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This study develops an innovative approach to homogenize discontinuities in both mean and variance in global subdaily radiosonde temperature data from 1958 to 2018. First, temperature natural variations and changes are estimated using reanalyses and removed from the radiosonde data to construct monthly and daily difference series. A penalized maximal F test and an improved Kolmogorov–Smirnov test are then applied to the monthly and daily difference series to detect spurious shifts in the mean and variance, respectively. About 60% (40%) of the changepoints appear in the mean (variance), and ~56% of them are confirmed by available metadata. The changepoints display a country-dependent pattern likely due to changes in national radiosonde networks. Mean segment length is 7.2 (14.6) years for the mean (variance)-based detection. A mean (quantile)-matching method using up to 5 years of data from two adjacent mean (variance)-based segments is used to adjust the earlier segments relative to the latest segment. The homogenized series is obtained by adding the two homogenized difference series back to the subtracted reference series. The homogenized data exhibit more spatially coherent trends and temporally consistent variations than the raw data, and lack the spurious tropospheric cooling over North China and Mongolia seen in several reanalyses and raw datasets. The homogenized data clearly show a warming maximum around 300 hPa over 30°S–30°N, consistent with model simulations, in contrast to the raw data. The results suggest that spurious changes are numerous and significant in the radiosonde records and our method can greatly improve their homogeneity.

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.

There is considerable import in creating more complete, better understood, holdings of early meteorological data. Such data permit an improved understanding of climate variability and long-term changes. Early records are particularly incomplete in the tropics, with implications for estimates of global and regional temperature. There is also a relatively low level of scientific understanding of how these measurements were made and, as a result, of their homogeneity and comparability to more modern techniques and measurements. Herein we describe and analyse a newly rescued set of long-term, up to six-way parallel measurements, undertaken over 1884–1903 in Mauritius, an island situated in the southern Indian Ocean. Data include: i) measurements from a well-ventilated room, ii) a shaded Thermograph; iii) instruments housed in a manner broadly equivalent to a modern Stevenson Screen; iv) a set of measurements by a Hygrometer mounted in a Stevenson Screen; and for a very much shorter period v) two additional Stevenson Screen configurations. All measurements were undertaken within roughly 80 metre radius. To our knowledge this is the first such multidecadal multi-instrument assessment of meteorological instrument transition impacts ever undertaken, providing potentially unique insights. The intercomparison also considers the impact of different ways of deriving daily and monthly averages. The long-term comparison is sufficient to robustly characterise systematic offsets between all the instruments and seasonally varying impacts. Differences between all techniques range from tenths of a degree Celsius to in excess of a degree Celsius and are considerably larger for maximum and minimum temperatures than for means or averages. Systematic differences of several tenths of a degree also exist for the different ways of deriving average / mean temperatures. All differences bar two average temperature series pairs are significant at the 0.01 level using a paired t-test. Given that all thermometers were regularly calibrated against a primary Kew standard thermometer this analysis highlights significant impacts of instrument exposure, housing, siting and measurement practices in early meteorological records. These results reaffirm the importance of thoroughly assessing the homogeneity of early meteorological records.

The likelihood of exceeding 1.5 °C of global warming relative to preindustrial depends on the warming observed so far, anthropogenic warming that may occur in the future, and the degree to which internal variability will either temporarily amplify or attenuate future anthropogenic warming. Here, we introduce a new framework that estimates the likelihood of exceeding 1.5 °C of global warming wherein uncertainties in each one of these factors is explicitly accounted for.In this new framework, we estimate the historical warming, and its uncertainty, from preindustrial to present using the recently-minted HadCRUT5 dataset. Future anthropogenic warming is estimated using an energy balance model tuned to an assessed range of climate sensitivity and applied to each of the core emissions scenarios (i.e. SSPs) underlying the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6). Finally, we estimate the influence of internal variability using a large ensemble of initial condition simulations. On this basis, we find that the largest uncertainty in estimates of the likelihood of exceeding 1.5°C of global warming is due to model-to-model differences in estimates of future anthropogenic warming, followed by historical warming uncertainty, and then uncertainty due to internal variability.Based on our analysis, we find that the earliest time for crossing 1.5 °C of global warming, here defined as the 5% likelihood, is approximately emissions-scenario independent. We define the 1.5 °C threshold without any overshoot: if a time series warms by more than 1.5 °C during any 20-year period before 2100, it is counted as having crossed 1.5 °C. In each considered scenario except SSP5-8.5, the 20-year average period that crosses the 1.5 °C threshold with a 5% likelihood is 2013 to 2032. On the other hand, the 50% likelihood does depend on the scenario, with the SSP5-8.5 crossing occurring in 2018 to 2037 and SSP1-1.9 crossing in 2022 to 2041. All scenarios except SSP1-1.9 have a likelihood close to 100% to cross 1.5 °C global warming before 2100. Even in SSP1-1.9, the scenario with the strongest emission reductions, there is a 71% likelihood to cross 1.5 °C by the end of this century. This implies that even in SSP1-1.9, the world may stay below 1.5 °C only if both climate sensitivity and historical warming are near the lower end of their respective distributions.These estimates, with their associated uncertainties, may have major implications for policy decisions.

The Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) concluded that warming of the climate system since the start of the instrumental record was unequivocal. While this conclusion does not solely rest upon the surface temperature record, this record is a key aspect underpinning this finding. In this chapter, the basis for the observational evidence for increases in global surface temperatures, since the start of the instrumental record, is discussed. The chapter begins with a discussion on how the measurements have been taken over time. This is followed by a description of the necessary efforts required to adjust the records to account for time varying biases. The resulting global estimates and attempts at quantifying uncertainties are then discussed. The changes in temperature extremes are briefly highlighted and the chapter closes with a view to future research directions and innovations.

Observations from the historical meteorological observing network contain many artefacts of non-climatic origin which must be accounted for prior to using these data in climate applications. State-of-the-art homogenisation approaches use various flavours of pairwise comparison between a target station and candidate neighbour station series. Such approaches require an adequate number of neighbours of sufficient quality and comparability - a condition that is met for most station series since the mid-20th Century. However, pairwise approaches have challenges where suitable neighbouring stations are sparse, as remains the case in vast regions of the globe and is common almost everywhere prior to the early 20th Century. Modern sparse-input centennial reanalysis products continue to improve and offer a potential alternative to pairwise comparison, particularly where and when observations are sparse. They do not directly ingest or use land-based surface temperature observations, so they are a formally independent estimate. This may be particularly helpful in cases where structurally similar changes exist across broad networks, which challenges current techniques in the absence of metadata. They also potentially offer a valuable methodologically distinct method, which would help explore structural uncertainty in homogenisation techniques. The present study compares the potential of spatially-interpolated sparse-input reanalysis products to neighbour-based approaches to perform homogenisation of global monthly land surface air temperature records back to 1850 based upon the statistical properties of station-minus-reanalysis and station-minus-neighbour series. This shows that neighbour-based approaches likely remain preferable in data dense regions and epochs. However, the most recent reanalysis product, NOAA-CIRES-DOE 20CRv3, is potentially preferable in cases where insufficient neighbours are available. This may in particular affect long-term global average estimates where a small number of long-term stations in data sparse regions will make substantial contributions to global estimates and may contain missed data artefacts in present homogenisation approaches.

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.

We explore the extent to which internal variability can reconcile discrepancies between observed and simulated warming in the upper tropical troposphere. We compare all extant radiosondebased estimates for the period 1958–2014 to simulations from the Coupled Model Intercomparison Project phase 5 multimodel ensemble and the 100 realization Max Planck Institute large ensemble. We consider annual mean temperatures and all available 30-and 15-year trends. Most observed trends fall within the ensemble spread for most of the record, and trends calculated over 15-year periods show better agreement than 30-year trends, with generally larger discrepancies for the older observational products. The simulated amplification of surface warming aloft in the troposphere is consistent with observations, and the linear correlation between surface and simultaneous tropospheric warming trends decreases with trend length. We conclude that trend differences between observations and simulations of tropical tropospheric temperatures are dominated by observational uncertainty and chaotic internal variability rather than by systematic errors in model performance.

It has been a decade since changes in diurnal temperature range (DTR) globally have been assessed in a stand-alone data analysis. The present study takes advantage of substantively improved basic data holdings arising from the International Surface Temperature Initiative’s databank effort and applies the National Centers for Environmental Information’s automated pairwise homogeneity assessment algorithm to reassess DTR records. It is found that breakpoints are more prevalent in DTR than other temperature elements and that the resulting adjustments have a broader distribution. This strongly implies that there is an overarching tendency, across the global meteorological networks, for nonclimatic artifacts to impart either random or anticorrelated rather than correlated biases in maximum and minimum temperature series. Future homogenization efforts would likely benefit from simultaneous consideration of DTR and maximum and minimum temperatures, in addition to average temperatures. Estimates of change in DTR are relatively insensitive to whether adjustments are calculated directly or inferred from adjustments returned for the maximum and minimum temperature series. The homogenized series exhibit a reduction in DTR since the midtwentieth century globally (-0.044 K/decade). Adjustments serve to approximately halve the long-term global reduction in DTR in the basic “raw” data. Most of the estimated DTR reduction occurred over 1960–1980. In several regions DTR has apparently increased over 1979–2012, while globally it has exhibited very little change (-0.016 K/decade). Estimated changes in DTR are an order of magnitude smaller than in maximum and minimum temperatures, which have both been increasing rapidly on multidecadal timescales (0.186 K/decade and 0.236 K/decade, respectively, since the midtwentieth century).

The uncertainty in Extended Reconstructed SST (ERSST) version 4 (v4) is reassessed based upon 1) reconstruction uncertainties and 2) an extended exploration of parametric uncertainties. The reconstruction uncertainty (Ur) results from using a truncated (130) set of empirical orthogonal teleconnection functions (EOTs), which yields an inevitable loss of information content, primarily at a local level. The Ur is assessed based upon 32 ensemble ERSST.v4 analyses with the spatially complete monthly Optimum Interpolation SST product. The parametric uncertainty (Up) results from using different parameter values in quality control, bias adjustments, and EOT definition etc. The Up is assessed using a 1000-member ensemble ERSST.v4 analysis with different combinations of plausible settings of 24 identified internal parameter values. At the scale of an individual grid box, the SST uncertainty varies between 0.3° and 0.7°C and arises from both Ur and Up. On the global scale, the SST uncertainty is substantially smaller (0.03°–0.14°C) and predominantly arises from Up. The SST uncertainties are greatest in periods and locales of data sparseness in the nineteenth century and relatively small after the 1950s. The global uncertainty estimates in ERSST.v4 are broadly consistent with independent estimates arising from the Hadley Centre SST dataset version 3 (HadSST3) and Centennial Observation-Based Estimates of SST version 2 (COBE-SST2). The uncertainty in the internal parameter values in quality control and bias adjustments can impact the SST trends in both the long-term (1901–2014) and “hiatus” (2000–14) periods.

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.

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.

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.

Using state-of-the-art observational datasets and results from a large archive of computer model simulations, a consortium of scientists from 12 different institutions has resolved a long-standing conundrum in climate science—the apparent discrepancy between simulated and observed temperature trends in the tropics. Research published by this group indicates that there is no fundamental discrepancy between modeled and observed tropical temperature trends when one accounts for: (1) the (currently large) uncertainties in observations; and (2) the statistical uncertainties in estimating trends from observations. These results refute a recent claim that model and observed tropical temperature trends “disagree to a statistically significant extent”. This claim was based on the application of a flawed statistical test and the use of older observational datasets.

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.

The global tropical cyclone (TC) intensity record, even in modern times, is uncertain because the vast majority of storms are only observed remotely. Forecasters determine the maximum wind speed using a patchwork of sporadic observations and remotely sensed data. A popular tool that aids forecasters is the Dvorak technique—a procedural system that estimates the maximum wind based on cloud features in IR and/or visible satellite imagery. Inherently, the application of the Dvorak procedure is open to subjectivity. Heterogeneities are also introduced into the historical record with the evolution of operational procedures, personnel, and observing platforms. These uncertainties impede our ability to identify the relationship between tropical cyclone intensities and, for example, recent climate change. A global reanalysis of TC intensity using experts is difficult because of the large number of storms. We will show that it is possible to effectively reanalyze the global record using crowdsourcing. Through modifying the Dvorak technique into a series of simple questions that amateurs (“citizen scientists”) can answer on a website, we are working toward developing a new TC dataset that resolves intensity discrepancies in several recent TCs. Preliminary results suggest that the performance of human classifiers in some cases exceeds that of an automated Dvorak technique applied to the same data for times when the storm is transitioning into a hurricane.

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.

[1] This analysis revisits detection and attribution of free atmosphere temperatures from radiosondes, almost a decade after previous studies. Since that time, data sets have not only become longer, but understanding of observational uncertainty has vastly improved. In addition, a coordinated set of experiments exploring the effects of human and natural forcings on past climate change has been made with a new generation of climate models. These advances allow a much more thorough analysis of the effects of modeling and observational uncertainty on attribution results than previously possible. Observational uncertainty is explored using multiple radiosonde reconstructions including those with ensembles of realizations exploring the effects of processing choices. Modeling uncertainty is explored by calculating multiple fingerprints of natural influence (from changes in solar irradiance and volcanic aerosols) and of human influence (due to greenhouse gases and due to the effects of combined anthropogenic forcings including stratospheric ozone depletion). With increased confidence over previous studies, human influences (both greenhouse gas and other anthropogenic forcings) have been detected in spatiotemporal changes in free atmosphere temperature from 1961 to 2010, irrespective of whether the full atmospheric column (30–850 hPa) is examined or purely the troposphere, with stratospheric ozone depletion dominating the cooling that has been observed in the lower stratosphere. Thus the advances of the last decade yield increased confidence that anthropogenic influences have made a substantial contribution to the evolution of free atmosphere temperatures.

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.

We review the scientific literature since the 1960s to examine the evolution of modeling tools and observations that have advanced understanding of global stratospheric temperature changes. Observations show overall cooling of the stratosphere during the period for which they are available (since the late 1950s and late 1970s from radiosondes and satellites, respectively), interrupted by episodes of warming associated with volcanic eruptions, and superimposed on variations associated with the solar cycle. There has been little global mean temperature change since about 1995. The temporal and vertical structure of these variations are reasonably well explained bymodels that include changes in greenhouse gases, ozone, volcanic aerosols, and solar output, although there are significant uncertainties in the temperature observations and regarding the nature and influence of past changes in stratospheric water vapor. As a companion to a recent WIREs review of tropospheric temperature trends, this article identifies areas of commonality and contrast between the tropospheric and stratospheric trend literature. For example, the increased attention over time to radiosonde and satellite data quality has contributed to better characterization of uncertainty in observed trends both in the troposphere and in the lower stratosphere, and has highlighted the relative deficiency of attention to observations in the middle and upper stratosphere. In contrast to the relatively unchanging expectations of surface and tropospheric warming primarily induced by greenhouse gas increases, stratospheric temperature change expectations have arisen from experiments with a wider variety of model types, showingmore complex trend patterns associated with a greater diversity of forcing agents.

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.

This report describes observations of precipitation, temperature, and other climatology metrics from different global regions.

Estimates of past climate change often involve teasing small signals from imperfect instrumental or proxy records. Success is often evaluated on the basis of the spatial or temporal consistency of the resulting reconstruction, or on the apparent prediction error on small space and time scales. However, inherent methodological trade-offs illustrated here can cause climate signal accuracy to be unrelated, or even inversely related, to such performance measures. This is a form of the classic conflict in statistics between minimum variance and unbiased estimators. Comprehensive statistical simulations based on climate model output are probably the best way to reliably assess whether methods of reconstructing climate from sparse records, such as radiosondes or paleoclimate proxies, actually work on longer time scales. Copyright © 2008 Royal Meteorological Society

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.

Reanalyses are used primarily for atmospheric model validation. However, reanalyses suffer from a number of limitations that unfortunately restrict their general use. A reanalysis workshop held recently at the University of Reading, United Kingdom, highlighted a number of central issues in climate research. Attendees discussed how these research needs may benefit from a dedicated reanalysis. Specific topics covered included a more in-depth understanding of the general circulation of the atmosphere and a more reliable assessment of climate trends, the hydrological cycle and the calculation of energy fluxes over the oceans.

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.

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.

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.

We use globally gridded radiosonde temperature datasets in a simple climate change study. Two climate models, when run with historical and, particularly, anthropogenic forcings, exhibit a degree of agreement with radiosonde temperature observations for 1958–1998.

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.

Changes in the circumstances behind in situ temperature measurements often lead to biases in individual station records that, collectively, can also bias regional temperature trends. Since these biases are comparable in magnitude to climate change signals, homogeneity “corrections” are necessary to make the records suitable for climate analysis. To quantify the effectiveness of U.S. surface temperature homogenization, a randomized perturbed ensemble of the USHCN pairwise homogenization algorithm was run against a suite of benchmark analogs to real monthly temperature data. Results indicate that all randomized versions of the algorithm consistently produce homogenized data closer to the true climate signal in the presence of widespread systematic errors. When applied to the real-world observations, the randomized ensemble reinforces previous understanding that the two dominant sources of bias in the U.S. temperature records are caused by changes to time of observation (spurious cooling in minimum and maximum) and conversion to electronic resistance thermometers (spurious cooling in maximum and warming in minimum). Error bounds defined by the ensemble output indicate that maximum temperature trends are positive for the past 30, 50 and 100 years, and that these maximums contain pervasive negative biases that cause the unhomogenized (raw) trends to fall below the lower limits of uncertainty. Moreover, because residual bias in the homogenized analogs is one-tailed under biased errors, it is likely that maximum temperature trends have been underestimated in the USHCN. Trends for minimum temperature are also positive over the three periods, but the ensemble error bounds encompass trends from the unhomogenized data.

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.

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 1973–2010. The full dataset consists of over 6000 stations, with 3375 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. 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). Some very initial analyses are performed to illustrate some of the types of problems to which the final data could be applied. Although the filtering has removed the poorest station records, no attempt has been made to homogenise the data thus far, due to the complexity of retaining the true distribution of high-resolution data when applying adjustments. 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. This dataset will allow the study of high frequency variations of temperature, pressure and humidity on a global basis over the last four decades. Both individual extremes and the overall population of extreme events could be investigated in detail to allow for comparison with past and projected climate.

[1] Temperature trends over 1979–2008 in the U.S. Historical Climatology Network (HCN) are compared with those in six recent atmospheric reanalyses. For the conterminous United States, the trend in the adjusted HCN (0.327 °C dec−1) is generally comparable to the ensemble mean of the reanalyses (0.342 °C dec−1). It is also well within the range of the reanalysis trend estimates (0.280 to 0.437 °C dec−1). The bias adjustments play a critical role, as the raw HCN dataset displays substantially less warming than all of the reanalyses. HCN has slightly lower maximum and minimum temperature trends than those reanalyses with hourly temporal resolution, suggesting the HCN adjustments may not fully compensate for recent non-climatic artifacts at some stations. Spatially, both the adjusted HCN and all of the reanalyses indicate widespread warming across the nation during the study period. Overall, the adjusted HCN is in broad agreement with the suite of reanalyses.

The consistency of tropical tropospheric temperature trends with climate model expectations remains contentious. A key limitation is that the uncertainties in observations from radiosondes are both substantial and poorly constrained. We present a thorough uncertainty analysis of radiosonde‐based temperature records. This uses an automated homogenization procedure and a previously developed set of complex error models where the answer is known a priori. We perform a number of homogenization experiments in which error models are used to provide uncertainty estimates of real‐world trends. These estimates are relatively insensitive to a variety of processing choices. Over 1979–2003, the satellite‐equivalent tropical lower tropospheric temperature trend has likely (5–95% confidence range) been between −0.01 K/decade and 0.19 K/decade (0.05–0.23 K/decade over 1958–2003) with a best estimate of 0.08 K/decade (0.14 K/decade). This range includes both available satellite data sets and estimates from models (based upon scaling their tropical amplification behavior by observed surface trends). On an individual pressure level basis, agreement between models, theory, and observations within the troposphere is uncertain over 1979 to 2003 and nonexistent above 300 hPa. Analysis of 1958–2003, however, shows consistent model‐data agreement in tropical lapse rate trends at all levels up to the tropical tropopause, so the disagreement in the more recent period is not necessarily evidence of a general problem in simulating long‐term global warming. Other possible reasons for the discrepancy since 1979 are: observational errors beyond those accounted for here, end‐point effects, inadequate decadal variability in model lapse rates, or neglected climate forcings.

Changes in atmospheric temperature have a particular importance in climate research because climate models consistently predict a distinctive vertical profile of trends. With increasing greenhouse gas concentrations, the surface and troposphere are consistently projected to warm, with an enhancement of that warming in the tropical upper troposphere. Hence, attempts to detect this distinct ‘fingerprint’ have been a focus for observational studies. The topic acquired heightened importance following the 1990 publication of an analysis of satellite data which challenged the reality of the projected tropospheric warming. This review documents the evolution over the last four decades of understanding of tropospheric temperature trends and their likely causes. Particular focus is given to the difficulty of producing homogenized datasets, with which to derive trends, from both radiosonde and satellite observing systems, because of the many systematic changes over time. The value of multiple independent analyses is demonstrated. Paralleling developments in observational datasets, increased computer power and improved understanding of climate forcing mechanisms have led to refined estimates of temperature trends from a wide range of climate models and a better understanding of internal variability. It is concluded that there is no reasonable evidence of a fundamental disagreement between tropospheric temperature trends from models and observations when uncertainties in both are treated comprehensively.  2010 Crown copyright WIREs Clim

[1] Measurements made by the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit (AMSU) provide a multidecadal record of global atmospheric temperature change, which have been used by several groups to produce long-term temperature records of thick layers of the atmosphere from the lower troposphere to the lower stratosphere. Here we present an internal uncertainty estimate for the Remote Sensing Systems data sets made using a Monte Carlo approach that includes contributions to the total uncertainty from sampling error, premerge adjustments to each individual satellite, and the merging procedure. The results can be used to estimate uncertainties in this product at all space and time scales of interest to any specific application. On small space and time scales sampling effects dominate. On the longer time scales intersatellite merging is important at all levels and the diurnal adjustment is a critical uncertainty for the two layers that have a significant surface component, particularly over land. A comparison of trends for the globe, tropics, and extratropics between the best estimate data set along with these error estimates and homogenized radiosonde estimates and available MSU/AMSU estimates from other groups is undertaken. This shows consistency between our product and those produced by others within the stated uncertainty for many regions and layers. In almost as many cases, however, the interdata set differences of the estimated trends are too large be accounted for by the internal uncertainty estimates derived herein.

Observed changes in the HadCRUH global land surface specific humidity and CRUTEM3 surface temperature from 1973 to 1999 are compared to CMIP3 archive climate model simulations with 20th Century forcings. Observed humidity increases are proportionately largest in the Northern Hemisphere, especially in winter. At the largest spatio-temporal scales moistening is close to the Clausius–Clapeyron scaling of the saturated specific humidity (~7% K − 1). At smaller scales in water-limited regions, changes in specific humidity are strongly inversely correlated with total changes in temperature. Conversely, in some regions increases are faster than implied by the Clausius–Clapeyron relation. The range of climate model specific humidity seasonal climatology and variance encompasses the observations. The models also reproduce the magnitude of observed interannual variance over all large regions. Observed and modelled trends and temperature–humidity relationships are comparable except for the extratropical Southern Hemisphere where observations exhibit no trend but models exhibit moistening. This may arise from: long-term biases remaining in the observations; the relative paucity of observational coverage; or common model errors. The overall degree of consistency of anthropogenically forced models with the observations is further evidence for anthropogenic influence on the climate of the late 20th century.

[1] Evidence is presented of a reduction in relative humidity over low-latitude and midlatitude land areas over a period of about 10 years leading up to 2008, based on monthly anomalies in surface air temperature and humidity from comprehensive European Centre for Medium-Range Weather Forecasts reanalyses (ERA-40 and ERA-Interim) and from Climatic Research Unit and Hadley Centre analyses of monthly station temperature data (CRUTEM3) and synoptic humidity observations (HadCRUH). The data sets agree well for both temperature and humidity variations for periods and places of overlap, although the average warming over land is larger for the fully sampled ERA data than for the spatially and temporally incomplete CRUTEM3 data. Near-surface specific humidity varies similarly over land and sea, suggesting that the recent reduction in relative humidity over land may be due to limited moisture supply from the oceans, where evaporation has been limited by sea surface temperatures that have not risen in concert with temperatures over land. Continental precipitation from the reanalyses is compared with a new gauge-based Global Precipitation Climatology Centre (GPCC) data set, with the combined gauge and satellite products of the Global Precipitation Climatology Project (GPCP) and the Climate Prediction Center (CPC), Merged Analysis of Precipitation (CMAP), and with CPC's independent gauge analysis of precipitation over land (PREC/L). The reanalyses agree best with the new GPCC and latest GPCP data sets, with ERA-Interim significantly better than ERA-40 at capturing monthly variability. Shifts over time in the differences among the precipitation data sets make it difficult to assess their longer-term variations and any link with longer-term variations in humidity.

A new analysis of historical radiosonde humidity observations is described. An assessment of both known and unknown instrument and observing practice changes has been conducted to assess their impact on bias and uncertainty in long-term trends. The processing of the data includes interpolation of data to address known sampling bias from missing dry day and cold temperature events, a first-guess adjustment for known radiosonde model changes, and a more sophisticated ensemble of estimates based on 100 neighbor-based homogenizations. At each stage the impact and uncertainty of the process has been quantified. The adjustments remove an apparent drying over Europe and parts of Asia and introduce greater consistency between temperature and specific humidity trends from day and night observations. Interannual variability and trends at the surface are shown to be in good agreement with independent in situ datasets, although some steplike discrepancies are apparent between the time series of relative humidity at the surface. Adjusted trends, accounting for documented and undocumented break points and their uncertainty, across the extratropical Northern Hemisphere lower and midtroposphere show warming of 0.1–0.4 K decade−1 and moistening on the order of 1%–5% decade−1 since 1970. There is little or no change in the observed relative humidity in the same period, consistent with climate model expectation of a positive water vapor feedback in the extratropics with near-constant relative humidity.

Biases and uncertainties in large-scale radiosonde temperature trends in the troposphere are critically reassessed. Realistic validation experiments are performed on an automatic radiosonde homogenization system by applying it to climate model data with four distinct sets of simulated breakpoint profiles. Knowledge of the “truth” permits a critical assessment of the ability of the system to recover the large-scale trends and a reinterpretation of the results when applied to the real observations. The homogenization system consistently reduces the bias in the daytime tropical, global, and Northern Hemisphere (NH) extratropical trends but underestimates the full magnitude of the bias. Southern Hemisphere (SH) extratropical and all nighttime trends were less well adjusted owing to the sparsity of stations. The ability to recover the trends is dependent on the underlying error structure, and the true trend does not necessarily lie within the range of estimates. The implications are that tropical tropospheric trends in the unadjusted daytime radiosonde observations, and in many current upper-air datasets, are biased cold, but the degree of this bias cannot be robustly quantified. Therefore, remaining biases in the radiosonde temperature record may account for the apparent tropical lapse rate discrepancy between radiosonde data and climate models. Furthermore, the authors find that the unadjusted global and NH extratropical tropospheric trends are biased cold in the daytime radiosonde observations. Finally, observing system experiments show that, if the Global Climate Observing System (GCOS) Upper Air Network (GUAN) were to make climate quality observations adhering to the GCOS monitoring principles, then one would be able to constrain the uncertainties in trends at a more comprehensive set of stations. This reaffirms the importance of running GUAN under the GCOS monitoring principles.

The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) concluded that global warming is ���unequivocal��� and that most of the observed increase since the mid-twentieth century is very likely due to the increase in anthropogenic greenhouse gas concentrations, with discernible human influences on ocean warming, continental-average temperatures, temperature extremes, wind patterns, and other physical and biological indicators, impacting both socioeconomic and ecological systems. It is now clear that we are committed to some level of global climate change, and it is imperative that this be considered when planning future climate research and observational strategies. The Global Climate Observing System program (GCOS), the World Climate Research Programme (WCRP), and the International Geosphere-Biosphere Programme (IGBP) therefore initiated a process to summarize the lessons learned through AR4 Working Groups I and II and to identify a set of high-priority modeling and observational needs. Two classes of recommendations emerged. First is the need to improve climate models, observational and climate monitoring systems, and our understanding of key processes. Second, the framework for climate research and observations must be extended to document impacts and to guide adaptation and mitigation efforts. Research and observational strategies specifically aimed at improving our ability to predict and understand impacts, adaptive capacity, and societal and ecosystem vulnerabilities will serve both purposes and are the subject of the specific recommendations made in this paper.