Sparse observational data in developing regions leads to uncertainty about how hydro-climatic factors influence crop phases and productivity, knowledge of which is essential to mitigating food security threats induced by climate change. In this study, NASA Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), and Global Land Data Assimilation System (GLDAS) data products bypass spatiotemporal limitations and drive machine learning algorithms developed to characterize hydro-climate-productivity interactions. Extensive feature engineering processes these products into nearly 4000 metrics, designed to decompose crop growing season hydro-climate conditions. Dimensionality reduction with bidirectional step-wise regression, Multi-Adaptive-Regression-Splines (MARS), and Random Forest algorithms are explored to determine key temporal hydro-climate drivers to agricultural productivity, with each method recognizing unique linear and non-linear predictors. Finally, multi-variate regression, MARS, and Random Forest models are trained on the drivers to predict seasonal crop yield. We apply this hydro-climate-productivity framework to investigate rabi wheat productivity on Pakistan’s Potohar Plateau. Here, we identify six of wheat’s ten phenological phases that display strong hydro-climate responses, with the shooting phase exhibiting sensitivity to precipitation intensity, minimum soil moisture, and sub-zero temperatures. In addition, the plateau’s heterogeneous climate-productivity connections are captured well by the calibrated models, strengthening their application for studying broader climate change impacts. The integration of remote sensing products and machine learning offers an effective framework to bypass in-situ data limitations and decompose climate-crop productivity relationships, thus improving drought onset recognition and food security forecasting.
Research on Atmospheric Rivers (ARs) has focused primarily on AR (thermo)dynamics and hydrological impacts over land. However, the evolution and potential role of nearshore air-sea fluxes during landfalling ARs are not well documented. Here, we examine synoptic evolutions of nearshore latent heat flux (LHF) during strong late-winter landfalling ARs (1979–2017) using 138 over-shelf buoys along the U. S. west coast. Composite evolutions show that ARs typically receive upward (absolute) LHF from the coastal ocean. LHF is small during landfall due to weak air-sea humidity gradients but is strongest (30–50 W/m^2 along the coast) 1–3 days before/after landfall. During El Niño winters, southern-coastal LHF strengthens, coincident with stronger ARs. A decomposition of LHF reveals that sea surface temperature (SST) anomalies modulated by the El Niño—Southern Oscillation dominate interannual LHF variations under ARs, suggesting a potential role for nearshore SST and LHF influencing the intensity of landfalling ARs.
Nudging is a ubiquitous capability of numerical weather and climate models that is widely used in a variety of applications (e.g. crude data assimilation, “intelligent’ interpolation between analysis times, constraining flow in tracer advection/diffusion simulations). Here, the focus is on the momentum nudging tendencies themselves, rather than the atmospheric state that results from application of the method. The initial intent was to interpret these tendencies as a quantitative estimation of model error (net parameterization error in particular). However, it was found that nudging tendencies depend strongly on the nudging time scale chosen, which is the primary result presented here. Reducing the nudging time scale reduces the difference between the model state and the target state, but much less so than the reduction in the nudging time scale, resulting in increased nudging tendencies. The dynamical core, in particular, appears to increasingly oppose nudging tendencies as the nudging time scale is reduced. These results suggest nudging tendencies cannot be quantitatively interpreted as model error. Still, nudging tendencies do contain some information on model errors and/or missing physical processes and still might be useful in model development and tuning, even if only qualitatively.
Intense snowfall sublimation was observed during a precipitation event over Davis in the Vestfold Hills, East Antarctica, from 08 to 10 January 2019. Radar observations and simulations from the Weather Research and Forecasting model revealed that orographic gravity waves (OGWs), generated by a north-easterly flow impinging on the ice ridge upstream of Davis, were responsible for snowfall sublimation through a Foehn effect. Despite the strong meridional moisture advection associated with an atmospheric river (AR) during this event, almost no precipitation reached the ground at Davis. We found that the direction of the synoptic flow with respect to the orography determined the intensity of OGWs over Davis, which in turn directly influenced the snowfall microphysics. Turbulence induced by the OGWs likely enhanced the aggregation process, as revealed by dual-polarization and dual-frequency radar observations. This study suggests that despite the intense AR, the precipitation distribution was determined by local processes tied to the orography. The mechanisms found in this case study could contribute to the extremely dry climate of the Vestfold Hills, one of the main Antarctic oasis.
US natural gas production increased by ~43% between 2005 and 2015, but there is disagreement in the scientific literature on whether this growth led to increased methane emissions. In this study, we evaluate the possible contributions of emissions versus meteorology to an upward trend in US atmospheric methane observations during 2007-2015. We find that interannual variability (IAV) in meteorology yields an apparent upward trend in atmospheric methane across much of the US. We further find that IAV in atmospheric methane at several observation sites is correlated with IAV in local wind speed. Overall, our results show that US trends in atmospheric methane largely reflect variability in meteorology, and are unlikely to be a direct reflection of trends in emissions. The results of this study therefore lend support for the conclusion that there was little upward trend in US methane emissions during this time.
Natural and non-natural factors have combined effects on the trajectory of COVID-19 pandemic, but it is difficult to make them separate. To address this problem, a two-stepped methodology is proposed. First, a compound natural factor (CNF) model is developed via assigning weight to each of seven investigated natural factors, i.e., temperature, humidity, visibility, wind speed, barometric pressure, aerosol and vegetation in order to show their coupling relationship with the COVID-19 trajectory. Onward, the empirical distribution based framework (EDBF) is employed to iteratively optimize the coupling relationship between trajectory and CNF to express the real interaction. In addition, the collected data is considered from the backdate, i.e., about 23 days—which contains 14-days incubation period and 9-days invalid human response time—due to the non-availability of prior information about the natural spreading of virus without any human intervention(s), and also lag effects of the weather change and social interventions on the observed trajectory due to the COVID-19 incubation period; Second, the optimized CNF-plus-polynomial model is used to predict the future trajectory of COVID-19.Results revealed that aerosol and visibility show the higher contribution to transmission, wind speed to death, and humidity followed by barometric pressure dominate the recovery rates, respectively. Consequently, the average effect of environmental change to COVID-19 trajectory in China is minor in all variables, i.e., about -0.3%, +0.3% and +0.1%, respectively. In this research, the response analysis of COVID-19 trajectory to the compound natural interactions presents a new prospect on the part of global pandemic trajectory to environmental changes.
The study was aimed to identify the relations between the severity of coronary artery disease and associated percutaneous coronary interventions with the changes in the local Earth magnetic field activity (LEMF). One-thousand-two-hundred-forty patients diagnosed with Acute coronary syndrome who underwent percutaneous coronary intervention within 2015-2016 were retrospectively included in this single centre study. The majority of acute coronary syndromes that occurred in females was associated with an increase in LEMF intensity in 3.5-32 Hz frequency ranges and were also associated with a higher number of diseased coronary arteries. Increased intensity in the same range was associated with a lower number of stented coronary arteries in males in 2015. Positive correlation coefficients were found between increased LEMF intensity in the 0-15 Hz range and the number of revascularized coronary arteries in females during the winter season in 2016. Stronger LEMF in low-medium frequency ranges is associated with acute coronary syndromes in males caused by less diffuse coronary artery disease resulting in lower number of coronary arteries segments needed for revascularisation, especially during winter. Stronger LEMF in high frequency range is associated with increased occurrence of ischaemic cardiovascular events, while stronger LEMF in low to moderate frequency ranges is associated with positive effect.
The calculations of atmospheric radiative transfer are among the most time-consuming components of the numerical weather prediction (NWP) models. Therefore, using deep learning to achieve fast radiative transfer has become a popular research direction. We propose a physics-incorporated framework for the radiative transfer model training, in which the thermal relationship between fluxes and heating rates is encoded as a layer of the network so that the energy conservation can be satisfied. Based on this framework, we compared various types of neural networks and found that the model structures with global receptive fields are more suitable for the radiative transfer problem, among which the Bi-LSTM model has the best performance.
Searching for life on other planets and planetary bodies poses a number of challenges, especially given that there is currently no clear evidence that lifeforms can only conform to characteristics observed on Earth. While current astrobiology missions operate under the assumption that any astrobiological entities of interest will have similar properties to organisms on Earth (‘canonical’ lifeforms), the current convention of searching for direct evidence of such lifeforms (e.g. organic compounds, genetic material, etc.) is largely exclusionary to any biologically valid lifeforms which are not currently a part of the canonical model of life that is used to drive exploratory efforts. It is proposed that the definition of life be broadened to include any entities capable of maintaining homeostasis relative to an entropic environment. Thus, instead of the traditional strategy of searching for direct evidence of life conforming to Earth-based standards, i.e., looking for specific organic compounds, a new strategy could be used to indirectly identify lifeforms through their utilization of environmental resources (e.g. as energy sources).
The polar vortices play a central role in vertically coupling the Sun-Earth system by facilitating the descent of reactive odd nitrogen (NOx = NO + NO2) produced in the atmosphere by energetic particle precipitation (EPP-NOx). Downward transport of EPP-NOx from the mesosphere-lower thermosphere (MLT) to the stratosphere inside the winter polar vortex is particularly impactful in the wake of prolonged sudden stratospheric warming events. This work is motivated by the fact that state-of-the-art global climate models severely underestimate this EPP-NOx transport in the Arctic. As a step toward understanding the transport pathways by which MLT air enters the top of the polar vortex, we explore the extent to which Lagrangian Coherent Structures (LCS) impact the geographic distribution of NO near the polar winter mesopause in the Whole Atmosphere Community Climate Model eXtended version with Data Assimilation Research Testbed (WACCMX+DART). We present planetary wave-driven enhanced NO descent near the polar winter mesopause during 14 case studies from the Arctic winters of 2005/2006 through 2018/2019. During all cases the model is in reasonable agreement with SABER temperatures and SOFIE and ACE-FTS NO. Results show consistent LCS formation at the top of the polar vortex during minor and major SSWs. LCSs act to confine air with elevated NO to high latitudes as it descends into the top of the polar vortex. Descent of NO tends to be enhanced in traveling planetary wave troughs. These results present a new conceptual model of transport in the polar winter mesosphere whereby regional-scale, long-lived LCSs, coincident with the troughs of planetary waves, act to sequester elevated NOx at high latitudes until the air descends to lower altitudes.
Brazil has been severely affected by the COVID-19 pandemic. Temperature and humidity have been purported as drivers of SARS-CoV-2 transmission, but no consensus has been reached in the literature regarding the relative roles of meteorology, governmental policy, and mobility on transmission in Brazil. We compiled data on meteorology, governmental policy, and mobility in Brazil’s 26 states and one federal district from June 2020 to August 2021. Associations between these variables and the time-varying reproductive number (Rt) of SARS-CoV-2 were examined using generalized additive models fit to data from the entire fifteen-month period and several shorter, three-month periods. Accumulated local effects and variable importance metrics were calculated to analyze the relationship between input variables and Rt. We found that transmission is strongly influenced by unmeasured sources of between-state heterogeneity and the near-recent trajectory of the pandemic. Increased temperature generally was associated with decreased transmission and specific humidity with increased transmission. However, the impact of meteorology, policy, and mobility on Rt varied in direction, magnitude, and significance across our study period. This time variance could explain inconsistencies in the published literature to date. While meteorology weakly modulates SARS-CoV-2 transmission, daily or seasonal weather variations alone will not stave off future surges in COVID-19 cases in Brazil. Investigating how the roles of environmental factors and disease control interventions may vary with time should be a deliberate consideration of future research on the drivers of SARS-CoV-2 transmission.
Previous work has found that as the surface warms the large-scale tropical circulations weaken, convective anvil cloud fraction decreases, and atmospheric static stability increases. Circulation changes inevitably lead to changes in the humidity and cloud fields which influence the surface energetics. The exchange of mass between the boundary layer and the midtroposphere has also been shown to weaken in global climate models. What has remained less clear is how robust these changes in the circulation are to different representations of convection, clouds, and microphysics in numerical models. We use simulations from the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) to investigate the interaction between overturning circulations, surface temperature, and atmospheric moisture. We analyze the underlying mechanisms of these relationships using a 21-member model ensemble that includes both general circulation models and cloud resolving models. We find a large spread in the change of intensity of the overturning circulation. Both the range of the circulation intensity, and its change with warming can be explained by the range of the mean upward vertical velocity. There is also a consistent decrease in the exchange of mass between the boundary layer and the midtroposphere. However, the magnitude of the decrease varies substantially due to the range of responses in both mean precipitation and mean precipitable water. This work implies that despite well understood thermodynamic constraints, there is still a considerable ability for the cloud fields and the precipitation efficiency to drive a substantial range of tropical convective responses to warming.
Robust and reliable projections of future streamflow are essential to create more resilient water resources, and such projections must first be bias corrected. Standard bias correction techniques are applied over calendar-based time windows and leverage statistical relations between observed and simulated data to adjust a given simulated datapoint. Motivated by a desire to connect the statistical process of bias correction to the underlying dynamics in hydrologic models, we introduce a novel windowing technique for projected streamflow wherein data are windowed based on hydrograph-relative time, rather than Julian day. We refer to this method as ‘seasonally anchored’. Four existing bias correction methods, each using both the standard day-of-year and the novel windowing technique, are applied to daily streamflow simulations driven by 10 global climate models across a diverse subset of six watersheds in California to investigate how these methods alter the model climate change signals. Among the methods, only PresRat preserves projected annual streamflow changes, and does so for both windowing techniques. The seasonally anchored window PresRat reduces the ensemble bias by a factor of two compared to quantile mapping (Qmap), cumulative distribution function transform (CDFt), and equidistant quantile matching (EDCDFm) methods. For wet season flows, PresRat with seasonally anchored windowing best preserves the original model change over the entire distribution, particularly at the highest quantiles, and the other three methods show improved performance using the novel windowing method. Concerning temporal shifts in seasonality, PresRat and CDFt preserve the original model signals with both the novel and standard windowing methods.
Increasing wildfire and declining snowpacks in mountain regions threaten water availability. We combine satellite-based fire detection with snow seasonality classifications to examine fire activity in California’s seasonal and ephemeral snow areas. We find a nearly tenfold increase in fire activity during 2020 and 2021 compared to 2001-2019 as measured by satellite data. Accumulation season snow albedo declined 17-77% in two burned sites as measured by in-situ data relative to un-burned conditions, with greater declines associated with increased soil burn severity. By enhancing snowpack susceptibility to melt, decreased snow albedo drove mid-winter melt during a multi-week midwinter dry spell in 2022. Despite similar meteorological conditions in 2013 and 2022, which we link to persistent high pressure weather regimes, minimal melt occurred in 2013. Post-fire differences are confirmed with satellite measurements. Our findings suggest larger areas of California’s snowpack will be increasingly impacted by the compounding effects of dry spells and wildfire.
The complex network is a method with a high flexibility and easy application. Complex Network allows extracting relevant information from the system, like its organization and dynamics, as well as different indices that allow obtaining particular characteristics. This work studies the communities present on the rain network in the Amazon basin for the austral summer. Summer was used due to the presence of the South American monsoon system (SAMS), since this is the greatest mechanism for modulating precipitation over South America. Once the communities were obtained, the minimum correlation value (MCV) was varied in order to verify the spatial variations of the communities. Where it was verified how certain communities are composed of subcommunities while others simply disappear. Finally, it is shown how the spatial distribution of the subcommunities shows a relationship with the presence of SAMS. However, more detailed analyzes are needed for each of these communities.
In this work, machine learning techniques were applied to detect clusters present in satellite and weather radar images. The technique used was the unsupervised clustering algorithm DBSCAN. This algorithm was used to extract the morphological characteristics of atmospheric systems that occurred between February 1 and March 30, 2014 (rainy season) and September 15 to October 15, 2014 (dry season). The morphological characteristics are extracted from different thresholds (235K, 220K and 210K) of cloud top brightness temperature observed in the infrared channel of GOES-13 satellite, and also the precipitation estimated at the reflectivity thresholds (20dBZ, 30dBZ and 40dBZ) of the SIPAM meteorological radar in the city of Manaus. The results present the number of clusters identified by the algorithm and described the characteristics of the clusters during the diurnal cycle and in both seasons.
Brazil is one of countries highest incidence of lightning in the world and the characterization of thus event can help in the development of public polices and decision-making by authorities to mitigate the socio-economic damage that may be caused. This work presents some analysis of spatio-temporal patterns of lightnings in Brazil in 2020, generated from Self-Organizing Map (SOM) technique. This analysis considers the activity of the lightning in the hourly, daily and monthly periods accumulated in the different Brazilian states. The seasonal variation of lightning was also evaluated, considering the four seasons of 2020. The results showed that the self-organizing maps were efficient in identifying spatio-temporal patterns of lightning, which are highly variability events. Thus, theses results can support the development of new tools or analysis in which the spatio-temporal information lightning is important, for example, in warning and forecasting systems.
Carbon fluxes from agroecosystems contribute to the variability in the carbon cycle and atmospheric [CO2]. In this study, we used the Integrated Science Assessment Model (ISAM) equipped with a spring wheat module to study carbon fluxes and their variability in spring wheat agroecosystems of India. First, ISAM was run in the site-scale mode to simulate the Gross Primary Production (GPP), Total Ecosystem Respiration (TER), and Net Ecosystem Production (NEP) over an experimental spring wheat site in the north India. Comparison with flux-tower observations showed that the spring wheat module in ISAM can match the observed flux patterns better than generic crop models. Next, regional-scale runs were conducted to simulate carbon fluxes across the country for the 1980-2016 period. Results showed that the fluxes vary widely, primarily due to variations in planting dates across regions. Fluxes peak earlier in the eastern and central parts of the country, where the crops are planted earlier. All fluxes show statistically significant increasing trends (p<.01) during the study period. The GPP, Net Primary Production (NPP), Autotrophic respiration (Ra), and Heterotrophic Respiration (Rh) increased at 1.272, 0.945, 0.579, 0.328, and 0.366 TgC/yr2, respectively. Numerical experiments were conducted to study how natural forcings like changing temperature and [CO2] and agricultural management practices like nitrogen fertilization and water availability could contribute to the increasing trends. The experiments revealed that increasing [CO2], nitrogen fertilization, and water added through irrigation contributed to the increase of carbon fluxes, with nitrogen fertilization having the strongest effect.