Radiosondes play a vital role in the validation of space-based observations of our atmosphere. These instruments provide high-resolution profile observations of pressure, temperature, relative humidity, and winds from the surface to the middle stratosphere. Radiosondes are launched at around 800 stations worldwide and by research organizations during intensive field campaigns. A detailed understanding of the sensor technology and sounding operation is essential for adequately interpreting these measurements. The history and the special considerations of using radiosonde observations for the validation and calibration of satellite instruments highlight the importance of these instruments. Radiosondes play an essential role in validating space-based observations of temperature, humidity, atmospheric motion vectors, and even surface winds by hyperspectral infrared and microwave radiometers. With the advent of space-based wind lidars, they also play an essential role in validating satellite-based wind profile observations. Numerical weather prediction models also play an important role in the validation and calibration of space-based observations but require anchoring, which among others is provided by radiosonde observations.
Miscalculating the volumes of water withdrawn for irrigation, the largest consumer of freshwater in the world, jeopardizes sustainable water management.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.
Study Region Forty-one river basins in Brazil and neighboring countries in South America. Study Focus In large river basins, on countrywide or continental scales, it is often difficult to have consistent and accurate long time series of spatially distributed precipitation data available. However, these are needed to calibrate hydrological models and to run hydrological simulations continuously in real-time streamflow forecasting. In this study, we assess two real-time precipitation products based on rain gauges and satellite data (TRMM-MERGE and CPC-NOAA) for their use in streamflow forecasting in the hydropower sector in Brazil. To take advantage of each precipitation data source and derive a unique dataset, a methodology is proposed to combine, extend, and validate the datasets. We consider the discharges at the river basin outlets as an independent and robust reference for hydrological applications. Observed discharges are used to quantify precipitation uncertainties and to weight the blending, while discharges obtained from hydrological modeling are used to validate the final precipitation product. New Hydrological Insights for the Region The proposed blending method, which uses the uncertainty of the original datasets to define the weighting factors, was efficient in generating a precipitation product that performs better than each dataset separately when used to force a hydrological model. The use of the double-mass curve correlation to extend the time series of the datasets beyond their common period allowed us to produce long time series of precipitation for South American basins and hydrological applications. The study shows that it is possible to rely on river discharge data and hydrological modeling to select and combine different precipitation products in the region and presents a step-by-step methodology to do so.
Space-based Earth observation (EO), in the form of long-term climate data records, has been crucial in the monitoring and quantification of slow changes in the climate system—from accumulating greenhouse gases (GHGs) in the atmosphere, increasing surface temperatures, and melting sea-ice, glaciers and ice sheets, to rising sea-level. In addition to documenting a changing climate, EO is needed for effective policy making, implementation and monitoring, and ultimately to measure progress and achievements towards the overarching goals of the United Nations Framework Convention on Climate Change (UNFCCC) Paris Agreement to combat climate change. The best approach for translating EO into actionable information for policymakers and other stakeholders is, however, far from clear. For example, climate change is now self-evident through increasingly intense and frequent extreme events—heatwaves, droughts, wildfires, and flooding—costing human lives and significant economic damage, even though single events do not constitute “climate”. EO can capture and visualize the impacts of such events in single images, and thus help quantify and ultimately manage them within the framework of the UNFCCC Paris Agreement, both at the national level (via the Enhanced Transparency Framework) and global level (via the Global Stocktake). We present a transdisciplinary perspective, across policy and science, and also theory and practice, that sheds light on the potential of EO to inform mitigation, including sinks and reservoirs of greenhouse gases, and adaptation, including loss and damage. Yet to be successful with this new mandate, EO science must undergo a radical overhaul: it must become more user-oriented, collaborative, and transdisciplinary; span the range from fiducial to contextual data; and embrace new technologies for data analysis (e.g., artificial intelligence). Only this will allow the creation of the knowledge base and actionable climate information needed to guide the UNFCCC Paris Agreement to a just and equitable success.
Since the 1980s, external forcing from increasing greenhouse gases and declining aerosols has had a large e ect on European summer temperatures. The forcing may therefore provide an important source of forecast skill, even for timescales as short as a season ahead. However, the relative importance of such forcing for seasonal forecasts has thus far not been quanti ed, particularly on a regional scale. In this study, we investigate forcing-induced skill by comparing the temperature skill of a multi-model ensemble of operational seasonal predictions from the Copernicus Climate Change Service (C3S) archive to that of an uninitialised ensemble of CMIP6 projections for European summers spanning the years 1993-2016. We show that in some regions, such as northern Europe, summer 2m temperature skill is relatively limited and the forced trend provides the primary source of skill in current seasonal forecast models at 2-4 month lead-times. Over large parts of northern Europe, summer temperature skill is actually higher in uninitialised predictions and in runs with long lead-times than at short lead-times suggesting that there may be problems with the initialisation. Conversely, 2m temperature in southern Europe is generally well predicted by seasonal forecast models out to 3-5 months due to a combination of dynamical skill and a strong forced trend. These results indicate that even uninitialised predictions can provide useful information for seasonal forecasts of European summer temperatures and secondly that the ability of models to capture dynamical signals for northern European summers requires further research.
One of the most important components of an atmospheric radiation scheme is its treatment of gas optical properties, which determines not only the accuracy of its radiative forcing calculations fundamental to climate prediction, but also its computational cost. This paper describes a free software tool “ecCKD” for generating fast gas‐optics models by optimally dividing the spectrum into pseudo‐monochromatic spectral intervals (known as k‐terms) according to a user‐specified error tolerance and the range of greenhouse‐gas concentrations that needs to be simulated. The models generated use the correlated k‐distribution method in user‐specified bands, but can also generate accurate “full‐spectrum correlated‐k” models that operate on the entire longwave or near‐infrared (NIR) parts of the spectrum. In the NIR, the large spectral variation in cloud absorption is represented by partitioning the parts of the spectrum where gases are optically thin into 2–6 sub‐bands, while allowing k‐terms for the optically thicker parts of the spectrum (where clouds and surface reflectance are less important) to span the entire NIR spectrum. Candidate models using only 16 and 32 k‐terms in each of the shortwave and longwave are evaluated against line‐by‐line calculations on clear and cloudy profiles. The 32‐term models are able to accurately capture the radiative forcing of varying greenhouse gases including CO2 concentrations spanning a factor of 12, and heating rates at pressures down to 1 Pa.
The collaboration between the Coordinated Regional Climate Downscaling Experiment (CORDEX) and the Earth System Grid Federation (ESGF) provides open access to an unprecedented ensemble of Regional Climate Model (RCM) simulations, across the 14 CORDEX continental-scale domains, with global coverage. These simulations have been used as a new line of evidence to assess regional climate projections in the latest contribution of the Working Group I (WGI) to the IPCC Sixth Assessment Report (AR6), particularly in the regional chapters and the Atlas. Here, we present the work done in the framework of the Copernicus Climate Change Service (C3S) to assemble a consistent worldwide CORDEX grand ensemble, aligned with the deadlines and activities of IPCC AR6. This work addressed the uneven and heterogeneous availability of CORDEX ESGF data by supporting publication in CORDEX domains with few archived simulations and performing quality control. It also addressed the lack of comprehensive documentation by compiling information from all contributing regional models, allowing for an informed use of data. In addition to presenting the worldwide CORDEX dataset, we assess here its consistency for precipitation and temperature by comparing climate change signals in regions with overlapping CORDEX domains, obtaining overall coincident regional climate change signals. The C3S CORDEX dataset has been used for the assessment of regional climate change in the IPCC AR6 (and for the interactive Atlas) and is available through the Copernicus Climate Data Store (CDS).
There is a high demand and expectation for sub-seasonal to seasonal (S2S) prediction which provides forecasts beyond 2 weeks, but less than 3 months ahead. To assess the potential benefit of artificial intelligence (AI) methods for S2S prediction through better postprocessing of ensemble prediction system outputs, the World Meteorological Organization (WMO) coordinated a prize challenge in 2021 to improve sub-seasonal prediction. The goal of this competition was to produce the most skillful forecasts of precipitation and two-meter temperature globally averaged over forecast weeks 3 and 4, and weeks 5 and 6 for the year 2020 using artificial intelligence techniques. The top three submissions, described in this article, succeeded in producing S2S forecasts significantly more skillful than the bias-corrected ECMWF operational reference forecasts, particularly for precipitation, through improved calibration of the ECMWF raw forecast outputs or multi-model combination. These forecast improvements should benefit the use of S2S forecasts in applications.
Mesoscale ocean eddies, an important element of the climate system, impact ocean circulation, heat uptake, gas exchange, carbon sequestration and nutrient transport. Much of what is known about ongoing changes in ocean eddy activity is based on satellite altimetry; however, the length of the altimetry record is limited, making it difficult to distinguish anthropogenic change from natural variability. Using a climate model that exploits a variable-resolution unstructured mesh in the ocean component to enhance grid resolution in eddy-rich regions, we investigate the long-term response of ocean eddy activity to anthropogenic climate change. Eddy kinetic energy is projected to shift poleward in most eddy-rich regions, to intensify in the Kuroshio Current, Brazil and Malvinas currents and Antarctic Circumpolar Current and to decrease in the Gulf Stream. Modelled changes are linked to elements of the broader climate including Atlantic meridional overturning circulation decline, intensifying Agulhas leakage and shifting Southern Hemisphere westerlies.
The Copernicus Climate Change Service (C3S) provides open and free access to state-of-the-art climate data and tools for use by governments, public authorities, and private entities around the world. It is fully funded by the European Union and implemented by the European Centre for Medium-Range Weather Forecasts ECMWF together with public and private entities in Europe and elsewhere. With over 120,000 registered users worldwide, C3S has rapidly become an authoritative climate service in Europe and beyond, delivering quality-assured climate data and information based on the latest science. Established in 2014, C3S became fully operational in 2018 with the launch of its Climate Data Store, a powerful cloud-based infrastructure providing access to a vast range of global and regional information, including climate data records derived from observations, the latest ECMWF reanalyses, seasonal forecast data from multiple providers and a large collection of climate projections. The system has been designed to be accessible to non-specialists, offering a uniform interface to all data and documentation as well as a Python-based toolbox that can be used to process and use the data online. C3S publishes European State of the Climate reports annually for policymakers, as well as monthly and annual summaries which are widely disseminated in the international press. Together with users, C3S develops customized indicators of climate impacts in economic sectors such as energy, water management, agriculture, insurance, health and urban planning. C3S works closely with national climate service providers, satellite agencies and other stakeholders on the improvement of its data and services.
The World Climate Research Programme (WCRP) envisions a world “that uses sound, relevant, and timely climate science to ensure a more resilient present and sustainable future for humankind.” This bold vision requires the climate science community to provide actionable scientific information that meets the evolving needs of societies all over the world. To realize its vision, WCRP has created five Lighthouse Activities to generate international commitment and support to tackle some of the most pressing challenges in climate science today. The overarching goal of the Lighthouse Activity on Explaining and Predicting Earth System Change is to develop an integrated capability to understand, attribute, and predict annual to decadal changes in the Earth system, including capabilities for early warning of potential high impact changes and events. This article provides an overview of both the scientific challenges that must be addressed, and the research and other activities required to achieve this goal. The work is organized in three thematic areas: (i) monitoring and modeling Earth system change; (ii) integrated attribution, prediction and projection; and (iii) assessment of current and future hazards. Also discussed are the benefits that the new capability will deliver. These include improved capabilities for early warning of impactful changes in the Earth system, more reliable assessments of meteorological hazard risks, and quantitative attribution statements to support the Global Annual to Decadal Climate Update and State of the Climate reports issued by the World Meteorological Organization.
During the pre-monsoon season (March–April–May), the eastern and northeastern parts of India, Himalayan foothills, and southern parts of India experience extensive lightning activity. Mean moisture, surface and upper-level winds, the sheared atmosphere in the lower level, and high positive values of vertically integrated moisture flux convergence (VIMFC) create favorable conditions for deep convective systems to occur, generating lightning. From mid-2018, the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) operationally introduced lightning flash density on a global scale. This study evaluates the ECMWF lightning forecasts over India during the pre-monsoon season of 2020 using the Indian Institute of Tropical Meteorology (IITM) Lightning Location Network (LLN) observation data. Qualitative and quantitative analysis of the ECMWF lightning forecast has shown that the lightning forecast with a 72-h lead time can capture the spatial and temporal variation of lightning with a 90% skill score.
Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land-atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-meter temperature over the TP in the LS4P experiment, results from a multi-model ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hot spot” regions identified here; the ensemble means in some “hot spots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.
The beginning of the 21 st century is marked by a rapid growth of land surface satellite data and model sophistication. This offers new opportunities to estimate multiple components of the water cycle via satellite-based land data assimilation (DA) across multiple scales. By resolving more processes in land surface models and by coupling the land, the atmosphere, and other Earth system compartments, the observed information can be propagated to constrain additional unobserved variables. Furthermore, access to more satellite observations enables the direct constraint of more and more components of the water cycle that are of interest to end users. However, the finer level of detail in models and data is also often accompanied by an increase in dimensions, with more state variables, parameters, or boundary conditions to estimate, and more observations to assimilate. This requires advanced DA methods and efficient solutions. One solution is to target specific observations for assimilation based on a sensitivity study or coupling strength analysis, because not all observations are equally effective in improving subsequent forecasts of hydrological variables, weather, agricultural production, or hazards through DA. This paper offers a perspective on current and future land DA development, and suggestions to optimally exploit advances in observing and modeling systems.
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. This study is conducted in the context of a novel SI shallow‐water model on the sphere, purposely designed to mimic numerical intricacies of modern all‐scale weather and climate (W&C) models. The governing algorithm of the shallow‐water model is based on the non‐oscillatory MPDATA methods for geophysical flows, whereas the resulting elliptic problem employs a strongly preconditioned non‐symmetric Krylov‐subspace Generalized Conjugated‐Residual (GCR) solver, proven in advanced atmospheric applications. The classical longitude/latitude grid is deliberately chosen to retain the stiffness of global W&C models. The analysis of the precision reduction is done on a software level, using an emulator, whereas the performance is measured on actual reduced precision hardware. The reduced‐precision experiments are conducted for established dynamical‐core test‐cases, like the Rossby‐Haurwitz wavenumber 4 and a zonal orographic flow. The study shows that selected key components of the elliptic solver, most prominently the preconditioning and the application of the linear operator, can be performed at the level of half precision. For these components, the use of half precision is found to yield a speed‐up of a factor 4 compared to double precision for a wide range of problem sizes.
Plain Language Summary Human activity is recognized as the primary cause of wildfires ignition. Still, lightning‐ignited fires are responsible for the majority of burned areas in remote regions. Unlike human behaviors, lightning activity can be predicted with a reasonable level of confidence as it is linked to weather conditions well represented in current forecasting models. Lighting predictions and environmental factors have been combined in machine‐learning based models to provide a quantitative measure to identify those episodes that are potentially conducive of fires. By providing the forecast in terms of probability of ignition rather than a binary (yes/no) outcome can highly increase the skill of the prediction. Still, the skill of a forecasting system not always equal its value. A skillful forecast can be useless for decision making if is not providing the right information, so it is important to verify that the increased skill brings real benefits. Using a simple cost‐loss model of economic value we found that for very low cost‐loss ratio (i.e., if we assume very high loss associated to the ignition) the use of probabilistic information would be also economically convenient to the decision‐maker ensuring almost 40% of the savings which would be obtained with perfect knowledge of future events.
Background and aim The associations between COVID-19 transmission and meteorological factors are scientifically debated. Several studies have been conducted worldwide, with inconsistent findings. However, often these studies had methodological issues, e.g., did not exclude important confounding factors, or had limited geographic or temporal resolution. Our aim was to quantify associations between temporal variations in COVID-19 incidence and meteorological variables globally. Methods We analysed data from 455 cities across 20 countries from 3 February to 31 October 2020. We used a time-series analysis that assumes a quasi-Poisson distribution of the cases and incorporates distributed lag non-linear modelling for the exposure associations at the city-level while considering effects of autocorrelation, long-term trends, and day of the week. The confounding by governmental measures was accounted for by incorporating the Oxford Governmental Stringency Index. The effects of daily mean air temperature, relative and absolute humidity, and UV radiation were estimated by applying a meta-regression of local estimates with multi-level random effects for location, country, and climatic zone. Results We found that air temperature and absolute humidity influenced the spread of COVID-19 over a lag period of 15 days. Pooling the estimates globally showed that overall low temperatures (7.5 °C compared to 17.0 °C) and low absolute humidity (6.0 g/m³ compared to 11.0 g/m³) were associated with higher COVID-19 incidence (RR temp =1.33 with 95%CI: 1.08; 1.64 and RR AH =1.33 with 95%CI: 1.12; 1.57). RH revealed no significant trend and for UV some evidence of a positive association was found. These results were robust to sensitivity analysis. However, the study results also emphasise the heterogeneity of these associations in different countries. Conclusion Globally, our results suggest that comparatively low temperatures and low absolute humidity were associated with increased risks of COVID-19 incidence. However, this study underlines regional heterogeneity of weather-related effects on COVID-19 transmission.
A deeper understanding of the intricate relationship between the two components of the Asian Summer Monsoon (ASM) – the Indian Summer Monsoon (ISM) and the East Asian Summer Monsoon (EASM) – is crucial to improve the subseasonal forecasting of extreme precipitation events. Using an innovative complex network‐based approach, we identify two dominant synchronization pathways between ISM and EASM – a southern mode between the Arabian Sea and south‐eastern China occurring in June, and a northern mode between the core ISM zone and northern China which peaks in July – and their associated large‐scale atmospheric circulation patterns. Furthermore, we discover that certain phases of the Madden‐Julian oscillation and the lower frequency mode of the boreal summer intraseasonal oscillation (BSISO) seem to favour the overall synchronization of extreme rainfall events between ISM and EASM while the higher frequency mode of the BSISO is likely to support the shifting between the modes of ISM‐EASM connection.
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