Climate2015, 3(2), 329-348; doi:10.3390/cli3020329 - published 28 April 2015 Show/Hide Abstract
Abstract: Satellite-based precipitation products have been shown to represent precipitation well over Nepal at monthly resolution, compared to ground-based stations. Here, we extend our analysis to the daily and subdaily timescales, which are relevant for mapping the hazards caused by storms as well as drought. We compared the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RT product with individual stations and with the gridded APHRODITE product to evaluate its ability to retrieve different precipitation intensities. We find that 3B42RT, which is freely available in near real time, has reasonable correspondence with ground-based precipitation products on a daily timescale; rank correlation coefficients approach 0.6, almost as high as the retrospectively calibrated TMPA 3B42 product. We also find that higher-quality ground and satellite precipitation observations improve the correspondence between the two on the daily timescale, suggesting opportunities for improvement in satellite-based monitoring technology. Correlation of 3B42RT and 3B42 with station observations is lower on subdaily timescales, although the mean diurnal cycle of precipitation is roughly correct. We develop a probabilistic precipitation monitoring methodology that uses previous observations (climatology) as well as 3B42RT as input to generate daily precipitation accumulation probability distributions at each 0.25° x 0.25° grid cell in Nepal and surrounding areas. We quantify the information gain associated with using 3B42RT in the probabilistic model instead of relying only on climatology and show that the quantitative precipitation estimates produced by this model are well calibrated compared to APHRODITE.
Climate2015, 3(2), 308-328; doi:10.3390/cli3020308 - published 16 April 2015 Show/Hide Abstract
Abstract: The Caucasus Region has been affected by an increasing number of heat waves during the last decades, which have had serious impacts on human health, agriculture and natural ecosystems. A dataset of 22 homogenized, daily maximum (Tmax) and minimum (Tmin) air temperature series is developed to quantify climatology and summer heat wave changes for Georgia and Tbilisi station between 1961 and 2010 using the extreme heat factor (EHF) as heat wave index. The EHF is studied with respect to eight heat wave aspects: event number, duration, participating heat wave days, peak and mean magnitude, number of heat wave days, severe and extreme heat wave days. A severity threshold for each station was determined by the climatological distribution of heat wave intensity. Moreover, heat wave series of two indices focusing on the 90th percentile of daily minimum temperature (CTN90p) and the 90th percentile of daily maximum temperature (CTX90p) were compared. The spatial distribution of heat wave characteristics over Georgia showed a concentration of high heat wave amplitudes and mean magnitudes in the Southwest. The longest and most frequently occurring heat wave events were observed in the Southeast of Georgia. Most severe heat wave events were found in both regions. Regarding the monthly distribution of heat waves, the largest proportion of severe events and highest intensities are measured during May. Trends for all Georgia-averaged heat wave aspects demonstrate significant increases in the number, intensity and duration of low- and high-intensity heat waves. However, for the heat wave mean magnitude no change was observed. Heat wave trend magnitudes for Tbilisi mainly exceed the Georgia-averages and its surrounding stations, implying urban heat island (UHI) effects and synergistic interactions between heat waves and UHIs. Comparing heat wave aspects for CTN90p and CTX90p, all trend magnitudes for CTN90p were larger, while the correlation between the annual time-series was very high among all heat wave indices analyzed. This finding reflects the importance of integrating the most suitable heat wave index into a sector-specific impact analysis.
Climate2015, 3(2), 283-307; doi:10.3390/cli3020283 - published 1 April 2015 Show/Hide Abstract
Abstract: With the refinement of grid meshes in regional climate models permitted by the increase in computing power, the grid telescoping or cascade method, already used in numerical weather prediction, can be applied to achieve very high-resolution climate simulations. The purpose of this study is two-fold: (1) to illustrate the perspectives offered by climate simulations on kilometer-scale grid meshes using the wind characteristics in the St. Lawrence River Valley (SLRV) as the test-bench; and (2) to establish some constraints to be satisfied for the physical realism and the computational affordability of these simulations. The cascade method is illustrated using a suite of five one-way nested, time-slice simulations carried out with the fifth-generation Canadian Regional Climate Model, with grid meshes varying from roughly 81 km, successively to 27, 9, 3 and finally 1 km, over domains centered on the SLRV. The results show the added value afforded by very high-resolution meshes for a realistic simulation of the SLRV winds. Kinetic energy spectra are used to document the spin-up time and the effective resolution of the simulations as a function of their grid meshes. A pragmatic consideration is developed arguing that kilometer-scale simulations could be achieved at a reasonable computational cost with time-slice simulations of high impact climate events. This study lends confidence to the idea that climate simulations and projections at kilometer-scale could soon become operationally feasible, thus offering interesting perspectives for resolving features that are currently out of reach with coarser-mesh models.
Climate2015, 3(2), 264-282; doi:10.3390/cli3020264 - published 26 March 2015 Show/Hide Abstract
Abstract: Climate change would significantly affect the temporal pattern and amount of annual precipitation at the regional level, which in turn would affect the regional water resources and future water availability. The Peace Region is a critical region for northern British Columbia’s social, environmental, and economic development, due to its potential in various land use activities. This study investigated the impacts of future climate change induced precipitation on water resources under the A2 and B1 greenhouse gas emission scenarios for 2020–2040 in a study area along the main river of the Kiskatinaw River watershed in the Peace Region as a case study using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) modeling system. The simulation results showed that climate change induced precipitation changes significantly affect monthly, seasonal and annual stream flows. With respect to the mean annual stream flow of the reference period (2000–2011), the mean annual stream flow from 2020 to 2040 under the A2 and B1 scenarios is expected to increase by 15.5% and 12.1%, respectively, due to the increased precipitation (on average 5.5% in the A2 and 3.5% in the B1 scenarios) and temperature (on average 0.76 °C in the A2 and 0.57 °C in the B1 scenarios) predicted, with respect to that under the reference period. From the seasonal point of view, the mean seasonal stream flow during winter, spring, summer and fall from 2020 to 2040 under the A2 scenario is expected to increase by 10%, 16%, 11%, and 11%, respectively. On the other hand, under the B1 scenario these numbers are 6%, 15%, 6%, and 8%, respectively. Increased precipitation also resulted in increased groundwater discharge and surface runoff. The obtained results from this study will provide valuable information for the study area in the long-term period for seasonal and annual water extractions from the river and allocation to the stakeholders for future water supply, and help develop a regional water resources management plan for climate change induced precipitation changes.
Climate2015, 3(1), 241-263; doi:10.3390/cli3010241 - published 13 March 2015 Show/Hide Abstract
Abstract: Infrastructure such as dams and reservoirs are critical water-supply features in several regions of the world. However, ongoing population growth, increased demand and climate variability/change necessitate the better understanding of these systems, particularly in terms of their long-term trends. The Sooke Reservoir (SR) of British Columbia, Canada is one such reservoir that currently supplies water to ~300,000 people, and is subject to considerable inter and intra-annual climatic variations. The main objectives of this study are to better understand the characteristics of the SR through an in-depth assessment of the contemporary water balance when the basin was intensively monitored (1996–2005), to use standardized runoff to select the best timescale to compute the Standard Precipitation (SPI) and Standard Precipitation Evaporation Indices (SPEI) to estimate trends in water availability over 1919 to 2005. Estimates of runoff and evaporation were validated by comparing simulated change in storage, computed by adding inputs and subtracting outputs from the known water levels by month, to observed change in storage. Water balance closure was within ±11% of the monthly change in storage on average when excluding months with spill pre-2002. The highest evaporation, dry season (1998) and lowest precipitation, wet season (2000/2001) from the intensively monitored period were used to construct a worst-case scenario to determine the resilience of the SR to drought. Under such conditions, the SR could support Greater Victoria until the start of the third wet season. The SPEI and SPI computed on a three-month timescale had the highest correlation with the standardized runoff, R2 equaled 0.93 and 0.90, respectively. A trend toward drier conditions was shown by SPEI over 1919 to 2005, while moistening over the same period was shown by SPI, although trends were small in magnitude. This study contributes a validated application of SPI and SPEI, giving more credit to their trends and estimated changes in drought.
Climate2015, 3(1), 227-240; doi:10.3390/cli3010227 - published 6 March 2015 Show/Hide Abstract
Abstract: Evidence is mounting that the temporal dynamics of the climate system are changing at the same time as the average global temperature is increasing due to multiple climate forcings. A large number of extreme weather events such as prolonged cold spells, heatwaves, droughts and floods have been recorded around the world in the past 10 years. Such changes in the temporal scaling behaviour of climate time-series data can be difficult to detect. While there are easy and direct ways of analysing climate data by calculating the means and variances for different levels of temporal aggregation, these methods can miss more subtle changes in their dynamics. This paper describes multi-scale entropy (MSE) analysis as a tool to study climate time-series data and to identify temporal scales of variability and their change over time in climate time-series. MSE estimates the sample entropy of the time-series after coarse-graining at different temporal scales. An application of MSE to Central European, variance-adjusted, mean monthly air temperature anomalies (CRUTEM4v) is provided. The results show that the temporal scales of the current climate (1960–2014) are different from the long-term average (1850–1960). For temporal scale factors longer than 12 months, the sample entropy increased markedly compared to the long-term record. Such an increase can be explained by systems theory with greater complexity in the regional temperature data. From 1961 the patterns of monthly air temperatures are less regular at time-scales greater than 12 months than in the earlier time period. This finding suggests that, at these inter-annual time scales, the temperature variability has become less predictable than in the past. It is possible that climate system feedbacks are expressed in altered temporal scales of the European temperature time-series data. A comparison with the variance and Shannon entropy shows that MSE analysis can provide additional information on the statistical properties of climate time-series data that can go undetected using traditional methods.