Using sub-limb observations to measure gravity waves excited by convection.

Convective gravity waves are a major driver of atmospheric circulation, including the stratospheric and mesospheric quasi-biennial oscillation (QBO) and the Brewer-Dobson circulation. Previous work shows clear evidence that these waves can be excited by both single convective cells and by mesoscale convective complexes acting as a single unit. However, the partitioning of the generated waves and, crucially for atmospheric model development, the flux of momentum they transport between these two types of excitation process remains highly uncertain due to a fundamental lack of suitable observations at the global scale.

A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts.

Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather models. Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems, that is, learning to add fine-scale structure to coarse images.

Mixed-Precision for Linear Solvers in Global Geophysical Flows.

Semi-implicit (SI) time-stepping schemes for atmosphere and ocean models require elliptic solvers that work efficiently on modern supercomputers. This paper reports our study of the potential computational savings when using mixed precision arithmetic in the elliptic solvers. Precision levels as low as half (16 bits) are used and a detailed evaluation of the impact of reduced precision on the solver convergence and the solution quality is performed.

Dynamical-statistical seasonal forecasts of winter and summer precipitation for the Island of Ireland.

Seasonal precipitation forecasting is highly challenging for the northwest fringes of Europe due to complex dynamical drivers. Hybrid dynamical-statistical approaches offer potential to improve forecast skill. Here, hindcasts of mean sea level pressure (MSLP) from two dynamical systems (GloSea5 and SEAS5) are used to derive two distinct sets of indices for forecasting winter (DJF) and summer (JJA) precipitation over lead-times of 1-4 months.

Using modelled relationships and satellite observations to attribute modelled aerosol biases over biomass burning regions.

Biomass burning (BB) is a major source of aerosols that remain the most uncertain components of the global radiative forcing. Current global models have great difficulty matching observed aerosol optical depth (AOD) over BB regions. A common solution to address modelled AOD biases is scaling BB emissions.

Fluid Simulations Accelerated With 16 Bits: Approaching 4x Speedup on A64FX by Squeezing ShallowWaters.jl Into Float16.

Most Earth-system simulations run on conventional central processing units in 64-bit double precision floating-point numbers Float64, although the need for high-precision calculations in the presence of large uncertainties has been questioned. Fugaku, currently the world's fastest supercomputer, is based on A64FX microprocessors, which also support the 16-bit low-precision format Float16. We investigate the Float16 performance on A64FX with ShallowWaters.

Variability of ENSO Forecast Skill in 2-Year Global Reforecasts Over the 20th Century.

In order to explore temporal changes of predictability of El Niño Southern Oscillation (ENSO), a novel set of global biennial climate reforecasts for the historical period 1901-2010 has been generated using a modern initialized coupled forecasting system. We find distinct periods of enhanced long-range skill at the beginning and at the end of the twentieth century, and an extended multi-decadal epoch of reduced skill during the 1930s-1950s. Once the forecast skill extends beyond the first spring barrier, the predictability limit is much enhanced and our results provide support for the feasibility of skillful ENSO forecasts up to 18 months.

Evaluation of the SPARTACUS-Urban Radiation Model for Vertically Resolved Shortwave Radiation in Urban Areas.

Unlabelled: The heterogenous structure of urban environments impacts interactions with radiation, and the intensity of urban-atmosphere exchanges. Numerical weather prediction (NWP) often characterizes the urban structure with an infinite street canyon, which does not capture the three-dimensional urban morphology realistically. Here, the SPARTACUS (Speedy Algorithm for Radiative Transfer through Cloud Sides) approach to urban radiation (SPARTACUS-Urban), a multi-layer radiative transfer model designed to capture three-dimensional urban geometry for NWP, is evaluated with respect to the explicit Discrete Anisotropic Radiative Transfer (DART) model.

Global warming is shifting the relationships between fire weather and realized fire-induced CO emissions in Europe.

Fire activity has significantly changed in Europe over the last decades (1980-2020s), with the emergence of summers attaining unprecedented fire prone weather conditions. Here we report a significant shift in the non-stationary relationship linking fire weather conditions and fire intensity measured in terms of CO emissions released during biomass burning across a latitudinal gradient of European IPCC regions. The reported trends indicate that global warming is possibly inducing an incipient change on regional fire dynamics towards increased fire impacts in Europe, suggesting that emerging risks posed by exceptional fire-weather danger conditions may progressively exceed current wildfire suppression capabilities in the next decades and impact forest carbon sinks.

The delusive accuracy of global irrigation water withdrawal estimates.

Miscalculating the volumes of water withdrawn for irrigation, the largest consumer of freshwater in the world, jeopardizes sustainable water management. Hydrological models quantify water withdrawals, but their estimates are unduly precise. Model imperfections need to be appreciated to avoid policy misjudgements.

Perspectives on the importance of complex systems in understanding our climate and climate change-The Nobel Prize in Physics 2021.

The Nobel Prize in Physics 2021 was awarded to Syukuro Manabe, Klaus Hasselmann, and Giorgio Parisi for their "groundbreaking contributions to our understanding of complex systems," including major advances in the understanding of our climate and climate change. In this Perspective article, we review their key contributions and discuss their relevance in relation to the present understanding of our climate. We conclude by outlining some promising research directions and open questions in climate science.

Global nature run data with realistic high-resolution carbon weather for the year of the Paris Agreement.

The CO Human Emissions project has generated realistic high-resolution 9 km global simulations for atmospheric carbon tracers referred to as nature runs to foster carbon-cycle research applications with current and planned satellite missions, as well as the surge of in situ observations. Realistic atmospheric CO, CH and CO fields can provide a reference for assessing the impact of proposed designs of new satellites and in situ networks and to study atmospheric variability of the tracers modulated by the weather. The simulations spanning 2015 are based on the Copernicus Atmosphere Monitoring Service forecasts at the European Centre for Medium Range Weather Forecasts, with improvements in various model components and input data such as anthropogenic emissions, in preparation of a CO Monitoring and Verification Support system.

The future of Earth system prediction: Advances in model-data fusion.

Predictions of the Earth system, such as weather forecasts and climate projections, require models informed by observations at many levels. Some methods for integrating models and observations are very systematic and comprehensive (e.g.

Delayed Antarctic sea-ice decline in high-resolution climate change simulations.

Despite global warming and Arctic sea-ice loss, on average the Antarctic sea-ice extent has not declined since 1979 when satellite data became available. In contrast, climate model simulations tend to exhibit strong negative sea-ice trends for the same period. This Antarctic sea-ice paradox leads to low confidence in 21st-century sea-ice projections.

Forecast-based attribution of a winter heatwave within the limit of predictability.

Attribution of extreme weather events has expanded rapidly as a field over the past decade. However, deficiencies in climate model representation of key dynamical drivers of extreme events have led to some concerns over the robustness of climate model-based attribution studies. It has also been suggested that the unconditioned risk-based approach to event attribution may result in false negative results due to dynamical noise overwhelming any climate change signal.

Technical note: AQMEII4 Activity 1: evaluation of wet and dry deposition schemes as an integral part of regional-scale air quality models.

We present in this technical note the research protocol for phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This research initiative is divided into two activities, collectively having three goals: (i) to define the current state of the science with respect to representations of wet and especially dry deposition in regional models, (ii) to quantify the extent to which different dry deposition parameterizations influence retrospective air pollutant concentration and flux predictions, and (iii) to identify, through the use of a common set of detailed diagnostics, sensitivity simulations, model evaluation, and reduction of input uncertainty, the specific causes for the current range of these predictions. Activity 1 is dedicated to the diagnostic evaluation of wet and dry deposition processes in regional air quality models (described in this paper), and Activity 2 to the evaluation of dry deposition point models against ozone flux measurements at multiple towers with multiyear observations (to be described in future submissions as part of the special issue on AQMEII4).

Compressing atmospheric data into its real information content.

Hundreds of petabytes are produced annually at weather and climate forecast centers worldwide. Compression is essential to reduce storage and to facilitate data sharing. Current techniques do not distinguish the real from the false information in data, leaving the level of meaningful precision unassessed.

Decadal climate variability in the tropical Pacific: Characteristics, causes, predictability, and prospects.

Climate variability in the tropical Pacific affects global climate on a wide range of time scales. On interannual time scales, the tropical Pacific is home to the El Niño–Southern Oscillation (ENSO). Decadal variations and changes in the tropical Pacific, referred to here collectively as tropical Pacific decadal variability (TPDV), also profoundly affect the climate system.

Regional trends and drivers of the global methane budget.

The ongoing development of the Global Carbon Project (GCP) global methane (CH ) budget shows a continuation of increasing CH emissions and CH accumulation in the atmosphere during 2000-2017. Here, we decompose the global budget into 19 regions (18 land and 1 oceanic) and five key source sectors to spatially attribute the observed global trends. A comparison of top-down (TD) (atmospheric and transport model-based) and bottom-up (BU) (inventory- and process model-based) CH emission estimates demonstrates robust temporal trends with CH emissions increasing in 16 of the 19 regions.

Seasonal Arctic sea ice forecasting with probabilistic deep learning.

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times.

Machine Learning Emulation of Gravity Wave Drag in Numerical Weather Forecasting.

We assess the value of machine learning as an accelerator for the parameterization schemes of operational weather forecasting systems, specifically the parameterization of nonorographic gravity wave drag. Emulators of this scheme can be trained to produce stable and accurate results up to seasonal forecasting timescales. Generally, networks that are more complex produce emulators that are more accurate.

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions.

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015-2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM, PM, PMC (coarse fraction of PM), NO, SO, NOx, CO, O and the total gaseous oxidant (OX = NO + O) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020.

Evaluation of the ERA5 reanalysis-based Universal Thermal Climate Index on mortality data in Europe.

Air temperature has been the most commonly used exposure metric in assessing relationships between thermal stress and mortality. Lack of the high-quality meteorological station data necessary to adequately characterize the thermal environment has been one of the main limitations for the use of more complex thermal indices. Global climate reanalyses may provide an ideal platform to overcome this limitation and define complex heat and cold stress conditions anywhere in the world.

The need for Pan-European automatic pollen and fungal spore monitoring: A stakeholder workshop position paper.

Background: Information about airborne pollen concentrations is required by a range of end users, particularly from the health sector who use both observations and forecasts to diagnose and treat allergic patients. Manual methods are the standard for such measurements but, despite the range of pollen taxa that can be identified, these techniques suffer from a range of drawbacks. This includes being available at low temporal resolution (usually daily averages) and with a delay (usually 3-9 days from the measurement).

COVID-19 Crisis Reduces Free Tropospheric Ozone Across the Northern Hemisphere.

Throughout spring and summer 2020, ozone stations in the northern extratropics recorded unusually low ozone in the free troposphere. From April to August, and from 1 to 8 kilometers altitude, ozone was on average 7% (≈4 nmol/mol) below the 2000-2020 climatological mean. Such low ozone, over several months, and at so many stations, has not been observed in any previous year since at least 2000.