Selected Work

The Atmospheric Bridge: ENSO teleconnections Between Ocean Basins

Moisture Transport/Heavy Precipitation in the Western US

Projected sea surface temperatures over the 21st century

The Ocean Response to Climate Change along the US East Coast and Gulf of Mexico

The Atmospheric Bridge: ENSO teleconnections Between Ocean Basins

While air–sea interactions responsible for El Nino and the Southern Oscillation (ENSO) are centered over the equatorial Pacific Ocean, changes in tropical convection (heavy rainfall) associated with ENSO influence the global atmosphere. The ENSO-driven atmospheric teleconnections alter the near-surface air temperature, humidity, clouds and wind far from the equatorial Pacific. The resulting variations in the surface heat, momentum, and freshwater fluxes can induce changes in sea surface temperature (SST), salinity, mixed layer depth, and ocean currents. Thus, the atmosphere acts as a bridge spanning from the equatorial Pacific to much of the global oceans. ENSO-related SST anomalies can also feed back on the original atmospheric response to ENSO.

a) Observed and b) simulated (in the MLM experiment described below) El Niño (warm) − La Niña (cold) composite of SLP (contour interval is 1 mb) and SST (shading interval is 0.2°C) for JJA and SON in YR0 (year when ENSO starts), DJF(0/1; when ENSO peaks), and MAM(1), where 0 indicates the ENSO year and 1 the year after. ENSO strongly influences the global atmospheric circulation, shown here as the difference between EL Nino and La Nina conditions For example there is a strong anomalous low (dashed contours) over the North Pacific during DJF(0/1). The winds are counter clockwise aroung a low resulting in stronger winds over the central north Pacific cooling the ocean. The ENSO-drive winds blow from southwest to northeast along the coast of North America warming the underlying ocean. (Note: El Niño and La Niña are not equal and opposite but computing the difference amplifies the pattern). Alexander et al. 2002, J. Climate

Composite El Niño–La Niña (*) SST (°C) during JFM (yr 1) for (a) observations 1950–99, (b) EKM, (c) MLM, and (d) EKM–MLM (Δ). The CI (CSI) is 0.25° (0.5°) C in (a)–(c). The shading in (d) indicates where the t test exceeds the 95% and 99% confidence levels for ΔSST (CI 0.1°C). The large box in (b)–(d) indicates the region of prescribed SST forcing. In the MLM experiment: a grid of one dimensional mixed layer models represent the global ocean outside of the tropical Pacific. The EKM simulation includes the MLM ocean model with the addition of the estimated heat transport by Ekman currents. Including Ekman heat transport strengthens the colling in the Central North Pacific and off the US east coast and increases the warming in the Gulf of Alaska (panel d), bringing the simulated ENSO SST pattern closer to observations. Alexander and Scott, 2008, J. Climate

El Niño–La Niña composite of 500 hPa heights (m) during JFM(1) for (a) observed (1950–99), (b) EKM, (c) MLM, and (d) Δ. The CI–CSI is 5 (10) m in (a)-(c). In (d), the shading indicates 95% and 99% confidence limits for the ΔZ500 (CI 5 m). In the MLM and EKM model experiments: observed SSTs are specified in the tropical Pacific underneath a global atmospheric model. The simulations show that the model generally reproduces the atmopsheric response to ENSO (SST anomalies in the tropical Pacific). Even though the ocean model is relatively simple it obtains many of the features found in observations. Including the influence of surface Ekman currents (EKM experiment) brings the model closer to observations. However, not all anomalies are reproduced. For example, the warming of the US west coast is much weaker than observed. This occurs because this model formulation does not include coastally trapped waves, which rpovide most of the near-shore warming Alexander and Scott, 2008, J. Climate

Relevant Publications

Xu, T., M. Newman, M., S.-I. Shin, A. Capotondi, D. J. Vimont, M. A. Alexander and E. Di Lorenzo, 2026: Persistent Northeast Pacific marine heatwaves are sensitive to the seasonality of tropical and North Pacific dynamics. Commun. Earth Environ., 7, 528. https://doi.org/10.1038/s43247-026-03442-x

Newman, M., M. A. Alexander, et al., 2016: The Pacific Decadal Oscillation, Revisited. J. Climate, 29, 4399-4427, https://doi.org/10.1175/JCLI-D-15-0508.1

Alexander, M. A. and J. D. Scott, 2008: The role of Ekman ocean heat transport in the Northern Hemisphere Response to ENSO. J. Climate, 21, 5688-5707. https://doi.org/10.1175/2008JCLI2382.1

Liu, Z. and M. A. Alexander, 2007: Atmospheric Bridge, Oceanic Tunnel and Global Climatic Teleconnections. Rev. of Geophys., 45, RG2005, doi:10.1029/2005RG000172.

Park, S., M. A. Alexander, and C. Deser, 2006: The impact of cloud radiative feedback, remote ENSO forcing, and entrainment on the persistence of North Pacific sea surface temperature anomalies. J. Climate, 19, 6243-6261. https://doi.org/10.1175/JCLI3957.1

Alexander, M. A., N.-C. Lau, and J. D. Scott, 2004: Broadening the atmospheric bridge paradigm: ENSO teleconnections to the North Pacific in summer and to the tropical west Pacific- Indian Oceans over the seasonal cycle. Earth Climate: The Ocean-Atmosphere Interaction, eds. C. Wang, S.-P. Xie and J. Carton. AGU Monograph. pp. 85-104. https://doi.org/10.1029/147GM05

Alexander, M. A., 1992: Midlatitude atmosphere-ocean interaction during El Niño. Part II: the Northern Hemisphere Atmosphere. J. Climate, 5, 959-972. https://doi.org/10.1175/1520-0442(1992)005<0959:MAIDEN>2.0.CO;2

Alexander, M. A., 1992: Midlatitude atmosphere-ocean interaction during El Niño. Part I: the North Pacific Ocean. J. Climate, 5, 944-958. https://doi.org/10.1175/1520-0442(1992)005<0944:MAIDEN>2.0.CO;2

Alexander, M. A., 1990: Simulation of the response of the North Pacific Ocean to the anomalous atmospheric circulation associated with El Niño. Climate Dyn., 5, 53-65. https://doi.org/10.1007/BF00195853

Alexander, M. A., and S. D. Schubert, 1990: Regional earth-atmosphere energy balance estimates based on assimilations with a GCM. J. Climate, 3, 15-31. https://doi.org/10.1007/BF00195853

The Reemergence Mechanism

The near-surface of the ocean is generally well mixed resulting in uniform temperature and salinity. The mean seasonal cycle of mixed layer depth (MLD) in the extratropical oceans has the potential to influence temperature, salinity and mixed layer depth anomalies from one winter to the next. Temperature and salinity anomalies that form at the surface and spread throughout the deep winter mixed layer are sequestered beneath the mixed layer when it shoals in spring, and are then re-entrained into the surface layer in the subsequent fall and winter. This ‘reemergence mechanism’, which occurs over wide areas of the extratropical oceans, enables sea surface temperature (SST) anomalies to recur from one winter to the next without persisting at the surface.

Composite El Niño − La Niña ocean temperatures from Nov(0) to Jun(2) and the composite MLD (m) during El Niño (red line) and La Niña (green line) from an ocean mixed layer model (MLM) coupled to a global atmospheric model for a region in the central North Pacific. The white arrows (schematically) show the path of the reemergence mechanism where the cold SST anomalies created by surface heat fluxes in winter are stored in the summer seasonal thermocline and are re-entrained into mixed layer when it deepens in the following fall and winter. The anomalies are damped in summer by upward heat fluxes. The mixed layer is deeper during El Nino (warm ENSO) events than La Nina events, as the water is anomalously cold in the central North Pacific in El Nino winters, thus is more dense and sinks further in the water column. Alexander et al. 2002.

Lead–lag regressions [°C/(1°C)] between SST anomalies in Apr–May, and temperature anomalies from the previous Jan through the following Apr in the east, central, and west Pacific regions. The contour interval is 0.1 and values greater than (a) 0.55, (b) 0.7, and (c) 0.75 are shaded red.

The winter mixed layer deepens from east to west across the North Pacific in midlatitudes and thus the reemerging signal extends further down into the ocean western region. Some of the temperature anomalies in the east region are transported deeper into the ocean (via subduction) and do not return to the surface in that region Alexander et al. 1999

Observed Mixed Layer Ocean Model Fixed depth Ocean Model Reemergence, No ENSO ENSO, no reemergence

The evolution of the leading pattern of SST variability over 20°N–60°N in the Pacific of monthly SST anomalies from January through April of the following year. The results are shown for March September and March of the following year. Results are presented for a) Observations, b) an atmosphere-ocean model (AGCM–MLM) simulation, without the dynamics for ENSO to occur in the tropical Pacific but with a grid of variable depth mixed layer models over the global ocean allowing for the reemergence mechanism and c) AGCM-slab ocean simulations where ENSO conditions are specified as atmospheric boundary conditions in the tropical Pacific but a fixed depth ("slab") model is used elsewhere over the global ocean, allowing for the atmospheric bridge but not reemergence. While the ENSO-driven atmospheric bridge influences the pattern of variability and its partial persistence through summer, reemergence is required for the pattern to strengthen rather than disappear in the following winter. Alexander et al. 2001.

(Left) The leading pattern of March SST anomalies in the North Atlantic Ocean obtained from empirical orthogonal function (EOF) analysis. It exhibits a tripole pattern shown in the phase with positive SST anomalies in the tropics and high latitudes and cold in the subtropics. (Right) The autocorrelation of the 1st principal component (PC), the time series indicating the amplitude and phase of the leading EOF of SST anomalies using all months. The peak in the correlation at about 10 months lag from March indicates the reemergence mechanism.

Relevant Publications

Sukhonos, P.A., and M. A. Alexander, 2024: Winter–to–winter recurrence of the tripole pattern of the sea surface temperature anomalies in the North Atlantic ocean and its interaction with the NAO. Climate Dynamics, 62, 7799–7817 (2024). https://doi.org/10.1007/s00382-024-07307-x

Sukhonos, P.A., and M. A. Alexander, 2022: The reemergence of the winter sea surface temperature tripole in the North Atlantic from ocean reanalysis data. Climate Dynamics https://doi.org/10.1007/s00382-022-06581-x

Byju, P., D. Dommenget, and M. A. Alexander, 2018: Widespread reemergence of sea surface temperature anomalies in the global oceans, including tropical regions forced by reemerging winds. Geophysical Research Letters, 45, 7683-7691. https://doi.org/10.1029/2018GL079137

Cassou, C., C. Deser and M. A. Alexander, 2007: Investigating the impact of reemerging sea surface temperature anomalies on the winter atmospheric circulation over the North Atlantic. J. Climate, 20, 3510-3526. https://doi.org/10.1175/JCLI4202.1

Deser, C., M. A. Alexander, and M. S. Timlin, 2003: Understanding the Persistence of Sea Surface Temperature Anomalies in Midlatitudes. J. Climate, 16, 57–72, https://doi.org/10.1175/1520-0442(2003)016<0057:UTPOSS>2.0.CO;2.

Timlin, M. S., M. A. Alexander, and C. Deser, 2002: On the reemergence of North Atlantic SST anomalies. J. Climate, 15, 9, 2707-2712. https://doi.org/10.1175/1520-0442(2002)015<2707:OTRONA>2.0.CO;2

Alexander, M. A., M. S. Timlin, and J. D. Scott, 2001: Winter-to-Winter recurrence of sea surface temperature, salinity and mixed layer depth anomalies. Progress in Oceanography, 49, 41-61. https://doi.org/10.1016/S0079-6611(01)00015-5.

Alexander, M. A., C. Deser, and M. S. Timlin, 1999: The Reemergence of SST Anomalies in the North Pacific Ocean. J. Climate, 12, 2419–2433, https://doi.org/10.1175/1520-0442(1999)012<2419:TROSAI>2.0.CO;2

Alexander, M. A., and C. Deser, 1995: A mechanism for the recurrence of midlatitude SST anomalies during winter. J. Phys. Oceanogr., 25, 122-137. https://doi.org/10.1175/1520-0485(1995)025<0122:AMFTRO>2.0.CO;2

Moisture Transport/Heavy Precipitation in the Western US

While generally dry, the interior of the western United States can experience extreme precipitation events, which can cause severe flooding, and avalanches potentially leading to safety and infrastructure problems. Determining how these events occur in the intermountain west (IMW, between the Sierra Nevada–Cascade Range and the Continental Divide) can help water managers and emergency planners be better prepared. The complex topography (land surface) makes moisture transport and the evolution of storms difficult to determine in the IMW. We have investigated paths of moisture transport using backward trajectories from where heavy precipitation occurred and by identifying the leading patterns of integrated water vapor transport (IVT) over land during winter, when the bulk of the precipitation falls. The results from both methods indicate that moisture from the Pacific leading to extreme precipitation in the IMW during winter takes distinct pathways and is influenced by gaps in the Sierra Nevada (California), and to a lesser degree in the Cascades (Oregon–Washington) and Peninsular ranges (Southern California through Baja California).

Examples of backward trajectories that were initiated near a surface station during one of the top 150 precipitation events in Southern Idaho. The dates and precipitation amounts are given above the panels for the four cases. The pressure (hPa) along a trajectory segment is shown by the color (blue–red) scale on the lower right and the terrain height (m) by the (green–white) scale on the bottom left. Higher pressure values, red colors, are closer to the surface. This example shows that the mositure from the Pacific can take several different paths to reach southern Idaho. Alexander et al. 2015

Left Count maps and Right cross sections indicating the number of back trajectories that originate in the (a),(b) Washington-northern Idaho (WA-nID); (c),(d) Oregon-southern Idaho (OR-sID); and (e),(f) Nevada (NV) regions. Trajectories are from CFSR initiated close to the surface 4 times per day when one of the top 150 (independent) precipitation events occurred. A total of 2400 trajectories were initiated in each region. The position of a trajectory is estimated at 1-hour intervals over the five previous days using the 6-hourly 3D wind fields.

Map and cross section back trajectory counts originating in the (a),(b) Utah-Colorado (UT-CO); (c),(d) Sourthern California (sCA); and (e),(f) Arizona-New Mexico (AZ-NM) regions. Topography is shown with black contours at 1000 m (3281 ft), 1500 m (4921 ft), and 2300 m (7546 ft) and stippling above 2300 m. Vertical cross section of the back-trajectory counts along the crest of the Cascades, the Sierra Nevada, and the Peninsular Ranges for the WA-nID, OR-sID, and NV regions. The terrain is shown in black.

Above: The three leading patterns (identified by EOF analysis) of the 6-hourly integrated water vapor transport (IVT) anomalies from CFSR, normalized by the local IVT monthly std dev, during October–March computed over the western United States. The anomalies are shown as a regression between the PC (time series) and the normalized anomaly. The percent of variance explained by each EOF is shown in the upper right. (d)–(f) Histograms of the three leading PC normalized values.

Right: Composite maps constructed from the top 1% (232 six-hourly values) of PC1 of the anomalous (a) IVT magnitude, (b) IVT direction (vectors) and convergence–divergence (mm day−1; shading; convergence = −∇ · IVT > 0), (c) 700-hPa specific humidity (g/kg), (d) 500-hPa height (dm), (e) precipitation from CFSR (mm/day), and (f) precipitation from Livneh (mm/day).

Composite maps constructed from the top 1% of PC2 values.

Composite maps based on the top 1% of PC3 values. To extract a realistic signal from this EOF, we also exclude periods that are in the top or bottom 1% of PC2 cases and retain events where the IVT anomaly is positive in the southwestern United States, isolating cutoff lows, leaving 116 events in the composite.

(a) Schematic map and (b) cross section (along the black line in (a)) showing the major moisture pathways from the Pacific Ocean into the intermountain west.

Summary of key findings

Results from both the trajectory and EOF methods clearly indicate that moisture originating from the Pacific that produces extreme precipitation in the IMW during winter takes dominant pathways that are influenced by gaps in the Cascade Range (Oregon–Washington), the Sierra Nevada (California), and the Peninsular Ranges (from Southern California tho Baja California). The following paths for different regions were identified (see schematic): 1) the Columbia River basin is a conduit for moisture to reach eastern Washington, northern Idaho, and western Montana; 2) a surprising path from central and Northern California, north of the high Sierra Nevada (~41°N), then north into eastern Oregon and Idaho, into the mountains of central Idaho and along the Snake River plain; 3) to the north and south (~35°N) of the high Sierra Nevada to reach Nevada; 4) just south of the Sierra Nevada into portions of Utah, Colorado, and Arizona; and 5) flow centered over gaps in the Peninsular Ranges near the United States–Mexico border at ~29°N and over the southern portion of the peninsula that has relatively low topography, bringing moisture to Arizona and western New Mexico. These pathways are consistent with recent studies of the penetration of atmospheric rivers, narrow bands of moisture ahead of cold fronts, into the IMW.

Relevant Publications

Hughes, M., Swales, D., Scott, J.D, Alexander, M., Mahoney, K., R. R. McCrary, R. R., Cifelli, R., Bukovsky, M., 2022: Changes in extreme integrated water vapor transport on the U.S. west coast in NA-CORDEX, and relationship to mountain and inland precipitation. Clim. Dyn. https://doi.org/10.1007/s00382-022-06168-6

Rutz, J. J., B. Guan, M. A. Alexander, et al., August 2020: Chapter 4. Global and Regional Perspectives. In Atmospheric Rivers, F. M. Ralph, M. D. Dettinger, J. J. Rutz, and D. E. Waliser (Eds.), Springer International Publishing, 89-140, ISBN 978-3-030-28905-8, 252 pp., https://doi.org/10.1007/978-3-030-28906-5.

Mahoney, K., D. Swales, M.J. Mueller, M. Alexander, M. Hughes, and K. Malloy, 2018: An Examination of an Inland-Penetrating Atmospheric River Flood Event under Potential Future Thermodynamic Conditions. J. Climate, 31, 6281-6297, https://doi.org/10.1175/JCLI-D-18-0118.1

Alexander, M. A., J. D. Scott, D. Swales, M. Hughes, K. Mahoney, C. A. Smith, 2015: Moisture Pathways into the US Intermountain West Associated with Heavy Winter Precipitation Events J. Hydromet., 16, 1184-1206, doi: http://dx.doi.org/10.1175/JHM-D-14-0139.1.

Hughes, M., K. M. Mahoney, P. J. Neiman, B. J. Moore, M. Alexander, F. M. Ralph, 2014: The Landfall and Inland Penetration of a Flood-Producing Atmospheric River in Arizona. Part II: Sensitivity of modeled precipitation to terrain height and atmospheric river orientation, J. Hydrometeorology, 15, 1954-1974. https://doi.org/10.1175/JHM-D-13-0176.1

Projected sea surface temperatures over the 21st century

The burning of fossil fuels and the resulting input of CO2 and other greenhouse gases into the atmosphere has already warmed the planet and will have a profound impact on the Earth, including the oceans, over the 21st century. sea surface temperatures (SST) is a key variable in the climate system, regulating thermal and dynamical interactions between the ocean and atmosphere. Climate change may not only manifest in mean SST trends but also in changes in the variability and extremes. Changing ocean temperatures, including seasonal differences in warming trends, and epsodes of extreme warming termed "marine heatwaves" may influence the behavior, growth, reproduction and survival of marine species. In this study, we used output from Global Climate Models to investigate changes in the mean, variability and extreme SSTs, with additional analyses of mixed layer depth (MLD), to better understand the changes in SSTs.

Ensemble mean SST trends from 26 GCMS in the Climate Model Intercomparison Project (CMIP5) and 30 simulations from the Community Earth System Model large ensemble project (CESM-LENS) over the period 1976–2099. Trends are shown for all months (a, b), for March (c, d) and for September (e, f) . Color bar indicates trends in °C/decade with positive (negative) values in shades of red (blue). Only trends that are significant at a 95% level using a Mann-Kendall test are shown. Trends are positive and significant in most areas except the North Atlantic and Arctic Oceans in March. Alexander et al. 2018

SST trends in Large Marine Ecosystems (LMEs) in the Arctic and around North America and Europe. Colors denote the CMIP5 ensemble mean area-averaged SST trends (°C/decade) during 1976–2099. All trends are significant at the 95% level using a Mann-Kendall test. Regions are numbered following the LME convention: 1) Bering Sea, 2) Gulf of Alaska, 3) California Current, 5) Gulf of Mexico, 6) Southeast US Shelf, 7) Northeast US Shelf, 8) Scotian Shelf, 9) Newfoundland-Labrador Shelf, 10) Hawaii, 18) West Greenland, 19) Greenland Sea, 20) Barents Sea, 21) Norwegian Sea, 22) North Sea, 24) Celtic-Biscay Shelf, 26) Mediterranean, 59) Iceland Shelf and Sea, and the 64) Central Arctic.

The mean seasonal cycle of SST (°C) for LMEs around North America. Observations for the historical period (1976–2005) are green, the CMIP5 ensemble mean during the historical period are black, and the ensemble mean CMIP5 RCP8.5 experiments in the future period (2070–2099) are red. Note the annual mean SST in each period has been subtracted. The percent change between the historical and future periods is shown in blue. The seasonal cycle is amplified in the future period.

The mean seasonal cycle of SST (°C) for LMEs in the Arctic and around Europe. Observations for the historical period (1976–2005) are green, the CMIP5 ensemble mean during the historical period are black, and the ensemble mean CMIP5 RCP8.5 experiments in the future period (2070–2099) are red. The annual mean SST in each period has been subtracted. The percent change between the historical and future periods is shown in blue. The seasonal cycle is amplified in the future period.

Probability distributions of CMIP5 monthly SST anomalies averaged over the LMEs around North America. The results are shown for the historical period (1976–2005, black lines) and future period (2070–2099, red lines), where the SSTs have been linearly detrended within each period. The red dashed line shows the future distribution of anomalies without the mean change to make it easier to compare the shapes of the future and historical distributions. Other than the change in the mean, the changes in the distributions are very small for most regions.

Probability distributions of CMIP5 monthly SST anomalies averaged over the LMEs around the Artic and Europe. The results are shown for the historical period (1976–2005, black lines) and future period (2070–2099, red lines), where the SSTs have been linearly detrended within each period. The red dashed line shows the future distribution of anomalies without the mean change to make it easier to compare the shapes of the future and historical distributions. Other than the change in the mean, the changes in the distributions are very small for most regions.

CMIP5 ensemble mean March mixed layer depths (MLDs) during 1976–2005 and 2070–2099. Shown are the time averaged March MLDs (m) during 1976–2005 (a) and 2070–2099 (c). The difference in March MLDs between the future (2070–2099) and historical (1976–2005) periods are shown in (e). A similar set of maps but for September are presented in panels (g–l). Changes in the MLD are cross-hatched where >80% of the models indicate a significant change based on a t-test at the 95% significance level.

CMIP5 ensemble mean September mixed layer depths (MLDs) during 1976–2005 and 2070–2099 and the difference between periods. The MLD exhibits significant decreases in the future relative to the historical period over much of the domain in both March and September.

Summary of Key Findings

Both CMIP5 and CESM-LENS show strong warming over the 21st century over most of the global oceans including the large marine ecosystems around North America, Europe, and the Arctic Ocean. The projected warming trends are generally larger in summer than in winter. The stronger SST trends in summer are partly due to the climatological seasonal cycle in MLD and to the shoaling mixed layer as the surface heating is confined to a shallower layer in summer. (Recent research suggests that air-sea feedback albd entrainment also favors stronger warming in summer). The SST changes by the end of the 21st century are primarily due to a mean warming, such that there will be a large increase in warm extremes and decrease in cold extremes relative to the historical period (1976–2005). The shift in the mean was so large in many regions that SSTs (in a given calendar month) during the last 30 years of the 21st century will always be warmer than the warmest year in the historical period.

Relevant Publications

Xu, T., M. Newman, A. Capotondi, S. Stevenson, E. DiLorenzo, and M. Alexander, 2022: An increase in marine heatwaves without significant changes in surface ocean temperature variability. Nat. Commun., 13, 7396, https://doi.org/10.1038/s41467-022-34934-x

Alexander MA, JD Scott, KD Friedland, KE Mills, JA Nye, AJ Pershing, AC Thomas, 2018: Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elementa: Science of the Anthropocene, 6(1):9, DOI: http://doi.org/10.1525/elementa.191

Le Bris, A., K. E. Mills, R. A. Wahle, Y. Chen, M. A. Alexander, A. J. Allyna, J. G. Schuetz, J. D. Scott, and A. J. Pershing, 2018: Climate vulnerability and resilience in the most valuable North American fishery. PNAS, https://doi.org/10.1073/pnas.1711122115.

Brady R.X., M.A. Alexander, N.S. Lovenduski and R.R. Rykaczewski, 2017: Emergent anthropogenic trends in California Current upwelling, Geophys. Res. Lett., 44, doi:10.1002/2017GL072945.

The Ocean Response to Climate Change along the US East Coast and Gulf of Mexico

The increase in greenhouse gases over the past century has contributed to the warming of most of the world’s oceans, including highly productive coastal regions responsible for the vast majority of global fish catch. The resolution of the global climate models (GCMs) used in CMIP5 is relatively coarse, with an ocean resolution on the order of 100 km, which does not resolve finescale topographic features and may not adequately represent aspects of the ocean dynamics. Here we use the regional ocean modeling system (ROMS) driven by three different GCMs to examine the response of the northwest Atlantic Ocean to greenhouse gas forcing under the RCP8.5 scenario during 2070-2099. Climate change will influence not only SST but also temperature, salinity, and currents throughout the water column. Thus, models and datasets with high spatial resolution may be necessary to fully diagnose and simulate the effects of climate change on the ocean along the US east coast and Gulf of Mexico.

SST in the CTRL (contours, interval of 2°C) and the SST response to climate change (RCP8.5 − CTRL; shaded, interval of 0.5°C) during (top) DJF and (bottom) JJA in ROMS driven by three GCMs, i.e., (a),(d) GFDL-ROMS, (b),(e) IPSL-ROMS, and (c),(f) HadGEM-ROMS. The surface and boundary conditions for the CTRL are obtained from reanalysis during 1976–2005 (historical period), with additional forcing added to the CTRL that is derived from the mean difference between 2070–99 and 1976–2005 in the three RCP8.5 experiments.The warming is strongest when ROMS is driven by the HadGem (UK) model and weakest when forced by the GFDL (US) model. The area of reduced warming east of the midAtlantic states is likely due to the wekening of the Gulf Stream and thus reduced northward transport of warm water. Alexander et al., 2020.

Bottom temperature response (RCP8.5 − CTRL; shaded, interval of 0.5°C) during (top) DJF and (bottom) JJA in (a),(d) GFDL-ROMS, (b),(e) IPSL-ROMS, and (c),(f) HadGEM-ROMS. The 200-m isobath (depth of the bottom), representing the shelf break, is indicated by the black curve. Only were the bottom depth is less than 400 m is shown. The warming due to climate change is very strong in all three models on the west Florida shelf.

Surface current response [RCP8.5 − CTRL; speed is shown by shading, interval of 5.0 cm s−1; vector scale is shown in (f)] in ROMS during (top) DJF and (bottom) JJA in (a),(d) GFDL-ROMS, (b),(e) IPSL-ROMS, and (c),(f) HadGEM-ROMs. The Gulf Stream weakens (blue colors) in all three ROMS simulations in both winter and summer

Relevant Publications

Kim, D., Ross, A. C., Shin, S.-I., Gomez, F. A., John, J. G., Volkov, D. L., Lee, S.-K., Alexander, M. A., and Stock, C. A., 2026: Dynamically downscaled future projections of the Northwest Atlantic Ocean across low to high emissions scenarios, Ocean Sci., 22, 1987–2009, https://doi.org/10.5194/os-22-1987-2026.

Clark, S., K. A. Hubbard, D. K. Ralston, D. J. McGillicuddy, C. Stock, M. A. Alexander, E. Curchitser, 2022: Projected effects of climate change on Pseudo-nitzschia bloom dynamics in the Gulf of Maine, Journal of Marine Systems, 230, 103737, ISSN 0924-7963, https://doi.org/10.1016/j.jmarsys.2022.103737.

Brickman D., M. A. Alexander, A. Pershing, J. D. Scott, and Z. Wang, 2021: Projections of Physical Conditions in the Gulf of Maine in 2050. Elementa Science of the Anthropocene. 9 (1): 00055. DOI: https://doi.org/10.1525/elementa.2020.20.00055

Drenkard, E., C. Stock, A. Adcroft, M. Alexander, and co-authors, 2021: Next-generation regional ocean projections for living marine resource management in a changing climate. ICES Journal of Marine Science. fsab100, https://doi.org/10.1093/icesjms/fsab100

Pozo Buil, M., M. G. Jacox, J. Fiechter, M. A. Alexander, S. J. Bograd, E. N. Curchitser, C. A. Edwards, R. R. Rykaczewski, and C. A. Stock, 2021: A dynamically downscaled ensemble of future projections for the California Current System. Frontiers in Marine Science, 8:612874. https://doi.org/10.3389/fmars.2021.612874

Alexander, M. A., S. Shin, J. D. Scott, E. Curchitser, C. Stock, 2020: The Response of the Northwest Atlantic Ocean to Climate Change. J. Climate, 33 (2): 405-428. DOI: https://doi.org/10.1175/JCLI-D-19-0117.1.

Shin, S., and M. A. Alexander, 2020: Dynamical Downscaling of Future Hydrographic Changes over the Northwest Atlantic Ocean J. Climate , 33 (7): 2871-2890. DOI: https://doi.org/10.1175/JCLI-D-19-0483.1.

Marine Heatwaves

Just like there can be heatwaves on land, heatwaves can also occur in the ocean. Warm ocean temperature extremes, termed marine heatwaves (MHWs), can dramatically impact the overall health of marine ecosystems around the globe, including changing the regional distribution of marine species, altering primary productivity, and increasing the risk of negative human-wildlife interactions. We have examined several aspects of MHWs including:

The influence of mixed layer depth trends on marine heatwaves.
Marine heatwaves on the bottom of the ocean along the continental shelves of North America
Additional aspects of MHWs are described in the publications below.

Relevant Publications

Suhas, D. L., W. Han, T. Shinoda, R. Sun, A. Subramanian, M. Bourassa, and M. Alexander, 2026: Marine Heatwaves in the Arabian Sea: Drivers and Impacts on Atmospheric Circulation and Extreme Precipitation. J. Climate, e250458, https://doi.org/10.1175/JCLI-D-25-0458.1, in press.

Jacox, M.G., D.J. Amaya, and M.A. Alexander, 2024: Marine Heatwaves in 2023 [in “State of the Climate in 2023”], Bulletin of the American Meteorological Society, 105 (8), S167-168, https://doi.org/10.1175/BAMS-D-24-0100.1

Amaya D.J., M.G. Jacox, M.R. Fewings, V.S. Saba, M.F. Stuecker, R. Rykaczewski, ... M.A. Alexander, et al., 2023: Marine heatwaves need clear definitions so coastal communities can adapt. Nature, 616(7955), 29-32. https://doi.org/10.1038/d41586-023-00924-2.

Amaya, D. J., M. G. Jacox, M. A. Alexander, J. D. Scott, C. Deser, A. Capotondi and A. S. Phillips, 2023: Bottom marine heatwaves along the continental shelves of North America. Nature Communications, 14, 1038. https://doi.org/10.1038/s41467-023-36567-0

Jacox, M. G., M. A. Alexander, D. Amaya, E. Becker, S. J. Bograd, S. Brodie, E. L. Hazen, M. Pozo Buil, and D. Tommasi, 2022: Global seasonal forecasts of marine heatwaves, Nature, 604, 486-490, doi:10.1038/s41586-022-04573-9.

Amaya, D. J., M. A. Alexander, A. Capotondi, C. Deser, K. B. Karnauskas, A.J. Miller, and N.J. Mantua, 2021: Are Long-Term Changes in Mixed Layer Depth Influencing North Pacific Marine Heatwaves? [in "Explaining Extremes of 2019 from a Climate Perspective"]. Bulletin of the American Meteorological Society, 102(1), S59-S66, https://doi.org/10.1175/BAMS-D-20-0144.1

Jacox, M. G., M. A. Alexander, S. J. Bograd, and J. D. Scott, 2020: Thermal displacement by marine heatwaves, Nature, 584, 82-86, doi:10.1038/s41586-020-2534-z.

Jacox, M. G., M. A. Alexander, N. J. Mantua, J. D. Scott, G. Hervieux, R. S. Webb, and F. E. Werner, 2017: Forcing of multiyear extreme ocean temperatures that impacted California Current living marine resources in 2016 [in "Explaining extreme events of 2016 from a climate perspective"], Bulletin of the American Meteorological Society, 98, S27-S33, doi:10.1175/BAMS-D-17-0119.1. https://www.mjacox.com/wp-content/uploads/2017/12/Jacox_BAMS_2017.pdf

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