The paper examines the multidecadal variability in the Northern Hemisphere, focusing on the North Atlantic region. Proxy-based reconstructions of surface temperatures over the past 330 years reveal a distinct oscillatory mode with a time scale of approximately 70 years. This variability is also observed in instrumental records, though the short length of these records makes it challenging to assess the oscillatory nature. The GFDL coupled atmosphere-ocean model, with a 1000-year integration, also shows significant multidecadal variability in the North Atlantic, involving fluctuations in the thermohaline circulation. The model's variability is compared to observed patterns using instrumental and proxy-based data, demonstrating substantial agreement in sea surface temperature (SST) but less so in sea level pressure (SLP). Seasonal analyses indicate that SST is the primary carrier of the multidecadal signal, while SLP patterns vary seasonally. The paper highlights the importance of understanding internal climate variability for long-range forecasting and societal decision-making, particularly in the context of anthropogenic impacts.The paper examines the multidecadal variability in the Northern Hemisphere, focusing on the North Atlantic region. Proxy-based reconstructions of surface temperatures over the past 330 years reveal a distinct oscillatory mode with a time scale of approximately 70 years. This variability is also observed in instrumental records, though the short length of these records makes it challenging to assess the oscillatory nature. The GFDL coupled atmosphere-ocean model, with a 1000-year integration, also shows significant multidecadal variability in the North Atlantic, involving fluctuations in the thermohaline circulation. The model's variability is compared to observed patterns using instrumental and proxy-based data, demonstrating substantial agreement in sea surface temperature (SST) but less so in sea level pressure (SLP). Seasonal analyses indicate that SST is the primary carrier of the multidecadal signal, while SLP patterns vary seasonally. The paper highlights the importance of understanding internal climate variability for long-range forecasting and societal decision-making, particularly in the context of anthropogenic impacts.