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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]

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]

Abstract Lake Bolsena, the fourth Italian lake for volume (9.2 × 109 m3), must be considered as highly sensitive to eutrophication for its extremely long water renewal time. In this paper, temperature and chemical characteristics of the lake measured in the period 2003–2017 are used to discuss the mixing pattern and the variation in the oxygen and algal nutrient concentrations, as indicators of the trophic level. In the analysed period the lake showed oligomictic characteristics, reaching the full overturn, with homogenization of the chemical profile over the whole water column, only in 4 out of the 15 considered years. A regular decrease of oxygen and increase of phosphorus concentrations in the deepest layers has been observed in the non-circulating multi-year periods. The mean total phosphorus concentration showed a regular increase, reaching values close to 16 μg P L−1 in early spring 2017, mostly because of the urban discharge from the watershed, not adequately collected from an existing sewage pipe. Chemical and mixing patterns are discussed in relation with a previous study, carried out in 1966–1971, confirming the recent increase of phosphorus concentrations and the lower frequency of full circulation. The progressive deterioration of lake water quality indicates the need of prompt actions to reduce the external nutrient load and of further studies on the physical and biological characteristics of the lake, still strongly missing. [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]

Daily minimum temperature (Tmin) and maximum temperature (Tmax) data of Huairou station in Beijing from 1960 to 2008 are examined and adjusted for inhomogeneities by applying the data of two nearby reference stations. Urban effects on the linear trends of the original and adjusted temperature series are estimated and compared. Results show that relocations of station cause obvious discontinuities in the data series, and one of the discontinuities for Tmin are highly significant when the station was moved from downtown to suburb in 1996. The daily Tmin and Tmax data are adjusted for the inhomogeneities. The mean annual Tmin and Tmax at Huairou station drop by 1.377°C and 0.271°C respectively after homogenization. The adjustments for Tmin are larger than those for Tmax, especially in winter, and the seasonal differences of the adjustments are generally more obvious for Tmin than for Tmax. Urban effects on annual mean Tmin and Tmax trends are −0.004°C/10 year and −0.035°C/10 year respectively for the original data, but they increase to 0.388°C/10 year and 0.096°C/10 year respectively for the adjusted data. The increase is more significant for the annual mean Tmin series. Urban contributions to the overall trends of annual mean Tmin and Tmax reach 100% and 28.8% respectively for the adjusted data. Our analysis shows that data homogenization for the stations moved from downtowns to suburbs can lead to a significant overestimate of rising trends of surface air temperature, and this necessitates a careful evaluation and adjustment for urban biases before the data are applied in analyses of local and regional climate change. [ABSTRACT FROM AUTHOR]

Climate trends analyses are studied through the analysis of long‐term records which usually are compromised by artificial non‐climatic factors (e.g., station relocation, instrumental replacements, etc). The impact of these factors on the analysis must be assessed and corrected in a procedure called “Homogenization,” before computing any trends. An unbiased analysis is essential for the East Mediterranean climate change, which suffers from scarcity of long and reliable datasets. Here, for the first time, we address these problems by jointly applying some of the state‐of‐the‐art homogenization methods, to long‐term Israeli temperature records (TX and TN) at five different meteorological stations throughout the period of 1950–2011. All of the studied time series were found to be inhomogeneous, where instrumentational issues were responsible for almost 50% of the breaks. The most frequent adjustments range between [−0.6, +0.6] oC while some larger adjustments do not fall within the range of the [−1, +1] °C interval. The adjustment of these breaks is crucial because they introduce large errors that may lead to wrong conclusions about the estimated trends. The difference in the seasonal and annual trends between raw and homogenized series was analysed applying the Mann–Kendall test. The general annual trend differences before and after homogenization, fell within the range of [−0.12, 0.16] oC/decade. Based on the homogenized dataset, a highly significant positive trend was found for the annual TN ¯ with 0.15 °C/decade (p = .002) whereas the TX ¯ trend was 0.10 °C/decade (p = .051). In general, the maximum temperature trends are lower and less statistically significant than those for minimum temperature. The most pronounced seasonal trends were recorded for the summer, which was characterized by significant positive trends for TX ¯ (0.15 °C/decade) and TN ¯ (0.23 °C/decade), while the winter had mainly no significant positive trends. [ABSTRACT FROM AUTHOR]

Instrumental climate records of the last centuries suffer from multiple breaks due to relocations and changes in measurement techniques. These breaks are detected by relative homogenization algorithms using the difference time series between a candidate and a reference. Modern multiple changepoint methods use a decomposition approach where the segmentation explaining most variance defines the breakpoints, while a stop criterion restricts the number of breaks. In this study a pairwise multiple breakpoint algorithm consisting of these two components is tested with simulated data for a range of signal-to-noise ratios (SNRs) found in monthly temperature station datasets. The results for low SNRs obtained by this algorithm do not differ much from random segmentations; simply increasing the stop criterion to reduce the number of breaks is shown to not be helpful. This can be understood by considering that, in case of multiple breakpoints, even a random segmentation can explain about half of the break variance. We derive analytical equations for the explained noise and break variance for random and optimal segmentations. From these we conclude that reliable break detection at low but realistic SNRs needs a new approach. The problem is relevant because the uncertainty of the trends of individual stations is shown to be climatologically significant also for these small SNRs. An important side result is a new method to determine the break variance and the number of breaks in a difference time series by studying the explained variance for random break positions. We further discuss the changes from monthly to annual scale which increase the SNR by more than a factor of 3. [ABSTRACT FROM AUTHOR]

ABSTRACT: Relative homogenization methods assume that measurements of nearby stations experience similar climate signals and rely therefore on dense station networks with high‐temporal correlations. In developing countries such as Peru, however, networks often suffer from low‐station density. The aim of this study is to quantify the influence of network density on homogenization. To this end, the homogenization method HOMER was applied to an artificially thinned Swiss network. Four homogenization experiments, reflecting different homogenization approaches, were examined. Such approaches include diverse levels of interaction of the homogenization operators with HOMER, and different application of metadata. To evaluate the performance of HOMER in the sparse networks, a reference series was built by applying HOMER under the best possible conditions. Applied in completely automatic mode, HOMER decreases the reliability of temperature records. Therefore, automatic use of HOMER is not recommended. If HOMER is applied in interactive mode, the reliability of temperature and precipitation data may be increased in sparse networks. However, breakpoints must be inserted conservatively. Information from metadata should be used only to determine the exact timing of statistically detected breaks. Insertion of additional breakpoints based solely on metadata may lead to harmful corrections due to the high noise in sparse networks. [ABSTRACT FROM AUTHOR]

Statistical models of habitat preference and species distribution (e.g., Resource Selection Functions and Maximum Entropy approaches) perform a quantitative comparison of the use of space with the availability of all habitats in an animal's environment. However, not all of space is accessible all of the time to all individuals, so availability is in fact determined by limitations in animal perception and mobility. Therefore, measuring habitat availability at biologically relevant scales is essential for understanding preference, but herein lies a trade-off: Models fitted at large spatial scales, will tend to average across the responses of different individuals that happen to be in regions with contrasting habitat compositions. We suggest that such models may fail to capture local extremes (hotspots and coldspots) in animal usage and call this potential problem, homogenization. In contrast, models fitted at smaller scales will vary stochastically depending on the particular habitat composition of their narrow spatial neighborhood, and hence fail to describe responses when predicting for different sampling instances. This is the now well-documented issue of non-transferability of habitat models. We illustrate this tradeoff, using a range of simulated experiments, incorporating variations in environmental gradients, richness and fragmentation. We propose diagnostics for detecting the two issues of homogenization and non-transferability and show that these scale-related symptoms are likely to be more pronounced in highly fragmented or steeply graded landscapes. Further, we address these problems by treating the neighborhood of each cell in the landscape grid as an individual sampling instance (with its own neighborhood), hence allowing coefficients to respond to the local expectations of environmental variables according to a Generalized Functional Response ( GFR). Under simulation this approach is consistently better at estimating robust (i.e., transferable) habitat models at smaller scales, and less susceptible to homogenization at larger scales. At the same time, it represents the first application of a GFR to continuous space (rather than multiple, spatially distinct datasets), allowing the predictive advantages of this extension of species distribution models to become available to data from large-scale but single-site field studies. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify any possible temperature changes within the last 50 years in the North-Western Italian Alps by examining data from 16 high-altitude weather stations in the period 1961-2010. The daily temperature time series were collected, digitized and subjected to a historical research to individuate discontinuities and retrieve metadata. We also carried out the data quality control and the homogenization which allowed the climatic indices trend detection. The analysis of the temperature values showed an increase in temperature, particularly at high-altitudes sites. In fact, the stations located above 1600 m asl revealed a rise in temperatures and a decrease in the number of cold periods. For the maximum temperatures, greater increases in spring and winter have been observed, for minimum temperatures in the summer. These trends confirm that climate change is occurring in an environment particularly sensitive to temperature changes, especially during the season of snow accumulation and vegetative growth. These results may be important for policy makers to define the best adaptation strategies in order to protect one of the most sensitive environments such as mountains. [ABSTRACT FROM AUTHOR]

In this study we present 19 new or re-analysed series of glacier-wide seasonal mass balance for the Swiss Alps based on direct measurements. The records partly start around 1920 and continue until today. Previously unpublished and unevaluated observations of point winter and annual balance are compiled from various sources and archives. These highly valuable datasets have not yet been consistently evaluated and were thus unavailable to the scientific community. Using distributed modelling for spatial interpolation and extrapolation and homogenization of the point measurements, we infer continuous series of area-averaged mass balance. The results are validated against independent decadal ice volume changes from photogrammetric surveys. Six of the new seasonal series cover 60 years and more and add a substantial amount of information on the variations of regional glacier mass change. This will strengthen the worldwide collection of glacier monitoring data, especially for the data-sparse period before the 1980s. We compare our results to existing long-term series and present an updated assessment of mass-balance variability and glacier sensitivity throughout the European Alps. [ABSTRACT FROM AUTHOR]

Long-term meteorological observation series are fundamental for reflecting climate changes. However, almost all meteorological stations inevitably undergo relocation or changes in observation instruments, rules, and methods, which can result in systematic biases in the observation series for corresponding periods. Homogenization is a technique for adjusting these biases in order to assess the true trends in the time series. In recent years, homogenization has shifted its focus from the adjustments to climate mean status to the adjustments to information about climate extremes or extreme weather. Using case analyses of ideal and actual climate series, here we demonstrate the basic idea of homogenization, introduce new understanding obtained from recent studies of homogenization of climate series in China, and raise issues for further studies in this field, especially with regards to climate extremes, uncertainty of the statistical adjustments, and biased physical relationships among different climate variables due to adjustments in single variable series. [ABSTRACT FROM AUTHOR]

The widely used Global Historical Climatology Network (GHCN) monthly temperature dataset is available in two formats—non-homogenized and homogenized. Since 2011, this homogenized dataset has been updated almost daily by applying the "Pairwise Homogenization Algorithm" (PHA) to the non-homogenized datasets. Previous studies found that the PHA can perform well at correcting synthetic time series when certain artificial biases are introduced. However, its performance with real world data has been less well studied. Therefore, the homogenized GHCN datasets (Version 3 and 4) were downloaded almost daily over a 10-year period (2011–2021) yielding 3689 different updates to the datasets. The different breakpoints identified were analyzed for a set of stations from 24 European countries for which station history metadata were available. A remarkable inconsistency in the identified breakpoints (and hence adjustments applied) was revealed. Of the adjustments applied for GHCN Version 4, 64% (61% for Version 3) were identified on less than 25% of runs, while only 16% of the adjustments (21% for Version 3) were identified consistently for more than 75% of the runs. The consistency of PHA adjustments improved when the breakpoints corresponded to documented station history metadata events. However, only 19% of the breakpoints (18% for Version 3) were associated with a documented event within 1 year, and 67% (69% for Version 3) were not associated with any documented event. Therefore, while the PHA remains a useful tool in the community's homogenization toolbox, many of the PHA adjustments applied to the homogenized GHCN dataset may have been spurious. Using station metadata to assess the reliability of PHA adjustments might potentially help to identify some of these spurious adjustments. [ABSTRACT FROM AUTHOR]

Climate changes within Cenozoic extreme climate events such as the Paleocene-Eocene Thermal Maximum and the First Oligocene Glacial provide good opportunities to estimate the global climate trends in our present and future life. However, quantitative paleotemperatures data for Cenozoic climatic reconstruction are still lacking, hindering a better understanding of the past and future climate conditions. In this contribution, quantitative paleotemperatures were determined by fluid inclusion homogenization temperature (Th) data from continental halite of the first member of the Shahejie Formation (SF1; probably late Eocene to early Oligocene) in Bohai Bay Basin, North China. The primary textures of the SF1 halite typified by cumulate and chevron halite suggest halite deposited in a shallow saline water and halite Th can serve as an temperature proxy. In total, one-hundred-twenty-one Th data from primary and single-phase aqueous fluid inclusions with different depths were acquired by the cooling nucleation method. The results show that all Th range from 17.7°C to 50.7°C,with the maximum homogenization temperatures (ThMAX) of 50.56C at the depth of 3028.04 mand 50.7°C at 3188.61 m, respectively. Both the ThMAX presented here are significantly higher than the highest temperature recorded in this region since 1954 and agree with global temperature models for the year 2100 predicted by the Intergovernmental Panel on Climate Change. [ABSTRACT FROM AUTHOR]

We analyzed the evolution of surface relative humidity (RH) and specific humidity (q) in Spain, based on complete records available from the State Meteorological Agency of Spain. The surface RH records used span the period 1920-2011, but because of spatial and temporal constraints in the dataset we used a subset of the data, covering the period 1961-2011. The subset contained 50 monthly series of RH, which were created through a process of quality control, reconstruction and homogenization. The data shows that there was a large decrease in RH over mainland Spain from 1961 to 2011, which was greatest in spring and summer. In contrast, there was no overall change in the specific humidity in this period, except in spring, when an increase was observed. The decrease in RH affected the entire country, but the changes in specific humidity were less homogeneous. For specific humidity there was a general increase in the northern and eastern parts of Spain, whereas negative trends dominated in the central and southern areas, mainly during the summer months. The results suggest that an increase in the water holding capacity of the atmosphere as a consequence of warming during recent decades has not been accompanied by an increase in the surface water vapor content, probably because the supply of water vapor from the main terrestrial and oceanic areas has been constrained. We discuss the implications of these findings for evapotranspiration processes, precipitation and water management in Spain. [ABSTRACT FROM AUTHOR]

Instrumental time series are often affected by inhomogeneities which can mask or amplify climate change signals. Various procedures for the detection and adjustment of breaks exist for monthly and annual time series. Homogenization methods on a daily basis are scarce and often disregard uncertainties accompanying the break adjustment. We present a complete homogenization procedure for daily extreme temperature series based on the break detection method PRODIGE and SPLIDHOM for break correction. Both parts of the homogenization rely on the existence of highly correlated reference stations. After the statistical comparison with neighbouring stations, detected breaks are verified and further localized by metadata. Uncertainties of the break adjustments are estimated by altering reference stations and by applying a bootstrapping technique providing an objective indication about the reliability of the homogenization. The method was tested and applied to 71 time series of daily minimum (TN) and maximum temperatures (TX) in Austria covering the period 1948-2009. For some series homogenization was not possible due to large uncertainties in the adjustments or a lack of suitable reference series. In the remaining 57 TN and 54 TX series a total number of 139 breaks were detected. Seventy-five percent of those breaks are documented in the metadata archive, with most of them being caused by station relocations and instrumentation changes. In general, the mean over the temperature dependent adjustments of all stations show a temperature reduction. However, the majority of breaks have mean amplitudes of less than 0.5 °C. A comprehensive analysis was performed on the new homogenized daily dataset, showing a widespread warming trend in both TN and TX series. The warming trend is in general amplified due to the homogenization. However, significant changes in the trend are only observed at very few stations. In autumn, however, the trend is reversed in many temperature based 'climate change detection indices'. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Evaluation and comparison of efficiencies of widely used objective homogenisation methods (OHOMs) are presented relying on some test-datasets and efficiency measures. Problems related to the choice of efficiency measure, creation of appropriate test-datasets and use of OHOM parameterisation are discussed. The detection parts of the OHOMs are examined only. Power of detection, false alarm rate, detection skill and skill of linear trend estimation are calculated and compared for eight OHOMs and six test-datasets. Each test-dataset comprises 10,000 100 year-long artificially simulated time series. In the simplest test dataset, each time series contains one inhomogeneity (IH), while a structure of inhomogeneities that is similar to that of real central European temperature time series is included in the most complex simulated dataset. Distinct attention is given to OHOMs that contain (1) cutting algorithm, (2) semihierarchic algorithm, (3) direct detection of multiple IHs, (4) detection of change-point and trend-line shaped IHs. Results show that Caussinus-Mestre method and Multiple Analysis of Series for Homogenization are the most powerful tools in detecting and correcting IHs in climatic time series. [ABSTRACT FROM AUTHOR]

Instrumental daily series of temperature are often affected by inhomogeneities. Several methods are available for their correction at monthly and annual scales, whereas few exist for daily data. Here, an improved version of the higher-order moments (HOM) method, the higher-order moments for autocorrelated data (HOMAD), is proposed. HOMAD addresses the main weaknesses of HOM, namely, data autocorrelation and the subjective choice of regression parameters. Simulated series are used for the comparison of both methodologies. The results highlight and reveal that HOMAD outperforms HOM for small samples. Additionally, three daily temperature time series from stations in the eastern Mediterranean are used to show the impact of homogenization procedures on trend estimation and the assessment of extremes. HOMAD provides an improved correction of daily temperature time series and further supports the use of corrected daily temperature time series prior to climate change assessment. [ABSTRACT FROM AUTHOR]

In comparisons of radiosonde vertical temperature trend profiles with comparable profiles derived from selected Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) general circulation models (GCMs) driven by major external forcings of the latter part of the twentieth century, model trends exhibit a positive bias relative to radiosonde trends in the majority of cases for both time periods examined (1960–99 and 1979–99). Homogeneity adjustments made in the Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC) and Hadley Centre Atmospheric Temperatures, version 2 (HadAT2), radiosonde datasets, which are applied by dataset developers to account for time-varying biases introduced by historical changes in instruments and measurement practices, reduce the relative bias in most cases. Although some differences were found between the two observed datasets, in general the observed trend profiles were more similar to one another than either was to the GCM profiles. In the troposphere, adjustment has a greater impact on improving agreement of the shapes of the trend profiles than on improving agreement of the layer mean trends, whereas in the stratosphere the opposite is true. Agreement between the shapes of GCM and radiosonde trend profiles is generally better in the stratosphere than the troposphere, with more complexity to the profiles in the latter than the former. In the troposphere the tropics exhibit the poorest agreement between GCM and radiosonde trend profiles, but also the largest improvement in agreement resulting from homogeneity adjustment. In the stratosphere, radiosonde trends indicate more cooling than GCMs. For the 1979–99 period, a disproportionate amount of this discrepancy arises several months after the eruption of Mount Pinatubo, at which time temperatures in the radiosonde time series cool abruptly by ∼0.5 K compared to those derived from GCMs, and this difference persists to the end of the record. [ABSTRACT FROM AUTHOR]

The removal of non-climatic biases, so-called inhomogeneities, from long climatic records needs sophistically developed statistical methods. One principle is that the differences between a candidate series and its neighbor series are usually analyzed instead of the candidate series directly, in order to neutralize the possible impacts of regionally common natural climate variation on the detection of inhomogeneities. In most homogenization methods, two main kinds of time series comparisons are applied, i.e., composite reference series or pairwise comparisons. In composite reference series, the inhomogeneities of neighbor series are attenuated by averaging the individual series, and the accuracy of homogenization can be improved by the iterative improvement of composite reference series. By contrast, pairwise comparisons have the advantage that coincidental inhomogeneities affecting several station series in a similar way can be identified with higher certainty than with composite reference series. In addition, homogenization with pairwise comparisons tends to facilitate the most accurate regional trend estimations. A new time series comparison method is presented here, which combines the use of pairwise comparisons and composite reference series in a way that their advantages are unified. This time series comparison method is embedded into the Applied Caussinus-Mestre Algorithm for homogenizing Networks of climatic Time series (ACMANT) homogenization method, and tested in large, commonly available monthly temperature test datasets. Further favorable characteristics of ACMANT are also discussed. [ABSTRACT FROM AUTHOR]

Biogeography is a vibrant academic discipline that plays an important role in framing and directing debates on some of the most important environmental issues of our time, such as the impacts of climate change on biodiversity, biotic homogenization and the effectiveness of global conservation policy. Despite this, biogeography suffers from a low public profile and may consequently be poorly understood by the public. Here I review the representation of the term biogeography, and a selection of biogeographers in the public sphere (newspapers, websites, blogs) and the academic sphere (journals and reports) to understand more fully the public perception of the discipline. As anticipated, the survey suggests that the term ‘biogeography’ has little public currency, and that the public still largely associate the subject area with either eminent Victorians such as A.R. Wallace or modern greats such as E.O. Wilson. I conclude by discussing the potential consequences of the virtual absence of biogeography from public discourse for education and professional biogeographers. [ABSTRACT FROM AUTHOR]

Sea surface temperatures form a vital part of global mean surface temperature records. Historical observation methods have changed substantially over time from buckets to engine‐room intake sensors, hull sensors and drifting buoys, rendering their use for climatological studies problematic. There are substantial uncertainties in the relative biases of different observations which may impact the global temperature record. Island and coastal weather stations can be compared to coastal sea surface temperature observations to obtain an assessment of changes in bias over time. The process is made more challenging by differences in the rate of warming between air temperatures and sea surface temperatures, and differences across coastal boundaries. A preliminary sea surface temperature reconstruction homogenized using coastal weather station data suggests significant changes to the sea surface temperature record, although there are substantial uncertainties, only some of which can be quantified. A large warm excursion in versions 4 and 5 of the NOAA Extended Reconstructed Sea Surface Temperature during World War 2 is rejected, as is a cool excursion around 1910 present in all existing records. The mid‐century plateau is cooler than in existing reconstructions. [ABSTRACT FROM AUTHOR]