ARlogo Annu. Rev. Astron. Astrophys. 2013. 51: 207-268
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In recent years there has been much progress in updating PDR models, and in combining hydrodynamic simulations, chemical modeling, and radiation transfer codes.

3.1. Photodissociation Regions

Since CO production mainly occurs through ion-neutral chemistry (van Dishoeck & Black 1988) the ionization structure through the PDR is important in setting the depth of the CO formation. Recombination of metal ions on PAHs can modify the abundance of free electrons. For a typical GMC with n ~ 103 cm-3 illuminated by a radiation field ~ 10 times the Galactic ISRF (the interstellar radiation field in the vicinity of the Sun), the tauCO = 1 surface is at a depth of AV ~ 1 when PAHs are included. Without PAHs the electron abundance stays high, and the ion-neutral chemistry is slowed due to H3+ recombination. Consequently, the tauCO = 1 surface is pushed deeper into the cloud (AV ~ 2). The PAH rates are estimated by Wolfire et al. (2008) by considering the C0 / C+ ratio in diffuse lines of sight, but there is considerable uncertainty both in the rates (Liszt 2011) and in the PAH abundance and their variation with cloud depth.

Bell et al. (2006) carry out a parameter study of XCO using PDR models with constant microturbulent line width. The calculated XCO versus AV plots have a characteristic shape with high XCO at low AV, dropping to a minimum value at AV ~ 2-4 and then slowly rising for increasing AV. The XCO dependence at low AV arises from molecular gas with low CO abundance. After the CO line intensity becomes optically thick, XCO slowly rises again as N(H2) increases. Increasing density up to the critical density of CO J = 1 → 0 (n(H2)cr,1 ~ 2200 cm-3) enhances the CO excitation and causes XCO to drop. In addition, the minimum moves closer to the cloud surface as does the tauCO = 1 surface. An increase in XCO by a factor of more than 100 is found when decreasing dust and metallicity to 1% of the local Galactic ISM, reflecting the larger column of H2 at a given AV. Bell, Viti & Williams (2007) suggest that the appropriate XCO value to use in various extragalactic environments can be estimated from the minimum in the XCO versus AV plot.

Wolfire, Hollenbach & McKee (2010) use an updated version of their PDR models, including a self-consistent calculation for the median density expected from a turbulent density distribution. The models provide a theoretical basis for predicting the molecular mass fraction outside of the CO emitting region in terms of incident radiation field and gas metallicity. They assume that the median density is given by < n >med = bar{n} exp(µ), where bar{n} is the volume averaged density distribution bar{n} propto 1/r, and µ = 0.5ln(1 + 0.25 M2) (Padoan, Jones & Nordlund 1997). The sound speed that enters in the Mach number, M, is calculated from the PDR model output while the turbulent velocity is given by the size-linewidth relation (Eq. 9).

3.2. Numerical Simulations

As an alternative to PDR models with simple geometries and densities, hydrodynamical models can be used to calculate the line width, and density, but with only limited spatial resolution and approximate chemistry and thermal balance. Glover & Mac Low ( 2007, 2007b) carry out simulations of the formation of molecular clouds using a modified version of the magnetohydrodynamical code ZEUS-MP. They include a time-dependent chemistry, in particular for H2 formation and destruction, and thermal balance. Glover et al. (2010) enhance the code to account for CO chemistry in a turbulent GMC. A turbulent picture of a GMC is not one with well defined clumps surrounded by an interclump medium but one with a continuous density distribution and constant mixing between low and high density regions. The CO abundance varies within the cloud depending on the gas density and penetration of the dissociating radiation field. Calculations of XCO are carried out by Glover & Mac Low (2011), and Shetty et al. (2011, 2011b). The latter two references include non-LTE CO excitation and line transfer in the LVG approximation. These authors carry out 3D turbulent simulations in a box of fixed size (20 pc), and turbulence generated with uniform power between wavenumbers 1 ≤ k ≤ 2, but with various initial densities and metallicities. For their standard cloud model the saturation amplitude of the 1D velocity dispersion is 2.4 km s-1. Each line of sight has a different CO intensity, and CO and H2 column density depending on the past and present physical conditions along it. Thus, for a given N(H2) there is a range in XCO values (see Figs. 5 and 6 in Shetty et al. 2011). The dispersion generally increases for clouds with lower density and lower metallicity.

Figure 7 in Shetty et al. (2011) shows the calculated mean XCO in different AV bins for several initial densities and metallicities. The variation in XCO with AV is qualitatively similar to that shown in Bell et al. (2006) for microturbulent models. Lower densities and metallicities drive XCO to higher values due to lower CO excitation and CO/H2 ratios respectively. At AV gtapprox 7 the models with different initial densities converge as the CO line becomes optically thick. The minimum in XCO occurs at larger AV in the hydrodynamic simulations compared to the one sided PDR models, likely due to the dissociating radiation incident on all sides of the box and due to its greater penetration along low density lines of sight. A range in density n = 100-1000 cm-3 and metallicity Z = 0.1-1 Zodot produces a range of only XCO,20 ~ 2-10. Thus although there can be large variations in XCO along different lines of sight in the cloud the emission weighted XCO is close to the typical Galactic value (Section 4). In addition, the low metallicity case has higher XCO at low AV but approaches the Galactic value for higher density (higher AV) lines of sight. Although the models were run with constant box size, the dependence on AV suggests that clouds with sufficiently small size so that CO does not become optically thick will have higher XCO. The results seem to confirm the suggestion by Bell et al. (2006) that the mean XCO value should be near the minimum when plotted as XCO versus AV.

Shetty et al. (2011b) investigate the results of varying the temperature, CO abundance, and turbulent line width. They find only a weak dependence on temperature with XCO propto Tkin-0.5 for 20 K < Tkin < 100 K, and thus over the range of temperatures typically found in Galactic clouds XCO is not expected to vary significantly due to temperature. At low (fixed) CO abundance (nCO / nH2 ~ 10-6) , the line does not become optically thick and thus W(CO) follows N(H2) with constant XCO up to at least logN(H2) = 22.5. Varying the turbulent line width increases the CO line intensity as expected since decreasing the self-absorption allows more CO line emission to escape the cloud. They also find, however, a decreasing brightness temperature and over the range of velocity dispersion sigma = 2-20 km s-1, W(CO) changes as W(CO) propto sigma0.5 instead of changing linearly. The higher turbulent velocities create dense shocks but also larger voids of low density material. The competing effects produce a dependence on sigma that is slower than linear. Increasing the column density as well as the line width might produce a model result closer to W(CO) propto sigma.

An interesting result is that XCO is not sensitive to the internal velocity profile within the cloud but only to the total line width, however that might be generated. A cloud need not obey a power law size-linewidth relation to have the same XCO as one with a Gaussian distribution and identical dispersion velocity. Thus, clouds need not be "virialized" nor obey a size-linewidth relation to have the same XCO. The modeled XCO converges for high AV clouds and thus the XCO factor is not expected to vary from cloud to cloud as long as the there is a large enough column density. Shetty et al. (2011b) conclude that a nearly constant XCO is the result of the limited range in column densities, temperatures, and linewidths found in Galactic molecular clouds and that applying a constant XCO is approximately correct to within a factor of ~ 2.

Global models of XCO using hydrodynamic simulations to investigate the variation due to galactic environment are carried out by Feldmann, Gnedin & Kravtsov (2012) and Narayanan et al. ( 2011, 2012). A significant problem in numerical simulations is how to handle the physical conditions and/or line emission within regions smaller than the spatial grid. Feldmann, Gnedin & Kravtsov (2012) use high resolution (~ 0.1 pc) simulations from Glover & Mac Low (2011) for "sub-grid" solutions to cosmological simulations of 60 pc resolution. These models used constant Tkin = 10 K, LTE excitation for CO, with either constant CO line width or one proportional to Sigmamol0.5. By comparing results at their highest resolutions with those at 1 kpc, and 4 kpc, they asses the effects of spatial averaging on XCO. The averaging tends to reduce the variation of XCO on N(H2) and UV radiation field intensity. At greater than kiloparsec scales, and H2 column densities between 1021 cm-2 and 1023 cm-2, XCO changes by only a factor of 2. They find essentially no variation in XCO with UV field strength between 0.1 and 100 times the Galactic ISRF. Feldmann, Gnedin & Kravtsov (2012) do find a significant variation with metallicity, with a scaling XCO propto Z-0.5 for virial line widths. Note that this relation is much shallower than found by, for example, (Genzel et al. 2012, Section 8.2). The authors suggest that low metallicity high-redshift galaxies may not obey the same gas surface-density to star formation relation observed in local disks.

The results of Narayanan et al. (2011) and Narayanan et al. (2012) will be discussed in Section 7.2. Here we note that their effective resolution of ~ 70 pc requires adopting sub-grid cloud properties. Although the surfaces of GMCs more massive than ~ 7× 105 Modot are resolved, their internal structure is not. The resulting line and continuum transfer, and temperature and chemical structure can only be approximate. The resolution problem also enters in simulations of individual clouds. We showed in Section 1 that CO becomes optically thick within a column N(H2) approx 2-3 × 1020 cm-2. Thus for sub parsec resolution ( ~ 0.1 pc) and density greater than n gtapprox 103 cm-3, the physical conditions (temperature, density, and abundances) are averaged over the line forming region and the calculated emitted intensity can be in error. The resolution problem is much more severe for H2 dissociation. Self-shielding of H2 starts within a column of only N(H2) ~ 1014 cm-2 (de Jong, Boland & Dalgarno 1980). Thus with a resolution of ~ 0.1 pc, and density of n gtapprox 10 cm-3, the optical depth to dissociating radiation is already tau gtapprox 104 in a resolution element. A complementary approach is to apply a state-of-the-art PDR code with well resolved H2 formation to the output of hydrodynamic simulations Levrier et al. 2012. With increasing computing power the issues of resolution will continue to improve. We suggest that high priority should be placed on creating large scale galactic simulations that are well matched to small scale simulations with resolved cloud structure. The former provide environmental conditions and cloud boundary conditions while the latter provide the chemistry and line emission in a realistic turbulent cloud. Expanding the library of GMC models with a range of column densities, line widths, and external heating, and thoroughly checking them against observations, would be most helpful.

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