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The unique properties of terrestrial isopods regarding responses to limiting factors such as drought and temperature have led to interesting distributional patterns along climatic and other environmental gradients at both species and community level. This paper will focus on the exploration of isopod distributions in evaluating climate change effects on biodiversity at different scales, geographical regions, and environments, in view of isopods' tolerances to environmental factors, mostly humidity and temperature. Isopod distribution is tightly connected to available habitats and habitat features at a fine spatial scale, even though different species may exhibit a variety of responses to environmental heterogeneity, reflecting the large interspecific variation within the group. Furthermore, isopod distributions show some notable deviations from common global patterns, mainly as a result of their ecological features and evolutionary origins. Responses to human disturbance are not always traceable, but a trend towards community homogenisation is often found under strong global urbanisation processes. In general, even though it is still not clear how predicted climate change will affect isopod distribution, there is evidence that mixed effects are to be expected, depending on the region under study. We still lack robust and extensive analyses of isopod distributions at different scales and at different biomes, as well as applications of distribution models that might help evaluate future trends. [ABSTRACT FROM AUTHOR]

This paper presents a method to homogenize China's surface solar radiation (SSR) data and uses the resulting homogenized SSR data to assess the SSR trend over the period 1958-2016. Neighboring surface sunshine duration (SSD) data are used as reference data to assess the SSR data homogeneity. A principal component analysis is applied to build a reference series, which is proven to be less sensitive to occasional data issues than using the arithmetic mean of data from adjacent stations. A relative or absolute test is applied to detect changepoints, depending on whether or not a suitable reference series is available. A quantile-matching method is used to adjust the data to diminish the inhomogeneities. As a result, 60 out of the 119 SSR stations were found to have inhomogeneity issues. These were mainly caused by changes in instrument and observation schedule. The nonclimatic changes exaggerated the SSR change rates in 1991-93 and resulted in a sudden rise in the national average SSR series, causing an unrealistically drastic trend reversal in the 1990s. This was diminished by the data homogenization. The homogenized data show that the national average SSR has been declining significantly over the period 1958-90; this dimming trend mostly diminished over the period 1991-2005 and was replaced by a brightening trend in the recent decade. From the homogenized SSR data, the 1958-90 and 1958-2005 dimming rate is estimated to be -6.13 ± 0.47 and -5.08 ± 0.27W m-2 decade-1, respectively, and the 2005-16 brightening rate is 6.13 ± 1.77W m-2 decade-1. [ABSTRACT FROM AUTHOR]

This paper develops a method for homogenizing daily temperature series. While daily temperatures are statistically more complex than annual or monthly temperatures, techniques and computational methods have been accumulating that can now model and analyze all salient statistical characteristics of daily temperature series. The goal here is to combine these techniques in an efficient manner for multiple changepoint identification in daily series; computational speed is critical as a century of daily data has over 36 500 data points. The method developed here takes into account 1) metadata, 2) reference series, 3) seasonal cycles, and 4) autocorrelation. Autocorrelation is especially important: ignoring it can degrade changepoint techniques, and sample autocorrelations of day-to-day temperature anomalies are often as large as 0.7. While daily homogenization is not conducted as commonly as monthly or annual homogenization, daily analyses provide greater detection precision as they are roughly 30 times as long as monthly records. For example, it is relatively easy to detect two changepoints less than two years apart with daily data, but virtually impossible to flag these in corresponding annually averaged data. The developed methods are shown to work in simulation studies and applied in the analysis of 46 years of daily temperatures from South Haven, Michigan. [ABSTRACT FROM AUTHOR]

Glaciological and geodetic methods provide independent observations of glacier mass balance. The glaciological method measures the surface mass balance, on a seasonal or annual basis, whereas the geodetic method measures surface, internal, and basal mass balances, over a period of years or decades. In this paper, we reanalyse the 10 glaciers with long-term mass-balance series in Norway. The reanalysis includes (i) homogenisation of both glaciological and geodetic observation series, (ii) uncertainty assessment, (iii) estimates of generic differences including estimates of internal and basal melt, (iv) validation, and, if needed, (v) calibration of mass-balance series. This study comprises an extensive set of data (484 mass-balance years, 34 geodetic surveys, and large volumes of supporting data, such as metadata and field notes). In total, 21 periods of data were compared and the results show discrepancies between the glaciological and geodetic methods for some glaciers, which are attributed in part to internal and basal ablation and in part to inhomogeneity in the data processing. Deviations were smaller than 0.2 m w.e.a-1 for 12 out of 21 periods. Calibration was applied to 7 out of 21 periods, as the deviations were larger than the uncertainty. The reanalysed glaciological series shows a more consistent signal of glacier change over the period of observations than previously reported: six glaciers had a significant mass loss (14-22 m w.e.) and four glaciers were nearly in balance. All glaciers have lost mass after the year 2000. More research is needed on the sources of uncertainty to reduce uncertainties and adjust the observation programmes accordingly. The study confirms the value of carrying out independent high-quality geodetic surveys to check and correct field observations. [ABSTRACT FROM AUTHOR]

Long term time series of climate observations measured at meteorological stations provide one of the most accurate records of climate in the past. Nevertheless, there are a number of factors that affect measurements of climatic parameters which may cause abrupt or more gradual shifts and trends in the corresponding time series. Thus in order to draw sound conclusions about the climate, homogenized data series are required. Because of the large number of different monthly homogenization methods a coordinated European initiative was launched namely the COST Action HOME ES0601: Advances in Homogenization Methods of Climate Series: an integrated approach ( HOME). Its main objective was to review and improve common homogenization methods and to assess their impact on climate time series. In this paper the homogenization method HOMER resulted by the COST Action ES0601, was applied to 52 monthly temperature series of synoptic stations for the period 1960-2004, belonging to the operational weather network of the Hellenic National Meteorological Service ( HNMS). Eight stations passed the homogeneity test successfully without any break point or outlier, for two stations only outliers were reported, but not any break points and the remaining 42 stations presented one or more breaks. Approximately 32% of the breaks could be explained by the stations' history. Station relocations as well as changes in observation practices caused most of the temperature anomalies. The adjustments in yearly temperature series were in the range from −1.2 to 2.0 °C. Seasonal and annual anomalies of raw and homogenized series were calculated using as reference the 30-year period 1961-1990. Also, seasonal and annual mean temperature trends before and after homogenization were analysed and their statistical significance calculated. A reduction of trend magnitude was found to be noticeable in all seasons. The most pronounced trend differences were found in summer followed by winter. [ABSTRACT FROM AUTHOR]

Creating a century-long homogenized near-surface wind speed (WS) observation dataset is essential to improve our knowledge about the uncertainty and causes of current WS stilling and recovery. Here, we rescued paper-based WS records dating back to the 1920s at 13 stations in Sweden and established a four-step homogenization procedure to generate the first 10-member centennial homogenized WS dataset (HomogWS-se) for community use. First, background climate variation in the rescued WS series was removed, using a verified reanalysis series as a reference series to construct a difference series. A penalized maximal F test at a significance level of 0.05 was then applied to detect spurious change-points. About 38 % of the detected change-points were confirmed by the known events recorded in metadata, and the average segment length split by the change-points is ~11.3 years. A mean-matching method using up to five years of data from two adjacent segments was used to adjust the earlier segments relative to the latest segment. The homogenized WS series was finally obtained by adding the homogenized difference series back onto the subtracted reference series. Compared with the raw WS data, the homogenized WS data is more continuous and lacks significant non-climatic jumps. The homogenized WS series presents an initial WS stilling and subsequent recovery until the 1990s, whereas the raw WS fluctuates with no clear trend before the 1970s. The homogenized WS shows a 25 % reduction in the WS stilling during 1990-2005 than the raw WS, and this reduction is significant when considering the homogenization uncertainty. The homogenized WS exhibits a significantly stronger correlation with the North Atlantic Oscillation (NAO) than that of the raw WS (0.54 vs 0.29). These results highlight the importance of the century-long homogenized WS series in increasing our ability to detect and attribute multidecadal variability and changes in WS. The proposed homogenization procedure enables other countries or regions to rescue their early climate data and jointly build global long-term high-quality datasets. HomogWS-se is publicly available from the Zenodo repository at http://doi.org/10.5281/zenodo.5850264 (Zhou et al., 2022). [ABSTRACT FROM AUTHOR]

Recent heightened concern regarding possible consequences of anthropogenically induced global warming has spurred analyses of data aimed at detection of climate change and more thorough characterization of the natural climate variability. However, there is greater concern regarding the extent and especially quality of the historical climate data. In this paper, rainfall records of 233 gauge stations over Ethiopia for the 1978-2007 period are employed in an analysis that involves homogenization, reconstruction, and gridding onto a regular 0.5° × 0.5° resolution grid. Inhomogeneity is detected and adjusted based on quantile matching. The regularized expectation-maximization and multichannel singular spectrum analysis algorithms are then utilized for imputation of missing values, and the latter has been determined to have a marginal advantage. Ordinary kriging is used to create a gridded monthly rainfall dataset. The spatial and temporal coherence of this dataset are assessed using harmonic analysis, self-organizing maps, and intercomparison with global datasets. The self-organizing map delineates Ethiopia into nine homogeneous rainfall regimes, which is consistent with seasonal and interannual rainfall variations. The harmonic analysis of the dataset reveals that the annual mode accounts for 55%-85% of the seasonal rainfall variability over western Ethiopia while the semiannual mode accounts for up to 40% over southern Ethiopia. The dataset is also intercompared with Global Precipitation Climatology Project (GPCP), Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), Climatic Research Unit time series version 3 (CRUTS3.0), Tropical Rainfall Measuring Mission (TRMM), and interim ECMWF Re-Analysis (ERA-Interim) rainfall. The correlation of the dataset with global datasets ranges from 0.52 to 0.95 over sparse to dense rain gauge regions. The GPCP rainfall has a small bias and good correlation with the new dataset whereas TRMM and ERA-Interim have relatively large dry and wet biases, respectively. [ABSTRACT FROM AUTHOR]