VRE for Northern Eurasia climatic studies Evgeny GORDOV SCERT/IMCES - - PowerPoint PPT Presentation

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VRE for Northern Eurasia climatic studies

Evgeny GORDOV SCERT/IMCES SB RAS and Tomsk State University

gordov@scert.ru

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OUTLINES

The state of the art:

• Big data
• Virtual research environment
• Integrated regional studies: SIRS/NEESPI/PEEX

Platform CLIMATE

• Functionality
• Some results of climatic extremes analysis
• Education

Plans/Perspectives

Data, information and knowledge (Richard Kenway) virtual data

– from a database somewhere – computed (on request) – measured (on request) – data: un-interpreted bits and bytes – information: data equipped with meaning – knowledge: information applied to solve a problem

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AGU FM 2013

• B. Lawrence and M. Juckes From petascale to

exascale, the future of simulated climate data Coleridge ought to have said: data, data, everywhere, and all the data centres groan, data data everywhere, nor any I should clone. Except of course, he didn't say it, and we do clone data!

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While we've been dealing with terabytes of simulated datasets, downloading ("cloning") and analysing, has been a plausible way forward. In the not too distant future we can imagine exabytes of data being produced, and all these problems will get worse. Arguably we have no plausible methods of effectively exploiting such data - particularly if the analysis requires intercomparison. Yet of course, we know full well that intercomparison is at the heart of climate science.

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In the past 10 years, NCDC’s digital archive experienced a six-fold increase from 1 petabyte to 6 petabytes. With increasing sophistication

• f data collection

equipment, such as new satellites and radars, data is expected to exceed 15 petabytes by 2020.

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Problems/Challanges

Data volumes are growing: Observations – up to 1 Tb/day CFSR NCEP – more then 66 Tb; ECMWF current status:

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Experience with managing a multi-petabyte meteorological archive Manuel Fuentes and Baudouin Raoult Meteorological Data Section ECMWF

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Virtual research environment

Increasingly global, multipolar and networked science is calling for research supporting environments where scattered scientists can seamlessly access data, software, and processing resources managed by diverse systems in separate administration domains through their web browser.

Virtual Research Environments/ Science Gateways (Portals)/ Collaboratories/ Digital Libraries/Inhabited Information Spaces

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Web-based services should be loosely combined into portals to provide a comprehensive infrastructure for the support of research across all academic disciplines. “VRE” portals should also leverage Web 2.0 technologies and social networking solutions to support collaboration and resource discovery.

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RIMS Rapid Integrated Mapping System

Гидрология метеорология: данные, модели, анализ

http://rims.unh.edu/

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Integrated Regional Studies in Northern Eurasia NEESPI Siberia Integrated Regional Studies

http://sirs.scert.ru/, http://iopscience.iop.org/1748-9326/5/1/015007/ PEEX MAIRS Major drivers and domain of applications

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Climate http://climate.scert.ru/

Software and hardware Web GIS platform for monitoring and analysis

• f regional climatic and

ecological changes

Climate| Solid knowledge

Ongoing climate changes have a significant impact on regional environment and socio-economic conditions. In some regions these changes go on much faster than at the global scale affecting the vegetation and permafrost, threatening northern infrastructure. They change the living conditions of the population, influence the spatial distribution of plant ecosystems and can cause serious socio-economic

• consequences. Under these conditions, the analysis of spatial data
• btained from modeling and observations is paramount for timely

decision-making at the municipal and federal levels. Software and hardware platform Climate is based on advanced technologies for development of modern web GIS applications. It allows to process and analyze significant amounts of spatial data to extract and present solid knowledge about ongoing regional climate changes and their socio-economical consequences using only a modern graphical web browser.

Architecture

One approach to all data kinds

Handling different data file formats

Platform Climate has built-in procedures to search and retrieve information from data sets, as well as providing the user with results in such popular formats as netCDF, HDF, ESRI Shapefile and GeoTIFF. Also access to the processing results using geo services is available according to the OGC standards.

GIS in a web browser window

Thanks to joint usage of Web and GIS technologies, the platform Climate includes tools for representation and manipulation of vector and raster data through a web interface typical for a desktop GIS, including adding, deleting, and overlapping of geolocated layers, zoom and pan.

Easy to handle large data sets

Volumes of modern data sets of climatic and meteorological data are tens and hundreds gigabytes. In

• rder to effectively operate them powerful computational

resources are required. Platform Climate includes high performance cloud storage and data processing systems, which power is available to users with ordinary personal computers.

Heterogeneous data integration

Platform Climate integrates in one graphical environment scientific tools for processing and visualization of heterogeneous data obtained from various sources, including data modeling, satellite imagery, observations at meteorological stations, boundaries of administrative areas and more in raster and vector formats.

Methods of processing and analysis

• f climatic and meteorological data

Statistical characteristics of meteorological data

Sample mean, variance, kurtosis, median, maximum and minimum value, the asymmetry

Derivative climatic indices

Length of the growing season, sum of effective temperatures, Selyaninov’s Hydrothermal Coefficient

Periodic variations

Mean square deviation, norms, deviation from norms, amplitude of diurnal and annual cycle

Non-periodic variations

Duration and frequency of occurrences of atmospheric phenomena with parameters above or below specified limits at different time scales

Spatial data with processing and rendering tools are accessible via a standard web browser from anywhere in the world

Графический интерфейс пользователя

Функциональность системы

Функциональность ЭО Платформы

Mean temperature (Merra,850mb, June 1990 – raster and NCEP2 – contour)

Функциональность ЭО Платформы

Integrated models

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Results presentations

Surface temperature, January means

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Results presentations

Wind velocity, January

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Google Maps and Landsat layers

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Data archives

Название набора данных Организация Временной период Разрешение данных NCEP/NCAR Reanalysis NCEP/NCAR 1951 – 2001 2.5°×2.5° 17 в. ур. давления NCEP/DOE AMIP II Reanalysis NCEP/DOE 1979 – 2003 2.5°×2.5° 17 в. ур. давления ERA-40 Reanalysis ECMWF 1957 – 2004 2.5°×2.5° 23 в. ур. давления JRA-25 Reanalysis JMA/CRIEPI 1979 – 2009 2.5°×2.5°; 23 в. ур. давления NOAA-CIRES 20th Century Global Reanalysis NOAA/OAR/ESRL PSD 1908 – 1958 2.0°×2.0°; 24 в. ур. давления APHRODITE Reanalysis RIHN-MRI/JMA 1951 - 2007 0.25°×0.25°; поверхность Merra Reanalysis ECMWF 1979 - 2000 0.67°×0.5°; 42 ур. давл. GPCC Reanalysis GPCC 1901 - 2009 0.5°×0.5°; INM CM4 dataset INM RAS 1950 - 2005 2.0°×1.5°; 8 ур. PlaSim dataset IMCES SB RAS 2000 - 2100 2.5°×2.5° 9092с Synoptic Network RIHMI-WDC/ NOAA CNDC ~ 1900 – 2000 83 метеостанций Сибири

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Модули вычислительного ядра

№ Тип обработки, которую предоставляет соответствующий модуль 1 Вычисление среднего значения метеовеличины по времени 2 Вычисление среднеквадратического отклонения метеовеличины 3 Вычисление коэффициента асимметрии 4 Вычисление коэффициента эксцесса 5 Вычисление абсолютного максимума метеовеличины 6 Вычисление абсолютного минимума метеовеличины 7 Вычисление размаха значений метеовеличины 8 Определение числа морозных дней 9 Определения числа теплых дней 10 Определения числа дней с заморозками 11 Определение числа тропических ночей 12 Определение продолжительности вегетационного периода 13 Вычисление суммы эффективных температур 14 Расчет индекса интенсивности осадков 15 Расчет числа дней с осадками выше заданных границ 16 Определения максимальной продолжительности периода без осадков 17 Определение максимальной продолжительности периода с осадками 18 Расчет гидротермического коэффициента Селянинова 19 Расчет значений линейного тренда 20 Расчет значений коэффициента корреляции 42

Observed climate extreme indices in Siberia T.M. Shulgina (stm@scert.ru), E.P. Gordov

To improve our understanding of observed climate extremes in Siberia and to provide to regional decision makers the reliable scientifically based information on climate state, we need to get accurate knowledge of last decade changes in the key surface atmospheric parameters over this area. This study covers the problem of accuracy of primary data, namely climate model

• utputs and gridded station observations, and corresponding

climatic characteristics used for studying changes in temperature and precipitation extremes. Here, past changes (1979 - 2010) in the extremes are estimated by linear trends of percentile-based threshold indices (Klein Tank et al., 2009) obtained from ECMWF ERA Interim and APHRODITE JMA data. All calculations are realized using information-computational web- GIS system “Climate” (http://climate.scert.ru/) developed to support collaborative multidisciplinary investigations of regional climatic changes and their impacts. ¡

Soft-ware support: All calculations have

been realized using information-computational web- GIS system “Climate” (http://climate.scert.ru/) developed to support collaborative multidisciplinary investigations of regional climatic changes and their impacts (Gordov et al, 2012). The archive of results is available for applications in the system.

Methodology:

In the context of this study the term ‘extreme’ is defined as a value in the tail of the variable’s probability distribution: Cold/Warm nights (TN10p/TN90p) Cold/ Warm days (TX10p/TX90p) The percentile thresholds have been calculated for the climatological base period 1961 – 1990.

What do we have today?

• 231 stations across Siberia: 139 of them provide

homogeneous time series of daily minimum temperature (TN) and 103 – daily maximum temperature (TX).

• To study the accuracy of modeled and gridded

temperature and precipitation daily extremes in relation to observed one;

• To analyze trends of temperature and precipitation

extremes in Siberia over the last decades, 1979-2010 What is this study for?

• To understand which data resources can be used for

reliable assessing of climate extremes in Siberia;

• To provide basis for decision making on adaptation and

mitigation measures to climate change. ¡

Results:

Reanalysis & gridded data vs. observations Figure 1: Mean bias of daily temperature extreme indices, TN10p (a), TN90p (b), TX10p (c), TX90p (d), calculated with annual output intervals based on ECMWF ERA Interim (left figure) and HadEX2 (right figure) data related to station observations from RIHMI-WDC/CDIAC 600 station network dataset and averaged over the period 1979-2010. Results obtained from GHCNDEX dataset are not presented because of their similarity of HadEX2 data results.

Fig.1.a. Mean bias (1979-2010), TN10p, ERA Interim vs. observations (left), HadEX2 vs. observations (right)

F i g u r e 2 : T a y l o r d i a g r a m d e s c r i b i n g pattern statistics the modeled (ERA Interim), gridded (HadEX2 and G H C N D E X ) a n d

• bserved (RIHMI-WDC/

CDIAC 600 station network dataset) annual climate extreme indices, TN10p (Red), TN90p (Blue), TX10p (Green), T X 9 0 p ( B r o w n ) , averaged over Siberia.

Linear winter (DFJ) and summer (JJA) trend estimates

Decadal dynamics (days / 10 years) is illustrated in colors. The arrows show daily maximum and minimum temperature changes (0C/10 years) for these extreme indices. Temperature extreme trends have been estimates over the period 1979–2012 (percentile thresholds were calculated from ECMWF ERA-40 reanalysis data).

Modeled and gridded data comparison indicates low mean biases (less 5 days per year) from observed data and high correlation with them (roughly 0.9). ERA Interim reanalysis outputs accurately reproduce both general and extreme dynamics of climate change in Siberia. Such ERA Interim data features as high spatial distribution (0,25°×0,25°) and powerful physical background of obtained meteorological fields promote us to select this dataset for our study.

Figure 3: Linear winter (DFJ) and summer (JJA) trend estimates

Decadal dynamics (days / 10 years) is illustrated in colors. The arrows show daily maximum and minimum temperature changes (0C/10 years) for these extreme indices. Temperature extreme trends have been estimates over the period 1979–2012 (percentile thresholds were calculated from ECMWF ERA-40 reanalysis data).

Dynamics of temperature extremes observed over the last decades (1979-2012) shows asymmetric changes according to determined tails of extreme indices distribution. Warming during winter cold nights is stronger than during warm nights, especially over the north of Siberia. Increases in minimum temperatures is more significant than in maximum temperatures. Warming determined at the high latitudes of the region is achieved mostly by winter temperature changes and less due to autumn temperature changes. South area of Siberia has slight cooling during winter (mostly out of cold temperature extremes) and during summer (associated with warm temperature extreme decrease).

Educational process in modern climatology within the web-GIS platform "Climate"

Yulia Gordova , et

• al. (yulia@scert.ru)

Prepara'on ¡of ¡future ¡professionals ¡in ¡Earth ¡system ¡ sciences ¡in ¡our ¡ins'tute: ¡ ¡

• ¡organiza'on ¡of ¡young ¡scien'sts ¡schools ¡(School ¡
• n ¡Computa'onal ¡Informa'on ¡Technologies ¡for ¡

Environmental ¡Sciences ¡CITES ¡every ¡two ¡years) ¡ ¡

• ¡usage ¡of ¡thema'c ¡informa'onal ¡computa'onal ¡

systems ¡(web-­‑GIS ¡based ¡plaDorm ¡“Climate”) ¡

To ¡organize ¡the ¡educa'onal ¡process ¡we ¡use ¡freeware ¡ Moodle ¡(hHp://moodle.com) ¡-­‑ ¡an ¡open-­‑source ¡course ¡ management ¡system. ¡

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Two ¡courses ¡of ¡educa-onal ¡block ¡of ¡the ¡pla5orm ¡ “Climate” ¡: ¡

• 1. ¡“Analysis ¡of ¡regional ¡climate ¡changes”: ¡ ¡
• ¡descrip'on ¡of ¡main ¡sta's'cal ¡methods ¡of ¡processing ¡and ¡

analysis ¡of ¡meteorological ¡data ¡which ¡are ¡used ¡to ¡ characterize ¡the ¡ongoing ¡changes ¡of ¡regional ¡climate. ¡ ¡

• ¡trainings ¡to ¡study ¡a ¡long-­‑term ¡behavior ¡of ¡clima'c ¡

variables ¡in ¡the ¡selected ¡regions. ¡ ¡

• 2. ¡“Analysis ¡of ¡future ¡climate”: ¡ ¡
• ¡analysis ¡of ¡changes ¡of ¡different ¡clima'c ¡characteris'cs ¡

and ¡their ¡correla'ons. ¡

• ¡trainings ¡with ¡results ¡of ¡the ¡model ¡“Planet ¡simulator” ¡

calcula'ons. ¡ ¡

Courses specific

Content

Theory

• Climatic characteristics
• Basic statistical techniques

Data

• Observations and datasets with

regular resolution (Reanalysis) (“Regional climate” course)

• “Planet Simulator” simulation data

for 9 scenarios: - Control scenario ( CO2-constsnt); - SRES (A1B, A2, B1, B2); - RCP (3.0, 4.5, 6.0, 8.5) (“Future climate” course) Practice

• Exercises for studying regional

climate changes

Advantages

• Wide set of characteristics for

meteorology, hydrology and ecosystem dynamics

• Possibility to compare data
• f observation and modeling
• Comparison of climate

responses to different radiative perturbations

• Work with different data and

indicators in one system

“Educational resources” window

INCLUDES: ¡

• ­‑ ¡Educa'onal ¡

courses ¡with ¡labs ¡

• ­‑ ¡Forums ¡
• ­‑ ¡FAQ ¡
• ­‑ Wiki ¡

Window of web-GIS system

Results

A group of Tomsk State University students were subscribed for both educational courses. After reading the theoretical part they have to pass through trainings within the systems. By now this group has passed the first course. Their results are sent to teachers to check their progress and to authors of the courses to find out weak points. Advantages for students

• fresh point of view on familiar disciplines
• acquaintance with modern research and modeling tools and

approaches

• chance to acquire new information and results by

themselves

• opportunity to conduct their own scientific research on the

basis of the available information and theoretical material.

Plans

• Regional and global climate models integration

CM4 INM RAS, RCM4, Arctic ocean ICMMG SB RAS, Polar WRF, JSBACH

• Data archive extension with new data sets (modeling and
• bservations)

Homemade “Northern” Reanalysis for 1960-2014

• “Geospatial Big data” storage and analysis support
• Development of new analytical modules for spatial data

analysis

• Strengthening of educational component with new

courses and labs

Partners

National Research Tomsk State University, Tomsk Institute of numerical mathematics of Russian academy of sciences (INM RAS), Moscow Research computing center of Moscow state university, Moscow Siberian regional hydrometeorological research institute, Novosibirsk Institute of computational technologies of Siberian branch of Russian academy

• f sciences (INM SB RAS), Tomsk

Support

Federal Programs of the Ministry of Education and Science (State contract N 07.514.11.4044 and agreement 8345 . Russian Foundation for Basic Research. Grants N 10-07-00547a, N 11-05-01190a, N 13-05-12034, and N 14-05-00502. Asia-Pacific Network for Global Change Research (APN). International project «Human Impact on Land-cover Changes in the Heart of Asia». Grants ARCP2008-14NMY and ARCP2009-02CMY. International project «Capacity Building to Study and Address Climate Change Induced Extremes in Northern Asia». Grant CBA2012-16NSY.

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Thank you for attention!

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Publications

• A. Titov, E. Gordov, I. Okladnikov, T. Shulgina. Web-system for processing and visualization of meteorological data

for Siberian environment research. // International Journal of Digital Earth, Vol.2, Issue S1 April 2009. Pp. 105 – 119. Gordov E.P., Fazliev A.Z., Lykosov V.N., Okladnikov I.G, Titov A.G. Development of web based information- computational infrastructure for Siberia Integrated Regional Study / Environmental Change in Siberia // Earth Observation, Field Studies and Modelling, Series: Advances in Global Change Research, Vol. 40, ISBN: 978-90-481-8640-2, Balzter, Heiko (Ed.) 1st Edition., 2010. Pp. 233 – 252. Evgeny Gordov, et. al. Development of Information-Computational Infrastructure for Environmental research in Siberia as a baseline component of the Northern Eurasia Earth Science Partnership Initiative (NEESPI) Studies / Regional Environmental Changes in Siberia and Their Global Consequences // Series: Springer Environmental Science and Engineering. Ed.: Groisman, Pavel Ya., Gutman, Garik. Vol. XII, 2012. Pp. 19 – 55. Gordova Yu.E., Genina E.Yu., Gorbatenko V.P., Gordov E.P., Kuzhevskaya I.V., Martynova Yu.V., Okladnikov I.G., Titov A.G., Shulgina T.M., Barashkova N.K. Support of educational process in the field of modern climatology based on web GIS platform "Climate" // Open and distance education. Tomsk, 2013. N1(49). Pp. 14 – 19. I.G. Okladnikov, A.G. Titov, T.M. Shulgina, E.P. Gordov, V.Yu. Bogomolov, Yu.V. Martynova, S.P. Sushchenko, A.V.

• Skvortsov. Software complex for analysis and visualization of data of climate change monitoring and

forecasting // Numerical methods and programming, 2013. V. 14. Pp. 123 – 131. A.G. Titov, E.P. Gordov, I.G. Okladnikov. Hardware and software platform "Climate" as the basis for geoportal of local spatial data infrastructure // Vestnik NGU. Series: Information technologies. 2012. V. 10. Issue 4. Pp. 104 – 111. T.M. Shulgina, E.P. Gordov, I.G. Okladnikov, A.G. Titov, E.Yu. Genina, V.P. Gorbatenko, I.V. Kuzhevskaya, A.S.

• Ahmetshina. Software complex for analysis of regional climate change // Vestnik NGU. Series: Information
• technologies. 2013. V. 11. Issue 1. Pp. 124 – 131.

Gordov E.P. Computing and information technologies of monitoring and modeling of climate change and its impacts / E.P. Gordov, V.N. Lykosov, V.N. Krupchatnikov, I.G. Okladnikov, A.G. Titov, T.M. Shulgina – Novosibirsk: Nauka,

• 2013. – 199 с.