Global climate models are complex computing tools that simulate the global climate system, and they are a major foundation of our group’s research. The following list of publications details our work to analyze, evaluate, and constrain global climate model output.
Purpose of Review
Highlight significant developments that have recently been made to enhance our understanding of how snow responds to climate forcing and the role that snow plays in the climate system.
Widespread snow loss has occurred in recent decades, with the largest decreases in spring. These changes are primarily driven by temperature and precipitation, but changes in vegetation, light-absorbing impurities, and sea ice also contribute to variability. Changes in snow cover can also affect climate through the snow albedo feedback (SAF). Recently, considerable progress has been made in better understanding the processes contributing to SAF. We also highlight advances in knowledge of how snow variability is linked to large-scale atmospheric changes. Lastly, large-scale snow losses are expected to continue under climate change in all but the coldest climates. These projected changes to snow raise considerable concerns over future freshwater availability in snow-dominated watersheds.
The results discussed here demonstrate the widespread implications that changes to snow have on the climate system and anthropogenic activity at large.
To understand the role of water vapor feedback in unperturbed surface temperature variability, a version of the Geophysical Fluid Dynamics Laboratory coupled ocean–atmosphere model is integrated for 1000 yr in two configurations, one with water vapor feedback and one without. For all spatial scales, the model with water vapor feedback has more low-frequency (timescale ≥ 2 yr) surface temperature variability than the one without. Thus water vapor feedback is positive in the context of the model’s unperturbed variability. In addition, water vapor feedback is more effective the longer the timescale of the surface temperature anomaly and the larger its spatial scale.
To understand the role of water vapor feedback in global warming, two 500-yr integrations were also performed in which CO2 was doubled in both model configurations. The final surface global warming in the model with water vapor feedback is 3.38°C, while in the one without it is only 1.05°C. However, the model’s water vapor feedback has a larger impact on surface warming in response to a doubling of CO2than it does on internally generated, low-frequency, global-mean surface temperature anomalies. Water vapor feedback’s strength therefore depends on the type of temperature anomaly it affects. The authors found that the degree to which a surface temperature anomaly penetrates into the troposphere is a critical factor in determining the effectiveness of its associated water vapor feedback. The more the anomaly penetrates, the stronger the feedback. It is also shown that the apparent impact of water vapor feedback is altered by other feedback mechanisms, such as albedo and cloud feedback. The sensitivity of the results to this fact is examined.
Finally, the authors compare the local and global-mean surface temperature time series from both unperturbed variability experiments to the observed record. The experiment without water vapor feedback does not have enough global-scale variability to reproduce the magnitude of the variability in the observed global-mean record, whether or not one removes the warming trend observed over the past century. In contrast, the amount of variability in the experiment with water vapor feedback is comparable to that of the global-mean record, provided the observed warming trend is removed. Thus, the authors are unable to simulate the observed levels of variability without water vapor feedback.