Trends and Significant Change against Natural Variability
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 Variability: Climatological averages and trends need to be seen relative to the inter-annual variability.
II – Variability and Change in Variability: 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 – Change and Significance: 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 1º x 1º resolution) in order to extract daily variability. See the embedded in each visual for discussion on how to interpret data presentations.
I – Trends within Variability
Climatological averages and trends need to be seen relative to the inter-annual variability
To visualize possible changes in the distribution of climate and its' variability, successive climatology periods can be compared through shifts in mean as well as the spread (width) of the variability. Each bell-shaped distribution represents a 20-year climatology interval. This can help to recognize if, for example, years are becoming hotter and/or more intense temperatures are occurring more frequently.
II – Variability and Change in Variability
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).
Plotting the evolution of climate across the seasons is illustrated through color representation of time from 2020-2099. Superposed is the Historical Reference Period, 1995-2014. Trends over time can be recognized by the color progression for each month. The important inter-annual variability is represented through the spread of similar colors. A rather clear warming trend can generally be seen for temperature variables where the hottest months of the entire period are commonly occurring closer to present day. A systematic trend in color progression is much harder to discern for precipitation.
A bubble graph is suited to illustrate the much more variable occurrence of short-term weather events (daily scale). A potential climate change signal is often more difficult to recognize in these noisy series. However, for some variables, a tendency can either be seen through changes in magnitude of events and/or through changes in frequency of significant events (see temperatures). But for other variables, a clear trend might not always be visible. Here, each bubble represents climate extremes in the form of standard deviations (SD) away from the monthly mean determined over the Historical Reference Period, 1995-2014.
III – Change and Significance
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.
Longer-term time series can show the changing dynamics of a variable selected. Climate change can be understood in relation to the statistically significant departure (year of significant change) from natural variability bounds (grey bar). Each SSP shows individual models used in the multi-model ensemble, with the dark line indicating the median of the multi-model ensemble. Some variables and regions of the world realize a strong departure from historical natural variability (1995-2014), while some never realize significant departure.
The map presents a spatial representation of the year (median) of significant change from departure from the established historical variability (1995-2014). 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). Transparent areas indicate where the median year of change is outside of the timescale bounds (> 2100).
Transparent areas indicate that the median year of change is outside of the timescale bounds (> 2100).