Suchergebnisse

Die Klimaforschung steht als politisch relevante Wissenschaft unter dem Druck, schnell Resultate zu liefern. - Und wo diese Resultate kontrovers sind, entsteht in der Öffentlichkeit rasch der Eindruck mangelnder Glaubwürdigkeit. Dieses Glaubwürdigkeitsproblem wurzelt einerseits in klassischen erkenntnis- und wissenschaftstheoretischen Schwierigkeiten wie Induktionsproblem, Unterbestimmtheitsthese und Theoriebeladenheit, andererseits in einer fehlgeleiteten Vorstellung von wertfreier Wissenschaft. Anna Leuschner zeigt: Nur wissenschaftlicher Pluralismus und intellektuelle Verantwortung der Wiss

Ponencia presentada en: VIII Congreso de la Asociación Española de Climatología celebrado en Salamanca entre el 25 y el 28 de septiembre de 2012. ; Climate Envelope Models (CEMs) are predictive tools widely used in ecological research to estimate the distribution of species by combining observations of their occurrence/abundance with bioclimatic indicators. In this contribution, we show that the resulting projections are highly sensitive to the quality of the baseline climate data, an aspect often overlooked in model criticism. Using distributional data of European beech in northern Spain (Cantabria region), we analyse the discrepancies in model performance and future projections using three public high-resolution climate datasets: WorldClim (WC), the University of Barcelona Atlas (UAB) and a new regional climate grid developed by Cantabria University (UC). We considered the future climate scenarios from several regional climate models (RCMs) of the EU-funded project ENSEMBLES. We demonstrate that the quality of the baseline climate used to derive the present and future bioclimatic indices has a great impact on the stability of the estimated CEMs, although commonly used performance metrics (AUC, Cohen's kappa) failed to detect this in the cross-validation experiments. WC models lead to unreliable future projections, whereas UAB models performed better but were outperformed by UC, demonstrating the paramount importance of reliable climate input data. ; This research has received funding from the European Union's Seventh Framework Programme (FP7/2007- 2013) under grant agreements 243888 (FUME Project) and 265192 (CLIMRUN Project) and from the CICYT project CGL2010-21869.

Abstract. Stochastic rainfall downscaling methods usually do not take into
account orographic effects or local precipitation features at spatial scales
finer than those resolved by the large-scale input field. For this reason
they may be less reliable in areas with complex topography or with sub-grid
surface heterogeneities. Here we test a simple method to introduce realistic
fine-scale precipitation patterns into the downscaled fields, with the
objective of producing downscaled data more suitable for climatological and
hydrological applications as well as for extreme event studies. The proposed
method relies on the availability of a reference fine-scale precipitation
climatology from which corrective weights for the downscaled fields are
derived. We demonstrate the method by applying it to the Rainfall Filtered
Autoregressive Model (RainFARM) stochastic rainfall downscaling algorithm. The modified RainFARM method is tested focusing on an area of complex
topography encompassing the Swiss Alps, first, in a "perfect-model
experiment" in which high-resolution (4 km) simulations performed with the
Weather Research and Forecasting (WRF) regional model are aggregated to a
coarser resolution (64 km) and then downscaled back to 4 km and compared with
the original data. Second, the modified RainFARM is applied to the E-OBS
gridded precipitation data (0.25∘ spatial resolution) over Switzerland,
where high-quality gridded precipitation climatologies and accurate in situ
observations are available for comparison with the downscaled data for the
period 1981–2010. The results of the perfect-model experiment confirm a clear improvement
in the description of the precipitation distribution when the RainFARM
stochastic downscaling is applied, either with or without the implemented
orographic adjustment. When we separately analyze grid points with
precipitation climatology higher or lower than the median calculated over the
neighboring grid points, we find that the probability density function (PDF)
of the real precipitation is better reproduced using the modified RainFARM
rather than the standard RainFARM method. In fact, the modified method
successfully assigns more precipitation to areas where precipitation is on
average more abundant according to a reference long-term climatology. The results of the E-OBS downscaling show that the modified RainFARM
introduces improvements in the representation of precipitation amplitudes.
While for low-precipitation areas the downscaled and the observed PDFs are in
good agreement, for high-precipitation areas residual differences persist,
mainly related to known E-OBS deficiencies in properly representing the
correct range of precipitation values in the Alpine region. The downscaling
method discussed is not intended to correct the bias which may be present in
the coarse-scale data, so possible biases should be adjusted before applying
the downscaling procedure.

A record spanning ten years of non-methane hydrocarbon (NMHC) data from the Pico Mountain Observatory (PMO), Pico Island, Azores, Portugal, was analyzed for seasonal NMHC behavior, atmospheric processing, and trends, focusing on ethane and propane. The location of this site in the central North Atlantic, at an elevation of 2225 m asl, allows these data to be used to investigate the background conditions and pollution transport events occurring in the lower free North Atlantic troposphere. The quantity ln([propane]/[ethane]) was used as an indicator of both photochemical processing and a marker for the occurrence of pollution transport events detected at the station. The Pico data were compared with three other continuous NMHC data sets from sites bordering the North Atlantic, i.e. the Global Atmospheric Watch (GAW) stations at Summit, Greenland, Hohenpeisssenberg, Germany, and Cape Verde, using ln([propane]/[ethane]) results as an indicator for the degree of photochemical processing ('aging') seen in the data. Comparisons of these three data sets showed some significant differences in the seasonal background and range of observed values. The statistical distribution of binned monthly data was determined, and individual sample events were then scaled to the monthly median observed value. Back trajectories, determined by the HYSPLIT model were used to investigate the geographic origin of the observed trace gases as a function of the degree of photochemical processing. Results show that PMO samples have been subjected to a diversity of air transport and aging, from highly processed air to freshly emitted air throughout the year, and in particular during summer months. The predominant air transport is from North America, with only occasional influence from continental areas located east and southeast (Europe and Africa). The available record was found to be too variable and still too short to allow deciphering NMHC trends from the data. Ethane and propane measurements at the PMO were compared with the MOZART-4 atmospheric chemistry and transport model at the appropriate time and location. The model was found to yield good agreement in the description of the lower range of atmospheric mole fractions observed, of the seasonal cycle, and the regional oxidation chemistry. However, ethane and propane enhancements in transport events were underestimated, indicating that after the ≥ 3 days of synoptic transport to PMO the spatial extent of plumes frequently is smaller than the 2.8° × 2.8° (∼300 km) model grid resolution. ; FUNDING INFORMATION : The PMO research has been supported by US National Science Foundation Awards #AGS-1011968, #AGS-1109568, #AGS-1110059, the NOAA Climate and Global Change Program grant NA03OAR4310072, and the U.S. Department of Energy Atmospheric Systems Research program, grant #DE-SC0006941. Funding was also received from the UK National Environment Research Council, grant number NE/F017391/1 and from PIP's Philip Leverhulme Prize. The Regional Government of Azores has supported the Pico Mountain Observatory and operation through the Regional Secretary for Science, Technology and Infrastructures, and the Secretary for the Environment and the Sea. The NMHC monitoring at Summit was supported through the NASA ROSES program, grant number NNX07AR26G. NCAR is operated by the University Corporation of Atmospheric Research under sponsorship of the National Science Foundation. ; info:eu-repo/semantics/publishedVersion

A reliable assessment of the optical properties of atmospheric black carbon is of crucial importance for an accurate estimation of radiative forcing. In this study we investigated the spatio-temporal variability of the mass absorption cross-section (MAC) of atmospheric black carbon, defined as light absorption coefficient (σap) divided by elemental carbon mass concentration (mEC). σap and mEC have been monitored at supersites of the ACTRIS network for a minimum period of one year. The 9 rural background sites considered in this study cover southern Scandinavia, central Europe and the Mediterranean. σap was determined using filter based absorption photometers and mEC using a thermal-optical technique. Homogeneity of the data-set was ensured by harmonization of all involved methods and instruments during extensive intercomparison exercises at the European Center for Aerosol Calibration (ECAC). Annual mean values of σap at a wavelength of 637 nm vary between 0.66 and 1.3 Mm−1 in southern Scandinavia, 3.7–11 Mm−1 in Central Europe and the British Isles, and 2.3–2.8 Mm−1 in the Mediterranean. Annual mean values of mEC vary between 0.084 and 0.23 μg m−3 in southern Scandinavia, 0.28–1.1 in Central Europe and the British Isles, and 0.22–0.26 in the Mediterranean. Both σap and mEC in southern Scandinavia and Central Europe have a distinct seasonality with maxima during the cold season and minima during summer, whereas at the Mediterranean sites an opposite trend was observed. Annual mean MAC values were quite similar across all sites and the seasonal variability was small at most sites. Consequently, a MAC value of 10.0 m2 g−1 (geometric standard deviation = 1.33) at a wavelength of 637 nm can be considered to be representative of the mixed boundary layer at European background sites, where BC is expected to be internally mixed to a large extent. The observed spatial variability is rather small compared to the variability of values in previous literature, indicating that the harmonization efforts resulted in substantially increased precision of the reported MAC. However, absolute uncertainties of the reported MAC values remain as high as ± 30–70% due to the lack of appropriate reference methods and calibration materials. The mass ratio between elemental carbon and non-light-absorbing matter was used as a proxy for the thickness of coatings around the BC cores, in order to assess the influence of the mixing state on the MAC of BC. Indeed, the MAC was found to increase with increasing values of the coating thickness proxy. This provides evidence that coatings do increase the MAC of atmospheric BC to some extent, which is commonly referred to as lensing effect. © 2016 The Authors ; The research leading to these results has received funding from the European Union Seventh Framework Programme (ACTRIS, FP7/2007-2013, grant agreement no. 262254 ). ACTRIS-2 is a European Project supported by the European Commission Horizon 2020 Research and Innovation Framework Programme (ACTRIS-2, H2020-INFRAIA-2014-2015, grant agreement no. 654109 ). This work was also supported by grants from Labex OSUG@2020 (PhD investissements d'avenir – ANR10 LABX56) and from the European Research Council (ERC-CoG 615922-BLACARAT). Appendix A. ; Peer reviewed

This reader is an Open Educational Resource, meant to accompany a graduate or higher-level undergraduate university course in climate change resilience, adaptation, and/or planning. While the material is geared toward students in urban and regional planning, it may also be of interest to students of urban studies, public health, geography, political science, sociology, risk management, and others. Each section of this volume includes (1) an introductory summary, (2) a reading list with full text articles, (3) student exercises meant to enhance understanding and facilitate in-class discussion, and (4) additional discussion prompts or activities for instructors to use in class. The format of materials is intended to convey key concepts, while leaving ample space for student exploration, discourse, and creativity. Lessons may culminate in an applied, imaginative final project, a sample framework of which is provided at the end of Section VI. Print on Demand Standard print copy (paperback) Adopt/Adapt If you are an instructor adopting or adapting this PDXOpen textbook, please help us understand your use by filling out this form The student exercises and reading lists for each section are available as Microsoft Word files. Instructors who wish to make changes to these can do so in Word, and insert their own versions of these pages into the PDF. ; https://pdxscholar.library.pdx.edu/pdxopen/1030/thumbnail.jpg

Historical Climatology draws from climatology and (environmental) history. lt aims at reconstructing climate and natural disasters for the period preceding the creation of meteorological networks. Moreover it investigates the impact of climate extremes on societies and it points out to past debates on social representations of climate. Up to 1989 no coherent methodology was available. Since then cooperation emerged in the framework of EU research projects. As a result common approaches and standards were developed. The article discusses the evidence and explains how long time series of monthly and seasonal temperature and precipitation indices were obtained from the data. Validation has revealed that such series are good substitutes for instrumental measurements. Recently climatologists have included this data into statistical models to construct charts of monthly surface pressure, temperature and precipitation in Europe back to 1659. Less efforts were made to investigate the effects of climatic variations and extremes on societies. lt is still not known how past societies perceived climatic extremes and natural disasters and how they adapted to them. Undoubtedly climate affected the use and availability of energy resources (food, fodder, fire-wood) and the outbreak of climate sensitive epidemics. Which climatic constellations mattered, needs to be assessed within the specific context. In any case the vulnerability of the society needs to be taken into account. The mental, legal and political setting affected the search for scapegoats in periods of crises. lt is demonstrated that extended witch-hunts took place in the late sixteenth century because a part of society held the witches directly responsible for the high frequency of climatic anomalies during this period. ; Historical Climatology draws from climatology and (environmental) history. lt aims at reconstructing climate and natural disasters for the period preceding the creation of meteorological networks. Moreover it investigates the impact of climate extremes on societies and it points out to past debates on social representations of climate. Up to 1989 no coherent methodology was available. Since then cooperation emerged in the framework of EU research projects. As a result common approaches and standards were developed. The article discusses the evidence and explains how long time series of monthly and seasonal temperature and precipitation indices were obtained from the data. Validation has revealed that such series are good substitutes for instrumental measurements. Recently climatologists have included this data into statistical models to construct charts of monthly surface pressure, temperature and precipitation in Europe back to 1659. Less efforts were made to investigate the effects of climatic variations and extremes on societies. lt is still not known how past societies perceived climatic extremes and natural disasters and how they adapted to them. Undoubtedly climate affected the use and availability of energy resources (food, fodder, fire-wood) and the outbreak of climate sensitive epidemics. Which climatic constellations mattered, needs to be assessed within the specific context. In any case the vulnerability of the society needs to be taken into account. The mental, legal and political setting affected the search for scapegoats in periods of crises. lt is demonstrated that extended witch-hunts took place in the late sixteenth century because a part of society held the witches directly responsible for the high frequency of climatic anomalies during this period.

Recent government in e-Infrastructure will transform aspects of environmental science by supporting both fundamental science and innovative uses of environmental data by the commmercial sector. The STFC Centre for Environmental Data Archival (CEDA) is heavily involved in two major projects: JASMIN - a NERC funded facility which will support both data archival and scientific data analysis, and CEMS - the Facility for Climate and Environmental Monitoring from Space - aimed at fostering knowledge exchange and commercial exploitation of environmental data. JASMIN and CEMS will share some hardware. In this presentation, we concentrate on JASMIN, which will consist of multi-Petabyte fast reliable storage and co-located data analysis compute at the STFC Rutherford Appleton Laboratory, with satellite installations at Reading, Leeds and Bristol Universities. JASMIN is a response to the growing use of direct numerical simulation in the environmental sciences resulting in much higher demand for high performance computing. This growth in HPC is accompanied by a transition in its nature, with data intensive HPC becoming an ever increasing part of the mix. (For example, at the time of writing CEDA is currently evaluating the requirements in terms of storage and co-located analysis compute for three grants each of which is expected to produce in excess of 0.5 PB of data over the next three years - this on top of known data acquisition already measured in PB. Clearly every grant round could bring similar requirements.) Such data intensive HPC is being carried out on on many different supercomputers, so it is no longer satisfactory to assume that putting storage alongside the HPC will solve the analysis problem (since such a solution, alone, could result in an NxN data transfer problem for data comparison between results on N supercomputers). Inevitably one needs to reduce the data transfer problem down to as close to Nx1 as possible - hence JASMIN - a facility configured for data storage AND analysis. For analysis, JASMIN will deploy a "private cloud" to allow the community to develop their own analysis environment using their favourite operating system configuration. JASMIN will also be used, along with a large tape facilities provided by STFC, to provide persistent storage for the archival and curation functions which CEDA also provides. These storage and computing advances will be supported by high-bandwidth network connectivity between key collaborating institutions (particularly supercomputing sites), both within the UK and in the Europe, and new light paths have been established alongside the JASMIN activity. JASMIN: Joint Analysis System Meeting e-Infrastructure Needs

Recent government in e-Infrastructure will transform aspects of environmental science by supporting both fundamental science and innovative uses of environmental data by the commmercial sector. The STFC Centre for Environmental Data Archival (CEDA) is heavily involved in two major projects: JASMIN - a NERC funded facility which will support both data archival and scientific data analysis, and CEMS - the Facility for Climate and Environmental Monitoring from Space - aimed at fostering knowledge exchange and commercial exploitation of environmental data. JASMIN and CEMS will share some hardware. In this presentation, we concentrate on JASMIN, which will consist of multi-Petabyte fast reliable storage and co-located data analysis compute at the STFC Rutherford Appleton Laboratory, with satellite installations at Reading, Leeds and Bristol Universities. JASMIN is a response to the growing use of direct numerical simulation in the environmental sciences resulting in much higher demand for high performance computing. This growth in HPC is accompanied by a transition in its nature, with data intensive HPC becoming an ever increasing part of the mix. (For example, at the time of writing CEDA is currently evaluating the requirements in terms of storage and co-located analysis compute for three grants each of which is expected to produce in excess of 0.5 PB of data over the next three years - this on top of known data acquisition already measured in PB. Clearly every grant round could bring similar requirements.) Such data intensive HPC is being carried out on on many different supercomputers, so it is no longer satisfactory to assume that putting storage alongside the HPC will solve the analysis problem (since such a solution, alone, could result in an NxN data transfer problem for data comparison between results on N supercomputers). Inevitably one needs to reduce the data transfer problem down to as close to Nx1 as possible - hence JASMIN - a facility configured for data storage AND analysis. For analysis, JASMIN will deploy a "private cloud" to allow the community to develop their own analysis environment using their favourite operating system configuration. JASMIN will also be used, along with a large tape facilities provided by STFC, to provide persistent storage for the archival and curation functions which CEDA also provides. These storage and computing advances will be supported by high-bandwidth network connectivity between key collaborating institutions (particularly supercomputing sites), both within the UK and in the Europe, and new light paths have been established alongside the JASMIN activity. JASMIN: Joint Analysis System Meeting e-Infrastructure Needs

This article presents a climatology of total cloud cover (TCC) in the area of the three inland Eurasian seas (Black, Caspian, and Aral Sea). Analyses are performed on the basis of 20 years of data (1991 2010), collected from almost 200 ground stations. Average TCC is 49%, with broad spatial and seasonal variability: minimum TCC values are found in summer and to the southeast, whereas maximum values correspond to winter and to the northwest. For the whole area, linear trend analyses show that TCC did not vary during the study period. We only detected a statistically significant positive trend (+1.2% decade−1) in autumn. We obtained different results for the regions delimited by means of a principal component analysis: a clear decrease, both for the annual, spring, and summer series, was detected for the south of Black Sea, while increasing TCC was found for the annual, autumn, and winter series in the north Caucasus and the west and north of Black Sea. We also analysed the TCC data from global gridded products, including satellite projects [International Satellite Cloud Climatology Project (ISCCP), Pathfinder Atmospheres Extended (PATMOS-x), cLoud, Albedo & Radiation (CLARA)], reanalyses [ERA-interim, National Centers for Environmental Prediction/Department of Energy (NCEP/DOE), Modern-Era Retrospective Analysis for Research and Applications (MERRA)], and surface observations [Climatic Research Unit (CRU)]. Although all these products capture the seasonal evolution over the study area, they differ substantially both among them and in relation to the ground observations: reanalyses produce much lower values of TCC, while ISCCP and CLARA provide a summer minimum that is too high. Trend analyses applied to these data generally showed a decrease in TCC; only CRU and NCEP/DOE tally with the ground data as regards the absence of overall trends. These results are discussed in relation to previous studies presenting trends of other variables such as sunshine duration, diurnal temperature range, or precipitation; we also discuss the connections with changes in synoptic patterns and environmental changes, in particular in the Aral Sea region ; This research was developed under the auspices of, and with funding from, the project 'CLIMSEAS: Climate Change and Inland Seas: Phenomena, Feedbacks, and Uncertainties. The Physical Science Basis', of the Seventh Framework Programme, European Union People-Marie Curie Actions, International Research Staff Exchange Scheme (FP7-PEOPLE-2009-IRSES N. 247512). Several authors are involved within the project NUCLIERSOL (CGL2010-18546), funded by the Spanish Ministry of Economy and Competitiveness. Aarón Enriquez-Alonso was given a grant from the FPI program (BES-2011-049095) of the same ministry. Arturo Sanchez-Lorenzo was supported by the 'Secretaria per a Universitats i Recerca del Departament d'Economia i Coneixement, de la Generalitat de Catalunya i del programa Cofund de les Accions Marie Curie del 7è Programa marc d'R+D de la Unió Europea' (2011 BP-B 00078) and the postdoctoral fellowship JCI-2012-12508. Partial support was provided to the Hydrometeorological Center of Russia by Russian Foundation for Basic Research (№13-05-00562). The ISCCP-D2 data were obtained from the International Satellite Cloud Climatology Project web site (http://isccp.giss.nasa.gov), maintained by the NASA Goddard Institute for Space Studies, New York. Data from EUMETSAT's Satellite Application Facility on Climate Monitoring (CM SAF) were used. PATMOS-x data are available via ftp from the University of Wisconsin, Space Science and Engineering Center (SSEC), and the Cooperative Institute for Meteorological Satellite Studies (CIMSS). ERA-Interim data are supported by the European Center for Medium-range Weather Forecast (ECMWF). NCEP Reanalysis data are provided by the National Oceanic and Atmospheric Administration (NOAA), Oceanic and Atmospheric Research (OAR). Earth System Research Laboratory (ESRL), Physical Sciences Division (PSD) (http://www.esrl.noaa.gov/psd/). MERRA files were obtained from the NASA Goddard Earth Sciences Data and Information Services Center. CRU TS3.20 Time-Series (TS) of High Resolution Gridded Data were provided by the University of East Anglia Climatic Research Unit (CRU)