Challenges

Top emitters have a large influence on the annual rate of GHG emissions. To develop an understanding of the anthropogenic contribution to future CO2 concentrations, there is a need for country specialists in the politics, energy policies, environmental regulations, and economics of each substantial emitter.

Complex statistical analysis and mathematical modelling involve multitudes of choices and assumptions. Recent “many analysts, one data set” studies show the danger of relying solely on one research team. Here we present several examples from this literature.

Unconditional forecasts of global temperature alone have an ambiguous interpretation. Suppose a forecast portends a small global temperature anomaly. Are future temperatures low because CO2 emissions will be tamed aggressively, or because climate turns out to be much less sensitive to atmospheric CO2 concentrations than currently believed?

Data gaps are particularly substantial and pressing for forward-looking risk information. And where forward-looking information exists, it is largely based on (i) announced intentions, or (ii) scenarios for which relative likelihood information currently does not exist.

The Climate Service Provider (CSP) industry is growing rapidly to meet demand. In this drive to meet growing demand, a number of issues rooted in adverse incentives have been overlooked.