Simple Precipitation Intensity Index Research Articles

The difference in the uncertainty sources between future projections of mean and extreme precipitation over East Asia

As the incidence of extreme precipitation events attributable to global climate change increases, providing policymakers with accurate model predictions is of the utmost importance. However, model projections have inherent uncertainties. The present study attempted to distinguish the sources of the uncertainty of the mean and extreme precipitation projections in the East Asia region using the mean boreal summer precipitation, simple precipitation intensity index (SDII), maximum cumulative 5 day precipitation, and annual maximum daily precipitation (Rx1d). The results show that while the mean precipitation was projected to change very little regardless of the scenario, more extreme indices were projected to increase considerably by the end of the century, particularly in the high-emissions scenarios. On average, model uncertainty accounted for the largest part of the uncertainty. However, for Rx1d in the 2030s, as well as mean and SDII in some regions until the 2060s, the internal variability was the largest contributor. In addition, whilst scenario uncertainty accounted for a negligible proportion of average precipitation variability, for the more extreme the precipitation indices, scenario uncertainty contribution to total variability by the end of the century was significant; namely, the scenario uncertainty contribution was overall highest for the maximum one-day precipitation. Additionally, comparatively wetter regions had greater overall projection uncertainties, especially uncertainty arising from internal variability, likely due to the influence of interannual variability from the EA summer monsoon.

Avian responses to climate extremes: insights into abundance curves and species sensitivity using the UK Breeding Bird Survey

Climate change remains one of the most urgent challenges for biodiversity conservation. Recent studies have highlighted that climate extremes (CLEXs) can lead to widespread and negative effects across all taxa and ecological levels, but most of these studies are based on short-term periods and small spatial scales and lack a multi-species approach. Here, using generalised additive models (GAMs) and the UK Breeding Bird Survey (BBS), we described response curves for the abundance of 100 resident bird species over large spatial and temporal scales and identified the species showing a greater sensitivity to CLEXs. We used five climatic indices computed at 1-km spatial resolution as proxies of CLEXs during the winter or breeding season and considered both 1- and 2-year lagged effects. The results demonstrated widespread and significant effects of CLEXs on bird abundances at both time lags and in both seasons. Winter frost days (FD0), summer days (SU25) during the breeding season and simple precipitation intensity index (SDII) during the breeding season mainly showed negative effects. Daily temperature range (DTR) in both winter and breeding season and dry days (DD) during the breeding season led to diversified responses across the species, with a prevalence of positive effects. A large proportion of species showed a high sensitivity to CLEXs, highlighting that these species may deserve attention in future studies aimed at biodiversity conservation. We demonstrated that CLEXs can represent a significant driver affecting population abundances over large spatial and temporal scales, emphasising the need for understanding mechanistic processes at the basis of the observed effects.

Evaluation of Extreme Precipitation Indices in Brazil’s Semiarid Region from Satellite Data

Brazil’s semiarid region (SAB) has a heterogeneous precipitation distribution, with the occurrence of periodic droughts and occasional extreme rainfall events. The precipitation monitoring system in this region is insufficient, but remote sensing products can provide information on rainfall in areas with low data coverage. Thus, the main objective of this study was to evaluate 12 extreme precipitation indices calculated using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data in comparison with indices calculated from data measured by rain gauges for different SAB locations. To evaluate the IMERG product, we used rainfall data measured by 56 rain gauges during the period from 1 January 2000 to 31 December 2020. The satellite product was evaluated through juxtaposition between the IMERG and actual rainfall data, by calculating the statistical indices bias, root-mean-squared error, Spearman correlation, and probability density function. The results showed that most of the extreme precipitation indices were well represented by the satellite data, except for the simple precipitation intensity index (SDII), in which case the correlation coefficient was 0.2. This result can be explained as this index is calculated from the exact value of daily precipitation, while the other indices are estimated by rainfall values above some thresholds. On the other hand, total annual precipitation and precipitation above 1 mm presented Spearman correlation reaching 0.97 in some locations. We conclude that the IMERG database is adequate to represent the maximum precipitation in the Brazilian semiarid region, and the extreme precipitation indices had good performance according to the region where the maximum rain occurred.

Evaluation of multi-satellite precipitation products in estimating precipitation extremes over mainland China at annual, seasonal and monthly scales

The wide and consistent global coverage of satellite-based quantitative precipitation estimates (QPEs) has shown great potential for monitoring precipitation (PR) at large spatial scales. Evaluation of QPEs in estimating PR extremes is vital to forecasting hydrologic extremes. Here, we present a systematic evaluation of four commonly used QPEs: (1) the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), (2) Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42 V7), (3) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and (4) Multi-Source Weighted-Ensemble Precipitation (MSWEP), in their abilities to detect PR extremes in mainland China at annual-seasonal-monthly scales. Four Standard Extreme Precipitation Indices (SEPIs) are chosen as the assessment metrics, including the maximum 1-day PR (Rx1day), the simple PR intensity index (SDII), the count of days with PR ≥ 20 mm (R20mm), and the consecutive dry days (CDD). Results indicate that PERSIANN-CDR performs best for all SEPIs, followed by CHIRPS and TRMM 3B42 V7. All QPEs (except MSWEP) perform better (worse) in capturing CDD (SDII) than other SEPIs at all three timescales. However, large differences among the performance of QPEs in estimated seasonal SEPIs are found - CHIRPS (PERSIANN-CDR) outperforms the other QPEs in spring (summer and autumn), while PERSIANN-CDR and CHIRPS outperform the other QPEs in winter. All QPEs perform better in detecting extreme PR occurrence in summer than in other seasons, and spatially most of them perform better in humid southeastern China than in arid northwestern China. CHIRPS and TRMM 3B42 V7 overestimate Rx1day, R20mm and SDII in most of China, while MSWEP notably underestimates CDD in most of China and overestimates Rx1day, R20mm and SDII in western China.

Spatiotemporal Variability in Precipitation Extremes in the Jianghuai Region of China and the Analysis of Its Circulation Features

In the context of global warming, changes in extreme-precipitation events are becoming increasingly complex, and investigating the spatial and temporal variation characteristics of extreme precipitation is extremely important for scientific water-resource planning, preventing new climate risks and maintaining ecosystem balances. Based on the daily precipitation from 1960–2017 at 15 meteorological stations in the Jianghuai region, the extreme-precipitation indices were calculated. The variations in 12 extreme-precipitation indices were detected by using the Mann–Kendall test in the Jianghuai region. The periodicity of indices was examined by wavelet analysis detecting significant time sections. Through the cross wavelet transform and wavelet coherence analyses, the nonlinear connections between extreme precipitation and atmospheric circulation were explored. The results indicate significant increasing trends in the max one-day precipitation amount (Rx1day), extreme wet days (R99p), and simple precipitation intensity index (SDII). The intensity of extreme precipitation increased significantly. The variation in extreme precipitation showed different trends in different regions, with a greater likelihood of increasing extreme-precipitation intensity and frequency in the southern region compared to the central and northern regions. The period of most oscillations of the indices tend toward be on a time scale of 2–4 years and are in the 1990s. The number of heavy precipitation days (R10 mm) and number of very heavy precipitation days (R20 mm) had, mainly, periods of 5.84 years. Additionally, there were significant resonance periods between the extreme-precipitation indices and the atmospheric circulation index; however, there were obvious differences in time domains. The North Atlantic Oscillation (NAO) and East Asian summer monsoon (EASM) had the most significant effect on the duration of extreme precipitation; Atlantic Oscillation (AO) and EASM had the most significant influence on the extreme-precipitation intensity. The results of the study can provide a scientific basis for water-resource management and disaster prevention and control in the Jianghuai region.

Spatial and Temporal Variations of Extreme Precipitation in Central Asia during 1982–2020

As one of the largest arid and semi-arid regions in the world, central Asia (CA) is very sensitive to changes in regional climate. However, because of the poor continuity of daily observational precipitation records in CA, the spatial and temporal variations of extreme precipitation in recent decades remain unclear. Considering their good spatial and temporal continuity, gridded data, such as Climate Prediction Center (CPC) global precipitation, and reanalysis data, such as ERA-Interim (ERA), are helpful for exploring the spatial–temporal variations of extreme precipitation. This study evaluates how well CPC and ERA can represent observed precipitation extremes by comparing the differences in eight extreme precipitation indices and observation data at 84 meteorological stations. The results indicate that the CPC (except for 1979–1981) is more suitable for depicting changes in precipitation extremes. Based on the CPC data for the period 1982–2020, we found that seven indices of precipitation extremes, including consecutive wet days (CWD), max1-day precipitation amount (Rx1day), max5-day precipitation amount (Rx5day), number of heavy precipitation days (R10), very wet days (R95p), annual total precipitation in wet days (PRCPTOT), and simple precipitation intensity index (SDII) have increased by 0.2 d/10a, 0.9 mm/10a, 1.8 mm/10a, 0.3 d/10, 8.4 mm/10a, 14.3 mm/10a and 0.1 mm/d/10a, respectively, and the consecutive dry days (CDDs) have decreased by −3.10 d/10a. It is notable that CDDs decreased significantly in the north of Xinjiang (XJ) but increased in Kyrgyzstan (KG), Tajikistan (TI), and eastern Turkmenistan (TX). The other indices increased clearly in the west of XJ, north of Kazakhstan (KZ), and east of KG but decreased in the south of KG, TI, and parts of XJ. For most indices, the largest change occurred in spring, the main season of precipitation in CA. Therefore, the large-scale atmospheric circulation in April is analyzed to contrast between the most and least precipitation years for the region. A typical circulation pattern in April for those extremely wet years includes an abnormal low-pressure center at 850 hpa to the east of the Caspian Sea, which enhances the southerly winds from the Indian Ocean and hence the transportation of water vapor required for precipitation into CA. This abnormal circulation pattern occurred more frequently after 2001 than before, thus partly explaining the recent increasing trends of precipitation extremes in CA.

Linking human activities and global climatic oscillation to phytoplankton dynamics in a subtropical lake

Human activities and climate change are two major stressors affecting lake ecosystems as well as phytoplankton communities worldwide. However, how the temporal dynamics of phytoplankton are directly or indirectly linked to anthropogenic activities and climatic oscillation remains unclear. We assessed the annual trends (1988–2018) in phytoplankton abundance (PA) in Lake Dongting, China and related it to five groups of variables characterizing human activities, global climate oscillation, water nutrients, hydrology, and meteorology. We found a significant increase in PA, urbanization (Upop), total nitrogen (TN), fertilizer application (FA), number of summer days (SU), and the warm speed duration index (WSDI) and a significant decrease in the water discharge of three inlets (TIWD) and the sediment discharge of three inlets (TISD) and four tributaries (FTSD) and the net sediment deposition (NSD). However, no significant annual trends were observed for the number of rainstorm days (R50mm), the simple precipitation intensity index (SDII) and yearly anomalies of El Niño-Southern oscillation events (ENSOi). Cross-correlation Function analyses demonstrated that the operation of the Three George Dam (TGD) strengthened the effects of hydrology, rainfall patterns and ENSOi on phytoplankton, but strongly weakened the association between water nutrients, human activities and phytoplankton abundance. Path analysis revealed that TP, TN, FA, R50 mm as well as WSDI had a direct positive effect on PA, while a direct negative effect was found for ENSOi, NSD and TISD. Human activities (Upop and FA), warming (WSDI and SU), and rainfall patterns (SDII and R50 mm) exerted indirect controls on phytoplankton through changes in water nutrients and hydrology. Climate change (ENSOi) had a direct effect on PA, but also showed twelve indirect pathways via changes in hydrology and meteorology (both positive and negative effects were found). Overall, meteorology contributed most markedly to the variations of PA (29.3%), followed by hydrology (25.3%), human activities (24%), water nutrients (10.5%), and ENSOi (1.9%). Our results highlight a strongly causal connection between human activities as well as global climate change and phytoplankton and the benefits of considering multiple environmental drivers in determining the temporal dynamics of lake biotic communities.

Do Extreme Climate Events Cause the Degradation of Malus sieversii Forests in China?

Frequent extreme climate events have attracted considerable attention around the world. Malus sieversii in Xinjiang is the ancestor of cultivated apple, and it is mainly distributed in the Ili river valley at end of the Tianshan Mountains. Wild fruit forests have been degraded, but the cause remains unclear. In order to identify whether extreme climate events caused this degradation reanalysis data and atmospheric circulation indices were used to determine the trends and the reasons for extreme climate changes. Subsequently, we further investigated the effect of extreme climate events on wild fruit forest using characteristics of extreme climate indices and tree-ring chronology. We found increasing trends in both extreme precipitation and warm indices, and decreasing trends in cool indices. Extreme climate events were mainly associated with the Atlantic Multidecadal Oscillation (AMO). Analysis of data of wind and geopotential height field at 500 hPa showed that strengthening wind, increasing geopotential height, cyclone and anti-cyclone circulation drivers contributed to extreme climate events. In the non-degraded region, there were significant positive correlations between tree-ring chronology and both extreme precipitation and extreme warm indices (except for warm spell duration indicator). The other extreme indices (except for heavy rain days) had a large correlation range with tree-rings in a 4–8-year period. These results indicated that extreme precipitation and extreme warm indices intensified M. sieversii growth of the non-degraded region on multi-time scales. In contrast, the degraded region showed insignificant negative relationship between tree-ring chronology and both extreme precipitation and extreme warm indices [except for warm spell duration index (WSDI)], and significant negative correlations in a 4–8-year period were detected between tree-ring chronology and most of the extreme precipitation indices, including heavy rain days, very wet days, cold spell duration indicator, simple precipitation intensity index (SDII), and annual total precipitation. Under the long disturbance of inappropriate anthropic activities, extreme climate has caused the outbreak of pests and diseases resulting in the degeneration of wild fruit forest. Our study provides scientific guidance for the ecosystem conservation in wild fruit forest in China, and also across the region.

An overview of precipitation climatology in Brazil: space-time variability of frequency and intensity associated with atmospheric systems

ABSTRACT The climatic characteristics of Brazil present marked spatial and temporal precipitation contrasts, which are reviewed according to four main indices: PRCPTOT – total precipitation; Rmm – number of days with precipitation; Rx1day – maximum 1-day precipitation; and SDII – simple precipitation intensity index. The study used data for 12 hydrographic basins from 1975 to 2005. The seasonal distribution of these precipitation indices in Brazil is summarized concerning the main atmospheric systems acting in South America. The austral summer and autumn present the maximum PRCPTOT, Rmm, and Rx1day, related to the South American Monsoon System and Intertropical Convergence Zone. However, in the winter, the extreme northern and eastern coast also present high Rx1day. Maximum values of SDII occur during the four seasons in southwestern Brazil, related to mesoscale convective complexes. Precipitation anomalies are also affected by the sea surface temperature of the Tropical Southern Atlantic and Equatorial Pacific Oceans.

Intraseasonal Precipitation Variability over West Africa under 1.5 °C and 2.0 °C Global Warming Scenarios: Results from CORDEX RCMs

This study assessed the performance of 24 simulations, from five regional climate models (RCMs) participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX), in representing spatiotemporal characteristics of precipitation over West Africa, compared to observations. The top five performing RCM simulations were used to assess future precipitation changes over West Africa, under 1.5 °C and 2.0 °C global warming levels (GWLs), following the representative concentration pathway (RCP) 8.5. The performance evaluation and future change assessment were done using a set of seven ‘descriptors’ of West African precipitation namely the simple precipitation intensity index (SDII), the consecutive wet days (CWD), the number of wet days index (R1MM), the number of wet days with moderate and heavy intensity precipitation (R10MM and R30MM, respectively), and annual and June to September daily mean precipitation (ANN and JJAS, respectively). The performance assessment and future change outlook were done for the CORDEX–Africa subdomains of north West Africa (WA-N), south West Africa (WA-S), and a combination of the two subdomains. While the performance of RCM runs was descriptor- and subregion- specific, five model runs emerged as top performers in representing precipitation characteristics over both WA-N and WA-S. The five model runs are CCLM4 forced by ICHEC-EC-EARTH (r12i1p1), RCA4 forced by CCCma-CanESM2 (r1i1p1), RACMO22T forced by MOHC-HadGEM2-ES (r1i1p1), and the ensemble means of simulations made by CCLM4 and RACMO22T. All precipitation descriptors recorded a reduction under the two warming levels, except the SDII which recorded an increase. Unlike the WA-N that showed less frequency and more intense precipitation, the WA-S showed increased frequency and intensity. Given the potential impact that these projected changes may have on West Africa’s socioeconomic activities, adjustments in investment may be required to take advantage of (and enhance system resilience against damage that may result from) the potential changes in precipitation.

The spatial pattern of selected extreme precipitation indices for Turkey (1975-2012)

Abstract This paper analyses extreme precipitation characteristics of Turkey based on selected WMO climate change indices. The indices – monthly total rainy days (RDays); monthly maximum 1-day precipitation (Rx1day); simple precipitation intensity index (SDII); and monthly count of days when total precipitation (represented by PRCP) exceeds 10 mm (R10mm) – were calculated for 98 stations for the 38-year overlapping period (1975–2012). Cluster analysis was applied to evaluate the spatial characterisation of the annual precipitation extremes. Four extreme precipitation clusters were detected. Cluster 1 corresponds spatially to Central and Eastern Anatolia and is identified with the lowest values of the indices, except rainy days. Cluster 2 is concentrated mainly on the west and south of Anatolia, and especially the coastal zone, and can be characterised with the lowest rainy days, and high and moderate values of other indices. These two clusters are the most prominent classes throughout the country, and include a total of 82 stations. Cluster 3 is clearly located in the Black Sea coastal zone in the north, and has high and moderate index values. Two stations on the north-east coast of the Black Sea region are identified as Cluster 4, which exhibits the highest values among all indices. The overall results reveal that winter months and October have the highest proportion of precipitation extremes in Turkey. The north-east part of the Black Sea region and Mediterranean coastal area from the south-west to the south-east are prone to frequent extreme precipitation events.

Heavy precipitation events over East Africa in a changing climate: results from CORDEX RCMs

The study assesses the performance of 24 model runs from five COordinated Regional climate Downscaling Experiment (CORDEX) regional climate models (RCMs) in simulating East Africa’s spatio-temporal precipitation characteristics using a set of eight descriptors: consecutive dry days (CDD), consecutive wet days (CWD), simple precipitation intensity index (SDII), mean daily annual (pr_ANN), seasonal (pr_MAM and pr_OND) precipitation, and representatives of heavy precipitation (90p) and very intense precipitation (99p) events. Relatively better performing RCM runs are then used to assess projected precipitation changes (for the period 2071–2099 relative to 1977–2005) over the study domain under the representative concentration pathway (RCP) 8.5 scenario. The performance of RCMs is found to be descriptor and scope specific. Overall, RCA4 (r1i1p1) forced by CNRM-CERFACS-CNRM-CM5 and MPI-M-MPI-ESM-LR, REMO2009 (r1i1p1) forced by MPI-M-MPI-ESM-LR, and RCA4 (r2i1p1) forced by MPI-M-MPI-ESM-LR emerge as the top four RCM runs. We show that an ensemble mean of the top four model runs outperforms an ensemble mean of 24 model simulations and ensemble means for all runs in an RCM. Our analysis of projections shows a reduction (increase) in mean daily precipitation for MAM(OND), an increase(decrease) in CDD(CWD) events, and a general increase in SDII and the width of the right tail of the precipitation distribution (99p–90p). An increase in SDII and 99p–90p implies a possibility of occurrence of heavy and extreme precipitation incidences by the end of the twenty-first century. Our findings provide important information to support the region’s climate change adaptation and mitigation efforts.

Evaluating the added values of regional climate modeling over China at different resolutions

Previous studies have suggested that dynamical downscaling to global climate models can produce improved climate simulations at regional and local scales. However, the expensive computational requirements of dynamical downscaling inevitably add a limit to the spatial resolution of the resulting regional climate simulations. In order to find a balance between computational requirements and simulation improvements, it is extremely important to investigate how the spatial resolution of regional climate simulation affects the added values of dynamical downscaling; yet, it is still not well understood. Therefore, in this study, we conduct long-term climate simulations for the entire country of China with the PRECIS regional climate model at two different spatial resolutions (i.e., 25 and 50 km). The purpose is to evaluate whether a fine-resolution model simulation, given its considerable requirements for computational resources, would add more valuable information for understanding regional climatology than a coarse-resolution model simulation. Our results show that the PRECIS can reasonably reproduce the spatial distribution of seasonal and monthly mean temperature and precipitation over the most of regions in China. However, in the process of downscaling, RCM with higher resolution cannot always produce more accurate output. In regard to precipitation simulations, compared with the host GCM, it is difficult to determine exactly a homogeneous improvement of performance in downscaling, both in terms of spatial patterns as well as magnitude of errors. For interannual variability, variations in temperature are closer to observation than precipitation and the high-resolution R25 has better skills over the northwest than R50. Moreover, except for the west, it is shown that PRECIS is able to better reproduce the probability distribution function of precipitation and some impact-relevant indices such as the number of consecutive wet days and simple precipitation intensity index in spatial distribution.

Recent disruptions in the timing and intensity of precipitation in Calakmul, Mexico

This study addresses changes in the timing and intensity of precipitation from 1982 to 2016 from three meteorological stations around Calakmul, Mexico, a landscape balancing biodiversity conservation and smallholder agricultural production. Five methods were used to assess changes in precipitation: the Mann-Kendall test of annual and wet season trends; a fuzzy-logic approach to determine the onset of the rainy season; the Gini Index and Precipitation Concentration Index (PCI) to evaluate the temporal distribution of precipitation; Simple Precipitation Intensity Index (SDII) to evaluate precipitation intensity; and the Rainfall Anomaly Index (RAI) to identify the deficit or surplus of rainfall compared with the long-term mean. Overall, rainfall trends in Calakmul over this period indicate a slight increase, though results of the indices (Gini, SDII, PCI) all indicate that rainfall has become more intense and more unevenly distributed throughout the year. There was no significant trend in the onset date of rainfall or the RAI overall, though there were more pronounced crests and troughs from 2004 to 2016. Higher interannual variability and more pronounced rainfall anomalies, both positive and negative, suggest that rainfall in the Calakmul region has become more extreme. This research informs for management and livelihood strategies in the local region and offers insights for analyses of regional patterns of seasonal precipitation events in tropical landscapes worldwide.

Performance of BCC-CSM Models with Different Horizontal Resolutions in Simulating Extreme Climate Events in China

In this study, the performance of the Beijing Climate Center (BCC) Climate System Model version 1.1 (BCC-CSM1.1) (280-km resolution) and the BCC-CSM1.1m (110-km resolution) in simulating extreme climate events over China in the last 40 years is compared. Both models capture the main spatial distribution features of heavy precipitation (R95T), the number of consecutive wet days (CWD), the annual count of days with precipitation mm (R1), the maximum consecutive 5-day precipitation (Rx5), and the numbers of frost days (FD) and summer days (SU). The BCC-CSM1.1m has a better ability to simulate the detailed distribution of extreme climate events than the BCC-CSM1.1, including R95T, CWD, R1, and the simple precipitation intensity index (SDII). However, the BCC-CSM1.1m does not show an improvement in simulating the number of days with extreme precipitation (R90N), the number of consecutive dry days (CDD), the heat wave duration index (HWDI), the warm day frequency (TX90P), and cold night frequency (TN10P). This indicates that the simulation of the R95T, CWD, R1, and SDII climate events is more sensitive to the resolution of the model. The improved BCC-CSM1.1m is used to explore the projection of extreme climate change in China during the 21st century under the RCP4.5 (Representative Concentration Pathways) and RCP8.5 scenarios. The results show that extreme precipitation will increase dramatically over North and Southwest China in the late 21st century. The CWD index will decrease on the Tibetan Plateau and in northeastern and central China and will increase in other parts of China; R1 will increase in northern China and decrease in southern China; Rx5 will increase dramatically in southern China; FD will decrease and SU will increase over China in the late 21st century under both emission scenarios, with larger amplitudes in RCP8.5.

Spatio-temporal variations of precipitation extremes in Hanjiang River Basin, China, during 1960–2015

Under the background of global warming, precipitation extremes are the main reasons for the cause of drought-flood disasters at local scales. Regional evaluations on the spatial and temporal variations of precipitation extremes are needed for comprehensively understanding the precipitation-related natural disasters. In this study, ten extreme precipitation indices from 17 meteorological stations in the Hanjiang River Basin, China, were computed and their spatial and temporal variations were analyzed using linear tendency method, based on daily precipitation data for the period of 1960–2015. Results indicated that consecutive dry days, number of heaviest precipitation days, maximum 1-day precipitation, very wet day precipitation, extremely wet day precipitation, and simple precipitation intensity index exhibited non-significant increasing trends. Consecutive wet days, number of heavy precipitation days, maximum 5-day precipitation, and total wet day precipitation exhibited non-significant decreasing trends. Using Mann-Kendall test, cumulative anomaly method, and moving t test for cross-validation, an abrupt increase was detected in 1978 for both very wet day precipitation and extremely wet day precipitation and for both these two indices, there were two significant periodic variation cycles, i.e., 1–2 years and 4 years, and in addition, extremely wet day precipitation had a change cycle of 8 years. The cross-wavelet transform method suggested that Pacific Decadal Oscillations and Sea Surface Temperature might be the two driving factors of extreme precipitation variation. The changing trends of precipitation extremes showed regional differences, but the stations with significant trends were not clustered. All the indices were highly correlated with each other, except for the number of consecutive dry days and consecutive wet days. The contribution of extreme precipitation to total precipitation revealed a non-significant increase in the study area. There were negative correlations between most of the indices and elevation with high level of significance. Changes of large-scale atmospheric circulation showed an increasing geopotential height and a weakened summer monsoon and had an impact on the variations of extreme precipitation.

The Spatiotemporal Characteristics of Extreme Precipitation Events in the Western United States

Changes in the frequency or intensity of extreme precipitation events would have profound impacts on both human society and the natural environment. In this paper, we present the results of a comprehensive analysis of the spatiotemporal changes of extreme precipitation in the western United States. The analyses explore the spatial characterization of the El Nino-Southern Oscillation (ENSO)-extreme precipitation response pattern and identify the multi-scale temporal variability in precipitation extremes in the western United States. Results indicate: (1) Extreme precipitation expressed in indices such as seasonal count of days when precipitation is large than 10 mm (R10), seasonal maximum 5-day precipitation (R5D), maximum length of dry spell (CDD), and seasonal total precipitation exceeding 95 percentile (R95) have a dipolar pattern and a transition zone which separates the west into two main dipolar centers regarded as Pacific Northwest and Desert Southwest. The simple precipitation intensity index (SDII) has little correlation with large scale natural oscillations over most of the west. (2) The spatial distributions of annual trend of R10, R5D, SDII, and R95 have seasonal variability in southern California and Lower Colorado River Basin. (3) There are consistent multi-year bands ranging from 2 to 20 years in the R10, R5D, CDD, and R95 winter time series which may be caused by the inter-decadal or multi-decadal modulation of ENSO effects on precipitation extremes. The results can provide beneficial reference to the prediction of precipitation extremes in the west.

Change point analysis of precipitation indices of Western Canada

The Markov chain statistical technique was employed to fill few missing values of daily precipitation observed at nine long-term (≥50 years of data) climate stations of Western Canada. Statistical properties of the gap filled precipitation data are compared with those of the original data to ensure that this approach preserves the statistical characteristics of the historical data. After gap filling missing data, 10 precipitation indices that represent magnitude and frequency of precipitation properties were computed from those 9 stations and the other 20 stations without missing records. Next, the Wilcoxon rank-sum test was used to detect possible change points of these precipitation indices. Based on the analysed results, there had been an increasing trend in the maximum number of consecutive wet days and the annual maximum 1-day precipitation and a decrease in the maximum number of consecutive dry days over most of British Columbia (BC). The annual total precipitation index of many stations had increased in southern BC and decreased in central parts of Canadian Prairies. Similar change was detected in the number of days when precipitation is equal and greater than 10 or 20 mm, and indices for the annual monthly maximum 1-day precipitation – precipitation that exceeds the 95 and 99 percentiles – and the simple precipitation intensity index. On the basis of the above results, the diurnal temperature range (DTR) of Western Canada has decreased, and predominant negative correlations between precipitation indices and DTR, it seems that Western Canada has generally become wetter since the middle of the 20th century partly because increase in the minimum daily temperature has been higher than the increase in the maximum daily temperature, leading to a decrease in DTR, which is likely related to climatic warming.

INCREASE IN SIMPLE PRECIPITATION INTENSITY INDEX IN PANAMA

The paper describes trends in climate change indices in the Republic of Panama by using long-term meteorological data sets from the Panama Canal and MRI-AGCM. Simple precipitation intensity index from eleven climate change indices shows the same trend, increasing at six meteorological stations around the Panama Canal. Forward projection (from 2080 to 2099) was carried out by using MRI-AGCM3.1S and 3.2S, which demonstrates an increase in simple precipitation intensity index in the entire area of Panama. The increase in simple precipitation intensity index may suggest that stronger precipitation will occur more frequently in the future in Panama.