1Lecture Ch. 7a• CAPE• Stability• Review of Ch.7 Concepts– “Homework” Ch. 7, Prob. 3• Cloud Classification• Precipitation ProcessesCurry and Webster, Ch. 7, 8For Tuesday: Finish reading Ch. 8CAPE2Connecting this course to current research…Start from two sections of Curry & Webster:Parameterization of Cloud Microphysical Processes(Section 8.6), pages 241 - 244. Understand the ideasbehind the equations on page 242 and note theremarks on page 244.Cloud-radiation Feedback (Section 13.4), especially thelast 2 paragraphs of Section 13.4.1 on pages 368, 369,and the last paragraph of Section 13.4 on page 374.What do we mean by “parameterization”?Some physical processes are too poorly understood,and/or they occur on too small space and time scales,so we cannot adequately represent them in globalnumerical climate models.For example, clouds and cloud-radiation interactionsare important to climate and to modeling climatechange.We don’t know how to include these processescorrectly, but we cannot afford to omit them.A working definition of “parameterization”:A parameterization is an algorithm or rule for obtainingthe statistical effect, of an ensemble of small-scaleprocesses (e. g., cloud processes), on the large-scaleprognostic fields computed explicitly in a model (e. g.,wind, pressure, temperature, humidity).In general, the parameterization must be explicit, in thesense that the statistical effect can be computed as afunction of the large-scale variables themselves.Example: cloud fraction might be a function of humidity.First, some background.We define “climate sensitivity” as the equilibriumchange in global average surface atmospherictemperature in response to doubling the presentatmospheric concentration of carbon dioxide. (Thereare many alternative definitions).Many (but not all) reputable models have sensitivitiesranging from about 1.5 deg C to 4.5 deg C. This rangeis an old result. It has not changed in nearly 30 years.Why?3Cloud effects dominate climate sensitivity.In a typical comparison of coupled models, some ofwhich have the same atmosphere, and some of whichhave the same ocean, the clear result is that it is theatmospheric model which largely determines thesensitivity, and in particular, it is clouds.This is another confirmation of something known for along time. Changing the cloud-radiation scheme in onemodel replicates the sensitivity spread of many models.The problem is we don’t know which scheme is best.Cloud algorithms require comprehensive approaches.Cloud radiative processes are determined BOTH bycloud macrophysics (cloud size, altitude, thickness,etc.) AND by cloud microphysics (water content,phase, particle shapes and size distributions, etc.).The old procedure of predicting clouds as a simplefunction of relative humidity, and then assigningradiative properties arbitrarily, is not good enough.Zeroth-order cloud effects are still unclear.Models don’t agree on even the simplest aspects ofcloud changes as climate warms. Some predict cloudamount increases; some predict it decreases.If in-situ and satellite interpretations can show anobserved large-scale secular trend in cloud amountin recent decades, our models must be able tosimulate that.To be believable, climate models must pass tests.It’s unacceptable that models with differentsensitivities all manage to reproduce the global mean20th-century temperature evolution by usingdramatically different assumptions on forcing.The temptation is to treat the 20th-centuryrecord of solar variability, volcanism, aerosols andgreenhouse gas changes as parameters that can betuned so the model produces the observed recordof surface temperature as a function of time.Climate sensitivity cannot be only 1 global numberThe annual cycle may be masked in average results orthrown away in perpetual-month simulations. In somemodels, cloud feedback in the longwave (terrestrial)is about the same in all seasons, but the shortwave(solar) cloud feedback actually changes sign over theannual cycle, due to cloud amount changes.The concept of cloud feedback should also includespatial as well as temporal variability.It may be a distraction to concentrate on global effects.We already know that aerosol effects are highlylocal. Perhaps cloud effects are largely local too.Even if cloud-radiation effects are not largeglobally, and we don’t know yet if this is true, theymay be very important locally.All this uncertainty about how clouds should bemodeled has motivated intense research, and onetheme of this research is to observe real clouds.4IPCC Synthesis ReportSimulated annual global mean surface temperaturesStratus CloudsStratusLow lying layer of cloud(called fog if on theground) with no structure.[http://www.fox8wghp.com/spacious.htm]10.2Ship Tracks• What are they?• aerosol “signatures” from ships• Ship tracks form in non polluted areas– Last 1-2 days• Counter global warmingWhy?10.2What are the characteristics of“ship tracks”?Ship Track Observations Remote/Optical In situ/AerosolConover 1966⇑ albedo = 20%Coakley, Bernstein, Durkee 1987⇑ R(3.7µm) = 3.9%⇑ R(0.63µm) = 1.6%R(11µm) = 0.0%Radke, Coakley, King 1989⇑R(0.63µm) = 13.6%⇑τ =260%⇓re = 21%King, Radke, Hobbs 1993⇑I(τ,-1)(0.74µm) = 220%⇓I(τ,-1)(2.20µm) = 87%⇑Ndrop = 220%⇑CN = 250%⇑LWC = 250%10.2Ship Track in Clean Air10.25Shiptrack in“clean”air12410241000.0012 40.012 40.12 41Diameter (µm)12410241000.0012 40.012 40.12 41Diameter (µm)371 cm-3In track82 cm-3BackgroundNOAA11, 3.7µm dndlogDp10.2Ship Track in Polluted Air10.2Ship trackin“polluted”airNOAA11, 3.7µm 10241002410000.0012 40.012 40.12 41Diameter (µm)10241002410000.0012 40.012 40.12 41Diameter (µm)1050 cm-3In track396 cm-3BackgrounddndlogDp10.2Processes Governing Ship TracksHypothesisRadke, Coakley, King 1989ship stacks ⇒ ⇑CCNAlbrecht 1989⇓Nprecip ⇒ ⇑LWC Hudson 1991⇓CCN ⇒ ⇑NprecipAckerman, Toon, Hobbs 1994⇓CCN ⇒ ⇓h,
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