO in two climate models, GFDL-CM2.1 and HadCM3. These models were able to accurately reproduce the spatial pattern and local impact of the SNAO over northwest Europe but not the remote influence in the eastern Mediterranean, which was weak or almost nonexistent. The error was tracked to the models’ inability to correctly capture the upper-level SNAO-related circulation. Moreover, because both models projected a strong upward SNAO trend in the future, the error in the surface SNAO signature then impacted the projected precipitation trends in the Mediterranean region, since the expected increase in precipitation, linearly associated with the SNAO trend, did not take place. These two models were chosen because of their realistic SNAO pattern and pronounced future SNAO trend but it is not known the extent to which this behavior is common to all models. On the other hand, Giorgi and Coppola (2007) have shown that the CMIP3 multi-model mean pattern of future summer SLP change displays increased pressure over the British Isles and decreased pressure in Greenland. This result suggests that the SNAO may indeed exhibit a future upward trend in most models, which would account for some of the consistent projected drying in northwest Europe. The goal of this paper is to document CMIP3 model performance with regards to the SNAO and to assess the contribution of SNAO trends to projections of precipitation in the Euro-Mediterranean region. We will show that many CMIP3 models correctly capture the spatial features of the SNAO as well as the strength of the associated rainfall anomalies in northwest Europe. Models also reproduce the widespread increase in precipitation in the Mediterranean that occurs during the positive SNAO phase, but the effect is consistently too weak. We also show that the future SNAO trend, and thus the part of the precipitation change that depends on this trend, varies considerably from model to model, although the trend is generally positive. Thus the SNAO emerges as an important contributor to intermodel consistency but also a large source of uncertainty in northwest Europe and a potentially large source of error in the Mediterranean. 2) Data and methodology The observational analysis is based on the 5o×5o gridded Trenberth SLP dataset [Trenberth and Paolino, 1980], covering the period 1899-2011, the 2.5o×2.5o global reconstructed PREC precipitation dataset (1948-2011) developed at NOAA CPC (available at http://www.esrl.noaa.gov/psd/data/gridded/data.prec.html) and NCEP/NCAR reanalysis 200-hPa geopotential height data. Over land, the PREC dataset is based on optimal interpolation of raingauge data, while over oceanic regions estimates are based on an EOF reconstruction of land gauge observations [Chen et al., 2002; 2004; Janowiak et al., 2003]. For model data, we use all available sequential 20C3M/SRESA1B simulations from the Coupled Model Intercomparison Project Phase 3 (CMIP3) developed for the IPCC AR4. Data for the individual 20C3M and SRESA1B runs were downloaded from the CMIP3 multi-model database (https://esg.llnl.gov:8443/index.jsp) and spliced into consecutive simulations extending from 1900 to 2099 (the common period to all runs), using the information contained in the metadata. At the start, all available simulations were used, except run 1 of GISS-ER, run 3 of ECHO-G and run 9 of CCSM3 (problems where found either in the metadata or in the data that prevented us from confidently concatenating the runs). In addition, we retrieved two supplementary GFDL-CM2.1 extended runs from the GFDL data portal (run 1 and run 3). In total, 24 models and 56 simulations were used at the onset (Table 1). Information on the models can be found at: http://www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php . l model data were re-gridded to a common grid (2.5o×2.5o for SLP, 0.5o×0.5o for precipitation) using bi-linear interpolation to facilitate model and observational data comparison. To avoid possible contamination from long-term trends, 20 century data are detrended prior to computing regressions and correlations. The results obtained, however, are very similar if undetrended data are used instead. Multi-model ensemble quantities are obtained by first averaging over all available ensemble members for each model and then averaging over all models, so that all models are given equal weight even if the number of ensemble members differs. All analyses are based on anomalies from the July-August mean, computed by subtracting the corresponding climatological long-term mean. EOFs are calculated as the eigenvectors of the area-weighted covariance matrix. To obtain (asymmetric) 95% confidence limits for linear correlation coefficients, a non-parametric bootstrap method is applied, in which 1000 independent pairs of data samples, with replacement, are drawn and an empirical distribution of the statistic is estimated (in some instances, for brevity, the 95% confidence interval is quoted as a symmetric interval, using the largest of the two deviations). Linear trends are estimated as the slope of a straight line fitted to the data, in a least-square sense. Trend significance is determined using a Monte Carlo technique (10000 random permutations of data). To compute ensemble-mean or area-mean correlations from a set of correlations with identical temporal sample size (which are not normally distributed and are therefore not additive), we apply a Fisher-Z transformation to each correlation value: Z = atanh(r). These Z values are approximately normally distributed, have equal standard deviations and can thus be averaged linearly [Wilks, 2006; Faller, 1981]. The resulting mean Z-value is inverse-transformed to yield an unbiased estimate of the average correlation. 3) The simulated SNAO pattern compared to observations In B2011, the summer NAO was defined as the leading EOF of mean July-August (“highsummer”) SLP in a restricted North Atlantic domain [40oN-70oN, 90oW-30oE], following recommendations by Folland et al. [2009; hereafter F2009] and Greatbatch and Rong [2006]. Because, prior to 1940, the SNAO pattern appeared weak and non-robust (likely because of the scarcity of Greenland data), a “baseline” SNAO was defined as the pattern obtained for the period 1950-2010. For this period, the leading EOF mode is robust, well separated from the second mode [North et al., 1982] and virtually identical in observational and NCEP reanalysis data. Thus specified, the SNAO is characterized by a SLP dipole with a SW/NE orientation, centers of action of comparable amplitude over Greenland and the UK and is such that, in the positive phase, anticyclonic conditions prevail over NW Europe (Fig. 2, inset). In this and the bottom maps, the EOF is displayed in terms of the regression between its normalized detrended principal component (PC) and SLP anomalies at every grid-point. To compare the simulated patterns of variability with the observed leading pattern, an identical EOF analysis of July-August SLP is performed for every individual simulation, also for the 1950-2010 period. Except in two simulations, the two leading EOFs are well separated from each other and the dominant mode generally consists of a north-south dipole, with the northern lobe situated over Greenland. The location of the southern lobe, however, is variable, with some models tending to position it west of the UK or even in the center of the Atlantic (e.g., IPSL-CM4, Fig. 2, bottom right). In a few simulations, the mode that most closely resembles the SNAO pattern is the second EOF (e.g., HadGEM1, consistent with F2009). Even when the simulated pattern is close to the observed, there may be large differences in the strength of the associated SLP anomalies, with the percent of explained variance ranging from 16% (GISS-AOM) to 50% (PCM), compared to 35% (observed). [The reader is referred to Table 1 for complete information on the SNAO in all simulations]. The resemblance between the simulated and observed patterns can be quantified by calculating the spatial anomaly correlation (rs) between the model’s closest analog of the SNAO (generally EOF1) and the observed pattern, together with the normalized magnitude relative to the observed pattern, or root-mean-square – rms – amplitude ratio, A= mi i 1 N / oi 2

Cite this paper

@inproceedings{Blad2012TheSN, title={The Summer North Atlantic Oscillation in Cmip3 Models and Related Uncertainties in Projected Summer Drying in Europe}, author={Ileana Blad{\'e} and Didac Fortuny and Geert Jan van Oldenborgh and Brant Liebmann}, year={2012} }