1. Introduction
Clouds play a central role in the Arctic climate system. On the one hand, they reflect solar radiation cooling the Earth’s surface (cloud albedo effect), on the other hand, they absorb and (re-)emit terrestrial radiation warming the Earth’s surface (cloud greenhouse effect). Following Schneider [
1], cloud radiative forcing
is defined as difference between the net radiative fluxes under all-sky (
) and clear-sky (
) conditions.
is the sum of the net longwave (LW) and shortwave (SW) radiative fluxes, while
denotes those fluxes in a cloud-free but otherwise identical atmosphere.
While SW CRF depends on cloud transmittance, surface albedo, and the solar zenith angle, LW CRF is a function of cloud temperature, height, and emissivity as well as the background moisture (e.g., [
2,
3]). Furthermore, CRF varies depending on cloud phase, aerosol loading and whether convective or stratiform clouds are treated [
4,
5,
6]. The radiative effects of clouds and their impact on climate have been addressed in many observational and/or modeling studies. Although Ramanathan
et al. [
7] and Schneider [
1] have shown that clouds have a net cooling effect on the global climate, the studies of Walsh and Chapman [
8] and Intrieri
et al. [
9] have identified the net warming effect of clouds on the Arctic surface, except for a short period during summer when the cloud albedo effect outweighs the greenhouse effect.
Due to low surface temperatures, especially during polar night when there is no solar insolation, and advection of warmer air, ground-based or elevated temperature inversions occur frequently in the Arctic boundary layer (ABL) as demonstrated by Kahl
et al. [
10] and Zhang
et al. [
11]. Low Arctic temperatures are accompanied by comparably low absolute humidities. Apart from the presence of sufficient water vapor, airborne aerosol particles are prerequisites for cloud formation at normal supersaturations, acting either as cloud condensation nuclei or ice nuclei. Although Arctic air masses are normally cold, comparably dry and unpolluted [
12], high-latitudes are mainly characterized by the occurrence of so-called boundary layer clouds (BLCs, [
13,
14,
15]). These BLCs show large seasonal and interannual variability, which is reversely related to Arctic sea-ice variability [
16,
17].
A major problem in climate modeling is the subgrid-scale treatment of cloud processes, requiring sophisticated parameterizations that ideally include the whole complexity of cloud microphysics like water phase changes and precipitation processes. One of the most relevant obstacles is the sparse availability of cloud observations, especially in the inner Arctic (e.g., [
18]), impeding the formulation of sufficient cloud parameterizations and validation of model results. The deficient representation of model cloudiness [
19,
20,
21,
22] plays a relevant role in why the net CRF, or more specifically the cloud-radiation feedback [
3,
23,
24], is not definitely understood to date. Solomon
et al. [
25] have concluded that cloud feedbacks represent the largest source of uncertainty in climate sensitivity estimates. A particular shortcoming in modeling the Arctic climate is the limited representation of prevailing mixed-phase BLCs (e.g., [
26,
27,
28]). Climate models have also difficulties in accurately modeling the vertical structure of Arctic clouds associated with the presence of multiple cloud layers, a strong temperature inversion accompanied by rapid moisture decrease above cloud top, and vertical fluxes within the cloud that are decoupled from the surface fluxes [
26,
29]. All that complicates the determination of surface radiation fluxes, which are very sensitive to the modeled cloud microphysical characteristics. An intercomparison of Arctic regional climate models (RCMs) has confirmed the large uncertainty in simulated cloud cover [
30].
However, various Arctic-specific studies (e.g., [
31,
32]) have identified sources of error, e.g., that layered cloud formation requires a higher-order ABL parameterization. The cloud droplet radius has been found to impact surface LW CRF significantly by changing the emissivity of Arctic clouds [
33,
34], where the liquid component of mixed-phase clouds dominates radiative properties in general. For the liquid phase Morrison
et al. [
35] have found in part reasonable agreement between modeled and observed microphysical properties of Arctic mixed-phase clouds, while the ice microphysical properties have been identified as significantly biased. Morrison and Pinto [
27] have demonstrated that only two-moment bulk microphysical schemes enable the adequate simulation of ice nucleation and snow formation. On the one hand, Morrison
et al. [
36] have shown that these more sophisticated schemes can better reproduce the observed ratio of liquid and solid water in Arctic clouds; on the other hand, these schemes contain plenty of “tuning” parameters derived from lower-latitude measurements which might be inapplicable to Arctic climate conditions.
Convective and stratiform clouds differ considerably in their formation, characteristics (e.g., vertical wind speed, lifetime, horizontal/vertical extent), and generation of precipitation, arguing for individual parameterizations in climate models. Cumulus convection and associated dynamic cloud processes like organized or turbulent en-/detrainment are often modeled by so-called “mass flux schemes”. While several studies (e.g., [
37,
38]) have stated the minor importance of convective clouds in the Arctic, Pinto and Curry [
39], Curry
et al. [
40] and Rozwadowska and Cahalan [
41] have shown that even over Arctic sea ice shallow convective clouds emanate from open water in leads or polynyas. Further, Sato
et al. [
42] have recently identified that these cumuliform clouds rather become more important in the Arctic due to the shift from ice-covered to ice-free Arctic ocean during autumn which can be associated with more well-mixed than stable ABL structures. Convective cloud processes are even important for stratiform cloud formation, such that, e.g., convective detrainment at the cloud top can be a direct source for stratiform cloud cover. To simulate stratiform clouds, current climate models commonly use complex bulk microphysics (e.g., [
43,
44]) and apply either relative humidity cloud schemes (RH-Schemes) or statistical cloud schemes as pointed out by Tompkins [
45] or Zhu and Zuidema [
46].
The motivation to this study was to evaluate and possibly adapt the subgrid-scale parameterization of Arctic clouds in the single-column climate model (SCM) HIRHAM5-SCM. SCMs are considered as a useful tool for developing and evaluating physical parameterizations of climate models, and thus have been exploited in various Arctic studies [
47,
48,
49,
50]. Here, the newly designed SCM version of the most recent RCM version HIRHAM5 [
51] was exploited to analyze the two selectable cloud schemes for inner-Arctic climate conditions. The HIRHAM5-SCM setup and the applied cloud parameterizations are described in
Section 2. Results of the model evaluation are presented and discussed in
Section 3. In the first subsection, modeled height profiles of temperature and relative humidity as well as total cloud cover are validated against observations from NP-35 followed by some statistics. In the second subsection, some cloud-related model variables are discussed with respect to their credibility. An evaluation of simulated cloudiness, using either the RH-Scheme by Sundquist
et al. [
52] or the prognostic statistical cloud scheme (PS-Scheme) by Tompkins [
45], with two satellite-derived cloud data sets is shown afterwards. In
Section 4 several model parameters are analyzed by means of sensitivity experiments for their potential to adapt the cloud parameterization to Arctic climate conditions. Finally,
Section 5 contains conclusions and gives an outlook.
4. Parameter Sensitivity Studies
Sensitivity studies were conducted to assess the effect of modified model adjustment parameters (listed in
Table 1) on cloud-related variables relative to the reference run (see
Figure 3). One of the main goals was to identify suitable tuning parameters, which are potentially able to reduce the systematic overestimation of Arctic clouds in HIRHAM5-SCM. While the values of PS-Scheme tuning parameters are originally based on cloud resolving model simulations, tunable parameters of the cloud microphysics have been estimated by detailed microphysical models. These “tuning” parameters obviously need to be adapted for the usage in large-scale models as stated by Tompkins [
45] and Roeckner
et al. [
54]. Further adjustment of these parameters is likely required again when changing from the global to the regional scale, thus necessitating our sensitivity experiments.
Each sensitivity experiment comprised a simulation over 13 months by analogy to the reference run but using a modified value of a single model parameter. Although every tuning parameter was varied within a certain parameter range (see
Table 1), the following discussions will basically be restricted to one lower and higher value, respectively, since the main conclusions remain unchanged. Based on the simulations, differences between respective sensitivity run (hereinafter “SENS”) and reference run (hereinafter “CTRL”) were computed, and zero-cases were neglected. To quantify the impact of a certain parameter change, relative frequencies of “positive differences” (
) were calculated for several cloud-related model variables both with respect to all 13 simulated months and the periods with moderate (WP) and high (SP)
in the reference run. Let
be the relative frequency of positive differences, then the percental decrease (
) or increase (
) of a certain model variable relative to the reference run can be computed using the formula
. The results are listed in
Table 4 and
Table 5 either with respect to
lower or
higher tuning parameters.
Table 4.
Percental decrease/increase of several model variables due to
lower parameter values (
,
,
,
,
,
,
,
) relative to the default (
Table 1) for the entire 13-month-long simulations (“all”) as well as the winter (WP) and summer (SP) periods as introduced in
Section 3.2.1.
Table 5.
Same as
Table 4 but for
higher parameter values (
,
,
,
,
,
,
,
).
4.1. Modified Adjustment Parameters of PS-Scheme
As introduced by
Section 2.2 the PS-Scheme includes the two adjustment parameters
and
K. The former was varied in the co-domain
, following the restrictions
and
for the beta distribution shape parameters to obtain only unimodal distributions. Also according to Tompkins [
45], the conditions
and
were retained to exclude distributions with negative skewness.
Figure 5.
Difference plots (SENS minus CTRL) of simulated fractional cloud cover (
top), cloud water content (
middle), and cloud ice content (
bottom) for one lower value of the PS-Scheme adjustment parameter
(
left column) and one higher value of the tunable parameter
(
right column), with
and
. These sensitivity experiments were conducted at the NP-35 start position simulating from 1 August 2007 to 31 August 2008 by analogy to the reference run (
Figure 3).
Figure 5.
Difference plots (SENS minus CTRL) of simulated fractional cloud cover (
top), cloud water content (
middle), and cloud ice content (
bottom) for one lower value of the PS-Scheme adjustment parameter
(
left column) and one higher value of the tunable parameter
(
right column), with
and
. These sensitivity experiments were conducted at the NP-35 start position simulating from 1 August 2007 to 31 August 2008 by analogy to the reference run (
Figure 3).
The third column of
Table 4 and
Table 5 reveal that overall a lower (higher) value of
leads to a reduction of (rise in)
. Indeed mid- and high-level clouds decrease significantly due to lower parameter values while low-level clouds tend to slightly increase (see
Figure 5(a)). One possible reason for the increase at lower levels might be very low saturation water contents due to cold temperatures in the relatively wet boundary layer over the Arctic Ocean favoring cloud formation. Although lower values of
are able to reduce
and rise
(overall increase in
suggested by
Figure 5(c)) as well as
, the overestimation of
is strengthened (overall increase in
suggested by
Figure 5(e)). Both
and
rise amplifying the overestimation of
as well.
The second adjustment parameter
K, which relates the increase in the skewness parameter
to the detrainment of cloud condensate, was varied in the co-domain
. However, modifying this parameter only leads to temporary local changes of
C (not shown), and overall
remains almost unaffected. This can be attributed to the minor role of convection in cloud formation over the ice-covered Arctic Ocean, unless open water areas in terms of polynyas or leads coexist. Other cloud-related model variables either remain almost unchanged or increase in part significantly by changes in
K. Both the overestimation of
(
) and
is strengthened. Furthermore, only higher parameter values of
K enable convective precipitation during wintertime (WP) explaining the increase of 100% in the fourth column of
Table 5.
4.2. Modified Tuning Parameters of Cloud Microphysics
The following discussions will basically concentrate on tuning parameters of the cloud microphysics, which can reduce
based on
Table 4 and
Table 5, and conclusions are summarized in
Table 6.
Table 6.
Overall effect on cloud-related model variables due to modification of a single model tuning parameter enabling the reduction of
relative to the default parameter value (see
Table 1 and
Figure 4). Effects that potentially improve model results are marked by a ‘+’, negative influences are indicated by a ‘−’.
Table 6.
Overall effect on cloud-related model variables due to modification of a single model tuning parameter enabling the reduction of relative to the default parameter value (see Table 1 and Figure 4). Effects that potentially improve model results are marked by a ‘+’, negative influences are indicated by a ‘−’.
Parameter | Changes due to lower parameter value | Changes due to higher parameter value |
---|
| 1.5 | 20 |
| reduction of and | rise in ( ) but reduction of ( ); |
| rise in ( ) | effect is small (large) for ( ) |
| rise in ( ) | rise in , , , and |
| rise in and | |
| | |
| rise in ( ) but reduction of ( ); | significant reduction of , |
| effect is more pronounced than for higher | reduction of |
| and more significant for | reduction of ( ) but rise in ( ) |
| rise in , , , and | and |
| 5 | 100 |
| ( ) rises but reduction of | rise in ( ) but reduction of ( ); |
| rise in all other regarded model variables | effect is large (small) for ( ) |
| | significant decrease in , |
| | rise in and |
| | |
| rise in ( ) but reduction of ( ), | ( ) rises |
| where effect is significant for and | rise in all remaining model variables |
| reduction of , | |
| increase in and | |
To assess the impact of modified
, sensitivity experiments were conducted in the wide range between zero and
. Note that
impacts model results by ensuring nonzero
and
regardless of the applied cloud scheme (see
Table 1). As a working hypothesis, lower (higher)
should result in rising (declining) cloud cover. This is generally confirmed by the fifth column of
Table 4 and
Table 5. Higher
lead to significant decrease in
, and very high parameter values are even able to prevent the formation of clouds. While low- and high-level clouds tend to decline monotonically, mid-level clouds first seem to increase but finally decrease as well (suggested by
Figure 5(b)). Despite the ability to reduce the overestimation of
, higher
amplify the over- and underestimation of
and
, respectively. During the entire simulation period
decreases below 900h but significantly increases above 900h, while
decreases monotonically (see
Figure 5(d,f)). Furthermore, higher
amplify the overestimation of
while
and
drop.
The autoconversion rate
, which controls the conversion from (supercooled) cloud droplets to rain drops and thus the cloud lifetime effect, was found to be the next promising tuning parameter. This parameter was varied in the co-domain
.
Figure 6(a) and the sixth column of
Table 4 and
Table 5 confirm that only higher parameter values might be able to improve simulated cloudiness. Here, the increase in
during wintertime (WP) is outweighed by the decrease during summertime (SP). Furthermore, higher
are able to reduce the over- and underestimation of
and
, respectively. For
and
this effect is more difficult to identify from
Figure 6(c,e) due to temporary local changes. As expected,
and
rise in case of higher
amplifying the overestimation of
, while
is more or less unaffected.
Finally, the cloud ice threshold
was identified as promising tuning parameter. This parameter controls the Bergeron–Findeisen process, which explains the growth of ice crystals at the expense of cloud droplets in mixed-phase clouds due to lower vapor pressures over ice than over water at subfreezing temperatures. Lohman
et al. [
66] have pointed out that as soon as the threshold of cloud ice content is exceeded a supercooled water cloud will glaciate immediately in the model. In the standard ECHAM5 code the remaining cloud water is not evaporated to deposit onto existing ice crystals but remaining cloud droplets have to either freeze or grow to precipitable sizes in subsequent time steps. For the sake of completeness
was varied from zero to
. As shown by
Figure 6(b) and the last column of
Table 4 and
Table 5, lower
are also able to reduce the overestimation of simulated Arctic clouds. Furthermore, a lower parameter value is most suitable to improve the modeled ratio of
to
(overall reduction of
but rise in
, see
Figure 6(d,f)), which can be associated with the most significant reduction of
but rise in
. While
decreases and
remains almost unchanged,
increases overall.
Figure 6.
Difference plots (SENS minus CTRL) of simulated fractional cloud cover (
top), cloud water content (
middle), and cloud ice content (
bottom) for one higher value of the tunable parameter
(
left column) and one lower value of the tunable parameter
(
right column), with
and
. These sensitivity experiments were conducted at the NP-35 start position simulating from 1 August 2007 to 31 August 2008 by analogy to the reference run (
Figure 3).
Figure 6.
Difference plots (SENS minus CTRL) of simulated fractional cloud cover (
top), cloud water content (
middle), and cloud ice content (
bottom) for one higher value of the tunable parameter
(
left column) and one lower value of the tunable parameter
(
right column), with
and
. These sensitivity experiments were conducted at the NP-35 start position simulating from 1 August 2007 to 31 August 2008 by analogy to the reference run (
Figure 3).
Figure 7 shows the comparison of monthly averaged
by analogy to
Section 3.2.2. Here, only the annual cycles of the best-fit parameters (
green curves), based on the best combination of lowest 13-month-mean of
and overall RMSE, are shown in addition to the annual cycle produced by MODIS (
black curve) and using the default values (
blue curve), respectively. Note that MODIS and the HIRHAM5-SCM reference run (using the PS-Scheme) produced averaged
of 64.8% and 78.2%, respectively with an overall RMSE of 18.4% (see
Table 3). Thus,
Figure 7 confirms that higher
(averaged
of 77.3% and overall RMSE of 17.2% for
) and
(averaged
of 77.8% and overall RMSE of 17.3% for
) as well as lower
(averaged
of 76.5% and overall RMSE of 17.9% for
) and
(averaged
of 74.9% and overall RMSE of 14.1% for
) reduce simulated Arctic cloud cover, where the latter might be the most promising tuning parameter to improve cloud-related variables in the model.
Figure 7.
Monthly means of
(in %) from August 2007 to August 2008 referring to the NP-35 start position. The results originate from MODIS (
black line) satellite observations, and HIRHAM5-SCM simulations using either the PS-Scheme and default model parameters (
blue line) or the PS-Scheme with a single modified tuning parameter (
green lines).
Figure 7.
Monthly means of
(in %) from August 2007 to August 2008 referring to the NP-35 start position. The results originate from MODIS (
black line) satellite observations, and HIRHAM5-SCM simulations using either the PS-Scheme and default model parameters (
blue line) or the PS-Scheme with a single modified tuning parameter (
green lines).
Figure 7 also reveals that all four tuning parameters are able to reduce
during May 2008 while only modified
and
improve the simulation of Arctic clouds during October 2007. In the former case, the enhanced cloud formation due to unrealistic turbulent moisture fluxes could be either partially compensated due to more efficient precipitation processes (for
and
) or through the partial suppression of cloud formation (for
and
). In the latter case, both the more deficient simulation of the ABL structure and the overestimated cloud top radiative cooling could not be significantly improved by changing tuning parameters of the cloud microphysics, except for
. The most significant impact through reduced
in both cases can be explained with the more efficient Bergeron–Findeisen process which results in faster growing cloud ice particles and finally enhanced snow fall.
5. Conclusions
A SCM version of the atmospheric RCM HIRHAM5 was developed to analyze the representation of Arctic clouds. HIRHAM5-SCM was exploited as test bed for evaluating the cloud cover schemes of the ECHAM5 parameterization package, the RH-Scheme by Sundquist
et al. [
52] and the more sophisticated PS-Scheme by Tompkins [
45].
Observations from the 35th Russian North Pole drifting station (NP-35) were used to validate the model. Above the Arctic boundary layer (ABL), simulated and observed temperature profiles agree fairly well. In contrast, the more pronounced vertical variability of relative humidity is inadequately reproduced. Primarily the RH-Scheme produces clouds at incorrect altitudes. The PS-Scheme enables to better simulate the observed correlation between the occurrence of boundary layer clouds (BLCs) and capping strong temperature inversion as well as rapid moisture decrease. Further, the PS-Scheme results in higher correlations between the simulated and the measured temperature and humidity profiles. Nevertheless, the model has difficulties in simulating the ABL, most likely due to unrealistic turbulent exchange under extremely stable conditions.
The evaluation of relative frequencies of simulated clear-sky, partially cloudy, and (totally) overcast cases revealed underestimation of clear-sky and overestimation of overcast conditions as compared to ground-based observations at NP-35. Both biases are significantly larger when using the RH-Scheme, even though the frequency of partially cloudy conditions agrees well. Overall, the overestimation of cloudy cases (sum of partially cloudy and overcast cases) is reduced by the PS-Scheme.
Independent from the used cloud scheme, the higher frequency of occurrence of modeled low-level clouds during summer (JJA) and autumn (SON) is in accordance with the frequently observed presence of summertime BLCs [
13,
14]. However, the PS-Scheme simulates cloud formation and dissipation more realistically, since the cloud cover is directly linked to sources and sinks (like turbulence, convection, and microphysics), enabling the simulation of frequently observed abrupt changes between overcast and clear-sky conditions.
The validation of the simulated annual cycle of total cloud cover against ISCCP-D2 and MODIS satellite observations showed qualitative agreement with MODIS in terms of higher cloud cover in summer and lower cloud cover in winter. The annual cycle of ISCCP-D2 is reversed and might be unrealistic as has already been pointed out by Schweiger
et al. [
71] and as is suggested by comparison with the ground-based observations at NP-35.
The qualitative agreement with MODIS is independent from the cloud cover scheme, but the RH-Scheme systematically overestimates the cloud cover, while the PS-Scheme shows reduced biases and pretty good agreement from November to January. However, the transition from high cloud cover in summer to lower cloud cover in winter and vice versa is shifted to the cold season in either case, accompanied by large biases in October and May.
All in all, the PS-Scheme enables an improved simulation of Arctic clouds as compared to the RH-Scheme, but it still shows a systematic overestimation of Arctic cloud cover. Several tunable parameters were analyzed by means of sensitivity studies to identify parameters which potentially enable the adaptation of the cloud parameterization to Arctic climate conditions. The resulting recommendations are summarized below:
• Lower values of
, the parameter that determines the shape of the symmetric beta distribution in the PS-Scheme, result in a reduction of total cloud cover (
best fit to MODIS), decreased underestimation of cloud ice, but increased overestimation of cloud water and precipitation.
• Higher values of the minimum cloud water content
result in a reduction of clouds (even up to their total disappearance) and consequently decreased overestimation of total cloud cover (
best fit to MODIS), but also in increased overestimation/underestimation of cloud water/cloud ice and increased overestimation of precipitation. Instead of applying the same value of
to cloud water and cloud ice, it is suggested using different thresholds, since cloud water contents are typically about one magnitude higher than cloud ice contents in Arctic clouds (e.g., [
28,
47]).
• Higher values of the autoconversion rate
, which controls the local rain production and thus the cloud lifetime, result in decreased overestimation of total cloud cover (
best fit to MODIS), decreased overestimation/underestimation of cloud water/cloud ice, but increased overestimation of precipitation as was expected.
• Lower values of the cloud ice threshold
, which controls the efficiency of the Bergeron–Findeisen process, turned out to be most suitable for reducing the overestimation of total cloud cover (
best fit to MODIS) and result additionally in decreased overestimation/underestimation of cloud water/cloud ice, but also increased overestimation of precipitation.
The best-fit parameters suggested by this study need to be examined for their performance in the three-dimensional model version HIRHAM5. Liu
et al. [
77] have identified the possible underestimation of MODIS relative to CloudSat-CALIPSO cloud amount especially over the ice-covered Arctic ocean. It is therefore planned to validate cloud-related variables simulated by HIRHAM5 against observations from the Multiangle Imaging SpectroRadiometer (MISR) and Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP). Since changes in modeled cloud fraction and total precipitation are anti-correlated, extensive validation against precipitation observations is required. This is currently ongoing work.