- Abstract
- 1. Introduction
- 2. Cloud feedbacks
- 3. Water vapor–lapse rate feedbacks
- 4. Cryosphere feedbacks
- 5. Summary and conclusions
- How Are Feedbacks Defined?
- How Are Global Radiative Feedbacks Diagnosed in GCMs?
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Fig . 1.Comparison of GCM climate feedback parameters (in W m−1 K−1) for water vapor (WV), cloud (C), surface albedo (A), lapse rate (LR), and the combined water vapor + lapse rate (WV + LR). ALL represents the sum of all feedbacks. Results are taken from Colman (2003; in blue), Soden and Held (2006, in red), and Winton (2006, in green). Closed and open symbols from Colman (2003) represent calculations determined using the PRP and the RCM approaches, respectively. Crosses represent the water vapor feedback computed for each model from Soden and Held (2006) assuming no change in relative humidity. Vertical bars depict the estimated uncertainty in the calculation of the feedbacks from Soden and Held (2006).
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Fig . 2.Composite of instantaneous infrared imagery from geostationary satellites (at 1200 UTC 29 Mar 2004) showing the contrast between the large-scale organization of the atmosphere and of the cloudiness in the Tropics and in the extratropics. [From SATMOS (©METEO-FRANCE and Japan Meteorological Agency)]
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Fig . 3.Two conceptual representations of the relationship between cloudiness and large-scale atmospheric circulation in the Tropics: (a) structure of the tropical atmosphere, showing the various regimes, approximately as a function of SST (decreasing from left to right) or mean large-scale vertical velocity in the midtroposphere (from mean ascending motions on the left to large-scale sinking motions on the right). [From Emanuel (1994).] (b) Two-box model of the Tropics used by Larson et al. (1999). The warm pool has high convective clouds and the cold pool has boundary layer clouds. Air is rising in the warm pool and sinking across the inversion in the cold pool.
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Fig . 4.(a) PDF Pω of the 500-hPa monthly mean large-scale vertical velocity ω500 in the Tropics (30°S–30°N) derived from ERA-40 meteorological reanalyses, and composite of the monthly mean (b) GPCP precipitation and (c) ERBE-derived longwave and shortwave (multiplied by −1) cloud radiative forcing in different circulation regimes defined from ERA-40 ω500 over 1985–89. Vertical bars show the seasonal standard deviation within each regime. [After Bony et al. (2004)]
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Fig . 5.(top) Schematic of a mature extratropical cyclone represented in the horizontal plane. Shaded areas are regions of precipitation. [From Cotton (1990).] (bottom) Schematic vertical cross section through an extratropical cyclone along the dashed line reported in the top showing typical cloud types and precipitation. [From Cotton (1990), after Houze and Hobbs (1982)]
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Fig . 6.Composite spatial distributions of 1000-hPa wind (arrows), SST (nearly horizontal lines), 500-hPa pressure vertical velocity (other solid and dashed lines), and ISCCP cloud anomalies (color) centered on locations within the region 30°–50°N, 155°–215°E where advection of the 1000-hPa wind over the SST gradient (−V · ∇SST) is (a) maximum positive during July, (b) maximum positive during January, (c) maximum negative during July, and (d) maximum negative during January. Composites were constructed from local noon data during 1984–2001. The SST contour interval is 2°C with a thick line for the 16°C isotherm. The vertical velocity contour interval is 20 hPa day−1 for July and 40 hPa day−1 for January with negative (upward) contours dashed, positive (downward) contours solid, and no zero contour. Each 2.5° × 2.5° grid box in the plot is filled with 25 pixels, and each pixel represents an additional 2% cloud amount or clear-sky frequency beyond the climatological value for the ISCCP category associated with that color (see legend in figure). Only cloud anomalies statistically significant at 95% are shown, and negative cloud anomalies are not plotted. [From Norris and Iacobellis (2005)]
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Fig . 7.Number of storm tracks per 90-day December–January–February season in each central pressure band (pr, pressure at storm center − 1000 hPa). Dark, medium dark, medium light, and light shadings (first, second, third, and fourth peak from left of each group) show the change in the number of storms (relative to the control experiment) in the experiment forced by both greenhouse gases and the direct effect of aerosols (SUL) or by only greenhouse gases (GHG) for two different time periods. Horizontal bars at the end of peaks show changes that are significant at the 1% level. [From Carnell and Senior (1998)]
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Fig . 8.Composites of the ERBE (a), (b) SW, (c), (d) LW, (e), (f) and NET cloud radiative forcing difference between the upper (warm) and lower (cold) SST terciles for July and January during 1985–89 in ω500 and advection intervals over the North Pacific (25°–55°N, 145°–225°E). The magnitude of SW CRF decrease with rising temperature under most conditions of vertical velocity and SST advection (the advection of the 1000-hPa wind over the SST gradient). The cloud amount and optical thickness also decrease with rising temperature (not shown). The magnitude of LW CRF decreases, but the change in SW CRF is larger, so net CRF becomes less negative (or more positive) with warmer temperature. [From Norris and Iacobellis (2005)]
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Fig . 9.Sensitivity (in W m−2 K−1) of the tropical (30°S–30°N) NET, SW, and LW CRF to SST changes associated with climate change (in a scenario in which the CO2 increases by 1% yr−1) derived from 15 coupled ocean–atmosphere GCMs participating in the AR4. The sensitivity is computed for different regimes of the large-scale atmospheric circulation (the 500-hPa large-scale vertical pressure velocity is used as a proxy for large-scale motions, negative values corresponding to large-scale ascent and positive values to large-scale subsidence). Results are presented for two groups of GCMs: models that predict a positive anomaly of the tropically averaged NET CRF in climate change (in red, eight models) and models that predict a negative anomaly (in blue, seven models). [From Bony and Dufresne (2005).]
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Fig . 10.Global change in the (left) NET, (middle) SW, and (right) LW CRF normalized by the change in global mean surface air temperature predicted by AR4 mixed layer ocean atmosphere models in 2xCO2 equilibrium experiments. For each panel, results (in W m−2 K−1) are shown for global (GL), tropical (TR, 30°S–30°N) and extratropical (EX) areas. The intermodel spread of the global CRF response to climate warming primarily arises from different model predictions of the change in tropical SW CRF. (Adapted from WEBB.)
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Fig . 11.(a) (top) ISCCP monthly mean cloud frequency sorted using the ω500 from ECMWF analysis, and divided into ISCCP cloud thickness categories: thin (0.02 ≤ τ ≤ 3.6), intermediate (3.6 ≤ τ ≤ 23), thick (τ ≥ 23), and (d) all optical depths. (bottom) Monthly mean cloud frequency from the ISCCP simulator for an AMIP simulation of (b) NCAR CAM 3.0 and (c) GFDL AM2.12b climate models over the period 1984–2000, sorted by ω500 and using similar thickness categories. (From WYANT)
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Fig . 12.(a) Progressive humidity profiles computed by reducing the free-tropospheric specific humidity of the Air Force Geophysical Laboratory profile between 800 and 100 hPa by multiplicative factors of 1.0, 0.4, 0.2, 0.1, and 0.05. This results in height-weighted average relative humidities in the free troposphere of 31%, 13%, 6%, 3%, and 1.6%, respectively. (b) Sensitivity of outgoing LW radiation to additive changes of relative humidity of 3% in 10-hPa-thick layers as a function of the humidity profiles shown in (a). (c) The nonlinear dependence of clear-sky outgoing LW radiation over this range of free-tropospheric relative humidity. [From Spencer and Braswell (1997)]
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Fig . 13.Illustration of Lagrangian trajectories through the atmosphere, showing the importance of microphysical processes in determining the water content of air. Diagram extends from (left) equator to (right) high latitudes and extends from surface to lower stratosphere. White clouds represent cumuli while the dark cloud represents sloping ascent in baroclinic systems. The total water content of air flowing out of clouds is set by the fraction of condensed water converted to precipitation, and subsequent moistening in the general subsiding branch is governed by detrainment from shallower clouds and by evaporation of precipitation. [From Emanuel and Zivkovic-Rothman (1999)]
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Fig . 14.Interannual variations in (a) surface temperature, (b) column-integrated water vapor, (c) atmospheric normalized greenhouse trapping, and (d) 6.7-μm brightness temperature for the SST-forced model (shaded), model with all known forcings (dashed), and observations (solid). Substantial differences between SST only forced experiments and “full forcing” experiments in (c) indicate that the model normalized greenhouse effect is very sensitive to the input of volcanic aerosols and changes in greenhouse gases. [From Allan et al. (2003)]
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Fig . 15.Comparison of the observed (black) and GCM-simulated (blue) changes in global-mean (90°S–90°N) 6.7-μm brightness temperature (Tb6.7). The observed anomalies are computed with respect to a 1979–90 base climatology and expressed relative to their preemption (January–May 1991) value. The GCM-simulated anomalies are computed as the ensemble-mean difference (Pinatubo − control) from three pairs of GCM simulations. The green curve depicts the GCM-simulated Tb6.7 computed under the assumption of a constant relative humidity change. The red curve depicts the GCM-simulated Tb6.7 computed under the assumption of a constant, seasonally varying water vapor mixing ratio (i.e., no drying of the upper troposphere). The thick lines depict the 7-month running mean of each time series. [From Soden et al. (2002)]
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Fig . 16.Estimates of water vapor feedback parameter (in W m−2 K−1) from the observations and from the HadCM3 climate model (note that the sign convention used in this figure for the definition of feedback parameters is opposite to that used in appendix A). The histogram is computed from 82 model estimates with a bin size of 0.5 and is shown in terms of probabilities. The shaded curve is a fitted normal distribution of model estimates with the 5% and 95% represented by darker shading. Observed estimates of the water vapor feedback parameter are indicated by the vertical lines, and lie in the range 0.9–2.5 W m−2 K−1 (using the sign convention of appendix A, i.e., positive feedbacks have positive sign). [From Forster and Collins (2004)]
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Fig . 17.The normalized zonally averaged surface air temperature change from 17 models participating in the AR4 of the IPCC. The temperature change is computed as the 2080–99 average from the so-called SRES AlB scenario minus the 1980–99 average from climate of the twentieth-century simulations. The zonally averaged change is normalized by the global average surface air temperature change.
