![]() L172: ".into the climate models" - I suggest rephrasing since this doesn't reflect what is happening in offline DA L166: An additional important category of errors are representativeness errors that arise from comparing proxy values at, e.g., points in space or time to model values representing time slices or large grid boxes L146: "time step": This is a bit confusing in reference to a proxy record is this not another example of a time slice, just referenced to a model? ![]() L138: I suggest ", a state vector might also." since this is not always the convention elsewhere where a state vector represents a particular time. L130: This expression describes offline approaches, but not necessarily online ones, where X_p is a function of observations at previous times L68: DA models can relax assumptions (they do not do so by default) L63: Given the discussion in 5.2, a caveat seems in order here that while nearly any parameter can be reconstructed, there is not necessarily a guarantee of skill in doing so An early example combining paleoceanographic data with a simple ocean model is Legrand and Wunsch (1995). See also the work of Jake Gebbie and Olivier Marchal using simplified transport models. 2014 Dail and Wunsch 2014 Kurahashi-Nakamura et al. Approaches using the adjoint of ocean models (4DVAR) include Winguth et al. ![]() L58: There is a substantial literature using DA approaches in ocean GCMs for paleoceanography not referenced here. is it the disequilibrium of climate that is at issue, or the imprint of anthropogenic forcing? 元9: I'm not sure about the context for "equilibrium" here. L1: I would not refer to paleoclimate DA as novel these approaches have been around for 20+ years Is DASH an acronym? If so, please spell it out. I also attended a workshop demonstrating the use of this code and found those (very similar) examples clear and well-executed. I did not work my way through the examples, but I believe that they are adequately well documented to allow future researchers to retrace their steps. I am suggesting major revisions given the number of comments, though I do not think that addressing them will be particularly onerous. In particular, I appreciate its readability and accessibility, which hopefully will help reduce some of the mystique and opacity surrounding DA methods and make them more broadly usable. I think that the manuscript is well deserving of publication in GMD. This toolbox and its accompanying documentation are excellent resources for the paleoclimate community. The manuscript provides a description of the key concepts underlying DA, a tutorial with direct reference to examples coded in MATLAB, and a discussion of some of the benefits and pitfalls of DA in the paleosciences. describe a new, publicly-available toolbox for paleoclimate data assimilation. This paper reviews the key components of the DASH toolbox and presents examples illustrating DASH's use for paleoclimate DA applications. ![]() The toolbox also provides optimized algorithms for implementing ensemble Kalman filters, particle filters, and optimal sensor analyses with variable and modular parameters. DASH includes tools for integrating and cataloguing data stored in disparate formats, building state vector ensembles, and running proxy (system) forward models. The toolbox is highly modular and is not built around any specific analysis, and thus DASH supports paleoclimate DA for a wide variety of time periods, spatial regions, proxy networks, and algorithms. DASH provides command line and scripting tools that implement common tasks in DA workflows. Here, we present DASH, a MATLAB toolbox designed to facilitate paleoclimate DA analyses. Despite the potential for DA to expand the scope of quantitative paleoclimatology, these methods remain difficult to implement in practice due to the multi-faceted requirements and data handling necessary for DA reconstructions, the diversity of DA methods, and the need for computationally efficient algorithms. Paleoclimate data assimilation (DA) is a novel tool for reconstructing past climates that directly integrates proxy records with climate model output. ![]()
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