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ATMOSPHERIC SCIENCE LETTERSAtmos. Sci. Let. 6: 145–147 (2005)Published online 5 August 2005 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/asl.107
Comment on the paper: On the design of practicablenumerical experiments to investigate stratospherictemperature change, by S. Hare et al. (2005)
John Austin1,2*1UCAR, Boulder, CO, USA2NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
*Correspondence to:John Austin, NOAA GeophysicalFluid Dynamics Laboratory,Princeton Forrestal Campus Rte.1, 201 Forrestal Rd., Princeton,NJ 08542-0308, USA.E-mail: [email protected]
Received: 19 May 2005Revised: 24 May 2005Accepted: 25 May 2005
AbstractIf stratospheric temperature trends are to be understood, coupled chemistry climate modelswill need to be run. Simulations with fixed ozone trends might provide a misleadingindication of future temperature trends. Copyright 2005 Royal Meteorological Society
In the above article, Hare et al. (2005) compare resultsfrom the coupled chemistry climate run of Austin andButchart (2003) and results from uncoupled modelsto conclude that the benefits of coupled chemistrymodels are disproportionate to their cost. They suggestthat it is preferable to complete a small ensembleof runs of an uncoupled model. In this comment Igive an alternative view, namely, that if stratospherictemperature trends are to be properly understood,coupled chemistry climate models will need to berun, whether as ensembles or not, and that simulationswith fixed ozone trends might provide a misleadingindication of future temperature trends.
1. Model details
First, I would like to clarify a few important detailsregarding UMETRAC (Unified Model with EulerianTransport and Chemistry). The chemistry is coupledevery physics timestep (30 min), although in practiceit is the radiation timestep (3 h) that is more signifi-cant. Improvements in transport such as using a semi-lagrangian method can reduce the overhead of runningchemistry, e.g. to a factor of 2.2 in the GeophysicalFluid Dynamics Laboratory (GFDL) Model. This isassuming that the sea surface temperatures (SSTs) andsea ice are fixed. In a coupled ocean model, the addi-tional resources for the ocean would mean that the costof coupled chemistry would be only about a factorof 1.6 above that of a model without chemistry. Thebalance of whether or not to include chemistry thenbecomes much less clear. In comparison, in the lasttwo years computer capability at GFDL has increased
effectively by a factor of about 6, divided evenlybetween hardware and software, so that here we cancontemplate both coupled chemistry and ensemblesat higher overall spatial resolution than UMETRAC.Institutions that are unable to keep up with world-classfacilities could consider investigating simpler prob-lems that do not require such facilities or to work inpartnership with those that do have the facilities.
An issue not raised by the authors is the qualityof the underlying chemistry and transport models,and this could be far more important than statisticaluncertainty in the trends. As indicated in Austinet al. (2003), different models can give a wide rangeof results, but some assumptions need to be maderegarding future ozone trends if future temperaturetrends are to be predicted.
2. Trend uncertainties
Although it was not our primary goal in Austinand Butchart (2003), we partially addressed the issueof trend attribution by comparing the model trendsfor the period 1980–2000 with those for the period2000–2020. The difference in temperature trend isdue primarily to changes in the ozone (Austin andButchart, 2003, Figure 5c). The globally averagedvalues are also compared with results from a slightlydifferent version of the Unified Model (Butchart et al.,2000), which indicated a very nearly linear trend intemperature for a slow exponential growth in CO2concentrations.
The UMETRAC results show a trend difference,attributable to ozone change, which is not quite sig-nificant throughout a large domain but which is much
Copyright 2005 Royal Meteorological Society
146 J. Austin
more coherent and can be more readily explained thanthe Hare et al. results. One would not expect a signif-icant change throughout the middle stratosphere sincemodel ozone trends there are small. A strong nega-tive signal exists in the lower stratosphere throughouta range of latitudes from about 45 ◦S to 60 ◦N, imply-ing larger cooling for the past than for the future.There is also a coherent model signal over Antarctica,implying stronger cooling in the past than predictedfor the future. The upper stratosphere throughout thebroad latitude range 45 ◦S to 90 ◦N shows strongercooling for the past than for the future. Except nearthe equator this is statistically significant at the 95%confidence level. This is a very basic result due tothe past chlorine change having a direct influence onthe ozone; yet in their Figure 3, Hare et al. indicate alower result than might be implied from Austin andButchart (2003) by a factor of two. Unfortunately, theanalysis of Austin and Butchart (2003) (as presum-ably is Hare et al.) was incomplete in not taking intoaccount the quasi-biennial oscillation (QBO) which isinterpreted as noise by the statistical package and canmask the underlying trend. The Rayleigh friction runof Austin and Butchart (2003) does not have a QBOand trend uncertainties are consequently smaller in thetropics (this comment, Figure 1). However, this runhas a different dynamical behaviour than the nonoro-graphic gravity wave forcing run; so the ozone trendwas different in the lower stratosphere in southernmid-latitudes (Austin and Butchart, 2003, Figures 5,7, 8).
Rather than calculating a linear trend in temperaturethroughout the 40-year period 1980–2020 as in Hareet al., an alternative approach is to recognise thechange in the physical processes from about thelate 1990s as the ozone trend changes from loss torecovery. This follows from the fact that the chlorinerate of change slowed considerably after the year 1997(Newchurch et al., 2003). Over the period 1980–2020the resulting change of temperature evolution wouldlead to increased uncertainty in the computed trends,since the statistical package assumes temperature isa linear function of time, and any divergence fromlinearity is interpreted as noise.
In compensation, a 40-year period has a longerdata record. However, ignoring the linearity issue,taking the simple model that ozone decreases from1980 to 2000 at a rate α with uncertainty ε andthen remains constant to 2020 and assuming thateach year’s data are independent, over the 40-yearperiod the trend would be α/2 and the uncertaintyapproximately ε/
√2. For the 20-year period, the trend
is α/ε standard errors, while for the 40-year periodthe trend is approximately 1/
√2. α/ε standard errors.
So, even assuming no recovery, it is more difficult todetect a trend over the full 40 years, than over thefirst 20, all else being equal. In some parts of theatmosphere, the model predicts significant recoveryafter 2000 thus making it even more difficult to detecta trend over the longer period. These arguments apply
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Figure 1. Zonal and annual average temperature trendscomputed for the Rayleigh friction run of Austin and Butchart(2003). (a) the period 1980 to 2000. (b) middle panel refersto the period 2000 to 2020. (c)the difference in trends forthe two periods 1980–2000 minus 2000–2020. Light shadingindicates a value significantly different from zero at the 80%confidence level. Dark shading indicates the regions of 95%statistical significance
to the ozone trend, but the same argument would alsoapply to the temperature trend, assuming CO2 has anoffset to the figures.
3. Dynamical consistency
Hare et al. raise the issue of dynamical consis-tency between the coupled and uncoupled simula-tions regarding the Antarctic ozone hole. However,the absence of anything resembling the Antarctic sig-nal in their Figure 3 is remarkable in comparison with
Copyright 2005 Royal Meteorological Society Atmos. Sci. Let. 6: 145–147 (2005)
Investigation of stratospheric temperature change 147
Austin and Butchart’s Figure 5 or Figure 1 in thiscomment. Dynamical inconsistencies between the sim-ulations of UMETRAC and imposed trends may bemore extensive and warrant further investigation. Per-haps the UMETRAC ozone results were imposed in away that is different to the coupled chemistry climatemodel. For example, the diurnal tides in the coupledand uncoupled models will not be identical. In thetropics at 1 hPa, the coupled model will have about 4%less ozone at noon than in the zonal average (Huanget al., 1997), although I do not see why this shouldnecessarily affect the trend results. It is also quite con-ceivable that there has been a trend in transient waveactivity which would have affected ozone amounts butwhich are unrepresented in the monthly averages usedby Hare et al. An increase in wave activity for exam-ple has been identified by Butchart and Scaife (2001)as contributing to an increase in the strength of theBrewer-Dobson circulation.
4. Cost analysis
For small ensemble sizes, the total cost of the coupledand uncoupled model simulations are not vastly dif-ferent, although the details will vary according to theindividual climate model. Taking the (GFDL) climatemodel (with specified SSTs) as unit cost 1 and thecoupled chemistry model as unit cost 2.2, four ensem-ble runs require a total expenditure of 6.2 (one cou-pled chemistry run and four uncoupled runs). For 5%more resources, three coupled runs would be available.The uncertainty in the trends would presumably scaleinversely as
√n − 1, where n is the number of runs.
Hence four uncoupled runs would have a trend uncer-tainty only about 20% lower than for the three coupledruns. In practice, the uncertainty reduction may well begreater as different chemistry runs might visit differ-ent chemical regimes introducing increased variabilitythan would occur in an ozone specified ensemble. So,statistical uncertainty might be less in uncoupled runs,but this a false sense of progress as there would stillbe confusion surrounding predicted future atmosphericvariability.
5. Chemistry simplifications
Simplifying the chemistry could also be problematic.Parameterised chemistry such as Cariolle and Deque(1986) sounds attractive, in principle, but with suchparameterisations you cannot be certain that the modelwill represent the correct physics, and it may proceedalong its own independent trajectory. The authors maycare to inspect Figure 9 of Austin et al. (2003). Theoutlyer in the upper panel is the only model withparameterised chemistry. It really depends on whatrisks one is prepared to take.
It is unclear to me how to apply offline chem-istry in order to reduce costs. The fact that chemistryand dynamics are coupled would mean that consider-able manual intervention is needed while models arerunning. The task of keeping models running continu-ously for many months of wall-clock time is already adifficult enough task, requiring considerable organisa-tion. In any case, for the Unified Model, the bulk of thecost of the chemistry lies in transporting the tracers.For the GFDL model, the tracer transport cost is some-what less and the resources here are excellent for thefull task. The one area that could significantly reducecosts is simply to do the chemistry on every otherhorizontal gridpoint and interpolate in between. In amassively parallel environment this requires carefulprogramming to ensure that results are independent ofthe number and distribution of processors. This couldlead to a very economical model with only modest lossof detail.
Acknowledgements
The UMETRAC simulations reported here and in Hare et al.were completed at the UK Meteorology Office and partiallyfunded by the CEC Framework 5 EuroSPICE project. NealButchart provided some helpful comments on the text andSian-Jian Lin clarified some of the details of the GFDL tracertransport technique.
References
Austin J, Butchart N. 2003. Coupled chemistry-climate modelsimulations for the period 1980 to 2020: ozone depletion and thestart of ozone recovery. Quarterly Journal of the Royal MeteorolicalSociety 129: 3225–3249.
Austin J, Shindell D, Bruhl C, Dameris M, Manzini E, Nagashima T,Newman P, Pawson S, Pitari G, Rozanov E, Schnadt C, Shep-herd TG. 2003. Uncertainties and assessments of chemistry-climatemodels of the stratosphere. Atmospheric Chemistry and Physics 3:1–27.
Butchart N, Scaife AA. 2001. Removal of chlorofluorocarbons byincreased mass exchange between the stratosphere and tropospherein a changing climate. Nature 410: 799–802.
Butchart N, Austin J, Knight JR, Scaife AA, Gallani ML. 2000. Theresponse of the stratospheric climate to projected changes in theconcentrations of the well-mixed greenhouse gases from 1992 to2051. Journal of Climate 13: 2142–2159.
Cariolle D, Deque M. 1986. Southern hemisphere medium-scale wavesand total ozone disturbances in a spectral general circulation model.Journal of Geophysical Research 91: 10 825–10 846.
Hare SHE, Gray LJ, Lahoz WA, O’Neill A. 2005. On the designof practicable numerical experiments to investigate stratospherictemperature change. Atmospheric Science Letters 6: 123–127. DOI:10.1002/asi.101
Huang FT, Reber CA, Austin J. 1997. Ozone diurnal variationsobserved by UARS and their model simulation. Journal ofGeophysical Research 102: 12 971–12 985.
Newchurch MJ, Yang ES, Cunnold DM, Reinsel GC, Zawodny JM,Russell JM III. 2003. Evidence for slowdown in stratospheric ozoneloss: first stage of ozone recovery. Journal of Geophysical Research108. DOI: 10.1029/2003JD003471.
Copyright 2005 Royal Meteorological Society Atmos. Sci. Let. 6: 145–147 (2005)
