Most notably,
in a global study Wahl et al (2017)
considered both extreme value analysis and numerical models that were used to simulate storm surges at coastline stretches where no
observations exist to quantify ESL and their
uncertainties.
These
observations should be
considered in the testing of cloud parameterizations
in climate models, which remain sources of substantial
uncertainty in global warming prediction.»
The reasons for that are many: the timid language of scientific probabilities, which the climatologist James Hansen once called «scientific reticence»
in a paper chastising scientists for editing their own
observations so conscientiously that they failed to communicate how dire the threat really was; the fact that the country is dominated by a group of technocrats who believe any problem can be solved and an opposing culture that doesn't even see warming as a problem worth addressing; the way that climate denialism has made scientists even more cautious
in offering speculative warnings; the simple speed of change and, also, its slowness, such that we are only seeing effects now of warming from decades past; our
uncertainty about
uncertainty, which the climate writer Naomi Oreskes
in particular has suggested stops us from preparing as though anything worse than a median outcome were even possible; the way we assume climate change will hit hardest elsewhere, not everywhere; the smallness (two degrees) and largeness (1.8 trillion tons) and abstractness (400 parts per million) of the numbers; the discomfort of
considering a problem that is very difficult, if not impossible, to solve; the altogether incomprehensible scale of that problem, which amounts to the prospect of our own annihilation; simple fear.