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  1. Here, we show that the Last Glacial Maximum (LGM) provides a stronger constraint on equilibrium climate sensitivity (ECS), the global warming from increasing greenhouse gases, after accounting for temperature patterns. Feedbacks governing ECS depend on spatial patterns of surface temperature (“pattern effects”); hence, using the LGM to constrain future warming requires quantifying how temperature patterns produce different feedbacks during LGM cooling versus modern-day warming. Combining data assimilation reconstructions with atmospheric models, we show that the climate is more sensitive to LGM forcing because ice sheets amplify extratropical cooling where feedbacks are destabilizing. Accounting for LGM pattern effects yields a median modern-day ECS of 2.4°C, 66% range 1.7° to 3.5°C (1.4° to 5.0°C, 5 to 95%), from LGM evidence alone. Combining the LGM with other lines of evidence, the best estimate becomes 2.9°C, 66% range 2.4° to 3.5°C (2.1° to 4.1°C, 5 to 95%), substantially narrowing uncertainty compared to recent assessments.

     
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    Free, publicly-accessible full text available April 19, 2025
  2. Abstract. Paleoclimate data assimilation (DA) is a tool for reconstructing past climates that directly integrates proxy records with climate model output. 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. Here, we present DASH, a MATLAB toolbox designed to facilitate paleoclimate DA analyses. DASH provides command line and scripting tools that implement common tasks in DA workflows. 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 includes tools for integrating and cataloguing data stored in disparate formats, building state vector ensembles, and running proxy (system) forward models. The toolbox also provides optimized algorithms for implementing ensemble Kalman filters, particle filters, and optimal sensor analyses with variable and modular parameters. This paper reviews the key components of the DASH toolbox and presents examples illustrating DASH's use for paleoclimate DA applications.

     
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  3. Reconstruction of the North Atlantic jet stream (NAJ) presents a critical, albeit largely unconstrained, paleoclimatic target. Models suggest northward migration and changing variance of the NAJ under 21st-century warming scenarios, but assessing the significance of such projections is hindered by a lack of long-term observations. Here, we incorporate insights from an ensemble of last-millennium water isotope–enabled climate model simulations and a wide array of mean annual water isotope (δ18O) and annually accumulated snowfall records from Greenland ice cores to reconstruct North Atlantic zonal-mean zonal winds back to the 8th century CE. Using this reconstruction we provide preobservational constraints on both annual mean NAJ position and intensity to show that late 20th- and early 21st-century NAJ variations were likely not unique relative to natural variability. Rather, insights from our 1,250 year reconstruction highlight the overwhelming role of natural variability in thus far masking the response of midlatitude atmospheric dynamics to anthropogenic forcing, consistent with recent large-ensemble transient modeling experiments. This masking is not projected to persist under high greenhouse gas emissions scenarios, however, with model projected annual mean NAJ position emerging as distinct from the range of reconstructed natural variability by as early as 2060 CE.

     
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