Mark Ringer

Cloud feedbacks and rapid adjustments in simplified climate model experiments

fig2_bot_revised2 — Relationship between the total radiative feedbacks (left, NET) and the cloud radiative feedbacks (right, NET CRE) calculated in the fully-coupled, y-axis, and idealized experiments, x-axis. The circles show the individual models, colour-coded by experiment; the one-to-one line is shown for reference.

An interesting component of the most recent coupled model inter-comparison project (CMIP5) is the inclusion of so-called “idealized” climate change experiments. These use simplified versions of the fully-coupled climate models used to perform the 21st century projections and other transient climate change experiments, such as either steadily or instantaneously increasing the atmospheric CO2 concentration. In the first of these, known as “amip” experiments, there is no coupling between the atmosphere and the oceans: in the control experiment the sea-surface temperatures (SSTs) are prescribed from observations over the 1979-2008 period and the atmospheric model responds to these. The climate change experiment then repeats this scenario except that the SSTs are increased: in one case by uniformally adding 4ºC to the SSTs everywhere, in another by imposing a geographical pattern of SST change. The second experiment is even more idealized. These are “aquaplanet” (water world ) simulations: there is no land, the prescribed SSTs are zonally symmetric and the solar insolation is for a fixed season. The climate change experiment with these models then consists of simply increasing the SSTs by 4ºC everywhere.

In our new paper, just published in Geophysical Research Letters, we calculate the global-mean radiative feedbacks in these idealized experiments and compare them to the values obtained from the fully-coupled models in response to increased CO2. We find that in all but two cases − two of the aquaplanet models − the cloud feedbacks match up almost perfectly (right hand figure). This is an interesting result because it suggests that factors such as the spatial pattern of the SST increase, the increased surface warming over land and the coupling between the atmosphere and ocean may not be especially relevant to determining the global-mean cloud feedbacks in models. It also implies that the simplified experiments should provide a useful framework for understanding the processes governing the cloud feedbacks. Note that the total radiative feedback is not so well characterized by the idealized experiments (left hand figure): this is primarily because they do not include the positive feedback due to reduced sea ice in response to warming. The paper also examines the relationship between the feedbacks and the CO2 radiative forcing. In particular we find an anti-correlation between the rapid cloud adjustments in response to increased CO2 and the cloud feedbacks. This strengthens as the experiments get progressively simpler, so that it is strongest in the aquaplanet model ensemble.

The abstract and citation are below. The paper can be accessed here.

Analysis of the available Coupled Model Intercomparison Project Phase 5 models suggests that sea surface temperature-forced, atmosphere-only global warming experiments (“amip4K,” “amipFuture,” and “aqua4K”) are a good guide to the global cloud feedbacks determined from coupled atmosphere-ocean CO2-forced simulations, including the intermodel spread. Differences in the total climate feedback parameter between the experiments arise primarily from differences in the clear-sky feedbacks which can largely be anticipated from the nature of the experimental design. The effective CO2 radiative forcing is anticorrelated with the total feedback in the coupled simulations. This anticorrelation strengthens as the experimental design becomes simpler, the number of potential degrees of freedom of the system’s response reduces, and the relevant physical processes can be identified. In the aquaplanet simulations the anticorrelation is primarily driven by opposing changes in the rapid cloud adjustment to CO2 and the net cloud response to increased surface warming. Establishing a physical explanation for this behavior is important future work.

Ringer, M. A., T. Andrews, and M. J. Webb (2014), Global-mean radiative feedbacks and forcing in atmosphere-only and coupled atmosphere-ocean climate change experiments, Geophys. Res. Lett., 41, doi:10.1002/2014GL060347.

Seeking inspiration…

NASA-Apollo8-Dec24-Earthrise — Earthrise – Dec 24, 1968

Like most people I occasionally get stuck in a rut and start thinking that everything I’m doing might be a complete waste of time.

Whenever this happens I usually find that taking a look at this image is enough to get me back on track and freshly motivated once again.

What do you do?

Paul Klee & the Earth’s radiation budget

Greeting 1922 Paul Klee — Greeting – Paul Klee (1922)

The wonderful Paul Klee exhibition currently at the Tate Modern in London contains this rather curious work which suggests that the great artist may have been inspired by the fluxes of infrared and solar radiation in the atmosphere! The colours of the arrows are right – red for the upwelling longwave, blue for the incoming shortwave – and the gradation from top to bottom suggests the decrease of temperature with altitude as one moves from the surface to the top-of-the-atmosphere. Just whimsical speculation on my part of course…but wouldn’t it be amazing if it were true?

Are cloud feedbacks reversible?

It is well-known that large changes in clouds often occur in response to increasing CO2 in climate models. What happens, however, if you then start to reverse the CO2 change? In a new paper by Tim Andrews and myself, just published online in the Journal of Climate, we outline a general method to separate rapid tropospheric adjustments and feedbacks in transient climate change experiments. We find that feedbacks – including cloud feedbacks – are almost entirely reversible under the idealised mitigation scenario we use, with no evidence for so-called “hysteresis” type behaviour. Our simple conceptual framework also appears to apply to regional feedback patterns. We do, however, stress the importance of interpreting the regional feedbacks carefully, especially in response to time-varying warming patterns.

The abstract and citation are below, the full paper is here. A recent talk I gave on this work is also available on the CFMIP website.

The HadGEM2-ES Earth system climate model is forced by a 1% per year compound increase in atmospheric CO2 for 140 years, followed by a 1% per year CO2 decrease back to the starting level. Analogous atmosphere-only simulations are performed to diagnose the component of change associated with the effective radiative forcing and rapid adjustments. The residual change is associated with radiative feedbacks that are shown to be linearly related to changes in global-mean surface-air-temperature and are found to be reversible under this experimental design, even for regional cloud feedback changes. The cloud adjustment is related to changes in cloud amount, with little indication of any large-scale changes in cloud optical depth. Plant physiological forcing plays a significant role in determining the cloud adjustment in this model and is the dominant contribution to the low-level cloud changes over land. Low-level cloud adjustments are associated with changes in surface turbulent fluxes and lower tropospheric stability, with significant adjustments in boundary layer cloud types and in the depth of the boundary layer itself. The linearity of simple forcing-response frameworks are examined and found to be generally applicable. Small regional departures from linearity occur during the early part of the ramp down phase, where the Southern Ocean and eastern tropical Pacific continue to warm for a few decades, despite the reversal in radiative forcing and global temperatures. We highlight the importance of considering time-varying patterns of warming and regional phenomena when diagnosing and understanding feedbacks in a coupled atmosphere-ocean framework.

Andrews, T. and M.A. Ringer, 2013, Cloud feedbacks, rapid adjustments and the forcing-response relationship in a transient CO2 reversibility scenario, J. Climate, in press. doi:http://dx.doi.org/10.1175/JCLI-D-13-00421.1

Cloud biases over the Southern Ocean in current climate models

Poor simulation of clouds may be the main reason why current climate models show sea-surface temperature biases over the Southern Ocean. This in turn could have implications for assessing the plausibility of modelled cloud feedbacks in this region under climate change. A new paper led by my colleague Alejandro Bodas-Salcedo – which has just been published as an early online release in the Journal of Climate – looks at the simulation of clouds over the Southern Ocean in the current generation of climate models. The abstract and citation are below, the paper can be accessed here.

Current climate models generally reflect too little solar radiation over the Southern Ocean, which may be the leading cause of the prevalent sea-surface temperature biases in climate models. We study the role of clouds the radiation biases in atmosphere-only simulations of the Cloud Feedback Model Intercomparison Project phase 2 (CFMIP2), as clouds have a leading role in controlling the solar radiation absorbed at those latitudes. We composite daily data around cyclone centres in the latitude band between 40°S and 70°S during the summer. We use cloud property estimates from satellite to classify clouds into different regimes, which allows us to relate the cloud regimes and their associated radiative biases to the meteorological conditions in which they occur. The cloud regimes are defined using cloud properties retrieved using passive sensors, and may suffer from the errors associated with this type of retrievals. We use information from the CALIPSO lidar to investigate in more detail the properties of the ‘mid-level’ cloud regime. Most of the model biases occur in the cold air side of the cyclone composite, and the cyclone composite accounts for most of the climatological error in that latitudinal band. The ‘mid-level’ regime is the main contributor to reflected shortwave radiation biases. CALIPSO data show that the ‘mid-level’ cloud regime is dominated by two main cloud types; cloud with tops actually at mid-level, and low-level cloud. Improving the simulation of these cloud types should help reduce the biases in the simulation of the solar radiation budget in the Southern Ocean in climate models.

Bodas-Salcedo, A., K.D. Williams, M.A. Ringer et al., 2013, Origins of the solar radiation biases over the Southern Ocean in CFMIP2 models, J. Climate, doi: http://dx.doi.org/10.1175/JCLI-D-13-00169.1

The seasonal variation of cloud regimes in CMIP5 models

A new paper led by my colleague Yoko Tsushima has just been published online in Climate Dynamics. The paper uses a clustering method to compare the latest CMIP5 models with satellite observations of clouds – the example above shows maps of the observed and model-simulated deep convective cloud cluster in the tropics for January and July. The focus of the paper is on how well the models are able to represent the seasonal cycle of such cloud regimes and whether these comparisons can provide us with a useful metric to assess model performance. The abstract and citation are below, the full paper is here.

An extended cloud-clustering method to assess the seasonal variation of clouds is applied to five CMIP5 models. The seasonal variation of the total cloud radiative effect (CRE) is dominated by variations in the relative frequency of occurrence of the different cloud regimes. Seasonal variations of the CRE within the individual regimes contribute much less. This is the case for both observations, models and model errors. The error in the seasonal variation of cloud regimes, and its breakdown into mean amplitude and time varying components, are quantified with a new metric. The seasonal variation of the CRE of the cloud regimes is relatively well simulated by the models in the tropics, but less well in the extra-tropics. The stratocumulus regime has the largest seasonal variation of shortwave CRE in the tropics, despite having a small magnitude in the climatological mean. Most of the models capture the temporal variation of the CRE reasonably well, with the main differences between models coming from the variation in amplitude. In the extra-tropics, most models fail to correctly represent both the amplitude and time variation of the CRE of congestus, frontal and stratocumulus regimes. The annual mean climatology of the CRE and its amplitude in the seasonal variation are both underestimated for the anvil regime in the tropics, the cirrus regime and the congestus regime in the extra-tropics. The models in this study that best capture the seasonal variation of the cloud regimes tend to have higher climate sensitivities.

Tsushima, Y., M.A. Ringer, M.J. Webb, K.D. Williams, 2012, Quantitative evaluation of the seasonal variations in climate model cloud regimes, Climate Dynamics, doi: 10.1007/ s00382-012-1609-4