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Measurements in seven sites in the Campus of the University of the Balearic Islands (UIB; Mallorca, Spain) during an experimental campaign to study the contribution of local surface heterogeneities on the surface energy budget at one point have been used to characterize the differences in extreme daily temperatures between the sites during a summer month. Absolute temperature differences in this month reached up to 1.92 (with a median of 0.73) and 2.02 (median of 1.21)°C for daily maximum and minimum, respectively. Higher differences in the minimum temperature can be attributed to the stably stratified and weak turbulent conditions at night that enhance local differences in the surface energy fluxes, especially in an area with strong variability of the surface characteristics like the UIB Campus. Instead, during daytime, maximum temperature differences are smoothed due to the convection and the horizontal advection due to the sea‐breeze. Two sites with longer records allowed to study the seasonal variations of these differences, which were substantially lower in the colder months. These results suggest that relocation of observatories, even at distances as short as 200 m, may introduce important inhomogeneities in the temperature series. Therefore, raw values of series from nearby stations should not be used to infill missing data in other series without adequate statistical adjustments. [ABSTRACT FROM AUTHOR]

The aim of time series homogenization is to remove nonclimatic effects, such as changes in station location, instrumentation, observation practices, and so on, from observed data. Statistical homogenization usually reduces the nonclimatic effects but does not remove them completely. In the Spanish "MULTITEST" project, the efficiencies of automatic homogenization methods were tested on large benchmark datasets of a wide range of statistical properties. In this study, test results for nine versions, based on five homogenization methods—the adapted Caussinus-Mestre algorithm for the homogenization of networks of climatic time series (ACMANT), "Climatol," multiple analysis of series for homogenization (MASH), the pairwise homogenization algorithm (PHA), and "RHtests"—are presented and evaluated. The tests were executed with 12 synthetic/surrogate monthly temperature test datasets containing 100–500 networks with 5–40 time series in each. Residual centered root-mean-square errors and residual trend biases were calculated both for individual station series and for network mean series. The results show that a larger fraction of the nonclimatic biases can be removed from station series than from network-mean series. The largest error reduction is found for the long-term linear trends of individual time series in datasets with a high signal-to-noise ratio (SNR), where the mean residual error is only 14%–36% of the raw data error. When the SNR is low, most of the results still indicate error reductions, although with smaller ratios than for large SNR. In general, ACMANT gave the most accurate homogenization results. In the accuracy of individual time series ACMANT is closely followed by Climatol, and for the accurate calculation of mean climatic trends over large geographical regions both PHA and ACMANT are recommended. [ABSTRACT FROM AUTHOR]

The Mid-Atlantic region of the USA has experienced increasing annual precipitation amounts in recent decades, along with more frequent extreme events of greater magnitude. Unlike many US regions that have suffered increasing drought conditions from higher evapotranspiration demand, positive trends in the Mid-Atlantic accumulated precipitation are greater than the recent increases in reference evapotranspiration. The temporal correlation between precipitation events and soil moisture capacity is essential for determining how the nature of drought has changed in the region. This analysis has shown that soil moisture scarcity has declined in nine of ten subregions of the Mid-Atlantic that were analyzed from 1985 to 2019. Two algorithms were deployed to draw this conclusion: Climatol enabled the use of the FAO-56 Penman-Monteith equation on daily observation station data for which complete records were unavailable, and the second algorithm calculated soil moisture levels on a daily basis, more accurately capturing drought conditions than common methods using weekly or monthly summaries. Although the declining drought trends were not statistically significant, a result of more extreme events and higher evapotranspiration rates, the inclusion of direct data from an expanded set of locations provides greater clarity from the trends, allowing policymakers and landowners to anticipate changes in future Mid-Atlantic irrigation water demand. [ABSTRACT FROM AUTHOR]

A dense monthly precipitation dataset of Ireland and Northern Ireland was homogenized with several modern homogenization methods. The efficiency of these homogenizations was tested by examining the similarity of homogenization results both in the real data homogenization and in the homogenization of a simulated dataset. The analysis of homogenization results shows that the real dataset is characterized by a large number of, but mostly small, non‐climatic biases, and a moderate reduction of such biases can be achieved with homogenization. Finally, a combination of the ACMANT and Climatol homogenization results was applied to improve the data accuracy before the trend calculations. These two methods were selected for their proven high accuracy, missing data tolerance and ability to complete time series via the infilling of missing values before the trend calculations. Metadata were used within the Climatol method. To facilitate this analysis the study area was split into smaller climatic regions by using the Ward clustering method. Five climatic zones consistent with the known spatial patterns of precipitation in Ireland were established. Linear regression fitting and the Mann‐Kendall test were applied. Low frequency fluctuations were also examined by applying a Gaussian filter. The results show that the precipitation amount generally increases in the study area, particularly in the northwestern region. The most significant increasing trends for the whole study period (1941–2010) are found for late winter and spring precipitation, as well as for the annual totals. In the period from the early 1970s the increase of precipitation is general in all seasons of the year except in winter, but the statistical significance of this increase is weak. [ABSTRACT FROM AUTHOR]

Time series homogenization for 299 of the available precipitation records for the island of Ireland (IENet) was performed. Four modern relative homogenization methods, that is, HOMER, ACMANT, CLIMATOL and AHOPS were applied to this network of station series where contiguous intact monthly records range from 30 to 70 years within the base period 1941–2010. Break detection results are compared between homogenization methods, and coincidences with available documentary information (metadata) were analysed. The lowest (highest) number of breaks were detected with HOMER (ACMANT). Large differences of break frequency were found, namely ACMANT and AHOPS detected 8 times as many breaks than HOMER, while the break frequency with CLIMATOL was intermediate. Also, the ratio of series classified to be homogeneous varies widely between the methods. It is 85% with HOMER, 60% with CLIMATOL, 31% with AHOPS, while only 22% with ACMANT. In a further experiment, all the available time series for Ireland and Northern Ireland, (910 series) were used with ACMANT and CLIMATOL to explore the stability of break frequency for the same 299 series examined in the base experiment. While overall break frequency slightly increased (by 6–13%), the break positions notably changed for individual time series. The number of breaks changed for 59% (23%) of the series with ACMANT (CLIMATOL). For the breaks detected coincidentally by at least three methods including ACMANT and CLIMATOL in the base experiment, the second experiment confirmed the break positions in 86–87% of the breaks. The consequences of these results in relation to the reliability of statistical homogenization are discussed. Sometimes markedly different step functions provide comparable good approaches. However, the accuracy of homogenized time series cannot be related directly to the instability of break detection results. [ABSTRACT FROM AUTHOR]

In the past years, several long-term (20+ years) reprocessed GNSS tropospheric delay and water vapor time series datasets have been produced and are available for climate studies. It includes worldwide datasets (e.g. the International GNSS Service 'IGS troposphere repro 1'), pan-European datasets (e.g. the EUREF Permanent Network 'EPN troposphere repro 2'), but also regional datasets. At the same time, several individual groups studied these reprocessed GNSS tropospheric delay and water vapor time series and found evidence for remaining inhomogeneities due to e.g. instrumental changes, environmental changes, etc. These inhomogeneities impact e.g. the calculation of the long-term water vapor trend (and its associated error), hence preventing a correct and precise interpretation of the dataset in terms of climate change. The homogenization of these datasets is thus mandatory prior any proper usage for studying the climate.In that context, several of these groups joined their efforts within the sub-WG "Data Homogenization" of the COST Action ES1206 "Advanced Global Navigation Satellite Systems tropospheric products for monitoring severe weather events and climate" (GNSS4SWEC), focusing first on the IGS repro 1 tropospheric dataset as a case study. The COST Action being ended, this work is now continued in the framework of the IAG WG 4.3.8 "GNSS Tropospheric Products for Climate".Amongst their activities, the working group (1) inventoried the existing homogenization tools that could be applied on the GNSS tropospheric delay and water vapor time series, (2) produced synthetic datasets (based on the true characteristics of the IGS repro 1 and ERA-Interim datasets, but with known offsets included) in order to (3) benchmark (blindly) the performance, weakness and advantages of each of these methods (via e.g. statistical scores and CRMEs, trend differences), and (4) finally assessed different methods for computing trends and their uncertainties in these time series.This presentation will summarize the working group activities, focusing on the most recent developments and achievements towards the homogenization of GNSS-based tropospheric delay and water vapor time series. [ABSTRACT FROM AUTHOR]