Archive for earth system models

The causes of the 90-ppm glacial atmospheric CO2 drawdown still strongly debated

Posted by mmaheigan 
· Tuesday, July 9th, 2019 

Joint feature with GEOTRACES

Figure: Illustration of the two main mechanisms identified by this study to explain lower atmospheric CO2 during glacial periods. Left: present-day conditions; right: conditions around 19,000 years ago during the Last Glacial Maximum. The obvious explanation for lower CO2 during glacial periods – cooler ocean temperatures (darker blue shade) making CO2 more soluble, much as a glass of sparkling wine will remain fizzier for longer when it is colder – has long been dismissed as not being a significant factor. However, previous calculations assumed that the ocean cooled uniformly and was saturated in dissolved CO2. The model, consistent with reconstructions of sea surface temperature, predicts more cooling at mid latitudes compared with polar regions and also accounts for undersaturation. This nearly doubles the effect of temperature change and accounts for almost half the 90 ppm glacial-interglacial atmospheric CO2 difference. Another quarter is explained in this model by increased growth of marine algae (green blobs and inset) in the waters off Antarctica. Algae absorb CO2 from the atmosphere during photosynthesis and “pump” it into the deep ocean when they die and sink. But their growth in the present-day ocean, especially the waters off Antarctica, is limited by the availability of iron, an essential micronutrient primarily supplied by wind-borne dust. In our model an increased supply of iron to the Southern Ocean, likely originating from Patagonia, Australia and New Zealand, enhances their growth and sucks CO2 out of the atmosphere. This “fertilization” effect was greatly underestimated by previous studies. The study also finds that, contrary to the current consensus, a large expansion of sea ice off Antarctica and reconfiguration of ocean circulation may have played only a minor role in glacial-interglacial CO2 changes. Credit: Illustration by Andrew Orkney, University of Oxford.

Using an observationally constrained earth system model, S. Khatiwala and co-workers compare different processes that could lead to the 90-ppm glacial atmospheric CO2 drawdown, with an important improvement on the deep carbon storage quantification (i.e. Biological Carbon Pump efficiency). They demonstrate that circulation and sea ice changes had only a modest net effect on glacial ocean carbon storage and atmospheric CO2, whereas temperature and iron input effects were more important than previously thought due to their effects on disequilibrium carbon storage.

Authors:
Samar Khatiwala (University of Oxford, UK)
Andreas Schmittner and Juan Muglia (Oregon State University)

Forecasting air-sea CO2 flux variations several years in advance

Posted by mmaheigan 
· Tuesday, July 9th, 2019 

Year-to-year changes in the flux of CO2 between the atmosphere and the ocean impact the global carbon cycle and climate system, and challenge our ability to verify fossil fuel CO2 emissions. A new study published in Earth System Dynamics suggests that these air-sea CO2 flux variations are predictable several years in advance.

A novel set of initialized forecasts of past air-sea CO2 flux from an Earth system model (Figure 1a) confidently predicts year-to-year variations in the globally-integrated flux up to two years in advance. At regional scales, the forecast lead time increases. The predictability of CO2 flux from the initialized forecast system exceeds that obtained solely from foreknowledge of variations in external forcing (e.g., volcanic eruptions) or a simple persistence forecast (e.g., CO2 flux this year will be the same as next year). The longest-lasting forecast enhancements are in the subantarctic Southern Ocean and the northern North Atlantic (Figure 1b).

Figure 1: (a) Forecasts of the past evolution of air-sea CO2 flux in the South Pacific using an Earth System model indicate the potential to predict the future evolution of this quantity. (b) In each biome, the maximum forecast lead time in which the initialized forecast of air-sea CO2 flux beats out other forecast methods.

These results are particularly meaningful for those forecasting year-to-year changes in the global carbon budget, especially as these forecasting efforts are blind to the externally-forced variability in advance (i.e., the external forcing of the future is unknown).  In this way, forecasts of air-sea CO2 flux variations can help to inform future predictions of land-air CO2 flux and atmospheric CO2 concentration.

Authors:
Nicole Lovenduski (University of Colorado Boulder)
Stephen G. Yeager (National Center for Atmospheric Research)
Keith Lindsay (National Center for Atmospheric Research)
Matthew C. Long (National Center for Atmospheric Research)

See also the OCB Ocean-Atmosphere Interactions: Scoping directions for U.S. research Workshop to be held in October 1-3, 2019

Suddenly shallow: A new aragonite saturation horizon will soon emerge in the Southern Ocean

Posted by mmaheigan 
· Monday, May 27th, 2019 

Earth System Models (ESMs) project that by the end of this century, the aragonite saturation horizon (the boundary between shallower, saturated waters and deeper, undersaturated waters that are corrosive to aragonitic shells) will shoal all the way to the surface in the Southern Ocean, yet the temporal evolution of the horizon has not been studied in much detail. Rather than a gradual shoaling, a new shallow aragonite saturation horizon emerges suddenly across many locations in the Southern Ocean between now and the end of the century (Figure 1, left), as detailed in a new study published in Nature Climate Change.

Figure 1: Maximum step-change in the depth of the aragonite saturation horizon (left), timing of the step-change (center), and cause of the change (right). Xs on the time axis (center) indicate when the shallow horizon emerges in each ensemble member. (click image to enlarge)

 

The emergence of the shallow aragonite saturation horizon is apparent in each member of an ensemble of climate projections from an ESM, but the step change occurs during different years (Figure 1, center). The shoaling is driven by the gradual accumulation of anthropogenic CO2 in the Southern Ocean thermocline, where the carbonate ion concentration exhibits a local minimum and approaches undersaturation (Figure 1, right).

The abrupt shoaling of the Southern Ocean aragonite saturation horizon occurs under both business-as-usual and emission-stabilizing scenarios, indicating an inevitable and sudden decrease in the volume of suitable habitat for aragonitic organisms such as shelled pteropods, foraminifers, cold-water corals, sea urchins, molluscs, and coralline algae. Widespread reductions in these habitats may have far-reaching consequences for fisheries, economies, and livelihoods.

Authors:
Gabriela Negrete-García (Scripps Institution of Oceanography)
Nicole Lovenduski (University of Colorado Boulder)

 

See also OCB2019 plenary session: Carbon cycle feedbacks from the seafloor (Wednesday, June 26, 2019)

Improved method to identify and reduce uncertainties in marine carbon cycle predictions

Posted by mmaheigan 
· Wednesday, September 26th, 2018 

Improved method to identify and reduce uncertainties in marine carbon cycle predictions

How well do contemporary Earth System Models (ESMs) represent the dynamics of the modern day ocean? Often we question the fidelity of biological and chemical processes represented in these ESMs. The fact is representations of biogeochemical processes in models are plagued with some degree of uncertainties; therefore, identifying and reducing such deficiencies could advance ESM development and improve model predictions.

An overview of several models with respect to each of the variables, using absolute (left) and relative (right) scores to determine the degree of uncertainty in relation to referenced datasets.

 

A recent publication in Atmosphere described the ongoing efforts to develop the International Ocean Model Benchmarking (IOMB) package to evaluate ESM skill sets in simulating marine biogeochemical variables and processes. Model performances were scored based on how well they captured the distribution and variability contained in high-quality observational datasets. The authors highlighted systematic model–data benchmarking as a technique to identify ocean model deficiencies, which could provide a pathway to improving representations of sub-grid-scale parameterizations. They have scaled the absolute score from zero to unity, where the red color tends toward zero to quantify weaknesses in the skill set of a particular model in capturing values from the observational datasets. On the other side of the spectrum, the green color signifies considerable temporal and spatial overlap between the predicted and the observational values. The authors also present the standard score to show the relative scores within two standard deviations from the model mean. The benchmarking package was employed in the published study to assess marine biogeochemical process representations, with a focus on surface ocean concentrations and sea–air fluxes of dimethylsulfide (DMS). The production and emission of natural aerosols remain one of the major limitations in estimating global radiative forcing. Appropriate representation of aerosols in the marine boundary layer (MBL) is essential to reduce uncertainty and provide reliable information on offsets to global warming. Results show that model–data biases increased as DMS enters the MBL, with models over-predicting sea surface concentrations in the productive region of the eastern tropical Pacific by almost a factor of two and the sea–air fluxes by a factor of three. The associated uncertainties with oceanic carbon cycle processes may be additive or antagonistic; in any case, a constructive effort to disentangle the subtleties begins with an objective benchmarking effort, which is focused specifically on marine biogeochemical processes. The tool in development will ensure we satisfy some of the Model Intercomparison Project (MIP) benchmarking needs for the sixth phase of Coupled Model Intercomparison Project (CMIP6).

 

Authors:
Oluwaseun Ogunro (ORNL)
Scott Elliott (LANL)
Oliver Wingenter (New Mexico Tech)
Clara Deal (University of Alaska)
Weiwei Fu (UC Irvine)
Nathan Collier (ORNL)
Forrest M. Hoffman (ORNL)

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