Evaluation of Polar-WRF Simulated Arctic Micro- and BL Meteorology the ASCOS Campaign

Evaluation of Polar-WRF simulated Arctic micro- and BL meteorology The ASCOS campaign

Oscar Fijneman September 16, 2019

Msc Thesis

Evaluation of Polar-WRF simulated Arctic micro- and BL meteorology The ASCOS campaign

Oscar Fijneman September 16, 2019

Supervised by: dr.ir. Laurens Ganzeveld Examined by: prof.dr. Jordi Vila-Guerau de Arellano

M.Sc. Thesis Meteorology and Air Quality Group Department of Environmental Sciences

Wageningen University & Research

Abstract Application of climate models to assess Arctic climate change poses a large challenge not only due to the role of feedback mechanisms but also especially due to the occurrence of meteorological conditions not easily captured by these relatively coarse models. Higher resolution regional models are developed to improve simulations of meteorological conditions for specific regions of the world such as the Arctic. The Polar version of the Weather Research and Forecasting model (PWRF) is such a regional model. Its application and further development of this model is planned as a contribution to analysis of observations to be collected in a large Arctic field campaign: the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC). A new feature that will be added to PWRF is that of climate-active trace gases exchange between the Arctic ocean-sea ice and atmosphere. However, before such modifications can be introduced in PWRF, an evaluation of the current state of the meteorological predictions is necessary. Consequently, PWRF was evaluated for the Arctic Summer Cloud Ocean Study (ASCOS) field campaign in August/September 2008. Overall, it can be concluded that PWRF performed reasonably well in representing the meteorological conditions during the ASCOS campaign, especially in representing the conditions prevailing for the first ten days of the campaign but it failed to reproduce the observed period associated with the presence of low level clouds. In the presence of the low level cloud regime, issues in cloud characteristics, boundary layer height and atmospheric state variables as incoming longwave radiation and near surface temperature were found. Furthermore, this study has revealed which model features, including the representation of albedo and boundary layer dynamics, require more focus to improve model performance for those specific conditions.

Table of Contents Abstract ...... 3 1. Introduction ...... 5 1.2 Research objective/question ...... 7 2. Background WRF and Polar WRF ...... 8 Polar WRF ...... 8 3. Method ...... 9 3.1 Measurement dataset ...... 9 3.2 Polar WRF configuration ...... 10 3.2.1 WRF 1-D set up ...... 10 3.2.2 PWRF 3-D default simulation and boundary layer schemes ...... 11 3.3 Evaluation ...... 12 4. Results ...... 13 4.1 WRF 1-D simulations ...... 13 4.1.1 Meteorological variables: Statistics ...... 13 4.1.2 Vertical profiles (PBL dynamics)...... 14 4.1.3 Clouds ...... 16 4.1.4 Discussion / Conclusion on 1-D simulations ...... 18 4.2 Default 3-D simulation and additional adjusted simulations ...... 19 4.2.1 Default simulation: meteorological variables ...... 19 4.2.2 Additional simulations: meteorological variables...... 22 4.2.2.1 Albedo ...... 22 4.2.2.2 Ice thermodynamics ...... 26 4.2.2.3 Gridded fields ...... 28 4.2.3 Vertical profiles (PBL dynamics)...... 29 4.2.4 Clouds ...... 32 4.3 Boundary layer schemes ...... 35 4.3.1 Meteorological variables: statistics & important differences ...... 36 4.3.2 Vertical profiles (PBL dynamics)...... 38 4.3.3 Clouds ...... 41 5. Discussion & Recommendations ...... 43 6. Conclusion...... 49 7. References ...... 50 Appendix A: additional and supporting figures and tables regarding the 1-D WRF simulations...... 54 Appendix B: additional and supporting figures and tables regarding the 3-D PWRF simulations. ... 57 Appendix C: Supporting figure regarding a measurement uncertainty in the discussion...... 65

1. Introduction The Arctic climate has been affected by rapid changes in the last decades. This region seems more affected by climate change than the lower latitudes, which is related to a process known as Arctic amplification (Serreze and Francis, 2006). Arctic amplification means that the temperature in the Arctic changes relatively more due to a change in forcing compared to lower latitudes. Increasing temperatures result in thawing permafrost and melting glaciers (Screen and Simmonds, 2010). Additionally, a decrease in sea ice cover of 15-20 % over the past 20 years is observed (Serreze et al., 2007). This results in an overall decrease in albedo, one of the main causes of this Arctic amplification. However, global climate models have difficulties simulating the rapid changes in sea ice decline and temperature in the Arctic, resulting in uncertainties in global climate simulations (Boe et al., 2009; Screen and Simmonds, 2010; Stroeve et al., 2007).

Higher resolution regional models are developed to eventually improve simulations of meteorological conditions in global models (Wilson et al., 2011). For the Arctic region, the polar version of the Weather Research and Forecasting (WRF) Model, called Polar WRF (PWRF) is developed to simulate the meteorological processers at resolutions up to ~10-20 km. Information about the WRF model and the adjustments for the Polar optimised version can be found in the background section (chapter 2).

Challenging features of simulating the Arctic meteorological conditions are related to the representation of: 1) boundary layer dynamics, 2) low level clouds and 3) surface physics. In particular, strong stably stratified boundary layers are difficult to reproduce in model simulations (Tjernström, 2011, Tastula et al., 2012), resulting in a misrepresentation of boundary layer depths in model simulations. This leads to a misrepresentation of the vertical mixing of reactive compounds reflected in their vertical concentration profiles but also in temperature and humidity vertical profiles. Furthermore, Tjernström (2011) indicated two main types of boundary layer structure in the Arctic, both with often low boundary layer heights: 1) well mixed, near neutral stratified boundary layers and 2) strong surface based inversions. The switch between these two boundary layer structures was often dictated by the presence of low level clouds. In presence of these low level clouds, often located in the lowest km of the atmosphere with the cloud base in the lowest 100m, a strong inversion layer is often present aloft the shallow boundary layers. Low level cloud layers tend to be difficult to simulate in models (Hines and Bromwich 2017; Listowski and Lachlan-Cope, 2017), which results in simulated biases in the radiation balance due to underestimation of super cooled liquid water content in the cloud layer (Tjernström, 2011). Especially one of the major issues in simulating Arctic meteorology is representation of the optically very thin cloud layer resulting in discrepancies in the radiation budget, especially in incoming longwave radiation (Tjernström, 2011). Furthermore, misrepresentation of the cloud layer can influence the representation of the mixed layer depths by turbulence due to presence of the cloud layer. Thirdly, the representation of the surface physics is important in relation to the boundary layer development. Development of sea ice concentration, thickness, snow cover and albedo are important features to properly represent in the model for correctly simulating the exchange of energy, momentum, moisture and trace gases near the surface (Marcq & Weiss, 2012). For example, a misrepresentation of sea ice cover has important implications for the representation of the exchange of trace gases such as dimethylsulfide (DMS) (Mungall et al., 2016). Furthermore, the heat flux through the sea ice as well as effects of melt ponds on the albedo, both effecting the surface energy balance and thereby sea ice cover, are often poorly represented in models (Tjernström, 2011). An evaluation of the meteorological features in the current PWRF model is therefore needed, to investigate the current issues in PWRF’s representation of surface and boundary layer meteorology and where improvement is necessary.

Another major challenge is related to evaluating model simulations of Arctic meteorological conditions by comparison with field observations. This is due to the limited amount of field data available for evaluation of the model simulations. The harsh conditions limit the number of field campaigns that have been conducted in the Arctic (Tjernström, 2011) and only a few in-situ measurement stations are present. An upcoming campaign to obtain additional field data is the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) (coordinated by Alfred Wegener Institut, Germany; https://www.mosaic-expedition.org/). The primary objective of this campaign and research project is to develop a better understanding of important coupled Arctic system processes, so they can be more accurately represented in regional- and global-scale models. A PhD project on analysis of observed and model simulated exchange of so-called climate-active trace gases between the Arctic ocean, sea ice and atmosphere as a contribution to MOSAiC, aims to further develop and apply PWRF- Chem (a version of PWRF further including a representation of atmospheric chemistry processes). Focus will be on the representation of the Arctic ocean-sea ice biochemistry-atmosphere interactions as the main driver of the exchange of ozone (O3), methane (CH4), carbon dioxide (CO2) and DMS. To properly represent the exchange and further atmospheric cycling of these trace gases, it is crucial to have a correct representation of boundary layer dynamics, ocean- sea ice- atmosphere exchange but also the generally prevailing meteorological conditions. The performance of PWRF(-Chem) in simulating these micro- and boundary layer meteorology and synoptic conditions must be evaluated before new additional Arctic climate change components, e.g., climate-active trace gas exchange, should be added.

Consequently, in this study, the micro- and boundary layer meteorology in PWRF was evaluated for the Arctic. The objective was to evaluate the model performance on meteorological features as: cloud formation, boundary layer dynamics and surface radiation and energy balance. Some of the individual components of this representation of near-surface meteorological processes in PWRF have already been evaluated (Hines and Bromwich, 2017; Sterk et al., 2013; Bromwich et al. 2009). However, according to our knowledge PWRF has not yet been extensively evaluated with a measurement dataset containing complementary information on all these features. The motivation to focus in this study on near-surface meteorological features at the site-scale was mainly based on 1) available observations to evaluate the model simulations, 2) adding to the knowledge of improving future simulations for the Arctic region and 3) the initial focus of activities on climate-active trace exchange processes in MOSAiC. To achieve the objective both 1-D and 3-D simulations with PWRF were evaluated with the available observations. 1-D simulations are a useful tool for evaluating boundary layer dynamics (Cuxart et al., 2006; Sterk et al., 2013). The 1-D simulations provide a first indication of the Arctic boundary layer and meteorological processes over sea ice that can be resolved at relative high vertical resolution compared to the 3-D model simulations, but also allowing to focus on the model’s representation of the column processes. Furthermore, 1-D simulations are computationally cheap and process relatively quick, therefore different physics schemes can be more easily evaluated, also important for the set-up of the 3-D simulations. The 3-D simulations are important because they include advection and are less influenced by the prescribed boundary conditions. 1-D simulation require quite some input to consider the role of advection which having a relatively large influence on the results, where in 3-D simulations the boundary conditions only influence directly the resolved meteorology of outer three cells from the edge of the domain directly. Consequently, because of these different (dis)advantages of both the 1- D and 3-D set-up of PWRF, we present in this thesis an extensive evaluation of both systems.

1.2 Research objective/question The main research objective was to evaluate the performance of PWRF in predicting the site-scale near-surface meteorology over the Arctic. The following research questions were used to reach this objective:

To what extent is Polar WRF able to reproduce the observed meteorological conditions during the ASCOS campaign?

- How well does PWRF represent standard meteorological variables (e.g. Temperature, wind, radiation etc.) and where can improvements be made in the model? - To what extent does PWRF simulations capture the vertical structure of the boundary layer in the Arctic region as being observed during ASCOS? o Will the use of different boundary layer schemes influence the results obtained from the simulation and where lie major differences between the results? - To what extent is PWRF able to correctly simulate cloud (and fog) conditions in the Arctic region? o Will the use of different boundary layer schemes influence the results obtained from the simulation and which boundary layer features explain the main differences between the results?

2. Background WRF and Polar WRF In this section, we will introduce the main features of the Polar optimised version of WRF. WRF is a mesoscale numerical weather prediction model (Skamarock et al., 2008), designed for both research and operational forecasting. It is designed for small and large scale simulations and is one of the commonly used models in atmospheric science (Powers et al., 2017). Though officially supported by the National Center for Atmospheric Research (NCAR), WRF mainly has grown as a community supported modelling system. It now provides a wide range of applications in different studies with foci that range from atmospheric chemistry, hydrology to regional climates studies (Powers et al., 2017). Polar WRF PWRF is mainly an updated version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5; Hines & Bromwich, 2008), in which MM5 was adjusted to a Polar optimised version. This included modifications regarding boundary-layer parameterisation, cloud microphysics, snow surface physics and sea ice treatment. Furthermore, additional updates regarding the representation of the Arctic Ocean (Bromwich et al., 2009; Hines et al., 2015) and Arctic land (Hines et al., 2011) have been introduced.

Two boundary layer schemes were tested during development of Polar WRF; the Yonsei University (YSU; Hong et al., 2006) and Mellor-Yamada-Janjic scheme (MYJ; Janjic, 2002). The most important features of the boundary-layer representation are improved parameterisations for the turbulent fluxes at the surface and calculation of the boundary layer height (Hines & Bromwich, 2008).

Regarding cloud microphysics, ice nuclei and CCN concentrations have been adopted to match pristine Arctic conditions during development of PWRF (Hines & Bromwich, 2008). In following research by Hines and Bromwich (2017), using the Morrison cloud microphysics scheme (Morrison et al., 2005; 2008), CCN concentrations were by default decreased to 50 cm-3 (from 250 cm-3) to more optimally match pristine Arctic conditions. Finally, radiative properties of clouds considering both the role of liquid and ice phase water to improve radiation forecasts were added (Hines & Bromwich, 2008).

Regarding the snow surface physics (Hines and Bromwich, 2008), the Noah land surface model was adjusted by setting the snow–ice longwave emissivity to a value of 0.98 and increasing the snow–ice albedo from 0.80 to 0.82 to improve simulations of radiation balance and temperature and better match Arctic observations. Furthermore, snow cover is treated as part of the first subsurface layer. The distribution of depths of the four subsurface layers is now equidistant. Heat transfer through the snowpack and heat storage quantities were set for the ice layers according to observations of Yen (1981). Furthermore, the surface energy balance equations do also account for latent heat release in the sublimation process over ice covered surfaces.

Finally, the representation of sea ice in PWRF can include fractional sea ice (Bromwich et al., 2009). This fractional sea ice cover is considered in PWRF in the boundary layer parameterisations calling these parameterizations to calculate separately the boundary layer depth for the ice covered and open water fractions of a grid box. For ice covered surfaces, PWRF calculates surface exchange fluxes of energy, momentum and moisture with the Land-Surface model (LSM) with modified surface properties to properly represent the transfer of energy through the ice and snow pack, whereas for the open water fraction PWRF uses the surface layer component from the boundary-layer parameterisations. Model parameters that differ between the LSM and boundary-layer parameterisation are surface roughness and albedo. Finally, grid box average surface fluxes and boundary depths are determined by weighting with the fractions.

3. Method In this section, we first discuss the ASCOS measurement dataset, used to evaluate the PWRF simulations, also since some features of these measurement have been considered in the set-up of PWRF. Afterwards, the set-up of the 1-D WRF simulations and default 3-D PWRF simulation are presented. Finally, we briefly discuss the statistical evaluation of the model results compared to the observations. 3.1 Measurement dataset The measurements to evaluate the model simulations originate from the Arctic Summer Cloud Ocean Study (ASCOS) (Tjernström et al., 2014). ASCOS was a field campaign in which the ice breaker Oden, equipped with multiple meteorological instruments, cruised along a track shown in Figure 1a from August 3th 2008 till September 7th 2008. Figure 1a also indicates the period where the ice breaker was drifting along the sea ice from August 14th till September 1st. During this period, extra measurements were taken from a measurement station set up on the large ice sheet near the ship called ‘Met Alley’ (Figure 1b). We focus in this study on analysis of the PWRF simulations for this period containing the most extensive measurement dataset.

Figure 1: Cruise track of the Oden during ASCOS (a; red) also showing the track of the ice drift (inset), which covers most of our simulation. The left-hand part of the track shows the initial northward track while the right-hand track shows the southward return track. Figure 1b shows the ice breaker Oden, while trapped along the large ice sheet. The circle with ‘Met Alley’ in the Figure is where the meteorological station was set up.

An overview of the measured variables taken from the ASCOS dataset, the instruments used for the measurements of these variables and the uncertainties of the measurements can be found in Table 1. Radiosonde measurements were performed every 6 hours from the ship deck. The vertical profiles from these radiosonde measurements were first of all used to estimate the boundary layer height. Furthermore, the radiosonde measurements were used for analysis of the vertical profiles for potential temperature, humidity, wind speed and direction. The time series from the weather stations at the ‘Met Alley’ site were used for a first evaluation of the basic meteorological variables and for the statistical evaluation. Finally, several instruments mounted on the Oden were used for evaluation of the role of clouds considering features such as cloud cover and liquid water content.

Table 1: Variables obtained from the different instruments used during ASCOS and corresponding uncertainty for the measurement instruments.

Measurement Equipment Variable Measurement uncertainty Vertical profiles Vaisala RS92-SGP Temperature ~ 0.2K (increasing with height) Radiosonde (6h) Humidity 5 % Wind speed ~ 0.15 m s-1 Wind direction ~ 2 degrees Time series - Eppley Pyranometer Shortwave Radiation < 10 W m-2 (single point) Weather stations Eppley Pyrgeometer Longwave Radiation 5 W m-2 (single point) Rotronic Temperature 0.2K LI-COR 7500 Humidity 3 % Gill R3 ultrasonic anemometer Wind speed 0.01 m s-1 Gill R3 ultrasonic anemometer Wind direction 1 degree Cloud data - Oden Doppler Millimeter Cloud Reflectivity ~ 2 dBZ (depending on height: at Radar higher altitude uncertainty becomes larger) Dual-wavelength radiometer Liquid water content ~ 10 g cm-2

An unfortunate shortcoming of the provided dataset is limited availability of surface energy flux measurements. Especially humidity profile measurement were often disturbed by icing of the sensors. Estimation of the latent heat flux was only possible for a very short time period during the expedition. Furthermore, there is only a limited number of sensible heat flux measurements because of issues involved in estimating the surface heat flux through the ice. In addition, these sensible heat flux measurements also suffered from icing issues and therefore the available measurements are also quite uncertain. Hence, assessing the surface energy balance for the Met alley site would have led to an energy balance with only few data points and a high uncertainty and it was therefore decided to omit this data. 3.2 Polar WRF configuration 3.2.1 WRF 1-D set up The goal of using the 1-D version of WRF (referred to as 1-D WRF) was to get a first insight in the development of boundary layers in this area and to arrive at an ideal set up regarding the selected physics schemes for the model. Given that some of the physics schemes in the model were updated specifically in the development of PWRF (Section 2.2), these physics schemes were selected for their application in the default set-up of the 3-D model simulations. In the next section, we elaborate upon choices made for the physics schemes in the 1-D WRF simulation.

YSU and MYJ were chosen as boundary layer schemes because they have previously been evaluated during the development of PWRF. Evaluations indicated that YSU performed better simulating boundary layer dynamics over Arctic ocean (Bromwich et al., 2009), whereas the MYJ boundary layer

10 parameterizations performed better over the Greenland ice sheet (Hines & Bromwich, 2008). For microphysics both the Thompson (Thompson et al., 2008) and Milbrandt (Milbrandt and Yau, 2005a, b) schemes were selected, given that these schemes performed well in PWRF simulations over Antarctica (Listowski and Lachlan-Cope, 2017). Their research furthermore included the Morrison microphysics scheme (Morrison et al., 2005; 2008), which was omitted in the 1-D WRF simulations where it required a reduction of 50 of the 200 vertical layers due to stability issues, largely reducing the desired vertical resolution, but will be used for the 3-D simulations. Additional information on the physics schemes used for the 1-D simulation, similar to those used in the 3-D simulations, can be found in Table 2.

The 1-D WRF simulations require certain input for the boundary conditions; Firstly, vertical profiles of potential temperature, humidity and both wind components are required for which the radiosonde data from the ASCOS campaign was used. Secondly, geostrophic wind profiles are required to consider the role of advection. These geostrophic wind profiles were obtained from estimations made from weather maps in combination with geostrophic wind data from the ASCOS campaign. Finally, ice temperatures for each layer, measured during ASCOS, complete the necessary boundary conditions for the 1-D WRF simulations. Finally, there are several options to consider advection in 1-D WRF, which then follows an upstream relaxation by Ghan et al. (1999), including horizontal and vertical transport. By default these options are turned off, but some test were carried out to evaluate these functions.

1-D WRF performs the model simulations on a 3x3 grid, resolving the physical and dynamical processes of the centre grid cell. In the simulations a 4x4 km grid size with 200 vertical levels was used. The lowest 125 vertical levels cover the first 1000m of the model, with a surface layer covering the lowest 1.5m of the model. The high vertical resolution, especially in the lowest 1-2km of the model, is important to capture the boundary layer development.

We conducted simulations with 1-D WRF for one day of the ASCOS campaign: the 22nd of August. This was a representative day during the ASCOS campaign, in between two passing weather systems. The day started with clear conditions, with clouds developing later during the day. This day could therefore provide us with some first insights about cloud formation and boundary layer development during the campaign. The simulation was centred at the position of the Oden for that day and covered 26 hours, also to capture the full development of the boundary layer during the day.

3.2.2 PWRF 3-D default simulation and boundary layer schemes We performed simulations with 3-D PWRF for the large ASCOS domain from August 12th till September 3rd. The first 48 hours were used as spin up for the model. Meteorological initial and boundary conditions, including sea-ice coverage, are obtained from the ECMWF ERA Interim database (Dee et al., 2011). The grid sizes used were 27 km by 27 km using a 50x50 grid system resulting in a domain of 1350 x 1350 km covering mostly a part of the Arctic ocean and a small part of Greenland (Figure 2). The domain and grid size is similar to the domain used by Hines and Bromwich (2017) for the ASCOS campaign also to allow a comparison of both studies. We used 50 vertical levels from which the lowest 30 were located in the first 1200m of the model and the surface layer covered the lowest 7m, to most optimally capture the boundary layer development and given recommended vertical resolution in the lower atmosphere in previous studies (Tjernström, 2011). Furthermore, a set of physics schemes will be used for the default simulation (Table 2). In a later stage, we evaluated the impact of applying different boundary layer schemes. Furthermore, we also have conducted more simulations with PWRF in which we have further modified specific features of the set-up of PWRF. This was also based on the results of the evaluation of the default model simulation. The introduced modifications are more extensively presented in the results section.

Table 2: Overview of the physics schemes used in the default simulation. In the additional 3-D simulations only the boundary layer scheme and according surface layers were altered.

Figure 2: Map of the Arctic domain covering mostly Arctic ocean and a small part of Greenland. As mentioned, different boundary layer schemes were tested to evaluate which one performs most optimally in terms of simulating the observed meteorological conditions. These boundary layer schemes are the already introduced YSU and MYJ parameterisations and the relatively new Mellor- Yamada-Nakanishi-Niino (MYNN, Nakanishi & Niino, 2006; 2009) BL parameterisation. MYNN was added in the 3-D simulations because this adjusted version of the MYJ scheme was used in several recent studies covering the Arctic region (Bromwich et al., 2018; Hines and Bromwich, 2017; Marelle et al., 2016). From their results it appears that MYNN performs well in reproducing Arctic meteorological conditions.

YSU is a first-order closure scheme where the PBL mixing depends on eddy diffusivity profiles of momentum, heat and moisture (Hong et al., 2006). The boundary-layer top is determined from the buoyancy profile using a critical Richardson number. MYJ is a 1.5 order closure model where the PBL mixing depends mostly on simulated Turbulent Kinetic Energy (TKE) as well as buoyancy (Janjic, 2002). The PBL top also depends on the TKE and this scheme tends to perform better in stable than in convective conditions (Hu et al., 2010). Finally, the MYNN scheme is a modification of the MYJ scheme mainly involving a modified representation of the stability functions and closure constants (Nakanishi & Niino, 2006; 2009). The MYNN scheme uses a 2.5 closure model. Improvements compared to the MYJ scheme were made in the calculation of turbulence by using closure constants from Large Eddy simulation (LES) studies (Nakanishi & Niino., 2009) instead of using observation-based closure constants in the MYJ scheme. 3.3 Evaluation To quantify differences between the observations and simulation the mean bias was calculated for the entire period for a selection of standard meteorological variables e.g. temperature, radiation, humidity, wind speed and direction. In addition the root mean squared error (RMSE) was calculated to further evaluate differences between the observations and simulations. These statistics were further evaluated considering the known uncertainties of the measurement equipment (Table 1).

4. Results In this section, we discuss the results of the PWRF simulations compared with the ASCOS observation. First, the experiments with the 1-D column model are evaluated. Second, the default 3-D simulation and the adjustments to the default simulation are discussed. Finally, we present the results of the sensitivity analysis comparing the simulated meteorology for three different boundary layer schemes. Each section will generally address the a detailed comparison of the following results : 1) Surface meteorological variables, 2) Vertical profiles, 3) Clouds. 4.1 WRF 1-D simulations 4.1.1 Meteorological variables: Statistics Figure 3 shows a comparison of the 1-D WRF simulated meteorological parameters with the ASCOS observations expressed by the mean bias and root mean square error in temperature, wind speed and direction and short- and longwave radiation simulated with both the YSU and MYJ boundary layer schemes for the 22nd of August 2008. Since this comparison indicates that these differences between the simulations for the two boundary layer schemes are apparently quite small, we focus in this section more on comparing the 1-D column model of WRF with the observations rather than analysing differences between the boundary layer schemes. Further analysis of the 1-D model simulations and observations is based on the 1-D WRF simulations using the YSU boundary layer scheme also because it performed most optimally in studies of Arctic meteorology over Arctic ocean in previous studies with PWRF (Bromwich et al., 2009).

Figure 3: Mean bias and RMSE of YSU and MYJ simulations over the 26h of the 1-D (SCM) simulations. Values are given only for the YSU simulation; Corresponding values for the MYJ simulation can be found in the Appendix A, table 1. Some variables are in different units, therefore comparison between different variables is not everywhere possible.

Near surface simulated temperatures were overestimated by 1-D WRF by 3-4 K (Figure 3). This can be partly explained by a simulated overestimation of the (short- and longwave) net radiation balance by 14-15 W m-2. This overestimation is rather high given the average observed net radiation flux for this day being around 5-6 W m-2. This small net radiation flux is however the result of observed average

13 gross shortwave radiation fluxes of ~100 W m-2 and longwave radiation fluxes of 300 W m-2 during ASCOS. Since the overestimation of net radiation balance only partly explains the temperature overestimation in the model, further analysis of what explains this overestimation follows in the section discussing the boundary layer development (Section 4.1.2).

All the individual components of the radiation balance show distinct differences with the observations (Figure 3). The simulated incoming and outgoing shortwave components show a positive bias compared to the observations ranging between 29 and 37 W m-2, respectively. Both the simulated incoming and outgoing longwave radiation show a clear negative bias in the range between 37 and 59 W m-2 compensating for the positive bias in simulated shortwave radiation. This explains the overall bias in simulated surface net radiation. Especially the shortwave components and the incoming longwave radiation biases are associated with the representation of cloud cover in 1-D WRF simulations, which will be discussed in more detail in Section 4.1.3.

Proper simulation of wind direction was one of the main issues in the 1-D WRF simulation. Modelled wind directions differed on average around 60-70 degrees compared to the observations. In addition, wind speeds in 1-D WRF were underestimated by ~2.5 m s-1 at the end of the 1-day simulation. The model mostly underestimated the wind speed at lower altitude, where the model showed a tendency to simulate near zero wind speeds near the surface in contrast to observed wind speeds of 2-3 m s-1. This substantial misrepresentation of both the wind direction and speeds in these 1-D simulations triggers the question to what extent this is due to a misrepresentation of the imposed boundary conditions or due to a misrepresentation of simulated model physics.

4.1.2 Vertical profiles (PBL dynamics) The development of the boundary layer is captured quite poorly by 1-D WRF based on a comparison of the simulated and observed potential temperature profiles shown in Figure 4 for 0, 12 and 24h on the 22nd of August. The initial profile (0h) fits well, except for being ~2K too cold in the lowest few meters of the profile. However, development of the mixed layer is not captured by the model resulting in too deep mixed layers with a positive model temperature bias near the surface up to 4-5K after 12 hours of simulation time. Furthermore, the simulated development in boundary layer height is inconsistent with the radiosonde observations. After 12 hours the shape of the profiles is quite similar. However, the mixing layer depth in the model is twice as large (140m) compared to the observed mixed layer depth (70m). Furthermore, the simulated potential temperature profile after 24 hours indicates that there is no distinct mixed layer, where the observations do show the presence of a mixed layer capped by an inversion around 225m.

14 a

Figure 4: Vertical profiles of potential temperatures after 0h (a), 12h (b) and 24h (c) of simulation time for the YSU simulation and the ASCOS radiosonde measurements in the lowest 400m of the atmosphere.

This misrepresentation of the vertical potential temperature profiles can also be found in a comparison of the profiles of specific humidity and wind speed given in Appendix A. Especially the vertical profiles for specific humidity (Appendix A, Figure 1) are quite similar to the potential temperature vertical profiles. At the start the observed and simulated profiles were relatively similar, however after 12 hours of simulation time, the simulated vertical mixing is over a too high vertical extent and the simulated humidity is too high compared to the observed humidity both at the surface and higher up in the atmosphere. This too high specific humidity is unexpected, because enhanced mixing normally would result in entrainment of colder and dryer air masses and hence a decrease in specific humidity.

As discussed, differences in the wind direction were rather large. Consequently, these profiles did not match well with the observations, indicating that advection in the model was not captured well. The simulated vertical wind speed profiles by 1-D WRF (Appendix A, Figure 2) did not capture the structure found in the observations. The general relatively low magnitude of the observed wind speeds during this day was better captured by 1-D WRF at higher altitude.

4.1.3 Clouds Cloud cover fraction simulated by 1-D WRF using the Milbrandt (Milbrandt and Yau, 2005a,b) microphysics scheme is shown in Figure 5 and can be compared to the radar reflectivity measured by the MilliMeter Cloud Radar (MMCR) shown in Figure 6. The Milbrandt scheme is shown because it showed more distinct cloud layers than the Thompson microphysics scheme, which lacked cloud cover throughout most of the simulation. The difference between the two microphysics schemes is most likely related to differences in the threshold values for ice formation or the difference in prescribed CCN amounts. 1-D WRF simulated three distinct cloud layers: 1) a fog layer at the surface till 05:30, reaching to around 10m deep; 2) a shallow cloud layer during the first 10 hours of the simulation time, located at around 50-60m altitude and; 3) A high cloud layer between 9000-9500m covering the full simulation time. The observed radar reflectivity of the MMCR actually shows two distinct layers: 1) A thick high cloud layer between 5000-9000m, which slowly thins out and which initially starts to decrease around 16:00 being completely dissolved around 19:00; 2) A fog / low cloud layer forming around 13:30, being present till the end of the simulation period. It is clear that the observed cloud cover from the MMCR and the simulated 1-D WRF cloud cover do not correspond well, regarding both

16 the timing of the presence of the lower clouds and the thickness and development of the higher cloud layer.

Figure 5: Cloud fraction over the full domain height obtained with PWRF. Note that the vertical axis is set to logarithmic scale, to be able to see the structures in the lower layers.

Figure 6: MilliMeter Cloud Radar (MMCR) measured reflectivity for the 22nd of August.

The differences in cloud cover between the MMCR observations and 1-D WRF can likely explain part of the differences found in the incoming and outgoing shortwave and incoming longwave radiation. The overestimation of the incoming shortwave is likely a result of the underestimation of the cloud cover by 1-D WRF. Consequently, this results in an overestimation of the simulated outgoing shortwave. Furthermore, underestimation of cloud cover in 1-D WRF can explain underestimation in simulated incoming longwave at the surface.

4.1.4 Discussion / Conclusion on 1-D simulations This part of the research aimed at identifying the ideal physics set up prior to conducting the 3-D simulations by simulating the meteorological conditions of the 22nd of August with 1-D WRF. The simulation resulted in small differences between the YSU and MYJ schemes, however did show clear differences between the model and the observations. Therefore, the various options regarding the selection of physics and dynamics representation for the 3-D simulations were based mainly on recommendations by previous studies with PWRF (see chapter 2) rather than on these results from the 1-D WRF simulation for this ASCOS case. Here we further discuss some of the differences found between 1-D WRF and the ASCOS observations and selections made regarding the set up options in 1- D WRF.

1-D WRF is a common used tool to evaluate physics settings in an ideal case set up (Cuxart et al., 2006; Steeneveld et al., 2006; Sterk et al., 2013). However, it is less used to simulate more complex real world cases. For example, in our case one of the problems was related to the rather complex initial wind profiles from the ASCOS radiosonde measurements, which clearly reflected the role of wind shear with rather large differences in wind direction and speed as a function of altitude. The 1-D WRF simulation had difficulties simulating these wind shear patterns and where it appears that this real Arctic condition’s case was not suited to be evaluated using 1-D WRF. As a result, wind direction was one of the major issues in the 1-D WRF simulation, having a bias between 60-70 degrees. Two additional causes for the large differences were identified: 1) The applied initial profile of the geostrophic wind, 2) representation of advection in these model simulations. Firstly, the geostrophic wind profiles were obtained from estimations using weather maps, which can lead to a relatively large uncertainty. Applying geostrophic wind fields using WRF 3-D simulations would have potentially been a more optimal approach to describe this specific boundary condition in these 1-D WRF simulations. Secondly, there are several options to consider advection in 1-D WRF, which then follows an upstream relaxation by Ghan et al. (1999), including horizontal and vertical transport. By default these options are turned off and testing of the options resulted in stability issues in our simulation. As a result, we decided to ignore the role of advection in these 1-D simulations, causing wind patterns to change dominantly based on processes within the centre grid cell. This would explain the simulated development of the vertical wind speed and direction profiles, which showed dominantly changes near the surface, but remained nearly consistent at higher altitude.

Though the longwave radiation biases were partly explained due to underestimation of the cloud cover in 1-D WRF, longwave radiation biases are more commonly found in WRF. Especially the identified underestimation in incoming longwave radiation is a commonly found issue (Wild et al., 2001; Guichard et al., 2003; Zhong et al., 2007; Kleczek et al., 2014), with values ranging from around 20 W m-2 during day till 40 W m-2 during night time conditions, which are in line with the biases for simulated incoming longwave found in this study. Considering the outgoing longwave bias, Kleczek et al. (2014) for example compared 1-D WRF simulations to weather station data using multiple boundary layer schemes for a location in the Netherlands and found an underestimation of around 20 W m-2 for the QNSE scheme, but also reasonably well simulated outgoing longwave for the YSU scheme. The underestimation of nearly 60 W m-2 found in this study is therefore substantially larger than found in other literature. As outgoing longwave radiation is largely determined by surface temperatures, this large bias in outgoing longwave radiation in our model would normally be associated with a large underestimation in near surface temperatures but this was apparently not the case. This triggers the question whether there is an issue in the outgoing longwave representation in the model as near surface temperatures were simulated rather well.

Snow surface coupling combined with the incoming longwave radiation can be the factors explaining the differences found in boundary layer development as observed from the vertical profiles of potential temperature. Sterk et al. (2013) stressed that for low wind regimes, which are observed in our case, WRF is sensitive for snow surface coupling and radiation regarding boundary layer structure and evolution. In regimes with higher wind speeds 1-D WRF is more sensitive to the representation of turbulent mixing. In the further development of PWRF, both boundary layer parameterisations and snow surface physics were improved for this region by the developers (see chapter 2). These PWRF options were in our 1-D simulations not yet installed, but are used in the 3-D simulations, therefore we can only speculate to what extend the improvements made for PWRF could have improved the boundary layer development and simulation of the meteorological variables in 1-D WRF. However, also given the results from Sterk et al. (2013) regarding the importance of snow surface coupling and radiation at low wind speed regime, we can argue that the used snow surface physics and biases in radiation have largely influenced the 1-D WRF representation of boundary layer development in our simulation. Furthermore, there is an additional uncertainty with the use of radiosonde measurements for evaluation of the vertical profiles. Radiosonde measurements do drift during their rise and rise at a relatively high speed, making the uncertainty in especially the (often shallow) boundary layer relatively high. Drifting could furthermore cause the radiosonde to pass different surfaces from sea ice to open ocean, leading to vertical profiles influenced by both surface types, where 1-D WRF only considered a full sea ice covered surface. However, the vertical profiles of temperature and humidity were similar to the observations at the comparison after 6 hours of simulation time, indicating that the first hours of boundary layer development were captured well.

Overall, it can be concluded that the 1-D WRF simulations performed relatively poor in representing the Arctic meteorology and boundary layer observed in the ASCOS dataset. The poor performance of the 1-D WRF simulations is a result of model issues, for example in the radiation components also linked to issues simulating cloud cover, and some of the imposed boundary conditions as for example the used wind data. 4.2 3-D simulations In this chapter, we focus on the default and adjusted 3-D PWRF simulations. The default PWRF 3-D simulation used the YSU boundary layer scheme in combination with the Morrison microphysics and the RRTM radiation scheme. Here, we first focus on an evaluation of the main meteorological variables also to assess some of the main discrepancies from this default simulation. Secondly, the focus is on the adjustments made to the default simulation. Thirdly, boundary layer heights and vertical profiles of the default simulation are discussed, ending this chapter with a section on cloud characteristics.

4.2.1 Default simulation: meteorological variables Results from a selection of simulated meteorological variables of the default 3-D simulation in terms of the mean bias and RMSE compared to the ASCOS observations are shown in Figure 7. Additionally to the RMSE and mean bias, a relative error based on the mean bias is given, to have a measure of relative differences between the meteorological variables.

The largest relative error is found regarding the simulated radiation balance (Figure 7). This can also be attributed to the relatively low average radiation balance of around 9 W m-2 during ASCOS. However, also a relatively large discrepancy of around -15 W m-2 in the outgoing shortwave radiation component adds to this bias in the simulated radiation balance. The simulated underestimation in outgoing shortwave is caused by a relatively large negative bias in simulated albedo of -0.15. Issues in the representation of albedo and, consequently in outgoing shortwave radiation, are discussed more extensively in Section 4.2.2.1, where we focus on the adjustments made to the calculation of the albedo and resulting meteorology in these PWRF simulations.

Figure 7: Mean bias, relative error based on mean bias, and RMSE of some meteorological variables for the default 3-D simulation. The relative error can be compared between the different variables to give an indication of the relative scale of the error.

Both incoming shortwave radiation and incoming and outgoing longwave radiation have relatively small mean biases ranging between -6 to almost 0 W m-2 (Figure 7). The simulated incoming shortwave is mostly influenced by cloud formation in the model (Section 4.2.4). Differences in the time series of shortwave radiation (Appendix B, figure 1) can occasionally be larger due to discrepancies in cloud cover, including small sub grid clouds, leading to the relatively higher RMSE (Figure 7). The simulated outgoing longwave has a bias close to 0, indicating that on average this parameter agrees well with the ASCOS observations further confirmed by a relatively low RMSE.

Simulated incoming longwave radiation also has a relatively small bias of around -5.5 W m-2 and thereby seems to be well simulated in PWRF. However, the time series of the incoming longwave radiation shown in Figure 8a, indicates a period from 26 till 31 August with a clear discrepancy between the default simulation and the observations. The observations indicate consistently higher incoming longwave as simulated in the default simulation. The time series of the near surface temperature shown in Figure 8b, display a similar pattern during the period from 26 till 31 August. Near surface temperatures during this period are consistently lower in the simulation than in the ASCOS observations. Another period where we see a clear overestimation in both simulated incoming longwave (Figure 8a) and near surface temperature (Figure 8b) is the 21st and 22nd of August. Incoming longwave radiation is dominantly determined by cloud characteristics, therefore we will discuss the potential explanation for these simulated discrepancies in more detail in Section 4.2.4 focussing on cloud characteristics.

Despite the simulated bias in surface temperatures for 26 till 31 August and around the 21st and 22nd of August (Figure 8b), in most of the default simulation, the development of the near surface temperature is captured relatively well by PWRF. This is confirmed by a mean bias of -0.32 K and a

20 relatively low RMSE of 2.53 K (Figure 7) in the PWRF simulated near surface temperature compared to the observations.

Figure 8: Incoming longwave radiation (a) and temperature (b) for the default simulation (PWRF YSU) and the Ascos observations. The temperature time series (b) includes some additional temperature measurements from the Oden, to evaluate the period following on the colder period.

The simulation of wind speed and wind direction in the default simulation performed rather well in PWRF (Figure 7), indicating that advection was simulated well. The near surface temperature time

21 series (Figure 8b) further indicates about this role of advection also showing the impact of the passage of frontal systems on the 21st and 23rd of August. Simulation of the wind speed performed especially well, with a bias of 0.42 m s-1 and a relatively low RMSE of 1.70 (Figure 7). Simulated wind direction in the default simulation also agreed reasonably well with the observations showing a positive bias < 10 degrees. The RMSE is rather high, due to some larger differences between individual data points of the simulation and observations. The latter becomes more clear when the ASCOS wind direction and default simulation wind direction are plotted in a 1:1 graph shown in Appendix B, Figure 2.

Finally, issues with the model representation of temperatures in the ice layers in the default simulation were identified (not shown in Figure 7). The results of some additional simulations regarding the thermodynamics of the ice layers will be addressed in more detail in Section 4.2.2.2.

4.2.2 Additional simulations: meteorological variables Based on the evaluation of the default simulation with PWRF, we have conducted a selection of additional simulations in which we have modified the various settings and representations of different parameters of relevance for the simulation of micro- and boundary layer meteorology. Here we present an overview of those different modelling experiments.

4.2.2.1 Albedo In this section, we address the issue regarding the representation of the albedo found in the default simulation. Figure 9 shows the time series of the albedo for both the ASCOS observations and the default simulation and also shows the precipitation intensity from ASCOS (measured from the 17th of August). The underestimation in albedo of the default simulation is clearly visible in the time series. Furthermore, the albedo of the ASCOS observations shows a direct response to precipitation events reflected by increases in albedo. The observations also indicate that following the precipitation events, the observed albedo either remains relatively stable or gradually decreases likely reflecting changes in the physical properties of the snow layer e.g. due to impaction, melting. The albedo of the default simulation albedo does not show a response to the simulated precipitation events which, interestingly, were rather similar in the default simulation compared to the ASCOS observed precipitation events in terms of timing, however sometimes were underestimating precipitation amounts.

Figure 9: Time series of the albedo for both the ASCOS observations and the default simulation (PWRF YSU). The secondary axis contains the precipitation intensity measurements (starting at the end of 17-08) from the ASCOS observations.

Additional simulations: Modified albedo The albedo of snow and sea ice is calculated in PWRF according to Mills (2011), using a snow and sea- ice albedo, weighted with the snow cover fraction and where the snow and sea-ice albedos are calculated from the skin temperature. The upper- and lower threshold values for snow albedo (0.82 and 0.65) are higher than those of sea ice (0.65 and 0.45). The albedo linearly fluctuates between these threshold values as a function of skin temperature between 268.15 – 273.15K for the snow albedo and between 273.15 – 278.15K for ice albedo.

An alternative approach where the albedo calculation depends on the snow height is also available in PWRF. This albedo calculation also contains two threshold values, which were set to match the ASCOS observations, and has a linear function were the albedo can fluctuate between two prescribed snow height values. This alternative representation of the albedo was also evaluated in this research.

We conducted a number of experiments with PWRF in which we evaluated the impact of four modifications of the calculation of the albedo compared to the default simulation. In a first experiment we initialized the model with a snow cover of 0.5m. The applied ECMWF input data to initialize the PWRF simulations do not include snow heights on the sea ice. Given that the ice albedo in PWRF is lower than that of snow, this results in higher initial albedos in the simulation. In a second experiment the thresholds for the minimum and maximum sea-ice and snow albedo were adjusted to match the observed albedo during ASCOS. The upper- (0.82) and lower threshold (0.65) values for snow albedo were increased to 0.85 and 0.70, respectively. The ice albedo maximum threshold value was also raised to 0.70, to better match the measured conditions during ASCOS. A third experiment used prescribed gridded fields for albedo obtained from Hines & Bromwich (2017), which follow a seasonal Arctic formula by Wilson et al. (2011). Finally, in a fourth experiment we conducted a simulation using the alternative PWRF option to infer albedo from snow height instead of temperature.

Modified albedo results The comparison of the simulated and observed albedo and outgoing shortwave radiation in terms of mean bias and RMSE for the 5 simulations are shown in Figure 10. Both the simulated albedo (Figure 10a) and the outgoing shortwave radiation (Figure 10b) are improved in all adjusted simulations, where

23 the simulations using the modified albedo threshold values as well as the simulation considering albedo as a function of snow depth showing the best performance with biases of the albedo at -0.01 and -0.02, respectively. The remaining bias of ~3 W m-2 in the outgoing shortwave in both simulations might not be a result of the albedo, but can be linked to incoming shortwave radiation biases related to cloud cover. The good performance of the modified albedo threshold value simulation and the snow height dependent albedo simulation are not surprising given that the albedo threshold values were modified to match conditions found in the observations. Furthermore, the case using the snow height dependent albedo was also developed using the ASCOS observations to fit the observed conditions.

Figure 10: Mean bias and RMSE for albedo (a) and outgoing shortwave radiation (b) for the default (PWRF YSU) and four altered simulations.

The simulation with an initial snow depth of 0.5m and the simulation with prescribed gridded fields for albedo did not improve the results quite as much, with biases for the albedo of -0.09 and -0.12, respectively. In the case of the initialized snow cover, this is mainly a consequence of the assumed minimum albedo threshold for sea ice and fresh snow, which were still similar to the default simulation. Given that the albedo of fresh snow of PWRF is assumed to be slightly higher than of ice, this explains the small improvement in this simulation compared to the default simulation. The prescribed fields for sea ice albedo from Hines & Bromwich (2017) follow a formula by Wilson et al. (2011), which originates from a climatological model and hence is based on Arctic climatological averages for August and not on the actual meteorological conditions prevailing during the ASCOS campaign.

The time series of the albedo for the five simulations are shown in Figure 11, as well as the observed precipitation events (station was set up at the 17th of August). The observed albedo increases gradually during the precipitation events till the 24th of August, after which albedo steadily decreases due to melting until the 28th of August when the observations indicate some increase again in albedo. All PWRF simulations do not capture these trends well, also because PWRF’s albedo parameterization does not represent increase in albedo related to fresh snowfall. The simulations using the initial snow depth and those using the modified albedo threshold values in which albedo depends on temperature, show sharp changes in albedo reflecting temperature changes with a positive temperature change leading to a decrease in albedo and vice versa. The snow height dependent simulation does simulate some gradual increase and decrease of the albedo in parts of the simulation, however also misses some observed temporal changes in albedo and does not respond to fresh snowfall as the observed albedo seem to do. Finally, the albedo for both the default simulation and the simulation using the gridded snow/ice property fields is consistently too low compared to the observed albedo and do not reproduce the observed increase to the high observed albedo values during the last 10 days of August. The simulation using the gridded fields for albedo simulation does show the gradual increase in albedo

25 according to the climatological mean, but never follows a similar trend seen in the observations. The default simulation only shows a slight increase in albedo after the start, remaining consistently too low also because of returning to mostly bare sea ice cover resulting in a relative low albedo compared to that of fresh snow cover.

Figure 11: Albedo obtained from the ASCOS observations and the default (PWRF YSU) and additional simulations. The secondary axis contains the precipitation intensity measurements (starting at the end of 17-08) from the ASCOS observations.

4.2.2.2 Ice thermodynamics In this section, we address the issues involved in the representation of ice layer temperatures found in the default simulation. In Figure 12 the observed and simulated ice temperatures are shown for the default simulation using the YSU boundary layer scheme. In the observations, a clear distinction can be seen between the temperature responses at 5cm depth compared to the deeper three other layers. The observed temperature at 5cm depth seems to reflect the response of a more porous medium, because it is affected quite rapidly by changes in skin temperature. From the description of the campaign (Tjernström et al, 2014), it could not be concluded whether the temperature sensor in this top layer was really located in an ice layer or in the more porous snow layer. The observed temperatures for the deeper layers all show the same rather small temperature changes. The simulated decreases in ice temperature in PWRF are largely overestimated, even though the first week ice temperatures seem rather stable. Simultaneous with the first decrease in near surface temperature in the model, around 21st of August, (Figure 8b), the modelled sea ice temperatures start to drop rapidly.

Figure 12: Ice layer temperatures for the observation and the PWRF default simulation (YSU) during the ASCOS campaign. Note that there is a difference in depth between the modelled and observed ice temperatures.

Modified ice thermodynamics The heat flux through the sea ice in PWRF depends on two main aspects: 1) the selected heat conductivity, specific heat capacity and sea ice density and 2) the temperature gradient between the subsequent ice layers. First, we compared the constants used in PWRF for properties of the sea ice with values found in literature. In Table 3 an overview of the most important ice properties for both the model and from research by Fukusako (1990) is given. The constants used in PWRF for the ice properties are quite in agreement with the values obtained from Fukusako (1990), however small adjustments in the given ranges of the ice properties were evaluated and compared to the default simulation. These results will however not be further discussed, because only very small improvements were seen in simulated ice temperatures in those experiments.

Table 3: Overview of the properties of the sea ice in both PWRF as research by Fukusako (1990)

Variable Fukusako (1990) (at 273.15 K) PWRF Conductivity 2.09 – 2.26 W m-2 2.2 W m-2 Density 916 – 918 kg m-3 917 kg m-3 Specific heat capacity 2000 – 2060 J kg-1 K-1 1880 J kg-1 K-1

Therefore, we will focus on the differences between the default simulation using the YSU and the simulation using the MYNN boundary layer scheme in representation of the sea ice temperatures focussing on differences in temperature gradient. It will become clear in Section 4.3 discussing the three used boundary layer schemes that surface temperatures in the simulation with MYNN were on average higher than in the simulation with YSU, which reduces the temperature gradient between the sea ice and the surface. Temperatures simulated with the MYJ boundary layer scheme were lower than with the YSU scheme and therefore were not expected to improve the sea ice temperatures and will not be further evaluated here.

Modified Ice thermodynamics results In Figure 13 the observed and simulated sea ice temperatures are shown for the simulation using the MYNN boundary layer scheme. The first clear difference between the simulation using MYNN and the simulation using YSU is that the decrease in sea ice temperatures is ~1.5K smaller for the top layer at the end of the simulation. This can be explained by the reduced temperature gradient at the surface due to higher near surface temperatures (Figure 21b). Furthermore, it is visible that the top simulated ice temperature at 25cm follows the regime of the observations relatively well for the first week. During the period in which PWRF significantly underestimates near surface temperatures (26-31 August) there is also a simulated continuous decrease in ice temperatures, indicating the importance of a proper simulated skin temperature for calculation of the sea ice temperatures in PWRF.

Figure 11: Ice layer temperatures for the observation and the PWRF default simulation (YSU) during the ASCOS campaign. Again note that there is a difference in depth between the modelled and observed ice temperatures

4.2.2.3 Gridded fields Given the various established shortcomings in the representation of the ice cover, snow cover on ice, ice thickness and the previously discussed albedo in PWRF, we also conducted one simulation using prescribed gridded fields for these quantities. These fields were made available by Hines & Bromwich (2017) for the ASCOS campaign and can be found at http://polarmet.osu.edu/hines/PWRF/. The fields were updated every 6 hours in our simulation, but are available for updates up to every 3 hours for the ASCOS campaign. Sea ice concentration- and thickness fields were created from the Scanning Radiometer for Earth Observing System (AMSR-E) observations with the help of the University of Illinois. Snow depth fields were created with the Pan-Arctic Ice-Ocean Modelling and Assimilation system (PIOMAS) (Lindsay et al. 2009; Zhang & Rothrock 2003). We omit the albedo fields here, as they are previously discussed.

The simulations that applied these prescribed snow and sea ice fields did not perform as well as expected. The gridded fields containing snow depths on sea ice were obtained from the PIOMAS model, but this model simulated no snowfall events (or not large enough to exceed the minimum threshold of 1cm of snow on sea ice in PWRF) during the ASCOS campaign, resulting in a constant snow

28 height of 1 cm on the ice in the simulation. As the observations clearly indicated the occurrence of several snowfall events during ASCOS, especially during the passage of frontal systems, the gridded fields for snow depth on sea ice did not provide the additional information to more realistically represent this feature in the PWRF simulations.

Application of the prescribed sea ice cover and thickness turned out to be of added value for these PWRF simulations of the ASCOS campaign. These fields were obtained using the AMSR-E, a scanning radiometer, and provided extra information regarding sea ice cover and thickness. These prescribed sea ice cover fields contained more open leads (not explicitly resolved in PWRF) and sea ice cover hence varied more throughout the domain, being slightly lower than 1.0 (around 0.98) at the ASCOS site. This sea ice fraction difference was too low to identify significantly large differences in meteorological state variables between both simulations and we will not further discuss the results here, though a small increase in boundary layer height was observed and is worth mentioning. Furthermore, the sea ice thickness added variation in the sea ice thickness to the model. PWRF normally takes an initially 3m thick layer for the Arctic as standard. The fields obtained from the AMSR- E contained varying sea ice thickness for the region, thereby creating a more realistic ice thickness field as boundary condition for the model and updating these fields every 6 hours into simulation.

4.2.3 Vertical profiles (PBL dynamics) In this part, we focus on boundary layer dynamics. First, boundary layer heights of the default simulation are compared to those estimated from the 6h radiosonde measurements. Furthermore, a few individual profiles of potential temperature are highlighted because they were especially different between the model and the observations.

Boundary layer heights from the default simulation, using the YSU boundary layer scheme, are compared to those estimated from the 6h radiosonde observations shown in Figure 14. Though all modelled boundary layer heights are in the right order of magnitude and a small cluster is present in the bottom left corner around the 1:1 line, many other points lay relatively far from the 1:1-line. The YSU scheme underestimates the boundary layer height for many data points, indicating an often too shallow mixed layer depth in the model. However, further investigation of the timing of these underestimated boundary layer heights indicated that most of the points associated with this

29 underestimation were occurring in the period from 26-31 August dominated by the low level cloud regime.

Figure 14: Modelled PBLH compared with estimated PBLH from the 6h radiosonde measurements during ASCOS.

During this period of 26-31 August, clear differences were visible between the PWRF simulated and observed potential temperature profiles. To illustrate this, a selection of vertical profiles of potential temperature in this period are shown in Figure 15. Especially the profiles from 27-29 August show a too shallow mixed layer in the model with boundary layers heights of 200-300m, where the observations indicate boundary layer heights ranging from 600-1100m. The vertical potential temperature profile from 30 August shows the underestimated mixing in the model less clear, given that the PWRF- YSU simulations resulted a second mixed layer with rather constant potential temperatures which reaches higher than the observed mixed layer depth. However, a general feature during the period of 26-31 August was an underestimation of boundary layer height by the default simulation. How this underestimation of boundary layer height can also be related to issues in the model representation of cloud cover, the simulated incoming longwave bias and corresponding temperature bias found in this period, is discussed in the following Section.

30 a

Figure 15: Potential temperature profiles for the radiosonde observations and the default PWRF simulation (YSU) during 4 central days associated with the incoming longwave / temperature bias (26-31 August). The displayed vertical profiles were taken around 11:30 UTC on the selected days: 27 August (a), 28 August (b), 29 August (c) and 30 August (d).

4.2.4 Clouds This section focusses on comparing simulated cloud parameters, e.g. cloud fraction and cloud water mixing ratios, with observations made by the MilliMeter Cloud Radar (MMCR) and the Dual Wavelength Radiometer (DWR).

A comparison of simulated cloud cover with the radar reflectivity data from the MMCR is shown in Figure 16. In general, simulated cloud cover (Figure 16b) is in reasonable agreement with the reflectivity measured from the MMCR (Figure 16a). For example, similar structures can be found just before Day of Year (DoY) 228 with vertical distribution of cloud cover up to around 5000m; the high clouds present at DoY 235 with limited low level clouds underneath; and the combination of presence of low and high clouds around DoY 243. An example for a day where structure in the simulated cloud layer corresponds less with observations from the radar reflectivity of the MMCR is around DoY 233.

The model simulations only result in presence of some low level clouds, where the measurements from the MMCR indicate a more vertically developed structure up to 4000-5000m on average and reaching maximum cloud cover up to 8000m. However, the PWRF simulations using the Morrison microphysics scheme seems to capture quite accurately most cloud cover during the ASCOS period.

Figure 16: Radar reflectivity (a) obtained from the MilliMeter Cloud Radar (MMCR) and Modelled cloud fraction (b) from the default simulation. DoY 226 = 13 August; 237 = 24 August.

Simulations of cloud water mixing ratios, shown in Appendix B Figure 3, give a further indication of where cloud cover was dominantly found in these model simulations. The cloud cover at higher altitude simulated by PWRF is less clear visible in these plots, where only a few vertical structures are visible in the simulations. This indicates that most of the thicker simulated clouds where located near

33 the surface, which is partly in line with the observations indicated by the radar reflectivity (Figure 16a). However, part of the vertical structures and higher clouds also appears to be thicker and this is not seen in the cloud water mixing ratios (Appendix B, Figure 3). Finally, the rather low obtained values for the cloud water mixing ratios in the model simulation need to be mentioned, with maximum values around 0.5 g kg-1 where values of at least 1-2 g kg-1 are expected.

As mentioned, the simulated longwave bias (Section 4.2.1) for the period 26-31 August (DoY 239 – 244) is most likely due to a misrepresentation of cloud characteristics. This period was dominated by low level clouds and therefore cloud water mixing ratios and radar reflectivity from the MMCR are shown up to 2000m in Figure 17. Simulated cloud water mixing ratios (Figure 17b) and observed radar reflectivity (Figure 17a) show reasonable agreement regarding the shape of the clouds in the period from DoY 239-244. However, the measured clouds by the MMCR shows generally a vertical cloud cover from the surface up to around 1000m altitude, where the default simulation shows clouds with the top being generally located around 300m altitude. A too thin simulated cloud layer can be the cause of the incoming longwave bias. Furthermore, the difference in cloud layer depth is in line with the found differences between modelled and observed boundary layer heights (Figure 15), indicating the relevance of mixing by these cloud layers in the model simulations.

34 a

Figure 17: Radar reflectivity obtained from the MMCR (a) and modelled cloud water mixing ratio (b) for the lowest 2000m of the atmosphere. DoY 237 = 24 August.

Finally, Liquid water path (LWP) in PWRF and the observations from the DWR were in rather good agreement throughout the simulation, which is visible in the time series of the LWP (Appendix B, Figure 4). The simulated and observed LWP are of comparable magnitude and most patterns were reasonably captured, indicating that the model simulated LWP in the atmosphere is reasonably well represented. Regarding the representation of LWP during the period of 26 till 31 August, there is no obvious discrepancy between the model and observations. However, one other period where the model and observations show a difference can be seen around 21 and 22 August. Here PWRF overestimates LWP, where the observations indicate nearly clear sky conditions. This discrepancy is in agreement with the found overestimation for incoming longwave radiation and near surface temperature (Figure 8; Section 4.2.1).

4.3 Boundary layer schemes In this section, we focus on the results of simulations performed with three different boundary layer schemes: YSU (default simulation), MYJ and MYNN. First, the statistical results in terms of the mean bias and RMSE for a selection of meteorological variables are compared between the simulations for

35 these different boundary layer schemes and the ASCOS observations. Second, results on the boundary layer dynamics are discussed. Finally, differences in cloud formation for the different boundary layer schemes are discussed.

4.3.1 Meteorological variables: statistics & important differences Results from a selection of meteorological variables of the simulations with the three boundary layer schemes in terms of the mean bias and RMSE compared to the ASCOS observations are shown in Figure 18. In general, most statistical attributes are rather similar between YSU and MYNN, with MYJ often having the largest RMSE and mean bias (Figure 18). This indicates that MYJ performs less regarding simulation of the ASCOS case compared to the simulations with the other boundary layer schemes.

Differences between the simulations using YSU and MYNN appear at first sight to be rather small, especially regarding variables such as wind direction, wind speed and incoming and outgoing shortwave radiation. However, comparison of the YSU and MYNN simulated near-surface temperature and incoming and outgoing longwave radiation show a change in sign for the mean bias, indicating larger differences between the simulations using the YSU and MYNN boundary layer schemes. In the next sections we focus on these large differences between these simulations and also address some interesting features in the simulation using MYJ.

Figure 18: Mean bias and RMSE for the three boundary layer schemes used in the model: YSU, MYJ and MYNN. Indicated values correspond for the RMSE of the YSU and MYNN scheme. Additional values for the RMSE of MYJ and mean bias for all boundary layer schemes can be found in Appendix B, table 1.

Largest RMSE differences between the YSU and MYNN simulations of ~4.5 W m-2 are found in the simulated incoming longwave radiation (Figure 18). Therefore, the time series of the incoming longwave radiation for the simulations with the three boundary layer schemes is shown in Figure 19a. Distinct differences in the simulated incoming longwave are seen around 26-31 August, with the simulation using MYNN performing the best compared to the observations but still underestimating the average incoming longwave radiation by ~15 W m-2. Note that the simulation using MYJ performed

36 worse than the simulations using YSU and MYNN during this period, however did perform better in simulating the lower incoming longwave radiation temporal variability around the 22nd of August. Incoming longwave radiation is largely determined by presence of clouds. Therefore this feature will be discussed in more detail in Section 4.3.3, where we focus on the cloud characteristics in the three simulations.

Previously, it was shown that simulated temporal variability in incoming longwave radiation and near surface temperature were rather similar for the default simulation. The time series of the incoming longwave radiation for the different boundary layer scheme simulations also shows some distinct differences and therefore we also show the differences in simulated near surface temperature time series for these simulations in Figure 19b. Differences in the near surface temperature mean bias between the simulations with YSU (-0.58 K) and MYNN (0.48 K, Figure 18) can be largely explained by the simulated differences in incoming longwave radiation (Figure 19a) showing extended periods where the temperature deviates from the ASCOS observations. Firstly, two periods are present where the simulation with MYNN has higher temperatures than the simulation with the YSU scheme: 1) 26- 31 August where the simulation using MYNN also better represents the ASCOS observations and 2) 1- 3 September where the simulation using MYNN overestimates temperatures compared to the ASCOS observations. The dominant differences are however in the period of 26-31 August, a period dominated by the presence of low level clouds when the longest consistent bias occurs. In this period, we furthermore see that the MYJ simulation performs worse in simulating the near surface temperature compared to the YSU and MYNN simulations. Around 21-22 August all three boundary layer schemes simulations result in overestimated near surface temperatures. In this period all boundary layer scheme simulations perform rather similar though the simulation using MYJ captured the observed lowest temperatures at the 22nd of August relatively well compared to the simulations with the other two BL schemes.

Finally, the simulated outgoing longwave indicated some larger differences comparing the three boundary layer scheme simulations. Outgoing longwave radiation is mainly determined by surface temperatures and therefore the temporal changes in simulated outgoing longwave radiation are rather similar to that of incoming longwave radiation and near surface temperature. Therefore, it is no surprise that the MYNN simulation has a positive bias of 2.11 W m-2 compared to a negative bias of - 1.23 W m-2 for the YSU simulation and the MYJ simulation having the largest bias of -7.02 W m-2.

Figure 19: Incoming longwave radiation (a) and near surface temperature (b) for the three used boundary layer schemes (similar set up as Figure 8).

4.3.2 Vertical profiles (PBL dynamics) Boundary layer heights calculated by the simulations using the MYJ and MYNN scheme are compared to the observed boundary layer heights inferred from the 6h radiosonde measurements, shown in Figure 20. We also included in this comparison the default simulation using the YSU boundary layer scheme shown in Figure 13. Boundary layer heights calculated by YSU, MYJ and MYNN differ quite a

38 lot and also deviate a lot from the observed boundary layer height not seeing a clear clustering around the 1:1-line. As discussed, the YSU simulation (Figure 13) resulted in underestimated boundary layer heights for extended periods compared to the observations, mainly during the period of 26-31 August with the presence of low level clouds. Looking at the MYJ simulation (Figure 20a), many of the diagnosed boundary layer heights are only ~15m deep, pointing at a simulation of very strong inversion conditions and which seems to be not realistic at all. Boundary layer heights calculated by MYNN (Figure 20b) show a more random spread, with both over- and underestimated boundary layer heights. Further investigation of the period of 26-31 August shows also large differences between the simulations using the three boundary layer schemes.

Figure 20: Modelled PBLH by MYJ (a) and MYNN (b) compared with estimated PBLH from the 6h radiosonde measurements during ASCOS.

A selection of potential temperature profiles corresponding to the period of 26-31 August were evaluated for the three boundary layer schemes and compared to the observations (Appendix B, Figure 5). These potential temperature vertical profiles from the MYJ simulation indicate generally lower average boundary layer heights (50-100m) compared to YSU simulation (200-300m), both not comparing well with the observed boundary layer heights from ASCOS (ranging between 600-1100m). Furthermore, potential temperature profiles from the MYJ simulation show the lowest surface temperatures and have less clear inversion layers compared to the potential temperature profiles calculated by the simulations with the YSU and MYNN boundary layer schemes as well as the radiosonde observations of potential temperature from ASCOS. Vertical potential temperature profiles from the MYNN simulation for the 26-31 August period show improved representations of the boundary layer height compared to the YSU and MYJ simulations. The extend of the mixed layer for the MYNN simulation reaches on average up to an altitude of ~600-700m, more comparable to the observed boundary layer heights of 600-1100m. This is actually also accompanied by improved simulated near surface temperatures, reducing the cold bias by ~3-4K compared to the simulations with the other BL schemes.

To further illustrate the differences between the boundary layer schemes, a timeseries of the boundary layer heights simulated by simulations using the three bounadary layer schemes is shown in Figure 21. The differences described for the individual profiles are in line with differences seen in the time series of the boundary layer height for the three boundary layer scheme simulations. Clearly, it is visible that the simulation using the MYNN scheme in this period resulted in larger boundary layer heigths (around 600m on average) compared to the simulations using YSU (200-300m) and MYJ (often at 15m). The MYNN simulation however still did not result in mixed layer depths close to those inferred seen from the ASCOS observations, which permanently show deeper mixed layers of ~800-1100m in the low level cloud period of 26-31 August.

Figure 21: Time series of the PBLH both from the model simulations and from the ASCOS observations, focussing on the period 25-08 till 01-09. The observations are on 6h interval between the radiosondes, the dashed line hence only indicates a possible height, but is not based on true measured points.

4.3.3 Clouds Previously, we discussed that the default simulation with the YSU boundary layer scheme simulated most cloud cover reasonable compared with the ASCOS observed cloud cover. In all boundary layer simulations the Morrison microphysics scheme was used. Therefore, it is understandable that similar structures regarding cloud cover are found in these different simulations. To further illustrate this similarity in the representation of clouds, cloud cover for the first 10 days of simulations for the three boundary layer scheme simulations are shown in Appendix B, Figure 6. Only small differences between the three simulations using the different boundary layer schemes can be found for this period, also compared to the ASCOS observations. For example, the clear sky conditions near the surface around the 22nd of August for the MYJ simulations, corresponding to the drop in incoming longwave radiation (Figure 19a), is one of those events that shows small differences. Larger differences between the boundary layer scheme simulations occur dominantly during the low level cloud regime from 26-31 August. However, in general the agreement between observed and simulated cloud cover is for all boundary layer schemes reasonable also indicating a reasonable performance of the Morrison microphysics scheme for the Arctic conditions prevailing during the ASCOS campaign.

The period with persistent low level clouds from 26-31 August was less well captured by the boundary layer scheme simulations showing some clear differences between the simulations regarding radiation fluxes, surface temperatures and cloud cover. To evaluate the differences in cloud cover, the last 9 days of the simulation are shown for each boundary layer scheme simulation in Figure 22, focussing on the lowest 2000m. As previously discussed, the ASCOS observations from the MMCR (Figure 16a) indicate that the cloud layer stretched from the surface up to about 800-1100m altitude on average during this period. None of the simulations with the different boundary layer schemes resulted in simulation of a cloud layer with such a vertical extent (Figure 21). Cloud cover simulated using MYNN performed best in terms of cloud layer depth, with on average a cloud layer reaching up to around 600m altitude, where the simulations using YSU resulted in cloud cover reaching up to on average around 300m and the simulation using MYJ often showing clear sky conditions. The simulated cloud layer depths seem to be related to simulated differences in boundary layer heights for each scheme during this period (Figure 20) affecting the incoming longwave radiation at the surface and corresponding near surface temperatures (Figure 18b). Looking at the structure from the simulations with MYNN and YSU, it seems that the YSU simulation agrees better with the observed cloud structure. The YSU simulation shows a cloud layer that extents all the way down to the surface, where the MYNN simulation shows a more open structure in the lowest 200m of the model, which is not in agreement with the ASCOS observations.

Figure 22: Cloud fraction in the lowest 2000m of the PWRF simulation for the three tested boundary layer schemes: YSU (a), MYJ (b) and MYNN (c) schemes. The displayed data contains the last part of the simulation starting from August 23 12:00 UTC till September 2. DoY 237 = August 24 42

Results Summary Overall, the results of this study indicated issues in both model physics and problems during certain meteorological conditions. However, the performance of PWRF in the first 10 days of the simulations was reasonable compared to the ASCOS observations. Issues in the model physics were found regarding the representations of the albedo and the ice layer temperatures. Finally, distinct differences between the ASCOS observations and the simulations were found during the low level cloud regime of 26-31 August, especially regarding incoming longwave, near surface temperature, cloud cover and boundary layer height.

5. Discussion & Recommendations In this section, we elaborate upon the identified main issues in PWRF’s representation of the meteorological conditions prevailing during the ASCOS campaign and compare the results with similar studies. Furthermore, this part contains recommendations for future research or to improve the model itself. Though the focus in this section is on explaining the main issues found in the PWRF’s model experiments, the overall results from the PWRF simulations were reasonable and therefore we start this section with a brief comparison to similar studies and afterwards focus on the recommended improvements and future research.

Default simulation PWRF simulations resulted in reasonable representation of several state variables, e.g., near surface temperature, wind speed and wind direction. Our results in terms of mean bias and RMSE between the default simulation and the ASCOS observations were in the same range as mean biases and RMSE found by the control simulation from Hines & Bromwich (2017), who performed an analysis focussing on cloud characteristics during ASCOS. They concluded that atmospheric state variables were reasonably simulated by PWRF over the ASCOS period. Furthermore, they concluded that cloud characteristics in the model were reasonably well simulated except for the persistent low level clouds, which is in agreement with our results. The low level cloud modelling issue is further confirmed by research of Sedlar et al. (2011), which focussed on the energy balance during ASCOS and also found biases in the representation of the radiation associated with the presence of persistent low level clouds occurring around 26-31 August. Furthermore, they concluded that the onset of a freeze period (from which moment the energy budget remains below 0 W m-2) seen in the ASCOS observations at the 24th of August was captured well by PWRF, similar as found in the simulations in our study. This confirms that PWRF simulates many of the ASCOS observed meteorological features reasonable except of the low level cloud regime from 26-31 August.

Before evaluating in more detail some of the issues found in the simulations, we need to address a main source of uncertainty regarding the observed boundary layer heights during ASCOS. The observed boundary layer heights were inferred from the vertical potential temperature and specific humidity profiles from the radiosonde measurements, whereas the simulated boundary layer heights are diagnosed from the simulated turbulent kinetic energy or eddy diffusivity profiles depending on the chosen scheme. These different approaches to infer the boundary layer heights between observations and simulation therefore introduced a bias that might also partly explain the large spread in the direct comparison of simulated and observed BL heights (Figure 14 & 20). Even though in the simulation the magnitude of the boundary layer height was on average comparable to the observations, except for the period of 26-31 August, the introduced bias should be considered in evaluation of the presented results in this research. In future research, it is recommended to also infer the BL height from the simulations based on the potential temperature and moisture profiles in addition to the calculation made by the boundary layer schemes, resulting in additional boundary layer height data determined in a similar way as done for the observations.

The temperature bias found in the period of 26-31 August in the simulation was linked to three possible causes. The first cause is an underestimation in the incoming longwave radiation, which is a rather common problem in WRF simulations ranging from 20 W m-2 during the day up to 40 W m-2 at night (Wild et al., 2001; Guichard et al., 2003; Zhong et al., 2007; Kleczek et al., 2014). However, the incoming longwave radiation in our simulations only consistently deviates by on average 20 W m-2 from the observations during the low level cloud period from 26-31 August and not during the entire simulation. Consequently, this did not fully explain the simulated incoming longwave bias. A second cause of the identified biases in surface temperature and longwave radiation for 26-31 August is a

44 misrepresentation of emitted longwave by the cloud layer, possibly being too low for this period. Sedlar et al. (2011) performed a simulation for the same period and concluded that changes in cloud emitted longwave radiation were the main cause of the incoming longwave radiation bias in the PWRF simulation. Furthermore, cloud characteristics in our simulation for the period were quite different than found in the ASCOS observations. Cloud mixing ratios were underestimated and the cloud layer depth was approximately a factor 2-3 too thin. Additionally, Tjernström (2011) discusses that the optically thin low level clouds are often poorly represented in model simulations and that such misrepresentations lead to biases in the radiation budget, dominated in the Arctic by the longwave radiation. This both indicates that an underestimation of longwave radiation from the cloud layer could be the cause of the simulated underestimation in incoming longwave radiation. A third likely cause of the temperature bias is the representation of the boundary layer during this period, which was clearly not in agreement with the observed boundary layers. Boundary layers during the 26-31 of August for ASCOS show relatively deep (ca 800-1000m) mixed profiles. The simulated boundary layers using the different boundary layer schemes resulted in underestimated boundary layer heights and that might also explain the issues on the representation of the cloud layers in the simulations. A capping inversion at the top of a cloud can occur due to large temperature differences as a result of the high reflectivity and cooling at the top of the cloud layer (Sedlar & Schupe, 2014). Development of the mixed layer within the cloud layer would be a combination of mechanical turbulence driven by wind and vertical motions in the cloud. Additionally, we previously discussed that with deeper mixed layers, near surface temperatures increased in the model simulations. Consequently, the most likely cause of the bias in incoming longwave radiation and near surface temperature seems to be a combination of the representation of the low level clouds, in terms of thickness of the cloud layer and emitted longwave radiation by the cloud layer, and boundary layer development induced by surface physics and the cloud layer.

A final note on some of the missing structures in cloud cover at higher altitude is possibly linked with the used vertical resolution. Especially, the misrepresentation of simulated cloud mixing ratios at higher altitude might be due to the relative coarse vertical resolution higher up in the model. With the largest number of vertical layers (30 of the in total 50 vertical layers) being used to resolve the lower 1200m, to most optimally capture the boundary layer, the layers > ~3000m have a vertical resolution up to several hundreds of meters and which limits the simulation of atmospheric features such as cloud mixing ratio. From the results of our simulations, the effect of this vertical layer distribution seemed less important for the simulated cloud cover, where it mainly resulted in some more coarse structures.

Albedo The calculation of albedo in PWRF should be adjusted depending on the type of research and temporal timescale. As discussed, the observed albedo during ASCOS shows gradual increases coinciding with fresh snowfall events and decreases for melting conditions. Though the PWRF simulated albedo for the modified threshold values agreed reasonably well with the observed albedo in terms of mean bias, the temporal variability was never reproduced. To potentially improve PWRF’s representation of snow and sea-ice albedo we need to assess beside the role of temperature changes also additional features such as presence of melting ponds on snow/ice, time since fresh snowfall, liquid water content of the snowpack, dust/particles depositing on the snow/ice and compaction of the snow (Curry et al., 1995; Kuipers Munneke et al., 2011; Willeit & Ganopolski, 2018). Improving the representation of albedo is especially important for research focussing more on short timescales (hours/days/weeks) such as presented in this thesis study. For studies focussing on longer timescales (months, years), the albedo calculation based on temperature currently used in PWRF might be a good enough estimation, especially when the threshold values are adjusted to the available observations. The albedo calculation from Wilson et al. (2011) based on climatological averages might also suffice for such studies focussing

45 on longer timescales. The snow height dependent albedo developed for the ASCOS case is not recommended for use in later studies. Though in terms of mean bias between the simulation and ASCOS observations the snow height dependent albedo performed relatively well, it failed at capturing the observed patterns in albedo. Furthermore, it was specifically designed for the ASCOS campaign and therefore it would require adjustments for different cases and also can likely not be used for long simulations because it depends highly on the threshold values used for the snow heights. In addition, for long simulations, the melting of the snow layer should be properly represented in PWRF also being sensitive to, but also potentially partly causing misrepresentations in the surface energy budget.

Ice sheet thermodynamics Further research should be performed regarding the temporal variability in the temperature gradients in the ice sheet. As discussed, main issues were identified in the representation of the observed temperature decrease of the ice layers, but also in initial temperature gradient between the ice layers. The latter is a rather technical issue given that the input files from ECMWF do not contain ice temperatures. Therefore, PWRF estimated the ice layer temperatures by linearly interpolating the difference between the skin temperature and the sea surface temperature, which resulted in this study in an initial temperature gradient in the sea ice which did not correspond to the observations. Prescribing the initial temperatures of the ice layers would solve this problem and is recommended for future research. The issue regarding the model’s representation of the observed temperature gradient in the ice layers is dominantly caused by the misrepresentation of the near surface temperature and not by the ice properties in the model. The default values for ice (2.2 W m-2) and snow conductivity (0.3 W m-2), heat capacity for sea ice (1880 J kg-1 K-1) and density of sea ice (917 kg m-3) are in line with common values found in research (Fukusako 1990; Lyle & Ackley, 1996; Pringle et al., 2007). Though these studies do indicate that some of these values depend on temperature and are not constant as in PWRF, this was not a main reason for the misrepresentation of the decreases in ice temperatures, because we found that the additional simulations altering the sea ice properties only led to minor improvements. A decrease in ice temperature comparable to what has been observed seems to be dominated by temperature gradient between the surface and the first subsurface layer. This could also be inferred from the differences in near surface temperature between the simulations of the boundary layer schemes, where higher near surface temperatures in the simulation using MYNN lead to a smaller simulated decreases in sea ice temperatures. To further evaluate the issue of the decrease in sea ice temperatures, it is recommended to impose the observed skin temperature to PWRF in combination with the initial prescribed ice temperature gradients. Note that there is a function to cope with the decreasing ice temperatures in PWRF (called tice2tsk_if2cold) which, when activated, sets the ice temperatures equal to the skin temperature. This indicates that the unrealistic cold ice temperatures are a commonly known problem in PWRF. For example a study of Steinhoff (2011), in which simulations resulted in sea ice temperatures of around 20K for small fractional ice concentrations. Gridded fields for albedo, snow on sea ice, sea ice cover and thickness In our simulations, which included the snow and sea ice input datasets by Hines and Bromwich (2017), it was found that only the sea ice cover and thickness fields obtained from the AMSR-E resulted in simulations that provided reasonable results. The albedo fields are based on climatological yearly averaged patterns (Wilson et al., 2011) and hence did not improve simulations regarding the observed rather short-term temporal variability. Furthermore, snow depth fields from PIOMAS (Lindsay et al., 2009; Zhang & Rothrock, 2003) were in poor agreement with the observations, because the PIOMAS model did not simulate snowfall events during the entire ASCOS period. However, sea ice thickness in PWRF usually is set to 3m as initial value, therefore the 6 hourly updated sea ice thickness fields did

46 give additional information to PWRF introducing a varying ice thickness over the domain, which is relevant for the representation of thermodynamics in the ice sheet.

Finally, the input sea ice cover fields do contain information on the presence of open leads. Application of these input data resulted in a sea ice fraction in PWRF slightly less than 1 (~0.98 at the ASCOS site) in these simulations. With the knowledge that PWRF does not consider explicitly the mechanisms that result in a representation of open leads, this additional boundary conditions can be valuable in future experiments also to evaluate the exchange of chemical compounds (for example DMS) between atmosphere and ocean, where open leads are important for transport (Zemmelink et al., 2005). Furthermore, in our simulation the spatial resolution of the model was set at 27 by 27 km. To capture the influence of open leads and cracks, a smaller grid size (~ a few 100m up to few kilometres) would be necessary. It can be seen from Figure 1b that open leads were largely present during ASCOS. It raises the question to what extent the observations are also affected by the presence of these leads which are anticipated to not only affect the exchange of trace gases but also especially the exchange of energy, water and momentum (Marcq & Weiss, 2012). In future experiments, the presence of such leads on all these exchange processes should be assessed, requiring model experiments at much higher resolutions to see how well the model could capture such sub-grid size processes for the spatial resolution of the modelling experiments presented in this study.

Boundary layer schemes Over the entire simulation period, differences between simulations with the YSU and MYNN boundary layer scheme were relatively small, but where it was obvious that the MYJ boundary layer scheme simulation performed less well than the other two simulations. The simulations using YSU and MYNN performed reasonably well in simulating most meteorological variables. Larger differences between the boundary layer scheme simulations occurred under conditions where low level stratocumulus cloud formation occurred, especially during the period of 26-31 August. These persistent low level cloud conditions are the most challenging conditions to simulate with PWRF, which is in line with research by Hines and Bromwich (2017) who found similar biases in the meteorological variables in their control run (using the MYNN scheme) compared to the ASCOS observations.

Research evaluating boundary layer schemes over the central Arctic ice sheet during persistent low level cloud conditions is limited. Hines and Bromwich (2017) evaluated the period of low level cloud conditions during ASCOS using the MYNN boundary layer and surface physics scheme, but focussed mainly on adjusting CCN concentrations within the Morrison microphysics scheme and not on different boundary layer schemes. In other simulations covering Arctic and Antarctic area, it was found that the YSU scheme often was preferred over the MYJ scheme (Bromwich et al., 2009; Tastula and Vihma, 2011). MYNN is a rather new boundary layer scheme, but the scheme is used commonly in recent studies using PWRF in the Arctic (Bromwich et al., 2018; Hines and Bromwich, 2017; Marelle et al., 2016). Their results indicate a reasonable performance of this boundary layer scheme and which can also be concluded from our results. However, we recommend that further research on both the YSU and MYNN boundary layer schemes is performed, especially regarding the low level cloud regimes and development of the mixed layer depth under these conditions. This also includes further evaluation of the additional options available in the MYNN scheme (Nakanishi & Niino, 2006; Nakanishi & Niino, 2009), where we only applied the default settings for this BL scheme in this study.

ASCOS observations Some notes on the uncertainty in the ASCOS observations should be made. It is shown from the wind direction measurements (Appendix C, Figure 1) that a clear bias exists between observed wind direction on the ship (Oden) and on the ice sheet (Met Alley). This bias was similar between our simulation and the Met Alley data (Appendix C, Figure 1a). Therefore, it was assumed that there was

47 an issue in the set-up of the meteorological station on the Met Alley site or by drifting of the ice sheet. Furthermore, icing of sensors was a common issue during the campaign (Tjernström et al., 2012). Especially the flux towers used to estimate the heat fluxes suffered from this, but it was occasionally also visible in other measurements such as the radiation measurements.

Furthermore, an uncertainty exist from the drifting of the radiosonde measurements, which could cause them to pass open leads/cracks (see Figure 1b) that might strongly affect the meteorological footprint. Another note regarding the open leads and cracks seen in Figure 1b, is that wind regimes are rather important, because it determines whether measured quantities are a result from winds over ice surfaces or open sea. This could lead to a bias between the ASCOS observations and PWRF simulations, especially considering the lack of representation of the this leads and cracks in the sea ice in PWRF.

An additional limitation of this study is that it focusses only on a period in August where the ASCOS dataset contains the most extensive dataset. Longer extensive meteorological datasets are however scarce for the Arctic (Tjernström, 2011). With the upcoming MOSAiC project an additional dataset will come available, covering a full year of Arctic meteorological data from the sea ice. It is recommended to evaluate PWRF with the upcoming MOSAiC dataset to verify PWRF and the results found in this study with a dataset covering an entire year.

6. Conclusion This study aimed to evaluate the simulated micro- and boundary layer meteorology in the meso-scale meteorological modelling system PWRF by comparison with the observations of the ASCOS campaign, August and September 2008. Multiple PWRF simulations were compared with the ASCOS data obtained during the ice drift from 14 August till 1 September, as this period contained the most extensive dataset of the campaign. Adjustments were made after evaluation of the default simulation and in a later stage the simulated micro- and boundary layer meteorology using three different boundary layer schemes was evaluated : YSU, MYJ and MYNN.

Most standard meteorological variables, e.g., near surface temperature, radiation and wind, were simulated reasonably well by PWRF, especially in the first 10 days of simulation. In the radiation balance, the simulated outgoing shortwave resulted in the largest bias due to the underestimation of the albedo in PWRF. Improvements in the representation of the albedo in PWRF, including features such as snow compaction, melting ponds, grain distribution, dust on the snow (time since snowfall) and water content in the snow should be considered especially for short (days-weeks) timescale simulations such as presented in this study. Furthermore, misrepresentation of the near surface temperature resulted in unrealistic ice temperatures. To what extent this misrepresentation of surface temperature actually explains the misrepresentation of the ice temperatures or, alternatively, to what extent issues on the simulation of energy transfer through the snow and ice layers explain this misrepresentation of ice temperatures, requires additional model simulations, e.g., nudging the model with observed surface temperatures.

The detailed comparison also showed that PWRF did not simulate well a period dominated by low level clouds (26-31 August) occurring later in the simulated period. Based on both our simulations and further review of other ASCOS studies this misrepresentation of observed conditions seems to be due to issues in the model representation of near surface temperature, incoming longwave radiation, boundary layer structure and cloud formation. All boundary layer schemes failed to represent boundary layer structure by underestimating boundary layer heights and near surface temperatures during the low level cloud regime although especially the MYNN scheme performed reasonably well in predicting boundary layer heights. Consequently, we advise to use PWRF with this boundary layer scheme for future research on Arctic micro- and boundary layer meteorology. Furthermore, cloud layer depths were underestimated in simulations with all boundary layer schemes during the low level cloud regime with cloud layer depths being comparable to the boundary layer heights for each scheme. Here the simulation with MYNN resulted in the smallest underestimation. In other meteorological conditions during the simulation, the Morrison microphysics scheme captured the cloud cover reasonably well for all boundary layer scheme simulations.

Overall, it can be concluded that PWRF performed reasonably well in representing the meteorological conditions during the ASCOS campaign, especially in representing the conditions prevailing for the first 10 days of the campaign, but it failed to reproduce the observed period associated with the presence of low level clouds. In the presence of the low level cloud regime, issues in cloud characteristics, boundary layer height and atmospheric state variables as incoming longwave radiation and near surface temperature were found, indicating that a realistic representation of these meteorological conditions in PWRF modelling experiments require additional attention in future research. Furthermore, this study has revealed issues in the representation of albedo and ice layer thermodynamics, which require additional research to improve model performance for the Arctic conditions.

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Appendix A additional and supporting figures and tables regarding the 1-D WRF simulations.

Table A1: Overview of the mean bias and RMSE for the 1-D WRF simulations. Results are shown for the YSU and MYJ scheme.

Temp 2m Wdir 10m windspd 10mSWin SWout LWin LWout Net radiation (K) (degree) (m/s) (W/m2) (W/m2) (W/m2) (W/m2) (W/m2) Mean bias YSU 3.76 67.52 -2.59 29.08 36.31 -37.22 -58.85 14.41 Mean bias MYJ 3.72 66.82 -1.98 29.08 36.30 -36.91 -58.60 14.47 RMSE YSU 4.18 68.29 3.58 62.92 49.20 42.68 60.30 22.24 RMSE MYJ 4.21 65.35 2.86 62.93 49.19 42.53 60.05 22.32 a

Figure A1: Vertical profiles of specific humidity after 0h (a), 12h (b) and 24h (c) of simulation for the YSU simulation and the ASCOS radiosonde measurements.

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Figure A2: Vertical profiles of wind speeds after 0h (a), 12h (b) and 24h (c) of simulation for the YSU simulation and the ASCOS radiosonde measurements. Appendix B additional and supporting figures and tables regarding the 3-D PWRF simulations.

Figure B1: Outgoing shortwave radiation for the default simulation and the ASCOS observations.

Figure B2: Wind direction from the default PWRF simulation compared to wind directions observed at the Oden shipdeck. The re-occurring structures in the top left and bottom right corners have to do with wind direction changes from 360 – 1 and were eliminated from the calculation of the mean bias and RMSE.

Figure B3: Cloud water mixing ratio for the first 10000m of the model simulation. DoY 226 = 13 August

Figure B4: LWP from ASCOS (green dots) and the default simulation (blue line) in PWRF.

Table B1: Mean bias and RMSE over the entire simulation period for several meteorological variables for the three used boundary layer schemes.

Temp 2m Winddir 10m Windspd 10m Swin Swout Lwin Lwout net radiation (K) (degree) (m/s) (W/m2) (W/m2) (W/m2) (W/m2) (W/m2) YSU bias -0.58 12.10 0.35 -3.93 -10.65 -3.71 -1.23 4.23 RMSE 2.48 55.17 1.58 29.31 22.28 19.39 8.87 12.76 MYJ bias -1.36 3.17 0.12 11.86 1.07 -19.91 -7.02 -5.11 RMSE 3.17 79.27 1.70 38.39 24.79 41.94 14.95 20.51 MYNN bias 0.49 15.38 0.36 -7.15 -13.84 0.79 2.11 4.17 RMSE 1.88 62.19 1.50 30.65 24.17 14.92 8.26 10.81

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Figure B5: Potential temperature profiles for YSU, MYNN, MYJ and the radiosonde observations during four central days in the period associated with the incoming longwave / temperature bias (26-31 August). The displayed vertical profiles were taken around 11:30 UTC on the selected days: 27 August (a), 28 August (b), 29 August (c) and 30 August (d).

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Figure B6: Cloud fraction for the three tested boundary layer schemes: YSU (a), MYJ (b) and MYNN (c). The period displayed contain 10 days of the simulated period from August 13 (DoY 226) till August 23 (DoY 236).

Appendix C Supporting figure regarding a measurement uncertainty in the discussion. a

Figure C1: 1:1 plots for the wind direction by the default PWRF simulation and observations of the Met Alley site and the Oden shipdeck. Default simulation against the Met Alley data (a), Oden shipdeck against the Met Alley data (b) and the default simulation against the Oden shipdeck (c). A bias seems to exist in the wind direction measured from the Met Alley station. The re-occurring structures in the top left and bottom right corners have to do with wind direction changes from 360 – 1 and were eliminated from the calculation of the mean bias and RMSE.