Emotion Recognition in Conversation (ERC) plays a key role in developing emotionally responsive conversational systems, and contextual modeling is central to this task. Several studies have developed contextual models that capture dialogue history, speaker interactions, and emotion dynamics to support emotion prediction. However, the specific role of affective atmosphere, a latent, conversation-level emotional tendency shaped by conversational content and speaker interactions, remains insufficiently understood. This paper investigates what role atmosphere plays in conversational emotion understanding. We first propose AtmosERC, a graph-based atmosphere extractor that utilizes conversational context and speaker interactions to produce a compact representation of affective atmosphere. We then evaluate two downstream uses of the extracted atmosphere: initializing lightweight ERC model and providing emotion cues for LLM-based ERC. Experiments on three widely used ERC benchmarks show that the estimated atmosphere improves emotion prediction across both applications and supports more reliable predictions when local emotions deviate from the overall affective tendency.

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