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We present a deep neural network based single site stochastic precipitation generator (SPG), capable of producing realistic time series of daily and hourly precipitation. The neural network outputs a wet day probability and precipitation distributions in the form of a mixture model. The SPG was tested in four different locations in New Zealand, and we found it accurately reproduced the precipitation depth, the autocorrelations seen in the original data, the observed dry-spell lengths and the seasonality in precipitation. We present two versions of the hourly and daily SPGs: (i) a stationary version of the SPG that assumes that the statistics of the precipitation are time independent (ii) a non-stationary version that captures the secular drift in precipitation statistics resulting from climate change. The latter was developed to be applicable to climate change impact studies, especially, studies reliant on SPG projections of future precipitation. We highlight many of the pitfalls associated with the training of a non-stationary SPG on observations alone, and offer an alternative method that replicates the secular drift in precipitation seen in a large-ensemble regional climate model. The SPG runs several orders of magnitude faster than a typical regional climate model, and permits the generation of very large ensembles of realistic precipitation time series under many climate change scenarios, these ensembles will also contain many extreme events not seen in the historical record.

MAPM (Mapping Air Pollution eMissions) is a project whose goal is to develop a method to infer airborne particulate matter (PM) emissions maps from in situ PM concentration measurements. In support of MAPM, a winter field campaign was conducted in New Zealand in 2019 (June to September) to obtain the measurements required to test and validate the MAPM methodology. Two different types of instruments measuring PM were deployed: ES-642 remote dust monitors (17 instruments) and Outdoor Dust Information Nodes (ODINs; 50 instruments). The measurement campaign was bracketed by two intercomparisons where all instruments were co-located, with a permanently installed tapered element oscillating membrane (TEOM) instrument, to determine any instrument biases. Changes in biases between the pre- and post-campaign intercomparisons were used to determine instrument drift over the campaign period. Once deployed, each ES-642 was co-located with an ODIN. In addition to the PM measurements, meteorological variables (temperature, pressure, wind speed, and wind direction) were measured at three automatic weather station (AWS) sites established as part of the campaign, with additional data being sourced from 27 further AWSs operated by other agencies. Vertical profile measurements were made with 12 radiosondes during two 24 h periods and complimented measurements made with a mini micropulse lidar and ceilometer. Here we present the data collected during the campaign and discuss the correction of the measurements made by various PM instruments. We find that when compared to measurements made with a simple linear correction, a correction based on environmental conditions improves the quality of measurements retrieved from ODINs but results in over-fitting and increases the uncertainties when applied to the more sophisticated ES-642 instruments. We also compare PM2.5 and PM10 measured by ODINs which, in some cases, allows us to identify PM from natural and anthropogenic sources. The PM data collected during the campaign are publicly available from https://doi.org/10.5281/zenodo.4542559 (Dale et al., 2020b), and the data from other instruments are available from https://doi.org/10.5281/zenodo.4536640 (Dale et al., 2020a).

Extreme temperature events (ETEs) have evolved alongside the warming climate over most parts of the world. This study provides a statistical quantification of how human influences have increased the likelihood and frequencies of ETEs in New Zealand, depending on the synoptic weather type. We use the simulation under pre-industrial conditions (natural scenarios with no rise in greenhouse gases (GHGs)) and present-day conditions (anthropogenic scenarios) from the weather@home regional climate model. The ensembles of simulations under these two scenarios are used to identify how increases in GHG concentrations have impacted the frequency and intensity of ETEs based on their connection to different large-scale circulation patterns derived using Self Organizing Maps (SOMs). Over New Zealand, an average 2-3 fold rise in frequencies of extremes occurs irrespective of seasons due to elevated GHG concentrations with a mean temperature increase close to 1℃. For some synoptic situations, the frequency and intensity of ETEs are enhanced. In particular, for low-pressure centers to the northeast of New Zealand, the frequency of occurrence of daily temperature extremes has increased by a factor of 7 between anthropogenic and natural simulations for the winter season, though these synoptic patterns rarely occur. For low-pressure centers to the northwest of New Zealand, we observe extreme temperatures frequently in both anthropogenic and natural simulations which we attribute to warm air advection from the tropics. The frequency of occurrence of these synoptic patterns has also increased by a factor of 2 between the natural and anthropogenic simulations. For these synoptic states, the extremes are observed in the North Island and along the east coast of the country with the highest temperature along the Canterbury coast and Northland. However, the change between the natural and anthropogenic simulations is largest on the west coast along the Southern Alps.

An assessment has been made of the ability of the UK Met Office Unified Model (UM) to simulate the Antarctic stratospheric circumpolar vortex and, in particular, the extent to which the vortex acts as a barrier to meridional transport. It is important that models simulate this barrier well as it determines spatial gradients in radiatively active gases, such as ozone, which then determine the spatial morphology of the radiative forcing field. The assessment was made by comparing metrics of meridional impermeability calculated from dynamical fields extracted from UM simulations and from analogous fields obtained from NCEP-CFSR reanalysis. Two different UM configurations were assessed: global atmosphere 3.0 (GA3.0) using the New Dynamics dynamical core, and GA7.0 using the newer ENDGame dynamical core, with both versions run at N96 resolution (1.25 $$^{\circ }$$ latitude by 1.875 $$^{\circ }$$ longitude). The GA7.0 configuration appears to better simulate the dynamical isolation of the Antarctic stratospheric vortex in the lower stratosphere up to about 600 K, while GA3.0 provides a better simulation in the upper stratosphere. However, neither UM configuration simulates the same degree of dynamical isolation suggested by the reanalysis. In particular the UM configurations produce a wider and more poleward meridional band of high wind-speed and steep PV gradients when compared with the NCEP-CFSR reanalysis, leading to a stronger barrier in GA7.0 and a weaker barrier in GA3.0. Possible causes of discrepancies between model simulations and reanalysis and between the two model configurations are discussed. It is pointed out that further work is needed to identify ways of resolving these discrepancies in model simulations.

MAPM (Mapping Air Pollution eMissions) is a two-year project whose goal is to develop a method to infer particulate matter (PM) emissions maps from in situ PM concentration measurements. Central to the functionality of MAPM is an inverse model. The input of the inverse model includes a spatially-distributed prior emissions estimate and PM measurement time series from instruments distributed across the desired domain. Here we describe the construction of this inverse model, the mathematics underlying the retrieval of the resultant posterior PM emissions maps, the way in which uncertainties are traced through the MAPM processing chain, and plans for future development of the processing chain. To demonstrate the capability of the inverse model developed for MAPM, we use the PM2.5 measurements obtained during a dedicated winter field campaign in Christchurch, New Zealand, in 2019 to infer PM2.5 emissions maps on city scale. The results indicate a systematic overestimation in the prior emissions for Christchurch of at least 40–60 %, which is consistent with some of the underlying assumptions used in the composition of the bottom-up emissions map used as the prior, highlighting the uncertainties in bottom-up approaches for estimating PM2.5 emissions maps. The paper also presents the results of two sets of observing system simulation experiments (OSSEs) that explore how measurement uncertainties affect the computation of the derived emissions maps, and the extent to which using emissions maps from one day as the prior for the next day improves the ability of the inversion system to characterize the emissions sources. We find in the first case that a smaller number of high-accuracy instruments performs significantly better than a higher number of low-accuracy instruments. In the second case, the results are ultimately inconclusive, showing the need for further investigations that are beyond the scope of this study.

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

Stratospheric ozone and water vapour are key components of the Earth system, and past and future changes to both have important impacts on global and regional climate. Here we evaluate long-term changes in these species from the pre- industrial (1850) to the end of the 21st century in CMIP6 models under a range of future emissions scenarios. There is good agreement between the CMIP multi-model mean and observations, although there is substantial variation between the individual CMIP6 models. For the CMIP6 multi-model mean, global total column ozone (TCO) has increased from ∼300 DU in 1850 to ∼305 DU in 1960, before rapidly declining in the 1970s and 1980s following the use and emission of halogenated ozone depleting substances (ODSs). TCO is projected to return to 1960s values by the middle of the 21st century under the SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0 and SSP5-8.5 scenarios, and under the SSP3-7.0 and SSP5-8.5 scenarios TCO values are projected to be ∼10 DU higher than the 1960s values by 2100. However, under the SSP1-1.9 and SSP1-1.6 scenarios, TCO is not projected to return to the 1960s values despite reductions in halogenated ODSs due to decreases in tropospheric ozone mixing ratios. This global pattern is similar to regional patterns, except in the tropics where TCO under most scenarios is not projected to return to 1960s values, either through reductions in tropospheric ozone under SSP1-1.9 and SSP1-2.6, or through reductions in lower stratospheric ozone resulting from an acceleration of the Brewer-Dobson Circulation under other SSPs. CMIP6 multi-model mean stratospheric water vapour mixing ratios in the tropical lower stratosphere have increased by ∼0.5 ppmv from the pre-industrial to the present day and are projected to increase further by the end of the 21st century. The largest increases (∼2 ppmv) are simulated under the future scenarios with the highest assumed forcing pathway (e.g. SSP5-8.5). Both TCO and tropical lower stratospheric water vapour show large variability following explosive volcanic eruptions.

As greenhouse gases continue to accumulate in Earth’s atmosphere, the nature of extreme weather events (EWEs) has been changing and is expected to change in the future. EWEs have contributions from anthropogenic climate change as well as from natural variability, which complicates attribution statements. EWERAM is a project that has been funded through the New Zealand Ministry of Business, Innovation and Employment Smart Ideas programme to develop the capability to provide, within days of an EWE having occurred over New Zealand, and while public interest is still high, scientifically defensible statements about the role of climate change in both the severity and frequency of that event. This is expected to raise public awareness and understanding of the effects of climate change on EWEs.A team of researchers from five institutions across New Zealand are participating in EWERAM. EWE attribution is a multi-faceted problem and different approaches are required to address different research aims. Although robustly assessing the contribution of changes in the thermodynamic state to an observed event can be more tractable than including changes in the dynamics of weather systems, for New Zealand, changes in dynamics have had a large impact on the frequency and location of EWEs. As such, we have initiated several lines of research to deliver metrics on EWE attribution, tailored to meet the needs of various stakeholders, that encompass the effects of both dynamical and thermodynamical changes in the atmosphere. This presentation will give an overview of EWERAM and present the methodologies and tools used in the project.

Frequency based climate change attribution of extreme weather events requires thousands of years worth of model output in order to obtain a statistically sound result. Additionally, extreme precipitation events in particular require a high resolution model as they can occur over a relatively small area. Unfortunately due storage and computational restrictions it is not feasible to run traditional models at a sufficiently high spatial resolution for the complete duration of these simulations. Instead, we suggest that deep learning could be used to emulate a proportion of a high resolution model, at a fraction of the computational cost. More specifically, we use a U-Net, a type of convolutional neural network. The U-Net takes as input, several fields from coarse resolution model output and is trained to predict corresponding high resolution precipitation fields. Because there are many potential precipitation fields associated with the coarse resolution model output, stochasticity is added to the U-Net and a generative adversarial network is employed in order to help create a realistic distribution of events. By sampling the U-Net many times, an estimate of the probability of a heavy precipitation event occurring on the sub-grid scale can be derived.

This paper describes the GRUAN-wide approach to manage the transition from the Vaisala RS92 to the Vaisala RS41 as the operational radiosonde. The goal of the GCOS Reference Upper-Air Network (GRUAN) is to provide long-term high-quality reference observations of upper air Essential Climate Variables (ECVs) such as temperature and water vapor. With GRUAN data being used for climate monitoring, it is vital that the change of measurement system does not introduce inhomogeneities in to the data record. The majority of the 27 GRUAN sites were launching the RS92 as their operational radiosonde, and following the end of production of the RS92 in the last quarter of 2017, most of these sites have now switched to the RS41. Such a large-scale change in instrumentation is unprecedented in the history of GRUAN and poses a challenge for the network. Several measurement programmes have been initiated to characterize differences in biases, uncertainties and noise between the two radiosonde types. These include laboratory characterization of measurement errors, extensive twin sounding studies with RS92 and RS41 on the same balloon, and comparison with ancillary data. This integrated approach is commensurate with the GRUAN principles of traceability and deliberate redundancy. A two-year period of regular twin soundings is recommended, and for sites that are not able to implement this burden sharing is employed, such that measurements at a certain site are considered representative of other sites with similar climatological characteristics. All data relevant to the RS92-RS41 transition are archived in a database that will be accessible to the scientific community for external scrutiny. Furthermore, the knowledge and experience gained about GRUAN's RS92-RS41 transition will be extensively documented to ensure traceability of the process. This documentation will benefit other networks in managing changes in their operational radiosonde systems. Preliminary analysis of the laboratory experiments indicates that the manufacturer's calibration of the RS41's temperature and humidity sensors is more accurate than for the RS92; with uncertainties of

A method, based on climate pattern scaling, has been developed to expand a small number of projections of fields of a selected climate variable (X) into an ensemble that encapsulates a wide range of indicative model structural uncertainties. The method described in this paper is referred to as the Ensemble Projections Incorporating Climate model uncertainty (EPIC) method. Each ensemble member is constructed by adding contributions from (1) a climatology derived from observations that represents the time-invariant part of the signal; (2) a contribution from forced changes in X, where those changes can be statistically related to changes in global mean surface temperature (Tglobal); and (3) a contribution from unforced variability that is generated by a stochastic weather generator. The patterns of unforced variability are also allowed to respond to changes in Tglobal. The statistical relationships between changes in X (and its patterns of variability) and Tglobal are obtained in a training phase. Then, in an implementation phase, 190 simulations of Tglobal are generated using a simple climate model tuned to emulate 19 different global climate models (GCMs) and 10 different carbon cycle models. Using the generated Tglobal time series and the correlation between the forced changes in X and Tglobal, obtained in the training phase, the forced change in the X field can be generated many times using Monte Carlo analysis. A stochastic weather generator is used to generate realistic representations of weather which include spatial coherence. Because GCMs and regional climate models (RCMs) are less likely to correctly represent unforced variability compared to observations, the stochastic weather generator takes as input measures of variability derived from observations, but also responds to forced changes in climate in a way that is consistent with the RCM projections. This approach to generating a large ensemble of projections is many orders of magnitude more computationally efficient than running multiple GCM or RCM simulations. Such a large ensemble of projections permits a description of a probability density function (PDF) of future climate states rather than a small number of individual story lines within that PDF, which may not be representative of the PDF as a whole; the EPIC method largely corrects for such potential sampling biases. The method is useful for providing projections of changes in climate to users wishing to investigate the impacts and implications of climate change in a probabilistic way. A web-based tool, using the EPIC method to provide probabilistic projections of changes in daily maximum and minimum temperatures for New Zealand, has been developed and is described in this paper.

Observing systems consisting of a finite number of in situ monitoring stations can provide high-quality measurements with the ability to quality assure both the instruments and the data but offer limited information over larger geographic areas. This paper quantifies the spatial coverage represented by a finite set of monitoring stations by using global data—data that are possibly of lower resolution and quality. For illustration purposes, merged satellite temperature data from Microwave Sounding Units are used to estimate the representativeness of the Global Climate Observing System Reference Upper-Air Network (GRUAN). While many metrics exist for evaluating the representativeness of a site, the ability to have highly accurate monthly averaged data is essential for both trend detection and climatology evaluation. The calculated correlations of the monthly averaged upper-troposphere satellite-derived temperatures over the GRUAN stations with all other pixels around the globe show that the current 9 certified GRUAN stations have moderate correlations (r ≥ 0.7) for approximately 10% of the earth, but an expanded network incorporating another 15 stations would result in moderate correlations for just over 60% of the earth. This analysis indicates that the value of additional stations can be quantified by using historical, satellite, or model data and can be used to reveal critical gaps in current monitoring capabilities. Evaluating the value of potential additional stations and prioritizing their initiation can optimize networks. The expansion of networks can be evaluated in a manner that allows for optimal benefit on the basis of optimization theory and economic analyses.

We examine the effects of non-linear interactions between surface albedo, water vapor and cloud cover (referred to as climate variables) on amplified warming of the polar regions, using a new energy balance model. Our simulations show that the sum of the contributions to surface temperature changes due to any variable considered in isolation is smaller than the temperature changes from coupled feedback simulations. This non-linearity is strongest when all three climate variables are allowed to interact. Surface albedo appears to be the strongest driver of this non-linear behavior, followed by water vapor and clouds. This is because increases in longwave radiation absorbed by the surface, related to increases in water vapor and clouds, and increases in surface absorbed shortwave radiation caused by a decrease in surface albedo, amplify each other. Furthermore, our results corroborate previous findings that while increases in cloud cover and water vapor, along with the greenhouse effect itself, warm the polar regions, water vapor also significantly warms equatorial regions, which reduces polar amplification. Changes in surface albedo drive large changes in absorption of incoming shortwave radiation, thereby enhancing surface warming. Unlike high latitudes, surface albedo change at low latitudes are more constrained. Interactions between surface albedo, water vapor and clouds drive larger increases in temperatures in the polar regions compared to low latitudes. This is in spite of the fact that, due to a forcing, cloud cover increases at high latitudes and decreases in low latitudes, and that water vapor significantly enhances warming at low latitudes.

Location information from long-duration super-pressure balloons flying in the Southern Hemisphere lower stratosphere during 2014 as part of X Project Loon are used to assess the quality of a number of different reanalyses including National Centers for Environmental Prediction Climate Forecast System version 2 (NCEP-CFSv2), European Centre for Medium-Range Weather Forecasts (ERA-Interim), NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA), and the recently released MERRA version 2. Balloon GPS location information is used to derive wind speeds which are then compared with values from the reanalyses interpolated to the balloon times and locations. All reanalysis data sets accurately describe the winds, with biases in zonal winds of less than 0.37 m s−1 and meridional biases of less than 0.08 m s−1. The standard deviation on the differences between Loon and reanalyses zonal winds is latitude-dependent, ranging between 2.5 and 3.5 m s−1, increasing equatorward. Comparisons between Loon trajectories and those calculated by applying a trajectory model to reanalysis wind fields show that MERRA-2 wind fields result in the most accurate simulated trajectories with a mean 5-day balloon–reanalysis trajectory separation of 621 km and median separation of 324 km showing significant improvements over MERRA version 1 and slightly outperforming ERA-Interim. The latitudinal structure of the trajectory statistics for all reanalyses displays marginally lower mean separations between 15 and 35° S than between 35 and 55° S, despite standard deviations in the wind differences increasing toward the equator. This is shown to be related to the distance travelled by the balloon playing a role in the separation statistics.

When computing climatological averages of atmospheric trace-gas mixing ratios obtained from satellite-based measurements, sampling biases arise if data coverage is not uniform in space and time. Homogeneous spatiotemporal coverage is essentially impossible to achieve. Solar occultation measurements, by virtue of satellite orbit and the requirement of direct observation of the sun through the atmosphere, result in particularly sparse spatial coverage. In this proof-of-concept study, a method is presented to adjust for such sampling biases when calculating climatological means. The method is demonstrated using carbonyl sulfide (OCS) measurements at 16 km altitude from the ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer). At this altitude, OCS mixing ratios show a steep gradient between the poles and Equator. ACE-FTS measurements, which are provided as vertically resolved profiles, and integrated stratospheric OCS columns are used in this study. The bias adjustment procedure requires no additional information other than the satellite data product itself. In particular, the method does not rely on atmospheric models with potentially unreliable transport or chemistry parameterizations, and the results can be used uncompromised to test and validate such models. It is expected to be generally applicable when constructing climatologies of long-lived tracers from sparsely and heterogeneously sampled satellite measurements. In the first step of the adjustment procedure, a regression model is used to fit a 2-D surface to all available ACE-FTS OCS measurements as a function of day-of-year and latitude. The regression model fit is used to calculate an adjustment factor that is then used to adjust each measurement individually. The mean of the adjusted measurement points of a chosen latitude range and season is then used as the bias-free climatological value. When applying the adjustment factor to seasonal averages in 30∘ zones, the maximum spatiotemporal sampling bias adjustment was 11 % for OCS mixing ratios at 16 km and 5 % for the stratospheric OCS column. The adjustments were validated against the much denser and more homogeneous OCS data product from the limb-sounding MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument, and both the direction and magnitude of the adjustments were in agreement with the adjustment of the ACE-FTS data.

Anthropogenic emissions of greenhouse gases and ozone-depleting substances are expected to continue to affect concentrations of ozone in the stratosphere through the 21st century. While a range of estimates for when stratospheric ozone is expected to return to unperturbed levels is available in the literature, quantification of the spread in results is sparse. Here we present the first probabilistic study of latitudinally resolved years of return of stratospheric ozone to 1960 levels. Results from our 180-member ensemble, simulated with a newly developed simple climate model, suggest that the spread in return years of ozone is largest around 40°N/S and in the southern high latitudes and decreases with increasing greenhouse gas emissions. The spread in projections of ozone is larger for higher greenhouse gas scenarios and is larger in the polar regions than in the midlatitudes, while the spread in ozone radiative forcing is smallest in the polar regions.

When computing climatological averages of atmospheric trace gas mixing ratios obtained from satellite-based measurements, sampling biases arise if data coverage is not uniform in space and time. Complete homogeneous spatio-temporal coverage is essentially impossible to achieve. Solar occultation measurements, by virtue of satellite orbits and the requirement of direct observation of the sun through the atmosphere, result in particularly sparse spatial coverage. In this study, a method is presented to adjust for such sampling biases when calculating climatological means. The method is demonstrated using carbonyl sulfide (OCS) measurements at 16 km altitude from the ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform 15 Spectrometer). At this altitude, OCS mixing ratios show a steep gradient between the poles and equator. ACE-FTS measurements, which are provided as vertically resolved profiles, and integrated stratospheric OCS columns are used in this study. The bias adjustment procedure requires no additional observations other than the satellite data product itself and is expected to be generally applicable when constructing climatologies of long-lived tracers from sparsely and heterogeneously sampled satellite data. In a first step of the adjustment procedure, a regression model is used to fit a 2-D surface to all available ACE-FTS OCS measurements as a function of day-of-year and latitude. The regression model fit is used to calculate an adjustment factor, 20 which is then used to adjust each measurement individually. The mean of the adjusted measurement points of a chosen spatio-temporal frame is then used as the bias-free climatological value. When applying the adjustment factor to seasonal averages in 30° zones, the maximum spatio-temporal sampling bias adjustment was 11 % for OCS mixing ratios at 16 km and 5 % for the stratospheric OCS column. The adjustments were validated against the much denser and more homogeneous OCS data product from the limb-sounding MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument, and both the direction and sign of the adjustments were in agreement with the adjustment of the ACE-FTS data.

An ozonesonde profile over the Network for Detection of Stratospheric Change (NDSC) site at Lauder (45.0° S, 169.7° E), New Zealand, for 24 December 1998 showed atypically low ozone centered around 24 km altitude (600 K potential temperature). The origin of the anomaly is explained using reverse domain filling (RDF) calculations combined with a PV/O3 fitting technique applied to ozone measurements from the Polar Ozone and Aerosol Measurement (POAM) III instrument. The RDF calculations for two isentropic surfaces, 550 and 600 K, show that ozone-poor air from the Antarctic polar vortex reached New Zealand on 24–26 December 1998. The vortex air on the 550 K isentrope originated in the ozone hole region, unlike the air on 600 K where low ozone values were caused by dynamical effects. High-resolution ozone maps were generated, and their examination shows that a vortex remnant situated above New Zealand was the cause of the altered ozone profile on 24 December. The maps also illustrate mixing of the vortex filaments into southern midlatitudes, whereby the overall mid-latitude ozone levels were decreased.Key words. Atmospheric composition and structure (middle atmosphere composition and chemistry) – Meteorology and atmospheric dynamics (middle atmosphere dynamics)

Gutt, Julian ... et al.-- 17 pages, 4 figures, The biodiversity, ecosystem services and climate variability of the Antarctic continent and the Southern Ocean are major components of the whole Earth system. Antarctic ecosystems are driven more strongly by the physical environment than many other marine and terrestrial ecosystems. As a consequence, to understand ecological functioning, cross-disciplinary studies are especially important in Antarctic research. The conceptual study presented here is based on a workshop initiated by the Research Programme Antarctic Thresholds – Ecosystem Resilience and Adaptation of the Scientific Committee on Antarctic Research, which focussed on challenges in identifying and applying cross-disciplinary approaches in the Antarctic. Novel ideas and first steps in their implementation were clustered into eight themes. These ranged from scale problems, through risk maps, and organism/ecosystem responses to multiple environmental changes and evolutionary processes. Scaling models and data across different spatial and temporal scales were identified as an overarching challenge. Approaches to bridge gaps in Antarctic research programmes included multi-disciplinary monitoring, linking biomolecular findings and simulated physical environments, as well as integrative ecological modelling. The results of advanced cross-disciplinary approaches can contribute significantly to our knowledge of Antarctic and global ecosystem functioning, the consequences of climate change, and to global assessments that ultimately benefit humankind, In addition to the employers of the authors this study was funded by the SCAR SRPs AnT-ERA, AntClim21 and AntEco

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.

Trends in the vertical distribution of ozone are reported and compared for a number of new and recently revised data sets. The amount of ozone-depleting compounds in the stratosphere (as measured by equivalent effective stratospheric chlorine – EESC) was maximised in the second half of the 1990s. We examine the periods before and after the peak to see if any change in trend is discernible in the ozone record that might be attributable to a change in the EESC trend, though no attribution is attempted. Prior to 1998, trends in the upper stratosphere (~ 45 km, 4 hPa) are found to be −5 to −10 % per decade at mid-latitudes and closer to −5 % per decade in the tropics. No trends are found in the mid-stratosphere (28 km, 30 hPa). Negative trends are seen in the lower stratosphere at mid-latitudes in both hemispheres and in the deep tropics. However, it is hard to be categorical about the trends in the lower stratosphere for three reasons: (i) there are fewer measurements, (ii) the data quality is poorer, and (iii) the measurements in the 1990s are perturbed by aerosols from the Mt Pinatubo eruption in 1991. These findings are similar to those reported previously even though the measurements for the main satellite and ground-based records have been revised. There is no sign of a continued negative trend in the upper stratosphere since 1998: instead there is a hint of an average positive trend of ~ 2 % per decade in mid-latitudes and ~ 3 % per decade in the tropics. The significance of these upward trends is investigated using different assumptions of the independence of the trend estimates found from different data sets. The averaged upward trends are significant if the trends derived from various data sets are assumed to be independent (as in Pawson et al., 2014) but are generally not significant if the trends are not independent. This occurs because many of the underlying measurement records are used in more than one merged data set. At this point it is not possible to say which assumption is best. Including an estimate of the drift of the overall ozone observing system decreases the significance of the trends. The significance will become clearer as (i) more years are added to the observational record, (ii) further improvements are made to the historic ozone record (e.g. through algorithm development), and (iii) the data merging techniques are refined, particularly through a more rigorous treatment of uncertainties.

In the framework of the SI2N (SPARC (Stratosphere-troposphere Processes And their Role in Climate)/IO3C (International Ozone Commission)/IGACO-O3 (Integrated Global Atmospheric Chemistry Observations – Ozone)/NDACC (Network for the Detection of Atmospheric Composition Change)) initiative, several long-term vertically resolved merged ozone data sets produced from satellite measurements have been analysed and compared. This paper presents an overview of the methods, assumptions, and challenges involved in constructing such merged data sets, as well as the first thorough intercomparison of seven new long-term satellite data sets. The analysis focuses on the representation of the annual cycle, interannual variability, and long-term trends for the period 1984–2011, which is common to all data sets. Overall, the best agreement amongst data sets is seen in the mid-latitude lower and middle stratosphere, with larger differences in the equatorial lower stratosphere and the upper stratosphere globally. In most cases, differences in the choice of underlying instrument records that were merged produced larger differences between data sets than the use of different merging techniques. Long-term ozone trends were calculated for the period 1984–2011 using a piecewise linear regression with a change in trend prescribed at the end of 1997. For the 1984–1997 period, trends tend to be most similar between data sets (with largest negative trends ranging from −4 to −8% decade−1 in the mid-latitude upper stratosphere), in large part due to the fact that most data sets are predominantly (or only) based on the SAGE-II record. Trends in the middle and lower stratosphere are much smaller, and, particularly for the lower stratosphere, large uncertainties remain. For the later period (1998–2011), trends vary to a greater extent, ranging from approximately −1 to +5% decade−1 in the upper stratosphere. Again, middle and lower stratospheric trends are smaller and for most data sets not significantly different from zero. Overall, however, there is a clear shift from mostly negative to mostly positive trends between the two periods over much of the profile., Atmospheric Chemistry and Physics, 15 (6), ISSN:1680-7375, ISSN:1680-7367

International audience; Equivalent effective stratospheric chlorine (EESC) construct of ozone regression models attributes ozone changes to EESC changes using a single value of the sensitivity of ozone to EESC over the whole period. Using space-based total column ozone (TCO) measurements, and a synthetic TCO time series constructed such that EESC does not fall below its late 1990s maximum, we demonstrate that the EESC-based estimates of ozone changes in the polar regions (70–90°) after 2000 may, falsely, suggest an EESC-driven increase in ozone over this period. An EESC-based regression of our synthetic “failed Montreal Protocol with constant EESC” time series suggests a positive TCO trend that is statistically significantly different from zero over 2001–2012 when, in fact, no recovery has taken place. Our analysis demonstrates that caution needs to be exercised when using explanatory variables, with a single fit coefficient, fitted to the entire data record, to interpret changes in only part of the record.

An extremely fast model to estimate the degree of stratospheric ozone depletion during polar winters is described. It is based on a set of coupled differential equations that simulate the seasonal evolution of vortex-averaged hydrogen chloride (HCl), nitric acid (HNO3), chlorine nitrate (ClONO2), active forms of chlorine (ClOx = Cl + ClO + 2 ClOOCl) and ozone (O3) on isentropic levels within the polar vortices. Terms in these equations account for the chemical and physical processes driving the time rate of change of these species. Eight empirical fit coefficients associated with these terms are derived by iteratively fitting the equations to vortex-averaged satellite-based measurements of HCl, HNO3 and ClONO2 and observationally derived ozone loss rates. The system of differential equations is not stiff and can be solved with a time step of one day, allowing many years to be processed per second on a standard PC. The inputs required are the daily fractions of the vortex area covered by polar stratospheric clouds and the fractions of the vortex area exposed to sunlight. The resultant model, SWIFT (Semi-empirical Weighted Iterative Fit Technique), provides a fast yet accurate method to simulate ozone loss rates in polar regions. SWIFT's capabilities are demonstrated by comparing measured and modeled total ozone loss outside of the training period.

Chemistry-climate models (CCMs) project an earlier return of northern mid-latitude total column ozone to 1980 values compared to the southern mid-latitudes. The chemical and dynamical drivers of this hemispheric difference are investigated in this study. The hemispheric asymmetry in return dates is a robust result across different CCMs and is qualitatively independent of the method used to estimate return dates. However, the differences in dates of return to 1980 levels between the southern and northern mid-latitudes can vary between 0 and 30 yr across the range of CCM projections analyzed. Positive linear trends in ozone lead to an earlier return of ozone than expected from the return of Cly to 1980 levels. This forward shift is stronger in the Northern than in the Southern Hemisphere because (i) trends have a larger effect on return dates if the sensitivity of ozone to Cly is lower and (ii) the trends in the Northern Hemisphere are stronger than in the Southern Hemisphere. An attribution analysis performed with two CCMs shows that chemically-induced changes in ozone are the major driver of the earlier return of ozone to 1980 levels in northern mid-latitudes; therefore transport changes are of minor importance. This conclusion is supported by the fact that the spread in the simulated hemispheric difference in return dates across an ensemble of twelve models is only weakly related to the spread in the simulated hemispheric asymmetry of trends in the strength of the Brewer–Dobson circulation. The causes for chemically-induced asymmetric ozone trends relevant for the total column ozone return date differences are found to be (i) stronger increases in ozone production due to enhanced NOx concentrations in the Northern Hemisphere lowermost stratosphere and troposphere, (ii) stronger decreases in the destruction rates of ozone by the NOx cycle in the Northern Hemisphere lower stratosphere linked to effects of dynamics and temperature on NOx concentrations, and (iii) an increasing efficiency of heterogeneous ozone destruction by Cly in the Southern Hemisphere mid-latitudes as a~result of decreasing lower stratospheric temperatures.

Climate models that do not simulate changes in stratospheric ozone concentrations require the prescription of ozone fields to accurately calculate UV fluxes and stratospheric heating rates. In this study, three different global ozone time series that are available for this purpose are compared: the data set of Randel and Wu (2007) (RW07), Cionni et al. (2011) (SPARC), and Bodeker et al. (2013) (BDBP). All three data sets represent multiple-linear regression fits to vertically resolved ozone observations, resulting in a spatially and temporally continuous stratospheric ozone field covering at least the period from 1979 to 2005. The main differences among the data sets result from regression models, which use different observations and include different basis functions. The data sets are compared against ozonesonde and satellite observations to assess how the data sets represent concentrations, trends and interannual variability. In the Southern Hemisphere polar region, RW07 and SPARC underestimate the ozone depletion in spring ozonesonde measurements. A piecewise linear trend regression is performed to estimate the 1979–1996 ozone decrease globally, covering a period of extreme depletion in most regions. BDBP overestimates Arctic and tropical ozone depletion over this period relative to the available measurements, whereas the depletion is underestimated in RW07 and SPARC. While the three data sets yield ozone concentrations that are within a range of different observations, there is a large spread in their respective ozone trends. One consequence of this is differences of almost a factor of four in the calculated stratospheric ozone radiative forcing between the data sets (RW07: −0.038 Wm−2, SPARC: −0.033 Wm−2, BDBP: −0.119 Wm−2), important in assessing the contribution of stratospheric ozone depletion to the total anthropogenic radiative forcing.

Two semi-empirical models were developed for the Antarctic stratosphere to relate the shift of species within total chlorine (Cly = HCl + ClONO2 + HOCl + 2 × Cl2 + 2×Cl2O2 + ClO + Cl) into the active forms (here: ClOx = 2×Cl2O2 + ClO), and to relate the rate of ozone destruction to ClOx. These two models provide a fast and computationally inexpensive way to describe the inter- and intra-annual evolution of ClOx and ozone mass deficit (OMD) in the Antarctic spring. The models are based on the underlying physics/chemistry of the system and capture the key chemical and physical processes in the Antarctic stratosphere that determine the interaction between climate change and Antarctic ozone depletion. They were developed considering bulk effects of chemical mechanisms for the duration of the Antarctic vortex period and quantities averaged over the vortex area. The model equations were regressed against observations of daytime ClO and OMD providing a set of empirical fit coefficients. Both semi-empirical models are able to explain much of the intra- and inter-annual variability observed in daily ClOx and OMD time series. This proof-of-concept paper outlines the semi-empirical approach to describing the evolution of Antarctic chlorine activation and ozone depletion.

We present a vertically resolved zonal mean monthly mean global ozone data set spanning the period 1901 to 2007, called HISTOZ.1.0. It is based on a new approach that combines information from an ensemble of chemistry climate model (CCM) simulations with historical total column ozone information. The CCM simulations incorporate important external drivers of stratospheric chemistry and dynamics (in particular solar and volcanic effects, greenhouse gases and ozone depleting substances, sea surface temperatures, and the quasi-biennial oscillation). The historical total column ozone observations include ground-based measurements from the 1920s onward and satellite observations from 1970 to 1976. An off-line data assimilation approach is used to combine model simulations, observations, and information on the observation error. The period starting in 1979 was used for validation with existing ozone data sets and therefore only ground-based measurements were assimilated. Results demonstrate considerable skill from the CCM simulations alone. Assimilating observations provides additional skill for total column ozone. With respect to the vertical ozone distribution, assimilating observations increases on average the correlation with a reference data set, but does not decrease the mean squared error. Analyses of HISTOZ.1.0 with respect to the effects of El Niño–Southern Oscillation (ENSO) and of the 11 yr solar cycle on stratospheric ozone from 1934 to 1979 qualitatively confirm previous studies that focussed on the post-1979 period. The ENSO signature exhibits a much clearer imprint of a change in strength of the Brewer–Dobson circulation compared to the post-1979 period. The imprint of the 11 yr solar cycle is slightly weaker in the earlier period. Furthermore, the total column ozone increase from the 1950s to around 1970 at northern mid-latitudes is briefly discussed. Indications for contributions of a tropospheric ozone increase, greenhouse gases, and changes in atmospheric circulation are found. Finally, the paper points at several possible future improvements of HISTOZ.1.0.

Through the 21st century, anthropogenic emissions of the greenhouse gases N2O and CH4 are projected to increase, thus increasing their atmospheric concentrations. Consequently, reactive nitrogen species produced from N2O and reactive hydrogen species produced from CH4 are expected to play an increasingly important role in determining stratospheric ozone concentrations. Eight chemistry-climate model simulations were performed to assess the sensitivity of stratospheric ozone to different emissions scenarios for N2O and CH4. Global-mean total column ozone increases through the 21st century in all eight simulations as a result of CO2-induced stratospheric cooling and decreasing stratospheric halogen concentrations. Larger N2O concentrations were associated with smaller ozone increases, due to reactive nitrogen-mediated ozone destruction. In the simulation with the largest N2O increase, global-mean total column ozone increased by 4.3 DU through the 21st century, compared with 10.0 DU in the simulation with the smallest N2O increase. In contrast, larger CH4 concentrations were associated with larger ozone increases; global-mean total column ozone increased by 16.7 DU through the 21st century in the simulation with the largest CH4 concentrations and by 4.4 DU in the simulation with the lowest CH4 concentrations. CH4 leads to ozone loss in the upper and lower stratosphere by increasing the rate of reactive hydrogen-mediated ozone loss cycles, however in the lower stratosphere and troposphere, CH4 leads to ozone increases due to photochemical smog-type chemistry. In addition to this mechanism, total column ozone increases due to H2O-induced cooling of the stratosphere, and slowing of the chlorine-catalyzed ozone loss cycles due to an increased rate of the CH4 + Cl reaction. Stratospheric column ozone through the 21st century exhibits a near-linear response to changes in N2O and CH4 surface concentrations, which provides a simple parameterization for the ozone response to changes in these gases.

The year 1980 has often been used as a benchmark for the return of Antarctic ozone to conditions assumed to be unaffected by emissions of ozone depleting substances (ODSs), implying that anthropogenic ozone depletion in Antarctica started around 1980. Here, the extent of anthropogenically-driven Antarctic ozone depletion prior to 1980 is examined using output from transient Chemistry-Climate Model (CCM) simulations from 1960 to 2000 with prescribed changes of ozone depleting substance concentrations in conjunction with observations. A regression model is used to attribute CCM modelled and observed changes in Antarctic total column ozone to halogen-driven chemistry prior to 1980. Winter-time Antarctic ozone is strongly affected by dynamical processes that vary in amplitude from year to year and from model to model. However, when the dynamical and chemical impacts on ozone are separated, all models consistently show a long-term, halogen-induced negative trend in Antarctic ozone from 1960 to 1980. The anthropogenically-driven ozone loss from 1960 to 1980 ranges between 26.4 ± 3.4 % and 49.8 ± 6.2 % of the total anthropogenic ozone depletion from 1960 to 2000. An even stronger ozone decline of 56.4 ± 6.8 % was estimated from ozone observations. This analysis of the observations and simulations from 17 CCMs clarifies that while the return of Antarctic ozone to 1980 values remains a valid milestone, achieving that milestone is not indicative of full recovery of the Antarctic ozone layer from the effects of ODSs.

A new fully spherical multiple scattering Monte Carlo radiative transfer model named NIMO (NIWA Monte Carlo model) is presented. The ray tracing algorithm is described in detail along with the treatment of scattering and absorption, and the simulation of backward adjoint trajectories. The primary application of NIMO is the calculation of box-air-mass factors (box-AMFs), which are used to convert slant column densities (SCDs) of trace gases, derived from UV–visible multiple axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements, into vertical column densities (VCDs). Box-AMFs are also employed as weighting functions for optimal estimation retrievals of vertical trace gas profiles from SCDs. Monte Carlo models are well suited to AMF calculations at high solar zenith angles (SZA) and at low viewing elevation angles where multiple scattering is important. Additionally, the object-oriented structure of NIMO makes it easily extensible to new applications by plugging in objects for new absorbing or scattering species. Box-AMFs and radiances, calculated for various wavelengths, SZAs, viewing elevation and azimuth angles and aerosol scenarios, are compared with results from nine other models using a set of exercises from a recent radiative transfer model intercomparison. NIMO results for these simulations are well within the range of variability of the other models.

Chemistry-climate models (CCMs) are commonly used to simulate the past and future development of Earth's ozone layer. The fully coupled chemistry schemes calculate the chemical production and destruction of ozone interactively and ozone is transported by the simulated atmospheric flow. Due to the complexity of the processes acting on ozone it is not straightforward to disentangle the influence of individual processes on the temporal development of ozone concentrations. A method is introduced here that quantifies the influence of chemistry and transport on ozone concentration changes and that is easily implemented in CCMs and chemistry-transport models (CTMs). In this method, ozone tendencies (i.e. the time rate of change of ozone) are partitioned into a contribution from ozone production and destruction (chemistry) and a contribution from transport of ozone (dynamics). The influence of transport on ozone in a specific region is further divided into export of ozone out of that region and import of ozone from elsewhere into that region. For this purpose, a diagnostic is used that disaggregates the ozone mixing ratio field into 9 separate fields according to in which of 9 predefined regions of the atmosphere the ozone originated. With this diagnostic the ozone mass fluxes between these regions are obtained. Furthermore, this method is used here to attribute long-term changes in ozone to chemistry and transport. The relative change in ozone from one period to another that is due to changes in production or destruction rates, or due to changes in import or export of ozone, are quantified. As such, the diagnostics introduced here can be used to attribute changes in ozone on monthly, interannual and long-term time-scales to the responsible mechanisms. Results from a CCM simulation are shown here as examples, with the main focus of the paper being on introducing the method., Geoscientific Model Development, 4 (2), ISSN:1991-9603, ISSN:1991-959X

International audience; Upper stratospheric ozone anomalies from the satellite-borne Solar Backscatter Ultra-Violet (SBUV), Stratospheric Aerosol and Gas Experiment II (SAGE II), Halogen Occultation Experiment (HALOE), Global Ozone Monitoring by Occultation of Stars (GOMOS), and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instruments agree within 5% or better with ground-based data from lidars and microwave radiometers at five stations of the Network for the Detection of Atmospheric Composition Change (NDACC), from 45°S to 48°N. From 1979 until the late 1990s, all available data show a clear decline of ozone near 40 km, by 10%–15%. This decline has not continued in the last 10 years. At some sites, ozone at 40 km appears to have increased since 2000, consistent with the beginning decline of stratospheric chlorine. The phaseout of chlorofluorocarbons after the International Montreal Protocol in 1987 has been successful, and is now showing positive effects on ozone in the upper stratosphere. Temperature anomalies near 40 km altitude from European Centre for Medium Range Weather Forecast reanalyses (ERA-40), from National Centers for Environmental Prediction (NCEP) operational analyses, and from HALOE and lidar measurements show good consistency at the five stations, within about 3 K. Since about 1985, upper stratospheric temperatures have been fluctuating around a constant level at all five NDACC stations. This non-decline of upper stratospheric temperatures is a significant change from the more or less linear cooling of the upper stratosphere up until the mid-1990s, reported in previous trend assessments. It is also at odds with the almost linear 1 K per decade cooling simulated over the entire 1979–2010 period by chemistry–climate models (CCMs). The same CCM simulations, however, track the historical ozone anomalies quite well, including the change of ozone tendency in the late 1990s.

This paper presents extensive validation analyses of ozone observations from the Atmospheric Chemistry Experiment (ACE) satellite instruments: the ACE Fourier Transform Spectrometer (ACE-FTS) and the Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation (ACE-MAESTRO) instrument. The ACE satellite instruments operate in the mid-infrared and ultraviolet-visible-near-infrared spectral regions using the solar occultation technique. In order to continue the long-standing record of solar occultation measurements from space, a detailed quality assessment is required to evaluate the ACE data and validate their use for scientific purposes. Here we compare the latest ozone data products from ACE-FTS and ACE-MAESTRO with coincident observations from satellite-borne, airborne, balloon-borne and ground-based instruments, by analysing volume mixing ratio profiles and partial column densities. The ACE-FTS version 2.2 Ozone Update product reports more ozone than most correlative measurements from the upper troposphere to the lower mesosphere. At altitude levels from 16 to 44 km, the mean differences range generally between 0 and +10% with a slight but systematic positive bias (typically +5%). At higher altitudes (45-60 km), the ACE-FTS ozone amounts are significantly larger than those of the comparison instruments by up to ~40% (typically +20%). For the ACE-MAESTRO version 1.2 ozone data product, agreement within ±10% (generally better than ±5%) is found between 18 and 40 km for the sunrise and sunset measurements. At higher altitudes (45-55 km), systematic biases of opposite sign are found between the ACE-MAESTRO sunrise and sunset observations. While ozone amounts derived from the ACE-MAESTRO sunrise occultation data are often smaller than the coincident observations (by as much as -10%), the sunset occultation profiles for ACE-MAESTRO show results that are qualitatively similar to ACE-FTS and indicate a large positive bias (+10 to +30%) in this altitude range. In contrast, there is no significant difference in bias found for the ACE-FTS sunrise and sunset measurements. These systematic effects in the ozone profiles retrieved from the measurements of ACE-FTS and ACE-MAESTRO are being investigated. This work shows that the ACE instruments provide reliable, high quality measurements from the tropopause to the upper stratosphere and can be used with confidence in this vertical domain

Comprehensive measures of ultraviolet radiation (UVR) exposure, concurrent activities and sun-protective practices are needed to develop and evaluate skin cancer prevention and sun protection interventions. The UVR exposures of 345 primary schoolchildren at 23 schools around New Zealand were measured using electronic UVR monitors for 1-week periods over 12 weeks in 2004 and 2005. In addition, ambient UVR levels on a horizontal surface were measured on-site at each school. Children completed activity diaries during the period UVR measurements were made and provided information on their indoor and outdoor status and clothing and sun protection worn. Mean total daily UVR exposure (7:00–20:00 h NZST + 1) at the body location where the UVR monitors were worn was 0.9 SED (standard erythemal dose, 1 SED = 100 J m )2 ). This was 4.9% of the ambient UVR on a horizontal surface. Mean time spent outdoors was 2.3 h day )1 . Differences in children’s UVR exposure could be explained in part by activity, where outdoor passive pursuits were associated with higher UVR exposure rates than outdoor active and outdoor travel pursuits. Compared with older children, the activities of younger children, although labeled the same, resulted in different UVR exposures, either as a result of reporting differences or a real difference in UVR exposure patterns. UVR exposure rates were generally higher on weekdays compared with the weekend, confirming the important role of school sun protection and skin cancer prevention programs. High UVR exposure activities included physical education, athletics and lunch break.

In the first study reported on here, requirements on random uncertainty of instantaneous temperature measurements, sampling frequency, season and pressure, required to ensure a minimum random uncertainty of monthly mean temperatures, have been explored. These results then inform analyses conducted in a second study which seeks to identify the optimal location of sites for detecting projected trends in upper-air temperatures in the shortest possible time. The third part of the paper presents a similar analysis for the optimal locations of sites to detect projected trends in total column ozone. Results from the first study show that only for individual measurement random uncertainties > 0.2 K does the measurement random uncertainty start to contribute significantly to the random uncertainty of the monthly mean. Analysis of the effects of the individual measurement random uncertainty and sampling strategy on the ability to detect upper-air temperature trends shows that only when the measurement random uncertainty exceeds 2 K, and measurements are made just once or twice a month, is the quality of the trend determination compromised. The time to detect a trend in some upper-air climate variable is a function of the unforced variance in the signal, the degree of autocorrelation, and the expected magnitude of the trend. For middle tropospheric and lower stratospheric temperatures, the first two quantities were derived from Microwave Sounding Unit (MSU) and Advanced Microwave Sounding Unit (AMSU) measurements while projected trends were obtained by averaging 21st century trends from simulations made by 11 chemistry–climate models (CCMs). For total column ozone, variance and autocorrelation were derived from the Bodeker Scientific total column ozone database with projected trends obtained from median values from 21 CCM simulations of total column ozone changes over the 21st century. While the optimal sites identified in this analysis for detecting temperature and total column ozone trends in the shortest time possible result from our use of only one of a wide range of objective strategies, these results provide additional incentives for initiating measurement programmes at these sites or, if already in operation, to continue to be supported.

A new database of monthly mean zonal mean (5° zones) temperature time series spanning 17 pressure levels from 300 to 7 hPa and extending from 2002 to 2012 was created by merging monthly mean time series from two satellite-based mid-infrared spectrometers (ACE-FTS and MIPAS), a microwave sounder (SMR), and from three satellite-based radio occultation experiments (GRACE, CHAMP, and TSX). The primary intended use of this new temperature data set is to validate the merging of the Microwave Sounding Unit channel 4 (MSU4), and Advanced Microwave Sounding Unit channel 9 (AMSU9) temperature time series conducted in previous studies. The six source data sets were merged by removing offsets and trends between the different measurement series. Weighted means were calculated of the six source data sets where the weights were a function of the uncertainty on the original monthly mean data. This new temperature data set of the upper troposphere and stratosphere has been validated by comparing it to RATPAC-A, COSMIC radio occultation data as well as the NCEPCFSR reanalyses. Differences in all three cases were typically < 2 K in the upper troposphere and lower stratosphere, but could reach up to 5 K in the mid-stratosphere. The data across the 17 pressure levels have then been vertically integrated, using the MSU4/AMSU9 weighting function, to provide a deep vertical layer temperature proxy of the merged MSU4+AMSU9 series. Differences between this vertically integrated data set and two different versions of the MSU4+AMSU9 data set – one from Remote Sensing Systems and one from the University of Alabama at Huntsville – were examined for discontinuities. No statistically significant discontinuities were found in either of those two data sets suggesting that the transition from the MSU4+AMSU9 data to AMSU9 data only does not introduce any discontinuities in the MSU4+AMSU9 climate data records that might compromise their use in temperature trend analyses.

UV measurements from instruments maintained by USDA at 16 mid-latitude sites were analysed to investigate geographic differences. Fifteen of the sites are in North America, and one is in New Zealand. The instruments measure erythemally weighted UV radiation, and the results are presented in terms of UV Index (UVI). The focus of this work is on data from 2003, but the main results are also shown for years 2002 and 2004. In the North American sites, the peak UVI values increase by approximately 15% between latitudes 47 degrees N and 40 degrees N, and they show an increase with altitude of approximately 15% in the first kilometer, but much smaller rates of increase above that level. Peak UV intensities in the New Zealand site (45 degrees S, alt. 0.37 km) exceed those at comparable latitudes and altitudes in North America by 41 +/- 5%, and are more comparable with those over 1 km higher and 5 degrees closer to the equator. The number of observations on these days that exceeded various thresholds of UVI showed similar patterns. Furthermore, the number of days in which the peak values exceeded various thresholds also showed similar patterns, with the number of extreme values in New Zealand being anomalously high. For example, the only sites in North America where UVI exceeded 12 were at the high altitude sites in Colorado and Utah, for which there were 53 days, 6 days and 2 days respectively at the 3.2 km, 1.6 and 1.4 km sites. By contrast, the peak UVI at Lauder (0.37 km) exceeded 12 on 17 days. Lauder was the only site under 1 km altitude where the UVI exceeded 11 on a regular basis (48 days). The optical depths at Lauder were significantly lower than at all North American sites. These, together with the lower ozone amounts and the closer Earth-Sun separation in summer all contribute to the relatively high UV intensities at the New Zealand site. Other sites in New Zealand show similar increases compared with corresponding sites in North America, and the differences persist from year to year. The contrast in UV between New Zealand and North America is similar to that observed previously between New Zealand and Europe. During winter months, the UVI in New Zealand is not particularly high, giving a larger summer/winter contrast in UVI, which may be important from a health perspective.

The Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) provides reference quality RS92 radiosonde measurements of temperature, pressure and humidity. A key attribute of reference quality measurements, and hence GRUAN data, is that each datum has a well characterised and traceable estimate of the measurement uncertainty. The long-term homogeneity of the measurement records, and their well characterised uncertainties, make these data suitable for reliably detecting changes in global and regional climate on decadal time scales. Considerable effort is invested in GRUAN operations to (i) describe and analyse all sources of measurement uncertainty to the extent possible, (ii) quantify and synthesize the contribution of each source of uncertainty to the total measurement uncertainty, and (iii) verify that the evaluated net uncertainty is within the required target uncertainty. However, if the climate science community is not sufficiently well informed on how to capitalize on this added value, the significant investment in estimating meaningful measurement uncertainties is largely wasted. This paper presents and discusses the techniques that will need to be employed to reliably quantify long-term trends in GRUAN data records. A pedagogical approach is taken whereby numerical recipes for key parts of the trend analysis process are explored. The paper discusses the construction of linear least squares regression models for trend analysis, boot-strapping approaches to determine uncertainties in trends, dealing with the combined effects of autocorrelation in the data and measurement uncertainties in calculating the uncertainty on trends, best practice for determining seasonality in trends, how to deal with co-linear basis functions, and interpreting derived trends. Synthetic data sets are used to demonstrate these concepts which are then applied to a first analysis of temperature trends in RS92 radiosonde upper air soundings at the GRUAN site at Lindenberg, Germany (52.21° N, 14.12° E).

To gauge the impact of clouds on erythemal (sunburn causing) UV irradiances under different surface albedo conditions, UV measurements from two Antarctic sites (McMurdo and South Pole Stations) and a midlatitude site (Lauder, New Zealand) are examined. The surface albedo at South Pole remains high throughout the year, at McMurdo it has a strong annual cycle, and at Lauder it is low throughout the year. The measurements at each site are divided into clear and cloudy subsets and are compared with modeled clear-sky irradiances to assess the attenuation of UV by clouds. A radiative transfer model is also used to interpret the observations. Results show increasing attenuation of UV with increasing cloud optical depth, but a high surface albedo can moderate this attenuation as a result of multiple scattering between the surface and cloud base. This effect is of particular importance at high latitudes where snow may be present during the summer months. There is also a tendency toward greater cloud attenuation with increasing solar zenith angle.

Total ozone growth rates are calculated using flexible ‘tendency curves’ that can follow ozone variations on all timescales greater than that of the quasi-biennial oscillation. This method improves on traditional trend analysis using straight line fits because it follows ozone variations more closely, providing visual information about the timing and global distribution of ozone variations. Results are compared from long-running Dobson sites and from two homogenized satellite data sets, one constructed at NASA/Goddard Space Flight Center and the other developed at New Zealand's National Institute of Water and Atmospheric Research. Although the most negative ozone trends in the Southern Hemisphere appear to be linked to polar vortex chemistry, those in the Northern Hemisphere have occurred between 35°N and 40°N and may be related to dynamical trends and/or chemistry on episodically occurring volcanic aerosols. A quasi-decadal cycle in total ozone was present since the mid-1920s and hence is independent of halogen chemistry. Its cause remains unknown. Including the deseasonalized and detrended local temperature in the ozone trend model decreases the standard error of the ozone trend over most of the globe.

A new ozone climatology, based on ozonesonde and satellite measurements, spanning the altitude region between the earth's surface and ~60 km is presented (TpO3 climatology). This climatology is novel in that the ozone profiles are categorized according to calendar month, latitude and local tropopause heights. Compared to the standard latitude–month categorization, this presentation improves the representativeness of the ozone climatology in the upper troposphere and the lower stratosphere (UTLS). The probability distribution of tropopause heights in each latitude–month bin provides additional climatological information and allows transforming/comparing the TpO3 climatology to a standard climatology of zonal mean ozone profiles. The TpO3 climatology is based on high-vertical-resolution measurements of ozone from the satellite-based Stratospheric Aerosol and Gas Experiment II (in 1984 to 2005) and from balloon-borne ozonesondes from 1980 to 2006. The main benefits of the TpO3 climatology are reduced standard deviations on climatological ozone profiles in the UTLS, partial characterization of longitudinal variability, and characterization of ozone profiles in the presence of double tropopauses. The first successful application of the TpO3 climatology as a priori in ozone profile retrievals from Ozone Monitoring Instrument on board the Earth Observing System (EOS) Aura satellite shows an improvement of ozone precision in UTLS of up to 10% compared with the use of conventional climatologies. In addition to being advantageous for use as a priori in satellite retrieval algorithms, the TpO3 climatology might be also useful for validating the representation of ozone in climate model simulations.