scholarly journals Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition

2021 ◽  
Vol 12 (4) ◽  
pp. 1295-1369
Author(s):  
Sebastian Landwehr ◽  
Michele Volpi ◽  
F. Alexander Haumann ◽  
Charlotte M. Robinson ◽  
Iris Thurnherr ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Environmental Data ◽  
Sea Surface ◽  
Latitudinal Gradients ◽  
Earth System ◽  
System Models ◽  
Environmental Processes ◽  
The Antarctic ◽  
Earth System Models

Abstract. The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90 d. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and “hotspots” of interaction between environmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean–atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question.

scholarly journals Biogeochemistry and Physics of the Southern Ocean-Atmosphere System Explored With Data Science

2021 ◽  
Author(s):  
Sebastian Landwehr ◽  
Michele Volpi ◽  
F. Alexander Haumann ◽  
Charlotte M. Robinson ◽  
Iris Thurnherr ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Latent Variables ◽  
Sea Surface ◽  
Latitudinal Gradients ◽  
Dynamic State ◽  
Environmental Processes ◽  
Holistic Understanding ◽  
Ocean Atmosphere ◽  
The Antarctic

Abstract. The Southern Ocean is a critical component of Earth’s climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the dynamic state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, ocean biogeochemistry and microbiology. This circumpolar cruise included visits to twelve remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter term variations, the geographic setting of environmental processes, and interactions between them over the duration of 90 days. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable, we applied a sparse Principal Component Analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. We identified temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and “hotspots” of interaction. Our results establish connections of oceanic, atmospheric, biological and terrestrial processes in an innovative way, while confirming many well known relations of the Southern Ocean system. More specifically, we identify: the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity; that sea ice predominantly controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilization and reduced light limitation; and clear regional patterns of aerosol characteristics emerged, stressing the role of the sea state, atmospheric chemical processing, as well as source processes near “hotspots” for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper ocean layer light intensity, surface wind speed and relative humidity, played an important role in the majority of latent variables, highlighting their importance for a large variety of processes and the necessity for Earth System Models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean-atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. The sPCA processing code is available as open-access and we believe that our approach is widely applicable to other environmental field studies.

GFDL’s ESM2 Global Coupled Climate–Carbon Earth System Models. Part I: Physical Formulation and Baseline Simulation Characteristics

2012 ◽  
Vol 25 (19) ◽  
pp. 6646-6665 ◽  
Author(s):  
John P. Dunne ◽  
Jasmin G. John ◽  
Alistair J. Adcroft ◽  
Stephen M. Griffies ◽  
Robert W. Hallberg ◽  
...  
Keyword(s):  
Climate Changes ◽  
Southern Oscillation ◽  
Heat Content ◽  
Ocean Dynamics ◽  
Thermocline Depth ◽  
Earth System ◽  
System Models ◽  
Model Version ◽  
Earth System Models

Abstract The physical climate formulation and simulation characteristics of two new global coupled carbon–climate Earth System Models, ESM2M and ESM2G, are described. These models demonstrate similar climate fidelity as the Geophysical Fluid Dynamics Laboratory’s previous Climate Model version 2.1 (CM2.1) while incorporating explicit and consistent carbon dynamics. The two models differ exclusively in the physical ocean component; ESM2M uses Modular Ocean Model version 4p1 with vertical pressure layers while ESM2G uses Generalized Ocean Layer Dynamics with a bulk mixed layer and interior isopycnal layers. Differences in the ocean mean state include the thermocline depth being relatively deep in ESM2M and relatively shallow in ESM2G compared to observations. The crucial role of ocean dynamics on climate variability is highlighted in El Niño–Southern Oscillation being overly strong in ESM2M and overly weak in ESM2G relative to observations. Thus, while ESM2G might better represent climate changes relating to total heat content variability given its lack of long-term drift, gyre circulation, and ventilation in the North Pacific, tropical Atlantic, and Indian Oceans, and depth structure in the overturning and abyssal flows, ESM2M might better represent climate changes relating to surface circulation given its superior surface temperature, salinity, and height patterns, tropical Pacific circulation and variability, and Southern Ocean dynamics. The overall assessment is that neither model is fundamentally superior to the other, and that both models achieve sufficient fidelity to allow meaningful climate and earth system modeling applications. This affords the ability to assess the role of ocean configuration on earth system interactions in the context of two state-of-the-art coupled carbon–climate models.

scholarly journals Harnessing big data to rethink land heterogeneity in Earth system models

2018 ◽  
Vol 22 (6) ◽  
pp. 3311-3330 ◽  
Author(s):  
Nathaniel W. Chaney ◽  
Marjolein H. J. Van Huijgevoort ◽  
Elena Shevliakova ◽  
Sergey Malyshev ◽  
Paul C. D. Milly ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Grid Cell ◽  
Environmental Data ◽  
Distributed Model ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Earth System ◽  
Carbon Cycles ◽  
System Models ◽  
Clustering Approach ◽  
Earth System Models

Abstract. The continual growth in the availability, detail, and wealth of environmental data provides an invaluable asset to improve the characterization of land heterogeneity in Earth system models – a persistent challenge in macroscale models. However, due to the nature of these data (volume and complexity) and computational constraints, these data are underused for global applications. As a proof of concept, this study explores how to effectively and efficiently harness these data in Earth system models over a 1/4∘ (∼ 25 km) grid cell in the western foothills of the Sierra Nevada in central California. First, a novel hierarchical multivariate clustering approach (HMC) is introduced that summarizes the high-dimensional environmental data space into hydrologically interconnected representative clusters (i.e., tiles). These tiles and their associated properties are then used to parameterize the sub-grid heterogeneity of the Geophysical Fluid Dynamics Laboratory (GFDL) LM4-HB land model. To assess how this clustering approach impacts the simulated water, energy, and carbon cycles, model experiments are run using a series of different tile configurations assembled using HMC. The results over the test domain show that (1) the observed similarity over the landscape makes it possible to converge on the macroscale response of the fully distributed model with around 300 sub-grid land model tiles; (2) assembling the sub-grid tile configuration from available environmental data can have a large impact on the macroscale states and fluxes of the water, energy, and carbon cycles; for example, the defined subsurface connections between the tiles lead to a dampening of macroscale extremes; (3) connecting the fine-scale grid to the model tiles via HMC enables circumvention of the classic scale discrepancies between the macroscale and field-scale estimates; this has potentially significant implications for the evaluation and application of Earth system models.

scholarly journals Two-way coupling between the sub-grid land surface and river networks in Earth system models

2020 ◽  
Author(s):  
Nathaniel W. Chaney ◽  
Laura Torres-Rojas ◽  
Noemi Vergopolan ◽  
Colby K. Fisher
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Environmental Data ◽  
Added Value ◽  
Earth System ◽  
Field Scale ◽  
System Models ◽  
Routing Scheme ◽  
Earth System Models ◽  
River Routing

Abstract. Over the past decade, there has been appreciable progress towards modeling the water, energy, and carbon cycles at field-scales (10–100 m) over continental to global extents. One such approach, named HydroBlocks, accomplishes this task while maintaining computational efficiency via sub-grid tiles, or Hydrologic Response Units (HRUs), learned via a hierarchical clustering approach from available global high-resolution environmental data. However, until now, there has yet to be a macroscale river routing approach that is able to leverage HydroBlocks' approach to sub-grid heterogeneity, thus limiting the added value of field-scale land surface modeling in Earth System Models (e.g., riparian zone dynamics, irrigation from surface water, and interactive floodplains). This paper introduces a novel dynamic river routing scheme in HydroBlocks that is intertwined with the modeled field-scale land surface heterogeneity. The primary features of the routing scheme include: 1) the fine-scale river network of each macroscale grid cell's is derived from very high resolution (

scholarly journals Harnessing Big Data to Rethink Land Heterogeneity in Earth System Models

2017 ◽  
Author(s):  
Nathaniel W. Chaney ◽  
Marjolein H. J. Van Huijgevoort ◽  
Elena Shevliakova ◽  
Sergey Malyshev ◽  
Paul C. D. Milly ◽  
...  
Keyword(s):  
Grid Cell ◽  
Environmental Data ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Fine Scale ◽  
Earth System ◽  
Carbon Cycles ◽  
System Models ◽  
Data Driven Approach ◽  
Scale Grid ◽  
Earth System Models

Abstract. The continual growth in the availability, detail, and wealth of environmental data provides an invaluable asset to improve the characterization of land heterogeneity in Earth System models – a persistent challenge in macroscale models. However, due to the nature of these data (volume and complexity) and the computational constraints of macroscale models, until now these data have been underutilized for global applications. As a proof of concept, this study explores over a 1/4 degree (~ 25 km) grid cell in southeastern California how to effectively and efficiently harness these data in Earth System models. First, a novel hierarchical multivariate clustering approach (HMC) is used to summarize the high dimensional environmental data space into hydrologically interconnected representative clusters (i.e., tiles). These tiles and their associated properties are then used to parameterize the sub-grid heterogeneity of the Geophysical Fluid Dynamics Laboratory (GFDL) LM4-HB land model. To assess how this data-driven approach to assemble the model tiles impacts the simulated water, energy, and carbon cycles, model experiments are run using a series of different tile configurations assembled by HMC. The results over the 1/4 degree macroscale grid cell and the underlying 30-meter fine-scale grid in southeastern California show that: 1) the observed similarity over the landscape makes it possible to robustly account for the role of multi-scale heterogeneity in the macroscale states and fluxes with around 300 sub-grid land model tiles; 2) assembling the sub-grid tiles from observed data, at times, leads to noticeable differences in the macroscale water, energy, and carbon cycles; for example, explicit subsurface interactions between the tiles leads to a dampening of macroscale extremes; 3) connecting the fine-scale grid to the model tiles via HMC enables circumventing the classic scale discrepancies between the macroscale and field-scale estimates; this has potentially significant implications for the evaluation and application of Earth System models.

scholarly journals Physics–Dynamics Coupling in Weather, Climate, and Earth System Models: Challenges and Recent Progress

2018 ◽  
Vol 146 (11) ◽  
pp. 3505-3544 ◽  
Author(s):  
Markus Gross ◽  
Hui Wan ◽  
Philip J. Rasch ◽  
Peter M. Caldwell ◽  
David L. Williamson ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Recent Progress ◽  
Earth System ◽  
System Models ◽  
Thermodynamic Compatibility ◽  
Emerging Issues ◽  
Microphysical Processes ◽  
Model Components ◽  
Earth System Models

Abstract Numerical weather, climate, or Earth system models involve the coupling of components. At a broad level, these components can be classified as the resolved fluid dynamics, unresolved fluid dynamical aspects (i.e., those represented by physical parameterizations such as subgrid-scale mixing), and nonfluid dynamical aspects such as radiation and microphysical processes. Typically, each component is developed, at least initially, independently. Once development is mature, the components are coupled to deliver a model of the required complexity. The implementation of the coupling can have a significant impact on the model. As the error associated with each component decreases, the errors introduced by the coupling will eventually dominate. Hence, any improvement in one of the components is unlikely to improve the performance of the overall system. The challenges associated with combining the components to create a coherent model are here termed physics–dynamics coupling. The issue goes beyond the coupling between the parameterizations and the resolved fluid dynamics. This paper highlights recent progress and some of the current challenges. It focuses on three objectives: to illustrate the phenomenology of the coupling problem with references to examples in the literature, to show how the problem can be analyzed, and to create awareness of the issue across the disciplines and specializations. The topics addressed are different ways of advancing full models in time, approaches to understanding the role of the coupling and evaluation of approaches, coupling ocean and atmosphere models, thermodynamic compatibility between model components, and emerging issues such as those that arise as model resolutions increase and/or models use variable resolutions.

scholarly journals Metrics for the Evaluation of the Southern Ocean in Coupled Climate Models and Earth System Models

2018 ◽  
Vol 123 (5) ◽  
pp. 3120-3143 ◽  
Author(s):  
Joellen L. Russell ◽  
Igor Kamenkovich ◽  
Cecilia Bitz ◽  
Raffaele Ferrari ◽  
Sarah T. Gille ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Climate Models ◽  
Earth System ◽  
System Models ◽  
Coupled Climate Models ◽  
Earth System Models

scholarly journals The seasonal cycle of <i>p</i>CO<sub>2</sub> and CO<sub>2</sub> fluxes in the Southern Ocean: diagnosing anomalies in CMIP5 Earth system models

2018 ◽  
Vol 15 (9) ◽  
pp. 2851-2872 ◽  
Author(s):  
N. Precious Mongwe ◽  
Marcello Vichi ◽  
Pedro M. S. Monteiro
Keyword(s):  
Southern Ocean ◽  
Seasonal Cycle ◽  
Dissolved Inorganic Carbon ◽  
Coupled Model ◽  
Dominant Mode ◽  
Cmip5 Models ◽  
Earth System ◽  
System Models ◽  
Rates Of Change ◽  
Earth System Models

Abstract. The Southern Ocean forms an important component of the Earth system as a major sink of CO2 and heat. Recent studies based on the Coupled Model Intercomparison Project version 5 (CMIP5) Earth system models (ESMs) show that CMIP5 models disagree on the phasing of the seasonal cycle of the CO2 flux (FCO2) and compare poorly with available observation products for the Southern Ocean. Because the seasonal cycle is the dominant mode of CO2 variability in the Southern Ocean, its simulation is a rigorous test for models and their long-term projections. Here we examine the competing roles of temperature and dissolved inorganic carbon (DIC) as drivers of the seasonal cycle of pCO2 in the Southern Ocean to explain the mechanistic basis for the seasonal biases in CMIP5 models. We find that despite significant differences in the spatial characteristics of the mean annual fluxes, the intra-model homogeneity in the seasonal cycle of FCO2 is greater than observational products. FCO2 biases in CMIP5 models can be grouped into two main categories, i.e., group-SST and group-DIC. Group-SST models show an exaggeration of the seasonal rates of change of sea surface temperature (SST) in autumn and spring during the cooling and warming peaks. These higher-than-observed rates of change of SST tip the control of the seasonal cycle of pCO2 and FCO2 towards SST and result in a divergence between the observed and modeled seasonal cycles, particularly in the Sub-Antarctic Zone. While almost all analyzed models (9 out of 10) show these SST-driven biases, 3 out of 10 (namely NorESM1-ME, HadGEM-ES and MPI-ESM, collectively the group-DIC models) compensate for the solubility bias because of their overly exaggerated primary production, such that biologically driven DIC changes mainly regulate the seasonal cycle of FCO2.

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