Abstract
Projections of global mean temperature changes (ΔT) in the future are associated with intrinsic uncertainties. Much climate policy discourse has been guided by “current knowledge” of the ΔTs uncertainty, ignoring the likely future reductions of the uncertainty, because a mechanism for predicting these reductions is lacking. By using simulations of Global Climate Models from the Coupled Model Intercomparison Project Phase 5 ensemble as pseudo past and future observations, we estimate how fast and in what way the uncertainties of ΔT can decline when the current observation network of surface air temperature is maintained. At least in the world of pseudo observations under the Representative Concentration Pathways (RCPs), we can drastically reduce more than 50% of the ΔTs uncertainty in the 2040 s by 2029, and more than 60% of the ΔTs uncertainty in the 2090 s by 2049. Under the highest forcing scenario of RCPs, we can predict the true timing of passing the 2 °C (3 °C) warming threshold 20 (30) years in advance with errors less than 10 years. These results demonstrate potential for sequential decision-making strategies to take advantage of future progress in understanding of anthropogenic climate change.
Highlights
The large uncertainty associated with projections of future climate change is one of the barriers to political agreement on mitigation policy
Using simple climate models have estimated learning rates by analyzing future pseudo observations (POs) from their simple models, their results are sensitive to the assumption of natural variability, which can not be simulated by simple models, and prior distributions of climate parameters[8,9,10]
Some studies have tried to incorporate the effects of possible future learning about climate change into mitigation analyses[8,11,12,13,14]
Summary
The large uncertainty associated with projections of future climate change is one of the barriers to political agreement on mitigation policy. By considering the uncertainties arising from the internal natural variability of the climate system, one can obtain observationally constrained confidence ranges of future ∆Ts. Various approaches including the ASK method have provided the “current knowledge” of future projections that have guided mitigation and adaptation studies[1,6,7]. Using simple climate models have estimated learning rates by analyzing future POs from their simple models, their results are sensitive to the assumption of natural variability, which can not be simulated by simple models, and prior distributions of climate parameters[8,9,10] These limitations of simple model analyses cause difficulties in usage of their estimates of learning rates in mitigation studies. We provide the information of the more plausible future reduction of uncertainties in climate change projection that can be useful for studies of sequential decision-making strategies
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