Climate change could affect botanical tourism by altering the plant phenology (e.g., flowering and leaf coloring date) and the physical comfort of tourists. To date, few studies have simultaneously considered the influence of plant phenology and physical comfort on the travel suitability of botanical tourism. Taking Beijing as an example, this study used phenological data of 73 species from 1963 to 2017 to construct a phenological ornamental index (POI) according to the flowering and leaf coloring date of ornamental plant. The climate comfort index (CCI) of tourism was calculated by using meteorological data of the corresponding periods. Finally, the travel suitability index (TSI) was constructed by integrating the two indices (POI and CCI). The POI showed that the best period for spring flower viewing was from April 4 to May 10, while the best period for autumn leaves viewing was from October 11 to November 6 on average. According to the variation of the CCI within the year, the most comfortable period for spring tourism was matched with the best period for spring flower viewing (April 4 to June 1), but the most comfortable period for autumn tourism (September 4 to October 19) was earlier than the best period for autumn leaves viewing. The TSI indicated that the best periods for spring and autumn botanical tourism were April 7 to May 10 and October 10 to November 7, respectively. Based on the climate data under different scenarios (representative concentration pathways 4.5 and 8.5), we simulated the climate and phenological suitability for botanical tourism in the next thirty years. The results showed that the best period for spring botanical tourism during 2040–2050 was earlier and the period for autumn botanical tourism was later than that in the past 55 years. Meanwhile, the duration would shorten by 2–7 days for both seasons. This study provided a reference for assessing the impact of global climate change on the best season of botanical tourism.

This paper investigates the spatiotemporal variability in hydrometeorological time-series to evaluate the current and future scenarios of water resources availability from upper Indus basin (UIB). Mann–Kendall and Sen’s slope estimator tests were used to analyze the variability in the temperature, precipitation, and streamflow time-series data at 27 meteorological stations and 34 hydrological stations for the period of 1963 to 2014. The time-series data of entire study period were divided into two equal subseries of 26 years each (1963–1988 and 1989–2014) to assess the overlapping aspect of climate change acceleration over UIB. The results showed a warming pattern at low altitude stations, while a cooling tendency was detected at high-altitude stations. An increase in streamflow was detected during winter and spring seasons at all hydrological stations, whereas the streamflow in summer and autumn seasons exhibited decreasing trends. The annual precipitation showed a significant decreasing trend at ten stations, while a significant increasing trend was observed at Kohat station during second subseries of the study period. The most significant winter drying trends were observed at Gupis, Chitral, Garidopatta, and Naran stations of magnitude of 47%, 13%, 25%, and 18%, respectively, during the second subseries. The annual runoff exhibited significant deceasing trends over Jhelum subbasin at Azad Pattan, Chinari, Domel Kohala, Muzaffarabad, and Palote, while within Indus basin at Chahan, Gurriala, Khairabad, Karora, and Kalam in the second time-series. It is believed that the results of this study will be helpful for the decision-makers to develop strategies for planning and development of future water resources projects.

This study investigates the performance of the latest version of RegCM4 in simulating summer precipitation over South Korea, comparing nine sensitivity experiments with different combinations of convective parameterization schemes (CPSs) between land and ocean. In addition to the gross pattern of seasonal and monthly mean precipitation, the northward propagation of the intense precipitation band and statistics from extreme daily precipitation are thoroughly evaluated against gridded and in situ station observations. The comparative analysis of 10-year simulations demonstrates that no CPS shows superiority in both quantitative and qualitative aspects. Furthermore, a nontrivial discrepancy among the different observation datasets makes a robust assessment of model performance difficult. Regardless of the CPS over the ocean, the simulations with the Kain–Fritsch scheme over land show a severe dry bias, whereas the simulations with the Tiedtke scheme over land suffer from a limited accuracy in reproducing spatial distributions due to the excessive orographic precipitation. In general, the simulations with the Emanuel scheme over land are better at capturing the major characteristics of summer precipitation over South Korea, despite not all statistical metrics showing the best performance. When applying the Emanuel scheme to both land and the ocean, precipitation tends to be slightly overestimated. This deficiency can be alleviated by using either the Tiedtke or Kain–Fritsch schemes over the ocean instead. As few studies have applied and evaluated the Tiedtke and Kain–Fritsch schemes to the Korean region within the RegCM framework, and this study introduces the potential of these new CPSs compared with the more frequently selected Emanuel scheme, which is particularly beneficial to RegCM users.

We evaluate the agreement between automated snow products generated from satellite observations in the microwave bands within NESDIS Microwave Integrated Retrieval System (MIRS) and Microwave Surface and Precipitation Products System (MSPPS), on the one hand, and snow cover maps produced with manual input by the NOAA’s Interactive Multisensor Snow and Ice Mapping System (IMS), on the other. MIRS uses physically based retrievals of atmospheric and surface state parameters to provide daily global maps of snow cover and snow water equivalent at 50 km resolution. The older MSPPS delivers daily global maps at the spatial resolution of 45 km and utilizes mostly simple empirical algorithms to retrieve information. IMS daily maps of snow and sea ice cover for the Northern Hemisphere are produced interactively through the analysis of satellite imagery in the visible, infrared, and microwave spectral bands. We compare the performances of these products across the Northern Hemisphere for 2014–2017, using IMS as the standard. In this intercomparison, the daily overall agreement of the automated snow products with IMS ranges between 88% and 99% for MIRS and 87% and 99% for MSPPS. However, daily snow sensitivity is lower, ranging between 36% and 90% for MIRS and 26% and 91% for MSPPS. We analyze this disagreement rate as a function of terrain and land cover type, finding that, relative to IMS, MIRS shows fewer false positives but more false negatives than MSPPS over high elevation and grassland areas.

Climate is a complex and chaotic system, and temperature prediction is a challenging problem. Accurate temperature prediction is also concerned in the fields of energy, environment, industry, and agriculture. In order to improve the accuracy of monthly mean temperature prediction and reduce the calculation scale of hybrid prediction process, a combined prediction model based on variational mode decomposition-differential symbolic entropy (VMD-DSE) and Volterra is proposed. Firstly, the original monthly mean meteorological temperature sequence is decomposed into finite mode components by VMD. The DSE is used to analyze the complexity and reconstruct the sequences. Then, the new sequence is reconstructed in phase space. The delay time and embedding dimension are determined by the mutual information method and G-P method, respectively. On this basis, the Volterra adaptive prediction model is established to modeling and predicting each component. Finally, the final predicted values are obtained by superimposing the predicted results. The monthly mean temperature data of Xianyang and Yan’an are used to verify the prediction performance of the proposed model. The experimental results show that the VMD-DSE-Volterra model shows better performance in the prediction of monthly mean temperature compared with other benchmark models in this paper. In addition, the combined forecasting model proposed in this paper can reduce the modeling time and improve the forecasting accuracy, so it is an effective forecasting model.

Drought is one of the most significant hazards in Sri Lanka. Status of drought in Sri Lanka was assessed using Standardized Precipitation Index (SPI) at 3, 6, and 12 months’ time scales using monthly rainfall (1970 to 2017) data of 54 weather stations. The frequency of drought events was evaluated using SPI, and trend of SPI was also detected using the Mann–Kendall (MK) test and Sen’s slope estimator. The result based on SPI identified hydrological years 1975-76, 1982-83, 1986-87, 1988-89, 2000-01, 2001-02, 2013-14, and 2016-17 as drought years for 52, 32, 35, 33, 33, 31, 31, and 31% of tested stations (54), respectively, at annual time scale. Comparison of the SPI at different time scales revealed that more drought events (SPI ≤ −1) occurred during Yala season than Maha cropping season. Considering the Thiessen polygon average rainfall, more frequent drought events occurred in the dry zone (57%) than the wet (49%) and intermediate zone (47%) at the annual time scale. SPI trend results showed greater increase in drought (59% of stations) during Yala seasons as compared to the Maha cropping season (15% of stations) in Sri Lanka.

A Multi-Model Ensemble (MME) based seasonal rainfall forecast customization tool called FOCUS was developed for Myanmar in order to provide improved seasonal rainfall forecast to the country. The tool was developed using hindcast data from 7 Global Climate Models (GCMs) and observed rainfall data from 49 meteorological surface observatories for the period of 1982 to 2011 from the Department of Meteorology and Hydrology. Based on the homogeneity in terms of the rainfall received annually, the country was divided into six climatological zones. Three different operational MME techniques, namely, (a) arithmetic mean (AM-MME), (b) weighted average (WA-MME), and (c) supervised principal component regression (PCR-MME), were used and built-in to the tool developed. For this study, all 7 GCMs were initialized with forecast data of May month to predict the rainfall during June to September (JJAS) period, which is the predominant rainfall season for Myanmar. The predictability of raw GCMs, bias-corrected GCMs, and the MMEs was evaluated using RMSE, correlation coefficients, and standard deviations. The probabilistic forecasts for the terciles were also evaluated using the relative operating characteristics (ROC) scores, to quantify the uncertainty in the GCMs. The results suggested that MME forecasts have shown improved performance (RMSE = 1.29), compared to the raw individual models (ECMWF, which is comparatively better among the selected models) with RMSE = 4.4 and bias-corrected RMSE = 4.3, over Myanmar. Specifically, WA-MME (CC = 0.64) and PCR-MME (CC = 0.68) methods have shown significant improvement in the high rainfall (delta) zone compared with WA-MME (CC = 0.57) and PCR-MME (CC = 0.56) techniques for the southern zone. The PCR method suggests higher predictability skill for the upper tercile (ROC = 0.78) and lower tercile categories (ROC = 0.85) for the delta region and is less skillful over lower rainfall zones like dry zones with ROC = 0.6 and 0.63 for upper and lower terciles, respectively. The model is thus suggested to perform relatively well over the higher rainfall (Wet) zones compared to the lower (Dry) zone during the JJAS period.

This study simulated the evolution of Typhoon Hato (2017) with the Weather Research and Forecasting model using three bulk schemes and one bin scheme. It was found that the track of the typhoon was insensitive to the microphysics scheme, whereas the degree of correspondence between the simulated precipitation and the cloud structure of the typhoon was closest to the observations when using the bin scheme. The different microphysical structure of the bin and three bulk schemes was reflected mainly in the cloud water and snow content. The three bulk schemes were found to produce more cloud water because the application of saturation adjustment condensed all the water vapor at the end of each time step. The production of more snow by the bin scheme could be attributed to several causes: (1) the calculations of cloud condensation nucleus size distributions and supersaturation at every grid point that cause small droplets to form at high levels, (2) different fall velocities of different sizes of particles that mean small particles remain at a significant height, (3) sufficient water vapor at high levels, and (4) smaller amounts of cloud water that reduce the rates of riming and conversion of snow to graupel. The distribution of hydrometeors affects the thermal and dynamical structure of the typhoon. The saturation adjustment hypothesis in the bulk schemes overestimates the condensate mass. Thus, the additional latent heat makes the typhoon structure warmer, which increases vertical velocity and enhances convective precipitation in the eyewall region.

A full analysis of 3-month Standardized Precipitation-Evapotranspiration index (SPEI-3) changes and attribution analyses are of significance for deeply understanding dryness/wetness evolutions and thus formulating specific measures to sustain regional development. In this study, we analyze monthly and annual SPEI-3 changes over Southwest China (SWC; including Sichuan (SC), Chongqing (CQ), Guizhou (GZ), Yunnan (YN), and west Guangxi (wGX)) during 1961–2012, using the SPEI model and routine meteorological measurements at 269 weather sites. For SWC and each subregion (excluding wGX), annual SPEI-3 during 1961–2012 tends to decrease, and drying is at most of months in January and September–December, but wetting is in February–August (excluding March for wGX). Additionally, more than 50% of sites show declined and increased SPEI-3 in January, April, June, and August–December and the remaining months, respectively. Except for wGX with dominant of ET0, annual SPEI-3 changes in SWC and other four subregions have dominant of precipitation. Spatially, annual SPEI-3 changes at 59% of sites are because of precipitation, generally located in southeast SC, south YN, CQ, GZ, and south and northeast wGX. Nevertheless, dominants at regional and site scales vary among months, e.g., SWC, SC, CQ, and GZ, having dominant of precipitation (ET0) during September–December (most of months during January–August), YN always with dominant of precipitation, and wGX with dominant of precipitation (ET0) in February–April and July–December (January, May, and June). Importantly, this study provides a reference for quantitatively evaluating spatiotemporal dryness/wetness variations with climate change, especially for regions with significant drying/wetting.