Magnetic field draping occurs when the magnetic field lines frozen in a plasma flow wrap around a body or plasma environment. The draping of the interplanetary magnetic field (IMF) around the Earth’s magnetosphere has been confirmed in the early days of space exploration. However, its global and three-dimensional structure is known from modeling only, mostly numerical. Here, this structure in the dayside of the Earth’s magnetosheath is determined as a function of the upstream IMF orientation purely from in-situ spacecraft observations. We show the draping structure can be organized in three regimes depending on how radial the upstream IMF is. Quantitative analysis demonstrates how the draping pattern results from the magnetic field being frozen in the magnetosheath flow, deflected around the magnetopause. The role of the flow is emphasized by a comparison of the draping structure to that predicted to a magnetostatic draping.
The thermal balance of forests regulates land-atmosphere feedbacks. Forests dominated by different plant functional types have contrasting energy balances, but little is known about the influence of forest structure and functional traits. By combining spaceborne measurements of land surface temperature from ECOSTRESS with ground-based meteorological data, we estimate the thermal balance at the surface (∆Tcan-air) during four summers in a region located at the Mediterranean-temperate ecotone in the NE Iberian Peninsula. We then analyze the spatiotemporal drivers of ∆Tcan-air by quantifying the effects of meteorology, forest structure (e.g. basal area, tree height) and ecophysiology (hydraulic traits, water use efficiency), during normal days and hot spells. Canopy temperatures fluctuate according to changes in air temperature but are on average 3.2˚K warmer than the near-surface air. During hot spells, ∆Tcan-air is smaller than normal periods because the advection of hot and dry air masses from the Sahara region results in a sudden increase in air temperature relative to the canopy temperature. Vapor pressure deficit (VPD) is negatively correlated to ∆Tcan-air, since the highest VPD values coincide with peaks in heat advection. Still canopy temperatures increase with VPD due to decreased transpiration and stomatal conductance and transpiration. Meanwhile, soil water availability is shown to enhance evaporative cooling. Importantly, we demonstrate that plot-scale forest structural and hydraulic traits are key determinants for the forest thermal balance. The integration of functional traits and forest structure over relevant spatial scales could improve our ability to understand and model land-atmosphere feedbacks in forested regions.
Dynamical cores used to study the circulation of the atmosphere employ various numerical methods ranging from finite-volume, spectral element, global spectral, and hybrid methods. In this work, we explore the use of Flux-Differencing Discontinuous Galerkin (FDDG) methods to simulate a fully compressible dry atmosphere at various resolutions. We show that the method offers a judicious compromise between high-order accuracy and stability for large-eddy simulations and simulations of the atmospheric general circulation. In particular, filters, divergence damping, diffusion, hyperdiffusion, or sponge-layers are not required to ensure stability; only the numerical dissipation naturally afforded by FDDG is necessary. We apply the method to the simulation of dry convection in an atmospheric boundary layer and in a global atmospheric dynamical core in the standard benchmark of Held and Suarez (1994).
The Solar Irradiance Science Team #2 (SIST-2) program is a competitively solicited National Aeronautics and Space Administration (NASA) Earth Science Division (ESD) science research program providing three-year awards beginning in July 2018 to quantify and understand the solar irradiance and its variability. A key motivation for the SIST-2 program is to understand the solar radiation variability and implications for Earth’s climate and atmospheric composition. The purpose for the SIST-2 program is limited to the accurate specification of the incoming solar irradiance into the Earth system considering the 43-year satellite data record as well as proxies to which the satellite record can be tied. The SIST-2 program funded eight research grants to study the variability of the total solar irradiance (TSI) and solar spectral irradiance (SSI) and to develop improved space-based data sets, solar proxies, and variability models of the solar irradiance. The SIST-2 projects are briefly introduced.
Evaluating historical simulations from global climate models (GCMs) remains an important exercise for better understanding future projections of climate change and variability in rapidly warming regions, such as the Arctic. As an alternative approach for comparing climate models and observations, we set up a machine learning classification task using a shallow artificial neural network (ANN). Specifically, we train an ANN on maps of annual mean near-surface temperature in the Arctic from a multi-model large ensemble archive in order to classify which GCM produced each temperature map. After training our ANN on data from the large ensembles, we input annual mean maps of Arctic temperature from observational reanalysis and sort the prediction output according to increasing values of the ANN’s confidence for each GCM class. To attempt to understand how the ANN is classifying each temperature map with a GCM, we leverage a feature attribution method from explainable artificial intelligence. By comparing composites from the attribution method for every GCM classification, we find that the ANN is learning regional temperature patterns in the Arctic that are unique to each GCM relative to the multi-model mean ensemble. In agreement with recent studies, we show that ANNs can be useful tools for extracting regional climate signals in GCMs and observations.
Leveraging aerosol data from multiple airborne and surface-based field campaigns encompassing diverse environmental conditions, we calculate statistics of the oxalate-sulfate mass ratio (median: 0.0217; 95% confidence interval: 0.0154 – 0.0296; R = 0.76; N = 2948). Ground-based measurements of the oxalate-sulfate ratio fall within our 95% confidence interval, suggesting the range is robust within the mixed layer for the submicrometer particle size range. We demonstrate that dust and biomass burning emissions can separately bias this ratio towards higher values by at least one order of magnitude. In the absence of these confounding factors, the 95% confidence interval of the ratio may be used to estimate the relative extent of aqueous processing by comparing inferred oxalate concentrations between air masses, with the assumption that sulfate primarily originates from aqueous processing.
Four-dimensional variational (4D-Var) data assimilation is an effective method for obtaining physically consistent time-varying states. In this study, a method using a neural network surrogate model obtained by machine learning is proposed to solve one of the most serious challenges in 4D-Var: to construct an adjoint model. The feasibility of the proposed method was demonstrated by a 4D-Var experiment using a surrogate model for the Lorenz 96 model. In the method, several effective procedures have been proposed to obtain an accurate surrogate model and the assimilated initial conditions, including two-stage learning (i.e., single- and multi-step learning) of neural networks, limiting the target states of the surrogate model to a small subspace of the state phase space, and updating the surrogate model during 4D-Var iterations.
With the utilization of a novel synergistic approach, we constrain the vertical distribution of water vapor on Mars with measurements from nadir-pointing instruments. Water vapor column abundances were retrieved simultaneously with PFS (sensing the thermal infrared range) and SPICAM (sensing the near-infrared range) on Mars Express, yielding distinct yet complementary sensitivity to different parts of the atmospheric column. We show that by exploiting a spectral synergy retrieval approach, we obtain more accurate water vapor column abundances compared to when only one instrument is used, providing a new and highly robust reference climatology from Mars Express. We present a composite global dataset covering all seasons and latitudes, assembled from co-located observations sampled from seven Martian years. The synergy also offers a way to study the vertical partitioning of water, which has remained out of the scope of nadir observations made by single instruments covering a single spectral interval. Special attention is given to the north polar region, with extra focus on the sublimation of the seasonal polar cap during the late spring and summer seasons. Column abundances from the Mars Climate Database were found to be significantly higher than synergistically retrieved values, especially in the summer Northern Hemisphere. Deviances between synergy and model in both magnitude and meridional variation of the vertical confinement were also discovered, suggesting that certain aspects of the transport and dynamics of water vapor are not fully captured by current models.
The diurnal-eastward propagating tide with zonal wavenumber 3 (DE3) has gained significant attention due to its ability to preferentially propagate to the ionosphere and thermosphere (IT) from the tropical troposphere, thus effectively coupling these atmospheric regions. In this work, we demonstrate the existence of a pronounced zonal wavenumber 4 (WN4) structure in the low-latitude ionosphere during May 27 - June 5, 2020 using concurrent in-situ total ion number density measurements from the Scintillation Observations and Response of The Ionosphere to Electrodynamics (SORTIE) and the Ionospheric Connection Explorer (ICON) satellites. Temperature observations from the Thermosphere Ionosphere Mesosphere Energetics Dynamics Sounding of the Atmosphere using Broadband Emission Radiometry (TIMED/SABER) instrument near 105 km and output from the Specified Dynamics Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension (SD/WACCM-X) demonstrate that this global-scale ionospheric WN4 structure is due to DE3 propagating through the lower thermosphere.
A new automated method to retrieve charge layer polarity from flashes, named Chargepol, is presented in this paper. Using data from the NASA Lightning Mapping Array (LMA) deployed during the RELAMPAGO field campaign in Cordoba, Argentina, from November 2018 to April 2019, this method estimates the polarity of vertical charge distributions and their altitudes and thicknesses (or vertical depth) using the very-high frequency (VHF) source emissions detected by LMAs. When this method is applied to LMA data for extended periods of time, it is capable of inferring a storm’s bulk electrical charge structure throughout its life cycle. This method reliably predicted the polarity of charge within which lightning flashes propagated and was validated in comparison to methods that require manual assignment of polarities via visual inspection of VHF lightning sources. Examples of normal and anomalous charge structures retrieved using Chargepol for storms in Central Argentina during RELAMPAGO are presented for the first time. Application of Chargepol to five months of LMA data in Central Argentina and several locations in the United States allowed for the characterization of the charge structure in these regions and for a reliable comparison using the same methodology. About 13.3% of Cordoba thunderstorms were defined by an anomalous charge structure, slightly higher than in Oklahoma (12.5%) and West Texas (11.1%), higher than Alabama (7.3%), and considerably lower than in Colorado (82.6%). Some of the Cordoba anomalous thunderstorms presented enhanced low-level positive charge, a feature rarely if ever observed in Colorado thunderstorms.
An analysis of concurrent extreme events of continental precipitation and Integrated Water Vapour Transport (IVT) is crucial to our understanding of the role of the major global mechanisms of atmospheric moisture transport, including that of the landfalling Atmospheric Rivers (ARs) in extratropical regions. For this purpose, gridded data on CPC precipitation and ERA-5 IVT at a spatial resolution of 0.5º were used to analyze these concurrent events, covering the period from Winter 1980/1981 to Autumn 2017. For each season, and for each point with more than 400 non-dry days, several copula models were fitted to model the joint distribution function of the two variables. At each of the analysed points, the best copula model was used to estimate the probability of a concurrent extreme. At the same time, within the sample of observed concurrent extremes, the proportion of days with landfalling ARs was calculated for the whole period and for two 15-year sub-periods, one earlier period and one more recent (warmer) period. Three metrics based on copulas were used to analyse carefully the influence of IVT on extreme precipitation in the main regions of occurrence of AR landfall. The results show that the probability of occurrence of concurrent extremes is strongly conditioned by the dynamic component of the IVT, the wind. The occurrence of landfalling ARs accounts for most of the concurrent extreme days of IVT and continental precipitation, with percentages of concurrent extreme days close to 90% in some seasons in almost all the known regions of maximum occurrence of landfalling ARs, and with percentages greater than 75% downwind of AR landfall regions. This coincidence was lower in tropical regions, and in monsoonal areas in particular, with percentages of less than 50%. With a few exceptions, the role of landfalling ARs as drivers of concurrent extremes of IVT and continental precipitation tends to show a decrease in recent (warmer) periods. For almost all the landfalling AR regions with high or very high probabilities of achieving a concurrent extreme, there is a general trend towards a lower influence of IVT on extreme continental precipitation in recent (warmer) periods.
Observed stratocumulus to cumulus transitions (SCT) and their sensitivity to aerosols are studied using a Large-Eddy Simulation (LES) model that simulates the aerosol lifecycle, including aerosol sources and sinks. To initialize, force, and evaluate the LES, we used a combination of reanalysis, satellite, and aircraft data from the 2015 Cloud System Evolution in the Trades field campaign over the Northeast Pacific. The simulations follow two Lagrangian trajectories from initially overcast stratocumulus to the tropical shallow cumulus region near Hawaii. The first trajectory is characterized by an initially clean, well-mixed stratocumulus-topped marine boundary layer (MBL), then continuous MBL deepening and precipitation onset followed by a clear SCT and a consistent reduction of aerosols that ultimately leads to an ultra-clean layer in the upper MBL. The second trajectory is characterized by an initially polluted and decoupled MBL, weak precipitation, and a late SCT. Overall, the LES simulates the observed general MBL features. Sensitivity studies with different aerosol initial and boundary conditions reveal aerosol-induced changes in the transition, and albedo changes are decomposed into the Twomey effect and adjustments of cloud liquid water path and cloud fraction. Impacts on precipitation play a key role in the sensitivity to aerosols: for the first case, runs with enhanced aerosols exhibit distinct changes in microphysics and macrophysics such as enhanced cloud droplet number concentration, reduced precipitation, and delayed SCT. Cloud adjustments are dominant in this case. For the second case, enhancing aerosols does not affect cloud macrophysical properties significantly, and the Twomey effect dominates.
For decades, the Arctic has been warming at least twice as fast as the rest of the globe. As a first step towards quantifying parametric uncertainty in Arctic feedbacks, we perform a variance-based global sensitivity analysis (GSA) using a fully-coupled, ultra-low resolution (ULR) configuration of version 1 of the Department of Energy’s Energy Exascale Earth System Model (E3SMv1). The study randomly draws 139 realizations of ten model parameters spanning three E3SMv1 components (sea ice, atmosphere and ocean), which are used to generate 75 year long projections of future climate using a fixed pre-industrial forcing. We quantify the sensitivity of six Arctic-focused quantities of interest (QOIs) to these parameters using main effect, total effect and Sobol sensitivity indices computed with a Gaussian process emulator. A sensitivity index-based ranking of model parameters shows that the atmospheric parameters in the CLUBB (Cloud Layers Unified by Binormals) scheme have significant impact on sea ice status and the larger Arctic climate. We also use the Gaussian process emulator to predict the response of varying each variable when the impact of other parameters are averaged out. These results allow one to assess the non-linearity of a parameter’s impact on a QOI and investigate the presence of local minima encountered during the spin-up tuning process. Our study confirms the necessity of performing global analyses involving fully-coupled climate models, and motivates follow-on investigations in which the ULR model is compared rigorously to higher resolution configurations to confirm its viability as a lower-cost surrogate in fully-coupled climate uncertainty analyses.
A new algorithm is proposed for estimating time-evolving global forcing in climate models. The method is a further development of the work of Forster et al. (2013), taking into account the non-constancy of the global feedbacks. We assume that the non-constancy of this global feedback can be explained as a time-scale dependence, associated with linear temperature responses to the forcing on different time scales. With this method we obtain stronger forcing estimates than previously assumed for the representative concentration pathway experiments in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The reason for the higher future forcing is that the global feedback parameter is more negative at shorter time scales than at longer time scales, consistent with the equilibrium climate sensitivity increasing with equilibration time. Our definition of forcing provides a clean separation of forcing and response, and we find that linear temperature response functions estimated from experiments with abrupt quadrupling of CO$_2$ can be used to predict responses also for future scenarios. In particular, we demonstrate that for most models, the response to our new forcing estimate applied on the 21st century scenarios provides a global surface temperature up to year 2100 consistent with the output of coupled model versions of the respective model.
Radiation schemes are physically important but computationally expensive components of weather and climate models. This has spurred efforts to replace them with a cheap emulator based on neural networks (NN), obtaining large speed-ups, but at the expense of accuracy, energy conservation and generalization. An alternative approach which is slower but more robust than full emulation is to use NNs to predict optical properties, without abandoning the radiative transfer equations. Recently, NNs were developed to replace the RRTMGP gas optics scheme, and shown to be accurate while improving speed.However, the evaluations were based solely on offline radiation computations. In this paper, we describe the implementation and prognostic evaluation of RRTMGP-NN in the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The new gas optics scheme was incorporated into ecRad, the modular ECMWF radiation scheme. Using a hybrid loss function designed to reduce radiative forcing errors, and an early stopping method based on monitoring fluxes and heating rates with respect to a line-by-line benchmark, new NN models were trained on RRTMGP k-distributions with reduced spectral resolutions. Offline evaluation shows a very high level of accuracy for clear-sky fluxes and heating rates; for instance the RMSE in shortwave surface downwelling flux is 0.78 W m−2 for RRTMGP and 0.80 W m−2 for RRTMGP-NN in a present-day scenario, while upwelling flux errors are actually smaller for the NN. Because our approach does not affect the treatment of clouds, no additional errors will be introduced for cloudy profiles. RRTMGP-NN closely reproduces radiative forcings for 5 important greenhouse gases across a wide range of concentrations such as 8x CO2. To assess the impact of different gas optics schemes in the IFS, four 1-year coupled ocean-atmosphere simulations were performed for each configuration. The results show that RRTMGP-NN and RRTMGP produce very similar model climates, with the differences being smaller than those between existing schemes, and statistically insignificant for zonal means of single-level quantities such as surface temperature. The use of RRTMGP-NN speeds up ecRad by a factor of 1.5 compared to RRTMGP (the gas optics being almost 3 times faster), and is also faster than the older and less accurate RRTMG which is used in the current operational cycle of the IFS
The January 2022 Hunga Tonga–Hunga Haʻapai eruption was one of the most explosive volcanic events of the modern era, producing a vertical plume which peaked > 50km above the Earth. The initial explosion and subsequent plume triggered atmospheric waves which propagated around the world multiple times. A global-scale wave response of this magnitude from a single source has not previously been observed. Here we show the details of this response, using a comprehensive set of satellite and ground-based observations to quantify it from surface to ionosphere. A broad spectrum of waves was triggered by the initial explosion, including Lamb waves5,6 propagating at phase speeds of 318.2+/-6 ms-1 at surface level and between 308+/-5 to 319+/-4 ms-1 in the stratosphere, and gravity waves propagating at 238+/-3 to 269+/-3 ms-1 in the stratosphere. Gravity waves at sub-ionospheric heights have not previously been observed propagating at this speed or over the whole Earth from a single source. Latent heat release from the plume remained the most significant individual gravity wave source worldwide for >12 hours, producing circular wavefronts visible across the Pacific basin in satellite observations. A single source dominating such a large region is also unique in the observational record. The Hunga Tonga eruption represents a key natural experiment in how the atmosphere responds to a sudden point-source-driven state change, which will be of use for improving weather and climate models.
Survey evidence has indicated that a significant percentage of the population does not fully embrace the scientific consensus regarding climate change. This paper assesses whether the hourly temperature data support this denial. The analysis examines the relationship between hourly CO2 concentration levels and temperature using hourly data from the NOAA-operated Barrow observatory in Alaska. At this observatory, the average annual temperature over the 2015-2020 period was about 3.37 oC higher than in 1985–1990. A time-series model to explain hourly temperature is formulated using the following explanatory variables: the hourly level of total downward solar irradiance, the CO2 value lagged by one hour, proxies for the diurnal variation in temperature, proxies for the seasonal temperature variation, and proxies for possible non-anthropomorphic drivers of temperature. The purpose of the time-series approach is to capture the data’s heteroskedastic and autoregressive nature, which would otherwise “mask” CO2’s “signal” in the data. The model is estimated using hourly data from 1985 through 2015. The results are consistent with the hypothesis that increases in CO2 concentration levels have nontrivial consequences for hourly temperature. The estimated annual contributions of factors exclusive of CO2 and downward total solar irradiance are very small. The model was evaluated using out-of-sample hourly data from 1 Jan 2016 through 31 Aug 2017. The model’s out-of-sample hourly temperature predictions are highly accurate, but this accuracy is significantly degraded if the estimated CO2 effects are ignored. In short, the results are consistent with the scientific consensus on climate change.
Moisture recycling via evapotranspiration (ET) is often invoked as a mechanism for the high deuterium excess signals observed in continental precipitation (dP). However, a global-scale analysis of precipitation monitoring station isotope data shows that metrics of ET contributions to precipitation (van der Ent et al., 2014) explain little dp variability on seasonal timescales. This occurs despite the fact that ET contributions increase by ~50% in continental locations such as the Eurasian interior from wet to dry seasons. To explain this apparent paradox, we hypothesize that the effects of ET on dP are dampened during dry seasons due to contributions from isotopically-evolved residual water storage that act to lower the d-excess of ET fluxes (dET), in combination with changes in transpiration fraction (T/ET). To test this hypothesis, we develop a parsimonious two-season (wet, dry) model for dET incorporating residual water storage and ET partitioning effects. We find that in environments with limited water storage, such as shallow-rooted grasslands, dry season dET is lower than wet season dET despite lower relative humidity. As global average ratios of annual water storage to precipitation are relatively low (Guntner et al., 2007), these dynamics may be widespread over continents. In environments where water storage is not limiting, such as groundwater-dependent ecosystems, dry season dET is still likely lower; however, this effect arises instead due to higher seasonal T/ET when energy-driven plant water use is enhanced and surface evaporation is relatively limited by water availability. Together, these analyses also indicate multiple mechanisms by which dET may be lower than dp during the same season, challenging the view that moisture recycling feedback increases the dp in continental interiors. This work demonstrates the potential complexity of seasonal dp dynamics and cautions against simple interpretations of dP as a process tracer for moisture recycling. References: Guntner et al., 2007. Water Resour. Res., 43, W05416. van der Ent et al., 2014. Earth Syst. Dynam., 5, 471–489.