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To continue your metaphor I guess it depends on the water on offer and those leading you to it. Quoting newspaper articles or indeed climate reports is like choosing television programs - it depends upon your perspective and the idiosyncrasies of the human psyche. There is a lot of work being undertaken by many people but how much "science" and is the "science" motivated to funding, power, status or all of these? For example, how does one negotiate the myriad pathways of the various "models" on offer - should I stick to the conclusions of the CMIP5 climate models or should I argue they are forced? If they are forced then how do I dissassmble the data sets and algorythms upon which the models are structured and how do I eliminate the programmed bias or do I just strip out and take the GFDL? There are many on offer. Peer reviewing these models is another matter entirely and depends upon the people undertaking the review, their ability their field of research or expertise and unfortunately their innate bias - which takes us back to the your orginal metaphor being who is leading you and to what end. A somewhat interesting interpretation of a complex issue. For example if you are considering energy budget and climate sensitivity models would you accept Armour's paper or question its methodology and if you question it are you stupid? I think calling people stupid because they don't agree with your conclusions or your innate bias is... well... rather stupid.
To continue your metaphor I guess it depends on the water on offer and those leading you to it. Quoting newspaper articles or indeed climate reports is like choosing television programs - it depends upon your perspective and the idiosyncrasies of the human psyche. There is a lot of work being undertaken by many people but how much "science" and is the "science" motivated to funding, power, status or all of these? For example, how does one negotiate the myriad pathways of the various "models" on offer - should I stick to the conclusions of the CMIP5 climate models or should I argue they are forced? If they are forced then how do I dissassmble the data sets and algorythms upon which the models are structured and how do I eliminate the programmed bias or do I just strip out and take the GFDL? There are many on offer. Peer reviewing these models is another matter entirely and depends upon the people undertaking the review, their ability their field of research or expertise and unfortunately their innate bias - which takes us back to the your orginal metaphor being who is leading you and to what end.
A somewhat interesting interpretation of a complex issue. For example if you are considering energy budget and climate sensitivity models would you accept Armour's paper or question its methodology and if you question it are you stupid? I think calling people stupid because they don't agree with your conclusions or your innate bias is... well... rather stupid.
