Not exact matches
As I said in # 626, Hansen et
al. plan to apply their method to
temperature projections in a future paper, so we'll be able to compare to their results at some point.
«Future
projections based on theory and high - resolution dynamical models consistently suggest that greenhouse warming will cause the globally averaged intensity of tropical cyclones to shift towards stronger storms,» Knutson et
al. (2010); Grinsted et
al. (2013) projected «a twofold to sevenfold increase in the frequency of Katrina magnitude events for a 1 °C rise in global
temperature.»
However, almost all climate change
projections predict increases in
temperature and decreases in Colorado River runoff [Vano et
al., 2014].
The
temperature reconstruction of Shakun et
al. (green — shifted manually by 0.25 degrees), of Marcott et
al. (blue), combined with the instrumental period data from HadCRUT4 (red) and the model average of IPCC
projections for the A1B scenario up to 2100 (orange).
«
Projections of Antarctic SMB changes over the 21st century thus indicate a negative contribution to sea level because of the projected widespread increase in snowfall associated with warming air
temperatures (Krinner et
al., 2007; Uotila et
al., 2007; Bracegirdle et
al., 2008).
«From 1910 - 1949 (pre-agricultural development, pre-DEV) to 1970 - 2009 (full agricultural development, full - DEV), the central United States experienced large - scale increases in rainfall of up to 35 % and decreases in surface air
temperature of up to 1 °C during the boreal summer months of July and August... which conflicts with expectations from climate change
projections for the end of the 21st century (i.e., warming and decreasing rainfall)(Melillo et
al., 2014).»
Ts + dSST.txt, updated May 2012;
projections from James Hansen et
al., «Global Surface
Temperature Change,» Reviews of Geophysics, vol.
Since then, despite a massive improvement in models and in our understanding of the mechanisms of climate change, the uncertainty in our
projections of
temperature change has stubbornly refused to narrow (Houghton et
al. 2001).
This point was also made by Schmidt et
al. (2014), which additionally showed that incorporating the most recent estimates of aerosol, solar, and greenhouse gas forcings, as well as the El Niño Southern Oscillation (ENSO) and
temperature measurement biases, the discrepancy between average GCM global surface warming
projections and observations is significantly reduced.
«extensive use [of GCMs] for the prediction and interpretation of the spatial patterns of
temperature change at the Earth's surface, and the use of such
projections in reports for policymakers (e.g. Parry et
al. 2007), leads us to the view that it is appropriate to assess their usefulness in this regard.»
it is found that global
temperature trends since 1998 are consistent with internal variability overlying the forced trends seen in climate model
projections (Easterling and Wehner, 2009; Mitchell et
al., 2012b); see also Figure 1.1, where differences between the observed and multimodel response of comparable duration occurred earlier.
England, et
al., reported that some CMIP5
projections approximated the air
temperature «hiatus» since 2000.
Analogize the multiple
temperature projections in Rowlands, et
al., Figure 1, that represent the ignorance widths of the parameter sets, onto the single hindcast line of SPM.5.
This method weights
projections by comparing their global mean surface
temperature projections to those of a probabilistic simple climate model, in this case (as in Rasmussen et
al., 2016) the MAGICC6 model (Meinshausen et
al., 2011).
Rosenzweig et
al. (2005) found that climate change based on downscaled general circulation model (GCM)
projections would exacerbate the New York City UHI by increasing baseline
temperatures and reducing local wind speeds.