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Modelling rainfall extremes and dry periods over the Southeast Asia (SEA) region is challenging due to the characteristics of the region, which consists of the Maritime Continent and a mountainous region; it also experiences monsoonal conditions, as it is located between the Asian summer monsoon and the Australian summer monsoon. Representing rainfall extremes is important for flood and drought assessments in the region. This paper evaluates extreme rainfall climatic indices from regional climate models from CORDEX Southeast Asia and compares them with the results of high-resolution global climate models with a comparable spatial resolution from the HighResMIP experiment. Observations indicate a high intensity of rainfall over areas affected by tropical cyclones and long consecutive dry day periods over some areas in Indochina and the southern end of Indonesia. In the model simulations, we find that both coupled and sea surface temperature-forced HighResMIP model experiments are more similar to the observations than CORDEX model results. However, the models produce a poorer simulation of precipitation intensity-related indices due to model biases in the rainfall intensity. This bias is higher in CORDEX than in HighResMIP and is evident in both the low- and high-resolution HighResMIP model versions. The comparable performances of HighResSST (atmosphere-only runs) and Hist-1950 (coupled ocean–atmosphere runs) demonstrate the accuracy of the ocean model. Comparable performances were also found for the two different resolutions of HighResMIP, suggesting that there is no improvement in the performance of the high-resolution HighResMIP model compared to the low-resolution HighResMIP model. © 2022 The Authors. International Journal of Climatology published by John Wiley Sons Ltd on behalf of Royal Meteorological Society.

This article provides high-resolution information on the projected changes in annual extreme rainfall and high and low streamflow events over Southeast Asia under extreme climate change. The analysis was performed using the bias-corrected result of the High-resolution Model Intercomparison Project (HighResMIP) multi-model experiment for the period 1971–2050. Eleven rainfall indices were calculated along with streamflow simulation using the PCR-GLOBWB hydrological model. The historical period 1981–2010 and the near-future period 2021–2050 were considered for this analysis. Results indicate that over Indochina, Myanmar faces more challenges in the near future. The east coast of Myanmar will experience more extreme high rainfall conditions, while northern Myanmar will have longer dry spells. Over the Indonesian maritime continent, Sumatra and Java will suffer from the increase in dry spell length of up to 40 %, while the increase of extreme high rainfall will occur over Borneo and mountainous areas in Papua. Based on the streamflow analysis, the impact of climate change is more prominent in a low flow event than in a high flow event. The majority of rivers in the central Mekong catchment, Sumatra, the Malaysian peninsula, Borneo, and Java will experience more extreme low flow events. More extreme dry conditions in the near future are also seen from the increasing probability of future low flow occurrences, which reaches 101 % and 122 % on average over Sumatra and Java, respectively. Finally, the changes in extreme high and low streamflow events are more pronounced in rivers with steep hydrographs, while rivers with shallow hydrographs have a higher risk in the probability of low flow change. Our study highlights the importance of catchment properties in aggregating and/or buffering the impact of extreme climate change.

During early 2021 (January to February), high rainfall intensity resulted in flooding over several locations over the western part of the Maritime Continent (East Aceh, South Borneo, Aceh Tamiang, and Jakarta). The atmospheric conditions were specifically different, but the pattern of strong northeasterly wind over the South China Sea (SCS) appears during these all flooding events. During the flooding event over East Aceh and South Borneo, the high rainfall intensity is mainly associated with the vortex over the Borneo Island, denoted by high wind speed (more than 10 m/s), closed-counterclockwise circulation, and high vorticity over Borneo (over 25 × 10–6 s−1). Moreover, during the Aceh Tamiang’s flooding, there was a local convective activity and strong wind from the Indian Ocean. The wind from the Indian Ocean deflected southward along the west coast of Sumatera Island. This northwesterly wind over the Indian Ocean (west part of Sumatra Island) was stronger than the other flooding cases. Furthermore, the vorticity magnitude over this area (18 × 10–5 s−1) was also considerably higher than the other cases. Moreover, during the Jakarta flooding event, the Borneo vortex did not appear, and the northeasterly wind over the South China Sea turned to southward wind and elongated to the southern part of Java Island. The meridional SST gradient along the 105° E resulted in high wind speed over the western part of the Java Island and played a role in low-level wind convergence at the beginning of the Jakarta flooding episode.

This first climate risk session will set the scene for the Climate Exp0 conference. With global temperatures now over 1°C above pre-industrial levels, and as the impacts of climate change are starting to be felt around the world, this session will present an overview of our current understanding of climate risk including systemic risks resulting from impacts cascading through inter-related systems and the risk of passing tipping points. Building on the Met Office conference “Science for a resilient future” held last week, the session will also point to the opportunities to create a more resilient and sustainable net zero future for the world.

Representing the rainy season of the maritime continent is a challenge for global and regional climate models. Here, we compare regional climate models (RCMs) based on the coupled model intercomparison project phase 5 (CMIP5) model generation with high-resolution global climate models with a comparable spatial resolution from the HighResMIP experiment. The onset and the total precipitation of the rainy season for both model experiments are compared against observational datasets for Southeast Asia. A realistic representation of the monsoon rainfall is essential for agriculture in Southeast Asia as a delayed onset jeopardizes the possibility of having three annual crops. In general, the coupled historical runs (Hist-1950) and the historical force atmosphere run (HighresSST) of the high-resolution model intercomparison project (HighResMIP) suite were consistently closer to the observations than the RCM of CMIP5 used in this study. We find that for the whole of Southeast Asia, the HighResMIP models simulate the onset date and the total precipitation of the rainy season over the region closer to the observations than the other model sets used in this study. High-resolution models in the HighresSST experiment showed a similar performance to their low-resolution equivalents in simulating the monsoon characteristics. The HighresSST experiment simulated the anomaly of the onset date and the total precipitation for different El Niño-southern oscillation conditions best, although the magnitude of the onset date anomaly was underestimated. Indonesia Endowment Fund for Education (LPDP), Grant/Award Number: S-353/LPDP.3/2019; H2020 Marie Skłodowska-Curie, Grant/Award Number: 748750; European Union's Horizon 2020 Research and Innovation Programme, Grant/Award Number: 641727

Simulation of past climate is an important tool for the validation of climate models. The comparison with observed daily values allows us to assess the reliability of their projections on climatic extremes in a future climate. The frequency and amplitude of extreme events are fundamental aspects that climate simulations need to reproduce as they have high impacts on economy and society. The ability to simulate them will help policy makers in taking better measures to face climate change. This work aims at evaluating how six models within the High Resolution Model Intercomparison Project reproduce the trends on extreme indices as they have been observed over Europe in the 1970–2014 period. Observed values are provided by the new homogenized version of the E-OBS gridded dataset. The comparison is performed through the use of indices based on seasonal averages and on exceedances of percentile-based thresholds, focusing on six subregions. Winter-average minimum temperature is generally underestimated by models (down to − 4 °C difference over Italy and Norway) while simulated trends in seasonal averages and extreme values are found to be too cold on Eastern Europe and too warm on Iberia and Southern Europe (e.g. up to a difference of − 4% per decade on the number of Cold Nights over Spain). On the other hand the models tend to overestimate summer maximum temperatures averages in the Mediterranean Area (up to + 5 °C over the Balkans) and underestimate these at higher latitudes. Iberia, Southern and Eastern Europe are simulated with too low trends in average summer temperatures. The simulated trends are too strong on the North West part and too weak on the South East part of Europe (down to − 3%/decade on the number of Warm Days over Italy and Western Balkans). These results corroborate the findings of previous studies about the underestimation of the warming trends of summer temperatures in Southern Europe, where these are more intense and have more impacts. The high-resolution versions of the models are compared to their lower-resolution counterparts, similar to those used in the CMIP5, showing a slight improvement for the simulation of extreme winter minimum temperatures, while no significant progresses have been found for extreme summer maximum temperatures.

We describe the approach taken to develop the United Kingdom's first community Earth system model, UKESM1. This is a joint effort involving the Met Office and the Natural Environment Research Council (NERC), representing the U.K. academic community. We document our model development procedure and the subsequent U.K. submission to CMIP6, based on a traceable hierarchy of coupled physical and Earth system models. UKESM1 builds on the well‐established, world‐leading HadGEM models of the physical climate system and incorporates cutting‐edge new representations of aerosols, atmospheric chemistry, terrestrial carbon, and nitrogen cycles and an advanced model of ocean biogeochemistry. A high‐level metric of overall performance shows that both models, HadGEM3‐GC3.1 and UKESM1, perform better than most other CMIP6 models so far submitted for a broad range of variables. We point to much more extensive evaluation performed in other papers in this special issue. The merits of not using any forced climate change simulations within our model development process are discussed. First results from HadGEM3‐GC3.1 and UKESM1 include the emergent climate sensitivity (5.5 and 5.4 K, respectively) which is high relative to the current range of CMIP5 models. The role of cloud microphysics and cloud‐aerosol interactions in driving the climate sensitivity, and the systematic approach taken to understand this role, is highlighted in other papers in this special issue. We place our findings within the broader modeling landscape indicating how our understanding of key processes driving higher sensitivity in the two U.K. models seems to align with results from a number of other CMIP6 models., Key Points The United Kingdom has developed its first community Earth system model and delivered a traceable hierarchy of models to CMIP6We applied a process‐based evaluation strategy in model development but chose not to use historic trends or measures of climate responseThe U.K. models exhibit higher climate sensitivity than seen in CMIP5 arising in part from more positive cloud feedbacks

Long and homogeneous series are a necessary requirement for reliable climate analysis. Relocation of measuring equipment from one station to another, such as from the city center to a rural area or a nearby airport, is one of the causes of discontinuities in these long series that may affect trend estimates. In this paper, an updated procedure for the composition of long series, by combining data from nearby stations, is introduced. It couples an evolution of the blending procedure already implemented within the European Climate Assessment and Dataset (ECA&D, which combines data from stations no more than 12.5 km apart from each other) with a duplicate removal, alongside the quantile matching homogenization procedure. The ECA&D contains approximately 3000 homogenized series for each temperature variable prior to the blending procedure, and approximately 820 of these are longer than 60 years; the process of blending increases the number of long series to more than 900. Three case studies illustrate the effects of the homogenization on single blended series, showing the effectiveness of separate adjustments on extreme and mean values (Geneva, Switzerland), on cases in which blending is complex (Rheinstetten, Germany), and on series that are completed by adding relevant portions of Global Telecommunications System synoptic data (Siauliai, Lithuania). A trend assessment on the whole European continent reveals the removal of negative and very large trends, demonstrating a stronger spatial consistency. The new blended and homogenized dataset will allow a more reliable use of temperature series for indices calculation and for the calculation of gridded datasets and it will be available online for users (https://www.ecad.eu).

High-resolution, regularly gridded air-temperature maps are frequently used in climatology, hydrology, and ecology. Within the Netherlands, 34 official automatic weather stations (AWSs) are operated by the National Met Service according to World Meteorological Organization (WMO) standards. Although the measurements are of high quality, the spatial density of the AWSs is not sufficient to reconstruct the temperature on a 1-km-resolution grid. Therefore, a new methodology for daily temperature reconstruction from 1990 to 2017 is proposed, using linear regression and multiple adaptive regression splines. The daily 34 AWS measurements are interpolated using eight different predictors: diurnal temperature range, population density, elevation, albedo, solar irradiance, roughness, precipitation, and vegetation index. Results are cross-validated for the AWS locations and compared with independent citizen weather observations. The RMSE of the reference method ordinary kriging amounts to 2.6 °C whereas using the new methods the RMSE drops below 1.0 °C. Especially for cities, a substantial improvement of the predictions is found. Independent predictions are on average 0.3 °C less biased than ordinary kriging at 40 high-quality citizen measurement sites. With this new method, we have improved the representation of local temperature variations within the Netherlands. The temperature maps presented here can have applications in urban heat island studies, local trend analysis, and model evaluation.

Homogenization of daily temperature series is a fundamental step for climatological analyses. In the last decades, several methods have been developed, presenting different statistical and procedural approaches. In this study, four homogenization methods (together with two variants) have been tested and compared. This has been performed constructing a benchmark dataset, where segments of homogeneous series are replaced with simultaneous measurements from neighboring homogeneous series. This generates inhomogeneous series (the test set) whose homogeneous version (the benchmark set) is known. Two benchmark datasets are created. The first one is based on series from the Czech Republic and has a high quality, high station density, and a large number of reference series. The second one uses stations from all Europe and presents more challenges, such as missing segments, low station density, and scarcity of reference series. The comparison has been performed with pre-defined metrics which check the statistical distance between the homogenized versions and the benchmark. Almost all homogenization methods perform well on the near-ideal benchmark (maximum relative root mean square error (rRMSE): 1.01), while on the European dataset, the homogenization methods diverge and the rRMSE increases up to 1.87. Analyses of the percentages of non-adjusted inhomogeneous data (up to 39%) and substantial differences in the trends among the homogenized versions helped identifying diverging procedural characteristics of the methods. These results add new elements to the debate about homogenization methods for daily values and motivate the use of realistic and challenging datasets in evaluating their robustness and flexibility.

This study introduces a new daily high-resolution land-only observational gridded dataset, called SA-OBS, for precipitation and minimum, mean, and maximum temperature covering Southeast Asia. This dataset improves upon existing observational products in terms of the number of contributing stations, in the use of an interpolation technique appropriate for daily climate observations, and in making estimates of the uncertainty of the gridded data. The dataset is delivered on a 0.25° × 0.25° and a 0.5° × 0.5° regular latitude–longitude grid for the period 1981–2014. The dataset aims to provide best estimates of grid square averages rather than point values to enable direct comparisons with regional climate models. Next to the best estimates, daily uncertainties are quantified. The underlying daily station time series are collected in cooperation between meteorological services in the region: the Southeast Asian Climate Assessment and Dataset (SACA&D). Comparisons are made with station observations and other gridded station or satellite-based datasets (APHRODITE, CMORPH, TRMM). The comparisons show that vast differences exist in the average daily precipitation, the number of rainy days, and the average precipitation on a wet day between these datasets. SA-OBS closely resembles the station observations in terms of dry/wet frequency, the timing of precipitation events, and the reproduction of extreme precipitation. New versions of SA-OBS will be released when the station network in SACA&D has grown further.

Climate indices are analyzed using a newly developed dataset with station-based daily data for Southeast Asia. With rice the staple food of the diet in the region, the indices used are aimed at agriculture, specifically rice production, and include the onset of the wet season and the nighttime temperature. Three indices are used to estimate the onset of the wet season. Despite a quantitative lack of similarity between these indices (although they are strongly correlated), the progression of the wet season over the area matches existing descriptions. Trends in the onset date of the wet season calculated over 1971–2012 are only statistically significant for a few stations; there are no signs that a wide spread delay as anticipated by future climate scenarios is already taking place. A positive trend in the nighttime temperature over the region is observed with values up to 0.7°C decade−1. For a selection of stations the change in distribution of nighttime temperatures is analyzed when comparing the 1971–90 period with the 1991–2010 period. They show a shift of the median to higher temperatures, and the decline in the number of relatively cool nights is stronger than the increase in the number of relatively warm nights.

In the Netherlands, there will be an urgent need for additional housing by the year 2040, which mainly has to be realized within the existing built environment rather than in the spatial extension of cities. In this data-driven study, we investigated the effects of different urban planning strategies on heat stress for the current climate and future climate scenarios (year 2050) for the urban agglomeration of The Hague. Heat stress is here expressed as the number of days exceeding minimum temperatures of 20 &deg, C in a year. Thereto, we applied a diagnostic equation to determine the daily maximum urban heat island based on routine meteorological observations and straightforward urban morphological properties including the sky-view factor and the vegetation fraction. Moreover, we utilized the Royal Netherlands Meteorological Institute&rsquo, s (KNMI) climate scenarios to transform present-day meteorological hourly time series into the future time series. The urban planning strategies differ in replacing low- and mid-rise buildings with high-rise buildings (which reduces the sky-view factor), and constructing buildings on green areas (which reduces the vegetation fraction). We found that, in most cases, the vegetation fraction is a more critical parameter than the sky-view factor to minimize the extra heat stress incurred when densifying the neighbourhood. This means that an urban planning strategy consisting of high-rise buildings and preserved green areas is often the best solution. Still, climate change will have a larger impact on heat stress for the year 2050 than the imposed urban densification.

[Departement_IRSTEA]Territoires [ADD1_IRSTEA]Adaptation des territoires au changement global; National audience; Historical in situ sub-daily rainfall observations are essential for the understanding of short-duration rainfall extremes but records are typically not readily accessible and data are often subject to errors and inhomogeneities. Furthermore, these events are poorly quantified in projections of future climate change making adaptation to the risk of flash flooding problematic. Consequently, knowledge of the processes contributing to intense, short-duration rainfall is less complete compared with those on daily timescales. The INTENSE project is addressing this global challenge by undertaking a data collection initiative that is coupled with advances in high-resolution climate modelling to better understand key processes and likely future change. The project has so far acquired data from over 23 000 rain gauges for its global sub-daily rainfall dataset (GSDR) and has provided evidence of an intensification of hourly extremes over the US. Studies of these observations, combined with model simulations, will continue to advance our understanding of the role of local-scale thermodynamics and large-scale atmospheric circulation in the generation of these events and how these might change in the future.

The International Climate Assessment & Dataset (ICA&D) concept provides climate services on a regional scale for users in participating countries and the broader scientific community. It builds on the expertise gained in Europe, where national meteorological services collaborate by sharing climate data in order to produce regional climate assessments. Universities and data-rescue initiatives have joined this collaboration. The result is a web-based information system that combines quality-controlled daily station data with derived climate indices. Indices are provided for mean and extreme climate conditions including droughts, heat waves, and heavy rainfall events. ICA&D systems currently exist in Europe and in three regions of the world vulnerable to climate change: Southeast Asia, Latin America, and West Africa. Historical perspectives on climate variability and change are integrated with the monitoring of current climate evolution through regular updates of the data series obtained from meteoro...

Indices for climate variability and extremes have been used for a long time, often by assessing days with temperature or precipitation observations above or below specific physically-based thresholds. While these indices provided insight into local conditions, few physically based thresholds have relevance in all parts of the world. Therefore, indices of extremes evolved over time and now often focus on relative thresholds that describe features in the tails of the distributions of meteorological variables. In order to help understand how extremes are changing globally, a subset of the wide range of possible indices is now being coordinated internationally which allows the results of studies from different parts of the world to fit together seamlessly. This paper reviews these as well as other indices of extremes and documents the obstacles to robustly calculating and analyzing indices and the methods developed to overcome these obstacles. Gridding indices are necessary in order to compare observations with climate model output. However, gridding indices from daily data are not always straightforward because averaging daily information from many stations tends to dampen gridded extremes. The paper describes recent progress in attribution of the changes in gridded indices of extremes that demonstrates human influence on the probability of extremes. The paper also describes model projections of the future and wraps up with a discussion of ongoing efforts to refine indices of extremes as they are being readied to contribute to the IPCC's Fifth Assessment Report. WIREs Clim Change 2011, 2:851–870. doi: 10.1002/wcc.147 For further resources related to this article, please visit the WIREs website.

Many climate studies assess trends and projections in heavy precipitation events using precipitation percentile (or quantile) indices. Here we investigate three different percentile indices that are commonly used. We demonstrate that these may produce very different results and thus require great care with interpretation. More specifically, consideration is given to two intensity-based indices and one frequency-based index, namely (a) all-day percentiles, (b) wet-day percentiles, and (c) frequency indices based on the exceedance of a percentile threshold. Wet-day percentiles are conditionally computed for the subset of wet events (with precipitation exceeding some threshold, e.g. 1 mm/d for daily precipitation). We present evidence that this commonly used methodology can lead to artifacts and misleading results if significant changes in the wet-day frequency are not accounted for. Percentile threshold indices measure the frequency of exceedance with respect to a percentile-based threshold. We show that these indices yield an assessment of changes in heavy precipitation events that is qualitatively consistent with all-day percentiles, but there are substantial differences in quantitative terms. We discuss the reasons for these effects, present a theoretical assessment, and provide a series of examples using global and regional climate models to quantify the effects in typical applications. Application to climate model output shows that these considerations are relevant to a wide range of typical climate-change applications. In particular, wet-day percentiles generally yield different results, and in most instances should not be used for the impact-oriented assessment of changes in heavy precipitation events., Climatic Change, 137 (1), ISSN:0165-0009, ISSN:1573-1480

In a statistical analysis of more than a century of data, a strong connection was found between strong warm El Nino winter events and high spring precipitation in a band from southern England eastwards into Asia. This relationship is an extension of the connection mentioned by Kiladis and Diaz (1989. ‘Global climatic anomalies associated with extremes in the Southern Oscillation’, J. Climate, 2, 1069–1090), and is much stronger than the winter season teleconnection that has been the subject of other studies. Correlation coefficients between December–January (DJF) NINO3 indices and March–May (MAM) precipitation are higher than r=0.3 for individual stations, and as high as r=0.49 for an index of precipitation anomalies around 50°N from 5°W to 35°E. The lagged correlation suggests that southeast Asian surface temperature anomalies may act as intermediate variables. Copyright © 2000 Royal Meteorological Society

This chapter addresses changes in weather and climate events relevant to extreme impacts and disasters. An extreme (weather or climate) event is generally defined as the occurrence of a value of a weather or climate variable above (or below) a threshold value near the upper (or lower) ends (‘tails’) of the range of observed values of the variable. Some climate extremes (e.g., droughts, floods) may be the result of an accumulation of weather or climate events that are, individually, not extreme themselves (though their accumulation is extreme). As well, weather or climate events, even if not extreme in a statistical sense, can still lead to extreme conditions or impacts, either by crossing a critical threshold in a social, ecological, or physical system, or by occurring simultaneously with other events. A weather system such as a tropical cyclone can have an extreme impact, depending on where and when it approaches landfall, even if the specific cyclone is not extreme relative to other tropical cyclones. Conversely, not all extremes necessarily lead to serious impacts. [3.1] Many weather and climate extremes are the result of natural climate variability (including phenomena such as El Nino), and natural decadal or multi-decadal variations in the climate provide the backdrop for anthropogenic climate changes. Even if there were no anthropogenic changes in climate, a wide variety of natural weather and climate extremes would still occur. [3.1] A changing climate leads to changes in the frequency, intensity, spatial extent, duration, and timing of weather and climate extremes, and can result in unprecedented extremes. Changes in extremes can also be directly related to changes in mean climate, because mean future conditions in some variables are projected to lie within the tails of present-day conditions. Nevertheless, changes in extremes of a climate or weather variable are not always related in a simple way to changes in the mean of the same variable, and in some cases can be of opposite sign to a change in the mean of the variable. Changes in phenomena such as the El Nino-Southern Oscillation or monsoons could affect the frequency and intensity of extremes in several regions simultaneously.

Surface temperature data – observed primarily for weather-related purposes – are disparate, originating from ever evolving instrument types and observational practices. Although several global databases are in use internationally, no comprehensive global repository exists and many data are undigitized or restricted. Scientists have painstakingly obtained vast quantities of data, carefully removed random errors and accounted for systematic biases. The 21st Century demands go further - requiring highly detailed (spatially and temporally), globally complete, long-term products, with quantified uncertainties, and created from freely available, fully traceable data. Many decisions of substantial socio-economic importance rely on the accuracy of such products. An international meeting was held to plan how best to facilitate such efforts. A central repository is to be created, where data are traceable from their origins to final product. Strategies are outlined to rescue non-digitized data and move towards entirely freely available data. Creation of multiple methodologically independent products is recommended for quantifying uncertainty. Methods of benchmarking and assessing multiple products to aid inter-comparison and end-user product selection are described. Data-products would be obtained and visualized using in-house tools from the planned data-portal. Structure and governance include engagement with bodies such as WMO and, importantly, with experts other than climatologists.

A new set of climate change scenarios for 2050 for the Netherlands was produced recently. The scenarios span a wide range of possible future climate conditions, and include climate variables that are of interest to a broad user community. The scenario values are constructed by combining output from an ensemble of recent General Climate Model (GCM) simulations, Regional Climate Model (RCM) output, meteorological observations and a touch of expert judgment. For temperature, precipitation, potential evaporation and wind four scenarios are constructed, encompassing ranges of both global mean temperature rise in 2050 and the strength of the response of the dominant atmospheric circulation in the area of interest to global warming. For this particular area, wintertime precipitation is seen to increase between 3.5 and 7% per degree global warming, but mean summertime precipitation shows opposite signs depending on the assumed response of the circulation regime. Annual maximum daily mean wind speed shows small changes compared to the observed (natural) variability of this variable. Sea level rise in the North Sea in 2100 ranges between 35 and 85 cm. Preliminary assessment of the impact of the new scenarios on water management and coastal defence policies indicate that particularly dry summer scenarios and increased intensity of extreme daily precipitation deserves additional attention in the near future.

We analyze century-long daily temperature and precipitation records for stations in Europe west of 60 degrees E. A set of climatic indices derived from the daily series, mainly focusing on extremes, is defined. Linear trends in these indices are assessed over the period 1901-2000. Average trends, for 75 stations mostly representing Europe west of 20 degrees E, show a warming for all temperature indices. Winter has, on average, warmed more (similar to 1.0 degrees C/100 yr) than summer (similar to 0.8 degrees C), both for daily maximum (TX) and minimum (TN) temperatures. Overall, the warming of TX in winter was stronger in the warm tail than in the cold tail (1.6 and 1.5 degrees C for 98th and 95th, but similar to 1.0 degrees C for 2nd, 5th and 10th percentiles). There are, however, large regional differences in temperature trend patterns. For summer, there is a tendency for stronger warming, both for TX and TN, in the warm than in the cold tail only in parts of central Europe. Winter precipitation totals, averaged over 121 European stations north of 40 degrees N, have increased significantly by similar to 12% per 100 years. Trends in 90th, 95th and 98th percentiles of daily winter precipitation have been similar. No overall long-term trend occurred in summer precipitation totals, but there is an overall weak (statistically insignificant and regionally dependent) tendency for summer precipitation to have become slightly more intense but less common. Data inhomogeneities and relative sparseness of station density in many parts of Europe preclude more robust conclusions. It is of importance that new methods are developed for homogenizing daily data.

The daily maximum and minimum temperature series of the European Climate Assessment & Dataset are homogenized using the quantile matching approach. As the dataset is large and the detail of metadata is generally missing, an automated method locates breaks in the series based on a comparison with surrounding series and applies adjustments which are estimated using homogeneous segments of surrounding series as reference. A total of 6,500 series have been processed and after removing duplicates and short series, about 2,100 series have been adjusted. Finally, the effect of the homogenization of daily maximum and minimum temperature on trend estimation is shown to produce a much more spatially homogeneous and then plausible picture.

Measurements of sulfur and nitrogen deposition by means of the throughfall method are compared with model estimates. The sulfur model appears to estimate deposition to deciduous forests very well, but underestimates deposition to conifers by 30 to 40%. The NH4-N deposition to forest is underestimated by the applied model. The model predictions of NO3-N deposition do not show any bias against observations, but are not better correlated with observations than NH4 calculations. Reduced nitrogen compounds are the dominant nitrogen form in throughfall in ammonia polluted areas and contribute up to 50% of the total nitrogen deposition in other areas. Deposition of alkaline material to forests is estimated by applying different methods. Basic cation deposition neutralizes about 31% of the acid sulfur deposition to forest, on the average. Because of the large spatial variability of base cation deposition, it is recommended to develop a model for atmospheric transport and deposition of calcium- and magnesium-bearing particles.