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A Planetary Boundary Layer Height Climatology derived from ECMWF Re-analysis Data

Journal of Climate, Vol. 26, , 2013, DOI: /JCLI-D A Planetary Boundary Layer Height Climatology derived from ECMWF Re-analysis Data Axel von Engeln 1, João Teixeira 2 Abstract.
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Journal of Climate, Vol. 26, , 2013, DOI: /JCLI-D A Planetary Boundary Layer Height Climatology derived from ECMWF Re-analysis Data Axel von Engeln 1, João Teixeira 2 Abstract. A Planetary Boundary Layer (PBL) height climatology from ECMWF re-analysis data is generated and analyzed. Different methods are first compared to derive PBL heights from atmospheric temperature, pressure, and relative humidity (RH), which mostly make use of profile gradients e.g., in RH, refractivity, virtual or potential temperature. Three methods based on the vertical gradient of RH, virtual, and potential temperature were selected for the climatology generation. The RH based method appears to capture the inversion that caps the convective boundary layer very well due to its temperature and humidity dependence, while the temperature based methods appear to capture the PBL better at high latitudes. A validation of the re-analysis fields with co-located radio sonde data shows generally good agreement in terms of PBL height mean and standard deviation for the RH based method. The generated ECMWF based PBL height climatology shows many of the expected climatological features such as a fairly low PBL height near the west coast of continents where stratus clouds are found, and the PBL growths as the air is advected over warmer waters towards the tropics along the trade winds. Large seasonal and diurnal variations are primarily found over land. The PBL height can exceed 3 km, mostly over desert areas during the day, although large values can also be found in areas such as the ITCZ. The robustness of the statistics was analyzed by using information on the percentage of outliers. Here, in particular the sea based PBL was found to be very stable. 1. Introduction The atmospheric boundary layer has a profound influence on the physics of the atmosphere and of the climate system as a whole. Cloud processes, land and ocean surface fluxes, and the atmospheric hydrological cycle in general, are strongly influenced by the boundary layer. Boundary layer processes play a role in the modulation of the atmosphere at weather and climate time-scales, and as such have an important role in extreme weather and climatic events. In spite of some recent progress, weather and climate prediction models still do not fully, realistically represent boundary layer processes, since most of the processes associated with the boundary layer occur at sub-grid scales when compared to typical climate and weather prediction horizontal grid-scales. As a consequence, boundary layer parametrizations that represent the statistical behavior of boundary layer processes at the model grid-scale have to be developed. The development of realistic boundary layer 1 EUMETSAT, Darmstadt, Germany 2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA parametrizations, representing the complex variety in the Earth s atmosphere, is still an unresolved challenge in climate and weather prediction [Teixeira et al., 2008]. One major issue in this context is the lack of global observational data sets. The atmospheric boundary layer has been notoriously difficult to observe from space, preventing a detailed understanding of its properties at global scales. One of the most relevant boundary layer properties to investigate is the planetary boundary layer (PBL) height (or inversion height) [Medeiros et al., 2005]. The PBL height characterizes the PBL in a fairly integrated manner and can be closely related to fundamental variables such as cloud cover and water, surface (and boundary layer) heat fluxes, contaminant dispersion, and ducting. A 10 year PBL climatology based on a pure general circulation model run is presented in Medeiros et al. [2005], along with a detailed analysis of the PBL drivers. In their model setup the PBL is synonymous with the lowest model layer. The horizontal resolution is rather coarse at 4 by 5. The authors identify several PBL regimes that tend to exhibit large-scale geographic organization where locally generated buoyancy fluxes and static stability control PBL 1 2 VON ENGELN and TEIXEIRA height nearly everywhere. However, the model uses no real observations and is based on early climate model developments. More recently, work by Seidel et al. [2010] used radio sonde data to lay some groundwork for the generation of a PBL climatology, albeit thus generally restricted to locations on land. A first climatology based on this earlier work is available in Seidel et al. [2012], focusing on the US and European Region. There have also been some studies of boundary layer properties from space-based remote sensing instruments, e.g. by von Engeln et al. [2005]; Sokolovskiy et al. [2006]; Basha and Ratnam [2009] who used radio occultation observations to infer the PBL height. This kind of observation has a high vertical resolution, down to below 100 m, albeit at a low horizontal resolution around a few hundred kilometers. Hence the derived PBL height is an average over larger horizontal areas. Other satellite based methods used e.g., infrared observations from the AIRS instrument [Fetzer et al., 2004; Martins et al., 2010] or Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) data to derive the PBL height in selected regions [Wood and Bretherton, 2004]. Other authors (e. g. [Karlsson et al., 2010]) have used highly accurate estimates of cloud top height from the Multi-angle Imaging SpectroRadiometer (MISR) to investigate the subtropical PBL height evolution. A global climatology for PBL height can be derived from re-analysis data, which combines information from observations and models; it can provide important insight into the physics of the boundary layer from a global perspective and can be used to evaluate weather and climate prediction models. Long term re-analysis runs (such as ERA- 40 or ERA-Interim [Simmons and Gibson, 2000; Simmons et al., 2006/07; Dee et al., 2011] of the European Centre for Medium-range Weather Forecasts (ECMWF)) are in several respects still the best source of global climatological information for many variables. Re-analysis systems assimilate a variety of measurements ranging from in-situ to space-based instruments into weather prediction models. In this work we generate a PBL height climatology from 20 years of re-analysis data. The ECMWF ERA-Interim reanalysis focuses on the data rich period from 1989 onwards, it is provided continuously in parallel to the operational analysis to support climate monitoring. It is thus well suited for such a task. The PBL height is derived from several atmospheric parameters, as detailed below. The primary focus is on relative humidity (RH), however virtual and potential temperatures are also discussed in more detail. The paper is structured as follows: we first introduce the used data sets for this study and the processing setup (Section 2), then discuss several possibilities to determine the PBL height (Section 3) where we pre-select different methods to determine the PBL height global climatology. A validation of the ECMWF fields with co-located radio sonde data is performed in Section 4. In Section 5 the general characteristics of the PBL climatology are presented, and Section 6 presents conclusions. 2. Input Data and General Processing Setup In this study, the ERA-Interim re-analysis and forecast fields [Simmons et al., 2006/07] are used. Climatological records are derived from 20 years of data (1990 to 2009). This includes the most recent and best quality observation period that starts from about 1989 onwards and already assimilates several standard satellite observations from geostationary orbit (Meteosat, GOES, GMS) and from Low-Earth- Orbit (NOAA and DMSP) and stretches into the modern era with hyperspectral sounders such as IASI, AIRS, and radio occultation instruments such as CHAMP, COSMIC, GRAS. For the most part of the study, temperature, surface pressure, geopotential, and specific humidity fields of ERA- Interim are used, and variables for PBL height determination are derived from these core fields. In addition, selected ERA-Interim fields such as the planetary boundary layer height variable are directly used for the year 2000 in order to perform some further validations and comparisons, these are available at 6 h and 12 h forecast times. The horizontal resolution of the ERA-Interim fields is 1.0 and in total 60 vertical hybrid levels are available (about 21 levels between 0 km and 5 km, starting with a resolution of about 25 m near the ground, decreasing to about 200 m around 1 km [Teixeira, 1999a]). Four analysis times are used at 00 UT, 06 UT, 12 UT, and 18 UT (Universal Time). Additionally, ERA-40 data for the years up to 2001 (at a lower resolution of 1.5 ) is also used for comparison. The methods to estimate the PBL height in this paper are generally making use of vertical gradients. All vertical profiles were thus interpolated to a 20 m resolution up to 5 km (a 20 m interpolation assures that the underlying ECMWF or sonde resolution is kept, but it has no influence on the PBL height calculation methods). In order to avoid selecting a very low PBL height caused by temperature inversions near the surface, we excluded all data below 50 m in our analysis, again both in ECMWF and in sonde data. Similar screening is also used by other authors to remove noisy data near the ground, e.g., Liu and Liang [2010]; Seidel et al. [2010]. Monthly averages for each analysis time and month were calculated initially. For seasonal averages, all analysis times of the corresponding months were averaged: DJF (December, January, February), MAM (March, April, May), JJA (June, July, August), SON (September, October, November). For UT time based averaging, all years and months were averaged at that specific time step. For a validation of the ERA-Interim gradients (primarily in RH) we use two sets of radio sondes, covering ocean and land conditions. For ocean observations, we used data from the Alfred Wegener Institute (AWI). For land observations, we also obtain data from selected sites through the University of Wyoming website, spanning 10 years from 2000 to AWI sondes are launched from the research vessel Po- PBL Climatology 3 larstern [König-Langlo and Marx, 1997]. Starting late 1982, VAISALA RS80 radiosondes were launched during research cruises of the ship. Cruises were mostly in the polar regions and the Atlantic, see e.g. Beyerle et al. [2006] for a typical location plot. In total, there are more than 7300 sondes available for the 1990 to 2009 time span, where about 900 are found at low latitudes, 1700 at mid-latitudes, and the majority at high latitudes. The vertical resolution is up to 30 m. The land based sonde observations covers more than 30,000 profiles, we did however limit the validation to specific times since most stations do not cover all investigated UT times. The pre-processing is essentially identical to the one of the ECMWF fields mentioned above, where the lowest 50 m are excluded, and all data is interpolated to a 20 m vertical grid. In addition, a further refinement was introduced for PBL heights based on the RH gradient, where the algorithm only searches first for altitudes with gradients 100.0[%/km]. The lowest found layer of this search is denoted as PBL height. Note that this additional step was introduced in order to remove the large variations in such a high resolution data set. The Wyoming data provides good annual coverage, this data was thus statistically evaluated per month and used to validate the averaged month-by-month variations. The AWI data was used as is, to validate the ECMWF fields at the time and location of the sonde observation. All mean and standard deviation calculations performed here are based on a robust estimator (Tukey s biweight [Hoaglin et al., 1983]). The robust estimator is an effective tool to deweight outliers from noisy distributions, returning standard deviation and percentage of data points falling into the ±2 σ interval. This would be 95 % for an ideal Gauss curve. This tool thus also allows us to determine how Gaussian or robust such a climatology is. Note that all PBL heights reported here are with respect to the local elevation. 3. Methods to determine PBL Height Several possibilities exist to determine the PBL height from atmospheric fields, mostly based on vertical gradients. Within this study we investigate the following gradient based methods on profiles derived from ERA-Interim re-analysis fields: (1) minimum gradient of relative humidity, PBL RH ; (2) maximum gradient of potential temperature, PBL Tp ; (3) maximum gradient of virtual temperature, PBL Tv ; (4) minimum gradient of specific humidity q, PBL q ; (5) minimum gradient of refractivityn, PBL N. In addition, we also investigated several other methods that are reported in the literature, or are used by researchers to estimate PBL growth and height: (6) scaled q that identifies the altitude where q decreases by 50 % from it s surface value, PBL sq ; (7) breakpoint method which searches for the break point in then profile, PBL BP. These methods rely on different thermodynamic properties of the vertical profile. The PBL Tp uses information on the vertical gradient of potential temperature, while PBL Tv also includes humidity information and in practice finds the strongest gradient in the profile of atmospheric buoyancy. The PBL q method identifies the sharp gradient in humidity at the top of the PBL while PBL sq uses the fact that within a well mixed PBL, the specific humidity varies little with height. PBL N is often used for deriving the PBL height from radio occultation data, see e.g., Ao et al. [2012], just as PBL BP, following a method developed by Sokolovskiy et al. [2006] and applied e.g. as well in Guo et al. [2011]. It is fair to say that there is no perfect way to determine the height of the PBL, and that the appropriateness of each method depends on the topic being investigated. In our case, the main interest is in characterizing in the best and most complete manner possible, boundary layers associated with clouds over the tropical, subtropical and mid-latitude oceans. Here, a method based on the gradient of RH provides a fairly robust way of determining the PBL inversion. This is due to the fact that RH combines both temperature and specific humidity, hence gradients in both temperature and specific humidity will be amplified when looking at RH. In addition, RH is intimately associated with clouds and the strong decrease in RH at the PBL top is a strong indication of the cloud top. Since we are particularly interested in capturing the properties of the cloudy boundary layer, this is an important property. Given the fact that the boundary layer is often associated with stronger mixing (as compared to the free troposphere) due to increased levels of turbulence, it would be natural to investigate properties associated with turbulence. Other authors have done this by, for example, associating the top of PBL with a certain value of the Richardson number (which characterizes the degree of turbulence). There are however several issues with this approach. For one, it is often more difficult to accurately reproduce the value of the Richardson number (which is based on ratios of both dynamic and thermodynamic vertical gradients) than just obtaining a value for the gradient of temperature and/or humidity. On the other hand, Richardson-based methods often use dry variables (as opposed to moist conserved variables) leading to results that may differ significantly from the real cloud or PBL top, in cloudy situations. Finally, an essential problem is that the Richardson number is just a measure of local turbulence, and is often unable to properly characterize the turbulent properties of convective boundary layers such a stratocumulus, shallow cumulus and dry convective PBLs. In order to properly address this final issue, a variable like turbulent kinetic energy (TKE) needs to be used, but TKE as a prognostic variable is rarely used in global models and as such is not available. An estimate of the PBL height based on the Richardson number (referred to as Boundary Layer Height) is however provided in the ERA-Interim data (PBL Ri ). It is only available at 6 hour and 12 hour forecast but it is likely very similar to the analysis period since forecasting skills over these short periods are well developed. Essentially it s based on 4 VON ENGELN and TEIXEIRA the level where the bulk Richardson number reaches a critical value of 0.25, using the difference between this level and the lowest level as an estimator for the vertical stability. But, as stated, since the stability estimation is based on dry thermodynamic variables (and not on moist conserved variables) there is a tendency (see below) for this method to provide an estimate of the PBL height that is often closer to the cloud base height in marine cloudy boundary layers, rather than the PBL height itself [Janssen and Bidlot, 2003]. Figure 1 shows these different PBL heights along a transect from the coast of California to the Equator, using only data from JJA This transect was introduced by the GEWEX/WGNE Pacific Cross-section Intercomparison (GPCI) to study important physical regimes and transitions in models and observations, e.g. stratocumulus, shallow cumulus, and deep convection [Teixeira et al., 2011]. The transect runs from 1 S, 173 W to 35 N, 125 W. In general, ERA- Interim data is used, although PBL RH also uses the ECMWF data set ERA-40 for illustration of improvements. Most PBL height definitions show fairly similar results for the transition from stratocumulus to cumulus in the area from 30 N to 20 N, although the PBL q and PBL N do not grow up to a mean value of about 1.6 km near 20 N as other estimates. More symptomatic of a potential problem with these two methods, and in addition the PBL BP one, is the significant decrease in PBL height south of 20 N, where the mean values of PBL height become about 600 m around 7 N. The refractivityn entering PBL N is dominated by the water vapor pressure at lower altitudes and decreases rapidly with height [von Engeln et al., 2007]. Thus a method based on a gradient search in a refractivity profile, or the break point method, can be dominated by water vapor pressure variations at lower altitudes when a strong gradient is found close to the warm ocean surface (this could also affect the PBL height determination using radio occultation data, however it is likely too close to the surface for this measurement technique to be affected, see also Ao et al. [2012]). This problem is also affecting the method based on q. Not excluding the lowest 50 m in the gradient search leads to much lower values for the methods based on N and q (the break point method automatically removes this lowest range). The method based on a scaled specific humidity (PBL sq ) is showing significantly larger PBL heights closer to the Intertropical Convergence Zone (ITCZ) where more water vapor is present at the surface and, because of deep convection, larger values of water vapor can be found deeper in the troposphere above the PBL. Although the boundary layer is not as well defined in its vertical structure in the deep convection regions as it is in the subtropics, a more detailed investigation also shows that the RH based method works better than the scaled humidity around the ITCZ (see discussion further below). More generally, the PBL sq method also results in unrealistically large PBL heights at polar latitudes where little water vapor is present. These generally high PBL heights are also found for the respective hemisphere winter time over polar regions for PBL q, it does though produce fairly realistic high values over desert regions. Methods based on virtual or potential temperature do not take into account the full cloud moist vertical structu
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