Motivation The main goal is to provide an overview of suitable data sources and the basic principles of methods that can be used to study global change The course introductory notes • Special attention will be given to the demonstration and explanation of selected statistical methods and techniques. • Students will conduct their own analyses of the data provided. • Practical part will use either internet resources or the R programming environment • Very basic knowledge of descriptive statistics is assumed • Written test at the end of the whole course • All materials available in IS (Information system - Study materials) The first lecture contents 1. Introductory terms 2. Selected data sources on climate change 3. Climate Explorer (CE) 4. Introductory statistics in CE A traditional view of climatology • Climate = weather statistics • Data in climatology - arithmetic means of meteorological measurements • Methods in climatology - descriptive statistics World MM|) of Kd|l[H-N-Oij:iT Climilll! dllSNifirillmn Hiiin rlimji«_s rrttipiatinii Ttmptranir Contemporary climatology • The climate system - several components. „ • Positive and negative feedbacks between components • External and internal climate drivers (forcings) I • Internal climate variability The climate system is changing h-Hps://www.e-educati0n.psu.edu/eOrth530/C0ntent/i3_p3.htmi https: // www.temperaturerecord.org/ Contemporary climatology Traditional and new sub-disciplines of climatology are given a new dimension in the current global change: Data assimilation, re-analysis o https://epic.awi.de/id/eprint/25075/l/D/\ ir.pdf Atribution analysis o World Weather Attribution - Exploring the contribution of climate change to extreme weather events o https://www.worldweQtherQttribution.org/ Urban climatology o Urban Climate Change Research Network o https://uccrn. ei. Columbia, edu/ „Open climate science" - Climate-lab book o http://www.climate-lab-book.ac.uk/about/ Contemporary climatology Among other things, it is typical for contemporary climatology: • High complexity of the studied phenomena in time and space • Their stochastic nature (some phenomena do not have a clear cause, there is internal climate variability) • It uses its own methodology with a strong statistical basis • It deals with defining uncertainties, it can only give a probabilistic statement about a number of phenomena • Climatology is not an experimental science (it does not have a "laboratory")- At a given time and place, only one realization of the course of the weather is available. • Numerical models play the role of "laboratory" in climatology Data sources in contemporary climatology Meteorological observations and measurements * Stations point measurements * spatial fields (remote sensing, interpolation) * Meteorological variables, e.g. eir temperature * climatological characteristics, e.g. number of tropical * climate indices, e.g. ENSO Index Model outputs (global, regional, local) Reana ly ses Paleoclimatology proxy reconstructions I days - - 5s Data sources in climatology • In most cases, data sources have a multidimensional character • This requires the use of special data formats (NetCDF) a PANOPLY http://www.giss.naSQ.gov/tools/panoply/. Climate Explorer https: //climexp. knmi. nl/ TU y, Climate Explorer " interface to access large amounts of data - a tool for climatological data analysis • the possibility of analyzing own data files https://climexp.knmi.nl/registerform.cgi Selection of average monthly air Climate Explorer temperatures from Brno, airport S>1 Climate Explorer - analysis example Is there a relationship between the average winter air temperature in Brno and theNAO index? First, verify the normality of the distribution of the temperature series Climate Explorer - analysis example CHI2 test a Q-Q graf Climate Explorer - analysis example Climate Explorer - analysis example Climate Explorer - analysis example What is the spatial representativeness of the Brno temperature series? Climate Explorer - analysis example Reanalyses • Reanalysis is an objective analysis of meteorological data applied backwards to the data and is also referred to as a method of physically consistent ("correct") interpolation. • It links meteorological measurements and observations (unevenly spaced, less frequent in the past) with a numerical prediction model that provides a "physically consistent" state of the atmosphere. • The connection is realized statistically (e.g. similar to LSM) with the use of the assimilation method (joining/linking) of data. • Unlike weather forecasting, in which the forecast model is constantly evolving, reanalysis is performed using a uniform approach - the used assimilation scheme does not evolve (it is so-called "frozen"). This enables the use of reanalyses, for example, in the study of climate change. • Outputs from reanalyses may also contain variables for which measurements are not available for thegiven period. • Current reanalyses cover the entire Earth three-dimensionally (in several layers), usually with a 6-hour step • In terms of time, some are available for the entire 20th century, also developing in paleoc lima to logy Reanalyses • Climate Explorer provides a simple interface allowing access to reanalyses at daily or monthly resolution • It also enables their visualization and basic processing Illustrative video https://www.voutube.com/watch?v=FAGobvUGI24 Further info m https://reanalyses.orq/ https://www.ecmwf.int/en/research/climate-reanalysis Climate Change Atlas TemoeraLire Czech "ep. Jun-Aug AR5 CMIP5 ľ;:g:i 2 0:1 2jSI ľim Temperature Czech Rep. Jun-Aug AR5 CMIP5 subset. On the left, for each scenario one line per model is shown p lus the multi-model mean, on the right percentiles of the whole dataset: the box extends from 25% to 75%, the whiskers from 5% to 95% and the horizontal line denotes the median (50%). Climate Change Atlas n rcp45 precipitation 2071-2100 minus 1981-2010 Jun-Aug AR5CMIP5 subset ^ fz-— Mm, k TTT 1 -0 i 0 0.1 0.2 0.5 1 2 Another data sources NOAA - National Centers for Environmental Information https://www.ncdc.nQQQ.gov/ © NOAA" Paleoclimatology Datasets Di ^ 1 I □1 m .'JL_