Can an ensemble climate simulation be used to separate climate change signals from internal unforced variability?

Abstract

The contribution of internal unforced variability to climate change is explored using a 100-member ensemble climate simulation for the period . The ensemble simulation is based on the Max Planck Institute for Meteorology climate model, ECHAM6, where all members have been exposed to the identically same radiative forcing. The range of global mean surface temperature warming over the 1850-2005 period, based on all members, is 0.65-1.10 °C. The distribution of the global mean surface temperature about the ensemble mean has a standard deviation (StD) of ~ 0.14 °C which slowly decreases in time. Regionally, the largest decrease in the ensemble StD occurs in the Northern Hemisphere winter. Comparing the temporal StD with that from the observed HadCRUT4 surface temperature data indicates that the majority of the ensemble members have a larger temporal StD than the observations suggesting that the model simulations might overestimate the variance. There are clear random 20-year linear trends in global mean surface temperature anomalies as well as significant regional 50-year linear trends. Even with an ensemble mean warming trend, typical of the early twenty-first century, a global hiatus in temperature of 20 years duration is possible to occur by chance. The results support the view that observed decadal and multi-decadal anomalies in the twentieth century were significantly influenced by internal processes of the climate system. This is particularly the case for the observed global warming trend of 1910-1940 and the global cooling trend of . Global mean precipitation hardly increases with time in the ensemble simulations, but in agreement with theory regional changes occur, with increasing precipitation in polar regions and in some tropical areas. In the subtropics there are reductions in precipitation. Long-lasting regional anomalies of significant amplitudes occur by chance in the ensemble integration.

The Max Planck Institute Grand Ensemble: Enabling the Exploration of Climate System Variability

“…The utility of MPI‐GE itself has also been demonstrated in previous studies (Bittner et al, ; Bengtsson & Hodges, ; Dessler et al, ; Hedemann et al, ; Li & Ilyina, ; Manzini et al, ; Maher et al, ; Marotzke, ; Niederdrenk & Notz, ; Plesca et al, ; Rädel et al, ; Stevens, ; Suárez‐Gutiérrez et al, , ; Zhang et al, ). Some high‐profile examples include the investigation into the 1998–2012 hiatus and extreme events; for example, Hedemann et al () used MPI‐GE to investigate the recent surface warming hiatus.…”

“…The first method is ideal for quantities that have good observational coverage over a long time span. This method puts the observations in the context of the transient model spread in time (e.g., Bengtsson & Hodges, ; Marotzke & Forster, ; Risbey et al, ) and additionally uses a rank histogram to evaluate the spread in the ensemble dimension (e.g., Marotzke & Forster, ). The second method can be used for observations where there is high quality data available but only for a short period of time.…”

“…The second method can be used for observations where there is high quality data available but only for a short period of time. Here we compare a single observational estimate with a histogram of the ensemble spread (e.g., Bengtsson & Hodges, ; Flato et al, ). The third method provides a novel way to assess the agreement of the model's internal variability and forced response with observations on a global map.…”

“…This is a novel model evaluation method, where we transfer the methodology used on European temperature (Suarez‐Gutierrez et al, , Supplementary Figure 3) to the globe. While previous methods to investigate this have used standard deviations and often detrended quantities (Bengtsson & Hodges, ; Lehner et al, ; McKinnon et al, ), this new method allows quantification of whether the whole distribution, including the extremes, agree well with observations. Additionally, by combining the map in Figure with the method from Figure , we can investigate exactly why the model does not agree with observations in specific regions identified on the map and can differentiate between discrepancies in internal variability and the forced response.…”

Quantifying the role of internal variability in the temperature we expect to observe in the coming decades

“…Practically, internal variability can be quantified and studied using climate models, with SMILEs effective tools to quantify the role of small perturbations in changing the short and long-term trajectory of the climate system. Individual SMILEs have been used in previous studies to investigate the role of internal variability in driving surface temperature projections, mainly on 35-60 year time-scales (Deser et al 2012b, Kay et al 2015, Bengtsson and Hodges 2018, with few studies investigating the shorter time-scales (e.g. Marotzke 2019).…”