This article introduces factors contributing significantly to climate change that have been largely neglected in both the scientific and popular press. These factors have immediate implications for public policy directed at slowing, halting and even reversing climate change and its effects. This article argues that in addition to the known contributions made by greenhouse gasses, climate change is also driven by shifts in the patterns of global atmospheric circulation which are influenced by persistent, large-scale vortices caused by the wake turbulence left by commercial air traffic. Because this traffic is highly concentrated along the most frequently traveled routes, the vortices aircraft create have transformed into semi-permanent atmospheric circulation which have widespread effects on how the atmosphere traps and releases heat. It is also possible that these changes alter the loss of water from the atmosphere. This would endanger all life on earth, not just the human population.
A new model validation and performance assessment tool is introduced, the sliding threshold of observation for numeric evaluation (STONE) curve. It is based on the relative operating characteristic (ROC) curve technique, but instead of sorting all observations in a categorical classification, the STONE tool uses the continuous nature of the observations. Rather than defining events in the observations and then sliding the threshold only in the classifier/model data set, the threshold is changed simultaneously for both the observational and model values, with the same threshold value for both data and model. This is only possible if the observations are continuous and the model output is in the same units and scale as the observations; the model is trying to exactly reproduce the data. The STONE curve has several similarities with the ROC curve – plotting probability of detection against probability of false detection, ranging from the (1,1) corner for low thresholds to the (0,0) corner for high thresholds, and values above the zero-intercept unity-slope line indicating better than random predictive ability. The main difference is that the STONE curve can be nonmonotonic, doubling back in both the x and y directions. These ripples reveal asymmetries in the data-model value pairs. This new technique is applied to modeling output of a common geomagnetic activity index as well as energetic electron fluxes in the Earth’s inner magnetosphere. It is not limited to space physics applications but can be used for any scientific or engineering field where numerical models are used to reproduce observations.
Climate models generally project an increase in the winter North Atlantic Oscillation (NAO) index under a future high-emissions scenario, alongside an increase in winter precipitation in northern Europe and a decrease in southern Europe. The extent to which future forced NAO trends are important for European winter precipitation trends and their uncertainty remains unclear. We show using the Multimodel Large Ensemble Archive that the NAO plays a small role in northern European mean winter precipitation projections for 2080-2099. Conversely, half of the model uncertainty in southern European mean winter precipitation projections is potentially reducible through improved understanding of the NAO projections. Extreme positive NAO winters increase in frequency in most models as a consequence of mean NAO changes. These extremes also have more severe future precipitation impacts, largely because of mean precipitation changes. This has implications for future resilience to extreme positive NAO winters, which frequently have severe societal impacts.
The Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) and the Global Precipitation Measuring (GPM) Microwave Imager (GMI) have been used as the radiometric transfer standard one after another for the GPM constellation radiometers, during the past nearly two decades. Given that GMI and TMI share only a 13-month common operational period, for the time there is no overlap in between, WindSat can serve as the calibration bridge to provide additional intercalibration for the realization of a consistent multi-decadal oceanic brightness temperature (Tb) product. Thus, we conducted the intercalibration of TMI/GMI for 13-month period, TMI/WindSat for >9 years’ overlap period, and WindSat/GMI XCAL for one year, to assess the Tb bias of one to another. A multi-decadal oceanic Tb dataset was thereafter achieved to ensure a consistent long-term precipitation record that covers TRMM and GPM eras. Moreover, a generic uncertainty quantification model (UQM) was developed by taking various sources of uncertainties into account rigorously and orderly. This UQM model was then applied to quantify the uncertainty estimates associated with these Tb biases. This allows the unified high-sampling-frequency and globally-covered Tb product with associated boundary uncertainties to be much improved for scientific utilization as compared to existing Tb products that are with ad-hoc uncertainties estimates. Moreover, based upon the results of uncertainty quantification process, it is recognized that there is room for improvement in the intercalibration for the water vapor sensitive channels. Further analysis indicates that the issue may be associated with the atmospheric water vapor profile input to the radiative transfer model. Suggestions are subsequently made to use water vapor profile retrieved from millimeter radiometer sounders’ measurements (rather than numerical weather predictions) to determine the impact on the Tb biases of these problematic channels.
Current lightning predictions are uncertain because they either rely on empirical diagnostic relationships based on the present climate or use coarse-scale climate scenario simulations in which deep convection is parameterized. Previous studies demonstrated that simulations with convection-permitting resolutions (km-scale) improve lightning predictions compared to coarser-grid simulations using convection parameterization for different geographical locations but not over the boreal zone. In this study, lightning simulations with the NASA Unified-Weather Research and Forecasting (NU-WRF) model are evaluated over a 955x540 km2 domain including the Great Slave Lake in Canada for six lightning seasons. The simulations are performed at convection-parameterized (9 km) and convection-permitting (3 km) resolution using the Goddard 4ICE and the Thompson microphysics (MP) schemes. Four lightning indices are evaluated against observations from the Canadian Lightning Detection Network (CLDN), in terms of spatiotemporal frequency distribution, spatial pattern, daily climatology, and an event-based overall skill assessment. Concerning the model configuration, regardless of the spatial resolution, the Thompson scheme is superior to the Goddard 4ICE scheme in predicting the daily climatology but worse in predicting the spatial patterns of lightning occurrence. Several evaluation metrics indicate the benefit of working at a convection-permitting resolution. The relative performance of the different lightning indices depends on the evaluation criteria. Finally, this study demonstrates issues of the models to reproduce the observed spatial pattern of lightning well, which might be related to an insufficient representation of land surface heterogeneity in the study area.
Chemical and biological composition of surface materials and physical structure and arrangement of those materials determine the intrinsic reflectance of Earth’s land surface. The apparent reflectance—as measured by a spaceborne or airborne sensor that has been corrected for atmospheric attenuation—depends also on topography, surface roughness, and the atmosphere. Especially in Earth’s mountains, estimating properties of scientific interest from remotely sensed data requires compensation for topography. Doing so requires information from digital elevation models (DEMs). Available DEMs with global coverage are derived from spaceborne interferometric radar and stereo-photogrammetry at ~30 m spatial resolution. Locally or regionally, lidar altimetry, interferometric radar, or stereo-photogrammetry produces DEMs with finer resolutions. Characterization of their quality typically expresses the root-mean-square (RMS) error of the elevation, but the accuracy of remotely sensed retrievals is sensitive to uncertainties in topographic properties that affect incoming and reflected radiation and that are inadequately represented by the RMS error of the elevation. The most essential variables are the cosine of the local solar illumination angle on a slope, the shadows cast by neighboring terrain, and the view factor, the fraction of the overlying hemisphere open to the sky. Comparison of global DEMs with locally available fine-scale DEMs shows that calculations with the global products consistently underestimate the cosine of the solar angle and underrepresent shadows. Analyzing imagery of Earth’s mountains from current and future spaceborne missions requires addressing the uncertainty introduced by errors in DEMs on algorithms that analyze remotely sensed data to produce information about Earth’s surface.
Earth System Models’ complex land components simulate a patchwork of increases and decreases in surface water availability when driven by projected future climate changes. Yet, commonly-used simple theories for surface water availability, such as the Aridity Index (P/E0) and Palmer Drought Severity Index (PDSI), obtain severe, globally dominant drying when driven by those same climate changes, leading to disagreement among published studies. In this work, we use a common modeling framework to show that ESM simulated runoff-ratio and soil-moisture responses become much more consistent with the P/E0 and PDSI responses when several previously known factors that the latter do not account for are cut out of the simulations. This reconciles the disagreement and makes the full ESM responses more understandable. For ESM runoff ratio, the most important factor causing the more positive global response compared to P/E0 is the concentration of precipitation in time with greenhouse warming. For ESM soil moisture, the most important factor causing the more positive global response compared to PDSI is the effect of increasing carbon dioxide on plant physiology, which also drives most of the spatial variation in the runoff ratio enhancement. The effect of increasing vapor-pressure deficit on plant physiology is a key secondary factor for both. Future work will assess the utility of both the ESMs and the simple indices for understanding observed, historical trends.
Reproducibility and replicability in analyzing data is one of the main requirements for the advancement of scientific fields that rely heavily on computational data analysis, such as atmospheric science. However, there are very few research activities that field in Indonesia that emphasize the principle of transparency of codes and data in the dissemination of the results. This issue is a major challenge for the Indonesian scientific community to verify the output of research activities from their peers. One common obstacle to the reproducibility of data-driven research is the portability issue of the computing environment used to reproduce the results. Therefore, in this article, we would like to offer a solution through Debian-based dockerized Jupyter Notebook that have been installed with several Python libraries that are often used in atmospheric science research. Through this containerized computing environment, we expect to overcome the portability and dependency constraints that often faced by atmospheric scientists and also to encourage the growth of research ecosystem in Indonesia through an open and replicable environment.
The observing system design of multi-disciplinary field measurements involves a variety of considerations on logistics, safety, and science objectives. Typically, this is done based on investigator intuition and designs of prior field measurements. However, there is potential for considerable increase in efficiency, safety, and scientific success by integrating numerical simulations in the design process. Here, we present a novel approach to observing system simulation experiments that aids surface-atmosphere synthesis at the interface of meso- and microscale meteorology. We used this approach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19). During pre-field simulation experiments, we considered the placement of 20 eddy-covariance flux towers, operations for 72 hours of low-altitude flux aircraft measurements, and integration of various remote sensing data products. High-resolution Large Eddy Simulations generated a super-sample of virtual ground, airborne, and satellite observations to explore two specific design hypotheses. We then analyzed these virtual observations through Environmental Response Functions to yield an optimal aircraft flight strategy for augmenting a stratified random flux tower network in combination with satellite retrievals. We demonstrate how this novel approach doubled CHEESEHEAD19’s ability to explore energy balance closure and spatial patterning science objectives while substantially simplifying logistics. Owing to its extensibility, the approach lends itself to optimize observing system designs also for natural climate solutions, emission inventory validation, urban air quality, industry leak detection and multi-species applications, among other use cases.
Water storage plays an important role in mitigating heat and flooding in urban areas. Assessment of the water storage capacity of cities remains challenging due to the inherent heterogeneity of the urban surface. Traditionally, effective storage has been estimated from runoff. Here, we present a novel approach to estimate effective water storage capacity from recession rates of observed evaporation during precipitation-free periods. We test this approach for cities at neighborhood scale with eddy-covariance based latent heat flux observations from fourteen contrasting sites with different local climate zones, vegetation cover and characteristics, and climates. Based on analysis of 583 drydowns, we find storage capacities to vary between 1.3-28.4 mm, corresponding to e-folding timescales of 1.8-20.1 days. This makes the storage capacity at least one order of magnitude smaller than the observed values for natural ecosystems, reflecting an evaporation regime characterised by extreme water limitation.
The 15 January 2022 eruption of the Hunga volcano (Tonga) generated a rich spectrum of waves, some of which achieved global propagation. Among numerous platforms mon- itoring the event, two stratospheric balloons flying over the tropical Pacific provided unique observations of infrasonic wave arrivals, detecting five complete revolutions. Combined with ground measurements from the infrasound network of the International Monitor- ing System, balloon-borne observations may provide additional constraint on the scenario of the eruption, as suggested by the correlation between bursts of acoustic wave emis- sion and peaks of maximum volcanic plume top height. Balloon records also highlight previously unobserved long-range propagation of infrasound modes and their dispersion patterns. A comparison between ground- and balloon-based measurements emphasizes superior signal-to-noise ratios onboard the balloons and further demonstrates their po- tential for infrasound studies.
Floods drive dynamic and deeply uncertain risks for people and infrastructures. Uncertainty characterization is a crucial step in improving the predictive understanding of multi-sector dynamics and the design of risk-management strategies. Current approaches to estimate flood hazards often sample only a relatively small subset of the known unknowns, for example the uncertainties surrounding the model parameters. This approach neglects the impacts of key uncertainties on hazards and system dynamics. Here we mainstream a recently developed method for Bayesian data-model fusion to calibrate a computationally expensive distributed hydrologic model. We compare three different calibration approaches: (1) stepwise line search, (2) precalibration or screening, and (3) the new Fast Model Calibrations (FaMoS) approach. FaMoS deploys a particle-based approach that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We quantify how neglecting known unknowns can drastically underestimate extreme flood events and risks. Accounting for parametric uncertainty improves model performance metrics over the best estimate parameters. Improving the characterization of model parametric uncertainty improves hindcasts and projections of flood risks.
Reducing the model spread in free-tropospheric relative humidity (RH) and its response to warming is a crucial step towards reducing the uncertainty in clear-sky climate sensitivity, a step that is hoped to be taken with recently developed global storm-resolving models (GSRMs). In this study we quantify the inter-model differences in tropical present-day RH across GSRMs, making use of DYAMOND, a first 40-day intercomparison. We find that the inter-model spread in tropical mean free-tropospheric RH is reduced compared to conventional atmospheric models, except from the the tropopause region and the transition to the boundary layer. We estimate the reduction to approximately 50-70% in the upper troposphere and 25-50% in the mid troposphere. However, the remaining RH differences still result in a spread of 1.2 Wm-2 in tropical mean clear-sky outgoing longwave radiation (OLR). This spread is mainly caused by RH differences in the lower and mid free troposphere, whereas RH differences in the upper troposphere have a minor impact. By examining model differences in moisture space we identify two regimes with a particularly large contribution to the spread in tropical mean clear-sky OLR: rather moist regimes at the transition from deep convective to subsidence regimes and very dry subsidence regimes. Particularly for these regimes a better understanding of the processes controlling the RH biases is needed.
Concurrent temperature and precipitation extremes during Indian summer monsoon generally have signicant effects on agriculture, society and ecosystems. Due to climate change, frequency and spatial extent of concurrent extremes have changed, and there is a need to advance our understanding in this domain. Quantication of individual extremes (temperature and precipitation) during the summer monsoon season and its teleconnections to climate indices have been studied comprehensively. But, less attention is devoted to the quantication of concurrent extremes and its teleconnections to climate indices. In this study, concurrent extremes (dry/hot and wet/cold) based on mean monthly temperature and total monthly precipitation during the Indian summer season from 1951 to 2019 over the Indian mainland are investigated. Next, the study uses wavelet coherence analysis to unravel the teleconnections of the spatial extent of concurrent extremes to climate indices (Nino 3.4, WEIO SST and SEEIO SST). Results show that the frequency of wet/hot concurrent extremes has increased signicantly, while the frequency of wet/cold concurrent has decreased for the time window 1985 to 2019 relative to 1951-1984. Also, a statistically signicant increase (decrease) in the spatial extent exists in concurrent dry/hot (wet/cold) extremes during the July, August and September months. The ndings of this study could advance our understanding of changes in concurrent extremes during the Indian summer monsoon due to climate change.
We investigate the atmospheric drivers of extreme precipitation over the Amundsen Sea Embayment (ASE) of West Antarctica using daily output from RACMO2 model and re- analysis data (1979-2016). Overall, 93.7% of days with extreme precipitation at the 2 coastal stations of ASE are associated with the 4 dominant Empirical Orthogonal Function (EOF) modes of geopotential height anomalies (at 850 hPa) over West Antarctica. The second EOF mode, associated with a coupled pattern consisting of Amundsen Sea Low and a blocking high to the east, is the main driver of extreme precipitation over ASE, linked to 44.75% of extreme precipitation days. This is followed by EOF-3 (associated with El Niño Southern Oscillation/PSA-1), EOF-4 (likely associated with more frequent ‘atmospheric river’ events) and EOF-1 (i.e., Southern Annular mode) with a contribution of 22.16%, 21.1% and 12%, respectively. Extreme precipitation linked to EOF-2 and EOF-4 are more intense (by ∼2 mm/day) than the rest.
The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes due in large part to their different lifetimes. Here, we discuss two key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed. Second, it demonstrated empirically that the response of atmospheric composition to emissions changes is heavily modulated by factors including carbon cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality.
Soft x-ray and EUV radiation from the Sun is absorbed by and ionizes the atmosphere, creating both the ionosphere and thermosphere. Temporal changes in irradiance energy and spectral distribution can have drastic impacts on the ionosphere, impacting technologies such as satellite drag and radio communication. Because of this, it is necessary to estimate and predict changes in Solar EUV spectral irradiance. Ideally, this would be done by direct measurement but the high cost of solar EUV spectrographs makes this prohibitively expensive. Instead, scientists must use data driven models to predict the solar spectrum for a given irradiance measurement. In this study, we further develop the Synthetic Reference Spectral Irradiance Model (SynRef). The SynRef model, which uses broadband EUV irradiance data from EUVM at Mars, was created to mirror the SORCE XPS model which uses data from the TIMED SEE instrument and the SORCE XPS instrument at Earth. Both models superpose theoretical Active Region and Quiet Sun spectra generated by CHIANTI to match daily measured irradiance data, and output a modeled solar EUV spectrum for that day. By adjusting the weighting of Active Region and Quiet Sun spectra, we update the SynRef model to better agree with the FISM model and with spectral data collected from sounding rocket flights. We also use the broadband EUVM measurements to estimate AR temperature. This will allow us to select from a library of AR reference spectra with different temperatures. We present this updated SynRef model to more accurately characterize the Solar EUV and soft x-ray spectra.
We investigate the CO2 flux calculated by the ISBA soil-vegetation-atmosphere transfer model (Noilhan and Planton, 1989)by comparing three different formulations for the plant (dark) respiration scheme applied to a soybean culture. The model includes CO2 flux/photosynthesis based on Jacobs (1994) in a manner similar to Calvet et al. (1998) (ISBA-A-gs). The first respiration scheme (M0) computed the autotrophic respiration Rd similarly to Jacobs (1994) but with an ad-hoc temperature correction calibrated by statistical parameter fitting using measured data. For the second model (M1), we implemented the respiration proposed by Joetzjer et al. (2015). Finally we implemented a third respiration scheme (M2) as in Wang (1996). The three models were calibrated and CO2 fluxes were compared with measurements made over a soybean culture using eddy covariance method between December, 2008 and March, 2009, at a farm near Buenos Aires, Argentina. The total CO2 maximum, minimum and mean measured flux values were respectively 0.9890, -0.2479 and 0.3087 mg m-2 s-1. For the sake of comparison, statistics were computed for the full daily cycle flux (total) and also for nighttime flux, as a means to avoid masking of the results due to the much larger daytime photosynthetic flux. We here present the Nash-Sutcliffe efficiency (NSE) coefficient for each model. M0 gave the best overall performance with 0.7568 for the total daily CO2 flux and 0.0795 for the dark flux. M1 gave similar predictions for the daily CO2 flux with 0.7582, butthe worst result for the nighttime period with -0.4965. M2 gave 0.7424 for the full daily flux and 0.0119 for the night CO2 flux. The results show a seemingly better performance of the models in predicting the total CO2 flux compared to the dark CO2 flux. This is due to several facts such as: respiration is less understood and harder to predict than photosynthesis; measurements are more difficult at nighttime due to the limitations of the eddy-covariance technique in low turbulent activity; in the measured data, it is difficult to identify and separate the portions of CO2 fluxes as soil respiration, autotrophic respiration and photosynthetic flux, without many auxiliary measurements. We also conclude that there is a clear influence of the temperature on the respiration, which can be suitably incorporated in the models.