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Presentation at AMS 15th Conference on polar meteorology and oceanography

Poster presented at the PAN-GASS meeting in Lorne, Australia, February 2018

Correct representation of physical processes, the parametrizations, and their interaction with the resolved circulation is crucial for the performance of numerical models. Here, focus is put on understanding model biases and developing tools to alleviate existing biases. Atmospheric blocking can divert the typical atmospheric flow for several days up to weeks and thereby impacts the mean climate of the region experiencing blocking. Models typically underestimate the frequency of atmospheric blocking. Based on results from the global climate model EC-Earth, it is found that the atmospheric model resolution is not strongly influencing the representation of atmospheric blocking once the grid reaches about 80 km grid length in the horizontal. Updating several physical parametrizations, and thereby the model version, is the largest contributor to advancements in simulating atmospheric blocking. The importance of the topography for the large-scale atmospheric flow is further investigated with the reanalysis ERA-Interim by applying a simplified theoretical analysis. It is found that the idealized topographic forcing theory can explain some part of the observed large-scale properties of the flow, though the method does mainly produce relative results. The explained part of the large-scale structure is increased during periods of northwesterly flow and when the flow impinges the mountain ridge almost orthogonally. Small-scale processes acting in air masses transported from midlatitudes to the Arctic are also discussed. Numerical models often struggle with representing the stable conditions in the Arctic and tend to underestimate the downward longwave impact during cloudy conditions. A comparison of single-column models (SCMs) indicates that most models can capture the bimodal longwave distribution which develops from alternating cloudy and clear-sky conditions. SCMs are often used for model development as they allow to decouple the parametrized physical processes from the large-scale environment and enable many parameter sensitivity tests. A new tool is presented which can be used for the development of physical parametrizations in marine and polar conditions. It combines one-dimensional models of the atmosphere and ocean, including sea-ice, into a coupled atmosphere-ocean SCM (AOSCM). The presented setup constitutes an advantage compared to SCMs of one component because the coupling is directly modelled and the interaction between the respective boundary layers does not dependent on prescribed boundary conditions. At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 4: Manuscript.

Single-column models (SCMs) have been used as tools to help develop numerical weather prediction and global climate models for several decades. SCMs decouple small-scale processes from large-scale forcing, which allows the testing of physical parameterisations in a controlled environment with reduced computational cost. Typically, either the ocean, sea ice or atmosphere is fully modelled and assumptions have to be made regarding the boundary conditions from other subsystems, adding a potential source of error. Here, we present a fully coupled atmosphere-ocean SCM (AOSCM), which is based on the global climate model EC-Earth3. The initial configuration of the AOSCM consists of the Nucleus for European Modelling of the Ocean (NEMO3.6) (ocean), the Louvain-la-Neuve Sea Ice Model (LIM3) (sea ice), the Open Integrated Forecasting System (OpenIFS) cycle 40r1 (atmosphere), and OASIS3-MCT (coupler). Results from the AOSCM are presented at three locations: the tropical Atlantic, the midlatitude Pacific and the Arctic. At all three locations, in situ observations are available for comparison. We find that the coupled AOSCM can capture the observed atmospheric and oceanic evolution based on comparisons with buoy data, soundings and ship-based observations. The model evolution is sensitive to the initial conditions and forcing data imposed on the column. Comparing coupled and uncoupled configurations of the model can help disentangle model feedbacks. We demonstrate that the AOSCM in the current set-up is a valuable tool to advance our understanding in marine and polar boundary layer processes and the interactions between the individual components of the system (atmosphere, sea ice and ocean). Funding Agencies|(O)IFS; OASIS; NEMO; LIM; ECMWF for providing ERA Interim data; European Union [727862]; EU FP7 [603521]

Weather and climate models struggle to represent lower tropospheric temperature and moisture profiles and surface fluxes in Arctic winter, partly because they lack or misrepresent physical processes that are specific to high latitudes. Observations have revealed two preferred states of the Arctic winter boundary layer. In the cloudy state, cloud liquid water limits surface radiative cooling, and temperature inversions are weak and elevated. In the radiatively clear state, strong surface radiative cooling leads to the build‐up of surface‐based temperature inversions. Many large‐scale models lack the cloudy state, and some substantially underestimate inversion strength in the clear state. Here, the transformation from a moist to a cold dry air mass is modeled using an idealized Lagrangian perspective. The trajectory includes both boundary layer states, and the single‐column experiment is the first Lagrangian Arctic air formation experiment (Larcform 1) organized within GEWEX GASS (Global atmospheric system studies). The intercomparison reproduces the typical biases of large‐scale models: some models lack the cloudy state of the boundary layer due to the representation of mixed‐phase microphysics or to the interaction between micro‐ and macrophysics. In some models, high emissivities of ice clouds or the lack of an insulating snow layer prevent the build‐up of surface‐based inversions in the radiatively clear state. Models substantially disagree on the amount of cloud liquid water in the cloudy state and on turbulent heat fluxes under clear skies. Observations of air mass transformations including both boundary layer states would allow for a tighter constraint of model behavior. ISSN:1942-2466

International audience; In the meteorological community, efforts converge towards implementation of high-resolution (< 12km) data-assimilating regional climate modelling/monitoring systems based on numerical weather prediction (NWP) cores. This is driven by requirements of improving process understanding, better representation of land surface interactions, atmospheric convection, orographic effects, and better forecasting on shorter timescales.This is relevant for the GRACE community since (1) these models may provide improved atmospheric mass separation / de-aliasing and smaller topography-induced errors, compared to global (ECMWF-Op, ERA-Interim) data, (2) they inherit high temporal resolution from NWP models, (3) parallel efforts towards improving the land surface component and coupling groundwater models; this may provide realistic hydrological mass estimates with sub-diurnal resolution, (4) parallel efforts towards re-analyses, with the aim of providing consistent time series. (5) On the other hand, GRACE can help validating models and aids in the identification of processes needing improvement.A coupled atmosphere - land surface – groundwater modelling system is currently being implemented for the European CORDEX region at 12.5 km resolution, based on the TerrSysMP platform (COSMO-EU NWP, CLM land surface and ParFlow groundwater models). We report results from Springer et al. (J. Hydromet., accept.) on validating the water cycle in COSMO-EU using GRACE and precipitation, evapotranspiration and runoff data; confirming that the model does favorably at representing observations. We show that after GRACE-derived bias correction, basin-average hydrological conditions prior to 2002 can be reconstructed better than before. Next, comparing GRACE with CLM forced by EURO-CORDEX simulations allows identifying processes needing improvement in the model. Finally, we compare COSMO-EU atmospheric pressure, a proxy for mass corrections in satellite gravimetry, with ERA-Interim over Europe at timescales shorter/longer than 1 month, and spatial scales below/above ERA resolution. We find differences between regional and global model more pronounced at high frequencies, with magnitude at sub-grid scale and larger scale corresponding to 1-3 hPa (1-3 cm EWH); relevant for the assessment of post-GRACE concepts.

Original research paper.

Presentation at AMS 15th Conference on polar meteorology and oceanography

The new tool, the Atmosphere-Ocean Single-Column Model (AOSCM), developed within APPLICATE and demonstrated in D2.1, is further developed to become an even more versatile tool for understanding model behaviour and aid model development. The enhancements of the model are mostly on the technical side with the goal to be able to run well-defined cases that can be compared with process observations. Procedures on how to set up cases have been defined and are described in a document appended to this deliverable. Observational process level data from the Arctic Ocean obtained in expeditions with the ice- breaker Oden is described and merged into files designed with the purpose of being easily accessible for model development. These observations are from three summer expeditions and provide data in the vertical column that is represented by the AOSCM. The methodology is developed in an international setting and will be utilized for data from the ongoing expedition MOSAiC that will provide data for a whole year October 2019 – October 2020. An experimental protocol is developed for the AOSCM and tested for a warm air advection case observed during the Oden expedition ACAS in 2014. The protocol covers perturbations of model setup and forcing and consists of 480 simulations of the three-day period. The perturbation analysis reveals that the net energy available at the surface during this period vary between 30 and 130 Wm-2. The results are most sensitive to the advection of moisture but substantial changes are seen when using all forcing and modifying other parameters such as the sea-ice properties, time step, model version etc. The methodology of using the AOSCM and combining it with observations are now mature enough to be expanded to cover the whole set of observational periods. This work contributes to Objective #2 of WP2 “Develop innovative methods, using observations and a variety of model configurations, to facilitate parameter optimization for physical processes in coupled model systems for NWP and climate”.

The new tool, the Atmosphere-Ocean Single-Column Model (AOSCM), developed within APPLICATE and demonstrated in D2.1, is further developed to become an even more versatile tool for understanding model behaviour and aid model development. The enhancements of the model are mostly on the technical side with the goal to be able to run well-defined cases that can be compared with process observations. Procedures on how to set up cases have been defined and are described in a document appended to this deliverable. Observational process level data from the Arctic Ocean obtained in expeditions with the ice- breaker Oden is described and merged into files designed with the purpose of being easily accessible for model development. These observations are from three summer expeditions and provide data in the vertical column that is represented by the AOSCM. The methodology is developed in an international setting and will be utilized for data from the ongoing expedition MOSAiC that will provide data for a whole year October 2019 – October 2020. An experimental protocol is developed for the AOSCM and tested for a warm air advection case observed during the Oden expedition ACAS in 2014. The protocol covers perturbations of model setup and forcing and consists of 480 simulations of the three-day period. The perturbation analysis reveals that the net energy available at the surface during this period vary between 30 and 130 Wm-2. The results are most sensitive to the advection of moisture but substantial changes are seen when using all forcing and modifying other parameters such as the sea-ice properties, time step, model version etc. The methodology of using the AOSCM and combining it with observations are now mature enough to be expanded to cover the whole set of observational periods. This work contributes to Objective #2 of WP2 “Develop innovative methods, using observations and a variety of model configurations, to facilitate parameter optimization for physical processes in coupled model systems for NWP and climate”.

Weather and climate models struggle to represent lower tropospheric temperature and moisture profiles and surface fluxes in Arctic winter, partly because they lack or misrepresent physical processes that are specific to high latitudes. Observations have revealed two preferred states of the Arctic winter boundary layer. In the cloudy state, cloud liquid water limits surface radiative cooling, and temperature inversions are weak and elevated. In the radiatively clear state, strong surface radiative cooling leads to the build‐up of surface‐based temperature inversions. Many large‐scale models lack the cloudy state, and some substantially underestimate inversion strength in the clear state. Here, the transformation from a moist to a cold dry air mass is modeled using an idealized Lagrangian perspective. The trajectory includes both boundary layer states, and the single‐column experiment is the first Lagrangian Arctic air formation experiment (Larcform 1) organized within GEWEX GASS (Global atmospheric system studies). The intercomparison reproduces the typical biases of large‐scale models: some models lack the cloudy state of the boundary layer due to the representation of mixed‐phase microphysics or to the interaction between micro‐ and macrophysics. In some models, high emissivities of ice clouds or the lack of an insulating snow layer prevent the build‐up of surface‐based inversions in the radiatively clear state. Models substantially disagree on the amount of cloud liquid water in the cloudy state and on turbulent heat fluxes under clear skies. Observations of air mass transformations including both boundary layer states would allow for a tighter constraint of model behavior., Journal of Advances in Modeling Earth Systems, 8 (3), ISSN:1942-2466

Original research paper.

© Copyright 2022 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy). The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development. The results discussed in this article were supported by the project APPLICATE (727862), funded by the European Union's Horizon 2020 research and innovation programme. PO was additionally supported by the Spanish fellowship RYC-2017-22772. Peer Reviewed Article signat per 29 autors/es: Pablo Ortega (1), Edward W. Blockley (2), Morten Køltzow (3), François Massonnet (4), Irina Sandu (5), Gunilla Svensson (6), Juan C. Acosta Navarro (1), Gabriele Arduini (5), Lauriane Batté (7), Eric Bazile (7), Matthieu Chevallier (8), Rubén Cruz-García (1), Jonathan J. Day (5), Thierry Fichefet (4), Daniela Flocco (9), Mukesh Gupta (4), Kerstin Hartung (6,10), Ed Hawkins (9), Claudia Hinrichs (11), Linus Magnusson (5), Eduardo Moreno-Chamarro (1), Sergio Pérez-Montero (1), Leandro Ponsoni (4), Tido Semmler (11), Doug Smith (2), Jean Sterlin (4), Michael Tjernström (6), Ilona Välisuo (7,12), and Thomas Jung (11,13) // (1) Barcelona Supercomputing Center, Barcelona, Spain | (2) Met Office, Exeter, UK | (3) Norwegian Meteorological Institute, Oslo, Norway | (4) Université catholique de Louvain, Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Louvain-la-Neuve, Belgium | (5) European Centre for Medium-Range Weather Forecasts, Reading, UK | (6) Department of Meteorology, Stockholm University, Stockholm, Sweden | (7) CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France | (8) Météo-France, Toulouse, France | (9) National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading, UK. | (10) Now at: Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany | (11) Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany | (12) Now at: Meteorology Unit, Finnish Meteorological Institute, Helsinki, Finland | (13) Department of Physics and Electrical Engineering, University of Bremen, Bremen, Germany