Econometric Measurement of Earth\u27s Transient Climate Sensitivity

How sensitive is Earth’s climate to a given increase in atmospheric greenhouse gas (GHG) concentrations? This long-standing and fundamental question in climate science was recently analyzed by dynamic panel data methods using extensive spatiotemporal data of global surface temperatures, solar radiation, and GHG concentrations over the last half century to 2010 (Storelvmo et al, 2016). These methods revealed that atmospheric aerosol effects masked approximately one-third of the continental warming due to increasing GHG concentrations over this period, thereby implying greater climate sensitivity to GHGs than previously thought. The present study provides asymptotic theory justifying the use of these methods when there are stochastic process trends in both the global forcing variables, such as GHGs, and station-level trend effects from such sources as local aerosol pollutants. These asymptotics validate con dence interval construction for econometric measures of Earth’s transient climate sensitivity. The methods are applied to observational data and to data generated from three leading global climate models (GCMs) that are sampled spatio-temporally in the same way as the empirical observations. The fi ndings indicate that estimates of transient climate sensitivity produced by these GCMs lie within empirically determined con dence limits but that the GCMs uniformly underestimate the effects of aerosol induced dimming. The analysis shows the potential of econometric methods to calibrate GCM performance against observational data and to reveal the respective sensitivity parameters (GHG and non-GHG related) governing GCM temperature trends

Using Satellite Observations to Evaluate Model Microphysical Representation of Arctic Mixed-Phase Clouds

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Exploring the Cloud Top Phase Partitioning in Different Cloud Types Using Active and Passive Satellite Sensors

One of the largest uncertainties in numerical weather prediction and climate models is the representation of mixed-phase clouds. With the aim of understanding how the supercooled liquid fraction (SLF) in clouds with temperature from -40$^\circ$C to 0$^\circ$C is related to temperature, geographical location, and cloud type, our analysis contains a comparison of four satellite-based datasets (one derived from active and three from passive satellite sensors), and focuses on SLF distribution near-globally, but also stratified by latitude and continental/maritime regions. Despite the warm bias in cloud top temperature of the passive sensor compared to the active sensor and the phase mismatch in collocated data, all datasets indicate, at the same height-level, an increase of SLF with cloud optical thickness, and generally larger SLF in the Southern Hemisphere than in the Northern Hemisphere (up to about 20\% difference), with the exception of continental low-level clouds, for which the opposite is true

Exploring the Cloud Top Phase Partitioning in Different Cloud Types Using Active and Passive Satellite Sensors

One of the largest uncertainties in numerical weather prediction and climate models is the representation of mixed-phase clouds. With the aim of understanding how the supercooled liquid fraction (SLF) in clouds with temperature from -40$^\circ$C to 0$^\circ$C is related to temperature, geographical location, and cloud type, our analysis contains a comparison of four satellite-based datasets (one derived from active and three from passive satellite sensors), and focuses on SLF distribution near-globally, but also stratified by latitude and continental/maritime regions. Despite the warm bias in cloud top temperature of the passive sensor compared to the active sensor and the phase mismatch in collocated data, all datasets indicate, at the same height-level, an increase of SLF with cloud optical thickness, and generally larger SLF in the Southern Hemisphere than in the Northern Hemisphere (up to about 20\% difference), with the exception of continental low-level clouds, for which the opposite is true

Importance of Orography for Greenland cloud and melt response to atmospheric blocking

Author Posting. © American Meteorological Society, 2020. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Hahn, L. C., Storelvmo, T., Hofer, S., Parfitt, R., & Ummenhofer, C. C. Importance of Orography for Greenland cloud and melt response to atmospheric blocking. Journal of Climate, 33(10), (2020): 4187-4206, doi:10.1175/JCLI-D-19-0527.1.More frequent high pressure conditions associated with atmospheric blocking episodes over Greenland in recent decades have been suggested to enhance melt through large-scale subsidence and cloud dissipation, which allows more solar radiation to reach the ice sheet surface. Here we investigate mechanisms linking high pressure circulation anomalies to Greenland cloud changes and resulting cloud radiative effects, with a focus on the previously neglected role of topography. Using reanalysis and satellite data in addition to a regional climate model, we show that anticyclonic circulation anomalies over Greenland during recent extreme blocking summers produce cloud changes dependent on orographic lift and descent. The resulting increased cloud cover over northern Greenland promotes surface longwave warming, while reduced cloud cover in southern and marginal Greenland favors surface shortwave warming. Comparison with an idealized model simulation with flattened topography reveals that orographic effects were necessary to produce area-averaged decreasing cloud cover since the mid-1990s and the extreme melt observed in the summer of 2012. This demonstrates a key role for Greenland topography in mediating the cloud and melt response to large-scale circulation variability. These results suggest that future melt will depend on the pattern of circulation anomalies as well as the shape of the Greenland Ice Sheet.This research was supported by the Woods Hole Oceanographic Institution Summer Student Fellow program, by the U.S. National Science Foundation under AGS-1355339 to C.C.U., and by the European Research Council through Grant 758005

Contributions of Mixed-Phase Clouds to Reduced Arctic Amplification

Earths Arctic is particularly sensitive to global warming. The climate record shows that Arctic changes in surface temperatures far exceed that of the global mean, a phenomenon referred to as Arctic amplification. Here, we show that warming of the Arctic atmosphere causes mixed-phase clouds in the region to contain less ice and more supercooled liquid, which in turn tends to increase their amount and thick- ness, thereby inducing a positive feedback mainly by increasing downward longwave (LW) radiation at the surface. The increased downward LW radiation decreases the positive lapse rate feedback in the Arctic, thus resulting in reduced Arctic amplification. The strength of this feedback depends on the initial mean-state supercooled liquid fraction (SLF) and the ice crystal effective radii. We also show that reduced precipitation rates can result from large mean-state ice effective radii being replaced by relatively more smaller liquid droplets in the cloud phase feedback, despite having high mean-state SLFs, demonstrating the importance of the representation of cloud microphysics in the Arctic

A process study on thinning of Arctic winter cirrus clouds with high‐resolution ICON‐ART simulations

In this study, cloud‐resolving simulations of a case study for a limited area of the hibernal Arctic were performed with the atmospheric modeling system ICON‐ART (ICOsahedral Nonhydrostatic‐Aerosol and Reactive Trace gases). A thorough comparison with data both from satellite as well as aircraft measurement is presented to validate the simulations. In addition, the model is applied to clarify the microphysical processes occurring when introducing artificial aerosol particles into the upper troposphere with the aim of modifying cirrus clouds in the framework of climate engineering. Former modeling studies investigating the climate effect of this method were performed with simplifying assumptions and much coarser resolution, reaching partly contradicting conclusions concerning the method's effectiveness. The primary effect of seeding is found to be a reduction of ice crystal number concentrations in cirrus clouds, leading to increased outgoing longwave radiative fluxes at the top of the atmosphere, thereby creating a cooling effect. Furthermore, a secondary effect is found, as ice crystals formed from the injected seeding aerosol particles lead to enhanced riming of cloud droplets within the planetary boundary layer. This effectively reduces the coverage of mixed‐phase clouds, thus generating additional cooling by increased upward longwave radiative fluxes at the surface. The efficacy of seeding cirrus clouds proves to be relatively independent from the atmospheric background conditions, scales with the number concentrations of seeding particles, and is highest for large aerosol particles

Intercomparison of the cloud water phase among global climate models

Mixed‐phase clouds (clouds that consist of both cloud droplets and ice crystals) are frequently present in the Earth's atmosphere and influence the Earth's energy budget through their radiative properties, which are highly dependent on the cloud water phase. In this study, the phase partitioning of cloud water is compared among six global climate models (GCMs) and with Cloud and Aerosol Lidar with Orthogonal Polarization retrievals. It is found that the GCMs predict vastly different distributions of cloud phase for a given temperature, and none of them are capable of reproducing the spatial distribution or magnitude of the observed phase partitioning. While some GCMs produced liquid water paths comparable to satellite observations, they all failed to preserve sufficient liquid water at mixed‐phase cloud temperatures. Our results suggest that validating GCMs using only the vertically integrated water contents could lead to amplified differences in cloud radiative feedback. The sensitivity of the simulated cloud phase in GCMs to the choice of heterogeneous ice nucleation parameterization is also investigated. The response to a change in ice nucleation is quite different for each GCM, and the implementation of the same ice nucleation parameterization in all models does not reduce the spread in simulated phase among GCMs. The results suggest that processes subsequent to ice nucleation are at least as important in determining phase and should be the focus of future studies aimed at understanding and reducing differences among the models. Key Points Phase partitioning of cloud water in GCMs is investigated Cloud water phase in GCMs is compared to satellite observations Ice nucleation parameterization influence on cloud water phase is investigatedPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106995/1/jgrd51239.pd

The Wegener-Bergeron-Findeisen process – Its discovery and vital importance for weather and climate

The Wegener-Bergeron-Findeisen process refers to the rapid growth of ice crystals at the expense of surrounding cloud droplets, which frequently occurs in atmospheric mixed-phase clouds. The process is a result of the difference in saturation vapor pressures with respect to liquid and ice, and may in some circumstances lead to abrupt and complete cloud glaciation at temperatures between −40 °C and 0 °C in the Earth's atmosphere. The process is named after three eminent scientists who were active in the first half of the 20th century, among them being German meteorologist Walter Findeisen (1909–1945). In his classical paper published in 1938, Findeisen described the contemporary understanding of the Wegener-Bergeron-Findeisen process and other key cloud microphysical processes. Here, we compare the understanding of the aforementioned processes at the time with that of the present, and find that they are remarkably similar. We also discuss how the Wegener-Bergeron-Findeisen process is implemented in state-of-the-art numerical models of the atmosphere, and highlight its importance for both weather and climate