Colaitis, A.

Forget, F.

González-Galindo, F.

Hourdin, F.

Lefèvre, F.

Lewis, S. R.

López-Valverde, M. A.

Madeleine, J.-B.

Millour, E.

Montabone, L.

Montmessin, F.

Mulholland, D.

Read, P.-L.

Spiga, A.

English

Forget, François

Gonzalez-Galindo, F.

Lefèvre, Franck

Montmessin, Franck

Lopez-Valverde, M.-A.

HAL-Ecole des Ponts ParisTech

Back to The Basics: Improving the Prediction of Temperature, Pressure and Winds in the LMD General Circulation Model

HAL-Polytechnique

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Open Research Online

Open Research OnlineThe Open University’s repository of research publicationsand other research outputsBack to the basics: improving the prediction oftemperature, pressure and winds in the LMD generalcirculation modelConference or Workshop ItemHow to cite:Forget, F.; Millour, E.; Madeleine, J.-B.; Colaitis, A.; Spiga, A.; Montabone, L.; Hourdin, F.; Lefe`vre, F.;Montmessin, F.; Gonza´lez-Galindo, F.; Lo´pez-Valverde, M. A.; Lewis, S. R.; Read, P.-L. and Mulholland, D. (2011).Back to the basics: improving the prediction of temperature, pressure and winds in the LMD general circulationmodel. In: Fourth International Workshop on the Mars Atmosphere: Modelling and Observations, 8-11 Feb 2011,Paris, France.For guidance on citations see FAQs.c© 2011 The AuthorsVersion: Version of RecordLink(s) to article on publisher’s website:http://www-mars.lmd.jussieu.fr/paris2011/Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyrightowners. For more information on Open Research Online’s data policy on reuse of materials please consult the policiespage.oro.open.ac.ukBACK TO THE BASICS: IMPROVING THE PREDICTION OF TEMPERATURE, PRESSURE AND WINDS IN THE LMD GENERAL CIRCULATION MODEL  F. Forget, E. Millour, J-B. Madeleine, A. Colaitis, A. Spiga, L. Montabone, F. Hourdin Laboratoire de Météorologie Dynamique, IPSL, Paris, France (forget@lmd.jussieu.fr), F. Lefèvre, F. Montmessin, Labora-toire Atmosphères, Milieux, Observations Spatiales (LATMOS), IPSL, Paris, France F. Gonzalez-Galindo, M.-A. Lopez-Valverde, Instituto de Astrofísica de Andalucía, Granada, Spain,  , S. R. Lewis, Department of Physics and Astronomy, The Open University, Milton Keynes, UK, P.-L. Read, D. Mulholland, Atmospheric, Oceanic & Planetary Physics, University of Oxford, UK.  Introduction: The Mars atmosphere General Circulation Model (GCM) [1] developed at the La-boratoire de Météorologie Dynamique in collabora-tion with several teams in europe (LATMOS, Uni-versity of Oxford, The Open University, the Instituto de Astrofisica de Andalucia), and with the support of ESA and CNES. is currently used for many kind of applications. It has become a “Mars System Model” which, for instance, includes the water cycle [see , e.g. abstract by Madeleine et al., this issue] the dust cycle [Mulholland et al., this issue], the Photochemi-stry [Lefevre et al. this issue], the release and trans-port of Radon [Meslin et al., this issue], a thermos-phere and a Ionosphere [Gonzalez-Galindo et al., Chauffray et al., this issue]. It can be also used to explore Mars past climates [see e.g. Wordsworth et al., this issue] or the transport of volcanic ashes [Kerber et al. this issue]. Moreover the outputs of the GCM are available to the community and to en-gineers through the Mars Climate Database, as de-scribed in Millour et al. [this issue].  For all these applications, it is more important than ever that the model accurately simulates the “funda-mentals” of the Martian meteorology: pressure, tem-perature, winds. Below we describe several im-provements that we have performed to improve the realism and thus the accuracy of the LMD GCM.   Improved Dust radiative properties  For many years, a recurrent problem we have faced was the inability to predict realistic tempera-tures while using both the observed radiative proper-ties and column dust opacities. For instance, we had to multiply the imposed dust opacity measured by TES by a tunable parameter to reach an agreement with the simultaneously measured temperatures, thereby losing consistency with part of the observa-tions. We found that the problem was partly due to our radiative transfer model (which we modified and validated against reference Monte-Carlo simula-tions), and partly to the dust radiative properties pre-viously used [e.g. 2, 3]. We found that using the most recent radiative properties derived by Mike Wolff [4,5] greatly improved this problem (fig-ure 1a, green curve).    Figure 1. (a) Zonally averaged equatorial temperature at 2 PM for the 0.5 hPa pressure level from TES climatology [6] (crosses), martian year 25 and 26, and as predicted by the new LMD/GCM for three different cases : a simulation using the Ockert-Bell radiative properties [2] (blue curve), another simulation using the Wolff et al. properties [4,5] (green curve) and a simulation using a semi-interactive dust model (red curve). (b) Dust opacity at 9.3 µm used to guide the model (in black), compared to the raw TES data-set at 2 PM (red points). (c) Analytical “top of the dust layer” used for simulation 1 and 2 (black line), compared to the dust layer top altitude actually predicted by the semi-interactive dust model. (d) Dust effective radius pre-dicted by the GCM at the 0.5 hPa level for the semi-interactive dust simulation. All the variables are taken at the equator. Figure from [7].   Improving the dust vertical distribution and par-ticle size using a dust transport model. When keeping a constant particle size in the en-tire atmosphere and using a prescribed vertical dis-tribution, we found that significant temperature bi-ases remained, especially above the 1 hPa pressure level.  To improve the realism of the model, we developed a semi-interactive dust model that simulates the ver-tical distribution and size of the dust particles but can use the observed dust column opacities to adjust the dust content. The evolution of dust particles sizes is computed using a so-called two moments scheme, in which in any atmospheric boxes the size of the dust particles is described by a lognormal distribu-tion. Then three parameters are sufficient to describe the dynamics of the whole particle population. Transporting the mass mixing ratio and the number mixing ratio assuming a constant variance of the distribution allows to realistically simulate the varia-tions or the particle sizes once the size segregation is properly calculated in the gravitational sedimenta-tion scheme.   The radiative properties are updated online and fol-low the 4D variations in dust particle sizes. We found that using this technique allows a better agreement with the observations, thereby underlin-ing the role of the dust layer thickness and particle sizes in shaping the thermal profile. The GCM is now able to predict good temperatures (figure 1, red curve) without any tuning of the dust opacity used to guide the model at least during the dusty, relatively cloud-free seasons. However, when clouds are known to be present, it was necessary to take into account their radiative effect.    Figure 2. Same as figure 1a for Martian year 26, showing the improvement of the thermal structure when taking into account the radiative effects of clouds predicted by the GCM. See Madeleine et al. [this issue] for more details.   Taking water ice clouds into account. The simulation of clouds and of their impact on the thermal structure of the atmosphere is detailed in Madeleine et al. [This issue]. As for dust, the radia-tive model takes into account the 3D variations of the clouds particles size and opacities. Clouds have a significant direct impact on the local thermal struc-ture (mostly by enhancing the exchange of thermal infrared radiation with the surface and space). Their presence also influences significantly the tempera-ture in the upper atmosphere even when they are not present through their impact on thermal tides and the Hadley circulation. As expected, parameterizing the radiative effect of clouds strongly improves the tem-perature predictions of the model (figure 2, blue curve).   A new parameterization of the near surface con-vection.  The Martian near-surface atmosphere has been shown to be alternatively “ultra-unstable” and “ultra-stable”. This is a challenge for boundary layer schemes originally developed for the Earth case. In particular, during the day, in the first meters above the ground, the atmosphere temperature profile is probably super-adiabatic, in spite of an active con-vection. In the previous versions of the model we employed a “convective adjustment” scheme which assumes instantaneous mixing of any unstable sec-tion of the atmosphere. Therefore, the convecting atmosphere was always close to the adiabatic lapse rate on average. This resulted in an unrealistic tem-perature profile in the boundary layer, i.e in the first kilometres above the surface (figure 3, orange and yellow curves).   Figure 3. Comparison of two potential temperature pro-files observed at two local times (11:59 and 14:55) near spring equinox by miniTES at the Opportunity Landing site [8] with predictions from the LMD GCM using the canonic convective adjustment parameterization (“con-vadj only”) and the new mass flux parameterization scheme (“gcm thermals”). The new scheme significantly improves our ability to simulate the  near-surface super-adiabatic lapse rate during daytime.    In order to improve the near surface daytime thermal structure, and also better simulate the transport and mixing by convection, we have implemented the mass flux parameterization of vertical transport in the convective boundary layer developed by [9] which greatly improve the performance of the GCM when compared to temperature profiles obtained by miniTES [8] (figure 3, green and red curves). The formulation of the new parameterization is based on an idealization of thermal cells or rolls. In practice, the atmospheric column is divided into two sub-columns of either rising or subsiding air. A diffusive scheme (based on a Mellor and Yamada 2.5 closure, see [1]) is still active to represent the turbulent mix-ing.  An improved CO2 cycle for better surface pres-sures and high latitude temperatures The atmospheric mass cycle resulting from the CO2 condensation and sublimation in the polar re-gions and which controls the surface pressure eve-rywhere on Mars must be well represented in a GCM which aims to realistically represent the Martian climate and meteorology. Moreover, an accurate simulation of the extension of the seasonal polar caps is necessary to accurately estimate the surface temperatures at the edge of the caps and to simulate the water cycle. To achieve this goal, in addition to the baseline CO2 condensation/sublimation scheme based on energy balance considerations, the LMD GCM now also includes subsurface water-ice tables in the polar regions, takes into account the impact of CO2 con-densation in the atmosphere (clouds) [10], includes the effect of non-condensable gases [11] and can be guided with the observed solar albedo as recorded by the TES solar band channel.  Non condensable gas enrichment and depletion:  The model includes a parameterization of non con-densable gas enrichment suitable for GCMs which is combined with a modified convection scheme able to simulate the convection forced by the enrichment of lighter gas near the surface. This scheme, com-bined with recent improvements of the GCM’s dy-namical core, yields non condensable gas enrich-ments which compare well with available data [11]. The variations of the CO2 ice temperature (and thus of the CO2 condensation rate) resulting from the changes of partial pressure can be taken into ac-count.    Thermal conduction in heterogeneous soil with a water ice table:  The model had always included thermal conduction in the Martian soil (which acts as a thermal reservoir which stores and restores heat to the surface) but until recently the subsurface was considered to be homogeneous (and of properties set to the known surface values).We have improved the representation of thermal conduction in the subsur-face of the model by switching to a scheme which includes the possibility of varying the thermal prop-erties (volumetric specific heat C and conductivity , or equivalently thermal inertia I=(C.)1/2 ) of the soil. With this implementation, we can now directly include some (high thermal inertia) water-ice tables in the GCM. The importance of the presence of subsurface po-lar ice tables which act as thermal reservoirs, storing and restoring large amounts of heat, and thus signifi-cantly affect the Martian CO2 cycle, has been dem-onstrated in [12]. It allows to match the observed global pressure variations without invoking unrealis-tically low CO2 ice emissivity to limit the CO2 con-densation rate. To include realistic maps of the ice tables in the model, we have used measurements by the Mars Odyssey Neutron Spectrometer (MONS) of the abundance of hydrogen near the surface [13,14] and an inversion model of the effective depth of the water ice table[15]. These maps describe the shape of the ice tables but not the exact geometrical depth at which they lay (to derive this depth accurately would require precise knowledge of the macroscopic absorption cross section of the ice-poor upper layer of soil). It is however possible to constrain the latter, as discussed in the next paragraphs.   Using the Viking Lander surface pressure mea-surements to constrain model parameters: The Vik-ing Landers have almost continuously monitored the Martian surface pressure over almost two Martian years for Viking Lander 2 and more than three Mar-tian years for Viking Lander 1. This dataset is ex-tremely useful to help constrain and validate GCM parameterizations.  Figure 4: An example of fit of the Viking lander 1 sur-face pressure (smoothed by a 10 sols boxcar averaged) by the LMD GCM after tuning ice depth correction coeffi-cient DN and DS and the CO2 ice cap albedoes in each hemisphere AN and AS. The CO2 ice emissivity is set to 0.9.  In [16] is described an automatic procedure which minimizes differences between model output   (a few control simulations) and Viking Lander 1 surface pressure and yields optimal values of “free” parameters such as polar cap albedos and emissivi-ties. Since (as mentioned above) the exact depth at which the polar H2O ice tables lay is not known, we have extended the best-fit procedure to include the ice depths parameter as parameters for the fit (the shapes of the ice tables remain those derived by [15]). In a first study, we applied a 4 dimensional fitting process (minimization with respect to North-ern cap albedo AN, Southern Cap albedo AS and coefficients DN and DS which represent proportio-nality constants to apply to the Mars Odyssey maps to determine the true depth at which the ice table of each hemisphere lays. We found that a best fit is obtained when DS is large (i.e. the ice table in the southern hemisphere is buried deep) and DN mini-mum (i.e. the ice table in the northern hemisphere is barely buried). Interestingly, we found that it was difficult to find any clear minimum for coefficients DS and DN, suggesting that we are still missing some physical processes in our simulations. In par-ticular, preliminary studies suggest that a better be-havior could be obtained assuming that the Northern subsurface ice thermal inertia is lower than the southern subsurface ice.   Guiding the seasonal cap ice albedo with TES solar band data. The CO2 ice albedo is known to strongly vary in time and space. To account for these variations and improve the realism of the modeled CO2 cycle, when CO2 ice is present on the surface, we set the surface albedo to the broadband albedo measured by TES solar channel (kindly provided by Tim Titus). The observed albedoes can be multiplied by a tunable coefficient to account for 1) the effect of airborne dust on TES albedo 2)  Non Lambertian behavior of the ice. In both case, the corrective fac-tor must be larger than 1 (the bond albedo of the surface is larger than the reflectance as seen from space at nadir through a dusty atmosphere).  Apply-ing our surface pressure best fit procedure to these ice albedo coefficient in addition to the water ice depth coefficient DN and DS showed that values up to 1.4 (i.e. TES albedoes increased by up to 40%) were necessary to yield a good match to the Viking Lander pressure observations.    Other improvements to the GCM include, an im-proved radiative transfer to calculate the 15 µm  in-frared cooling in the upper atmosphere [Lopez-Valverde et al. this issue], the inclusion of a new surface rugosity map [see Listowski et al., this issue], the updating of the surface albedo and thermal iner-tia with the latest available maps from the TES team. In the near future, we also plan to include a pa-rametrization of the scavenging of dust by water ice clouds. This will be directly feasible by coupling the semi-interactive dust model that simulates the verti-cal distribution of the dust with the water ice clouds parametrization. This phenomenon may play a key role in the vertical structure of the Martian atmos-phere [see e.g. Benson et al., Heavens et al., this issue]. Taking into account the radiative effects of clouds and the scavenging of dust by the water ice particles will also be of key importance when simu-lating past climates with much thicker clouds and precipitation.   References:  [1] Forget F. et al. (1999) JGR, 90, 104, E10  [2] Ockert-Bell et al. Journal of Geophysical Re-search, 102 :9039–9050 (1997) [3] Clancy and Lee Icarus, 93 :135–158 (1991). [4] Wolff et al. Journal of Geophysical Research (Planets) 111 :12 (2006).,  [5] Wolff et al. Journal of Geophysical Research (Planets), vol. 114, p. E00D04 (2009) [6] Smith, M. D. Icarus, 167 :148–165 (2004). [7] Madeleine et al. Revisiting the radiative impact of dust on Mars using the LMD Global Climate Model. Submitted to JGR (2011). [8] Smith et al. Journal of Geophysical Research (Planets) 111, E10 (2006). [9]  Hourdin, Couvreux, and Menut, Journal of At-mospheric Sciences, 59, Issue 6, pp.1105-1123 (2002). [10] Forget F. et al. (1998) Icarus, 131, 302-316  [11] Forget F. et al. Non condensable gas enrichment and depletion in the Martian polar regions in “Mars atmosphere modeling and observations”, abstract#9106 Williamsburg workshop (2008)  [12] Haberle R. M. et al. Planetary and Space Science, 56, 251-255 (2008) [13] Feldman W. C. et al. Science, 297, 75-78 (2002) [14] Feldman W. C. et al. JGR, 109, E09006 (2004) [15] Diez B. et al. Icarus, 196, 409-421 (2008) [16] Hourdin F. et al. JGR, 100, 5501-5523(1995)     

Back to the basics: improving the prediction of temperature, pressure and winds in the LMD general circulation model

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http://oro.open.ac.uk/30724/1/forget1_paris2011.pdf

Back to the basics: improving the prediction of temperature, pressure and winds in the LMD general circulation model

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