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Understanding carbon sources and sinks across the Earth's surface is fundamental in climate science and policy; thus, these topics have been extensively studied but have yet to be fully resolved and are associated with massive debate regarding the sign and magnitude of the carbon budget from global to regional scales. Developing new models and estimates based on state-of-the-art algorithms and data constraints can provide valuable knowledge and contribute to a final ensemble model in which various optimal carbon budget estimates are integrated, such as the annual global carbon budget paper. Here, we develop a new atmospheric inversion system based on the 4D local ensemble transform Kalman filter (4D-LETKF) coupled with the GEOS-Chem global transport model to infer surface-to-atmosphere net carbon fluxes from Orbiting Carbon Observatory-2 (OCO-2) V10r XCO 2 retrievals. The 4D-LETKF algorithm is adapted to an OCO-2-based global carbon inversion system for the first time in this work. On average, the mean annual terrestrial and oceanic fluxes between 2015 and 2020 are estimated as - 2.02 and - 2.34 GtC yr -1 , respectively, compensating for 21 % and 24 %, respectively, of global fossil carbon dioxide (CO 2) emissions (9.80 GtC yr -1). Our inversion results agree with the CO 2 atmospheric growth rates reported by the National Oceanic and Atmospheric Administration (NOAA) and reduce the modeled CO 2 concentration biases relative to the prior fluxes against surface and aircraft measurements. Our inversion-based carbon fluxes are broadly consistent with those provided by other global atmospheric inversion models, although discrepancies still occur in the land–ocean flux partitioning schemes and seasonal flux amplitudes over boreal and tropical regions, possibly due to the sparse observational constraints of the OCO-2 satellite and the divergent prior fluxes used in different inversion models. Four sensitivity experiments are performed herein to vary the prior fluxes and uncertainties in our inversion system, suggesting that regions that lack OCO-2 coverage are sensitive to the priors, especially over the tropics and high latitudes. In the further development of our inversion system, we will optimize the data-assimilation configuration to fully utilize current observations and increase the spatial and seasonal representativeness of the prior fluxes over regions that lack observations. [ABSTRACT FROM AUTHOR]

A 30-year (1980–2010) climatology of the major variables and terms of the transformed Eulerian-mean (TEM) momentum and thermodynamic equations is constructed by using four global atmospheric reanalyses: the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2); the Japanese 55-year Reanalysis (JRA-55); the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim); and the Climate Forecast System Reanalysis (CFSR). Both the reanalysis ensemble mean (REM) and the differences in each reanalysis from the REM are investigated in the latitude–pressure domain for December–January–February and for June–July–August. For the REM investigation, two residual vertical velocities (the original one and one evaluated from residual meridional velocity) and two mass streamfunctions (from meridional and vertical velocities) are compared. Longwave (LW) radiative heating and shortwave (SW) radiative heating are also shown and discussed. For the TEM equations, the residual terms are also calculated and investigated for their potential usefulness, as the residual term for the momentum equation should include the effects of parameterized processes such as gravity waves, while that for the thermodynamic equation should indicate the analysis increment. Inter-reanalysis differences are investigated for the mass streamfunction, LW and SW heating, the two major terms of the TEM momentum equation (the Coriolis term and the Eliassen–Palm flux divergence term), and the two major terms of the TEM thermodynamic equation (the vertical temperature advection term and the total diabatic heating term). The spread among reanalysis TEM momentum balance terms is around 10 % in Northern Hemisphere winter and up to 50 % in Southern Hemisphere winter. The largest uncertainties in the thermodynamic equation (about 50 %) are found in the vertical advection, for which the structure is inconsistent with the differences in heating. The results shown in this paper provide basic information on the degree of agreement among recent reanalyses in the stratosphere and upper troposphere in the TEM framework. [ABSTRACT FROM AUTHOR]

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO 2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a spectral surface reflectivity climatology based on observations made by TROPOMI on board the Sentinel-5P satellite. The database contains the directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface for 21 wavelength bands ranging from 328 to 2314 nm and for each calendar month. The spatial resolution of the database grid is 0.125° × 0.125°. A recently developed cloud shadow detection technique is implemented to avoid dark scenes due to cloud shadow. In the database, the anisotropy of the surface reflection is described using a third-order parameterisation of the viewing angle dependence. The viewing angle dependence of the DLER is analysed globally and for a selection of surface type regions. The dependence is found to agree with the viewing angle dependence found in the GOME-2 surface DLER database. Differences exist, related to the actual solar position. On average, the viewing angle dependence in TROPOMI DLER is weaker than for GOME-2 DLER, but still important. Validation of the new database was first performed by comparison of the non-directional TROPOMI surface LER with heritage LER databases based on GOME-1 , OMI, SCIAMACHY, and GOME-2 data. Agreement was found within 0.002–0.02 in the UV-VIS (below 500 nm), up to 0.003 in the NIR (670–772 nm), and below 0.001 in the short-wave infrared (SWIR) (2314 nm). These performance numbers are dominated by the performance over ocean, but they are in most cases also representative for land surfaces. For the validation of the directional TROPOMI surface DLER, we made use of comparison with the MODIS surface bi-directional reflectance distribution function (BRDF) for a selection of surface type regions. In all cases the DLER performed significantly better than the traditional LER, and we found good agreement with the MODIS surface BRDF. The TROPOMI surface DLER database is a clear improvement on previous surface albedo databases and can be used as input not only for satellite retrievals from TROPOMI observations, but also for retrievals from observations from other polar-orbiting satellite instruments provided that their equator crossing time is close to that of TROPOMI. The algorithm that is introduced in this paper can be used for the retrieval of surface reflectivity climatologies from other polar satellite missions as well, including Ocean and Land Colour Instrument (OLCI) on the Sentinel-3 satellites, Sentinel-5, and Multi-viewing Multi-channel Multi-polarisation imager (3MI) on the MetOp-SG-A1 satellite to be launched in 2025, as well as the future CO2M mission. [ABSTRACT FROM AUTHOR]

In the online seminar series "Perspectives on climate sciences: from historical developments to future frontiers", which took place during 2020–2021, well-known and established scientists from several fields – including mathematics, physics, climate science and ecology – presented their perspectives on the evolution of climate science and on relevant scientific concepts. This special issue aims to create a platform for a more detailed elaboration of the topics discussed in the seminars but also to publish new scientific findings. In this paper, we first give an overview of the content of the seminar series, and then we introduce the written contributions to this special issue. In line with the spirit of the seminar series, this paper is structured along thematic areas of the broad field of climate science, conveying different perspectives on the climate system: geophysical fluid dynamics, dynamical systems theory, multiscale processes, statistical physics, paleoclimate and the human dimension. [ABSTRACT FROM AUTHOR]

Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction. [ABSTRACT FROM AUTHOR]

The combination of precipitation formation and fallout affects atmospheric flows through the release of latent heat and through the removal of mass from the atmosphere, but because the mass of water vapor is only a small fraction of the total mass of Earth's atmosphere, precipitation mass sinks are often neglected in theory and models. However, a small number of modeling studies suggest that water mass sources and sinks can intensify heavily precipitating weather systems. These studies point to a need to more systematically verify the impact of neglecting precipitation mass sinks, particularly for warmer and moister climates in which precipitation rates can be much higher. In this paper, we add precipitation mass sources and sinks to an idealized general circulation model and examine their effects on steady-state midlatitude storm track statistics. The model has several idealizations, including that all condensates immediately fall out of the atmosphere, and is run across a wide range of climates, including very warm climates. We find that modifying the model to include mass sources and sinks has no detectable effect on midlatitude variability or extremes, even in climates much warmer and moister than the modern. However, we find that a 10-fold exaggeration of mass sources and sinks is sufficient to produce more intense midlatitude weather extremes and increase surface pressure variance. This result is consistent with theoretical potential vorticity analysis, which suggests that the dynamical effects of mass sources and sinks are much smaller than the dynamical effects of accompanying latent heating unless mass sinks are artificially amplified by at least a factor of 10. Finally, we use simulations of "tropical cyclone worlds" to attempt to reconcile our results with earlier work showing stronger deepening in a simulation of a tropical cyclone case study when precipitation mass sinks were included. We demonstrate that abruptly "turning on" mass sources and sinks can lead to stronger transient deepening in some individual storms (consistent with results of past work) but weaker transient deepening in other storms, without modifying the steady-state statistics of storms in equilibrium with the large-scale environment (consistent with our other results). Our results provide a firmer foundation for using general circulation models that neglect moist mass sources and sinks in climate simulations, even in climates much warmer than today, while leaving open the possibility that their inclusion might lead to short-term improvements in forecast skill. [ABSTRACT FROM AUTHOR]

In this paper we introduce a spectral surface reflectivity climatology based on observations made by the TROPOMI instrument on board the Sentinel-5P satellite. The database contains the directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface for 21 wavelength bands ranging from 328nm to 2314nm and for each calendar month. The spatial resolution of the database grid is 0.125° × 0.125°. A recently developed cloud shadow detection technique is implemented to avoid dark scenes due to cloud shadow. In the database, the anisotropy of the surface reflection is described using a third-order parameterisation of the viewing angle dependence. The viewing angle dependence of the DLER is analysed globally and for a selection of surface type regions. The dependence is found to agree with the viewing angle dependence found in the GOME-2 surface DLER database. Differences exist, related to the actual solar position. On average, the viewing angle dependence in TROPOMI DLER is weaker than for GOME-2 DLER, but still important. Validation of the new database was first performed by comparison of the non-directional TROPOMI surface LER with heritage LER databases based on GOME-1, OMI, SCIAMACHY, and GOME-2 data. Agreement was found within 0.002-0.02 in the UV-VIS (below 500nm), up to 0.003 in the NIR (670-772nm), and below 0.001 in the SWIR (2314nm). These performance numbers are dominated by the performance over ocean, but they are in most cases also representative for land surfaces. For the validation of the directional TROPOMI surface DLER we made use of comparison with MODIS surface BRDF for a selection of surface type regions. In all cases the DLER performed significantly better than the traditional LER and we found good agreement with MODIS surface BRDF. The TROPOMI surface DLER database is a clear improvement on previous surface albedo databases and can be used as input not only for satellite retrievals from TROPOMI observations, but also for retrievals from observations from other polarorbiting satellite instruments provided that their equator crossing time is close to that of TROPOMI. The algorithm that is introduced in this paper can be used for the retrieval of surface reflectivity climatologies from other polar satellite missions as well, including OLCI on the Sentinel-3 satellites, Sentinel-5 and 3MI on the MetOp-SG-A1 satellite to be launched in 2025, and the future CO2M mission. [ABSTRACT FROM AUTHOR]

Integrated water vapour (IWV) measurements from similar or different techniques are often inter-compared for calibration and validation purposes. Results are usually assessed in terms of bias (difference of the means), standard deviation of the differences, and linear fit slope and offset (intercept) estimates. When the instruments are located at different elevations, a correction must be applied to account for the vertical displacement between the sites. Empirical formulations are traditionally used for this correction. In this paper we show that the widely used correction model based on a standard, exponential, profile for water vapour cannot properly correct the bias, slope, and offset parameters simultaneously. Correcting the bias with this model degrades the slope and offset estimates and vice versa. This paper proposes an improved correction method that overcomes these limitations. It implements a multiple linear regression method where the slope and offset parameters are provided from a radiosonde climatology. It is able to predict monthly mean IWVs with a bias smaller than 0.1 kgm-2 and a root-mean-square error smaller than 0.5 kgm-2 for height differences up to 500 m. The method is applied to the inter-comparison of GPS IWV data in a tropical mountainous area and to the inter-validation of GPS and satellite microwave radiometer data. This paper also emphasizes the need for using a slope and offset regression method that accounts for errors in both variables and for correctly specifying these errors. [ABSTRACT FROM AUTHOR]

Non-equilibrium is dominant in geophysical and climate phenomena. However the study of non-equilibrium is much more difficult than equilibrium and the relevance of probabilistic simplified models has been emphasized. Large deviation rates have been used recently in climate science. In this paper after recalling progress during the last decades in understanding the role of large deviations in a class of non-equilibrium systems we point out differences between equilibrium and non-equilibrium. For example in non-equilibrium: a) large deviation rates may be extensive but not simply additive. b) In non-equilibrium there are generically long range space correlations so large deviation rates are non-local. c) Singularities in large deviation rates denote the existence of phase transitions often not possible in equilibrium. To exemplify we shall refer to lattice gas models like the symmetric simple exclusion process and other models which are playing an important role in the understanding of non-equilibrium physics. Reasons why all this may be of interest in climate physics will be briefly indicated. [ABSTRACT FROM AUTHOR]

The climate science community faces a major challenge with respect to communicating the risks associated with climate change within a heavily politicised landscape that is characterised by varying degrees of denial, scepticism, distrust in scientific enterprise, and an increased prevalence of misinformation ("fake news"). This issue is particularly significant given the reliance on conventional "deficit" communication approaches, which are based on the assumption that scientific information provision will necessarily lead to desired behavioural changes. Indeed, the constrained orthodoxy of scientific practices in seeking to maintain strict objectivity and political separation imposes very tangible limits on the potential effectiveness of climate scientists for communicating risk in many contemporary settings. To address these challenges, this paper uses insights from a collaboration between UK climate scientists and artist researchers to argue for a more creative and emotionally attentive approach to climate science engagement and advocacy. In so doing, the paper highlights innovative ways in which climate change communication can be reimagined through different art forms to enable complex concepts to become knowable. We suggest that in learning to express their work through forms of art, including print-making, theatre and performance, song-writing, and creative writing, researchers experienced not only a sense of liberation from the rigid communicative framework operating in their familiar scientific environment but also a growing self-confidence in their ability and willingness to engage in new ways of expressing their work. As such, we argue that scientific institutions and funding bodies should recognise the potential value of climate scientists engaging in advocacy through art–science collaborations and that these personal investments and contributions to science engagement by individuals should be rewarded and valued alongside conventional scientific outputs. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) and reanalysis datasets provide valuable information for assessing the vulnerability of ice shelves to collapse over Antarctica, which is important for future global sea level rise estimates. Within this context, this paper examines variability in snowfall, near-surface air temperature and melt across products from the Met Office Unified Model (MetUM), Regional Atmospheric Climate Model (RACMO) and Modèle Atmosphérique Régional (MAR) RCMs, as well as the ERA-Interim and ERA5 reanalysis datasets. Seasonal and trend decomposition using LOESS (STL) is applied to split the monthly time series at each model grid cell into trend, seasonal and residual components. Significant systematic differences between outputs are shown for all variables in the mean and in the seasonal and residual standard deviations, occurring at both large and fine spatial scales across Antarctica. Results imply that differences in the atmospheric dynamics, parametrisation, tuning and surface schemes between models together contribute more significantly to large-scale variability than differences in the driving data, resolution, domain specification, ice sheet mask, digital elevation model and boundary conditions. Despite significant systematic differences, high temporal correlations are found for snowfall and near-surface air temperature across all products at fine spatial scales. For melt, only moderate correlation exists at fine spatial scales between different RCMs and low correlation between RCM and reanalysis outputs. Root mean square deviations (RMSDs) between all outputs in the monthly time series for each variable are shown to be significant at fine spatial scales relative to the magnitude of annual deviations. Correcting for systematic differences results in significant reductions in RMSDs, suggesting the importance of observations and further development of bias-correction techniques. [ABSTRACT FROM AUTHOR]

In the online seminar series "Perspectives on Climate Sciences: from Historical Developments to Future Frontiers", which took place during 2020-2021, well-known and established scientists from several fields - including mathematics, physics, climate science and ecology - presented their perspectives on the evolution of climate science and on relevant scientific concepts. This special issue aims to create a platform for a more detailed elaboration of the topics discussed in the seminars but also to publish new scientific findings. In this paper, we first give an overview of the content of the seminar series, and then we introduce the written contributions to this special issue. In line with the spirit of the seminar series, this paper is structured along thematic areas of the broad field of climate science, conveying different perspectives on the climate system: geophysical fluid dynamics, dynamical systems theory, multiscale processes, statistical physics, paleoclimate and the human dimension. [ABSTRACT FROM AUTHOR]

The 2014-2018 drought over South Africa's winter rainfall zone (WRZ) created a critical water crisis which highlighted the region's drought and climate change vulnerability. Consequently, it is imperative to better understand the climatic characteristics of the drought in order to inform regional adaptation to projected climate change. In this paper we investigate the spatio-temporal patterns of drought intensity and the recent rainfall trends, focusing on assessing the consistency of the prevailing conceptual model of drought drivers with observed patterns. For this we use the new spatial subdivision for the region encompassing the WRZ introduced in our companion paper (Conradie et al., 2022). Compared to previous droughts since 1979, the 2014-2018 drought in the WRZ core was characterised by a markedly lower frequency of very wet days (exceeding the climatological 99.5th percentile daily rainfall -- including dry days) and of wet months (SPI1 > 0:5), a sub-seasonal attribute not previously reported. There was considerable variability in the spatial footprint of the drought. Short-term drought began in the south-western core WRZ in spring 2014. The peak intensity gradually spread north-eastward, although a spatially near-uniform peak is seen during mid-2017. The overall drought intensity for the 2015--2017 period transitions radially from most severe in the WRZ core to least severe in the surroundings. During 2014 and 2015, the drought was most severe at those stations receiving the largest proportion of their rainfall from westerly and north-westerly winds; by 2018, those stations receiving the most rain from the south and south-east were most severely impacted. This indicates an evolving set of dynamic drivers associated with distinct rain-bearing synoptic flows. No evidence is found to support the suggestion that the drought was more severe in the mountain catchments of Cape Town's major supply reservoirs than elsewhere in the core nor that rain day frequency trends since 1979 are more negative in this subdomain. Rainfall and rain day trend rates also exhibit some connections to the spatial seasonality structure of the WRZ, although this is weaker than for drought intensity. Caution should be applied in assessing South African rain day trends given their high sensitivity to observed data shortcomings. Our findings suggest an important role for zonally asymmetric dynamics in the region's drought evolution. This analysis demonstrates the utility of the spatial subdivisions proposed in the companion paper by highlighting spatial structure in drought intensity evolution linked to rainfall dynamics. [ABSTRACT FROM AUTHOR]

Non-equilibrium is dominant in geophysical and climate phenomena. However the study of non-equilibrium is much more difficult than equilibrium, and the relevance of probabilistic simplified models has been emphasized. Large deviation rates have been used recently in climate science. In this paper, after recalling progress during the last decades in understanding the role of large deviations in a class of non-equilibrium systems, we point out differences between equilibrium and non-equilibrium. For example, in non-equilibrium (a) large deviation rates may be extensive but not simply additive. (b) In non-equilibrium there are generically long-range space correlations, so large deviation rates are non-local. (c) Singularities in large deviation rates denote the existence of phase transitions often not possible in equilibrium. To exemplify, we shall refer to lattice gas models like the symmetric simple exclusion process and other models which are playing an important role in the understanding of non-equilibrium physics. The reasons why all this may be of interest in climate physics will be briefly indicated. [ABSTRACT FROM AUTHOR]

Cloud radars are capable of providing continuous high-resolution observations of clouds and now offer new capabilities within fog layers thanks to the development of frequency-modulated continuous-wave 95 GHz cloud radars. These observations are related to the microphysical properties of clouds. Power law relations in the form of Z=a⋅LWCb are generally used to estimate liquid water content (LWC) profiles. The constants a and b from the power law relation vary with the cloud type and cloud characteristics. Due to the variety of such parameterizations, selecting the most appropriate Z –LWC relation for a continuous cloud system is complicated. Additional information such as liquid water path (LWP) from a co-located microwave radiometer (MWR) is used to scale the LWC of the cloud profile. An algorithm for estimating the LWC of fog and warm clouds using 95 GHz cloud radar–microwave radiometer synergy in a variational framework is presented. This paper also aims to propose an algorithm configuration that retrieves the LWC of clouds and fog using radar reflectivity and a climatology of the power law parameters. To do so, variations in the scaling factor ln⁡a (the logarithm of pre-factor a from power law relation) when MWR observations are available are allowed in each cloud profile to build a climatology of the scaling factor ln⁡a that can be used when MWR observations are not available. The algorithm also accounts for attenuation due to cloud droplets. In this algorithm formulation, the measure of uncertainty in the observations, the forward model, and the a priori information of desired variables acts as weights in the retrieved quantities. These uncertainties in the retrieval are analyzed in the sensitivity analysis of the algorithm. The retrieval algorithm is first tested on a synthetic profile for different perturbations in sensitivity parameters. The sensitivity study has shown that this method is susceptible to LWP information because LWP is point information for the whole cloud column. By further investigating the sensitivity analysis of various biases in LWP information, it was found that it is beneficial to incorporate LWP, even if it is biased, rather than not assimilate any LWP. The algorithm is then implemented in various cloud and fog cases at the SIRTA observatory to estimate LWC and the scaling factor. The scaling factor (ln⁡a) changes for each cloud profile, and the range of ln⁡a is consistent with suggested values in the literature. The validation of such an algorithm is challenging, as we need reference measurements of LWC co-located with the retrieved values. During the SOFOG-3D campaign (southwest of France, October 2019 to March 2020), in situ measurements of LWC were collected in the vicinity of a cloud radar and a microwave radiometer, allowing comparison of retrieved and measured LWC. The comparison demonstrated that the cloud–fog heterogeneity played a key role in the assessment. The proposed synergistic retrieval algorithm is applied to 39 cloud and fog cases at SIRTA, and the behavior of the scaling factor is studied. This statistical analysis of scaling is carried out to develop a radar-only retrieval method. The climatology revealed that the scaling factor can be linked to the maximum reflectivity of the profile. From climatology, the statistical relations for the scaling factor are proposed for fog and clouds. Thanks to the variational framework, a stand-alone radar version of the algorithm is adapted from the synergistic retrieval algorithm, which incorporates the climatology of the scaling factor as a priori information to estimate the LWC of warm clouds. This method allows the LWC estimation using only radar reflectivity and climatology of the scaling factor. [ABSTRACT FROM AUTHOR]

Weighted positive matrix factorization (PMF) has been used by scientists to find small sets of underlying factors in environmental data. However, as the size of the data has grown, increasing computational costs have made it impractical to use traditional methods for this factorization. In this paper, we present a new weighting method to dramatically decrease computational costs for these traditional algorithms. We then apply this weighting method with the Randomized Hierarchical Alternating Least Squares (RHALS) algorithm to a large environmental dataset, where we show that interpretable factors can be reproduced using these methods. We show this algorithm results in a computational speedup of 38, 67, and 634 compared to the Multiplicative Update (MU), deterministic Hierarchical Alternating Least Squares (HALS), and non-negative Alternating Least Squares (ALS) algorithms, respectively. We also investigate rotational ambiguity in the solution, and present a simple "pulling" method to rotate a set of factors. This method is shown to find alternative solutions, and in some cases, lower the weighted residual error of the algorithm. [ABSTRACT FROM AUTHOR]

The assessment of tropical cyclone (TC) statistics requires the direct, objective, and automatic detection and tracking of TCs in reanalyses and model simulations. Research groups have independently developed numerous algorithms during recent decades in order to answer that need. Today, there is a large number of trackers that aim to detect the positions of TCs in gridded datasets. The questions we ask here are the following: does the choice of tracker impact the climatology obtained? And, if it does, how should we deal with this issue? This paper compares four trackers with very different formulations in detail. We assess their performances by tracking TCs in the ERA5 reanalysis and by comparing the outcome to the IBTrACS observations database. We find typical detection rates of the trackers around 80 %. At the same time, false alarm rates (FARs) greatly vary across the four trackers and can sometimes exceed the number of genuine cyclones detected. Based on the finding that many of these false alarms (FAs) are extra-tropical cyclones (ETCs), we adapt two existing filtering methods common to all trackers. Both post-treatments dramatically impact FARs, which range from 9 % to 36 % in our final catalogs of TC tracks. We then show that different traditional metrics can be very sensitive to the particular choice of tracker, which is particularly true for the TC frequencies and their durations. By contrast, all trackers identify a robust negative bias in ERA5 TC intensities, a result already noted in previous studies. We conclude by advising against using as many trackers as possible and averaging the results. A more efficient approach would involve selecting one or a few trackers with well-known and complementary properties. [ABSTRACT FROM AUTHOR]

We developed a new version of the Alfred Wegener Institute Climate Model (AWI-CM3), which has higher skills in representing the observed climatology and better computational efficiency than its predecessors. Its ocean component FESOM2 (Finite-volumE Sea ice–Ocean Model) has the multi-resolution functionality typical of unstructured-mesh models while still featuring a scalability and efficiency similar to regular-grid models. The atmospheric component OpenIFS (CY43R3) enables the use of the latest developments in the numerical-weather-prediction community in climate sciences. In this paper we describe the coupling of the model components and evaluate the model performance on a variable-resolution (25–125 km) ocean mesh and a 61 km atmosphere grid, which serves as a reference and starting point for other ongoing research activities with AWI-CM3. This includes the exploration of high and variable resolution and the development of a full Earth system model as well as the creation of a new sea ice prediction system. At this early development stage and with the given coarse to medium resolutions, the model already features above-CMIP6-average skills (where CMIP6 denotes Coupled Model Intercomparison Project phase 6) in representing the climatology and competitive model throughput. Finally we identify remaining biases and suggest further improvements to be made to the model. [ABSTRACT FROM AUTHOR]

In this study, we assess the skill of a stochastic weather generator (SWG) to forecast precipitation in several cities in western Europe. The SWG is based on a random sampling of analogs of the geopotential height at 500 hPa (Z500). The SWG is evaluated for two reanalyses (NCEP and ERA5). We simulate 100-member ensemble forecasts on a daily time increment. We evaluate the performance of SWG with forecast skill scores and we compare it to ECMWF forecasts. Results show significant positive skill score (continuous rank probability skill score and correlation) compared with persistence and climatology forecasts for lead times of 5 and 10 d for different areas in Europe. We find that the low predictability episodes of our model are related to specific weather regimes, depending on the European region. Comparing the SWG forecasts to ECMWF forecasts, we find that the SWG shows a good performance for 5 d. This performance varies from one region to another. This paper is a proof of concept for a stochastic regional ensemble precipitation forecast. Its parameters (e.g., region for analogs) must be tuned for each region in order to optimize its performance. [ABSTRACT FROM AUTHOR]

Radio Occultation (RO) using the Global Navigation Satellite Systems (GNSS) can be used to infer atmospheric profiles of microwave refractivity in the Earth's atmosphere. GNSS RO data are now assimilated into numerical weather prediction models and used for climate monitoring. New remote sensing applications are being considered that fuse GNSS RO soundings and passive nadir-scanned radiance soundings. Collocating RO soundings and nadir-scanned radiance soundings, however, is computationally expensive, especially as new commercial GNSS RO constellations greatly increase the number of global daily RO soundings. This paper develops a new and efficient technique, called the "rotation-collocation-method", for collocating RO and nadir-scanned radiance soundings in which all soundings are rotated into the time-dependent reference frame in which the nadir sounder's scan pattern is stationary. Collocations with RO soundings are then found when the track of an RO sounding crosses the line corresponding to the nadir sounder's scan pattern. When applied to finding collocations between RO soundings from COSMIC-2, Metop-B-GRAS, and Metop-C-GRAS and the passive microwave soundings of ATMS on NOAA-20, Suomi-NPP, and AMSU-A on Metop-B and Metop-C for the month of January, 2021, the rotation- collocation method proves to be 99.0 % accurate and is hundreds to thousands of times faster than traditional approaches to finding collocations. [ABSTRACT FROM AUTHOR]

Models of the carbon cycle and climate on geologic (>104 year) timescales have improved tremendously in the last 50 years due to parallel advances in our understanding of the Earth system and the increase in computing power to simulate its key processes. Despite these advances, balancing the Earth System's vast complexity with a model's computational expense is a primary challenge in model development. Running longer simulations spanning hundreds of thousands of years or more generally requires reducing the complexity of the modeled climate system. However, simpler model frameworks often leave out certain features of the climate system, such as radiative feedbacks, shifts in atmospheric circulation, and the expansion and decay of ice sheets, which can have profound effects on the long-term carbon cycle. Here, we present a model for climate and the long-term carbon cycle that captures many fundamental features of global climate while retaining the computational efficiency needed to simulate millions of years of time. The Carbon- H2O Coupled HydrOlOgical model with Terrestrial Runoff And INsolation, or CH2O-CHOO TRAIN, couples a one-dimensional (latitudinal) moist static energy balance model of climate with a model for rock weathering and the long-term carbon cycle. The key advantages of this framework are (1) it simulates fundamental climate forcings and feedbacks; (2) it accounts for geographic configuration; and (3) it is highly customizable, equipped to easily add features, change the strength of feedbacks, and prescribe conditions that are often hard-coded or emergent properties of more complex models, such as climate sensitivity and the strength of meridional heat transport. The CH2O-CHOO TRAIN is capable of running million-year-long simulations in about thirty minutes on a laptop PC. This paper outlines the model equations, presents a sensitivity analysis of the climate responses to varied climatic and carbon cycle perturbations, and discusses potential applications and next stops for the CH2O-CHOO TRAIN. [ABSTRACT FROM AUTHOR]

In contrast to fixed measuring devices, ship-based lidar systems provide highly reliable wind observations within extensive regions. Therefore, this kind of reference dataset provides a great potential for evaluating the performance of mesoscale numerical models in resembling mesoscale flow phenomena such as low-level jets -- essential for an optimal development and operation of wind turbines. This paper presents a comparison between numerical output data from two state-of-the-art numerical datasets (ERA5 and NEWA) and the shipmounted lidar measurements from the NEWA Ferry Lidar Experiment. The comparison was performed along the route covered by the vessel, as well as in specific locations within this route, to better understand the capabilities and limitations of the numerical models to precisely resemble the occurrence and main properties of low-level jets (LLJs) in different locations. The findings of this study show that the non-stationary nature of ship-based lidar measurements allows evaluating the accuracy of the models when retrieving jets' characteristics and occurrence under different temporal and spatial effects. Numerical models underestimate the occurrence of LLJs, and they struggle to accurately describe their main characteristics, with a particularly large underestimation of the falloff. The found results are to be seen in relation to the characteristics of the observations, such as the data availability, the time--position relation of the selected vessel's route, or the profile height limitation, as well as the features of the jets, with a particular relevance of core height and falloff. Additionally, the results illustrate the temporal and spatial shift between the LLJ events detected by the measurements and the models and the potential benefit of considering such deviations when studying LLJs' climatology through numerical modes. [ABSTRACT FROM AUTHOR]

Billions of historical climatological observations remain unavailable to science as they exist only on paper, stored in numerous archives around the world. The conversion of these data from paper to digital could transform our understanding of historical climate variations, including extreme weather events. Here we demonstrate how the rescue of such paper observations has improved our understanding of a severe windstorm that occurred in February 1903 and its significant impacts. By assimilating newly rescued atmospheric pressure observations, the storm is now credibly represented in an improved reanalysis of the event. In some locations this storm produced stronger winds than any event during the modern period (1950–2015) and it is in the top-4 storms for strongest winds anywhere over land in England & Wales. As a result, estimates of risk from severe storms, based on modern period data, may need to be revised. Examining the atmospheric structure of the storm suggests that it is a classic Shapiro-Keyser-type cyclone with 'sting jet' precursors and associated extreme winds at locations and times of known significant damage. Comparison with both independent observations and qualitative information, such as photographs and written accounts, provides additional evidence of the credibility of the atmospheric reconstruction, including of sub-daily rainfall variations. Simulations of the storm surge resulting from this storm show a large coastal surge of around 2.5 m, comparing favourably with newly rescued tide gauge observations and adding to our confidence in the reconstruction. Combining historical rescued weather observations with modern reanalysis techniques has allowed us to plausibly reconstruct a severe windstorm and associated storm surge from more than 100 years ago, establishing an invaluable end-to-end tool to improve assessments of risks from extreme weather. [ABSTRACT FROM AUTHOR]

A 30-year (1980–2010) climatology of the major variables and terms of the transformed Eulerian-mean (TEM) momentum and thermodynamic equations is constructed by using four global atmospheric reanalyses, MERRA-2, JRA-55, ERA-Interim, and CFSR. Both the reanalysis ensemble mean (REM) and the differences of each reanalysis from the REM are investigated in the latitude-pressure domain for December-January-February and for June-July-August. For the REM investigation, two residual vertical velocities (the original one and one evaluated from residual meridional velocity) and two mass streamfunctions (from meridional and vertical velocities) are compared, and longwave (LW) and shortwave (SW) radiative heatings are also shown and discussed. For the TEM equations, the residual terms are also calculated and investigated for their potential usefulness, as the residual term for the momentum equation should include the effects of parameterised processes such gravity waves, while that for the thermodynamic equation should indicate the analysis increment. Inter-reanalysis differences are investigated for the mass streamfunction, LW and SW heatings, the two major terms of the TEM momentum equation (the Coriolis term and the Elliassen-Palm flux divergence term), and the two major terms of the TEM thermodynamic equation (the vertical temperature advection term and the total diabatic heating term). The spread among reanalysis TEM momentum balance terms is around 10 % in Northern-Hemisphere winter and up to 50 % in Southern-Hemisphere winter. The largest uncertainties in the thermodynamic equation (about 50 %) are found in the vertical advection, which does not show a structure consistent with the differences in heatings. The results shown in this paper provide basic information on the degree of agreement among recent reanalyses in the stratosphere and in the upper troposphere in the TEM framework. [ABSTRACT FROM AUTHOR]

Systematic, routine, and comprehensive evaluation of Earth System Models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The PCMDI Metrics Package (PMP) is an open-source Python software package that provides 'quick-look' objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as El Niño-Southern Oscillation (ENSO) and Madden-Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including extremes. The PMP results are produced in the context of all model simulations contributed to CMIP6 and earlier CMIP phases. An important priority of the PMP is to document evaluation statistics for all Historical and AMIP simulations submitted to recent phases of CMIP, providing version-controlled information for all data sets and software packages being used. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. In this paper, we present an overview of the PMP including its history to date, capabilities, recent updates, and future direction. [ABSTRACT FROM AUTHOR]

In this study, we utilized a high-resolution (4 km) convection-permitting Weather Research Forecasting (WRF) simulation spanning a 13-year period (2000–2013) to investigate the climatological features of Low-level Jets (LLJs) over North America. The 4-km simulation enabled us to represent the effects of orography and the underlying surface on the boundary layer winds better. Focusing on the continental US and the adjacent border regions of Canada and Mexico, this study characterizes the spatial distribution, seasonal patterns, and diurnal fluctuations of northerly/southerly LLJ occurrence frequencies. This paper not only identified several well-known large-scale LLJs in North America, such as the southerly Great Plains LLJ and the summer northerly California coastal LLJ, but also the Quebec northerly LLJ, which gets less focus before. Moreover, the high-resolution simulation revealed climatic characteristics of weaker and smaller-scale LLJs or low-level wind maxima in regions with complex terrains, such as the northerly LLJs in the foothill regions of the Rocky Mountains and the Appalachian during the winter. Additionally, the different thermal and dynamic mechanisms forming significant LLJs near the Great Plains, California, and Quebec are investigated. This study provides valuable insights into the climatological features of LLJs in North America and the high-resolution simulation offers a more detailed understanding of LLJ behavior near complex terrains and other smaller-scale features. [ABSTRACT FROM AUTHOR]

This paper presents an updated estimation of the bottom-up global surface seawater dimethyl sulfide (DMS) climatology. This update, called DMS-Rev3, is the third of its kind and includes five significant changes from the last climatology, L11 (Lana et al., 2011), that was released about a decade ago. The first change is the inclusion of new observations that have become available over the last decade, creating a database of 873 539 observations leading to an ∼ 18-fold increase in raw data as compared to the last estimation. The second is significant improvements in data handling, processing, and filtering, to avoid biases due to different observation frequencies which result from different measurement techniques. Thirdly, we incorporate the dynamic seasonal changes observed in the geographic boundaries of the ocean biogeochemical provinces. The fourth change involves the refinement of the interpolation algorithm used to fill in the missing data. Lastly, an upgraded smoothing algorithm based on observed DMS variability length scales (VLS) helps to reproduce a more realistic distribution of the DMS concentration data. The results show that DMS-Rev3 estimates the global annual mean DMS concentration to be ∼ 2.26 nM (2.39 nM without a sea-ice mask), i.e., about 4 % lower than the previous bottom-up L11 climatology. However, significant regional differences of more than 100 % as compared to L11 are observed. The global sea-to-air flux of DMS is estimated at ∼ 27.1 TgS yr -1 , which is about 4 % lower than L11, although, like the DMS distribution, large regional differences were observed. The largest changes are observed in high concentration regions such as the polar oceans, although oceanic regions that were under-sampled in the past also show large differences between revisions of the climatology. Finally, DMS-Rev3 reduces the previously observed patchiness in high productivity regions. The new climatology, along with the algorithm, can be found in the online repository: 10.17632/hyn62spny2.1 (Mahajan, 2021). [ABSTRACT FROM AUTHOR]

Climate science depends upon accurate measurements of air temperature and humidity, the majority of which are still derived from sensors exposed within passively ventilated louvred Stevenson-type thermometer screens. It is well-documented that, under certain circumstances, air temperatures measured within such screens can differ significantly from "true" air temperatures measured by other methods, such as aspirated sensors. Passively ventilated screens depend upon wind motion to provide ventilation within the screen and thus airflow over the sensors contained therein. Consequently, instances of anomalous temperatures occur most often during light winds when airflow through the screen is weakest, particularly when in combination with strong or low-angle incident solar radiation. Adequate ventilation is essential for reliable and consistent measurements of both air temperature and humidity, yet very few systematic comparisons to quantify relationships between external wind speed and airflow within a thermometer screen have been made. This paper addresses that gap by summarizing the results of a 3-month field experiment in which airflow within a UK-standard Stevenson screen was measured using a sensitive sonic anemometer and comparisons made with simultaneous wind speed and direction records from the same site. The mean in-screen ventilation rate was found to be 0.2 m s -1 (median 0.18 m s -1), well below the 1 m s -1 minimum assumed in meteorological and design standard references, and only about 7 % of the scalar mean wind speed at 10 m. The implications of low in-screen ventilation on the uncertainty of air temperature and humidity measurements from Stevenson-type thermometer screens are discussed, particularly those due to the differing response times of dry- and wet-bulb temperature sensors and ambiguity in the value of the psychrometric coefficient. [ABSTRACT FROM AUTHOR]

The paper investigates systematic changes of the global atmospheric circulation during midlatitude heat waves. The global balanced circulation is defined in terms of the Rossby wave solutions of the linearized primitive equations. The circulation variability is assessed by the probability density functions (PDFs) of the normalized total energy anomalies with respect to their climatological values. The evaluation is performed in wavenumber space defined by the zonal wavenumbers, meridional modes and vertical structure functions. Heat waves are defined by spatially averaged surface temperatures in Eurasia 5 (bounded by the Ural) above the 95% percentile on at least three consecutive days. Normalized energy anomalies of the Rossby waves are found to be 2-distributed with a skewness associated with the number of degrees of freedom. The PDFs of energy anomalies during heat waves are compared with their climatological distributions for the zonal mean flow, for the planetary and for the synoptic scales. During heat waves, the skewness of PDFs of planetary-scale circulation is increased up to a factor of two. The increase is associated with a drastic reduction of the number of degrees of freedom down to 1/4 compared to 10 climatology. This reduction explains the coarse structure of blocking events in the midlatitude troposphere as confirmed by the reconstructed circulation in physical space. The changes in variability are also assessed by investigating submonthly circulation variance across scales. Planetary waves are found to be more persistent during heat waves, while the synoptic waves vary more, which is consistent with the idea of circulation blocking during the extreme heat events. The presented diagnostics can be applied for other extreme events in the atmosphere. [ABSTRACT FROM AUTHOR]

Even the most advanced climate models struggle to reproduce the observed wintertime circulation of the atmosphere over the North Atlantic and western Europe. During winter, the large-scale motions of this particularly challenging region are dominated by eddy-driven and highly non-linear flows, whose low-frequency variability is often studied from the perspective of regimes – a small number of qualitatively distinct atmospheric states. Poor representation of regimes associated with persistent atmospheric blocking events, or variations in jet latitude, degrades the ability of models to correctly simulate extreme events. In this paper we leverage a recently developed hybrid approach – which combines both jet and geopotential height data – to assess the representation of regimes in 8400 years of historical climate simulations drawn from the Coupled Model Intercomparison Project (CMIP) experiments, CMIP5, CMIP6, and HighResMIP. We show that these geopotential-jet regimes are particularly suited to the analysis of climate data, with considerable reductions in sampling variability compared to classical regime approaches. We find that CMIP6 has a considerably improved spatial regime structure, and a more trimodal eddy-driven jet, relative to CMIP5, but it still struggles with under-persistent regimes and too little European blocking when compared to reanalysis. Reduced regime persistence can be understood, at least in part, as a result of jets that are too fast and eddy feedbacks on the jet stream that are too weak – structural errors that do not noticeably improve in higher-resolution models. [ABSTRACT FROM AUTHOR]

In 2016–2018, the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) project undertook 3-month-long deployments to the southeastern (SE) Atlantic Ocean using research aircraft to better understand the impact of biomass burning (BB) aerosol transport to the SE Atlantic Ocean on climate. In this (part 1 of the meteorological overview) paper, the climatological features at monthly timescales are investigated. The southern African easterly jet (AEJ-S), defined as the zonal easterlies over 600–700 hPa exceeding 6 m s -1 around 5–15 ∘ S, is a characteristic feature of the mid-level circulation over southern Africa that was also during the deployment months of August 2017, September 2016, and October 2018. Climatologically, the AEJ-S develops at lower altitudes (∼ 3 km; 700 hPa) between 5–10 ∘ S in August, while it develops at around 4 km (∼ 600 hPa) and further south (5–15 ∘ S) in September and October, largely driven by the strong sensible heating over the African plateau. Notable meteorological anomalous characteristics during the 3 deployment months, compared to climatology (2000–2018), include the following: (1) during August 2017, the AEJ-S was weaker than the climatological mean, with an additional anomalous upper-level jet aloft (∼ 6 km) around 10 ∘ S. August 2017 was also drier over the SE Atlantic at 600–700 hPa than climatology, with a stronger Benguela low-level jet (LLJ) at 925–950 hPa along the Namibian coast of the SE Atlantic. Consistent with this, the southern Atlantic anticyclone was also stronger and closer to the coast than the August climatological mean. (2) During September 2016, the AEJ-S intensity was similar to the climatological mean, although the heat low and vertical motion over the land was slightly stronger compared to the September climatology. The LLJ and the large-scale southern Atlantic anticyclone were stronger than the climatological mean. (3) During October 2018, the AEJ-S was slightly weaker compared to the climatological mean, as was the LLJ and the southern Atlantic anticyclone. October 2018 was wetter over the Benguela coastal region at 600 hPa than the climatological mean. During all the deployment months, the sea surface temperatures (SST) over the SE Atlantic were warmer than the climatological means, but the monthly mean low cloud fraction was only noticeably reduced in August 2017. A weak August 2017 AEJ-S can explain low offshore black carbon (BC) mixing ratios within the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, although the BC peak altitude, at 2–3 km, is below that of the AEJ-S. The upper-level wave disturbance and the associated anomalous circulation also explain the weakening of AEJ-S through the reduction of the strength of the heat low over the land during August 2017. [ABSTRACT FROM AUTHOR]

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts to better understand past, present and future climate changes in a multi-model context. A total of 21 Model Intercomparison Projects (MIPs) were endorsed in its 6th phase (CMIP6), which included 190 different experiments that were used to simulate 40000 years and produced around 40 PB of data in total. This paper shows the main results obtained from the collection of performance metrics done for CMIP6 (CPMIP). The document provides the list of partners involved, the CPMIP metrics per institution/model, and the approach used for the collection and the coordination behind this process. Furthermore, a section has been included to analyze the results and prove the usefulness of the metrics for the community. Moreover, we describe the main difficulties faced during the collection and propose recommendations for future exercises. [ABSTRACT FROM AUTHOR]

HydroGFD3 (Hydrological Global Forcing Data) is a data set of bias-adjusted reanalysis data for daily precipitation and minimum, mean, and maximum temperature. It is mainly intended for large-scale hydrological modelling but is also suitable for other impact modelling. The data set has an almost global land area coverage, excluding the Antarctic continent and small islands, at a horizontal resolution of 0.25 ∘ , i.e. about 25 km. It is available for the complete ERA5 reanalysis time period, currently 1979 until 5 d ago. This period will be extended back to 1950 once the back catalogue of ERA5 is available. The historical period is adjusted using global gridded observational data sets, and to acquire real-time data, a collection of several reference data sets is used. Consistency in time is attempted by relying on a background climatology and only making use of anomalies from the different data sets. Precipitation is adjusted for mean bias as well as the number of wet days in a month. The latter is relying on a calibrated statistical method with input only of the monthly precipitation anomaly such that no additional input data about the number of wet days are necessary. The daily mean temperature is adjusted toward the monthly mean of the observations and applied to 1 h time steps of the ERA5 reanalysis. Daily mean, minimum, and maximum temperature are then calculated. The performance of the HydroGFD3 data set is on par with other similar products, although there are significant differences in different parts of the globe, especially where observations are uncertain. Further, HydroGFD3 tends to have higher precipitation extremes, partly due to its higher spatial resolution. In this paper, we present the methodology, evaluation results, and how to access the data set at 10.5281/zenodo.3871707. [ABSTRACT FROM AUTHOR]

The three IASI instruments on-board the Metop family of satellites have been sounding the atmospheric composition since 2006. More than 30 atmospheric gases can be measured from the IASI radiance spectra, allowing the improvement of weather forecasting, and the monitoring of atmospheric chemistry and climate variables. The early detection of extreme events such as fires, pollution episodes, volcanic eruptions, or industrial releases is key to take safety measures to protect inhabitants and the environment in the impacted areas. With its near real time observations and good horizontal coverage, IASI can contribute to the series of monitoring systems for the systematic and continuous detection of exceptional atmospheric events, in order to support operational decisions. In this paper, we describe a new approach for the near real time detection and characterization of unexpected events, which relies on the principal component analysis (PCA) of IASI radiance spectra. By analysing both the IASI raw and compressed spectra, we applied a PCA-granule based method on various past well documented extreme events such as volcanic eruptions, fires, anthropogenic pollutions and industrial accidents. We demonstrate that the method is well suited to detect spectral signatures for reactive and weak absorbing gases, even for sporadic events. Long-term records are also generated for fire and volcanic events, by analysing the available IASI/Metop-B data record. [ABSTRACT FROM AUTHOR]

Water productivity (or efficiency) data informs water policy, zoning and planning along with water allocation decisions under water scarcity pressure. This paper demonstrates that different water productivity metrics lead to different conclusions about who is using water more effectively. In addition to supporting the population's drinking and sanitation needs, water generates many other public and private social, environmental, and economic values. For the group of municipalities comprising the Phoenix Metropolitan Area we compare several water productivity metrics by calculating the Water Value Intensity (WVI) of potable water delivered by the municipality to its residential and non-residential customers. Core cities with more industrial water uses are less productive by the conventional efficiency measure of water used per capita, but core cities generate more tax revenues, business revenues, and payroll revenues per unit of water delivered, achieving a higher water productivity by these measures. We argue that policymakers should consider a more diverse set of socio-economic water productivity measures to ensure that a broader set of values are represented in water allocation policies. [ABSTRACT FROM AUTHOR]

Meteorological fronts are important due to their associated surface impacts, including extreme precipitation and extreme winds. Objective identification of fronts is therefore of interest in both operational weather prediction and research settings. The aim of this study is to produce a front identification algorithm based on earlier studies that is portable and scalable to different resolution datasets. We have made a number of changes to an earlier objective front identification algorithm, applied these to reanalysis datasets, and present the improvements associated with these changes. First, we show that a change in the order of operations yields smoother fronts with fewer breaks. Next, we propose the selection of the front identification thresholds in terms of climatological quantiles of the threshold fields. This allows for comparison between datasets of differing resolutions. Finally, we include a number of numerical improvements in the implementation of the algorithm and better handling of short fronts, which yield further benefits in the smoothness and number of breaks. This updated version of the algorithm has been made fully portable and scalable to different datasets in order to enable future climatological studies of fronts and their impacts. [ABSTRACT FROM AUTHOR]

Ice clouds are a crucial component of the Earth's weather system, and their representation remains a principal challenge for current weather and climate models. Several past and future satellite missions were explicitly designed to provide observations offering new insights into cloud processes, but these specialized cloud sensors are limited in their spatial and temporal coverage. Geostationary satellites have been observing clouds for several decades and can ideally complement the sparse measurements from specialized cloud sensors. However, the geostationary observations that are continuously and globally available over the full observation record are restricted to a small number of wavelengths, which limits the information they can provide on clouds. The Chalmers Cloud Ice Climatology (CCIC) is a novel cloud-property dataset that aims to provide an improved climate record of ice hydrometeor concentrations by applying state-of-the-art machine-learning techniques to retrieve ice cloud properties from globally gridded, single-channel geostationary observations that are readily available from 1980 onwards. CCIC offers a novel perspective on the record of geostationary IR observations by providing spatially and temporally continuous retrievals of the vertically integrated and vertically resolved concentrations of frozen hydrometeors, typically referred to as ice water path (IWP) and ice water content (IWC). In addition to that, CCIC provides 2D and 3D cloud masks and a 3D cloud classification. A fully convolutional quantile regression neural network constitutes the core of the CCIC retrieval, providing probabilistic estimates of IWP and IWC. The network is trained against CloudSat retrievals using 3.5 years of global collocations. Assessed on a held-out test dataset, the CCIC-provided IWP and IWC estimates achieve correlations exceeding 0.7 and 0.6, respectively, and biases better than - 5 % and - 2 % demonstrating considerable skill in estimating both IWP and IWC. In addition, CCIC is extensively validated against both in situ and remote sensing measurements from two flight campaign series and a ground-based radar. The results of this independent validation confirm the ability of CCIC to retrieve IWP and IWC. CCIC thus ideally complements temporally and spatially more limited measurements from dedicated cloud sensors by providing spatially and temporally continuous estimates of ice cloud properties. The CCIC network and its associated software are made accessible to the scientific community. [ABSTRACT FROM AUTHOR]

The eastern equatorial Atlantic (EEA) seasonal cycle and interannual variability strongly influence the climate of the surrounding continents. It is thus crucial that models used in both climate predictions and future climate projections are able to simulate them accurately. In that context, the EEA monthly climatology and interannual variability are evaluated over the period 1985–2004 for models participating in the Ocean Model Intercomparison Project Phases 1 and 2 (OMIP1 and OMIP2). The main difference between OMIP1 and OMIP2 simulations is their atmospheric forcing: CORE-II and JRA55-do, respectively. Monthly climatologies of the equatorial Atlantic zonal wind, sea level anomaly, and sea surface temperature in OMIP1 and OMIP2 are comparable to reanalysis products. Yet, some discrepancies exist in both OMIP ensembles: the thermocline is too diffusive, and there is a lack of cooling during the development of the Atlantic cold tongue. The EEA interannual sea surface temperature variability during May–June–July in the OMIP1 ensemble mean is found to be 51 % larger (0.62 ± 0.04 °C) than that in the OMIP2 ensemble mean (0.41 ± 0.03 °C). Likewise, the May–June–July interannual sea surface height variability in the EEA is 33 % larger in the OMIP1 ensemble mean (0.02 ± 0.002 m) than in the OMIP2 ensemble mean (0.015 ± 0.002 m). Sensitivity experiments demonstrate that the discrepancies in interannual sea surface temperatures and sea surface height variabilities between OMIP1 and OMIP2 are mainly attributable to their wind forcings and, specifically, to their variability. While the April–May–June zonal wind variability in the western equatorial Atlantic is similar in both forcings, the zonal wind variability peaks in April for JRA55-do and in May for CORE-II. [ABSTRACT FROM AUTHOR]

Understanding extreme precipitation events (EPEs) and their underlying dynamical processes and moisture transport patterns is essential to mitigating EPE-related risks. In this study, we investigate the dynamics of 82 EPEs (≥100 mm d -1) over the territory of Ukraine in the recent decades (1979–2019), of which the majority occurred in summer. The EPEs are identified based on precipitation observations from 215 meteorological stations and posts in Ukraine. The atmospheric variables for the case study analysis of selected EPEs and for climatological composites and trajectory calculations were taken from ERA5 reanalyses. Moisture sources contributing to the EPEs in Ukraine are identified with kinematic backward trajectories and the subsequent application of a moisture source identification scheme based on the humidity mass budget along these trajectories. The large-scale atmospheric circulation associated with EPEs was studied for a selection of representative EPEs in all seasons and with the aid of composites of all events per season. Results show that EPEs in summer occur all across Ukraine, but in other seasons EPE hotspots are mainly in the Carpathians and along the Black Sea and Sea of Azov. All EPEs were associated with a surface cyclone, with most having an upper-level trough, except for the winter events that occurred in situations with very strong westerly jets. Isentropic potential vorticity anomalies associated with EPEs in Ukraine show clear dipole structures in all seasons, however, interestingly with a different orientation of these anomaly dipoles between seasons. The analysis of moisture sources revealed a very strong case-to-case variability and often a combination of local and remote sources. Oceanic sources dominate in winter, but land evapotranspiration accounts for 60 %–80 % of the moisture that rains out in EPEs in the other seasons. Taken together, these findings provide a novel insight into large-scale characteristics of EPEs in Ukraine, a region with a unique geographical setting and with moisture sources as diverse as Newfoundland, the Azores, the Caspian Sea, and the Arctic Ocean. [ABSTRACT FROM AUTHOR]

Trace gases and aerosols play a crucial role in shaping Arctic climate through their impacts on radiation and chemistry. The concentration of these substances over the Arctic is largely determined by long-range transport originating from midlatitude and tropical source regions. In this study, we explore how atmospheric circulation modulates the interannual variability of long-range transport into the Arctic by utilizing a chemistry–climate model. Idealized tracers, which have fixed lifetimes and spatially varying but temporally fixed surface emissions corresponding to the climatology of anthropogenic emissions of the year 2000, are employed to isolate the role of atmospheric transport from emission and chemistry in modulating interannual variability. Tracers emitted from different source regions are tagged to quantify their relative contributions. Model simulations reveal that tracers from Europe, East Asia, and North America contribute the most to Arctic tracer mass, followed by those from the Tibetan Plateau and South Asia, as well as the Middle East. These regional tracers are predominantly transported into the Arctic middle to upper troposphere, with the exception of tracers from Europe during winter, which are transported into the Arctic lower troposphere. Our analysis shows that the interannual variability of transport into the Arctic for each regional tracer is determined by the atmospheric circulation over the corresponding emission region; i.e., anomalous poleward and eastward winds over the source region promote transport into the Arctic. Considering tracers with global emissions, a southward shift of the midlatitude jet during winter favors increased transport into the Arctic, particularly for tracers emitted over Asia, aligning with previous studies. Comparisons of tracers with different lifetimes indicate that the interannual variability of shorter lifetime tracers is predominantly influenced by regional tracers with shorter transport pathways into the Arctic (e.g., Europe), while the interannual variability of longer lifetime tracers is more contributed by regional tracers with higher emissions (e.g., East Asia). [ABSTRACT FROM AUTHOR]

Variations in the isotopologic composition of water vapour are fundamental for understanding the relative importance of different mechanisms of water vapour transport from the tropical upper troposphere to the lower stratosphere. Previous comparisons obtained from observations of H2O and HDO by satellite instruments showed discrepancies. In this work, newer versions of H2O and HDO retrievals from Envisat/MIPAS and SCISAT/ACE-FTS are compared. Specifically, MIPAS-IMK V5, MIPAS-ESA V8 and ACE-FTS V4.1/4.2 for the common period from February 2004 to April 2012 are compared for the first time through a profile-to-profile approach and comparison based on climatological structures. The comparison is essential for the scientific community to assess the quality of new satellite data products, a necessary procedure to validate further scientific work. Averaged stratospheric H2O profiles reveal general good agreement between 16 and 30 km. Biases derived from the profile-to-profile comparison are around zero between 16 and 30 km for MIPAS-IMK and ACE-FTS comparison. For HDO and δ D, low biases are found in the MIPAS-ESA and ACE-FTS comparison in the same range of altitudes, even if associated with a larger de-biased standard deviation. The zonally averaged cross sections of H2O and HDO exhibit the expected distribution that has been established in previous studies. For δ D the tropical depletion in MIPAS-ESA occurs at the top of the dynamical tropopause, but this minimum is found at higher altitudes in the ACE-FTS and MIPAS-IMK dataset. The tape recorder signal is present in H2O and HDO for the three databases with slight quantitative differences. The δ D annual variation for ACE-FTS data and MIPAS-ESA data is weaker compared to the MIPAS-IMK dataset, which shows a coherent tape recorder signal clearly detectable up to at least 30 km. The observed differences in the climatological δ D composites between databases could lead to different interpretations regarding the water vapour transport processes toward the stratosphere. Therefore, it is important to further improve the quality of level 2 products. [ABSTRACT FROM AUTHOR]

This study investigates the validity of wine must quality as an April-to-August temperature proxy between 1420 and 2019 based on expert ratings and quality measurements from Germany, Luxembourg, eastern France, and the Swiss Plateau. This is highly relevant as uncertainties remain on past climate variations during this period. The evidence was reviewed according to the best practice of historical climatology. Expert ratings tended to agree with Oechsle density measurements that gradually replaced them from the 1840s. A statistical model calibrated to predict wine must quality from climate data explains 75 % of the variance, underlining the potential value of wine must quality as a climate proxy. Premium crops were collected in years of early harvest involving high insolation during maturation, while poor crops resulted from very late harvests in cold and wet summers. An analysis of daily weather types for high- and low-quality years after 1763 shows marked differences. On a decadal timescale, the average quality was highest from 1470 to 1479, from 1536 to 1545, and from 1945 to 1954. Poor crops were collected in periods with prevailing cold and wet summers such as 1453 to 1466, 1485 to 1494, 1585 to 1614, 1685 to 1703, 1812 to 1821, and 1876 to 1936. In the period of enhanced warming after 1990, high quality became the rule. [ABSTRACT FROM AUTHOR]

A climatology of snow water equivalent (SWE) based on data collected at 240 gauging sites was performed for the Italian Alps over the 1967–2020 period, when Enel routinely conducted snow depth and density measurements with homogeneous methods. Six hydrological sub-regions were investigated spanning from the eastern Alps to the western Alps at altitudes ranging from 1000 to 3000 m a.s.l. Measurements were conducted at fixed dates at the beginning of each month from 1 February to 1 June and on 15 April. To our knowledge, this is the most comprehensive and homogeneous dataset of measured snow depth and density for the Italian Alps. Significant decreasing trends over the years at all fixed dates and elevation classes were identified for both snow depth, equal to -0.12 ± 0.06 m per decade, and snow water equivalent, equal to -51 ± 37 mm per decade, on average in the six macro-basins we selected. The analysis of bulk snow density data showed a temporal evolution along the snow accumulation and melt season, but no altitudinal trends were found. A Moving Average and Running Trend Analysis (MARTA triangles), combined with a Pettitt's test change-point detection, highlighted a decreasing change of snow climatology occurring around the end of the 1980s. The comparison with winter temperature and precipitation data from the HISTALP dataset identified a major role played by temperature on the long-term decrease and changing points of snow depth and SWE with respect to precipitation, mainly responsible for its variability. Correlation with climatic indexes indicates significant negative values of the Pearson correlation coefficient with winter North Atlantic Oscillation (NAO) and positive values with winter Western Mediterranean Oscillation (WeMO) for some areas and elevation classes. Results of this climatology are synthesized in a temporal polynomial model that is useful for climatological studies and water resources management in mountain areas. [ABSTRACT FROM AUTHOR]

We present the new Cloud Class Climatology (CCClim) dataset, quantifying the global distribution of established morphological cloud types over 35 years. CCClim combines active and passive sensor data with machine learning (ML) and provides a new opportunity for improving the understanding of clouds and their related processes. CCClim is based on cloud property retrievals from the European Space Agency's (ESA) Cloud_cci dataset, adding relative occurrences of eight major cloud types, designed to be similar to those defined by the World Meteorological Organization (WMO) at 1° resolution. The ML framework used to obtain the cloud types is trained on data from multiple satellites in the afternoon constellation (A-Train). Using multiple spaceborne sensors reduces the impact of single-sensor problems like the difficulty of passive sensors to detect thin cirrus or the small footprint of active sensors. We leverage this to generate sufficient labeled data to train supervised ML models. CCClim's global coverage being almost gapless from 1982 to 2016 allows for performing process-oriented analyses of clouds on a climatological timescale. Similarly, the moderate spatial and temporal resolutions make it a lightweight dataset while enabling straightforward comparison to climate models. CCClim creates multiple opportunities to study clouds, of which we sketch out a few examples. Along with the cloud-type frequencies, CCClim contains the cloud properties used as inputs to the ML framework, such that all cloud types can be associated with relevant physical quantities. CCClim can also be combined with other datasets such as reanalysis data to assess the dynamical regime favoring the occurrence of a specific cloud type in association with its properties. Additionally, we show an example of how to evaluate a global climate model by comparing CCClim with cloud types obtained by applying the same ML method used to create CCClim to output from the icosahedral nonhydrostatic atmosphere model (ICON-A). CCClim can be accessed via the following digital object identifier: 10.5281/zenodo.8369202. [ABSTRACT FROM AUTHOR]

There are indications that the reference climatology underlying meteorological drought has shown nonstationarity at seasonal, decadal, and centennial timescales, impacting the calculation of drought indices and potentially having ecological and economic consequences. Analyzing these trends in meteorological drought climatology beyond 100 years, a time frame which exceeds the available period of observation data, contributes to a better understanding of the nonstationary changes, ultimately determining whether they are within the range of natural variability or outside this range. To accomplish this, our study introduces a novel approach to integrate unevenly scaled tree-ring proxy data from the North American Seasonal Precipitation Atlas (NASPA) with instrumental precipitation datasets by first temporally downscaling the proxy data to produce a regular time series and then modeling climate nonstationarity while simultaneously correcting model-induced bias. This new modeling approach was applied to 14 sites across the continental United States using the 3-month standardized precipitation index (SPI) as a basis. The findings showed that certain locations have experienced recent rapid shifts towards drier or wetter conditions during the instrumental period compared to the past 1000 years, with drying trends generally found in the west and wetting trends in the east. This study also found that seasonal shifts have occurred in some regions recently, with seasonality changes most notable for southern gauges. We expect that our new approach provides a foundation for incorporating various datasets to examine nonstationary variability in long-term precipitation climatology and to confirm the spatial patterns noted here in greater detail. [ABSTRACT FROM AUTHOR]

The acquisition and dissemination of essential information for understanding global biogeochemical interactions between the atmosphere and ecosystems and how climate–ecosystem feedback loops may change atmospheric composition in the future comprise a fundamental prerequisite for societal resilience in the face of climate change. In particular, the detection of trends and seasonality in the abundance of greenhouse gases and short-lived climate-active atmospheric constituents is an important aspect of climate science. Therefore, easy and fast access to reliable, long-term, and high-quality observational environmental data is recognised as fundamental to research and the development of environmental forecasting and assessment services. In our opinion article, we discuss the potential role that environmental research infrastructures in Europe (ENVRI RIs) can play in the context of an integrated global observation system. In particular, we focus on the role of the atmosphere-centred research infrastructures ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure), IAGOS (In-service Aircraft for a Global Observing System), and ICOS (Integrated Carbon Observation System), also referred to as ATMO-RIs, with their capabilities for standardised collection and provision of long-term and high-quality observational data, complemented by rich metadata. The ATMO-RIs provide data through open access and offer data interoperability across different research fields including all fields of environmental sciences and beyond. As a result of these capabilities in data collection and provision, we elaborate on the novel research opportunities in atmospheric sciences which arise from the combination of open-access and interoperable observational data, tools, and technologies offered by data-intensive science and the emerging collaboration platform ENVRI-Hub, hosted by the European Open Science Cloud (EOSC). [ABSTRACT FROM AUTHOR]

Climate science depends upon accurate measurements of air temperature and humidity, the majority of which are still derived from sensors exposed within passively-ventilated louvred Stevenson-type thermometer screens. It is well-documented that, under certain circumstances, air temperatures measured within such screens can differ significantly from 'true' air temperatures measured by other methods, such as aspirated sensors. Passively-ventilated screens depend upon wind motion to provide ventilation within the screen, and thus airflow over the sensors contained therein. Consequently, instances of anomalous temperatures occur most often during light winds when airflow through the screen is weakest, particularly when in combination with strong or low-angle incident solar radiation. Adequate ventilation is essential for reliable and consistent measurements of both air temperature and humidity, yet very few systematic comparisons to quantify relationships between external wind speed and airflow within a thermometer screen have been made. This paper addresses that gap by summarising the results of a three month field experiment in which airflow within a UK-standard Stevenson screen was measured using a sensitive sonic anemometer, and comparisons made using simultaneous wind speed and direction records from the same site. The average in-screen ventilation rate was found to be 0.2 m s-1, well below the 1 m s-1 minimum assumed in meteorological and design standard references, and only about 7 % of the scalar mean wind speed at 10 m. The implications of low in-screen ventilation on the uncertainty of air temperature and humidity measurements from Stevenson-type thermometer screens are discussed, particularly those due to the differing response times of dry- and wet-bulb temperature sensors, and ambiguity in the value of the psychrometric coefficient. [ABSTRACT FROM AUTHOR]

The contrail formation potential as well as its temporal and spatial distribution is estimated using meteorological conditions of temperature and relative humidity from the ERA5 re-analysis provided by the European Centre for Medium-Range Weather Forecasts. Contrail formation is estimated with the Schmidt–Appleman criterion (SAc), solely considering thermodynamic effects. The focus is on a region ranging from the Eastern United States (110–65° W) to central Europe (5° W–30° E). Around 18 000 flight trajectories from the In-service Aircraft for a Global Observing System (IAGOS) are used as a representative subset of transatlantic, commercial flights. The typical crossing distance through a contrail-prone area is determined based on IAGOS measurements of temperature T and relative humidity r and then based on co-located ERA5 simulations of the same quantities. Differences in spatial resolution between IAGOS and ERA5 are addressed from an aircraft-centered perspective, using 1 km segments, and a model-centered perspective, using 19 km flight sections. Using the aircraft-centered approach, 50 % of the crossings of persistent contrail (PC) regions based on IAGOS are shorter than 9 km, while in ERA5 the median is 155 km. Time-averaged IAGOS data lead to a median crossing length of 66 km. The difference between the two data sets is attributed to the higher variability of r in IAGOS compared to ERA5. The model-centered approach yields similar results, but typical crossing lengths are larger by only up to 10 %. Binary masks of PC formation are created by applying the SAc on the two-dimensional fields of T and r from ERA5. In a second step the morphology of PC regions is also assessed. Half of the PC regions in ERA5 are found to be smaller than ≈ 35 000 km2 (at 200 hPa), and the median of the maximum dimension is shorter than 760 km (at 200 hPa). Furthermore, PC regions tend to be of near-circular shape with a tendency to a slight oval shape and a preferred alignment along the dominant westerly flow. Seasonal, vertical distributions of PC formation potential P are characterized by a maximum between 250 and 200 hPa. P is subject to seasonal variations with a maximum in magnitude and extension during the winter months and a minimum during summer. The horizontal distribution of PC regions suggests that PC regions are likely to appear in the same location on adjacent pressure levels. Climatologies of T , r , wind speed U , and resulting PC formation potential are calculated to identify the constraining effects of T and r on P. PC formation is primarily limited by conditions that are too warm below and conditions that are too dry above the formation region. The distribution of PCs is slanted towards lower altitudes from 30 to 70° N, following lines of constant T and r. For an observed co-location of high U and P , it remains unclear whether PC formation and the jet stream are favored by the same meteorological conditions or if the jet stream itself favors PC occurrence. This analysis suggests that some PC regions will be difficult to avoid by rerouting aircraft because of their large vertical and horizontal extents. [ABSTRACT FROM AUTHOR]

Atmospheric waveguides have been linked to amplified atmospheric Rossby waves and therefore to extreme weather events in the mid-latitudes. Waveguides have often been calculated on zonal-mean data, and/or on timescales of a month or longer. Here, I develop an objective algorithm to detect barotropic waveguides, and create a dataset of time- and spatially-varying waveguides in both summer and winter for both the Northern and Southern Hemisphere (NH/SH), including a metric of waveguide depth. In this dataset, waveguides for waves of zonal wavenumber 5 exist in the extra-tropics on more than 40 % of days across many longitudes, with the frequency of occurrence reducing for higher zonal wavenumbers. Waveguides tend to be more frequent, and deeper, in summer than in winter, and more frequent in the NH than the SH. Composites of days with high spatial mean waveguide depth over particular regions show a double jet structure associated with strong waveguide occurrence, consistent with previous research. Significant positive correlations exist between waveguide depth and the presence/strength of quasi-stationary waves. In the SH these correlations are strong across much of the mid-latitudes in both seasons, whilst in the NH significant correlations are found only over the Atlantic, Europe and Asia during NH summer, with the strongest correlations over the Atlantic and western Europe, a region notable for its strong positive trend in extreme heat temperature events in recent decades. [ABSTRACT FROM AUTHOR]