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The focus of this perspective piece is on memory, persistence, and explainable outreach of forced systems, with greenhouse gas (GHG) emissions into the atmosphere serving as our case in point. In the light of the continued increase in emissions globally vis-à-vis the reductions required without further delay until 2050 and beyond, we conjecture that, being ignorant of memory and persistence, we may underestimate the “inertia” with which global GHG emissions will continue on their increasing path beyond today, thus, also leading to the amount of reduction that can be achieved in the future being overestimated. This issue is at the heart of mitigation and adaptation. For a practitioner, this translates to the problem of how persistently an emissions system behaves when subjected to a specified mitigation measure and which emissions level to adapt to for precautionary reasons in the presence of uncertainty. Memory allows us to reference how strongly the past can influence the “near-term future” of the system or (what we define as) its explainable outreach. We consider memory to be an intrinsic property of a system, retrospective in nature; and persistence to be a consequential (i.e., observable) feature of memory, prospective in nature and reflecting the tendency of a system to preserve a current state (including trend). Persistence depends on the system’s memory which, in turn, reflects how many historical states directly influence the current one. The nature of this influence can range from purely deterministic to purely stochastic. Different approaches exist to capture memory. We capture memory generically with the help of three characteristics: its temporal extent and both its weight and quality over time. The extent of memory quantifies how many historical data directly influence the current data point. The weight of memory describes the strength of this influence (fading of memory), while the quality of memory steers how well we know the latter (blurring of memory). Capturing fading and blurring of memory in combination is novel. In a numerical experiment with the focus on systemic insight, we cast a glance far ahead by illustrating one way to capture memory, and to understand how persistence plays out and how an explainable outreach of the system can be derived even under unfavorable conditions. We look into the following two questions: (1) Do we learn properly from the past? That is, do we have the right science in place to understand and treat memory appropriately? And (2) being aware that memory links a system’s past with its near-term future, do we quantify this outreach in a way that is useful for prognostic modelers and decision-makers? The latter question implies another question, namely, whether we can differentiate between and specify the various characteristics of memory (i.e., those mentioned above) by way of diagnostic data-processing alone? Or, in other words, how much system understanding do we need to have and to inject into the data-analysis process to enable such differentiation? Although the prime intention of our perspective piece is to study memory, persistence, and explainable outreach of forced systems and, thus, to expand on the usefulness of GHG emission inventories, our insights indicate the high chance of our conjecture proving true: being ignorant of memory and persistence, we underestimate, probably considerably, the “inertia” with which global GHG emissions will continue on their historical path beyond today and thus overestimate the amount of reductions that we might achieve in the future.

The crux with reducing emissions in the long-term: The underestimated “now” versus the overestimated “then”