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We investigate how the ocean responds to 10-yr persistent surface heat flux forcing over the subpolar North Atlantic (SPNA) associated with the observed winter NAO in three CMIP6-class coupled models. The experiments reveal a broadly consistent ocean response to the imposed NAO forcing. Positive NAO forcing produces anomalously dense water masses in the SPNA, increasing the southward lower (denser) limb of the Atlantic meridional overturning circulation (AMOC) in density coordinates. The southward propagation of the anomalous dense water generates a zonal pressure gradient overlying the models’ North Atlantic Current that enhances the upper (lighter) limb of the density-space AMOC, increasing the heat and salt transport into the SPNA. However, the amplitude of the thermohaline process response differs substantially between the models. Intriguingly, the anomalous dense-water formation is not primarily driven directly by the imposed flux anomalies, but rather dominated by changes in isopycnal outcropping area and associated changes in surface water mass transformation (WMT) due to the background surface heat fluxes. The forcing initially alters the outcropping area in dense-water formation regions, but WMT due to the background surface heat fluxes through anomalous outcropping area decisively controls the total dense-water formation response and can explain the intermodel amplitude difference. Our study suggests that coupled models can simulate consistent mechanisms and spatial patterns of decadal SPNA variability when forced with the same anomalous buoyancy fluxes, but the amplitude of the response depends on the background states of the models., This study is supported by the joint UK-NERC/US-NSF WISHBONE project (NSF-2040020). YRR is supported by a “la Caixa” Foundation fellowship (ID 100010434) and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 847648. NCAR is a major facility sponsored by NSF under Cooperative Agreement No. 1852977., Peer Reviewed, Postprint (published version)

Author Posting. © American Meteorological Society, 2021. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Liang, Y.-C., Frankignoul, C., Kwon, Y.-O., Gastineau, G., Manzini, E., Danabasoglu, G., Suo, L., Yeager, S., Gao, Y., Attema, J. J., Cherchi, A., Ghosh, R., Matei, D., Mecking, J., Tian, T., & Zhang, Y. Impacts of Arctic sea ice on cold season atmospheric variability and trends estimated from observations and a multimodel large ensemble. Journal of Climate, 34(20), (2021): 8419–8443, https://doi.org/10.1175/JCLI-D-20-0578.s1., To examine the atmospheric responses to Arctic sea ice variability in the Northern Hemisphere cold season (from October to the following March), this study uses a coordinated set of large-ensemble experiments of nine atmospheric general circulation models (AGCMs) forced with observed daily varying sea ice, sea surface temperature, and radiative forcings prescribed during the 1979–2014 period, together with a parallel set of experiments where Arctic sea ice is substituted by its climatology. The simulations of the former set reproduce the near-surface temperature trends in reanalysis data, with similar amplitude, and their multimodel ensemble mean (MMEM) shows decreasing sea level pressure over much of the polar cap and Eurasia in boreal autumn. The MMEM difference between the two experiments allows isolating the effects of Arctic sea ice loss, which explain a large portion of the Arctic warming trends in the lower troposphere and drive a small but statistically significant weakening of the wintertime Arctic Oscillation. The observed interannual covariability between sea ice extent in the Barents–Kara Seas and lagged atmospheric circulation is distinguished from the effects of confounding factors based on multiple regression, and quantitatively compared to the covariability in MMEMs. The interannual sea ice decline followed by a negative North Atlantic Oscillation–like anomaly found in observations is also seen in the MMEM differences, with consistent spatial structure but much smaller amplitude. This result suggests that the sea ice impacts on trends and interannual atmospheric variability simulated by AGCMs could be underestimated, but caution is needed because internal atmospheric variability may have affected the observed relationship., We acknowledge support by the Blue-Action Project (the European Union’s Horizon 2020 research and innovation programme, #727852, http://www.blue-action.eu/index.php?id=3498). The WHOI–NCAR group was supported by the U.S. National Science Foundation (NSF) Office of Polar Programs Grants 1736738 and 1737377. Their computing and data storage resources, including the Cheyenne supercomputer (doi:10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory at NCAR. NCAR is a major facility sponsored by the U.S. NSF under Cooperative Agreement No. 1852977. Guillaume Gastineau was granted access to the HPC resources of TGCC under the allocations A5-017403 and A7-017403 made by GENCI. The SST and SIC data were downloaded from the U.K. Met Office Hadley Centre Observations Datasets (http://www.metoffice.gov.uk/hadobs/hadisst). The work by NLeSC was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative. The simulations of IAP AGCM were supported by the National Key R&D Program of China 2017YFE0111800. The NorESM2-CAM6 simulations were performed on resources provided by UNINETT Sigma2–the National Infrastructure for High Performance Computing and Data Storage in Norway (nn2343k, NS9015K).

Climate prediction on subseasonal to decadal time scales is a rapidly advancing field that is synthesizing improvements in climate process understanding and modeling to improve and expand operational services worldwide. Weather and climate variations on subseasonal to decadal timescales can have enormous social, economic and environmental impacts, making skillful predictions on these timescales a valuable tool for decision makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) timescales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) timescales, while the focus broadly remains similar, (e.g., on precipitation, surface and upper ocean temperatures and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal and externally-forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these area, The International Conferences on Subseasonal to Decadal Prediction on which this paper is based were sponsored by: US CLIVAR, NSF, UCAR, NCAR and its Climate and Global Dynamics Laboratory (CGD), NOAA’s Climate Variability and Predictability (CVP) and Modeling, Analysis, Predictions and Projections (MAPP) Programs, Copernicus Climate Change Service, IPSL, and WWRP/WCRP’s Subseasonal-to-Seasonal (S2S) Prediction Project., Postprint (author's final draft)

Author Posting. © American Meteorological Society, 2019. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 32(13), (2019): 3883-3898, doi:10.1175/JCLI-D-18-0735.1., While it has generally been understood that the production of Labrador Sea Water (LSW) impacts the Atlantic meridional overturning circulation (MOC), this relationship has not been explored extensively or validated against observations. To explore this relationship, a suite of global ocean–sea ice models forced by the same interannually varying atmospheric dataset, varying in resolution from non-eddy-permitting to eddy-permitting (1°–1/4°), is analyzed to investigate the local and downstream relationships between LSW formation and the MOC on interannual to decadal time scales. While all models display a strong relationship between changes in the LSW volume and the MOC in the Labrador Sea, this relationship degrades considerably downstream of the Labrador Sea. In particular, there is no consistent pattern among the models in the North Atlantic subtropical basin over interannual to decadal time scales. Furthermore, the strong response of the MOC in the Labrador Sea to LSW volume changes in that basin may be biased by the overproduction of LSW in many models compared to observations. This analysis shows that changes in LSW volume in the Labrador Sea cannot be clearly and consistently linked to a coherent MOC response across latitudes over interannual to decadal time scales in ocean hindcast simulations of the last half century. Similarly, no coherent relationships are identified between the MOC and the Labrador Sea mixed layer depth or the density of newly formed LSW across latitudes or across models over interannual to decadal time scales., FL and MSL are thankful for the financial support from the National Science Foundation (NSF) Physical Oceanography Program (NSF-OCE-12-59102, NSF-OCE-12-59103). The NCAR contribution was supported by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office (CPO) under Climate Variability and Predictability Program (CVP) Grant NA13OAR4310138 and by the NSF Collaborative Research EaSM2 Grant OCE-1243015. NCAR is sponsored by the NSF. NPH is supported by NERC programs U.K. OSNAP (NE/K010875) and ACSIS (National Capability, NE/N018044/1). Y-OK is supported by NOAA CPO CVP (NA17OAR4310111) and NSF EaSM2 grant (OCE-1242989). AR is supported by NASA-ROSES Modeling, Analysis and Prediction 2016 NNX16AC93G-MAP. RZ is supported by NOAA/OAR. Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (http://www.argo.ucsd.edu, http://argo.jcommops.org). The Argo Program is part of the Global Ocean Observing System (http://doi.org/10.17882/42182). Data from the RAPID-MOCHA-WBTS array funded by NERC, NSF and NOAA are freely available from www.rapid.ac.uk/rapidmoc. We thank Stephen Griffies for providing access to the GFDL-MOM025 COREII simulation output and Matthew Harrison and Xiaoqin Yan for their comments on the manuscript. We also thank the anonymous reviewers for their valuable comments., 2020-06-11

Author Posting. © American Meteorological Society, 2012. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 25 (2012): 7781–7801, doi:10.1175/JCLI-D-11-00442.1., Air–sea fluxes from the Community Climate System Model version 4 (CCSM4) are compared with the Coordinated Ocean-Ice Reference Experiment (CORE) dataset to assess present-day mean biases, variability errors, and late twentieth-century trend differences. CCSM4 is improved over the previous version, CCSM3, in both air–sea heat and freshwater fluxes in some regions; however, a large increase in net shortwave radiation into the ocean may contribute to an enhanced hydrological cycle. The authors provide a new baseline for assessment of flux variance at annual and interannual frequency bands in future model versions and contribute a new metric for assessing the coupling between the atmospheric and oceanic planetary boundary layer (PBL) schemes of any climate model. Maps of the ratio of CCSM4 variance to CORE reveal that variance on annual time scales has larger error than on interannual time scales and that different processes cause errors in mean, annual, and interannual frequency bands. Air temperature and specific humidity in the CCSM4 atmospheric boundary layer (ABL) follow the sea surface conditions much more closely than is found in CORE. Sensible and latent heat fluxes are less of a negative feedback to sea surface temperature warming in the CCSM4 than in the CORE data with the model’s PBL allowing for more heating of the ocean’s surface., The CESM project is supported by the National Science Foundation and the Office of Science (BER) of the U.S. Department of Energy. S. Stevensonwas supported byNASAGrantNNX09A020H and B. Fox-Kemper by Grants NSF 0934737 and NASA NNX09AF38G., 2013-05-15

Author Posting. © American Meteorological Society, 2012. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 25 (2012): 1361–1389, doi:10.1175/JCLI-D-11-00091.1., The ocean component of the Community Climate System Model version 4 (CCSM4) is described, and its solutions from the twentieth-century (20C) simulations are documented in comparison with observations and those of CCSM3. The improvements to the ocean model physical processes include new parameterizations to represent previously missing physics and modifications of existing parameterizations to incorporate recent new developments. In comparison with CCSM3, the new solutions show some significant improvements that can be attributed to these model changes. These include a better equatorial current structure, a sharper thermocline, and elimination of the cold bias of the equatorial cold tongue all in the Pacific Ocean; reduced sea surface temperature (SST) and salinity biases along the North Atlantic Current path; and much smaller potential temperature and salinity biases in the near-surface Pacific Ocean. Other improvements include a global-mean SST that is more consistent with the present-day observations due to a different spinup procedure from that used in CCSM3. Despite these improvements, many of the biases present in CCSM3 still exist in CCSM4. A major concern continues to be the substantial heat content loss in the ocean during the preindustrial control simulation from which the 20C cases start. This heat loss largely reflects the top of the atmospheric model heat loss rate in the coupled system, and it essentially determines the abyssal ocean potential temperature biases in the 20C simulations. There is also a deep salty bias in all basins. As a result of this latter bias in the deep North Atlantic, the parameterized overflow waters cannot penetrate much deeper than in CCSM3., NCAR is sponsored by the National Science Foundation. The CCSM is also sponsored by the Department of Energy. SGY was supported by the NOAA Climate Program Office under Climate Variability and Predictability Program Grant NA09OAR4310163., 2012-09-01

Author Posting. © American Meteorological Society, 2012. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 25 (2012): 5153–5172, doi:10.1175/JCLI-D-11-00463.1., Atlantic meridional overturning circulation (AMOC) variability is documented in the Community Climate System Model, version 4 (CCSM4) preindustrial control simulation that uses nominal 1° horizontal resolution in all its components. AMOC shows a broad spectrum of low-frequency variability covering the 50–200-yr range, contrasting sharply with the multidecadal variability seen in the T85 × 1 resolution CCSM3 present-day control simulation. Furthermore, the amplitude of variability is much reduced in CCSM4 compared to that of CCSM3. Similarities as well as differences in AMOC variability mechanisms between CCSM3 and CCSM4 are discussed. As in CCSM3, the CCSM4 AMOC variability is primarily driven by the positive density anomalies at the Labrador Sea (LS) deep-water formation site, peaking 2 yr prior to an AMOC maximum. All processes, including parameterized mesoscale and submesoscale eddies, play a role in the creation of salinity anomalies that dominate these density anomalies. High Nordic Sea densities do not necessarily lead to increased overflow transports because the overflow physics is governed by source and interior region density differences. Increased overflow transports do not lead to a higher AMOC either but instead appear to be a precursor to lower AMOC transports through enhanced stratification in LS. This has important implications for decadal prediction studies. The North Atlantic Oscillation (NAO) is significantly correlated with the positive boundary layer depth and density anomalies prior to an AMOC maximum. This suggests a role for NAO through setting the surface flux anomalies in LS and affecting the subpolar gyre circulation strength., The CCSM project is supported by NSF and the Office of Science (BER) of the U.S. Department of Energy. SGY and YOK were supported by the NOAA Climate Program Office under Climate Variability and Predictability Program Grants NA09OAR4310163 and NA10OAR4310202, respectively., 2013-02-01

Author Posting. © American Meteorological Society, 2007. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 37 (2007): 1918-1938, doi:10.1175/jpo3089.1., The interannual variability in upper-ocean (0–400 m) temperature and governing mechanisms for the period 1968–97 are quantified from a global ocean hindcast simulation driven by atmospheric reanalysis and satellite data products. The unconstrained simulation exhibits considerable skill in replicating the observed interannual variability in vertically integrated heat content estimated from hydrographic data and monthly satellite sea surface temperature and sea surface height data. Globally, the most significant interannual variability modes arise from El Niño–Southern Oscillation and the Indian Ocean zonal mode, with substantial extension beyond the Tropics into the midlatitudes. In the well-stratified Tropics and subtropics, net annual heat storage variability is driven predominately by the convergence of the advective heat transport, mostly reflecting velocity anomalies times the mean temperature field. Vertical velocity variability is caused by remote wind forcing, and subsurface temperature anomalies are governed mostly by isopycnal displacements (heave). The dynamics at mid- to high latitudes are qualitatively different and vary regionally. Interannual temperature variability is more coherent with depth because of deep winter mixing and variations in western boundary currents and the Antarctic Circumpolar Current that span the upper thermocline. Net annual heat storage variability is forced by a mixture of local air–sea heat fluxes and the convergence of the advective heat transport, the latter resulting from both velocity and temperature anomalies. Also, density-compensated temperature changes on isopycnal surfaces (spice) are quantitatively significant., This work was supported in part from NOAA Office of Global Programs ACCP Grant NA86GP0290, NSF Grant OCE96-33681, and the WHOI Ocean and Climate Change Institute.