资源环境科技发展态势分析平台

Arctic moisture intrusions have played an important role in warming the Arctic over the past few decades. A prior study found that Coupled Model Intercomparison Project Phase 5 (CMIP5) models exhibit large regional biases in the moisture flux across 70 degrees N. It is shown here that the systematic misrepresentation of the moisture flux is related to the models' overprediction of zonal wavenumber k = 2 contribution and underprediction of k = 1 contribution to the flux. Models with a warmer tropical upper troposphere and El-Nino-like tropical surface temperature tend to simulate stronger k = 2 flux, while k = 1 flux is uncorrelated with tropical upper tropospheric temperature and is associated with La-Nina-like surface temperature. The models also overpredict the transient eddy moisture flux while underpredicting the stationary eddy flux. Moreover, future projections in Representative Concentration Pathway 8.5 (RCP8.5) simulations show trends in moisture flux that is consistent with biases in historical simulations, suggesting that these CMIP5 projections reflect the same error(s) that cause the model biases.

Plain Language Summary The Arctic is the region where climate change has been most rapid. A growing body of work indicates that moisture intrusions into Arctic have played an important role in warming the Arctic over the past decades. Coupled Model Intercomparison Project Phase 5 (CMIP5) models have served as a critical tool for projecting future climate changes. Therefore, it is imperative to evaluate whether the physical processes governing moisture intrusions are accurately represented by the models. It is shown here that there is a systematic misrepresentation of the moisture flux into the Arctic related to the models' biased representation of tropical temperatures. Moreover, future projections in Representative Concentration Pathway 8.5 (RCP8.5) simulations show moisture flux trends that are consistent with biases in historical simulations, suggesting that these CMIP5 projections reflect the same error(s) that cause the model biases. It is common practice to regard averages across climate model as being the true response to climate forcing. The findings here question this widespread assumption and underscore the need to pay close attention to model biases and their causes.