A High-Resolution Global Gridded Historical Dataset of Climate Extreme Indices

Climate extreme indices (CEIs) are important metrics that not only assist in the analysis of regional and global extremes in meteorological events, but also aid climate modellers and policymakers in the assessment of sectoral impacts. Global high-spatial-resolution CEI datasets derived from quality-controlled historical observations, or reanalysis data products are scarce. This study introduces a new high-resolution global gridded dataset of CEIs based on sub-daily temperature and precipitation data from the Global Land Data Assimilation System (GLDAS). The dataset called "CEI_0p25_1970_2016" includes 71 annual (and in some cases monthly) CEIs at 0.25 ∘ × 0.25 ∘ gridded resolution, covering 47 years over the period 1970–2016. The data of individual indices are publicly available for download in the commonly used Network Common Data Form 4 (NetCDF4) format. Potential applications of CEI_0p25_1970_2016 presented here include the assessment of sectoral impacts (e.g., Agriculture, Health, Energy, and Hydrology), as well as the identification of hot spots (clusters) showing similar historical spatial patterns of high/low temperature and precipitation extremes. CEI_0p25_1970_2016 fills gaps in existing CEI datasets by encompassing not only more indices, but also by being the only comprehensive global gridded CEI data available at high spatial resolution

Trends of precipitation extreme indices over a subtropical semi-arid area using PERSIANN-CDR

In this study, satellite-based daily precipitation estimation data from precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)-climate data record (CDR) are being evaluated in Iran. This dataset (0.25°, daily), which covers over three decades of continuous observation beginning in 1983, is evaluated using rain-gauge data for the period of 1998–2007. In addition to categorical statistics and mean annual amount and number of rainy days, ten standard extreme indices were calculated to observe the behavior of daily extremes. The results show that PERSIANN-CDR exhibits reasonable performance associated with the probability of detection and false-alarm ratio, but it overestimates precipitation in the area. Although PERSIANN-CDR mostly underestimates extreme indices, it shows relatively high correlations (between 0.6316–0.7797) for intensity indices. PERSIANN-CDR data are also used to calculate the trend in annual amounts of precipitation, the number of rainy days, and precipitation extremes over Iran covering the period of 1983–2012. Our analysis shows that, although annual precipitation decreased in the western and eastern regions of Iran, the annual number of rainy days increased in the northern and northwestern areas. Statistically significant negative trends are identified in the 90th percentile daily precipitation, as well as the mean daily precipitation from wet days in the northern part of the study area. The positive trends of the maximum annual number of consecutive dry days in the eastern regions indicate that the dry periods became longer in these arid areas

Using a Gridded Global Dataset to Characterize Regional Hydroclimate in Central Chile

Central Chile is facing dramatic projections of climate change, with a consensus for declining precipitation, negatively affecting hydropower generation and irrigated agriculture. Rising from sea level to 6000 m within a distance of 200 km, precipitation characterization is difficult because of a lack of long-term observations, especially at higher elevations. For understanding current mean and extreme conditions and recent hydroclimatological change, as well as to provide a baseline for downscaling climate model projections, a temporally and spatially complete dataset of daily meteorology is essential. The authors use a gridded global daily meteorological dataset at 0.25° resolution for the period 1948–2008, adjusted by monthly precipitation observations interpolated to the same grid using a cokriging method with elevation as a covariate. For validation, daily statistics of the adjusted gridded precipitation are compared to station observations. For further validation, a hydrology model is driven with the gridded 0.25° meteorology and streamflow statistics are compared with observed flow. The high elevation precipitation is validated by comparing the simulated snow extent to Moderate Resolution Imaging Spectroradiometer (MODIS) images. Results show that the daily meteorology with the adjusted precipitation can accurately capture the statistical properties of extreme events as well as the sequence of wet and dry events, with hydrological model results displaying reasonable agreement with observed streamflow and snow extent. This demonstrates the successful use of a global gridded data product in a relatively data-sparse region to capture hydroclimatological characteristics and extremes

Intercomparison of PERSIANN-CDR and TRMM-3B42V7 precipitation estimates at monthly and daily time scales

In the first part of this paper, monthly precipitation data from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission 3B42 algorithm Version 7 (TRMM-3B42V7) are evaluated over Iran using the Generalized Three-Cornered Hat (GTCH) method which is self-sufficient of reference data as input. Climate Data Unit (CRU) is added to the GTCH evaluations as an independent gauge-based dataset thus, the minimum requirement of three datasets for the model is satisfied. To ensure consistency of all datasets, the two satellite products were aggregated to 0.5° spatial resolution, which is the minimum resolution of CRU. The results show that the PERSIANN-CDR has higher Signal to Noise Ratio (SNR) than TRMM-3B42V7 for the monthly rainfall estimation, especially in the northern half of the country. All datasets showed low SNR in the mountainous area of southwestern Iran, as well as the arid parts in the southeast region of the country. Additionally, in order to evaluate the efficacy of PERSIANN-CDR and TRMM-3B42V7 in capturing extreme daily-precipitation amounts, an in-situ rain-gauge dataset collected by the Islamic Republic of the Iran Meteorological Organization (IRIMO) was employed. Given the sparsity of the rain gauges, only 0.25° pixels containing three or more gauges were used for this evaluation. There were 228 such pixels where daily and extreme rainfall from PERSIANN-CDR and TRMM-3B42V7 could be compared. However, TRMM-3B42V7 overestimates most of the intensity indices (correlation coefficients; R between 0.7648–0.8311, Root Mean Square Error; RMSE between 3.29mm/day-21.2mm/5day); PERSIANN-CDR underestimates these extremes (R between 0.6349–0.7791 and RMSE between 3.59mm/day-30.56mm/5day). Both satellite products show higher correlation coefficients and lower RMSEs for the annual mean of consecutive dry spells than wet spells. The results show that TRMM-3B42V7 can capture the annual mean of the absolute indices (the number of wet days in which daily precipitation >10 mm, 20 mm) better than PERSIANN-CDR. The results of daily evaluations show that the similarity of Empirical Cumulative Density Function (ECDF) of satellite products and IRIMO gauges daily precipitation, as well as dry spells with different thresholds in some selected pixels (include at least five gauges), are significant. The results also indicate that ECDFs become more significant when threshold increases. In terms of regional analyses, the higher SNR of the products on monthly (based on the GTCH method) and daily evaluations (significant ECDFs) is mostly consistent

Environmental science applications with Rapid Integrated Mapping and analysis System (RIMS)

The Rapid Integrated Mapping and analysis System (RIMS) has been developed at the University of New Hampshire as an online instrument for multidisciplinary data visualization, analysis and manipulation with a focus on hydrological applications. Recently it was enriched with data and tools to allow more sophisticated analysis of interdisciplinary data. Three different examples of specific scientific applications with RIMS are demonstrated and discussed. Analysis of historical changes in major components of the Eurasian pan-Arctic water budget is based on historical discharge data, gridded observational meteorological fields, and remote sensing data for sea ice area. Express analysis of the extremely hot and dry summer of 2010 across European Russia is performed using a combination of near-real time and historical data to evaluate the intensity and spatial distribution of this event and its socioeconomic impacts. Integrative analysis of hydrological, water management, and population data for Central Asia over the last 30 years provides an assessment of regional water security due to changes in climate, water use and demography. The presented case studies demonstrate the capabilities of RIMS as a powerful instrument for hydrological and coupled human-natural systems research

Challenges in quantifying changes in the global water cycle

Human influences have likely already impacted the large-scale water cycle but natural variability and observational uncertainty are substantial. It is essential to maintain and improve observational capabilities to better characterize changes. Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time-series over land but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols, and due to large climate variability presently limits confidence in attribution of observed changes

Quantifying the reliability of four global datasets for drought monitoring over a semiarid region

Drought is one of the most relevant natural disasters, especially in arid regions such as Iran. One of the requirements to access reliable drought monitoring is long-term and continuous high-resolution precipitation data. Different climatic and global databases are being developed and made available in real time or near real time by different agencies and centers; however, for this purpose, these databases must be evaluated regionally and in different local climates. In this paper, a near real-time global climate model, a data assimilation system, and two gridded gauge-based datasets over Iran are evaluated. The ground truth data include 50 gauges from the period of 1980 to 2010. Drought analysis was carried out by means of the Standard Precipitation Index (SPI) at 2-, 3-, 6-, and 12-month timescales. Although the results show spatial variations, overall the two gauge-based datasets perform better than the models. In addition, the results are more reliable for the western portion of the Zagros Range and the eastern region of the country. The analysis of the onsets of the 6-month moderate drought with at least 3 months’ persistence indicates that all datasets have a better performance over the western portion of the Zagros Range, but display poor performance over the coast of the Caspian Sea. Base on the results of this study, the Modern-Era Retrospective Analysis for Research and Applications (MERRA) dataset is a preferred alternative for drought analysis in the region when gauge-based datasets are not available

Historical global gridded degree‐days: A high‐spatial resolution database of CDD and HDD

Cooling and heating degree‐days (CDD/HDD) are important metrics used in energy studies as a proxy for determining demand and consumption patterns of residential/commercial buildings and work spaces. Driven by the requirements of energy impact modellers, policymakers and building design experts; a new historical high‐spatial resolution, global gridded dataset of degree‐days constructed using various base (threshold) temperatures (Tb) is presented in this study. Derived using sub‐daily temperature from a quality‐controlled reanalysis data product (Global Land Data Assimilation System—GLDAS), the dataset called ‘DegDays_0p25_1970_2018’ includes monthly and annual (i) CDD; (ii) HDD; and (iii) CDD computed using wet‐bulb temperature (CDDwb) at 0.25° × 0.25° gridded resolution, covering 49 years over the period 1970–2018. The Tb used for assembling DegDays_0p25_1970_2018 include 18, 18.3, 22, 23, 24, 25°C for CDD and CDDwb; and 10, 15, 15.5, 16, 17 and 18°C for HDD, respectively. The data of individual indices are made publicly available in the commonly used scientific Network Common Data Form 4 (NetCDF4) and Georeferenced Tagged Image File (GeoTIFF) formats. DegDays_0p25_1970_2018 fills gaps in existing energy indicators’ datasets by being the only high‐resolution historical global gridded time series based on multiple threshold temperatures, thus offering applications in wide‐ranging climate zones and thermal comfort environments. The richness of DegDays_0p25_1970_2018 lies in its flexibility by allowing users to aggregate the degree‐days not only at varying spatial scales (such as administrative levels, national boundaries, economic organizations e.g. OECD; with or without population weights), but also at varying temporal scales (such as seasons), thereby offering climatologists with a potential to examine global teleconnection patterns more discretely

SEASONAL IMPACTS OF CLIMATE CHANGE ON FUTURE PEAK RIVER DISCHARGE IN THE U.S. NORTHEAST

This research uses projected changes in future precipitation to calculate the changes in the magnitude, frequency, and timing of streamflow, particularly peak annual flows, in the U.S. Northeast through 20 representative watersheds. Temperature and precipitation data on a 3-hourly time step from five climate projection from the North American Regional Climate Change Assessment Program (NARCCAP) are downscaled and bias- corrected using the Spatial Downscaling and Bias Correction (SDBC) method. These data are used to force a regional hydrological model (WRF-Hydro) to estimate daily future streamflow. The changes in magnitude at various return intervals of peak flow events are determined through the comparison between peak annual flow values during the historical period (1968-1999) and during the future period (2038-2070). The frequencies of high daily streamflow in each month are evaluated using a peak-over-threshold (POT) analysis of both high precipitation days and high streamflow days to understand the correlation (if any) between the two in this particular region. The results indicate an overall average increase of 10%, 15%, and 18 % in the 2-year, 50-year, and 100-year return interval magnitude of peak floods in the U.S. Northeast region, respectively. The POT analysis reveals increases in the number of extreme precipitation days during the winter months (DJF) which is expected to result in higher peaks in streamflow. This correlation is less apparent during the summer (JJA) months, suggesting a significant role of soil moisture and snowpack. The degree of climate change impacts vary by season, lending to differing flow regimes. Shifts in the seasonality of future peak flow events are observed in the results and further explain the changes in flood magnitudes and frequencies. They suggest similar trends in the inundation processes that directly influence soil moisture; consequently exacerbating flood and drought events that require new adaptation and mitigation strategies in the region