Climate processes

Abstract:

<p><strong>Abstract.</strong> Previous study of shallow convection has generally suffered from having to balance domain size with resolution, resulting in high resolution studies which do not capture large scale behaviour of the cloud fields. In this work we hope to go some way towards addressing this by carrying out cloud resolving simulations on large domains. Simulations of trade wind cumulus are carried out using the Met Office Unified Model (UM), based on a case study from the Rain In Cumulus over the Ocean (RICO) field campaign. The UM is run with a nested domain of 500&amp;thinsp;km with 500&amp;thinsp;m resolution, in order to capture the large scale behaviour of the cloud field, and with a double-moment interactive microphysics scheme. Simulations are run using baseline aerosol profiles based on observations from RICO, which are then perturbed. We find that the aerosol perturbations result in changes to the convective behaviour of the cloud field, with higher aerosol leading to an increase (decrease) in the number of deeper (shallower) clouds. However, despite this deepening, there is little increase in the frequency of higher rain rates. This is in contrast to the findings of previous work making use of idealised simulation setups. In further contrast, we find that increasing aerosol results in a persistent increase in domain mean liquid water path and decrease in precipitation, with little impact on cloud fraction.</p>

Abstract:

One of the most pressing questions in climate science is that of the effect of anthropogenic1 aerosol on the Earth’s energy balance. Aerosols provide the ‘seeds’ on which cloud droplets form, and changes in the amount of aerosol available to a cloud can change its brightness and other physical properties such as optical thickness and spatial extent. Clouds play a critical role in moderating global temperatures and small perturbations can lead to significant amounts of cooling or warming. Uncertainty in this effect is so large it is not currently known if it is negligible, or provides a large enough cooling to largely negate present-day warming by CO2. This work uses deep convolutional neural networks to look for two particular perturbations in clouds due to anthropogenic aerosol and assess their properties and prevalence, providing valuable insights into their climatic effects.

Abstract:

<p><strong>Abstract.</strong> We introduce tobac (Tracking and Object-Based Analysis of Clouds), a newly developed framework for tracking and analysing individual clouds in different types of datasets, such as cloud-resolving model simulations and geostationary satellite retrievals. The software has been designed to be used flexibly with any two- or three-dimensional time-varying input. The application of high-level data formats, such as iris cubes or xarray arrays, for input and output allows for convenient use of metadata in the tracking analysis and visualisation. Comprehensive analysis routines are provided to derive properties like cloud lifetimes or statistics of cloud properties along with tools to visualise the results in a convenient way. The application of tobac is presented in two examples. We first track and analyse scattered deep convective cells based on maximum vertical velocity and the three-dimensional condensate mixing ratio field in cloud-resolving model simulations. We also investigate the performance of the tracking algorithm for different choices of time resolution of the model output. In the second application, we show how the framework can be used to effectively combine information from two different types of datasets by simultaneously tracking convective clouds in model simulations and in geostationary satellite images based on outgoing longwave radiation. tobac provides a flexible new way to include the evolution of the characteristics of individual clouds in a range of important analyses like model intercomparison studies or model assessment based on observational data.</p>

Abstract:

<p><strong>Abstract.</strong> This study assesses the change in anthropogenic aerosol forcing from the mid-1970s to the mid-2000s. Both decades had similar global-mean anthropogenic aerosol optical depths but substantially different global distributions. For both years, we quantify (i) the forcing spread due to model-internal variability and (ii) the forcing spread among models. Our assessment is based on new ensembles of atmosphere-only simulations with five state-of-the-art Earth system models. Four of these models will be used in the sixth Coupled Model Intercomparison Project (CMIP6; <span class="cit" id="xref_altparen.1"><a href="#bib1.bibx14">Eyring et al.</a>, <a href="#bib1.bibx14">2016</a></span>). Here, the complexity of the anthropogenic aerosol has been reduced in the participating models. In all our simulations, we prescribe the same patterns of the anthropogenic aerosol optical properties and associated effects on the cloud droplet number concentration. We calculate the instantaneous radiative forcing (RF) and the effective radiative forcing (ERF). Their difference defines the net contribution from rapid adjustments. Our simulations show a model spread in ERF from <span class="inline-formula">−0.4</span> to <span class="inline-formula">−0.9</span>&amp;thinsp;W&amp;thinsp;m<span class="inline-formula">⁻²</span>. The standard deviation in annual ERF is 0.3&amp;thinsp;W&amp;thinsp;m<span class="inline-formula">⁻²</span>, based on 180 individual estimates from each participating model. This result implies that identifying the model spread in ERF due to systematic differences requires averaging over a sufficiently large number of years. Moreover, we find almost identical ERFs for the mid-1970s and mid-2000s for individual models, although there are major model differences in natural aerosols and clouds. The model-ensemble mean ERF is <span class="inline-formula">−0.54</span>&amp;thinsp;W&amp;thinsp;m<span class="inline-formula">⁻²</span> for the pre-industrial era to the mid-1970s and <span class="inline-formula">−0.59</span>&amp;thinsp;W&amp;thinsp;m<span class="inline-formula">⁻²</span> for the pre-industrial era to the mid-2000s. Our result suggests that comparing ERF changes between two observable periods rather than absolute magnitudes relative to a poorly constrained pre-industrial state might provide a better test for a model's ability to represent transient climate changes.</p>