Search

El Niño‐Southern Oscillation (ENSO) variability affects year‐to‐year changes in North American hydroclimate. Extra‐tropical teleconnections are not always consistent between El Niño events due to stochastic atmospheric variability and diverse sea surface temperature anomalies, making it difficult to quantify teleconnections using only instrumentally‐based records. Here we use two paleoclimate data assimilation (DA) products spanning the Last Millennium (LM) to compare changes in amplitudes and frequencies of diverse El Niño events during the pre‐industrial period and 20th century, and to assess the stationarity of their North American hydroclimate impacts on multi‐decadal to centennial timescales. Using several definitions for Central Pacific (CP) and Eastern Pacific (EP) El Niño, we find a marked increase in 20th century EP El Niño intensity, but no significant changes in CP or EP El Niño frequencies in response to anthropogenic forcing. The associated hydroclimate anomalies indicate (a) dry conditions across the eastern‐central and northwestern U.S. during CP El Niño and wetter conditions in the same regions during EP El Niño; (b) wet conditions over the southwestern U.S. for both El Niño types. The magnitude of regional hydroclimate teleconnections also shows large natural variability on multi‐decadal to centennial timescales. However, when the entire LM is considered, mean hydroclimate anomalies in North America during CP or EP El Niño are consistent in terms of sign (wet vs. dry). Results are sensitive to proxy data and model priors used in DA products. Inconsistencies between El Niño classification methods underscore the need for improved ENSO diversity classification when assessing precipitation teleconnections. Key Points: Last Millennium data assimilation products show intensification of Eastern Pacific (EP) El Niño events in the 20th century, but no change in Central Pacific (CP) frequencyShifts in El Niño hydroclimate teleconnections over North America are variable in magnitude, but consistent in sign over the Last MillenniumInconsistent results between CP and EP El Niño definitions suggest a need for more robust, spatially dependent classification schemes [ABSTRACT FROM AUTHOR]

Reconstructing past climates remains a difficult task because pre‐instrumental observational networks are composed of geographically sparse and noisy paleoclimate proxy records that require statistical techniques to inform complete climate fields. Traditionally, instrumental or climate model statistical relationships are used to spread information from proxy measurements to other locations and to other climate variables. Here ensembles drawn from single climate models and from combinations of multiple climate models are used to reconstruct temperature variability over the last millennium in idealized experiments. We find that reconstructions derived from multi‐model ensembles produce lower error than reconstructions from single‐model ensembles when reconstructing independent model and instrumental data. Specifically, we find the largest decreases in error over regions far from proxy locations that are often associated with large uncertainties in model physics, such as mid‐ and high‐latitude ocean and sea‐ice regions. Furthermore, we find that multi‐model ensemble reconstructions outperform single‐model reconstructions that use covariance localization. We propose that multi‐model ensembles could be used to improve paleoclimate reconstructions in time periods beyond the last millennium and for climate variables other than air temperature, such as drought metrics or sea ice variables. Plain Language Summary: Understanding past climate variability is important for contextualizing climate change as well as for testing the ability of climate models to simulate the climate system before global warming. However, reconstructing past climate variability remains a complex task because pre‐instrumental paleoclimate proxy records, such as tree rings, corals, ice cores, and sediment cores, are geographically sparse and are not perfect recorders of climate information. Exactly how to extrapolate information from paleoclimate proxies to other locations and climate variables remains an outstanding issue. Traditionally, information about how one location varies with another location or variable (covariance) is derived from one climate model or from one instrumental data source. Here we find that reconstructions using covariance estimated from combinations of multiple climate models produce less error than reconstructions that use just one climate model. Key Points: Ensembles drawn from single climate models and combinations of multiple models are used to reconstruct surface air temperature variabilityReconstructions from multi‐model ensembles produce lower error than reconstructions from single‐model ensemblesReconstructions from multi‐model ensembles show the largest decreases in error in regions with few observations such as high‐latitude oceans [ABSTRACT FROM AUTHOR]

Paleoclimate reconstruction relies on estimates of spatiotemporal relationships among climate quantities to interpolate between proxy data. This work quantifies how structural uncertainties in those relationships translate to uncertainties in reconstructions of past climate. We develop and apply a data assimilation uncertainty quantification approach to paleoclimate networks and observational uncertainties representative of data for the last millennium. We find that structural uncertainties arising from uncertain spatial covariance relationships typically contribute 10% of the total uncertainty in reconstructed temperature variability at small (∼200 km), continental, and hemispheric length scales, with larger errors (50% or larger) in regions where long‐range climate covariances are least certain. These structural uncertainties contribute far more to errors in uncertainty quantification, sometimes by a factor of 5 or higher. Accounting for and reducing uncertainties in climate model dynamics and resulting covariance relationships will improve paleoclimate reconstruction accuracy. Plain Language Summary: Reconstructing past climate conditions is important to characterize climate variability and extremes, with implications for understanding future variability and change. An important source of information is data from proxy sources like tree rings, ice cores, ocean sediment cores, and other sources, but those data are not available everywhere on the globe. Extracting climate information from these data requires information about how climate properties covary in space and time, and that information is inevitably flawed because we lack a complete picture of how the climate behaves. This work seeks to address how much those flaws matter, how they propagate to reconstructions of past climate, and how we can reduce them. We use different physically plausible representations of climate physics from three different coupled climate models in a series of reconstruction experiments and suggest that uncertain physics typically accounts for 10% of reconstruction errors in the particular case of reconstructing surface air temperature over the Last Millennium, with regionally higher values (50% or larger). We can reduce these errors by improving climate models and by improving how we represent uncertain physics in climate reconstructions. Key Points: Perfect model experiments show how uncertain climate covariance relationships lead to uncertainties in paleoclimate reconstructionsThese uncertainties typically account for 10% of total error in reconstructed Last Millennium air temperature, with large regional variabilityImproving climate model physics and accounting for its uncertainty will improve reconstruction accuracy [ABSTRACT FROM AUTHOR]

Accurate assessments of future climate impacts require realistic simulation of interannual-century-scale temperature and precipitation variability. Here, well-constrained paleoclimate data and the latest generation of Earth system model data are used to evaluate the magnitude and spatial consistency of climate variance distributions across interannual to centennial frequencies. It is found that temperature variance generally increases with time scale in patterns that are spatially consistent among models, especially over the mid- and high-latitude oceans. However, precipitation is similar to white noise across much of the globe. When Earth system model variance is compared to variance generated by simple autocorrelation, it is found that tropical temperature variability in Earth system models is difficult to distinguish from variability generated by simple autocorrelation. By contrast, both forced and unforced Earth system models produce variability distinct from a simple autoregressive process over most high-latitude oceans. This new analysis of tropical paleoclimate records suggests that low-frequency variance dominates the temperature spectrum across the tropical Pacific and Indian Oceans, but in many Earth system models, interannual variance dominates the simulated central and eastern tropical Pacific temperature spectrum, regardless of forcing. Tropical Pacific model spectra are compared to spectra from the instrumental record, but the short instrumental record likely cannot provide accurate multidecadal-centennial-scale variance estimates. In the coming decades, both forced and natural patterns of decade-century-scale variability will determine climate-related risks. Underestimating low-frequency temperature and precipitation variability may significantly alter our understanding of the projections of these climate impacts. [ABSTRACT FROM AUTHOR]