Building Ensemble-Based Data Assimilation Systems with Coupled Models

Discussed is the construction of programs for efficient ensemble data assimilation systems based on a direct connection between a coupled simulation model and ensemble data assimilation software. The strategy allows us to set up a data assimilation program with high flexibility and parallel scalability with only small changes to the model. The direct connection is obtained by first extending the source code of the coupled model so that it is able to run an ensemble of model states. In addition, a filtering step is added using a combination of in-memory access and parallel communication to create an online-coupled ensemble assimilation program. The direct connection avoids the common need to stop and restart a whole coupled model system to perform the assimilation of observations in the analysis step of ensemble-based filter methods like ensemble Kalman or particle filters. Instead, the analysis step is performed in between time steps and is independent of the actual model coupler. This strategy allows us to perform both in-compartment (for weakly coupled assimilation) and cross-compartment (for strongly coupled assimilation) assimilation. The assimilation frequency can be kept flexible, so that assimilation of observations from different compartments can be performed at different time intervals. Using the parallel data assimilation framework (PDAF, http://pdaf.awi.de), the direct connection strategy will be exemplified for the ocean-atmosphere model ECHAM6-FESOM

Building a Scalable Ensemble Data Assimilation System for Coupled Models

Efficient ensemble data assimilation with coupled models poses particular challenges due to the comp lexity of the model system and due to its high computational cost. On the methodological side, one h as to account for different time scales, but also distinct correlation lengths, of different model c ompartments like the ocean and the atmosphere. Computationally, one often has to deal with multiple program executables, a coupler software, observation handling for different model compartments, and a large number of processors required to compute a complex coupled model. This contribution focuses on the computational aspects. Discussed are the steps required to build a highly scalable and flexible data assimilation system can be built on the basis of the Parallel Data Assimilation Framework (PDAF, http://pdaf.awi.de) using the example of the coupled climate model AW I-CM (Sidorenko et al., Climate Dynamics, 44 (2015) 757-780). AWI-CM consists of the finite-element sea ice-ocean model FESOM, which uses an unstructured model grid, and the model ECHAM6 for the atmosphere. The model coupling is implemented with OASIS-MCT and the model system consists of two separate executable programs for the ocean and atmosphere. Next to the implementation steps, the scalability of the assimilation system is discussed with a realistic configuration of AWI-CM. The high scalability is obtained by an online-connection strategy for the data assimilation system. First, the parallelization of the coupled model system is modified so that the coupled model can perform ensemble forecasts. Second, the analysis (solver) step is directly inserted into the time-stepping loops of each model compartment. Augmenting the coupled model in this online way, the ensemble information is kept in memory and transferred by parallel communication when necessary. Thus, one avoids the need to repeatedly write an ensemble of model fields into files and read them again for the analysis step. Further, the coupled model is only started once and there is no need to stop and restart the whole coupled model to compute the analysis step. Instead, the analysis step is performed in between time steps and is independent of the actual model coupler. These modifications of the model are supported by the framework structure of PDAF. In addition to the parallel online connection for the data assimilation system, the analysis step has to be parallelized. Here, the different model compartments are treated like parallel subdomains of the model. In this way, one can one can use the data assimilation algorithms provided by PDAF and can implement and perform the analysis step in analogy to uncoupled models. However, one has to take into account the different model grids and possible distinct ways in which the model compartments store their model fields. This results in a data assimilation system that can perform the assimilations both in-compartment (for weakly coupled assimilation) and cross-compartment (for strongly coupled assimilation)

Scalable Coupled Ensemble Data Assimilation with AWI-CM and PDAF

We discuss a strategy to build a highly scalable and flexible data assimilation system on the basis of the Parallel Data Assimilation Framework (PDAF, http://pdaf.awi.de) using the example of the coupled climate model AWI-CM (Sidorenko et al., Climate Dynamics, 44 (2015) 757-780). AWI-CM consists of the finite-element sea ice-ocean model FESOM, which uses an unstructured model grid, and the model ECHAM6 for the atmosphere. The model compartments are coupled using OASIS3-MCT. The model system consists of two separate executable programs for the ocean and atmosphere. The assimilation system is generated by online-coupling of AWI-CM and PDAF. This modifies AWI-CM to perform ensemble forecasting and data assimilation and allows to fully keep the ensemble information in memory avoiding costly file operations and model restarts. The resulting assimilation system supports to apply the assimilation both in-compartment (i.e. weakly-coupled) as well as cross-compartment (i.e. strongly-coupled). Discussed are the structure and computational performance of the assimilation system as well as results from the assimilation of sea surface temperature and ocean profile data sets into a realistic configuration of AWI-CM

An extreme event of enhanced Arctic Ocean export west of Greenland caused by the pronounced dynamic sea level drop in the North Atlantic subpolar gyre in the mid-to-late 2010s

The North Atlantic subpolar gyre influences the climate in many different ways. Here, we identified that it is also responsible for a recent extreme event of Arctic Ocean freshwater export west of Greenland. A shift in climate regimes occurred in the mid-2000s, with a significant negative trend in the dynamic sea level in the subpolar gyre since then. We found that the dynamic sea level drop induced a strong increase in freshwater export west of Greenland, in particular from 2015 to 2017, when the sea level was close to the minimum. Sea ice melting and atmospheric variability in the Arctic had only a small contribution to this event. As the exported water from the Arctic Ocean has low salinity and constituents of chemical tracers very different from those in the North Atlantic, such events might have impacts on the North Atlantic ecosystem and the climate as well. Our study suggests that such events might be predictable if the subpolar gyre sea level has certain predictability

Building an Efficient Ensemble Data Assimilation System for Coupled Models with the Parallel Data Assimilation Framework

We discuss how to build an ensemble data assimilation system using a direct connection between a coupled model system and the ensemble data assimilation software PDAF (Parallel Data Assimilation Framework, http://pdaf.awi.de). The direct connection results in a data assimilation program with high flexibility, efficiency, and parallel scalability. For this we augment the source code of the coupled model by data assimilation routines and hence create an online-coupled assimilative model. This first modifies the coupled model to be able to simulate an ensemble. Using a combination of in-memory access and parallel communication with the Message Passing Interface (MPI) standard we can further add the analysis step of ensemble-based filter methods, which compute the assimilation of observations, without the need to stop and restart the whole coupled model system. Instead, the analysis step is performed in between time steps and is independent of the actual model coupler that couples the different model compartments. This strategy to build the assimilation system allows us to perform both weakly coupled (in-compartment) and strongly coupled (cross-compartment) assimilation. The assimilation frequency can be kept flexible, so that the assimilation of observations from different compartments can be performed at different intervals. Further, the reading and writing of disk files is minimized. The resulting assimilative model can be run in the same way as the regular coupled model, but with additional parameters controlling the assimilation and with a higher number of processors to simulate the ensemble. Using the example of the coupled climate model AWI-CM that contains the FESOM model for the ocean and sea ice and ECHAM6 for the atmosphere, both coupled through the OASIS-MCT coupler, we discuss the features of the online assimilation coupling strategy and the performance of the resulting assimilative model

Efficient Ensemble Data Assimilation For Earth System Models with the Parallel Data Assimilation Framework (PDAF)

We discuss how to build an ensemble data assimilation system using a direct connection between a coupled Earth system model (ESM) and the ensemble data assimilation software PDAF (Parallel Data Assimilation Framework, http://pdaf.awi.de). The direct connection results in a data assimilation program with high flexibility, efficiency, and parallel scalability. For this we augment the source code of the coupled model by data assimilation routines and hence create an online-coupled assimilative model. This first modifies the coupled model to be able to simulate an ensemble. Using a combination of in-memory access and parallel communication with the Message Passing Interface (MPI) standard we can further add the analysis step of ensemble-based assimilation methods. Thus the assimilation of observations is computed without the need to stop and restart the whole coupled model system. Instead, the analysis step is performed in between time steps and is independent of the actual model coupler that couples the different model compartments. This strategy to build the assimilation system allows us to perform both weakly coupled (in-compartment) and strongly coupled (cross-compartment) assimilation. The assimilation frequency can be kept flexible, so that the assimilation of observations from different compartments of the ESM can be performed at different intervals. Further, the reading and writing of disk files is minimized. The resulting assimilative model can be run in the same way as the regular ESM, but with additional parameters controlling the assimilation and with a higher number of processors to simulate the ensemble. Using the example of the coupled climate model AWI-CM that contains the FESOM model for the ocean and sea ice and ECHAM6 for the atmosphere, both coupled through the OASIS-MCT coupler, we discuss the features of the online assimilation coupling strategy and the performance of the resulting assimilative model

Early-Holocene simulations using different forcings and resolutions in AWI-ESM.

The earliest part of the Holocene, from 11.5k to 7k (k = 1000 years before present), is a critical transition period between the relatively cold last deglaciation and the warm middle Holocene. It is marked by more pronounced seasonality and reduced greenhouse gases (GHGs) than the present state, as well as by the presence of the Laurentide Ice Sheet (LIS) and glacial meltwater perturbation. This paper performs experiments under pre-industrial and different early-Holocene regimes with AWI-ESM (Alfred Wegener Institute–Earth System Model), a state-of-the-art climate model with unstructured mesh and varying resolutions, to examine the sensitivity of the simulated Atlantic meridional overturning circulation (AMOC) to early-Holocene insolation, GHGs, topography (including properties of the ice sheet), and glacial meltwater perturbation. In the experiments with early-Holocene Earth orbital parameters and GHGs applied, the AWI-ESM simulation shows a JJA (June–July–August) warming and DJF (December–January–February) cooling over the mid and high latitudes compared with pre-industrial conditions, with amplification over the continents. The presence of the LIS leads to an additional regional cooling over the North America. We also simulate the meltwater event around 8.2k. Big discrepancies are found in the oceanic responses to different locations and magnitudes of freshwater discharge. Our experiments, which compare the effects of freshwater release evenly across the Labrador Sea to a more precise injection along the western boundary of the North Atlantic (the coastal region of LIS), show significant differences in the ocean circulation response, as the former produces a major decline of the AMOC and the latter yields no obvious effect on the strength of the thermohaline circulation. Furthermore, proglacial drainage of Lakes Agassiz and Ojibway leads to a fast spin-down of the AMOC, followed, however, by a gradual recovery. Most hosing experiments lead to a warming over the Nordic Sea and Barents Sea of varying magnitudes, because of an enhanced inflow from lower latitudes and a northward displacement of the North Atlantic deep convection. These processes exist in both of our high- and low-resolution experiments, but with some local discrepancies such as (1) the hosing-induced subpolar warming is much less pronounced in the high-resolution simulations; (2) LIS coastal melting in the high-resolution model leads to a slight decrease in the AMOC; and (3) the convection formation site in the low- and high-resolution experiments differs, in the former mainly over northeastern North Atlantic Ocean, but in the latter over a very shallow subpolar region along the northern edge of the North Atlantic Ocean. In conclusion, we find that our simulations capture spatially heterogeneous responses of the early-Holocene climate

Simple algorithms to compute meridional overturning and barotropic streamfunctions on unstructured meshes

Abstract. Computation of barotropic and meridional overturning streamfunctions for models formulated on unstructured meshes is commonly preceded by interpolation to a regular mesh. This operation destroys the original conservation, which can be then artificially imposed to make the computation possible. An elementary method is proposed that avoids interpolation and preserves conservation in a strict model sense. The method is described as applied to the discretization of the Finite volumE Sea ice – Ocean Model (FESOM2) on triangular meshes. It, however, is generalizable to colocated vertex-based discretization on triangular meshes and to both triangular and hexagonal C-grid discretizations. </jats:p