FY 2013 Performance Metrics

Use new climate model simulations to quantify the interactions between cloud and climate changes

Overall Performance Measures

1st Quarter Metric - COMPLETED

Objective

Quantify the response of high clouds to climate change in new global climate model simulations

Product Definition
One of the largest uncertainties in projections of future climate warming involves changes to clouds, which strongly impact the planet's energy budget. Cloud responses vary depending on cloud type. This quarter is focused on the changes to high clouds, including both the rapid response that occurs following CO2 quadrupling and on high cloud feedbacks that evolve more slowly as the planet warms. The radiative impacts of cloud changes are derived from an ensemble of current generation atmosphere-ocean climate models simulating a step-function quadrupling of CO2.  
Product Documentation
Upon quadrupling of CO2, the high cloud changes cause some planetary cooling and some warming, with warming effects dominating. For the rapid responses, the high clouds in the ensemble of climate models decrease in spatial coverage and in optical depth (reflectivity), while increasing in altitude, all enhancing the planetary heating above and beyond that due directly to CO2.  As the planet subsequently warms, eventually the heating effect of high clouds increases further in most models due primarily to increases in cloud top altitude, which are slightly opposed by increases in cloud optical depth. Thus, the changes that high clouds undergo both enhance the heating from CO2 and act as a positive feedback on the subsequent warming. 

2nd Quarter Metric - COMPLETED

Objective

Quantify the response of marine stratocumulus cloud amount to climate change from new global climate model simulations

Product Definition
Marine low clouds play a critical role in regulating the global energy budget. Changes in low clouds associated with simulated anthropogenic climate change likewise have a large effect on the anthropogenic perturbation to the global energy budget. These changes exhibit a large spread across models participating in the previous and current phases of the Coupled Model Intercomparison Project (CMIP). As part of the efforts to reduce this spread, we examine the response of low cloud amount to climate change in several low cloud‐dominated oceanic regions and in 18 CMIP5 models. We focus on two changes in the low clouds’ large‐scale environment‐‐‐the strength of the inversion that caps the planetary boundary layer (PBL) and sea surface temperature (SST). We develop a heuristic model designed to tease out the respective contributions of the two factors to the cloud response in the models. We explore to what extent the intermodel spread in the cloud response is linked to differences in PBL and cloud parameterizations used in the models.

Product Documentation
In the CMIP5 ensemble, marine low cloud cover (LCC) responds mainly to changes in SST. The correlation between the SST contribution and actual LCC changes across CMIP5 models is 0.69, so that about 50% of the intermodel variance of the LCC changes is accounted for by the SST contribution. In contrast, the contribution of inversion strength is only weakly correlated to the actual LCC changes, suggesting its contribution to LCC spread is relatively small. Both SST change and SST slope (which measures the sensitivity of LCC to SST change) contribute approximately equally to differences in the LCC changes in CMIP5 ensemble. The LCC changes are positively correlated with the SST slope, while the LCC changes are anticorrelated with SST changes. Moreover, we find a clear association between parameterizations and the cloud response via SST slope. Schemes that favor a large negative SST slope exhibit a similarly large decrease in LCC, while schemes that favor a small negative or positive SST slope often exhibit an increase in LCC.

3rd Quarter Metric - COMPLETED

Objective

Quantify the response of low-cloud optical depths to climate change from new global climate model simulations

Product Definition

The ability for clouds to reflect solar radiation, the cloud optical depth, is a key element of the global energy budget. As the climate warms from increased greenhouse gases, it is important to understand how the reflective ability of clouds responds to temperature increases, either exacerbating or mitigating anthropogenic warming. We calculate the sensitivity of the cloud optical depth to both surface and atmospheric temperature variability in the pre-industrial control climate integrations of six fully coupled ocean-atmosphere global climate models from the Coupled Model Intercomparison Project (CMIP). In addition, we calculate how low-level cloud optical depth changes between the model’s control and climate change integrations. We then use the cloud variability at time scales we observe, such as seasonal to interannual, to infer cloud changes expected for future climate simulations on longer time scales of decades to centuries.

Product Documentation

Similar to behavior observed from satellites, clouds tend to increase optical depth, and cloud reflectivity, with increasing atmospheric and surface temperature for relatively cold clouds, and clouds outside the tropics and subtropics. This increase in optical depth stems primarily from increasing cloud water content, as warmer air holds more water vapor, and the clouds that form in a warmer atmosphere contain more condensed liquid and ice. The response of low-level clouds to warming temperatures therefore primarily represents a negative feedback on the climate system, tending to offset the warming by greenhouse gases. It is also shown that optical depth variability on time scales that are observable may be used to constrain future cloud changes.

4th Quarter Metric - COMPLETED

Objective

Quantify the response of marine stratocumulus cloud amount simulated by a mixed-layer model to climate change when driven by boundary conditions from new global climate model simulations

Product Definition

Large swaths of the subtropics are covered by stratocumulus clouds, which exert a strong cooling influence on the planet by reflecting sunlight back to space. Differences in the simulation of subtropical stratocumulus have long been identified as a primary source of disagreement among climate model predictions of future warming. This quarter’s goal is to test the impact of replacing the cloud physics parameterizations in a variety of state-of-the-art global climate models (GCMs) with a single, specialized stratocumulus parameterization called a mixed-layer model (MLM). Using one cloud parameterization allows us to clarify the relative importance of cloud physics versus large-scale circulation (e.g., broad-scale sinking of warm air) components of the GCMs in contributing to inter-model disagreement. If cloud parameterization is most important (as expected), this approach should also extract better, less variable predictions of future stratocumulus change from the GCMs studied. 
Product Documentation

Observations and theory indicate that increased stratocumulus cloud coverage would strongly cool the planet, acting as a “negative feedback" on global warming. However, GCMs show little consistency in their predictions of how these clouds will change, leading to large uncertainty in climate projections. The GCMs studied here are found to be unable to reproduce the relationship between cloud fraction and the strength of the temperature inversion at cloud top which explains >80% of observed cloud fraction variability in current climate. This problem is apparently related to GCM cloud parameterizations when these GCMs’ large-scale conditions are used to drive a physically more realistic mixed layer model (MLM), the observed relationship re-appears. However, even when using a common cloud physics parameterization (the MLM) for all models, the inter-model spread in low cloud response remains large. This is because the MLM displays an increased (more realistic) sensitivity to inversion strength. In general the MLM predicts stratocumulus change to be more positive than the GCMs, leading to weaker predictions of global warming. This implies that most GCMs would over-predict warming because their stratocumulus clouds do not increase enough; but model spread and uncertainty is large (mainly due to differences in model parameterizations of these low clouds).