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  • View in gallery

    Frequency distribution of liquid water fraction as a function of temperature for the diagnostic temperature-dependent mixed-phase scheme (solid black line) and the new prognostic ice/liquid scheme (shading) during January 2011 (a) for the whole globe, (b) for the Arctic region (75°–90°N), and (c) for the tropics (10°S–10°N).

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    Time-averaged profiles of (left) liquid water content and (right) total ice water content for different sensitivity experiments in the IFS single-column model simulations for a single mixed-phase boundary layer cloud during a 3-day period of the MPACE observational campaign at the Arctic NSA site in October 2004. (a) Impact of vertical resolution (60 levels and 137 levels). (b) Impact of the Prenni et al. (2007) ice nuclei concentration parameterization compared to the control based on Meyers et al. (1992). (c) Impact of the cloud-top deposition rate reduction (PROG+) for different vertical resolutions. (d) Sensitivity to the parameters in the PROG+ formulation.

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    (a) Ice nuclei number concentration as a function of temperature for various parameterizations: Meyers et al. (1992), Prenni et al. (2007), Cooper (1986), and DeMott et al. (2010). (b) Bulk ice deposition rate as a function of ice water content for the Rotstayn et al. (2000) (solid line) and Wilson and Ballard (1999) (dashed line) parameterizations assuming water saturation at a temperature of −10°C and pressure of 950 hPa.

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    (left) Cloud fraction, (middle) cloud liquid water contents, and (right) cloud ice water contents for a single-layer mixed-phase cloud observed during 9–10 Oct 2004 at the NSA site during MPACE. (a) Observations, (b) the DIAG model version, (c) the PROG model version, and (d) the PROG+ model version. Note the color scale is a factor of 5 different between the liquid and ice water content panels.

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    Liquid water path vs ice water path observed and modeled during the 48-h period from 0000 LT 9 Oct to 2300 LT 10 Oct. DIAG, PROG, and PROG+ are water paths from the three model versions at the land point closest to the NSA site at Barrow. The values for the closest ocean point are indicated by the gray markers and “o” suffix. MICROBASE and WANG stand for water paths retrieved using the Microbase and Wang algorithms, respectively. Two versions of the Shupe–Turner retrieval are shown, the standard retrieval (S-T/VAR) and a modified retrieval (S-T/MPACE) to better match in situ observations. AIR refers to the water paths derived from aircraft observations.

  • View in gallery

    (a) Surface irradiance and (b) downward longwave radiation time series, for the single-layer mixed-phase Arctic cloud at NSA during 9 and 10 Oct 2004 for the observations (black) and the three model versions, DIAG (blue), PROG (green), and PROG+ (red).

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    Net surface longwave radiation frequency distribution at the NSA site for (a) the observations (CMBE product, black) and the operational model with diagnostic mixed-phase (DIAG, red) for January–December 2009, and (b) the observations (QCRAD product, black) and operational model with prognostic mixed phase (PROG+, red) for January–December 2013.

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    Sample track of cloud phase categorization from (a) DARDAR product, and (b) the DIAG, (c) PROG, and (d) PROG+ model versions, for a CALIPSO/CloudSat track across the Southern Ocean on 2 Jan 2007. Black shading indicates the presence of supercooled liquid (with or without additional ice), while gray shading indicates the presence of frozen hydrometeors (cloud ice for DIAG, cloud ice or snow for PROG and PROG+).

  • View in gallery

    Annual mean top-of-the-atmosphere net shortwave radiation (net SW) for (a) CERES observations, and the difference from CERES for the 4-member ensemble 1-yr IFS model simulations at T159 resolution (125-km grid resolution, 91 levels), for (b) DIAG, (c) PROG, and (d) PROG+. Hatched areas indicate regions of significance at the 5% level using a two-tailed t test.

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    2-m temperature (°C) over northern Europe at 0000 UTC 4 Jan 2011 for (a) SYNOP observation analysis, and 60-h forecasts for (b) DIAG simulation, (c) PROG simulation, and (d) PROG+ simulation.

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    Impact on 2-m temperature over land (72-h forecasts for January 2011) for supercooled liquid water changes (PROG+ minus PROG) for (a) mean temperature change (°C) and (b) change in mean absolute error (°C) when compared to 2-m temperature analyses using SYNOP stations.

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On the Representation of High-Latitude Boundary Layer Mixed-Phase Cloud in the ECMWF Global Model

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  • 1 European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
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Abstract

Supercooled liquid water (SLW) layers in boundary layer clouds are abundantly observed in the atmosphere at high latitudes, but remain a challenge to represent in numerical weather prediction (NWP) and climate models. Unresolved processes such as small-scale turbulence and mixed-phase microphysics act to maintain the liquid layer at cloud top, directly affecting cloud radiative properties and prolonging cloud lifetimes. This paper describes the representation of supercooled liquid water in boundary layer clouds in the European Centre for Medium-Range Weather Forecasts (ECMWF) global NWP model and in particular the change from a diagnostic temperature-dependent mixed phase to a prognostic representation with separate cloud liquid and ice variables. Data from the Atmospheric Radiation Measurement site in Alaska and from the CloudSat/Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite missions are used to evaluate the model parameterizations. The prognostic scheme shows a more realistic cloud structure, with an SLW layer at cloud top and ice falling out below. However, because of the limited vertical and horizontal resolution and uncertainties in the parameterization of physical processes near cloud top, the change leads to an overall reduction in SLW water with a detrimental impact on shortwave and longwave radiative fluxes, and increased 2-m temperature errors over land. A reduction in the ice deposition rate at cloud top significantly improves the SLW occurrence and radiative impacts, and highlights the need for improved understanding and parameterization of physical processes for mixed-phase cloud in large-scale models.

Corresponding author address: Maike Ahlgrimm, European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading RG2 9AX, United Kingdom. E-mail: maike.ahlgrimm@ecmwf.int

Abstract

Supercooled liquid water (SLW) layers in boundary layer clouds are abundantly observed in the atmosphere at high latitudes, but remain a challenge to represent in numerical weather prediction (NWP) and climate models. Unresolved processes such as small-scale turbulence and mixed-phase microphysics act to maintain the liquid layer at cloud top, directly affecting cloud radiative properties and prolonging cloud lifetimes. This paper describes the representation of supercooled liquid water in boundary layer clouds in the European Centre for Medium-Range Weather Forecasts (ECMWF) global NWP model and in particular the change from a diagnostic temperature-dependent mixed phase to a prognostic representation with separate cloud liquid and ice variables. Data from the Atmospheric Radiation Measurement site in Alaska and from the CloudSat/Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite missions are used to evaluate the model parameterizations. The prognostic scheme shows a more realistic cloud structure, with an SLW layer at cloud top and ice falling out below. However, because of the limited vertical and horizontal resolution and uncertainties in the parameterization of physical processes near cloud top, the change leads to an overall reduction in SLW water with a detrimental impact on shortwave and longwave radiative fluxes, and increased 2-m temperature errors over land. A reduction in the ice deposition rate at cloud top significantly improves the SLW occurrence and radiative impacts, and highlights the need for improved understanding and parameterization of physical processes for mixed-phase cloud in large-scale models.

Corresponding author address: Maike Ahlgrimm, European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading RG2 9AX, United Kingdom. E-mail: maike.ahlgrimm@ecmwf.int

1. Introduction

Mixed-phase clouds are found across the globe at temperatures down to −40°C, but with decreasing frequency as temperatures drop (Hogan et al. 2003, 2004; Shupe 2011). Both ground-based observations (Shupe et al. 2011; Shupe 2011) and satellite data (Hogan et al. 2004; Hu et al. 2010) show that a high proportion of high-latitude low-level clouds contain supercooled liquid. The presence of liquid in clouds has a large radiative impact as liquid is much more opaque in the longwave spectrum than ice, and the clouds’ albedo increases in the presence of liquid drops (e.g., Hogan et al. 2003). Thus, an accurate representation of the mixed phase is critical to model the cloud–radiation interaction correctly and reduce uncertainty in weather and climate predictions. This is particularly true in areas with extensive persistent mixed-phase boundary layer clouds at high latitudes such as found in the Arctic (Morrison et al. 2012) and over the Southern Hemisphere oceans (Huang et al. 2012). A summary of available ground-based observations of Arctic clouds is provided in Shupe et al. (2008b) and in situ observations from aircraft are also available from several measurement campaigns [the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment-Arctic Cloud Experiment/Surface Heat Budget of the Arctic Ocean (FIRE-ACE/SHEBA; Curry et al. 2000); the Mixed-Phase Arctic Cloud Experiment (MPACE; Verlinde et al. 2007); and the Indirect and Semi-Direct Aerosol Campaign (ISDAC; McFarquhar et al. 2011)]. These observations suggest that when supercooled liquid is present in Arctic clouds, it is commonly arranged in thin layers a few hundred meters deep at cloud top with ice crystals forming in the cloud and precipitating out below. In the western Arctic during the fall season, mixed-phase clouds can persist for several days (Shupe 2011). This is perhaps unexpected, as the Wegener–Bergeron–Findeisen process suggests that supercooled liquid in the presence of ice will rapidly evaporate and deposit onto ice crystals because of subsaturated liquid but supersaturated ice vapor pressure (Wegener 1911; Bergeron 1935; Findeisen 1939), and ice particles will then sediment out of the cloud removing ice nuclei. Yet, the supercooled layers persist, indicating that the ice growth rate (acting as a liquid sink) must be balanced by an equally strong condensate supply rate (liquid source) and a continuous supply of new or recycled ice nuclei must be available. As persistent mixed-phase clouds have been observed in subsiding conditions (Zuidema et al. 2005; Morrison et al. 2012), large-scale ascending motion to bring the air to water saturation is not necessary for the maintenance of supercooled liquid. However, turbulent vertical motion may be driven by cloud-top radiative cooling, evaporative cooling due to entrainment, or shear, and often weak updrafts of a few tens of centimeters per second are sufficient for the maintenance of the liquid layer (Rauber and Tokay 1991; Shupe et al. 2008a; Korolev and Field 2008).

Large-eddy simulations (LES) that have high enough resolution to resolve the turbulent motions generated by radiative cooling are able to represent the supercooled liquid layers at cloud top (Harrington et al. 1999; Jiang et al. 2000; Marsham et al. 2006; Hill et al. 2014; Field et al. 2013). The cloud top is a unique part of mixed-phase cloud where the ice deposition rate may also be reduced significantly as newly formed ice particles are small and quickly sediment as they grow through deposition, physically separating the liquid and frozen condensate and promoting the persistence of supercooled liquid water (SLW; Rauber and Tokay 1991). Aerosol abundance and properties will also influence ice microphysics, including nucleation and growth rates (McFarquhar et al. 2011). The interaction and balance between all these processes is complex and not completely understood, so their representation in models is still subject to many uncertainties. In addition, for global NWP and climate models, not only is the horizontal resolution much too coarse to resolve the turbulent motions, which must be parameterized, but the vertical resolution is often similar to the thickness of a supercooled liquid layer (a few hundred meters) so that the vertical structure in the cloud is not well resolved. This can have important consequences, for example, on the unresolved supersaturation profile and the separation of supercooled liquid and ice at cloud top due to the sedimentation of growing ice particles (Barrett et al. 2014, manuscript submitted to J. Geophys. Res., hereafter BHF14).

Many global circulation models (GCMs) used for weather forecasting and climate studies still have an empirical approach to the representation of mixed-phase cloud with a single cloud condensate variable and a fixed diagnostic temperature-dependent function partitioning of the liquid/ice phase with varying thresholds and functional forms (Del Genio et al. 1996; Zhao and Carr 1997; Lopez 2002; Boville et al. 2006; Bélair et al. 2009; Wu et al. 2010). However, observations show that a wide range of different partitions of supercooled liquid water and ice can occur for a given temperature and therefore a temperature-dependent phase partitioning does not allow enough flexibility to reproduce observed conditions (Ryan 1996). In particular, these diagnostic phase schemes cannot be expected to represent the thin supercooled liquid water layers often observed at the colder temperatures at the cloud top in the boundary layer cloud. A number of GCMs do include separate prognostic variables for cloud liquid and ice with explicit mixed-phase microphysics (Wilson and Ballard 1999; Rotstayn et al. 2000; Lohmann et al. 2007; Morrison and Gettelman 2008) with increased degrees of freedom to represent mixed-phase conditions, but this does not guarantee an adequate representation of the high-latitude mixed-phase boundary layer cloud in a GCM, even with a more complex representation of microphysical processes (Liu et al. 2011).

The purpose of this paper is threefold: 1) to document a significant upgrade to the European Centre for Medium-Range Weather Forecasts (ECMWF) global model mixed-phase physics with a change from a diagnostic temperature-dependent representation of the mixed phase to prognostic cloud liquid and ice variables and an explicit representation of the ice deposition process; 2) to describe the important changes to cloud structure in mixed-phase boundary layer cloud and its impacts on radiative fluxes and near-surface temperature for weather forecasts and the climate of the model; and 3) to illustrate how increasing the complexity of the mixed-phase parameterization can lead to deterioration in the forecasts if all the processes that contribute to the mixed-phase parameterization are not adequately represented in the model.

Section 2 describes those parts of the ECMWF global model parameterization schemes relevant for mixed-phase cloud processes and the modifications to the cloud parameterization. Section 3 discusses some of the uncertainties in the representation of mixed-phase processes and impacts of different assumptions in the Integrated Forecast System (IFS) with a pragmatic approach to improve the representation of cloud top supercooled liquid water layers in the boundary layer cloud.

Section 4 provides an evaluation of the model with the different parameterizations for a mixed-phase Arctic cloud case study, the impact on low cloud decks over the Southern Hemisphere high-latitude ocean using CloudSat/Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite data, and the impact on near-surface temperatures over land in the Northern Hemisphere high latitudes. The conclusions are given in section 5.

2. ECMWF model representation of supercooled liquid water cloud

The cloud parameterization in the ECMWF global IFS is based on Tiedtke (1993) with prognostic subgrid cloud fraction and condensate variables and sources and sinks that describe the major generation and destruction processes of cloud and precipitation, including the effects of detrainment from subgrid convection, boundary layer turbulence, radiation, and microphysics. The scheme has evolved over time and notably includes a representation of ice supersaturation (Tompkins et al. 2007). Significant changes to the mixed-phase representation in the operational model in 2010, from a diagnostic temperature-dependent phase partition to separate prognostic variables for cloud liquid and ice, are described below.

a. Diagnostic cloud liquid/ice condensate (DIAG)

Prior to 9 November 2010 (IFS cycle 36r3 and earlier), the operational IFS cloud scheme had one prognostic variable for subgrid cloud fraction and one for cloud condensate, and a diagnostic representation of precipitating rain and snow determined by autoconversion, evaporation, and melting parameterizations (Tiedtke 1993). In this scheme (referred to here as DIAG), a fixed diagnostic temperature-dependent function is used to partition the cloud condensate variable into liquid and ice in the mixed-phase temperature regime. The fraction of liquid water α in the total condensate amount is derived from data in Matveev (1984) and defined in the model as
eq1
where T0 = 0°C and Tice = −23°C represent the threshold temperatures between which a mixed-phase cloud is allowed to exist. The fraction of supercooled liquid in the cloud ice/liquid mixture (solid line in Fig. 1) therefore decreases with decreasing temperature from all liquid at 0°C to all ice at −23°C and is a first approximation to the global mean distribution of supercooled liquid water occurrence. This has been a common approach for GCMs over the years and is still present in cloud parameterization schemes in a number of NWP and climate models (e.g., Del Genio et al. 1996; Zhao and Carr 1997; Lopez 2002; Boville et al. 2006; Bélair et al. 2009; Wu et al. 2010).
Fig. 1.
Fig. 1.

Frequency distribution of liquid water fraction as a function of temperature for the diagnostic temperature-dependent mixed-phase scheme (solid black line) and the new prognostic ice/liquid scheme (shading) during January 2011 (a) for the whole globe, (b) for the Arctic region (75°–90°N), and (c) for the tropics (10°S–10°N).

Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00325.1

b. Prognostic cloud liquid and cloud ice condensate (PROG)

A major upgrade to the parameterization of cloud and precipitation was implemented in IFS cycle 36r4, operational at ECMWF from 9 November 2010. The new scheme (referred to here as PROG) has separate prognostic variables for cloud liquid condensate and cloud ice condensate, as well as prognostic variables for both rain and snow.

This removes the diagnostic temperature dependent liquid/ice phase split in the previous scheme and allows additional degrees of freedom to represent the wider variability of supercooled liquid water observed in the atmosphere. Cloud liquid water drops are formed when water saturation is reached, then the processes of ice crystal nucleation and diffusional growth determine the rate of conversion of supercooled liquid water to ice cloud. The parameterizations of ice nucleation and diffusional growth follow Rotstayn et al. (2000) [their Eqs. (2)–(5)], as summarized below.

Following Pruppacher and Klett (1997), the rate of growth of an ice crystal of mass Mi is
e1
where C is the capacitance of the particle (related to the shape), Si = (e/esi − 1) is the ice supersaturation (ratio of vapor pressure e to the saturated vapor pressure with respect to ice, esi), T is the air temperature, Ls is the latent heat of sublimation, Rυ is the gas constant for vapor, Ka is the heat conductivity of air, and χ is the diffusivity of water vapor in air, which varies inversely with pressure p, as χ = 2.21/p.
Equation (1) is integrated over the assumed ice particle size distribution to determine the gridbox mean ice deposition rate. In the current scheme, as in Rotstayn et al. (2000), the ice crystals are assumed to be monodispersed with all particles having equal diameter Di and equal mass Mi (and therefore also equal density where ρi = 700 kg m−3). The cloud ice specific water content is therefore qi =MiNi/ρ, where ρ is the density of air and Ni is the ice crystal number concentration defined below. The capacitance term C in Eq. (1) assumes ice crystals are spherical (C = Di/2) where Di = (6Mi/πρi)1/3. Elimination of C in Eq. (1) then gives an equation for the rate of depositional growth of ice qi as a function of number concentration, supersaturation, and specific ice water content:
e2
where β is a function of air density and temperature. The ice crystal number concentration Ni is given by the parameterization of Meyers et al. (1992) and, as the cloud is considered to be at water saturation in the model when considering the nucleation process and subsequent depositional growth, this is defined as
e3

The vapor pressures at ice saturation esi and water saturation esl are dependent only on temperature so in this implementation the number of activated ice nuclei is also purely a function of temperature. The deposition process is active when there is liquid water present and so assumes that the vapor pressure e is at water saturation esl. When the liquid water is depleted, the deposition process becomes inactive, an approximation that will be addressed in the future. Equations (2) and (3) show the deposition rate is dependent on the amount of ice mass already in the grid box, increasing for higher ice water contents, and on the temperature through the ice crystal number concentration and supersaturation terms, increasing with decreasing temperature. The validity of the approximations and uncertainties in the ice nuclei concentration and deposition rate formulations described here are discussed in section 3.

The new scheme with separate liquid and ice prognostic variables and parameterized depositional growth allows supercooled liquid water to exist at all temperatures between 0°C and the homogeneous freezing threshold temperature defined as −38°C. At temperatures colder than this water droplets are assumed to freeze instantaneously and homogeneous nucleation of ice particles occurs when a critical supersaturation with respect to ice is reached in the clear air part of the grid box, as described in detail by Tompkins et al. (2007). For temperatures warmer than −38°C when supercooled liquid and ice coexist, they are assumed to be well mixed and distributed uniformly through the cloud, although alternative liquid-ice overlap assumptions can be made (Rotstayn et al. 2000).

It is the process of deposition representing the Wegener–Bergeron–Findeisen (WBF) process that largely determines the partition between liquid and ice in mixed-phase clouds in the scheme. Figure 1a shows the frequency distribution of the phase partition between ice and liquid cloud for a model forecast using the new scheme (PROG) compared to the previous diagnostic version (DIAG) for all cloud globally. It is seen that the “S” shape is reproduced from observations (e.g., Rotstayn et al. 2000). Compared to the default model, which sets liquid water fraction diagnostically to zero at −23°C, there are more occurrences of liquid water at temperatures colder than this threshold. The mode of the distribution still approximately follows the temperature-dependent function due to the diagnostic phase assumption remaining in the cloud source term from the convection parameterization, but variability is significantly increased. For example, 100% of cloud is all ice at −38°C, 100% is all liquid at +10°C, but at −10°C the cloud can vary from all ice, through varying fractions of supercooled liquid water, to all water. There is no liquid water at temperatures colder than around −38°C because of homogeneous freezing and there is increased occurrence of pure ice cloud at all temperatures colder than 0°C. Small ice fractions (i.e., liquid water fractions close to one) at temperatures warmer than 0°C are due to the fact that ice is allowed to fall and takes a finite time to melt. To highlight the regional variations of the phase partitioning, Figs. 1b and 1c show the frequency distribution of water fraction for the Arctic (75°–90°N) and the tropics (10°S–10°N), respectively. The impact of the diagnostic temperature-dependent mixed-phase function in the convective parameterization as a source of condensate is very clear in the tropics, and the higher occurrence of supercooled liquid water at colder temperatures is evident in the Arctic cloud.

The change to separate prognostic variables for liquid and ice is a major step forward in the representation of mixed-phase cloud in the IFS, but as will be shown in section 4, the additional degrees of freedom can also be detrimental to the forecast if all processes are not represented adequately. In particular, simulating the commonly observed thin liquid water layers at cloud top is difficult for typical global models with resolutions that are too coarse to represent the small-scale processes as well as deficiencies in the formulation of parameterizations. Some of these issues for the IFS are discussed in the following section.

3. Uncertainties in the ECMWF model representation of mixed-phase processes and impacts on supercooled liquid water layers

There remain many uncertainties in our understanding of the detailed physical processes that are important in the formation, maintenance, and dissipation of supercooled liquid water layers commonly observed in subfreezing boundary layer cloud. As discussed earlier, the small-scale dynamical and microphysical processes are unique at cloud top due to radiative cooling enhancing the turbulent production of supercooled water, the rate of ice nucleation and deposition controlling the depletion of the liquid water through the WBF mechanism, and the fall out of the growing ice crystals reducing the number of crystals and limiting the amount of ice mass in the shallow layer at cloud top. In the PROG version of the IFS, supercooled water is produced if there is sufficient resolved vertical motion or vertical transport by the subgrid parameterizations, or local longwave radiative cooling for part of the model grid box to reach water saturation. Ice nucleation will then occur and the deposition process will start to evaporate the supercooled water and increase the ice mass. The sedimentation term then removes a proportion of the ice from the grid box, depending on the assumed fall speed for the ice, the depth of the model layer, and time step of the model.

The model may have deficiencies in all of the above aspects. A comprehensive study is beyond the scope of this paper, but a single-column model (SCM) version of the IFS with the PROG scheme is used here to illustrate some of the sensitivities of supercooled liquid water and ice to vertical resolution and different aspects of the parameterization formulations for a single-layer high-latitude boundary layer mixed-phase cloud. The chosen case study is from M-PACE, an observational campaign at the U.S. Department of Energy ARM program North Slope of Alaska (NSA) observational site during October 2004 (Verlinde et al. 2007). This case is discussed in more detail in section 4.

Figure 2 shows the specific cloud liquid water content (left column) and combined specific ice and snow water content (right column) profiles averaged over a 3-day period from 10 to 12 October 2004 where a persistent single mixed-phase cloud layer was observed. The SCM is forced with 3-hourly wind, temperature, and humidity data from the operational IFS model. The period is shifted by a day compared to the case discussed in section 4 to avoid the first day (9 October), during which the DIAG model (the operational version in 2004 used to derive the SCM forcing) produces a deeper cloud layer with hints of a multilayer structure. The period is also extended for a third day where the single-layer cloud persisted for slightly more robust results.

Fig. 2.
Fig. 2.

Time-averaged profiles of (left) liquid water content and (right) total ice water content for different sensitivity experiments in the IFS single-column model simulations for a single mixed-phase boundary layer cloud during a 3-day period of the MPACE observational campaign at the Arctic NSA site in October 2004. (a) Impact of vertical resolution (60 levels and 137 levels). (b) Impact of the Prenni et al. (2007) ice nuclei concentration parameterization compared to the control based on Meyers et al. (1992). (c) Impact of the cloud-top deposition rate reduction (PROG+) for different vertical resolutions. (d) Sensitivity to the parameters in the PROG+ formulation.

Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00325.1

a. Vertical resolution

The poorly resolved vertical profile of ice water content and supersaturation in the top few hundred meters of the cloud is likely to have a detrimental impact on the cloud-top supercooled liquid water layer. Observations, large-eddy simulations, and single-column model studies show ice water content increases, while the supersaturation decreases with distance downward from cloud top (Shupe et al. 2008b; BHF14). As seen in Eq. (2), the deposition rate is the product of these two terms. BHF14 describe a single-column model sensitivity study for a shallow supercooled liquid-water-topped cloud showing that the fastest deposition growth rates occur significantly below cloud top and that a higher proportion of the ice should sediment out of the grid layer due to the increasing profile of ice toward the base of the layer. They show that the persistence of the cloud-top SLW layer is particular sensitive to the vertical resolution of the model. When the cloud is well resolved with high vertical resolution the thermodynamic profile, deposition rate, and sedimentation are accurately represented and the smaller deposition rate at cloud top leads to a supercooled liquid water layer that is able to persist. However, with coarser vertical resolution representing the cloud with only one or two model layers, as is common in many GCMs, the assumption that the ice water content is uniform within the layer and the underestimate of ice sedimentation away from the cloud top leads to an overestimate of deposition rate and a more rapid depletion of the supercooled water layer.

Figure 2a shows the sensitivity of the supercooled liquid profile to the model’s vertical resolution. The control SCM run (black curve) with 60 vertical levels (layer thickness of about 210 m at 1-km height) produces a maximum liquid water content of about 0.08 g kg−1. Increasing the vertical resolution to 137 levels (100-m-layer thickness at 1 km, red curve) almost triples the water amount in the topmost cloud layer. Ice water content is also increased, though only by about 50%.

b. Ice particle number concentration

The ice nuclei concentration (IN) is a large uncertainty due to the wide variability observed in the atmosphere (e.g., DeMott et al. 2010). The formulation of the deposition rate in Eq. (2) in the PROG version of the model makes it particularly sensitive to the parameterization of the number of nucleated ice particles. Equation (3) based on Meyers et al. (1992) is used here, but there are significant spatial and temporal variations and uncertainties with many proposed alternative parameterizations as a function of temperature and supersaturation (Cooper 1986; Prenni et al. 2007; DeMott et al. 2010). Prenni et al. (2007) describe observations of IN in the Arctic during M-PACE in the fall of 2004 with much lower IN concentrations observed than predicted by the Meyers et al. (1992) parameterization [see Eq. (3)]. They propose an alternative formulation that provides a better fit to the M-PACE data:
e4

Figure 3a shows the two IN parameterizations from Eqs. (3) and (4), as well as other proposed formulations in the literature, as a function of temperature and assuming water saturation. For a given temperature, the number of IN can be different by orders of magnitude with a direct impact on the deposition rate in Eq. (2). The cloud in Fig. 2 is at about −10°C and so the Prenni et al. (2007) formulation decreases the deposition rate by about a factor of 5. The impact is to significantly increase the amount of SLW throughout the cloud, which leads to a more persistent and deeper Arctic boundary layer cloud as shown in Fig. 2b (blue curve versus black curve). However, although the Prenni et al. (2007) formulation may be a better representation of the observations for the M-PACE case, its validity throughout the entire global troposphere across different meteorological regimes is uncertain, as is also the case for other alternative formulations.

Fig. 3.
Fig. 3.

(a) Ice nuclei number concentration as a function of temperature for various parameterizations: Meyers et al. (1992), Prenni et al. (2007), Cooper (1986), and DeMott et al. (2010). (b) Bulk ice deposition rate as a function of ice water content for the Rotstayn et al. (2000) (solid line) and Wilson and Ballard (1999) (dashed line) parameterizations assuming water saturation at a temperature of −10°C and pressure of 950 hPa.

Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00325.1

c. Deposition rate formulation

The ice deposition growth rate parameterization from Rotstayn et al. (2000) described in section 2b has a number of simplifying assumptions such as the monodisperse size distribution, constant ice density, spherical particles, and lack of a ventilation enhancement term. An alternative formulation following Wilson and Ballard (1999) with an exponential size distribution, ventilation factor, and variable ice density is implemented in the single-column model to assess the impact on the SLW layer. Forbes and Clark (2003) [their Eqs. (1)–(12)] provide the details of the formulation of the deposition rate and show that it can be written in a similar form to Eq. (2):
e5
where β′ is a function of air density and temperature, and the exponent γ takes a value close to ⅔ for small ice water contents and a value closer to 1 for larger ice water contents due to the dependence on the ice terminal fall speed in the ventilation factor. A comparison of the ice deposition rate for the Rotstayn et al. (2000) and Wilson and Ballard (1999) schemes as a function of ice water content is shown in Fig. 3b. The different exponent for the ice water content of ⅓ in Eq. (2) and ⅔ toward 1 in Eq. (5) result in large differences of the deposition rate for high and low ice water contents. The impact of the new deposition rate on the SCM Arctic case is shown in Fig. 2b (red line) and there is significantly less liquid water and ice in the profile. In fact, the initial ice water content is about 10−5 g kg−1 and this results in a factor of 3 higher deposition rate (Fig. 3b) that removes all the SLW in the first few time steps and the cloud collapses. The main point of this result is to highlight the sensitive balance of processes in the cloud and the need for appropriate parameterizations of both the sources and sinks of SLW for a realistic simulation of mixed-phase boundary layer cloud.

d. Turbulent production of water saturation

In the absence of large-scale ascent, turbulent vertical motion can be sufficient to bring an air parcel to saturation and produce liquid condensate. Currently, the IFS represents enhanced turbulence driven by strong cloud-top cooling only in its stratocumulus boundary layer scheme (Köhler et al. 2011). This scheme requires a positive surface buoyancy flux to trigger, otherwise, the boundary layer is considered to be stable. This is problematic in the case of decoupled stratiform layers, such as might be found during the night over the winter continents or in the Arctic. It is likely that turbulence, and thus liquid production, will be underestimated in these cases. Inspiration for an improved treatment of turbulent-driven production of water saturation in mixed-phase clouds can be found, for example, in the works of Korolev and Field (2008), Hill et al. (2014), and Field et al. (2013). A revision of the boundary layer turbulent production of supercooled liquid water is part of future development plans for the IFS and cannot yet be included in the sensitivity tests here. Although clearly an important part of the mixed-phase problem, it is one of many.

e. A pragmatic reduction of cloud-top deposition (PROG+)

After implementation of the new prognostic (PROG) cloud scheme in the operational global ECMWF model in November 2010, a cold bias was reported in the forecast of 2-m temperatures over parts of Scandinavia during January 2011 (discussed in more detail in section 4c). This prompted an investigation that found a correlation between the increased temperature bias and regions of subfreezing boundary layer cloud with too little supercooled liquid water at cloud top.

The uncertainties in the mixed-phase processes discussed above were all possible contributors to the bias. However, any change to the model has to be appropriate for all the different meteorological regimes across the whole globe. In particular, supercooled liquid water is present in deep convective clouds in the tropics, clouds associated with frontal cyclones in midlatitudes, as well as high-latitude cloud layers. Initial testing of changes to the ice nucleation and deposition rate beneficial to the high-latitude boundary layer cloud were detrimental elsewhere in different meteorological regimes, suggesting too simplified an approach, missing processes or deficiencies in the parameterizations. Given the unique environment of the mixed-phase cloud top in the single-layer cloud, it was reasonable to also look at the role that vertical resolution plays in the cloud-top physics in the GCM, as highlighted in section 3a.

One solution in a GCM would be to increase the effective vertical grid resolution for the deposition and sedimentation part of the microphysical parameterization, with an associated increase in complexity and computational cost. An alternative is to formulate correction factors for the deposition rate and sedimentation rate, as described in BHF14. However, here a pragmatic solution was first investigated to assess the impacts of a reduction in cloud-top ice deposition rate on supercooled liquid water layers in the IFS, given the many uncertainties that affect this part of the cloud. In this formulation, the deposition rate is multiplied by a factor Fdep, which reduces the conversion rate from supercooled liquid water to ice near cloud top, defined as
e6
where Fref is a reference deposition rate factor of 0.1, Δzcldtop is the distance in meters from the cloud-top layer (defined by the presence of supercooled liquid water and a cloud fraction threshold of 1%), Δzref is a reference depth set to 500 m, and FT is a function of temperature from 0° to −23°C to reduce the impact of the modification with decreasing temperature (increasing altitude). This temperature dependence is rather arbitrary, but for this initial implementation was found to be beneficial, avoiding an increase in supercooled liquid water at very cold temperatures.

A modified version of the model (referred to here as PROG+) includes the above changes to the deposition parameterization to represent the effects of unresolved processes and deficiencies in the model parameterization in the mixed-phase cloud-top region.

Figure 2c shows the impact of the PROG+ modification on the SCM simulation of the Arctic boundary layer cloud at three different vertical resolutions. As expected, the SLW toward cloud top is increased significantly, by a factor of 3 to 0.23 g kg−1 for the L60 model and increasing with vertical resolution to a value of 0.45 g kg−1 for the L137 model. The cloud layer is deeper for all three resolutions and the depth more consistent across the resolutions. In addition, the ice water profile is largely unchanged and rather insensitive to the resolution.

Figure 2d illustrates the sensitivity of PROG+ to the parameter choices for Fref and Δzref. The formulation is fairly insensitive to the choice of Δzref, which was chosen as 500 m based on the typical observed depth of cloud-top SLW layers (e.g., Shupe et al. 2008a). The scheme is more sensitive to the choice of Fref. Following BHF14, a value of 0.8 (rather than the 0.1 chosen here) might be more appropriate to account for just the impact of assuming vertical homogeneity of ice condensate and temperature on the deposition rate. With this value, the cloud condensate still increases versus the control, but to a lesser degree.

It is apparent that the key factor of the above change to the deposition parameterization is to significantly reduce the deposition rate in the topmost layer of the cloud. Although a pragmatic approach, the aim is to compensate for some of the deficiencies in the parameterizations and inadequate vertical resolution in the mixed-phase cloud-top region, and to increase the SLW in high-latitude boundary layer cloud for an improved radiative impact. The modified scheme was implemented in the global model IFS cycle 37r3, operational from 15 November 2011 as an interim solution to address the near-surface temperature bias and is included in the evaluation results described in the next section.

4. Evaluation of the mixed-phase cloud parameterizations

For a global weather forecast model such as the operational ECMWF IFS, evaluation against a wide range of observations is vital for assessment of the operational performance of the model. Of particular relevance to this paper is the assessment of clouds and their radiative impacts including the near-surface temperature over land. Any change to the model has to be appropriate for different meteorological regimes across the whole globe. The two versions of the IFS global model described in section 2 (DIAG and PROG) and the modified version described in section 3e (PROG+) are assessed for their representation of mixed-phase boundary layer cloud using ground and satellite-based observations.

The focus is on higher latitudes where boundary layer supercooled water cloud is more predominant (Hu et al. 2010). First, a case study of mixed-phase boundary layer cloud from the Atmospheric Radiation Measurement (ARM) program is described to highlight the representation of mixed-phase cloud structure in the different model versions. Second, lidar observations from the CALIPSO satellite are used to evaluate larger-scale impacts on the occurrence of mixed-phase boundary layer cloud over the Southern Oceans. Finally, the impact on global forecasts of cloud, radiation, and 2-m temperature over land are assessed.

a. Impact on Arctic mixed-phase boundary layer clouds during M-PACE

The U.S. Department of Energy ARM program operates observational sites at the North Slope of Alaska (NSA) at Barrow and Oliktok Point. Observations from NSA have been used previously to evaluate the ECMWF model’s performance in Arctic conditions. Beesley et al. (2000) note that the operational IFS in 1997 underestimated the fraction of liquid water in clouds observed during the SHEBA intensive observing period. When low clouds were present, the downwelling longwave radiation was underestimated. Xie et al. (2006) found a similar underestimate of liquid water fraction in the IFS for boundary layer clouds observed during M-PACE in the fall of 2004, and also linked the lack of cloud water with a substantial underestimation of surface downwelling longwave radiation. A single-layer mixed-phase cloud was observed during M-PACE on 9–10 October 2004 with a cloud-top temperature of about −15°C. This period was chosen for a model intercomparison study discussed in Klein et al. (2009). The IFS in single-column mode was part of the intercomparison and consistent with the earlier studies, the model was unable to partition the integrated water path correctly into liquid and ice phase, and significantly underestimated the amount of liquid in the cloud, underestimated the downward longwave, and overestimated the downward shortwave radiation at the surface.

To evaluate the IFS performance with the recent cloud scheme changes, the same two-day M-PACE single-layer cloud case study is revisited. A full description of M-PACE, including details on the ground-based active and passive sensors as well as in situ aircraft observations can be found in Verlinde et al. (2007). The observed cloud fraction, cloud liquid, and ice water contents for 9–10 October 2004 are shown in Fig. 4a. The cloud fraction is derived from the millimeter wavelength cloud radar (MMCR) and the micropulse lidar (MPL) and is available as part of the Cloud Modeling Best Estimate (CMBE) value-added product (Xie et al. 2010). The cloud water content shown here is derived using the Shupe–Turner algorithm (Shupe 2007; Turner 2007) and the 60-s in-cloud water content profiles have been averaged for each hour. The cloud liquid water mass is highest in the upper half of the cloud. The retrieval does not distinguish between suspended and precipitating ice, thus the ice water content in the right-hand panel of Fig. 4a shows a combination of both. It should be noted that the uncertainty associated with cloud water retrievals is not (yet) well defined and probably significant (Zhao et al. 2012). Klein et al. (2009) suggest rough uncertainty estimates of 20 g m−2 for the liquid water path and a factor of 2 for the ice water content and path, although differences between retrievals can be larger than this.

Fig. 4.
Fig. 4.

(left) Cloud fraction, (middle) cloud liquid water contents, and (right) cloud ice water contents for a single-layer mixed-phase cloud observed during 9–10 Oct 2004 at the NSA site during MPACE. (a) Observations, (b) the DIAG model version, (c) the PROG model version, and (d) the PROG+ model version. Note the color scale is a factor of 5 different between the liquid and ice water content panels.

Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00325.1

The ECMWF model is initialized at 1200 UTC 8 October 2004 and integrated for 48 h from the operational analysis. To leave a spinup period and to define the time series so that maximum solar radiation is at the locally defined noon, forecast lead times from 21.5 to 45.5 h are used to form a 24-h time series for 9 October. A second forecast initialized at 1200 UTC 9 October is concatenated with the first to form the 2-day time series shown in Fig. 4. The forecast lead time is not critical for this case study as shorter lead times also gave similar results. Model data from the land and ocean grid points nearest to the Barrow site are chosen for the comparison with the ARM observations. Although the aircraft flights in the M-PACE campaign were over the ocean, the remote sensing and radiation instruments were on land. Figure 4 (and Fig. 6), therefore, show the data from the land point, whereas the liquid and ice water paths in Fig. 5 contain the model data for both land and ocean points for comparison with the remote sensing and aircraft data.

Fig. 5.
Fig. 5.

Liquid water path vs ice water path observed and modeled during the 48-h period from 0000 LT 9 Oct to 2300 LT 10 Oct. DIAG, PROG, and PROG+ are water paths from the three model versions at the land point closest to the NSA site at Barrow. The values for the closest ocean point are indicated by the gray markers and “o” suffix. MICROBASE and WANG stand for water paths retrieved using the Microbase and Wang algorithms, respectively. Two versions of the Shupe–Turner retrieval are shown, the standard retrieval (S-T/VAR) and a modified retrieval (S-T/MPACE) to better match in situ observations. AIR refers to the water paths derived from aircraft observations.

Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00325.1

The DIAG cloud scheme with the diagnostic temperature-dependent mixed-phase function produces a layer cloud with condensate primarily in the ice phase (Fig. 4b). Little liquid is present and it is distributed throughout the depth of the cloud, as the cloud is within the model defined mixed-phase from 0° to −23°C temperature regime. As precipitating snow is diagnostic in the DIAG cloud scheme, Fig. 4b only includes a contribution from cloud ice, so the total ice including the snow would be even higher and farther from the observed ice water path. In contrast, the PROG cloud scheme, with separate liquid water, ice and snow prognostic variables, significantly improves the phase partitioning of the modeled cloud (Fig. 4c). There is now more cloud liquid water than the combined frozen water content from ice and snow, and the supercooled liquid water layer is concentrated near the cloud top in closer agreement with the observed structure. Yet, the total condensate amount remains lower than observed and the model is unable to maintain the liquid layer throughout the 2-day period. The enhancement to the PROG version of the cloud scheme with reduced deposition rate near the cloud top (PROG+) increases the supercooled liquid water content and allows the liquid layer to persist throughout the 48-h period (Fig. 4d).

Figure 5 shows the vertically integrated liquid water path (LWP) and ice water path (IWP) from the IFS model versions for the land and the ocean grid points together with the available aircraft and remote sensing observations for the 2-day period. The IWP from DIAG contains only the contribution from the cloud ice whereas the PROG versions include both ice and snow. Three ground-based retrievals—MICROBASE (Dunn et al. 2011), Shupe–Turner [Shupe et al. (2005) and Turner (2005), referred to here as “S-T/VAR”], and Wang et al. (2004, referred to as “WANG”) are available as part of the ARM Cloud Retrieval Ensemble Dataset (Zhao et al. 2011, 2012). A second, modified version of the Shupe–Turner retrieval for the MPACE period is also included (“S-T/MPACE”). The ice retrieval has been adjusted in this latter version to yield results more consistent with in situ observations from the aircraft. The standard retrieval uses a variable, seasonally-dependent coefficient to the radar reflectivity power law (0.079 during M-PACE). However, comparison with in situ observations suggests that this coefficient is overestimated for the M-PACE case, and a lower coefficient (~0.04) is more appropriate (M. D. Shupe 2012, personal communication). It is this modified version (“S-T/MPACE”) that is used in Fig. 4a. Klein et al. (2009) derive an integrated water path from two research flights during the period, which are marked as “AIR” in Fig. 5. Note that in situ aircraft observations underestimate IWP, as the aircraft totals do not include all the ice from the subcloud air that the aircraft did not sample (Klein et al. 2009). The spread of observed IWP in particular illustrates the large uncertainty in ice water retrievals for Arctic mixed-phase clouds. The agreement for LWP is somewhat better. Nonetheless, all retrievals point toward a mixed-phase cloud dominated by the liquid water content.

As highlighted in Fig. 4, the DIAG version of the model with diagnostic mixed-phase assumption has much less supercooled liquid water than observed, around 10 g m−2 for the land point compared to the observed estimates of 100–200 g m−2, and a liquid water path fraction (ratio of LWP to total water path) of about 0.25, consistent with the temperature of the cloud of around −10° to −15°C (see diagnostic mixed-phase function in Fig. 1). The PROG model increases the LWP by a factor of 4 to 40 g m−2, closer to observations but still significantly underestimated. The IWP is also slightly reduced, despite including the contribution from the prognostic snow category (not included in the DIAG version), giving a liquid water path fraction of about 0.6. The PROG+ version, with cloud-top enhancement, doubles the LWP without affecting the IWP giving a liquid water path fraction of about 0.75, closer to the observed fraction in the range 0.85–0.9 (based on the WANG and S-T/MPACE and aircraft retrievals in Fig. 5). However, the LWP over land is still underestimated compared to the observed estimates by up to a factor of 2. For the ocean point, where the surface is warmer and boundary layer turbulence is more active, all three model versions have higher IWP and the PROG versions also show a 50% increase in LWP.

Figure 6 shows a comparison of the corresponding surface irradiance and downward longwave radiation from the IFS model versions and observations during the 2-day period at the Barrow site. The DIAG model overestimates the surface irradiance by a factor of 2 (by 50 W m−2) and the downwelling longwave radiation at the surface is underestimated by 30 W m−2 or more, consistent with earlier studies. The PROG model has an improved surface irradiance on the first day, but worse on the second day when the liquid cloud is depleted. Similarly, the downward longwave is significantly improved when supercooled liquid cloud is present, but has a worse agreement with observations than DIAG on the second day when the liquid cloud has dissipated. The closest fit to observations is the PROG+ model with just a 20 W m−2 overestimate of surface irradiance on the first day and less than 10 W m−2 on the second. The downward longwave difference from the PROG+ model is consistently the best throughout the period and only slightly underestimates the observations by about 10 W m−2. These results highlight the importance of representing the supercooled liquid water at the cloud top for the correct radiation budget in the model.

Fig. 6.
Fig. 6.

(a) Surface irradiance and (b) downward longwave radiation time series, for the single-layer mixed-phase Arctic cloud at NSA during 9 and 10 Oct 2004 for the observations (black) and the three model versions, DIAG (blue), PROG (green), and PROG+ (red).

Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00325.1

This case study covers only two days. However, a much longer record of surface radiation measurements is available from the NSA site at Barrow. Previous studies have shown that two persistent regimes are commonly observed at the site: a clear or nearly clear state with few optically thin clouds, and a cloudy state with liquid-containing stratiform clouds that have an emissivity close to one (Stramler et al. 2011; Morrison et al. 2012). These states are reflected in the surface longwave radiation with small net longwave radiation for the cloudy state and large negative net radiation (i.e., a surface heat loss) for the clear state. Figure 7 shows a histogram of the observed (black) and modeled (red) hourly mean net longwave surface radiation at NSA for two years: 2009 and 2013. In both years, the observations have a clear bimodal distribution with a peak around −15 W m−2 corresponding to the cloudy state, and a peak near −50 W m−2 corresponding to the clear state. The CMBE product is not yet available for the more recent period (2013), so the hourly mean surface net longwave radiation has been calculated from the 1-min quality controlled surface radiation product (QCRAD; Shi and Riihimaki 1994). The cloudy state occurs about twice as often as the clear state. The two years are chosen to coincide with years where the operational forecast model at ECMWF used the DIAG and PROG+ cloud schemes, though additional model changes were implemented between 2009 and 2013. Results are similar if different years (with DIAG/PROG+ in use) are chosen. The DIAG model does produce a bimodal distribution, but the relative frequency of the two modes is not captured well. The cloudy, warm state does not occur often enough, which is likely due to the scheme’s poor representation of supercooled liquid layers. The PROG+ model produces the cloudy state more frequently, but the two modes of the distribution are not as well separated as in the observations. The net longwave radiation associated with the cloudy state is still underestimated (i.e., surface heat loss overestimated). This may be due to a remaining underestimate of LWP (as seen in the 2-day MPACE case study), though incorrect cloud occurrence and base height are also potential error sources. The long-term radiation record confirms that the new cloud scheme is a step toward a more realistic representation of the cloudy state occurrence at NSA, though there is room for further improvements.

Fig. 7.
Fig. 7.

Net surface longwave radiation frequency distribution at the NSA site for (a) the observations (CMBE product, black) and the operational model with diagnostic mixed-phase (DIAG, red) for January–December 2009, and (b) the observations (QCRAD product, black) and operational model with prognostic mixed phase (PROG+, red) for January–December 2013.

Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00325.1

b. Impact on mixed-phase boundary layer clouds over the Southern Ocean

The advent of satellite-borne active sensors has provided new information about the vertical distribution of clouds over the Southern Ocean. In particular, cloud structure and cloud phase can be determined from combined radar and lidar observations from the CloudSat (Stephens et al. 2008) and CALIPSO (Winker et al. 2007) satellite data. The observations show that boundary layer clouds are the most common cloud type and dominate the cloud-radiative effect in the Southern Ocean region (Mace 2010; Verlinden et al. 2011; Haynes et al. 2011). Supercooled liquid water is often present in the areas dominated by boundary layer clouds (Hu et al. 2010). The Southern Ocean is also an area where many global models, including the IFS, struggle to represent the radiative budget correctly (Trenberth and Fasullo 2010; Bodas-Salcedo et al. 2012). This section examines the impact of the mixed-phase parameterization changes on cloud and radiation in the Southern Ocean region.

A product combining CALIPSO lidar and CloudSat radar observations with Moderate Resolution Imaging Spectroradiometer (MODIS) for a comprehensive cloud mask and target categorization [DARDAR (raDAR/liDAR)] is described in Delanoë and Hogan (2010). Strong lidar backscatter distinguishes boundary layer clouds containing liquid water from those with ice only. In combination with model-derived temperature information, cloud volumes containing supercooled liquid may be identified. The algorithm distinguishes hydrometeor targets containing ice, ice plus supercooled liquid, warm liquid, supercooled liquid without ice, and rain. Here, the two categories containing supercooled liquid (with or without ice) are grouped together, as are the categories for warm liquid and rain. There is just one ice category, with no distinction between “suspended ice” particles and “precipitating snow,” so the ice phase prognostic variables from the model are combined for the comparison.

Data from model forecasts initialized at 1200 UTC (using forecast hours 12–36) are extracted for grid points along the satellite track between 55° and 70°S. The 3-hourly model output is used, such that collocated observations and model results are never more than 1.5 h apart. A grid box falls into the “supercooled” category if the cloud liquid water content exceeds a threshold of 10−7 kg kg−1 and the temperature is below freezing. A box is categorized as “ice” if the ice water content (ice only for DIAG and ice+snow for PROG and PROG+) exceeds the threshold while the liquid water content remains below the threshold.

Figure 8a shows a sample vertical cross section along the CALIPSO/CloudSat flight track over the Southern Ocean on 2 January 2007 for the “ice only” and “supercooled liquid” target categorization derived from the satellite products. The sea surface temperature along the track is below 0°C south of 60°S and rises to just under 3°C at 55°S at the far right of the section. This particular scene has all cloud below freezing and does not include any targets identified as warm liquid/rain, although in reality there is likely to be some mixed ice/rain close to the surface beneath the deeper frontal cloud in the northern part of the section. The main feature of the scene is the mixed-phase boundary layer cloud extending across 10° of latitude with supercooled liquid present in multiple layers but primarily near the cloud top. Note that the vertical extent and exact base of the lower layers is difficult to ascertain as the lidar signal is rapidly attenuated in the presence of liquid, and the radar retrieval becomes uncertain due to the sensitivity threshold and ground clutter near the surface.

Fig. 8.
Fig. 8.

Sample track of cloud phase categorization from (a) DARDAR product, and (b) the DIAG, (c) PROG, and (d) PROG+ model versions, for a CALIPSO/CloudSat track across the Southern Ocean on 2 Jan 2007. Black shading indicates the presence of supercooled liquid (with or without additional ice), while gray shading indicates the presence of frozen hydrometeors (cloud ice for DIAG, cloud ice or snow for PROG and PROG+).

Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00325.1

The model data for the same cross section are shown in Figs. 8b–d. Even though the modeled clouds are displaced a few degrees in latitude, all three versions of the model show cloud structures similar to the observations with extensive mixed-phase boundary layer cloud across the scene, deeper ice-dominated cloud systems to the north and south and some ice cloud present toward the center of the scene. As a consequence of the diagnostic split used in the DIAG model to partition cloud water into ice and liquid, clouds between 0° and −23°C will always contain some liquid. Thus the boundary layer cloud in the DIAG model (Fig. 8b) is categorized as supercooled throughout its depth. Note, the shading indicates the target classification only, not the cloud fraction. Figure 8c shows the corresponding track for the PROG model version and includes the prognostic snow water content in the target categorization to correspond more closely with the hydrometeors in the observed ice category. The depth of the ice cloud, therefore, extends farther toward the surface. The PROG model produces a boundary layer cloud with multiple supercooled layers, with similarities to the observations. Yet, liquid is not consistently present near the cloud top (Fig. 8c) in contrast to that observed. This highlights the deficiencies in the model parameterizations that are unable to maintain enough supercooled liquid water at the cloud top. With the additional cloud-top deposition rate modification in the PROG+ model version, a consistent supercooled liquid layer is maintained at the cloud top throughout the scene (Fig. 8d), in closer agreement with the observations.

The track shown in Fig. 8 is a typical example for the Southern Ocean region, showing the model improvement in a qualitative manner. A quantitative comparison of the model and observations is performed for boundary layer clouds in the region. Nearly 15 000 grid boxes with boundary layer clouds over ocean (i.e., cloud base below 2.5 km and cloud no thicker than 3 km) were identified along the CloudSat/CALIPSO track between 55° and 70°S during January 2007. A comparison of the frequency of occurrence of supercooled liquid at the boundary layer cloud top gives a 76% occurrence in the observations and an 85% occurrence in the PROG+ version of the model. Huang et al. (2012) also found a range of frequency of occurrence, 80%–90%, of supercooled/mixed-phase low-altitude cloud tops derived from different data products over this region in summer from 4 years of data (CALIPSO, MODIS, and the combined DARDAR data used here). The results for January 2007 suggest the PROG+ model slightly overestimates the occurrence of supercooled liquid water at boundary layer cloud top, but it is in the right range given the uncertainties in the observations and in the assumed thresholds for the model water contents.

The radiative impact of the model changes in the Southern Ocean region is evaluated by comparison of the top-of-the-atmosphere net shortwave (SW) radiative fluxes against the Clouds and the Earth’s Radiant Energy System (CERES) satellite observations (Wielicki et al. 1998). An ensemble of four 1-yr low-resolution free-running simulations with the IFS (spectral truncation of T159 equivalent to a 125-km grid spacing) are performed for each of the model versions. Figure 9 shows the annual mean net SW from CERES and the model differences from the observations. The DIAG version in Fig. 9b highlights the main systematic errors, with too much reflection between 30°N and 30°S, particularly in regions of deep convection and shallow cumulus, and too little reflection in the coastal maritime stratocumulus regions and across the Southern Ocean south of 50°S. These differences are robust errors in the IFS, seen at different model horizontal resolutions and forecast time periods and have been persistent in the IFS model across many model versions.

Fig. 9.
Fig. 9.

Annual mean top-of-the-atmosphere net shortwave radiation (net SW) for (a) CERES observations, and the difference from CERES for the 4-member ensemble 1-yr IFS model simulations at T159 resolution (125-km grid resolution, 91 levels), for (b) DIAG, (c) PROG, and (d) PROG+. Hatched areas indicate regions of significance at the 5% level using a two-tailed t test.

Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00325.1

The change to the prognostic mixed phase (PROG) in Fig. 9c leads to less supercooled liquid water, with a reduction in the net SW error in the tropics (also due to warm-phase cloud microphysics changes in the model upgrade), but an increase in the net SW error in the Southern Ocean of around 10 W m−2. The supercooled liquid water decreased in this region by up to 15 g m−2 in the annual mean (not shown), particularly in the regions dominated by boundary layer cloud below 0°C. Figure 9d shows the PROG+ model difference versus CERES, which has a specific impact on the net SW in this band across the Southern Ocean, consistent with the regions of observed supercooled liquid water from the CALIPSO lidar (Hu et al. 2010). The liquid water path and net SW changes are reversed from the PROG simulation, increasing the LWP at high latitudes by 10–20 g m−2 in the annual mean where the temperatures are below freezing, and reducing the net SW error by up to 10 W m−2 (increased reflection) with a slight improvement compared to the original DIAG model version in Fig. 9b. The sensitivity of the shortwave to supercooled liquid water in this region has been suggested by others (e.g., Bodas-Salcedo et al. 2012), but the impact is quantified here, highlighting the need to have a realistic representation of mixed-phase clouds, the need to characterize these clouds from observations, and the need to improve our understanding of mixed-phase physical processes and how to formulate their parameterization in GCMs.

c. Impact on Northern Hemisphere high-latitude 2-m temperature over land

After implementation of the new prognostic (PROG) cloud scheme in the operational global ECMWF model in November 2010, a cold bias was reported in the forecast of 2-m temperatures over parts of Scandinavia during January 2011. This prompted an investigation, which found a correlation between the increased temperature bias and regions of low-level mixed-phase cloud, and motivated the cloud-top deposition rate modifications in the PROG+ model version described in this paper for an operational solution to the problem.

Figure 10 shows the 2-m temperature for 0000 UTC 4 January 2011 as an example of the problem. This was a month where there was persistent stratiform boundary layer cloud over Finland at temperatures below freezing. Figure 10a shows the operational analysis of 2-m temperature. The model with diagnostic mixed-phase (DIAG) has supercooled liquid throughout the cloud, as the temperatures are between −10° and −20°C, and is able to produce a reasonable 2-m temperature forecast (Fig. 10b). However, the PROG version of the model, operational at the time, is unable to retain much supercooled water in the cloud, resulting in excessive longwave cooling of the snow-covered land surface and a significant cold bias developing of up to −10°C (Fig. 10c). The increase in longwave cooling (more negative) in the PROG simulation is consistent with the second day of the M-PACE case described in section 3a, due to the lack of supercooled liquid in the cloud. The PROG+ model in Fig. 10d increases supercooled liquid water content in the stratiform cloud and reduced longwave surface cooling leads to near-surface temperatures over Finland in much closer agreement with observations and the analysis (Fig. 10a).

Fig. 10.
Fig. 10.

2-m temperature (°C) over northern Europe at 0000 UTC 4 Jan 2011 for (a) SYNOP observation analysis, and 60-h forecasts for (b) DIAG simulation, (c) PROG simulation, and (d) PROG+ simulation.

Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00325.1

Figure 11 shows the impact of the cloud-top deposition change in the PROG+ version compared to the PROG parameterization on the 2-m temperature for the whole month of January 2011. The impact of the increased supercooled liquid condensate and warming in the PROG+ simulation shown in the 4 January case study is clearly reflected in the monthly average over Scandinavia and eastern Europe (Fig. 11a) with an average warming of 1°–2°C, as well as over western Russia and North America. There is not just a change in the mean 2-m temperature, but also a decrease in the mean absolute error of 2-m temperature in large parts of Europe and North America (Fig. 11b) by a similar magnitude. Calculating the root-mean-square error of the 1000-hPa temperature for the Northern Hemisphere for the winter season 2010/11 also shows a significant 2% improvement throughout the first 5 days of the forecast (not shown). This example highlights the importance for weather forecasting of focusing future parameterization development on improving the representation of physical processes that determine the SLW topped boundary layer cloud.

Fig. 11.
Fig. 11.

Impact on 2-m temperature over land (72-h forecasts for January 2011) for supercooled liquid water changes (PROG+ minus PROG) for (a) mean temperature change (°C) and (b) change in mean absolute error (°C) when compared to 2-m temperature analyses using SYNOP stations.

Citation: Monthly Weather Review 142, 9; 10.1175/MWR-D-13-00325.1

5. Conclusions

Mixed-phase clouds are abundant in the atmosphere and supercooled liquid water (SLW) is particularly prevalent in boundary layer clouds at higher latitudes. These clouds can be extensive and persistent in the Arctic, Northern Hemisphere mid- to high-latitude land areas during winter, and across the Southern Ocean region (Hu et al. 2010). The vertical structure often consists of a shallow (few hundred meters) layer of supercooled liquid at cloud top with ice falling out below (Shupe et al. 2008b). The supercooled liquid water layers are the result of a fine balance between radiative cooling driving small-scale turbulent motions, production of water saturation and cloud liquid water droplets, the availability of ice nuclei, nucleation of ice crystals, deposition growth removing water vapor, and fall-out of ice particles under gravity. The presence of supercooled water has a significant radiative impact in boundary layer clouds and it is important to adequately represent the cloud phase and water contents in NWP and climate models in order to capture the correct radiative response and effects on a cloud’s lifetime. This study evaluates the impact of recent changes to the parameterization of mixed-phase microphysics in the ECMWF global NWP model with a focus on high-latitude boundary layer clouds.

The original mixed-phase scheme in the Tiedtke (1993) cloud parameterization, operational in the ECMWF global model from 1995 to 2010, uses a diagnostic temperature-dependent phase partitioning (DIAG), in common with many other NWP and climate models. Such schemes attempt to capture the global average statistics of mixed-phase cloud, but cannot represent the correct vertical structure and the wide variation of cloud phase partitioning across temperatures as observed in the atmosphere.

A major upgrade to the cloud scheme in the operational ECMWF model in 2010 allowed separate prognostic variables for cloud ice and liquid (PROG). This new scheme provides the framework needed for a more realistic representation of mixed-phase clouds with a flexible partitioning of the cloud condensate controlled by the physical processes of supercooled water condensation, ice deposition, and sedimentation. However, in some circumstances it was found that the new prognostic formulation was reducing the supercooled liquid water at cloud top too rapidly, resulting in a lack of supercooled cloud and associated errors in radiative fluxes and surface temperatures over land. There remain many uncertainties and deficiencies in the parameterization of the turbulent production of SLW and the complex mixed-phase microphysical processes as well as limitations due to the vertical resolution of the model (where the vertical grid spacing is of the order of the depth of the supercooled liquid water layers). In particular, without very high vertical resolution, the model is likely to overestimate the deposition rate and underestimate the sedimentation rate due to the use of a vertically mean ice water content in the parameterization in the deep model layers (BHF14). In reality, the small ice particles at cloud top and vertical separation between the newly formed supercooled liquid water droplets and sedimentation of the larger rapidly growing ice crystals lead to a reduced effective ice deposition rate (BHF14). An additional pragmatic modification to the parameterization is therefore tested to represent the effects of unresolved processes near the cloud top (PROG+). This significantly increases the occurrence of cloud-top supercooled liquid water with consequent improvements in radiative properties and highlights the importance of continuing to improve our parameterizations of mixed-phase cloud processes in GCMs.

Three examples are used to evaluate the effects of the different parameterizations on supercooled liquid water cloud and radiative impacts in the ECMWF global model. An evaluation of the vertical structure of persistent Arctic boundary layer cloud during the M-PACE campaign (Verlinde et al. 2007) at the ARM NSA site highlights the deficiencies of the diagnostic temperature-dependent mixed-phase assumption and the improvement in the vertical structure of the cloud with the introduction of the separate prognostic liquid and ice variables. The ice water path is reduced and the liquid water path is increased in closer agreement with the observed estimates. With the additional deposition rate reduction at cloud top, surface irradiance and downwelling longwave radiation are both significantly improved, with the increased supercooled liquid water modifying both shortwave and longwave by 20–30 W m−2. The liquid water path is enhanced but is still likely to be an underestimate compared to observations.

A comparison with the cloud phase mask derived from CloudSat/CALIPSO satellite observations over the Southern Ocean confirms that the structure of boundary layer cloud with supercooled liquid layers near cloud top are better represented with the new cloud scheme. However, systematic errors in the annual mean net top-of-the-atmosphere shortwave radiation of about 10 W m−2 in the DIAG model increased to around 20 W m−2 over the ocean south of 50°S in the PROG model due to reduced occurrence of SLW. The PROG+ modification shows that improving the SLW occurrence in the boundary layer cloud can significantly reduce these errors to below 10 W m−2. This again highlights the need for improved parameterizations of mixed-phase cloud processes in NWP and climate models, many of which have systematic errors in radiation over this region (Trenberth and Fasullo 2010; Bodas-Salcedo et al. 2012), also important for coupled models where ocean temperature bias can be affected.

A third example highlights the impact of the mixed-phase cloud parameterization on radiation and 2-m temperature over land in the Northern Hemispheric winter using the ECMWF operational 2-m temperature analysis based on surface synoptic observations (SYNOP) reports. If the occurrence of supercooled liquid water in persistent mixed-phase boundary layer cloud is underestimated by the model (as in the PROG model version), downwelling longwave radiation is reduced and a cold bias of up to −10°C locally in near-surface temperatures can develop. The PROG+ scheme with increased supercooled liquid water at cloud top has a positive impact in operational ECMWF forecasts, with an average warming of forecast 2-m temperature of 1°–2°C and a reduction of root-mean-square errors over large parts of northern North America and Europe during the Northern Hemispheric winter.

This paper shows the importance of having separate liquid and ice prognostic variables in a GCM for the representation of mixed-phase cloud, but also that introducing more degrees of freedom to represent the variability of the real atmosphere can lead to an increase in model errors in certain regimes. Understanding and diagnosing the cause of such discrepancies is important for the continued improvement of the model forecasts. The modification to the cloud top parameterization of ice deposition represents a pragmatic attempt within the constraints of an operational global NWP environment to increase the occurrence of supercooled liquid water at cloud top. It should be noted that the new scheme as a whole with separate variables for liquid and ice has a much stronger physical basis than the assumption of a diagnostic mixed-phase partition based on temperature, still in use in many operational NWP and climate models. However, it is imperative that the development of GCM parameterizations for mixed-phase cloud builds on the improvements in our understanding of the relevant physical processes from a combination of observations, theory, and LES modeling.

For the ECMWF model, further work is needed on representing the impact of small-scale turbulence on supercooled liquid water formation, on improving the formulation of ice nucleation and ice deposition parameterizations, on including a better representation of the vertically unresolved liquid-ice-sedimentation processes at cloud top (BHF14), and on addressing the issue of subgrid variability in the horizontal (including horizontal separation of supercooled liquid and ice). The question of how important it is to capture the complexity and detailed characteristics of the ice nucleation process, as well as the role of ice nuclei depletion in sedimenting particles, is still an area of open research. The apparent resilience of mixed-phase boundary layer cloud (Morrison et al. 2012) suggests that small-scale processes interact to stabilize the system and the relative role of the small-scale processes versus large-scale environmental conditions in modulating the persistence or breakup of the cloud also needs to be quantified.

Acknowledgments

This work was supported by U.S. Department of Energy’s Office of Science via the Atmospheric System Research program under Grant DE-SC0005259. The NASA CloudSat Project, and CALIPSO programme are acknowledged for the satellite data used in the DARDAR-MASK product, courtesy of Julien Delanoë and ICARE.

We thank Adrian Tompkins for his initial work on the mixed-phase microphysics, and Andrew Barrett, Robin Hogan, and colleagues at ECMWF for helpful and informative discussions. We would also like to thank the three anonymous reviewers for their insightful comments, which led to an improved manuscript.

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