Search

Climate changes across the past 24,000 years provide key insights into Earth system responses to external forcing. Climate model simulations.sup.1,2 and proxy data.sup.3-8 have independently allowed for study of this crucial interval; however, they have at times yielded disparate conclusions. Here, we leverage both types of information using paleoclimate data assimilation.sup.9,10 to produce the first proxy-constrained, full-field reanalysis of surface temperature change spanning the Last Glacial Maximum to present at 200-year resolution. We demonstrate that temperature variability across the past 24 thousand years was linked to two primary climatic mechanisms: radiative forcing from ice sheets and greenhouse gases; and a superposition of changes in the ocean overturning circulation and seasonal insolation. In contrast with previous proxy-based reconstructions.sup.6,7 our results show that global mean temperature has slightly but steadily warmed, by ~0.5 °C, since the early Holocene (around 9 thousand years ago). When compared with recent temperature changes.sup.11, our reanalysis indicates that both the rate and magnitude of modern warming are unusual relative to the changes of the past 24 thousand years. Paleoclimate datasets are integrated with a climate model to reconstruct global surface temperature since the Last Glacial Maximum, showing sustained warming until the mid-Holocene., Author(s): Matthew B. Osman [sup.1] , Jessica E. Tierney [sup.1] , Jiang Zhu [sup.2] , Robert Tardif [sup.3] , Gregory J. Hakim [sup.3] , Jonathan King [sup.1] , Christopher J. [...]

Climate field reconstruction (CFR) refers to the estimation of spatiotemporal climate fields (such as surface temperature) from a collection of pointwise paleoclimate proxy datasets. The climate fields can provide rich information on climate dynamics and provide an out-of-sample validation of climate models. However, most CFR workflows are complex and timeconsuming, as they involve: (i) preprocessing of the proxy records, climate model simulations, and instrumental observations, (ii) application of one or more statistical methods, and (iii) analysis and visualization of the reconstruction results. Historically, this process has lacked transparency and accessibility, limiting reproducibility and experimentation by non-specialists. This article presents an open-source and object-oriented Python package called cfr that aims to make CFR workflows easy to understand and conduct, saving climatologists from technical details and facilitating efficient and reproducible research. It provides user-friendly utilities for common CFR tasks such as proxy and climate data analysis and visualization, proxy system modeling, and modularized workflows for multiple reconstruction methods, enabling methodological intercomparisons within the same framework. The package is supported with an extensive documentation of the application interface (API) and a growing number of tutorial notebooks illustrating its usage. As an example, we present two cfr -driven reconstruction experiments using the PAGES 2k temperature database: applying the last millennium reanalysis (LMR) paleoclimate data assimilation (PDA) framework and the Graphical Expectation-Maximization (GraphEM) algorithm, respectively. [ABSTRACT FROM AUTHOR]

Climate field reconstruction (CFR) refers to the estimation of spatiotemporal climate fields (such as surface temperature) from a collection of pointwise paleoclimate proxy datasets. The climate fields can provide rich information on climate dynamics and provide an out-of-sample validation of climate models. However, most CFR workflows are complex and timeconsuming, as they involve: (i) preprocessing of the proxy records, climate model simulations, and instrumental observations, (ii) application of one or more statistical methods, and (iii) analysis and visualization of the reconstruction results. Historically, this process has lacked transparency and accessibility, limiting reproducibility and experimentation by non-specialists. This article presents an open-source and object-oriented Python package called cfr that aims to make CFR workflows easy to understand and conduct, saving climatologists from technical details and facilitating efficient and reproducible research. It provides user-friendly utilities for common CFR tasks such as proxy and climate data analysis and visualization, proxy system modeling, and modularized workflows for multiple reconstruction methods, enabling methodological intercomparisons within the same framework. The package is supported with an extensive documentation of the application interface (API) and a growing number of tutorial notebooks illustrating its usage. As an example, we present two cfr-driven reconstruction experiments using the PAGES 2k temperature database: applying the last millennium reanalysis (LMR) paleoclimate data assimilation (PDA) framework and the Graphical Expectation-Maximization (GraphEM) algorithm, respectively. [ABSTRACT FROM AUTHOR]

To gain insights into the poorly understood phenomenon of precipitation fog, this study assesses the evaporation of freely falling drops departing from equilibrium as a possible contributing factor to fog formation in rainy conditions. The study is based on simulations performed with a microphysical column model describing the evolution of the temperature and mass of evaporating raindrops within a Lagrangian reference frame. Equilibrium defines a state where the latent heat loss of an evaporating drop is balanced by the sensible heat flux from the ambient air, hence defining a steady-state drop temperature. Model results show that the assumption of equilibrium leads to small but significant errors in calculated precipitation evaporation rates for drops falling in continuously varying ambient near-saturated or saturated conditions. Departure from equilibrium depends on the magnitude of the vertical gradients of the ambient temperature and moisture as well as the drop-size-dependent terminal velocity. Contrasting patterns of behavior occur depending on the stratification of the atmosphere. Raindrops falling in inversion layers remain warmer than the equilibrium temperature and lead to enhanced moistening, with supersaturation achieved when evaporation proceeds in saturated inversions. Dehydration occurs in layers with temperature and water vapor increasing with height due to the vapor flux from the environment to the colder drops. These contrasts are not represented when equilibrium is assumed. The role of nonequilibrium raindrop evaporation in fog occurrences is further emphasized with simulations of a case study characterized by fog forming under light rain falling in a developing frontal inversion. Good agreement is obtained between fog water content observations and simulations representing only the effects of rainfall evaporation. This study demonstrates the need to take into account the nonequilibrium state of falling raindrops for a proper representation of an important mechanism contributing to precipitation fog occurrences. DOI: 10.1175/2009JAS3149.1