Whereas humidity, wind patterns, solar radiation, and atmospheric pressure typically vary within hours or days, precipitation and temperature are the main climatic variables most affected by variability on timescales from a month to a season.

Variability at longer timescales
High interannual and seasonal variability of precipitation is characteristic of some climates more than others. It is particularly common in regions with arid, semi-arid, and monsoonal climates, where rainfall patterns can fluctuate significantly from year to year. For example, desert regions or areas affected by El Niño and La Niña cycles often experience large variations in precipitation. In contrast, more temperate or tropical rainforest climates tend to have more stable and predictable precipitation patterns with lower interannual variability.

At these timescales, climatic variability is commonly assessed through statistical analysis of meteorological data. The main techniques are:
• Time series analysis: identifying patterns, trends, and cycles in climate variables such as temperature and precipitation, with techniques such as moving averages, autocorrelation, and spectral (Fourier) analysis.
• Anomaly detection: calculating deviations from long-term averages (climatology) to identify unusual or extreme values in climate variables.
• Probability distribution analysis: examining the frequency and distribution of specific climatic events, such as heavy rainfall or drought. More on this topic in Precipitation statistics

These statistical analyses can be consolidated into climate indices, such as the Standardized Precipitation Index (SPI), which measures the deviation of precipitation from the long-term mean, and is often used to assess drought conditions or excess rainfall.
Indices based on large-scale atmospheric pressure differences, such as the Southern Oscillation Index (SOI) and the North Atlantic Oscillation (NAO), track major atmospheric and oceanic patterns that drive climate variability. These indices aid in understanding and predicting events like El Niño and La Niña, which typically occur every 2 to 7 years.

Short-term variability: dry and wet spells, hot and cold spells
On shorter time frames, precipitation anomalies can be expressed in terms of periods of dry or wet spells, consecutive days without or with precipitation. Temperature extremes can equally be described in terms of consecutive days with temperature above or below user-defined thresholds.

Short-term variability can be anayzed using standard descriptive statistics (means, median, standard deviation, frequency distribution) and summarized in graphs. When high-quality precipitation time series are available, the occurrence and duration of dry and wet spells can be predicted through more sophisticated approaches, such as Markov chains and stochastic models.

An alternative approach, valuable for evaluating crop suitability in areas with high rainfall variability, is to analyze year-by-year variations in the growing period. The concept of dependable growing period was developed for that purpose.
More on this topic in ‘Assessing variability of the growing period‘

Markov chains is a probabilistic method that models the likelihood of transitioning from a dry to wet day (or vice versa) based on the current state (first-order Markov chains) or on the state of multiple preceding days (higher-order Markov chains).
Stochastic models generate synthetic precipitation data that mimic real-world variability, allowing for the simulation and analysis of dry and wet spells under different conditions.

These methods can be used individually or in combination to gain a comprehensive understanding of the likelihood of dry and wet spells of different duration, as well as their impact on agricultural systems. An excellent example of integration of these methods is presented in the software application “ICARDA Agroclimate Tool”.