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The occurrence of urban flooding following strong rainfall events may increase as a result of climate change. Urban expansion, aging infrastructure and an increasing number of impervious surfaces are further exacerbating flooding. To increase resilience and support flood mitigation, bespoke accurate flood modelling and reliable prediction is required. However, flooding in urban areas is most challenging. State-of-the-art flood inundation modelling is still often based on relatively low-resolution 2.5 D bare earth models with 2–5 m GSD. Current systems suffer from a lack of precise input data and numerical instabilities and lack of other important data, such as drainage networks. Especially, the quality and resolution of the topographic input data represents a major source of uncertainty in urban flood modelling. A benchmark study is needed that defines the accuracy requirements for highly detailed urban flood modelling and to improve our understanding of important threshold processes and limitations of current methods and 3D mapping data alike.This paper presents the first steps in establishing a new, innovative multiscale data set suitable to benchmark urban flood modelling. The final data set will consist of high-resolution 3D mapping data acquired from different airborne platforms, focusing on the use of drones (optical and LiDAR). The case study includes residential as well as rural areas in Dudelange/Luxembourg, which have been prone to localized flash flooding following strong rainfall events in recent years. The project also represents a cross disciplinary collaboration between the geospatial and flood modelling community. In this paper, we introduce the first steps to build up a new benchmark data set together with some initial flood modelling results. More detailed investigations will follow in the next phases of this project.

Estimating how much water is flowing through rivers at the global scale is challenging due to a lack of observations in space and time. A way forward is to optimally combine the global network of Earth system observations with advanced Numerical Weather Prediction (NWP) models to generate consistent spatio-temporal maps of land, ocean, and atmospheric variables of interest, known as a reanalysis. While the current generation of NWP output runoff at each grid cell, they currently do not produce river discharge at catchment scales directly, and thus have limited utility in hydrological applications such as flood and drought monitoring and forecasting. This is overcome in the Global Flood Awareness System (GloFAS; http://www.globalfloods.eu/) by coupling surface and sub-surface runoff from the HTESSEL land surface model used within ECMWF’s latest global atmospheric reanalysis (ERA5) with the LISFLOOD hydrological and channel routing model.This work presents the new GloFAS-ERA5 global river discharge reanalysis dataset launched on 5 November 2019 (version 2.1 release). The river discharge reanalysis is a global gridded dataset with a horizontal resolution of 0.1° at a daily time step. An innovative feature is that it is produced in an operational environment so is available to users from 1 January 1979 until near real time (2 to 5 days behind real time). The reanalysis was evaluated against a global network of 1801 river discharge observation stations. Results found that the GloFAS-ERA5 reanalysis was skilful against a mean flow benchmark in 86 % of catchments according to the modified Kling-Gupta Efficiency Skill Score, although the strength of skill varied considerably with location. The global median Pearson correlation coefficient was 0.61 with an interquartile range of 0.44 to 0.74. The long-term and operational nature of the GloFAS-ERA5 reanalysis dataset provides a valuable dataset to the user community for large scale hydrology applications ranging from monitoring global flood and drought conditions, understanding hydroclimatic variability and change, initialising hydrological forecasts, and as raw input to post-processing and machine learning methods that can add further value.Data availibility: The dataset is openly available from the Copernicus Climate Change Service (C3S) Climate Data Store (C3S): https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical?tab=overview and further details and the evaluation of the dataset can be found in the accompanying data description paper: Data paper: Harrigan, S., Zsoter, E., Alfieri, L., Prudhomme, C., Salamon, P., Wetterhall, F., Barnard, C., Cloke, H., and Pappenberger, F.: GloFAS-ERA5 operational global river discharge reanalysis 1979–present, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-232, 2020.

The recent paper by Shen et al. (2018) (referred to hereafter as SHEN) made a sweeping statement on the winter haze pollution in Beijing by claiming “Insignificant effect of climate change on winter haze in Beijing”. We argue that the paper contains three serious flaws. Either one of the three flaws can nullify the claim of SHEN.

Characterizing the temporal uncertainty in palaeoclimate records is crucial for analysing past climate change, correlating climate events between records, assessing climate periodicities, identifying potential triggers and evaluating climate model simulations. The first global compilation of speleothem isotope records by the SISAL (Speleothem Isotope Synthesis and Analysis) working group showed that age model uncertainties are not systematically reported in the published literature, and these are only available for a limited number of records (ca. 15 %, n = 107/691). To improve the usefulness of the SISAL database, we have (i) improved the database’s spatio-temporal coverage and (ii) created new chronologies using seven different approaches for age– depth modelling. We have applied these alternative chronologies to the records from the first version of the SISAL database (SISALv1) and to new records compiled since the release of SISALv1. This paper documents the necessary changes in the structure of the SISAL database to accommodate the inclusion of the new age models and their uncertainties as well as the expansion of the database to include new records and the qualitycontrol measures applied. This paper also documents the age–depth model approaches used to calculate the new chronologies. The updated version of the SISAL database (SISALv2) contains isotopic data from 691 speleothem records from 294 cave sites and new age–depth models, including age–depth temporal uncertainties for 512 speleothems. SISALv2 is available at https://doi.org/10.17864/1947.256 (Comas-Bru et al., 2020a)., The design and creation of v2 of the database were supported by funding to Sandy P. Harrison from the ERCfunded project GC2.0 (Global Change 2.0: Unlocking the past for a clearer future; grant no. 694481) and the Geological Survey Ireland Short Call 2017 (Developing a toolkit for model evaluation using speleothem isotope data; grant no. 2017-SC-056) award to Laia Comas-Bru. Sandy P. Harrison and Laia Comas-Bru received additional support from the ERC-funded project GC2.0 and from the JPI-Belmont project “PAlaeo-Constraints on Monsoon Evolution and Dynamics (PACMEDY)” through the UK Natural Environmental Research Council (NERC). Laia Comas-Bru and Belen Martrat received support from the CSIC scientific international collaboration programme I-LINKA20102 IBCC-lo2k. Kira Rehfeld and Denis Scholz acknowledge support by the Deutsche Forschungsgemeinschaft (DFG; codes RE3994/2-1 and SCHO 1274/11-1).

Surface water, as the largest part of water resources, plays an important role on China's agricultural production and food security. And surface water is vulnerable to climate change. This paper aims to examine the status of the supply reliability of surface water irrigation, and discusses how it is affected by climate change in rural China. The field data we used in this study was collected from a nine-province field survey during 2012 and 2013. Climate data are offered by China's National Meteorological Information Center which contains temperature and precipitation in the past 30 years. A Tobit model (or censored regression model) was used to estimate the influence of climate change on supply reliability of surface water irrigation. Descriptive results showed that, surface water supply reliability was 74 % in the past 3 years. Econometric results revealed that climate variables significantly influenced the supply reliability of surface water irrigation. Specifically, temperature is negatively related with the supply reliability of surface water irrigation; but precipitation positively influences the supply reliability of surface water irrigation. Besides, climate influence differs by seasons. In a word, this paper improves our understanding of the impact of climate change on agriculture irrigation and water supply reliability in the micro scale, and provides a scientific basis for relevant policy making.

The members of the ACER project wish to thank the QUEST-DESIRE (UK and France) bilateral project, the INQUA International Focus Group ACER and the INTIMATE-COST action for funding a suite of workshops to compile the ACER pollen and charcoal database and the workshop on ACER chronology that allow setting the basis for harmonizing the chronologies. Josué M. Polanco-Martinez was funded by a Basque Government postdoctoral fellowship (POS_2015_1_0006) and Sandy P. Harrison by the ERC Advanced Grant GC2.0: unlocking the past for a clearer future. Quaternary records provide an opportunity to examine the nature of the vegetation and fire responses to rapid past climate changes comparable in velocity and magnitude to those expected in the 21st-century. The best documented examples of rapid climate change in the past are the warming events associated with the Dansgaard–Oeschger (D–O) cycles during the last glacial period, which were sufficiently large to have had a potential feedback through changes in albedo and greenhouse gas emissions on climate. Previous reconstructions of vegetation and fire changes during the D–O cycles used independently constructed age models, making it difficult to compare the changes between different sites and regions. Here, we present the ACER (Abrupt Climate Changes and Environmental Responses) global database, which includes 93 pollen records from the last glacial period (73–15 ka) with a temporal resolution better than 1000 years, 32 of which also provide charcoal records. A harmonized and consistent chronology based on radiometric dating (14C, 234U∕230Th, optically stimulated luminescence (OSL), 40Ar∕39Ar-dated tephra layers) has been constructed for 86 of these records, although in some cases additional information was derived using common control points based on event stratigraphy. The ACER database compiles metadata including geospatial and dating information, pollen and charcoal counts, and pollen percentages of the characteristic biomes and is archived in Microsoft AccessTM at https://doi.org/10.1594/PANGAEA.870867 Publisher PDF

Reduced visibility is an indicator of poor air quality. Moreover, degradation in visibility can be hazardous to human safety; for example, low visibility can lead to road, rail, sea and air accidents. In this paper, we explore the combined influence of atmospheric aerosol particle and gas characteristics, and meteorology, on long-term visibility. We use visibility data from eight meteorological stations, situated in the UK, which have been running since the 1950s. The site locations include urban, rural and marine environments. Most stations show a long-term trend of increasing visibility, which is indicative of reductions in air pollution, especially in urban areas. Additionally, the visibility at all sites shows a very clear dependence on relative humidity, indicating the importance of aerosol hygroscopicity on the ability of aerosol particles to scatter radiation. The dependence of visibility on other meteorological parameters, such as wind speed and wind direction, is also investigated. Most stations show long-term increases in temperature which can be ascribed to climate change, land-use changes (e.g. urban heat island effects) or a combination of both; the observed effect is greatest in urban areas. The impact of this temperature change upon local relative humidity is discussed. To explain the long-term visibility trends and their dependence on meteorological conditions, the measured data were fitted to a newly developed light-extinction model to generate predictions of historic aerosol and gas scattering and absorbing properties. In general, an excellent fit was achieved between measured and modelled visibility for all eight sites. The model incorporates parameterizations of aerosol hygroscopicity, particle concentration, particle scattering, and particle and gas absorption. This new model should be applicable and is easily transferrable to other data sets worldwide. Hence, historical visibility data can be used to assess trends in aerosol particle properties. This approach may help constrain global model simulations which attempt to generate aerosol fields for time periods when observational data are scarce or non-existent. Both the measured visibility and the modelled aerosol properties reported in this paper highlight the success of the UK's Clean Air Act, which was passed in 1956, in cleaning the atmosphere of visibility-reducing pollutants.

The Nordic Centre of Excellence CRAICC (Cryosphere–Atmosphere Interactions in a Changing Arctic Climate), funded by NordForsk in the years 2011–2016, is the largest joint Nordic research and innovation initiative to date, aiming to strengthen research and innovation regarding climate change issues in the Nordic region. CRAICC gathered more than 100 scientists from all Nordic countries in a virtual centre with the objectives of identifying and quantifying the major processes controlling Arctic warming and related feedback mechanisms, outlining strategies to mitigate Arctic warming, and developing Nordic Earth system modelling with a focus on short-lived climate forcers (SLCFs), including natural and anthropogenic aerosols. The outcome of CRAICC is reflected in more than 150 peer-reviewed scientific publications, most of which are in the CRAICC special issue of the journal Atmospheric Chemistry and Physics. This paper presents an overview of the main scientific topics investigated in the centre and provides the reader with a state-of-the-art comprehensive summary of what has been achieved in CRAICC with links to the particular publications for further detail. Faced with a vast amount of scientific discovery, we do not claim to completely summarize the results from CRAICC within this paper, but rather concentrate here on the main results which are related to feedback loops in climate change–cryosphere interactions that affect Arctic amplification., The CRAICC team acknowledges the following institutions for financial support: the Finnish Cultural Foundation grant, Markku Kulmala “International Working Groups”; Russian mega-grant no. 11.G34.31.0048 (University of Nizhny Novgorod); Academy of Finland contracts 259537, 257411, and 254195; Beautiful Beijing (Finland–China collaboration project) funded by TEKES; Nordforsk CRAICC-PEEX (amendment to contract 26060); CRAICC-CRUCIAL (project no. 81257); Icelandic Research Fund (Rannis) grant no. 152248-051; Danish Environmental Protection Agency with means from the Dancea fund for environmental support to the Arctic region (M 112 002700); the Villum Foundation; the Carlsberg Foundation (project 009_1_0515); COST1303 (TOPROF); COST ES1404 (HarmoSnow); and the Pan-Eurasian Experiment (PEEX). The development and use of NorESM1 was supported by the Norwegian Research Council through the projects EarthClim (207711/E10), EVA (grant no. 229771), NOTUR (nn2345k), and NorStore (ns2345k) and through the Nordic Centre of Excellence eSTICC (57001) and the EU H2020 project CRESCENDO (grant no. 641816). The CRAICC team also thanks Rogier Floors for providing Fig. 8 and Christoph Münkel for Fig. 9. The authors and entire CRAICC community would like to thank and acknowledge the work and inspiration of Jon Egill Kristjansson, whose life was cut short during these collaborations. Jon Egill Kristjansson is deeply missed, but his scientific legacy continues.

This paper presents a review of a large number of research studies performed during the last few decades that focused on the investigation of cold season extreme precipitation events (EPEs) in the Mediterranean region (MR). The publications demonstrate the important role of anomalously intense transports of moist air from the tropical and subtropical Atlantic in the occurrence of EPEs in the MR. EPEs in the MR are directly or indirectly connected to narrow bands with a high concentration of moisture in the lower troposphere, i.e., atmospheric rivers, along which a large amount of moisture is transported from the tropics to midlatitudes. Whereas in a significant fraction of the EPEs in the western MR moisture is transported to the MR from the tropical Atlantic, EPEs in the central, and especially the eastern, MR are more often associated with intense tropical moisture transports over North Africa and the Red Sea. The moist air for the EPEs in the latter part of the MR also mainly originates from the tropical Atlantic and Indian oceans, and in many cases it serves as a temporary moisture reservoir for future development. The paper is supplemented by the results of a test for a possible connection between declining Arctic sea ice and the climatology of intense precipitation in the eastern MR. Based on the results of the evaluation supporting those from the earlier climate change analyses and modeling studies, it is concluded that a further anthropogenic global warming may lead a greater risk of higher rainfall totals and therefore larger winter floods in western and central parts of the MR as a consequence of stronger and more numerous Atlantic atmospheric rivers, possibly accompanied by a decline in the number of EPEs in the eastern part of the MR.

Global climate change has numerous implications for members of mountain communities who feel the impacts in both physical and social dimensions. In the western Himalayas of India, a majority of residents maintain a livelihood strategy that includes a combination of subsistence or small-scale agriculture, livestock rearing, seasonal or long-term migration, and localized natural resource extraction. While warming temperatures, irregular patterns of precipitation and snowmelt, and changing biological systems present challenges to the viability of these traditional livelihood portfolios in general, we find that climate change is also undermining local communities' livelihood assets in gender-specific ways. In this paper, we present a case study from the Nanda Devi Biosphere Reserve (Uttarakhand, India) that both outlines the implications of climate change for women farmers in the area and highlights the potential for ecotourism (as a form of livelihood diversification) to strengthen both key livelihood assets of women and local communities' adaptive capacity more broadly. The paper intentionally employs a categorical focus on women but also addresses issues of inter-group and gender diversity. With this special issue in mind, suggestions for related research are proposed for consideration by climate scientists and social systems and/or policy modelers seeking to support gender justice through socially transformative perspectives and frameworks.

Few hydrological studies have been made in Greenland, other than on glacial hydrology associated with the ice sheet. Understanding permafrost hydrology and hydroclimatic change and variability, however, provides key information for understanding climate change effects and feedbacks in the Arctic landscape. This paper presents a new extensive and detailed hydrological and meteorological open access dataset, with high temporal resolution from a 1.56 km2 permafrost catchment with a lake underlain by a through talik close to the ice sheet in the Kangerlussuaq region, western Greenland. The paper describes the hydrological site investigations and utilized equipment, as well as the data collection and processing. The investigations were performed between 2010 and 2013. The high spatial resolution, within the investigated area, of the dataset makes it highly suitable for various detailed hydrological and ecological studies on catchment scale. The dataset is availble for all users via the PANGAEA database, http://doi.pangaea.de/10.1594/PANGAEA.836178. Please note this dataset is under review and recommended not to be used before the final version of the manuscript is accepted for publication.

Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between global climate models (GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) data sets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to present a proof-of-concept approximation of within-GCM uncertainty for monthly precipitation and temperature projections and to assess the impact of within-GCM uncertainty on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. We adopt stochastic replicates of available GCM runs to approximate within-GCM uncertainty because large ensembles, hundreds of runs, for a given GCM and scenario are unavailable, other than the Climateprediction.net data set for the Hadley Centre GCM. To date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2015) sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP), mean annual temperature (MAT), mean annual runoff (MAR), the standard deviation of annual precipitation (SDP), standard deviation of runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 worldwide catchments. Based on 100 stochastic replicates of each GCM run at each catchment, within-GCM uncertainty was assessed in relative form as the standard deviation expressed as a percentage of the mean of the 100 replicate values of each variable. The average relative within-GCM uncertainties from the 17 catchments and 5 GCMs for 2015–2044 (A1B) were MAP 4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The Gould–Dincer Gamma (G-DG) procedure was applied to each annual runoff time series for hypothetical reservoir capacities of 1 × MAR and 3 × MAR and the average uncertainties in reservoir yield due to within-GCM uncertainty from the 17 catchments and 5 GCMs were 25.1% (1 × MAR) and 11.9% (3 × MAR). Our approximation of within-GCM uncertainty is expected to be an underestimate due to not replicating the GCM trend. However, our results indicate that within-GCM uncertainty is important when interpreting climate change impact assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and reservoir yield from 1 × MAR or 3 × MAR capacity reservoirs are expected to fall within twice their respective relative uncertainty (standard deviation/mean). Within-GCM uncertainty has significant implications for interpreting climate change impact assessments that report future changes within our range of uncertainty for a given variable – these projected changes may be due solely to within-GCM uncertainty. Since within-GCM variability is amplified from precipitation to runoff and then to reservoir yield, climate change impact assessments that do not take into account within-GCM uncertainty risk providing water resources management decision makers with a sense of certainty that is unjustified.

Human activities have caused various changes to the Earth system, and hence the interconnections between human activities and the Earth system should be recognized and reflected in models that simulate Earth system processes. One key anthropogenic activity is water resource management, which determines the dynamics of human–water interactions in time and space and controls human livelihoods and economy, including energy and food production. There are immediate needs to include water resource management in Earth system models. First, the extent of human water requirements is increasing rapidly at the global scale and it is crucial to analyze the possible imbalance between water demands and supply under various scenarios of climate change and across various temporal and spatial scales. Second, recent observations show that human–water interactions, manifested through water resource management, can substantially alter the terrestrial water cycle, affect land–atmospheric feedbacks and may further interact with climate and contribute to sea-level change. Due to the importance of water resource management in determining the future of the global water and climate cycles, the World Climate Research Program's Global Energy and Water Exchanges project (WRCP-GEWEX) has recently identified gaps in describing human–water interactions as one of the grand challenges in Earth system modeling (GEWEX, 2012). Here, we divide water resource management into two interdependent elements, related firstly to water demand and secondly to water supply and allocation. In this paper, we survey the current literature on how various components of water demand have been included in large-scale models, in particular land surface and global hydrological models. Issues of water supply and allocation are addressed in a companion paper. The available algorithms to represent the dominant demands are classified based on the demand type, mode of simulation and underlying modeling assumptions. We discuss the pros and cons of available algorithms, address various sources of uncertainty and highlight limitations in current applications. We conclude that current capability of large-scale models to represent human water demands is rather limited, particularly with respect to future projections and coupled land–atmospheric simulations. To fill these gaps, the available models, algorithms and data for representing various water demands should be systematically tested, intercompared and improved. In particular, human water demands should be considered in conjunction with water supply and allocation, particularly in the face of water scarcity and unknown future climate.

This guide to estimating daily and monthly actual, potential, reference crop and pan evaporation covers topics that are of interest to researchers, consulting hydrologists and practicing engineers. Topics include estimating actual evaporation from deep lakes and from farm dams and for catchment water balance studies, estimating potential evaporation as input to rainfall-runoff models, and reference crop evapotranspiration for small irrigation areas, and for irrigation within large irrigation districts. Inspiration for this guide arose in response to the authors' experiences in reviewing research papers and consulting reports where estimation of the actual evaporation component in catchment and water balance studies was often inadequately handled. Practical guides using consistent terminology that cover both theory and practice are not readily available. Here we provide such a guide, which is divided into three parts. The first part provides background theory and an outline of the conceptual models of potential evaporation of Penman, Penman–Monteith and Priestley–Taylor, as well as discussions of reference crop evapotranspiration and Class-A pan evaporation. The last two sub-sections in this first part include techniques to estimate actual evaporation from (i) open-surface water and (ii) landscapes and catchments (Morton and the advection-aridity models). The second part addresses topics confronting a practicing hydrologist, e.g. estimating actual evaporation for deep lakes, shallow lakes and farm dams, lakes covered with vegetation, catchments, irrigation areas and bare soil. The third part addresses six related issues: (i) automatic (hard wired) calculation of evaporation estimates in commercial weather stations, (ii) evaporation estimates without wind data, (iii) at-site meteorological data, (iv) dealing with evaporation in a climate change environment, (v) 24 h versus day-light hour estimation of meteorological variables, and (vi) uncertainty in evaporation estimates. This paper is supported by a Supplement that includes 21 sections enhancing the material in the text, worked examples of many procedures discussed in the paper, a program listing (Fortran 90) of Morton's WREVAP evaporation models along with tables of monthly Class-A pan coefficients for 68 locations across Australia and other information.

Sea level rise, changes in storms and wave climate as a consequence of global climate change are expected to increase the size and magnitude of flooded and eroding coastal areas, thus having profound impacts on coastal communities and ecosystems. River deltas, beaches, estuaries and lagoons are considered particularly vulnerable to the adverse effects of climate change, which should be studied at the regional/local scale. This paper presents a regional vulnerability assessment (RVA) methodology developed to analyse site-specific spatial information on coastal vulnerability to the envisaged effects of global climate change, and assist coastal communities in operational coastal management and conservation. The main aim of the RVA is to identify key vulnerable receptors (i.e. natural and human ecosystems) in the considered region and localize vulnerable hot spot areas, which could be considered as homogeneous geographic sites for the definition of adaptation strategies. The application of the RVA methodology is based on a heterogeneous subset of bio-geophysical and socio-economic vulnerability indicators (e.g. coastal topography, geomorphology, presence and distribution of vegetation cover, location of artificial protection), which are a measure of the potential harm from a range of climate-related impacts (e.g. sea level rise inundation, storm surge flooding, coastal erosion). Based on a system of numerical weights and scores, the RVA provides relative vulnerability maps that allow to prioritize more vulnerable areas and targets of different climate-related impacts in the examined region and to support the identification of suitable areas for human settlements, infrastructures and economic activities, providing a basis for coastal zoning and land use planning. The implementation, performance and results of the methodology for the coastal area of the North Adriatic Sea (Italy) are fully described in the paper.

This paper aims at helping synergistic studies in combining data from different satellites for gaining new insights into two critical yet poorly understood aspects of anthropogenic climate change, aerosol-cloud interactions and aerosol radiative effects. In particular, the paper examines the way cloud information from the MODIS (MODerate resolution Imaging Spectroradiometer) imager can refine our perceptions based on CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar measurements about the systematic aerosol changes that occur near clouds. The statistical analysis of a yearlong dataset of co-located global maritime observations from the Aqua and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellites reveals that MODIS's multispectral imaging ability can greatly help the interpretation of CALIOP observations. The results show that imagers on Aqua and CALIPSO yield very similar pictures, and that the discrepancies – due mainly to wind drift and differences in view angle – do not significantly hinder aerosol measurements near clouds. By detecting clouds outside the CALIOP track, MODIS reveals that clouds are usually closer to clear areas than CALIOP data alone would suggest. The paper finds statistical relationships between the distances to clouds in MODIS and CALIOP data, and proposes a rescaling approach to statistically account for the impact of clouds outside the CALIOP track even when MODIS cannot reliably detect low clouds, for example at night or over sea ice. Finally, the results show that the typical distance to clouds depends on both cloud coverage and cloud type, and accordingly varies with location and season. In maritime areas perceived cloud free, the global median distance to clouds below 3 km altitude is in the 4–5 km range.

The Canadian Earth System Model version 5 (CanESM5) developed at Environment and Climate Change Canada's Canadian Centre for Climate Modelling and Analysis (CCCma) is participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). A 40-member ensemble of CanESM5 retrospective decadal forecasts (or hindcasts) is integrated for 10 years from realistic initial states once a year during 1961 to the present using prescribed external forcing. The results are part of CCCma's contribution to the Decadal Climate Prediction Project (DCPP) component of CMIP6. This paper evaluates CanESM5 large ensemble decadal hindcasts against observational benchmarks and against historical climate simulations initialized from pre-industrial control run states. The focus is on the evaluation of the potential predictability and actual skill of annual and multi-year averages of key oceanic and atmospheric fields at regional and global scales. The impact of initialization on prediction skill is quantified from the hindcasts decomposition into uninitialized and initialized components. The dependence of potential and actual skill on ensemble size is examined. CanESM5 decadal hindcasts skillfully predict upper-ocean states and surface climate with a significant impact from initialization that depend on climate variable, forecast range, and geographic location. Deficiencies in the skill of North Atlantic surface climate are identified and potential causes discussed. The inclusion of biogeochemical modules in CanESM5 enables the prediction of carbon cycle variables which are shown to be potentially skillful on decadal timescales, with a strong long-lasting impact from initialization on skill in the ocean and a moderate short-lived impact on land.

The Arctic region is responding heavily to climate change, and yet, the air temperature of ice-covered areas in the Arctic is heavily under-sampled when it comes to in situ measurements, resulting in large uncertainties in existing weather and reanalysis products. This paper presents a method for estimating daily mean clear-sky 2 m air temperatures (T2m) in the Arctic from satellite observations of skin temperature, using the Arctic and Antarctic ice Surface Temperatures from thermal Infrared (AASTI) satellite dataset, providing spatially detailed observations of the Arctic. The method is based on a linear regression model, which has been tuned against in situ observations to estimate daily mean T2m based on clear-sky satellite ice surface skin temperatures. The daily satellite-derived T2m product includes estimated uncertainties and covers the Arctic sea ice and the Greenland Ice Sheet during clear skies for the period 2000–2009, provided on a 0.25∘ regular latitude–longitude grid. Comparisons with independent in situ measured T2m show average biases of 0.30 and 0.35∘C and average root-mean-square errors of 3.47 and 3.20 ∘C for land ice and sea ice, respectively. The associated uncertainties are verified to be very realistic for both land ice and sea ice, using in situ observations. The reconstruction provides a much better spatial coverage than the sparse in situ observations of T2m in the Arctic and is independent of numerical weather prediction model input. Therefore, it provides an important supplement to simulated air temperatures to be used for assimilation or global surface temperature reconstructions. A comparison of T2m derived from satellite and ERA-Interim/ERA5 estimates shows that the satellite-derived T2m validates similar to or better than ERA-Interim/ERA5 against in situ measurements in the Arctic.

While there is agreement that global warming over the 21st century is likely to influence the biological pump, Earth system models (ESM) display significant divergence in their projections of future new production. This paper quantifies and interprets the sensitivity of projected changes in new production in an idealized global ocean-biogeochemistry model. The model includes two tracers that explicitly represent nutrient transport, light- and nutrient-limited nutrient uptake by the ecosystem (new production), and export via sinking organic particles. Globally, new production declines with warming due to reduced surface nutrient availability, as expected. However, the magnitude, seasonality, and underlying dynamics of the nutrient uptake are sensitive to the light and nutrient dependencies of uptake, which we summarize in terms of a single biological timescale that is a linear combination of the partial derivatives of production with respect to light and nutrients. Although the relationships are non-linear, this biological timescale is correlated with several measures of biogeochemical function: shorter timescales are associated with greater global annual new production and higher nutrient utilization. Shorter timescales are also associated with greater declines in global new production in a warmer climate and greater sensitivity to changes in nutrient than light. Future work is needed to characterize more complex ocean biogeochemical models in terms of similar timescale generalities to examine their climate change implications.

This paper presents a novel dataset of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. This is a unique and physically consistent dataset, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100×10-year simulations and 25×10-year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. To demonstrate how adaptation-relevant information can be derived from the HAPPI dataset, changes in four climate indices for periods with 1.5 and 2.0 ∘C global warming are quantified. These indices include number of days per year with daily mean near-surface apparent temperature of >28 ∘C (ATG28); the yearly maximum 5-day sum of precipitation (RX5day); the daily precipitation intensity of the 50-year return period (RI50yr); and the annual consecutive dry days (CDDs). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature-related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 ∘C.

This paper describes the latest reprocessed data record (version 7.1) over 2007 to 2020 from the WegenerNet climate station networks, which since 2007 have been providing measurements with very high spatial and temporal resolution of hydrometeorological variables for two regions in the state of Styria, southeastern Austria: (1) the WegenerNet Feldbach Region, in the Alpine forelands of southeastern Styria, which extends over an area of about 22 km × 16 km and comprises 155 meteorological stations placed on a tightly spaced grid with an average spatial density of 1 station per ∼ 2 km2 and a temporal sampling of 5 min, and (2) the WegenerNet Johnsbachtal, which is a smaller “sister network” of the WegenerNet Feldbach Region in the mountainous Alpine region of upper Styria that extends over an area of about 16 km × 17 km and comprises 13 meteorological stations and 1 hydrographic station at altitudes ranging from below 600 m to over 2100 m and with a temporal sampling of 10 min. These networks operate on a long-term basis and continuously provide quality-controlled station time series for a multitude of hydrometeorological near-surface and surface variables, including air temperature, relative humidity, precipitation, wind speed and direction, wind gust speed and direction, soil moisture, soil temperature, and others like pressure and radiation variables at a few reference stations. In addition, gridded data are available at a resolution of 200 m × 200 m for air temperature, relative humidity, precipitation, and heat index for the Feldbach region and at a resolution of 100 m × 100 m for the wind parameters for both regions. Here we describe this dataset (the most recent reprocessing version 7.1) in terms of the measurement site and station characteristics as well as the data processing, from raw data (level 0) via quality-controlled basic station data (level 1) to weather and climate data products (level 2). In order to showcase the practical utility of the data, we also include two illustrative example applications, briefly summarize and refer to scientific uses in a range of previous studies, and briefly inform about the most recent WegenerNet advancements in 2020 towards a 3D open-air laboratory for climate change research. The dataset is published as part of the University of Graz Wegener Center's WegenerNet data repository under the DOI https://doi.org/10.25364/WEGC/WPS7.1:2021.1 (Fuchsberger et al., 2021) and is continuously extended.

Future changes in the climate system could have significant impacts on the natural environment and human activities, which in turn affect changes in the climate system. In the interaction between natural and human systems under climate change conditions, land use is one of the elements that play an essential role. On the one hand, future climate change will affect the availability of water and food, which may impact land-use change. On the other hand, human-induced land-use change can affect the climate system through biogeophysical and biogeochemical effects. To investigate these interrelationships, we developed MIROC-INTEG-LAND (MIROC INTEGrated LAND surface model version 1), an integrated model that combines the land surface component of global climate model MIROC (Model for Interdisciplinary Research on Climate) with water resources, crop production, land ecosystem, and land-use models. The most significant feature of MIROC-INTEG-LAND is that the land surface model that describes the processes of the energy and water balance, human water management, and crop growth incorporates a land use decision-making model based on economic activities. In MIROC-INTEG-LAND, spatially detailed information regarding water resources and crop yields is reflected in the prediction of future land-use change, which cannot be considered in the conventional integrated assessment models. In this paper, we introduce the details and interconnections of the submodels of MIROC-INTEG-LAND, compare historical simulations with observations, and identify various interactions between the submodels. By evaluating the historical simulation, we have confirmed that the model reproduces the observed states well. The future simulations indicate that changes in climate have significant impacts on crop yields, land use, and irrigation water demand. The newly developed MIROC-INTEG-LAND could be combined with atmospheric and ocean models to develop an integrated earth system model to simulate the interactions among coupled natural–human earth system components.

The 2018–2020 consecutive drought events in Germany resulted in impacts related with several sectors such as agriculture, forestry, water management, industry, energy production and transport. A major national operational drought information system is the German Drought Monitor (GDM), launched in 2014. It provides daily soil moisture (SM) simulated with the mesoscale hydrological model (mHM) and its related soil moisture index at a spatial resolution of 4 × 4 km2. Key to preparedness for extreme drought events are high-resolution information systems. The release of the new soil map BUEK200 allowed to increase the model resolution to ~1.2 × 1.2 km2, which is used in the second version of the GDM. In this paper, we explore the ability to provide drought information on the one-kilometer scale in Germany. Therefore, we compare simulated SM dynamics using homogenized and deseasonalized SM observations to evaluate the high-resolution drought simulations of the GDM. These SM observations are obtained from single profile measurements, spatially distributed sensor networks, cosmic-ray neutron stations and lysimeters at 40 sites in Germany. The results show that the agreement of simulated and observed SM dynamics is especially high in the vegetation period (0.84 median correlation R) and lower in winter (0.59 median R). Lower agreement in winter results from methodological uncertainties in simulations as well as in observations. Moderate but significant improvements between the first and second GDM version to observed SM were found in correlations for autumn (+0.07 median R) and winter (+0.12 median R). The annual drought intensity ranking and the spatial structure of drought events over the past 69 years is comparable for the two GDM versions. However, the higher resolution of the second GDM version allows a much more detailed representation of the spatial variability of SM, which is particularly beneficial for local risk assessments. Furthermore, the results underline that nationwide drought information systems depend both on appropriate simulations of the water cycle and a broad, high-quality observational soil moisture database.

Biosphere Reserves are archetypal parts of natural and cultural landscapes encompassing over large area of different ecosystem, it represents bio-geographic zones of an region. Globally, the areas of biosphere reserve is shrinking and exploiting due to the extreme climatic condition, natural calamities and anthropogenic activities, which leads to environmental and land degradation. In this paper Nilgiri Biosphere Reserve (NBSR) area has been selected and it represents a biodiversity-rich ecosystem in the Western Ghats and includes two of the ten biogeographical provinces of India. Amongst the most insubstantial ecosystems in the world, the Nilgiri Biosphere Reserve is bearing the substance of climate change evident in increasingly unpredictable rainfall and higher temperatures during recent years. The region was mostly unscathed till two centuries ago, but has witnessed large-scale destruction ever since. In this scenario, a need of application of remote sensing and advance machine learning techniques to monitor environmental degradation and its ecosystem in NBSR is more essential. The objective of the present study is to develop satellite image classification techniques that can reliably to map forest cover and land use, and provide the basis for long-term monitoring. Advanced image classification techniques on the cloud-based platform Google Earth Engine (GEE) for mapping vegetation and land use types, and analyse their spatial distributions. To restore degraded ecosystems to their natural conditions through proper management and conservation practices. In order to understand the nature of environmental degradation and its ecosystem in Nilgiri Biosphere Reserve; following thematic criteria’s were grouped in to four major indicators such as Terrain Indicator (TI), Environmental Indicator (EI), Hydro-Meteorological Indicator (HMI) and Socio-Economic Indicator (SEI). The utilisation of remote sensing product of huge datasets and various data product in analysis and advanced machine learning algorithm through Google earth engine are indispensable. After extraction of all the thematic layers by using multi criteria decision and fuzzy linear member based weight and ranks were assigned and overlay in GIS environment at a common pixel size of 30 m. Based on the analysis the resultant layer has been classified into five environmental degraded classes i.e., very high, high, moderate, slight and no degradation. This study is help to identify the degradation and long term monitoring and suggest the appropriate conservation, management and policies, it is a time to implement and protect the Nilgiri biosphere reserves without hindering present stage of natural environment in a sustainable manner.

The authors group is within the Glacier Lab of Politecnico di Torino (part of the CC-LAB, a laboratory for climate change monitoring), which is working on glacier monitoring since 2016, mainly exploiting Geomatics techniques to measure the extent and to model the surface of glaciers over the years. Measurement campaigns were carried out within the ASP (Alta Scuola Politecnica – Poliecnico di Torino e Milano) DREAM projects (Drone tEchnnology for wAter resources and hydrologic hazard Monitoring) The manuscript is focused on a specific case study related to the Belvedere glacier, a valley glacier located in northern Italy.In the framework of the Belvedere glacier monitoring, several Geomatics approaches have already been applied in the last four years by the cc-glacier-lab and DREAM Projects with the goal to monitor both the extent of the glacier and its surface. Such monitoring enables the multi-temporal comparison of the glacier digital surface model (DSM), highlighting areas of ice loss and gain. Considering the limitations of aerial surveys in high altitude environments, the authors started assessing the suitability of a satellite based approach, mainly focusing on positional accuracy assessment. The paper is focused on a monitoring based on a high resolution (0.5 m) satellite optical stereo pair. Several tests were carried out with the goal to test the 3D positional accuracies, assessing the impact of different configurations of Ground Control Point (GCP) in terms of numerosity and distribution and focusing on the DSM validation. The results demonstrated the fit-for-purpose of a satellite-based approach for glacier monitoring.

Global warming plays a principal role on the continuous increasing sea-level rise, which exposes coastal regions worldwide to flooding threat. However, the challenge is that the regional impact of SLR flooding can be variable, especially when considering multiple effects of land subsidence, long-term general sea-level rise and extreme weather conditions like storm surge. In this paper, we build module with high-resolution InSAR-derived precision DEMs with resolution of 4 m, long-term SLR trend and episodic signals of climate change to calculate the relative sea level in AD 2100 on various scenarios over the Jiaozhou Bay, one typical region of the biggest peninsula in China and an important economic centre adjoining to the Yellow Sea. The potential of TanDEM-X DEM for coastal vulnerability mapping in the Qingdao coastal area were evaluated in order to investigate the effect of the accuracy and resolution of coastal topography on the reliability and usefulness of elevation-based sea-level rise assessments. The results reveal that coastal lowland areas over the JiaozhouBay are extremely vulnerable in the following years within 21st century with use of high-accuracy TanDEM-X DEM data, which would be an advantage for further elevation-based dynamic assessments of coastal inundation events considering storm surges, abnormal high tides, and extreme precipitation events. which would be vital for locally coastal protection and decision-making.

Given the multiple abiotic and biotic stressors resulting from global changes, management systems and practices must be adapted in order to maintain and reinforce the resilience of forests. Among others, the transformation of monocultures into uneven-aged and mixed stands is an avenue to improve forest resilience. To explore the forest response to these new silvicultural practices under a changing environment, one needs models combining a process-based approach with a detailed spatial representation, which is quite rare. We therefore decided to develop our own model (HETEROFOR for HETEROgeneous FORest) according to a spatially explicit approach, describing individual tree growth based on resource sharing (light, water and nutrients). HETEROFOR was progressively elaborated within Capsis (Computer-Aided Projection for Strategies in Silviculture), a collaborative modelling platform devoted to tree growth and stand dynamics. This paper describes the carbon-related processes of HETEROFOR (photosynthesis, respiration, carbon allocation and tree dimensional growth) and evaluates the model performances for three broadleaved stands with different species compositions (Wallonia, Belgium). This first evaluation showed that HETEROFOR predicts well individual radial growth (Pearson's correlation of 0.83 and 0.63 for the European beech and sessile oak, respectively) and is able to reproduce size–growth relationships. We also noticed that the net to gross primary production (npp to gpp) ratio option for describing maintenance respiration provides better results than the temperature-dependent routine, while the process-based (Farquhar model) and empirical (radiation use efficiency) approaches perform similarly for photosynthesis. To illustrate how the model can be used to predict climate change impacts on forest ecosystems, we simulated the growth dynamics of the mixed stand driven by three IPCC climate scenarios. According to these simulations, the tree growth trends will be governed by the CO2 fertilization effect, with the increase in vegetation period length and the increase in water stress also playing a role but offsetting each other.

Climate change affects forest growth in numerous and sometimes opposite ways, and the resulting trend is often difficult to predict for a given site. Integrating and structuring the knowledge gained from the monitoring and experimental studies into process-based models is an interesting approach to predict the response of forest ecosystems to climate change. While the first generation of models operates at stand level, one now needs spatially explicit individual-based approaches in order to account for individual variability, local environment modification and tree adaptive behaviour in mixed and uneven-aged forests that are supposed to be more resilient under stressful conditions. The local environment of a tree is strongly influenced by the neighbouring trees, which modify the resource level through positive and negative interactions with the target tree. Among other things, drought stress and vegetation period length vary with tree size and crown position within the canopy. In this paper, we describe the phenology and water balance modules integrated in the tree growth model HETEROFOR (HETEROgenous FORest) and evaluate them on six heterogeneous sessile oak and European beech stands with different levels of mixing and development stages and installed on various soil types. More precisely, we assess the ability of the model to reproduce key phenological processes (budburst, leaf development, yellowing and fall) as well as water fluxes. Two two-phase models differing regarding their response function to temperature during the chilling period (optimum and sigmoid functions) and a simplified one-phase model are used to predict budburst date. The two-phase model with the optimum function is the least biased (overestimation of 2.46 d), while the one-phase model best accounts for the interannual variability (Pearson's r=0.68). For the leaf development, yellowing and fall, predictions and observations are in accordance. Regarding the water balance module, the predicted throughfall is also in close agreement with the measurements (Pearson's r=0.856; bias =-1.3 %), and the soil water dynamics across the year are well reproduced for all the study sites (Pearson's r was between 0.893 and 0.950, and bias was between −1.81 and −9.33 %). The model also reproduced well the individual transpiration for sessile oak and European beech, with similar performances at the tree and stand scale (Pearson's r of 0.84–0.85 for sessile oak and 0.88–0.89 for European beech). The good results of the model assessment will allow us to use it reliably in projection studies to evaluate the impact of climate change on tree growth in structurally complex stands and test various management strategies to improve forest resilience.

Cold region hydrology is very sensitive to the impacts of climate warming. Impacts of warming over recent decades in western Canada include glacier retreat, permafrost thaw, and changing patterns of precipitation, with an increased proportion of winter precipitation falling as rainfall and shorter durations of snow cover, as well as consequent changes in flow regimes. Future warming is expected to continue along these lines. Physically realistic and sophisticated hydrological models driven by reliable climate forcing can provide the capability to assess hydrological responses to climate change. However, the provision of reliable forcing data remains problematic, particularly in data-sparse regions. Hydrological processes in cold regions involve complex phase changes and so are very sensitive to small biases in the driving meteorology, particularly in temperature and precipitation, including precipitation phase. Cold regions often have sparse surface observations, particularly at high elevations that generate a large amount of runoff. This paper aims to provide an improved set of forcing data for large-scale hydrological models for climate change impact assessment. The best available gridded data in Canada are from the high-resolution forecasts of the Global Environmental Multiscale (GEM) atmospheric model and outputs of the Canadian Precipitation Analysis (CaPA), but these datasets have a short historical record. The EU WATCH ERA-Interim reanalysis (WFDEI) has a longer historical record but has often been found to be biased relative to observations over Canada. The aim of this study, therefore, is to blend the strengths of both datasets (GEM-CaPA and WFDEI) to produce a less-biased long-record product (WFDEI-GEM-CaPA) for hydrological modelling and climate change impact assessment over the Mackenzie River Basin. First, a multivariate generalization of the quantile mapping technique was implemented to bias-correct WFDEI against GEM-CaPA at 3 h ×0.125∘ resolution during the 2005–2016 overlap period, followed by a hindcast of WFDEI-GEM-CaPA from 1979. The derived WFDEI-GEM-CaPA data are validated against station observations as a preliminary step to assess their added value. This product is then used to bias-correct climate projections from the Canadian Centre for Climate Modelling and Analysis Canadian Regional Climate Model (CanRCM4) between 1950 and 2100 under RCP8.5, and an analysis of the datasets shows that the biases in the original WFDEI product have been removed and the climate change signals in CanRCM4 are preserved. The resulting bias-corrected datasets are a consistent set of historical and climate projection data suitable for large-scale modelling and future climate scenario analysis. The final historical product (WFDEI-GEM-CaPA, 1979–2016) is freely available at the Federated Research Data Repository at https://doi.org/10.20383/101.0111 (Asong et al., 2018), while the original and corrected CanRCM4 data are available at https://doi.org/10.20383/101.0162 (Asong et al., 2019).

In China, historical documents record a large quantity of information related to climate change and grain harvest. This information can help to explore the impacts of extreme drought or flood on crop production, which can provide implications for the adaptation of agriculture to higher-probability extreme climate in the context of global warming. In this paper, reported extreme drought/flood chronologies and reconstructed grain harvest series derived from historical documents were adopted in order to investigate the association between the reported frequency of extreme drought/flood in eastern China and reconstructed poor harvests during 801–1910. The results show that extreme droughts were reported more often in 801–870, 1031–1230, 1481–1530, and 1581–1650 over the whole of eastern China. On a regional scale, extreme droughts were reported more often in 1031–1100, 1441–1490, 1601–1650, and 1831–1880 in the North China Plain, 801–870, 1031–1120, 1161–1220, and 1471–1530 in Jianghuai, and 991–1040, 1091–1150, 1171–1230, 1411–1470, and 1481–1530 in Jiangnan. The grain harvest was reconstructed to be generally poor in 801–940, 1251–1650, and 1841–1910, but the reconstructed harvests were bumper in 951–1250 and 1651–1840, approximately. During the entire period from 801 to 1910, the frequency of reporting of extreme droughts in any subregion of eastern China was significantly associated over the long term with lower reconstructed harvests. The association between reported frequency of extreme floods and reconstructed low harvests appeared to be much weaker, while reconstructed harvest was much worse when extreme drought and extreme flood in different subregions were reported in the same year. The association between reconstructed poor harvests and reported frequency of regional extreme droughts was weak during the warm epoch of 920–1300 but strong during the cold epoch of 1310–1880, which could imply that a warm climate could weaken the impact of extreme drought on poor harvests; yet other historical factors may also contribute to these different patterns extracted from the two datasets.

The Canadian Earth System Model version 5 (CanESM5) is a global model developed to simulate historical climate change and variability, to make centennial-scale projections of future climate, and to produce initialized seasonal and decadal predictions. This paper describes the model components and their coupling, as well as various aspects of model development, including tuning, optimization, and a reproducibility strategy. We also document the stability of the model using a long control simulation, quantify the model's ability to reproduce large-scale features of the historical climate, and evaluate the response of the model to external forcing. CanESM5 is comprised of three-dimensional atmosphere (T63 spectral resolution equivalent roughly to 2.8∘) and ocean (nominally 1∘) general circulation models, a sea-ice model, a land surface scheme, and explicit land and ocean carbon cycle models. The model features relatively coarse resolution and high throughput, which facilitates the production of large ensembles. CanESM5 has a notably higher equilibrium climate sensitivity (5.6 K) than its predecessor, CanESM2 (3.7 K), which we briefly discuss, along with simulated changes over the historical period. CanESM5 simulations contribute to the Coupled Model Intercomparison Project phase 6 (CMIP6) and will be employed for climate science and service applications in Canada.

The Global Land and Marine Observations Database aims to produce a comprehensive land based meteorological data archive and inventory. This requires the compilation of available land-based station meteorological data information from all known available in-situ meteorological data repositories/sources at multiple timescales (e.g. sub-daily, daily and monthly). During this process the service team members have identified that many of the data sources contained stations with incorrect location coordinates. These stations cannot be included in the processing to be served via the Copernicus Climate Change Service until the issues are satisfactorily resolved. Many of these stations are located in regions of the world where a sparsity of climate data currently exists, such as Southeast Asia and South America. As such, resolving these issues would provide important additional climate data, but this is a very labour-intensive task. Therefore, we have developed the Geo-locate project enrolling the help of undergraduate Geography students at Maynooth University, Ireland, to resolve some of the land-based station geolocation issues. We have successfully run two Geo-locate projects, the first in the second semester of the 2017/18 academic year and the second in the 2018/19 academic year. Both iterations to date have been very successful with 1926 out of 2168 total candidate stations ostensibly resolved, which equates to an 88 % success rate. At the same time, students gained critical skills helping to meet the expected pedagogical outcomes of the second-year curriculum, while producing a lasting scientific legacy. We asked the class of 2018/19 to reflect critically upon the outcomes and present the results herein which provide important feedback on what students felt that they gained from their participation and how we may improve the experience and learning outcomes in future. We will be continuing to run Geo-locate projects over the next few years. Due to the success of the Geo-locate project we encourage other organisations to investigate the potential for engaging university students to help resolve similar data issues while enriching the student experience and aiding the delivery of learning outcomes. This paper provides details of the project, and all supporting information such as project guidelines and templates to enable this.

Environmental change monitoring in earth sciences needs land use land cover change (LULCC) modeling to investigate the impact of climate change phenomena such as droughts and floods on earth surface land cover. As land cover has a direct impact on Land Surface Temperature (LST), the Land cover mapping is an essential part of climate change modeling. In this paper, for land use land cover mapping (LULCM), image classification of Sentinel-1A Synthetic Aperture Radar (SAR) Ground Range Detected (GRD) data using two machine learning algorithms including Support Vector Machine (SVM) and Random Forest (RF) are implemented in R programming language and compared in terms of overall accuracy for image classification. Considering eight different scenarios defined in this research, RF and SVM classification methods show their best performance with overall accuracies of 90.81 and 92.09 percent respectively.

Due to climate changes, wildfire breakouts get more frequent and difficult to control. In the mid-July 2017, the wildfire spread from wildland to the city of Split, the second-largest city in Croatia. This unpredictable spread almost caused the collapse of emergency response systems. Fortunately, a greater tragedy was avoided with the composure of the responsible services and the help of the citizens. The citizens helped in extinguishing the fire and timely provided the significant amount of disaster-related information on different platforms and through social media. In this paper, we address the problem of identifying useful Volunteered Geographic Information (VGI) and georeferenced social media, for improving situation awareness while the wildfire was reaching the Croatian city of Split. Additionally, we combine social media with other external data sources (e. g. Sentinel-2 satellite images) and authoritative data (e.g. Croatian National Protection and Rescue Directorate official data and Public Fire Department of Split data) to establish the geographical relations between the wildfire phenomena and social media messages. In this manner, we seek to leverage the existing knowledge and data about the spatiotemporal characteristics of the Split wildfire in order to improve the identification of useful information from georeferenced social media with other integrated data sources that can be valuable for improving situation awareness in wildfire events.

Coal fire has been found to be a major problem worldwide in coal mining areas. The surrounding areas get hugely affected, and significant amount of reserves are wasted due to the burning of coal. This severely affects the environment condition, which leads to a rise in temperature of the region, which is a major reason for climate change. Greenhouse gases like CO2 SO2 NO CH4 are also emitted from the cracks and fissures. Large masses of the burning of coal also causes land subsidence and collapse. Underground coal fires ignited by natural causes or human error leads to atmospheric pollution, acid rain, land subsidence, and increased coronary and respiratory diseases. They consume a valuable energy resource, destroy floral and faunal habitats, and promote human suffering because of heat, subsidence, and pollution. Jharia Coalfield, Jharkhand, India, is well known for being the storehouse of prime coking coal and for accommodating the maximum number of coal fires among all the coalfields in the country. In this paper, some of the important issues of coal fire mapping from satellite thermal infrared data have been addressed in particular reference to Jharia Coalfield. Namely, these are: retrieval of spectral radiance from raw digital satellite data using scene-specific calibration coefficients of the detectors from metadata, thermal emissivity of surface to obtain kinetic temperature at each ground resolution cell of satellite data, field-based modelling of pixel-integrated temperature for differentiating surface and subsurface fire pixels in Landsat 8 thermal IR data, identification of surface coal fire locations from infrared data and lateral propagation of coal fire.

Appraisal of potential evapotranspiration (PET) is needed for estimating the agricultural water requirement and understanding hydrological processes in an arena. Therefore, aim of the paper was to estimate the PET in the upper Beas basin, situated in the Western Indian Himalaya, under future climate change scenarios (by mid-21st century). Climate data (1969–2010) of Manali, Bhuntar and Katrain were obtained from India Meteorological Department (IMD) and the Indian Agricultural Research Institute (IARI). Landsat data were used for mapping land use/land cover (LULC) conditions of the basin through decision tree technique. Elevation detail of the catchment area is derived from the Cartosat-1 digital elevation model (DEM). Simulations of PET were done by the Soil and Water Assessment Tool (SWAT) model. The model was calibrated using the average monthly discharge data from Thalout station. The study found fluctuations in PET under different climate change scenarios. It is likely to increase in near future owing to the rise in temperature. The higher water demand can be met from the excess snowmelt water reaching the lower basin area during the cropping seasons. This study will be helpful to understand water availability conditions in the upper Beas basin in the near future.

Rice is an important cereal crop and part of daily diet not only in India but also throughout Asia. Agriculture is highly dependent on the variations in temperature, precipitation and solar radiation and long-term climate patterns. The pattern of changing climate in last 30 years indicate that predictable and possible changes in rainfall and temperature can reduce agriculture outputs and yields particularly for rice and wheat significantly. The main aim of this research paper is to study the Kharif Rice Productivity in Hisar, Haryana under changing climate. The study focuses on the impacts of climate change namely intensity, timings and spatial distribution of rainfall. Kharif rice is more vulnerable to meteorological drought due to growing uncertainties in monsoon rains under the changing climate patterns. The study of seasonal changes in precipitation at decadal scale for observed and CORDEX projected precipitation for the selected region was done to understand the impacts of climate change. Along with this, DSSAT Crop Simulation Model was run to quantify the water stress in the years with more negative rainfall departure and to identify the reasons for variability in yield. DSSAT model was able to simulate phenological events and final grain yield at maturity stage with reasonable accuracy under varied weather conditions. The analysis of the simulated results indicates the association between yield and rainfall amount and its distribution during the season and different phenological growth stages of rice. The results further indicate that water stress at important stages such as Booting, Heading and Flowering majorly impact the final yield.

Temperature and ozone changes in the upper troposphere and lower stratosphere (UTLS) are important components of climate change. In this paper, variability and trends of temperature and ozone in the UTLS are investigated for the period 2002–2017 using high-quality, high vertical resolution Global Navigation Satellite System radio occultation (GNSS RO) data and improved merged satellite data sets. As part of the Stratosphere-troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP), three reanalysis data sets, including the ERA-I, MERRA2 and the recently released ERA5, are evaluated for their representation of temperature and ozone in the UTLS. The recent temperature and ozone trends are updated with a multiple linear regression (MLR) method and related to sea surface temperature (SST) changes based on model simulations made with NCAR's Whole Atmosphere Community Climate Model (WACCM). All reanalysis temperatures show good agreement with the GNSS RO measurements in both absolute value and annual cycle. Interannual variations in temperature related to Quasi-Biennial Oscillation (QBO) and the El Niño–Southern Oscillation (ENSO) processes are well represented by all reanalyses. However, evident biases can be seen in reanalyses for the linear trends of temperature since they are affected by discontinuities in assimilated observations and methods. Such biases can be corrected and the estimated trends can be significantly improved. ERA5 is significantly improved compared to ERA-I and shows the best agreement with the GNSS RO temperature. The MLR results indicate a significant warming of 0.2–0.3 K per decade in most areas of the troposphere, with a stronger increase of 0.4–0.5 K per decade at midlatitudes of both hemispheres. In contrast, the stratospheric temperature decreases at a rate of 0.1–0.3 K per decade, which is most significant in the Southern Hemisphere (SH). Positive temperature trends of 0.1–0.3 K per decade are seen in the tropical lower stratosphere (100–50 hPa). Negative trends of ozone are found in the Northern Hemisphere (NH) at 150–50 hPa, while positive trends are evident in the tropical lower stratosphere. Asymmetric trends of ozone can be found in the midlatitudes of two hemispheres in the middle stratosphere, with significant ozone decrease in the NH and increase in ozone in the SH. Large biases exist in reanalyses, and it is still challenging to do trend analysis based on reanalysis ozone data. According to single-factor-controlled model simulations with WACCM, the temperature increase in the troposphere and the ozone decrease in the NH stratosphere are mainly connected to the increase in SST and subsequent changes of atmospheric circulations. Both the increase in SSTs and the decrease in ozone in the NH contribute to the temperature decrease in the NH stratosphere. The increase in temperature in the lower stratospheric tropics may be related to an increase in ozone in that region, while warming SSTs contribute to a cooling in that area.

Climate change impacts on watershed ecosystems and hydrologic processes are complex. The key significant parameters responsible for balancing the watershed ecosystems are temperature and rainfall. Though these parameters are uncertain, they play a prime role in the projections of dimensional climate change studies. The impact of climate change is more dependent on temperature and precipitation which contributes at a larger magnitude for characterising global warming issues. This paper aims to forecast the variations of temperature and precipitation during the period of 2020–2050 for the northern part of Thenpennar sub basin. This study is modelled using SWAT (Soil and Water Assessment Tool) – a scale model developed to predict the impact of changes that occurs in land, soil and water over a period of time. This study is validated using the base period from 1980–2000 which shows the distribution of rainfall and temperature among 38 watersheds. The results from this study show that there is a decrease in the rainfall for a maximum of about 20% in the month of December during the predicted period of 2020 and 2050. This study assesses the possible adverse impact of climate change on temperature and precipitation of Thenpennai sub-basin. This kind of predictions will help the government agencies, rulers and decision makers in policy making and implementing the adaptation strategies for the changing climatic conditions.

This paper presents a new global burned area (BA) product, generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) red (R) and near-infrared (NIR) reflectances and thermal anomaly data, thus providing the highest spatial resolution (approx. 250 m) among the existing global BA datasets. The product includes the full times series (2001–2016) of the Terra-MODIS archive. The BA detection algorithm was based on monthly composites of daily images, using temporal and spatial distance to active fires. The algorithm has two steps, the first one aiming to reduce commission errors by selecting the most clearly burned pixels (seeds), and the second one targeting to reduce omission errors by applying contextual analysis around the seed pixels. This product was developed within the European Space Agency's (ESA) Climate Change Initiative (CCI) programme, under the Fire Disturbance project (Fire_cci). The final output includes two types of BA files: monthly full-resolution continental tiles and biweekly global grid files at a degraded resolution of 0.25°. Each set of products includes several auxiliary variables that were defined by the climate users to facilitate the ingestion of the product into global dynamic vegetation and atmospheric emission models. Average annual burned area from this product was 3.81 Mkm2, with maximum burning in 2011 (4.1 Mkm2) and minimum in 2013 (3.24 Mkm2). The validation was based on a stratified random sample of 1200 pairs of Landsat images, covering the whole globe from 2003 to 2014. The validation indicates an overall accuracy of 0.9972, with much higher errors for the burned than the unburned category (global omission error of BA was estimated as 0.7090 and global commission as 0.5123). These error values are similar to other global BA products, but slightly higher than the NASA BA product (named MCD64A1, which is produced at 500 m resolution). However, commission and omission errors are better compensated in our product, with a tendency towards BA underestimation (relative bias −0.4033), as most existing global BA products. To understand the value of this product in detecting small fire patches ( https://www.esa-fire-cci.org/, last access: 10 November 2018) and their repositories (pixel at full resolution: https://doi.org/cpk7, and grid: https://doi.org/gcx9gf).

Tools for estimating probabilities of flooding hazards caused by the simultaneous effect of sea level and waves are needed for the secure planning of densely populated coastal areas that are strongly vulnerable to climate change. In this paper we present a method for combining location-specific probability distributions of three different components: (1) long-term mean sea level change, (2) short-term sea level variations, and (3) wind-generated waves. We apply the method in two locations in the Helsinki Archipelago to obtain run-up level estimates representing the joint effect of the still water level and the wave run-up. These estimates for the present, 2050 and 2100 are based on field measurements and mean sea level scenarios. In the case of our study locations, the significant locational variability of the wave conditions leads to a difference in the safe building levels of up to one meter. The rising mean sea level in the Gulf of Finland and the uncertainty related to the associated scenarios contribute significantly to the run-up levels for the year 2100. We also present a sensitivity test of the method and discuss its applicability to other coastal regions. Our approach allows for the determining of different building levels based on the acceptable risks for various infrastructure, thus reducing building costs while maintaining necessary safety margins.

The purpose of the Intergovernmental Panel on Climate Change (IPCC) is to provide policy-relevant assessments of the scientific evidence about climate change. Policymaking necessarily involves risk assessments, so it is important that IPCC reports are designed accordingly. This paper proposes a specific idea, illustrated with examples, to improve the contribution of IPCC Working Group I to informing climate risk assessments.

Small-scale floods are a consequence of high precipitation rates in small areas that can occur along frontal activity and convective storms. This situation is expected to become more severe due to a warming climate, when single precipitation events resulting from deep convection become more intense (super Clausius–Clapeyron effect). Regional climate model (RCM) evaluations and inter-comparisons have shown that there is evidence that an increase in RCM resolution and, in particular, at the convection-permitting scale will lead to a better representation of the spatial and temporal characteristics of heavy precipitation at small and medium scales. In this paper, the benefits of grid size reduction and bias correction in climate models are evaluated in their ability to properly represent flood generation in small- and medium-sized catchments. The climate models are sequentially coupled with a distributed hydrological model. The study area is the Eastern Alps, where small-scale storms often occur along with heterogeneous rainfall distributions leading to a very local flash flood generation. The work is carried out in a small multi-model framework using two different RCMs (CCLM and WRF) in different grid sizes. Bias correction is performed by the use of the novel scaled distribution mapping (SDM), which is similar to the usual quantile mapping (QM) method. The results show that, in the investigated RCM ensemble, no clear added value of the usage of convection-permitting RCMs for the purpose of flood modelling can be found. This is based on the fact that flood events are the consequence of an interplay between the total precipitation amount per event and the temporal distribution of rainfall intensities on a sub-daily scale. The RCM ensemble is lacking in one and/or the other. In the small catchment ( km2), a favourable superposition of the errors leads to seemingly good CCLM 3 km results both for flood statistics and seasonal occurrence. This is, however, not systematic across the catchments. The applied bias correction only corrects total event rainfall amounts in an attempt to reduce systematic errors on a seasonal basis. It does not account for errors in the temporal dynamics and deteriorates the results in the small catchment. Therefore, it cannot be recommended for flood modelling.

Geoengineering, or climate intervention, describes methods of deliberately altering the climate system to offset anthropogenic climate change. As an idealized representation of near-surface solar geoengineering over the ocean, such as marine cloud brightening, this paper discusses experiment G1ocean-albedo of the Geoengineering Model Intercomparison Project (GeoMIP), involving an abrupt quadrupling of the CO2 concentration and an instantaneous increase in ocean albedo to maintain approximate net top-of-atmosphere radiative flux balance. A total of 11 Earth system models are relatively consistent in their temperature, radiative flux, and hydrological cycle responses to this experiment. Due to the imposed forcing, air over the land surface warms by a model average of 1.14 K, while air over most of the ocean cools. Some parts of the near-surface air temperature over ocean warm due to heat transport from land to ocean. These changes generally resolve within a few years, indicating that changes in ocean heat content play at most a small role in the warming over the oceans. The hydrological cycle response is a general slowing down, with high heterogeneity in the response, particularly in the tropics. While idealized, these results have important implications for marine cloud brightening, or other methods of geoengineering involving spatially heterogeneous forcing, or other general forcings with a strong land–ocean contrast. It also reinforces previous findings that keeping top-of-atmosphere net radiative flux constant is not sufficient for preventing changes in global mean temperature. © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License.

As the impacts by climate changes and human activities are intensified, variability may occur in river's annual runoff as well as flood and low water characteristics. In order to understand the characteristics of variability in hydrological series, diagnosis and identification must be conducted specific to the variability of hydrological series, i.e., whether there was variability and where the variability began to occur. In this paper, the mainstream of Yangtze River was taken as the object of study. A model was established to simulate the impounding and operation of upstream cascade reservoirs so as to obtain the runoff of downstream hydrological control stations after the regulation by upstream reservoirs in different level years. The Range of Variability Approach was utilized to analyze the impact of the operation of upstream reservoirs on the variability of downstream. The results indicated that the overall hydrologic alterations of Yichang hydrological station in 2010 level year, 2015 level year and the forward level year were 68.4, 72.5 and 74.3 % respectively, belonging to high alteration in all three level years. The runoff series of mainstream hydrological stations presented variability in different degrees, where the runoff series of the four hydrological stations including Xiangjiaba, Gaochang and Wulong belonged to high alteration in the three level years; and the runoff series of Beibei hydrological station in 2010 level year belonged to medium alteration, and high alteration in 2015 level year and the forward level year. The study on the impact of the operation of cascade reservoirs in Upper Yangtze River on hydrological variability of the mainstream had important practical significance on the sustainable utilization of water resources, disaster prevention and mitigation, safe and efficient operation and management of water conservancy projects and stable development of the economic society.

In this study we use the term moisture transport for precipitation for a target region as the moisture coming to this region from its major moisture sources resulting in precipitation over the target region (MTP). We have identified changes in the pattern of moisture transport for precipitation over the Arctic region, the Arctic Ocean, and its 13 main subdomains concurrent with the major sea ice decline that occurred in 2003. The pattern consists of a general decrease in moisture transport in summer and enhanced moisture transport in autumn and early winter, with different contributions depending on the moisture source and ocean subregion. The pattern is statistically significant and consistent with changes in the vertically integrated moisture fluxes and frequency of circulation types. The results of this paper also reveal that the assumed and partially documented enhanced poleward moisture transport from lower latitudes as a consequence of increased moisture from climate change seems to be less simple and constant than typically recognised in relation to enhanced Arctic precipitation throughout the year in the present climate. Secretaría de Estado de Investigación, Desarrollo e Innovación | Ref. CGL2015-65141-R

water resources management and sustainable development strategy, but also provide reference for assessing the impact of climate change and human activities. This paper selects three inland lakes in Northwest China, using Landsat MSS/TM/ETM+/OLI data from 1970 to 2015, Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) were used to extract lake area and analysed the dynamic trends. Meteorological station rainfall, evaporation and other meteorological data of the lakes were used to analyse reasons for the area change. The results showed that area of Hongjiannao Lake in the past 40 a was reduced, the groundwater impoundment and underground coal mining are the main cause of area reduction; the area of Bosten Lake in recent 40 a showed a decreasing trend after the first increase, the area was mainly affected by the surface runoff and snowmelt; the area of Qinghai Lake in the past 40 a shows a trend of decreasing first and then increasing, the change of its area is mainly affected by regional precipitation and the inflow.

Urban heat island (UHI) effect is considered to be one of the key indicators of the impacts of urbanization and the climate changes on the environment. Thus, the growing interest in studying the impacts of urbanization on changes in land surface temperature (LST). The literature on LST indicates the need for more studies on the relationship between changes in LST and land use types, especially in the arid environment. This paper examines the spatial and temporal changes in land surface temperature influenced by land use/land cover types in Riyadh, Saudi Arabia. Multi-temporal Landsat images of the study area, 1985, 1995, 2002 and 2015, were processed to derive land surface temperatures. UHI index was computed for the different land use/land cover types (high-density residential, medium-density residential, low-density residential, industrial, vegetation, and desert) in the study area. The results indicate a trend of rising temperatures in all the land use types in the study area. This is probably due to climate change. The industrial area has the highest temperatures among the land use types. The lowest temperatures are found in the vegetation area as expected. There is a need to implement mitigating measures to reduce the effects of rising temperatures in the study area.

Development of wind energy resource (WER) is a key to deal with climate change and energy structure adjustment. A crucial issue is to obtain the distribution and variability of WER, and mine the suitable location to exploit it. In this paper, a multicriteria evaluation (MCE) model is constructed by integrating resource richness and stability, utilization value and trend of resource, natural environment with weights. The global resource richness is assessed through wind power density (WPD) and multi-level wind speed. The utilizable value of resource is assessed by the frequency of effective wind. The resource stability is assessed by the coefficient of variation of WPD and the frequency of prevailing wind direction. Regression slope of long time series WPD is used to assess the trend of WER. All of the resource evaluation indicators are derived from the atmospheric reanalysis data ERA-Interim with spatial resolution 0.125°. The natural environment factors mainly refer to slope and land-use suitability, which are derived from multi-resolution terrain elevation data 2010 (GMTED 2010) and GlobalCover2009. Besides, the global WER utilization potential map is produced, which shows most high potential regions are located in north of Africa. Additionally, by verifying that 22.22 % and 48.8 9% operational wind farms fall on medium-high and high potential regions respectively, the result can provide a basis for the macroscopic siting of wind farm.

The water temperature in the drinking water distribution system and at customers' taps approaches the surrounding soil temperature at a depth of 1 m. Water temperature is an important determinant of water quality. In the Netherlands drinking water is distributed without additional residual disinfectant and the temperature of drinking water at customers' taps is not allowed to exceed 25 °C. In recent decades, the urban (sub)surface has been getting more occupied by various types of infrastructures, and some of these can be heat sources. Only recently have the anthropogenic sources and their influence on the underground been studied on coarse spatial scales. Little is known about the urban shallow underground heat profile on small spatial scales, of the order of 10 m × 10 m. Routine water quality samples at the tap in urban areas have shown up locations – so-called hotspots – in the city, with relatively high soil temperatures – up to 7 °C warmer – compared to the soil temperatures in the surrounding rural areas. Yet the sources and the locations of these hotspots have not been identified. It is expected that with climate change during a warm summer the soil temperature in the hotspots can be above 25 °C. The objective of this paper is to find a method to identify heat sources and urban characteristics that locally influence the soil temperature. The proposed method combines mapping of urban anthropogenic heat sources, retrospective modelling of the soil temperature, analysis of water temperature measurements at the tap, and extensive soil temperature measurements. This approach provided insight into the typical range of the variation of the urban soil temperature, and it is a first step to identifying areas with potential underground heat stress towards thermal underground management in cities.