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Given the increasing use of climate projections and multi-model ensemble weighting for a diverse array of applications, this project assesses the sensitivities of climate model weighting strategies and their resulting ensemble means to multiple components, such as the weighting schemes, climate variables, or spatial domains of interest. The purpose of this study is to assess the sensitivities associated with multi-model weighting strategies. The analysis makes use of global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their statistically downscaled counterparts created with the localized constructed analogs (LOCA) method. This work focuses on historical and projected future mean precipitation and daily high temperatures of the south-central United States. Results suggest that the model weights and the corresponding weighted model means can be sensitive to the weighting strategy that is applied. For instance, when estimating model weights based on Louisiana precipitation, the weighted projections show a wetter and cooler south-central domain in the future compared to other weighting strategies. Alternatively, for example, when estimating model weights based on New Mexico temperature, the weighted projections show a drier and warmer south-central domain in the future. However, when considering the entire south-central domain in estimating the model weights, the weighted future projections show a compromise in the precipitation and temperature estimates. As for uncertainty, our matrix of results provided a more certain picture of future climate compared to the spread in the original model ensemble. If future impact assessments utilize weighting strategies, then our findings suggest that how the specific weighting strategy is used with climate projections may depend on the needs of an impact assessment or adaptation plan.

Despite an urgent demand for reliable seasonal prediction of precipitation in California (CA) due to the recent recurrent and severe drought conditions, our predictive skill for CA winter precipitation remains limited. October hindcasts by the coupled dynamical models typically show a correlation skill of about 0.3 for CA winter (November–March) precipitation. In this study, an attempt is made to understand the underlying processes that limit seasonal prediction skill for CA winter precipitation. It is found that only about 25% of interannual variability of CA winter precipitation can be attributed to influences by El Niño–Southern Oscillation (ENSO). Instead, the year-to-year CA winter precipitation variability is primarily due to circulation anomalies independent from ENSO, featuring a circulation center over the west coast United States as a portion of a short Rossby wave train pattern over the North Pacific. Analyses suggest that dynamical models show nearly no skill in predicting these ENSO-independent circulation anomalies, thus leading to limited predictive skill for CA winter precipitation. Low predictability of these ENSO-independent circulation anomalies is further demonstrated by a large ensemble of atmospheric-only climate model simulations. While low predictability of the ENSO-independent circulation anomalies could be due to chaotic internal atmospheric processes over the mid- to high latitudes, possible underexploited predictability sources for CA precipitation in models are also discussed. This study pinpoints an urgent need for improved understanding of the formation mechanisms of ENSO-independent circulation anomalies over the U.S. West Coast for a breakthrough in seasonal prediction of CA winter precipitation.

Leveraging the concept of atmospheric rivers (ARs), a detection technique based on a widely utilized global algorithm to detect ARs (Guan and Waliser, 2019, 2015; Guan et al., 2018) was recently developed to detect aerosol atmospheric rivers (AARs) using the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis (Chakraborty et al., 2021a). The current study further characterizes and quantifies various details of AARs that were not provided in that study, such as the AARs' seasonality, event characteristics, vertical profiles of aerosol mass mixing ratio and wind speed, and the fraction of total annual aerosol transport conducted by AARs. Analysis is also performed to quantify the sensitivity of AAR detection to the criteria and thresholds used by the algorithm. AARs occur more frequently over, and typically extend from, regions with higher aerosol emission. For a number of planetary-scale pathways that exhibit large climatological aerosol transport, AARs contribute up to a maximum of 80 % to the total annual transport, depending on the species of aerosols. Dust (DU) AARs are more frequent in boreal spring, sea salt AARs are often more frequent during the boreal winter (summer) in the Northern (Southern) Hemisphere, carbonaceous (CA) AARs are more frequent during dry seasons, and often originate from the global rainforests and industrial areas, and sulfate AARs are present in the Northern Hemisphere during all seasons. For most aerosol types, the mass mixing ratio within AARs is highest near the surface. However, DU and CA AARs over or near the African continent exhibit peaks in their aerosol mixing ratio profiles around 700 hPa. AAR event characteristics are mostly independent of species with the mean length, width, and length / width ratio around 4000 km, 600 km, and 7–8, respectively.

A barrier to utilizing machine learning in seasonal forecasting applications is the limited sample size of observational data for model training. To circumvent this issue, here we explore the feasibility of training various machine learning approaches on a large climate model ensemble, providing a long training set with physically consistent model realizations. After training on thousands of seasons of climate model simulations, the machine learning models are tested for producing seasonal forecasts across the historical observational period (1980-2020). For forecasting large-scale spatial patterns of precipitation across the western United States, here we show that these machine learning-based models are capable of competing with or outperforming existing dynamical models from the North American Multi Model Ensemble. We further show that this approach need not be considered a ‘black box’ by utilizing machine learning interpretability methods to identify the relevant physical processes that lead to prediction skill. Seasonal forecasting skill in machine learning methods that are trained on large climate model ensembles can compete with, or out-compete, existing dynamical models, while retaining physical interpretability.

Surface wind plays a crucial role in many local/regional weather and climate processes, especially through the exchanges of energy, mass, and momentum across Earth’s surface. However, there is a lack of consistent observations with continuous coverage over the global tropical ocean. To fill this gap, the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016, consisting of a constellation of eight small spacecrafts that remotely sense near-surface wind speed over the tropical and subtropical oceans with relatively high sampling rates both temporally and spatially. This current study uses data obtained from the Tropical Moored Buoy Arrays to quantitatively characterize and validate the CYGNSS derived winds over the tropical Indian, Pacific, and Atlantic Oceans. The validation results show that the uncertainty in CYGNSS wind speed, as compared with these tropical buoy data, is less than 2 m s−1 root-mean-square difference, meeting the NASA science mission level-1 uncertainty requirement for wind speeds below 20 m s−1. The quality of the CYGNSS wind is further assessed under different precipitation conditions, and in convective cold-pool events, identified using buoy rain and temperature data. Results show that CYGNSS winds compare fairly well with buoy observations in the presence of rain, though at low wind speeds the presence of rain appears to cause a slight positive wind speed bias in the CYGNSS data. The comparison indicates the potential utility of the CYGNSS surface wind product, which in turn may help to unravel the complexities of air–sea interaction in regions that are relatively undersampled by other observing platforms.

The joint WWRP–WCRP Subseasonal to Seasonal Prediction Project (e.g., Robertson et al. 2014) created a global repository of experimental or operational near-real-time S2S forecasts and reforecasts (hindcasts) from 11 international meteorological institutions, cohosted by ECMWF and CMA (Vitart et al. 2017). These data are publicly accessible by researchers and users (https://apps.ecmwf.int/datasets/data/s2s and http://s2s.cma.cn/index). With the exception of the fourth case study, which uses GloSea5 forecasts (MacLachlan et al. 2015), all case studies use selected S2S forecasts and reforecasts that are available from this repository, providing a consistent basis for S2S forecast skill assessment and evaluation of their utility. The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale. DD gratefully acknowledges support from the Swiss National Science Foundation through project PP00P2_170523. For case study 1, ACP and WTKH were funded by the U.K. Climate Resilience Programme, supported by the UKRI Strategic Priorities Fund. RWL was funded by NERC Grant NE/P00678/1 and by the BER DOE Office of Science Federal Award DE-SC0020324. TS was funded by NERC Independent Research Fellowship (NE/P018637/1). CMG and DB were funded by the Helmholtz Young Investigator Group “SPREADOUT” Grant VH-NG-1243. Case study 2 was supported by the U.K. Global Challenges Research Fund NE/P021077/1 (GCRF African SWIFT) and the Tertiary Education Trust Fund (TETFUND) of Nigeria TETFund/DR&D/CE/NRF/STI/73/VOL.1. EO thanks Adrian Tomkins of ICTP, Italy, for his contribution. Case study 3 was undertaken as part of the Columbia World Project, ACToday, Columbia University (https://iri.columbia.edu/actoday/). Case study 4 was supported by the ForPAc (Towards Forecast-based Preparedness Action) project within the NERC/FCDO SHEAR Programme NE/P000428/1, NE/P000673/1, and NE/P000568/1. Case study 5 was undertaken as part of the International Research Applications Project, funded by the U.S. National Oceanic and Atmospheric Administration. EO thanks IRAP project colleagues at The University of Arizona, Indian Meteorological Department, Regional Integrated Multi-Hazard Early Warning System for Africa and Asia, and two of Bihar’s State Agricultural Universities for their contributions. For case study 6, CASC thanks Conselho Nacional de Desenvolvimento Científico e Tecnológico Process 305206/2019-2 and Fundação de Amparo à Pesquisa do Estado de São Paulo Process 2015/50687-8 (CLIMAX Project) for their support. For case study 7, DW’s contributions were carried out under contract with the National Aeronautics and Space Administration. Case study 8 was funded by the EU Horizon 2020 Research and Innovation Programme Grant 7767874 (S2S4E). We also acknowledge the Subseasonal-to-Seasonal Project’s Real-Time Pilot Initiative for providing access to real-time forecasts. For case study 9, TIC-LCPE Hydro-04 was funded by the University of Strathclyde’s Low Carbon Power and Energy program. JB was supported by EPSRC Innovation Fellowship EP/R023484/1. We thank Andrew Low and Richard Hearnden from SSE Renewables for their input. Case study 10 was supported by the Earth Systems and Climate Change Hub under the Australian Government’s National Environmental Science Program, and the Decadal Climate Forecasting Project (CSIRO). Case study 11 was funded by the Technologies for Sustainable Built Environments Centre, Reading University, in conjunction with the EPSRC Grant EP/G037787/1 and BT PLC. Case study 12 was funded through the framework service contract for operating the EFAS Computational Center Contract 198702 and the Copernicus Fire Danger Computations Contract 389730 295 in support of the Copernicus Emergency Management Service and Early Warning Systems between the Joint Research Centre and ECMWF. Peer Reviewed "Article signat per 60 autors/es: Christopher J. White, Daniela I. V. Domeisen, Nachiketa Acharya, Elijah A. Adefisan, Michael L. Anderson, Stella Aura, Ahmed A. Balogun, Douglas Bertram, Sonia Bluhm, David J. Brayshaw, Jethro Browell, Dominik Büeler, Andrew Charlton-Perez, Xandre Chourio, Isadora Christel, Caio A. S. Coelho, Michael J. DeFlorio, Luca Delle Monache, Francesca Di Giuseppe, Ana María García-Solórzano, Peter B. Gibson, Lisa Goddard, Carmen González Romero, Richard J. Graham, Robert M. Graham, Christian M. Grams, Alan Halford, W. T. Katty Huang, Kjeld Jensen, Mary Kilavi, Kamoru A. Lawal, Robert W. Lee, David MacLeod, Andrea Manrique-Suñén, Eduardo S. P. R. Martins, Carolyn J. Maxwell, William J. Merryfield, Ángel G. Muñoz, Eniola Olaniyan, George Otieno, John A. Oyedepo, Lluís Palma, Ilias G. Pechlivanidis, Diego Pons, F. Martin Ralph, Dirceu S. Reis Jr., Tomas A. Remenyi, James S. Risbey, Donald J. C. Robertson, Andrew W. Robertson, Stefan Smith, Albert Soret, Ting Sun, Martin C. Todd, Carly R. Tozer, Francisco C. Vasconcelos Jr., Ilaria Vigo, Duane E. Waliser, Fredrik Wetterhall, and Robert G. Wilson"

Our success with environmental prediction could be considered among humankind’s most remarkable developments over the past 50 years. It protects lives and property, and helps us advance the well-being of society. Vast changes have occurred recently to the complexity and scope of our weather enterprise. Given its importance to society, there is reason to optimize our approaches for advancing its scope and capabilities. This essay highlights three points that may help facilitate this optimization, with an overarching suggestion to more overtly embrace a systems (engineering) approach. 1) Continued emphasis should be placed on advancing Earth system science as the foundational knowledge that advances weather prediction as well as the more holistic scope of environmental prediction (EP). 2) The complexity and coupling of the social, programmatic, observation, modeling, analytic, and interdisciplinary landscapes within the EP enterprise suggest adding a system engineering perspective/approach to further optimize outcomes and limit vulnerabilities. 3) The consideration of the enterprise as a data to information flow problem highlights opportunities and focal points to leverage that could help to advance the societal benefits derived from the EP enterprise. A generalized, highly simplified systems perspective on the advancement of Earth science and environmental prediction is offered by framing a simple equation involving the synthesis of observations, models, and programmatics that in turn yield science and applications benefits. Simplifications and derivatives of this equation are used to distill challenges and opportunities for further advancing enterprise benefits and to motivate considerations of scope and priorities related to our community’s decadal survey(s).

Atmospheric rivers (ARs) are long, narrow filamentary regions of enhanced vertically integrated water vapor transport (IVT) that play an important role in regional water supply and hydrometeorological extremes. Here, an AR detection algorithm is applied to global reanalysis from Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), to objectively and consistently characterize ARs regionally across the continental United States (CONUS). The characteristics of AR and associated precipitation are computed at the gridpoint scale and summarized over the seven U.S. National Climate Assessment regions. ARs are most frequent in the autumn and winter in the West, spring in the Great Plains, and autumn in the Midwest and Northeast. ARs show regional and seasonal variability in basic geometry and IVT. AR IVT composites reveal annually consistent northeastward-directed moisture transport from the Pacific Ocean in the West, whereas moisture transport patterns vary seasonally across the Southern Great Plains and Midwest. Linked AR precipitation characteristics suggest that a substantial proportion of extreme events, defined as the top 5% of 3-day precipitation totals, are associated with ARs over many parts of CONUS, including the East. Regional patterns of AR-associated precipitation highlight that seasonally varying moisture transport and lifting mechanisms differ between the East and the West where orographic lifting is key. Our study aims to contribute a comprehensive and consistent CONUS-wide, regional-scale analysis of ARs in support of ongoing NCA efforts. Given the CONUS-wide role ARs play in extreme precipitation, findings motivate continued study of associated climate change impacts.

Combined airborne, shipboard, and satellite measurements provide the first observational assessment of all major terms of the vertically integrated water vapor (IWV) budget for a 150 km × 160 km region within the core of a strong atmospheric river over the northeastern Pacific Ocean centered on 1930 UTC 5 February 2015. Column-integrated moisture flux convergence is estimated from eight dropsonde profiles, and surface rain rate is estimated from tail Doppler radar reflectivity measurements. Dynamical convergence of water vapor (2.20 ± 0.12 mm h−1) nearly balances estimated precipitation (2.47 ± 0.41 mm h−1), but surface evaporation (0.0 ± 0.05 mm h−1) is negligible. Advection of drier air into the budget region (−1.50 ± 0.21 mm h−1) causes IWV tendency from the sum of all terms to be negative (−1.66 ± 0.45 mm h−1). An independent estimate of IWV tendency obtained from the difference between IWV measured by dropsonde and retrieved by satellite 3 h earlier is less negative (−0.52 ± 0.24 mm h−1), suggesting the presence of substantial temporal variability that is smoothed out when averaging over several hours. The calculation of budget terms for various combinations of dropsonde subsets indicates the presence of substantial spatial variability at ~50-km scales for precipitation, moisture flux convergence, and IWV tendency that is smoothed out when averaging over the full budget region. Across subregions, surface rain rate is linearly proportional to dynamical convergence of water vapor. These observational results improve our understanding of the thermodynamic and kinematic processes that control IWV in atmospheric rivers and the scales at which they occur.

This study utilizes Bayesian model averaging (BMA) as a framework to constrain the spread of uncertainty in climate projections of precipitation over the contiguous United States (CONUS). We use a subset of historical model simulations and future model projections (RCP8.5) from the Coupled Model Intercomparison Project phase 5 (CMIP5). We evaluate the representation of five precipitation summary metrics in the historical simulations using observations from the NASA Tropical Rainfall Measuring Mission (TRMM) satellites. The summary metrics include mean, annual and interannual variability, and maximum and minimum extremes of precipitation. The estimated model average produced with BMA is shown to have higher accuracy in simulating mean rainfall than the ensemble mean (RMSE of 0.49 for BMA versus 0.65 for ensemble mean), and a more constrained spread of uncertainty with roughly a third of the total uncertainty than is produced with the multimodel ensemble. The results show that, by the end of the century, the mean daily rainfall is projected to increase for most of the East Coast and the Northwest, may decrease in the southern United States, and with little change expected for the Southwest. For extremes, the wettest year on record is projected to become wetter for the majority of CONUS and the driest year to become drier. We show that BMA offers a framework to more accurately estimate and to constrain the spread of uncertainties of future climate, such as precipitation changes over CONUS.

Statistical relationships between atmospheric rivers (ARs) and extratropical cyclones and anticyclones are investigated on a global scale using objectively identified ARs, cyclones, and anticyclones during 1979–2014. Composites of circulation and moisture fields around the ARs show that a strong cyclone is located poleward and westward of the AR centroid, which confirms the close link between the AR and extratropical cyclone. In addition, a pronounced anticyclone is found to be located equatorward and eastward of the AR, whose presence together with the cyclone leads to strong horizontal pressure gradient that forces moisture to be transported along a narrow corridor within the warm sector of the cyclone. This anticyclone located toward the downstream equatorward side of the cyclone is found to be missing for cyclones not associated with ARs. These key features are robust in composites performed in different hemispheres, over different ocean basins, and with respect to different AR intensities. Furthermore, correlation analysis shows that the AR intensity is much better correlated with the pressure gradient between the cyclone and anticyclone than with the cyclone/anticyclone intensity alone, although stronger cyclones favor the occurrence of AR. The importance of the horizontal pressure gradient in the formation of the AR is also consistent with the fact that climatologically ARs are frequently found over the region between the polar lows and subtropical highs in all seasons.

There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0–14-day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer time scales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden–Julian oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, reevaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.

Persistent winter ridging events are a consistent feature of meteorological drought across the western and southwestern United States. In this study, a ridge detection algorithm is developed and applied on daily geopotential height anomalies to track and quantify the diversity of individual ridge characteristics (e.g., position, frequency, magnitude, extent, and persistence). Three dominant ridge types are shown to play important, but differing, roles for influencing the location of landfalling atmospheric rivers (ARs), precipitation, and subsequently meteorological drought. For California, a combination of these ridge types is important for influencing precipitation deficits on daily through seasonal time scales, indicating the various pathways by which ridging can induce drought. Furthermore, both the frequency of ridge types and reduced AR activity are necessary features for explaining drought variability on seasonal time scales across the western and southwestern regions. The three ridge types are found to be associated in different ways with various remote drivers and modes of variability, highlighting possible sources of subseasonal-to-seasonal (S2S) predictability. A comparison between ridge types shows that anomalously large and persistent ridging events relate to different Rossby wave trains across the Pacific with different preferential upstream locations of tropical heating. For the “South-ridge” type, centered over the Southwest, a positive trend is found in both the frequency and persistence of these events across recent decades, likely contributing to observed regional drying. These results illustrate the utility of feature tracking for characterizing a wider range of ridging features that collectively influence precipitation deficits and drought.

Atmospheric rivers (ARs) are responsible for the vast majority (approximately 88%) of flood damage in the Western U.S, an annual average of USD$1.1 billion. Here, using historical flood insurance d...

Author(s): Rutz, JJ; Shields, CA; Lora, JM; Payne, AE; Guan, B; Ullrich, P; O’Brien, T; Leung, LR; Ralph, FM; Wehner, M; Brands, S; Collow, A; Goldenson, N; Gorodetskaya, I; Griffith, H; Kashinath, K; Kawzenuk, B; Krishnan, H; Kurlin, V; Lavers, D; Magnusdottir, G; Mahoney, K; McClenny, E; Muszynski, G; Nguyen, PD; Prabhat, M; Qian, Y; Ramos, AM; Sarangi, C; Sellars, S; Shulgina, T; Tome, R; Waliser, D; Walton, D; Wick, G; Wilson, AM; Viale, M | Abstract: Atmospheric rivers (ARs) are now widely known for their association with high-impact weather events and long-term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR-related metrics based on 20+ different AR identification and tracking methods applied to Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria-based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all-method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR-related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR-related research to consider.

Atmospheric rivers (ARs) are narrow jets of integrated water vapor transport that are important for the global water cycle and also have large impacts on local weather and regional hydrology. Uniformly weighted multi‐model averages have been used to describe how ARs will change in the future, but this type of estimate does not consider skill or independence of the climate models of interest. Here, we utilize information from various model averaging approaches, such as Bayesian model averaging (BMA), to evaluate 21 global climate models from the Coupled Model Intercomparison Project Phase 5. Model ensemble weighting strategies are based on model independence and AR performance skill relative to ERA‐Interim reanalysis data and result in higher accuracy for the historic period, for example, root mean square error for AR frequency (in % of time steps) of 0.69 for BMA versus 0.94 for the multi‐model ensemble mean. Model weighting strategies also result in lower uncertainties in the future estimates, for example, only 20–25% of the total uncertainties seen in the equal weighting strategy. These model averaging methods show, with high certainty, that globally the frequency of ARs is expected to have average relative increases of ~50% (and ~25% in AR intensity) by the end of the century.

The NASA Cyclone Global Navigation Satellite System (CYGNSS) constellation of eight satellites was successfully launched into low Earth orbit on 15 December 2016. Each satellite carries a radar receiver that measures GPS signals scattered from the surface. Wind speed over the ocean is determined from distortions in the signal caused by wind-driven surface roughness. GPS operates at a sufficiently low frequency to allow for propagation through all precipitation, including the extreme rain rates present in the eyewall of tropical cyclones. The spacing and orbit of the satellites were chosen to optimize frequent sampling of tropical cyclones. In this study, we characterize the CYGNSS ocean surface wind speed measurements by their uncertainty, dynamic range, sensitivity to precipitation, spatial resolution, spatial and temporal sampling, and data latency. The current status of each of these properties is examined and potential future improvements are discussed. In addition, examples are given of current science investigations that make use of the data.

Climate model evaluation is complicated by the presence of observational uncertainty. In this study we analyze daily precipitation indices and compare multiple gridded observational and reanalysis products with regional climate models (RCMs) from the North American component of the Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) multimodel ensemble. In the context of model evaluation, observational product differences across the contiguous United States (CONUS) are also deemed nontrivial for some indices, especially for annual counts of consecutive wet days and for heavy precipitation indices. Multidimensional scaling (MDS) is used to directly include this observational spread into the model evaluation procedure, enabling visualization and interpretation of model differences relative to a “cloud” of observational uncertainty. Applying MDS to the evaluation of NA-CORDEX RCMs reveals situations of added value from dynamical downscaling, situations of degraded performance from dynamical downscaling, and the sensitivity of model performance to model resolution. On precipitation days, higher-resolution RCMs typically simulate higher mean and extreme precipitation rates than their lower-resolution pairs, sometimes improving model fidelity with observations. These results document the model spread and biases in daily precipitation extremes across the full NA-CORDEX model ensemble. The often-large divergence between in situ observations, satellite data, and reanalysis, shown here for CONUS, is especially relevant for data-sparse regions of the globe where satellite and reanalysis products are extensively relied upon. This highlights the need to carefully consider multiple observational products when evaluating climate models.

We assess the vertical distribution of radiative heating rates (RHRs) in climate models using a multimodel experiment and A-Train satellite observations, for the first time. As RHRs rely on the representation of cloud amount and properties, we first compare the modeled vertical distribution of clouds directly against lidar–radar combined cloud observations (i.e., without simulators). On a near-global scale (50°S–50°N), two systematic differences arise: an excess of high-level clouds around 200 hPa in the tropics, and a general lack of mid- and low-level clouds compared to the observations. Then, using RHR profiles calculated with constraints from A-Train and reanalysis data, along with their associated maximum uncertainty estimates, we show that the excess clouds and ice water content in the upper troposphere result in excess infrared heating in the vicinity of and below the clouds as well as a lack of solar heating below the clouds. In the lower troposphere, the smaller cloud amount and the underestimation of cloud-top height is coincident with a shift of the infrared cooling to lower levels, substantially reducing the greenhouse effect, which is slightly compensated by an erroneous excess absorption of solar radiation. Clear-sky RHR differences between the observations and the models mitigate cloudy RHR biases in the low levels while they enhance them in the high levels. Finally, our results indicate that a better agreement between observed and modeled cloud profiles could substantially improve the RHR profiles. However, more work is needed to precisely quantify modeled cloud errors and their subsequent effect on RHRs.

Emerging methods that improve precipitation forecasting over weeks to months could support more informed resource management and increase lead times for responding to droughts and floods.

Atmospheric rivers (ARs) are long and narrow regions of strong horizontal water vapor transport. Upon landfall, ARs are typically associated with heavy precipitation and strong surface winds. A quantitative understanding of the atmospheric conditions that favor extreme surface winds during ARs has implications for anticipating and managing various impacts associated with these potentially hazardous events. Here, a global AR database (1999–2014) with relevant information from MERRA-2 reanalysis, QuikSCAT and AIRS satellite observations are used to better understand and quantify the role of near-surface static stability in modulating surface winds during landfalling ARs. The temperature difference between the surface and 1 km MSL (ΔT; used here as a proxy for near-surface static stability), and integrated water vapor transport (IVT) are analyzed to quantify their relationships to surface winds using bivariate linear regression. In four regions where AR landfalls are common, the MERRA-2-based results indicate that IVT accounts for 22-38% of the variance in surface wind speed. Combining ΔT with IVT increases the explained variance to 36-52%. Substitution of QuikSCAT surface winds and AIRS ΔT in place of the MERRA-2 data largely preserves this relationship (e.g., 44% compared to 52% explained variance for USA West Coast). Use of an alternate static stability measure–the bulk Richardson number–yields a similar explained variance (47%). Lastly, AR cases within the top and bottom 25% of near-surface static stability indicate that extreme surface winds (gale or higher) are more likely to occur in unstable conditions (5.3%/14.7% during weak/strong IVT) than in stable conditions (0.58%/6.15%).

We investigate how the Madden‐Julian Oscillation (MJO), the dominant mode of tropical subseasonal variability, modulates the lifecycle of cool‐season North Pacific atmospheric rivers (ARs). When the enhanced (suppressed) convection center is located over the Indian Ocean (western Pacific), more AR events originate over eastern Asia and with fewer over the subtropical northern Pacific. When the enhanced (suppressed) convection is over the western Pacific (Indian Ocean), the opposite changes occur, with more AR events originate over the subtropical northern Pacific and fewer over eastern Asia. Dynamical processes involving anomalous MJO wind and seasonal mean moisture are found to be the dominant factors impacting these variations in AR origins. The MJO‐related anomalous geopotential height patterns are also shown to modulate the propagation of the AR events. These MJO–AR lifecycle relationships are further supported by model simulations.

Recently, assessments of global climate model (GCM) ensembles have transitioned from using unweighted means to weighted means designed to account for skill and interdependence among models. Although ensemble-weighting schemes are typically derived using a GCM ensemble, statistically downscaled projections are used in climate change assessments. This study applies four ensemble-weighting schemes for model averaging to precipitation projections in the south-central United States. The weighting schemes are applied to (1) a 26-member GCM ensemble and (2) those 26 members downscaled using Localized Canonical Analogs (LOCA). This study is distinct from prior research because it compares the interactions of ensemble-weighting schemes with GCMs and statistical downscaling to produce summarized climate projection products. The analysis indicates that statistical downscaling improves the ensemble accuracy (LOCA average root mean square error is 100 mm less than the CMIP5 average root mean square error) and reduces the uncertainty of the projected ensemble-mean change. Furthermore, averaging the LOCA ensemble using Bayesian Model Averaging reduces the uncertainty beyond any other combination of weighting schemes and ensemble (standard deviation of the mean projected change in the domain is reduced by 40&ndash, 50 mm). The results also indicate that it is inappropriate to assume that a weighting scheme derived from a GCM ensemble matches the same weights derived using a downscaled ensemble.

This study investigates the historical climatology and future projected change of atmospheric rivers (ARs) and precipitation for the Middle East and North Africa (MENA) region. We use a suite of models from the Coupled Model Intercomparison Project Phase 5 (CMIP5, historical and RCP8.5 scenarios) and other observations to estimate AR frequency and mean daily precipitation. Despite its arid-to-semi-arid climate, parts of the MENA region experience strong ARs, which contribute a large fraction of the annual precipitation, such as in the mountainous areas of Turkey and Iran. This study shows that by the end of this century, AR frequency is projected to increase (~20&ndash, 40%) for the North Africa and Mediterranean areas (including any region with higher latitudes than 35 N). However, for these regions, mean daily precipitation (i.e., regardless of the presence of ARs) is projected to decrease (~15&ndash, 30%). For the rest of the MENA region, including the Arabian Peninsula and the Horn of Africa, minor changes in AR frequency (&plusmn, 10%) are expected, yet mean precipitation is projected to increase (~50%) for these regions. Overall, the projected sign of change in AR frequency is opposite to the projected sign of change in mean daily precipitation for most areas within the MENA region.

The Observations for Model Intercomparison Project (Obs4MIPs) was initiated in 2010 to facilitate the use of observations in climate model evaluation and research, with a particular target being the Coupled Model Intercomparison Project (CMIP), a major initiative of the World Climate Research Programme (WCRP). To this end, Obs4MIPs (1) targets observed variables that can be compared to CMIP model variables; (2) utilizes dataset formatting specifications and metadata requirements closely aligned with CMIP model output; (3) provides brief technical documentation for each dataset, designed for nonexperts and tailored towards relevance for model evaluation, including information on uncertainty, dataset merits, and limitations; and (4) disseminates the data through the Earth System Grid Federation (ESGF) platforms, making the observations searchable and accessible via the same portals as the model output. Taken together, these characteristics of the organization and structure of obs4MIPs should entice a more diverse community of researchers to engage in the comparison of model output with observations and to contribute to a more comprehensive evaluation of the climate models. At present, the number of obs4MIPs datasets has grown to about 80; many are undergoing updates, with another 20 or so in preparation, and more than 100 are proposed and under consideration. A partial list of current global satellite-based datasets includes humidity and temperature profiles; a wide range of cloud and aerosol observations; ocean surface wind, temperature, height, and sea ice fraction; surface and top-of-atmosphere longwave and shortwave radiation; and ozone (O3), methane (CH4), and carbon dioxide (CO2) products. A partial list of proposed products expected to be useful in analyzing CMIP6 results includes the following: alternative products for the above quantities, additional products for ocean surface flux and chlorophyll products, a number of vegetation products (e.g., FAPAR, LAI, burned area fraction), ice sheet mass and height, carbon monoxide (CO), and nitrogen dioxide (NO2). While most existing obs4MIPs datasets consist of monthly-mean gridded data over the global domain, products with higher time resolution (e.g., daily) and/or regional products are now receiving more attention. Along with an increasing number of datasets, obs4MIPs has implemented a number of capability upgrades including (1) an updated obs4MIPs data specifications document that provides additional search facets and generally improves congruence with CMIP6 specifications for model datasets, (2) a set of six easily understood indicators that help guide users as to a dataset's maturity and suitability for application, and (3) an option to supply supplemental information about a dataset beyond what can be found in the standard metadata. With the maturation of the obs4MIPs framework, the dataset inclusion process, and the dataset formatting guidelines and resources, the scope of the observations being considered is expected to grow to include gridded in situ datasets as well as datasets with a regional focus, and the ultimate intent is to judiciously expand this scope to any observation dataset that has applicability for evaluation of the types of Earth system models used in CMIP.

A large amount of fresh water resources are stored in the snowpack, which is the primary source of water for streamflow in many places at middle-to-high latitude areas. Therefore, snow water equivalent (SWE) is a key parameter in the water cycle. Active and passive microwave remote sensing methods have been used to retrieve SWE due to relatively poor resolution of current in situ interpolated maps with good accuracy. However, estimation of SWE has proved challenging, despite several decades of efforts to develop retrieval approaches. Active sensors provide higher-resolution observations. Two recent promising retrieval algorithms using active data are dual frequency dual polarization backscattered power and differential interferometry. These retrieval algorithms have some restrictions on snow characteristics, the environment, and instrument properties. The restrictions limit the snow that is suitable for the specific retrieval algorithm. In order to better understand how much of the snowpack satisfies the precondition of these retrieval approaches, we use a 4 km gridded snowpack product over the contiguous US for years 1997 and 2015. We use a simple scattering model to simulate the scattering characteristics of snow. The snow property maps, simulated scattering characteristics of snow, and environmental conditions are used to filter the suitable snow for each retrieval algorithm. We show that snow wetness and vegetation coverage are the two main limiting conditions for these retrieval algorithms. We show that 39% and 44% of the grid-points with snow satisfy the preconditions of dual polarization dual frequency retrieval algorithms at 13.5 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important limiting factors for dual polarization dual frequency retrieval method are dryness of snow, penetration depth, and vegetation-free constraints. The backscattered power in dual polarization dual frequency method is more sensitive to snow density and grain radius rather than to snow depth. We also show that 55% and 53% of the grid-points with snow satisfy the precondition of differential interferometry retrieval algorithms at 1 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important precondition-limiting factors for differential interferometry are dryness of snow and vegetation-free constraints. The differential interferometry phase retrieval algorithm is equally sensitive to snow height and snow density variations and is independent of snow grain radius.