Across Europe, a wide variety of public and private organisations require climate hazard and impact data both to improve understanding of current and changing risks and inform adaptation measures. Climate hazard and impact data currently require considerable technical expertise to access, download and interrogate creating a barrier for policy makers, local authorities, non-governmental and citizen organisations, and other interested parties. In the UK, the Met Office, in partnership with ESRI, has created a Climate Data Portal to address this issue. It provides a selection of climate data and supporting documentation in user friendly, ready-to-use data formats. Built using ArcGIS Hub, Esri’s cloud-based data engagement platform, the portal makes it much easier for users to view climate data geospatially and analyse climate change projections alongside their own data. Datasets currently available include a selection of historical climate records, climate projections and climate change impact metrics. The authors have also been exploring ways to use the platform to provide bespoke information for different sectors, including Local Authorities which are elected bodies that provide a range of services for particular geographical areas. This presentation will include a brief demonstration of the portal.

Crowdsourced observation networks are typically much more dense than those maintained by National Meteorological Services, and sample a much wider range of local climates. This offers an opportunity to build observed climatologies that are more representative of lived experience, particularly in cities. This study provides a worked example to show their potential for improving operational climate services, and to identify the challenges to realizing that potential. To demonstrate the concept, data from personal weather stations, obtained through citizen science, are used to build an observed record of daily maximum temperatures in 2020 in Manchester (UK). This record is compared to the standard baseline used in a current climate service, showing a substantial increase in the estimated heat hazard. If such potential benefits are to be realized in a climate service, it will be necessary to first build an alternative observed baseline of decadal length and at national or international scale. This requires further work to acquire, quality-control, exposure-control and map the crowdsourced observations. Crowdsourced observations may be used to enhance the spatial detail in observed climatologies, making urban temperatures more representative of lived experience. Their use may increase the estimated heat hazard (days above 25 degrees C) in a current urban climate service by up to 3 weeks in Greater Manchester (UK, outlined) in 2020. image

<div> <div>Crowdsourced observations have the potential to bring a step-change in urban climatology. This rapid prototyping project explores their potential for improving the standard observed grids, and the likely consequences for urban climate services. Basic quality control procedures are applied to WOW, Davis, Netatmo and Met Office sites around Manchester (UK), and site records of daily minimum and maximum temperatures are built. These are interpolated onto a set of daily observed grids of temperature for Manchester at 1km resolution for summer (JJA) 2020, thus obtaining a crowdsourced alternative to HadUK-Grid. The number of tropical nights (minimum > 20 degrees) is counted in these two gridded products. This provides the baseline for a current climate service for partners in local government that projects possible future changes in heat hazards. Thus the comparison of the standard and crowdsourced products gives some insight into the potential for observations from citizen science to improve gridded observations, an observed hazard metric, future projections of that metric, and so influence public policy decisions related to extreme heat.</div> </div>

The Conway-Maxwell-Poisson distribution improves the precision with which seasonal counts of tropical cyclones may be modelled. Conventionally the Poisson is used, which assumes that the formation and transit of tropical cyclones is the result of a Poisson process, such that their frequency distribution has equal mean and variance (‘equi-dispersion’). However, earlier studies of observed records have sometimes found over-dispersion, where the variance exceeds the mean, indicating that tropical cyclones are clustered in particular years. The evidence presented here demonstrates that at least some of this over-dispersion arises from observational inhomogeneities. Once this is removed, and particularly near the coasts, there is evidence for equi-dispersion or under-dispersion. In order to more accurately model numbers of tropical cyclones, we investigate the use of the Conway-Maxwell-Poisson as an alternative to the Poisson that represents any dispersion characteristic. An example is given for east China where using it improves the skill of a prototype seasonal forecast of tropical cyclone landfall.

Changes in climate pose major challenges to society, and so decision-makers need actionable climate information to inform their planning and policies to make society more resilient to climatic changes. Climate services are being developed to provide such actionable climate information. The successful development and use of climate services benefits greatly from close engagement between developers, providers, and users of the services. The Climate Science for Service Partnership China (CSSP China) is a China-UK collaboration fostering closer engagement between climate scientists, providers of climate services, and users of climate services. We describe the process within CSSP China of co-developing climate services through trials with users to revise and improve a prototype. Examples are provided covering various scientific capabilities, user needs, and parts of China. The development process is yielding many benefits, such as increasing the engagement between providers and users, making users more aware of how climate information can be of use in their decision-making, giving the climate service providers a better understanding of the users' requirements for climate information, and shaping future scientific research and development. In addition to the benefits, we also document some challenges that have emerged, along with ways of alleviating them. We have two key recommendations from our experiences: make the time and space for effective engagement between the users and developers of any climate service; bring the needs of the users in to the design and delivery of the climate service as early as possible and throughout the development cycle.

A prototype climate service was developed and trialled in early 2019 to provide seasonal forecast of the June-July-August (JJA) tropical cyclone (TC) landfall risk for the East China region ahead of the forthcoming typhoon season. Test forecasts were produced in both March and April 2019 and a final forecast was released to the China Meteorological Administration (CMA) on 1 May 2019. The trial service was produced by using the Met Office Global Seasonal forecast system (GloSea5), and a forecast of the western Pacific subtropical high (WPSH) index was used to infer the TC landfall risk based on a simple linear regression between historical model WPSH indices and observed TC landfalls in East China. The forecast method shows significant skill for forecasting the JJA TC landfall risk in East China with up to three-month lead time, with the greatest skill for predictions initialized in May. The 2019 forecast provided good guidance of the near-average TC activity observed in East China in JJA 2019. Success of the forecast adds confidence to an improved climate service ahead of the 2020 typhoon season.

This paper describes the development and first results of the “Community Integrated Assessment System” (CIAS), a unique multi-institutional modular and flexible integrated assessment system for modelling climate change. Key to this development is the supporting software infrastructure, SoftIAM. Through it, CIAS is distributed between the communities of institutions which has each contributed modules to the CIAS system. At the heart of SoftIAM is the Bespoke Framework Generator (BFG) which enables flexibility in the assembly and composition of individual modules from a pool to form coupled models within CIAS, and flexibility in their deployment onto the available software and hardware resources. Such flexibility greatly enhances modellers' ability to re-configure the CIAS coupled models to answer different questions, thus tracking evolving policy needs. It also allows rigorous testing of the robustness of IA modelling results to the use of different component modules representing the same processes (for example, the economy). Such processes are often modelled in very different ways, using different paradigms, at the participating institutions. An illustrative application to the study of the relationship between the economy and the earth's climate system is provided.

A database of monthly climate observations from meteorological stations is constructed. The database includes six climate elements and extends over the global land surface. The database is checked for inhomogeneities in the station records using an automated method that refines previous methods by using incomplete and partially overlapping records and by detecting inhomogeneities with opposite signs in different seasons. The method includes the development of reference series using neighbouring stations. Information from different sources about a single station may be combined, even without an overlapping period, using a reference series. Thus, a longer station record may be obtained and fragmentation of records reduced. The reference series also enables 1961-90 normals to be calculated for a larger proportion of stations.The station anomalies are interpolated onto a 0.5 degrees grid covering the global land surface (excluding Antarctica) and combined with a published normal from 1961-90. Thus, climate grids are constructed for nine climate variables (temperature, diurnal temperature range, daily minimum and maximum temperatures, precipitation, wet-day frequency, frost-day frequency, vapour pressure, and cloud cover) for the period 1901-2002. This dataset is known as CRU TS 2.1 and is publicly available (http://www.cru.uea.ac.uk/). Copyright (c) 2005 Royal Meteorological Society.

Global change will alter the supply of ecosystem services that are vital for human well-being. To investigate ecosystem service supply during the 21st century, we used a range of ecosystem models and scenarios of climate and land-use change to conduct a Europe-wide assessment. Large changes in climate and land use typically resulted in large changes in ecosystem service supply. Some of these trends may be positive (for example, increases in forest area and productivity) or offer opportunities (for example, “surplus land” for agricultural extensification and bioenergy production). However, many changes increase vulnerability as a result of a decreasing supply of ecosystem services (for example, declining soil fertility, declining water availability, increasing risk of forest fires), especially in the Mediterranean and mountain regions.

A fully probabilistic, or risk, assessment of future regional climate changeand its impacts involves more scenarios of radiative forcing than can besimulated by a general (GCM) or regional (RCM) circulation model. Additionalscenarios may be created by scaling a spatial response pattern from a GCM bya global warming projection from a simple climate model. I examine thistechnique, known as pattern scaling, using a particular GCM (HadCM2).Thecritical assumption is that there is a linear relationship between the scaler(annual global-mean temperature) and the response pattern. Previous studieshave found this assumption to be broadly valid for annual temperature; Iextend this conclusion to precipitation and seasonal (JJA) climate. However,slight non-linearities arise from the dependence of the climatic response onthe rate, not just the amount, of change in the scaler. These non-linearitiesintroduce some significant errors into the estimates made by pattern scaling,but nonetheless the estimates accurately represent the modelled changes. Aresponse pattern may be made more robust by lengthening the period from whichit is obtained, by anomalising it relative to the control simulation, and byusing least squares regression to obtain it. The errors arising from patternscaling may be minimised by interpolating from a stronger to a weaker forcingscenario.

AreaVolume 34, Issue 1 p. 103-112 Climate data for political areas Timothy D. Mitchell, Timothy D. Mitchell Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwicht.mitchell@uea.ac.ukSearch for more papers by this authorMike Hulme, Mike Hulme Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwicht.mitchell@uea.ac.ukSearch for more papers by this authorMark New, Mark New School of Geography and the Environment, University of OxfordSearch for more papers by this author Timothy D. Mitchell, Timothy D. Mitchell Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwicht.mitchell@uea.ac.ukSearch for more papers by this authorMike Hulme, Mike Hulme Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwicht.mitchell@uea.ac.ukSearch for more papers by this authorMark New, Mark New School of Geography and the Environment, University of OxfordSearch for more papers by this author First published: 16 December 2002 https://doi.org/10.1111/1475-4762.00062Citations: 93AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume34, Issue1March 2002Pages 103-112 RelatedInformation