468 research outputs found
Construction of a surface air temperature series for Qingdao in China for the period 1899 to 2014
Abstract. We present a homogenized surface air temperature (SAT) time series at 2 m height for the city of Qingdao in China from 1899 to 2014. This series is derived from three data sources: newly digitized and homogenized observations of the German National Meteorological Service from 1899 to 1913, homogenized observation data of the China Meteorological Administration (CMA) from 1961 to 2014 and a gridded dataset of Willmott and Matsuura (2012) in Delaware to fill the gap from 1914 to 1960. Based on this new series, long-term trends are described. The SAT in Qingdao has a significant warming trend of 0.11 ± 0.03 °C decade−1 during 1899–2014. The coldest period occurred during 1909–1918 and the warmest period occurred during 1999–2008. For the seasonal mean SAT, the most significant warming can be found in spring, followed by winter. The homogenized time series of Qingdao is provided and archived by the Deutscher Wetterdienst (DWD) web page under overseas stations of the Deutsche Seewarte (http://www.dwd.de/EN/ourservices/overseas_stations/ueberseedoku/doi_qingdao.html) in ASCII format. Users can also freely obtain a short description of the data at https://doi.org/https://dx.doi.org/10.5676/DWD/Qing_v1 And the data can be downloaded at http://dwd.de/EN/ourservices/overseas_stations/ueberseedoku/data_qingdao.txt
On the Role of Global Warming on the Statistics of Record-Breaking Temperatures
We theoretically study long-term trends in the statistics of record-breaking
daily temperatures and validate these predictions using Monte Carlo simulations
and data from the city of Philadelphia, for which 126 years of daily
temperature data is available. Using extreme statistics, we derive the number
and the magnitude of record temperature events, based on the observed Gaussian
daily temperatures distribution in Philadelphia, as a function of the number of
elapsed years from the start of the data. We further consider the case of
global warming, where the mean temperature systematically increases with time.
We argue that the current warming rate is insufficient to measurably influence
the frequency of record temperature events over the time range of the
observations, a conclusion that is supported by numerical simulations and the
Philadelphia temperature data.Comment: 11 pages, 6 figures, 2-column revtex4 format. For submission to
Journal of Climate. Revised version has some new results and some errors
corrected. Reformatted for Journal of Climate. Second revision has an added
reference. In the third revision one sentence that explains the simulations
is reworded for clarity. New revision 10/3/06 has considerable additions and
new results. Revision on 11/8/06 contains a number of minor corrections and
is the version that will appear in Phys. Rev.
Against politicization of science: Comment on S. Keller: Scientization: putting global climate change on the scientific agenda since 1970 and the role of the IPCC
Modeling long-range memory with stationary Markovian processes
In this paper we give explicit examples of power-law correlated stationary
Markovian processes y(t) where the stationary pdf shows tails which are
gaussian or exponential. These processes are obtained by simply performing a
coordinate transformation of a specific power-law correlated additive process
x(t), already known in the literature, whose pdf shows power-law tails 1/x^a.
We give analytical and numerical evidence that although the new processes (i)
are Markovian and (ii) have gaussian or exponential tails their autocorrelation
function still shows a power-law decay =1/T^b where b grows with a
with a law which is compatible with b=a/2-c, where c is a numerical constant.
When a<2(1+c) the process y(t), although Markovian, is long-range correlated.
Our results help in clarifying that even in the context of Markovian processes
long-range dependencies are not necessarily associated to the occurrence of
extreme events. Moreover, our results can be relevant in the modeling of
complex systems with long memory. In fact, we provide simple processes
associated to Langevin equations thus showing that long-memory effects can be
modeled in the context of continuous time stationary Markovian processes.Comment: 5 figure
Variability in the Slope Water and its relation to the Gulf Stream path
Author Posting. © American Geophysical Union, 2008. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 35 (2008): L03606, doi:10.1029/2007GL032183.Sea Surface Temperature (SST) and Sea Surface Height (SSH) data for 1993–2007 for the North Atlantic are combined with hydrographic data at 69W to investigate the relation between the Gulf Stream (GS) position and the Slope Water properties. SST anomalies north of the GS are correlated with changes in its path. The lag of this correlation is such that positive temperature anomalies precede northward shifts of the GS, and suggests that SST anomalies move westward with speeds of several cm/s. EOF analysis of the SST and SSH fields shows that cooling and strengthening of the SW flow are in phase over the Slope Water, which is mirrored in the vertical structure of these fields at 69W, indicating larger transports in the Deep Western Boundary Current lead to southward shifts of the mean GS path. This relation between the Slope Water and the GS path provides some predictability for the latter
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Exploring the meteorological potential for planning a high performance European Electricity Super-grid: optimal power capacity distribution among countries
The concept of a European Super-grid for electricity presents clear advantages for a reliable and affordable renewable power production (photovoltaics and wind). Based on the mean-variance portfolio optimization analysis, we explore optimal scenarios for the allocation of new renewable capacity at national level in order to provide to energy decision-makers guidance about which regions should be mostly targeted to either maximize total production or reduce its day-to-day variability.
The results show that the existing distribution of renewable generation capacity across Europe is far from optimal: i.e., a 'better' spatial distribution of resources could have been achieved with either a ~31% increase in mean power supply (for the same level of day-to-day variability) or a ~37.5% reduction in day-to-day variability (for the same level of mean productivity). Careful planning of additional increments in renewable capacity at the European level could, however, act to significantly ameliorate this deficiency. The choice of where to deploy resources depends, however, on the objective being pursued – on the one hand, if the goal is to maximize average output, then new capacity is best allocated in the countries with highest resources, whereas investment in additional capacity in a north/south dipole pattern across Europe would act to most reduce daily variations and thus decrease the day-to-day volatility of renewable power supply
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