Summary

This document analyzes climate variability patterns, including seasonal cycles, long-term trends, and inter-annual fluctuations. Climate indices are used to simplify and interpret complex atmospheric-ocean conditions. The analysis covers various aspects of climate dynamics.

Full Transcript

Random/noise Seasonality Trend Observed • Time series analysis The decomposition of time series is a statistical task that deconstructs a time series into several components, each representing one of the underlying categories of patterns. With this we will be able to see the trend, seasonal, an...

Random/noise Seasonality Trend Observed • Time series analysis The decomposition of time series is a statistical task that deconstructs a time series into several components, each representing one of the underlying categories of patterns. With this we will be able to see the trend, seasonal, and residual components of our data. Identify climate patterns of variability 2 A statistical general tendency/drift of a set of data as related to time It is the general direction of the variable in the observation period, that is, the long-term change of the series mean. • Long-term Trends. • Inter-annual/decadal irregular fluctuations. There are no two summers/winters exactly alike in the same place (some are warmer, stormier, etc). This phenomenon is the main climate variability of the climate system and is mainly due to internal feedbacks of the climate system. It usually refers to time periods ranging from several months to as many as 30 years. Interannual variation in climate is partly responsible for year-to-year variations in many business. For example, interannual variability can cause variation up to 30% of the energy consumption in Scandinavian countries. Interannual to decadal variability can also explain anomalies related to extremes and higher impacts. Variations of any parameter within the calendar year (with the months/seasons) Usually refers to regular and predictable changes (periodic fluctuations) Seasonal fluctuations in time series can be modelled with cyclical patterns. • Seasonality.   CLIMATE VARIABILITY arrow direction? greater wind energy from south direction during summer, local scale AUTUMN (SOC) SPRING (MAM) Mean Wind energy of wind at 10 m height (W m-2) SUMMER (JJA) WINTER (DEF)  Seasonal wind climatology higher wind speed from the north in fall - winter higher wind speed from south-west in spring - summer  Seasonal wind climatologies 8 7 Physical processes of short-term: monthly maximum values • Monthly annual pattern SEASONALITY INTERANUAL VARIABILITY Seasonal regular cyclic variation can be estimated by using a sinusoidal model with one or more sinusoids whose period-lengths may be known (annual cycle, semi-annual cycle,..) • Seasonality adjustment Identify the time seasonal scale of variation. Calculate mean/median values from the historical info. Remove the statistics from the time series • Removing mean seasonal averages A simple way to correct for a seasonal component. E.g. a seasonal component at the level of one month can be removed it on an observation today by subtracting the value from last month  Characterization, modelling • Differencing  Detection •A time series plot will often show seasonality •A within-a-year time series (showing all the daily-monthly-season scale data overlapped) •Multiple (monthly) box plots •An autocorrelation plot (ACF) and spectral frequency analysis plots can help identify seasonal periods. Seasonality is a characteristic of a time series in which the data experiences regular and predictable changes that recur every calendar year. Any predictable fluctuation or pattern that recurs or repeats over a one-year period is said to be seasonal • Estacionalidad steady condition of high significant wave height in southern hemisphere during march-november high significant wave in northern hemisphere during boreal winter  Seasonal wave climatology requires long time series data, N > 30 year? Extreme wave seasonaity Climate indices Define the guide variables to characterise the climate pattern. Obtain the quality database of the guide variables in the necessary time domain. Data processing (anomalies, averages, ...) to obtain the fluctuation information of interest. Standardisation of the data. 1. 2. 3. 4. Calculation of a Climate index: It is challenging to comprehend and describe the atmospheric-ocean motion and climate conditions at each point on Earth, at all altitudes. A climate index helps to simplifies vast amounts of data about current, past, and future atmospheric conditions to improve communication regarding specific circulation phenomena and their impacts A climate index describes the position of the climate cycle within its range at a given moment in time. • are often represented as time series • Show a defined spatial structure. • They can be constructed from any atmosphere-ocean variable (temperature anomalies, monsoon rainfall, pressure gradients,...). • Tools to describe the state of the climate system. (teleconective indices) Teleconnections but they are still irregular “Modes” o “Oscillations” Circulation patterns Teleconnections = the relationship between climate anomalies at two locations at some distance from each other. (Early 20th century) "in the atmosphere, apparently unrelated events occurring in distant regions are connected by different mechanisms". Climate indices (teleconective indices) • Cycles • Irregular fluctuations Mid-term variations definitions - (from NAO (North Atlantic Oscillation) •Monthly •Annual (Winter NAO index) Time scale of the NAO index: The simplest and most common way: The difference in atmospheric pressure between two measuring stations located in the Icelandic low and the Azores high. Multiple possible and accepted measurements, EOFs from models, etc.). • How to calculate it? + NAO: North Atlantic Oscillation AO: pressure difference between Arctic and mid-latitudes. The NAO (discovered before 1950) and the AO (defined after the 1990s) are very closely related. Although they are not the same thing, either is often referred to in explaining climate dynamics in the Northern Hemisphere. The scientific community considers the NAO to be more physically based. NAO vs. AO (Artic Oscillation / Annular mode) What is? Spatial pattern of climate variability in the North Atlantic region What does it do? Changes in atmospheric air masses (seesaw) between the Polar Low and the Subtropical High. What does it cause? Causes variations in the tracks and intensity of storm systems crossing the Atlantic from the East Coast of the USA to Europe. (most noticeable during winter (Nov-April). NAO (North Atlantic Oscillation) Dominant mode of winter climate variability in the North Atlantic region. NAO: North Atlantic Oscillation SST climate index > 0.5 -> El Niño < -0.5 -> La Niña ENSO phenomenon: El Niño-Southern Oscillation warmer sea surface temperature ENSO phenomenon: El Niño-Southern Oscillation colder SST En: Temperature, precipitations, storms, Agriculture, transport, Waves, Energy SOCIO-ECONOMIC IMPACT Occurrence rate of El Niño: 3-7 yrs (on average ~4 años) Southern Oscillation: a variant system that shapes the atmosphereocean interaction in the tropical Pacific. ENSO phenomenon: El Niño-Southern Oscillation NAO IMPACTS •Niño3.4 •Atlantic Multi-decadal Oscillation (AMO) •Indian Ocean Dipole (IOD) •North Pacific Mode (NPM) •Pacific Decadal Oscillation (PDO) Other examples of SST climate indices: •North Atlantic Oscillation (NAO) •Arctic Oscillation (AO) •Pacific North America pattern (PNA) •Eastern Pacific Oscillation (EPO) •Western Pacific Oscillation (WPO) •Scandinavian Pattern (SCAND) •East Atlantic / Western Russia pattern (EA/WR) •Southern Oscillation Index (SOI) •Southern Annular Mode (SAM) or Antarctic Oscillation (AAO) Examples of pressure-based climate indices: ENSO vs. Extreme SEA LEVELS cm of extreme sea level per ENSO index Meters of extreme sea level per ENSO index •The Pacific Decadal Oscillation (PDO) is a pattern of Pacific climate variability similar to ENSO, but which varies over a much longer time scale. •The PDO can remain in the same phase for 20 to 30 years, while ENSO cycles typically last only 6 to 18 months. •The PDO, like ENSO, consists of a warm and cold phase that alters the upper-level atmospheric winds. Changes in the PDO phase can have significant implications for global climate, affecting hurricane activity in the Pacific and Atlantic, droughts and floods in the Pacific basin, the productivity of marine ecosystems and global land temperature patterns. •Experts also believe that the PDO can intensify or diminish the impacts of ENSO depending on its phase. If both ENSO and the PDO are in the same phase, it is believed that El Niño/La Niña impacts may be magnified. Conversely, if ENSO and the PDO are out of phase, it has been proposed that they may offset each other, preventing "true" ENSO impacts from occurring. PDO: Pacific Decadal Oscillation Southern Oscillation Index, air pressure based ENSO phenomenon: El Niño-Southern Oscillation SOI Correlation patterns between wave climate and Climate Indices SAM  Interannual wind climatology 31  Interannual wind climatology Inter-annual contribution to wind resources 30 • Accelerated rises • Lineal trends Long-term processes: LONG-TERM TRENDS

Use Quizgecko on...
Browser
Browser