An official website of the United States government
Abstract Historical observations of Earth’s climate underpin our knowledge and predictions of climate variability and change. However, the observations are incomplete and uncertain, and existing datasets based on these observations typically do not assimilate observations simultaneously across different components of the climate system, yielding inconsistencies that limit understanding of coupled climate dynamics. Here, we use coupled data assimilation, which synthesizes observational and dynamical constraints across all climate fields simultaneously, to reconstruct globally resolved sea surface temperature (SST), near-surface air temperature (T), sea level pressure (SLP), and sea ice concentration (SIC), over 1850–2023. We use a Kalman filter and forecasts from an efficient emulator, the linear inverse model (LIM), to assimilate observations of SST, landT, marine SLP, and satellite-era SIC. We account for model error by training LIMs on eight CMIP6 models, and we use the LIMs to generate eight independent reanalyses with 200 ensemble members, yielding 1600 total members. Key findings in the tropics include post-1980 trends in the Walker circulation that are consistent with past variability, whereas the tropical SST contrast (the difference between warmer and colder SSTs) shows a distinct strengthening since 1975. El Niño–Southern Oscillation (ENSO) amplitude exhibits substantial low-frequency variability and a local maximum in variance over 1875–1910. In polar regions, we find a muted cooling trend in the Southern Ocean post-1980 and substantial uncertainty. Changes in Antarctic sea ice are relatively small between 1850 and 2000, while Arctic sea ice declines by 0.5 ± 0.1 (1σ) million km2during the 1920s. Significance StatementThe key advance in our reconstruction is that the ocean, atmosphere, and sea ice are dynamically consistent with each other and with observations across all components, thus forming a true climate reanalysis. Existing climate datasets are typically derived separately for each component (e.g., atmosphere, ocean, and sea ice), leading to spurious trends and inconsistencies in coupled climate variability. We use coupled data assimilation to unify observations and coupled dynamics across components. We combine forecasts from climate models with observations from ocean vessels and weather stations to produce monthly state estimates spanning 1850–2023 and a novel quantification of globally resolved uncertainty. This reconstruction provides insights into historical variability and trends while motivating future efforts to reduce uncertainties in the climate record.
more »
« less
The Impacts of Optimizing Model‐Dependent Parameters on the Antarctic Sea Ice Data Assimilation
Abstract Given the role played by the historical and extensive coverage of sea ice concentration (SIC) observations in reconstructing the long‐term variability of Antarctic sea ice, and the limited attention given to model‐dependent parameters in current sea ice data assimilation studies, this study focuses on enhancing the performance of the Data Assimilation System for the Southern Ocean in assimilating SIC through optimizing the localization and observation error estimate, and two assimilation experiments were conducted from 1979 to 2018. By comparing the results with the sea ice extent of the Southern Ocean and the sea ice thickness in the Weddell Sea, it becomes evident that the experiment with optimizations outperforms that without optimizations due to achieving more reasonable error estimates. Investigating uncertainties of the sea ice volume anomaly modeling reveals the importance of the sea ice‐ocean interaction in the SIC assimilation, implying the necessity of assimilating more oceanic and sea‐ice observations.
more »
« less
- PAR ID:
- 10520588
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 50
- Issue:
- 22
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Abstract. Ocean–sea-ice coupled models constrained by various observations provide different ice thickness estimates in the Antarctic. We evaluatecontemporary monthly ice thickness from four reanalyses in the Weddell Sea: the German contribution of the project Estimating the Circulation and Climate ofthe Ocean Version 2 (GECCO2), the Southern Ocean State Estimate (SOSE), the Ensemble Kalman Filter system based on the Nucleus for European Modelling of the Ocean (NEMO-EnKF) and the Global Ice–Ocean Modeling and Assimilation System (GIOMAS). The evaluation is performed againstreference satellite and in situ observations from ICESat-1, Envisat, upward-looking sonars and visual ship-based sea-ice observations. Compared withICESat-1, NEMO-EnKF has the highest correlation coefficient (CC) of 0.54 and lowest root mean square error (RMSE) of 0.44 m. Compared within situ observations, SOSE has the highest CC of 0.77 and lowest RMSE of 0.72 m. All reanalyses underestimate ice thickness near the coast ofthe western Weddell Sea with respect to ICESat-1 and in situ observations even though these observational estimates may be biased low. GECCO2 andNEMO-EnKF reproduce the seasonal variation in first-year ice thickness reasonably well in the eastern Weddell Sea. In contrast, GIOMAS ice thicknessperforms best in the central Weddell Sea, while SOSE ice thickness agrees most with the observations from the southern coast of the Weddell Sea. Inaddition, only NEMO-EnKF can reproduce the seasonal evolution of the large-scale spatial distribution of ice thickness, characterized by the thickice shifting from the southwestern and western Weddell Sea in summer to the western and northwestern Weddell Sea in spring. We infer that the thickice distribution is correlated with its better simulation of northward ice motion in the western Weddell Sea. These results demonstrate thepossibilities and limitations of using current sea-ice reanalysis for understanding the recent variability of sea-ice volume in the Antarctic.more » « less
-
Abstract This paper describes the new Regional Arctic Ocean/sea ice Reanalysis (RARE) with a domain that spans a subpolar/polar cap poleward of 45°N. Sequential data assimilation constrains temperature and salinity using World Ocean Database profiles as well as in situ and satellite SST, and PIOMAS sea ice thickness estimates. The 41-yr (1980–2020) RARE1.15.2 reanalysis with resolution varying between 2 and 5 km horizontally and 1–10 m vertically in the upper 100 m is examined. To explore the impact of resolution RARE1.15.2 is compared to a coarser-resolution SODA3.15.2, which uses the same modeling and data assimilation system. Improving resolution in the reanalysis system improves agreement with observations. It produces stronger more compact currents, enhances eddy kinetic energy, and strengthens along-isopycnal heat and salt transports, but reduces vertical exchanges and thus strengthens upper ocean haline stratification. RARE1.15.2 and SODA3.15.2 are also compared to the Hadley Center EN4.2.2 statistical objective analysis. In regions of reasonable data coverage such as the Nordic seas the three products produce similar time-mean distributions of temperature and salinity. But in regions of poor coverage and in regions where the coverage changes in time EN4.2.2 suffers more from those inhomogeneities. Finally, the impact on the Arctic of interannual temperature fluctuations in the subpolar gyres on the Arctic Ocean is compared. The influence of the subpolar North Pacific is limited to a region surrounding Bering Strait. The influence of the subpolar North Atlantic, in contrast, spreads throughout the Nordic seas and Barents Sea in all three products within two years. Significance StatementThe Arctic Ocean/sea ice system plays crucial roles in climate variability and change by controlling the northern end of the oceanic overturning circulation, the equator to pole air pressure gradient, and Earth’s energy balance. Yet the historical ocean observation set is sparse and inhomogeneous, while ocean dynamics has challengingly fine horizontal and vertical scales. This paper introduces a new Regional Arctic Ocean/sea ice Reanalysis (RARE) whose goal is to use the combined constraints of mesoscale ocean dynamics, historical observations, surface meteorology, and continental runoff in a data assimilation framework to reconstruct historical variability. RARE is used to produce a 41-yr ocean/sea ice reanalysis 1980–2020 whose results are described here.more » « less
-
Abstract The Arctic region is experiencing significant changes due to climate change, and the resulting decline in sea ice concentration and extent is already impacting ocean dynamics and exacerbating coastal hazards in the region. In this context, numerical models play a crucial role in simulating the interactions between the ocean, land, sea ice, and atmosphere, thus supporting scientific studies in the region. This research aims to evaluate how different sea ice products with spatial resolutions varying from 2 to 25 km influence a phase averaged spectral wave model results in the Alaskan Arctic under storm conditions. Four events throughout the Fall to Winter seasons in 2019 were utilized to assess the accuracy of wave simulations generated under the dynamic sea ice conditions found in the Arctic. The selected sea ice products used to parameterize the numerical wave model include the National Snow and Ice Data Center (NSIDC) sea ice concentration, the European Centre for Medium‐Range Weather Forecasts (ECMWF) Re‐Analysis (ERA5), the HYbrid Coordinate Ocean Model‐Community Ice CodE (HYCOM‐CICE) system assimilated with Navy Coupled Ocean Data Assimilation (NCODA), and the High‐resolution Ice‐Ocean Modeling and Assimilation System (HIOMAS). The Simulating WAves Nearshore (SWAN) model's accuracy in simulating waves using these sea ice products was evaluated against Sea State Daily Multisensor L3 satellite observations. Results show wave simulations using ERA5 consistently exhibited high correlation with observations, maintaining an accuracy above 0.83 to the observations across all events. Conversely, HIOMAS demonstrated the weakest performance, particularly during the Winter, with the lowest correlation of 0.40 to the observations. Remarkably, ERA5 surpassed all other products by up to 30% in accuracy during the selected storm events, and even when an ensemble was assessed by combining the selected sea ice products, ERA5's individual performance remained unmatched. Our study provides insights for selecting sea ice products under different sea ice conditions for accurately simulating waves and coastal hazards in high latitudes.more » « less
-
The top 2000 m of the Southern Ocean has freshened and warmed over recent decades. However, the high-latitude (south of 50°S) southeast Pacific was observed to be cooler and fresher in the years 2008–10 compared to 2005–07 over a wide depth range including surface, mode, and intermediate waters. The causes and impacts of this event are analyzed using the ocean–sea ice data-assimilating Southern Ocean State Estimate (SOSE) and observationally based products. In 2008–10, a strong positive southern annular mode coincided with a negative El Niño–Southern Oscillation and a deep Amundsen Sea low. Enhanced meridional winds drove strong sea ice export from the eastern Ross Sea, bringing large amounts of ice to the Amundsen Sea ice edge. In 2008, together with increased precipitation, this introduced a strong freshwater anomaly that was advected eastward by the Antarctic Circumpolar Current (ACC), mixing along the way. This anomaly entered the ocean interior not only as Antarctic Intermediate Water, but also as lighter Southeast Pacific Subantarctic Mode Water (SEPSAMW). A numerical particle release experiment carried out in SOSE showed that the Ross Sea sector was the dominant source of particles reaching the SEPSAMW formation region. This suggests that large-scale climate fluctuations can induce strong interannual variability of volume and properties of SEPSAMW. These fluctuations act at different time scales: instantaneously via direct forcing and also lagged over advective time scales of several years from upstream regions.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1029/2023GL105690
