Sierra Leone

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 with land surface changes and aerosols 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 Historical Climate pages. 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 0.25º x 0.25º resolution) in order to extract also the daily variability. 

01 - Trends within Natural Variability and Significance

Climatological averages and long-term trends need to be seen relative to the inter-annual variability.

What you can see in this figure
The trend-per-decade maps visually represent the annual or seasonal linear trends of key climate variables across 1971-2020. The country or administrative unit selected here affects the plots below. 

Regions where natural variability overshadows the trend are indicated by gray shading. This shading denotes areas where changes aren't statistically significant (at a 95% confidence level, accounting for autocorrelation), ensuring you focus on robust signals. Conversely, areas where changes surpass the significance threshold are left unshaded, allowing for a clear view of the color-coded trend intensity.

Understanding the Data: Implications and Utility
You'll often observe greater warming in higher latitudes, particularly prominent in the Northern Hemisphere.

It's important to note that precipitation is considerably more variable than temperature, making it much harder to detect a statistically significant trend. However, the theoretical expectation for precipitation is that dry regions and seasons will become drier, while wet regions and seasons will become wetter.

What are some caveats and potential limitations to consider?
Here we show the trend for the last 50 years. The trend is slightly different depending on the period selected. 

What you can see in this figure
Here we show the most recent climatological average, 1991-2020, in black. Superposed to the average seasonal cycle, you can see each annual average from 1951 to 2020, colored according to time. 

Understanding the Data: Implications and Utility
In this graphic, you can observe the seasonal evolution of a climate variable across different years, allowing for a visual comparison of interannual variability and long-term trends.

Trends over time are revealed by the progression of colors in a consistent direction, signaling a shift in values through the years. Meanwhile, interannual variability, or year-to-year fluctuations, is depicted by the spread or dispersion of similar colors within each monthly segment, per each decade.

For temperature variables, a distinct warming trend is generally discernible, with the warmest months of the entire 50-year period frequently occurring closer to the present day. Conversely, a systematic and clear trend in color progression is much more challenging to identify for precipitation, reflecting its inherently more complex and less predictable behavior over time.

02 - Spatially Aggregated Trends and Significance

This section presents visualizations of spatially aggregated time series (at the country or subnational level), alongside linear trend calculations for different temporal periods and seasons. This approach allows for an examination of whether, and to what extent, the observed trend is accelerating. Here, 'significance' should be understood as statistically significant at the 95th percentile.

What you can see in this figure
Here you can see the full annual time series for the selected variable together with fitted linear trends for three different periods.

Understanding the Data: Implications and Utility
Long-term time series can show the changing dynamics of a variable selected. Over the historical period, the emergence of the climate change signal increases towards the present, especially for temperature. 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 linear 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.

What are some caveats and potential limitations to consider?
Please note that the labeled trend is based on a robust linear regression that accounts for outliers, while the displayed trend line represents a simple linear regression for visual purposes. If there is a discrepancy, please refer to the labeled value.​

What you can see in this figure
The heat plot displays the monthly anomalies for various 10-year periods, relative to the monthly average over the historical recent period (1995-2014). For example, the value shown under 1980-1990 for March represents the difference between the average March temperature during 1980-1990 and the average March temperature across the reference period (1995-2014). 

Understanding the Data: Implications and Utility
Use this plot to understand how some seasons are warming/getting drier (wetter) than others, which will have implications for agriculture planning, flooding resilience, etc. 

03 - The Influence of Climate Change on Variability and Extreme Events

Here, we examine the evolution of the entire climate distribution over time, not solely the changes in the climate mean. This is critical because extreme events, located at the tails of the distribution, typically exert the most significant impacts and are often the first noticeable manifestations of a changing climate.

These plots are designed to help you understand if climate variability is increasing, which would indicate more pronounced fluctuations and erratic weather patterns. Furthermore, they illustrate how extreme events are becoming more common, particularly focusing on the increasing frequency and intensity of extreme temperature and precipitation events.

What you can see in this figure
We compare successive 30-year climatology periods by visualizing their 30-year mean and spread (standard deviation) using a bell-shaped distribution.

Understanding the Data: Implications and Utility
This approach allows us to discern, for example, if years are becoming consistently hotter/drier/wetter or if more intense temperatures are occurring with greater frequency or if variability is becoming larger or smaller, even in cases where the average temperature (the mean) shows little change. Extreme events have the most impact over human populations. This plot gives as a first idea on the historical change in extremes. 

What you can see in this figure
In these graphs, each bubble represents a daily climate extreme, specifically showing its deviation in standard deviations (SD) away from the respective monthly mean. These standard deviations are calculated over the recent historical climatology period of 1991-2020.

Understanding the Data: Implications and Utility
Here, we delve into the extremes of the climate distribution by analyzing daily extreme events. Bubble graphs serve as an effective visualization tool, illustrating trends in these highly variable, short-term weather phenomena, which often have the most significant impact. Due to the inherent "noise" in daily time series, detecting a clear climate change signal within them can often be challenging.

We expect these extreme events, in both temperature and precipitation, to become increasingly frequent over time. Generally, identifying potential long-term changes in climate extremes becomes easier with longer historical records. Locations with higher natural variability will face greater difficulty in distinguishing true extreme events from their 'normal' range of natural fluctuations.