How well do operational Numerical Weather Prediction configurations represent hydrology?

Land surface models (LSMs) have traditionally been designed to focus on providing lower boundary conditions to the atmosphere with less focus on hydrological processes. State of the art application of LSMs include land data assimilation system (LDAS) which incorporates available land surface observations to provide an improved realism of surface conditions. While improved representations of the surface variables (such as soil moisture and snow depth) make LDAS an essential component of any Numerical Weather Prediction (NWP) system, the related increments remove or add water, potentially having a negative impact on the simulated hydrological cycle by opening the water budget. This paper focuses on evaluating how well global NWP configurations are able to support hydrological applications, in addition to the traditional weather forecasting. River discharge simulations from two climatological reanalyses are compared: one ‘online’ set which includes land-atmosphere coupling and LDAS with an open water budget, and also an ‘offline’ set with a closed water budget and no LDAS. It was found that while the online version of the model largely improves temperature and snow depth conditions, it caused poorer representation of peak river flow, particularly in snowmelt-dominated areas in the high latitudes. Without addressing such issues there will never be confidence in using LSMs for hydrological forecasting applications across the globe. This type of analysis should be used to diagnose where improvements need to be made; considering the whole Earth System in the data assimilation and coupling developments is critical for moving towards the goal of holistic Earth System approaches

Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System

Operational global-scale hydrological forecasting systems are used to help manage hydrological extremes such as floods and droughts. The vast amounts of raw data that underpin forecast systems and the ability to generate information on forecast skill have, until now, not been publicly available. As part of the Global Flood Awareness System (GloFAS; https://www.globalfloods.eu/, last access: 3 December 2022) service evolution, in this paper daily ensemble river discharge reforecasts and real-time forecast datasets are made free and openly available through the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). They include real-time forecast data starting on 1 January 2020 updated operationally every day and a 20-year set of reforecasts and associated metadata. This paper describes the model components and configuration used to generate the real-time river discharge forecasts and the reforecasts. An evaluation of ensemble forecast skill using the continuous ranked probability skill score (CRPSS) was also undertaken for river points around the globe. Results show that GloFAS is skilful in over 93 % of catchments in the short (1 to 3 d) and medium range (5 to 15 d) against a persistence benchmark forecast and skilful in over 80 % of catchments out to the extended range (16 to 30 d) against a climatological benchmark forecast. However, the strength of skill varies considerably by location with GloFAS found to have no or negative skill at longer lead times in broad hydroclimatic regions in tropical Africa, western coast of South America, and catchments dominated by snow and ice in high northern latitudes. Forecast skill is summarised as a new headline skill score available as a new layer on the GloFAS forecast Web Map Viewer to aid user interpretation and understanding of forecast quality.</p

Atmospheric predictability revisited

This article examines the potential to improve numerical weather prediction (NWP) by estimating upper and lower bounds on predictability by re-visiting the original study of Lorenz (1982) but applied to the most recent version of the European Centre for Medium Range Weather Forecasts (ECMWF) forecast system, for both the deterministic and ensemble prediction systems (EPS). These bounds are contrasted with an older version of the same NWP system to see how they have changed with improvements to the NWP system. The computations were performed for the earlier seasons of DJF 1985/1986 and JJA 1986 and the later seasons of DJF 2010/2011 and JJA 2011 using the 500-hPa geopotential height field. Results indicate that for this field, we may be approaching the limit of deterministic forecasting so that further improvements might only be obtained by improving the initial state. The results also show that predictability calculations with earlier versions of the model may overestimate potential forecast skill, which may be due to insufficient internal variability in the model and because recent versions of the model are more realistic in representing the true atmospheric evolution. The same methodology is applied to the EPS to calculate upper and lower bounds of predictability of the ensemble mean forecast in order to explore how ensemble forecasting could extend the limits of the deterministic forecast. The results show that there is a large potential to improve the ensemble predictions, but for the increased predictability of the ensemble mean, there will be a trade-off in information as the forecasts will become increasingly smoothed with time. From around the 10-d forecast time, the ensemble mean begins to converge towards climatology. Until this point, the ensemble mean is able to predict the main features of the large-scale flow accurately and with high consistency from one forecast cycle to the next. By the 15-d forecast time, the ensemble mean has lost information with the anomaly of the flow strongly smoothed out. In contrast, the control forecast is much less consistent from run to run, but provides more detailed (unsmoothed) but less useful information

Global predictability of temperature extremes

Extreme temperatures are one of the leading causes of death and disease in both developed and developing countries, and heat extremes are projected to rise in many regions. To reduce risk, heatwave plans and cold weather plans have been effectively implemented around the world. However, much of the world’s population is not yet protected by such systems, including many data-scarce but also highly vulnerable regions. In this study, we assess at a global level where such systems have the potential to be effective at reducing risk from temperature extremes, characterizing (1) long-term average occurrence of heatwaves and coldwaves, (2) seasonality of these extremes, and (3) short-term predictability of these extreme events three to ten days in advance. Using both the NOAA and ECMWF weather forecast models, we develop global maps indicating a first approximation of the locations that are likely to benefit from the development of seasonal preparedness plans and/or short-term early warning systems for extreme temperature. The extratropics generally show both short-term skill as well as strong seasonality; in the tropics, most locations do also demonstrate one or both. In fact, almost 5 billion people live in regions that have seasonality and predictability of heatwaves and/or coldwaves. Climate adaptation investments in these regions can take advantage of seasonality and predictability to reduce risks to vulnerable populations

Selective Attenuation of Norepinephrine Release and Stress-Induced Heart Rate Increase by Partial Adenosine A1 Agonism

The release of the neurotransmitter norepinephrine (NE) is modulated by presynaptic adenosine receptors. In the present study we investigated the effect of a partial activation of this feedback mechanism. We hypothesized that partial agonism would have differential effects on NE release in isolated hearts as well as on heart rate in vivo depending on the genetic background and baseline sympathetic activity. In isolated perfused hearts of Wistar and Spontaneously Hypertensive Rats (SHR), NE release was induced by electrical stimulation under control conditions (S1), and with capadenoson 6 · 10−8 M (30 µg/l), 6 · 10−7 M (300 µg/l) or 2-chloro-N6-cyclopentyladenosine (CCPA) 10−6 M (S2). Under control conditions (S1), NE release was significantly higher in SHR hearts compared to Wistar (766+/−87 pmol/g vs. 173+/−18 pmol/g, p<0.01). Capadenoson led to a concentration-dependent decrease of the stimulation–induced NE release in SHR (S2/S1 = 0.90±0.08 with capadenoson 6 · 10−8 M, 0.54±0.02 with 6 · 10−7 M), but not in Wistar hearts (S2/S1 = 1.05±0.12 with 6 · 10−8 M, 1.03±0.09 with 6 · 10−7 M). CCPA reduced NE release to a similar degree in hearts from both strains. In vivo capadenoson did not alter resting heart rate in Wistar rats or SHR. Restraint stress induced a significantly greater increase of heart rate in SHR than in Wistar rats. Capadenoson blunted this stress-induced tachycardia by 45% in SHR, but not in Wistar rats. Using a [35S]GTPγS assay we demonstrated that capadenoson is a partial agonist compared to the full agonist CCPA (74+/−2% A1-receptor stimulation). These results suggest that partial adenosine A1-agonism dampens stress-induced tachycardia selectively in rats susceptible to strong increases in sympathetic activity, most likely due to a presynaptic attenuation of NE release