A cloud event in the altitude range of 53-65 km was observed with lidars over Yanqing (40.5°N, 116°E) and Pingquan (41°N, 118.7°E) on 30 October 2018. Clouds with a multilayer structure first occurred within the line view of lidar at dawn (03:40-06:00LT). They were faint and tenuous, and the maximum volume backscatter coefficient (VBSC) was 1.4×10-10m-1sr-1. At twilight, clouds with multilayer structures were reobserved via lidars, but they became much thicker, with a maximum VBSC of 11.2×10-10m-1sr-1. The structure of the cloud layers varied with time, and they faded completely at approximately ~00:30 LT (+1 day). Measurements from SABER/TIMED were utilized for analysis, and it was found that before the onset of cloud event, a temperature anomaly occurred in the mesosphere over Beijing, and water vapor was also very abundant. The frost point temperature profile of water vapor was estimated, and lidar measurements showed that the atmospheric temperature was close to the frost point of water vapor in the vicinity of the stratopause when the mesosphere was undergoing a low-temperature phase. It was a rare mesospheric cloud event observed with lidars at rather low latitudes, and the clouds probably resulted from the nucleation of saturated water vapor due to the occurrence of a temperature anomaly in the mesosphere.
[This presentation is published at https://doi.org/10.1111/1440-1703.12317] Dead organic matter (DOM), which consists of leaf litter, fine woody debris (FWD; < 3 cm diameter), downed coarse woody debris (CWDlog), and standing or suspended coarse woody debris (CWDsnag), plays a crucial role in forest carbon cycling. However, the contributions of each DOM type on stand-scale carbon storage (necromass) and stand-scale CO2 efflux (Rstand) estimates are not well understood. In addition, there is little knowledge of the effect of each DOM type on the accuracy of stand-scale estimates of total necromass and Rstand. This study investigated characteristics of necromass and Rstand from DOM in a subtropical forest in Okinawa island, Japan, to quantify the effect of each DOM type on total necromass, total Rstand, and estimate error of total necromass and Rstand. The CWDsnag accounted for the highest proportion (54%) of total necromass (1499.7 g C m–2), followed by CWDlog (24%), FWD (11%), and leaf litter (11%). Leaf litter accounted for the highest proportion (37%) of total Rstand (340.6 g C m–2 yr–1), followed by CWDsnag (25%), CWDlog (20%), and FWD (17%). The CWDsnag was distributed locally with 173% of the coefficient of variation for necromass, which was approximately two times higher than those of leaf litter and FWD (72–73%). Our spatial analysis revealed, for accurate estimates of CWDsnag and CWDlog necromass, sampling areas of ≥ 28750 m2 and ≥ 2058‒42875 m2 were required, respectively, under the condition of 95% confidence level and 0.1 of accepted error. In summary, CWD considerably contributed to stand-scale carbon storage and efflux in this subtropical forest, resulting in a major source of errors in the stand-scale estimates. In forests where frequent tree death is likely to occur, necromass and Rstand of CWD are not negligible in considering the carbon cycling as in this study, and therefore need to be estimated accurately.
We studied the topographic heating center over the Tibetan and Persian plateaus that forms the Asian high to try to understand its effects on circulation in the stratosphere. The results show that the heating center at 300 hPa excites planetary waves which can be propogated into the southern hemisphere. The u wind regression field demonstrates the planetary wave propogates from the northern to the southern hemisphere. Once the planetary wave is propagated to the southern hemisphere, then it forms another three-wave train, which strengthens as it propagates upward. The wave propogation channel is at the upper troposphere above the equator. The results also denote that the heating center of Asian high may contribute to the QBO in the stratosphere which mechanism needs to be argued deeply. These results partially explain why there are planetary waves in the stratosphere of the southern hemisphere.
The Terrestrial Gamma-ray Flashes (TGF) to lightning ratio, computed over the 3 tropical chimneys, presents a paradox: African thunderstorms produce the most lightning but yield the lowest fraction of TGF when compared to American and Southeast Asian thunderclouds. To understand the physical insights into this asymmetry, TRMM Precipitation Radar measurements are used to depict the vertical precipitation structure of the observed thunderstorms in the 3 regions and the thunderstorms during TGF occurrences detected by AGILE, Fermi-GBM and RHESSI sensors. African thunderstorms are taller, smaller and have higher concentration of dense ice particles above the freezing level. TGF thunderstorms are taller and less intense (0.5-2dBZ), besides presenting similar radar reflectivity decay with height independent of the region. In addition, these storms show thicker electrical charge layers separated by 4.7-5.2 km and also a positive charge fraction reduction between -20 o C and -40 o C and enhancement above -50 o C when compared to the overall thunderstorms.
Biomass burning (BB) is one of the largest sources of absorbing aerosols globally and accounts for about 40% of black carbon in the atmosphere. The Southern African region contributes approximately 35% of Earth’s BB aerosol emissions. During the Southern Hemisphere winter, smoke is transported over the southeast Atlantic Ocean, overlying and mixing with a semi-permanent stratocumulus cloud deck. Aerosol-cloud interactions contribute the largest uncertainty to anthropogenic forcing, and the southeast Atlantic region exhibits a large model-to-model divergence of climate forcing. This makes the region particularly valuable for understanding these interactions and was one of the factors motivating the three-year NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) mission. Previous studies using ORACLES datasets have explored the distribution of aerosol and cloud particles, however, changes in some aerosol properties during transport are not well documented. This study investigates the evolution of biomass burning aerosol properties from emission within Southern Africa, transport over land, and then over the Atlantic. Measurements from a collection of airborne in situ and remote-sensing instruments including 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) along with ground-based AERONET (Aerosol Robotic Network) are combined with results from two regional models, the WRF-AAM and WRF-CAM5 to explore the changes in the optical properties of these smoke plumes as they age. The aerosol age is determined using tracers from the WRF-AAM configured with 12 km resolution over the region’s spatial domain (41 ºS – 14 ºN, 34 ºW – 51 ºE). Changes in extinction, single scattering Albedo (SSA) and angstrom exponent (AE) with age as well as a comparative analysis between observations and model results were carried out using datasets from airborne PSAP (Particle Soot Absorption Photometer) and nephelometers, 4STAR, AERONET, and WRF-CAM5.
The variability of the Southern Hemisphere (SH) extratropical large-scale circulation is dominated by the Southern Annular Mode (SAM), whose timescale is extensively used as a key metric in evaluating state-of-the-art climate models. Past observational and theoretical studies suggest that the SAM lacks any internally generated (intrinsic) periodicity. Here, we show, using observations and a climate model hierarchy, that the SAM has an intrinsic 150-day periodicity. This periodicity is robustly detectable in the power spectra and principal oscillation patterns (aka dynamical mode decomposition) of the zonal-mean circulation, and in hemispheric-scale precipitation and ocean surface wind stress. The 150-day period is consistent with the predictions of a new reduced-order model for the SAM, which suggests that this periodicity is tied with a complex interaction of turbulent eddies and zonal wind anomalies, as the latter propagate from low to high latitudes. These findings present a rare example of periodic oscillations arising from the internal dynamics of the extratropical turbulent circulations. Based on these findings, we further propose a new metric for evaluating climate models, and show that some of the previously reported shortcomings and improvements in simulating SAM’s variability connect to the models’ ability in reproducing this periodicity. We argue that this periodicity should be considered in evaluating climate models and understanding the past, current, and projected Southern Hemisphere climate variability.
Ice nucleation in mixed-phase clouds has recently been identified as a critical factor in projections of future climate. Here we explore how this process influences climate sensitivity using the Community Earth System Model 2 (CESM2). We find that ice nucleation affects simulated cloud feedbacks over most regions and levels of the troposphere, not just extratropical low clouds. Ice nucleation’s impact on climate sensitivity is found to primarily operate through this process’s role setting global-scale cloud phase. Conversely, whether ice nucleation is treated as aerosol-sensitive is of limited importance. In satellite-constrained model experiments, dissimilar ice nucleation realizations all result in a strongly positive total cloud feedback, as in the default CESM2. A microphysics update from CESM1 to CESM2 had substantially weakened ice nucleation, due partly to a model issue. Our findings suggest that this contributed to increased climate sensitivity by reducing global cloud phase bias, resulting in more realistic mixed-phase clouds.
Canada’s boreal forests and tundra ecosystems are responding to unprecedented climate change with implications for the global carbon (C) cycle and global climate. However, our ability to model the response of Canada’s terrestrial ecosystems to climate change is limited and there has been no comprehensive, process-based assessment of Canada’s terrestrial C cycle. We tailor the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) to Canada and evaluate its C cycling performance against independent reference data. We utilize skill scores to assess model performance against reference data alongside benchmark scores that quantify the level of agreement between the reference data sets to aid in interpretation. Our results demonstrate CLASSIC’s sensitivity to prescribed vegetation cover. They also show that the addition of five region-specific PFTs improves CLASSIC’s skill at simulating the Canadian C cycle. CLASSIC performs well when tailored to Canada, falls within the range of the reference data sets, and meets or exceeds the benchmark scores for most C cycling processes. New region-specific land cover products, well-informed plant functional type (PFT) parameterizations, and more detailed reference data sets will facilitate improvements to the representation of the terrestrial C cycle in regional and global land surface models (LSMs). Incorporating a parameterization for boreal disturbance processes and explicitly representing peatlands and permafrost soils will improve CLASSIC’s future performance in Canada and other boreal regions. This is an important step toward a comprehensive process-based assessment of Canada’s terrestrial C cycle and evaluating Canada’s net C balance under climate change.
Antarctic landfast sea ice (fast ice) is stationary sea ice that is attached to the coast, grounded icebergs, ice shelves, or other protrusions on the continental shelf. Fast ice forms in narrow (generally up to 200 km wide) bands, and ranges in thickness from centimeters to tens of meters. In most regions, it forms in autumn, persists through the winter and melts in spring/summer, but can remain throughout the summer in particular locations. Despite its relatively limited horizontal extent (comprising between about 4 and 13 \% of overall sea ice), its presence, variability and seasonality are drivers of a wide range of physical, biological and biogeochemical processes, with both local and far-ranging ramifications for various Earth systems. Antarctic fast ice has, until quite recently, been overlooked in studies, likely due to insufficient knowledge of its distribution, leading to its reputation as a “missing piece of the Antarctic puzzle”. This review presents a synthesis of current knowledge of the physical, biogeochemical and biological aspects of fast ice, based on the sub-domains of: fast ice growth, properties and seasonality; remote-sensing and distribution; interactions with the atmosphere and the ocean; biogeochemical interactions; its role in primary production; and fast ice as a habitat for grazers. Finally, we consider the potential state of Antarctic fast ice at the end of the 21st Century, underpinned by Coupled Model Intercomparison Project model projections. This review also gives recommendations for targeted future work to increase our understanding of this critically-important element of the global cryosphere.
In the atmosphere, there is an intimate relationship between clouds, atmospheric radiative cooling/heating, and radiatively induced circulations at various temporal and spatial scales. This coupling remains not well under- stood, which contributes to limiting our ability to model and predict clouds and climate accurately. Cloud liquid and ice particles interact with both shortwave (SW) and longwave (LW) radiation, leading to cloud radiative effect (CRE). The CRE includes perturbations of the radiative fluxes at the top of the atmosphere (TOA) and the surface, as well as perturbations of the radiative cooling pro- file within the atmosphere. The effect of clouds that results in atmospheric radiative heating or cooling that is distinct from the clear-sky radiative cooling profile will be termed the CRE on atmospheric heating, or CRE-AH. The CRE-AH can significantly modify the horizontal and vertical gradients of the diabatic heating profile, inducing circulations at various scales in the atmosphere. In turn, circulations govern cloud formation and evolution processes and therefore the properties and distribution of clouds.
Heatwaves damage societies globally and are intensifying with global warming. Several mechanistic drivers of heatwaves, such as atmospheric blocking and soil moisture-atmosphere feedback, are well-known for their ability to raise surface air temperature. However, what limits the maximum surface air temperature in heatwaves remains unknown; this became evident during recent Northern Hemisphere heatwaves which achieved temperatures far beyond the upper tail of the observed statistical distribution. Here, we present the hypothesis, with corroborating evidence, that convective instability limits annual maximum surface air temperatures (TXx) over midlatitude land. We provide a theory for the upper bound of midlatitude temperatures, which accurately describes the observed relationship between temperatures at the surface and in the mid-troposphere. Known heatwave drivers shift the position of the atmospheric state in the phase space described by the theory, changing its proximity to the upper bound.Our theory suggests that the upper bound for midlatitude TXx should increase 1.9 times as fast as 500-hPa temperatures. Using empirical 500-hPa warming, we project that the upper bound of TXx over Northern Hemisphere midlatitude land (40°N-65°N) will increase about twice as fast as global mean surface air temperature, and TXx will increase faster than this bound over regions that dry on the hottest days.
Through machine learning and remote sensing, a high-end model with a finer resolution for groundwater recharge has been developed for the region of South-East Asia. The groundwater recharge coefficient can be found by the application of Random Forest regression followed by the implication of the water budget method to calculate the Groundwater Recharge values. Climatic factors such as precipitation and actual evapotranspiration to map Groundwater Recharge has been framed with a sophisticated machine learning method to be considered as a scale predicting model. A comprehensive visualization of the dataset has been done; the accuracy of the model is noted through random forest regression. Thus, the model can be used for various regions of the dataset specifically for the area where there is a lack of reach for data. It can be successfully used to form a sophisticated end-to-end ML model. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.
Machine Learning and Remote sensing method to determine the relationship between Climate and Groundwater Recharge. Adya Aiswarya Dash1, Abhijit Mukherjee1,2,3. 1Department of Geology and Geophysics, Indian Institute of Technology Kharagpur, West Bengal 721302, India 2School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India 3Applied Policy Advisory for Hydrogeoscience (APAH) Group, Indian Institute of Technology Kharagpur, West Bengal 721302, India Abstract Through machine learning and remote sensing, a high-end model with a finer resolution for groundwater recharge has been developed for the region of South-East Asia. The groundwater recharge coefficient can be found by the application of Random Forest regression followed by the implication of the water budget method to calculate the Groundwater Recharge values. Climatic factors such as precipitation and actual evapotranspiration to map Groundwater Recharge has been framed with a sophisticated machine learning method to be considered as a scale predicting model. A comprehensive visualization of the dataset has been done; the accuracy of the model is noted through random forest regression. Thus, the model can be used for various regions of the dataset specifically for the area where there is a lack of reach for data. It can be successfully used to form a sophisticated end-to-end ML model. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.
This editorial aims to improve awareness of the current best practices in open research, and stimulate discussion on the practical implementation of AGU's data and software policy in key areas of space weather research. We also further aim to encourage authors to take additional steps to ensure clear credit to all contributors to the work, whether that is underlying data, key software, or direct contributions to the manuscript.
The analysis of the proton flux variations observed by the Energetic Particle Telescope (EPT) at energies > 9.5 MeV from the launch of PROBA-V satellite on 7 May 2013 up to October 2022 shows an anti-correlation between the proton fluxes and the solar phase. At solar minimum, the fluxes are higher at low L corresponding to the northern border of the South Atlantic Anomaly (SAA). This solar cycle modulation of the inner belt is mainly due to losses by increased atmospheric interactions during solar maximum. Strong Solar Energetic Particle (SEP) events, like in January 2014, June 2015 and September 2017, inject energetic protons at high latitudes, but not in the inner belt where protons are trapped at long term at low L. Nevertheless, big geomagnetic storms, including those following SEP a few days after, can cause losses of protons at the outer border of the proton belt, due to magnetic field perturbations. A double peak in the proton belt is observed during long period of measurements for the EPT channel of 9.5-13 MeV. The narrow gap between the two peaks in the inner belt is located around L=2. This resembles to a splitting of the proton belt, separating the SAA into two different parts, North and South. The high-resolution measurements of PROBA-V/EPT allow the observation of small-scale structures that brings new elements to the understanding of the different source and loss mechanisms acting on the proton radiation belt at LEO.
Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.
Low marine clouds are a major source of uncertainty in cloud feedbacks across climate models and in forcing by aerosol-cloud interactions. The evolution of these clouds and their response to aerosol are sensitive to the ambient environmental conditions, so it is important to be able to determine different responses over a representative set of conditions. Here, we propose a novel approach to encompassing the broad range of conditions present in low marine cloud regions, by building a library of observed environmental conditions. This approach can be used, for example, to more systematically test the fidelity of Large Eddy Simulations (LES) in representing these clouds. ERA5 reanalysis and various satellite observations are used to extract and derive macrophysical and microphysical cloud-controlling variables (CCVs) such as SST, estimated inversion strength (EIS), subsidence, and cloud droplet number concentrations. A few locations in the stratocumulus (Sc) deck region of the Northeast Pacific during summer are selected to fill out a phase space of CCVs. Thereafter, Principal Component Analysis (PCA) is applied to reduce the dimensionality and to select a reduced set of components that explain most of the variability among CCVs in order to efficiently select cases for LES simulations that encompass the observed CCV phase space. From this phase space, 75-100 cases with distinct environmental conditions will be selected and used to initialize 2-day LES modeling to provide a spectrum of aerosol-cloud interactions and Sc-to-Cumulus transition under observed ambient conditions. Such a large number of simulations will help create statistics to assess how well the LES can simulate the cloud lifecycle when constrained by the ‘best estimate’ of the environmental conditions, and how sensitive the modeled clouds are to changes in these driving fields.