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 the last 70-, 50- and 30-year periods. It is meant as an informational tool to augment the views from the climatology pages (Current Climatology- Climatology tab). 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 the ERA5 reanalysis (here used at 0.5º x 0.5º resolution) in order to extract also the daily variability. See 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
(95th Percentile Not Met) :
Plotting the evolution of climate across the seasons is illustrated through color representation of time from 1971 to 2020. Superposed is the most recent climatological average, 1991-2020. 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 50-year period are commonly occurring closer to present day. A systematic trend in color progression is much harder to discern for precipitation.
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).
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 30-year climatology interval. This can help to recognize if, for example, years are becoming hotter and/or more intense temperatures are occurring more frequently.
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. The longer the records, the easier it is to recognize potential changes. Here, each bubble represents climate extremes in the form of standard deviations (SD) away from the monthly mean determined over the current climatology of 1991-2020.
III – Change and Significance
Trends determined on different length of timeseries can be a good indicator of change. 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 (solid black line). Over the historical period, the emergence of the climate change signal increases towards the present. Therefore, comparing a full period with trends over more recent intervals can demonstrate the intensification of the forced change over the natural variability. Here, through the three trend lines, 1951-2020, 1971-2020, and 1991-2020, one can identify progressive changes in the trend towards present day. This can be identified most strongly in temperature variables.
A somewhat more smoothed version of time series is presented by heatplots. They allow for the visualization of monthly anomaly (change) over longer-term time horizons using a 10-year averaging. This helps illustrate the difference in magnitude across the seasons, something that can be hidden in annual timeseries.