Glossary

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A

Accuracy
The average distance between a set of measurements and the 'true' value of the object being measured. For climate prediction this can be defined as the average distance between a set of forecasts and an estimate of the observational reference.
Anomalies
These represent the departures of specific measurements and/or forecasts from their long-term climatological values. Anomalies describe how much a specific variable differs from its normal state.

B

Bias
The average difference between the values of the forecasts and the observations in the long term. While  accuracy is always positive the bias could be either positive or negative depending on the situation.
Bias-correction or bias-adjustment
Methods  to 'calibrate' model simulations to ensure their statistical properties are  similar to those of the corresponding observed values.

C

Calibration
in climate predictions this is the procedure to make the forecasts reliable. This often comes at the cost of the accuracy and the skill of the forecasts.
Climate
Climate in a narrow sense is usually defined as the average weather, or more rigorously as the statistical description in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. The classical period for averaging these variables is 30 years, as defined by the World Meteorological Organization. The relevant quantities are most often surface variables such as temperature, precipitation and wind. Climate in a wider sense is the state, including a statistical description, of the climate system.
Climate forecast
Is the result of an attempt to produce (starting from a particular state of the climate system) an estimate of the actual evolution of the climate in the future, for example, at seasonal, interannual or decadal time scales. Since the future evolution of the climate system may be highly sensitive to initial conditions, such predictions are usually probabilistic in nature. See also Climate projection, Climate scenario, Model initialization and Predictability.
Climate model simulations
These are numerical solutions of sets of equations that represent the most relevant processes describing the climate system. Climate models can be of very different levels of complexity but the most elaborated ones appear to be able to reproduce realistically the key meteorological and climatological phenomena.
Climate prediction
is the result of an attempt to produce (starting from a particular state of the climate system) an estimate of the actual evolution of the climate in the future, for example, at seasonal, interannual or decadal time scales. Since the future evolution of the climate system may be highly sensitive to initial conditions, such predictions are usually probabilistic in nature. See also Climate projection, Climate scenario, Model initialization and Predictability.
Climate services
Climate services involve the production, translation, transfer, and use of climate knowledge and information for decision making, policy and planning.
Climate variability

Climate variability refers to variations in the mean state and other statistics (such as standard deviations, the occurrence of extremes, etc.) of the climate on all spatial and temporal scales beyond that of individual weather events. Variability may be due to natural internal processes within the climate system (internal variability), or to variations in natural or anthropogenic external forcing (external variability ). See also Climate change

Climatology
Can be defined as the science of climate, but is also used in the meaning of the normal state such as a base line over the normal period. Climatology is often taken as the mean value for a given month for example over 1961-1990.
Confidence
The validity of a finding based on the type, amount, quality, and consistency of evidence (e.g. mechanistic understanding, theory, data, models, expert judgment) and on the degree of agreement. Confidence's expressed qualitatively (Mastrandrea et al., 2010).

D

Downscaling
Downscaling is a method that derives local- to regional-scale (10 to 100 km) information from larger-scale models or data analyses. Two main methods exist: dynamical downscaling and empirical/statistical downscaling. The dynamical method uses the output of regional climate models, global models with variable spatial resolution or high-resolution global models. The empirical/statistical methods develop statistical relationships that link the large-scale atmospheric variables to local/regional climate variables. In all cases, the quality of the driving model remains an important limitation on the quality of the downscaled information.

E

El Niño-Southern Oscillation (ENSO)
The term El Niño was initially used to describe a warm-water current that periodically flows along the coast of Ecuador and Peru, disrupting the local fishery. Since then, it has become identified with a basin-wide warming of the tropical Pacific Ocean east of the dateline. This oceanic event is associated with a fluctuation of a global-scale tropical and subtropical surface pressure pattern called the Southern Oscillation. This coupled atmosphere-ocean phenomenon, with preferred time scales of 2 to about 7 years, is collectively known as the El Niño-Southern Oscillation. It is often measured by the surface pressure anomaly difference between Darwin and Tahiti and the sea surface temperatures in the central and eastern equatorial Pacific. During an ENSO event, the prevailing trade winds weaken, reducing upwelling and altering ocean currents such that the sea surface temperatures warm, further weakening the trade winds. This event has a great impact on the wind, sea surface temperature, and precipitation patterns in the tropical Pacific. It has climatic effects throughout the Pacific region and in many other parts of the world, through global teleconnections. The cold phase of ENSO is called La Niña.
Ensemble
A collection of model simulations characterizing a climate prediction (or projection). Differences in the initial conditions and model formulations result in different evolution of the modelled systems and may give information on uncertainty associated with model error, error in the initial conditions and with the internally generated climate variability
Extreme (weather or climate) event
The occurrence of a value of a weather or climate variable above (or below) a threshold value near the upper (or lower) ends ('tails') of the range of observed values of the variable. Extreme events comprise a facet of climate variability under stable or changing climate conditions.

F

Flood

The overflowing of the normal confines of a stream or other body of water, or the accumulation of water over areas that are not normally submerged. Floods include river (fluvial) floods, flash floods, urban floods, pluvial floods, sewer floods, coastal floods, and glacial lake outburst floods.

Forecast time
It's the time elapsed since the beginning of the forecast. This can be a time range (e.g. month 2-4).
Forecasts
A climate forecast is a statement about the future evolution of some aspects of the climate system encompassing both forced and internally generated components.  Climate forecasts are generally used as a synonym of climate predictions. At the same time some authors like to use prediction in a more general sense while referring to forecasts as a specific prediction which  provides guidance on future climate and can take the form of quantitative outcomes, maps or text.

G

GCM
Global Climate Models or General Circulation Models (GCMs) are computer codes used to solve a set of mathematical equations describing the laws of physics relevant to the atmospheric and oceanic circulation, the distribution of heat, and the interaction between electromagnetic radiation and atmospheric gases and aerosols. Climate models represent an implementation of our theoretical knowledge of the climate system, describing interconnections between processes. They consist of different modules describing the atmosphere, oceans, sea-ice/snow and land surface, and represent the world in terms of boxes stacked next to and on top of one another. The values for temperature, motion and mass are solved in each of these boxes, based on well known physical laws.

H

Hindcast
A forecast made for a period in the past using only information available before the beginning of the forecast. A set of hindcasts can be used to bias-correct and/or calibrate the forecast and/or provide a measure of the skill.

L

Likelihood
A probabilistic estimate of the occurrence of a single event or of an outcome, for example, of a climate parameter, an observed trend, or a projected change lying in a given range. Likelihood may be based on statistical or modelling analyses, elicitation of expert views, or other quantitative analyses.

N

North Atlantic Oscillation (NAO)
A recurring spatial pattern of mean sea-level pressure (MSLP) over the north Atlantic region characterised by low MSLP over Iceland and high MSLP over the Azores/Lisbon. The NAO expresses climate variability associated with variations in the large-scale temperature and precipitation pattern over Northern Europe.

P

Predictability

The extent to which future states of a system may be predicted based on knowledge of current and past states of the system. Since knowledge of the climate system’s past and current states is generally imperfect, as are the models that utilise this knowledge to produce a climate prediction, and since the climate system is inherently nonlinear and chaotic, predictability of the climate system is inherently limited. Even with arbitrarily accurate models and observations, there may still be limits to the predictability of such a nonlinear system (AMS, 2000).

Prediction
is generally used as a synonym of forecast. At the same time some authors like to use prediction in a more general sense while referring to forecasts as to a specific prediction which provides guidance on future climate and can take the form of quantitative outcomes, maps or text.
Probabilistic forecast
A forecast that specifies the future probability of one or more events occurring.
Probability density function (PDF)
A probability density function (PDF) is a function that indicates the relative chances of occurrence of different outcomes of a variable. The function integrates to unity over the domain for which it is defined and has the property that the integral over a sub-domain equals the probability that the outcome of the variable lies within that sub-domain. For example, the probability that a temperature anomaly defined in a particular way is greater than zero is obtained from its PDF by integrating the PDF over all possible temperature anomalies greater than zero. Probability density functions that describe two or more variables simultaneously are similarly defined.
Projection

A projection is a potential future evolution of a quantity or set of quantities, often computed with the aid of a climate model. Unlike predictions, projections are conditional on assumptions concerning, for example, future socioeconomic and technological developments that may or may not be realised. See also Climate prediction and Climate projection.

R

Reanalyses

Reanalyses are estimates of historical atmospheric or hydrographic or other climate relevant quantities, created by processing past climate data using fixed state-of-the-art weather forecasting or ocean circulation models with data assimilation techniques.

Regional climate models (RCMs)
Climate model at higher resolution over a limited area. Such models are used in downscaling global model simulations over specific regions.
Reliable
Is a characteristic of a forecast system for which the probabilities issued for a specific event vary a proportion of times equal to the climatological frequency of the event. A reliable system which predicts, for example 50 % (or 20 %, 73 %) probability of rain, should, on average,  be correct 50 % (or 20 %, 73 %)  of the times, no more, no less.
Retrospective forecasts (also known as hindcasts)
Forecasts made for a period in the past using only information available before the beginning of the forecast. A set of hindcasts can be used to bias-correct and/or calibrate the forecast and/or provide a measure of the skill.
Return value
The highest (or, alternatively, lowest) value of a given variable, on average occurring once in a given period of time (e.g. in 10 years).
Risk
Often taken to be the product of the probability of an event and the severity of its consequences. In statistical terms, this can be expressed as Risk(Y) = Pr(X) C(Y|X), where Pr is the probability, C is the cost, X is a variable describing the magnitude of the event, and Y is a sector or region.

S

Skill

Measures of the success of a prediction against observationally-based information. No single measure can summarize all aspects of forecast quality and a suite of metrics is considered. Metrics will differ for forecasts given in deterministic and probabilistic form.

Skill score
A relative measure of the quality of the forecasting system compared to the benchmark or reference forecast (e.g. climatology, persistence, etc.).
Statistical significance
Describes the likelihood of an observation or a result being due to pure chance. It is often used in connection with a null-hypothesis (an alternative explanation, usually such as there is no correlation or no causal relationship), and gives the odds that the null-hypothesis is correct.

T

Theory
A well-established fact concerning interrelations and physical laws. These include quantum physics, the general theory of relativity, Newton’s laws, the ideal gas laws, thermodynamics, electromagnetism, conservation of energy and mass and mathematical truths. Whereas theories are seen as facts, hypotheses are more tentative and speculative and are not yet well-established.
Trends
Long-term evolution, such as climate change and global warming. Trend analysis is used to describe trends, and can involve linear or multiple regression with time as a covariate. A trend model may be a straight line (linear) or more complex (polynomial), and the long-term rate of change can be described in terms of the time derivative from the trend model.

U

Uncertainty
Means lack of precision or that the exact value for a given time is not predictable, but it does not usually imply lack of knowledge. Often, the future state of a process may not be predictable, such as a roll with dice, but the probability of finding it in a certain state may be well known (the probability of rolling a six is 1/6 and flipping tails with a coin is 1/2). In climate science, the dice may be loaded, and we may refer to uncertainties even with perfect knowledge of the odds. Uncertainties can be modelled statistically in terms of PDFs, extreme value theory and stochastic time series models.

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RESILIENCE aims to strengthen the efficiency and security of wind power supply within energy networks, by providing robust information of the future variability in wind power resources based on probabilistic climate predictions.