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We provide an updated sea level dataset for Dublin for the period 1938–2016 at yearly resolution. Using a newly collated sea level record for Dublin Port, as well as two nearby tide gauges at Arklow and Howth Harbour, we perform data quality checks and calibration of the Dublin Port record by adjusting the biased high water level measurements that affect the overall calculation of mean sea level (MSL). To correct these MSL values, we use a novel Bayesian linear regression that includes the mean low water values as a predictor in the model. We validate the re-created MSL dataset and show its consistency with other nearby tide gauge datasets. Using our new corrected dataset, we estimate a rate of sea level rise of 1.1 mm yr−1 during 1953–2016 (95 % credible interval from 0.6 to 1.6 mm yr−1), and a rate of 7 mm yr−1 during 1997–2016 (95 % credible interval from 5 to 8.8 mm yr−1). The overall sea level rise is in line with expected trends, but large multidecadal variability has led to higher rates of rise in recent years.

C.R.'s portion of the work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). This work used JASMIN, the UK collaborative data analysis facility. Anthropogenic forcing is driving energy accumulation in the Earth system, including increases in the sensible heat content of the atmosphere, as measured by dry-bulb temperature—the metric that is almost universally used for communications about climate change. The atmosphere is also moistening, though, representing an accumulation of latent heat, which is partly concealed by dry-bulb temperature trends. We highlight that, consistent with basic theory, latent heat gains are outpacing sensible heat gains over about half of the Earth's surface. The difference is largest in the tropics, where global “hotspots” of total heat accumulation are located, and where regional disparities in heating rates are very poorly represented by dry-bulb temperatures. Including latent heat in climate-change metrics captures this heat accumulation and therefore improves adaptation-relevant understanding of the extreme humid heat and precipitation hazards that threaten these latitudes so acutely. For example, irrigation can lower peak dry-bulb temperatures, but amplify latent heat content by a larger margin, intensifying dangerous heat stress. Based on a review of the research literature, our Perspective therefore calls for routine use of equivalent temperature, a measure that expresses the combined sensible and latent heat content of the atmosphere in the familiar units of °C or K. We recognize that dry-bulb air temperature must remain a key indicator of the atmospheric state, not least for the many sectors that are sensitive to sensible heat transfer. However, we assert here that more widespread use of equivalent temperature could improve process understanding, public messaging, and adaptation to climate change. Publisher PDF

We provide an updated sea level dataset for Dublin for the period 1938 to 2016 at yearly resolution. Using a newly collated sea level record for Dublin Port, as well as two nearby tide gauges at Arklow and Howth Harbour, we perform data quality checks and calibration of the Dublin Port record by adjusting the biased high water level measurements that affect the overall calculation of mean sea level (MSL). To correct these MSL values, we use a novel Bayesian linear regression that includes the Mean Low Water values as a predictor in the model. We validate the re-created MSL dataset and show its consistency with other nearby tide gauge datasets. Using our new corrected dataset, we estimate a rate of 1.08 mm/yr sea level rise at Dublin Port between 1953–2016 (95 % CI from 0.62 to 1.55 mm/yr), and a rate of 6.48 mm/yr between 1997–2016 (95 % CI 4.22 to 8.80 mm/yr). Overall sea level rise is in line with expected trends but large multidecadal varaibility has led to higher rates of rise in recent years.

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.

This paper describes the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) approach to managing the transition from the Vaisala RS92 to the Vaisala RS41 as the operational radiosonde. The goal of GRUAN is to provide long-term high-quality reference observations of upper-air essential climate variables (ECVs) such as temperature and water vapor. With GRUAN data being used for climate monitoring, it is vital that the change of measurement system does not introduce inhomogeneities to the data record. The majority of the 27 GRUAN sites were launching the RS92 as their operational radiosonde, and following the end of production of the RS92 in the last quarter of 2017, most of these sites have now switched to the RS41. Such a large-scale change in instrumentation is unprecedented in the history of GRUAN and poses a challenge for the network. Several measurement programs have been initiated to characterize differences in biases, uncertainties, and noise between the two radiosonde types. These include laboratory characterization of measurement errors, extensive twin sounding studies with RS92 and RS41 on the same balloon, and comparison with ancillary data. This integrated approach is commensurate with the GRUAN principles of traceability and deliberate redundancy. A 2-year period of regular twin soundings is recommended, and for sites that are not able to implement this, burden-sharing is employed such that measurements at a certain site are considered representative of other sites with similar climatological characteristics. All data relevant to the RS92–RS41 transition are archived in a database that will be accessible to the scientific community for external scrutiny. Furthermore, the knowledge and experience gained regarding GRUAN's RS92–RS41 transition will be extensively documented to ensure traceability of the process. This documentation will benefit other networks in managing changes in their operational radiosonde systems. Preliminary analysis of the laboratory experiments indicates that the manufacturer's calibration of the RS41 temperature and humidity sensors is more accurate than for the RS92, with uncertainties of  K for the temperature and  % RH (RH: relative humidity) for the humidity sensor. A first analysis of 224 RS92–RS41 twin soundings at Lindenberg Observatory shows nighttime temperature differences  K between the Vaisala-processed temperature data for the RS41 (TRS41) and the GRUAN data product for the RS92 (TRS92-GDP.2). However, daytime temperature differences in the stratosphere increase steadily with altitude, with TRS92-GDP.2 up to 0.6 K higher than TRS41 at 35 km. RHRS41 values are up to 8 % higher, which is consistent with the analysis of satellite–radiosonde collocations.

Societal dependence on, and commercial and scientific exploitation of Earth-Oriented remote sensing from satellites is growing at an exponential rate. The comprehensive EU Copernicus programme provides a major contribution to the global effort, but even so, to achieve the necessary global and temporal coverage requires synergistic cooperation and associated interoperability of the Worlds sensors. For a user to exploit Earth Observation (EO) data there must exist confidence in data characteristics, quality and reliable delivery. Although long-term data records for climate may be the most demanding in nature, generation of analysis-ready operational data sets for applications, as diverse as food security to pollution monitoring, all require the user to have some quantitative level of confidence in the data and derived information. A long-term Calibration/Validation (Cal/Val) vision necessitates clear ownership and long-term funding. Delineating the roles of the European Commission (EC), space agencies and member states in long-term Cal/Val would provide clarity. It is clear that the space agencies have the responsibility to meet the mission requirement of their spaceborne instruments but long-term validation is often entrusted to interested parties bringing their own resources to the task. Furthermore, there is a critical need for Fiducial Reference Measurements (FRMs), acquired in operational mode, and comprehensive in coverage both spatially and temporally, to assure that the satellite product accuracies are met. This paper discusses the current status, gaps and challenges regarding long-term Cal/Val of EO satellites and recommends the creation of a European coordinating entity for satellite product calibration and validation. The proposed entity would be an integrative organization coordinating the European Cal/Val activities in partnership with the member states and the space agencies and working together with existing data providers to secure access to satellite and in-situ data of traceable FRM standards.

In the last two decades, technological progress has not only seen improvements to the quality of atmospheric upper-air observations but also provided the opportunity to design and implement automated systems able to replace measurement procedures typically performed manually. Radiosoundings, which remain one of the primary data sources for weather and climate applications, are still largely performed around the world manually, although increasingly fully automated upper-air observations are used, from urban areas to the remotest locations, which minimize operating costs and challenges in performing radiosounding launches. This analysis presents a first step to demonstrating the reliability of the automatic radiosonde launchers (ARLs) provided by Vaisala, Meteomodem and Meisei. The metadata and datasets collected by a few existing ARLs operated by the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) certified or candidate sites (Sodankylä, Payerne, Trappes, Potenza) have been investigated and a comparative analysis of the technical performance (i.e. manual versus ARL) is reported. The performance of ARLs is evaluated as being similar or superior to those achieved with the traditional manual launches in terms of percentage of successful launches, balloon burst and ascent speed. For both temperature and relative humidity, the ground-check comparisons showed a negative bias of a few tenths of a degree and % RH, respectively. Two datasets of parallel soundings between manual and ARL-based measurements, using identical sonde models, provided by Sodankylä and Faa'a stations, showed mean differences between the ARL and manual launches smaller than ±0.2 K up to 10 hPa for the temperature profiles. For relative humidity, differences were smaller than 1 % RH for the Sodankylä dataset up to 300 hPa, while they were smaller than 0.7 % RH for Faa'a station. Finally, the observation-minus-background (O–B) mean and root mean square (rms) statistics for German RS92 and RS41 stations, which operate a mix of manual and ARL launch protocols, calculated using the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast model, are very similar, although RS41 shows larger rms(O–B) differences for ARL stations, in particular for temperature and wind. A discussion of the potential next steps proposed by GRUAN community and other parties is provided, with the aim to lay the basis for the elaboration of a strategy to fully demonstrate the value of ARLs and guarantee that the provided products are traceable and suitable for the creation of GRUAN data products.

Globally, few precipitation records extend to the 18th century. The England Wales Precipitation (EWP) series is a notable exception with continuous monthly records from 1766. EWP has found widespread use across diverse fields of research including trend detection, evaluation of climate model simulations, as a proxy for mid‐latitude atmospheric circulation, a predictor in long‐term European gridded precipitation data sets, the assessment of drought and extremes, tree‐ring reconstructions and as a benchmark for other regional series. A key finding from EWP has been the multi‐centennial trends towards wetter winters and drier summers. We statistically reconstruct seasonal EWP using independent, quality‐assured temperature, pressure and circulation indices. Using a sleet and snow series for the UK derived by Profs. Gordon Manley and Elizabeth Shaw to examine winter reconstructions, we show that precipitation totals for pre‐1870 winters are likely biased low due to gauge under‐catch of snowfall and a higher incidence of snowfall during this period. When these factors are accounted for in our reconstructions, the observed trend to wetter winters in EWP is no longer evident. For summer, we find that pre‐1820 precipitation totals are too high, likely due to decreasing network density and less certain data at key stations. A significant trend to drier summers is not robustly present in our reconstructions of the EWP series. While our findings are more certain for winter than summer, we highlight (a) that extreme caution should be exercised when using EWP to make inferences about multi‐centennial trends, and; (b) that assessments of 18th and 19th Century winter precipitation should be aware of potential snow biases in early records. Our findings underline the importance of continual re‐appraisal of established long‐term climate data sets as new evidence becomes available. It is also likely that the identified biases in winter EWP have distorted many other long‐term European precipitation series., The trend towards wetter winters and drier summers in EWP can be explained by changes in measurement practice and data biases. For winter (Figure below), the timing of divergence aligns with the introduction of the Snowdon Pattern Rain Gauge in the UK around 1863. Given the widespread use of EWP across environmental and climate research, we expect that our findings will challenge understanding of multi‐centennial trends in precipitation in the UK. These changes in measurement practice are also likely to affect other long‐term precipitation records in north‐west Europe.

HadISDH.2.0.0 is the first gridded, multi-variable humidity and temperature in situ observations-only climate-data product that is homogenised and annually updated. It provides physically consistent estimates for specific humidity, vapour pressure, relative humidity, dew point temperature, wet bulb temperature, dew point depression and temperature. It is a monthly mean gridded (5° by 5°) product with uncertainty estimates that account for spatio-temporal sampling, climatology calculation, homogenisation and irreducible random measurement effects. It provides a tool for the long-term monitoring of a variety of humidity-related variables which have different impacts and implications for society. It is also useful for climate model evaluation and reanalyses validation. HadISDH.2.0.0 is shown to be in good agreement both with other estimates and with theoretical understanding. The data set is available from 1973 to the present. The theme common to all variables is of a warming world with more water vapour present in the atmosphere. The largest increases in water vapour are found over the tropics and the Mediterranean. Over the tropics and high northern latitudes the surface air over land is becoming more saturated. However, despite increasing water vapour over the mid-latitudes and Mediterranean, the surface air over land is becoming less saturated. These observed features may be due to atmospheric circulation changes, land–sea warming disparities and reduced water availability or changed land surface properties.

Background\ud Global mean surface temperature is widely used in the climate literature as a measure of the impact of human activity on the climate system. While the concept of a spatial average is simple, the estimation of that average from spatially incomplete data is not. Correlation between nearby map grid cells means that missing data cannot simply be ignored. Estimators that (often implicitly) assume uncorrelated observations can be biased when naively applied to the observed data, and in particular, the commonly used area weighted average is a biased estimator under these circumstances. Some surface temperature products use different forms of infilling or imputation to estimate temperatures for regions distant from the nearest observation, however the impacts of such methods on estimation of the global mean are not necessarily obvious or themselves unbiased. This issue was addressed in the 1970s by Ruvim Kagan, however his work has not been widely adopted, possibly due to its complexity and dependence on subjective choices in estimating the dependence between geographically proximate observations.\ud \ud Objectives\ud The aim of this work is to present a simple estimator for global mean surface temperature from spatially incomplete data which retains many of the benefits of the work of Kagan, while being fully specified by two equations and a single parameter. The main purpose of the simplified estimator is to better explain to users of temperature data the problems associated with estimating an unbiased global mean from spatially incomplete data, however the estimator may also be useful for problems with specific requirements for reproducibility and performance.\ud \ud Methods\ud The new estimator is based on generalized least squares, and uses the correlation matrix of the observations to weight each observation in accordance with the independent information it contributes. It can be implemented in fewer than 20 lines of computer code. The performance of the estimator is evaluated for different levels of observational coverage using reanalysis data with artificial noise.\ud \ud Results\ud For recent decades the generalized least squares estimator mitigates most of the error associated with the use of a naive area weighted average. The improvement arises from the fact that coverage bias in the historical temperature record does not arise from an absolute shortage of observations (at least for recent decades), but rather from spatial heterogeneity in the distribution of observations, with some regions being relatively undersampled and others oversampled. The estimator addresses this problem by reducing the weight of the oversampled regions, in contrast to some existing global temperature datasets which extrapolate temperatures into the unobserved regions. The results are almost identical to the use of kriging (Gaussian process interpolation) to impute missing data to global coverage, followed by an area weighted average of the resulting field. However, the new formulation allows direct diagnosis of the contribution of individual observations and sources of error.\ud \ud Conclusions\ud More sophisticated solutions to the problem of missing data in global temperature estimation already exist. However the simple estimator presented here, and the error analysis that it enables, demonstrate why such solutions are necessary. The 2013 Fifth Assessment Report of the Intergovernmental Panel on Climate Change discussed a slowdown in warming for the period 1998-2012, quoting the trend in the HadCRUT4 historical temperature dataset from the United Kingdom Meteorological Office in collaboration with the Climatic Research Unit of the University of East Anglia, along with other records. Use of the new estimator for global mean surface temperature would have reduced the apparent slowdown in warming of the early 21st century by one third in the spatially incomplete HadCRUT4 product.

A continuous 305-year (1711–2016) monthly rainfall series is created for the Island of Ireland. Two overlapping data sources are employed: i) a previously unpublished UK Meteorological Office note containing annual rainfall anomalies and corresponding proportional monthly totals based on weather diaries and early observational records for the period 1711–1977 and; ii) a long-term, homogenised monthly rainfall series for the island of Ireland for the period 1850–2016. Using estimates of long-term average precipitation from the homogenised series to merge these sources, the new 305-year record is constructed and insights drawn about notable extremes, climate variability and change. The consistency of the resulting series was evaluated by comparison with independent long-term observations and reconstructions of precipitation, temperature and circulation indices from across the British-Irish Isles. Strong decadal consistency is evident throughout the record amongst all series in spring, summer and autumn. The winter series is probably too dry from the 1740s to the 1780s, but strong consistency with other records strengthens confidence from 1790 onwards. The new Island of Ireland series reveals remarkably wet winters during the 1730s, concurrent with a period of strong westerly airflow, glacial advance throughout Scandinavia and near unprecedented warmth in the Central England Temperature record – all consistent with the strong phase of the North Atlantic Oscillation. The driest winter decade in the series coincides with the Laki eruption of 1783–1784. Unusually wet summers occurred in the 1750s, consistent with proxy (tree-ring) reconstructions of summer precipitation in the region. In the annual series, the most recent decade (2006–2015) is found to be the wettest in over 300 years. The new series reveals statistically significant (0.05 level) multi-centennial trends in winter (increasing) and summer (decreasing) seasonal precipitation. However, given uncertainties in the early winter record, the former should be treated as tentative. Importantly, we show that the years 1940 to present – the period with the most widely available digitised records – is unrepresentative of long-term changes in all seasons. Although there are recognized uncertainties in the early record, the derived series offers valuable insights for understanding multi-decadal and centennial rainfall variability in Ireland, and provides a firm basis for benchmarking other long-term records and future reconstructions.

The accurate monitoring of climate change im- poses strict requirements upon observing systems, in partic- ular regarding measurement accuracy and long-term stability. Currently available data records of the essential climate vari- ables (temperature- T , geopotential- p , humidity-RH, wind, and cloud properties) in the upper-air generally fail to fulfil such requirements. This raises serious issues about the abil- ity to detect, quantify and understand recent climate changes and their causes. GCOS is currently implementing a Ref- erence Upper-Air Network (GRUAN) in order to fill this major void within the global observing system. As part of the GRUAN implementation plan we provide herein funda- mental guidelines for establishing and maintaining reference quality atmospheric observations which are based on prin- cipal concepts of metrology, in particular traceability. It is argued that the detailed analysis of the uncertainty budget of a measurement technique is the critical step for achieving this goal. As we will demonstrate with an example, detailed knowledge of the calibration procedures and data process- ing algorithms are required for determining the uncertainty of each individual data point. Of particular importance is the careful assessment of the uncertainties introduced by correc- tion schemes adjusting for systematic effects.

Since the mid-twentieth century, radiosonde and satellite measurements show that the troposphere has warmed and the stratosphere has cooled. These changes are primarily due to increasing concentrations of well-mixed greenhouse gases and the depletion of stratospheric ozone. In response to continued greenhouse gas increases and stratospheric ozone depletion, climate models project continued tropospheric warming and stratospheric cooling over the coming decades. Global average satellite observations of lower stratospheric temperatures exhibit no significant trends since the turn of the century. In contrast, an analysis of vertically resolved radiosonde measurements from 60 stations shows an increase of lower stratospheric temperature since the turn of the century at altitudes between 15 and 30 km and over most continents. Trend estimates are somewhat sensitive to homogeneity assessment choices, but all investigated radiosonde data sets suggest a change from late twentieth century cooling to early 21st century warming in the lower stratosphere, which is consistent with a reversal from ozone depletion to recovery from the effects of ozone-depleting substances. In comparison, satellite observations at the radiosonde locations show only minor early 21st century warming, possibly due to the compensating effects of continued cooling above the radiosonde altitude range.

There is overwhelming evidence that the climate system has warmed since the instigation of instrumental meteorological observations. The Fifth Assessment Report of the Intergovernmental Panel on Climate Change concluded that the evidence for warming was unequivocal. However, owing to imperfect measurements and ubiquitous changes in measurement networks and techniques, there remain uncertainties in many of the details of these historical changes. These uncertainties do not call into question the trend or overall magnitude of the changes in the global climate system. Rather, they act to make the picture less clear than it could be, particularly at the local scale where many decisions regarding adaptation choices will be required, both now and in the future. A set of high-quality long-term fiducial reference measurements of essential climate variables will enable future generations to make rigorous assessments of future climate change and variability, providing society with the best possible information to support future decisions. Here we propose that by implementing and maintaining a suitably stable and metrologically well-characterized global land surface climate fiducial reference measurements network, the present-day scientific community can bequeath to future generations a better set of observations. This will aid future adaptation decisions and help us to monitor and quantify the effectiveness of internationally agreed mitigation steps. This article provides the background, rationale, metrological principles, and practical considerations regarding what would be involved in such a network, and outlines the benefits which may accrue. The challenge, of course, is how to convert such a vision to a long-term sustainable capability providing the necessary well-characterized measurement series to the benefit of global science and future generations.

Projections of future climate do not typically include the effects of volcanic activity. By incorporating a range of volcanic futures into a coupled model, it is shown that volcanic forcing has quantifiable impacts on the time at which anthropogenic signatures emerge across various climate metrics. Volcanic activity plays a strong role in modulating climate variability1. Most model projections of the twenty-first century, however, under-sample future volcanic effects by not representing the range of plausible eruption scenarios2,3,4. Here, we explore how sixty possible volcanic futures, consistent with ice-core records5, impact climate variability projections of the Norwegian Earth System Model (NorESM)6 under RCP4.5 (ref. 7). The inclusion of volcanic forcing enhances climate variability on annual-to-decadal timescales. Although decades with negative global temperature trends become ∼50% more commonplace with volcanic activity, these are unlikely to be able to mitigate long-term anthropogenic warming. Volcanic activity also impacts probabilistic projections of global radiation, sea level, ocean circulation, and sea-ice variability, the local-scale effects of which are detectable when quantifying the time of emergence8. These results highlight the importance and feasibility of representing volcanic uncertainty in future climate assessments.

The monthly global 2° × 2° Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.077°C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore, high-latitude SSTs are decreased by 0.1°–0.2°C by using sea ice concentration from HadISST2 over HadISST1. Changes arising from remaining innovations are mostly important at small space and time scales, primarily having an impact where and when input observations are sparse. Cross validations and verifications with independent modern observations show that the updates incorporated in ERSSTv5 have improved the representation of spatial variability over the global oceans, the magnitude of El Niño and La Niña events, and the decadal nature of SST changes over 1930s–40s when observation instruments changed rapidly. Both long- (1900–2015) and short-term (2000–15) SST trends in ERSSTv5 remain significant as in ERSSTv4.

We assess the most probable causes of late twentieth century (1960-1994) tropospheric temperature changes. Optimal detection techniques are used to compare observed spatio-temporal patterns of near-surface and tropospheric temperature change with results from experiments performed with two different versions of the Hadley Centre climate model. We detect anthropogenic forcings, particularly well-mixed greenhouse-gases, with a less certain sulfate aerosol cooling influence. More limited evidence exists for a detectable volcanic influence. Our principal results do not depend upon the choice of model. Both models, but particularly HadCM3, appear to overestimate the simulated climate response to greenhouse gases (especially at the surface) and volcanoes. This result may arise, at least in part, due to errors in the forcings (especially sulfate) and technical details of our approach, which differs from previous studies. We use corrected and uncorrected versions of the radiosonde record to assess sensitivity of our detection results to observational uncertainties. We find that previous corrections applied to the radiosonde temperature record are likely to have been sub-optimal in only taking into account temporal consistency. However, the choice of corrected or uncorrected version has no systematic effect upon our main conclusions. We show that both models are potentially internally consistent explanations of observed tropospheric temperatures.

Changes in diurnal temperature range (DTR) over global land areas are compared from a broad range of independent data sets. All data sets agree that global‐mean DTR has decreased significantly since 1950, with most of that decrease occurring over 1960–1980. The since‐1979 trends are not significant, with inter‐data set disagreement even over the sign of global changes. Inter‐data set spread becomes greater regionally and in particular at the grid box level. Despite this, there is general agreement that DTR decreased in North America, Europe, and Australia since 1951, with this decrease being partially reversed over Australia and Europe since the early 1980s. There is substantive disagreement between data sets prior to the middle of the twentieth century, particularly over Europe, which precludes making any meaningful conclusions about DTR changes prior to 1950, either globally or regionally. Several variants that undertake a broad range of approaches to postprocessing steps of gridding and interpolation were analyzed for two of the data sets. These choices have a substantial influence in data sparse regions or periods. The potential of further insights is therefore inextricably linked with the efficacy of data rescue and digitization for maximum and minimum temperature series prior to 1950 everywhere and in data sparse regions throughout the period of record. Over North America, station selection and homogeneity assessment is the primary determinant. Over Europe, where the basic station data are similar, the postprocessing choices are dominant. We assess that globally averaged DTR has decreased since the middle twentieth century but that this decrease has not been linear.

As stated by GCOS "Long-term, high-quality and uninterrupted observations of the atmosphere, land and ocean are vital for all countries, as their economies and societies become increasingly affected by climate variability and change". High-quality observation is possible only if based on a sustained traceability to SI and with documented uncertainties associated to the measured values. Following the signature of the MRA by the WMO , in April 2010, the CCT of the CIPM , in its XXV meeting of May 2010 submitted a recommendation to CIPM. The document highlighted the need to encourage National Metrology Institutes (NMIs) [...] to face new perspectives, needs, projects and activities related to the traceability, quality assurance, calibration procedures and definitions for those quantities involved in climate studies and meteorological observations and to support a strong cooperation between NMIs and Meteorological Institutions at local, national and international levels. In response to this call, several Joint Research Projects (JRPs) in metrology have been established. Their objective is to improve calibration procedures and measurement techniques for some Essential Climate Variables (ECVs), focussing especially on temperature, pressure and water vapour. Additional objectives have included investigations of sensor characteristics and the improvement of measurement devices and their use in the field. The impact effort is demonstrated also by the involvement of key international scientific Institutions such as GRUAN ,ISTI , IAPWS , and prominent Manufacturers and Universities. The overall aim is to make a further step towards establishing full data comparability, consistency and long-term continuity, through a comprehensive evaluation of the measurement uncertainties for the quantities involved in the global climate observations. The improvement of quality of ECVs records, through the inclusion of measurement uncertainty budgets, will also highlight possible strategies for the reduction of the uncertainty. This contribution will report on JRPs advances, events and task group activities, with the vision to establish a permanent bridge between metrologists and climatologists, through which to strengthen and develop collaborations, joint activities/projects and results dissemination to the whole society.

The monthly Extended Reconstructed Sea Surface Temperature (ERSST) dataset, available on global 2° × 2° grids, has been revised herein to version 4 (v4) from v3b. Major revisions include updated and substantially more complete input data from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) release 2.5; revised empirical orthogonal teleconnections (EOTs) and EOT acceptance criterion; updated sea surface temperature (SST) quality control procedures; revised SST anomaly (SSTA) evaluation methods; updated bias adjustments of ship SSTs using the Hadley Centre Nighttime Marine Air Temperature dataset version 2 (HadNMAT2); and buoy SST bias adjustment not previously made in v3b. Tests show that the impacts of the revisions to ship SST bias adjustment in ERSST.v4 are dominant among all revisions and updates. The effect is to make SST 0.1°–0.2°C cooler north of 30°S but 0.1°–0.2°C warmer south of 30°S in ERSST.v4 than in ERSST.v3b before 1940. In comparison with the Met Office SST product [the Hadley Centre Sea Surface Temperature dataset, version 3 (HadSST3)], the ship SST bias adjustment in ERSST.v4 is 0.1°–0.2°C cooler in the tropics but 0.1°–0.2°C warmer in the midlatitude oceans both before 1940 and from 1945 to 1970. Comparisons highlight differences in long-term SST trends and SSTA variations at decadal time scales among ERSST.v4, ERSST.v3b, HadSST3, and Centennial Observation-Based Estimates of SST version 2 (COBE-SST2), which is largely associated with the difference of bias adjustments in these SST products. The tests also show that, when compared with v3b, SSTAs in ERSST.v4 can substantially better represent the El Niño/La Niña behavior when observations are sparse before 1940. Comparisons indicate that SSTs in ERSST.v4 are as close to satellite-based observations as other similar SST analyses.

To assess published hypotheses surrounding the recent slowdown in surface warming (hiatus), we compare five available global observational surface temperature estimates to two 30-member ensembles from the Norwegian Earth System Model (NorESM). Model ensembles are initialized in 1980 from the transient historical runs and driven with forcings used in the CMIP5 experiments and updated forcings based upon current observational understanding, described in Part 1. The ensembles’ surface temperature trends are statistically indistinguishable over 1998–2012 despite differences in the prescribed forcings. There is thus no evidence that forcing errors play a significant role in explaining the hiatus according to NorESM. The observations fall either toward the lower portion of the ensembles or, for some observational estimates and regions, outside. The exception is the Arctic where the observations fall toward the upper ensemble bounds. Observational data set choices can make a large difference to findings of consistency or otherwise. Those NorESM ensemble members that exhibit Nino3.4 Sea Surface Temperature (SST) trends similar to observed also exhibit comparable tropical and to some extent globalmean trends, supporting a role for El Nino Southern Oscillation in explaining the hiatus. Several ensemble members capture the marked seasonality observed in Northern Hemispheremidlatitude trends, with cooling in the wintertime and warming in the remaining seasons. Overall, we find that we cannot falsify NorESM as being capable of explaining the observed hiatus behavior. Importantly, this is not equivalent to concluding NorESM could simultaneously capture all important facets of the hiatus. Similar experiments with further, distinct, Earth System Models are required to verify our findings.

Described herein is the parametric and structural uncertainty quantification for the monthly Extended Reconstructed Sea Surface Temperature (ERSST) version 4 (v4). A Monte Carlo ensemble approach was adopted to characterize parametric uncertainty, because initial experiments indicate the existence of significant nonlinear interactions. Globally, the resulting ensemble exhibits a wider uncertainty range before 1900, as well as an uncertainty maximum around World War II. Changes at smaller spatial scales in many regions, or for important features such as Niño-3.4 variability, are found to be dominated by particular parameter choices. Substantial differences in parametric uncertainty estimates are found between ERSST.v4 and the independently derived Hadley Centre SST version 3 (HadSST3) product. The largest uncertainties are over the mid and high latitudes in ERSST.v4 but in the tropics in HadSST3. Overall, in comparison with HadSST3, ERSST.v4 has larger parametric uncertainties at smaller spatial and shorter time scales and smaller parametric uncertainties at longer time scales, which likely reflects the different sources of uncertainty quantified in the respective parametric analyses. ERSST.v4 exhibits a stronger globally averaged warming trend than HadSST3 during the period of 1910–2012, but with a smaller parametric uncertainty. These global-mean trend estimates and their uncertainties marginally overlap. Several additional SST datasets are used to infer the structural uncertainty inherent in SST estimates. For the global mean, the structural uncertainty, estimated as the spread between available SST products, is more often than not larger than the parametric uncertainty in ERSST.v4. Neither parametric nor structural uncertainties call into question that on the global-mean level and centennial time scale, SSTs have warmed notably.

The recent Intergovernmental Panel on Climate Change report, along with numerous studies since, has suggested that the apparent global warming hiatus results from some combination of natural variability and changes to external forcings. Herein the external forcings for greenhouse gases (GHGs), long-lived trace gases, volcanic and tropospheric aerosols, and solar irradiance have been replaced in the Norwegian Earth System Model using recent observational estimates. The potential impact of these alternative forcings, and by residual the internally generated variability, is examined through two 30-member ensembles covering the period 1980 to 2012. The Reference ensemble uses the Coupled Model Intercomparison Project phase 5 historical forcings extended with the Representative Concentration Pathway 8.5 (RCP8.5) scenario, while the Sensitivity ensemble uses the alternative forcings. Over the hiatus period de fi ned herein as 1998 – 2012, all of the forcings show some change between the Sensitivity and Reference experiments and have a combined net forcing change of - 0.03 W m - 2 . The GHG forcing is 0.012Wm - 2 higher in the Sensitivity forcings. The alternative solar forcing differs from the Reference forcing by - 0.08 W m - 2 , the same as the alternative volcanic forcing that was based on the latest estimates from NASA Goddard Institute for Space Studies. Anthropogenic aerosol emissions were replaced using the EU-EclipseV4a data set and produce a mean forcing change of 0.11Wm - 2 over the period. Part 1 details the creation of the two 30-member ensembles and their characterization for parameters of particular relevance to the explanation of the hiatus. A detailed investigation of the two resulting ensembles global surface temperature behavior is given in Part 2, along with comparisons to observational data sets.

A recent observation in NOAA's National Climatic Data Center's monthly assessment of the state of the climate was that contiguous U.S. average monthly temperatures were in the top third of monthly ranked historical temperatures for 13 straight months from June 2011 to June 2012. The chance of such a streak occurring randomly was quoted as (1/3)13, or about one in 1.6 million. The streak continued for three more months before the October 2012 value dropped below the upper tercile. The climate system displays a degree of persistence that increases this probability relative to the assumption of independence. This paper puts forth different statistical techniques that more accurately quantify the probability of this and other such streaks. We consider how much more likely streaks are when an underlying warming trend is accounted for in the record, the chance of streaks occurring anywhere in the record, and the distribution of the record's longest streak.

Described herein is the first version release of monthly temperature holdings of a new Global Land Surface Meteorological Databank. Organized under the auspices of the International Surface Temperature Initiative (ISTI), an international group of scientists have spent three years collating and merging data from numerous sources to create a merged holding. This release in its recommended form consists of over 30 000 individual station records, some of which extend over the past 300 years. This article describes the sources, the chosen merge methodology, and the resulting databank characteristics. Several variants of the databank have also been released that reflect the structural uncertainty in merging datasets. Variants differ in, for example, the order in which sources are considered and the degree of congruence required in station geolocation for consideration as a merged or unique record. Also described is a version control protocol that will be applied in the event of updates. Future updates are envisaged with the addition of new data sources, and with changes in processing, where public feedback is always welcomed. Major updates, when necessary, will always be accompanied by a new journal paper. This databank release forms the foundation for the construction of new global land surface air temperature analyses by the global research community and their assessment by the ISTI's benchmarking and assessment working group.

The International Surface Temperature Initiative (ISTI) is striving towards substantively improving our ability to robustly understand historical land surface air temperature change at all scales. A key recently completed first step has been collating all available records into a comprehensive open access, traceable and version-controlled databank. The crucial next step is to maximise the value of the collated data through a robust international framework of benchmarking and assessment for product intercomparison and uncertainty estimation. We focus on uncertainties arising from the presence of inhomogeneities in monthly mean land surface temperature data and the varied methodological choices made by various groups in building homogeneous temperature products. The central facet of the benchmarking process is the creation of global-scale synthetic analogues to the real-world database where both the "true" series and inhomogeneities are known (a luxury the real-world data do not afford us). Hence, algorithmic strengths and weaknesses can be meaningfully quantified and conditional inferences made about the real-world climate system. Here we discuss the necessary framework for developing an international homogenisation benchmarking system on the global scale for monthly mean temperatures. The value of this framework is critically dependent upon the number of groups taking part and so we strongly advocate involvement in the benchmarking exercise from as many data analyst groups as possible to make the best use of this substantial effort.

The International Surface Temperature Initiative (ISTI) consists of an end-to-end process for land surface air temperature analyses. The foundation is the establishment of a global land surface Databank. This builds upon the groundbreaking efforts of scientists in the 1980s and 1990s. While using many of their principles, a primary aim is to improve aspects including data provenance, version control, openness and transparency, temporal and spatial coverage, and improved methods for merging disparate sources. The initial focus is on daily and monthly timescales. A Databank Working Group is focused on establishing Stage-0 (original observation forms) through Stage-3 data (merged dataset without quality control). More than 35 sources of data have already been added and efforts have now turned to development of the initial version of the merged dataset. Methods have been established for ensuring to the extent possible the provenance of all data from the point of observation through all intermediate steps to final archive and access. Databank submission procedures were designed to make the process of contributing data as easy as possible. All data are provided openly and without charge. We encourage the use of these data and feedback from interested users. Fil: Lawrimore, J. H.. National Oceanic and Atmospheric Administration; Estados Unidos Fil: Rennie, J.. Nansen Environmental and Remote Sensing Center; Noruega Fil: Gambi de Almeida, W.. Centro de Previsao de Tempo e Estudos Climáticos. Instituto Nacional de Pesquisas Espaciais; Brasil Fil: Christy, J.. University of Alabama; Estados Unidos Fil: Flannery, M.. Bureau of Meteorology; Australia Fil: Gleason, B.. National Oceanic and Atmospheric Administration; Estados Unidos Fil: Klein Tank, A.. Royal Netherlands Meteorological Institute; Países Bajos Fil: Mhanda, A.. frican Centre of Meteorological Applications for Development; Nigeria Fil: Ishihara, K.. Japan Meteorological Agency; Japón Fil: Lister, D.. Climatic Research Unit; Reino Unido Fil: Menne, M. J.. National Oceanic and Atmospheric Administration; Estados Unidos Fil: Razuvaev, V.. Russian Research Institute of Hydrometeorological Information; Rusia Fil: Renom, M.. Universidad de la República; Uruguay Fil: Rusticucci, Matilde Monica. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Tandy, J.. Met Office Hadley Centre; Reino Unido Fil: Thorne, P. W.. Nansen Environmental and Remote Sensing Center; Noruega Fil: Worley, S.. National Center for Atmospheric Research; Estados Unidos

Surface temperature is a key aspect of weather and climate, but the term may refer to different quantities that play interconnected roles and are observed by different means. In a community-based activity in June 2012, the EarthTemp Network brought together 55 researchers from five continents to improve the interaction between scientific communities who focus on surface temperature in particular domains, to exploit the strengths of different observing systems and to better meet the needs of different communities. The workshop identified key needs for progress towards meeting scientific and societal requirements for surface temperature understanding and information, which are presented in this community paper. A "whole-Earth" perspective is required with more integrated, collaborative approaches to observing and understanding Earth's various surface temperatures. It is necessary to build understanding of the relationships between different surface temperatures, where presently inadequate, and undertake large-scale systematic intercomparisons. Datasets need to be easier to obtain and exploit for a wide constituency of users, with the differences and complementarities communicated in readily understood terms, and realistic and consistent uncertainty information provided. Steps were also recommended to curate and make available data that are presently inaccessible, develop new observing systems and build capacities to accelerate progress in the accuracy and usability of surface temperature datasets.

The year 2012 marks a decade of observations undertaken by the U.S. Climate Reference Network (USCRN) under the auspices of NOAA's National Climatic Data Center and Atmospheric Turbulence and Diffusion Division. The network consists of 114 sites across the conterminous 48 states, with additional sites in Alaska and Hawaii. Stations are installed in open (where possible), rural sites very likely to have stable land-cover/use conditions for several decades to come. At each site a suite of meteorological parameters are monitored, including triple redundancy for the primary air temperature and precipitation variables and for soil moisture/temperature. Instrumentation is regularly calibrated to National Institute for Standards and Technology (NIST) standards and maintained by a staff of expert engineers. This attention to detail in USCRN is intended to ensure the creation of an unimpeachable record of changes in surface climate over the United States for decades to come. Data are made available without restriction for all public, private, and government use. This article describes the rationale for the USCRN, its implementation, and some of the highlights of the first decade of operations. One critical use of these observations is as an independent data source to verify the existing U.S. temperature record derived from networks corrected for nonhomogenous histories. Future directions for the network are also discussed, including the applicability of USCRN approaches for networks monitoring climate at scales from regional to global. Constructive feedback from end users will allow for continued improvement of USCRN in the future and ensure that it continues to meet stakeholder requirements for precise climate measurements.

The GCOS Reference Upper Air Network (GRUAN) is an international reference observing network, designed to meet climate requirements and to fill a major void in the current global observing system. Upper air observations within the GRUAN network will provide long-term high-quality climate records, will be used to constrain and validate data from space based remote sensors, and will provide accurate data for the study of atmospheric processes. The network covers measurements of a range of key climate variables including temperature. Implementation of the network has started, and as part of this process a number of scientific questions need to be addressed in order to establish a viable climate reference upper air network, in addition to meeting the other objectives for the network measurements. These include quantifying collocation issues for different measurement techniques including the impact on the overall uncertainty of combined measurements; change management requirements when switching between sensors; assessing the benefit of complementary measurements of the same variable using different measurement techniques; and establishing the appropriate sampling strategy to determine long-term trends. This paper reviews the work that is currently underway to address these issues.

Since the late 1970s, satellite-based instruments have monitored global changes in atmospheric temperature. These measurements reveal multidecadal tropospheric warming and stratospheric cooling, punctuated by short-term volcanic signals of reverse sign. Similar long- and short-term temperature signals occur in model simulations driven by human-caused changes in atmospheric composition and natural variations in volcanic aerosols. Most previous comparisons of modeled and observed atmospheric temperature changes have used results from individual models and individual observational records. In contrast, we rely on a large multimodel archive and multiple observational datasets. We show that a human-caused latitude/altitude pattern of atmospheric temperature change can be identified with high statistical confidence in satellite data. Results are robust to current uncertainties in models and observations. Virtually all previous research in this area has attempted to discriminate an anthropogenic signal from internal variability. Here, we present evidence that a human-caused signal can also be identified relative to the larger "total" natural variability arising from sources internal to the climate system, solar irradiance changes, and volcanic forcing. Consistent signal identification occurs because both internal and total natural variability (as simulated by state-of-the-art models) cannot produce sustained global-scale tropospheric warming and stratospheric cooling. Our results provide clear evidence for a discernible human influence on the thermal structure of the atmosphere.

The observational climate record is a cornerstone of our scientific understanding of climate changes and their potential causes. Existing observing networks have been designed largely in support of operational weather forecasting and continue to be run in this mode. Coverage and timeliness are often higher priorities than absolute traceability and accuracy. Changes in instrumentation used in the observing system, as well as in operating procedures, are frequent, rarely adequately documented and their impacts poorly quantified. For monitoring changes in upper-air climate, which is achieved through in-situ soundings and more recently satellites and ground-based remote sensing, the net result has been trend uncertainties as large as, or larger than, the expected emergent signals of climate change. This is more than simply academic with the tropospheric temperature trends issue having been the subject of intense debate, two international assessment reports and several US congressional hearings. For more than a decade the international climate science community has been calling for the instigation of a network of reference quality measurements to reduce uncertainty in our climate monitoring capabilities. This paper provides a brief history of GRUAN developments to date and outlines future plans. Such reference networks can only be achieved and maintained with strong continuing input from the global metrological community.

Since the 1990s a number of national institutions have developed and maintained global data sets of land surface air temperature [Peterson and Vose, 1997; Hansen et al., 2010; Jones et al., 2012; Menne et al., 2012]. These efforts have led to great advances in understanding how Earth's temperatures have varied and changed. They also serve as essential sources of a fundamental climate variable, which is crucial for interpreting climate evolution in response to the interplay of radiative forcing, climate feedbacks, and ocean heating [e.g., Hansen et al., 2011]. However, more can be done to improve global surface temperature collections while enhancing data management, access, and public transparency with which data are collected, processed, and converted into climate information. To address these needs, the International Surface Temperature Initiative (ISTI), which began through a partnership of scientists from around the world [Thorne et al., 2011], released its first beta version of a global land surface databank in October 2012.

HadISDH is a near-global land surface specific humidity monitoring product providing monthly means from 1973 onwards over large-scale grids. Presented herein to 2012, annual updates are anticipated. HadISDH is an update to the land component of HadCRUH, utilising the global high-resolution land surface station product HadISD as a basis. HadISD, in turn, uses an updated version of NOAA's Integrated Surface Database. Intensive automated quality control has been undertaken at the individual observation level, as part of HadISD processing. The data have been subsequently run through the pairwise homogenisation algorithm developed for NCDC's US Historical Climatology Network monthly temperature product. For the first time, uncertainty estimates are provided at the grid-box spatial scale and monthly timescale. HadISDH is in good agreement with existing land surface humidity products in periods of overlap, and with both land air and sea surface temperature estimates. Widespread moistening is shown over the 1973–2012 period. The largest moistening signals are over the tropics with drying over the subtropics, supporting other evidence of an intensified hydrological cycle over recent years. Moistening is detectable with high (95%) confidence over large-scale averages for the globe, Northern Hemisphere and tropics, with trends of 0.089 (0.080 to 0.098) g kg−1 per decade, 0.086 (0.075 to 0.097) g kg−1 per decade and 0.133 (0.119 to 0.148) g kg−1 per decade, respectively. These changes are outside the uncertainty range for the large-scale average which is dominated by the spatial coverage component; station and grid-box sampling uncertainty is essentially negligible on large scales. A very small moistening (0.013 (−0.005 to 0.031) g kg−1 per decade) is found in the Southern Hemisphere, but it is not significantly different from zero and uncertainty is large. When globally averaged, 1998 is the moistest year since monitoring began in 1973, closely followed by 2010, two strong El Niño years. The period in between is relatively flat, concurring with previous findings of decreasing relative humidity over land.

We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.

[Abridged] This paper describes the creation of HadISD: an automatically quality-controlled synoptic resolution dataset of temperature, dewpoint temperature, sea-level pressure, wind speed, wind direction and cloud cover from global weather stations for 1973--2011. The full dataset consists of over 6000 stations, with 3427 long-term stations deemed to have sufficient sampling and quality for climate applications requiring sub-daily resolution. As with other surface datasets, coverage is heavily skewed towards Northern Hemisphere mid-latitudes. The dataset is constructed from a large pre-existing ASCII flatfile data bank that represents over a decade of substantial effort at data retrieval, reformatting and provision. These raw data have had varying levels of quality control applied to them by individual data providers. The work proceeded in several steps: merging stations with multiple reporting identifiers; reformatting to netCDF; quality control; and then filtering to form a final dataset. Particular attention has been paid to maintaining true extreme values where possible within an automated, objective process. Detailed validation has been performed on a subset of global stations and also on UK data using known extreme events to help finalise the QC tests. Further validation was performed on a selection of extreme events world-wide (Hurricane Katrina in 2005, the cold snap in Alaska in 1989 and heat waves in SE Australia in 2009). Although the filtering has removed the poorest station records, no attempt has been made to homogenise the data thus far. Hence non-climatic, time-varying errors may still exist in many of the individual station records and care is needed in inferring long-term trends from these data. A version-control system has been constructed for this dataset to allow for the clear documentation of any updates and corrections in the future., Comment: Published in Climate of the Past, www.clim-past.net/8/1649/2012/. 31 pages, 23 figures, 9 pages. For data see http://www.metoffice.gov.uk/hadobs/hadisd