Uncertainty in climate change projections: the role of internal variability

Abstract

Uncertainty in future climate change presents a key challenge for adaptation planning. In this study, uncertainty arising from internal climate variability is investigated using a new 40-member ensemble conducted with the National Center for Atmospheric Research Community Climate System Model Version 3 (CCSM3) under the SRES A1B greenhouse gas and ozone recovery forcing scenarios during 2000-2060. The contribution of intrinsic atmospheric variability to the total uncertainty is further examined using a 10,000-year control integration of the atmospheric model component of CCSM3 under fixed boundary conditions. The global climate response is characterized in terms of air temperature, precipitation, and sea level pressure during winter and summer. The dominant source of uncertainty in the simulated climate response at middle and high latitudes is internal atmospheric variability associated with the annular modes of circulation variability. Coupled ocean-atmosphere variability plays a dominant role in the tropics, with attendant effects at higher latitudes via atmospheric teleconnections. Uncertainties in the forced response are generally larger for sea level pressure than precipitation, and smallest for air temperature. Accordingly, forced changes in air temperature can be detected earlier and with fewer ensemble members than those in atmospheric circulation and precipitation. Implications of the results for detection and attribution of observed climate change and for multi-model climate assessments are discussed. Internal variability is estimated to account for at least half of the inter-model spread in projected climate trends during 2005-2060 in the CMIP3 multi-model ensemble.

Mapping (dis)agreement in hydrologic projections

“…Accounting for the internal climate variability implies considering different realizations of the same GCM. It has been demonstrated that slightly different initial conditions can lead to a substantially different GCM realization (Deser et al, 2012;Fatichi et al, 2016). Furthermore, land-use and soil parameters have been kept constant in the hydrologic model for both modelling periods, although it is very likely that land use will change in the future.…”

Exacerbation of the 2013–2016 Pan‐Caribbean Drought by Anthropogenic Warming

“…Our estimates of the contribution of anthropogenic warming to the Pan‐Caribbean did not consider anthropogenic effects on precipitation trends and variability nor how these affected the Pan‐Caribbean drought, as these effects are likely too complex to be approximated by calculating empirical trends (Abatzoglou & Williams, 2016; Deser et al, 2012; Williams et al, 2015). Notably, climate models consistently simulate significant decreases in precipitation in the Caribbean as anthropogenic greenhouse‐gas concentrations increase in the future (IPCC, 2014; Neelin et al, 2006), and if those trends are already underway, then the total contribution of anthropogenic climate change would be greater than that estimated here.…”

“…Despite its simplicity, we used this approach because we aimed to specifically quantify the anthropogenic warming trend, which models are likely to accurately characterize. However, we did not attempt to identify the anthropogenic component of other variables used in the calculation of PET and scPDSI (such as precipitation) because of the difficulty of separating forced and internal variability in these variables (Cook et al, 2014; Deser et al, 2012; Williams et al, 2015). …”

Weather, Climate and the Nature of Predictability

“…As in NWP, the use of ensembles in climate model simulations is common. In this case, however, the ensemble is typically used to sample several different sources of uncertainty 4 : natural climate variability (Deser et al 2012), initial condition uncertainty (Scaife et al 2014), parametric uncertainty (Stainforth et al 2005) or model structural uncertainty (Taylor et al 2012).…”