On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles

Global and local feedback analysis techniques have been applied to two ensembles of mixed layer equilibrium CO 2 doubling climate change experiments, from the CFMIP (Cloud Feedback Model Intercomparison Project) and QUMP (Quantifying Uncertainty in Model Predictions) projects. Neither of these new ensembles shows evidence of a statistically significant change in the ensemble mean or variance in global mean climate sensitivity when compared with the results from the mixed layer models quoted in the Third Assessment Report of the IPCC. Global mean feedback analysis of these two ensembles confirms the large contribution made by inter-model differences in cloud feedbacks to those in climate sensitivity in earlier studies; net cloud feedbacks are responsible for 66% of the inter-model variance in the total feedback in the CFMIP ensemble and 85% in the QUMP ensemble. The ensemble mean global feedback components are all statistically indistinguishable between the two ensembles, except for the clear-sky shortwave feedback which is stronger in the CFMIP ensemble. While ensemble variances of the shortwave cloud feedback and both clear-sky feedback terms are larger in CFMIP, there is considerable overlap in the cloud feedback ranges; QUMP spans 80% or more of the CFMIP ranges in longwave and shortwave cloud feedback. We introduce a local cloud feedback classification system which distinguishes different types of cloud feedbacks on the basis of the relative strengths of their longwave and shortwave components, and interpret these in terms of responses of different cloud types diagnosed by the International Satellite Cloud Climatology Project simulator. In the CFMIP ensemble, areas where low-top cloud changes constitute the largest cloud response are responsible for 59% of the contribution from cloud feedback to the variance in the total feedback. A similar figure is found for the QUMP ensemble. Areas of positive low cloud feedback (associated with reductions in low level cloud amount) contribute most to this figure in the CFMIP ensemble, while areas of negative cloud feedback (associated with increases in low level cloud amount and optical thickness) contribute most in QUMP. Classes associated with high-top cloud feedbacks are responsible for 33 and 20% of the cloud feedback contribution in CFMIP and QUMP, respectively, while classes where no particular cloud type stands out are responsible for 8 and 21%.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45863/1/382_2006_Article_111.pd

The Role of Clouds: An Introduction and Rapporteur Report

This paper presents an overview of discussions during the Cloud s Role session at the Observing and Modelling Earth s Energy Flows Workshop. N. Loeb and B. Soden convened this session including 10 presentations by B. Stevens, B. Wielicki, G. Stephens, A. Clement, K. Sassen, D. Hartmann, T. Andrews, A. Del Genio, H. Barker, and M. Sugi addressing critical aspects of the role of clouds in modulating Earth energy flows. Presentation topics covered a diverse range of areas from cloud microphysics and dynamics, cloud radiative transfer, and the role of clouds in large-scale atmospheric circulations patterns in both observations and atmospheric models. The presentations and discussions, summarized below, are organized around several key questions raised during the session. (1) What is the best way to evaluate clouds in climate models? (2) How well do models need to represent clouds to be acceptable for making climate predictions? (3) What are the largest uncertainties in clouds? (4) How can these uncertainties be reduced? (5) What new observations are needed to address these problems? Answers to these critical questions are the topics of ongoing research and will guide the future direction of this area of research

Small global-mean cooling due to volcanic radiative forcing

In both the observational record and atmosphere-ocean general circulation model (AOGCM) simulations of the last ∼∼ 150 years, short-lived negative radiative forcing due to volcanic aerosol, following explosive eruptions, causes sudden global-mean cooling of up to ∼∼ 0.3 K. This is about five times smaller than expected from the transient climate response parameter (TCRP, K of global-mean surface air temperature change per W m−2 of radiative forcing increase) evaluated under atmospheric CO2 concentration increasing at 1 % yr−1. Using the step model (Good et al. in Geophys Res Lett 38:L01703, 2011. doi:10.​1029/​2010GL045208), we confirm the previous finding (Held et al. in J Clim 23:2418–2427, 2010. doi:10.​1175/​2009JCLI3466.​1) that the main reason for the discrepancy is the damping of the response to short-lived forcing by the thermal inertia of the upper ocean. Although the step model includes this effect, it still overestimates the volcanic cooling simulated by AOGCMs by about 60 %. We show that this remaining discrepancy can be explained by the magnitude of the volcanic forcing, which may be smaller in AOGCMs (by 30 % for the HadCM3 AOGCM) than in off-line calculations that do not account for rapid cloud adjustment, and the climate sensitivity parameter, which may be smaller than for increasing CO2 (40 % smaller than for 4 × CO2 in HadCM3)

Importance of the mixed-phase cloud distribution in the control climate for assessing the response of clouds to carbon dioxide increase: a multi-model study

We have conducted a multi-model intercomparison of cloud-water in five state-of-the-art AGCMs run for control and doubled carbon dioxide climates. The most notable feature of the differences between the control and doubled carbon dioxide climates is in the distribution of cloud-water in the mixed-phase temperature band. The difference is greatest at mid and high latitudes. We found that the amount of cloud ice in the mixed phase layer in the control climate largely determines how much the cloud-water distribution changes for the doubled carbon dioxide climate. Therefore evaluation of the cloud ice distribution by comparison with data is important for future climate sensitivity studies. Cloud ice and cloud liquid both decrease in the layer below the melting layer, but only cloud liquid increases in the mixed-phase layer. Although the decrease in cloud-water below the melting layer occurs at all latitudes, the increase in cloud liquid in the mixed-phase layer is restricted to those latitudes where there is a large amount of cloud ice in the mixed-phase layer. If the cloud ice in the mixed-phase layer is concentrated at high latitudes, doubling of carbon dioxide might shift the center of cloud water distribution poleward which could decrease solar reflection because solar insolation is less at higher latitude. The magnitude of this poleward shift of cloud water appears to be larger for the higher climate sensitivity models, and it is consistent with the associated changes in cloud albedo forcing. For the control climate there is a clear relationship between the differences in cloud-water and relative humidity between the different models, for both magnitude and distribution. On the other hand the ratio of cloud ice to cloud-water follows the threshold temperature which is determined in each model. Improved measurements of relative humidity could be used to constrain the modeled representation of cloud water. At the same time, comparative analysis in global cloud resolving model simulations is necessary for further understanding of the relationships suggested in this paper.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45864/1/382_2006_Article_127.pd

Phytoplankton responses to marine climate change – an introduction

Phytoplankton are one of the key players in the ocean and contribute approximately 50% to global primary production. They serve as the basis for marine food webs, drive chemical composition of the global atmosphere and thereby climate. Seasonal environmental changes and nutrient availability naturally influence phytoplankton species composition. Since the industrial era, anthropogenic climatic influences have increased noticeably – also within the ocean. Our changing climate, however, affects the composition of phytoplankton species composition on a long-term basis and requires the organisms to adapt to this changing environment, influencing micronutrient bioavailability and other biogeochemical parameters. At the same time, phytoplankton themselves can influence the climate with their responses to environmental changes. Due to its key role, phytoplankton has been of interest in marine sciences for quite some time and there are several methodical approaches implemented in oceanographic sciences. There are ongoing attempts to improve predictions and to close gaps in the understanding of this sensitive ecological system and its responses

Beyond equilibrium climate sensitivity

ISSN:1752-0908ISSN:1752-089