Time-mean precipitation and evaporation as a function of latitude as simulated by an aqua-planet version of an atmospheric GCM (GFDL’s AM2.1) with a homogeneous “slab-ocean” lower boundary (saturated surface with small heat capacity), forced by annual mean insolation.
One often hears the statement the “strength of the hydrological cycle” increases with global warming. But this phrase seems to mean different things in different contexts.
From Hall and Qu, 2006. Each number corresponds to a model in the CMIP3 archive. Vertical axis is a measure of the strength of surface albedo feedback due to snow cover change over the 21st century (surface albedo change divided by change in surface temperature over land in April). Horizontal axis is measure of surface albedo feedback over land in seasonal cycle (April to May changes in albedo divided by change in temperature). The focus is on springtime since this is a period in which albedo feedback tends to be strongest.
There are a lot of uncertainties in how to simulate climate, so, if you ask me, it is self-evident that we need a variety of climate models. The ensembles of models that we consider are often models that different groups around the world have come up with as their best shots at climate simulation. Or they might be “perturbed physics” ensembles in which one starts with a given model and perturbs a set of parameters. The latter provides a much more systematic approach to parametric uncertainty, while the former give us an impression of structural uncertainty– ie, these models often don’t even agree on what the parameters are. The spread of model responses is useful as input into attempts at characterizing uncertainty, but I want to focus here, not on characterizing uncertainty, but on reducing it.
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