N. Kameshwari, T. V. S. Udaya Bhaskar, Krishna K. Osuri
et al.
Abstract The marine atmospheric boundary layer influences the evolution of evaporation duct height (EDH) by altering the vertical profile of the refractive index. The EDH is a crucial parameter for marine and naval communication. Many studies have demonstrated the sensitivity of boundary layer similarity theory in estimating the vertical profile of refractive index. The current study modifies the widely accepted EDH formulation of Babin’s model A to suit the air–sea interaction in moderate wind conditions of the tropical Indian Ocean (TIO), referred to as a modified model A (MMA). The EDH estimated by the MMA model has reduced the error by around 3.5 m than that of the operational Paulus–Jeske (PJ) model, when validated against observations. The present study revealed that the PJ model fails in an upwelling region and also modifies the seasonality of computed EDH. Shapley additive explanations (SHAP) analysis brings out that the EDH is sensitive to moisture transfer coefficient (with R 2 = −0.83) and surface layer stability (with R 2 = 0.8) in the TIO, which was supported by the hourly time series constructed from ocean buoy data. This study brings confidence in replacing the existing operational PJ model with the MMA model in naval applications for the TIO region. Significance Statement To compute evaporation duct height (EDH), Babin’s model A is a well-accepted model to overcome the problems with the previous Paulus–Jeske (PJ) model. To suit the wind conditions over the tropical Indian Ocean, the model A has been slightly modified (modified model A). Comparison analysis showed that the PJ model precisely fails in an upwelling region rather than in a stable surface layer region as found in previous studies. Comparing all the marine atmospheric boundary layer (MABL) variables, surface layer stability is found to be the most dominant variable determining the EDH. In an unstable and neutral surface layer, the sea–air humidity difference and moisture transfer coefficient affect the EDH, and in a stable surface layer, additionally, humidity and sea surface temperature affect the duct height variability.
Abstract Crewed missions to Mars will be a milestone of future space exploration programs. However, the absence of Earth's magnetic field leaves astronauts directly exposed to unattenuated energetic particles in deep space, primarily galactic cosmic rays (GCRs), resulting in significantly higher radiation levels and enhanced health risks. Understanding and quantifying these radiation hazards is thus essential for evaluating the feasibility and safety of long‐duration Mars missions. Based on the dose data from the Trace Gas Orbiter mission and the Cosmic Ray Telescope for the Effects of Radiation (CRaTER), we perform correlation analyses between the measured dose rate and solar modulation conditions, parameterized as solar modulation potential, and develop empirical models that can be extrapolated to a broader range of solar activities. Using these models, the GCR‐cumulative dose for mission scenarios following three different transfer trajectories under varying solar modulation conditions during the past ∼60 years are calculated. Our results indicate that missions operated during solar maximum accumulate 30%–55% less GCR effective dose than those during solar minimum, with the specific percentage depending on their execution period and trajectory. Under similar shielding conditions of measurements used here, missions following the minimum energy trajectory and conducted during relatively active solar cycles can generally maintain the cumulative radiation effective dose below 1,000 mSv, but keeping it below the NASA's new limit of 600 mSv requires restricting the mission duration to the solar maximum. Nevertheless, faster transfer orbits can help satisfy this limit during solar minimum years.
Cameron R. Homeyer, Matthew J. Bunkers, John T. Allen
et al.
Abstract Supercell storms are recognized to account for a disproportionately large amount of severe weather occurring in the United States and elsewhere. Despite their importance, few studies document their climatological behavior, and those that do focus predominantly or entirely on severe supercells. This study presents an objective and comprehensive radar-based climatology of supercell storms during a 14-yr period over the contiguous United States. Approximately 56 000 supercells are identified, half of which are likely nonsevere. All objectively identified supercells (strongly and persistently rotating storms) are diagnosed as either “right moving” (RM) or “left moving” (LM) based primarily on the deviance of storm motion relative to the 0–6-km environmental wind shear vector. RM supercells outnumber LM supercells at a rate of approximately 3:1 and also live longer. RM supercells are more frequently severe than LM supercells, accounting for ≈99.8% of supercell tornadoes, ≈76.9% of supercell severe hail events, and ≈68.9% of supercell severe wind events. Common to both supercell configurations, numerous sensitivities are identified between storm characteristics and storm severity. Namely, storm severity increases with increasing velocities of storm motion and increasing midlevel rotation. Severity maximizes when storm motion deviates ≈30° left or right of the 0–6-km environmental wind shear vector. Last, severe and nonsevere supercell storm characteristics are compared, and the greatest discriminatory indicators are based on metrics of the depth of high radar reflectivity magnitudes. Significance Statement Rotating thunderstorms (supercells) are responsible for the vast majority of the most significant tornadoes and severe hail events in the United States. Despite their recognized importance, many basic details about them remain unknown, such as their frequency, spatial distribution, and dominant character of motion (right or left moving). In this study, a comprehensive 14-yr statistical analysis of United States supercell storms based on objectively analyzed radar observations is used to address this gap in understanding. Numerous sensitivities between supercell severity and storm characteristics are found and have potential to advance the warning decision-making process.
Muhammed Haziq Muhammed Nor, Mohd Aftar Abu Bakar, Noratiqah Mohd Ariff
et al.
The Environmental Performance Index (EPI) is widely used to assess a country’s environmental sustainability in terms of climate, environmental health, and ecosystem health. However, significant missing data can lead to biased outcomes, potentially resulting in unfair penalties or rewards for certain countries. By ensuring equal treatment of missing values, the EPI could enhance its usefulness. This study aimed to compare multiple imputation methods, specifically MICEForest, k NN, and MissForest imputation, on EPI data with missing data ranging from 1% to over 50%. The study also evaluated these methods’ ability to handle Missing Not At Random (MNAR) issues, specifically for fisheries and maritime activities indicators in landlocked countries. While it was assumed that landlocked countries could access the sea through various means, limited information was available. Our results showed that MICEForest, k NN, and MissForest imputation methods produced imputed data closely matching the original data, with minimal impact on central tendencies, as indicated by low MAE, RMSE, MAPE, and WAPE values. Sensitivity analysis revealed that MissForest and k NN were more stable and consistent than MICEForest across all error metrics when parameters were adjusted. Future research may explore deep learning techniques for handling missing data in environmental datasets like EPI.
Sanjoy Kumar Pal, Soumen Sarkar, Kousik Nanda
et al.
The G5 geomagnetic storm of May 2024 provided a significant opportunity to investigate global ionospheric disturbances using vertical total electron content (VTEC) data derived from 422 GNSS-IGS stations and GIM. This study presents a comprehensive spatio-temporal analysis of VTEC modulation before, during, and after the storm, focusing on hemispheric asymmetries and longitudinal variations. The primary objective of this study is to analyze the spatial and temporal modulation of VTEC under extreme geomagnetic conditions, assess the hemispheric asymmetry and longitudinal disruptions, and evaluate the influence of geomagnetic indices on storm-time ionospheric variability. The indices examined reveal intense geomagnetic activity, with the dst index plunging to −412 nT, the Kp index reaching 9, and significant fluctuations in the auroral electrojet indices (AE, AL, AU), all indicative of severe space weather conditions. The results highlight storm-induced hemispheric asymmetries, with positive storm effects (VTEC enhancement) in the Northern Hemisphere and negative storm effects (VTEC depletion) in the Southern Hemisphere. These anomalies are primarily attributed to penetration electric fields, neutral wind effects, and composition changes in the ionosphere. The storm’s peak impact on DoY 132 exhibited maximum disturbances at ±90° and ±180° longitudes, emphasizing the role of geomagnetic forces in plasma redistribution. Longitudinal gradients were strongly amplified, disrupting the usual equatorial ionization anomaly structure. Post-storm recovery on DoY 136 demonstrated a gradual return to equilibrium, although lingering effects persisted at mid- and high latitudes. These findings are crucial for understanding space weather-induced ionospheric perturbations, directly impacting GNSS-based navigation, communication systems, and space weather forecasting.
Sarah Clark, Zack Guido, Laura T. Cabrera-Rivera
et al.
Climate extremes can generate impacts in one sector that cascade or amplify the impacts in others. Developing strategies that build resilience to these compound hazards requires collaboration among diverse stakeholders to understand hazard dynamics and the synergies and tradeoffs in adaptation activities. In many regions, community-based organizations (CBOs) lead in local climate adaptation, and their engagement in research can help inform research agendas and capacity-strengthening activities that support locally led adaptation. In this paper, we describe a co-produced, collaborative research project that convened CBOs working in climate adaptation, public health, and energy resilience in Puerto Rico. The goals were to identify knowledge gaps and opportunities for immediate action. Based on interviews, a participatory workshop, and a survey, we report on the CBO activities, their networks and their views on the relationships between climate, public health, and energy. We also describe their perspectives on priorities to address compound hazards. Drawing on these results, we discuss five strategies that can help research projects collaborate, co-produce, and engage with CBOs. They include understanding the network to inform engagement, paying attention to differential impacts and justice, employing flexible planning to accommodate multiple goals and perspectives, focusing on information sharing to advance collaboration, exploring narratives of change to understand adaptation and maladaptation, and confronting the question of “what next.” This study informs how research can more effectively engage CBOs in climate adaptation studies, which, in turn, can contribute to building plans and systems that are better equipped to build resilience to compound extreme events.
Forecasting is essential for improving aviation safety, with air humidity being a critical factor influenced by air temperature. This study analyzes daily humidity data from I Gusti Ngurah Rai Airport, one of Indonesia’s busiest air stations, using two time series modeling approaches: Autoregressive (AR) and high-order fuzzy modeling. The objective is to evaluate and compare their forecasting accuracy. Historical daily data from the Meteorology, Climatology, and Geophysics Agency of Indonesia were used to build the forecasting models. The optimal linear AR model served as the foundation for constructing the AR high-order fuzzy model, which incorporates linguistic rules to capture nonlinear patterns. Both models were implemented and evaluated using the Mean Squared Error (MSE) metric. Results show that the AR(2) model outperforms the AR high-order fuzzy model, achieving a lower MSE of 13.23. This suggests that the AR(2) model provides more accurate humidity forecasts over the observed period. These findings offer practical insights for policymakers and decision-makers in forecasting daily humidity levels and supporting aviation operations. While the study confirms the effectiveness of traditional AR modeling, it also highlights limitations of the fuzzy approach, particularly its sensitivity to parameter tuning and data sparsity. The integration of high-order fuzzy modeling represents a novel contribution to this domain, though further refinement is needed to enhance its forecasting performance.
Timothy O Ogunbode, Emmanuel K Odusina, Victor O Oyebamiji
et al.
Water demand at any given time, particularly at the household level, depends on various factors, including climatic variables, social, economic, and demographic factors. Achieving consistent availability and accessibility, coupled with effective resource management, is crucial, particularly in tropical regions. Understanding all these variables is essential to achieving these goals. This research aimed to assess the impact of ten socio-demographic variables on predicting household water demand in Iwo. Two hundred and twenty-five households were randomly selected, with 196 completing and returning the survey. Both descriptive and multivariate analyses, specifically Factor and Regression Analysis, were employed to analyse the data. Factor analysis (FA) identified four variables: (i) housing characteristics; (ii) marital status; (iii) income level; and (iv) gender distribution, in that sequence. These four variables collectively accounted for 68.608% of the variance in household water demand in Iwo. This outcome underscores the importance of giving due consideration to these variables in water supply planning by relevant authorities. The study contributes to a better understanding and quantification of the significant variables influencing household water demand. However, it is advisable that future investigations into household water use incorporate additional variables beyond socio-demographic factors to comprehensively comprehend factors influencing water demand at the household level.
Since the reform and opening up, the scale of foreign direct investment (FDI) inflows into China has been continuously expanding, but the imperfect environmental governance mechanism has led to increasingly severe environmental problems in China. This paper studies the impact and mechanism of public environmental participation (PEP) on FDI. The results show that PEP has a significant negative impact on the FDI inflow of enterprises. Hindrance effect of PEP on enterprise FDI is more obvious in economically developed eastern regions, coastal cities and first-tier cities. PEP has a greater impact on FDI in high-tech industries. The economic growth target has a restraining effect on China’s environmental protection, weakening public supervision of FDI. The constraint of economic growth targets increases the pressure to develop the economy, and weakens the inhibitory effect of PEP on corporate FDI. This study provides important empirical evidence for improving China’s environmental governance system and high-quality utilization of foreign investment.
Abstract Over the past two decades, more frequent and intense climate events have seriously threatened the operation of water transfer projects in the Pacific Rim region. However, the role of climatic change in driving runoff variations in the water source areas of these projects is unclear. We used tree-ring data to reconstruct changes in the runoff of the Hanjiang River since 1580 CE representing an important water source area for China’s south-north water transfer project. Comparisons with hydroclimatic reconstructions for the southwestern United States and central Chile indicated that the Pacific Rim region has experienced multiple coinciding droughts related to ENSO activity. Climate simulations indicate an increased likelihood of drought occurrence in the Pacific Rim region in the coming decades. The combination of warming-induced drought stresses with dynamic El Niño (warming ENSO) patterns is a thread to urban agglomerations and agricultural regions that rely on water transfer projects along the Pacific Rim.
Abstract An interaction of trends in a multitude of climate indicators dictate how agricultural production and resource use will be affected. Turkish agroecosystems have not been evaluated for climate trends, especially focusing on spatial and temporal domains relevant for agricultural production. Long-term (1981–2017) temporal trends in agriculturally relevant climate indicators [maximum (Tmax), minimum (Tmin), and mean (Tavg) air temperatures, diurnal temperature range (DTR), growing degree-days (GDD), precipitation, incoming shortwave radiation (Rs), relative humidity (RH), wind speed (u2), saturated and actual vapor pressure (es and ea), vapor pressure deficit (VPD), grass- and alfalfa-reference evapotranspiration (ETo and ETr), and aridity index (AI)] across Turkey (Turkiye) were quantified and analyzed using the NASA-POWER dataset at 0.5° × 0.5° grid cells (n = 323) for nine agricultural zones (AZs) in Turkey. At the growing-season scale, Tmin, Tmax, Tavg, GDD, es, ea, VPD, Rs, precipitation, RH, and AI showed statistically significant positive trends at 100%, 76%, 100%, 100%, 94%, 98%, 22%, 83%, 33%, 10%, and 13% of Turkey’s terrestrial area, respectively. Negative trends were observed in growing-season-scale DTR, u2, ETo, and ETr at 38%, 38%, 10%, and 18% of the total terrestrial area, respectively. At the annual scale, ETo and ETr showed increasing trends over 37% and 19% of the area, respectively. Evaporative demand showed national mean trends of −2.6 and −4.1 mm yr−1 during the growing season, respectively. Aegean AZ showed the most negative trends in growing-season ETo and ETr. The national mean magnitude in annual total precipitation (4.7 mm yr−1) was 39% greater than that in growing-season total precipitation (3.4 mm yr−1).
Alina Bărbulescu, Cristian Stefan Dumitriu, Iulia Ilie
et al.
Nowadays, observing, recording, and modeling the dynamics of atmospheric pollutants represent actual study areas given the effects of pollution on the population and ecosystems. The existence of aberrant values may influence reports on air quality when they are based on average values over a period. This may also influence the quality of models, which are further used in forecasting. Therefore, correct data collection and analysis is necessary before modeling. This study aimed to detect aberrant values in a nitrogen oxide concentration series recorded in the interval 1 January–8 June 2016 in Timisoara, Romania, and retrieved from the official reports of the National Network for Monitoring the Air Quality, Romania. Four methods were utilized, including the interquartile range (IQR), isolation forest, local outlier factor (LOF) methods, and the generalized extreme studentized deviate (GESD) test. Autoregressive integrated moving average (ARIMA), Generalized Regression Neural Networks (GRNN), and hybrid ARIMA-GRNN models were built for the series before and after the removal of aberrant values. The results show that the first approach provided a good model (from a statistical viewpoint) for the series after the anomalies removal. The best model was obtained by the hybrid ARIMA-GRNN. For example, for the raw NO<sub>2</sub> series, the ARIMA model was not statistically validated, whereas, for the series without outliers, the ARIMA(1,1,1) was validated. The GRNN model for the raw series was able to learn the data well: R<sup>2</sup> = 76.135%, the correlation between the actual and predicted values (r<sub>ap</sub>) was 0.8778, the mean standard errors (MSE) = 0.177, the mean absolute error MAE = 0.2839, and the mean absolute percentage error MAPE = 9.9786. Still, on the test set, the results were worse: MSE = 1.5101, MAE = 0.8175, r<sub>ap</sub> = 0.4482. For the series without outliers, the model was able to learn the data in the training set better than for the raw series (R<sup>2</sup> = 0.996), whereas, on the test set, the results were not very good (R<sup>2</sup> = 0.473). The performances of the hybrid ARIMA–GRNN on the initial series were not satisfactory on the test (the pattern of the computed values was almost linear) but were very good on the series without outliers (the correlation between the predicted values on the test set was very close to 1). The same was true for the models built for O<sub>3.</sub>
Shedrack R. Nayebare, Omar S. Aburizaiza, Azhar Siddique
et al.
Urban air pollution is rapidly becoming a major environmental problem of public concern in several developing countries of the world. Jeddah, the second-largest city in Saudi Arabia, is subject to high air pollution that has severe implications for the health of the exposed population. Fine particulate matter (PM<sub>2.5</sub>) samples were collected for 24 h daily, during a 1-year campaign from 2013 to 2014. This study presents a detailed investigation of PM<sub>2.5</sub> mass, chemical composition, and sources covering all four seasons of the year. Samples were analyzed for black carbon (BC), trace elements (TEs), and water-soluble ionic species (IS). The chemical compositions were statistically examined, and the temporal and seasonal patterns were characterized using descriptive analysis, correlation matrices, and elemental enrichment factor (EF). Source apportionment and source locations were performed on PM<sub>2.5</sub> samples using the positive matrix factorization (PMF) model, elemental enrichment factor, and air-mass back trajectory analysis. The 24-h mean PM<sub>2.5</sub> and BC concentrations ranged from 33.9 ± 9.1–58.8 ± 25 µg/m<sup>3</sup> and 1.8 ± 0.4–2.4 ± 0.6 µg/m<sup>3</sup>, respectively. Atmospheric PM<sub>2.5</sub> concentrations were well above the 24-h WHO guideline of 15 µg/m<sup>3</sup>, with overall results showing significant temporal and seasonal variability. EF defined two broad categories of TEs: anthropogenic (Ni, V, Cu, Zn, Cl, Pb, S, Lu, and Br), and earth-crust derived (Al, Si, Mg, K, Ca, Ti, Cr, Mn, Fe, and Sr). The five identified factors resulting from PMF were (1) fossil-fuels/oil combustion (45.3%), (2) vehicular emissions (19.1%), (3) soil/dust resuspension (15.6%), (4) industrial mixed dust (13.5%), and (5) sea-spray (6.5%). This study highlights the importance of focusing control strategies, not only on reducing PM concentration but also on the reduction of components of the PM as well, to effectively protect human health and the environment.
<p>The physical understanding and timely prediction of extreme weather events are of enormous importance to society due to their associated impacts. In this article, we highlight several types of weather extremes occurring in Europe in connection with a particular atmospheric flow pattern, known as atmospheric blocking. This flow pattern effectively blocks the prevailing westerly large-scale atmospheric flow, resulting in changing flow anomalies in the vicinity of the blocking system and persistent conditions in the immediate region of its occurrence. Blocking systems are long-lasting, quasi-stationary and self-sustaining systems that occur frequently over certain regions. Their presence and characteristics have an impact on the predictability of weather extremes and can thus be used as potential indicators. The phasing between the surface and the upper-level blocking anomalies is of major importance for the development of the extreme event. In summer, heat waves and droughts form below the blocking anticyclone primarily via large-scale subsidence that leads to cloud-free skies and, thus, persistent shortwave radiative warming of the ground. In winter, cold waves that occur during atmospheric blocking are normally observed downstream or south of these systems. Here, meridional advection of cold air masses from higher latitudes plays a decisive role. Depending on their location, blocking systems also may lead to a shift in the storm track, which influences the occurrence of wind and precipitation anomalies. Due to these multifaceted linkages, compound events are often observed in conjunction with blocking conditions. In addition to the aforementioned relations, the predictability of extreme events associated with blocking and links to climate change are assessed. Finally, current knowledge gaps and pertinent research perspectives for the future are discussed.</p>
Many studies have shown that air pollutants have complex impacts on urban precipitation. Meteorological weather station and satellite Aerosol Optical Depth (AOD) product data from the last 20 years, combined with simulation results from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), this paper focuses on the effects of air pollutants on summer precipitation in different regions of Beijing. These results showed that air pollution intensity during the summer affected the precipitation contribution rate (PCR) of plains and mountainous regions in the Beijing area, especially in the plains. Over the past 20 years, plains PCR increased by ~10% when the AOD augmented by 0.15, whereas it decreased with lower pollution levels. In contrast, PCR in mountainous areas decreased with higher pollution levels and increased with lower pollution levels. Our analysis from model results indicated that aerosol increases reduce the effective particle size of cloud droplets and raindrops. Smaller cloud raindrops more readily transport to high air layers and participate in the generation of ice-phase substances in the clouds, increasing the total amount of cloud water in the air in a certain time, which ultimately enhanced precipitation intensity on the plains. The removal of pollutants caused by increased precipitation in the plains decreased rainfall levels in mountainous areas.
Sabina Licen, Luisa Zupin, Lorenzo Martello
et al.
The airborne route of transmission of SARS-CoV-2 was confirmed by the World Health Organization in April 2021. There is an urge to establish standardized protocols for assessing the concentration of SARS-CoV-2 RNA in air samples to support risk assessment, especially in indoor environments. Debates on the airborne transmission route of SARS-CoV-2 have been complicated because, among the studies testing the presence of the virus in the air, the percentage of positive samples has often been very low. In the present study, we report preliminary results on a study for the evaluation of parameters that can influence SARS-CoV-2 RNA recovery from quartz fiber filters spotted either by standard single-stranded SARS-CoV-2 RNA or by inactivated SARS-CoV-2 virions. The analytes were spiked on filters and underwent an active or passive sampling; then, they were preserved at −80 °C for different numbers of days (0 to 54) before extraction and analysis. We found a mean recovery of 2.43%, except for the sample not preserved (0 days) that showed a recovery of 13.51%. We found a relationship between the number of days and the recovery percentage. The results presented show a possible issue that relates to the quartz matrix and SARS-CoV-2 RNA recovery. The results are in accordance with the already published studies that described similar methods for SARS-CoV-2 RNA field sampling and that reported non-detectable concentrations of RNA. These outcomes could be false negatives due to sample preservation conditions. Thus, until further investigation, we suggest, as possible alternatives, to keep the filters: (i) in a sealed container for preservation at 4 °C; and (ii) in a viral transport medium for preservation at a temperature below 0 °C.
To enhance our understanding of fog processes over complex terrain, various fog events that occurred during the International Collaborative Experiments for Pyeongchang 2018 Winter Olympics and Paralympics (ICE-POP) campaign were selected. Investigation of thermodynamic, dynamic, and microphysical conditions within fog layers affected by quasi-periodic oscillation of atmospheric variables was conducted using observations from a Fog Monitor-120 (FM-120) and other in-situ meteorological instruments. A total of nine radiation fog cases that occurred in the autumn and winter seasons during the campaign over the mountainous region of Pyeongchang, Korea were selected. The wavelet analysis was used to study quasi-period oscillations of dynamic, microphysical, and thermodynamic variables. By decomposing the time series into the time-frequency space, we can determine both dominant periods and how these dominant periods change in time. Quasi-period oscillations of liquid water content (LWC), pressure, temperature, and horizontal/vertical velocity, which have periods of 15−40 min, were observed during the fog formation stages. We hypothesize that these quasi-periodic oscillations were induced by Kelvin−Helmholtz instability. The results suggest that Kelvin−Helmholtz instability events near the surface can be explained by an increase in the vertical shear of horizontal wind and by a simultaneous increase in wind speed when fog forms. In the mature stages, fluctuations of the variables did not appear near the surface anymore.
To implement deterministic short-range numerical weather forecast error correction, this study develops a novel approach using the variational method and historical data. Based on time-dependency characteristic of nonsystematic forecast error, variational approach is adopted to establish the mapping relation between nonsystematic error series and the prior period nonsystematic error series, so as to estimate nonsystematic error in the future and revise the forecast under the premise of the revision for forecast systematic forecast error. According to the hindcast daily data of geopotential height on 500 hPa generated by GRAPES model on January and July from 2002 to 2010, preliminary analysis is carried out on characteristics of forecast error in East Asia. Further estimation and forecast correction test are conducted for nonsystematic error. The result shows that the nonsystematic forecast error in the GRAPES model has obvious characteristic of state dependency. Nonsystematic forecast error changes along season and the state of weather and accounts for great proportion in total forecast error. Nonsystematic forecast error estimated by variational approach is relatively close to the real forecast error. After nonsystematic correction, the corrected 24 h and 48 h forecast of majority samples has a smaller RMSE. Further study on temperature shows a similar result, even comparing to the observational upper air MICAPS data.
Applying qualitative and quantitative methods, this article explains the driving forces behind U.S. President Donald Trump's decision to withdraw from the Paris Agreement, assesses the impacts of this withdrawal on the compliance prospects of the agreement, and proposes how China should respond. The withdrawal undercuts the foundation of global climate governance and upsets the process of climate cooperation, and the impacts are manifold. The withdrawal undermines the universality of the Paris Agreement and impairs states' confidence in climate cooperation; it aggravates the leadership deficit in addressing global climate issues and sets a bad precedent for international climate cooperation. The withdrawal reduces other countries' emission space and raises their emission costs, and refusal to contribute to climate aid makes it more difficult for developing countries to mitigate and adapt to climate change. Cutting climate research funding will compromise the quality of future IPCC reports and ultimately undermine the scientific authority of future climate negotiations. China faces mounting pressure from the international community to assume global climate leadership after the U.S. withdraws, and this article proposes that China should reach the high ends of its domestic climate targets under the current Nationally Determined Contributions; internationally, China should facilitate the rebuilding of shared climate leadership, replacing the G2 with C5. Meanwhile, China needs to keep the U.S. engaged in climate cooperation.
Meteorology. Climatology, Social sciences (General)