scholarly journals Baltic Sea Operational Oceanography—A Stimulant for Regional Earth System Research

2020 ◽  
Vol 8 ◽  
Author(s):  
Jun She ◽  
H. E. Markus Meier ◽  
Miroslaw Darecki ◽  
Patrick Gorringe ◽  
Vibeke Huess ◽  
...  
2021 ◽  
Author(s):  
Matthias Gröger ◽  
Christian Dieterich ◽  
Jari Haapala ◽  
Ha Thi Minh Ho-Hagemann ◽  
Stefan Hagemann ◽  
...  

Abstract. Non-linear responses to externally forced climate change are known to dampen or amplify the local climate impact due to complex cross compartmental feedback loops in the earth system. These feedbacks are less well represented in traditional standalone atmosphere and ocean models on which many of today's regional climate assessments rely on (e.g. EuroCordex, NOSCCA, BACC II). This promotes the development of regional climate models for the Baltic Sea region by coupling different compartments of the earth system into more comprehensive models. Coupled models more realistically represent feedback loops than the information imposed into the region by using prescribed boundary conditions, and thus, permit a higher degree of freedom. In the past, several coupled model systems have been developed for Europe and the Baltic Sea region. This article reviews recent progress of model systems that allow two way communication between atmosphere and ocean models, models for the land surface including the terrestrial biosphere, as well as wave models at the air sea interface and hydrology models for water cycle closure. However, several processes that have so far mostly been realized by one way coupling such as marine biogeochemistry, nutrient cycling and atmospheric chemistry (e.g. aerosols) are not considered here.Compared to uncoupled standalone models, coupled earth system models models can modify mean near surface air temperatures locally up to several degrees compared to their standalone atmospheric counterparts using prescribed surface boundary conditions. Over open ocean areas, the representation of small scale oceanic processes such as vertical mixing, and sea ice dynamics appear essential to accurately resolve the air sea heat exchange in the Baltic Sea region and can only be provided by online coupled high resolution ocean models. In addition, the coupling of wave models at the ocean-atmosphere interface allows a more explicit formulation of small-scale to microphysical processes with local feedbacks to water temperature and large scale processes such as oceanic upwelling. Over land, important climate feedbacks arise from dynamical terrestrial vegetation changes as well as the implementation of land use scenarios and afforestation/deforestation that further alter surface albedo, roughness length and evapotranspiration. Furthermore, a good representation of surface temperatures and roughness length over open sea and land areas is critical for the representation of climatic extremes like e.g. heavy precipitation, storms, or tropical nights, and appear to be sensitive to coupling.For the present-day climate, many coupled atmosphere-ocean and atmosphere-land surface models demonstrate added value with respect to single climate variables in particular when low quality boundary data were used in the respective standalone model. This makes coupled models a prospective tool for downscaling climate change scenarios from global climate models because these models often have large biases on the regional scale. However, the coupling of hydrology models for closing the water cycle remains problematic as the accuracy of precipitation provided by the atmosphere models is in most cases insufficient to realistically simulate the runoff to the Baltic Sea without bias adjustments.Many regional standalone ocean and atmosphere models are tuned to well represent present day climatologies rather than accurately simulate climate change. More research is necessary about how the regional climate sensitivity (e.g. the models’ response to a given change in global mean temperature) is affected by coupling and how the spread is altered in multi-model and multi-scenario ensembles of coupled models compared to uncoupled ones.


2011 ◽  
Vol 4 (2) ◽  
pp. 264
Author(s):  
Madson Tavares Silva ◽  
Stephany C. F. Do Egito Costa ◽  
Manoel Francisco Gomes Filho ◽  
Daisy B. Lucena

Apresenta-se neste estudo a avaliação da metodologia de Análises Multivariadas: Análises em Componente Principal (ACP) e de Agrupamento (AA), aos dados de Temperatura da Superfície do Mar (TSM) para os Oceanos Atlântico (Norte (NATL), Tropical (TROP) e Sul (SATL)) e Pacifico (NIÑO1+2, NIÑO3.4, NIÑO3 e NIÑO4). Foram utilizados dados mensais de janeiro de 1950 a dezembro de 2010 de TSM obtidos na NOAA (National Oceanic and Atmospheric Administration/Earth System Research Laboratory). As regiões TROP e NIÑO4 apresentam as maiores TSM para os meses entre dezembro-julho. A região NATL apresenta no período de agosto-outubro seu maiores valores de TSM. A região NIÑo1+2 apresentou os menores valores de TSM. Os resultados da Análise em Componente Principal (ACP) identificaram maiores pesos na variação total explicada pelas duas primeiras componentes, que representam cerca de 100% da variância total dos dados de TSM. A Análise de Agrupamento (AA), pelo método Ward, permitiu o agrupamento das estações em três grupos homogêneos. Palavras - chave: Análises Multivariadas, Mudanças climáticas, Aquecimento Global.   Study of Sea Surface Temperature for the Atlantic and Pacific Oceans Using the Technique of Principal Component Analysis and Cluster   ABSTRACT Presented in this study was to evaluate the methodology of Multivariate Analysis: Principal Component Analysis (PCA) and cluster analysis (CA), the data of sea surface temperature (SST) for the Atlantic (North (NATL), Tropical (TROP) and South (Satler)) and Pacific (+2 NIÑO1, NIÑO3.4, and NIÑO3 NIÑO4). We used monthly data from January 1950 to December 2010 SST obtained from NOAA (National Oceanic and Atmospheric Administration / Earth System Research Laboratory). TROP and NIÑO4 regions have the highest SST for the months from December to July. NATL The region has in the period August-October SST your highest values +2 NIÑo1 The region had the lowest values of TSM. Results on Principal Component Analysis (PCA) identified higher weights in the total variation explained by the first two components, which represent about 100% of the total variance of SST. The Cluster Analysis (AA), the Ward method, allowed the grouping of stations into three homogeneous groups. Keywords: Multivariate Analysis, Climate Change, Global Warming.


2021 ◽  
Vol 12 (3) ◽  
pp. 939-973
Author(s):  
Matthias Gröger ◽  
Christian Dieterich ◽  
Jari Haapala ◽  
Ha Thi Minh Ho-Hagemann ◽  
Stefan Hagemann ◽  
...  

Abstract. Nonlinear responses to externally forced climate change are known to dampen or amplify the local climate impact due to complex cross-compartmental feedback loops in the Earth system. These feedbacks are less well represented in the traditional stand-alone atmosphere and ocean models on which many of today's regional climate assessments rely (e.g., EURO-CORDEX, NOSCCA and BACC II). This has promoted the development of regional climate models for the Baltic Sea region by coupling different compartments of the Earth system into more comprehensive models. Coupled models more realistically represent feedback loops than the information imposed on the region by prescribed boundary conditions and, thus, permit more degrees of freedom. In the past, several coupled model systems have been developed for Europe and the Baltic Sea region. This article reviews recent progress on model systems that allow two-way communication between atmosphere and ocean models; models for the land surface, including the terrestrial biosphere; and wave models at the air–sea interface and hydrology models for water cycle closure. However, several processes that have mostly been realized by one-way coupling to date, such as marine biogeochemistry, nutrient cycling and atmospheric chemistry (e.g., aerosols), are not considered here. In contrast to uncoupled stand-alone models, coupled Earth system models can modify mean near-surface air temperatures locally by up to several degrees compared with their stand-alone atmospheric counterparts using prescribed surface boundary conditions. The representation of small-scale oceanic processes, such as vertical mixing and sea-ice dynamics, appears essential to accurately resolve the air–sea heat exchange over the Baltic Sea, and these parameters can only be provided by online coupled high-resolution ocean models. In addition, the coupling of wave models at the ocean–atmosphere interface allows for a more explicit formulation of small-scale to microphysical processes with local feedbacks to water temperature and large-scale processes such as oceanic upwelling. Over land, important climate feedbacks arise from dynamical terrestrial vegetation changes as well as the implementation of land-use scenarios and afforestation/deforestation that further alter surface albedo, roughness length and evapotranspiration. Furthermore, a good representation of surface temperatures and roughness length over open sea and land areas is critical for the representation of climatic extremes such as heavy precipitation, storms, or tropical nights (defined as nights where the daily minimum temperature does not fall below 20 ∘C), and these parameters appear to be sensitive to coupling. For the present-day climate, many coupled atmosphere–ocean and atmosphere–land surface models have demonstrated the added value of single climate variables, in particular when low-quality boundary data were used in the respective stand-alone model. This makes coupled models a prospective tool for downscaling climate change scenarios from global climate models because these models often have large biases on the regional scale. However, the coupling of hydrology models to close the water cycle remains problematic, as the accuracy of precipitation provided by atmosphere models is, in most cases, insufficient to realistically simulate the runoff to the Baltic Sea without bias adjustments. Many regional stand-alone ocean and atmosphere models are tuned to suitably represent present-day climatologies rather than to accurately simulate climate change. Therefore, more research is required into how the regional climate sensitivity (e.g., the models' response to a given change in global mean temperature) is affected by coupling and how the spread is altered in multi-model and multi-scenario ensembles of coupled models compared with uncoupled ones.


2020 ◽  
Author(s):  
Stan Benjamin ◽  
Joseph Joseph Olson ◽  
Shan Sun ◽  
Georg Georg Grell ◽  
Curtis Curtis Alexander

<p>Subgrid-scale cloud representation and the closely related surface-energy balance continue to be a central challenge from subseasonal-to-seasonal models down to storm-scale models applied for forecast duration of only a few hours. Previously, NOAA/ESRL confirmed this issue from 3-km model (HRRR using WRF-ARW) for short-range forecasting including sub-grid-scale cloud representation up to a 25-km subseasonal model (FV3-GFS) testing a common suite of scale-aware physical parameterizations.  </p><p>In a major physics suite component -- modified representation of subgrid cloud water resulted in much improved agreement with radiation measurements as shown with 2018-2020 testing of the 3km HRRR model. Latest results will be shown using SURFRAD radiation and METAR ceiling observations, indicating much improved bias in downward solar radiation and in cloud location (via mean absolute error metric), as well as with 2m temperature and precipitation.</p><p>In addition, new evaluations with the same convection-allowing suite (“mesoscale” suite) of physical parameterizations revised further for subseasonal 30-day tests over summer and winter periods with the 25km NOAA FV3-GFS model. These results are compared with CERES-estimated cloud and downward solar radiation fields. The radiation results from this very preliminary subseasonal test with the ESRL-HRRR physics suite will be compared with previous subseasonal tests using the GFS physics suite and at different horizontal resolution.  This global application now confirms much better downward solar-radiation results over oceans for both January and June from a Nov-2019 version over a 2018 of the “mesoscale” suite.</p><p>Background: NOAA Earth System Research Laboratory, together with NCAR, has developed this parameterization suite (turbulent mixing, deep/shallow convection, 9-layer land/snow/vegetation/lake model) to improve PBL biases (temperature and moisture) including better representation of clouds and precipitation. This parameterization suite development has been accompanied by an effort for improved data assimilation of clouds, near-surface observations and radar for the atmosphere-land system.</p><p>Subgrid-scale cloud representation continues to be a central challenge from subseasonal-to-seasonal models down to storm-scale models applied for forecast duration of only a few hours.   Previously, NOAA/ESRL confirmed this issue from 3-km model (HRRR) for short-range forecasting including sub-grid-scale cloud representation up to a 60-km subseasonal model testing a common suite of scale-aware physical parameterizations.   Some progress has been made in 2018-2019 to substantially reduce cloud deficiency and excessive downward solar radiation at least over land areas.</p><p>Recent development and refinements to this common suite of physical parameterizations for scale-aware deep/shallow convection and boundary-layer mixing over this wide range of time and spatial scales will be reported in this presentation showing some progress. Evaluation of components of this suite is being evaluated for cloud/radiation (using SURFRAD, CERES, METAR ceiling) and near-surface (METAR, mesonet, aircraft, rawinsonde).</p><p>NOAA Earth System Research Laboratory, together with NCAR, has developed this parameterization suite (turbulent mixing, deep/shallow convection, 9-layer land/snow/vegetation model) to improve PBL biases (temperature and moisture) including better representation of clouds and precipitation. This parameterization suite development has been accompanied by an effort for improved data assimilation of clouds, near-surface observations and radar for the atmosphere-land system.  </p><p>The MYNN boundary-layer EDMF scheme (Olson, et al 2019), RUC land-surface model (Smirnova et al. 2016 MWR), Grell-Freitas scheme (2014, Atmos. Chem. Phys.), and aerosol-aware cloud microphysics (Thompson and Eidhammer 2015) have been applied and tested extensively for the NOAA hourly updated 3-km High-Resolution Rapid Refresh (HRRR) and 13-km Rapid Refresh model/assimilation systems over the United States and North America.   This mesoscale but also scale-aware suite is being tested,</p>


2013 ◽  
Vol 30 (8) ◽  
pp. 1635-1655 ◽  
Author(s):  
Ryan R. Neely ◽  
Matthew Hayman ◽  
Robert Stillwell ◽  
Jeffrey P. Thayer ◽  
R. Michael Hardesty ◽  
...  

Abstract Accurate measurements of cloud properties are necessary to document the full range of cloud conditions and characteristics. The Cloud, Aerosol Polarization and Backscatter Lidar (CAPABL) has been developed to address this need by measuring depolarization, particle orientation, and the backscatter of clouds and aerosols. The lidar is located at Summit, Greenland (72.6°N, 38.5°W; 3200 m MSL), as part of the Integrated Characterization of Energy, Clouds, Atmospheric State, and Precipitation at Summit Project and NOAA's Earth System Research Laboratory's Global Monitoring Division's lidar network. Here, the instrument is described with particular emphasis placed upon the implementation of new polarization methods developed to measure particle orientation and improve the overall accuracy of lidar depolarization measurements. Initial results from the lidar are also shown to demonstrate the ability of the lidar to observe cloud properties.


2020 ◽  
Author(s):  
Ethan Grossman ◽  
◽  
Michael M. Joachimski ◽  
Cristina Krause ◽  
Wolfgang Kiessling

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