EarthCARE Exploitation

Active satellite instruments providing a vertically resolved characterization
of clouds bring new observation types to be used in numerical weather
prediction (NWP). Whether information on clouds extracted from such data can
be beneficial for NWP analyses and forecasts has been studied at the European
Centre for Medium-Range Weather Forecasts (ECMWF). Assimilation experiments
for cloud radar and lidar observations have been performed using a two-step
technique which combines one-dimensional (1D-Var) with four-dimensional
variational (4D-Var) data assimilation. All developments and testing
were done using CloudSat and CALIPSO data, which are currently
the only available data from space-borne radar and lidar with global
coverage. The performed work paves the way towards the use of similar data
from the future EarthCARE mission. The feasibility to assimilate such
observations has been demonstrated in the ECMWF studies and the achieved
results triggered the desire to use these new types of cloud
observations for assimilation. Future activities will focus on developments
towards a direct (in-line) data assimilation and monitoring systems to allow
extensive research beneficial for future applications of EarthCARE
cloud radar and lidar observations on the global scale.

Unresolved issues involving Clouds, aerosols and their interactions remain  a major  factor contributing   to the uncertainty of our understanding of Climate change. The ESA-JAXA satellite mission EarthCARE (Earth Cloud Aerosol and Radiation Explorer) aims to help to better understand the impacts of clouds and aerosols on climate change by retrieving vertical profiles of clouds and aerosols and their micro-physical and radiative properties. The EarthCARE satellite will carry four instruments onboard, among them the High Spectral Resolution Lidar (HSRL) called ATLID. A-AER is one of the four  algorithms within the so-called ATLID profile (A-Prof) processor. The A-AER algorithm's objective is to retrieve, on a large horizontal scale (50-100 km) four aerosol optical properties (extinction and backscatter coefficients depolarization and lidar ratios). Although retrieving the extinction coefficient, in principle, is a relatively simple and direct procedure, in practice a number of issues must be addressed. One of the issues that must be addressed is the proper averaging of the data, this is mainly due to the low Signal to Noise Ratio (SNR) associated with the EarthCARE Rayleigh channel. In addition to an adaptive averaging scheme, A-AER will use a multiple scattering correction. In this presentation, we  give an overview of the A-AER algorithm, as well as preliminary results of the output products for different simulated atmospheric scenes.

The Coupled Model Intercomparison Project (CMIP) is now moving into its 6^th Phase (Meehl et al., 2014). One goal of CMIP6 is a more routine evaluation of the models as soon as the output is submitted to the Earth System Grid Federation (ESGF). We present here the contribution of the ESA Climate Change Initiative (CCI) Climate Model User Group (CMUG) to the development of the Earth System Model Evaluation Tool (ESMValTool, Eyring et al. (2015)), a community diagnostics and performance metrics tool for the systematic evaluation of Earth system models (ESMs). The tool allows for routine benchmarking of single or multiple models, either against predecessor versions or against observations. It is being developed in such a way that additional analyses and more in-depth process-oriented diagnostics can be easily added. The priority of the effort so far has been to target specific scientific themes focusing on selected Essential Climate Variables (ECVs) and a range of known systematic biases common to ESMs. Here, we show results with ESA CCI data that are implemented into the ESMValTool to benchmark ESMs in the era of CMIP6. Within CMUG Phase 2, we will include and use most of the current 14 ESA CCI ECVs in the ESMValTool to evaluate the models, but the presentation focuses on results from the evaluation of CMIP5 models with data from a subset of the ESA CCIs (e.g., aerosol, cloud, sea ice, and soil moisture) in comparison to other observations. We also present results from a climate variability study of ENSO using ESA CCI data and other observations. Our activities will foster the use of ESA CCI data with the goal to demonstrate the merit of these new datasets in the frame of CMIP6 and other related model intercomparison projects. We envisage running the ESMValTool routinely on the CMIP model output utilizing observations available through the ESGF in standard formats (obs4MIPs) and including ESA CCI data.

References:

Eyring, V., Righi, M., Evaldsson, M., Lauer, A., Wenzel, S., Jones, C., Anav, A., Andrews, O., Cionni, I., Davin, E. L., Deser, C., Ehbrecht, C., Friedlingstein, P., Gleckler, P., Gottschaldt, K.-D., Hagemann, S., Juckes, M., Kindermann, S., Krasting, J., Kunert, D., Levine, R., Loew, A., Mäkelä, J., Martin, G., Mason, E., Phillips, A., Read, S., Rio, C., Roehrig, R., Senftleben, D., Sterl, A., van Ulft, L. H., Walton, J., Wang, S., and Williams, K. D.: ESMValTool (v1.0) – a community diagnostic and performance metrics tool for routine evaluation of Earth System Models in CMIP, Geosci. Model Dev. Discuss., 8, 7541-7661, doi:10.5194/gmdd-8-7541-2015, 2015.

Meehl, G. A., R. Moss, K. E. Taylor, V. Eyring, R. J. Stouffer, S. Bony and B. Stevens, Climate Model Intercomparisons: Preparing for the Next Phase, Eos Trans. AGU, 95(9), 77, 2014.

The APRES3 project (“Antarctic Precipitation : Remote Sensing from Surface and Space”, see the partner presentation by Genthon et al. in this conference) aims at providing constraints on the precipitation rates over Antarctica by using the CloudSat CPR data, and paves the way for a similar use of the EarthCare data. A key application of precipitation observations in Antarctica is the improvement of global climate models (also known as GCMs), which are poorly constrained in this part of the world. GCMs show multiple responses in Antarctica and do not agree on the future precipitation rates over the ice sheet. Indeed, their cold microphysics schemes are often built on concepts and datasets developed in non-polar regions, and the mixed phase and ice phase microphysical processes of polar environments are still poorly known. These microphysical processes play an important role in polar precipitation but also more generally in predicting accurately the cloud radiative properties and resulting energy budget. For example, many GCMs underestimate cloud cover and therefore miss essential climate features in polar regions.

In this study, the measurements of precipitation made by CloudSat in the context of the APRES3 project are compared to the results of the LMDz GCM (Laboratoire de Météorologie Dynamique zoomable GCM) and MAR model (Modèle Atmosphérique Régional). The LMDz GCM is used as the atmospheric component of the IPSL Earth System Model, which is involved in the IPCC CMIP exercises. The LMDz simulations are run using prescribed sea surface temperatures over the CloudSat observation period. In order to distinguish the influence on the results of dynamics from that of the physical parameterizations, the GCM is also run in nudging mode where the winds are adjusted toward the ECMWF reanalyses. The main biases of the LMDz GCM as well as the misrepresented physical processes are identified. Comparisons are also made to simulations performed using the MAR model. MAR is a limited area model developed for polar regions which can computationally afford a more advanced microphysics scheme than the LMDz GCM. The formation of cold clouds and solid precipitation in the LMDz GCM is often based on laws deduced from observations of high tropical clouds, and improving the representation of precipitation in polar regions might be necessary. Developments are underway to improve the cold microphysical processes of the LMDz GCM in polar regions while keeping the short computational time that is required by global climate simulations.

Aerosols interfer in several processes of the Earth system, like the radiative budget and the hydrological cycle and impact air quality. Also, they degrade the retrieval of atmospheric and surface parameters from satellite observations, which is significant given that only satellites provide global coverage. The vertical distribution of aerosols is important because together with the optical depth and the single scattering albedo define the aerosols’ impact on the abovementioned processes. The vertical distribution is not easily measurable except from lidars and with limited sensitivity from passive remote sensors. In general passive satellite instruments provide one piece of information, i.e. the altitude of the plume, but they offer better coverage of the Earth in comparison to spaceborne lidars. Depending on the capabilities of each passive instrument, advantage is taken of measurements at UV, O₂ absorption A-band and IR spectra or multi-angle observations. In order to facilitate the validation of the retrieved aerosol altitude from passive satellite instruments, here observations from CALIOP on board CALIPSO are used to calculate the mean altitude of aerosols and dust globally (60^oS-60^oN). The depolarizing channel at 532 nm allows the discrimination between dust and other types of aerosols that weakly depolarize light. Despite the 16 days repetition cycle of CALIPSO, CALIOP's beam diameter of 70 m at the surface provides only limited coverage of the Earth. In order to overcome this difficulty use is made of the L2 5 km aerosol layer product (version 3) to calculate the mean altitude at seasonal scale with a horizontal resolution of 1 degree. Only night-time data are used (because of the better signal to noise ratio) during 7 years (June 2006 – May 2013). For the case of aerosols all six subtypes of the L2 product are combined, while desert dust and polluted dust are merged for the case of dust. The fine mode aerosols optical depth from the space instrument PARASOL is used to indicate the regions that are affected by non-dust aerosols, such as smoke or continental. Results show that the mean altitude presents an obvious seasonal dependence with lower values during winter and higher values during summer in the case of dust aerosols. The mean altitude of aerosols is lower outside the dust regions where dust is transported and other aerosol types, like marine or pollution, are located below the dust plumes. In the Southern Ocean the mean altitude of aerosols is increasing towards 60^oS, probably due to the occurrence of high winds. Over the fire regions of S. America and S. Africa during the biomass burning season the mean altitude is about 2 and 3 km, respectively.