Search arXivSearch

arXiv · 1711.10880

The Arctic Ocean seasonal cycles of heat and freshwater fluxes: observation-based inverse estimates

Abstract

This paper presents the first estimate of the seasonal cycle of ocean and sea ice net heat and freshwater (FW) fluxes around the boundary of the Arctic Ocean. The ocean transports are estimated primarily using 138 moored instruments deployed in September 2005 to August 2006 across the four main Arctic gateways: Davis, Fram and Bering Straits, and the Barents Sea Opening (BSO). Sea ice transports are estimated from a sea ice assimilation product. Monthly velocity fields are calculated with a box inverse model that enforces volume and salinity conservation. The resulting net ocean and sea ice heat and FW fluxes (annual mean $\pm$ 1 standard deviation) are 175 $\pm$48 TW and 204 $\pm$85 mSv (respectively; 1 Sv = 10$^{6} m^{3} s^{-1}$). These boundary fluxes accurately represent the annual means of the relevant surface fluxes. Oceanic net heat transport variability is driven by temperature variability in upper part of the water column and by volume transport variability in the Atlantic Water layer. Oceanic net FW transport variability is dominated by Bering Strait velocity variability. The net water mass transformation in the Arctic entails a freshening and cooling of inflowing waters by 0.62$\pm$0.23 in salinity and 3.74$\pm$0.76C in temperature, respectively, and a reduction in density by 0.23$\pm$0.20 kg m$^{-3}$. The volume transport into the Arctic of waters associated with this water mass transformation is 11.3$\pm$1.2 Sv, and the export is -11.4$\pm$1.1 Sv. The boundary heat and FW fluxes provide a benchmark data set for the validation of numerical models and atmospheric re-analyses products.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Takamasa Tsubouchi, Sheldon Bacon, Yevgeny Aksenov, Alberto C. Naveira Garabato, Agnieszka Beszczynska-Möller, Edmond Hansen, Laura de Steur, Beth Curry, Craig M. Lee. 2017-11-29. The Arctic Ocean seasonal cycles of heat and freshwater fluxes: observation-based inverse estimates. https://doi.org/10.1175/jpo-d-17-0239.1

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting

Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics numerical weather prediction models can represent the processes governing RI, resolving storm-environment interactions remains computationally expensive, while purely data-driven approaches often lack physical interpretability. We present FAST-ML, a hybrid framework that bridges data-driven efficiency with physical constraints. A physically informed dual-stream neural parameterization ingests 3D ERA5 fields to diagnose ventilation controls---environmental wind shear and mid-level entropy deficit. By optimizing these parameters end-to-end through a differentiable FAST intensity model, this architecture establishes a robust new paradigm for observation-driven parameter optimization, ensuring storm evolution remains strictly governed by thermodynamic principles. By better capturing the storm's continuous intensity evolution, FAST-ML improves upon its physical baseline, reducing ensemble CRPS across forecast lead times, with a reduction of approximately 31% at 60 h and nearly halving the RI false alarm ratio without sacrificing detection skill. In a 100-member ensemble configuration, FAST-ML produces intensity forecasts comparable to FNV3 for selected storms under the evaluated input configurations. Furthermore, zero-shot tests on selected Eastern Pacific storms provide encouraging evidence of cross-basin transferability. FAST-ML provides a modular intensity forecasting framework that can be coupled with externally supplied storm tracks and environmental fields. It demonstrates that observation-driven parameter learning within physically constrained dynamics simultaneously enhances accuracy, interpretability, and computational efficiency.

physics.ao-ph

West-WRF AI 2-km: High-Resolution Prediction of Integrated Vapor Transport and Precipitation

We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resolution elsewhere globally. Forecasting over the western U.S. is challenging because complex topography and atmospheric rivers (ARs) strongly influence orographic precipitation. West-WRF AI 2-km builds on a global model pretrained with a 40-year European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset and is fine-tuned with the Center for Western Weather and Water Extremes (CW3E) 2-km regional reanalysis to produce autoregressive 6-hourly forecasts of precipitation and integrated vapor transport (IVT). Forecasts are evaluated over winters 2020-2023 using gridded precipitation observations, rain gauges, and AR Reconnaissance dropsondes and are benchmarked against coarser-resolution AI forecasts and regional and global numerical weather prediction (NWP) systems. West-WRF AI 2-km reproduces observed precipitation-intensity distributions, retains fine-scale spectral variability, and produces sharper narrow coastal precipitation bands and localized, terrain-sensitive extremes. Its broader-scale performance remains comparable to coarser-resolution configurations while preserving large-scale skill despite higher resolution. Dropsonde verification shows lower errors and improved categorical skill at the most extreme IVT threshold. Overall, West-WRF AI 2-km provides its greatest value for localized precipitation extremes and intense AR-related moisture transport.

physics.ao-ph

Hydrological Constraints on Temperature Sensitivities of Precipitation Frequency and Intensity

To improve understanding of how precipitation frequency and intensity change with warming, we combine the coupled land--atmosphere water balance with a stochastic hydrological model that retains explicit dependence on precipitation frequency and event depth. We find a linear relationship between co-variations of rainfall frequency and intensity, where the slope is a hydrological sensitivity determined by the dryness and storage indices and the intercept is a forcing term associated with changes in moisture convergence, potential evapotranspiration, and effective water-storage capacity. While a given relative change in either rainfall frequency or intensity produces an identical relative change in the mean rate, rainfall intensity additionally influences the runoff coefficient by altering terrestrial water storage relative to rainfall depth. This means frequency and intensity have different impacts on the water cycle, allowing the frequency--intensity slope to depart from -1. Analysis of long-term MOPEX records shows that the forcing term is dominated by moisture convergence and that the frequency--intensity relation becomes steeper from wet to dry regimes, consistent with the slope predicted by the theory. The framework provides a physical interpretation of the observed negative covariation between precipitation frequency and intensity and clarifies the roles of hydrological partitioning and atmospheric moisture supply in their responses to warming.

physics.ao-ph