SPEAR: The Next Generation GFDL Modeling System for Seasonal to Multidecadal Prediction and Projection

We document the development and simulation characteristics of the next generation modeling system for seasonal to decadal prediction and projection at the Geophysical Fluid Dynamics Laboratory (GFDL). SPEAR (Seamless System for Prediction and EArth System Research) is built from component models recently developed at GFDL—the AM4 atmosphere model, MOM6 ocean code, LM4 land model, and SIS2 sea ice model. The SPEAR models are specifically designed with attributes needed for a prediction model for seasonal to decadal time scales, including the ability to run large ensembles of simulations with available computational resources. For computational speed SPEAR uses a coarse ocean resolution of approximately 1.0° (with tropical refinement). SPEAR can use differing atmospheric horizontal resolutions ranging from 1° to 0.25°. The higher atmospheric resolution facilitates improved simulation of regional climate and extremes. SPEAR is built from the same components as the GFDL CM4 and ESM4 models but with design choices geared toward seasonal to multidecadal physical climate prediction and projection. We document simulation characteristics for the time mean climate, aspects of internal variability, and the response to both idealized and realistic radiative forcing change. We describe in greater detail one focus of the model development process that was motivated by the importance of the Southern Ocean to the global climate system. We present sensitivity tests that document the influence of the Antarctic surface heat budget on Southern Ocean ventilation and deep global ocean circulation. These findings were also useful in the development processes for the GFDL CM4 and ESM4 models

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

GFDL's CM2 Global Coupled Climate Models. Part IV: Idealized Climate Response

Abstract The climate response to idealized changes in the atmospheric CO2 concentration by the new GFDL climate model (CM2) is documented. This new model is very different from earlier GFDL models in its parameterizations of subgrid-scale physical processes, numerical algorithms, and resolution. The model was constructed to be useful for both seasonal-to-interannual predictions and climate change research. Unlike previous versions of the global coupled GFDL climate models, CM2 does not use flux adjustments to maintain a stable control climate. Results from two model versions, Climate Model versions 2.0 (CM2.0) and 2.1 (CM2.1), are presented. Two atmosphere–mixed layer ocean or slab models, Slab Model versions 2.0 (SM2.0) and 2.1 (SM2.1), are constructed corresponding to CM2.0 and CM2.1. Using the SM2 models to estimate the climate sensitivity, it is found that the equilibrium globally averaged surface air temperature increases 2.9 (SM2.0) and 3.4 K (SM2.1) for a doubling of the atmospheric CO2 concentration. When forced by a 1% per year CO2 increase, the surface air temperature difference around the time of CO2 doubling [transient climate response (TCR)] is about 1.6 K for both coupled model versions (CM2.0 and CM2.1). The simulated warming is near the median of the responses documented for the climate models used in the 2001 Intergovernmental Panel on Climate Change (IPCC) Working Group I Third Assessment Report (TAR). The thermohaline circulation (THC) weakened in response to increasing atmospheric CO2. By the time of CO2 doubling, the weakening in CM2.1 is larger than that found in CM2.0: 7 and 4 Sv (1 Sv ≡ 106 m3 s−1), respectively. However, the THC in the control integration of CM2.1 is stronger than in CM2.0, so that the percentage change in the THC between the two versions is more similar. The average THC change for the models presented in the TAR is about 3 or 4 Sv; however, the range across the model results is very large, varying from a slight increase (+2 Sv) to a large decrease (−10 Sv)