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1. Title Page Research grant proposal to NOAA’s ESS Program – Atlantic Meridional Overturning Circulation (AMOC)–Mechanisms and Decadal Predictability Federal Funding Opportunity Number: NOAA-OAR-CPO-2013-2003445

Title: Modeling Effects of Greenland Ice Sheet Melting on AMOC Variability and Predictability.

Names and Institutions of Collaborators Lead-PI: Dr. Andreas Schmittner, College of Earth, Ocean, and Atmospheric Sciences, Oregon

State University, CEOAS Admin Bldg, Corvallis OR 97331-5503, USA, phone: 541 737 9952, email: aschmitt@coas.oregonstate.edu

Institutional Representative: Patricia Hawk, Sponsored Programs, Oregon State University, phone: 541 737 4933, email: sponsored.programs@oregonstate.edu

Co-PIs: Dr. Aixue Hu, National Center for Atmospheric Research, Boulder, CO, P.O. Box 3000, Boulder, Colorado 80307-3000, phone: (303) 497-1334, email: ahu@ucar.edu; Dr. Sebastian H. Mernhild, Computational Physics and Methods (CCS-2), Mail Stop B296, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, Los Alamos National Laboratory, New Mexico, USA

Collaborators: Dr. R. Stouffer, Geophysical Fluid Dynamics Laboratory, Princeton University Forrestal Campus, 201 Forrestal Road, Princeton, NJ 08540-6649, phone: 609 452-6576, email: Ronald.Stouffer@noaa.gov; Drs. A. Abe-Ouchi and M. Yoshimori, Atmosphere and Ocean Research Institute, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-8568, Japan, phone 81-4-7136-4395, emails: abeouchi@aori.u-tokyo.ac.jp, masakazu@aori.u-tokyo.ac.jp, Dr. U. Mikolajewicz, Max-Planck-Institute for Meteorology, Hamburg, Germany, email: uwe.mikolajewicz@zmaw.de; Dr. D. Swingedouw, Laboratoire des Sciences du Climat et de l’Environment, Institut Pierre Simon Laplace, 91191 Gif-sur-Yvette, France, email: Didier.swingedouw@lsce.fr; Dr. S. Drijfhout, Royal Netherlands Meteorological Institute (KNMI), Netherlands, phone: 31 30 220 6395, email: sybren.drijfhout@knmi.nl, Dr. O. Saenko, Canadian Center for Climate Modeling and Analysis, Victoria, Canada, phone: 250-363-8267, email: oleg.saenko@ec.gc.ca; Dr. K. Taylor, Program for Climate Model Diagnosis and Intercomparison (PCMDI), Lawrence Livermore National Laboratory, Livermore, CA, phone: 925 423-3623, email: taylor13@llnl.gov

Total amount of Federal funds requested: total = $364,686

year 1: $95,240; year 2: $163,038; year 3: $106,408

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2. Abstract Name of the Competition: Earth System Science Program, Atlantic Meridional Overturning Circulation (AMOC)–Mechanisms and Decadal Predictability

Recent observations reveal accelerated melting of the Greenland Ice Sheet (GrIS). Projections of future effects suggest continuing ice loss at increasing rates for business-as-usual anthropogenic greenhouse gas emissions scenarios. Additional meltwater fluxes into the surrounding North Atlantic ocean will increase the buoyancy of surface waters, which may reduce their rates of convection, subduction and sinking to the deep ocean and hence slow down the AMOC. Detailed estimates of the GrIS mass balance show that it is influenced by North Atlantic climate variability, suggesting a possible feedback between the GrIS and the AMOC. However, most comprehensive climate models currently do not include interactive ice sheets. Thus projections of future climate change performed with these models (including CMIP5) do not consider impacts of GrIS melting on AMOC variability although it is well known that the AMOC is sensitive to freshwater fluxes to the North Atlantic. The probabilities of AMOC reduction and shutdown for a given greenhouse gas emission scenario are therefore poorly known. Moreover, previous studies of AMOC internal variability and predictability did not consider feedbacks between the GrIS and the AMOC. Here we propose to organize a model intercomparison project, involving the major climate modeling centers around the world, aimed at quantifying the effects of GrIS mass balance changes on current and future AMOC variability and predictability including uncertainty estimates. Realistic meltwater scenarios will be developed based on a new approximation of GrIS surface mass balance changes. The meltwater will be distributed to the ocean along the Greenland coast using a realistic runoff scheme. The range of meltwater scenarios will consider uncertainties associated with estimating future mass balance changes. Different state-of-the-science climate models will be forced with these scenarios in addition to standard radiative forcing in order to quantify the AMOC response to warming and meltwater input as well as the uncertainty of model AMOC sensitivities to the imposed forcings. Probabilistic AMOC projections will be computed based on the multi-model ensemble. Simulations with an interactive scheme of GrIS mass balance changes will be used to quantify the effect of ice sheet – ocean interactions on AMOC variability and predictability on decadal to centennial time scales. The model experiments will be carefully analyzed in order to understand responses and model differences. The probability of an AMOC shutdown in the coming two centuries will be quantified. The project will lead to international collaboration between scientists at different modeling centers and a new collaboration between global climate modelers and an expert on observations and detailed mass balance modeling of the GrIS.

Relevance to NOAA’s Long Term Goal of Climate Adaptation and Mitigation: Scientific understanding of the interactions between the cryosphere and the ocean will be advanced through realistic modeling of feedbacks between the GrIS and the AMOC. The combined potential impacts of global warming and melting of the GrIS on the AMOC will be assessed on decadal to centennial time scales. The project will lead to useful predictions of likely climate impacts including associated uncertainties, which can support mitigation and adaptation choices by decision makers. The PIs are actively engaged in education and outreach activities that will continue to improve public climate literacy. Results of this project will be published in the peer-reviewed literature and disseminated as broadly as possible, e.g. through press releases via Oregon State University’s News and Research Communications office and interviews with reporters.

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3. Results from prior research NSF Marine Geology & Geophysics (0728315-OCE) “Ocean Circulation, Oxygen and Nutrient Cycles During the Last Glacial Period”, 09/2007 – 08/2010 Schmittner ($363,481) The objective of this project was to develop a model of nitrogen isotopes, and to examine millennial time scale variability of nutrients, oxygen, and the greenhouse gas nitrous oxide (N2O) during the last glacial interval in the context of changing ocean circulation and climate. A major accomplishment is the successful development of the first (to our knowledge) detailed, three-dimensional global model of nitrogen isotope cycling in the ocean. A database of contemporary δ15NNO3 observations has been compiled and is available for download on the project web site (mgg.coas.oregonstate.edu/~andreas/Nitrogen). Three papers have been published: a detailed model description and comparison to observations [Somes et al., 2010b], a description of effects of iron limitation on the distribution of nitrogen isotopes in the modern ocean [Somes et al., 2010a], and a study on the effects of AMOC variations during the last glacial period on marine production of N2O and resulting changes in atmospheric N2O concentrations [Schmittner et al., 2011a]. A fourth paper analyzing the effect of post-depositional alteration in the sediments is in press [Robinson et al., in press]. Two manuscripts describing modern and paleo results are currently in review or in preparation.

NSF Paleoclimate Program (0602395-ATS) “Collaborative Research: Project PALEOVAR—Past Climate Variability: Understanding Mechanisms and Interactions with the Mean State”, 07/2006-06/2011 Pisias, Bartlein, Brook, Clark, Edwards, Hostetler, Mix, and Schmittner ($283,269 Schmittner part) For this project we have developed a new coupled climate model suitable for paleoclimate applications. The model, called OSUVic, is available at http://mgg.coas.oregonstate.edu/~andreas/OSUVic. First applications with that model have led to new insights into the fundamental driving forces of the AMOC. In a paper published in the Journal of Climate [Schmittner et al., 2011b] we have shown that the AMOC is driven by the effects of orography (mountains and ice sheets) on atmospheric freshwater fluxes and wind stress. A study published in Science has pioneered a new method to estimate climate sensitivity from temperature reconstructions of the Last Glacial Maximum [Schmittner et al., 2011a]. A paper published in Nature has shown that during the last deglaciation CO2 preceded global temperature [Shakun et al., 2012]. Other published results are a paper in PNAS on interactions between the AMOC and ice-shelfs in the glacial North Atlantic [Marcott et al., 2011], two AGU monograph chapters, one on AMOC and climate variability during the last glacial period [Clark et al., 2007], and one on the effects of AMOC variations on ocean carbon cycle and atmospheric CO2 [Schmittner et al., 2007], and a description and evaluation of the climate model GENMOM [Alder et al., 2011].

NSF Polar Programs (0944764-PP) “Atmospheric CO2 and Abrupt Climate Change” 01/2010 – 12/2013 (includes one year no cost extension) PI J. Ahn ($448,074) The goal of this project is to improve our understanding of abrupt variations of climate, AMOC, and atmospheric CO2 by measuring CO2 samples in bubbles from Antarctic ice cores in high resolution and comparing the measurements to model simulations. Initial results published in Geophysical Research Letters show abrupt increases in atmospheric CO2 during the last glacial period, the origin of which remains unclear [Ahn et al., 2012].

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4. Statement of Work

4.1 Identification of the Problem The AMOC is important for global heat transport and biogeochemical cycles. Since it is driven to a large degree by surface buoyancy fluxes it is sensitive to warming and freshening in the North Atlantic. Future projections examining the effects of continued release of anthropogenic greenhouse gases into the atmosphere suggest that both processes will lead to a reduction in the AMOC strength over the coming decades [e.g. Manabe and Stouffer, 1994]. Model simulations performed for the previous (fourth) assessment report (AR4) of the Intergovenmental Panel on Climate Change (IPCC) showed a 25(±25)% reduction of the AMOC by the year 2100 for an intermediate (A1B) carbon emission scenario [Schmittner et al., 2005]. Fig. 1 shows that similar results are emerging from the ongoing analysis of new climate model simulations performed for AR5 as part of the Coupled Model Intercomparison Project 5 (CMIP5). By the end of the 21st century the AMOC decreases in these projections by 5-40% (RCP4.5) and 15-60% (RCP8.5) [Cheng et al., in review].

It is known that the AMOC can respond highly non-linear to freshwater forcing and possibly exhibits multiple steady states separated by thresholds and hysteresis behavior [Stommel, 1961; Bryan, 1986; Manabe and Stouffer, 1988; Stocker and Wright, 1991; Rahmstorf, 1996; Hawkins et al., 2011]. Stouffer et al. [2006], using a multi-model ensemble, show that freshwater input to the North Atlantic of 0.1 Sv leads to a 30(±25)% AMOC decline, whereas a 1 Sv forcing leads to a collapse in all models. These modeling results, taken together with evidence from the paleoclimate record that indicate abrupt transitions happened in the past [e.g. Broecker et al., 1985; Shakun et al., 2012], make the AMOC one of the prime candidates for rapid and potentially irreversible climate change. A collapse of the AMOC would have large impacts on climate [Wood et al., 2003], ecosystems [Scholze et al., 2003; Schmittner, 2005], and biogeochemical cycles [e.g. Schmittner and Galbraith, 2008]. What is the probability of a future AMOC collapse? The AR4 concluded “it is very unlikely that the MOC will undergo a large abrupt transition during the course of the 21st century” but that “at this stage, it is too early to assess the likelihood of a large abrupt change of the MOC beyond the end of the 21st century” [Meehl et al., 2007].

An important reason for the current difficulties to assess AMOC predictability is the fact that projections with comprehensive climate models performed for the IPCC assessment reports do not include meltwater runoff, or distributed runoff, from the GrIS even though recent observations show rapid melting (Fig. 2c). Rignot et al. [2011] estimate the ice mass loss from Greenland to be ~300 Gt/yr in 2010 with an acceleration of 22 Gt/yr2 during the past 18 years. The accelerated melting is partly due to changes in atmospheric circulation [Fettweis et al., 2012a; Hanna et al., 2012]. If this trend continued the freshwater flux from the GrIS to the ocean in 2100 would be about 0.067 Sv [Swingedouw et al., 2012]. Ice sheet and mass balance modeling studies also indicate accelerated melting in the 21st century (Fig. 3) [Huybrechts et al., 2004; Mernild et al., 2010; Fettweis et al., 2012b; Yoshimori and Abe-Ouchi, 2012]. A few uncoordinated studies assessing the effect of Greenland melting on the AMOC in comprehensive climate models have used either various idealized distributions and/or rates of freshwater fluxes [Swingedouw et al., 2006, 2012; Hu et al., 2011] or interactive coupling to an ice sheet model [Fichefet et al., 2003; Driesschaert et al., 2007; Mikolajewicz et al., 2007a,b]. Fichefet et al. [2003] and Swingedouw et al. [2006] find a much larger AMOC reduction if land ice melt is

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included than when it is neglected. In contrast, Mikolajewicz et al. [2007a, 2007b] estimate that freshwater fluxes due to ice sheet melting are much smaller than those due to changes in atmospheric moisture transport in their model (ECHAM3-LSG2-SICOPOLIS) and find little effect of ice sheet melting on the initial AMOC reduction although the melting affected the recovery phase more, in line with other studies [Huybrechts et al., 2002; Ridley et al., 2005]. Hu et al. [2011] find a significant effect of Greenland melting on the AMOC in NCAR’s CCSM3 only for very large meltwater fluxes (~0.2 Sv) using the A1B scenario. For the same carbon scenario Jungclaus et al. [2006] find a 35% (42%) AMOC reduction for a “conservative” (0.03 Sv) and “high” (0.09 Sv) estimate of the meltwater fluxes compared with a 30% decrease without Greenland melting. The reasons for these different responses are not known although it could be that models with a weaker AMOC [Jungclaus et al., 2006] or with less leakage of the freshwater anomaly from the subpolar gyre [Swingedeouw et al., 2012] are more sensitive than others. Together these studies suggest that the AMOC response depends both on the forcing and on the sensitivity of the climate models to that forcing.

Since these uncoordinated studies all use different magnitudes and distributions of the meltwater forcings they are difficult to compare. The first, to our knowledge, coordinated effort is the recent study by Swingedeouw et al. [2012], who used five European climate model simulations of the historical period (20th century) forced for 40 years with 0.1 Sv of freshwater equally distributed around Greenland. The AMOC decreases by 1-4 Sv in these simulations. However, no coordinated experiments of future scenarios with comprehensive models have been performed to date. Here we propose to organize a model intercomparison project aimed at quantifying the effect of GrIS melting on AMOC variability and predictability over the next two centuries.

Observations from the historical period show that Arctic and Greenland temperature variations on multidecadal time scales are highly correlated with the Atlantic Multidecadal Oscillation (AMO) and anti-correlated with Antarctic temperatures [Chylek et al., 2010; Hanna et al., 2012]. This indicates that AMOC variability, which has been linked to the AMO and is known to modulate interhemispheric heat transport [Crowley, 1992], has an important influence on Greenland. Detailed simulations show that a stronger AMO leads to a more negative surface mass balance (SMB) of the GrIS [Mernild and Liston, 2012]. The correlation coefficient for the data shown in Fig. 2 is -0.51 and the slope is -220 km3/yr w.e. per unit AMO. The implied impacts of AMOC variability on the GrIS SMB are consistent with results from modeling studies [Vizcaíno et al., 2008; Yoshimori and Abe-Ouchi, 2012]. This suggests a possible negative feedback between the AMOC and the GrIS SMB, whereby an increased AMOC/AMO leads to higher GrIS SMB loss, which will lead to increased freshwater input to the North Atlantic and decrease the AMOC/AMO. Currently, however, it is unknown if this feedback is important in modulating multidecadal AMOC variability.

4.2 Scientific Objectives 1. Estimate the effect of GrIS melting on AMOC variability.

2. Estimate the effect of GrIS melting on AMOC predictability.

3. Quantify the probability of an AMOC shutdown in the next two centuries.

4.3 Proposed Methodology Our methodology is guided by the following principles:

- projections should be as realistic as possible, and

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- uncertainties should be quantified to the best of our ability.

Advancements in the realism of projections will be achieved by using state-of-the-science climate models, the best possible estimates of GrIS mass balance, including spatial detailed runoff distributions and freshwater fluxes to the ocean. From the discussion in subsection 4.1 we conclude that there are two main sources of uncertainty. First, there is forcing uncertainty due to estimating GrIS SMB and associated freshwater fluxes to the ocean. Second, there is uncertainty in the sensitivity of the AMOC to a given forcing. Forcing uncertainty will be quantified by using a range of different estimates of GrIS melting. AMOC sensitivity uncertainty will be quantified by using a range of structurally and parametrically different climate models.

We plan to organize an international intercomparison project involving many of the major climate modeling groups around the world. We have already contacted several groups. Currently scientists from six international centers have indicated their interest to collaborate (for names see collaborators listed on title page). The groups are NCAR (US), GFDL (US), IPSL (France), MIROC (Japan), CCCma (Canada), and MPI (Germany). If this project is funded we will reach out to all other major modeling centers as well in order to ensure a maximum number of models participate.

4.3.1 Estimating Meltwater Fluxes from the Greenland Ice Sheet The GrIS gains mass mainly from snowfall and loses mass from surface ablation (evaporation, sublimation, and runoff through melting) as well as calving of icebergs into the ocean and submarine melting of glaciers. There is also mass gain from refreezing rain and mass loss from subglacial melting, but those are minor contributions to the total ice sheet mass balance. Current mass loss and its recent acceleration from ice calving are of similar magnitude as those from surface melting [van den Broeke et al., 2009; Rignot et al., 2011]. Future changes of the GrIS on century timescales, however, are likely to be dominated by surface melting. Calving and ice dynamics are deemed less important on these timescales because they are controlled mainly by slowly changing ice sheet topography and because calving can only increase until the glacier becomes land locked, at which point its fate is controlled by surface processes. Some recent observations suggest that surface melting and penetration of meltwater to the base of the ice sheet may lead to lubrication, glacial sliding and accelerated ice flow [Zwally et al., 2002]. However, these processes are not well understood. It is controversial whether or not the observed recent acceleration of ice flow is caused by this mechanism and if it will or will not continue into the future [Nick et al., 2009; Parizek and Alley, 2004; Sundal et al., 2011]. Submarine melting of outlet glaciers and ice shelfs has also been implicated for accelerated ice flow [Holland et al., 2008; Rignot et al., 2010; Rignot and Steffen, 2008] but the circulation in the fjords and melt rates are not well understood [Straneo et al., 2011]. Hence ice-sheet-wide quantitative estimates of the effects of submarine melting on the recently observed accelerated flow remain lacking and the importance of this process for future mass balance changes remains unknown. For these reasons we will focus on estimating changes in the SMB, although we will also consider possible changes in ice dynamics.

Different detailed SMB and runoff models of the GrIS have recently been developed. There are two general approaches in order to resolve the narrow and steep ablation zone around the ice sheet margin. (1) Direct downscaling of GCM results on a high resolution grid used for SMB calculations [Huybrechts et al., 2004; Yoshimori and Abe-Ouchi, 2012], and (2) dynamic downscaling using a high resolution regional climate model (RCM) forced at its boundaries by

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GCM output [Mernild et al., 2010; Fettweis et al., 2012b]. Method (2) is physically more realistic but also computationally more expensive compared with method (1). For the calculation of the SMB two general approaches can be distinguished. (i) Empirical relationships such as the temperature-index [e.g. Yoshimori and Abe-Ouchi, 2012] or positive-degree-day methods [e.g. Huybrechts et al., 2004] and (ii) more detailed and process based energy balance models [Mernild et al., 2010; Mernild and Liston, 2012; Fettweis et al., 2012b].

All studies, no matter which method used, agree that in future global warming scenarios snowfall will increase in the interior of the ice sheet and that ablation will increase near the margins. They also agree that the increase in snowfall will be less than the increase in melting (Fig. 2), which implies that the entire ice sheet will loose mass leading to enhanced meltwater flux into the ocean. However, different methods yield quantitatively different results for the total GrIS SMB changes even for the same climate model and scenario as illustrated in the following example. Mernild et al. [2010] (MEA10 in the following) use output from the SRES A1B scenario simulation of the global ECMAM5/MPI-OM1 climate model downscaled with a high-resolution (25 km) regional climate model (HIRHAM4) and then bias corrected using observations to drive a detailed, high-resolution process based model of snowpack evolution (SnowModel). Between the 1950s and 2070s they estimate a decrease in the SMB of ~350 km3yr-1 or ~1.2 mm SLE yr-1. Yoshimori and Abe-Ouchi [2012] (Y&A-O12), on the other hand, use global climate model anomalies directly to force a high-resolution (2 km) empirical model of the GrIS SMB, which results in about twice the freshwater flux (~2 mm SLE yr-1) for the same model and scenario. Fettweis et al. [2012b] (FEA12), using non-bias corrected output from the same model and scenario downscaled with the regional climate model MAR, calculate ~600 Gt yr-1 (1 Gt ≅ 1 km3) of ice mass loss until year 2100. Although the methods of FEA12 and MEA10 are similar (dynamical downscaling and energy balance SMB model) their results are very different, whereas Y&A-O12 use a different method (direct downscaling and temperature-index SMB) their result is similar to the one by FEA12. The difference between FEA12 and MEA10 suggests that in this case the bias correction is important, whereas the difference between MEA10 and Y&A-O12 indicates the importance of the downscaling and SMB schemes. Bias correction is important due to non-linearities in the system associated with the ice/snow albedo feedback and changes in the equilibrium line altitude (ELA). As an example consider a climate model that is too warm over Greenland (like ECHAM5, see FEA12). Such a model has its ELA on too high elevations, where the surface slope is lower. A given warming associated with a fixed upward shift of the ELA will expose a larger area to melting than it would if the ELA was at lower elevations, where the surface slope is steeper. Thus, a warm biased model can lead to overestimated ablation.

The use of empirical SMB schemes can also be associated with biases. The analysis by Y&A-O12 indicates that the temperature index method can lead to underestimated ablation in areas of the ice sheet that are below the freezing point (not melting) in the downscaled SMB grid but at the freezing point (melting) in the GCM grid. They suggest that in this case GCM temperatures are affected by the latent heat that goes into melting ice, which is not accounted for in the temperature index method. The use of dynamical downscaling and energy balance methods for the SMB calculations avoids these biases. Whereas direct downscaling leads to maximum warming over the highest part of the GrIS, dynamical downscaling shows a different pattern with increased warming along the northern and eastern margins and less, and more uniformly distributed warming over the interior of the ice sheet [Fettweis et al., 2012b; Mernild et al.,

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2010]. The warming patters resulting from dynamical downscaling have been attributed to the disappearance of Arctic sea ice [Mernild et al., 2010] amplified by snow albedo feedback over tundra-covered areas [Fettweis et al., 2012b]. FEA12 find that RCM simulated surface temperature anomalies cannot be predicted from the GCM surface temperature anomalies due to differences in the surface schemes, whereas they can be predicted from GCM temperature anomalies at higher elevations (600 hPa) because these are not affected by the surface scheme.

FEA12 show that changes in the SBM of the entire GrIS can be well predicted by the following equation:

ΔSMB ΔSF − 84.2 ⋅ ΔT 600JJA − 2.4 ⋅(ΔT 600JJA )2 −1.6 ⋅(ΔT 600JJA )

3 , (1)

where ΔSF is the GCM predicted snowfall anomaly and ΔT 600JJA is the GCM predicted summer (JJA) temperature anomaly at 600 hPa. A comparison between SMB estimates based on eq. (1) and RCM simulated SMB (Figs. 3, 4) shows that this is a good approximation valid for a wide range of SMB changes and climates. Annual (decadal) averages have correlation coefficients of 0.89 (0.98) and the root-mean-squared-error (RMSE) for the GCM-derived SMB is 87 (35) Gt yr-1 [Fettweis et al., 2012b]. Here, we propose to use eq. (1) to estimate GrIS SMB changes because running a RCM for each CMIP5 GCM would be prohibitively expensive.

Decreases in GrIS SMB estimated using CMIP5 models forced with scenario RCP8.5 and eq. (1) show a large spread ranging from ~200 to more than 1,200 Gt yr-1 (Fig. 3), corresponding to a freshwater flux to the ocean of less than 0.01 Sv to more than 0.04 Sv. The single most important factor determining this spread is the global mean near surface air temperature change (ΔSAT) of the GCM [Fettweis et al., 2012b; Yoshimori and Abe-Ouchi, 2012]. It is well known that ΔSAT in transient experiments is strongly related to the equilibrium climate sensitivity for a doubling of atmospheric CO2 (ΔSAT2xC). Numerous studies have calculated probability density functions (PDFs) for ΔSAT2xC [e.g. Knutti and Hegerl, 2008; Schmittner et al., 2011a]. The relationship between ΔSAT2xC and the GrIS SMB will allow us to assign probabilities to the meltwater scenarios based on published PDFs. The resulting probabilities will be used in the estimation of the probability of AMOC projections including those for an AMOC shutdown.

FEA12 show that the seasonality of the SMB does not change much in the future. The melting season remains limited from May to September even for extreme scenarios and the largest changes in runoff occur in July and August. Thus, for our purpose it is warranted to apply the runoff anomaly to a fixed summer season.

We will use a detailed runoff model for the GrIS [Liston and Mernild, 2012; Mernild and Liston, 2012] for a realistic distribution of the meltwater into the ocean (Fig. 5). The modern fraction of total GrIS runoff will be calculated for each catchment. Projected changes in runoff will then be distributed according to this fixed fraction and enter the ocean at the corresponding climate model grid points. A sensitivity experiment with a runoff fractions calculated from a RCM simulated SMB distribution at the end of the century will quantify the error induced due to the assumption of a fixed runoff fraction.

The European project EMBRACE uses a similar approach to the one proposed here and will estimate GrIS mass balance changes corresponding to the RCP8.5 scenario. The four European models participating in EMBRACE are MPI, IPSL, HadGEM, and ECEarth. We are already in contact with and will continue to collaborate and coordinate with Sybren Drijfhout (KNMI) who

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leads that project. Dr. Drijfhout will also include estimates of changes in ice dynamics and iceberg calving based on the modern distribution of fluxes derived from a high-resolution ocean model that tracks icebergs [Marsh et al., 2010]. EMBRACE will assume a constant distribution and estimate Greenland-wide meltwater fluxes based upon an update of the previous study of Katsman et al. [2011].

4.3.2 Estimating the Probabilistic AMOC Response to Warming and GrIS Melting Global warming is already increasing the buoyancy of surface waters at high latitudes both by raising their temperatures and by decreasing their salinities due to an enhanced hydrological cycle and increased meridional water vapor flux [Levitus, 2005; Boyer et al., 2007]. Both effects decrease the density of surface waters in the North Atlantic and, if continued into the future, will lead to a reduction in the AMOC [Manabe et al., 1994; Stocker and Schmittner, 1997; Gregory et al., 2005]. Due to the long time scales associated with ice sheet responses to climate change ice sheet models have so far not been coupled interactively to most comprehensive ocean-atmosphere models. Thus, AMOC projections with these models (e.g. Fig. 1) are conservative. If ice sheet melting was included we would expect an additional freshwater flux into the ocean and presumably enhanced AMOC reduction.

High-resolution ocean modeling indicates that meltwater from Greenland initially (in the first few years) stays close to Greenland and may have little effect on the circulation [Marsh et al., 2010]. However, after a few years the meltwater begins to be dispersed into the North Atlantic subpolar gyre and affects the AMOC similarly in high-resolution models as in coarse resolution models [Weijer et al., 2012]. This suggests that coarse resolution global climate models are suitable tools to study the potential effects of Greenland meltwater on the AMOC. Here we will use state-of-the-art coupled ocean-atmosphere-sea ice models from some of the major climate modeling centers around the world.

We propose to use two climate-forcing scenarios, the intermediate RCP4.5 and the high RCP8.5. For both climate-forcing scenarios three meltwater scenarios will be developed as described in subsection 4.3.1. A “best estimate” meltwater scenario will be based on the multi-model ensemble mean and a “maximum plausible” scenario based on the 95 percentile of the CMIP5 distribution. These four experiments will use the same forcing for each model, which will allow us to quantify the uncertainty due to different AMOC sensitivities. Two additional simulations are proposed in which each model will be forced with the meltwater scenario calculated from that model and/or applying an interactive scheme of GrIS mass balance changes based on eq. (1). These will be the most realistic simulations for that particular model since its meltwater forcing will be most consistent with its climate projection. We will attempt to develop the meltwater scenarios at least until year 2200 since this is the time of minimum AMOC in the RCP8.5 scenario (Fig. 1). A Wiki page will be created where the scenarios and forcings will be discussed with the modeling groups and where data will be made available. In close collaboration with the participating modeling groups we will set priorities for the proposed experiments. Tentatively we will propose higher priority for the larger climate and meltwater forcing scenarios, because the smaller forcing scenarios may not yield a significant effect on the AMOC. In case the higher forcing scenarios result in a significant effect on the AMOC the lower scenarios will also be run (see Table in subsection 4.3.4 below).

Differences between the simulations with GrIS melting and without will be used to quantify the effect of GrIS melting on AMOC variability and predictability. The experiments without GrIS

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melting have already been performed for most models and are available for analysis on the Earth System Grid. Probabilistic AMOC projections will be calculated by fitting appropriate generic probability density functions to the results from the model ensemble. E.g. analysis of the model results may suggest that fits to a Gaussian with time varying mean and standard deviation is appropriate. Sensitivity tests with different probability density functions will be performed. The median and 95 percentiles from the models with and without GrIS melting will be compared and tested for statistical significance of being equal. This will be done for each combination of climate and GrIS meltwater forcing scenarios. The different GrIS meltwater forcings will then be combined according to their probabilities to give the time varying probability density for the AMOC response to a given climate-forcing scenario. This distribution will again be compared with the one resulting from no GrIS melting to test for statistical significance of being equal. These constitute tests of the null-hypothesis that GrIS melting does not influence AMOC variability. If for certain scenarios the null-hypothesis is rejected we will test the alternative hypothesis that the AMOC is reduced due to GrIS melting. Our default analysis will assume equal weight for each climate model. However, we will also consider assigning weights based on model performance (e.g. comparison of simulated to observed AMOC) or climate sensitivity. Perfect model experiments, in which one model is assumed to be the real climate system and all other models are evaluated by comparison to the “perfect” model, will be used to test whether or not such weighing improves projections.

A definition of an AMOC shutdown will be developed based on literature survey, the results of the above experiments and consultations with the collaborators. E.g. we could decide that a decrease by more than 75% from 20th century levels for more than two decades constitutes an appropriate definition. For each climate scenario and GrIS melting scenario we will subsequently calculate the probability of an AMOC shutdown based on the probabilistic AMOC projections.

Warming and freshwater forcing may interact in interesting, complex, and unanticipated ways as illustrated in Fig. 6, which shows unpublished results from experiments with the intermediate complexity UVic (University of Victoria) climate model [Weaver et al., 2001]. Two experiments have been performed both with identical freshwater forcing: a linear increase from zero at model year 0 to 0.4 Sv at model year 140. After year 140 the freshwater forcing is set back to zero. The two simulations differ only in the climate forcing, which is zero for one experiment (H for hosing, black lines in Fig. 6) and follows a 1% to 4x pre-industrial CO2 scenario for the other experiment (H+CO2, red lines). Global air temperature warms by ΔSAT≅ 5°C in experiment H+CO2, whereas it does not change appreciably in experiment H. We may expect that the AMOC will be reduced more in experiment H+CO2 because two forcings that both individually lead to an AMOC reduction are added together. However, contrary to our expectation experiment H leads to a shutdown of the AMOC, whereas experiment H+CO2 does not. This implies that warming leads to a decreased sensitivity of the AMOC to freshwater forcing in this model.

Analysis indicates that the depth integrated steric height [Godfrey, 1989] difference between the South and North Atlantic Δφ, which has been previously suggested to control the AMOC strength [Hughes and Weaver, 1994] consistent with simple theoretical considerations [Stommel, 1961], decreases not as much in exp. H+CO2 compared with H mainly because warming leads to a larger increase in φ in the South Atlantic. These results are in line with previous findings that the AMOC responds not only to buoyancy fluxes to the North Atlantic but also to forcings elsewhere, e.g. in the Southern Ocean [Toggweiler and Samuels, 1995], and that the AMOC

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recovery may be controlled by density changes in the South Atlantic [Manabe and Stouffer, 1994] or Southern Ocean available potential energy [Saenko, 2012].

Note that the UVic model uses a simple energy-moisture balance atmospheric component with fixed wind velocities. We plan to conduct similar sensitivity experiments with some of the more comprehensive GCMs and/or the OSUVic model [Schmittner et al., 2011b] in order to test the robustness of this result. Nevertheless, this example shows that heat and freshwater forcing may interact in interesting and unexpected ways. We will consider experiments in which the GrIS meltwater flux will be applied to the control simulation. The differences between this experiment and the one that includes CO2 increase will quantify the importance of the reduced sensitivity due to warming in more realistic models.

Model results will be analyzed carefully in order to better understand the AMOC response. This analysis will include meridional gradients in densities and depth integrated steric height as illustrated above as well as kinetic and available potential energy calculations as outlined in previous studies [Gregory and Tailleux, 2011].

4.3.3 Estimating GrIS melting effects on AMOC variability and predictability Predictability of the atmosphere is generally limited to about a week due to the chaotic nature of atmospheric motions [Lorenz, 1963]. The ocean, however, introduces longer memory [Hasselmann, 1976], which can make climate predictable on longer timescales. Mid- and high-latitudes in particular hold promise for longer timescale predictability because there the surface ocean is in contact with deeper layers [Boer and Lambert, 2008]. Upper ocean temperatures in the North Atlantic are potentially predictable for up to about a decade. Beyond about one decade North Atlantic climate may become more predictable if external forcing is strong enough [Branstator and Teng, 2010]. For low to intermediate strengths of the forcing the AMOC will likely undergo a transient decrease followed by a resumption (Fig. 1), whereas for a very strong forcing it may experience an irreversible transition to a state without deep water formation in the North Atlantic [e.g. Manabe and Stouffer, 1994; Mikolajewicz et al., 2007b]. Both of these cases are potentially predictable. However, if the forcing is in a range to push the AMOC close to a bifurcation point predictability may get lost [Knutti and Stocker, 2002].

Observations [Chylek et al., 2012; Delworth and Mann, 2000; Enfield et al., 2001; Kushnir, 1994; Schlesinger and Ramankutty, 1994] and climate models [Danabasoglu et al., 2012; Delworth et al., 1993; Delworth and Mann, 2000; Griffies and Bryan, 1997; Knight et al., 2005; Latif et al., 2004; Timmermann et al., 1998] show pronounced multi-decadal climate variability in the North Atlantic referred to as the Atlantic Multidecadal Oscillation AMO [Kerr, 2000]. The observations show SST variations of about ± 0.3°C and periods of anywhere between 50 and 80 years [Delworth and Mann, 2000; Kushnir, 1994]. In the models decadal variability of North Atlantic surface temperatures is strongly influenced by AMOC fluctuations. Similarity between simulated temperature anomalies and observations suggests that this may also apply to the real world [Delworth et al., 1993; Delworth and Mann, 2000; Griffies and Bryan, 1997; Knight et al., 2005; Latif et al., 2004; Timmermann et al., 1998]. However, climate models show large differences in amplitudes and frequencies of the simulated multi-decadal variability. Models exhibit various spectral peaks ranging in periods from ~300 years [Delworth and Zeng, 2012] over 70-100 years [Pohlmann et al., 2004; Dong and Sutton, 2005], ~60 years [Delworth and Mann, 2000], to ~20 years [Danabasoglu, 2008; Delworth and Zeng, 2012; Dong and Sutton, 2005]. Some of the peaks are very narrow and highly statistically significant indicating

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oscillatory behavior [Danabasoglu, 2008; Delworth and Mann, 2000], whereas others are broader and not significantly different from a stochastic red noise process [Kwon and Frankignoul, 2012]. Even the same model can exhibit different behavior during different phases of the simulation [Danabasoglu, 2008; Kwon and Frankignoul, 2012; Zhu and Jungclaus, 2008]. Some models simulate too large amplitudes of the multi-decadal SST variability compared to observations [Danabasoglu, 2008; Latif et al., 2004], whereas others appear more realistic [Delworth and Mann, 2000; Knight et al., 2005].

Mechanisms of internal AMOC variability in models remain controversial [Kwon and Frankignoul, 2012]. While some studies indicate temperature and heat fluxes as the dominate causes [Eden and Jung, 2001; Zhu and Jungclaus, 2008], others find salinity and freshwater fluxes more important [Delworth et al., 1993; Dong and Sutton, 2005; Jungclaus et al., 2005]. Different models emphasize the role of changes in density and convection in the Labrador Sea on the AMOC variability [Delworth et al., 1993; Jungclaus et al., 2005]. Jungclaus et al. [2005] find changes in freshwater fluxes between the Arctic and the Labrador Sea via the East Greenland Current important mechanisms for decadal AMOC variability in their model. However, none of these studies have considered the effects of GrIS interactions on AMOC variability.

Here we will perform experiments with selected GCMs in which an interactive scheme of Greenland melting is included according to eq. (1), which should capture the observed relation between the AMO and GrIS SMB (Fig. 2). Long (multi-centennial) control integrations without variations in external forcing with these models will be conducted. Results from these experiments will be compared with existing control simulations performed for CMIP5. Spectral analysis of the AMOC for both sets of simulations will be compared and tested for significant differences. We will use different established spectral methods for the analysis such as periodogram based methods, Empirical Orthogonal Function -, Principal Oscillation Pattern -, Singular Spectrum -, and Wavelet analysis [von Storch and Zwiers, 2002; Ghil et al., 2002]. An outcome of this analysis will be a test of the null-hypothesis that GrIS interactions do not influence AMOC variability. If the null-hypothesis is rejected we will analyze how variability is changed by GrIS interactions. An alternative hypothesis after which AMOC variability is reduced due to the negative feedback with the GrIS will be tested and additional experiments will be conducted geared toward quantifying how AMOC predictability is changed by GrIS interactions. These predictability experiments will follow previous studies in which an ensemble of short (10-20 year) simulations with one particular model is conducted in which each ensemble member is started with similar but slightly different initial conditions. A common approach is to use the same ocean initial conditions but vary the atmospheric initial state (Griffies and Bryan, 1997; Collins et al., 2006).

4.3.4 Summary of Work Plan Year 1: Hire postdoctoral researcher (PR). Contact all modeling groups and set up project web site. Prioritize planned experiments. Develop scenarios and calculate freshwater forcing. Calculate runoff distribution for each model. Implement forcing and runoff distribution in individual models. Begin running high priority simulations with prescribed GrIS meltwater forcings. (PR, AS, SM)

Year 2: Continue running simulations and begin analyzing results. Implement interactive GrIS SMB coupling in selected models. Start experiments with interactive scheme. Calculate

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probabilistic AMOC projections including the probability of an AMOC shutdown in next two centuries. Organize session at AGU fall meeting on AMOC variability and predictability. (PR, AS, AH) Year 3: Finish analyzing results. Publication. (all)

Table: Model experiments matrix. Tentative priorities are given by the numbers such that a low number corresponds to a high priority.

Greenland Meltwater Flux Scenario

High Medium Interactive

piControl 3 4 1

RCP8.5 1 2 3

Climate Scenario

RCP4.5 2 3 4

4.4 Relevance to NOAA’s Long Term Goal of Climate Adaptation and Mitigation This project will contribute to improved scientific understanding of interactions between the GrIS and the AMOC. It will assess possible impacts of climate change by quantifying the probability of a future AMOC collapse due to global warming and GrIS melting. Quantifying this probability is important for decision makers and society in assessing the risks of future greenhouse gas emission scenarios.

4.5 Relevance to the Priorities of the ESS Program This project will contribute to improved climate model predictions. It will advance the understanding of the behavior and predictability of land-atmosphere-ocean-cryosphere system interactions giving rise to climate variability and change. Specifically, it will improve the understanding of interactions between the GrIS and the AMOC and how these interactions affect AMOC variability and predictability on decadal to centennial time scales.

4.6 Benefits to the General Public and the Scientific Community The PIs are actively engaged in outreach activities such as presentations for K12 students and teachers, public events, discussions, and interviews with the press. (Some of Schmittner’s recent activities are noted on his homepage http://mgg.coas.oregonstate.edu/~andreas/.) This project would support these ongoing activities to improve public climate literacy. Results from this project can be expected to be of interest to the public and policy makers. We will make efforts to disseminate relevant results as widely as possible.

The scientific community will benefit from new international collaborations and new model simulations that will be made available for other researchers to analyze.

4.7 Data Sharing Procedures A website will be created that includes a Wiki page, which can be edited by all collaborators. For internal data sharing between collaborators this web site will be used. E.g. the GrIS meltwater flux scenarios will be available there for each modeling group to download. Local ftp servers and/or servers connected to the Earth System Grid (esgf.org), which is currently used for the distributed CMIP5 data base, will be used to make model data available not only for project members and collaborators but also for other scientists and the public. We have already contacted Karl Taylor from PCMDI (Program for Climate Model Diagnosis and

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Intercomparison), who will provide critical pieces of the technology they have developed for CMIP5 at no cost to this project. The project web site will include links to the databases from where the data can be downloaded. Analyzed results of low data density (e.g. small files containing AMOC time series) will be made available directly from the project web site. Schmittner’s philosophy is to share all of his model code and as much data as possible. He has practiced this approach in the past and has a track record of data sharing (see e.g. Results from Prior Research section above). 4.8 References Ahn, J., E. J. Brook, A. Schmittner, and K. Kreutz (2012), Abrupt change in atmospheric CO2

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Schmittner, a., M. Latif, and B. Schneider (2005), Model projections of the North Atlantic thermohaline circulation for the 21st century assessed by observations, Geophysical Research Letters, 32(23), 2001–2004, doi:10.1029/2005GL024368.

Schmittner, A. (2005), Decline of the marine ecosystem caused by a reduction in the Atlantic overturning circulation, 434, doi:10.1038/nature03429.1.

Schmittner, A., and E. D. Galbraith (2008), Glacial greenhouse-gas fluctuations controlled by ocean circulation changes, Nature, 456(7220), 373–6, doi:10.1038/nature07531.

Schmittner, A., N. M. Urban, J. D. Shakun, N. M. Mahowald, P. U. Clark, P. J. Bartlein, A. C. Mix, and A. Rosell-Melé (2011a), Climate sensitivity estimated from temperature reconstructions of the Last Glacial Maximum., Science (New York, N.Y.), 334(6061), 1385–8, doi:10.1126/science.1203513.

Schmittner, A., T. A. M. Silva, K. Fraedrich, E. Kirk, and F. Lunkeit (2011b), Effects of mountains and ice sheets on global ocean circulation, Journal of Climate, 24(11), 2814–2829, doi:doi:10.1175/2010JCLI3982.1.

Scholze, M., W. Knorr, and M. Heimann (2003), Modelling terrestrial vegetation dynamics and carbon cycling for an abrupt climatic change event, The Holocene, 3, 327–333.

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Shakun, J. D., P. U. Clark, F. He, S. a Marcott, A. C. Mix, Z. Liu, B. Otto-Bliesner, A. Schmittner, and E. Bard (2012), Global warming preceded by increasing carbon dioxide concentrations during the last deglaciation., Nature, 484(7392), 49–54, doi:10.1038/nature10915.

Somes, C. J., A. Schmittner, and M. A. Altabet (2010a), Nitrogen isotope simulations show the importance of atmospheric iron deposition for nitrogen fixation across the Pacific Ocean, Geophysical Research Letters, 37, L23605, doi:doi: 10.1029/2010gl044537.

Somes, C. J., A. Schmittner, E. D. Galbraith, M. F. Lehmann, M. A. Altabet, J. P. Montoya, R. M. Letelier, A. C. Mix, A. Bourbonnais, and M. Eby (2010b), Simulating the global distribution of nitrogen isotopes in the ocean, Global Biogeochemical Cycles, 24, GB4019, doi:doi: 10.1029/2009gb003767.

Stocker, T., and D. Wright (1991), Rapid transitions of the ocean’s deep circulation induced by changes in surface water fluxes, Nature, 351, 729–732.

Stocker, T. F., and A. Schmittner (1997), Influence of CO2 emission rates on the stability of the thermohaline circulation, 388, 4–7.

Stommel, H. (1961), Thermohaline convection with two stable regimes of flow, Tellus, 13, 224–230.

von Storch, H., and Zwiers, F. W. (2002). Statistical Analysis in Climate Research (p. 496). Cambridge University Press.

Straneo, F., R. G. Curry, D. A. Sutherland, G. S. Hamilton, C. Cenedese, K. Vage, and L. A. Stearns (2011), Impact of fjord dynamics and glacial runoff on the circulation near Helheim Glacier, Nat Geosci, 4(5), 322-327.

Stouffer, R. J. et al. (2006), Investigating the causes of the response of the thermohaline circulation to past and future climate changes, Journal of Climate, 19(8), 1365–1387.

Sundal, A. V., A. Shepherd, P. Nienow, E. Hanna, S. Palmer, and P. Huybrechts (2011), Melt-induced speed-up of Greenland Ice Sheet offset by efficient subglacial drainage., Nature, 469(7331), 521–4, doi:10.1038/nature09740.

Swingedouw, D., P. Braconnot, and O. Marti (2006), Sensitivity of the Atlantic Meridional Overturning Circulation to the melting from northern glaciers in climate change experiments, Geophysical Research Letters, 33(7), 1–4, doi:10.1029/2006GL025765.

Swingedouw, D., C. B. Rodehacke, E. Behrens, M. Menary, S. M. Olsen, Y. Gao, U. Mikolajewicz, J. Mignot, and A. Biastoch (2012), Decadal fingerprints of freshwater discharge around Greenland in a multi-model ensemble, Climate Dynamics, in press, doi:10.1007/s00382-012-1479-9.

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Weijer, W., M. E. Maltrud, M. W. Hecht, H. A. Dijkstra, and M. A. Kliphuis (2012), Response of the Atlantic Ocean circulation to Greenland Ice Sheet melting in a strongly-eddying ocean model, Geophys Res Lett, 39.

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Zwally, H. J., W. Abdalati, T. Herring, K. Larson, J. Saba, and K. Steffen (2002), Surface melt-induced acceleration of Greenland ice-sheet flow., Science (New York, N.Y.), 297(5579), 218–22, doi:10.1126/science.1072708.

4.9 Figures

Fig. 1. AMOC (at 30°N) simulations for the historical period (1850-2005) and scenario RCP8.5 (after year 2005) from AR5/CMIP5 models. Top panel: 5-year running mean (units: Sv).

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Bottom: percent change from historical mean with 11-year smoothing. From Cheng et al. [in review].

Fig. 2. Time series of AMO index (top; detrended SST over N. Atlantic 0-70°N; Enfield et al., 2001) and estimated historical GrIS surface mass balance (SMB), precipitation, and runoff (bottom) [Mernild and Liston, 2012].

Fig. 3: Projected changes in GrIS SMB for emission scenario RCP8.5 [Fettweis et al., 2012b]. Solid lines are based on eq. (1) applied to CMIP5 GCMs. Yellow, red, and green lines correspond to NorESM1-M, MIROC5, and CanESM2, three models that simulate GrIS climate particularly well. Dashed lines are the corresponding SMB anomalies calculated using the

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regional climate model (RCM). (The purple line corresponds to model BCC-CSM1-1.) The solid black line is the CMIP5 ensemble mean and the gray shading indicates the standard deviation. 100 Gt/yr correspond to a freshwater flux of about 0.0032 Sv.

Fig. 4. Changes in GrIS SMB simulated by a high-resolution RCM versus GCM predicted SMB using eq. (1). From Fettweis et al. [2012b].

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Fig. 5: Greenland drainage basins (left) and spatial distribution of freshwater fluxes (right) of the combined SMB (SnowModel) and runoff (HydroFlow) models [Mernild and Liston, 2012].

Fig. 6. Time series of unpublished results from simulations with the UVic model in which identical freshwater hosing is applied to the North Atlantic (center panel on the left; red and black lines overlap) in two simulations. One simulation includes increasing atmospheric CO2 at 1%/yr to 4x pre-industrial levels (red lines in all panels) and the other simulation (black lines in all panels) has CO2 fixed (top panel on left). The simulation with increasing CO2 experiences warming of global surface air temperatures (SAT, bottom left). Panels on the right show the AMOC response (top), changes in depth-integrated (between 0 and 1 km) steric height gradient between the South (40°S-30°S) and North (55°N-65°N) Atlantic (second from top), as well as the changes in depth-integrated steric height in the North (third from top) and in the South Atlantic (bottom) including the temperature (T, dotted) and salinity (S, dashed) contributions to the changes.

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5. Budget Justification a. Personnel The College of Earth, Ocean, and Atmospheric Sciences is funded largely through external grants and contracts. Tenure-track faculty are expected to raise the majority of their salary from non-state sources. For associate professors, the expectation is that 65% of their 12-month salary will be derived from grants and contracts. Schmittner requests salary support for one month in all years. This constitutes 13% of his required grant funded salary support for research of 7.8 months/year. Schmittner is in charge of overall project lead, will supervise the postdoctoral researcher, and write papers. We request a total of two years (6 months in year 1, 12 months in year 2, and 6 months in year 3) of salary support for one postdoctoral researcher (PR). His/her responsibility will be development of the meltwater scenarios, design of the model experiments, communication with the involved modeling groups, model analysis and writing publications. b. Fringe Benefits are payroll and personnel assessment expenditures such as Federal Insurance Contributions Act (FICA), Public Employees’ Retirement System (PERS); State Accident Insurance Fund (SAIF); Medical, Dental, and Life Insurance; and assessments from the Personnel Division Workers’ Compensation Board and Employee Relations Board. The fringe benefit rates used are estimates for proposal budget calculations only. Actual fringe benefit rates are charged to grants as they are incurred. Additional information may be found on OSU’s Office of Sponsored Programs website: http://oregonstate.edu/research/osprc/submission/index.htm. For further questions, please contact the Office of Sponsored Programs at (541) 737-4933 or by e-mail at sponsored.programs@oregonstate.edu. Rates for Schmittner are 0.5, 0.51, and 0.52 leading to $4,162, $4,412, and $4,681 for years 1-3, respectively. For the PR the rates are 0.62, 0.64, and 0.66 leading to $15,475, $33,227, and $17,818 for years 1-3, respectively. c. Travel: We request $8,813 in year 1, $8,756 in year 2, and $10,884 in year 3 for travel expenses. We plan to attend the US AMOC annual PI meeting each year. In years 1 and 3 travel and accommodation for the PR, Schmittner, and Hu to Miami, FL, in year 2 for PR and Schmittner to Denver, CO is budgeted for this purpose. In year 1 the PR will travel to Valdivia, Chile in order to work for one week with Mernild on the runoff model. Early 2013 Mernild will move to Valdivia, where he will work at the Centro de Estudios Cientificos (CEC). The PR will attend the AGU Fall Meeting in San Francisco in all three years. In year 2 Schmittner and Mernild will also attend the AGU Fall Meeting. In year 3 the PR and Mernild will attend the EGU general assembly meeting in Vienna. A detailed budget spreadsheet is attached. Airfares were estimated from travelocity.com. Per diem and lodging have been estimated from OSU’s business affairs guidelines available at http://oregonstate.edu/fa/businessaffairs/travel/tres/per_diem_us. d. Equipment NA e. Supplies Laboratory supplies of $2,000 are requested in year 1 for a computer for data analysis specially dedicated to this project. f. Contractual NA g. Construction NA h. Other $2,000 are requested in years 2 and 3 for publication costs (page and color figure charges). Computer services are prorated to months of PI salary, based on fractional research effort. Total network charges are $1,500 in year 1, $2,700 in year 2, and $1,500 in year 3. j. Indirect costs: The indirect cost rate is 46%.

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6. Budget

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7. IDCRA

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8. Vitae

Andreas Schmittner October 2012

College of Earth, Ocean and Atmospheric Sciences, Oregon State University, 104 CEOAS Admin Bldg, Corvallis, OR 97331-5503, (541) 737-9952, aschmitt@coas.oregonstate.edu

(a) Professional Preparation University Giessen, Germany Physics Vordiplom (Pre-diploma), 1993

University Bremen, Germany Physics Diplom (Diploma), 1996

University Bern, Switzerland Physics Ph.D., 1999

(b) Appointments Associate Professor, College of Earth, Ocean, and Atmospheric Sciences, OSU, 2011-pres.

Assistant Professor, College of Oceanic & Atmospheric Sciences, Oregon State University, 2005-2011

Postdoctoral Scholar, Institute of Geosciences, University Kiel, Germany, 2003-2005

Postdoctoral Scholar, Max-Planck-Institute for Biogeochemistry, Jena, Germany, 2002-2003

Lecturer, Department of Physics and Astronomy, University of Victoria, Canada, 2001-2002

Postdoctoral Scholar, School of Earth and Ocean Sciences, University of Victoria, Canada, 1999-2002

(c) Publications (electronic documents available at http://mgg.coas.oregonstate.edu/~andreas/)

last 3 years 1. Ahn, J., Brook, E. J., Schmittner, A., and Kreutz, K., 2012, Abrupt change in atmospheric

CO2 during the last ice age, Geophys. Res. Lett. 39, L18711, doi:10.1029/2012GL53018.

2. Schmittner, A., Urban N. M., Shakun, J. D., Mahowald, N. M., Clark, P. U., Bartlein, P. J., Mix, A. C., and Rosell-Melé, A., 2011, Response to Comment on “Climate Sensitivity Estimated From Temperature Reconstructions of the Last Glacial Maximum”, Science, 337, 1294, doi: 10.1126/science.1221634.

3. Pinsonneault, A. J., Matthews, H. D., Galbraith, E. D., and A. Schmittner, 2012, Calcium carbonate production response to future ocean warming and acidification, Biogeosciences, 9, 2351-2364, doi:10.5194/bg-9-2351-2012.

4. Ross, A., Matthews, H. D., Schmittner, A., and Kothavala, Z., 2012, Assessing the Effects of Ocean Diffusivity and Climate Sensitivity on the Rate of Global Climate Change, Tellus B, 64, 17733, doi:10.3402/tellusb.v64i0.17733.

5. Shakun, J. D., Clark, P. U., He, F., Marcott, S. A., Mix, A. C., Liu, Z., Otto-Bliesner, B., Schmittner, A., and Bard, E., 2011, Global warming preceded by increasing carbon dioxide concentrations during the last deglaciation, Nature, 484, 49-54, doi:10.1038/nature10915.

6. Zhang, X., Prange, M., Steph, S., Butzin, M., Krebs, U., Lunt, D. J., Nisancioglu, K. H., Park, W., Schmittner, A., Schneider, B., Schulz, M., 2012, Changes in equatorial Pacific thermocline depth in response to Panamanian Seaway closure: Insights from a multi-model study, Earth and Planet. Sci. Lett., 317-318, 76-84, doi:10.1016/j.epsl.2011.11.028.

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7. Schmittner, A., Urban N. M., Shakun, J. D., Mahowald, N. M., Clark, P. U., Bartlein, P. J., Mix, A. C., and Rosell-Melé, A., 2011, Climate Sensitivity Estimated From Temperature Reconstructions of the Last Glacial Maximum, Science, 334, 1385-1388, doi: 10.1126/science.1203513.

8. Marcott, S. A., Clark, P. U., Padman, L., Klinkhammer, G. P., Springer, S., Liu, Z., Otto-Bliesner, B. L., Carlson, A. E., Ungerer, A., Padman, J., He, F., Cheng, J. and Schmittner, A., 2011, Ice-shelf collapse from subsurface warming as a trigger for Heinrich events, Proc. Nat. Acad. Sci., 108 (33), 13415-13419, doi:10.1073/pnas.1104772108.

9. Zickfeld, K., Eby, M., Matthews, H. D., Schmittner, A., and Weaver, A. J., 2011, Nonlinearity of carbon cycle feedbacks, J. Climate, 24, 4255–4275, doi: 10.1175/2011JCLI3898.1.

10. Schmittner, A., Silva, T. A. M., Fraedrich, K., Kirk, E., and Lunkeit, F., Effects of mountains and ice sheets on global ocean circulation, 2011, J. Climate, 24, 2814-2829, doi:10.1175/2010JCLI3982.1.

11. Alder, C. J., Hostetler, S. W., Pollard, D., and Schmittner, A., 2011, Evaluation of a present-day climate simulation with a new couled atmosphere-ocean model GENMOM, Geosci, Model Dev., 4, 69-83, doi:10.5194/gmd-4-69-2011.

12. Goes, M., Urban, N. M., Tonkonojenkov, R., Haran, M., Schmittner, A., Keller, K., 2010, What is the skill of ocean tracers in reducing uncertainties about ocean diapycnal mixing and projections of the Atlantic Meridional Overturning Circulation, J. Geophys. Res., 115, C12006, doi:10.1029/2010JC006407.

13. Somes, C. J., Schmittner, A., and Altabet, M., 2010, Nitrogen isotope simulations confirm the importance of atmospheric iron deposition for nitrogen fixation across the Pacific ocean, Geophys. Res. Lett., 37, L23605, doi:10.1029/2010GL044537.

14. Somes, C. J., Schmittner, A., Mix, A., Letelier, R., Galbraith, E., Lehmann, M., Altabet, M., Montoya, J., Bourbonnais, A., and Eby, M., 2010, Simulating the global distribution of nitrogen isotopes in the ocean. Global Biogeochem. Cycles, 24, GB4019, doi:10.1029/2009GB003767.

5 other publications 1. Schmittner, A., N. M. Urban, K. Keller, and D. Matthews, 2009, Using tracer observations

to reduce the uncertainty of ocean diapycnal mixing and climate–carbon cycle projections, Global Biogeochem. Cycles, 23, GB4009, doi:10.1029/2008GB003421.

2. Sarnthein, M., G. Bartoli, M. Prange, A. Schmittner, B. Schneider, M. Weinelt, N. Andersen, and D. Garbe-Schonberg, 2009, Mid-Pliocene shifts in ocean overturning circulation and the onset of Quaternary-style climates, Clim. Past, 5, 269-283.

3. Schmittner, A., A. Oschlies, H. D. Matthews, and E. D. Galbraith, 2008, Future changes in climate, ocean circulation, ecosystems and biogeochemical cycling simulated for a business-as-usual CO2 emission scenario until year 4000 AD, Glob. Biogeochem. Cycles, 22, GB1013, doi:10.1029/2007GB002953.

4. Schmittner, A., E. D. Galbraith, S. W. Hostetler, T. F. Pedersen, and R. Zhang, 2007, Large fluctuations of dissolved oxygen in the Indian and Pacific oceans during Dansgaard-

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Oeschger oscillations caused by variations of North Atlantic Deep Water subduction, Paleoceanogr., 22, PA3207.

5. Schmittner, A., E. D. Galbraith, 2008, Glacial greenhouse gas fluctuations controlled by ocean circulation changes, Nature, 456, 373-376, doi:10.1038/nature07531.

Sebastian H. Mernild Los Alamos, November 1, 2012 Climate, Ocean, and Sea Ice Modeling Group Computational Physics and Methods (CCS-2) Mail Stop B296 Los Alamos National Laboratory (LANL) Los Alamos, New Mexico 87545 United States of America

Email: mernild@lanl.gov and smernild@gmail.com Affiliations after Ph.D. degree Sep 2009 to present Climate, Ocean, and Sea Ice Modeling Group

(http://climate.lanl.gov/index.shtml#top), Department of Computational Physics and Methods (CCS-2), Los Alamos National Laboratory, New Mexico, United States of America.

Nov 2006 to Sep 2009 International Arctic Research Center (http://www.iarc.uaf.edu/) and Water and Environment Research Center (http://ine.uaf.edu/werc/), University of Alaska Fairbanks, Alaska, United States of America.

Scientific educations and degrees Nov 2006 Ph.D., Department of Geography, University of Copenhagen,

Denmark. (Research areas of interest: High-latitude climatology, glaciology, and

hydrology) Oct 2001 M.Sc., Department of Geography, University of Copenhagen,

Denmark. (Research areas of interest: Mid-latitude climatology and hydrology) Jun 1999 B.S., Department of Geography, University of Copenhagen, Denmark. (Research areas of interest: High-latitude climatology and glaciology. List of publications Hirsch index: 11 Refereed International Scientific Reports 3. IPCC AR5 2013. Sea level Change. S. H. Mernild is a contributing Author on Chapter 13,

IPCC WGI Fifth Assessment Report, Pp. 1–107. 2a. Mernild, S. H., J. Cappelen, B. V. Jørgensen, W. H. Lipscomb, J. E. Box, and E. Hanna,

2011. Greenland Coastal Precipitation. In Richter-Menge, J., M.O. Jeffries, and J.E. Overland. The Arctic Report Card, Greenland. NOAA Report 2012. (http://www.arctic.noaa.gov/report11/greenland_ice_sheet.html).

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2b. Mernild, S. H., N. T. Knudsen, and E. Hanna 2011. Mittivakkat Gletscher, SE Greenland. In Richter-Menge, J., M.O. Jeffries, and J.E. Overland. The Arctic Report Card, Greenland. NOAA Report 2011 (http://www.arctic.noaa.gov/report11/greenland_ice_sheet.html).

1. Dahl-Jensen, D., S. H. Mernild and K. Steffen 2009. Melting ICE – the Greenland Ice Sheet. In: Melting Snow and Ice: A call for action. A state-of-the-art report on the status of the future scenarios for the melting of ice in affected areas worldwide.

Refereed Scientific Papers Mernild, S. H.. and J. K. Malmros. Multi decadal climate related land terminating glacier

fluctuations in the Pan-Arctic. The Cryosphere Discussion, 6, 4417–4446. Mernild, S. H., N. T. Knudsen, J. C. Yde, M. J. Hoffman, W. L. Lipscomb, E. Hanna, J. K.

Malmros, and R. S. Fausto 2012. Thinning and slowdown of Greenland’s Mittivakkat Gletscher. The Cryosphere Discussion, 6, 4387–4415.

Hanna, E., S. H. Mernild, J. Cappelen, and K. Steffen 2012. Recent warming in Greenland in a long-term instrumental (1881–2012) climatic context. Part 1: Evaluation of surface air temperature records. Environmental Research Letters, 7, 045404, doi:10.1088/1748-9326/7/4/045404.

Liston, G. E. and S. H. Mernild 2012. Greenland freshwater runoff. Part I: A runoff routing model for glaciated and non-glaciated landscapes (HydroFlow). Journal of Climate, 25(17): 5997–6014.

Mernild, S. H. and G. E. Liston 2012. Greenland freshwater runoff. Part II: Distribution and trends, 1960–2010. Journal of Climate, 25(17): 6015–6035.

Fausto, R. S., S. H. Mernild, B. Hasholt, A. P. Ahlstrøm, and N. T. Knudsen 2012. Modelling suspended sediment concentration and transport for the hydrological years 2004–2006,

Mittivakkat Glacier, Southeast Greenland. Arctic, Antarctic, and Alpine Research, 44(3), 306–318. doi: 10.1657/1938-4246-44.3.

Mernild, S. H., J. K. Malmros, J. C. Yde, and N. T. Knudsen 2012. Multi-decadal marine and land-terminating glacier retreat in Ammassalik region, Southeast Greenland. The Cryosphere, 6, 625–639, doi:10.5194/tc-6-625-2012.

Hanna, E., J. M. Jones, J. Cappelen, S. H. Mernild, L. Wood, K. Steffen, P. Huybrechts 2012. Discerning the influence of North Atlantic atmospheric and oceanic forcing effects on 1900–2010 Greenland summer climate and melt. International Journal of Climatology. doi: 10.1002/joc.3475.

Mernild, S. H., G. E. Liston, and M. van den Broeke 2012. Simulated internal storage build-up, release, and runoff from Greenland Ice Sheet at Kangerlussuaq, West Greenland. Arctic, Antarctic, and Alpine Research, 44(1), 83–94. doi: 10.1657/1938-4246-44.1.

Mernild, S. H., M.-S. Seidenkrantz, P. Chylek, G. E. Liston, and B. Hasholt 2012. Climate-driven fluctuations in freshwater to Sermilik Fjord, East Greenland, during the last 4000 years. The Holocene, 22(2), 155–164. doi: 10.1177/0959683611431215.

Mernild, S. H., T. Mote, and G. E. Liston 2011. Greenland Ice Sheet surface melt extent and trends, 1960–2010. Journal of Glaciology, 57(204): 621–628.

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Mernild, S. H., N. T. Knudsen, W. H. Lipscomb, J. C. Yde, J. K. Malmros, B. H. Jakobsen, and B. Hasholt 2011. Increasing mass loss from Greenland’s Mittivakkat Gletscher. The Cryosphere, 5, 341–348, doi:10.5194/tc-5-341-2011.

Mernild, S. H., G. E. Liston, C. A. Hiemstra, J. H. Christensen, M. Stendel, and B. Hasholt 2011. Surface mass-balance and runoff modeling using HIRHAM4 RCM at Kangerlussuaq (Søndre Strømfjord), West Greenland, 1950–2080. Journal of Climate, 24(3): 609–623, doi: 10.1175/2010 JCLI3560.1.

Mernild, S. H., I. M. Howat, Y. Ahn, G. E. Liston, K. Steffen, B. H. Jakobsen, B. Hasholt, B. Fog, and D. van As 2010. Freshwater flux to Sermilik Fjord, SE Greenland. The Cryosphere, 4, 453–465, doi:10.5194/tc-4-453-2010.

McGrath, D., K. Steffen, I. Overeem, S. H. Mernild, B. Hasholt, and M. van den Broeke 2010. Sediment Plumes in Kangerlussuaq, West Greenland, as a proxy for runoff from the Greenland Ice Sheet. Journal of Glaciology, 56(199): 813–821.

Mernild, S. H., G. E. Liston, K. Steffen, M van den Broeke, and B. Hasholt 2010. Runoff and mass-balance simulations from the Greenland Ice Sheet at Kangerlussuaq (Søndre Strømfjord) in a 30-year perspective, 1979–2008. The Cryosphere, 4, 231–242, doi:10.5194/tc-4-231-2010.

Mernild, S. H., G. E. Liston, C. A. Hiemstra, and J. H. Christensen 2010. Greenland Ice Sheet surface mass-balance modeling in a 131-year perspective 1950–2080. Journal of Hydrometeorology, 11(1): 3–25.

Mernild, S. H. and G. E. Liston 2010. The influence of air temperature inversion on snow melt and glacier surface mass-balance simulations, SW Ammassalik Island, SE Greenland. Journal of Applied Meteorology and Climate, 49(1): 47–67.

Mernild, S. H., G. E. Liston, K. Steffen, and P. Chylek 2010. Meltwater flux and runoff modeling in the ablation area of the Jakobshavn Isbræ, West Greenland. Journal of Glaciology, 56(195): 20–32.

Mernild, S. H. and B. Hasholt 2009. Observed runoff, jökulhlaups, and suspended sediment load from the Greenland Ice Sheet at Kangerlussuaq, West Greenland, for 2007 and 2008. Journal of Glaciology, 55(193): 855–858.

Mernild, S. H., G. E. Liston, C. A. Hiemstra, K. Steffen, E. Hanna and J. H. Christensen 2009. Greenland Ice Sheet surface mass-balance modeling and freshwater flux for 2007, and in a 1995–2007 perspective. Hydrological Processes, doi: 10.1002/hyp.7354.

Mernild, S. H., G. E. Liston, C. A. Hiemstra, and K. Steffen 2009. Record 2007 Greenland Ice Sheet surface melt-extent and runoff. Eos Trans. AGU, 90(2): 13–14.

Mernild, S. H., G. E. Liston, C. A. Hiemstra and K. Steffen 2008. Surface Melt Area and Water Balance Modeling on the Greenland Ice Sheet 1995–2005. Journal of Hydrometeorology, 9(6): 1191–1211.

Mernild, S. H., B. Hasholt, D. L. Kane and A. C. Tidwell 2008. Jökulhlaup Observed at Greenland Ice Sheet. Eos Trans. AGU, 99(35): 321–322.

Mernild, S. H., G. E. Liston, B. Hasholt and N. T. Knudsen 2006. Snow distribution and melt modeling for Mittivakkat Glacier, Ammassalik Island, SE Greenland. Journal of Hydrometeorology, 7: 808–824.

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NATIONAL CENTER FOR ATMOSPHERIC RESEARCH P.O. Box 3000 • Boulder, Colorado 80307-3000

Aixue  Hu,  PhD  

Climate & Global Dynamics Division (CGD) Telephone: (303) 497-1334 • FAX: (303) 497-1348

Email: ahu@ucar.edu Education: 2001 Ph.D in Meteorology and Physical Oceanography, MPO/RSMAS, U. of Miami, Miami, Florida 1998 M.S. in Meteorology and Physical Oceanography, MPO/RSMAS, U. of Miami, Miami, Florida 1992 M.S. in Climatology, Department of Geophysics, Peking University, Beijing, China 1986 B.S. equivalent in Meteorology, Beijing Institute of Meteorology, Beijing, China

Professional Experience: 10/2010-present Project Scientist II, CCR/ CGD, NCAR, Boulder, CO 03/2006-09/2010 Project Scientist I, CCR/ CGD, NCAR, Boulder, CO 11/2001-02/2006 Associate Scientist III, CCR/CGD, NCAR, Boulder, CO Professional membership: American Geophysical Union, American Meteorological Society Publications:

1. Hu, A. 2012, Cryosphere, Climate Change Effects. DOI:10.1007/SpringerReference_327090. In Encyclopedia of Remote Sensing, edited by Eni G. Njoku. Published by Spinger.

2. Meehl, G. A., A. Hu, C. Tebaldi, J. M. Arblaster, W. M. Washington, H. Teng, B. Sanderson, T. Ault, W. G. Strand, and J. B. White III, 2012, Relative outcomes of climate change mitigation related to global temperature versus sea level rise, Nature Climate Change, 576-580, doi:10.1038/NCLIMAT1529.

3. Hu, A., G. A. Meehl, W. Han, A. Timmermann, B. Otto-Bliesner, Z. Liu, W. M. Washington, W. Large, A. Abe-Ouchi, M. Kimoto, K. Lambeck, and B. Wu, 2012, Role of the Bering Strait on the hysteresis of the ocean conveyor belt circulation and glacial climate stability, Proceedings of the National Academy of Sciences, 17, 6417-6422, doi:10.1073/pnas.1116014109.

4. Hu, A., G. A. Meehl, W. Han, A. Abe-Ouchi, C. Morrill, Y. Okazaki, M. O. Chikamoto, 2012, The Pacific-Atlantic Seesaw and the Bering Strait, Geophys. Res. Lett., L03702, doi:10.1029/2011GL050567 (AGU Research Spotlight paper).

5. Hu, A., G. A. Meehl, W. Han and J. Yin (2011), Effect of the potential melting of the Greenland Ice Sheet on the meridional overturning circulation and global climate, Deep Sea Research II, 58, 1914-1926, doi:10.1016/j.dsr.2010.10.069.

6. Meehl, A. G., A. Hu and C. Tebaldi (2010), Decadal prediction in the Pacific region, J. Climate, 23, DOI: 10.1175/2010JCLI3296.1, 2959-2973.

7. Hu, A., G.A. Meehl, B. Otto-Bliesner, C. Waelbroeck, W. Han, M.-F. Loutre, K. Lambeck, J. X Mitrovica, and Nan Rosenbloom (2010), Influence of Bering Strait flow and North

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Atlantic circulation on glacial sea level changes, Nature Geoscience, doi:10.1038/NGEO729, 3, 118-121 (NCAR, NSF press release).

8. Hu, A., G. A. Meehl, W. Han and J. Yin (2009), Transient Response of the MOC and Climate to Potential Melting of the Greenland Ice Sheet in the 21st Century, Geophys. Res. Lett., 36, L10707, doi:10.1029/2009GL037998 (NCAR, AGU, NSF joint press release).

9. Hu, A. and G. A. Meehl (2009), Effect of the Atlantic hurricanes on the oceanic meridional overturning circulation and heat transport, Geophys. Res. Lett., 36, L03702, doi:10.1029/2008GL036680.

10. Meehl, G. A., A. Hu and B. Santer (2009), The mid-1970s climate shift in the Pacific and the relative roles of forced versus inherent decadal variability, J. Clim, in press.

11. Hu, A., B. L. Otto-Bliesner, G. A. Meehl, W. Han, C. Morrill, E. C. Brady and B. Briegleb (2008), Response of thermohaline circulation to freshwater forcing under present day and LGM conditions, J. Clim, 21, 2239-2258.

12. Hu, A., G.A. Meehl and W. Han (2007), Role of Bering Strait in the thermohaline circulation and abrupt climate change,Geopyhys. Res. Lett., 34, L05704, doi:10.1029/2006GL028906.

13. Hu, A., G.A. Meehl and W. Han (2007), Causes of a fresher, colder northern North Atlantic in the late 20th century in a coupled model, Progress in Oceanography, 73, 384-405, doi:10.1016/j.pocean.2006.07.008.

14. Meehl, G. A. and A. Hu (2006), Megadroughts in the Indian monsoon and southwest North America and a mechanism for associated multi-decadal Pacific sea surface temperature anomalies, J. Climate, 19, 1605-1623.

15. Hu, A. and G.A. Meehl (2005), Bering Strait throughflow and the thermohaline circulation, Geophys. Res. Lett., 32, L24610,doi:10.1029/2005GL024424.

16. Hu, A. and G. A. Meehl, 2005: Reasons for a fresher northern North Atlantic in the late 20th Century. Geophys. Res. Lett., 32, L11701,doi:1029/2005GL022900.

17. Dai, Aiguo, A. Hu, G.A. Meehl, W.M. Washington and W.G. Strand, 2005: Atlantic thermohaline circulation in a coupled general circulation model: Unforced variations vs. forced changes. J. Clim., 18, 2990-3013.

18. Hu, A., Gerald A. Meehl, and Weiqing Han, 2004: Detecting thermohaline circulation changes from ocean properties in a coupled model. Geophys. Res. Lett, 31, L13204, doi:10.1029/2004GL020218.

19. Hu, A., G.A. Meehl, W.M. Washington, A. Dai, 2004: Response of the Atlantic thermohaline circulation to increased atmospheric CO2 in a coupled model , J. Climate, 17, 4267-4279.

20. Hu, A., Claes Rooth, Rainer Bleck and Clara Deser 2002: NAO influence on sea ice extent in the Eurasian coastal region, Geopys. Res. Lett., 29(22), 2053, doi:10.1029/2001GL014293.

9. Current and Pending Support

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Andreas Schmittner

Current Co-PI: Atmospheric CO2 and Abrupt Climate Change, PI: Ahn, NSF 0944764-ANT, $448,074,

01/2010-12/2012 (plus one year no cost extension), 0.5-0.5-1.25 months/a.

PI: Reconstructing Glacial Nitrogen and Carbon Cycling Using Isotopes, NSF 1131834-OCE, $508,279 08/2011-07/2014, 3 months/a.

Co-PI: Data-Model Synthesis: Gulf of Alaska Sea-Surface Paleotemperature, Freshwater Input, and the Dynamics of Deglacial Climate Variability, PI A. C. Mix, NSF OCE 1204204, $549,263, 04/2012-03/2014, 1 month/a.

PI: Estimating Climate Sensitivity from Temperature Reconstructions of the Last Glacial Maximum, NSF AGS $345,345, 05/2012 – 04/2015, 2 months/a.

PI: Collaborative Research: Assessing Climate Model Simulations of Last Glacial Maximum Ocean Circulation with Carbon Isotopes, NSF OCE, $95,391, 09/2012-08/2015, 0.5/1/1.5 months/a.

Pending

PI: Quantifying the effect of the lunar nodal tide on North Pacific climate variability, NSF OCE, $234,761, 03/2013-02/2015, 1 month/a.

PI: Modeling Effects of Greenland Ice Sheet Melting on AMOC Variability and Predictability, NOAA CPO, $353,944, 1 month/a. (THIS PROPOSAL)

Aixue Hu none

Sebastian Mernild none

10. DUNS number: 05359908