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With the ongoing warming of the globe, it is important to quantify changes in the recent behaviour of extreme events given their impacts on human health, infrastructure and the natural environment. We use the sub-daily, multi-variate, station-based HadISD dataset to study the changes in the statistical distributions of temperature, dewpoint temperature and wind speeds. Firstly, we use zonally averaged quantities to show that the lowest temperatures during both day and night are changing more rapidly than the highest, with the effect more pronounced in the northern high latitudes. Along with an increases in the zonally-averaged mean temperature, the standard deviation has decreased and the skew increased (increasing positive tail/decreasing negative tail) over the last 45 years, again with a stronger, more robust signal at higher latitudes. Changes in the distribution of dewpoint temperature are similar to those of temperature. However, changes in the distribution of wind speeds indicate a more rapid change at higher speeds than at lower. Secondly, to assess in more detail the spatial distribution of changes as well as across seasons and hours of the day we study each station individually. For stations which show clear indications of change in the statistical moments, the higher the statistical moment, generally the more spatially heterogenous the patterns of change. The standard deviation of temperatures are increasing in a band stretching from Europe through to China, but are decreasing across North America and in the high northern latitudes, indicating broadening and narrowing of the distributions respectively. Large seasonal differences are found in the change of standard deviations of temperatures over North America and eastern China. Temperatures in Eastern Asia also have increasing skew in the winter in contrast to the remainder of the year. The dewpoint temperatures show smaller variation in all of the moments, but similar patterns to the temperatures. For wind speeds, apart from the USA, standard deviations are decreasing across the world, indicating a decrease in variability. Finally, we use quantile regression to show changes in the percentiles of distributions over time. These show an increase of high quantiles of temperature in eastern Europe during the summer, and also in northern Europe for low quantiles in the winter, also indicating broadening and narrowing of the distributions respectively. In North America, the largest changes are at the lower quantiles in northern latitudes for autumn and winter. Quantiles of dewpoint temperature are changing most in the autumn and winter, especially in the northern parts of Europe.

Global surface-temperature is a fundamental measure of climate change. We discuss bias estimation for sea-surface temperature and recommend the improvements to data, observational metadata, and uncertainty modeling needed to make progress.Global surface-temperature changes are a fundamental expression of climate change. Recent, much-debated, variations in the observed rate of surface-temperature change have highlighted the importance of uncertainty in adjustments applied to sea-surface temperature (SST) measurements. These adjustments are applied to compensate for systematic biases and changes in observing protocol. Better quantification of the adjustments and their uncertainties would increase confidence in estimated surface-temperature change and provide higher-quality gridded SST fields for use in many applications.Bias adjustments have been based either on physical models of the observing processes or on the assumption of an unchanging relationship between SST and a reference data set such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and timescales, but regional differences can exceed the estimated uncertainty. We describe challenges to improving our understanding of SST biases. Overcoming these will require clarification of past observational methods, improved modeling of biases associated with each observing method, and the development of statistical bias estimates that are less sensitive to the absence of metadata regarding the observing method.New approaches are required that embed bias models, specific to each type of observation, within a robust statistical framework. Mobile platforms and rapid changes in observation type require biases to be assessed for individual historic and present-day platforms (i.e., ships or buoys) or groups of platforms. Lack of observational metadata and of high-quality observations for validation and bias model development are likely to remain major challenges.

Patients suffering from medical conditions resulting in hand impairment experience difficulty in performing simple daily tasks, like getting dressed or using a pencil, resulting in a poorer quality of life. Rehabilitation attempts to help such individuals regain a sense of control and normalcy. In this context, recent advances in robotics have manifested in multiple designs of hand exoskeletons and exosuit gloves for assistance and rehabilitation. These designs are typically actuated using pneumatic, shape memory alloys and motor-tendon actuators. The proposed Motor Tendon Actuated Exosuit Glove (MTAEG) with an open palm is a soft material glove capable of both flexion and extension of all four fingers of the human hand. Its minimally invasive design maintains an open palm to facilitate haptic and tactile interaction with the environment. The MTAEG achieves flexion-extension motion with joint angles of 45° at the metacarpal joint which is 57% of the desired motion; 90° at the proximal interphalangeal joint which is 100% of the desired motion; and 50° at the distal interphalangeal joint which is 96% of the desired motion. The paper discusses the challenges in achieving the desired motion without the ability to directly model human tendons, and the inability to actuate joints individually.

Indian monsoon depressions are synoptic scale events typically spun up in the Bay of Bengal. They usually last 4–6 days, during which they propagate northwestward across the Indian subcontinent before dissipating over northwest India or Pakistan. They can have a significant effect on monsoon precipitation, particularly in primarily agrarian northern India, and therefore quantifying their structure and variability and evaluating these in NWP models and GCMs is of critical importance. In this study, satellite data from the CloudSat and recently concluded TRMM missions are used in conjunction with an independently evaluated tracking algorithm to form a three-dimensional composite image of cloud structure and precipitation within monsoon depressions. The composite comprises 34 depressions from the 1998–2014 TRMM mission and 12 from the 2007-present CloudSat mission, and is statistically robust enough to allow significant probing of the spatiotemporal characteristics of moisture and hydrometeor fields. Among the key results of this work are the discovery and characterisation of a bimodal, diurnal cycle in surface precipitation; the first picture of the structure of cloud type and density in depressions, showing that deep convection dominates south of the centre and prominent cirrus throughout; the first composite picture of vertical hydrometeor structure in depressions, showing significant precipitation for hundreds of kilometres outside the centre and well past the mid-troposphere; and novel discussion of drop size distributions (showing significant uniformity across the depression) and resulting latent heat profiles, showing average heating rates near the centre can reach 2K/hr.

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.

We report on preliminary steps in the homogenisation of HadISD, a sub-daily, station-based data set covering 1973–2013. Using temperature, dew point temperature, sea-level pressure and wind speeds, change points are detected using the Pairwise Homogenisation Algorithm from Menne and Williams Jr. (2009). Monthly-mean values and monthly-mean diurnal ranges (temperature and dew point temperature) or monthly-maximum values (wind speeds) are processed using the full network of 6103 stations in HadISD. Where multiple change points are detected within 1 year, they are combined and the average date is used. Under the assumption that the underlying true population of inhomogeneity magnitudes is Gaussian, inhomogeneity magnitudes as small as around 0.5 °C, 0.5 hPa or 0.5 m s−1 have been successfully detected. The change point dates and inhomogeneity magnitudes for each of the calculation methods will be provided alongside the data set to allow users to select stations which have different levels of homogeneity. We give an example application of this change point information in calculating global temperature values from HadISD and comparing these to CRUTEM4. Removing the most inhomogeneous stations results in a better match between HadISD and CRUTEM4 when matched to the same coverage. However, further removals of stations with smaller and fewer inhomogeneities worsen the match.

Our analyses provide empirical explanations for slowdowns and irregularities in global surface temperature variation, 1891–2015., The time series of monthly global mean surface temperature (GST) since 1891 is successfully reconstructed from known natural and anthropogenic forcing factors, including internal climate variability, using a multiple regression technique. Comparisons are made with the performance of 40 CMIP5 models in predicting GST. The relative contributions of the various forcing factors to GST changes vary in time, but most of the warming since 1891 is found to be attributable to the net influence of increasing greenhouse gases and anthropogenic aerosols. Separate statistically independent analyses are also carried out for three periods of GST slowdown (1896–1910, 1941–1975, and 1998–2013 and subperiods); two periods of strong warming (1911–1940 and 1976–1997) are also analyzed. A reduction in total incident solar radiation forcing played a significant cooling role over 2001–2010. The only serious disagreements between the reconstructions and observations occur during the Second World War, especially in the period 1944–1945, when observed near-worldwide sea surface temperatures (SSTs) may be significantly warm-biased. In contrast, reconstructions of near-worldwide SSTs were rather warmer than those observed between about 1907 and 1910. However, the generally high reconstruction accuracy shows that known external and internal forcing factors explain all the main variations in GST between 1891 and 2015, allowing for our current understanding of their uncertainties. Accordingly, no important additional factors are needed to explain the two main warming and three main slowdown periods during this epoch.

The spatial and temporal pattern of sub-daily temperature change in mainland China was analysed for the period from 1973 to 2011 using a 3-hourly dataset based on 408 stations. The increase in surface air temperature was more significant by night between 1973 and 1992, with the fastest upward trend around local midnight being about 0.27 °C/decade, while it was more significant by day between 1992 and 2011, with the fastest upward trend being about 0.46 °C/decade in mid-late morning. The season with rapid temperature increase also shifted from winter in 1973–1992 (the largest increase happened near midnight in December, 0.75 °C/decade) to spring in 1992–2011 (the largest increase happened at in the early afternoon in March, 0.82 °C/decade). The change in the spatial distributions of the sub-daily temperature trends shows that Northeast China warmed more significantly in 1973–1992 than elsewhere, but it cooled in 1992–2011, when Southwest China was the new focus of temperature increase whereas it had previously been cooling. A preliminary analysis of the possible causes implies that changes in solar radiation, cloud cover, aerosols and the observational environments near the stations might have contributed to these observed temperature changes.

Based on the homogenized data set, we analyze changes in mean temperature and some extreme temperature indices over China since 1961 and especially during the recent warming hiatus period (1998–2012) in a global average context. The result shows that the decrease of annual mean maximum has contributed most to the decreases in overall mean temperature and in diurnal temperature range (DTR) during the warming hiatus period. In most parts of China except the southwest, the summer mean maximum temperature (TxS) shows the largest increase, while the winter mean minimum temperature (TnW) indicates slight cooling trends. These changes have augmented the seasonal cycle and increased the likelihood of extreme warm and cold events. Further analyses reveal that the increases in TxS are significantly correlated with concurrent increases in solar radiation. In southwest China, the annual mean temperature, TxS, TnW, and DTR increased during 1998–2012, possibly related to increased dryness in this region during the hiatus period.

[1] Human influence on atmospheric sea level pressure (SLP) has previously been detected globally, but the contributions of greenhouse gas, aerosol, and ozone changes to the observed trends have not been separately identified. We use simulations from eight climate models to show that greenhouse gas, aerosol, and ozone changes each drive distinct seasonal and geographical patterns of trends, which are separately detectable in observed seasonal SLP trends over the 1951–2011 period. This detection is driven by significant low-latitude SLP responses to greenhouse gas, aerosol, and ozone changes, as well as the more frequently-studied high latitude responses. These results aid in understanding past atmospheric circulation changes, and have potential to improve projections of future circulation changes.

] We discuss 13 real-time forecasts of global annual-mean surface temperature issued by the United KingdomMet Office for 1year ahead for 2000–2012. These involvestatistical, and since 2008, initialized dynamical forecastsusing the Met Office DePreSys system. For the periodwhen the statistical forecast system changed little, 2000–2010, issued forecasts had a high correlation of 0.74 withobservations and a root mean square error of 0.07 C.However, the HadCRUT data sets against which issuedforecasts were verified were biased slightly cold, especiallyfrom 2004, because of data gaps in the strongly warmingArctic. This observational cold bias was mainly responsiblefor a statistically significant warm bias in the 2000–2010forecasts of 0.06 C. Climate forcing data sets used in thestatistical method, and verification data, have recently beenmodified, increasing hindcast correlation skill to 0.80 withno significant bias. Dynamical hindcasts for 2000–2011 havea similar correlation skill of 0.78 and skillfully hindcastannual mean spatial global surface temperature patterns.Such skill indicates that we have a good understandingof the main factors influencing global mean surfacetemperature.

[1] An updated version of the Met Office Hadley Centre's monthly night marine air temperature data set is presented. It is available on a 5° latitude-longitude grid from 1880 as anomalies relative to 1961–1990 calendar-monthly climatological average night marine air temperature (NMAT). Adjustments are made for changes in observation height; these depend on estimates of the stability of the near surface atmospheric boundary layer. In previous versions of the data set, ad hoc adjustments were also made for three periods and regions where poor observational practice was prevalent. These adjustments are re-examined. Estimates of uncertainty are calculated for every grid box and result from measurement errors, uncertainty in adjustments applied to the observations, uncertainty in the measurement height, and under-sampling. The new data set is a clear improvement over previous versions in terms of coverage because of the recent digitization of historical observations from ships' logbooks. However, the periods prior to about 1890 and around World War II remain particularly uncertain, and sampling is still sparse in some regions in other periods. A further improvement is the availability of uncertainty estimates for every grid box and every month. Previous versions required adjustments that were dependent on contemporary measurements of sea surface temperature (SST); to avoid these, the new data set starts in 1880 rather than 1856. Overall agreement with variations of SST is better for the updated data set than for previous versions, supporting existing estimates of global warming and increasing confidence in the global record of temperature variability and change.

On 13 September 1922, a temperature of 58°C (136.4°F) was purportedly recorded at El Azizia (approximately 40 km south-southwest of Tripoli) in what is now modern-day Libya. That temperature record of 58°C has been cited by numerous world-record sources as the highest recorded temperature for the planet. During 2010–11, a World Meteorological Organization (WMO) Commission of Climatology (CCl) special international panel of meteorological experts conducted an in-depth investigation of this record temperature for the WMO World Archive of Weather and Climate Extremes (http://wmo.asu.edu/). This committee identified five major concerns with the 1922 El Azizia temperature extreme record, specifically 1) potentially problematical instrumentation, 2) a probable new and inexperienced observer at the time of observation, 3) unrepresentative microclimate of the observation site, 4) poor correspondence of the extreme to other locations, and 5) poor comparison to subsequent temperature values recorded at the site. Based on these concerns, the WMO World Archive of Weather and Climate Extremes rejected this temperature extreme of 58°C as the highest temperature officially recorded on the planet. The WMO assessment is that the highest recorded surface temperature of 56.7°C (134°F) was measured on 10 July 1913 at Greenland Ranch (Death Valley), California.

The global average temperature near the surface of the earth calculated from the third version of the Hadley Centre and Climatic Research Unit’s (HadCRUT3) (Brohan et al., 2006) data set in 2010 was 0.50 ± 0.09 degC above the 1961–1990 average (Figure 1(a)). 2010 is nominally the second warmest year in HadCRUT3, but the uncertainties are such that it is statistically indistinguishable from any of the seven warmest years. The largest component of the uncertainty in recent years arises from large areas of missing data at high latitudes where there are few observing stations. The National Climate Data Center (NCDC) and the National Aeronautics and Space Administration’s Goddard Institute for Space Studies (NASA GISS) data sets estimate temperature anomalies in these regions, with GISS extrapolating temperatures the most extensively. The Arctic has warmed much faster than the rest of the globe and so GISS has reported higher global average temperatures than NCDC and HadCRUT3 in recent years. The analyses produced by NASA GISS (Hansen et al., 2010) and NCDC (Smith et al., 2008) rank 2010 as the joint warmest year. The warmth of 2010 was due in part to the El Nino that developed in 2009: El Nino events normally lead to a rise in global average temperature. The effects of El Nino on global temperature typically lag temperature changes in the tropical Pacific (Figure 2) by a few months (Trenberth et al., 2002). The recent El Nino reached its peak strength in December 2009 with an average sea-surface temperature anomaly in the Nino 3 region (150°–90°W, 5°S–5°N, Figure 2) of around +1.5 degC. There was a rapid transition from El Nino to La Nina conditions in 2010 and

Radiosonde humidity records represent the only in situ observations of tropospheric water vapor content with multidecadal length and quasi-global coverage. However, their use has been hampered by ubiquitous and large discontinuities resulting from changes to instrumentation and observing practices. Here a new approach is developed to homogenize historical records of tropospheric (up to 100 hPa) dewpoint depression (DPD), the archived radiosonde humidity parameter. Two statistical tests are used to detect changepoints, which are most apparent in histograms and occurrence frequencies of the daily DPD: a variant of the Kolmogorov–Smirnov (K–S) test for changes in distributions and the penalized maximal F test (PMFred) for mean shifts in the occurrence frequency for different bins of DPD. These tests capture most of the apparent discontinuities in the daily DPD data, with an average of 8.6 changepoints (∼1 changepoint per 5 yr) in each of the analyzed radiosonde records, which begin as early as the 1950s and ended in March 2009. Before applying breakpoint adjustments, artificial sampling effects are first adjusted by estimating missing DPD reports for cold (T < −30°C) and dry (DPD artificially set to 30°C) conditions using empirical relationships at each station between the anomalies of air temperature and vapor pressure derived from recent observations when DPD reports are available under these conditions. Next, the sampling-adjusted DPD is detrended separately for each of the 4–10 quantile categories and then adjusted using a quantile-matching algorithm so that the earlier segments have histograms comparable to that of the latest segment. Neither the changepoint detection nor the adjustment uses a reference series given the stability of the DPD series. Using this new approach, a homogenized global, twice-daily DPD dataset (available online at www.cgd.ucar.edu/cas/catalog/) is created for climate and other applications based on the Integrated Global Radiosonde Archive (IGRA) and two other data sources. The adjusted-daily DPD has much smaller and spatially more coherent trends during 1973–2008 than the raw data. It implies only small changes in relative humidity in the lower and middle troposphere. When combined with homogenized radiosonde temperature, other atmospheric humidity variables can be calculated, and these exhibit spatially more coherent trends than without the DPD homogenization. The DPD adjustment yields a different pattern of change in humidity parameters compared to the apparent trends from the raw data. The adjusted estimates show an increase in tropospheric water vapor globally.

We have used the China Homogenized Historic Temperature dataset and some long-term station series of the neighbor countries from CRUTEM3, a 5°×5° gridded dataset of monthly mean temperature since 1900, to provide a 107-year record of surface temperature trends and variability. We derived a comprehensive set of uncertainty estimates to accompany the data: measurement and sampling errors, uncertainties in temperature bias estimates, and uncertainties arising from limited observational coverage on large-scale averages have all been estimated. We reanalysed the temperature changes during the period of record. The best estimates of trends for 1900–2006 with uncertainties at 95% confidence range are about 0.09±0.017°C/decade for the year as a whole, and 0.14±0.021°C/decade, 0.11±0.021°C/decade, 0.04±0.017°C/decade, and 0.07±0.017°C/decade for winter, spring, summer and autumn respectively. For 1954–2006, the trends for annual, winter, spring, summer and autumn are: 0.26±0.032°C/decade, 0.35±0.046°C/decade, 0.25±0.051°C/decade, 0.16±0.037°C/decade and 0.22±0.055°C/decade. Winter saw the most significant warming trend in both 1900–2006 and 1954–2006, while during the most recent period (the satellite era, 1979–2006), all the seasons show similar warming trends: 0.45±0.13°C/decade, 0.51±0.11°C/decade, 0.52±0.16°C/decade, 0.37±0.10°C/decade and 0.50±0.16°C/decade for annual, winter, spring, summer and autumn. Trends arising from urbanization have been evaluated as less than 5% of the total warming trend for 1951–2001, so this bias was not removed.

Urban heat islands are a result of the physical properties of buildings and other structures, and the emission of heat by human activities. They are most pronounced on clear, calm nights; their strength depends also on the background geography and climate, and there are often cool islands in parks and less-developed areas. Some old city centers no longer show warming trends relative to rural neighbourhoods, because urban development has stabilised. This article reviews the effects that urban heat islands may have on estimates of global near-surface temperature trends. These effects have been reduced by avoiding or adjusting urban temperature measurements. Comparisons of windy weather with calm-weather air temperature trends for a worldwide set of observing sites suggest that global near-surface temperature trends have not been greatly affected by urban warming trends; this is supported by comparisons with marine surface temperatures. The use of dynamical-model-based reanalyses to estimate urban influences has been hindered by the heterogeneity of the data input to the reanalyses and by biases in the models. However, improvements in reanalyses are increasing their utility for assessing the surface air temperature record. High-resolution climate models and data on changing land use offer potential for future assessment of worldwide urban warming influences. The latest assessments of the likely magnitude of the residual urban trend in available global near-surface temperature records are summarized, along with the uncertainties of these residual trends. Copyright © 2010 John Wiley & Sons, Ltd. This article is categorized under: Paleoclimates and Current Trends > Detection and Attribution

Optical holographic interferometry was used to study standing sound waves in air inside a resonance tube driven by a small loudspeaker at one end. The front face of the resonance tube was constructed with plexiglass, allowing optical interrogation of the tube interior. The object beam of the holographic setup was directed through the plexiglass and reflected off the back wall of the resonator. When driven at resonance, the fluctuations in the air density at the antinodes altered the refractive index of the air in the tube, causing interference patterns in the resulting holographic images. Real-time holography was used to determine resonance frequencies and to measure the wavelengths of the standing waves. Time-average holography was used to observe the effect of increasing the sound pressure level on the resulting fringe pattern. A simple theory was developed to successfully predict the fringe pattern.

Uncertainties in historical climate records constrain our understanding of natural variability of climate, but estimation of these uncertainties enables us to place recent climate events and extremes into a realistic historical perspective. Uncertainties in Central England temperature (CET) since 1878 have already been estimated; here we estimate uncertainties back to the start of the record in 1659, using Manley's publications and more recently developed techniques for estimating spatial sampling errors. Estimated monthly standard errors are of the order of 0.5 °C up to the 1720s, but 0.3 °C subsequently when more observing sites were used. Corresponding annual standard errors are up to nearly 0.4 °C in the earliest years but around 0.15 °C after the 1720s. Daily standard errors from 1772, when the daily series begins, up to 1877 are of the order of 1 °C because only a single site was used at any one time. Inter-diurnal variability in the daily CET record appears greater before 1878 than subsequently, partly because the sites were in the Midlands or southern England where day-to-day temperature variability exceeds that in the Lancashire part of Manley's CET. Copyright © 2009 Royal Meteorological Society

Daily anomalies of mean central England temperature (CET), relative to daily 1961–90 climatology, are analyzed in terms of the source of the air estimated from fields of mean sea level pressure. The average CET anomaly for a given source and calendar month during 1961–90 is taken as an estimate of the influence of atmospheric circulation for that source and calendar month, and the uncertainty in this influence is provided by the associated standard error. The atmospheric circulation influences are subtracted from the daily CET anomalies since the late nineteenth century to yield “residual anomalies,” which represent the influence of forcings other than atmospheric circulation. The use of air sources captures more circulation-related daily CET variance than the airflow indices used in previous studies. The warming in central England since the 1970s is not predominantly a result of atmospheric circulation changes, and the long-term changes of CET for air from major source regions are on the whole very similar to each other and to the overall long-term changes.

Uncertainties in observed records of atmospheric temperature aloft remain poorly quantified. This has resulted in considerable controversy regarding signals of climate change over recent decades from temperature records of radiosondes and satellites. This work revisits the problems associated with the removal of inhomogeneities from the historical radiosonde temperature records, and provides a method for quantifying uncertainty in an adjusted radiosonde climate record due to the subjective choices made during the data homogenization. This paper presents an automated homogenization method designed to replicate the decisions made by manual judgment in the generation of an earlier radiosonde dataset [i.e., the Hadley Centre radiosonde temperature dataset (HadAT)]. A number of validation experiments have been conducted to test the system performance and impact on linear trends. Using climate model data to simulate biased radiosonde data, the authors show that limitations in the homogenization method are sufficiently large to explain much of the tropical trend discrepancy between HadAT and estimates from satellite platforms and climate models. This situation arises from the combination of systematic (unknown magnitude) and random uncertainties (of order 0.05 K decade−1) in the radiosonde data. Previous assessment of trends and uncertainty in HadAT is likely to have underestimated the systematic bias in tropical mean temperature trends. This objective assessment of radiosonde homogenization supports the conclusions of the synthesis report of the U.S. Climate Change Science Program (CCSP), and associated research, regarding potential bias in tropospheric temperature records from radiosondes.

For over a century, wooden thermometer screens of the Stevenson design have been used in the meteorological station network administered by the UK Met Office. A screen is now being manufactured of the same dimensions but made from plastic. This requires minimal maintenance and has the potential for a much longer life than the wooden type. In order to determine whether a changeover to the plastic type would be acceptable for climatological applications, in particular for studies of climate change, a 12-month trial was carried out at 3 contrasting UK locations. Temperature and relative humidity measurements were made at 1-minute intervals in plastic and wooden trial screens installed at each site, as well as in the existing operational wooden screens. At the start of the trial, acceptance criteria based on Global Climate Observing System (GCOS) standards were set for mean and extreme temperature values. The temperature differences between the trial wooden and plastic screens were all found to be within these acceptance criteria. The largest differences occurred between pairs of wooden screens, not between wooden and plastic screens. Analysis of humidity differences between wooden and plastic screens did not yield clear results. To help understand the temperature and humidity differences, the screen data were analysed in terms of other current weather conditions, principally solar radiation and wind speed. We recommend that, in temperate climates, the plastic thermometer screen is a climatologically consistent alternative to the wooden design, requiring no adjustments to long-term records. © Crown Copyright 2006. Reproduced with the permission of the Controller of HMSO. Published by John Wiley & Sons, Ltd.

DFID is considering a new programme on climate information and services in Sub-Saharan Africa (SSA), building on existing initiatives and a providing a step change in the use of climate information to support poverty reduction and promote socio-economic development. As part of the scoping for the design of the Climate and Information Services for Africa programme (CIASA), this report provides a short literature review on user needs, gaps and potential intervention options. It adopts the structure of the Global Framework for Climate Services (GFCS, see Section 1) with individual sections on ‘Users’, ‘User Interface Platforms (UIPs)’, ‘Climate Services Information Systems’, ‘Observations and Monitoring’, ‘Research, Modelling and Prediction’ and ‘Capacity Development’. The review identified a number of gaps in climate services, which were then considered alongside other evidence in a synthesis report and for the design of intervention options. The literature review found that the requirements of users of climate information are demanding relative to current levels of provider capacity in Africa (Section 2, Section 7). Needs are differentiated across a wide range of users working across decision time- and geographic scales (Section 2). Climate services should be reliable in terms of accuracy, robust in terms of operational delivery, relevant in terms of content and format and accessible in terms of channels of communication and language employed. To deliver the best possible climate services, at scales down to sub-national, synthesis of a wide range of information from global, regional and national centres is required together with the means for effective two-way communication. The appropriate operational hardware and expertise, institutional linkages, data exchanges (Section 4) and institutional capacity (Section 7) require development to facilitate this synthesis and a reliable operational service.

On the premise that urban heat islands are strongest in calm conditions but are largely absent in windy weather, daily minimum and maximum air temperatures for the period 1950–2000 at a worldwide selection of land stations are analyzed separately for windy and calm conditions, and the global and regional trends are compared. The trends in temperature are almost unaffected by this subsampling, indicating that urban development and other local or instrumental influences have contributed little overall to the observed warming trends. The trends of temperature averaged over the selected land stations worldwide are in close agreement with published trends based on much more complete networks, indicating that the smaller selection used here is sufficient for reliable sampling of global trends as well as interannual variations. A small tendency for windy days to have warmed more than other days in winter over Eurasia is the opposite of that expected from urbanization and is likely to be a consequence of atmospheric circulation changes.

A new flexible gridded dataset of sea surface temperature (SST) since 1850 is presented and its uncertainties are quantified. This analysis [the Second Hadley Centre Sea Surface Temperature dataset (HadSST2)] is based on data contained within the recently created International Comprehensive Ocean–Atmosphere Data Set (ICOADS) database and so is superior in geographical coverage to previous datasets and has smaller uncertainties. Issues arising when analyzing a database of observations measured from very different platforms and drawn from many different countries with different measurement practices are introduced. Improved bias corrections are applied to the data to account for changes in measurement conditions through time. A detailed analysis of uncertainties in these corrections is included by exploring assumptions made in their construction and producing multiple versions using a Monte Carlo method. An assessment of total uncertainty in each gridded average is obtained by combining these bias-correction-related uncertainties with those arising from measurement errors and undersampling of intragrid box variability. These are calculated by partitioning the variance in grid box averages between real and spurious variability. From month to month in individual grid boxes, sampling uncertainties tend to be most important (except in certain regions), but on large-scale averages bias-correction uncertainties are more dominant owing to their correlation between grid boxes. Changes in large-scale SST through time are assessed by two methods. The linear warming between 1850 and 2004 was 0.52° ± 0.19°C (95% confidence interval) for the globe, 0.59° ± 0.20°C for the Northern Hemisphere, and 0.46° ± 0.29°C for the Southern Hemisphere. Decadally filtered differences for these regions over this period were 0.67° ± 0.04°C, 0.71° ± 0.06°C, and 0.64° ± 0.07°C.

There is no single reference dataset of long-term global upper-air temperature observations, although several groups have developed datasets from radiosonde and satellite observations for climate-monitoring purposes. The existence of multiple data products allows for exploration of the uncertainty in signals of climate variations and change. This paper examines eight upper-air temperature datasets and quantifies the magnitude and uncertainty of various climate signals, including stratospheric quasi-biennial oscillation (QBO) and tropospheric ENSO signals, stratospheric warming following three major volcanic eruptions, the abrupt tropospheric warming of 1976‐77, and multidecadal temperature trends. Uncertainty estimates are based both on the spread of signal estimates from the different observational datasets and on the inherent statistical uncertainties of the signal in any individual dataset. The large spread among trend estimates suggests that using multiple datasets to characterize large-scale upperair temperature trends gives a more complete characterization of their uncertainty than reliance on a single dataset. For other climate signals, there is value in using more than one dataset, because signal strengths vary. However, the purely statistical uncertainty of the signal in individual datasets is large enough to effectively encompass the spread among datasets. This result supports the notion of an 11th climate-monitoring principle, augmenting the 10 principles that have now been generally accepted (although not generally implemented) by the climate community. This 11th principle calls for monitoring key climate variables with multiple, independent observing systems for measuring the variable, and multiple, independent groups analyzing the data.

Deep-layer temperatures derived from satellite-borne microwave sensors since 1979 are revised (version 5.0) to account for 1) a change from microwave sounding units (MSUs) to the advanced MSUs (AMSUs) and 2) an improved diurnal drift adjustment for tropospheric products. AMSU data, beginning in 1998, show characteristics indistinguishable from the earlier MSU products. MSU–AMSU error estimates are calculated through comparisons with radiosonde-simulated bulk temperatures for the low–middle troposphere (TLT), midtroposphere (TMT), and lower stratosphere (TLS.) Monthly (annual) standard errors for global mean anomalies of TLT satellite temperatures are estimated at 0.10°C (0.07°C). The TLT (TMT) trend for January 1979 to April 2002 is estimated as +0.06° (+0.02°) ±0.05°C decade–1 (95% confidence interval). Error estimates for TLS temperatures are less well characterized due to significant heterogeneities in the radiosonde data at high altitudes, though evidence is presented to suggest that since 19...

We report on preliminary steps in the homogenisation of HadISD, a sub-daily, station-based dataset covering 1973–2013. Using temperature, dewpoint temperature, sea-level pressure and wind speeds, change points are detected using the Pairwise Homogenisation Algorithm from Menne and Williams Jr. (2009). Monthly mean values and monthly mean diurnal ranges (temperature and dewpoint temperature) or monthly maximum values (wind speeds) are processed using the full network of 6103 stations in HadISD. Where multiple change points are detected within one year, they are combined and the average date used. Under the assumption that the underlying true population of inhomogeneity magnitudes is Gaussian, adjustments as small as around 0.5 °C, 0.5 hPa or 0.5 m s−1 have been successfully detected. No strong biases are present in the distributions of the adjustment values. The change point dates and adjustment values for each of the calculation methods will be provided alongside the dataset to allow users to select stations which have different levels of homogeneity. We give an example application of this change point information in calculating global temperature values from HadISD and comparing these to CRUTEM4. Removing the most inhomogeneous stations results in a better match between HadISD and CRUTEM4 when matched to the same coverage. However, further removals of stations with smaller and fewer inhomogeneities worsens the match.

In 2013, the vast majority of the monitored climate variables reported here maintained trends established in recent decades. ENSO was in a neutral state during the entire year, remaining mostly on the cool side of neutral with modest impacts on regional weather patterns around the world. This follows several years dominated by the effects of either La Niña or El Niño events. According to several independent analyses, 2013 was again among the 10 warmest years on record at the global scale, both at the Earths surface and through the troposphere. Some regions in the Southern Hemisphere had record or near-record high temperatures for the year. Australia observed its hottest year on record, while Argentina and New Zealand reported their second and third hottest years, respectively. In Antarctica, Amundsen-Scott South Pole Station reported its highest annual temperature since records began in 1957. At the opposite pole, the Arctic observed its seventh warmest year since records began in the early 20th century. At 20-m depth, record high temperatures were measured at some permafrost stations on the North Slope of Alaska and in the Brooks Range. In the Northern Hemisphere extratropics, anomalous meridional atmospheric circulation occurred throughout much of the year, leading to marked regional extremes of both temperature and precipitation. Cold temperature anomalies during winter across Eurasia were followed by warm spring temperature anomalies, which were linked to a new record low Eurasian snow cover extent in May. Minimum sea ice extent in the Arctic was the sixth lowest since satellite observations began in 1979. Including 2013, all seven lowest extents on record have occurred in the past seven years. Antarctica, on the other hand, had above-average sea ice extent throughout 2013, with 116 days of new daily high extent records, including a new daily maximum sea ice area of 19.57 million km2 reached on 1 October. ENSO-neutral conditions in the eastern central Pacific Ocean and a negative Pacific decadal oscillation pattern in the North Pacific had the largest impacts on the global sea surface temperature in 2013. The North Pacific reached a historic high temperature in 2013 and on balance the globally-averaged sea surface temperature was among the 10 highest on record. Overall, the salt content in nearsurface ocean waters increased while in intermediate waters it decreased. Global mean sea level continued to rise during 2013, on pace with a trend of 3.2 mm yr-1 over the past two decades. A portion of this trend (0.5 mm yr-1) has been attributed to natural variability associated with the Pacific decadal oscillation as well as to ongoing contributions from the melting of glaciers and ice sheets and ocean warming. Global tropical cyclone frequency during 2013 was slightly above average with a total of 94 storms, although the North Atlantic Basin had its quietest hurricane season since 1994. In the Western North Pacific Basin, Super Typhoon Haiyan, the deadliest tropical cyclone of 2013, had 1-minute sustained winds estimated to be 170 kt (87.5 m s-1) on 7 November, the highest wind speed ever assigned to a tropical cyclone. High storm surge was also associated with Haiyan as it made landfall over the central Philippines, an area where sea level is currently at historic highs, increasing by 200 mm since 1970. In the atmosphere, carbon dioxide, methane, and nitrous oxide all continued to increase in 2013. As in previous years, each of these major greenhouse gases once again reached historic high concentrations. In the Arctic, carbon dioxide and methane increased at the same rate as the global increase. These increases are likely due to export from lower latitudes rather than a consequence of increases in Arctic sources, such as thawing permafrost. At Mauna Loa, Hawaii, for the first time since measurements began in 1958, the daily average mixing ratio of carbon dioxide exceeded 400 ppm on 9 May. The state of these variables, along with dozens of others, and the 2013 climate conditions of regions around the world are discussed in further detail in this 24th edition of the State of the Climate series. © 2014, American Meteorological Society. All rights reserved.

Annual and seasonal gridded ocean surface temperature anomalies show an increase in warm extremes and a decrease in cold extremes since the late 19th century attributable entirely to the overall warming trend. Over land, however, a reduction in the total incidence of extremes may reflect improved instrumental exposures. Our estimates of extremes are made by deriving percentiles from fits of anomalies on 5° latitude ×5° longitude resolution to modified 2-parameter gamma distributions. A non-parametric method is used to check the validity of the results. Fields of percentiles created using this technique can be used to map the distribution of unusual temperature anomalies across the globe on any time scale from a month to about a decade, from 1870 onwards. We apply a similar technique to assess changes in the incidence of extreme daily Central England temperature anomalies. The incidence of these extremes, relative to individual monthly average temperatures, has declined.

A variety of measurements indicate that the rate of atmospheric warming in the tropics since 1979 is less than the observed warming of the sea surface. This result is further examined using the high quality buoys monitored by the Pacific Marine Environmental Laboratory in the Tropical Pacific Ocean. These buoys show cooling (most cases being statistically significant) of the air at 3m height relative to the sea at l m depth over 8 to 20-year periods in the eastern region. A global surface temperature dataset which uses only near-surface air temperature over both land and ocean, indicates less warming since 1979 than those using SSTs over the oceans, though large uncertainties remain with marine air temperatures.

Climate models predict that the surface and the troposphere will warm in response to increasing concentrations of greenhouse gases. Both have warmed since 1958, but radiosonde and satellite observations suggest that the atmosphere has not followed the strong surface warming over the last 20 years especially in the tropics. In the light of this, we investigate observed tropical lower-tropospheric lapse rate variability on decadal and longer time scales using radiosonde data since 1958. We find evidence of interdecadal changes in the surface minus 700 hPa temperature difference with reduced values throughout the tropics in the late 1970s to mid 1980s followed by an increase to the present day. This ubiquitous signal implies that the lapse rate changes are real rather than artefacts of the radiosonde record. Thus, real variability may explain much of the discrepancy in tropical temperature trends.

This paper examines trend uncertainties in layer-average free atmosphere temperatures arising from the use of different trend estimation methods. It also considers statistical issues that arise in assessing the significance of individual trends and of trend differences between data sets. Possible causes of these trends are not addressed. We use data from satellite and radiosonde measurements and from two reanalysis projects. To facilitate intercomparison, we compute from reanalyses and radiosonde data temperatures equivalent to those from the satellite-based Microwave Sounding Unit (MSU). We compare linear trends based on minimization of absolute deviations (LA) and minimization of squared deviations (LS). Differences are generally less than 0.05°C/decade over 1959–1996. Over 1979–1993, they exceed 0.10°C/decade for lower tropospheric time series and 0.15°C/decade for the lower stratosphere. Trend fitting by the LA method can degrade the lower-tropospheric trend agreement of 0.03°C/decade (over 1979–1996) previously reported for the MSU and radiosonde data. In assessing trend significance we employ two methods to account for temporal autocorrelation effects. With our preferred method, virtually none of the individual 1979–1993 trends in deep-layer temperatures are significantly different from zero. To examine trend differences between data sets we compute 95% confidence intervals for individual trends and show that these overlap for almost all data sets considered. Confidence intervals for lower-tropospheric trends encompass both zero and the model-projected trends due to anthropogenic effects. We also test the significance of a trend in d(t), the time series of differences between a pair of data sets. Use of d(t) removes variability common to both time series and facilitates identification of small trend differences. This more discerning test reveals that roughly 30% of the data set comparisons have significant differences in lower-tropospheric trends, primarily related to differences in measurement system. Our study gives empirical estimates of statistical uncertainties in recent atmospheric temperature trends. These estimates and the simple significance testing framework used here facilitate the interpretation of previous temperature trend comparisons involving satellite, radiosonde, and reanalysis data sets.

A survey is given of the available instrumental data for monitoring and analysis of climatic variations. We focus on temperature measurements, both over land and ocean, at the surface and aloft. Over land, the older observations were subject to exposure changes which may not have been fully compensated. The effects of urbanization have been largely avoided in studies of climatic change over the last 150 years. There are few records for pre-1850 outside Europe and eastern North America, and the global network shows a recent decline. Over the ocean, sea surface temperature (SST) has been measured using buckets, engine intakes, hull sensors, buoys, and satellites. Many of these data have been effectively homogenized, but new challenges arise as observing systems evolve. Available SST and marine air temperature datasets begin in the 1850s. The data are concentrated in shipping lanes especially before 1900, and very sparse during the world wars, but additional historical data are being digitized. The radiosonde record is short (∼40 years) and has major gaps over the oceans, tropics and Southern Hemisphere. Instrumental heterogeneities are beginning to be assessed and removed using physical and statistical techniques. The MSU record is complete but only began in 1979, and is not highly resolved in the vertical: major biases, mainly affecting the lower-tropospheric retrieval, have been reduced as a result of recent analyses. Advanced interpolation or data-assimilation techniques are being applied to these data, but the results must be interpreted with care.