Showing papers by "Cooperative Institute for Research in the Atmosphere published in 2020"

Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability

Abstract: Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have often been used within the geosciences to most accurately identify a desired output given a set of inputs, with the interpretation of what the network learns used as a secondary metric to ensure the network is making the right decision for the right reason. Neural network interpretation techniques have become more advanced in recent years, however, and we therefore propose that the ultimate objective of using a neural network can also be the interpretation of what the network has learned rather than the output itself. We show that the interpretation of neural networks can enable the discovery of scientifically meaningful connections within geoscientific data. In particular, we use two methods for neural network interpretation called backwards optimization and layerwise relevance propagation, both of which project the decision pathways of a network back onto the original input dimensions. To the best of our knowledge, LRP has not yet been applied to geoscientific research, and we believe it has great potential in this area. We show how these interpretation techniques can be used to reliably infer scientifically meaningful information from neural networks by applying them to common climate patterns. These results suggest that combining interpretable neural networks with novel scientific hypotheses will open the door to many new avenues in neural network-related geoscience research.

GrSMBMIP: intercomparison of the modelled 1980-2012 surface mass balance over the Greenland Ice Sheet

Abstract: . The Greenland Ice Sheet (GrIS) mass loss has been accelerating at a rate of about 20 ± 10 Gt/yr2 since the end of the 1990's, with around 60 % of this mass loss directly attributed to enhanced surface meltwater runoff. However, in the climate and glaciology communities, different approaches exist on how to model the different surface mass balance (SMB) components using: (1) complex physically-based climate models which are computationally expensive; (2) intermediate complexity energy balance models; (3) simple and fast positive degree day models which base their inferences on statistical principles and are computationally highly efficient. Additionally, many of these models compute the SMB components based on different spatial and temporal resolutions, with different forcing fields as well as different ice sheet topographies and extents, making inter-comparison difficult. In the GrIS SMB model intercomparison project (GrSMBMIP) we address these issues by forcing each model with the same data (i.e., the ERA-Interim reanalysis) except for two global models for which this forcing is limited to the oceanic conditions, and at the same time by interpolating all modelled results onto a common ice sheet mask at 1 km horizontal resolution for the common period 1980–2012. The SMB outputs from 13 models are then compared over the GrIS to (1) SMB estimates using a combination of gravimetric remote sensing data from GRACE and measured ice discharge, (2) ice cores, snow pits, in-situ SMB observations, and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Our results reveal that the mean GrIS SMB of all 13 models has been positive between 1980 and 2012 with an average of 340 ± Gt/yr, but has decreased at an average rate of −7.3 Gt/yr2 (with a significance of 96 %), mainly driven by an increase of 8.0 Gt/yr2 (with a significance of 98 %) in meltwater runoff. Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting the need for accurate representation of the GrIS ablation zone extent and processes driving the surface melt. In addition, a higher density of in-situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 mWE/yr due to large discrepancies in modelled snowfall accumulation. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of same order than RCMs with observations and remain then useful tools for long-term simulations. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present day SMB relative to observations, suggesting that biases are not systematic among models.

A Lagrangian Snow-Evolution System for Sea-Ice Applications (SnowModel-LG): Part I—Model Description

Abstract: A Lagrangian snow-evolution model (SnowModel-LG) was used to produce daily, pan-Arctic, snow-on-sea-ice, snow property distributions on a 25 × 25-km grid, from 1 August 1980 through 31 July 2018 (38 years). The model was forced with NASA's Modern Era Retrospective-Analysis for Research and Applications-Version 2 (MERRA-2) and European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis-5th Generation (ERA5) atmospheric reanalyses, and National Snow and Ice Data Center (NSIDC) sea ice parcel concentration and trajectory data sets (approximately 61,000, 14 × 14-km parcels). The simulations performed full surface and internal energy and mass balances within a multilayer snowpack evolution system. Processes and features accounted for included rainfall, snowfall, sublimation from static-surfaces and blowing-snow, snow melt, snow density evolution, snow temperature profiles, energy and mass transfers within the snowpack, superimposed ice, and ice dynamics. The simulations produced horizontal snow spatial structures that likely exist in the natural system but have not been revealed in previous studies spanning these spatial and temporal domains. Blowing-snow sublimation made a significant contribution to the snowpack mass budget. The superimposed ice layer was minimal and decreased over the last four decades. Snow carryover to the next accumulation season was minimal and sensitive to the melt-season atmospheric forcing (e.g., the average summer melt period was 3 weeks or 50% longer with ERA5 forcing than MERRA-2 forcing). Observed ice dynamics controlled the ice parcel age (in days), and ice age exerted a first-order control on snow property evolution.

Evaluation, Tuning, and Interpretation of Neural Networks for Working with Images in Meteorological Applications

Abstract: The method of neural networks (aka deep learning) has opened up many new opportunities to utilize remotely sensed images in meteorology. Common applications include image classification, e.g., to determine whether an image contains a tropical cyclone, and image-to-image translation, e.g., to emulate radar imagery for satellites that only have passive channels. However, there are yet many open questions regarding the use of neural networks for working with meteorological images, such as best practices for evaluation, tuning and interpretation. This article highlights several strategies and practical considerations for neural network development that have not yet received much attention in the meteorological community, such as the concept of receptive fields, underutilized meteorological performance measures, and methods for neural network interpretation, such as synthetic experiments and layer-wise relevance propagation. We also consider the process of neural network interpretation as a whole, recognizing it as an iterative meteorologist-driven discovery process that builds on experimental design and hypothesis generation and testing. Finally, while most work on neural network interpretation in meteorology has so far focused on networks for image classification tasks, we expand the focus to also include networks for image-to-image translation. 12

A global analysis of climate-relevant aerosol properties retrieved from the network of Global Atmosphere Watch (GAW) near-surface observatories

Abstract: . Aerosol particles are essential constituents of the Earth's atmosphere, impacting the earth radiation balance directly by scattering and absorbing solar radiation, and indirectly by acting as cloud condensation nuclei. In contrast to most greenhouse gases, aerosol particles have short atmospheric residence times, resulting in a highly heterogeneous distribution in space and time. There is a clear need to document this variability at regional scale through observations involving, in particular, the in situ near-surface segment of the atmospheric observation system. This paper will provide the widest effort so far to document variability of climate-relevant in situ aerosol properties (namely wavelength dependent particle light scattering and absorption coefficients, particle number concentration and particle number size distribution) from all sites connected to the Global Atmosphere Watch network. High-quality data from almost 90 stations worldwide have been collected and controlled for quality and are reported for a reference year in 2017, providing a very extended and robust view of the variability of these variables worldwide. The range of variability observed worldwide for light scattering and absorption coefficients, single-scattering albedo, and particle number concentration are presented together with preliminary information on their long-term trends and comparison with model simulation for the different stations. The scope of the present paper is also to provide the necessary suite of information, including data provision procedures, quality control and analysis, data policy, and usage of the ground-based aerosol measurement network. It delivers to users of the World Data Centre on Aerosol, the required confidence in data products in the form of a fully characterized value chain, including uncertainty estimation and requirements for contributing to the global climate monitoring system.

A decade of GOSAT Proxy satellite CH 4 observations

Abstract: . This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction ( XCH4 ) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4∕XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of −0.84 ppb. These data are available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb ( Parker and Boesch , 2020 ) .

Indicator patterns of forced change learned by an artificial neural network

Abstract: Many problems in climate science require the identification of signals obscured by both the "noise" of internal climate variability and differences across models. Following previous work, we train an artificial neural network (ANN) to identify the year of input maps of temperature and precipitation from forced climate model simulations. This prediction task requires the ANN to learn forced patterns of change amidst a background of climate noise and model differences. We then apply a neural network visualization technique (layerwise relevance propagation) to visualize the spatial patterns that lead the ANN to successfully predict the year. These spatial patterns thus serve as "reliable indicators" of the forced change. The architecture of the ANN is chosen such that these indicators vary in time, thus capturing the evolving nature of regional signals of change. Results are compared to those of more standard approaches like signal-to-noise ratios and multi-linear regression in order to gain intuition about the reliable indicators identified by the ANN. We then apply an additional visualization tool (backward optimization) to highlight where disagreements in simulated and observed patterns of change are most important for the prediction of the year. This work demonstrates that ANNs and their visualization tools make a powerful pair for extracting climate patterns of forced change.

Multidecadal trend analysis of in situ aerosol radiative properties around the world

Abstract: . In order to assess the evolution of aerosol parameters affecting climate change, a long-term trend analysis of aerosol optical properties was performed on time series from 52 stations situated across five continents. The time series of measured scattering, backscattering and absorption coefficients as well as the derived single scattering albedo, backscattering fraction, scattering and absorption Angstrom exponents covered at least 10 years and up to 40 years for some stations. The non-parametric seasonal Mann–Kendall (MK) statistical test associated with several pre-whitening methods and with Sen's slope was used as the main trend analysis method. Comparisons with general least mean square associated with autoregressive bootstrap (GLS/ARB) and with standard least mean square analysis (LMS) enabled confirmation of the detected MK statistically significant trends and the assessment of advantages and limitations of each method. Currently, scattering and backscattering coefficient trends are mostly decreasing in Europe and North America and are not statistically significant in Asia, while polar stations exhibit a mix of increasing and decreasing trends. A few increasing trends are also found at some stations in North America and Australia. Absorption coefficient time series also exhibit primarily decreasing trends. For single scattering albedo, 52 % of the sites exhibit statistically significant positive trends, mostly in Asia, eastern/northern Europe and the Arctic, 22 % of sites exhibit statistically significant negative trends, mostly in central Europe and central North America, while the remaining 26 % of sites have trends which are not statistically significant. In addition to evaluating trends for the overall time series, the evolution of the trends in sequential 10-year segments was also analyzed. For scattering and backscattering, statistically significant increasing 10-year trends are primarily found for earlier periods (10-year trends ending in 2010–2015) for polar stations and Mauna Loa. For most of the stations, the present-day statistically significant decreasing 10-year trends of the single scattering albedo were preceded by not statistically significant and statistically significant increasing 10-year trends. The effect of air pollution abatement policies in continental North America is very obvious in the 10-year trends of the scattering coefficient – there is a shift to statistically significant negative trends in 2009–2012 for all stations in the eastern and central USA. This long-term trend analysis of aerosol radiative properties with a broad spatial coverage provides insight into potential aerosol effects on climate changes.

Evaluation of climate model aerosol trends with ground-based observations over the last 2 decades – an AeroCom and CMIP6 analysis

Abstract: . This study presents a multi-parameter analysis of aerosol trends over the last two decades at regional and global scales. Regional time series have been computed for a set of nine optical, chemical composition and mass aerosol properties by using the observations of several ground-based networks. From these regional time series the aerosol trends have been derived for different regions of the world. Most of the properties related to aerosol loading exhibit negative trends, both at the surface and in the total atmospheric column. Significant decreases of aerosol optical depth (AOD) are found in Europe, North America, South America and North Africa, ranging from −1.3 %/yr to −3.1 %/yr. An error and representativity analysis of the incomplete observational data has been performed using model data subsets in order to investigate how likely the observed trends represent the actual trends happening in the regions over the full study period from 2000 to 2014. This analysis reveals that significant uncertainty is associated with some of the regional trends due to time and space sampling deficiencies. The set of observed regional trends has then been used for the evaluation of the climate models and their skills in reproducing the aerosol trends. Model performance is found to vary depending on the parameters and the regions of the world. The models tend to capture trends in AOD, column Angstrom exponent, sulfate and particulate matter well (except in North Africa), but show larger discrepancies for coarse mode AOD. The rather good agreement of the trends, across different aerosol parameters between models and observations, when co-locating them in time and space, implies that global model trends, including those in poorly monitored regions, are likely correct. The models can help to provide a global picture of the aerosol trends by filling the gaps in regions not covered by observations. The calculation of aerosol trends at a global scale reveals a different picture from the one depicted by solely relying on ground based observations. Using a model with complete diagnostics (NorESM2) we find a global increase of AOD of about 0.2 %/yr between 2000 and 2014, primarily caused by an increase of the loads of organic aerosol, sulfate and black carbon.

Key rules of life and the fading cryosphere: Impacts in alpine lakes and streams

Abstract: Alpine regions are changing rapidly due to loss of snow and ice in response to ongoing climate change. While studies have documented ecological responses in alpine lakes and streams to these changes, our ability to predict such outcomes is limited. We propose that the application of fundamental rules of life can help develop necessary predictive frameworks. We focus on four key rules of life and their interactions: the temperature dependence of biotic processes from enzymes to evolution; the wavelength dependence of the effects of solar radiation on biological and ecological processes; the ramifications of the non-arbitrary elemental stoichiometry of life; and maximization of limiting resource use efficiency across scales. As the cryosphere melts and thaws, alpine lakes and streams will experience major changes in temperature regimes, absolute and relative inputs of solar radiation in ultraviolet and photosynthetically active radiation, and relative supplies of resources (e.g., carbon, nitrogen, and phosphorus), leading to nonlinear and interactive effects on particular biota, as well as on community and ecosystem properties. We propose that applying these key rules of life to cryosphere-influenced ecosystems will reduce uncertainties about the impacts of global change and help develop an integrated global view of rapidly changing alpine environments. However, doing so will require intensive interdisciplinary collaboration and international cooperation. More broadly, the alpine cryosphere is an example of a system where improving our understanding of mechanistic underpinnings of living systems might transform our ability to predict and mitigate the impacts of ongoing global change across the daunting scope of diversity in Earth's biota and environments.

Local anomalies in the column-averaged dry air mole fractions of carbon dioxide across the globe during the first months of the coronavirus recession.

Abstract: We use a global transport model and satellite retrievals of the carbon dioxide (CO2) column average to explore the impact of CO2 emissions reductions that occurred during the economic downturn at the start of the Covid-19 pandemic. The changes in the column averages are substantial in a few places of the model global grid, but the induced gradients are most often less than the random errors of the retrievals. The current necessity to restrict the quality-assured column retrievals to almost cloud-free areas appears to be a major obstacle in identifying changes in CO2 emissions. Indeed, large changes have occurred in the presence of clouds and, in places that were cloud-free in 2020, the comparison with previous years is hampered by different cloud conditions during these years. We therefore recommend to favor all-weather CO2 monitoring systems, at least in situ, to support international efforts to reduce emissions.

Improving Operational Radar Rainfall Estimates Using Profiler Observations Over Complex Terrain in Northern California

Abstract: Quantitative precipitation estimation (QPE) using operational weather radars in the western United States is still a challenging issue due to the beam blockage in the mountainous areas and complex rainfall microphysics induced by the orographic enhancement. This article aims to improve operational radar rainfall estimates in complex terrain by incorporating auxiliary remote sensing observations. An innovative vertical profile of reflectivity (VPR) correction scheme is developed for operational radar using observations from multiple vertically pointing profilers to represent the vertical structure of precipitation at various locations. A demonstration study in the Russian River basin in Northern California is detailed. Results show that the QPE performance is significantly improved after VPR correction, and this new VPR correction approach is superior to the conventional approach currently applied in the operational radar rainfall system. The normalized standard error of hourly rainfall estimates for the two precipitation events presented in this article is improved by ~20% after applying the proposed VPR correction scheme.

Use of a high-resolution-satellite-based precipitation product in mapping continental-scale rainfall erosivity: A case study of the United States

Abstract: A rainfall erosivity map is useful for understanding the spatial variability of rainfall erosivity, and for identifying regions vulnerable to soil erosion by rainfall. This study addresses a new approach to mapping rainfall erosivity on a continental scale, based on a high-resolution-satellite-based precipitation product—the National Oceanic and Atmospheric and Atmospheric Administration’s Climate Precipitation Center morphing technique (CMORPH). For this purpose, a rainfall erosivity map of the contiguous United States is experimentally developed, and is analyzed from the perspectives of the corresponding hydrological basins and climate features. In general, we conclude that the CMORPH precipitation product is useful for mapping rainfall erosivity on a continental scale. In the contiguous United States, the mean of rainfall erosivity was 1260 MJ mm ha−1 h−1 yr−1, with high variability by region. The coastal regions showed the highest rainfall erosivity, at 20%. The seasonality of the rainfall erosivity was evident in most coastal regions (rainfall erosivity depends on climates). The rainfall erosivity in the tropical climate zone was the highest, whereas it was the lowest in the arid climate zone (and spatially homogeneous). However, corrections were required for improving the accuracy of the CMORPH precipitation in most coastal regions, i.e., to secure a better rainfall erosivity product. Compared to a rain gauge-based rainfall erosivity map, the CMORPH’s rainfall erosivity map tended to underestimate the rainfall erosivity in coastal regions near the Gulf of Mexico and Atlantic Ocean, but overestimated it in coastal regions near the Pacific Ocean.

Wide discrepancies in the magnitude and direction of modeled solar-induced chlorophyll fluorescence in response to light conditions

Abstract: . Recent successes in passive remote sensing of far-red solar-induced chlorophyll fluorescence (SIF) have spurred the development and integration of canopy-level fluorescence models in global terrestrial biosphere models (TBMs) for climate and carbon cycle research. The interaction of fluorescence with photochemistry at the leaf and canopy scales provides opportunities to diagnose and constrain model simulations of photosynthesis and related processes, through direct comparison to and assimilation of tower, airborne, and satellite data. TBMs describe key processes related to the absorption of sunlight, leaf-level fluorescence emission, scattering, and reabsorption throughout the canopy. Here, we analyze simulations from an ensemble of process-based TBM–SIF models (SiB3 – Simple Biosphere Model, SiB4, CLM4.5 – Community Land Model, CLM5.0, BETHY – Biosphere Energy Transfer Hydrology, ORCHIDEE – Organizing Carbon and Hydrology In Dynamic Ecosystems, and BEPS – Boreal Ecosystems Productivity Simulator) and the SCOPE (Soil Canopy Observation Photosynthesis Energy) canopy radiation and vegetation model at a subalpine evergreen needleleaf forest near Niwot Ridge, Colorado. These models are forced with local meteorology and analyzed against tower-based continuous far-red SIF and gross-primary-productivity-partitioned (GPP) eddy covariance data at diurnal and synoptic scales during the growing season (July–August 2017). Our primary objective is to summarize the site-level state of the art in TBM–SIF modeling over a relatively short time period (summer) when light, canopy structure, and pigments are similar, setting the stage for regional- to global-scale analyses. We find that these models are generally well constrained in simulating photosynthetic yield but show strongly divergent patterns in the simulation of absorbed photosynthetic active radiation (PAR), absolute GPP and fluorescence, quantum yields, and light response at the leaf and canopy scales. This study highlights the need for mechanistic modeling of nonphotochemical quenching in stressed and unstressed environments and improved the representation of light absorption (APAR), distribution of light across sunlit and shaded leaves, and radiative transfer from the leaf to the canopy scale.

Evaluating Geostationary Lightning Mapper Flash Rates Within Intense Convective Storms

Abstract: The Geostationary LightningMapper (GLM)marks the first time that lightning observations at storm‐scale resolution are operationally available from geostationary orbit. We evaluate GLM detection efficiency (DE) for a special class of convective storms characterized by anomalous charge structures. These storms are anomalous as their internal layered charge structure departs from the tripole charge structure model, where midlevel negative charge is situated between upper and lower positive charge layers. Anomalous storms are characterized by extreme flash rates, low median flash heights, and intense precipitation. Ground truth information on lightning flash rates is provided by Lightning Mapping Arrays (LMA), which measure VHF radio frequency emissions produced by electrical breakdown. This study contrasts two regions: Colorado, where electrically “anomalous” storms are numerous, and Alabama, where they are rare. This study analyzes GLM DE as a function of the precipitation water path, cloud water path, and lightning properties from LMA. The GLM DE is found to vary with the geometric size of the flash and with cloud water path, the latter depending on flash height and cloud water content. Optical scattering (attenuation) by precipitation‐sized particles does not appear to be a factor since precipitation particles contain much less surface area than cloud particles. The size of the flash is correlated with its optical brightness, and the cloud water path is correlated with optical extinction. Regional differences in GLM DE remain that appear to be related to sensor viewing geometry and day versus night sensitivity differences.

An application of upscaled optimal foraging theory using hidden Markov modelling: year-round behavioural variation in a large arctic herbivore

Abstract: In highly seasonal environments, animals face critical decisions regarding time allocation, diet optimisation, and habitat use. In the Arctic, the short summers are crucial for replenishing body reserves, while low food availability and increased energetic demands characterise the long winters (9–10 months). Under such extreme seasonal variability, even small deviations from optimal time allocation can markedly impact individuals’ condition, reproductive success and survival. We investigated which environmental conditions influenced daily, seasonal, and interannual variation in time allocation in high-arctic muskoxen (Ovibos moschatus) and evaluated whether results support qualitative predictions derived from upscaled optimal foraging theory. Using hidden Markov models (HMMs), we inferred behavioural states (foraging, resting, relocating) from hourly positions of GPS-collared females tracked in northeast Greenland (28 muskox-years). To relate behavioural variation to environmental conditions, we considered a wide range of spatially and/or temporally explicit covariates in the HMMs. While we found little interannual variation, daily and seasonal time allocation varied markedly. Scheduling of daily activities was distinct throughout the year except for the period of continuous daylight. During summer, muskoxen spent about 69% of time foraging and 19% resting, without environmental constraints on foraging activity. During winter, time spent foraging decreased to 45%, whereas about 43% of time was spent resting, mediated by longer resting bouts than during summer. Our results clearly indicate that female muskoxen follow an energy intake maximisation strategy during the arctic summer. During winter, our results were not easily reconcilable with just one dominant foraging strategy. The overall reduction in activity likely reflects higher time requirements for rumination in response to the reduction of forage quality (supporting an energy intake maximisation strategy). However, deep snow and low temperatures were apparent constraints to winter foraging, hence also suggesting attempts to conserve energy (net energy maximisation strategy). Our approach provides new insights into the year-round behavioural strategies of the largest Arctic herbivore and outlines a practical example of how to approximate qualitative predictions of upscaled optimal foraging theory using multi-year GPS tracking data.

Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003--2018) for carbon and climate applications

Abstract: . Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide ( CO2 ) and methane ( CH4 ), denoted XCO2 and XCH4 , respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO2 ) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5 ∘ × 5 ∘ data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO2 or XCH4 , corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO2 Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error ( 1σ ): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy ( 1σ ): 0.66 ppm (0.70 ppm). The corresponding values for the XCH4 products are single-observation random error ( 1σ ): 17.4 ppb (monthly: 8.7 ppb); global bias: −2.0 ppb ( −2.9 ppb); and spatiotemporal bias ( 1σ ): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO2 and XCH4 growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from early 2020 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/ , last access: 10 January 2020) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/ , last access: 10 January 2020).

Quantifying the Potential for Snow‐Ice Formation in the Arctic Ocean

Abstract: We examine the regional variations and long‐term changes of the potential for snow‐ice formation for level Arctic sea ice from 1980 to 2016. We use daily sea ice motion data and implement a 1‐D snow/ice thermodynamic model that follows the ice trajectories while forcing the simulations with Modern‐Era Retrospective analysis for Research and Applications, Version 2 and ERA‐Interim reanalyses. We find there is potential for snow‐ice formation in level ice over most of the Arctic Ocean; this is true since the 1980s. In addition, the regional variations are very strong. The largest potential is typically found in the Atlantic sector of the Arctic Ocean, particularly in the Greenland Sea, where precipitation is highest. We surmise that, in addition to the annual amount of solid precipitation, potential for snow‐ice formation is controlled by two main factors: the initial second‐year/multiyear ice thickness in the autumn and the timing of first‐year ice formation.

Quality controls, bias, and seasonality of CO 2 columns in the boreal forest with Orbiting Carbon Observatory-2, Total Carbon Column Observing Network, and EM27/SUN measurements

Abstract: . Seasonal CO2 exchange in the boreal forest plays an important role in the global carbon budget and in driving interannual variability in seasonal cycles of atmospheric CO2 . Satellite-based observations from polar orbiting satellites like the Orbiting Carbon Observatory-2 (OCO-2) offer an opportunity to characterize boreal forest seasonal cycles across longitudes with a spatially and temporally rich data set, but data quality controls and biases still require vetting at high latitudes. With the objective of improving data availability at northern, terrestrial high latitudes, this study evaluates quality control methods and biases of OCO-2 retrievals of atmospheric column-averaged dry air mole fractions of CO2 ( X CO 2 ) in boreal forest regions. In addition to the standard quality control (QC) filters recommended for the Atmospheric Carbon Observations from Space (ACOS) B8 (B8 QC) and ACOS B9 (B9 QC) OCO-2 retrievals, a third set of quality control filters were specifically tailored to boreal forest observations (boreal QC) with the goal of increasing data availability at high latitudes without sacrificing data quality. Ground-based reference measurements of X CO 2 include observations from two sites in the Total Carbon Column Observing Network (TCCON) at East Trout Lake, Saskatchewan, Canada, and Sodankyla, Finland. OCO-2 retrievals were also compared to ground-based observations from two Bruker EM27/SUN Fourier transform infrared spectrometers (FTSs) at Fairbanks, Alaska, USA. The EM27/SUN spectrometers that were deployed in Fairbanks were carefully monitored for instrument performance and were bias corrected to TCCON using observations at the Caltech TCCON site. The B9 QC were found to pass approximately twice as many OCO-2 retrievals over land north of 50 ∘ N than the B8 QC, and the boreal QC were found to pass approximately twice as many retrievals in May, August, and September as the B9 QC. While boreal QC results in a substantial increase in passable retrievals, this is accompanied by increases in the standard deviations in biases at boreal forest sites from ∼1.4 parts per million (ppm) with B9 QC to ∼1.6 ppm with boreal QC. Total average biases for coincident OCO-2 retrievals at the three sites considered did not consistently increase or decrease with different QC methods, and instead, responses to changes in QC varied according to site and satellite viewing geometries. Regardless of the quality control method used, seasonal variability in biases was observed, and this variability was more pronounced at Sodankyla and East Trout Lake than at Fairbanks. Long-term coincident observations from TCCON, EM27/SUN, and satellites from multiple locations would be necessary to determine whether the reduced seasonal variability in bias at Fairbanks is due to geography or instrumentation. Monthly average biases generally varied between −1 and +1 ppm at the three sites considered, with more negative biases in spring (March, April, and May – MAM) and autumn (September and October – SO) but more positive biases in the summer months (June, July, and August – JJA). Monthly standard deviations in biases ranged from approximately 1.0 to 2.0 ppm and did not exhibit strong seasonal dependence, apart from exceptionally high standard deviation observed with all three QC methods at Sodankyla in June. There was no evidence found to suggest that seasonal variability in bias is a direct result of air mass dependence in ground-based retrievals or of proximity bias from coincidence criteria, but there were a number of retrieval parameters used as quality control filters that exhibit seasonality and could contribute to seasonal dependence in OCO-2 bias. Furthermore, it was found that OCO-2 retrievals of X CO 2 without the standard OCO-2 bias correction exhibit almost no perceptible seasonal dependence in average monthly bias at these boreal forest sites, suggesting that seasonal variability in bias is introduced by the bias correction. Overall, we found that modified quality controls can allow for significant increases in passable OCO-2 retrievals with only marginal compromises in data quality, but seasonal dependence in biases still warrants further exploration.

Assessing United States County-Level Exposure for Research on Tropical Cyclones and Human Health.

Abstract: Background: Tropical cyclone epidemiology can be advanced through exposure assessment methods that are comprehensive and consistent across space and time, as these facilitate multiyear, multistorm ...

Faster decline and higher variability in the sea ice thickness of themarginal Arctic seas

Abstract: . Mean sea ice thickness is a sensitive indicator of Arctic climate change and in long-term decline despite significant interannual variability. Current thickness estimations from satellite radar altimeters employ a snow climatology for converting range measurements to sea ice thickness, but this introduces unrealistically low interannual variability and trends. When the sea ice thickness in the period 2002–2018 is calculated using new snow data with more realistic variability and trends, we find mean sea ice thickness in three of the seven marginal seas to be declining between 70–100 % faster than when calculated with the conventional method. When analysed as an aggregate, the mean ice thickness in the marginal seas is now in statistically significant decline for four of seven winter months. This is observed despite a 58% increase in interannual variability between the methods in the same time period. On a seasonal timescale we find that snow data exerts an increasingly strong control on thickness variability over the growth season, contributing only 20 % in October but 72 % by April. Higher variability and faster decline in the sea ice thickness of the marginal seas has wide implications for our understanding of the polar climate system and our predictions for its change, as well as for stakeholders involved in Arctic shipping and natural resource extraction.

Commercial-Aircraft-Based Observations for NWP: Global Coverage, Data Impacts, and COVID-19

Abstract: Weather observations from commercial aircraft constitute an essential component of the global observing system and have been shown to be the most valuable observation source for short-range numerical weather prediction (NWP) systems over North America However, the distribution of aircraft observations is highly irregular in space and time In this study, we summarize the recent state of aircraft observation coverage over the globe and provide an updated quantification of its impact upon short-range NWP forecast skill Aircraft observation coverage is most dense over the contiguous United States and Europe, with secondary maxima in East Asia and Australia/New Zealand As of late November 2019, 665 airports around the world had at least one daily ascent or descent profile observation;400 of these come from North American or European airports Flight reductions related to the COVID-19 pandemic have led to a 75% reduction in aircraft observations globally as of late April 2020 A set of data denial experiments with the latest version of the Rapid Refresh NWP system for recent winter and summer periods quantifies the statistically significant positive forecast impacts of assimilating aircraft observations A special additional experiment excluding approximately 80% of aircraft observations reveals a reduction in forecast skill for both summer and winter amounting to 30%–60% of the degradation seen when all aircraft observations are excluded These results represent an approximate quantification of the NWP impact of COVID-19-related commercial flight reductions, demonstrating that regional NWP guidance is degraded as a result of the decreased number of aircraft observations © 2020 American Meteorological Society