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1 Dynamical Polar Warming Amplification and a New Climate Feedback Analysis Framework Ming Cai Florida State University Tallahassee, FL 32306 cai@met.fsu.edu cai@met.fsu.edu Cai (2005, GRL); Cai (2006, Climate Dynamics) Cai and Lu (2007, Climate Dynamics) Lu and Cai, and Cai and Lu (2008, Climate Dynamics)

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2 Observed global warming pattern (NCEP R2) 0.22 K/decade 1st EOF (30% var.) Latitude T(K) Meridional Profile of global warming in IPCC AR4 Climate Simulations

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3 Questions Science: What are the roles of atmospheric motions (turbulences, convections, large-scale motions) for the spatial (vertical and horizontal) variations of the warming pattern? Specifically, can a change in the atmospheric circulation alone explain a larger warming in high latitudes? Technique : How do we incorporate atmospheric/oceanic dynamics in the climate feedback analysis?

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4 Coupled Atmosphere-Surface Climate Feedback-Response Analysis Method (CFRAM) for CGCM feedback analysis

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5 Mathematical formulation of CFRAM The radiation flux change only due to a change in the atmosphere-surface column temperature + = Radiative energy input due to the external forcing Energy flux perturbations that are not due to the radiation change associated with temperature changes Planck feedback matrix

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6 Planck Feedback Matrix Positive along diagonal Off-diagonals are negative and numerically are much smaller than diagonal Not degenerated!! Positive along diagonal Off-diagonals are negative and numerically are much smaller than diagonal Not degenerated!!

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7 Mathematical formulation of CFRAM RHS: external forcing plus energy flux perturbations due to each of (thermodynamic, local, and non-local dyn. feedbacks

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8 Feedback Gain Matrices in CFRAM Feedback gain matrix Initial gain matrix= inverse of the Planck feedback matrix Both feedbacks and their effects are additive!

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9 Demonstration of the CFRAM in the context of a dry GCM without hydrological cycle (manuscript in preparation) The science question: Can the surface warming in response to anthropogenic greenhouse gases be still stronger in high latitudes than in low latitudes in the absence of ice-albedo and evaporation feedbacks in a dry GCM model?

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10 The key features of the coupled GCM model Dynamical core: Suarez and Held (1992) Physics: Fu et al. (1992)s radiation model. Dry convection adjustment so that maximum lapse rate cannot exceed a preset meridional profile (6.5K/1km in tropics and 9.8K/1km outside). Atmospheric relative humidity is kept at a prescribed vertical and meridional profile. The surface energy balance model that exchanges sensible heat flux, emits long wave radiation out, and absorbs downward radiation at the surface. The annual mean solar forcing. 1CO2 versus 2CO2 climate simulations

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11 Climatology of the dry GCM E E E W W

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12 2CO2 Climate forcing (w/m2) in the GCM 700 hPa 150 hPa (or IPCC TOA) At the surface At the models TOA

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13 (Total) Warming pattern Pressure 1.7 K 2.9 K 2.1 K 0.9 K

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14 2CO2 only Partial T due to 2CO2 & H2O 1.8 K < 0 1.8 K < 0 H2O change only 3.1 K < 0 2CO2 + H2O) 1.8 K 0.6 K

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15 Partial T due to dynamics Vertical transport 2.8 K 0.9 K Total dynamical feedback 1.0 K 2.0 K Horizontal transport 1.9 K 0.5 K 1.2 K

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16 Sum of partial Ts 2CO2 + H2O) Total dynamical feedback 1.0 K 2.0 K 3.1 K < 0 Total change 1.7 K 2.9 K 2.1 K 0.9 K

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17 Summary Radiative forcing of greenhouse gases (including water vapor) tend to cause a stronger warming in low latitudes and weaker warming in high latitudes in atmos. and surf.. Vertical convection reduces the surface warming in tropics An enhanced poleward heat transport results in a greenhouse-plus feedback (more back radiation from the air to the surface) => a large SURFACE warming in high latitudes than low-latitudes even without evaporation and ice-albedo feedbacks! The CFRAM allows us to explicitly isolate contributions to the global warming and its spatial pattern from external forcing alone and individual thermodynamic and dynamical feedback processes. Part of the total effects of individual thermodynamic and non-local dynamical feedbacks and the total effects of all local dynamical feedbacks are lumped into the lapse rate feedback in a TOA-based framework. CFRAM can be used identify the physical origins of climate projection uncertainties in IPCC AR4 models.

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18 In a realistic model with hydrological cycle: A much stronger reduction of the surface warming in tropics/subtropics due to the evaporation feedback (about 1-2 K warming reduction). Stronger moist convection in the deep tropics brings energy to further up => stronger poleward heat transport (including the latent heat transport) => a larger dynamical amplification of polar warming. Ice albedo feedback => further strengthens the polar warming amplification. Role of clouds? But the CFRAM can help to answer that question! We expect:

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