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© Copyright 2022. Sociedad Meteorológica Estadounidense (AMS). Para obtener permiso para reutilizar cualquier parte de este Trabajo, comuníquese con permisos@ametsoc. org . Cualquier uso del material en este Trabajo que se determine como "uso justo" según la Sección 107 de la Ley de derechos de autor de EE. UU. (17 Código de EE. UU. § 107) o que cumpla las condiciones especificadas en la Sección 108 de la Ley de derechos de autor de EE. ) no requiere el permiso de la AMS. La republicación, la reproducción sistemática, la publicación en forma electrónica, como en un sitio web o en una base de datos de búsqueda, u otros usos de este material, excepto los exentos de la declaración anterior, requieren un permiso por escrito o una licencia de la AMS. Todas las publicaciones periódicas y monográficas de AMS están registradas en el Centro de Autorización de Derechos de Autor (https://www.copyright.com ). Se proporcionan detalles adicionales en la declaración de política de derechos de autor de AMS, disponible en el sitio web de AMS ( https://www.ametsoc.org/PUBSCopyrightPolicy ) ., In 2021, both social and economic activities began to return towards the levels preceding the COVID-19 pandemic for some parts of the globe, with others still experiencing restrictions. Meanwhile, the climate has continued to respond to the ongoing increase in greenhouse gases and resulting warming. La Niña, a phenomenon which tends to depress global temperatures while changing rainfall patterns in many regions, prevailed for all but two months of the year. Despite this, 2021 was one of the six-warmest years on record as measured by global mean surface temperature with an anomaly of between +0.21° and +0.28°C above the 1991–2020 climatology., Lake surface water temperatures from satellite data have been generated within the GloboLakes project funded by the UK National Environment Research Council (NE/J023345/2), with extensions funded by the EU Copernicus Climate Change Service (C3S) programme...

Evaluating the trends of extreme indices (EI) is crucial to detect and attribute extreme events (EE) and establish adaptation and mitigation strategies to the current and future climate conditions. However, the observational climate data used for the calculation of these indices often contains many missing values and leads to incomplete and inaccurate EI. This problem is even greater as we go back in time due to the scarcity of the older measurements.To tackle this problem, interpolation techniques such as the kriging method are often used to fill in the gaps. However, it has been shown that such techniques are inadequate to reconstruct specific climatic patterns [1]. Deep-learning based technologies give the possibility to surpass standard statistical methods by learning complex patterns and features in climate data.In this work, we are using an inpainting technique based on a U-Net neural network made of partial convolutional layers and a loss function designed to produce semantically meaningful predictions [1]. Models are trained using vast amounts of climate model data and can be used to reconstruct large and irregular regions of missing data with few computational resources.The efficiency of the method is well demonstrated through its application to the HadEX3 dataset [2]. This dataset contains gridded land surface EI, among which the TX90p index that measures the monthly (or annual) frequency of warm days (defined as a percentage of days where daily maximum temperature is above the 90th percentile). As for other EI, there is a lack of TX90p values in many regions of the world, even in recent years. It is particularly true when looking at an intermediate product of HadEX3 where the station-based indices have been combined without interpolation. This is illustrated by the left map of the figure where the gray pixels correspond to missing values. By training our model using data from the CMIP6 archive, we have been able to reconstruct the missing TX90p values for all the time steps of HadEX3 (see right map in the figure) and detect EE that were not included in the original dataset. The reconstructed dataset is being prepared for the community in the framework of the H2020 CLINT project [3] for further detection and attribution studies.[1] Kadow C. et al., Nat. Geosci., 13, 408-413 (2020)[2] Dunn R.J.H. et al., J. Geophys. Res. Atmos., 125, 1 (2020)[3] https://climateintelligence.eu/

We present a new data set of air temperature change across land and ocean extending back to the late-18th century. This new data set uses marine air temperature observations rather than the sea surface temperature measurements typically used by pre-existing data sets. This allows the new data set to extend further into the past than existing instrumental temperature records, which typically have start dates in the mid-to-late 19th century. The new data set brings together advances in understanding of measurement biases affecting all-day marine air temperature observations with a new assessment of the effects of non-standard thermometer enclosures used at land meteorological stations in the early instrumental record. A further innovation is the use of kriging to obtain localised temperature estimates that allow land air temperature records to be converted into anomalies even for stations without observations during the baseline period. Global and hemispheric series show close agreement with those based on sea-surface temperature for much of the overlapping period of their records, some of the interesting differences will be presented. This data set has been developed under the GloSAT project (https://www.glosat.org/).

Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC), including the first global stocktake under the Paris Agreement that will conclude at COP28 in December 2023. Evidence-based decision making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We base this update on the assessment methods used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report, updating the monitoring datasets and to produce updated estimates for key climate indicators including emissions, greenhouse gas concentrations, radiative forcing, surface temperature changes, the Earth’s energy imbalance, warming attributed to human activities, the remaining carbon budget and estimates of global temperature extremes. The purpose of this effort, grounded in an open data, open science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi.org/10.5281/zenodo.7883758, Smith et al., 2023). As they are traceable and consistent with IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that human induced warming reached 1.14 [0.9 to 1.4] °C over the 2013–2022 period and 1.26 [1.0 to 1.6] °C in 2022. Human induced warming is increasing at an unprecedented rate of over 0.2 °C per decade. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 57 ± 5.6 GtCO2e over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there are signs that emission levels are starting to stabilise, and we can hope that a continued series of these annual updates might track a real-world change of direction for the climate over this critical decade.

Global Climate is one chapter from the State of the Climate in 2019 annual report and is avail- able from https://doi.org/10.1175/BAMS-D-20-0104.1 Compiled by NOAA’s National Centers for Environmental Information, State of the Climate in 2019 is based on contributions from scien- tists from around the world. It provides a detailed update on global climate indicators, notable weather events, and other data collected by environmental monitoring stations and instru- ments located on land, water, ice, and in space. The full report is available from https://doi.org/10.1175/2020BAMSStateoftheClimate.1.

Surface temperature is a vital metric of Earth’s climate state but is incompletely observed in both space and time: over half of monthly values are missing from the widely used HadCRUT4.6 global surface temperature dataset. Here we apply the graphical expectation–maximization algorithm (GraphEM), a recently developed imputation method, to construct a spatially complete estimate of HadCRUT4.6 temperatures. GraphEM leverages Gaussian Markov random fields (also known as Gaussian graphical models) to better estimate covariance relationships within a climate field, detecting anisotropic features such as land–ocean contrasts, orography, ocean currents, and wave-propagation pathways. This detection leads to improved estimates of missing values compared to methods (such as kriging) that assume isotropic covariance relationships, as we show with real and synthetic data. This interpolated analysis of HadCRUT4.6 data is available as a 100-member ensemble, propagating information about sampling variability available from the original HadCRUT4.6 dataset. A comparison of Niño-3.4 and global mean monthly temperature series with published datasets reveals similarities and differences due in part to the spatial interpolation method. Notably, the GraphEM-completed HadCRUT4.6 global temperature displays a stronger early twenty-first-century warming trend than its uninterpolated counterpart, consistent with recent analyses using other datasets. Known events like the 1877/78 El Niño are recovered with greater fidelity than with kriging, and result in different assessments of changes in ENSO variability through time. Gaussian Markov random fields provide a more geophysically motivated way to impute missing values in climate fields, and the associated graph provides a powerful tool to analyze the structure of teleconnection patterns. We close with a discussion of wider applications of Markov random fields in climate science.

Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.

The new HadCRUT5 data set combines meteorological station air temperature records with sea-surface temperature measurements in a data set of near-surface temperature anomalies from the year 1850 to present. Major developments in HadCRUT5 include: updates to underpinning observation data holdings; use of an updated assessment of the impacts of changing marine measurement methods; and adoption of a statistical gridding method to extend estimates into sparsely observed regions of the globe, such as the Arctic. The data are presented as a 200-member ensemble that spans the assessed uncertainty associated with adjustments for long-term observational biases, observing platform measurement errors and the interaction of observational sampling with gridding methods. The impacts of methodological changes in HadCRUT5 on diagnostics of the global climate will be discussed and compared to results derived from other state-of-the-art global data sets.

We present a new version of the Met Office Hadley Centre/Climatic Research Unit global surface temperature dataset, HadCRUT5. HadCRUT5 presents monthly average near-surface temperature anomalies, relative to the 1961-1990 period, on a regular 5° latitude by 5° longitude grid from 1850 to 2018. HadCRUT5 is a combination of sea-surface temperature measurements over the ocean from ships and buoys and near-surface air temperature measurements from weather stations over the land surface. These data have been sourced from updated compilations and the adjustments applied to mitigate the impact of changes in sea-surface temperature measurement methods have been revised. Two variants of HadCRUT5 have been produced for use in different applications. The first represents temperature anomaly data on a grid for locations where measurement data are available. The second, more spatially complete, variant uses a Gaussian process based statistical method to make better use of the available observations, extending temperature anomaly estimates into regions for which the underlying measurements are informative. Each is provided as a 200-member ensemble accompanied by additional uncertainty information. The combination of revised input datasets and statistical analysis results in greater warming of the global average over the course of the whole record. In recent years, increased warming results from an improved representation of Arctic warming and a better understanding of evolving biases sea-surface temperature measurements from ships. These updates result in greater consistency with other independent global surface temperature datasets, despite their different approaches to dataset construction, and further increase confidence in our understanding of changes seen.

Climatic Research Unit temperature version 5 (CRUTEM5) is an extensive revision of our land surface air temperature data set. We have expanded the underlying compilation of monthly temperature records from 5,583 to 10,639 stations, of which those with sufficient data to be used in the gridded data set has grown from 4,842 to 7,983. Many station records have also been extended or replaced by series that have been homogenized by national meteorological and hydrological services. We have improved the identification of potential outliers in these data to better capture outliers during the reference period; to avoid classifying some real regional temperature extremes as outliers; and to reduce trends in outlier counts arising from climatic warming. Due to these updates, the gridded data set shows some regional increases in station density and regional changes in temperature anomalies. Nonetheless, the global‐mean timeseries of land air temperature is only slightly modified compared with previous versions and previous conclusions are not altered. The standard gridding algorithm and comprehensive error model are the same as for the previous version, but we have explored an alternative gridding algorithm that removes the under‐representation of high latitude stations. The alternative gridding increases estimated global‐mean land warming by about 0.1°C over the course of the whole record. The warming from 1861–1900 to the mean of the last 5 years is 1.6°C using the standard gridding (with a 95% confidence interval for errors on individual annual means of −0.11 to +0.10°C in recent years), while the alternative gridding gives a change of 1.7°C.

A number of studies have noted that the use of distinct reference periods when comparing indices measuring the frequency of days exceeding a particular temperature percentile threshold leads to apparently different behaviour. We show that these differences arise because of the interplay between the increasing temperatures and the choice of reference period. The time series of the indicators calculated using the different reference periods are offset, as expected, but also diverge. Linear trends calculated over the same period from the same underlying data but where different reference periods have been used are substantially different if a change in climatological conditions has occurred between the two reference periods. We show this not only occurs in our simple empirical approach, but also for the averages of gridded observational and reanalysis datasets and also at a station level. This has implications for data set comparisons using trends in temperature percentile indices that are based on different reference periods. It also has implications for updates to standard reference periods used to monitor the climate.

2016 was another very warm year globally. Global temperatures in 2015 and 2016 were clearly the warmest two years on record. The record heat arose from the combined effect of the long-term temperature rise, attributed largely to greenhouse gases, and a temporary boost from the strong 2015/2016 El Nino event. Record low sea-ice extent was observed in a number of months in the Arctic, and December also saw record low extent for the Antarctic. Winter 2015/2016 was the second wettest winter on record for the UK. European-average temperatures in 2016 were nominally the third highest on record, slightly cooler than 2014 and 2015.

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

Time series of global and regional mean surface air temperature (SAT) anomalies are a common metric used to estimate recent climate change. Various techniques can be used to create these time series from meteorological station data. The degree of difference arising from using five different techniques, based on existing temperature anomaly dataset techniques, to estimate Arctic SAT anomalies over land and sea ice was investigated using reanalysis data as a test bed. Techniques that interpolated anomalies were found to result in smaller errors than noninterpolating techniques relative to the reanalysis reference. Kriging techniques provided the smallest errors in estimates of Arctic anomalies, and simple kriging was often the best kriging method in this study, especially over sea ice. A linear interpolation technique had, on average, root-mean-square errors (RMSEs) up to 0.55 K larger than the two kriging techniques tested. Noninterpolating techniques provided the least representative anomaly estimates. Nonetheless, they serve as useful checks for confirming whether estimates from interpolating techniques are reasonable. The interaction of meteorological station coverage with estimation techniques between 1850 and 2011 was simulated using an ensemble dataset comprising repeated individual years (1979–2011). All techniques were found to have larger RMSEs for earlier station coverages. This supports calls for increased data sharing and data rescue, especially in sparsely observed regions such as the Arctic.

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

A systematic review is provided on artificial agent methodologies applicable to control engineering of autonomous vehicles and robots. The paper focuses on some fundamentals that make a machine autonomous: decision making that involves modelling the environment and forming data abstractions for symbolic processing and logic-based reasoning. Most relevant capabilities such as navigation, autonomous path planning, path following control, and communications, that directly affect decision making, are treated as basic skills of agents. Although many autonomous vehicles have been engineered in the past without using the agent-oriented approach, most decision making onboard of vehicles is similar to or can be classified as some kind of agent architecture, even if in a naïve form. First the ANSI standard of intelligent systems is recalled then a summary of the fundamental types of possible agent architectures for autonomous vehicles are presented, starting from reactive, through layered, to advanced architectures in terms of beliefs, goals, and intentions. The review identifies some missing links between computer science results on discrete agents and engineering results of continuous world sensing, actuation, and path planning. In this context design tools for ‘abstractions programming’ are identified as needed to fill in the gap between logic-based reasoning and sensing. Finally, research is reviewed on autonomous vehicles in water, on the ground, in the air, and in space with comments on their methods of decision making. One of the main conclusions of this review is that standardization of decision making through agent architectures is desirable for the future of intelligent vehicle developments and their legal certification.

This paper describes the application of geometric bounding techniques to range-only navigation of an underwater vehicle. A geometric technique is defined to obtain a position fix of an underwater vehicle using a combination of dead-reckoning navigation and acoustic measurements of range between the underwater vehicle and a global positioning system (GPS)-equipped ship. An assessment is made of the accuracy to which navigational parameters can be estimated using these methods.

This paper describes the application of geometric bounding techniques to navigation of an underwater vehicle. A technique is described for obtaining a fix of submersible position based on measurements of range from a supporting ship and the propagation of dead-reckoning errors. Also described is the estimation of bounds on a constant drift rate and the use of these bounds in the determination of velocity sensor misalignment angle. An assessment is made of the accuracy to which navigational parameters can be estimated using these methods.

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

[1] Recent developments in observational near-surface air temperature and sea-surface temperature analyses are combined to produce HadCRUT4, a new data set of global and regional temperature evolution from 1850 to the present. This includes the addition of newly digitized measurement data, both over land and sea, new sea-surface temperature bias adjustments and a more comprehensive error model for describing uncertainties in sea-surface temperature measurements. An ensemble approach has been adopted to better describe complex temporal and spatial interdependencies of measurement and bias uncertainties and to allow these correlated uncertainties to be taken into account in studies that are based upon HadCRUT4. Climate diagnostics computed from the gridded data set broadly agree with those of other global near-surface temperature analyses. Fitted linear trends in temperature anomalies are approximately 0.07°C/decade from 1901 to 2010 and 0.17°C/decade from 1979 to 2010 globally. Northern/southern hemispheric trends are 0.08/0.07°C/decade over 1901 to 2010 and 0.24/0.10°C/decade over 1979 to 2010. Linear trends in other prominent near-surface temperature analyses agree well with the range of trends computed from the HadCRUT4 ensemble members.

A terrain aided navigation correction system has been developed for use with the Autonomous Underwater Vehicle (AUV) Autosub 6000. This system allows drift in dead-reckoning navigation to be estimated by matching bathymetry obsevered in multibeam echosounder (MBE) data to a reference map. The reference map consists of a single line of bathymetric data, one swath width across, spanning the operational area of a survey mission. This map is collected by Autosub prior to undertaking the survey. This paper presents a discussion of the biases in typical AUV navigational sensors and their influence on navigation on a map that is created in-mission by a submersible. Based on this discussion a filter has been implemented and has been used to analyse the navigational errors accumulated during an Autosub 6000 survey mission and computational limitations for realtime application are assessed.