Greenland climate simulations show high Eemian surface meltwhich could explain reduced total air content in ice cores Climate of the Past DOI 10.5194/cp-17-317-2021 16 February 2021 This study presents simulations of Greenland surface melt for the Eemian interglacial period (∼130,000 to 115, 000 years ago) derived from regional climate simulations with a coupled surface energy balance model. Read more
The Eocene–Oligocene transition: a review of marine and terrestrial proxy data, models and model–data comparisons Climate of the Past DOI 10.5194/cp-17-269-2021 15 February 2021 We find that CO2 forcing involving a large decrease in CO2 of ca. 40 % (∼325 ppm drop) provides the best fit to the available proxy evidence,with ice sheet and palaeogeographic changes playing a secondary role. Read more
Macroscopic water vapor diffusion is not enhanced in snow The Cryosphere DOI 10.5194/tc-15-389-2021 12 February 2021 Here we show using theory and numerical simulations of idealized and measured snow microstructures that, although sublimation and deposition of water vapor onto snow crystal surfaces do enhance microscopic diffusion in the pore space, this effect is more than countered by the restriction of diffusion space due to ice. Read more
Experiments on magnetic interference for a portable airborne magnetometry system using a hybrid unmanned aerial vehicle (UAV) Geoscientific Instrumentation, Methods and Data Systems DOI 10.5194/gi-10-25-2021 11 February 2021 An experiment concerning the static magnetic interference of the UAV was conducted to assess the severity of the interference of a hybrid vertical take-off and landing (VTOL) UAV. UAV)">Read more
Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity Climate of the Past DOI 10.5194/cp-17-253-2021 10 February 2021 Here we conduct a suite of Last Glacial Maximum (LGM) simulations using the Community Earth System Model version 1.2 (CESM1.2) to quantify the forcing and efficacy of land ice sheets (LISs) and greenhouse gases (GHGs) in order to estimate equilibrium climate sensitivity. LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity">Read more
Review article: Earth’s ice imbalance The Cryosphere DOI 10.5194/tc-15-233-2021 9 February 2021 We combine satellite observations and numerical models to show that Earth lost 28 trillion tonnes of ice between 1994 and 2017. Read more
A methodology for attributing the role of climate change in extreme events: a global spectrally nudged storyline Natural Hazards and Earth System Sciences DOI 10.5194/nhess-21-171-2021 8 February 2021 Extreme weather events are generally associated with unusual dynamical conditions, yet the signal-to-noise ratio of the dynamical aspects of climate change that are relevant to extremes appears to be small, and the nature of the change can be highly uncertain. Read more
Opinion: Cloud-phase climate feedback and the importance of ice-nucleatingparticles Atmospheric Chemistry and Physics DOI 10.5194/acp-21-665-2021 5 February 2021 Shallow clouds covering vast areas of the world’s middle- and high-latitude oceans play a key role in dampening the global temperaturerise associated with CO2. These clouds, which contain both ice andsupercooled water, respond to a warming world by transitioning to a statewith more liquid water and a greater albedo, resulting in a negative“cloud-phase” climate feedback component. Here we argue that the magnitudeof the negative cloud-phase feedback component depends on the amount andnature of the small fraction of aerosol particles that can nucleate icecrystals. We propose that a concerted research effort is required to reducesubstantial uncertainties related to the poorly understoodsources, concentration, seasonal cycles and nature of these ice-nucleatingparticles (INPs) and their rudimentary treatment in climate models. Thetopic is important because many climate models may have overestimated themagnitude of the cloud-phase feedback, and those with better representationof shallow oceanic clouds predict a substantially larger climate warming. Wemake the case that understanding the present-day INP population in shallowclouds in the cold sector of cyclone systems is particularly critical fordefining present-day cloud phase and therefore how the clouds respond towarming. We also need to develop a predictive capability for future INPemissions and sinks in a warmer world with less ice and snow and potentiallystronger INP sources. Read more
ModIs Dust AeroSol (MIDAS): a global fine-resolution dust optical depth data set Atmospheric Measurement Techniques DOI 10.5194/amt-14-309-2021 4 February 2021 Monitoring and describing the spatiotemporal variability in dust aerosols is crucial for understanding their multiple effects, related feedbacks, and impacts within the Earth system. This study describes the development of the ModIs Dust AeroSol (MIDAS) data set. MIDAS provides columnar daily dust optical depth (DOD) at 550 nm at a global scale and fine spatial resolution (0.1∘ × 0.1∘) over a 15-year period (2003–2017). This new data set combines quality filtered satellite aerosol optical depth (AOD) retrievals from MODIS-Aqua at swath level (Collection 6.1; Level 2), along with DOD-to-AOD ratios provided by the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis to derive DOD on the MODIS native grid. The uncertainties of the MODIS AOD and MERRA-2 dust fraction, with respect to the AEronet RObotic NETwork (AERONET) and LIdar climatology of vertical Aerosol Structure for space-based lidar simulation (LIVAS), respectively, are taken into account for the estimation of the total DOD uncertainty. MERRA-2 dust fractions are in very good agreement with those of LIVAS across the dust belt in thetropical Atlantic Ocean and the Arabian Sea; the agreement degrades in North America and the Southern Hemisphere, where dust sources are smaller. MIDAS, MERRA-2, and LIVAS DODs strongly agree when it comes to annual and seasonal spatial patterns, with colocated global DOD averages of 0.033, 0.031, and 0.029, respectively; however, deviations in dust loading are evident and regionally dependent. Overall, MIDAS is well correlated with AERONET-derived DODs (R=0.89) and only shows a small positive bias (0.004 or 2.7 %). Among the major dust areas of the planet, the highest R values (>0.9) are found at sites of North Africa, the Middle East, and Asia. MIDAS expands, complements, and upgrades the existing observational capabilities of dust aerosols, and it is suitable for dust climatological studies, model evaluation, and data assimilation. MIDAS): a global fine-resolution dust optical depth data set">Read more
On the ice-nucleating potential of warm hydrometeors in mixed-phase clouds Atmospheric Chemistry and Physics DOI 10.5194/acp-21-561-2021 3 February 2021 The question as to whether or not the presence of warm hydrometeors in clouds mayplay a significant role in the nucleation of new ice particles has been debatedfor several decades. While the early works of and indicated that it might be irrelevant, the more recentstudy of suggested otherwise. In this work, weattempt to quantify the ice-nucleating potential using high-fidelity flowsimulation techniques around a single hydrometeor and use favorable considerationsto upscale the effects to a collective of ice particles in clouds. While we find that ice nucleationmay be significantly enhanced in the vicinity of a warm hydrometeorand that the affected volume of air is much larger than previously estimated, it isunlikely that this effect alone causes the rapidenhancement of ice nucleation observed in some types of clouds, mainly due to the lowinitial volumetric ice concentration. Furthermore, it is demonstrated that the excess nucleationrate does not primarily depend on the rate at which cloud volume is sampled by the meteors’wakes but is rather limited by the exposure time of ice-nucleating particles to the wake,which is estimated to be of the order of few microseconds. It is suggested to further investigatethis phenomenon by tracking the trajectories of ice-nucleating particles in order to obtaina parametrization which can be implemented into existing cloud models to investigate second-order effectssuch as ice enhancement after the onset of glaciation. Read more
