Theoretical and Applied Climatology

, Volume 106, Issue 3–4, pp 473–479 | Cite as

What will a new generation of world climate research and computing facilities bring to climate long-term predictions?

Original Paper


Based on issues recently raised on the future of climate science, I present here a critical discussion which embraces the crucial aspects of the communication between climate scientists and laypersons, of the role confusing statements may exert on possible advancements in climate research, and of scientific priorities in climate science. I start distinguishing between different applications of climate models and identifying confusing uses of the words “prediction” and “projection” in recent discussions on climate modeling. Numerical models like those used in climate simulations are not assimilable to truly theories, nor can obtained results be considered as truly experimental evidences. Hence, it is hard to envisage the feasibility of crucial experiments through climate models. Increasing model resolution and complexity, although undoubtedly helpful for many applications related to a deeper understanding of the complex climate system and to substantial improvement of short-term forecasts, is not destined to change this fundamental limitation, to tackle the impossibility of predicting prominent climate forcings and to facilitate result comparisons against observations. Finally, as an example describing possible alternative resource allocations, I propose to devote more energy to strengthen the observational part of climate research, to focus on midterm forecasts, and to implement a new employment policy for climate scientists. In particular, through an increased and truly global in situ and remote sensing climate observing network, crucial experiments could emerge to challenge the fundamental basis of the conjecture of a great anthropogenic climate change, which, as known, is largely based on high climate sensitivities simulated by numerical models.


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Department of Environmental Sciences, Informatics and StatisticsUniversity of VeniceVeniceItaly

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