Changes In Atmospheric Conditions Research Articles

Optimal stomatal theory predicts CO2 responses of stomatal conductance in both gymnosperm and angiosperm trees.

Optimal stomatal theory predicts that stomata operate to maximise photosynthesis (Anet ) and minimise transpirational water loss to achieve optimal intrinsic water-use efficiency (iWUE). We tested whether this theory can predict stomatal responses to elevated atmospheric CO2 (eCO2 ), and whether it can capture differences in responsiveness among woody plant functional types (PFTs). We conducted a meta-analysis of tree studies of the effect of eCO2 on iWUE and its components Anet and stomatal conductance (gs ). We compared three PFTs, using the unified stomatal optimisation (USO) model to account for confounding effects of leaf-air vapour pressure difference (D). We expected smaller gs , but greater Anet , responses to eCO2 in gymnosperms compared with angiosperm PFTs. We found that iWUE increased in proportion to increasing eCO2 in all PFTs, and that increases in Anet had stronger effects than reductions in gs . The USO model correctly captured stomatal behaviour with eCO2 across most datasets. The chief difference among PFTs was a lower stomatal slope parameter (g1 ) for the gymnosperm, compared with angiosperm, species. Land surface models can use the USO model to describe stomatal behaviour under changing atmospheric CO2 conditions.

Atmospheric conditions favouring extreme precipitation and flash floods in temperate regions of Europe

Abstract. In recent years, flash floods have repeatedly occurred in temperate regions of central western Europe. Unlike in Mediterranean catchments, this flooding behaviour is unusual. In the past (especially in the 1990s), floods have been characterized by predictable, slowly rising water levels during winter and driven by westerly atmospheric fluxes. Here, we explore potential links and causes between the recent occurrence of flash floods in central western Europe to extreme precipitation and specific atmospheric conditions. We hypothesize that a change in atmospheric conditions has led to more frequent extreme precipitation events that have subsequently triggered flash flood events in central western Europe. To test this hypothesis, we compiled data on flash floods in central western Europe and selected precipitation events above 40 mm h−1 from radar data (the RADOLAN “Radar-Online-Adjustment” dataset from the German Weather Service). Moreover, we identified proxy parameters representative of extreme precipitation favouring atmospheric conditions from the ERA5 reanalysis dataset. High specific humidity (q) in the lower troposphere (q≥0.004 kg kg−1), sufficient latent instability (convective available potential energy (CAPE) ≥ 327 J kg−1), and weak wind speeds between 10 m a.g.l. and 500 hPa (WS10m-500hPa≤6 m s−1) proved to be characteristic of intense rainfall that can potentially trigger flash floods. We relied on linear models to analyse 40 years worth (1981–2020) of atmospheric parameters as well as related precipitation events. We found significant increases in the atmospheric moisture content and increases in atmospheric instability. Parameters representing the motion and organization of convective systems remained largely unchanged in the considered period (1981–2020); however, the number of precipitation events, their maximum 5 min intensities, and their hourly sums were characterized by large interannual variations, and no trends could be identified between 2002 and 2020. Our study shows that there is no single mechanistic path leading from atmospheric conditions to extreme precipitation and subsequently to flash floods. The interactions between the processes involved are so intricate that more analyses which consider other potentially relevant factors, such as intra-annual precipitation patterns or catchment-specific parameters, are required.

A non-isolated MIC for PV application with wide input voltage range

ABSTRACT Photovoltaic (PV) systems are widely recognised as one of the most essential renewable energy sources for meeting future energy demands. The use of various multi-input DC–DC converters (MICs) to interface PV systems for output voltage regulation has been reported in the literature. PVs have inherent limitations, such as being extremely sensitive to changes in atmospheric conditions and human design/installation errors. Therefore, output voltage regulation suffers because of wide input voltage variations which have been ignored. In this regard, a novel MIC topology is proposed in this paper and it can operate under wide input voltage variation in buck, boost, and buck-boost modes of operation to achieve desired output voltage. The proposed topology is capable to integrate multiple energy sources and it offers an individual or simultaneous operation of the connected energy sources. Further, it also has additional features including high voltage gain, modularity, and control flexibility. The steady-state and thermal analysis have been presented, as well as a comparison with the state-of-the-art literature to prove the suitability of the proposed topology for PV application. The proposed MIC topology has been validated experimentally with wide input voltage variation.

Performance comparison between proportional–integral and backstepping control of maximum power in photovoltaic system

The major problem with photovoltaic systems is to extract the maximum power with better performance and good efficiency as long as there are large variations in atmospheric conditions. To do this, we made a comparative analysis between two controllers, Proportional-Integrator (PI) and backstepping based on a photovoltaic system made up of two parts, the first is the Photovoltaic (PV) panels which are used to convert solar irradiation to an electric current, the second is a boost converter which is used to provide the maximum power. The Perturb and Observe algorithm is used to generate the reference voltage in order to follow it by the two controllers (PI and backstepping) and therefore generate the maximum power. We are considered some parameters for this comparison such asthe efficiency of the controllersto follow the reference power despite the rapid change in atmospheric conditions as well as the response time of the system and the ripples in the transient phase.

Interactive impacts of atmospheric, soil, and vegetation conditions on land surface energy partitioning over a coastal wetland in Northern China

Elucidating environmental controls on land surface energy partitioning at ecosystem levels is critical for better understanding water and energy cycles across landscapes; however, the underlying effects of changing atmospheric, soil, and vegetation conditions on the energy partitioning in coastal wetlands are not well understood. Here, the temporal variations in surface evaporative fraction (EF; defined as latent heat flux-LE divided by the sum of LE and sensible heat flux-H) and its responses to environmental conditions were analyzed at half-hourly and daily timescales, using measurements from a Bowen ratio tower situated in an herbaceous coastal wetland in Tianjin, Northern China. Results showed that net radiation (Rn) was mainly allocated to H with an average H/Rn of 0.90 and daily EF was averaged at 0.14 during the study period. The EF patterns in the studied coastal wetland were significantly different from those in terrestrial ecosystems, which was mainly attributed to the complex interactions in local atmospheric, soil, and vegetation conditions. Specifically, significant directional relationships among environmental variables were detected through the Granger causality analysis (GCA), especially at half-hourly timescales. The GCA and boosted regression tree methods showed that energy-related factors (i.e., air temperature and Rn) were the dominant controls on EF. Poor vegetation status, which was affected by salinity and flood stress, primarily limited LE and subsequently temporal EF variability. During the wet period, atmospheric conditions changed with the prevailing onshore winds, resulting in the dominant control of relative humidity on EF variations. Overall, the EF variability in coastal wetlands was not solely controlled by energy but was more tightly constrained by atmospheric conditions and vegetation dynamics, which offers new insights into energy exchanges in coastal wetland ecosystems, especially for determining the coupling strength between atmosphere and land surface processes.

Mathematical models of non-stationary random processes in the SVVP representation

The work examines methods and mathematical models that provide the possibility of researching the statistical characteristics of complex and non-stationary random processes describing a wide class of physical phenomena. The proposed models can be used to study the processes observed in various fields of human activity, namely, to analyze the trajectories of unmanned aerial vehicles, their acoustic signals, meteorological processes reflecting the state of the atmosphere.
 Real and simulated non-stationary random processes considered in the work are represented by the complex vector random process (CVRP) model. In this case, the length of the subvector is equal to the period of the seasonal component. In fact, in such a representation, the time series readings are replaced by their aggregate, i.e. subvectors. Statistical relationships are analyzed for subvectors, and not, as usual, for process counts. If the length of the subvector is equal to one, all operations in the SVVP representation are equivalent to the usual operations for time series.
 The mathematical apparatus developed in the article was used to analyze changes in time series of atmospheric temperature observed over a long period of time; average annual temperatures were estimated with subsequent smoothing with a low-pass filter. The results obtained can be used to analyze medium-term and long-term changes in atmospheric conditions, refine the results obtained by traditional methods of mathematical statistics, analyze and predict data flows in mobile communication networks, as well as in other areas of human activity.

Comparison of deep learning and regression-based MPPT algorithms in PV systems

Solar energy systems (SES) and photovoltaic (PV) modules should be operated at the maximum power point (MPP) to achieve the highest efficiency in the energy generation processes. Maximum power point tracking (MPPT) applications using conventional methods may not be able to follow the global MPP (GMPP) of the PV system under changing atmospheric conditions and they could oscillate around the local MPP. In this study, a machine learning and deep learning (DL) based long short-term memory (LSTM) model is proposed as an innovative solution for MPPT. Contrary to the traditional MPPT applications using current and voltage sensors, the output resistance of the PV module estimation was made by using environmental parameters (such as temperature and radiation) and artificial intelligence algorithms in this study.The LSTM model was compared with artificial neural networks (ANN) and regression methods regarding mean square error (MSE), root mean square error(RMSE) and mean absolute error (MAE) parameters. It has been determined that the LSTM model has a better performance and could more successfully follow MPP compared to the other methods. Finally, after the comparison with the ANN method, it is proved that LSTM gives 37%, 21%, and 31% more successful MSE, RMSE, and MAE results, respectively.

Decentralized adaptive neuro-fuzzy dynamic surface control for maximum power point tracking of a photovoltaic system

The current work proposes a decentralized adaptive dynamic surface control approach for extracting the maximum power from a photovoltaic (PV) system and then regulating the required voltage for charging the battery. In this regard, two cascaded direct current-direct current (DC-DC) converters are utilized. The boost converter is interposed between the PV system and the load to help extract the maximum power. The buck-boost converter is then exploited to maintain the output voltage at a specified level which must meet the battery demand. Therefore, to handle the interactions between the cascaded converters, a decentralized control approach is developed. In the suggested approach, by introducing a nonlinear filter, an effective dynamic surface control (DSC) scheme is proposed with guaranteeing asymptotic tracking convergence. Further, by incorporating a nonlinear compensation term into the proposed control approach, the robustness of the resulting controller is improved. In addition, since the model of the converters is nonlinear with unknown uncertainties, the neuro-fuzzy system is used to estimate lumped uncertainties. The proposed control method has good attributes in terms of having a low tracking error, an excellent transition response, and a quick response to changes in atmospheric conditions. The stability of the whole control system is proved by the Lyapunov stability theorem. Finally, comprehensive simulation results are performed to validate the effectiveness of the suggested control approach.

Accurate water extraction using remote sensing imagery based on normalized difference water index and unsupervised deep learning

Large-scale monitoring of surface water bodies is of great significance to the sustainable development of regional ecosystems. Remote sensing is currently the main method of global earth observation. On the one hand, the traditional water index is simple and efficient, but it relies on a fixed global threshold, which leads to low accuracy for water extraction. On the other hand, deep learning has achieved state-of-the-art results in classification of spectral data, but it consumes a substantial amount of manpower and time to label sufficient high-quality samples. Furthermore, spectral characteristics of water bodies in different areas vary greatly due to changes in atmospheric conditions and viewing geometry. In this paper, we propose a new accurate water extraction framework based on unsupervised deep learning and NDWI of multispectral images. Binarized NDWI images are used to identify potential water bodies, and deep learning training is performed using these pseudo samples and labels. This process realizes the conversion from unlabeled learning to noisy label learning. First, we proposed a simple and fast binarization algorithm to segment as many real water bodies as possible from NDWI images. Then a set of water confidence assessment rules was constructed from the four aspects of the spectrum, shape, agglomeration, and range. The water segments were scored and sorted to make the model start learning from easy samples and gradually advance to complex samples. Finally, the adjusted co-teaching learning strategy is adopted to filter errors introduced by noisy labels through peer networks with different learning capabilities during training. We tested the accuracy of our method using Gaofen Image Dataset (GID), Sentinel-2 and Landsat images of several water bodies in China. Compared with other methods, our method improved the F1 score by 18.1–40.3% and 6.8–22.2% for GID and Sentinel-2 images, respectively. In addition, our method is more stable in long-term water monitoring. The proposed method has the potential to be used for extracting water bodies with high accuracy on a large scale, especially in areas with complex environments and a lack of deep learning samples. And it provides a new idea for unsupervised learning in the current remote sensing field by fully combining remote sensing expertise and spectral information of ground objects.

Regional patterns of vegetation, temperature, and rainfall trends in the coastal mountain range of Chiapas, Mexico

Changes in atmospheric CO2, ocean temperature, and regional vegetation conditions in Mesoamerica indicate that significant trends in temperature and rainfall may have occurred in the Sierra Madre de Chiapas, Mexico. This is an important region for flora and fauna which could be affected by climate trends. We aimed to determine if and where climate trends had occurred in the Sierra Madre and lower elevation regions between 1990 and 2016 (27 years), if these trends were part of longer term (1960-2016 [57 years]) changes, and how changes in large-scale and regional/local conditions may be influencing these trends. In the Sierra Madre, overall minimum daily temperatures increased, maximum temperatures decreased, and the most significant mean temperature trends were cooler during the 27-yr period. Both the start and end of the wet season trended earlier in the year, and wet season rainfall increased significantly. Trends were not significant during the 57-yr period in the Sierra Madre; however, in the adjacent Pacific coast region, significant warmer temperature trends continued during this period. Within regions, there was large variation in temperature and rainfall changes and some local trends were opposite to the regional averages. Large-scale processes of warming sea surface temperatures in the east coast of Mexico, a change from the positive to negative phase of the Pacific Decadal Oscillation and increases in atmospheric CO2 may be influencing these trends. At the regional scale, increases in dense vegetation and evapotranspiration since 1990 may have created characteristics favoring a positive feedback of higher ocean-based moisture and vegetation-based precipitation cycling.

A New FL-MPPT High Voltage DC-DC Converter for PV Solar Application

To reduce the effects of global warming, there is an increasing need for renewable energy sources. Several studies have been carried out on photovoltaic (PV) systems to maximize their potential as an alternative electricity generator. However, various power converters for high voltage ratio applications have multiple drawbacks. This research was carried out to develop a power converter topology connected between the PV and the load for the need. In this research, the high step-up DC-DC converter for high-voltage gain conversion ratio and high efficiency is proposed. Furthermore, the fuzzy logic-based Maximum Power Point Tracking (MPPT) technique connected to the power converter was used to maximize the power converted from PV in changing atmospheric conditions. The MPPT control with fuzzy logic controller (FLC) was analysed and compared with the perturb and observe (P&O) algorithm. The results showed that the FLC algorithm could control the high step-up (HSU) DC-DC converter with an output voltage of 29% higher than the P&O algorithm.

Variety-specific response of bulk stomatal conductance of grapevine canopies to changes in net radiation, atmospheric demand, and drought stress.

In wine growing regions around the world, climate change has the potential to affect vine transpiration and overall vineyard water use due to related changes in daily atmospheric conditions and soil water deficits. Grapevines control their transpiration in response to such changes by regulating conductance of water through the soil-plant-atmosphere continuum. The response of bulk stomatal conductance, the vine canopy equivalent of stomatal conductance, to such changes were studied on Cabernet-Sauvignon, Merlot, Tempranillo, Ugni blanc, and Semillon vines in a non-irrigated vineyard in Bordeaux France. Whole-vine sap flow, temperature and humidity in the vine canopy, and net radiation absorbed by the vine canopy were measured on 15-minute intervals from early July through mid-September 2020, together with periodic measurements of leaf area, canopy porosity, and predawn leaf water potential. From these data, bulk stomatal conductance was calculated on 15-minute intervals, and multiple linear regression analysis was performed to identify key variables and their relative effect on conductance. For the regression analysis, attention was focused on addressing non-linearity and collinearity in the explanatory variables and developing a model that was readily interpretable.Variability of vapour pressure deficit in the vine canopy over the day and predawn water potential over the season explained much of the variability in bulk stomatal conductance overall, with relative differences between varieties appearing to be driven in large part by differences in conductance response to predawn water potential between the varieties. Transpiration simulations based on the regression equations found similar differences between varieties in terms of daily and seasonal transpiration. These simulations also compared well with those from an accepted vineyard water balance model, although there appeared to be differences between the two approaches in the rate at which conductance, and hence transpiration is reduced as a function of decreasing soil water content (i.e., increasing water deficit stress). By better characterizing the response of bulk stomatal conductance, the dynamics of vine transpiration can be better parameterized in vineyard water use modeling of current and future climate scenarios.

Heterogeneous Graphical Model for Non-Negative and Non-Gaussian PM2.5 data

AbstractStudies on the conditional relationships between PM2.5 concentrations among different regions are of great interest for the joint prevention and control of air pollution. Because of seasonal changes in atmospheric conditions, spatial patterns of PM2.5 may differ throughout the year. Additionally, concentration data are both non-negative and non-Gaussian. These data features pose significant challenges to existing methods. This study proposes a heterogeneous graphical model for non-negative and non-Gaussian data via the score matching loss. The proposed method simultaneously clusters multiple datasets and estimates a graph for variables with complex properties in each cluster. Furthermore, our model involves a network that indicate similarity among datasets, and this network can have additional applications. In simulation studies, the proposed method outperforms competing alternatives in both clustering and edge identification. We also analyse the PM2.5 concentrations' spatial correlations in Taiwan's regions using data obtained in year 2019 from 67 air-quality monitoring stations. The 12 months are clustered into four groups: January–March, April, May–September and October–December, and the corresponding graphs have 153, 57, 86 and 167 edges respectively. The results show obvious seasonality, which is consistent with the meteorological literature. Geographically, the PM2.5 concentrations of north and south Taiwan regions correlate more respectively. These results can provide valuable information for developing joint air-quality control strategies.

Reconstructed Centennial Mass Balance Change for Golubin Glacier, Northern Tien Shan

Mass balance measurements for Golubin glacier in Northern Tien Shan, Kyrgyzstan, have been discontinuous over the last century, with significant data gaps. We provide a unique over 100-year-long mass balance series on daily resolution. We applied a temperature index model calibrated with glaciological measurements and validated with secular mass balances derived from independent length change observations. A comparison with other recent geodetic studies reveals good agreement. Golubin lost −0.16 ± 0.45 m w.e. a−1 from 1900/1901 to 2020/2021. From the long-term mass balance time series, we identify a shift to a more negative/less positive regime with time, with a steepening of the ablation and accumulation gradients, especially for the past two decades. We observe a parallel shift of the mass balance gradient accompanied by a rotation of the ablation gradient due to increased ablation at the glacier tongue and accumulation above the equilibrium line altitude. This tendency is believed to intensify in the future, affecting glaciers’ mass balance sensitivity to changes in atmospheric conditions and year-to-year variability and resulting in irregular melt water release feeding the rivers that provide water to Bishkek. These kinds of datasets are sparse for Tien Shan and, yet, indispensable to enhancing our understanding of glacier changes in High Mountain Asia.

Intercomparison of holographic imaging and single-particle forward light scattering in situ measurements of liquid clouds in changing atmospheric conditions

Abstract. Upon a new measurement technique, it is possible to sharpen the determination of microphysical properties of cloud droplets using high resolving power imaging. The shape, size, and position of each particle inside a well-defined, three-dimensional sample volume can be measured with holographic methods without assumptions of particle properties. In situ cloud measurements were carried out at the Puijo station in Kuopio, Finland, focusing on intercomparisons between cloud droplet analyzers over 2 months in September–November 2020. The novel holographic imaging instrument (ICEMET) was adapted to measure microphysical properties of liquid clouds, and these values were compared with parallel measurements of a cloud droplet spectrometer (FM-120) and particle measurements using a twin-inlet system. When the intercomparison was carried out during isoaxial sampling, our results showed good agreement in terms of variability between the instruments, with the averaged ratios between ICEMET and FM-120 being 0.6 ± 0.2, 1.0 ± 0.5, and 1.2 ± 0.2 for the total number concentration (Nd) of droplets, liquid water content (LWC), and median volume diameter (MVD), respectively. This agreement during isoaxial sampling was also confirmed by mutual correlation and Pearson correlation coefficients. The ICEMET-observed LWC was more reliable than FM-120 (without a swivel-head mount), which was verified by comparing the estimated LWC to measured values, whereas the twin-inlet DMPS system and FM-120 observations of Nd showed good agreement both in variability and amplitude. Field data revealed that ICEMET can detect small cloud droplets down to 5 µm via geometric magnification.

Decrease of winter cyclone passage over northern Japan due to the reduction in the regional cyclogenesis associated with cold air outbreak

AbstractIn East Asia, a strong winter monsoon and high baroclinicity cause enhanced cyclone activity, which has been the subject of intensive study. Despite the high frequency of genesis of mesoscale cyclones in this area, most of the studies on the decadal timescale have focused only on synoptic‐scale cyclones. To clarify the interannual variation of the passage of winter cyclones including synoptic‐scale and mesoscale cyclones over and around Japan, a tracking algorithm was applied to 62 winter seasons (December–March) from 1958/1959 to 2019/2020. During the study period, the passage of cyclones around northern Japan showed a decreasing trend. Most cyclones that pass around northern Japan are generated over the northern part of the Sea of Japan, which is an area where cyclogenesis has decreased during the study period. Weather pattern classification based on synoptic scale atmospheric conditions revealed that passage of upper‐level troughs and occurrence of lower‐level cold air outbreaks are the preferential conditions for cyclogenesis in this region. In addition, the intensity of the cold air outbreak in this region was weakened during the study period. These results suggest that changes in atmospheric conditions due to climate change have reduced mesocyclone genesis over the northern Sea of Japan, resulting in the reduction in the number of cyclones passing over and around northern Japan.

The climatic interdependence of extreme-rainfall events around the globe.

The identification of regions of similar climatological behavior can be utilized for the discovery of spatial relationships over long-range scales, including teleconnections. Additionally, it provides insights for the improvement of corresponding interaction processes in general circulation models. In this regard, the global picture of the interdependence patterns of extreme-rainfall events (EREs) still needs to be further explored. To this end, we propose a top-down complex-network-based clustering workflow, with the combination of consensus clustering and mutual correspondences. Consensus clustering provides a reliable community structure under each dataset, while mutual correspondences build a matching relationship between different community structures obtained from different datasets. This approach ensures the robustness of the identified structures when multiple datasets are available. By applying it simultaneously to two satellite-derived precipitation datasets, we identify consistent synchronized structures of EREs around the globe, during boreal summer. Two of them show independent spatiotemporal characteristics, uncovering the primary compositions of different monsoon systems. They explicitly manifest the primary intraseasonal variability in the context of the global monsoon, in particular, the "monsoon jump" over both East Asia and West Africa and the mid-summer drought over Central America and southern Mexico. Through a case study related to the Asian summer monsoon, we verify that the intraseasonal changes of upper-level atmospheric conditions are preserved by significant connections within the global synchronization structure. Our work advances network-based clustering methodology for (i) decoding the spatiotemporal configuration of interdependence patterns of natural variability and for (ii) the intercomparison of these patterns, especially regarding their spatial distributions over different datasets.

Comparison of Extreme Coastal Flooding Events between Tropical and Midlatitude Weather Systems in the Delaware and Chesapeake Bays for 1980–2019

Abstract Coastal flooding is one of the most costly and deadly natural hazards facing the U.S. mid-Atlantic region today. Impacts in this heavily populated and economically significant region are caused by a combination of the location’s exposure and natural forcing from storms and sea level rise. Tropical cyclones (TCs) and midlatitude (ML) weather systems each have caused extreme coastal flooding in the region. Skew surge was computed over each tidal cycle for the past 40 years (1980–2019) at several tide gauges in the Delaware and Chesapeake Bays to compare the meteorological component of surge for each weather type. Although TCs cause higher mean surges, ML weather systems can produce surges just as severe and occur much more frequently, peaking in the cold season (November–March). Of the top 10 largest surge events, TCs account for 30%–45% in the Delaware and upper Chesapeake Bays and 40%–45% in the lower Chesapeake Bay. This percentage drops to 10%–15% for larger numbers of events in all regions. Mean sea level pressure and 500-hPa geopotential height (GPH) fields of the top 10 surge events from ML weather systems show a low pressure center west-southwest of “Delmarva” and a semistationary high pressure center to the northeast prior to maximum surge, producing strong easterly winds. Low pressure centers intensify under upper-level divergence as they travel eastward, and the high pressure centers are near the GPH ridges. During lower-bay events, the low pressure centers develop farther south, intensifying over warmer coastal waters, with a south-shifted GPH pattern relative to upper-bay events. Significance Statement Severe coastal flooding is a year-round threat in the U.S. mid-Atlantic region, and impacts are projected to increase in magnitude and frequency. Research into the meteorological contribution to storm surge, separate from mean sea level and tidal phase, will increase the scientific understanding and monitoring of changing atmospheric conditions. Tropical cyclones and midlatitude weather systems both significantly impact the mid-Atlantic region during different times of year. However, climate change may alter the future behavior of these systems differently. Understanding the synoptic environment and quantifying the surge response and subbay geographic variability of each weather system in this region will aid in public awareness, near-term emergency preparation, and long-term planning for coastal storms.

Synoptic Time Scale Variability in Precipitation and Streamflows for River Basins over Northern South America

The synoptic mode of variability (SMV) refers to changes in atmospheric conditions over periods ranging from 2 to 10 days. In tropical regions, this variability is driven by tropical waves that have a clear signal on the wavenumber–frequency power spectra of precipitation. This study uses the ensemble empirical mode decomposition (EEMD) method to identify the SMV in daily precipitation and streamflows in 47 river basins over northern South America. We found the presence of the frequency bands with periods of 3–12 days and 6–18 days, which agrees with the SMV associated with tropical waves that modulate precipitation over the region. Furthermore, our results reveal that variance explained by the SMV in rainfall over each catchment is greater than the variance explained by those SMV in streamflows, which suggests that catchments efficiently filter out this variability. We found that SMV explains from 5% to 20% of streamflow variability for catchments ranging from 1000 km2 to 5000 km2. Additionally, the variance explained by SMV decreases as a power fit with the catchment area. Thus, this study characterizes the SMV for potential applications on regional hydrology, diagnosis, modeling, short-time forecasting, prediction, and management of water resources.

Mount Everest's photogenic weather during the post‐monsoon

Mount Everest's photogenic weather during the post‐monsoon