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Trends in climate — past, present and future — always need to be understood in the context of the naturally occurring variability. Climate variability here, refers to the ways how climate conditions (e.g., temperature and precipitation) “flicker” from year to year within their respective typical “range of variability”. The cause for this natural variability can be due to quasi random internal variability of the coupled atmosphere-ocean-land-ice system (as weather variability is drawn out over many years). A prime example for a cause of that category is the variability induced by El Niño – Southern Oscillation. Other causes can be the influence from periodic “forcing” events of non-human nature, such as explosive volcanic eruptions. These natural factors (internal as well as natural forcing) are summarized under “internal climate variability”. This internal climate variability is always present, sometimes a bit more exaggerated, sometimes a bit less. A climatology, therefore, has to be understood as a mean with variability around it. Variability can be very large from year to year (i.e., the high latitudes), and in a few locations, and for specific variables, it can be small (i.e., temperatures in the tropics).

In contrast to natural variability, anthropogenic emissions of greenhouse gases and resulting changes in atmospheric concentrations (i.e., CO2, methane) together land surface changes and aerosol impose a different forcing on the climate system. The search for climate change signals tries to separate their effects from the natural background variability. That signal can show as changes in the magnitude of the variability as well as through a systematic trend overtime.

This page offers three themes in which to explore and understand differences in variability, trends, and significance of change across future climate scenarios (SSPs).

I – Trends within Natural Variability and Significance: Climatological averages and trends need to be seen relative to the inter-annual variability.

II – The Influence of Climate Change on Variability and Extreme Events: Trends don’t necessarily imply a simple shift in climate and its variability envelope. Changes in variability can be very important for both climatic means as well as at the weather scale (extremes).

III – The Emergence of Trends Above Natural Variability: Change in relation to evolving trends provides insight into when (year of significant change) a changing climate departs from historical natural variability bounds. A period dominated by natural variability (low trend) can be seen in contrast to the emergence of an (anthropogenically) forced trend.

This page is meant as an informational tool to augment the views from the climatology pages (Climate Projections - Mean Projections). The three sections present different aspects of how variability might need to be taken into account. For simplicity of navigation, the variables presented are only a subset of the full indicator catalog.  Data used on this page is derived from CMIP6 (presented here at 0.25º x 0.25º resolution) in order to extract daily variability. See the  embedded in each visual for discussion on how to interpret data presentations.

01 - Trends Within Natural Variability and Significance

Long-term trends and their significance need to be seen relative to the inter-annual variability. Trends in climate don't always mean a simple shift in the average. We also expect changes in variability, which could lead to extreme events becoming more common.

What you can see in this figure
These trend-per-decade maps illustrate the annual or seasonal linear trend of specific climate variables within their spatial context. You can view these trends for various 50-year periods, including 1971-2020, 2001-2050, and 2051-2100.

Understanding the Data: Implications and Utility
You'll often notice a greater magnitude of temperature change in higher latitudes. The p10 and p90 bounds give you an idea of the broader spread of model variability for these trends. The primary goal is to observe how this linear trend changes depending on the period. For instance, in high emissions scenarios, the trend for the latter half of the 21st century (2051-2100) is often larger, indicating an acceleration of changes.

What are some caveats and potential limitations to consider?
This plot is complementary to the anomaly calculations found under 'Mean Projections', though it's derived using a slightly different method. While the anomaly shows the absolute change from a historical period to a future one, here we present the result of fitting a linear trend over the selected 50-year period. Since the actual trend might not be perfectly linear, the results could vary slightly. 

What you can see in this figure
This figure uses a bell curve to represent the 20-year average and associated annual variability for different periods.

Understanding the Data: Implications and Utility
You can observe how the climate mean is projected to change (or remain stable) over time. Simultaneously, changes in the width of the curve indicate shifts in climate variability. A widening curve suggests increasing variability and more associated extreme events.

What are some caveats and potential limitations to consider?
It's important to note that approximating 20-year variability with a simple bell curve is a significant simplification, so interpret these results with caution. We plan to characterize variability with a more suitable method in future iterations.

02 - Individual Models: Natural Variability and Extreme Events

Here we explore for each model how long-term trends emerge from interannual variability and how extreme events become more common.

What you can see in this figure
This figure displays the annual average for a selected climate variable from 2020 to 2100 for each month, for each chosen spatial unit (as selected on the map) and future scenario (SSP). This is shown for every single climate model to highlight interannual variability, since the multi-model ensemble washes out the interannual variability.  

Understanding the Data: Implications and Utility
You can observe the year-to-year fluctuations in the annual average, which represent interannual variability (and may differ across models). Over longer periods, a long-term climate trend can emerge, especially for temperature variables. While the nature of this long-term trend will generally be consistent across models, its strength and prominence relative to interannual variability may vary.

What you can see in this figure
Here, you can observe the projected changes in daily extremes for each month and model, from the present until the end of the 21st century, under various climate scenarios. These deviations are plotted as departures from the historical period's standard deviations.

Understanding the Data: Implications and Utility
Climate change typically shifts the climate system to a new mean and to increased variability. These two effects commonly result in more frequent and intense temperature maximums, fewer minimum extremes, and an increase in extreme precipitation events in most locations.

03 - The Emergence of Trends Above Natural Variability

By analyzing changes relative to evolving trends, we can pinpoint when a slowly shifting long-term climate begins to depart from the bounds of historical natural variability, contrasting periods dominated by natural variability with the emergence of an anthropogenically forced long-term trend.

What you can see in this figure
Here, we display the complete multi-model median time series (from the historical period to the end of the 21st century) for each scenario. The individual model time series are also shown with thinner lines. Vertical lines indicate the year of departure for each scenario and the p10-p90 uncertainty range. You can select or de-select desired scenarios from the legend for a clearer view.

Understanding the Data: Implications and Utility
The earlier the departure from historical norms, the sooner the population will experience the consequences of a changing climate. The timing of this departure varies by variable and region. While temperature changes are already clearly moving beyond natural variability, this trend is more complex and less straightforward for precipitation.

What you can see in this figure
The map presents a spatial representation of the year (median) of significance change from departure from the established historical variability (1995-2014). Red colors indicate an earlier departure from natural variability denoting more urgency. Transparent areas indicate where the median year of change is outside of the timescale bounds (>2100). 

Understanding the Data: Implications and Utility
Some variables and regions of the world realize a strong departure from natural variability, while some never realize significant departure within the established time bounds (1971-2100). See the 10th percentile (p10) and the 90th percentile (p90) to better understand the uncertainty associated to this departure date.