Data Record Length Research Articles

Evaluation of the sensitivity of meteorological drought in the Mediterranean region to different data record lengths.

The division and evaluation of data series used in monitoring drought into different time intervals is a practical approach to detecting the spatial and temporal extent of drought spread. This study aimed to determine meteorological drought's spatial and temporal distribution using overlapping and consecutive periods and cycles of the standardized precipitation index (SPI) time series in the Mediterranean region, Turkey. In the scope of the research, SPI values for the SPI12, SPI6 (1), and SPI6 (2) seasons were calculated for consecutive and overlapping hydrological years (1978-1998/21years, 1978-2008/31years, and 1978-2018/41years) at 28 meteorological stations. Autocorrelation, Mann-Kendall, and Sen slope trend tests were applied at a 5% significance level for each season (SPI12, SPI6 (1), and SPI6 (2)) and different time scales (21, 31, and 41years). For each season and period, maps of the SPI drought class, average formation of drought class, Mann-Kendall (MK) trend, and Sen's slope (SS) trend test statistics for the Mediterranean region were obtained, and the spatial distribution rate of trends was determined by drawing hypsometric curves. Changes in drought occurrence at different time scales were thoroughly evaluated with the changing length of data recording.Consequently, it was determined that the mild wet (MIW) and mild drought (MID) classes dominate the study area in the Mediterranean region. Significant and nonstationary changes detected in extreme wet and drought occurrences (extreme wet, EW; severe wet, SW; extreme drought, ED; severe drought, SD) were found to pose a risk in the study area. It was observed that there were spatially and temporally insignificant decreasing drought trends in the Mediterranean basin, considering that the time scales of these trends slowed down. Despite a nonsignificant trend from the MID drought class to the MIW drought class, it is predicted that the MIW and MID classes will maintain their dominance in the Mediterranean region. The central part of the study area (central Mediterranean basin) is the region with the highest drought risk.

Quantifying compound flood event uncertainties in a wave and tidally dominated coastal region: The impacts of copula selection, sampling, record length, and precipitation gauge selection

AbstractCoastal flooding is a growing hazard. Compound event characterization and uncertainty quantification are critical to accurate flood risk assessment. This study presents univariate, conditional, and joint probabilities for observed water levels, precipitation, and waves. Design events for 10‐ and 100‐year marine water level and precipitation events are developed. A total water level formulation explicitly accounting for wave impacts is presented. Uncertainties associated with sampling method, copula selection, data record length, and utilized rainfall gauge are determined. Eight copulas are used to quantify multivariate uncertainty. Generally, copulas present similar results, except the BB5. Sampling method uncertainty was quantified using four sampling types; annual maximum, annual coinciding, wet season monthly maximum, and wet season monthly coinciding sampling. Annual coinciding sampling typically produced the lowest event magnitude estimates. Uncertainty associated with record length was explored by partitioning a 100‐year record into various subsets. Withholding 30 years of observations (i.e., records of less than 70 years) resulted in substantial variability of both the 10‐ and 100‐year return period estimates. Approximately equidistant rainfall gauges led to large event estimate differences, suggesting microclimatology and gauge selection play a key role in characterizing compound events. Generally, event estimate uncertainty was dominated by sampling method and rainfall gauge selection.

A global gridded dataset for cloud vertical structure from combined CloudSat and CALIPSO observations

Abstract. The vertical structure of clouds has a profound effect on the global energy budget, the global circulation, and the atmospheric hydrological cycle. The CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) missions have taken complementary, colocated observations of cloud vertical structure for over a decade. However, no globally gridded dataset is available to the public for the full length of this unique combined data record. Here we present the 3S-GEOPROF-COMB product (Bertrand et al. 2023, https://doi.org/10.5281/zenodo.8057791), a globally gridded (level 3S) community data product summarizing geometrical profiles (GEOPROF) of hydrometeor occurrence from combined (COMB) CloudSat and CALIPSO data. Our product is calculated from the latest release (R05) of per-orbit (level-2) combined cloud mask profiles. We process a set of cloud cover, vertical cloud fraction, and sampling variables at 2.5, 5, and 10° spatial resolutions and monthly and seasonal temporal resolutions. We address the 2011 reduction in CloudSat data collection with Daylight-Only Operations (DO-Op) mode by subsampling pre-2011 data to mimic DO-Op collection patterns, thereby allowing users to evaluate the impact of the reduced sampling on their analyses. We evaluate our data product against CloudSat-only and CALIPSO-only global-gridded data products as well as four comparable surface-based sites, underscoring the added value of the combined product. Interest in the product is anticipated for the study of cloud processes, cloud–climate interactions, and as a candidate baseline climate data record for comparison to follow-up satellite missions, among other uses.

Single-cycle P Class phasor estimation based on harmonics whitening and off-nominal frequency offset adjustment

Phasor Measurement Units (PMUs) are essential for real-time power system monitoring and control. In this paper, a novel algorithm for protection (i.e., P Class) PMUs is presented. The proposed algorithm consists of four steps: preliminary narrowband disturbance whitening; estimation of the off-nominal fundamental frequency offset through the Interpolated Discrete Time Fourier Transform (IpDFT); data record length adjustment to refine narrowband disturbance whitening over an integer number of fundamental periods; time-domain estimation of the Taylor's series coefficients of the synchrophasor function through a weighted least-squares estimator (after correction of possible static off-nominal deviations). The key novel contribution of the proposed approach is its superior ability to smooth harmonic disturbances over shorter observation intervals than many existing algorithms (which typically require at least two-cycle-long data records to meet P Class accuracy requirements), while ensuring a good tracking capability of transient events with a reasonable execution time. Several simulation and experimental results confirm that, in the case of multi-harmonic distortion, the proposed approach returns more accurate results than both the basic Taylor-series-based Weighted Least-Squares (TWLS) estimator and other whitening-based techniques, while ensuring compliance with P Class specifications even over one-cycle-long data records.

Exploring Climate Sensitivity in Hydrological Model Calibration

In the context of hydrological model calibration, observational data play a central role in refining and evaluating model performance and uncertainty. Among the critical factors, the length of the data records and the associated climatic conditions are paramount. While there is ample research on data record length selection, the same cannot be said for the selection of data types, particularly when it comes to choosing the climatic conditions for calibration. Conceptual hydrological models inherently simplify the representation of hydrological processes, which can lead to structural limitations, which is particularly evident under specific climatic conditions. In this study, we explore the impact of climatic conditions during the calibration period on model predictive performance and uncertainty. We categorize the inflow data from AnDong Dam and HapCheon Dam in southeastern South Korea from 2001 to 2021 into four climatic conditions (dry years, normal years, wet years, and mixed years) based on the Budyko dryness index. We then use data from periods within the same climatic category to calibrate the hydrological model. Subsequently, we analyze the model’s performance and posterior distribution under various climatic conditions during validation periods. Our findings underscore the substantial influence of the climatic conditions during the calibration period on model performance and uncertainty. We discover that when calibrating the hydrological model using data from periods with wet climatic conditions, achieving comparable predictive performance in validation periods with different climatic conditions remains challenging, even when the calibration period exhibits excellent model performance. Furthermore, when considering model parameters and predicted streamflow uncertainty, it is advantageous to calibrate the hydrological model under dry climatic conditions to achieve more robust results.

MULTIVARIATE COASTAL FLOOD RISK ALONG THE US PACIFIC

Urban coastal flooding is an increasing humanitarian and socioeconomic hazard. Flood assessments do not typically consider multivariate (marine and hydrologic) and multi-pathway (e.g., precipitation-surge, overtopping- overflow, surface–sewer) flooding, which may amplify coastal hazards and vulnerability. Notably, flood extent is underestimated when compounded hydrologic and marine impacts are not considered (Chen and Liu, 2014). Previous multivariate flooding studies typically consider the joint impacts of storm surge and fluvial sources. This study explores multivariate flooding caused by marine water levels, precipitation, and waves along the U.S. Pacific Coast while considering uncertainties in data sampling, precipitation source, record length, and distribution selection impart on risk estimates.

Assessing Models of Sea Level Rise and Mean Sea Surface with Sentinel-3B and Jason-3 Altimeter Data near Taiwan: Impacts of Data Quality and Length

Studying the local sea level rise (SLR) is vital for coastal sustainability and resilience amid climate change. Using the latest altimeter data from the Sentinel-3B (S3B) and Jason-3 (J3) missions, we investigated the regional variability and accuracy of SLR rates around Taiwan and evaluated the accuracies of three mean sea surface (MSS) models: NCTUMSS, CLS15, and DTU18. NCTUMSS is a regional model for defining the new depth datum of Taiwan. The least-squares method with outlier removal was used to compute the along-track SLRs and MSS heights from S3B and J3. Our results show that the SLR rates around Taiwan in the recent 3–6 years from S3B and J3 were 2.0–3.0 mm/year higher than the global rate of 3.3 mm/year. Positive SLR rates were observed in most waters around Taiwan, but negative rates occurred in some parts of the Taiwan Strait. Short-term SLR rates from S3B and J3 were significantly different from the long-term rates determined using altimeter data from the TOPEX/POSEIDON (TP), Jason-1, Jason-2, and J3 missions from 1992.96 to 2021.92. The comparison between the along-track MSS heights from S3B and J3 and the modeled MSS heights showed that CLS15 had the highest accuracy. The DTU18 model exhibited lower sea surface heights near Penghu in the Taiwan Strait compared with CLS15 and NCTUMSS. The NCTUMSS model incorporates tide gauge measurements and Taiwan’s hybrid geoid for a smooth transition from ocean to land. It was concluded that the SLR rates around Taiwan were not uniform, and the rates provided by the three global models, as well as the along-track S3B and J3 altimeter data, suffered from problems like limited spatial resolutions and accuracies, which originated from limited altimeter data qualities and record lengths. One must be cautious about the accuracy of an MSS model for constructing a depth datum and the accuracy of an SLR model for mitigating SLR-induced hazards. We recommend updating the MSS model around Taiwan every 7 years to ensure a 2 cm accuracy requirement, considering the average SLR rate of 3.3 mm/year around Taiwan. Short-term data reflects recent sea level rise but lacks accuracy, while the long-term sea level record may be more precise but may not capture recent rates, necessitating a comprehensive approach that considers both factors for producing accurate assessments and the planning of sea level rise impacts.

Database of the Italian disdrometer network

Abstract. In 2021, a group of seven Italian institutions decided to bring together their know-how, experience, and instruments for measuring the drop size distribution (DSD) of atmospheric precipitation, giving birth to the Italian Group of Disdrometry (in Italian named Gruppo Italiano Disdrometria, GID, https://www.gid-net.it/, last access: 16 May 2023). GID has made freely available a database of 1 min records of DSD collected by the disdrometer network along the Italian peninsula. At the time of writing, the disdrometer network was composed of eight laser disdrometers belonging to six different Italian institutions (including research centres, universities, and environmental regional agencies). This work aims to document the technical aspects of the Italian DSD database consisting of 1 min sampling data from 2012 to 2021 in a uniform standard format defined within GID. Although not all the disdrometers have the same data record length, the DSD data collection effort is the first of its kind in Italy, and from here onwards, it opens up new opportunities in the surface characterization of microphysical properties of precipitation in the perspective of climate records and beyond. The Version 01 GID database can be downloaded at https://doi.org/10.5281/zenodo.6875801 (Adirosi et al., 2022), while Version 02 can be downloaded at https://doi.org/10.5281/zenodo.7708563 (Adirosi et al., 2023). The difference among the two versions is the diameter–fall velocity relation used for the DSD computation.

Assessment of the Uncertainty Associated with Statistical Modeling of Precipitation Extremes for Hydrologic Engineering Applications in Amman, Jordan

Estimates of extreme precipitation are commonly associated with different sources of uncertainty. One of the primary sources of uncertainty in the statistical modeling of precipitation extremes comes from extreme data series (i.e., sampling uncertainty). Therefore, this research aimed to quantify the sampling uncertainty in terms of confidence intervals. In addition, this article examined how the data record length affects predicted extreme precipitation estimates and data set statistics. A nonparametric bootstrap resample was utilized to quantify the precipitation quantile sampling distribution at a particular non exceedance probability. This sampling distribution can provide a point estimation of the precipitation quantile and the confidence interval at a particular non exceedance probability. It has been shown that the different types of probability distributions fit the extreme precipitation data series of various weather stations. Therefore, the uncertainty analysis should be conducted using the best-fit probability distribution for extreme precipitation data series rather than a predefined single probability distribution for all stations based on modern extreme value theory. According to the 95% confidence intervals, precipitation quantiles are subject to significant uncertainty and the band of the uncertainty intervals increases with the return period. These uncertainty bounds need to be integrated into any frequency analysis from historical data. The average, standard deviation, skewness and kurtosis are highly affected by the data record length. Thus, a longer record length is desirable to decrease the sampling uncertainty and, therefore, decrease the error in the predicted quantile values. Moreover, the results suggest that a series of at least 40 years of data records is needed to obtain reasonably accurate estimates of the distribution parameters and the precipitation quantiles for 100 years return periods and higher. Using only 20 to 25 years of data to obtain estimates of the higher return period quantile is risky, since it created high sampling variability relative to the full data length.

Uncertainty Analysis of Hydrological Drought Due to Record Length, Time Scale, and Probability Distribution Functions Using Monte Carlo Simulation Method

Standardized Runoff Index (SRI), as one of the well-known hydrological drought indices, may contain uncertainties caused by the employment of the distribution function, time scale, and record length of statistical data. In this study, the uncertainty in the SRI estimation of monthly discharge data of 30- and 49-year lengths from the Minab Dam watershed, south of Iran, was investigated. Four probability distribution functions (Gamma, Weibull, Lognormal, and Normal) were used to fit the cumulative discharge data at 3-, 6-. 9-, 12-, 24-, and 48-month time scales, with their goodness-of-fit and normality evaluated by K-S and normality tests, respectively. Using Monte Carlo sampling, 50,000 statistical data were generated for each event and each time scale, followed by 95% confidence interval. The width of the confidence interval was used as uncertainty, and sources of uncertainty were investigated using miscellaneous factors. It was found that the maximum uncertainty was related to Normal and Lognormal distributions and the minimum uncertainty to Gamma and Weibull distributions. Furthermore, the increase in both time scale and record length led to the decrease in uncertainty.

Meta-Analysis in Using Satellite Precipitation Products for Drought Monitoring: Lessons Learnt and Way Forward

In recent years, satellite precipitation products (SPPs) have emerged as an essential source of data and information. This work intends to summarize lessons learnt on using SPPs for drought monitoring and to propose ways forward in this field of research. A thorough literature review was conducted to review three aspects: effects of climate type, data record length, and time scale on SPPs performance. The conducted meta-analysis showed that the performance of SPPs for drought monitoring largely depends upon the climate type of the location and length of the data record. SPPs drought monitoring performance was shown to be higher in temperate and tropical climates than in dry and continental ones. SPPs were found to perform better with an increase in data record length. From a general standpoint, SPPs offer great potential for drought monitoring, but the performance of SPPs needs to be improved for operational purposes. The present study discusses blending SPPs with in situ data and other lessons learned, as well as future directions of using SPPs for drought applications.

New, improved analysis of correlation ECE data to accurately determine turbulent electron temperature spectra and magnitudes (invited).

Turbulent electron temperature fluctuation measurement using a correlation electron cyclotron emission (CECE) radiometer has become an important diagnostic for studying energy transport in fusion plasmas, and its use is widespread in tokamaks (DIII-D, ASDEX Upgrade, Alcator C-Mod, Tore Supra, EAST, TCV, HL-2A, etc.). The CECE diagnostic typically performs correlation analysis between two closely spaced (within the turbulent correlation length) ECE channels that are dominated by uncorrelated thermal noise emission. This allows electron temperature fluctuations embedded in the thermal noise to be revealed and fluctuation level and spectra determined. We have demonstrated a new, improved CECE coherency-based analysis for calculating the temperature fluctuation frequency spectrum and level, which has been verified both numerically through the simulation of synthetic ECE radiometer data and through analysis of experimental data from the CECE system on DIII-D. The new formulation places coherency-based analysis on a firm foundational footing and corrects some currently published methodologies. This new method accurately accounts for bias error in the coherence function and correctly calculates noise levels for a fixed data record length. It provides excellent accuracy in determining temperature fluctuation level (e.g., <10% error) even for a small realization number in the ensemble average. The method also has a smaller uncertainty (i.e., error bar) in the power spectrum when compared to the more standard cross-power method when evaluated at low coherency. Direct calculation of system noise level using correlation between randomized intermediate frequency signals is recommended.

Uncertainty in SPI Calculation and its Impact on Drought Assessment in Different Climate Regions over China

AbstractUncertainty in the calculation of a Standardized Precipitation Index (SPI) attracted growing concerns in the hydrometeorology research community in the last decade. This issue is addressed in the present study from the perspective of candidate probability distributions, the data record length, the cumulative timescale and the selection of a reference period with the bootstrap and Monte Carlo methods using daily precipitation data observed in four climate regions across China. The impacts of the uncertainty in an SPI calculation on drought assessment are also investigated. Results show that the Gamma distribution is optimal in describing the cumulative precipitation in China; among the four timescales investigated in the present study, the minimal timescale appropriate for SPI calculation is 20 days for the humid region, 30 days for the semi-humid/semi-arid region and Tibetan Plateau (mostly its eastern part), and 90 days for the arid region. The uncertainty in SPI calculation decreases with the increase of timescale and record length, essentially as a consequence of the decrease of the confidence interval width of Gamma distribution parameters with the increase of timescale and record length. But there is little improvement for the parameter estimation with record length longer than 70 years. There is greater uncertainty for high absolute SPI values than for small ones, consequently there is greater uncertainty in assessing extreme droughts than moderate droughts. Reference period selection has large impacts on drought assessment, especially in the context of climate change. The uncertainty of the SPI calculation has large impacts on categorizing droughts, but no impact on assessing the temporal features of drought variation.

Variability of precipitation areal reduction factors in the conterminous United States

Many hydrologic and hydraulic (H&H) engineering applications require spatial rainfall distribution over a watershed, but point precipitation frequency estimates, such as those provided by NOAA Atlas 14, are only applicable for relatively small areas. For larger areas, areal reduction factors (ARFs) are commonly used to transform a point precipitation frequency estimate of a given duration and frequency to a corresponding areal estimate. The most common source of ARFs for the United States is Technical Paper 29 (TP-29), published in 1958, although there have been significant increases in record length and types of available data and several new methods for computing ARFs have been proposed over the last several decades. This study applied up-to-date precipitation data products and analysis methods with a watershed-based approach to investigate factors that affect ARF variabilities, and to compare ARFs across multiple US hydrologic regions. Our overall findings are in line with other recent studies showing that ARFs decrease with increasing area, increase with increasing duration, and decrease with increasing return period. In particular, we found a strong geographical variability across different US hydrologic regions, suggesting that ARF are specific to regional climate patterns and geographical characteristics and should not be applied arbitrarily to other locations. The results also reveal the importance of record length, especially for long return period ARFs. The study demonstrates the need to improve ARFs with new data and methods to support more reliable areal precipitation frequency estimates for H&H applications.

Assessing the benefit of satellite-based Solar-Induced Chlorophyll Fluorescence in crop yield prediction

Large-scale crop yield prediction is critical for early warning of food insecurity, agricultural supply chain management, and economic market. Satellite-based Solar-Induced Chlorophyll Fluorescence (SIF) products have revealed hot spots of photosynthesis over global croplands, such as in the U.S. Midwest. However, to what extent these satellite-based SIF products can enhance the performance of crop yield prediction when benchmarking against other existing satellite data remains unclear. Here we assessed the benefits of using three satellite-based SIF products in yield prediction for maize and soybean in the U.S. Midwest: gap-filled SIF from Orbiting Carbon Observatory 2 (OCO-2), new SIF retrievals from the TROPOspheric Monitoring Instrument (TROPOMI), and the coarse-resolution SIF retrievals from the Global Ozone Monitoring Experiment-2 (GOME-2). The yield prediction performances of using SIF data were benchmarked with those using satellite-based vegetation indices (VIs), including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIRv), and land surface temperature (LST). Five machine-learning algorithms were used to build yield prediction models with both remote-sensing-only and climate-remote-sensing-combined variables. We found that high-resolution SIF products from OCO-2 and TROPOMI outperformed coarse-resolution GOME-2 SIF product in crop yield prediction. Using high-resolution SIF products gave the best forward predictions for both maize and soybean yields in 2018, indicating the great potential of using satellite-based high-resolution SIF products for crop yield prediction. However, using currently available high-resolution SIF products did not guarantee consistently better yield prediction performances than using other satellite-based remote sensing variables in all the evaluated cases. The relative performances of using different remote sensing variables in yield prediction depended on crop types (maize or soybean), out-of-sample testing methods (five-fold-cross-validation or forward), and record length of training data. We also found that using NIRv could generally lead to better yield prediction performance than using NDVI, EVI, or LST, and using NIRv could achieve similar or even better yield prediction performance than using OCO-2 or TROPOMI SIF products. We concluded that satellite-based SIF products could be beneficial in crop yield prediction with more high-resolution and good-quality SIF products accumulated in the future.

An Interference-Tolerant Algorithm for Wide-Band Moving Source Passive Localization

A new technique for locating a moving source radiating a wide-band almost-cyclostationary signal is proposed. For this purpose, the signals received on two possibly moving sensors are modeled as jointly spectrally correlated, a new nonstationarity model that allows one to describe the Doppler effect accounting for a time-scale or time-stretch factor in the complex envelopes of the received signals. The proposed approach relaxes the narrow-band condition constraint under which the Doppler effect is modeled just as a frequency-shift of the carrier. The typical interference-tolerance property of cyclostationarity-based algorithms is shown to be valid under mild conditions also for (jointly) spectrally correlated signals. With respect to classical source location methods, removing the constraint of the narrow-band condition allows the adoption of larger signal bandwidths and data-record lengths, lower signal-to-noise and signal-to-interference ratios, and the capability to operate in scenarios with higher mobility. The new source location method, dubbed wide-band spectral coherence alignment (WB-SPECCOA), is exploited in a location problem involving low Earth orbit satellites.

Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities

Drought is a common hydrometeorological phenomenon and a pervasive global hazard. As our climate changes, it is likely that drought events will become more intense and frequent. Effective drought monitoring is therefore critical, both to the research community in developing an understanding of drought, and to those responsible for drought management and mitigation. Over the past 50 years remote sensing has shifted the field away from reliance on traditional site-based measurements and enabled observations and estimates of key drought-related variables over larger spatial and temporal scales than was previously possible. This has proven especially important in data poor regions with limited in-situ monitoring stations. Available remotely sensed data products now represent almost all aspects of drought propagation and have contributed to our understanding of the phenomena. In this review we chart the rise of remote sensing for drought monitoring, examining key milestones and technologies for assessing meteorological, agricultural and hydrological drought events. We reflect on challenges the research community has faced to date, such as limitations associated with data record length and spatial, temporal and spectral resolution. This review then looks ahead to the future in terms of new technologies, such as the ESA Sentinel satellites, analytical platforms and approaches, such as Google EarthEngine, and the utility of existing data in new drought monitoring applications. We look forward to the continuation of 50 years of progress to provide effective, innovative and efficient drought monitoring solutions utilising remote sensing technology.

The Effect of Climate, Precipitation Regime and Data Record Length on the Bhalme &amp; Mooley Drought Index in Iran

The Effect of Climate, Precipitation Regime and Data Record Length on the Bhalme &amp; Mooley Drought Index in Iran

Comparisons of fire weather indices using Canadian raw and homogenized weather data

Modifications to the environment around a weather station or changes in instrument result in discontinuities or shift in weather data. This paper asks the often ignored questions such as, “what are the impacts of inhomogenized data?” and “does using homogenized weather data affect the conclusions of environmental research?” To answer these questions, we used the Canadian Forest Fire Weather Index (CFFWI) System for our studies. Weather station data are used to calculate wildfire danger indices. The homogenized data and raw (inhomogenized) observations for sixteen weather stations spread across Canada were used to calculate the CFFWI indices. The sixteen weather stations were further divided into three subset of stations based on the length of the accessible data during the fire season (April to end of September). The first set included stations that covered just 27 years of data and the second data sets had 49 years of data, while the third set included only five stations with the longest time period of 66 years. The majority of the stations, as measured by the Wilcoxon signed-rank test, rejected the null hypothesis (difference between the pairs follows a symmetric distribution around zero). The rejection rate increases to 100% as the length of data record increases from 27 to 66 years. Homogenization of 66 years data reduced the indices values approximately 0.7–8.4% and also reversed the long-term trend in some stations such as Kapuskasing.

A Revisit of Global Dimming and Brightening Based on the Sunshine Duration

AbstractObservations show that the surface incident solar radiation (Rs) decreased over land from the 1950s to the 1980s and increased thereafter, known as global dimming and brightening. This claim has been questioned due to the inhomogeneity and low spatial‐temporal coverage of Rs observations. Based on direct comparisons of ~200 observed and sunshine duration (SunDu) derived Rs station pairs, meeting data record lengths exceeding 60 months and spatial distances less than 110 km, we show that meteorological observations of SunDu can be used as a proxy for measured Rs. Our revised results from ~2,600 stations show global dimming from the 1950s to the 1980s over China (−1.90 W/m2 per decade), Europe (−1.36 W/m2 per decade), and the United States (−1.10 W/m2 per decade), brightening from 1980 to 2009 in Europe (1.66 W/m2 per decade) and a decline from 1994 to 2010 in China (−1.06 W/m2 per decade). Even if 1994–2010 is well known as a period of global brightening, the observed and SunDu‐derived Rs over China still exhibit declining trends. Trends in Rs from 1923 to 1950 are also found over Europe (1.91 W/m2 per decade) and the United States (−1.31 W/m2 per decade), but the results in Europe may not well represent the actual trend for the European continent due to poor spatial sampling.