Much of the predictability of seasonal climate anomalies around the globe is due to the predictability of tropical El Ni & ntilde;o-Southern Oscillation (ENSO) events and their global teleconnections. Despite decades of research, however, the relative roles of the dominant positive and negative feedbacks on predictable ENSO event magnitudes, patterns, and durations remain unclear. Here, an attempt is made to estimate these feedbacks directly from observational data. A 15-component linear inverse model (LIM) of the coupled Indo-Pacific atmosphere-ocean climate system is constructed for this purpose using reanalysis data for 1979-2017, and its predictable dynamics are clarified through a series of feedback-denial experiments. This approach yields, for the first time from data, a clear picture of the dominant competition between the destabilization of ENSO by positive near-surface zonal wind and subsurface oceanic feedbacks and its stabilization by a negative surface shortwave flux feedback associated with cloud shielding in cloudy areas. The results suggest that it is primarily this negative feedback that renders ENSO asymptotically stable. They also suggest that its underrepresentation is likely behind the tendency of climate models to extend the equatorial Pacific warming during El Ni & ntilde;o (and cooling during La Ni & ntilde;a) too far west into the western Pacific, compromising seasonal and longer-term predictions around the globe. An underrepresentation of this negative feedback over the Maritime Continent is also consistent with the mean sea surface temperature (SST) cold tongue and easterly trade wind biases of many climate models over the western equatorial Pacific.

We explore whether improvements to the Twentieth Century Reanalysis (20CR) in the most recent version (v3) have improved representation of the Southern Annular Mode (SAM). The negative SAM index bias in the first half of the 20th century compared to instrumental SAM reconstructions in previous 20CR versions (due to a systematic high latitude high pressure bias) is still present. It is reduced in summer and autumn, but not in winter and spring. Correlations between reanalysis and reconstructed SAM indices through the full series in all seasons do show improvements in v3 compared to previous versions. A reduction in SAM index ensemble spread is evident during periods with higher numbers of assimilated observations, in particular in summer and autumn. Analysis of the spatial distribution of assimilated observations shows clear improvement in years/periods with greater numbers of ships observations in the Southern Ocean and Antarctic observations (e.g. early 20th century Antarctic expeditions). However it is not until the advent of greater numbers of ships observations in the Southern Ocean and regular data from Antarctic meteorological stations in the late 1940s that there are enough high latitude observations to realistically constrain the reanalysis. Enhancements have been enough to improve how the reanalysis follows the observations temporally, highlighting the benefit of data rescue, but due to the bias, we recommend that the 20CR should still not be used for the analysis of long-term SAM trends, and caution should be exerted when using SLP data from the high latitude Southern Hemisphere from all 20CR versions prior to 1957.

The daily likelihood of High Tide Flooding (HTF) predicted by the National Oceanic and Atmospheric Administration (NOAA) for leads up to one year is expressed as the sum of a long-term trend, tides, and nontidal residuals (NTRs) whose probability density functions (PDFs) are assumed to be Gaussian (i.e., normally distributed). We analyzed observed detrended hourly NTR distributions at 148 NOAA tide gauges along the U.S. coastline and show that 98.7% of them are better characterized by ‘Stochastically Generated Skewed’ (SGS) distributions, a class of non-Gaussian (skewed, heavy-tailed) PDFs. In contrast to other methods that generate PDFs by fitting observed raw histograms, SGS distributions are determined through time series analysis. Observations are fit to a simple linear (autoregressive) time series model, driven by stochastic noise with a linear dependence upon the NTR anomaly. The PDF is then determined from the fitted model parameters. The SGS distributions improve upon the Gaussian PDF high-water probabilities at varying thresholds throughout the year along all U.S. coasts, with significantly better estimates along the U.S. East and Gulf coasts during summer (apart from large hurricane events) and along the U.S. West Coast during winter (even though variability there is often dominated by monthly time scales and many locations have nearly Gaussian PDFs). For evaluating extreme high-water event probabilities, the SGS distribution is no more sensitive to limited observations than kernel density estimation or Generalized Extreme Value methods. Tail probabilities for all three methods are generally similar. Our results may contribute to more robust and accurate HTF forecasts and, more broadly, provide additional insight in developing adaptation and mitigation strategies for future sea level conditions.

The spatial and temporal characteristics of wind and solar energy droughts across the contiguous U.S. and most of Canada for the period 1959-2022 are investigated using bias-corrected values of daily wind and solar power generation derived from the ERA5 meteorological reanalysis. The analysis domain has been divided into regions that correspond to four major interconnects and nine sub-regions. Droughts are examined for wind alone, solar alone, or a mix of wind and solar in which each provides 50% of the long-term mean energy produced, for durations of 1-90 days. Wind and solar energy droughts and floods are characterized on a regional basis through intensity-duration-frequency curves. Wind and solar generation are shown to be weakly anti-correlated over most of the analysis domain, with the exception of the southwest U.S. The intensities of wind and solar droughts are found to be strongly dependent on region. In addition, the wind resource in the central U.S. and the solar resource in the southwestern U.S. are sufficiently good that over-weighting capacity in those areas would help mitigate droughts that span the contiguous United States for most duration lengths. The correlation of droughts for the 50%-50% mix of wind and solar generation with temperature shows that the most intense droughts occur when temperatures exhibit relatively moderate values, not when energy demand will be largest. Finally, for all regions except the southeast U.S., winter droughts will have a larger impact on balancing the electric grid than summer droughts. (c) 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC) license.

The applicability of the ERA5 reanalysis for estimating wind and solar energy generation over the contiguous United States is evaluated using wind speed and irradiance variables from multiple observational data sets. After converting ERA5 and observed meteorological variables into wind power and solar power, comparisons demonstrate that significant errors in the ERA5 reanalysis exist that limit its direct applicability for a wind and solar energy analysis. Overall, ERA5-derived solar power is biased high, while ERA5-derived wind power is biased low. During winter, the ERA5-derived solar power is biased high by 23% on average, while on an annual basis, the ERA5-derived wind power is biased low by 20%. ERA5-derived solar power errors are found to have consistent characteristics across the contiguous United States. Errors for the shortest duration and most extreme solar negative anomaly events are relatively small in the ERA5 when completely overcast conditions occur in both the ERA5 and observations. However, longer-duration anomaly events on weekly to monthly timescales, which include partially cloudy days or a mix of cloudy and sunny days, have significant ERA5 errors. At 10 days duration, the ERA5-derived average solar power produced during the largest negative anomaly events is 62% greater than observed. The ERA5 wind speed and derived wind power negative biases are largely consistent across the central and northwestern U.S., and offshore, while the northeastern U.S. has an overall small net bias. For the ERA5-derived most extreme negative anomaly wind power events, at some sites at 10 days duration, the ERA5-derived wind power produced can be less than half of that observed. Corrections to ERA5 are derived using a quantile–quantile method for solar power and linear regression of wind speed for wind power. These methods are shown to avoid potential over-inflation of the reanalysis variability resulting from differences between point measurements and the temporally and spatially smoother reanalysis values. The corrections greatly reduce the ERA5 errors, including those for extreme events associated with wind and solar energy droughts, which will be most challenging for electric grid operation.

Since 2011, the Global Energy and Water cycle Exchanges (GEWEX) Water Vapor Assessment (G-VAP) has provided performance analyses for state-of-the-art reanalysis and satellite water vapour products to the GEWEX Data and Analysis Panel (GDAP) and the user community in general. A significant component of the work undertaken by G-VAP is to characterise the quality and uncertainty of these water vapour records to (i) ensure full exploitation and (ii) avoid incorrect use or interpretation of results. This study presents results from the second phase of G-VAP, where we have extended and expanded our analysis of total column water vapour (TCWV) from phase 1, in conjunction with updating the G-VAP archive. For version 2 of the archive, we consider 28 freely available and mature satellite and reanalysis data products, remapped to a regular longitude–latitude grid of 2° × 2° and on monthly time steps between January 1979 and December 2019. We first analysed all records for a “common” short period of 5 years (2005–2009), focusing on variability (spatial and seasonal) and deviation from the ensemble mean. We observed that clear-sky daytime-only satellite products were generally drier than the ensemble mean, and seasonal variability/disparity in several regions up to 12 kg m−2 related to original spatial resolution and temporal sampling. For 11 of the 28 data records, further analysis was undertaken between 1988–2014. Within this “long period”, key results show (i) trends between −1.18 ± 0.68 to 3.82 ± 3.94 kg m−2 per decade and −0.39 ± 0.27 to 1.24 ± 0.85 kg m−2 per decade were found over ice-free global oceans and land surfaces, respectively, and (ii) regression coefficients of TCWV against surface temperatures of 6.17 ± 0.24 to 27.02 ± 0.51 % K−1 over oceans (using sea surface temperature) and 3.00 ± 0.17 to 7.77 ± 0.16 % K−1 over land (using surface air temperature). It is important to note that trends estimated within G-VAP are used to identify issues in the data records rather than analyse climate change. Additionally, breakpoints have been identified and characterised for both land and ocean surfaces within this period. Finally, we present a spatial analysis of correlations to six climate indices within the long period, highlighting regional areas of significant positive and negative correlation and the level of agreement among records.

State‐of‐the‐art homogenisation approaches for any test site rely upon the availability of a sufficient number of neighbouring sites with similar climatic conditions and a sufficient quantity of overlapping measurements. These conditions are not always met, particularly in poorly sampled regions and epochs. Modern sparse‐input reanalysis products which are constrained by observed sea surface temperatures, sea‐ice and surface pressure observations, continue to improve, offering independently produced surface temperature estimates back to the early 19th century. This study undertakes an exploratory analysis on the applicability of sparse‐input reanalysis to identify breakpoints in available basic station data. Adjustments are then applied using a variety of reanalysis and neighbour‐based approaches to produce four distinct estimates. The methodological independence of the approach may offer valuable insights into historical data quality issues. The resulting estimates are compared to Global Historical Climatology Network version 4 (GHCNMv4) at various aggregations. Comparisons are also made with five existing global land surface monthly time series. We find a lower rate of long‐term warming which principally arises in differences in estimated behaviour prior to the early 20th century. Differences depend upon the exact pair of estimates, varying between 15 and 40% for changes from 1850–1900 to 2005–2014. Differences are much smaller for metrics starting after 1900 and negligible after 1950. Initial efforts at quantifying parametric uncertainty suggest this would be substantial and may lead to overlap between these new estimates and existing estimates. Further work would be required to use these data products in an operational context. This would include better understanding the reasons for apparent early period divergence including the impact of spatial infilling choices, quantification of parametric uncertainty, and a means to update the product post‐2015 when the NOAA‐CIRES‐DOE 20CRv3 sparse input reanalysis product, upon which they are based, presently ceases.

The Madden-Julian oscillation (MJO) significantly impacts North Atlantic hurricanes, with increased hurricane activity occurring when the MJO enhances convection over Africa and the tropical Indian Ocean and suppressed hurricane activity occurring when the MJO enhances convection over the tropical Pacific. Using data from 1905 to 2015, we find more tropical cyclones (TCs) make landfall in the continental United States when the MJO enhances tropical Indian Ocean convection. In addition, when the MJO enhances Western Pacific and Western Hemisphere convection, TC activity is preferentially favored in the Caribbean, leading to more Gulf Coast landfalls. As MJO-enhanced convection moves to the Indian Ocean and Maritime Continent, more storms form in the tropical Atlantic, favoring Florida Peninsula and East Coast landfalls. The MJO's TC steering wind modulation appears to be secondary to its genesis location modulation.

Billions of historical climatological observations remainunavailable to science as they exist only on paper, stored in numerousarchives around the world. The conversion of these data from paper todigital could transform our understanding of historical climate variations,including extreme weather events. Here we demonstrate how the rescue of suchpaper observations has improved our understanding of a severe windstorm thatoccurred in February 1903 and its significant impacts. By assimilating newlyrescued atmospheric pressure observations, the storm is now crediblyrepresented in an improved reanalysis of the event. In some locations thisstorm produced stronger winds than any event during the modern period(1950-2015) and it is in the top-4 storms for strongest winds anywhere overland in England and Wales. As a result, estimates of risk from severestorms, based on modern period data, may need to be revised. Examining theatmospheric structure of the storm suggests that it is a classicShapiro-Keyser-type cyclone with "sting-jet" precursors and associatedextreme winds at locations and times of known significant damage. Comparisonwith both independent observations and qualitative information, such asphotographs and written accounts, provides additional evidence of thecredibility of the atmospheric reconstruction, including sub-dailyrainfall variations. Simulations of the storm surge resulting from thisstorm show a large coastal surge of around 2.5 m, comparing favourably withnewly rescued tide gauge observations and adding to our confidence in thereconstruction. Combining historical rescued weather observations withmodern reanalysis techniques has allowed us to plausibly reconstruct asevere windstorm and associated storm surge from more than 100 years ago,establishing an invaluable end-to-end tool to improve assessments of risksfrom extreme weather.

A new reanalysis-based approach is proposed to examine how extreme weather events differ in a warmer or cooler counter-factual world. This approach offers a novel way to develop plausible storylines for some types of extreme event that other methods may not be suitable for. As a proof-of-concept, a reanalysis of a severe windstorm that occurred in February 1903 is translated into a warmer world where it produces higher wind speeds and increased rainfall, suggesting that this storm would be more damaging if it occurred today rather than 120 years ago.

Extreme wind events are among the costliest natural disasters in Europe. Significant effort is dedicated to understanding the risk of such events, usually analysing observed storms in the modern era. However, it is likely that some historical windstorms were more extreme and/or followed different tracks from those in the modern era. Producing plausible reanalyses of such events would improve the quantification of current and future windstorm risks.Billions of historical climatological observations remain unavailable to science as they exist only on paper, stored in numerous archives around the world. We demonstrate how the rescue of such paper observations has improved our understanding of an extreme windstorm that occurred in February 1903 and its significant impacts. By assimilating newly rescued atmospheric pressure observations into the 20th Century Reanalysis system, the storm is now credibly represented in an improved reanalysis of the event. In some locations this storm produced stronger winds than any event during the modern era. As a result, estimates of risk from severe storms, based on modern period data, may need to be revised. Simulations of the storm surge resulting from this storm show a large coastal surge of around 2.5m, comparing favourably with newly rescued tide gauge observations and increasing our confidence in the quality of the reconstruction.In addition, we use novel reanalysis experiments to translate this windstorm into a warmer world to quantify how it might be different both in the present and in the future. We find that the same storm produces more intense rainfall and stronger winds in a warmer climate, providing a new approach to quantifying how extreme weather events will change as the world is warming.

It is well known that randomly perturbing an atmospheric model's diabatic tendencies can increase its prob-abilistic forecast skill, mainly by increasing the spread of ensemble forecasts and making it more consistent with the errors of ensemble-mean forecasts. Less obvious and less well established is that such perturbations can also reduce the errors of the ensemble-mean forecasts and improve the model's mean climate, variability, and sensitivity to forcing. A clear reduc-tion in ensemble-mean forecast errors is demonstrated here in large ensembles of 15-day forecasts made with NOAA's Global Forecast System model. The nearly ubiquitous reduction around the globe, obtained throughout the forecast range, is interpreted as arising in effect from a modification of the model's deterministic evolution operator by a stochastic noise-induced drift. The effect is general in systems with state-dependent noise, and occurs even if the noise is not white. In the atmospheric context considered here, the effect is suggested to arise largely from noise-induced reductions of mechanical and thermal damping by chaotic boundary layer and cloud-radiative processes, which also tend to increase model sensitiv-ity to forcing. The results presented here are consistent with many previous studies performed with models ranging from simple stochastically forced models to comprehensive global weather and climate models. They suggest that the diabatic in-teractions in most current global atmospheric models may not be sufficiently chaotic and this deficiency could be partly remedied by specifying additional stochastic terms. Using some empirical guidance in such specifications may be unavoid-able, given the generally intractable complexity of the diabatic interactions.

In this study, we detect and track extratropical cyclones using 6-hourly mean sea level pressure data taken from the Twentieth Century Reanalysis version 3 (20CRv3) over the period 1951–2015 and compare them with those in the Interim and fifth generation of ECMWF reanalyses over the period 1979–2018. Three indices were employed to characterize cyclone activity, including cyclone count, cyclone intensity, and a cyclone activity index (CAI) that combines the count and intensity. The results show that the cyclone indices in the three datasets have comparable annual climatologies and seasonal evolution over the northern extratropical land and ocean in recent decades. Based on the cyclone indices over the period 1951–2010 in 80 ensemble members of 20CRv3, cyclone count and intensity are negatively correlated in winter and tend to be positively and weakly correlated in summer. The interannual CAI variability is dominated by the cyclone count variability. Regional mean cyclone activity can be well represented using the ensemble average cyclone index. We then examined the linkage of the cyclone activity in 20CRv3 and observed cold and warm extremes over Eurasia and North America over the period 1951–2010. In winter, the principal components of interannual cold and warm extreme anomalies are more correlated with the regional mean cyclone count index over Eurasia, while they are more correlated with the cyclone intensity index over North America. The temperature anomalies associated with the regional and ensemble mean cyclone count index explain about 10% (20%) of interannual cold (warm) extreme variances averaged over Eurasia. The temperature anomalies associated with the mean cyclone intensity explain about 10% of interannual cold and warm extreme variances over North America. Large-scale atmospheric circulation anomalies in association with cyclone activity and the induced temperature advection drive temperature anomalies over Eurasia and North America. In summer, circulation and thermal advection anomalies associated with cyclone activity are weak over the two continents. Hence, that season’s relationship between cyclone activity and extreme temperature variability is weak.

The U.S. operational global data assimilation system provides updated analysis and forecast fields every 6 h, which is not frequent enough to handle the rapid error growth associated with hurricanes or other storms. This motivates development of an hourly updating global data assimilation system, but observational data latency can be a barrier. Two methods are presented to overcome this challenge: "catch-up cycles," in which a 1-hourly system is reinitialized from a 6-hourly system that has assimilated high-latency observations; and "overlapping assimilation windows," in which the system is updated hourly with new observations valid in the past 3 h. The performance of these methods is assessed in a nearoperational setup using the Global Forecast System by comparing forecasts with in situ observations. At short forecast leads, the overlapping windows method performs comparably to the 6-hourly control in a simplified configuration and outperforms the control in a full-input configuration. In the full-input experiment, the catch-up cycle method performs similarly to the 6-hourly control; reinitializing from the 6-hourly control does not appear to provide a significant benefit. Results suggest that the overlapping windows method performs well in part because of the hourly update cadence, but also because hourly cycling systems can make better use of available observations. The impact of the hourly update relative to the 6-hourly update is most significant during the first forecast day, while impacts on longer-range forecasts were found to be mixed and mostly insignificant. Further effort toward an operational global hourly updating system should be pursued.

Daily weather reconstructions (called “reanalyses”) can help improve our understanding of meteorology and long-term climate changes. Adding undigitized historical weather observations to the datasets that underpin reanalyses is desirable; however, time requirements to capture those data from a range of archives is usually limited. Southern Weather Discovery is a citizen science data rescue project that recovered tabulated handwritten meteorological observations from ship log books and land-based stations spanning New Zealand, the Southern Ocean, and Antarctica. We describe the Zooniverse-hosted Southern Weather Discovery campaign, highlight promotion tactics, and replicate keying levels needed to obtain 100% complete transcribed datasets with minimal type 1 and type 2 transcription errors. Rescued weather observations can augment optical character recognition (OCR) text recognition libraries. Closer links between citizen science data rescue and OCR-based scientific data capture will accelerate weather reconstruction improvements, which can be harnessed to mitigate impacts on communities and infrastructure from weather extremes.

Accurate estimation of the potential "upper limit" for extreme precipitation is critical for dam safety and water resources management, as dam failures pose significant risks to life and property. Methods used to estimate the theoretical upper limit of precipitation are often outdated and in need of updating. The rarity of extreme events means that old storms with limited observational data are often used to define the upper bound of precipitation. Observations of many important old storms are limited in spatial and temporal coverage, and sometimes of dubious quality. This reduces confidence in flood hazard assessments used in dam safety evaluations and leads to unknown or uncertain societal risk. This paper describes a method for generating and applying ensembles of high-resolution, state-of-the-art numerical model simulations of historical past extreme precipitation events to meet contemporary stakeholder needs. The method was designed as part of a research-to-application-focused partnership project to update state dam safety rules in Colorado and New Mexico. The results demonstrated multiple stakeholder and user benefits that were applied directly into storm analyses utilized for extreme rainfall estimation, and diagnostics were developed and ultimately used to update Colorado state dam safety rules, officially passed in January 2020. We discuss how what started as a prototype research foray to meet a specific user need may ultimately inform wider adoption of numerical simulations for water resources risk assessment, and how the historical event downscaling method performed offers near-term, implementable improvements to current dam safety flood risk estimates that can better serve society today.

European flood frequency and intensity change on a multidecadal scale. Floods were more frequent in the 19th (central Europe) and early 20th century (western Europe) than during the mid-20th century and again more frequent since the 1970s. The causes of this variability are not well understood and the relation to climate change is unclear. Palaeoclimate studies from the northern Alps suggest that past flood-rich periods coincided with cold periods. In contrast, some studies suggest that more floods might occur in a future, warming world. Here we address the contribution of atmospheric circulation and of warming to multidecadal flood variability. For this, we use long series of annual peak streamflow, daily weather data, reanalyses, and reconstructions. We show that both changes in atmospheric circulation and moisture content affected multidecadal changes of annual peak streamflow in central and western Europe over the past two centuries. We find that during the 19th and early 20th century, atmospheric circulation changes led to high peak values of moisture flux convergence. The circulation was more conducive to strong and long-lasting precipitation events than in the mid-20th century. These changes are also partly reflected in the seasonal mean circulation and reproduced in atmospheric model simulations, pointing to a possible role of oceanic variability. For the period after 1980, increasing moisture content in a warming atmosphere led to extremely high moisture flux convergence. Thus, the main atmospheric driver of flood variability changed from atmospheric circulation variability to water vapour increase.