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. The authors study the role of clouds on 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. The authors composite daily data around cyclone centers in the latitude band between 40° and 70°S during the summer. They use cloud property estimates from satellite to classify clouds into different regimes, which allow them 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. The authors use information from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar to investigate in more detail the properties of the “midlevel” 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 midlevel regime is the main contributor to reflected shortwave radiation biases. CALIPSO data show that the midlevel cloud regime is dominated by two main cloud types: cloud with tops actually at midlevel 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.

Effective climate sensitivity is often assumed to be constant (if uncertain), but some previous studies of general circulation model (GCM) simulations have found it varying as the simulation progresses. This complicates the fitting of simple models to such simulations, as well as having implications for the estimation of climate sensitivity from observations. This study examines the evolution of the feedbacks determining the climate sensitivity in GCMs submitted to the Coupled Model Intercomparison Project. Apparent centennial-time-scale variations of effective climate sensitivity during stabilization to a forcing can be considered an artifact of using conventional forcings, which only allow for instantaneous effects and stratospheric adjustment. If the forcing is adjusted for processes occurring on time scales that are short compared to the climate stabilization time scale, then there is little centennial-time-scale evolution of effective climate sensitivity in any of the GCMs. Here it is suggested that much of the apparent variation in effective climate sensitivity identified in previous studies is actually due to the comparatively fast forcing adjustment.Persistent differences are found in the strength of the feedbacks between the coupled atmosphere-ocean (AO) versions and their atmosphere-mixed layer ocean (AML) counterparts (the latter are often assumed to give the equilibrium climate sensitivity of the AOGCM). The AML model can typically only estimate the equilibrium climate sensitivity of the parallel AO version to within about 0.5 K. The adjustment to the forcing to account for comparatively fast processes varies in magnitude and sign between GCMs, as well as differing between AO and AML versions of the same model. There is evidence from one AOGCM that the forcing adjustment may take a couple of decades, with implications for observationally based estimates of equilibrium climate sensitivity. It is suggested that at least some of the spread in twenty-first-century global temperature predictions between GCMs is due to differing adjustment processes, hence work to understand these differences should be a priority.

No abstract

Abstract. Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive. A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (“CTWN”) for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive. For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.