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Extratropical cyclones, including winter westerly disturbances (WWD) over central Asia, are fundamental features of the atmosphere that maintain energy, momentum, and moisture at global scales while intimately linking large-scale circulation to regional-scale meteorology. Within high mountain Asia, WWD are the primary contributor to regional precipitation during winter. In this work, we present a novel WWD tracking methodology, which provides an inventory of location, timing, intensity, and duration of events, allowing for a comprehensive study of the factors that relate WWD to orographic precipitation, on an individual event basis and in the aggregate. We identify the relationship between the strength of disturbances, the state of the background environment during their propagation, and precipitation totals in the Karakoram/western Himalaya. We observe significant differences in convective and mechanical instability contributions to orographic precipitation as a function of the relationship between the intensity of WWD and the background temperature and moisture fields, which exhibit strong intraseasonal variability. Precipitation is primarily orographically forced during intense WWD with strong cross-barrier winds, while weaker WWD with similar precipitation totals are observed to benefit from enhanced instability due to high moisture content and temperature at low levels, occurring primarily in the late winter/premonsoon. The contribution of these factors is observed to fluctuate on a per-case basis, indicating important influences of intraseasonal oscillations and tropical-extratropical interactions on regional precipitation.

Abstract: The Indian monsoon system (IMS) is among the most complex and important climatic features on land. This study proposes a simple and robust method to investigate large-scale variations and changes in the IMS that accounts for fluctuations in amplitude, onset, and duration of the summer monsoon, including active and break phases, and the postmonsoon season. This study uses 35 years (1979–2013) of daily data from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) at 1° resolution and indicates great potential for application to other reanalyses and climate model datasets. The method is based on combined EOF (CEOF) analysis of variables associated with the IMS’s seasonal cycle (precipitation, circulation at 10 m, and temperature and specific humidity at 2 m). The first CEOF (CEOF-1) explains ~40% of the total variance and represents the continental-scale Asian monsoon. The second CEOF (CEOF-2) explains 11% of the variance and characterizes the Indian monsoon variability, including increased precipitation over western, central, and northern parts of India and the monsoon onset and demise over those regions. Thus, CEOF-2 is referred to as the large-scale index for the Indian monsoon system (LIMS). It is shown that LIMS’s amplitude is strongly correlated with the total June–September precipitation over India. LIMS is continuous in time and can be used to evaluate significant postmonsoon rainfall events that often affect the Indian subcontinent. Moreover, LIMS exhibits spectral variance on intraseasonal time scales that are associated with active and break phases of the monsoon during summer and enhanced rainfall in the postmonsoon period.

Western disturbances (WDs) are synoptic-scale weather systems embedded within the subtropical westerly jet. Manifesting as upper-level troughs often associated with a lower-tropospheric low over Western India, they share some dynamical features with extratropical cyclones. WDs are most common during the boreal winter (December to March), during which they bring the majority of precipitation – both rain and snow – to the Western Himalaya, as well as to surrounding areas of north India, Pakistan and the Tibetan Plateau. WDs are also associated with weather hazards such as heavy snowfall, hailstorms, fog, cloudbursts, avalanches, frost, and coldwaves. In this paper, we review the recent understanding and development on WDs. Recent studies have collectively made use of novel data, novel analysis techniques, and the increasing availability of high-resolution weather and climate models. This review is separated into six main sections – structure and thermodynamics, precipitation and impacts, teleconnections, modelling experiments, forecasting at a range of scales, and paleoclimate and climate change – each motivated with a brief discussion of the accomplishments and limitations of previous research. A number of step changes in understanding are synthesised. Use of new modelling frameworks and tracking algorithms has significantly improved knowledge of WD structure and variability, and a more frequentist approach can now be taken. Improved observation systems have helped quantification of water security over the Western Himalaya. Convection-permitting models have improved our understanding of how WDs interact with the Himalayas to trigger natural hazards. Improvements in paleoclimate and future climate modelling experiments have helped to explain how WDs and their impacts over the Himalaya respond to large-scale natural and anthropogenic forcings. We end by summarising unresolved questions and outlining key future WD research topics. [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]

We analyze uncertainty in model-based estimates of probable maximum precipitation (PMP) as used in dam spillway design. Our focus is on model-based PMP derived from Weather Research and Forecasting (WRF) Model reconstructions of severe historical storms, amplified by the addition of moisture in the boundary conditions [so-called relative humidity maximization (RHM)]. By scaling moisture and predicting the resulting precipitation, the model-based approach arguably is more realistic than currently used techniques [documented in NOAA's Hydrometeorological Reports (HMRs)], which assume that precipitation scales linearly with moisture. Despite the important improvement this represents, model-based PMP is subject to several sources of uncertainty that have slowed adoption in operational settings. We analyze an ensemble of PMP simulations that reflect recognized sources of uncertainty including the following: 1) initial condition error, 2) choice of physics parameterizations, and 3) upscale propagating model errors. We apply this ensemble approach to the Feather River watershed (Oroville Dam) in California for the storms of February 1986 and January 1997, which produced some of the largest floods on record at that location, after carrying out in-depth evaluations of model reconstructions. Differences in the maximized 72-h precipitation totals across the 56 ensemble members we produced for each storm are modest, ranging from ±7% of ensemble mean. Our results suggest that while model-based PMP estimates should be interpreted as a range of values, model uncertainty appears to be relatively small for the major atmospheric river–driven flood events we investigated. [ABSTRACT FROM AUTHOR]

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