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The dynamic and non-linear nature of complex chemical processes often leads to low prediction accuracy of key quality variables by traditional soft sensors, thus affecting the overall system control accuracy and operational efficiency. Therefore, this paper proposes a semi-supervised soft sensor modelling method based on improved the northern goshawk optimization (INGO)-variable mode decomposition (VMD)-echo state network (ESN). Firstly, a new semi-supervised fusion method is proposed to address the problem of model training difficulty due to the scarcity of labelled samples and process dynamics, which reconstructs the sample dataset by fusing labelled and unlabelled samples into more representative new samples, improving the model's generalization ability. Secondly, for the noise interference present in the reconstructed data, the input data is denoised using the VMD method to improve the quality of data. Then, a soft sensor model is built based on ESN. Additionally, the denoising and prediction performance of VMD and ESN is significantly affected by parameters, therefore the paper utilizes the INGO algorithm to achieve parameter rectification for VMD and ESN. Finally, the method is validated based on actual sulphur recovery data from a refinery. The results demonstrate that the method effectively mitigates the impact of dynamics and nonlinearity in the complex chemical process which enhances prediction accuracy. [ABSTRACT FROM AUTHOR]

This study investigates the inter-annual changes in tropical cyclone season onset (TCSO) over the western North Pacific (WNP) in neutral El Niño-Southern Oscillation (ENSO) conditions during 1979–2019. The average TCSO during early TCSO years (~ March 27) is significantly earlier than during late TCSO years (~ June 18). In response to inter-annual changes of TCSO in neutral ENSO years, TC activity shows distinct seasonal features. Although there are no significant differences in total TC counts, more TCs occur in March–May while fewer TCs occur in September–November during early TCSO years. Additionally, TC genesis locations tend to move eastward and equatorward during early TCSO years. These TCs are also found to have stronger lifetime maximum intensities. Changes in seasonal TC activity are closely associated with changes in the large-scale environmental pattern changes, which are driven by the seasonal evolution of sea surface temperature anomalies (SSTAs). Low-level vorticity and mid-level humidity changes are found likely to be the two primary factors inducing the seasonal feature differences between early and late TCSO years. Further analyses suggested that the seasonal evolution of sea surface temperature anomaly patterns over the tropical Indian Ocean and tropical Pacific accompanied by TCSO in neutral years is closely linked to changes in the spring Pacific Meridional Mode. Observational analyses and numerical simulations suggest that the Pacific Meridional Mode mainly causes changes in the low-level large-scale circulation over the tropical WNP via a Rossby wave Gill-type response, thus affects inter-annual variability of TCSO during neutral ENSO years. This response is found to be distinct from the previously documented strong relationship between TCSO and strong ENSO events. [ABSTRACT FROM AUTHOR]

An urban heat island (UHI) is a phenomenon where the temperatures within cities are greater than those of surrounding rural areas due to human activity and physical properties of urban surfaces. One method that has been investigated as a way to mitigate the UHI phenomenon is to increase the surface albedo in cities, which reflects a greater amount of solar radiation away from these surfaces compared to conventional materials (e.g., dark asphalt shingles). In this study, we utilize the Weather Research and Forecasting (WRF) model to investigate the UHI under different scenarios during a July 2012 heat wave event in the Kansas City metropolitan area (KCMA). Two cool roof simulations are implemented to determine the effectiveness of this mitigation strategy on reducing temperatures within the KCMA. The first scenario represents "newly installed" cool roofs with an albedo of 0.8 and the second with "aged" cool roofs with an albedo of 0.5. Results indicate that cool roof materials were able to mitigate the UHI effect by up to 0.64 °C during the evening, causing the onset of the UHI effect to be delayed until later in the day. Cool roofs were also shown to have important impacts on the surface energy balance, affecting both sensible and ground storage heat fluxes, and the planetary boundary layer. [ABSTRACT FROM AUTHOR]

An extreme and persistent heat wave event hit Missouri in summer 2012. Current operational forecast models failed to predict such an event at a long lead. The objective of the current study is to simulate this extreme event using a high-resolution Weather Research and Forecasting (WRF) model with eight combined physical (including longwave/shortwave radiation, microphysics, and planetary boundary layer) parameterization packages. Integrated for one month, the model successfully simulates the spatial pattern and temporal evolution of surface air temperature, compared to in-situ observations. The interesting feature is an oscillatory development of the surface air temperature, with a pronounced synoptic timescale. Such a temperature evolution is consistent with the local zonal wind fluctuation, implying the important role of anomalous temperature advection. An overall skill score that combines the performance of 2-m air temperature, relative humidity, and precipitation fields is defined. The result shows that the combination of Thompson, Rapid Radiative Transfer Model for GCMs (RRTMG), and Mellor-Yamada-Nakanishi-Niino level-3 (MYNN3) schemes presents the best WRF simulation. A further analysis of this best simulation shows that the model successfully reproduces the urban heat island (UHI) effect in the Kansas City Metropolitan Area with realistic diurnal variation of 2-m air temperature in the urban and nonurban areas with a larger UHI effect at nighttime. [ABSTRACT FROM AUTHOR]

In this study, we investigated the urbanization‐induced summer rainfall changes in the Yangtze River Delta (YRD) by analyzing long‐term observations and numerical simulations. The observation‐based analysis showed that long‐term urbanization increased the region's summer rainfall, particularly through the intensification of heavy rainfall, which is noted as the urban rain island (URI) effect. A series of numerical sensitivity experiments with three historical land use and land cover scenarios (1990, 2000, and 2010) were designed to further understand the urbanization impacts on rainfall. The observed URI effect was well reproduced by the numerical simulations, and on average, urban expansion during 1990–2010 increased summer rainfall over urban areas by 51.91 mm. The URI effect slightly weakened in the late stage of urbanization (2000–2010) compared to the early stage (1990–2000). We conclude that the strengthening of precipitation‐inhibiting effects during the late period offset the precipitation‐enhancing effects, which led to the weakening of the URI effect. Plain Language Summary: In this study, urbanization‐induced summertime precipitation changes over the Yangtze River Delta (YRD) region were investigated using both long‐term observational data and numerical model experiments. Observations showed that over the past decades, the summer precipitation over the YRD region has generally experienced a significant increasing trend, which was mainly caused by the intensification of strong‐intensity rainfalls. We further showed that urbanization in the region has played key roles in the trend by an urban rain island (URI) effect. The URI effect was found to have slightly weakened in the late stage of the study period (2000–2010) compared to the early stage (1990–2000). We also found that precipitation‐inhibiting effects (e.g., atmospheric humidity loss), overshadowed the precipitation‐enhancing effects (e.g., the urban heat island effect), which resulted in the weakened URI effect in the late stage. Key Points: The urban modification on summertime rainfall in YRD was investigated based on both long‐term observations and numerical experimentsLong‐term urbanization increased summer rainfall, yet the urban rain island (URI) effect slightly weakened in the late stage of urbanizationPrecipitation‐inhibiting effects strengthened in the late stage of urbanization and led to a weakened URI effect [ABSTRACT FROM AUTHOR]

This study uses dynamical and statistical methods to understand end‐of‐century mean changes to Sierra Nevada snowpack. Dynamical results reveal that middle‐elevation watersheds experience considerably more rain than snow during winter, leading to substantial snowpack declines by spring. Despite some high‐elevation watersheds receiving slightly more snow in January and February, the warming signal still dominates across the wet season and leads to notable declines by springtime. A statistical model is created to mimic dynamical results for 1 April snowpack, allowing for an efficient downscaling of all available general circulation models from the Coupled Model Intercomparison Project phase 5. For all general circulation models and emission scenarios, dramatic 1 April snowpack loss occurs at elevations below 2,500 m, despite increased precipitation in many general circulation models. Only 36% (±12%) of historical 1 April total snow water equivalent volume remains at the century's end under a "business‐as‐usual" emission scenario, with 70% (±12%) remaining under a realistic "mitigation" scenario. Plain Language Summary: The Sierra Nevada is one of California's most beloved natural treasures, and mountain snowpack snow is an important water resource. As climate change continues, scientists and water managers have become increasingly concerned about the future of the frozen reservoir Californian depend on. Global climate models are the best tools we have for projecting future climate change. But they are too coarse in spatial resolution to accurately simulate future climate in topographically complex areas like the Sierra Nevada, where different elevations experience different climatic conditions. This study utilizes an innovative hybrid high‐resolution downscaling method to understand spatial and temporal patterns of snowpack changes for certain watersheds and different elevations in the Sierra Nevada. A full range of global climate models and future greenhouse emission scenarios are investigated to quantify the uncertainties. Dramatic decreases in total Sierra Nevada snowpack are projected by century's end, even under a realistic mitigation emission scenario. The results are intended to provide water resource and management agencies information to help plan for the impacts of future climate change on the reliability and inhomogeneity of water supplies. Key Points: Dynamically downscaled projections are used to understand spatial and temporal patterns of snowpack changes for Sierra Nevada watershedsA hybrid dynamical‐statistical downscaling technique is developed to project 1 April snowpack change by century's end for all GCMs and RCPsDramatic decreases in total snowpack volume are projected by century's end, even under a realistic mitigation emission scenario [ABSTRACT FROM AUTHOR]

Asignificant increase of tropical cyclone (TC) frequency is observed over the North Atlantic (NATL) basin during the recent decades (1995-2014). In this study, the changes in large-scale controls of the NATL TC activity are compared between two periods, one before and one since 1995, when a regime change is observed. The results herein suggest that the significantly enhanced NATL TC frequency is related mainly to the combined effect of changes in the magnitudes of large-scale atmospheric and oceanic factors and their association with TC frequency. Interdecadal changes in the role of vertical wind shear and local sea surface temperatures (SSTs) over the NATLappear to be two important contributors to the recent increase of NATL TC frequency. Low-level vorticity plays a relatively weak role in the recent increase of TC frequency. These changes in the role of large-scale factors largely depend on interdecadal changes of tropical SST anomalies (SSTAs). Enhanced low-level westerlies to the east of the positive SSTAs have been observed over the tropical Atlantic since 1995, with a pattern nearly opposite to that seen before 1995. Moreover, the large-scale contributors to the NATL TC frequency increase since 1995 are likely related to both local and remote SSTAs. Quantification of the impacts of local and remote SSTAs on the increase of TC frequency over the NATL basin and the physical mechanisms require numerical simulations and further observational analyses. [ABSTRACT FROM AUTHOR]

The dynamic character of an enstrophy-based diagnostic, previously used in the study of atmospheric blocking, is examined here, in near-term future simulations from the Institut Pierre Simon Laplace Climate Model version 4 (IPSL-CM4) and version 5 (IPSL-CM5) climate models of the Northern Hemisphere flow for moderate climate change scenarios. Previous research has shown that integrated regional enstrophy (IE) increases during blocking onset and decay, which is a reflection of planetary-scale instability. In addition, IE has been shown previously to increase during flow regime transitions in general, even those not associated with blocking events. Here, a 31-year IE diagnostic time series is examined for changes in short term (5-40 days) planetary-scale variability that may correspond flow regime changes in an increased carbon dioxide environment. The time-series analysis herein indicates that the IE diagnostic provides evidence for approximately 30-35 atmospheric flow regime transitions per year in a warmer climate, which is similar to that of the control run and the latest 30-year observed climate, as derived from re-analyses. This result has implications regarding the predictability of weather in a warmer world. [ABSTRACT FROM AUTHOR]

California's Sierra Nevada is a high-elevation mountain range with significant seasonal snow cover. Under anthropogenic climate change, amplification of the warming is expected to occur at elevations near snow margins due to snow albedo feedback. However, climate change projections for the Sierra Nevada made by global climate models (GCMs) and statistical downscaling methods miss this key process. Dynamical downscaling simulates the additional warming due to snow albedo feedback. Ideally, dynamical downscaling would be applied to a large ensemble of 30 or more GCMs to project ensemble-mean outcomes and intermodel spread, but this is far too computationally expensive. To approximate the results that would occur if the entire GCM ensemble were dynamically downscaled, a hybrid dynamical-statistical downscaling approach is used. First, dynamical downscaling is used to reconstruct the historical climate of the 1981-2000 period and then to project the future climate of the 2081-2100 period based on climate changes from five GCMs. Next, a statistical model is built to emulate the dynamically downscaled warming and snow cover changes for any GCM. This statistical model is used to produce warming and snow cover loss projections for all available CMIP5 GCMs. These projections incorporate snow albedo feedback, so they capture the local warming enhancement (up to 3°C) from snow cover loss that other statistical methods miss. Capturing these details may be important for accurately projecting impacts on surface hydrology, water resources, and ecosystems. [ABSTRACT FROM AUTHOR]

Future snowfall and snowpack changes over the mountains of Southern California are projected using a new hybrid dynamical-statistical framework. Output from all general circulation models (GCMs) in phase 5 of the Coupled Model Intercomparison Project archive is downscaled to 2-km resolution over the region. Variables pertaining to snow are analyzed for the middle (2041-60) and end (2081-2100) of the twenty-first century under two representative concentration pathway (RCP) scenarios: RCP8.5 (business as usual) and RCP2.6 (mitigation). These four sets of projections are compared with a baseline reconstruction of climate from 1981 to 2000. For both future time slices and scenarios, ensemble-mean total winter snowfall loss is widespread. By the mid-twenty-first century under RCP8.5, ensemble-mean winter snowfall is about 70% of baseline, whereas the corresponding value for RCP2.6 is somewhat higher (about 80% of baseline). By the end of the century, however, the two scenarios diverge significantly. Under RCP8.5, snowfall sees a dramatic further decline; 2081-2100 totals are only about half of baseline totals. Under RCP2.6, only a negligible further reduction from midcentury snowfall totals is seen. Because of the spread in the GCM climate projections, these figures are all associated with large intermodel uncertainty. Snowpack on the ground, as represented by 1 April snow water equivalent is also assessed. Because of enhanced snowmelt, the loss seen in snowpack is generally 50% greater than that seen in winter snowfall. By midcentury under RCP8.5, warming-accelerated spring snowmelt leads to snow-free dates that are about 1-3 weeks earlier than in the baseline period. [ABSTRACT FROM AUTHOR]

In this study (Part I), the mid-twenty-first-century surface air temperature increase in the entire CMIP5 ensemble is downscaled to very high resolution (2 km) over the Los Angeles region, using a new hybrid dynamical-statistical technique. This technique combines the ability of dynamical downscaling to capture finescale dynamics with the computational savings of a statistical model to downscale multiple GCMs. First, dynamical downscaling is applied to five GCMs. Guided by an understanding of the underlying local dynamics, a simple statistical model is built relating the GCM input and the dynamically downscaled output. This statistical model is used to approximate the warming patterns of the remaining GCMs, as if they had been dynamically downscaled. The full 32-member ensemble allows for robust estimates of the most likely warming and uncertainty resulting from intermodel differences. The warming averaged over the region has an ensemble mean of 2.3°C, with a 95% confidence interval ranging from 1.0° to 3.6°C. Inland and high elevation areas warm more than coastal areas year round, and by as much as 60% in the summer months. A comparison to other common statistical downscaling techniques shows that the hybrid method produces similar regional-mean warming outcomes but demonstrates considerable improvement in capturing the spatial details. Additionally, this hybrid technique incorporates an understanding of the physical mechanisms shaping the region's warming patterns, enhancing the credibility of the final results. [ABSTRACT FROM AUTHOR]

Using the hybrid downscaling technique developed in part I of this study, temperature changes relative to a baseline period (1981-2000) in the greater Los Angeles region are downscaled for two future time slices: midcentury (2041-60) and end of century (2081-2100). Two representative concentration pathways (RCPs) are considered, corresponding to greenhouse gas emission reductions over coming decades (RCP2.6) and to continued twenty-first-century emissions increases (RCP8.5). All available global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are downscaled to provide likelihood and uncertainty estimates. By the end of century under RCP8.5, a distinctly new regional climate state emerges: average temperatures will almost certainly be outside the interannual variability range seen in the baseline. Except for the highest elevations and a narrow swath very near the coast, land locations will likely see 60-90 additional extremely hot days per year, effectively adding a new season of extreme heat. In mountainous areas, a majority of the many baseline days with freezing nighttime temperatures will most likely not occur. According to a similarity metric that measures daily temperature variability and the climate change signal, the RCP8.5 end-of-century climate will most likely be only about 50% similar to the baseline. For midcentury under RCP2.6 and RCP8.5 and end of century under RCP2.6, these same measures also indicate a detectable though less significant climatic shift. Therefore, while measures reducing global emissions would not prevent climate change at this regional scale in the coming decades, their impact would be dramatic by the end of the twenty-first century. [ABSTRACT FROM AUTHOR]

A new hybrid statistical-dynamical downscaling technique is described to project mid- and end-of-twenty-first-century local precipitation changes associated with 36 global climate models (GCMs) in phase 5 of the Coupled Model Intercomparison Project archive over the greater Los Angeles region. Land-averaged precipitation changes, ensemble-mean changes, and the spread of those changes for both time slices are presented. It is demonstrated that the results are similar to what would be produced if expensive dynamical downscaling techniques were instead applied to all GCMs. Changes in land-averaged ensemble-mean precipitation are near zero for both time slices, reflecting the region's typical position in the models at the node of oppositely signed large-scale precipitation changes. For both time slices, the intermodel spread of changes is only about 0.2-0.4 times as large as natural interannual variability in the baseline period. A caveat to these conclusions is that interannual variability in the tropical Pacific is generally regarded as a weakness of the GCMs. As a result, there is some chance the GCM responses in the tropical Pacific to a changing climate and associated impacts on Southern California precipitation are not credible. It is subjectively judged that this GCM weakness increases the uncertainty of regional precipitation change, perhaps by as much as 25%. Thus, it cannot be excluded that the possibility that significant regional adaptation challenges related to either a precipitation increase or decrease would arise. However, the most likely downscaled outcome is a small change in local mean precipitation compared to natural variability, with large uncertainty on the sign of the change. [ABSTRACT FROM AUTHOR]

We examine the influence of the South-American land-mass and its mountains on the significant cyclic diurnal and semidiurnal components of the average circulation in the adjacent area of the southeastern tropical Pacific (SEP). Our approach is based on a number of numerical simulations with the regional atmospheric model weather research and forecasting forced by the National Centers for Environmental Prediction's final analysis operational analysis data. In the control simulation the model domain covers the SEP and a large part of South America. In several sensitivity experiments the domain is reduced to progressively exclude continental areas. We find that the mean diurnal cycle is sensitive to model domain in ways that reveal the existence of different contributions originating from the Chilean and Peruvian land-masses. The experiments suggest that diurnal variations in circulations and thermal structures over the SEP (mainly forced by local insolation) are influenced by convection over the Peruvian sector of the Andes cordillera, while the mostly dry mountain-breeze circulations force an additional component that results in semi-diurnal variations near the coast. A series of numerical tests, however, reveal sensitivity of the simulations to the choice of vertical grid, limiting the possibility of solid quantitative statements on the amplitudes and phases of the diurnal and semidiurnal components across the domain. [ABSTRACT FROM AUTHOR]

This study examines the slow modulation of El Niño–Southern Oscillation (ENSO) intensity and its underlying mechanism. A 10–15-yr ENSO intensity modulation cycle is identified from historical and paleoclimate data by calculating the envelope function of boreal winter Niño-3.4 and Niño-3 sea surface temperature (SST) indices. Composite analyses reveal interesting spatial asymmetries between El Niño and La Niña events within the modulation cycle. In the enhanced intensity periods of the cycle, El Niño is located in the eastern tropical Pacific and La Niña in the central tropical Pacific. The asymmetry is reversed in the weakened intensity periods: El Niño centers in the central Pacific and La Niña in the eastern Pacific. El Niño and La Niña centered in the eastern Pacific are accompanied with basin-scale surface wind and thermocline anomalies, whereas those centered in the central Pacific are accompanied with local wind and thermocline anomalies. The El Niño–La Niña asymmetries provide a possible mechanism for ENSO to exert a nonzero residual effect that could lead to slow changes in the Pacific mean state. The mean state changes are characterized by an SST dipole pattern between the eastern and central tropical Pacific, which appears as one leading EOF mode of tropical Pacific decadal variability. The Pacific Walker circulation migrates zonally in association with this decadal mode and also changes the mean surface wind and thermocline patterns along the equator. Although the causality has not been established, it is speculated that the mean state changes in turn favor the alternative spatial patterns of El Niño and La Niña that manifest as the reversed ENSO asymmetries. Using these findings, an ENSO–Pacific climate interaction mechanism is hypothesized to explain the decadal ENSO intensity modulation cycle. [ABSTRACT FROM AUTHOR]

The Community Climate System Model, version 3 (CCSM3), is known to produce many aspects of El Niño–Southern Oscillation (ENSO) realistically, but the simulated ENSO exhibits an overly strong biennial periodicity. Hypotheses on the cause of this excessive biennial tendency have thus far focused primarily on the model's biases within the tropical Pacific. This study conducts CCSM3 experiments to show that the model's biases in simulating the Indian Ocean mean sea surface temperatures (SSTs) and the Indian and Australian monsoon variability also contribute to the biennial ENSO tendency. Two CCSM3 simulations are contrasted: a control run that includes global ocean–atmosphere coupling and an experiment in which the air–sea coupling in the tropical Indian Ocean is turned off by replacing simulated SSTs with an observed monthly climatology. The decoupling experiment removes CCSM3's warm bias in the tropical Indian Ocean and reduces the biennial variability in Indian and Australian monsoons by about 40% and 60%, respectively. The excessive biennial ENSO is found to reduce dramatically by about 75% in the decoupled experiment. It is shown that the biennial monsoon variability in CCSM3 excites an anomalous surface wind pattern in the western Pacific that projects well into the wind pattern associated with the onset phase of the simulated biennial ENSO. Therefore, the biennial monsoon variability is very effective in exciting biennial ENSO variability in CCSM3. The warm SST bias in the tropical Indian Ocean also increases ENSO variability by inducing stronger mean surface easterlies along the equatorial Pacific, which strengthen the Pacific ocean–atmosphere coupling and enhance the ENSO intensity. [ABSTRACT FROM AUTHOR]