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Journal ArticleDOI

Investigating physical constraints on climate feedbacks using a perturbed parameter ensemble

TLDR
In this article, a large parameter-perturbed ensemble of the Met Office climate model is used to explore the relationship between radiative feedbacks and the present-day simulation of the associated physical processes.
Abstract
A large parameter-perturbed ensemble (PPE) of the Met Office climate model is used to explore the relationship between radiative feedbacks and the present-day simulation of the associated physical processes. We highlight three tropical regimes (deep convection over ocean and land, and marine stratocumulus) in which the same set of processes drives the present-day simulation of clouds and their feedbacks. In each case, the amount of the dominant cloud types reduces in response to warming and the reduction is approximately proportional to the amount simulated in the present day. In deep convective regions, convective process parameters lead the spread among multiple contributing processes, with vegetation processes contributing as well for the land regions. Multiple parameters, such as boundary layer processes, drive stratocumulus regions. However, the low-thick clouds are systematically overestimated, suggesting a structural error in their process representations which would limit the efficacy of the constraint. The influence of convection is largely confined to the tropical deep convective regions in the present day but extends to mid-latitudes under warming. Because of this, contributing processes to the spread in the present-day and the response are different in the extra-tropics, making it much more difficult to establish links between the present-day and the feedback within the region. This suggests that identifying a constraint on convective processes in the tropics for the present-day simulations could constrain both the tropical feedbacks and feedbacks in the extra-tropics. A parameter representing deep-convective entrainment links the present-day tropical mean high cloud and clear-sky longwave flux to their feedbacks in our model, suggesting a potential process constraint from the observations. Understanding and improving the detailed processes controlling feedbacks is ultimately only possible in individual models. Different process-based constraints might be inferred for different models. The approach described here could usefully extended to other single model ensembles and the collective understandings could be valuable for improving model process and feedbacks more generally.

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Citations
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Reliability of multi-model and structurally different single-model ensembles

TL;DR: In the MMEs, the climate variables investigated are broadly reliable on the global scale, with a tendency towards overdispersion, and the application of rank histograms to assess reliability when designing and running perturbed physics SMEs is recommended.
Journal ArticleDOI

An underestimated negative cloud feedback from cloud lifetime changes

TL;DR: In this article, the authors modified a climate model to better simulate warm-rain probability and found that it exhibits a cloud lifetime feedback nearly three times larger than the default model, which suggests that model errors in cloud-precipitation processes may bias cloud feedbacks by as much as the CMIP5-to-CMIP6 climate sensitivity difference.

Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator

TL;DR: In more recent models, there is widespread reduction of a bias associated with too many highly reflective clouds, with the best models having eliminated this bias as mentioned in this paper, with increased amounts of clouds with lesser reflectivity, the compensating errors that permit models to simulate the time-mean radiation balance have been reduced.

Reducing the uncertainty in subtropical cloud feedback

TL;DR: In this article, an observationally constrained formulation of the response of subtropical clouds to greenhouse forcing is proposed, where the observed interannual sensitivity of cloud solar reflection to varying meteorological conditions suggests that increasing sea surface temperature and atmospheric stability in the future climate will have largely canceling effects on subturbical cloudiness, overall leading to a weak positive shortwave cloud feedback.

Reconciling Simulated and Observed Views of Clouds: MODIS, ISCCP, and the Limits of Instrument Simulators in Climate Models

TL;DR: This work examines the extent to which instrument simulators are able to capture essential differences between MODIS and ISCCP, two similar but independent estimates of cloud properties.
References
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Journal ArticleDOI

An Overview of CMIP5 and the Experiment Design

TL;DR: The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance the authors' knowledge of climate variability and climate change.
Journal ArticleDOI

Robust Responses of the Hydrological Cycle to Global Warming

TL;DR: In this paper, the authors examined some aspects of the hydrological cycle that are robust across the models, including the decrease in convective mass fluxes, the increase in horizontal moisture transport, the associated enhancement of the pattern of evaporation minus precipitation and its temporal variance, and decrease in the horizontal sensible heat transport in the extratropics.
Journal ArticleDOI

Advances in understanding clouds from ISCCP

TL;DR: The progress report on the International Satellite Cloud Climatology Project (ISCCP) describes changes made to produce new cloud data products (D data), examines the evidence that these changes are improvements over the previous version (C data), summarizes some results, and discusses plans for the ISCCP through 2005.
Journal ArticleDOI

Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms

TL;DR: Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) is a two-wavelength polarization lidar that performs global profiling of aerosols and clouds in the troposphere and lower stratosphere as discussed by the authors.
Book ChapterDOI

Evaluation of climate models

TL;DR: In this article, an overview of model capabilities as assessed in this chapter, including improvements, or lack thereof, relative to models assessed in the AR4, is presented, along with an assessment of recent work connecting model performance to the detection and attribution of climate change as well as to future projections.
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