Impact of modifying the longwave water vapor continuum absorption model on community Earth system model simulations

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117,, doi: /2011jd016440, 2012 Impact of modifying the longwave water vapor continuum absorption model on community Earth system model simulations D. D. Turner, 1 A. Merrelli, 2 D. Vimont, 2 and E. J. Mlawer 3 Received 20 June 2011; revised 23 December 2011; accepted 27 December 2011; published 24 February [1] The far-infrared (wavelengths longer than 17 mm) has been shown to be extremely important for radiative processes in the earth s atmosphere. The strength of the water vapor continuum absorption in this spectral region has largely been predicted using observations at other wavelengths that have been extrapolated using semiempirical approaches such as the Clough-Kneizys-Davies (CKD) family of models. Recent field experiments using new far-infrared instrumentation have supported a factor of 2 decrease in the modeled strength of the foreign continuum at 50 mm and a factor of 1.5 increase in the self-continuum at 24 mm in the Clough-Kneizys-Davies continuum model (CKD v2.4); these changes are incorporated in the Mlawer-Tobin-CKD continuum model (MT_CKD v2.4). The water vapor continuum in the Community Earth System Model (CESM v1.0) was modified to use the newer model, and the impacts of this change were investigated by comparing output from the original and modified CESM for 20 year integrations with prescribed sea surface temperatures. The change results in an increase in the net upward longwave flux of order 0.5 W m 2 between 300 and 400 mb, and a decrease in this flux of about the same magnitude for altitudes below 600 mb. The radiative impact results in a small but statistically significant change in the mean temperature and humidity fields, and also a slight decrease (order 0.5%) of high-cloud amount. The change in the cloud amount modified the longwave cloud radiative forcing, which partially offset the radiative heating caused by the change in the water vapor continuum absorption model. Citation: Turner, D. D., A. Merrelli, D. Vimont, and E. J. Mlawer (2012), Impact of modifying the longwave water vapor continuum absorption model on community Earth system model simulations, J. Geophys. Res., 117,, doi: /2011jd Introduction [2] There is a balance between the net solar radiation received by the earth and the longwave radiation that is emitted to space. This balance is achieved via a wide variety of mechanisms that absorb and scatter the incoming solar energy, transport energy through many different forms both vertically through the atmosphere and ocean as well as horizontally to different regions of the globe, and ultimately radiate the energy back to space. All these processes must be properly modeled in order to accurately simulate climate in general circulation models (GCMs). As many of these processes are encapsulated within radiative transfer models (RTMs) and their components, changes to components of RTMs may have significant impacts on GCM simulations. [3] A very important spectral region for radiative processes in the Earth s atmosphere is the far-infrared (FIR), 1 NOAA National Severe Storms Laboratory, Norman, Oklahoma, USA. 2 Atmospheric and Oceanic Sciences Department, University of Wisconsin Madison, Madison, Wisconsin, USA. 3 Atmospheric and Environmental Research, Inc., Lexington, Massachusetts, USA. Copyright 2012 by the American Geophysical Union /12/2011JD which is defined here as wavelengths longer than 17 mm (i.e, wave numbers less than about 600 cm 1 ). The rotational water vapor absorption band is the dominant spectral feature in the FIR and is responsible for nearly all of the absorption and emission in this spectral region in clear skies. This spectral region has been shown to be responsible for approximately 40% of the total outgoing longwave radiation emitted by the planet [Harries et al., 2008]; furthermore, it is also responsible for the majority of the radiative cooling in the middle-to-upper troposphere by water vapor [Clough et al., 1992]. [4] The most accurate RTMs are models that treat gaseous absorption in a line-by-line manner, utilizing all the information (e.g., line position, strength, width, and temperature dependence) in spectral databases such as HITRAN [Rothman et al., 2005]. These line-by-line RTMs typically use all pertinent spectroscopic line parameters to compute the optical depth due to the local component of each line within 25 cm 1 of its line center, thereby accounting for its relatively high spectral variability in the region nearby the line center. For molecules such as water vapor and carbon dioxide, it is also important to consider the contribution from other than the local line component of an absorption line as the aggregate optical depth from all such contributions is not 1of11

2 negligible for current atmospheric abundances of these species (see the sidebar in study by Turner and Mlawer [2010] for a simplified summary). The optical depths from this aggregate do not have a great deal of spectral variability and scales simply with pressure and temperature, so it is typically dealt with as continuum absorption by line-by-line RTMs. For water vapor, the definition of the continuum that has been adopted by many is the total observed absorption because of water vapor minus the contribution from the local component of the lines (i.e., the contribution of the all of the absorption lines within 25 cm 1 of line center assuming a Voigt lineshape but excluding the basement term). Perhaps the most commonly used water vapor continuum formulation is the Clough-Kneizys-Davies (CKD) family of models [Clough et al., 1989, 2005], which includes the newer Mlawer- Tobin-Clough-Kneizys-Davies (MT_CKD) parameterizations. These are semiempirical formulations that fit a small number of adjustable lineshape-related parameters to atmospheric and laboratory observations made in different spectral regions [Mlawer et al., 2012]. The water vapor continuum is divided into two parts: the self-component and foreign component, where the water vapor molecule interacts with another water vapor or non water vapor molecule (nitrogen or oxygen), respectively. This division of self-absorption versus foreign absorption is useful because (1) the strength of the selfabsorption is proportional to the square of the water vapor density, whereas the foreign absorption is linearly proportional; (2) the two components have markedly different relative spectral behavior; and (3) there are differences in the temperature dependence of the absorption. (It should be noted that recent evidence [e.g., Paynter et al., 2007; Scribano and Leforestier, 2007] suggests that water vapor dimers contribute to water vapor continuum absorption; however, this study focuses on the impact of a change to the commonly used CKD/MT_CKD models that was based on the newly acquired observations.) [5] The FIR has been historically under-observed compared to other spectral regions [Turner and Mlawer, 2010; Harries, 1996]; this was primarily because of the lack of instruments that had the ability to make accurate spectrally resolved radiance measurements in this band. However, advances in FIR instrumentation have been made recently [e.g., Knuteson et al., 2004a, 2004b; Serio et al., 2008a; Mlynczak et al., 2006], and consequently, some new field experiments have been conducted to gather data sets to evaluate and improve RTMs in the FIR. Owing to the strong absorption by water vapor, these experiments were conducted at surface locations that have very small amounts of precipitable water vapor (PWV), so that the FIR spectrum is semitransparent. Surface-based experiments allow a large complement of instrumentation to be brought to bear to characterize the atmosphere and radiation. [6] One of these experiments, the first Radiative Heating in Underexplored Bands Campaign (RHUBC-I), was performed at the Atmospheric Radiation Measurement site in Barrow, Alaska, from February to March 2007 [Turner and Mlawer, 2010]. A second separate experiment, called the Earth Cooling by Water Vapor Radiation (ECOWAR), was conducted in the Italian Alps in March 2007 [Bhawar et al., 2008]. RHUBC-I suggested that the representation of the foreign water vapor continuum absorption in the FIR was too strong, while the self-continuum was too weak in the MT_CKD model, and subsequently, an updated version of the water vapor continuum model (MT_CKD v2.4) was developed [Delamere et al., 2010]. The ECOWAR results were in reasonable agreement with the RHUBC-I results [Serio et al., 2008b]. [7] Radiative transfer calculations are among the most time consuming of processes that are simulated in GCMs. Line-by-line RTMs are too computationally expensive to be used in GCMs, and thus, detailed line-by-line RTMs are used to build drastically faster parameterized RTMs. An example of this is the rapid RTM (RRTM) model [Mlawer et al., 1997; Iacono et al., 2008], which is a correlated-k model that separates the longwave spectrum into 16 bands (there is also a shortwave version of the RRTM, but this will not be discussed here). Recent versions of both the longwave and shortwave RRTM models were implemented in the Community Atmospheric Model (CAM5) of the Community Earth System Model (CESM v1.0), and earlier versions of the longwave RRTM model were evaluated in CAM3 and other models [e.g., Iacono et al., 2000]. The version of the water vapor continuum implemented in CAM5 via RRTM is CKD v2.4. (An accelerated version of RRTM, which is called RRTM GCM (RRTMG), is implemented in CAM5. As the two models have the same origin and provide effectively the same fluxes and heating rates [Iacono et al., 2008], we will simply refer to the RTM in CAM5 as RRTM. More information on these models, as well as the models themselves, is available at [8] CKD v2.4 accounted for the analysis of spectral infrared (IR) radiance data in the mm region in research by Tobin et al. [1999]; however, there were significant uncertainties in the water vapor measurements (estimated to be 25% or larger in PWV) made during that study. The recent RHUBC-I and ECOWAR experiments had significantly better water vapor measurements (estimated to be much smaller than 5% in PWV [Delamere et al., 2010]), and thus, the accuracy of the derived water vapor continuum coefficients in the FIR is much better. However, observations from these two experiments do not provide measurements of the continuum over the entire FIR spectral region, and thus, the MT_CKD v2.4 model interpolates across this spectral region based on the observations near 400 cm 1 from RHUBC-I and microwave observations at 5 cm 1 (see Delamere et al. [2010] and Mlawer et al. [2012] for more details on this interpolation). Figure 1 shows the two components of the water vapor continuum of CKD v2.4 and MT_CKD v2.4 (which resulted from the RHUBC-I analysis). In the FIR, the foreign component is responsible for 60% 90% of the total absorption because of the continuum [Delamere et al., 2010]. There is an approximate factor of 2 decrease in the magnitude of the foreign component at 200 cm 1 (50 mm) in MT_CKD v2.4 relative to CKD v2.4, and an approximate 50% increase in the strength of the selfcontinuum at 420 cm 1 (24 mm). Changes to the water vapor continuum absorption model were also made in the mid-ir between 8 and 13 mm between the CKD and MT_CKD models based on observations at a midlatitude site over a range of water vapor [Turner et al., 2004]. A full description of the CKD and MT_CKD models, including details of the mathematical implementation and the evolution of the different versions, is provided in the study by Mlawer et al. [2012]. 2of11

3 Figure 1. (top) The foreign (C f ) and self (C s ) water vapor continuum coefficients for the CKD v2.4 (solid) and MT_CKD v2.4 (dashed) models and (bottom) the ratio of MT_CKD to CKD for C f and C s. Note that even though the change to the foreign continuum between the two models is nearly a factor of 20 in the cm 1 region, the magnitude of the continuum absorption is so small that this change has negligible impact on the radiative field or heating rates in this spectral region. [9] This large change between these two continuum models across the thermal IR (Figure 1) results in different clear-sky net longwave flux and heating rate profiles for environmental conditions ranging from the tropics to the Arctic (Figure 2), with increased longwave net upward flux for the MT_CKD model between 200 and 500 mb and decreased net flux below 600 mb relative to the CKD model. Interestingly, the height of the peak in the difference in the net flux profiles from the two continuum models corresponds extremely well to the height z i where 0.1 mm = (1/r L ) R z TOA i r w (z)dz, r w (z) is water vapor density profile, and r L is the density of liquid water at the surface. In other words, z i is the height that corresponds to the uppermost 0.1 mm of PWV and provides a convenient way to gauge the altitude level of the impact of the water vapor continuum model change at different climatic locations. The change in the net flux profiles results in changes in the radiative heating rate profile, and thus, has the potential to change vertical circulation globally as longwave radiative processes associated with water vapor absorption are continuously active in the atmosphere. 2. Experiment Design [10] Figure 2 illustrates the change in the clear-sky longwave net flux and hence the clear-sky longwave (wavelengths longer than 3 mm) radiative heating rate (QRLC) profile over a range of clear-sky atmospheric conditions, with respect to the changes between the CKD v2.4 and MT_CKD v2.4 water vapor continuum models. Figure 2 demonstrates that the impact is sizable for all atmospheres including humid tropical profiles, not just atmospheres that have low PWV amounts (as was needed to evaluate the accuracy of the continuum absorption using observations). The question we wanted to answer is simple: Do these large changes in the two components of the water vapor continuum absorption in the longwave make a substantial change in a multidecadal climate simulation through its impact on the diabatic heating profile, and, if so, what atmospheric properties are affected? For this exercise, we used the CAM5 embedded in CESM, as it was relatively easy to update the water vapor continuum model in RRTM from CKD v2.4 to MT_CKD v2.4. [11] Several other groups have investigated the impact of changes to water vapor continuum representations in the IR on climate simulations using an approach similar to ours. For example, Schwarzkopf and Ramaswamy [1999] and Collins et al. [2002] replaced the early Roberts et al. [1976] water vapor continuum model, which only included the selfcomponent in the 8 13 mm window, with the CKD v2.1 water vapor continuum model that included both self-component and foreign component over the entire IR (wavelengths longer than 3 mm). Zhong and Haigh [1999] performed a similar study, replacing the Roberts et al. [1976] continuum model with the CKD v2.2 continuum model. The three studies utilized three different GCMs, yet the general results were very similar, with an approximate 0.5 K day 1 (+0.25 K day 1 ) change in the upper (mid-to-lower) longwave tropospheric heating rates. Furthermore, all three studies demonstrated that the changes to the RTMs impacted the dynamics of the model simulations, resulting in changes in the atmospheric temperature and humidity profiles and the vertical circulation patterns. [12] To evaluate the impact of the modified water vapor continuum model, we replaced both the self water vapor continuum coefficient and foreign water vapor continuum coefficient in the CAM5/CESM 1.0 model for a subset of the RRTM bands. In particular, we replaced the CKD v2.4 continuum coefficients with the MT_CKD v2.4 coefficients in all bands for wavelengths longer than 5.5 mm with the 3of11

4 Figure 2. (left) The longwave (wavelengths longer than 3 mm) net upward flux difference (MT_CKD minus CKD) profile, (middle) radiative heating rate difference profile (in K day 1 ), and (right) radiative heating rate difference profile (in % change), computed using RRTM for standard clear-sky atmospheres: tropical (red), midlatitude summer (blue), midlatitude winter (green), sub-arctic summer (brown), and sub-arctic winter (purple). The horizontal lines under the arrow indicate the height that corresponds to the uppermost 0.1 mm of PWV. Note that the maximum change in the longwave heating rate profile is 5%. exception of the 9.6 mm ozone band and the 15 mm carbon dioxide band. The impact of not including all the spectral regions in the modification has a maximum impact of less than 0.01 K day 1 ; this maximum impact was achieved in the atmospheres with the most water vapor (e.g., tropical and midlatitude summer), below 900 mb, and the impact of not including all of the spectral regions was negligible above 900 mb for all atmospheres and for all altitudes in dry atmospheres (e.g., midlatitude and arctic regions). [13] There are differences in both the self-component and foreign component of the water vapor continuum model between CKD v2.4 and MT_CKD v2.4, and while the changes from RHUBC-I were primarily in the far-ir, there were also some changes in the mid-ir (i.e., in the 8 13 mm window) (Figure 1). To understand the relative contributions of the change to the self-component versus foreign component of the water vapor continuum model to the overall change in the longwave radiative heating rate profile, we have computed the difference in the QRLC profiles between RRTM using CKD 2.4 and modified versions of RRTM that use portions of the MT_CKD v2.4 continuum (Figure 3). The calculations were performed for two different atmospheres with different amounts of PWV to demonstrate the varying relative impact of the foreign continuum component (scales linearly with water vapor concentration) and selfcontinuum component (scales quadratically). These results demonstrate that the changes to the foreign and self-continuum largely offset each other below 700 mb in both the wet and dry atmospheres resulting in very little change to the total longwave heating rate profile, even though changing each component separately has a large impact. Figure 3 also shows that the increase in the radiative heating in the upper troposphere (375 to 200 mb) is due entirely to the change in the foreign continuum. However, the decrease in the total longwave heating rate profile in the middle troposphere (700 to 375 mb) is caused by a combination of the change to both the self-component and the foreign component, with the contribution from the self-component having a larger influence, between approximately 700 and 550 mb, and contribution from the foreign having the larger influence, between roughly 550 and 375 mb. Figure 3 also demonstrates that making only the MT_CKD v2.4 far-ir (i.e., wavelengths longer than 17 mm) change provides almost the same change in the total longwave heating rate profile for altitudes above approximately 700 mb; this suggests that the differences between the CKD v2.4 and MT_CKD v2.4 mid-ir water vapor continuum absorption coefficients provide a relatively small contribution to the changes to the diabatic heating rate profile in the mid-to-upper troposphere for these atmospheres. Figure 3. The difference of the QRLC profile between various modified versions of RRTM (using different combinations of the water vapor continuum) and the RRTM using CKD v2.4. The (left) midlatitude summer and (right) midlatitude winter profiles have PWV amounts of 29 and 9 mm, respectively. The four modified RRTM models used the CKD v2.4 model as the baseline continuum with modifications by (1) using the MT_CKD v2.4 foreign continuum (dashed line), (2) using the MT_CKD v2.4 self-continuum (dotted line), (3) using both components of the MT_CKD v2.4 in only the far-ir (thin solid line), and (4) using the entire MT_CKD v2.4 continuum (thick solid line) models. 4of11

5 Figure 4. (left) The zonal distribution of the difference (experiment minus control) of the mean QRLC profile and (right) the mean residual heating rate difference. See the text for a definition of the residual heating rate. The zero K day 1 difference contour is denoted with black lines in both panels. [14] We performed two multidecadal simulations with CAM5. The first simulation, the control run, used the standard CAM5 physics configuration, which includes RRTM and CKD v2.4. The second simulation, the experimental run, used RRTM modified to use MT_CKD v2.4 as discussed above. All other characteristics were identical in both simulations. The F_2000 component set was used in CESM, which uses a prescribed climatology for sea surface temperature and sea ice, with an active land model (the Community Land Model, CLM) coupled to the atmosphere model (CAM5). The atmospheric composition is constant at year 2000 amounts (e.g., the carbon dioxide concentration a constant ppm during both simulations). The simulations used a finite volume grid, with 30 hybrid sigma pressure levels, and a 30 min integration time step. The default CAM5 physics package was used [N. C. A. R., 2010], other than our modifications to the RRTM radiation parameterization as discussed earlier. Each simulation was a 22 year integration, with the first two years ignored to allow for spin-up, yielding a 20 year total integration for model comparison. As no coupled ocean or ice models were used, the variability is introduced by the coupled atmosphere and land models. After removing the annual cycle, the atmospheric variability should be approximately white noise on time scales longer than a few months [Barsugli and Battisti, 1998], so the choice of a two year spin-up time is conservative and allows for potential adjustment because of the coupling to the land model. Also, other similar studies, such as the studies by Schwarzkopf and Ramaswamy [1999] and Collins et al. [2002], both of which also used fixed sea surface temperatures, used a 1 year period for model spin-up. The 20 year integration time was chosen to allow a large reduction in atmospheric variability by comparing the mean climatologies of each simulation. [15] To validate our choice of two years for the model spin-up time, we examined each model for any evidence of temporal trends in the primary geophysical variables (temperature, winds, humidity, heating rates, and cloud fractions) within the 20 year integration period. No evidence of temporal trends was observed in any of these variables, so the model runs appear to be stationary for the full 20 year period. 3. Results [16] We compared the two simulations by looking at differences in the 20 year mean atmospheric properties. These mean property differences were also averaged seasonally, zonally, and over different geographic regions to help identify any significant differences and gain insight into how the perturbed longwave radiation calculations may have impacted the multidecade simulation. [17] Figure 4 shows the annual mean of the zonal distribution of the difference of the mean QRLC profiles between the two CESM simulations, where the difference is defined here and throughout this article as the experimental run that used MT_CKD minus the control run that used CKD. The zonal distribution of the difference of the mean radiative heating rate profiles agrees very well with those computed using the standard atmospheres in Figure 2; for example, the maximum heating rate difference in the tropics is approximately 0.05 K day 1 and occurs around 200 mb, and the altitude at which the maximum radiative heating rate difference occurs decreases toward the poles. The similarity of the differences between the mean 20 year heating rate profiles by latitude (Figure 4) and the static calculation differences using idealized atmospheres (Figure 2) suggested that there was little adjustment to the radiative heating rate perturbation from the initial conditions; a year-by-year analysis showed very little change in the shape or magnitude of the radiative heating rate profile difference at any location (not shown). If temperature was the only geophysical variable to respond to this radiative heating rate change, then the temperature profile would adjust so that the radiative heating rate profile would be identical between the experiment and control; Schwarzkopf and Ramaswamy [1999] showed this to be approximately true in the stratosphere. That this is not the case for our simulations implies that multiple geophysical 5of11

6 Figure 5. (left) The mean difference profile (experiment minus control) for QRLC; shortwave clear-sky radiative heating (QRSC); the QRLCF, which is computed as the all-sky radiative heating profile minus the QRLC profile; and the difference profile for the precipitation physics tendency (DTCOND). (Note that the x axis scales are identical for these heating rate profiles.) (right) The mean difference in the temperature (T), specific humidity (Q), relative humidity (RH), and cloud fraction amount (CLOUD) profiles for tropical data averaged over the latitude bands 15 S to 15 N over the entire 20 year simulation period. Portions of the profile that are in bold are regions where the difference is statistically different than zero; that is, a Student s t test would reject the hypothesis that the difference between the two simulations is zero at the p = 0.05 level. variables are responding to this change in the longwave radiative heating rate profile (i.e., the model is dynamically responding), thereby allowing the perturbed longwave heating rate profile to be maintained. [18] In order to assess statistical significance of differences between the two simulations, we estimated the variance of each geophysical field by computing the sample variances assuming each year is an independent realization. The assumption of no interannual correlation is physically motivated by the use of a prescribed ocean and ice fields rather than an interactive ocean model (see discussion in section 2). To verify this assumption was reasonable, we computed lagged autocorrelations of the annual mean temperature fields in both climate model runs (not shown). The correlation values were consistent with a null hypothesis of zero correlation between years. [19] For a specific example of the model comparison calculation, consider the difference between the monthly mean 200 mb temperature in the tropical atmosphere. For each simulation, the monthly mean is computed for all grid cells in the tropical zone (within 15 latitude) for each monthly mean field. The result is a 240 element time series. The total monthly mean is then computed by averaging the mean for that month across all 20 years, and simultaneously the standard deviation of these 20 values is computed to estimate the true variance. As we assume no correlation between these values, we use the standard expression for standard deviation, which assumes N 1 degrees of freedom. The statistical significance of the difference between a variable in the experimental and control runs is assessed by comparing the two sample means with a t test, with the independently computed standard deviation from each sample. The result is deemed significant if it passes a two-sided 95% t test (e.g., 2.5% in each tail); otherwise, we cannot reject the null hypothesis that the two simulations produce the same mean value for that geophysical field. [20] Prior to performing this experiment, we had hypothesized that the change in the water vapor continuum absorption would have an impact on the vertical circulation of the atmosphere. However, our analysis of vertical motion and atmospheric transport did not show any statistically significant differences globally, seasonally, or zonally. [21] There are differences in several other variables that are statistically significant, and the magnitude and the altitude of the differences have a zonal dependence; these differences are shown in Figures 5 8. The changes made to the water vapor continuum absorption resulted in a statistically significant change in the QRLC profiles in all four of the zonal regions (tropics, subtropics, midlatitudes, and highlatitudes in Figures 5, 6, 7, and 8, respectively). However, we also noticed that there were smaller but statistically significant differences in the clear-sky shortwave radiative heating rate. To understand why the shortwave radiative heating rate profile was affected, we need to consider the differences in other variables. [22] Figures 5 8 demonstrate that there are small but statistically significant differences in the temperature, specific humidity, and relative humidity (RH) profiles. All four zonal 6of11

7 Figure 6. Same as Figure 5, except the average is over the subtropical regions from 15 S to 40 S and 15 N to 40 N. regions show both statistically significant warming and cooling in the temperature profile: the warming layer is found at and slightly above the region where there is a positive change in the QRLC profile and is about mb thick, whereas the cooling layer is found at and below the region where the difference in the QRLC is negative and typically is about mb thick. The specific humidity shows a statistically significant increase at the level associated with the largest negative difference in the longwave heating rate in the tropical and subtropical regions, but in the midlatitudes and high latitudes, the specific humidity is actually significantly decreased at the level of largest negative difference in longwave heating rate. Interestingly, in all zonal regions but the high-latitudes, there is a decrease in the specific humidity below the region of significant cooling in the midtroposphere, where this region of drying is quite thick in the tropics (300 mb) but decreases to about 50 mb in the midlatitudes. The change in the specific humidity results in a change in the clear-sky shortwave radiative heating rate, as well as impacting the QRLC profiles. [23] Changes in the temperature and specific humidity profiles resulted in statistically significant differences in the Figure 7. Same as Figure 5, except the average is over the midlatitude regions from 40 S to 60 S and 40 N to 60 N. 7of11

8 Figure 8. Same as Figure 5, except the average is over the high-latitude regions from 60 S to the South Pole and 60 N to the North Pole. RH profiles (the bolded regions of the profiles shown in Figures 5 8). As the parameterizations for liquid and ice stratus cloud fractions in CAM5 are directly related to RH [Neale et al., 2010; Park and Bretherton, 2009; Gettelman et al., 2010], the changes in RH result in corresponding changes in cloud amount. The experimental CAM5 run shows an increase in the mid-cloud amount in all four zonal regions and a decrease in the high-cloud amount. [24] The change in the cloud amount impacts the all-sky longwave radiative heating, and consequentially, the longwave cloud radiative forcing (denoted QRLCF; computed as the all-sky radiative heating rate profile minus the QRLC profile). The QRLCF profiles are strongly correlated with the cloud amount profiles above 500 mb in the tropics and subtropics (Figures 5 and 6), with increases (decreases) in cloud amount leading to increases (decreases) in QRLCF just below the cloud location. The changes in the QRLCF are opposite in sign to the QRLC profiles, and thus the cloud radiative feedback partially offsets the impact of the change in the water vapor continuum absorption. [25] Another feedback related to cloud physics is shown in the mean precipitation physics tendency (DTCOND) profiles. The DTCOND variable includes all temperature tendencies from moist physics parameterizations. Within CAM, this variable is calculated by computing the change in temperature caused by the various moist physics parameterizations (e.g., deep convective, shallow convective, and stratiform clouds). This temperature change is caused primarily by latent heating or cooling; however, it also includes vertical transport through the subgrid convection parameterizations. The DTCOND profile change in the tropical and subtropical regions shows an additional negative feedback to the QRLC change, with a similar shape to the QRLCF profile change discussed earlier. The magnitude of the DTCOND change is significantly larger in the tropical profile where moist convection is much more prevalent. In the midlatitude and arctic regions, the DTCOND change is small in magnitude and not well correlated to the cloud fraction amount (CLOUD) and QRLCF changes. [26] A reasonable question to ask is this: if the dynamical response of the model resulted in a change in temperature and water vapor profiles, what is the impact of the changed thermodynamic state on the QRLC profile? To address this question, we used the mean zonal averaged profiles of temperature and humidity (T/Q) from both the experimental and control CESM simulation in an off-line calculation with the RRTM using both the CAM5 default continuum model (CKD v2.4) and the updated model (MT_CKD v2.4). The radiative surface temperature for these calculations was assumed to be equal to the lowest layer air temperature. The resulting zonal difference of the QRLCs is shown in Figure 9a. The similarity of this difference field to Figure 4 (left; the mean difference from the experimental minus control CESM run) shows that the impact of the computing the heating rates from mean profiles versus computing the mean heating rate from instantaneous unaveraged T/Q fields (i.e., the nonlinear behavior of the radiative transfer) is small. Figure 9b shows the mean QRLC differences that result if only the water vapor continuum model is changed, whereas Figure 9c illustrates the differences if only the mean T/Q profiles are changed (from mean experimental CESM profiles to mean control profiles), while using the same RTM. These two panels illustrate that the source of the change in the QRLC difference from the CESM runs (Figure 4, left) is caused by the change in the radiation model, and not caused by the resulting change in the T/Q profiles. In fact, Figure 9c demonstrates that the changed T/Q profiles results in a small amount of radiative heating with an opposite sign as what is shown in Figure 4 (left), and thus serves to partially offset the change in the vertical distribution of heating in a similar manner as the change in cloud forcing. Finally, Figure 9d illustrates the zonal mean difference of the 8of11

9 Figure 9. The mean zonal difference in the QRLC profile computed with the RRTM using the zonal and temporal averaged T/Q fields from the experimental and control CESM runs. (a) The updated continuum model (MT_CKD v2.4) with mean T/Q fields from the experimental CESM run minus the CAM5 default continuum model (CKD v2.4) with mean T/Q fields from the control CESM run. (b) The difference between the updated and default continuum models, where both used the same mean T/Q profiles from the control CESM run. (c) The difference that arises when the default continuum model is applied to the mean T/Q fields from the experimental versus control CESM run. (d) The mean zonal difference in the shortwave clear-sky radiative heating between the experimental and control CESM run. Note that the display range for Figure 9d is one quarter the size of the range used in Figures 9a 9c. shortwave clear-sky radiative heating, which as discussed earlier, is responding to the change in the T/Q profiles. [27] Figure 10 shows the monthly global mean difference and zonal mean difference in high-cloud amount to illustrate the uniformity and distribution of the change in the cloudiness between the experiment and control runs. When averaged over the entire globe, all months except February and May show a statistically significant decrease in the highcloud amount, with the mean decrease of approximately 0.5% for the experimental run as shown in Figure 10 (top). However, as this statistic is dominated by the tropics and subtropics (which cover a much larger fraction of the globe than the poles), Figure 10 (bottom) shows the change in high-cloud amount in equal latitude bins, which shows a relatively uniform decrease in the high-cloud amount from 60 S to 60 N, with increased variability in the mean difference and larger uncertainties at higher latitudes. [28] The primary balancing of the imposed change in heating appears in the heating associated with moist processes (DTCOND) and longwave cloud forcing. The balancing effect is most clearly seen in the tropical profiles (Figure 5), where these profiles (QRLCF and DTCOND) are anticorrelated with QRLC. The balance is less complete in the other zonal regions (Figures 6 8). We have defined the residual atmospheric heating rate to be the sum of all the heating terms: the longwave and shortwave clear-sky heating rates, the longwave and shortwave cloud-forcing contributions, the heating because of the moist convective processes, and the heating because of the vertical temperature diffusion parameterizations. As the experimental model reached a steady state (there are no temporal trends in any variable), the residual heating rate has an expectation value of zero when integrated over the entire globe. The actual residual heating rate will be nonzero because of the specific realization of the 9of11

10 Figure 10. (top) The mean monthly difference (experiment minus control) of global high-cloud amount (CLDHGH) and (bottom) the mean annual difference in CLDHGH as a function of latitude. atmospheric variability within the model run. Figure 4 (right) shows the mean residual heating rate difference between the experimental and control simulations. As expected, the regions of strong change in the clear-sky longwave heating (Figure 4, left) are suppressed. At low altitudes ( mb), the residual heating changes are larger than the clear-sky longwave heating changes, but these are largely dependent on highly spatially variable heating terms such as cloud forcing, vertical diffusion, and moist physics parameterizations. Thus, these changes at low altitudes are dominated by weather variability and are not statistically significant. There remains a low-amplitude pattern in the residual heating rate change above 700 mb that has a strong dependence on latitude, similar to the change in the clear-sky longwave heating. The magnitude of this residual heating is quite small; order of 0.02 to 0.04 K day 1 where the full clear-sky cooling rate is 0.5 to 2.0 K day 1. However, the presence of this residual suggests that there is a dynamical adjustment occurring in the experimental simulation (relative to the control simulation); however, we are unable to find any statistically significant differences in the mean meridional or vertical heat transport between the simulations. It is possible that the dynamical changes are still hidden in the atmospheric variability. 4. Discussion and Conclusions [29] The RHUBC-I field experiment established that the magnitude of the foreign water vapor continuum absorption was too large in the CKD v2.4 model, while the magnitude of the self-continuum in this spectral region was underestimated. Modifying the longwave water vapor continuum absorption in the CAM5 to account for the RHUBC-I results shows small (<5%), yet statistically significant, changes in the QRLC profile. The changed QRLC profile affects the profiles of temperature and specific humidity and hence RH. This, in turn, affects the profiles of cloud amount and clearsky shortwave radiative heating. Furthermore, the change in the cloud amount impacts the longwave cloud forcing, which partially counteracts changes in the QRLC profile, especially in the tropics and subtropics. Additional compensation of the longwave heating change was seen in the moist convective processes and the clear-sky shortwave radiative heating. These changes are statistically significant for the 20 year integration that was performed for this study; if the integration period was shorter, then some of these differences would not have been statistically significant because of the weather noise variability. [30] These results may be model dependent; in other words, other GCMs may exhibit different sensitivities to the large change in the longwave water vapor absorption because of different parameterizations and the interactions between the physics and dynamics in these other models. In particular, the response of the cloud properties to the changed clear-sky radiative properties, and thus the potential change in the cloud forcing, may be significantly different among different models. Thus, this same test should be conducted with other GCM models to test their sensitivity. [31] This work concentrated on the impact of the changes in the longwave foreign component and longwave selfcomponents of the water vapor continuum absorption model used inside RRTM, especially the large changes in the FIR. However, there was a 300% change made to the foreign continuum absorption in the FIR from the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment in 1998 [Tobin et al., 1999], which was subsequently further refined by RHUBC-I (Figure 11). Thus, if this GCM sensitivity study was repeated using the pre-sheba version of the water vapor continuum model (CKD v2.1), the impact on the simulated atmosphere should be much larger. Thus, GCMs that are using older versions of RTMs that do not have the updates that have arisen from these field experiments should be updated. This was also illustrated by the large changes in atmospheric properties as well as top-of-atmosphere and surface-longwave fluxes when an early version of RRTM was implemented in an early version of CAM in the late 1990s [Iacono et al., 2000]. [32] The RHUBC-I and ECOWAR results are not the final word, however. Members from these two communities recently teamed up to conduct a second version of the Figure 11. (left) A comparison of the difference in the clear-sky longwave net flux and (right) radiative heating rate profile for the standard subarctic winter atmosphere for MT_CKD v2.4 and CKD v2.4 (dashed) and MT_CKD v2.4 and CKD v2.1 (solid). CKD v2.1 was the water vapor continuum model that was available prior to Surface Heat Budget of the Arctic Ocean (SHEBA) in 1998, CKD v2.4 was available prior to RHUBC-I in 2007, and MT_CKD v2.4 was released in 2009 after RHUBC-I. 10 of 11

11 Radiative Heating in Underexplored Bands Campaign (RHUBC-II) in an extremely dry region of the Atacama Desert in northern Chile in the fall of 2009 [Turner and Mlawer, 2010]. The PWV during RHUBC-II was nearly 5 times drier than the driest case observed during RHUBC-I, and thus the atmosphere is semitransparent in the FIR from 17 mm to nearly 42 mm. Other experiments, especially aircraft observations that collect spectral radiance and in situ observations of atmospheric state [e.g., Green et al., 2012], have also been conducted to provide data that can be used in radiative closure studies in the far-ir. These data sets, which are currently being analyzed, should provide an additional refinement to our knowledge of water vapor continuum absorption in the FIR, but it is anticipated that any future changes will have a much smaller impact on climate simulations than the changes between MT_CKD v2.4 and CKD v2.4 that were analyzed in this study. [33] Acknowledgments. 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Haigh (1999), The sensitivity of long-wave radiation fields and the response of a GCM to water-vapour continuum absorption, Q. J. R. Meteorol. Soc., 125, , doi: /smsqj A. Merrelli and D. Vimont, Atmospheric and Oceanic Sciences Department, University of Wisconsin Madison, 1225 West Dayton St., Madison, WI 53706, USA. E. J. Mlawer, Atmospheric and Environmental Research, Inc., Lexington, MA , USA. D. D. Turner, NOAA/NSSL, 120 David L. Boren Blvd., Norman, OK 73072, USA. (dave.turner@noaa.gov) 11 of 11