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Andrea Toreti
,
Franz G. Kuglitsch
,
Elena Xoplaki
,
Jürg Luterbacher
, and
Heinz Wanner

Abstract

Instrumental daily series of temperature are often affected by inhomogeneities. Several methods are available for their correction at monthly and annual scales, whereas few exist for daily data. Here, an improved version of the higher-order moments (HOM) method, the higher-order moments for autocorrelated data (HOMAD), is proposed. HOMAD addresses the main weaknesses of HOM, namely, data autocorrelation and the subjective choice of regression parameters. Simulated series are used for the comparison of both methodologies. The results highlight and reveal that HOMAD outperforms HOM for small samples. Additionally, three daily temperature time series from stations in the eastern Mediterranean are used to show the impact of homogenization procedures on trend estimation and the assessment of extremes. HOMAD provides an improved correction of daily temperature time series and further supports the use of corrected daily temperature time series prior to climate change assessment.

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Andrea Toreti
,
Franz G. Kuglitsch
,
Elena Xoplaki
, and
Jürg Luterbacher

Abstract

Sudden changes caused by nonclimatic factors (inhomogeneities) usually affect instrumental time series of climate variables. To perform robust climate analyses based on observations, a proper identification of such changes is necessary. Here, an approach (named the “GAHMDI” method, after its components and purpose) that is based on a genetic algorithm and hidden Markov models is proposed for detection of inhomogeneities caused by changes in the mean and variance. Simulated series and a case study (winter precipitation from a weather station located in Milan, Italy) are set up to compare GAHMDI with existing methodologies and to highlight its features. For the identification of a single changepoint, GAHMDI performs similarly to other methods (e.g., standard normal homogeneity test). However, for the identification of multiple inhomogeneities and changes in variance, GAHMDI returns better results than three widespread methods by avoiding overdetection. For future applications and research in the homogenization of climate datasets (temperature and precipitation) the use of GAHMDI is encouraged, preferably in combination with another detection procedure (e.g., the method of Caussinus and Mestre) when metadata are not available. Since GAHMDI is developed in the generic context of time series segmentation, it can be applied to series of generic variables—for instance, those related to economics, biology, and informatics.

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Anne Schindler
,
Andrea Toreti
,
Matteo Zampieri
,
Enrico Scoccimarro
,
Silvio Gualdi
,
Sophie Fukutome
,
Elena Xoplaki
, and
Jürg Luterbacher

Abstract

Climate model simulations are currently the main tool to provide information about possible future climates. Apart from scenario uncertainties and model error, internal variability is a major source of uncertainty, complicating predictions of future changes. Here, a suite of statistical tests is proposed to determine the shortest time window necessary to capture the internal precipitation variability in a stationary climate. The length of this shortest window thus expresses internal variability in terms of years. The method is applied globally to daily precipitation in a 200-yr preindustrial climate simulation with the CMCC-CM coupled general circulation model. The two-sample Cramér–von Mises test is used to assess differences in precipitation distribution, the Walker test accounts for multiple testing at grid cell level, and field significance is determined by calculating the Bejamini–Hochberg false-discovery rate. Results for the investigated simulation show that internal variability of daily precipitation is regionally and seasonally dependent and that regions requiring long time windows do not necessarily coincide with areas with large standard deviation. The estimated time scales are longer over sea than over land, in the tropics than in midlatitudes, and in the transitional seasons than in winter and summer. For many land grid cells, 30 seasons suffice to capture the internal variability of daily precipitation. There exist regions, however, where even 50 years do not suffice to sample the internal variability. The results show that diagnosing daily precipitation change at different times based on fixed global snapshots of one climate simulation might not be a robust detection method.

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Naiming Yuan
,
Minghu Ding
,
Yan Huang
,
Zuntao Fu
,
Elena Xoplaki
, and
Juerg Luterbacher

Abstract

In this study, observed temperature records of 12 stations from Antarctica island, coastline, and continental areas are analyzed by means of detrended fluctuation analysis (DFA). After Monte Carlo significance tests, different long-term climate memory (LTM) behaviors are found: temperatures from coastal and island stations are characterized by significant long-term climate memory whereas temperatures over the Antarctic continent behave more like white noise, except for the Byrd station, which is located in the West Antarctica. It is argued that the emergence of LTM may be dominated by the interactions between local weather system and external slow-varying systems (ocean), and therefore the different LTM behaviors between temperatures over the Byrd station and that over other continental stations can be considered as a reflection of the different climatic environments between West and East Antarctica. By calculating the trend significance with the effect of LTM taken into account, and further comparing the results with those obtained from assumptions of autoregressive (AR) process and white noise, it is found that 1) most of the Antarctic stations do not show any significant trends over the past several decades, and 2) more rigorous trend evaluation can be obtained if the effect of LTM is considered. Therefore, it is emphasized that for air temperatures over Antarctica, especially for the Antarctica coastline, island, and the west continental areas, LTM is nonnegligible for trend evaluation.

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Stella Dafka
,
Andrea Toreti
,
Juerg Luterbacher
,
Prodromos Zanis
,
Evangelos Tyrlis
, and
Elena Xoplaki

Abstract

Episodes of extremely strong northerly winds (known as etesians) during boreal summer can cause hazardous conditions over the Aegean Archipelago (Greece) and represent a threat for the safe design, construction, and operation of wind energy turbines. Here, these extremes are characterized by employing a peak-over-threshold approach in the extended summer season (May–September) from 1989 to 2008. Twelve meteorological stations in the Aegean are used, and results are compared with 6-hourly wind speed data from five ERA-Interim–driven regional climate model (RCM) simulations from the European domain of the Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX). The main findings show that, in the range of wind speeds for the maximum power output of the turbine, the most etesian-exposed stations could operate 90% at a hub height of 80 m. The central and northern Aegean are identified as areas prone to wind hazards, where medium- to high-wind (class II or I according to the International Electrotechnical Committee standards) wind turbines could be more suitable. In the central Aegean, turbines with a cutout wind speed > 25 m s−1 are recommended. Overall, RCMs can be considered a valuable tool for investigating wind resources at regional scale. Therefore, this study encourages a broader use of climate models for the assessment of future wind energy potential over the Aegean.

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Henry F. Diaz
,
Ricardo Trigo
,
Malcolm K. Hughes
,
Michael E. Mann
,
Elena Xoplaki
, and
David Barriopedro

Developing accurate reconstructions of past climate regimes and enhancing our understanding of the causal factors that may have contributed to their occurrence is important for a number of reasons; these include improvements in the attribution of climate change to natural and anthropogenic forcing, gaining a better appreciation for the range and magnitude of low-frequency variability and previous climatic regimes in comparison with the modern instrumental period, and developing greater insights into the relationship between human society and climatic changes. This paper examine upto- date evidence regarding the characteristics of the climate in medieval times (A.D. ~950–1400). Long and high-resolution climate proxy records reported in the scientific literature, which form the basis for the climate reconstructions, have greatly expanded in the last few decades, with greater numbers of sites that now cover more areas of the globe. Some comparisons with the modern climate record and discussion of potential mechanisms associated with the patterns of medieval climate are presented here, but our main goal is to provide the reader with some appreciation of the richness of past natural climate variability in terms of its spatial and temporal characteristics.

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Lorine Behr
,
Niklas Luther
,
Simon A. Josey
,
Jürg Luterbacher
,
Sebastian Wagner
, and
Elena Xoplaki

Abstract

Accurate representation of the Atlantic–Mediterranean exchange in climate models is important for a reliable simulation of the circulation in the North Atlantic Ocean. We evaluate the performance of 10 global climate models in representing Mediterranean Overflow Water (MOW) over the recent period 1986–2005 by using various performance metrics. The metrics are based on the representation of the climatological mean state and the spatiotemporal variability of temperature, salinity, and volume transports. On the basis of analyses and observations, we perform a model ranking by calculating absolute, relative, and total relative errors Ej over each performance metric and model. The majority of models simulate at least six metrics well. The equilibrium depth of the MOW, the mean Atlantic–Mediterranean exchange flow, and the dominant pattern of the MOW are represented reasonably well by most of the models. Of those models considered, MPI-ESM-MR, MPI-ESM-LR, CSIRO Mk3.6.0, and MRI-CGCM3 provide the best MOW representation (Ej = 0.14, 0.19, 0.19, and 0.25, respectively). They are thus likely to be the most suitable choices for studies of MOW-dependent processes. However, the models experience salinity, temperature, and transport biases and do not represent temporal variability accurately. The implications of our results for future model analysis of the Mediterranean Sea overflow are discussed.

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Jianping Duan
,
Lun Li
,
Zhuguo Ma
,
Jan Esper
,
Ulf Büntgen
,
Elena Xoplaki
,
Dujuan Zhang
,
Lily Wang
,
Hong Yin
, and
Jürg Luterbacher

Abstract

Large volcanic eruptions may cause abrupt summer cooling over large parts of the globe. However, no comparable imprint has been found on the Tibetan Plateau (TP). Here, we introduce a 400-yr-long temperature-sensitive network of 17 tree-ring maximum latewood density sites from the TP that demonstrates that the effects of tropical eruptions on the TP are generally greater than those of extratropical eruptions. Moreover, we found that large tropical eruptions accompanied by subsequent El Niño events caused less summer cooling than those that occurred without El Niño association. Superposed epoch analysis (SEA) based on 27 events, including 14 tropical eruptions and 13 extratropical eruptions, shows that the summer cooling driven by extratropical eruptions is insignificant on the TP, while significant summer temperature decreases occur subsequent to tropical eruptions. Further analysis of the TP August–September temperature responses reveals a significant postvolcanic cooling only when no El Niño event occurred. However, there is no such cooling for all other situations, that is, tropical eruptions together with a subsequent El Niño event, as well as extratropical eruptions regardless of the occurrence of an El Niño event. The averaged August–September temperature deviation (T dev) following 10 large tropical eruptions without a subsequent El Niño event is up to −0.48° ± 0.19°C (with respect to the preceding 5-yr mean), whereas the temperature deviation following 4 large tropical eruptions with an El Niño association is approximately 0.23° ± 0.16°C. These results indicate a mitigation effect of El Niño events on the TP temperature response to large tropical eruptions. The possible mechanism is that El Niño events can weaken the Indian summer monsoon with a subsequent decrease in rainfall and cooling effect, which may lead to a relatively high temperature on the TP, one of the regions affected by the Indian summer monsoon.

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