Abstract
In this paper, we examine differences in cloud adjustments (often called rapid adjustments) that occur as a direct result of abruptly increasing the solar constant by 4% or abruptly quadrupling of atmospheric CO2. In doing so, we devise a novel method for calculating the cloud adjustments for the abrupt solar forcing simulations that uses differences between coupled model simulations with abrupt solar and CO2 forcing, in combination with uncoupled, atmosphere-only, abrupt CO2 forced experiments that have prescribed sea-surface temperature. Our main findings are that (a) there are substantial differences in the responses of stratocumulus and cumulus clouds to solar and CO2 forcing, which follow the differences in the direct radiative effect that solar and CO2 forcing have at cloud top, and (b) there are differences in the adjustment of the average optical depth of high clouds to solar and CO2 forcing that we speculate are driven by the differences in the vertical profile of radiative heating and differences in the pattern of sea-surface temperature change (for a fixed global mean temperature). These cloud adjustments contribute significantly to the total net cloud radiative effect, even after 150 years of simulation.
Abstract
The atmospheric large-scale environment determines the occurrence of local extreme precipitation, and it is unclear how climate change affects this relationship. Here we investigate the present-day relationship between large-scale circulation types (CTs) and daily precipitation extremes over Scandinavia and its future change. A 50-member EC-Earth3 large ensemble is used to assess future changes against internal variability. We show that CTs are related to extreme precipitation over the entire domain. The intensity of extreme daily precipitation increases in all seasons in the future climate, generally following the strength of warming in the six different future scenarios considered. However, no significant future change is found in the relationship between extreme precipitation and the CTs in any season or scenario. The results have important implications for applications that rely on the stability of this relationship, such as statistical and event-based dynamical downscaling of future weather and climate predictions and long-term climate projections.
Abstract
We study the statistical features of magnetosphere-ionosphere (M-I) coupling using a two-way M-I model, the GT configuration of the Multiscale Atmosphere Geospace Environment (MAGE) model. The M-I coupling characteristics, such as field-aligned current, polar cap potential, ionospheric Joule heating, and downward Alfvénic Poynting flux, are binned according to the interplanetary magnetic field clock angles over an entire Carrington Rotation event between 20 March and 16 April 2008. The MAGE model simulates similar distributions of field-aligned currents compared to empirical Weimer/AMPS models and Iridium observations and reproduces the Region 0 current system. The simulated convection potential agrees well with the Weimer empirical model and displays consistent two-cell patterns with SuperDARN observations, which benefit from more extensive data sets. The Joule heating structure in MAGE is generally consistent with both empirical Cosgrove and Weimer models. Moreover, our model reproduces Joule heating enhancements in the cusp region, as presented in the Cosgrove model and observations. The distribution of the simulated Alfvénic Poynting flux is consistent with that observed by the FAST satellite in the dispersive Alfvén wave regime. These M-I coupling characteristics are also binned by the Kp indices, indicating that the Kp dependence of these patterns in the M-I model is more effective than the empirical models within the Carrington Rotation. Furthermore, the MAGE simulation exhibits an improved M-I current-voltage relation that closely resembles the Weimer model, suggesting that the updated global model is significantly improved in terms of M-I coupling.
Abstract
Solar wind is an intermediary in energy transfer from the Sun into the Earth's magnetosphere, and is considered as a decisive driver of energetic electron dynamics at the geosynchronous orbit (GEO). Based on machine learning technology, several models driven by solar wind parameters have been established to predict GEO electron fluxes. However, the relative contributions of different solar wind parameters on GEO electron fluxes are still unclear. Recently, a feature attribution method, Deep SHapley Additive exPlanations (Deep SHAP) is proposed to open black boxes of machine learning models. In this study, we use the Deep SHAP method to quantify contributions of different solar wind parameters with an artificial neural network (ANN) model. Backpropagating the prediction results of this ANN model from 2011 to 2020, SHAP values for four solar wind parameters (interplanetary magnetic field (IMF) B
Z, solar wind speed, solar wind dynamic pressure, and proton density) are calculated and comprehensively analyzed. The results suggest that solar wind speed with a lag of 1 day is the most important driver. We further investigate relative roles of different parameters in three specific electron fluxes variation events (corresponding to electron fluxes reaching a local maximum, a local minimum, and unchanged, respectively). The results suggest that high solar wind speed and southward IMF B
Z (high dynamic pressures) facilitate increases (decreases) of electron fluxes. These findings help reveal the underlying physical mechanisms of GEO electron dynamics and help develop more accurate prediction models for GEO electron fluxes.
Compound droughts under climate change in Switzerland
Christoph Nathanael von Matt, Regula Muelchi, Lukas Gudmundsson, and Olivia Martius
Nat. Hazards Earth Syst. Sci., 24, 1975–2001, https://doi.org/10.5194/nhess-24-1975-2024, 2024
The simultaneous occurrence of meteorological (precipitation), agricultural (soil moisture), and hydrological (streamflow) drought can lead to augmented impacts. By analysing drought indices derived from the newest climate scenarios for Switzerland (CH2018, Hydro-CH2018), we show that with climate change the concurrence of all drought types will increase in all studied regions of Switzerland. Our results stress the benefits of and need for both mitigation and adaptation measures at early stages.
Estimating the refractivity bias of FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) radio occultation in the deep troposphere
Gia Huan Pham, Shu-Chih Yang, Chih-Chien Chang, Shu-Ya Chen, and Cheng Yung Huang
Atmos. Meas. Tech., 17, 3605–3623, https://doi.org/10.5194/amt-17-3605-2024, 2024
This research examines the characteristics of low-level GNSS radio occultation (RO) refractivity bias over ocean and land and its dependency on the RO retrieval uncertainty, atmospheric temperature, and moisture. We propose methods for estimating the region-dependent refractivity bias. Our methods can be applied to calibrate the refractivity bias under different atmospheric conditions and thus improve the applications of the GNSS RO data in the deep troposphere.
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, 2024
This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Stability requirements of satellites to detect long-term stratospheric ozone trends based upon Monte Carlo simulations
Mark Weber
Atmos. Meas. Tech., 17, 3597–3604, https://doi.org/10.5194/amt-17-3597-2024, 2024
We investigate how stable the performance of a satellite instrument has to be to be useful for assessing long-term trends in stratospheric ozone. The stability of an instrument is specified in percent per decade and is also called instrument drift. Instrument drifts add to uncertainties of long-term trends. From simulated time series of ozone based on the Monte Carlo approach, we determine stability requirements that are needed to achieve the desired long-term trend uncertainty.
Design and evaluation of BOOGIE: a collector for the analysis of cloud composition and processes: Biological, Organics, Oxidants, soluble Gases, inorganic Ions and metal Elements
Mickael Vaitilingom, Christophe Bernard, Mickael Ribeiro, Christophe Berthod, Angelica Bianco, and Laurent Deguillaume
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-95,2024
Preprint under review for AMT (discussion: open, 0 comments)
The new collector BOOGIE has been designed and evaluated to sample cloud droplets. Computational fluid dynamic simulations are performed to evaluate the sampling efficiency for different droplets size. In situ measurements show very good water collection rates and sampling efficiency. BOOGIE is compared to other cloud collectors and the efficiency is comparable, as well as the chemical and biological compositions.
Incorporating Oxygen Isotopes of Oxidized Reactive Nitrogen in the Regional Atmospheric Chemistry Mechanism, version 2 (ICOIN-RACM2)
Wendell W. Walters, Masayuki Takeuchi, Nga L. Ng, and Meredith G. Hastings
Geosci. Model Dev., 17, 4673–4687, https://doi.org/10.5194/gmd-17-4673-2024, 2024
The study introduces a novel chemical mechanism for explicitly tracking oxygen isotope transfer in oxidized reactive nitrogen and odd oxygen using the Regional Atmospheric Chemistry Mechanism, version 2. This model enhances our ability to simulate and compare oxygen isotope compositions of reactive nitrogen, revealing insights into oxidation chemistry. The approach shows promise for improving atmospheric chemistry models and tropospheric oxidation capacity predictions.
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, 2024
We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Multivariate adjustment of drizzle bias using machine learning in European climate projections
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, 2024
This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
2021 Alaska Earthquake: entropy approach to its precursors and aftershock regimes
Eugenio E. Vogel, Denisse Pastén, Gonzalo Saravia, Michel Aguilera, and Antonio Posadas
Nat. Hazards Earth Syst. Sci. Discuss., https//doi.org/10.5194/nhess-2024-106,2024
Preprint under review for NHESS (discussion: open, 0 comments)
For the first time, an entropy analysis has been performed in Alaska, a seismic-rich region located in a subduction zone that shows non-trivial behavior: the subduction arc changes seismic activity from the eastern zone to the western zone, showing a decrease in this activity along subduction. This study shows how an entropy approach can help understand seismicity in subduction zones.
Scale size estimation and flow pattern recognition around a magnetosheath jet
Adrian Pöppelwerth, Georg Glebe, Johannes Z. D. Mieth, Florian Koller, Tomas Karlsson, Zoltán Vörös, and Ferdinand Plaschke
Ann. Geophys., 42, 271–284, https://doi.org/10.5194/angeo-42-271-2024, 2024
In the magnetosheath, a near-Earth region of space, we observe increases in plasma velocity and density, so-called jets. As they propagate towards Earth, jets interact with the ambient plasma. We study this interaction with three spacecraft simultaneously to infer their sizes. While previous studies have investigated their size almost exclusively statistically, we demonstrate a new method of determining the sizes of individual jets.
Abstract
There are many uncertainties in future climate, including how the Earth may react to different types of radiative forcing, such as CO2, aerosols, and even geoengineered changes in the amount of sunlight absorbed by Earth's surface. Here, we analyze model simulations where the climate system is subjected to an abrupt change of the solar constant by ±4%, and where the atmospheric CO2 concentration is abruptly changed to quadruple and half its preindustrial value. Using these experiments, we examine how clouds respond to changes in solar forcing, compared to CO2, and feedback on global surface temperature. The total cloud response can be decomposed into those responses driven by changes in global surface temperature, called the temperature mediated cloud feedbacks, and responses driven directly by the forcing that are independent of the global surface temperature. In this paper, we study the temperature mediated cloud changes to answer two primary questions: (a) How do temperature mediated cloud feedbacks differ in response to abrupt changes in CO2 and solar forcing? And (b) Are there symmetrical (equal and opposite) temperature mediated cloud feedbacks during global warming and global cooling? We find that temperature mediated cloud feedbacks are similar in response to increasing solar and increasing CO2 forcing, and we provide a short review of recent literature regarding the physical mechanisms responsible for these feedbacks. We also find that cloud responses to warming and cooling are not symmetric, due largely to non-linearity introduced by phase changes in mid-to-high latitude low clouds and sea ice loss/formation.
