Impact of Decadal Cloud Variations on the Earth’s Energy Budget
Scientists in the Lawrence Livermore National Laboratory analyzed the cloud radiative effect during the instrumental period with a climate model that simulates tropical low-level cloud cover well. They showed that the cloud variations induced by changes in spatial pattern of sea surface temperature oscillates dramatically and contribute a cooling effect to recent climate change. This conclusion is further confirmed by a combination of observations and AMIP simulations.
This study suggests that clouds have likely contributed to the slowdown of global warming in the 2000s, and that the climate sensitivity calculated from recent trends is probably low biased. The study also provides the physical mechanism of how the spatial pattern of surface warming affects the magnitude of cloud feedback.
Feedbacks of clouds on climate change strongly influence the magnitude of global warming. Cloud feedbacks, in turn, depend on the spatial patterns of surface warming, which vary on decadal timescales. Therefore, the magnitude of the decadal cloud feedback could deviate from the long-term cloud feedback. Here we present climate model simulations to show that the global mean cloud feedback in response to decadal temperature fluctuations varies dramatically due to time variations in the spatial pattern of sea surface temperature (SST). We find that cloud anomalies associated with these patterns significantly modify the Earth’s energy budget. Specifically, the decadal cloud feedback between the 1980s and 2000s is substantially more negative than the long-term cloud feedback. This is a result of cooling in tropical regions where air descends, relative to warming in tropical ascent regions, which strengthens low-level atmospheric stability. Under these conditions, low-level cloud cover and its reflection of solar radiation increase, despite an increase in global mean surface temperature. These results suggest that SST pattern-induced low cloud anomalies could have contributed to the period of reduced warming between 1998 and 2013, and offer a physical explanation of why climate sensitivities estimated from recently observed trends are probably biased low.