A basic effect of cloud radiative effects on tropical sea-surface temperature variability

Cloud radiative effects (CREs) are known to play a central role in governing the long-term mean distribution of sea-surface temperatures (SSTs). Very recent work suggests that CREs may also play a role in governing the variability of SSTs in the context of the El Niño/Southern Oscillation. Here, the authors exploit numerical simulations in the Max Planck Institute Earth System Model with two different representations of CREs to demonstrate that coupling between CREs and the atmospheric circulation has a much more general and widespread effect on tropical climate than that indicated in previous work.The results reveal that coupling between CREs and the atmospheric circulation leads to robust increases in SST variability on timescales longer than a month throughout the tropical oceans. Remarkably, cloud/circulation coupling leads to more than a doubling of the amplitude of decadal-scale variability in tropical-mean SSTs. It is argued that the increases in tropical SST variance derive primarily from the coupling between SSTs and shortwave CREs: Coupling increases the memory in shortwave CREs on hourly and daily timescales, and thus reddens the spectrum of shortwave CREs and increases their variance on timescales spanning weeks to decades. Coupling between SSTs and CREs does not noticeably affect the variance of SSTs in the extratropics, where the effects from variability in CREs on the surface energy budget are much smaller than the effects from the turbulent heat fluxes. The results indicate a basic but critical role of CREs in climate variability throughout the tropics

Consistently estimating internal climate variability from climate model simulations

AbstractThis paper introduces and applies a new method to consistently estimate internal climate variability for all models within a multi-model ensemble. The method regresses each model?s estimate of internal variability from the preindustrial control simulation on the variability derived from a model?s ensemble simulations, thus providing practical evidence of the quasi-ergodic assumption. The method allows one to test in a multi-model consensus view how the internal variability of a variable changes for different forcing scenarios. Applying the method to the CMIP5 model ensemble shows that the internal variability of global-mean surface air temperature remains largely unchanged for historical simulations and might decrease for future simulations with a large CO2 forcing. Regionally, the projected changes reveal likely increases in temperature variability in the tropics, subtropics and polar regions and extremely likely decreases in mid-latitudes. Applying the method to sea-ice volume and area shows that their internal variability decreases extremely likely or likely and proportionally to their mean state, except for Arctic sea-ice area, which shows no consistent change across models. For the evaluation of CMIP5 simulations of Arctic and Antarctic sea ice the method confirms that internal variability can explain most of the models? deviation from observed trends, but often not the models? deviation from the observed mean states. Our method benefits from a large number of models and long pre-industrial control simulations, but requires only a small number of ensemble simulations. The method allows for a consistent consideration of internal variability in multi-model studies and thus fosters our understanding of the role of internal variability in a changing climate

How large does a large ensemble need to be

Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool to quantify the forced response and internal variability in various components of the climate system. However, there is no consensus on the ideal or even sufficient ensemble size for a large ensemble. Here, we introduce an objective method to estimate the required ensemble size that can be applied to any given application and demonstrate its use on the examples of global mean surface temperature, local surface temperature and precipitation and variability in the ENSO region and central America. Where possible, we base our estimate of the required ensemble size on the pre-industrial control simulation, which is available for every model. First, we determine how much of an available ensemble size is interpretable without a substantial impact of resampling ensemble members. Then, we show that more ensemble members are needed to quantify variability than the forced response, with the largest ensemble sizes needed to detect changes in internal variability itself. Finally, we highlight that the required ensemble size depends on both the acceptable error to the user and the studied quantity

The Max Planck Institute Grand Ensemble - Enabling the Exploration of Climate System Variability

The Max Planck Institute Grand Ensemble (MPI-GE) is the largest ensemble of a single comprehensive climate model currently available, with 100 members for the historical simulations (1850-2005) and each of four forcing scenarios. It is currently the only large ensemble available that includes scenario representative concentration pathway (RCP) 2.6 and a 1% CO2 scenario. The ensemble also has the advantage that it is initialized by sampling the control state for all model components, meaning that for most variables the ensemble can be directly investigated from the beginning of the simulations. These advantages make MPI-GE a powerful tool. We present an overview of MPI-GE and its components and detail the experiments completed. We provide an example of how to compare multiple scenarios and discuss the value of having a large ensemble to do so. We then demonstrate multiple ways both to evaluate MPI-GE and compare observations to large ensembles, including a novel approach for comparing model internal variability with estimated observed variability. We demonstrate how to separate the forced response from internal variability in a large ensemble. This separation allows the quantification of both the forced signal under climate change and the internal variability to unprecedented precision. Additionally we emphasize the capacity of using the ensemble dimension to quantify variability and its potential changes in a transient forcing scenario. Finally, sea level pressure is used to demonstrate how MPI-GE can be utilized to estimate the ensemble size needed for a given scientific problem and provide insights for future ensemble projects