A new analysis by Clara Deser and colleagues (accepted for Nature Climate Change), provides some fantastic visualisations of the crucial role of natural variability in how we will experience climate.
Essentially, Deser et al. perform 40 simulations with the same climate model and the same radiative forcings, but only change the initial state of the atmosphere. The only difference between the simulations will therefore be due to chaotic and rather unpredictable variability (the butterfly effect).
However, the trends in the simulations over the next 50 years can be remarkably different, with some showing little warming, and some showing a strong warming. It must be cautioned that they only use a single model, and other models might show different ranges of behaviour, but there is no indication that the variability in CCSM3 – the model used – is wildly wrong.
The examples they show are for North America (Fig. 1 below) – showing the average, warmest and coolest simulation for the U.S. with maps of the trend in winter (DJF) temperature. The timeseries show the maximum and minimum trends for the locations indicated, with the histograms showing the distribution of simulated trends. Globally, all the simulations warm by very similar amounts, but regionally the trends are extremely diverse. Versions of the figures for summer temperature and winter precipitation are also shown in the paper.
You could probably select any region of the globe and make similar plots, so I made a timeseries for annual temperature for the UK (Fig. 2 below). Here I have used just 10 simulations with a single climate model (CSIRO 3.6, different from the one used by Deser et al.), with identical radiative forcings, but focussed on the UK (using Central England Temperature, CET).
Again, there is a wide range of behaviours for the different simulations – one (in red) shows warming, followed by a flatter period, whereas another (blue) shows a sharp cooling, followed by a rapid warming. Each of these paths could be considered equally plausible, and demonstrates the uncertainty in future climate which is attributable to rather random fluctuations in the weather. It also highlights that the climate experienced in one location is not necessarily representative, as in all these simulations the globe as a whole is warming by a similar amount.
Fig. 1: Maps and timeseries of winter (DJF) North American temperatures. Maps show average, warmest and coolest trends (units of K per 55 years) of 40 simulations which are identical except for the atmospheric initial conditions. Timeseries show highest and lowest trends from the ensemble for particular locations. Histograms show distributions of possible trends.
Fig. 2: Projections of Central England Temperature (CET) from a single model (CSIRO 3.6) and identical radiative forcings. The only difference is the initial conditions.
This piece was originally posted on the National Centre for Atmospheric Science (NCAS)’s blog, Climate Lab Book, and was reproduced with kind permission from the author.
Ed Hawkins is a climate scientist in NCAS-Climate at the Department of Meteorology, University of Reading. His research interests are in decadal variability and predictability of climate, especially in the Atlantic region, and in quantifying the different sources of uncertainty in climate predictions and impacts.