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Dramatic increases in climate data underlie a gradual paradigm shift in knowledge acquisition methods from physically based models to data-based mining approaches. One of the most popular data clustering/mining techniques is k-means, and it has been used to detect hidden patterns in climate systems; k-means is established based on distance metrics for pattern recognition, which is relatively ineffective when dealing with “structured” data, that is, data in time and space domains, which are dominant in climate science. Here, we propose (i) a novel structural-similarity-recognition-based k-means algorithm called structural k-means or S k-means for climate data mining and (ii) a new clustering uncertainty representation/evaluation framework based on the information entropy concept. We demonstrate that the novel S k-means could provide higher-quality clustering outcomes in terms of general silhouette analysis, although it requires higher computational resources compared with conventional algorithms. The results are consistent with different demonstration problem settings using different types of input data, including two-dimensional weather patterns, historical climate change in terms of time series, and tropical cyclone paths. Additionally, by quantifying the uncertainty underlying the clustering outcomes we, for the first time, evaluated the “meaningfulness” of applying a given clustering algorithm for a given dataset. We expect that this study will constitute a new standard of k-means clustering with “structural” input data, as well as a new framework for uncertainty representation/evaluation of clustering algorithms for (but not limited to) climate science.

This study contributes to the body of current knowledge about the urban effect on extreme precipitation (EP) by investigating the city–EP interaction over Lagos, Nigeria. This is a unique, first-time study that adds a “missing piece” of this information about the African continent to the comprehensive global urban precipitation “puzzle.” The convection-permitting Weather Research and Forecasting (WRF) Model is employed within an ensemble simulation framework using combinations of different physical schemes and boundary/initial conditions to detect the urban signal on an extreme rainfall event that occurred on 30 May 2006. WRF simulations are verified against satellite-estimated and in situ observations, and the results from the best-performing ensemble members are used for analysis. The results show that the control simulation with urban representation generated 20%–30% more rainfall over the urban area than the nonurban sensitivity simulation, in which the city is replaced by forest. Physical mechanisms behind the differences were revealed. We found that the urbanization in Lagos reduced evapotranspiration, resulting in the increase of sensible heating (by 75 W m−2). This further enhances the urban heat-island effect (+1.5 K of air surface temperature), facilitating horizontal convergence and boosting daytime sea breeze. As a result, more moisture is transported from the southern sea area to inland areas; the moisture then converges over Lagos city, creating favorable conditions for enhancing convection and extreme-rainfall-generating processes.

Dramatic increases in climate data underlie a gradual paradigm shift in knowledge-acquisition methods from physical-based models to data-based mining techniques. k-Means is one of the most popular data clustering/mining techniques, and it has been used to detect hidden patterns in climate systems. k-Means is established based on distance metrics for pattern recognition, which is relatively ineffective when dealing with “structured” data that are dominant in climate science, that is, data in time and space domains. Here, we propose (i) a novel structural similarity recognition-based k-means algorithm called structural k-means or S k-means for climate data mining and (ii) a new clustering uncertainty representation/evaluation framework based on the information entropy concept. We demonstrated that the novel S k-means could provide higher-quality clustering outcomes in terms of general silhouette analysis, although it requires higher computational resources compared with conventional algorithms. The results are consistent with different demonstration problem settings using different types of input data, including two-dimensional weather patterns, historical climate change in terms of time series, and tropical cyclone paths. Additionally, by quantifying the uncertainty underlying the clustering outcomes we for the first time evaluated the “meaningfulness” of applying a given clustering algorithm for a given dataset. We expect that this study will constitute a new standard of k-means clustering with “structural” input data, as well as a new framework for uncertainty representation/evaluation of clustering algorithms for (but not limited to) climate science.

Simulations of five November months (2010–2014) using the urban version of the numerical weather prediction system of the Meteorological Service Singapore (uSINGV) are used to analyse the urban effect on convective precipitation over Singapore and Johor Bahru (Malaysia). The model is able to closely predict locations where rainfall peaks occur, but rainfall totals are overestimated compared to radar data. The temporal variability of rainfall in the region shows that urban areas increase the frequency and severity of rainfall events and that such impact increases with the rainfall intensity. Results show that low-level moisture advection is enhanced in this coastal conurbation as a result of the strengthening of wind convergence. The latter is likely caused by increasing sea-breeze strength due to lower surface pressure over the urban area, and higher urban surface roughness, respectively. As a consequence, more precipitable water is available in the region, enhancing convection and increasing the probability of heavy rainfall over the centre and north of Singapore island and Johor Bahru. Stronger convection further increases moisture advection from the vicinity. By studying the temporal variability and the spatial distribution of rainfall events, the present study provides new insights on the urban impact on heavy rainfall in tropical areas. The conclusions are only valid for the November inter-monsoon period, when local forcing, rather than large-scale influences, dominates rainfall generation. National Environment Agency - Singapore, R-109-000-338 236-490

This study proposes a novel structural self-organizing map (S-SOM) algorithm for synoptic weather typing. A novel feature of the S-SOM compared with traditional SOMs is its ability to deal with input data with spatial or temporal structures. In detail, the search scheme for the best matching unit (BMU) in a S-SOM is built based on a structural similarity (S-SIM) index rather than by using the traditional Euclidean distance (ED). S-SIM enables the BMU search to consider the correlation in space between weather states, such as the locations of highs or lows, that is impossible when using ED. The S-SOM performance is evaluated by multiple demo simulations of clustering weather patterns over Japan using the ERA-Interim sea-level pressure data. The results show the S-SOM's superiority compared with a standard SOM with ED (or ED-SOM) in two respects: clustering quality based on silhouette analysis and topological preservation based on topological error. Better performance of S-SOM versus ED is consistent with results from different tests and node-size configurations. S-SOM performs better than a SOM using the Pearson correlation coefficient (or COR-SOM), though the difference is not as clear as it is compared to ED-SOM.

India responded to the severe acute respiratory syndrome (SARS) coronavirus disease 2019 (COVID-19) pandemic through a three-phase nationwide lockdown: 25 March–14 April, 15 April–3 May, and 4–17 May 2020. We utilized this unique opportunity to assess the impact of restrictions on the air quality of Indian cities. We conducted comprehensive statistical assessments for the air quality index (AQI) and criteria pollutant concentrations for 91 cities during the lockdown phases relative to the preceding seven days (prelockdown phase of 18–24 March 2020) and to corresponding values from the same days of the year in 2019. Both comparisons show statistically significant countrywide mean decrease in AQI (33%), PM2.5 (36%), PM10 (40%), NO2 (58%), O3 (5%), SO2 (25%), NH3 (28%), and CO (60%). These reductions represent a background or the lower bound of air quality burden of industrial and transportation sectors. The northern region was most impacted by the first two phases of the lockdown, whereas the southern region was most affected in the last phase. The northeastern region was least affected, followed by the eastern region, which also showed an increase in O3 during the lockdown. Analysis of satellite-retrieved aerosol optical depth (AOD) shows that effects of restrictions on particulate pollution were variable—locally confined in some areas or having a broader impact in other regions. Anomalous behavior over the eastern region suggests a differing role of regional societal response or meteorological conditions. The study results have policy implications because they provide the observational background values for the industrial and transportation sector’s contribution to urban pollution.

Combined effects of global warming and rapid urbanization replace green spaces with urban facilities. Children in urban areas are at a higher risk of heat-related adverse health effects. Our study aimed to examine the protective effect of urban green space on heat-related respiratory hospitalization among children under 5 years of age in Hanoi, the capital city of Vietnam. We estimated district-specific meteorological conditions from 2010 to 2014 by using a dynamic downscaling approach with a fine-resolution numerical climate model. The green space in each district was calculated using satellite data. The attributable fraction of heat-related respiratory hospitalization was estimated using a two-stage model, including a distributed lag non-linear model (DLNM) coupled with multivariate meta-analysis. The association between heat-related respiratory hospitalization and green spaces at the district level was explored using a linear regression model. The central districts were more crowded and hotter, with less green spaces than the outer districts. At temperatures 34 °C (extreme heat threshold), the hospitalizations in the central districts increased significantly; however, in the outer districts, the hospitalization rate was insignificant. On average, extreme heat attributed 0.33% to citywide hospitalization, 0.35% in the center, and 0.32% in the outer region. Every 1% increase in the green space fraction will reduce heat-related respiratory hospitalization risk by 3.8%. Heat significantly increased the risk of respiratory hospitalization among children under 5 years in Hanoi, Vietnam. These findings are valuable for authorities to consider strategies to protect children's health against the effects of heat, including increasing green space.

Broadband passive daytime radiative cooling (PDRC) materials exhibit sub-ambient surface temperatures and contribute highly to mitigating extreme urban heat during the warm period. However, their application may cause undesired overcooling problems in winter. This study aims to assess, on a city scale, different solutions to overcome the winter overcooling penalty derived from using PDRC materials. Furthermore, a mesoscale urban modeling system assesses the potential of the optical modulation of reflectance (ρ) and emissivity (ε) to reduce, minimize, or reverse the overcooling penalty. The alteration of heat flux components, air temperature modification, ground and roof surface temperature, and the urban canopy temperature are assessed. The maximum decrease of the winter ambient temperature using standard PDRC materials is 1.1 °C and 0.8 °C for daytime and nighttime, respectively, while the ρ+ε-modulation can increase the ambient temperature up to 0.4 °C and 1.4 °C, respectively, compared to the use of conventional materials. Compared with the control case, the maximum decrease of net radiation inflow occurred at the peak hour, reducing by 192.7 Wm−2 for the PDRC materials, 5.4 Wm−2 for ρ-modulated PDRC materials, and 173.7 Wm−2 for ε-PDRC materials; nevertheless, the ρ+ε-modulated PDRC materials increased the maximum net radiation inflow by 51.5 Wm−2, leading to heating of the cities during the winter.

Understanding the future offshore wind energy is crucial for making an appropriate energy strategy. Yet how offshore wind energy will respond to the rapidly intensifying global warming remains inconclusive. Focusing on a tropical sea in Vietnam, this study assesses the climatic impact of global warming on the local offshore wind potential. Dynamically downscaled wind data obtained by an edge-cutting fine-resolution regional climate model with two greenhouse-gases Representative Concentration Pathway (RCP) scenarios 8.5 and 4.5 were biased-corrected with a quantile mapping method before using for assessment. Results revealed a changing trend of the annual-mean offshore wind speed and power potential in Vietnam under both scenarios. However, the change is seasonally asymmetric with decreases of about 2 – 10% in September-October-November and slight increases of about 2 – 4% in December-January-February. The turbine-based assessment showed that the cumulative annual energy obtained from the 12 MW and 8 MW turbines could reach the highest about 50.9 GWh and 30.7 GWh (2080–2099 average under scenario RCP8.5) but the difference in their capacity factor is much smaller, suggesting that turbine 8 MW should be considered in the sense of cost-effectiveness in some areas.

Understanding the response of extreme precipitation (EP) at a city scale to global warming is critical to reducing the respective risk of urban flooding. Yet, current knowledge on this issue is limited. Here, focusing on an urban agglomeration in the tropics, Singapore, we reveal that future global warming enhances both frequency and intensity of EP, based on simulations with a state-of-the-art convection-permitting regional climate model. EP intensification can reach maximum "super" Clausius-Clapeyron rate (>= $\ge $+7% per K warming) rate, implying a "new normal" of "extreme events get more extreme," which is consistently for both Representative Concentration Pathways 8.5 and 4.5. The intensification is lower for moderate and light precipitation. Also, global warming was found to dampen the urban effect on EP events. The EP enhancement is attributed to the increased atmospheric moisture and convective inhibition due to enhanced low-level stratification that delays a convection to develop until it becomes more intense., Earth's Future, 10 (6), ISSN:2328-4277

Urban development exacerbates urban heat island effects of cities and thermal discomfort of the residents worldwide. However, such urban effects vary with the geographical and climatic conditions of the respective cities. This study is the first to quantitatively estimate the impact of past (1878), present (2012), and future (2050) urbanization on surface air temperatures and human thermal comfort in Sofia, the largest city of Bulgaria. We used the state‐of‐the‐art Weather Research and Forecasting (WRF) climate model, with a 1‐km horizontal resolution for three cases: LU1878, LU2012, and LU2050. We first validated the results of the control simulation (case LU2012) against actual observations. The model satisfactorily reproduced the diurnal variation of the observed monthly mean surface air temperatures for July (2011–2013) with a mean bias of ±0.6°C. We then estimated the potential impacts of past and future urbanization on surface temperature. Our results suggest that because of urbanization of Sofia, the surface air temperature at 0600 Local Standard Time (LST) has increased approximately 4.0°C in the past (1878–2012) and approximately 0.4°C in future (2012–2050). We also evaluated the impact of the urban thermal environment on human comfort levels. Considering the LU2050 case in the central part of Sofia, the temperature–humidity index during mid‐afternoon (1400–1700 LST) is estimated to be 24–26, indicating that approximately 50% of the residents could feel perceptibly uncomfortable.

Groundwater salinization is one of the most severe environmental problems in coastal aquifers worldwide, causing exceeding salinity in groundwater supply systems for many purposes. High salinity concentration in groundwater can be detected several kilometers inland and may result in an increased risk for coastal water supply systems and human health problems. This study investigates the impacts of groundwater pumping practices and regional groundwater flow dynamics on groundwater flow and salinity intrusion in the coastal aquifers of the Vietnamese Mekong Delta using the SEAWAT model—a variable-density groundwater flow and solute transport model. The model was constructed in three dimensions (3D) and accounted for multi-aquifers, variation of groundwater levels in neighboring areas, pumping, and paleo-salinity. Model calibration was carried for 13 years (2000 to 2012), and validation was conducted for 4 years (2013 to 2016). The best-calibrated model was used to develop prediction models for the next 14 years (2017 to 2030). Six future scenarios were introduced based on pumping rates and regional groundwater levels. Modeling results revealed that groundwater pumping activities and variation of regional groundwater flow systems strongly influence groundwater level depletion and saline movement from upper layers to lower layers. High salinity (>2.0 g/L) was expected to expand downward up to 150 m in depth and 2000 m toward surrounding areas in the next 14 years under increasing groundwater pumping capacity. A slight recovery in water level was also observed with decreasing groundwater exploitation. The reduction in the pumping rate from both local and regional scales will be necessary to recover groundwater levels and protect fresh aquifers from expanding paleo-saline in groundwater.

In this study, we propose a novel structural self-organizing map (S-SOM) algorithm for synoptic weather typing. A novel feature of the S-SOM compared with traditional SOMs is its ability to deal with input data that have spatial or temporal structures. In detail, the search scheme for the best matching unit (BMU) in a S-SOM is built based upon a structural similarity (S-SIM) index rather than by using the traditional Euclidean distance (ED). S-SIM enables the BMU search to consider the correlation in space between weather states, such as the location of highs of lows, that is impossible when using ED. The S-SOM performance is evaluated by multiple demo simulations of clustering weather patterns over Japan using the ERA-Interim sea-level pressure data. The results show the superiority of the S-SOM compared with a standard SOM with ED (or ED-SOM) in two respects: clustering quality based on silhouette analysis and topological preservation based on topological error analysis. The superior performance of the S-SOM compared with an ED-SOM is probably independent of both the input data and SOM configuration.

As an effect of climate change, cities need detailed information on urban climates at decision scale that cannot be easily delivered using current observation networks, nor global and even regional climate models. A review is presented of the recent literature and recommendations are formulated for future work. In most cities, historical observational records are too short, discontinuous, or of too poor quality to support trend analysis and climate change attribution. For climate modeling, on the other hand, specific dynamical and thermal parameterization dedicated to the exchange of water and energy between the atmosphere and the urban surfaces have to be implemented. Therefore, to fully understand how cities are impacted by climate change, it is important to have (1) simulations of the urban climate at fine spatial scales (including coastal hazards for coastal cities) integrating global climate scenarios with urban expansion and population growth scenarios and their associated uncertainty estimates, (2) urban climate observations, especially in Global South cities, and (3) spatial data of high resolution on urban structure and form, human behavior, and energy consumption.

The extraction of urban built-up areas is an important aspect of urban planning and understanding the complex drivers and biophysical mechanism of urban climate processes. However, built-up...

Summary Extreme urban heat causes serious climatic, environmental, and economic problems. Daytime radiative cooling technologies are the most promising cooling technology in recent years. Here, we report experimental and numerical analysis to assess the cooling performance of scalable broadband radiative coolers, evaluate their heat mitigation potential in cities, and investigate their impact on local climate and the atmospheric dynamics. We show that, when air temperature reached its peak, the sub-ambient surface temperature of 8.2°C was achievable for samples with a wind cover. The maximum reductions of peak ambient temperature, average daytime ambient temperature, and highest urban canopy temperature are 5.3°C, 3.6°C, and 13°C, respectively. Collectively, we show that broadband coolers can significantly mitigate urban heat, but the cooling island effect they generate would increase side effects such as a decrease in the mixing layer of the atmosphere and an increase in pollutant concentration.

Short-term extreme rainfall events (EREs) often produce urban flooding and result in the loss of life and property. In this study, three high spatial-temporal resolution precipitation products were used to analyze hourly EREs during May-October from 2001 to 2018 over the Yangtze River Delta (YRD). To quantify the possible link between EREs and environmental factors (such as urbanization, climate change, and orography), the Hovmoller diagrams and Generalized additive model for location, scale, and shape (GAMLSS) nonstationary model were used. The results showed that the eastward propagating precipitation systems created a heavy rainfall center in the southwest mountainous of YRD due to orographic lifting, and enhanced in the plains over urbanized areas, especially in the vicinity of Nanjing, Changzhou, and Nantong metropolitan regions. The variability of hourly rainfall extreme in the urbanized regions is high correlated to the multivariate ENSO index (MEI), and shows a greater magnitude in the urban than rural regions. This phenomenon was intensified by large-scale circulation patterns due to strong-ENSO over the Nanjing-Changzhou-Nantong (NCN) urban regions, but weakened over the Shanghai-Hangzhou-Ningbo (SHN) urban regions. An analysis of nonstationary GAMLSS also suggests the large-scale circulation presents a greater impact on the hourly extreme rainfall than the urbanization, while the hourly rainfall extremes in the urban areas are still higher than that in rural regions under the nonstationary scenario. Those results suggest intense rainfall occurred more frequently in the urban region under observed climate change and urbanization in YRD.

This study examines the climate impact of future urbanization on the urban heat island effect over one of the developing megacities in tropical Southeast Asia, the greater Ho Chi Minh City area, using a dynamical downscaling approach with a high-resolution regional climate model coupled to urban canopy model. Simulations incorporate different land use/cover and anthropogenic heat release data sets for the current and future periods, while the boundary condition was fixed in the current time to detect only the impact of urbanization. Furthermore, human thermal comfort due to changes in the thermal environment was examined by analyzing the temperature-humidity index. Simulated results show that the increase in the surface air temperature is approximately 0.22 °C in the preexisting urbanized area and approximately 0.41 °C in new highly urbanized areas. The main factor in these changes is conversion from agricultural or grassland to urban structures, which results in increased sensible heating and decreased latent heating. Simulated results also show that despite the increase in air temperature, there is no significant change in thermal comfort. This is because human comfort is not only determined by temperature alone, but also by relative humidity. Urbanization increases temperatures, but decreases evaporation, which then decreases the relative humidity.

This study examines climatic impact of urbanization on the variability of the urban heat island (UHI) effect over Greater Ho Chi Minh City metropolitan area (GHCM), since the late 1980s, using the dynamical downscaling with very high-resolution regional climate model coupled to an urban canopy model (RCM/UCM). This is the first application of RCM/UCM to a city in developing countries in Southeast Asia in assessing the impacts of the past land-use and anthropogenic heat release during the selected three periods (ca. 1989, 1999, and 2009). The main findings are as follows: First, agreement between simulated results (for case of ca. 2009 urban) and observation demonstrates that the RCM/UCM is able to reproduce the urban climate of GHCM. Second, the evolution of spatial distribution of UHI is closely associated with urban expansion. The increase in the surface air temperature was about 0.3 °C in the pre-existing urbanized area and about 0.6 °C in newly urbanized area in the last 20 years. Main factor of these changes is conversion of agriculture or grassland into urban structure, which results in increase in sensible heating and decrease in latent heating. In addition, in the central GHCM, the urbanization impact was estimated at 0.31 °C, while the temperature increase was observed at 0.64 °C in the last 20 years. This suggests that the urbanization may contribute about half to the increase of surface air temperature in the central GHCM.

In this paper, we present the current exploitation status of the wind resources in general and of the offshore wind in particular. In addition, we introduce the initial and basic calculation of wind energy density at the height of 100 m, the method for development of wind resources, and the development of wind energy responding to climate change. The Vietnam sea region has a huge potential for wind energy resource in 0–30 m deep-water with an area of 111,000 km2 and a potential capacity of producing 64,000 GW and in 30–60 m deep-water with an area of 142,000 km2 and a potential capacity of producing 106,000 GW. The most promising region is the coastal area of Binh Thuan - Ca Mau provinces with a density of nearly 1000 W/m2, which is one of the high potential regions in the world. Wind farms have been deployed in this region with a total capacity of 1 GW and is expected to be 9 GW in 2030. We suggest that more research on and development of wind energy and zoning is needed to ensure energy security, reduce greenhouse gas emissions and mitigate the impact of climate change.

Tropical cities are more susceptible to the suggested fall outs from projected global warming scenarios as they are located in the Torrid Zone and growing at rapid rates. Therefore, research on the mitigation of urban heat island (UHI) effects in tropical cities has attained much significance and increased immensely over recent years. The UHI mitigation strategies commonly used for temperate cities need to be examined in the tropical context since the mechanism of attaining a surface energy balance in the tropics is quite different from that in the mid-latitudes. The present paper evaluates the performance of four different mitigation strategies to counterbalance the impact of UHI phenomena for climate resilient adaptation in the Kolkata Metropolitan Area (KMA), India. This has been achieved by reproducing the study sites, selected from three different urban morphologies of open low-rise, compact low-rise and mid-rise residential areas, using ENVI-met V 4.0 and simulating the effects of different mitigation strategies- cool pavement, cool roof, added urban vegetation and cool city (a combination of the three former strategies), in reducing the UHI intensity. Simulation results show that at a diurnal scale during summer, the green city model performed best at neighborhood level to reduce air temperature (Ta) by 0.7 °C, 0.8 °C and 1.1 °C, whereas the cool city model was the most effective strategy to reduce physiologically equivalent temperature (PET) by 2.8° – 3.1 °C, 2.2° – 2.8 °C and 2.8° – 2.9 °C in the mid-rise, compact low-rise and open low-rise residential areas, respectively. It was observed that (for all the built environment types) vegetation played the most significant role in determining surface energy balance in the study area, compared to cool roofs and cool pavements. This study also finds that irrespective of building environments, tropical cities are less sensitive to the selected strategies of UHI mitigation than their temperate counter parts, which can be attributed to the difference in magnitude of urbanness.