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Projections of future climate are sensitive to the representation of upper-ocean diurnal variability, including the diurnal cycle of winds. Two different methods suitable for time series with missing data are used here to characterize how observed diurnal winds vary over the year. One is based on diurnal composites of mooring data, and the other is based on harmonic analysis via a least squares fit and is able to isolate annual (i.e., 1 cycle per year) modulation of diurnal variability. Results show that the diurnal amplitude in meridional winds is larger than in zonal winds and peaks in the tropical Pacific, where diurnal variability in zonal winds is overall weaker compared to other basins. Furthermore, the amplitude and phasing of diurnal winds in the tropical oceans are not uniform in time, with overall larger differences through the year in the meridional component of tropical winds. Estimating the annual modulation of the diurnal signal implies resolving both the diurnal energy peak and also the modulation of this peak by the annual cycle. This leads to a recommendation for sampling at least 6 times per day and for a duration of at least 3 years.

Abstract. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the MERRA-2 reanalysis from January 1980 to June of 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month proof of concept trial run designed to illustrate the utility and feasibility of the ARTMIP project.

Upper ocean processes play a key role in air-sea coupling, with variability on both short and long time scales. The diurnal cycle associated with diurnal solar insolation and nighttime cooling, may act, along with stochastic wind variability, on upper ocean temperatures and stratification resulting in a diurnal warm layer and a nonlinear rectified effect on longer time scales. This study describes diurnal changes in upper ocean temperature for a location in the equatorial Indian Ocean, using observations from the Dynamics of the Madden-Julian Oscillation field campaign, a high vertical resolution 1-D process model, and a diurnal cycling scheme. Solar forcing is the main driver of diurnal variability in upper ocean temperature and stratification. Yet except during nighttime convection, winds with variability on the order of hours (here referred to as “wind gusts”) regulate how fast surface water is mixed to greater depths when daily mean winds are weak. Wind gusts are much stronger than diurnal winds. Even using stochastic wind gusts but no diurnal winds as input in a 1-D process model yields an estimate of diurnal temperature that compares well with observations. A new version of the Large and Caron (2015) scheme (LC2015) provides an estimate of upper ocean diurnal temperature that is consistent with observations. LC2015 has the advantage of being suitable for implementation in a climate model, with the goal to improve SST estimates, hence the simulated heat flux at the air-sea interface. Yet LC2015 is not very sensitive to the inclusion or omission of the high-frequency component of the wind.

Accurate forecasts of weather conditions have the potential to mitigate the social and economic damages they cause. To make informed decisions based on forecasts, it is important to determine the extent to which they could be skillful. This study focuses on subseasonal forecasts out to a lead time of four weeks. We examine the differences between the potential predictability, which is computed under the assumption of a "perfect model," of integrated vapor transport (IVT) and precipitation under extreme conditions in subseasonal forecasts across the northeast Pacific. Our results demonstrate significant forecast skill of extreme IVT and precipitation events (exceeding the 90th percentile) into week 4 for specific areas, particularly when anomalously wet conditions are observed in the true model state. This forecast skill during weeks 3 and 4 is closely associated with a zonal extension of the North Pacific jet. These findings of the source of skillful subseasonal forecasts over the U.S. West Coast could have implications for water management in these regions susceptible to drought and flooding extremes. Additionally, they may offer valuable insights for governments and industries on the U.S. West Coast seeking to make informed decisions based on extended weather prediction. Significance Statement: The purpose of this study is to understand the differences between the ability to predict high amounts of the transport of water vapor and precipitation over the North Pacific 3 and 4 weeks into the future. The results indicate that differences do exist in a region that is relevant to precipitation on the U.S. West Coast. To physically explain why differences in predictability exist, the relationship between weekly extremes of the extension of the jet stream, IVT, and precipitation over the North Pacific is explored. These findings may impact decisions relevant to water management on the U.S. West Coast susceptible to drought and flooding extremes. [ABSTRACT FROM AUTHOR]

Arctic sea ice has been declining rapidly in recent decades. We investigate how the poleward transport of moisture and heat from lower latitudes through atmospheric rivers (ARs) influences Arctic sea ice variations. We use hourly ERA5 (fifth-generation European Reanalysis) data for 1981–2020 at 0.25 ∘ × 0.25 ∘ resolution to examine the meteorological conditions and sea ice changes associated with ARs in the Arctic. In the years 2012 and 2020, which had an extremely low summer Arctic sea ice extent, we show that the individual AR events associated with large cyclones initiate a rapid sea ice decrease through turbulent heat fluxes and winds. We carry out further statistical analysis of the meteorological conditions and sea ice variations for 1981–2020 over the entire Arctic Ocean. We find that on weather timescales the atmospheric moisture content anticorrelates significantly with the sea ice concentration tendency almost everywhere in the Arctic Ocean, while the dynamic sea ice motion driven by northward winds further reduces the sea ice concentration. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of impactful winter weather in California, including ARs, SAWs and heat extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1–4 lead. This methodology uses dynamical model information considered most reliable, that is, planetary/synoptic‐scale atmospheric circulation, filters for dynamical model error/uncertainty at longer lead times and increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support. Plain Language Summary: California winter weather can alternate between very wet conditions from atmospheric rivers making landfall along the Pacific coast to hot, dry, and windy conditions brought by Santa Ana winds blowing in from the Southwest interior. Atmospheric rivers are important for water resources while also causing flooding, whereas Santa Ana winds are often associated with wildfire, especially following prolonged dry periods. Preparing for these types of weather events is important for managing resources and protecting life and property, yet reliable forecasts beyond about 7–10 days remain a challenge. We have developed a new prediction system that combines information about approaching atmospheric weather patterns from weather forecast models along with historical information relating those patterns to impacts over California to predict the likelihood of impactful weather at 1–4 weeks lead time. By extending the window of opportunity to take management action, this new approach should aid in resource and emergency planning in water, land, and fire sectors as well as protecting residents through improved warning systems. Key Points: A hybrid dynamical‐statistical model is described for 1–4‐week forecasts of impactful California winter weather using circulation regimesThis hybrid framework reduces the number of forecasts produced, but the ones issued can be interpreted with higher confidenceThis new methodology provides skillful subseasonal forecasts with potential to improve early warnings for impactful weather events [ABSTRACT FROM AUTHOR]

The tropical Pacific plays an important role in modulating the global climate through its prevailing sea-surface temperature spatial structure and dominant climate modes like El Niño-Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), and their teleconnections. These modes of variability, including their oceanic anomalies, are considered to provide sources of prediction skill on subseasonal timescales in the Tropics. Therefore, this study aims to examine how assimilating in situ ocean observations influences the initial ocean sea-surface temperature (SST) and mixed-layer depth (MLD) and their subseasonal forecasts. We analyze two subseasonal forecast systems generated with the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), where the ocean states were initialized using two Observing-System Experiment (OSE) reanalyses. We find that the SST differences between forecasts with and without ocean data assimilation grow with time, resulting in a reduced cold-tongue bias when assimilating ocean observations. Two mechanisms related to air-sea coupling are considered to contribute to this growth of SST differences. One is a positive feedback between the zonal SST gradient, pressure gradient, and surface wind. The other is the difference in Ekman suction and mixing at the Equator due to surface wind-speed differences. While the initial mixed-layer depth (MLD) can be improved through ocean data assimilation, this improvement is not maintained in the forecasts. Instead, the MLD in both experiments shoals rapidly at the beginning of the forecast. These results emphasize how initialization and model biases influence air-sea interaction and the accuracy of subseasonal forecasts in the tropical Pacific. [ABSTRACT FROM AUTHOR]

Modern climate models use both deterministic and stochastic parametrization schemes to represent uncertainties in their physics and inputs. This work considers the problem of estimating the involved parameters of such systems simultaneously with their state through data assimilation. Standard state‐parameter filtering schemes cannot be applied to such systems, owing to the posterior dependence between the stochastic parameters and the "dynamical" augmented state, defined as the state augmented by the deterministic parameters. We resort to the variational Bayesian (VB) approach to break this dependence, by approximating the joint posterior probability density function (pdf) of the augmented state and the stochastic parameters with a separable product of two marginal pdfs that minimizes the Kullback–Leibler divergence. The resulting marginal pdf of the augmented state is then sampled using a one‐step‐ahead smoothing‐based ensemble Kalman filter (EnKF‐OSAS), whereas a closed form is derived for the marginal pdf of the stochastic parameters. The proposed approach combines the effectiveness of the OSAS filtering approach to mitigate inconsistency issues that often arise with the joint ensemble Kalman filter (EnKF), with the advantage of obtaining a full posterior pdf for the stochastic parameters, which is not possible with the traditional maximum‐likelihood method. We demonstrate the relevance of the proposed approach through extensive numerical experiments with a one‐scale Lorenz‐96 model, which includes a stochastic parametrization representing subgrid‐scale effects. [ABSTRACT FROM AUTHOR]

In this work, we integrated the WaveWatch III model into the regional coupled model SKRIPS (Scripps-KAUST Regional Integrated Prediction System). TheWaveWatch III model is implemented with flexibility, meaning the coupled system can run with or without the wave component. To demonstrate the impact of coupling we performed a case study using a series of coupled and uncoupled simulations of tropical cyclone Mekunu, which occurred in the Arabian Sea in May 2018. We examined the skill of the coupled model against the stand-alone WRF model and further investigated the impact of Langmuir turbulence in the coupled system. We found that the coupled model better captures the minimum pressure and maximum wind speed compared with the stand-alone WRF model. The characteristics of the tropical cyclone do not change significantly when using different options to parameterize the influence of waves on the ocean and the atmosphere. However, in the region of the cold wake, when Langmuir turbulence is considered in the coupled system, the sea surface temperature is about 0.5◦C colder and the mixed layer is about 20 meters deeper. This indicates the ocean model is sensitive to the parameterization of Langmuir turbulence in the coupled simulations. [ABSTRACT FROM AUTHOR]