Unveiling the Indian Ocean forcing on winter eastern warming – western cooling pattern over North America

Domain: earth_science
Published: 2024-11-07
Editorial outcome: major_revision

Abstract
While the tropical Pacific teleconnection to North America has been studied extensively, the impact of the Indian Ocean on North American climate has received less attention. Here, through observational analysis and hierarchy atmospheric model simulations with different complexity, we find that the Indian Ocean plays a crucial role in North American winter climate through a teleconnection termed the Indian Ocean-North America pattern. We show that in the warm Indian Ocean phase, this teleconnection contributes to anomalously cold winters along the west coast of the United States through advection with increased mountain snowfall, while simultaneously leading to warmer conditions over the Great Lakes region. Snow-albedo feedback amplifies these Rossby wave-induced surface anomalies. Remarkably, this teleconnection pattern is at work on both interannual and multi-decadal timescales, with its climatic impact being slightly less pronounced than that induced by tropical Pacific sea surface temperature anomalies. Our findings underscore the significance of the Indian Ocean in both the prediction and future projection of North American climate.

Introduction
In recent decades, severe cold spells and heavy snowfalls frequently struck North America in boreal winter1–3. In early 2023, record snowfall buried California towns at Mammoth Mountain, causing power outages for more than 100,000 residents. Exceptionally heavy snow and cold events also caused many serious problems by disrupting transportation, damaging agricultural production, and threatening public health and personal safety. It is thus important to investigate the main drivers and physical mechanisms of these cold events, and to further improve their prediction skills.

Recent studies have indicated that a series of physical mechanisms, including the atmospheric internal variability and regional feedbacks, may significantly influence the North American climate. For example, snow cover exerts an important effect on surface temperature anomalies over North America through snow-albedo feedback4,5, while cloud feedback plays a crucial role in modulating the surface temperature via its radiative effects6,7. Furthermore, atmospheric internal variability is also important for mid-latitude temperature and precipitation variability over North America8–11. Remote forcings from the tropics and the Arctic also play pivotal roles in driving climate variations across North America by influencing atmospheric circulations. The tropical-extratropical teleconnection, recognized for over four decades, has been a focal point in climate dynamics research, examining how changes in tropical oceans affect extratropical atmospheric circulation over both hemispheres12,13. The tropical Pacific sea surface temperature (SST) variability14, particularly the El Niño-Southern Oscillation (ENSO), significantly impacts North American winter climate via the Pacific-North America (PNA) teleconnection pattern15–17. During the positive phase of PNA, often associated with the warm phase of ENSO, North America experiences a marked temperature contrast, with cold-air outbreaks in the southeast and warmer air in the northwest18. Moreover, SST anomalies in the tropical19 and North Atlantic20,21 contribute to North American climate variability by altering large-scale atmospheric circulation through Rossby wave dynamics, while sea-ice loss over the Barents-Kara Seas usually heats the North American continent through thermal advection22.

In addition, numerous studies have investigated Rossby wave trains generated by heat sources across the Indo-Pacific Oceans and their impacts on climate patterns over the Northern Hemisphere23–27. On intraseasonal timescales, convective activities in the tropical Indo-Pacific Oceans during different phases of the Madden-Julian Oscillation (MJO) can trigger Rossby wave trains28, which propagate toward the North Pacific and North America, contributing to surface temperature and precipitation anomalies in these regions29–31. Based on Rossby wave dynamics32–35, similar teleconnection patterns also work for interannual and longer time scales. However, the interannual variability over the Indian Ocean is often considered less important than ENSO events in affecting North American climate, partly due to the strong interaction between the Indian Ocean Basin Mode (IOBM)36,37 and ENSO16,38–40. An El Niño episode typically drives a warm anomaly over the Indian Ocean from early winter through the following summer41, which may obscure the Rossby wave train induced by Indian Ocean warming and the ENSO-related PNA pattern42–45. As a result, the remote impact of Indian Ocean warming has long been viewed as a passive effect of ENSO events.

Here, we aim to isolate the impact of Indian Ocean warming on the North American climate from the ENSO-induced teleconnections, further clarifying the effects of regional feedbacks. Our statistical analyses and numerical model simulations reveal that Indian Ocean warming can generate a stationary Rossby wave train, driving seesaw-like variations in SAT and snowfall across the North American continent, with snow-albedo feedback amplifying this process. Overall, the impact of Indian Ocean warming on the North American winter climate is comparable to, or even greater than, that of ENSO in some regions, especially along the densely populated and economically developed west and east coasts.

Results
The Indian Ocean–North America teleconnection pattern
We first retrieve the North American winter (December-January-February, DJF) SAT anomalies associated with the IOBM index (Supplementary Fig. 1b) through linear partial regression analysis using the ERA5 reanalysis dataset. Linear trends and the Niño 3.4 index have been removed prior to the regression analysis to isolate the impact of the Indian Ocean on North America. The results show that the Indian Ocean warming can trigger a western cooling-eastern warming pattern over North America (Fig. 1a), characterized by significant cooling over Alaska and the western United States, extending from the west coast to the eastern edge of the Rocky Mountain (about 100°W). In contrast, a warming signal is observed over northeastern North America, particularly across Quebec and the Great Lakes region.

We find the same western cooling-eastern warming anomalies over the North American continent when repeating the regression analysis with four additional state-of-the-art reanalysis datasets: JRA55, MERRA2, NCEP2 and CFSR (Supplementary Fig. 2). To further assess the robustness of this linkage, we select three regions—the west coast (blue box in Fig. 1a), the east coast (red box), and Alaska (cyan box)—and quantify the Indian Ocean-induced cooling and warming signals over these areas using the five reanalysis datasets (Fig. 2a). Across all datasets, Indian Ocean warming consistently drives significant warming over eastern North America (Fig. 2a, bottom panel), and significant cold anomalies over Alaska and the west coast (Fig. 2a, upper and middle panels). Specifically, one standard deviation of the Indian Ocean basin warming is associated with a 0.69–0.73°C increase in temperature (among the five reanalyses) over the east coast of North America, a 0.41–0.56°C decrease over the west coast, and approximately a 1.01–1.2°C decrease over Alaska.

This Indian Ocean-induced seesaw-like SAT anomaly is accompanied by a contrasting snowfall pattern over North America (Fig. 1b), with heavier snowfall in northern California, Oregon, Washington, and southwestern British Columbia, but reduced snowfall over broader regions, including the Great Lakes. Particularly, one standard deviation of the IOBM index corresponds to an increase of up to 12 mm in snowfall (water equivalent) along the west coast of the United States during the winter season (Fig. 1b). The anomalies in SAT and snowfall further lead to snow cover changes over the North American continent (Fig. 1c), contributing to a 3% increase (by area) in snow cover over western North America (blue box in Fig. 1c), but a 2% decrease around the Great Lakes region (red box).

In order to validate the robustness of the impacts of Indian Ocean on North American winter climate, we applied both the Student’s t-test and a more stringent field significance test46 on the regression results for SAT and snow cover. All SAT and snow cover regression results over the west and east coasts of the North America can pass the Student’s t-test (indicated by magenta plus signs in Fig. 1a, c). Furthermore, the SAT regression results for the west (cyan box) and east (red box) coasts, as well as Alaska (blue box), also passed the field significance test (black circles in Fig. 1a), while the snow cover regression results for the west coast (blue box) of the United States also passed the field significance test (black circles in Fig. 1c). These findings highlight the robustness of the linkage between the Indian Ocean and North American winter climate, especially along the west coast of the United States.

To further identify the impact of Indian Ocean warming on North American climate and compare it with that of ENSO events, we evaluate the effect of the Niño 3.4 index on North American SAT through linear regression analysis (Supplementary Fig. 3). Consistent with previous studies47,48, the warming in the equatorial Pacific and its associated PNA-like pattern typically produce a northern warming-southern cooling pattern over the North American continent (Supplementary Fig. 3a). This temperature distribution is distinct from the Indian Ocean warming-induced western cooling-eastern warming pattern. One standard deviation of the Niño 3.4 index is associated with a 0.89°C warming (bottom panel in Supplementary Fig. 4a) over the northern parts of North America (red box in Supplementary Fig. 3a), but a 0.32°C cooling (upper panel in Supplementary Fig. 4a) over the southern regions (blue box in Supplementary Fig. 3a). This suggests that the amplitude of the Indian Ocean-induced temperature anomaly is only slightly smaller than that of the ENSO-induced anomaly. In addition, although the area influenced by the Indian Ocean is relatively small (Fig. 1a and Supplementary Fig. 3a), it encompasses the west coast and the Great Lakes region, both of which are densely populated economic and industrial hubs.

Climate teleconnections are often mediated by large-scale atmospheric circulation adjustments, especially through Rossby wave dynamics15,17. To further clarify the physical pathway of the Indian Ocean - North America (INA) teleconnection, we conduct partial regression analysis of sea level pressure (SLP, shading in Fig. 3c) and geopotential height (GPH) at 200-hPa (Fig. 3a) and 500-hPa (Fig. 3b) levels against the IOBM index in winter. The negative anomalies of the observed outgoing longwave radiation related to Indian Ocean warming (light blue contours in Fig. 3c) indicate intensified tropical convection in the western Indian Ocean. The convective heating drives atmospheric circulation anomalies, with a strong barotropic structure in the extratropics (Fig. 3a–c), manifesting as a Rossby wave train along the great circle originating from the Indian Ocean to North America (dashed arrow in Fig. 3a). To trace the propagation of this stationary Rossby wave train, we diagnosed the anomalous wave flux activity at 200 hPa associated with the tropical Indian Ocean warming (Supplementary Fig. 5). The pathway of the wavetrain is influenced by the waveguide effect of the Asian-Pacific jet49. In addition, the wave train is further amplified near the international dateline (Supplementary Fig. 5) due to barotropic instability in the jet exit region12,33. In the lower troposphere (Fig. 3c), the circulation adjustment is characterized by an anomalous high-pressure center over the mid-latitude North Pacific, extending from the Sea of Japan to the west coast of the United States, and an anomalous low-pressure center covering the Bering Sea, Alaska, and western Canada.

Remarkably, the INA pattern is robust across all five reanalysis datasets (Supplementary Figs. 6 and 7). Statistical analysis indicates that the Indian Ocean warming may contribute to an increase of 11.3–12.1 gpm in geopotential height at 500 hPa over the mid-latitude North Pacific (red box in Fig. 3b) and a decrease of 9.8–10.6 gpm over North America (blue box in Fig. 3b) among the five reanalysis datasets (Fig. 2b). The atmospheric circulation adjustment further contributes to the SAT and snowfall anomalies through thermal advection.

Reproducing the INA teleconnection by numerical model experiments
Although the statistical analysis reveals the linkage between the tropical Indian Ocean and North American climate, it does not identify a causal relationship. For instance, Kumar and Hoerling42 argued that Indian Ocean SST anomalies are induced by ENSO, incapable of inducing an atmospheric response. Furthermore, the linear assumption adopted in partial regression analysis may not be valid, raising questions about the linear decomposition of the Indian Ocean’s effects from those of the Pacific. To further clarify causality and isolate the impact of the Indian Ocean, we conduct an ensemble model experiment using the Community Atmospheric Model (CAM5), driven only by the observed Indian Ocean SST variability from 1979 to 2020 (see Methods). We then regress the simulated North American SAT against the IOBM index. The simulated SAT response is very similar to that of the partial regression analysis (Fig. 1a), with cold anomalies over Alaska and the west coast of the United States, as well as warm anomalies over the eastern half of North America (Fig. 1d). The numerical model experiment also well reproduces the east-west contrast in snowfall and snow cover (Fig. 1e, f), showing heavier snow over the west coast and lighter snow in eastern North America, especially around the Great Lakes.

Despite a strong similarity in regression patterns between reanalysis datasets and model simulation, the SAT pattern in response to the Indian Ocean warming in the CAM5 experiment exhibits a westward shift in comparison to that in the partial regression analysis. In the simulation, the cold anomaly over mid-latitude North America is confined to a narrow band near the west coast of the United States (Fig. 1d), while the warm anomaly extends from the east coast to central North America (Fig. 1d). Similar westward shifts are also seen in the simulated snowfall and snow cover patterns (Fig. 1e, f). Notably, in both the reanalyses and numerical model experiment, the west coast of North America consistently experiences anomalously severe winters and heavier snowfall in response to the Indian Ocean basin-scale warming.

Importantly, the numerical experiment well simulates the Indian Ocean-induced Rossby wave train. The regression patterns of simulated SLP and GPH at multiple pressure levels (Fig. 3d–f) against the IOBM index reveal a stationary Rossby wave pattern from the tropical Indian Ocean to North America (Fig. 3d). Its three-dimensional structure (Fig. 3d–f) closely resembles the partial regression patterns in the reanalysis datasets (Fig. 3a–c). The anomalous tropical convection in the experiment (light blue contours in Fig. 3f), driven by Indian Ocean SSTs, generates a stationary Rossby wave train propagating poleward and eastward to the North Pacific and North America, forming an anomalous high-pressure center over the mid-latitude North Pacific and a low-pressure center across the North American continent (Fig. 3d–f), albeit with a slight westward shift of the wavetrain and an excessively strong response over the Arctic compared to the reanalyses. We note that there are other mechanisms possibly working to form the Rossby wave response. For example, the barotropic energy conversion from the subtropical jet to the eddy field likely amplifies the response, primarily in regions of strong climatological deformation12. Also, the synoptic transient eddies may feedback into the linear Rossby wave response to Indian Ocean warming13. These two mechanisms are resolved in the CAM5 simulation. Nonetheless, it will be shown later that the stationary Rossby wave response can be reproduced in an initial value calculation of GFDL dry dynamical core.

The shift in the circulation pattern also contributes to the westward shift of the seesaw-like SAT and snowfall patterns in simulation results through the adjustment of thermal advection.

Over the past half-century, the Indian Ocean has experienced rapid and steady warming50, which may potentially drive multi-decadal changes in the global climate system through atmospheric bridges involving the INA pattern. However, this multi-decadal impact could be overwhelmed by other climate factors, such as the global warming signal51 and the global impacts of the Pacific52,53 and Atlantic54,55 Oceans. As a result, it may be challenging to identify the Indian Ocean-induced teleconnection signal from observational and reanalysis datasets. Nevertheless, the ensemble model experiment provides a valuable reference for isolating the multi-decadal climate response to Indian Ocean warming.

We thus illustrate the multi-decadal trends of SLP, GPH, and SAT (Supplementary Fig. 8) in the CAM5 experiment forced by Indian Ocean SSTs. These results show that, on multi-decadal timescales, the Indian Ocean warming can trigger a stationary Rossby wave train, whose horizontal pattern is nearly identical to that of the INA teleconnection pattern on interannual timescales. The convective heating anomalies in the Indian Ocean generate an anomalous high-pressure center over the mid-latitude North Pacific (Supplementary Fig. 8b–d) and a low-pressure center over western Canada, leading to cooling in Alaska and along the west coast of North America, meanwhile warming the central-eastern regions of the continent (Supplementary Fig. 8a). The simulation results indicate that the INA pattern works on both interannual and multi-decadal time scales, contributing to recent multi-decadal climate changes. Our findings thus qualitatively demonstrate the effects of the Indian Ocean warming on climate variations over the North Pacific and North America. In view of the complexity of the Earth’s climate system, this result also underscores the urgent need to quantify the impact of the Indian Ocean warming and to further clarify its interactions with changes in other ocean basins.

Given that the source of the Rossby wave train is not fully isolated in the partial regression analysis of reanalysis data (Fig. 3a) or in the simulated regression pattern (Fig. 3d), we further simulate the evolution of the atmospheric circulation response to the Indian Ocean forcing during winter using an idealized atmospheric model, the GFDL dry dynamical core, to clarify the physical dynamics. In this simulation, the model is perturbed by an idealized convective heating over the tropical Indian Ocean, while the winter climatological basic state is maintained as a balanced state of the model’s governing equations (see Methods). The experimental design has been employed to study the atmospheric response to a thermal forcing56–58. The model simulation illustrates a clear picture of the INA pattern (Supplementary Fig. 9), demonstrating that convective heating over the tropical Indian Ocean triggers a stationary Rossby wave train, which propagates along a great circle linking the Indian Ocean to North America. In addition to this direct pathway, another wave source appears in the subtropical North Pacific near the international dateline, which reflects the barotropic instability in the jet exit region12, thus intensifying the stationary wave train and forming a teleconnection pattern similar to those in the reanalysis dataset (Fig. 3a) and the CAM5 model simulation (Fig. 3d), albeit with a slight phase shift in the wavetrains.

The coherence between the reanalysis datasets (Fig. 3a), the comprehensive atmospheric model (Fig. 3d), and the idealized atmospheric model (Supplementary Fig. 9) verifies the robustness of the INA teleconnection pattern. These results reveal that the Indian Ocean heating contributes to the large-scale circulation adjustment over the North Pacific and North America through Rossby wave dynamics, leading to temperature and snowfall changes across the North American continent in winter.

Physical mechanisms for North American response
Our statistical analysis and numerical model simulations suggest that the Indian Ocean warming can drive a pair of anomalous high- and low-pressure centers over the mid-latitude North Pacific and North America (Fig. 3). The circulation adjustment further induces changes in SAT and snow cover in North America. Here, we evaluate the physical mechanisms involved in this process. We first illustrate the thermal advection anomaly at the 850-hPa level (color shading in Fig. 4a) associated with the Indian Ocean warming using linear partial regression analysis on the ERA5 dataset, together with the GPH and wind anomalies at the same pressure level (contours and vectors in Fig. 4a). These results show that the anticyclonic circulation anomaly over the mid-latitude North Pacific drives anomalous cold advection toward the west coast of North America, while the cyclonic circulation anomaly across western Canada induces anomalous warm advection in central-eastern North America, thereby contributing to the western cooling-eastern warming pattern.

We further calculate each term of the surface energy budget related to the Indian Ocean warming. The large-scale atmospheric circulation anomalies forced by the Indian Ocean warming may not induce prominent changes in local sensible and latent heat fluxes over North America in winter, as these signals are relatively weak and statistically insignificant across most of the continent (not shown). In contrast, the radiation anomalies play a key role in driving North American wintertime SAT changes. The surface net shortwave radiation (Fig. 4c, positive downward) decreases significantly by approximately 2.2 W·m−2 from the west coast of the United States to the eastern edge of the Rocky Mountains in response to one standard deviation of the Indian Ocean basin warming, which likely contributes to the cooling signal in this region (Fig. 1a). The longwave radiation anomaly induced by Indian Ocean warming (Fig. 4d) is relatively smaller, with a spatial distribution opposite to that of the shortwave radiation, partially offsetting its effects (Fig. 4c).

We further investigate regional feedbacks involved in these radiation changes. Previous studies have shown that cloud feedback6,7 and snow-albedo feedback4,5 can facilitate the temperature anomalies through local radiation effects. Hence, we examine the cloud cover changes in response to Indian Ocean warming through partial regression analysis (Supplementary Fig. 10). We also calculate the Indian Ocean warming-induced surface albedo anomalies over North America (Fig. 4b) and compare them with the spatial patterns of the surface net shortwave radiation anomaly (Fig. 4c) and the snow cover anomaly (Fig. 1c).

The cloud cover anomaly induced by Indian Ocean warming does not exert a dominant influence on radiation flux changes over most of the United States (Supplementary Fig. 10). However, the atmospheric circulation adjustment over the North Pacific and North America related to Indian Ocean warming enhances moisture convergence (not shown) over northern Mexico. This provides favorable conditions for increased cloud cover and resultant changes in longwave (Fig. 4d) and shortwave (Fig. 4e) radiation fluxes in this region. While these radiative effects offset each other and do not result in a pronounced SAT anomaly over Mexico (Fig. 1a), the effect of cloud feedback is limited in areas where significant Indian Ocean-induced SAT changes occur over North America.

Notably, the snow-albedo feedback appears to play an important role in cooling the western parts of the United States (Fig. 1a). The Indian Ocean warming drives increased snow cover over mid-latitude North America (Fig. 1c), extending from the west coast of the United States to the Rocky Mountains. The enhanced snow cover leads to higher surface albedo over the same area (Fig. 4b) and associated increased upward shortwave radiation, further contributing to a reduction in surface net shortwave radiation in the western United States (Fig. 4c). The close correspondence among the spatial patterns of snow cover, surface albedo, and surface shortwave radiation emphasize the importance of snow-albedo feedback in cold anomalies over the western United States (Fig. 1a), in response to the Indian Ocean-induced teleconnection.

On the other hand, the surface cooling may in return slow the melting of snow over mid-latitude North America. This positive feedback amplifies the effect of the cold advection driven by the Indian Ocean warming-induced circulation anomalies (Fig. 4a), leading to more pronounced cooling over the western United States (Fig. 1a).

Methods
Datasets
We employ the SST data for the period 1979–2020 from the Met Office Hadley Centre Global SST dataset (HadISST)62 in the statistical analyses and numerical model simulations (serving as the lower boundary forcing). The outgoing longwave radiation data63 from National Oceanic and Atmospheric Administration is utilized to examine the tropical convection anomalies. We obtain monthly mean atmospheric variables during 1979–2020, including SAT, SLP, and GPH, etc., from five state-of-the-art reanalysis datasets: (1) the European Centre for Medium Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5)64, (2) the Japanese 55 year Reanalysis (JRA55)65, (3) the Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA2)66, (4) the NCEP-DOE Reanalysis II (NCEP2)67, and (5) the National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR)68. The monthly averaged snowfall and snow cover data used in this study are from the ERA5-Land dataset69.

Definition of SST index
The IOBM is the leading mode of interannual SST variability over the tropical Indian Ocean on interannual timescales. The IOBM index is defined as the area-weighted mean SST anomaly over 20°S-20°N, 40°E-110°E (Supplementary Fig. 1), with the climatological mean SST during the period of 1979–2020 removed. The Niño 3.4 index is defined as the SST anomaly averaged over 5°N-5°S, 170°W-120°W.

Regression analysis and significance level
In this study, we use a linear regression analysis to examine the linkage between the tropical Indian Ocean variability and North American wintertime climate changes. In particular, a partial regression analysis is applied to remove the ENSO influence in the reanalysis datasets through Gram–Schmidt orthogonalization70. We have standardized the IOBM and Niño 3.4 time series before these regressions. The Student’s t-test is used to assess the statistical significance level. Given the multiple testing problem and spatial autocorrelation, we further conduct field significance test46 by calculating adjusted p-values according to the control of false discovery rate (FDR) method. We display local significant grid points with black circles that satisfy the FDR criterion in the linear partial regression patterns of Figs. 1 and 4.

Wave activity flux
The wave activity flux proposed by Takaya and Nakamura71 is utilized to investigate the propagation characteristics of stationary Rossby wave trains. This wave activity flux is typically parallel to the local group velocity of the stationary Rossby wave train. The formula for the horizontal component of the wave activity flux is as follows:

W = 1 / 2|U| [ U (ψx^2 - ψψxx) + V (ψxψy - ψψxy) , U (ψxψy - ψψxy) + V (ψy^2 - ψψyy) ] , (1)

Here, ψ represents the perturbed geostrophic streamfunction; U=(U,V) denotes the climatological mean of horizontal wind velocity during DJF from 1979 to 2020; The pressure, p, is standardized by 1000 hPa.

Sen’s slope method and Mann–Kendall test
Sen’s slope72 method is used to calculate the simulated multi-decadal trends of the SAT, the SLP, and the GPH, and the Mann-Kendall test is used to estimate the confidence intervals72 of trends.

CAM5 model experiment
The National Center for Atmospheric Research (NCAR) atmospheric model, the Community Atmosphere Model version 5 (CAM5)73, is used to identify the atmospheric responses to the Indian Ocean SST forcing. The CAM5 is the atmospheric component of the Community Earth System Model (CESM), with a horizontal resolution of 2.5°×1.9° and 26 vertical layers.

The HadISST is used as the lower boundary condition of the atmospheric model. The model experiment is forced by the observed monthly SST over the tropical Indian Ocean (north of 20°S) from 1979 to 2020, with linear buffer zones extending from 20°S to 30°S over the Indian Ocean basin. The sea ice concentration and the SSTs outside the target region are set to the climatological mean state during the period of 1981–2010 with an annual cycle. Other forcings, including the greenhouse gas concentration, aerosols, and solar radiation, are all set to fixed values during the entire integration period. We perform an ensemble experiment with 12 ensemble members driven by the same external forcing, but starting from different initial conditions. The ensemble mean of these simulation results is considered to be the CAM5 response to the observed Indian Ocean SST variability.

GFDL dry-dynamical-core experiment
We use the dry spectral dynamical core of the Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric general circulation model74 to investigate the evolution of the atmospheric response to Indian Ocean warming. The model is run at triangular 42 horizontal resolution with 19 vertical levels. The model is initialized with the winter climatological background flow of the ERA5 reanalysis in the DJF season and is run with an additional forcing that makes the initial background flow as the balanced state of the model. This additional forcing is obtained by integrating the model with the background flow for a few time steps56,75. Once the model is balanced by the background flow, a convective heating is introduced to the thermodynamic energy equation for the first 5 days to mimic the tropical Indian Ocean warming as shown in Fig. 4 of Li, Holland57. Temporally, the convective heating is ramped up from day 0 to day 1, maintained for 5 days, and ramped down from day 5 to day 6. Horizontally, the heating follows a cosine function in both latitude and longitude direction, centered at 0°, 67.5°E. The latitude and longitude widths are set to 20° and 45°, respectively, to cover the tropical Indian Ocean57. Vertically, the heating is maximized at 500 hPa with a magnitude of 1×10−4 K·s−1 and extends between 100–1000 hPa following a cosine function. The model is run for 20 days, and the Supplementary Fig. 9 shows the dynamical response to an initial perturbation mimicking the tropical Indian Ocean warming at day 13.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript explores the relationship between Indian Ocean Sea Surface Temperature (SST) variability and climate variabilities across North America during the boreal winter. The authors illuminate how Indian Ocean warming initiates a stationary Rossby wave train, thereby influencing the contrasting east-west patterns in Surface Air Temperature (SAT) and snowfall during winter, a process further intensified by snow-albedo feedback.\nWhile the climate teleconnection between the tropical Pacific and North America has long been recognized as a primary driver of climate variability in the region, the role of the tropical Indian Ocean has been somewhat overlooked. This manuscript convincingly underscores the significance of the Indian Ocean in shaping winter-time North American SAT and snowfall patterns, particularly along the west and east coasts of the United States. The rigorous statistical analysis and numerical simulations presented in the study are robust, offering compelling evidence that may profoundly impact climate prediction across America.\nI believe this paper merits consideration for publication in Nature Communications, and I offer the following detailed comments and suggestions:\n1. The authors reveal the Indian ocean-induced Rossby wave pattern linking Indian Ocean and North America in boreal winter. I am curious whether this teleconnection triggered by Indian Ocean also active for other seasons. Exploring the seasonality of this teleconnection would provide valuable insights.\n2. I suggest the authors provide a more comprehensive description of the numerical model experiments' setup. What is the horizontal and vertical resolution of the models used in this study? What is the detailed setting of the external forcing in each sensitivity experiments? Furthermore, please elucidate the differences between the CAM and the GFDL Model.", "reviewer_2": "In this study, the authors report that they have identified a teleconnection pattern referred to as the Indian Ocean / North American (INA) pattern in boreal winter, which is induced by sea surface temperature (SST) anomalies over the Indian Ocean. Through this teleconnection, a warmer than normal Indian Ocean leads to an anomalously cold winter along the west coast of the United States and a warmer condition over the Great Lake region. The results are based on analysis of several reanalysis datasets and a global atmospheric general circulation model (AGCM) simulation with prescribed SST variability over the Indian Ocean region, as well as a linearized model experiment with an idealized Indian Ocean forcing. In general, the analysis and model experiments are reasonably well designed and performed, and the conclusions are reasonably well supported.\nUnfortunately, I cannot recommend publication of this manuscript in Nature Communications. The main reason is that it has limited novelty. In particular, the so-called INA pattern is not new, which is in fact one of the preferred patterns of atmospheric response to tropical forcing that is already known. This pattern has also been found to be linked to forcing in the Indian Ocean in previous studies (See Major comment 1 for detail). The paper has several other shortcomings as well. Relevant references with similar work on atmospheric teleconnections associated with the Indian Ocean forcing are not mentioned and discussed (Major comment 2). A more objective approach is needed when demonstrating the Rossby wave pathway, and other possible mechanisms need to be explored in explaining the teleconnection (Major comments 3 and 4). More detailed information needs to be provided on the linearized model experiment, so that the result can be reproduced (Major comment 5).\nMajor comments:\n1) The so-called Indian Ocean / North American (INA) pattern in boreal winter is not new. In an early study by Simmons et al. (1983), for example, it was demonstrated that the extratropical atmospheric response to tropical forcing takes a few preferred patterns. One of them is in the Pacific – North American sector, which is, to some extent, like the Pacific-North American pattern (PNA). The North Pacific atmospheric response to a heating source over the Indian Ocean is similar to that to a cooling source in the tropical western Pacific (their Fig. 5). Similarly, Ting and Sardeshmukh (1993) found that the extratropical response to a heating source at 100°E is out of phase with that to a heating source at 140°E (their Figure 8 a and e). Furthermore, Lin et al. (2010) did a set of linearized experiments studying the sensitivity of atmospheric response to the longitudinal location of an equatorial forcing. They found that when the forcing is over the Indian Ocean there is a strong North Pacific response. The INA in the present study has a good similarity to the extratropical response pattern to a forcing over the Indian Ocean region in these earlier studies. For example, Supplementary Fig. 8 of this manuscript is strikingly similar to Fig. 14b of Lin et al. (2010). Therefore, the INA is not a new teleconnection pattern. Instead, it is a negative PNA-like response to a heating source over the Indian Ocean, which is known in the literature. The mechanism of this pattern has been explored and discussed in these previous studies.\n2) There are several similar studies on atmospheric teleconnections associated with the Indian Ocean forcing that are not referenced and discussed in this manuscript (e.g., Molteni et al. 2015, 2020; Abid et al. 2023). The study of Molteni et al. (2015) is particularly relevant, which compares the impacts of anomalous heat sources in the Indian Ocean and the tropical Pacific and shows that the interannual teleconnection is consistent with those detected on the sub-seasonal time scale. There is a large body of tropical-extratropical teleconnection studies on the sub-seasonal time scale (e.g., Stan et al. 2017), which is also of relevance.\n3) A more objective approach should be used, and more care should be taken in explaining the results. The Rossby wave theory is likely a good candidate for explaining the teleconnection linking the Indian Ocean and North America. However, the wave trains indicated on the figures (e.g., the blue arrows in Figs. 3 and Supplementary Fig. 8) appear to be subjective. The arrows shown do not even pass through the GPH200 anomaly centers (e.g., Fig. 3d, Supp Fig. 8). This gives the impression that the authors are plotting the Rossby wave pathway according to what they like. The anomaly centers upstream of the central North Pacific are weak and Fig. 5 is somehow misleading. A more appropriate and objective approach would be to calculate and plot the wave activity flux.\n4) There are possibly other mechanisms contributing to the teleconnection pattern observed in the reanalysis data and the AGCM simulation in addition to the Rossby wave propagation, which are not mentioned and discussed in this manuscript. For example, the 2-dimentional zonally varying mean flow could provide kinetic energy to the growth of the extratropical anomalies (e.g., Simmons et al. 1983). The synoptic-scale transient eddies could feedback to reinforce or modify the linear Rossby wave response (e.g., Held et al. 1989).\n5) The idealized linear model experiment does help support the observed and the AGCM simulated teleconnection, but its description needs to be clarified. Is it a spectral or finite-difference model? What is the resolution? On lines 454-455, it says “At each time step, an additional forcing is added to the background flow in order to …”. How is the forcing added to the background flow? Is it added to the vorticity, divergence, or temperature equation? How is it determined? Is it time independent? About the convective heating, what is its shape, vertical profile, location, and magnitude? On line 456 it says, “added as an initial perturbation”. Is it really at the initial step only? Do you keep it there during the integration? In Supplementary Fig. 8, how many days have you integrated the model or is it a stationary response?\nMinor comments:\n1) Paragraph starting from line 95: The variability of winter climate in North America may not be related to any external forcing. It could be caused by atmospheric internal dynamics.\n2) Paragraph starting from line 142 and Fig. 1b: The snowfall signal near the west coast is in a small area and not statistically significant. In the whole study, there is no explanation on why the snowfall increases there associated with the Indian Ocean induced teleconnection.\n3) Line 177 and Fig. 3c: There is reduced precipitation in the eastern Indian Ocean associated with warm SST (red contours). It would be helpful to look at the outgoing longwave radiation (OLR) that better represents tropical convections.\n4) Fig. 3f: Why not show the regression of precipitation (or OLR) for the model simulation? It is the diabatic heating of convection that drives the Rossby wave, which is a more direct forcing than SST. It would be helpful to demonstrate if the model can produce a reasonable local convection or precipitation response to the prescribed SST anomaly. It should also be kept in mind that in an AGCM simulation with prescribed SST, there is no interaction between SST and convection, which affects the simulation of precipitation over the warm pool region.\n5) Paragraph starting from line 327: It is unclear how this helps explain the cold anomaly to the north of the reduced cloud cover. It is also unclear what causes the cloud cover reduction near northern Mexico.\n6) Line 337: What is the relative contribution of snow-albedo feedback comparing to the cold advection, to justify that it plays “a crucial role”?\n7) Line 409: Please clarify the snowfall and snow cover data.\n8) Lines 426-427: Please provide a reference for the Gram-Schmidt method.\n9) Paragraph starting from 439: How is the sea-ice concentration specified in the AGCM simulation?\n10) Fig. 3: There are differences between the observations and the AGCM simulation over the North Atlantic sector. This may not be important for the discussion of North American impact but might be of interest to those studying the North Atlantic Oscillation and climate variability in Europe.\nReferences:\nAbid, M.A., F. Kucharski, F. Molteni, and M. Almazroui, 2023: Predictability of Indian Ocean precipitation and its North Atlantic teleconnections during early winter. npj Clim. Atmos. Sci., 6, 17. https://doi.org/10.1038/s41612-023-00328-z.\nHeld, I. M., S. W. Lyons, and S. Nigam, 1989: Transients and the extratropical response to El Nino. J. Atmos. Sci., 46, 163-174. https://doi.org/10.1175/1520-0469(1989)046<0163:TATERT>2.0.CO;2.\nLin, H., G. Brunet and R. Mo, 2010: Impact of the Madden-Julian Oscillation on wintertime precipitation in Canada. Mon. Wea. Rev., 138, 3822–3839. https://doi.org/10.1175/2010MWR3363.1.\nMolteni, F., T. N. Stockdale, and F. Vitart, 2015: Understanding and modelling extra-tropical teleconnections with the Indo-Pacific region during the northern winter. Clim. Dyn., 45, 3119–3140. https://doi.org/10.1007/s00382-015-2528-y\nMolteni, F., and Coauthers. 2020: Boreal-winter teleconnections with tropical Indo-Pacific rainfall in HighResMIP historical simulations from the PRIMAVERA project. Clim. Dyn., 55, 1843–1873. https://link.springer.com/article/10.1007/s00382-020-05358-4.\nSimmons, A. J., J. M. Wallace, and G. W. Branstator, 1983: Barotropic wave propagation and instability, and atmospheric teleconnection patterns. J. Atmos. Sci., 40 , 1363–1392. http://dx.doi.org/10.1175/1520-0469(1983)040<1363:BWPAIA>2.0.CO;2.\nStan, C., and Coauthors, 2017: Review of tropical-extratropical teleconnections on intraseasonal time scales. Rev. Geophys. 55 (4), 902-937. https://doi.org/10.1002/2016RG000538.\nTing, M., and P. D. Sardeshmukh, 1993: Factors determining the extratropical response to equatorial diabatic heating anomalies. J. Atmos. Sci., 50, 907–918. https://doi.org/10.1175/1520-0469(1993)050<0907:FDTERT>2.0.CO;2", "reviewer_3": "Review of “Unveiling the Indian Ocean Forcing on Winter Eastern Warming – Western Cooling Pattern over North America” by Hou et al.\nThe authors examined the potential impact of Indian Ocean Warming on North American surface air temperature during boreal winter. To explore this potential link, they used partial regression analysis (attempting to remove the ENSO signal) and idealised atmospheric model simulation with prescribed SST variability only over the Indian Ocean. The authors argue that the convective heating associated with the Indian Ocean Warming triggers a Rossby Wave pattern which, guided by the prevailing jet, sets up a barotropic atmospheric response over North Pacific and North America. The resulting atmospheric circulation pattern advects warm air from south over the eastern North America and cold air from north over western North America; leading to a zonal sea-saw pattern in surface air temperature. It is further argued that this thermal advection due to large-scale atmospheric circulation changes is supported by local snow-albedo feedback. This further helps set up the observed surface air temperature pattern, particularly over western North America.\nThe findings are plausible and partially convincing based on the analysis performed in the study. However, before acceptance, I would request the authors to clarify some of the concerns below:\nFirst of all, as there is no line numbering or at least paragraph numbering, it is a bit difficult to report the review.\nMethods:\n1. Is the IOBM index detrended before doing the partial regression analysis? It is not clear from the description whether the detrended timeseries in Supplemetary Fig 1b is used or the one in Supplemetary Fig 1a.\n2. With such a small spatial extent of the significant regression slope values, the authors should consider checking the field significance of the statistical results for gridded data.\ne.g.: Wilks, D. S., 2016: \"The Stippling Shows Statistically Significant Grid Points\": How Research Results are Routinely Overstated and Overinterpreted, and What to Do about It. Bull. Amer. Meteor. Soc., 97, 2263-2273, https://doi.org/10.1175/BAMS-D.\n3. The GFDL dynamical dry core experiment is not well described. How much heating has been applied, and where? (heating profile and/or region of induced heating should be shown). Is that induced heating comparable to the observed pattern and magnitude?\nPage 5, last para and Fig 1:\nThe snow cover and snowfall associated with the Indian Ocean Warming are barely significant in Fig 1b,c. Why are there different boxes in those figures? Please specify the box lat/lon boundaries. How significant is the 2-3% change in the area of snow cover in terms of climatic impact?\nFigure 2:\nConsider specifying North America/ North Pacific in Fig 2b, instead of ‘positive’ and ‘negative’.\nFigure 3:\nThe SLP pattern over north pacific resembles a negative PDO phase. The surface weather responses over North America (which authors argue due to the large-scale atmospheric caused by remote forcing from the Indian Ocean) may be largely influenced by PDO-related surface atmospheric circulation changes?\nFigure 4:\nThermal advection figure shows barely any statistically significant regions over North America, particularly over regions of signifincat SAT changes. Also no significant changes are seen in Fig 4c over western part of North America, where albedo shows some significnat changes (4b). I feel to make this part more reliable and robust, authors should consider performing field significance tests. Also, it would be better if Figure 1 and 4 are plotted in same map projection.\nFinally, the precipitation pattern shown in Figure 1c shows a clear zonal dipole, resembling the IOD related precipitation pattern. Although IOD in general is not that active during the studied period (DJF), since the authors argue that the source of the large scale atmospheric circulation changes are convective heating related to this precipitaion pattern, one can expect the same rossby wave dynamics would be triggered during IOD as well, or the prevailing structure of the jet stream during the IOD season doesn’t allow to set up such an Rossby wave dynamics?"}, "author_response": "We are grateful to the reviewer’s comments, which greatly helped to improve our manuscript. The detailed responses are listed as follows.\n1. The authors reveal the Indian ocean-induced Rossby wave pattern linking Indian Ocean and North America in boreal winter. I am curious whether this teleconnection triggered by Indian Ocean also active for other seasons. Exploring the seasonality of this teleconnection would provide valuable insights.\nResponse:\nThank you for your comment. Based on your advice we conduct partial regression analyses of the GPH200 from the ERA5 dataset against the IOBM (Fig. R1) index across all seasons, with the Niño 3.4 index removed. During boreal winter (DJF), the IOBM-related atmospheric circulation anomaly over the Northern Hemisphere exhibits a high-pressure center in North Pacific and a low-pressure across North America (Fig. R1a). In spring (MAM), the regressed GPH200 displays meridionally low- and high-pressure centers over North Pacific, implying the poleward shift of the jet stream (Fig. R1b). In addition, in autumn (SON), the atmospheric circulation anomaly associated with IOBM events shows a similar Rossby wave pattern, however, which is out of phase with that in winter (Fig. R1d). Considering the wave-flow interaction and the strong seasonality of the background state, this teleconnection linking the tropical Indian Ocean to North America has complex seasonality, which will be a main focus in our future study.\n2. I suggest the authors provide a more comprehensive description of the numerical model experiments' setup. What is the horizontal and vertical resolution of the models used in this study? What is the detailed setting of the external forcing in each sensitivity experiments? Furthermore, please elucidate the differences between the CAM and the GFDL Model.\nResponse:\nWe are so sorry for not describing the setup details of numerical model experiments. The CAM5 is run with a horizontal resolution of 2.5°×1.9° and 37 vertical layers. The CAM5 includes both the barotropic energy conversion, the moisture processes, and the transient eddy feedback mechanisms, which represents the real physical world. The GFDL model basically misses the eddy effect and the moisture processes, which represent the Rossby dynamics. The strong agreement between the CAM model and the GFDL model results highlights the importance of the Rossby wave dynamics in the INA teleconnection. We have further revised the modelling description in the original manuscript.\n[...]"}, {"round": 2, "reviewer_comments": {"reviewer_2": "Round 2 review on “Unveiling the Indian Ocean Forcing on Winter Eastern Warming – Western Cooling Pattern over North America” authored by Yurong Hou et al.\nI appreciate the authors’ effort addressing my concerns and comments raised in the 1st round of review. However, the response to my comments is not fully satisfactory.", "reviewer_3": "I think the authors have answered my queries, and the results are convincing, given the robust analysis used here with several reanalysis datasets and supported by numerical experiments. The methodology section has been improved which adds to the reproducibility of the study. I recommend the publication of the study in Nature Communications."}, "author_response": "We really appreciate the reviewer for this important suggestion. We have further calculated the TN wave activity flux based on the GFDL model results, as shown in Figure R1. As anticipated by the reviewer, the TN flux intensifies significantly over the mid-latitude central Pacific, near the dateline. This is because this area is located in the exit region of the subtropical jet, where enhanced barotropic energy conversion allows additional kinetic energy to be transferred from the mean flow to the eddy field1-3.\n[...]"}, {"round": 3, "reviewer_comments": {"reviewer_1": "The manuscript explores the relationship between Indian Ocean Sea Surface Temperature (SST) variability and climate variabilities across North America during the boreal winter. The authors illuminate how Indian Ocean warming initiates a stationary Rossby wave train, thereby influencing the contrasting east-west patterns in Surface Air Temperature (SAT) and snowfall during winter, a process further intensified by snow-albedo feedback.\nWhile the climate teleconnection between the tropical Pacific and North America has long been recognized as a primary driver of climate variability in the region, the role of the tropical Indian Ocean has been somewhat overlooked. This manuscript convincingly underscores the significance of the Indian Ocean in shaping winter-time North American SAT and snowfall patterns, particularly along the west and east coasts of the United States. The rigorous statistical analysis and numerical simulations presented in the study are robust, offering compelling evidence that may profoundly impact climate prediction across America.\nI believe this paper merits consideration for publication in Nature Communications, and I offer the following detailed comments and suggestions:\nWe are grateful to the reviewer’s comments, which greatly helped to improve our manuscript. The detailed responses are listed as follows.\n1. The authors reveal the Indian ocean-induced Rossby wave pattern linking Indian Ocean and North America in boreal winter. I am curious whether this teleconnection triggered by Indian Ocean also active for other seasons. Exploring the seasonality of this teleconnection would provide valuable insights.", "reviewer_2": "The authors have addressed my concerns. I think that it is acceptable for publication."}, "author_response": ""}]

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