Sentences with phrase «greatest uncertainties in climate models»

«A cloud system - resolved model can reduce one of the greatest uncertainties in climate models, by improving the way we treat clouds,» Wehner said.

Not exact matches

Although there is still some disagreement in the preliminary results (eg the description of polar ice caps), a lot of things appear to be quite robust as the climate models for instance indicate consistent patterns of surface warming and rainfall trends: the models tend to agree on a stronger warming in the Arctic and stronger precipitation changes in the Topics (see crude examples for the SRES A1b scenarios given in Figures 1 & 2; Note, the degrees of freedom varies with latitude, so that the uncertainty of these estimates are greater near the poles).

The aerosol forcing is the biggest tuning in this regard in many climate models, although the GISS model uses published forward calculations (the right thing to do, but still fraught with great uncertainty), whereas many climate models use an inverse method to get aerosol forcing that matches.

If one uses the historical record of warming to help tune your climate model, you are assuming that 100 % of warming is due to the forcing we know about (with a great deal of uncertainty in the case of aerosols).

«The assessment is supported additionally by a complementary analysis in which the parameters of an Earth System Model of Intermediate Complexity (EMIC) were constrained using observations of near - surface temperature and ocean heat content, as well as prior information on the magnitudes of forcings, and which concluded that GHGs have caused 0.6 °C to 1.1 °C (5 to 95 % uncertainty) warming since the mid-20th century (Huber and Knutti, 2011); an analysis by Wigley and Santer (2013), who used an energy balance model and RF and climate sensitivity estimates from AR4, and they concluded that there was about a 93 % chance that GHGs caused a warming greater than observed over the 1950 — 2005 period; and earlier detection and attribution studies assessed in the AR4 (Hegerl et al., 2007b).&rModel of Intermediate Complexity (EMIC) were constrained using observations of near - surface temperature and ocean heat content, as well as prior information on the magnitudes of forcings, and which concluded that GHGs have caused 0.6 °C to 1.1 °C (5 to 95 % uncertainty) warming since the mid-20th century (Huber and Knutti, 2011); an analysis by Wigley and Santer (2013), who used an energy balance model and RF and climate sensitivity estimates from AR4, and they concluded that there was about a 93 % chance that GHGs caused a warming greater than observed over the 1950 — 2005 period; and earlier detection and attribution studies assessed in the AR4 (Hegerl et al., 2007b).&rmodel and RF and climate sensitivity estimates from AR4, and they concluded that there was about a 93 % chance that GHGs caused a warming greater than observed over the 1950 — 2005 period; and earlier detection and attribution studies assessed in the AR4 (Hegerl et al., 2007b).»

This should, in theory, lead to more realistic projections for the future, but many of the climate modellers I spoke to were keen to point out that simulating the climate with more complex models may well lead to greater uncertainty about what the future holds.

The uncertainties in global climate models vs empirical observations are equally great ranging from 10 C by 2100!