The Design Document page provides a description of the algorithms, implementation and planned testing including unit, verification, validation and performance testing. Please read Step 1.3 Performance Expectations that explains feature documentation requirements from the performance group point of view.
Design Document
In the table below 4.Equ means Equations and Algorithms, 5.Ver means Verification, 6.Perf - Performance, 7. Val - Validation, 
- competed, 
- in progress, 
- not done
Overview table for the owner and an approver of this feature
1.Description | Corrects inadequacies in sea ice nitrogen dynamics |
| 2.Owner | Nicole Jeffery |
| 3.Created | 08/26/2019 |
| 4.Equ | |
| 5.Ver | |
| 6.Perf | |
| 7.Val | |
| 8.Approver | |
| 9.Approved Date |
Title: Improved nitrogen cycling in sea ice
Requirements and Design
E3SM Sea Ice Group
Date:Aug 26, 2019
Summary
The E3SMv1.1 CBGC simulations of sea ice biogeochemistry (BGC) drastically underestimate ice algal primary production in the Arctic. This is in large part due to biases in the upper ocean nitrate pool. However, sea ice algal primary production is also underestimated in the Southern Ocean, albeit not as drastically, where surface nutrients do not play a role. This suggests insufficiencies in our modelled biochemical interactions. Several studies (Fripiat et al. 2013, New insights into sea ice nitrogen biogeochemical dynamics from the nitrogen isotopes papers; Baer et al. 2015, Nitrogen uptake dynamics in landfast sea ice of the Chukchi Sea) point to dissolved organic nitrogen (DON) remineralization and nitrate production from nitrification as important nutrient sources for algal production. These sources are important for two reasons. First, DON is found in high concentrations in the polar oceans and, unlike nitrate, DON adsorbs to the ice crystals during sea ice growth and so can accumulate to high concentrations in the sea ice. Remineralization of DON produces ammonium which is readily taken up by ice algae, in fact, preferentially to nitrate. Thus DON remineralization to ammonium provides a new and abundant source of nitrogen for ice algae that will almost surely mitigate our production biases. Second, nitrate produced from nitrification is distinct from the ocean source of nitrate because it is trapped in a biofilm which adsorbs to ice crystals (Deman et al. 2019, High production going along with respiration, Impact of bio-film formation for sea ice biogeochemistry, IGS Sea Ice Symposium). This process allows for build-up of nitrate in the sea ice brine, a feature observed in sea ice cores (Baer et al., 2015; Duarte et al., 2017 Sea ice thermohaline dynamics and biogeochemistry in the Arctic Ocean: Empirical and model results) but that is virtually impossible in our current model design.
The current model has a DON pool that follows the transport physics of adsorbing BGC tracers. Fluxes between ocean and sea ice have already been implemented. In terms of biochemistry, DON is produced in situ during algal mortality and has a loss due to bacterial reminerialization, however that loss is tracked but not included as a source of ammonium. This source will be added and tuned based on observations. Nitrification is already implemented in the v1 model but with a default rate constant of zero. Observations indicate a rate constant of 0.046 per day. The impacts of the bio-film, however, still need to be added. This requires some minor coding to allow for adsorption of nitrate during the biogeochemical reaction process but not, as occurs for DON, during brine transport.
All code changes will involve a single subroutine in the MPAS-SI column package. Implementation and tuning will require updates to the namelist mpassi_in. It is expected that these changes will reduce ice algal primary production biases in both polar regions, though we should still see underestimations in the Arctic.
Requirements
Requirement: Add remineralization and nitrification fluxes to sea ice biogeochemical reactions.
Date last modified: Aug 27, 2019
Contributors: Nicole Jeffery
Currently, dissolved organic nitrogen (DON), ammonium (NH4), nitrate (NO3), and algal nitrogen (N) are the four fundamental pools in the sea ice nitrogen cycle. They satisfy coupled non-linear advection-diffusion-reaction equations. As modelled, this cycle is not closed, though losses are tracked in a diagnostic zooplankton pool (ZOO). Although not the focus of this development, we are working towards a model structure that conserves all sea ice biogeochemical tracers. With conservation in mind, we will modify the DON equation source terms so that together with ammonium sources they balance nitrogen losses from algae during grazing and mortality. We will then add a source term to the NH4 equation that can be made equivalent (via a parameter, fdon) to the remineralization loss term already present in the DON equation. Finally, we will change namelist parameters to turn on nitrification at observed rates.
This development will be first tested algebraically to show conservation of nitrogen. In addition, the ZOO diagnostic will be used to print an error message if nitrogen is not conserved in the reaction terms. We can test this in short stand alone MPAS-SI runs similar to the standard-bgc testsuite.
Requirement: Add adsorption of nitrate produced from nitrification
Date last modified: Aug 27, 2019
Contributors: Nicole Jeffery
Nitrate tracers are currently purely mobile. This means that they never adhere to the ice crystals and cannot build up in the brine in significant excess of the ocean concentration. Tracers that adhere, such as DON, move between the mobile and stationary phases via prescribed timescales with the condition that the mobile to stationary transformation occurs only during sea ice growth and the stationary to mobile transformation occurs during sea ice melt. In the case of nitrate, we first must define a stationary fraction of nitrate equal to the nitrification source term. Secondly, we need to implement the latter transformation, stationary to mobile, while preventing mobile to stationary transformations.
With the nitrification parameter set to 0, the code should be identical up to round off error in the biogeochemical tracers. With nitrification at observed values, we expect to see higher nitrate concentrations in some polar sea ice, but no unphysical accumulations. This can be tested by activating a warning flag already implemented that will indicate higher than expected concentrations of BGC tracers.
Algorithmic Formulations
Design solution: modify the reaction terms in the NH4 and DON equations
Date last modified:Aug 27, 2019
Contributors: Nicole Jeffery
The improved formulation of the reaction equations for NH4, DON, algal nitrogen (N) and NO3 are described in NitrogenCycleMPAS_SI.pdf
The v1 code does not include DONremin in equation (3) and the fraction of grazing contributing to DON in equation (4) has been modified from fdon fgs to (1-fgs + fng fgs) to close the grazing pathway. In addition, Algal mortality is now a source of DON.
For each requirement, there is a design solution that is intended to meet that requirement. Design solutions can include detailed technical discussions of PDEs, algorithms, solvers and similar, as well as technical discussion of performance issues. In general, this section should steer away from a detailed discussion of low-level software issues such as variable declarations, interfaces and sequencing.
Design and Implementation
Implementation: short-desciption-of-implementation-here
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This section should detail the plan for implementing the design solution for requirement XXX. In general, this section is software-centric with a focus on software implementation. Pseudo code is appropriate in this section. Links to actual source code are appropriate. Project management items, such as svn branches, timelines and staffing are also appropriate. How do we typeset pseudo code?
Planned Verification and Unit Testing
Verification and Unit Testing: short-desciption-of-testing-here
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How will XXX be tested? i.e. how will be we know when we have met requirement XXX. Will these unit tests be included in the ongoing going forward?
Planned Validation Testing
Validation Testing: short-desciption-of-testing-here
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How will XXX be tested? What observational or other dataset will be used? i.e. how will be we know when we have met requirement XXX. Will these unit tests be included in the ongoing going forward?
Planned Performance Testing
Performance Testing: short-desciption-of-testing-here
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Contributors: (add your name to this list if it does not appear)
How will XXX be tested? i.e. how will be we know when we have met requirement XXX. Will these unit tests be included in the ongoing going forward?