Why itcz migrate

The solid band of clouds may extend for many hundreds of miles and is sometimes broken into smaller line segments. It exists because of the convergence of the trade winds. In the northern hemisphere the northeast trade winds converge with southeast winds from the Southern Hemisphere.

The point at which the trade winds converge forces the air up into the atmosphere, forming the ITCZ. The tendency for convective storms in the tropics is to be short in their duration, usually on a small scale.

But these short lived storms can produce intense rainfall. It is estimated that 40 percent of all tropical rainfall rates exceed one inch per hour. The ITCZ follows the sun in that the position varies seasonally.

It moves north in the Northern Hemisphere summer and south in the Northern Hemisphere winter. Therefore, the ITCZ is responsible for the wet and dry seasons in the tropics. The sun crosses the equator twice a year in March and September, and consequently makes for two wet seasons each year.

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Harris, I. Levine, X. Response of the Hadley circulation to climate change in an aquaplanet GCM coupled to a simple representation of ocean heat transport. Download references. Fasullo and K. Trenberth from the National Center for Atmospheric Research provided the energy flux data we used in Figs 1b and 5 and in some of the estimates in the text. Marcott provided the temperature reconstructions in Fig. Hell drew Figs 4 and 5. We are grateful for discussions with D.

Sigman and N. Meckler and for comments on drafts by F. Ait-Chaalal, A. Donohoe, R. Ferrari, and J. You can also search for this author in PubMed Google Scholar. All authors discussed the central concepts and ideas and processed and analysed data.

Correspondence to Tapio Schneider. Reprints and Permissions. Migrations and dynamics of the intertropical convergence zone. Nature , 45—53 Download citation. Received : 25 November Accepted : 01 July Published : 03 September Issue Date : 04 September Anyone you share the following link with will be able to read this content:.

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Abstract Rainfall on Earth is most intense in the intertropical convergence zone ITCZ , a narrow belt of clouds centred on average around six degrees north of the Equator. Access through your institution. Buy or subscribe. Rent or Buy article Get time limited or full article access on ReadCube.

Figure 1: Annual-mean precipitation, surface winds, and atmospheric energy balance. Figure 5: Atmospheric meridional energy flux and energy flux equator. References 1 Waliser, D. Change 54 , — Google Scholar 20 Chiang, J. Google Scholar 31 Frierson, D. Google Scholar 41 Pauluis, O. Acknowledgements J. Haug Authors Tapio Schneider View author publications. View author publications. Ethics declarations Competing interests The authors declare no competing financial interests. PowerPoint slides.

PowerPoint slide for Fig. Rights and permissions Reprints and Permissions. About this article. Cite this article Schneider, T. Copy to clipboard. Further reading Emergence of seasonal delay of tropical rainfall during — Fengfei Song , L. Ayantika , Ramesh K. Vellore , T. Sabin , K. Comments By submitting a comment you agree to abide by our Terms and Community Guidelines. Delworth , : Simulated tropical response to a substantial weakening of the Atlantic thermohaline circulation. Times series in the member ensemble mean of the forced simulations.

See section 2 for definitions and details of the indices. Thin and thick lines are the yearly values and the yr running mean, respectively. Cross-wavelet coherence between the yearly indices in the forced ensemble in Fig. The first and second time series are indicated above each panel to help identify leads or lags between the two.

Wavelet coherence highlights regions in time—frequency space with a large common power and consistent phase relationship between two time series, which suggests periods when there might exist causality between their underlying processes.

Significant coherence values, however, do not necessarily mean actual causality. The arrow direction indicates the phase relationship between the two wavelet transforms: arrows pointing right left indicate that the two series are in phase out of phase , while arrows pointing down up indicate that the second series lags leads the first one, if the two series are positively correlated.

The cone of influence white dashed line defines the area in which the border effect does not influence the wavelet spectra. As in Fig. By construction, this component highlights the asymmetric pattern about the equator. Time series in the control, as in Fig. Thin and thick lines are the and the yr running means, respectively. No lead or lag is applied to the PDO. Adapted from McGee et al. We use a coupled climate model that allows us to integrate over climate noise and assess underlying mechanisms.

Such linkages occur on decadal time scales in the ensemble driven by the imposed forcing, and internally on multicentennial time scales in the control. Regional precipitation anomalies differ between the ensemble and the control for a zonally averaged ITCZ shift of similar magnitude, which suggests a dependence on time scale.

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy www. The strong moisture convergence in the lower branch of the Hadley circulation sets the position of the intertropical convergence zone ITCZ , a narrow band of intense precipitation that encircles Earth near the equator. In the present-day climate the NH is heated more strongly than the SH in the annual mean [as described in Frierson et al.

To compensate for this imbalance, the Hadley circulation—the main driver of atmospheric meridional energy transport in the tropics—and the ITCZ are centered north of the equator, thus allowing a net southward atmospheric energy transport across the equator. Over the seasonal cycle the Hadley circulation and the ITCZ migrate meridionally following the solar-driven heating imbalance, transporting energy into the colder, winter hemisphere e.

On longer time scales, a variety of modeling studies have shown that the Hadley cells shift meridionally as a result of atmospheric heating imbalances driven by changes in the Atlantic Ocean heat transport Vellinga and Wu ; Zhang and Delworth ; Broccoli et al. In observations over the twentieth century, the ITCZ position appears to be connected to an interhemispheric heating imbalance driven by multidecadal variability in the extratropical sea surface temperature SST of the North Atlantic [Atlantic multidecadal variability AMV ] Green et al.

Yet, whether this link can ultimately be extended to AMOC multidecadal variability is difficult to answer with the inadequate observational record. The AMV is a same-signed, basin-scale, multidecadal fluctuation of the North Atlantic SST that has been observed in the twentieth century and is superimposed on the long-term warming trend Kerr The PDO has been attributed to midlatitude ocean—atmosphere feedbacks relating the Aleutian low and surface winds over the North Pacific [as reviewed in Liu and Newman et al.

This is perhaps because interhemispheric heating differences are unlikely to arise from changes in the Pacific Ocean heat transport, which is smaller than in the Atlantic Trenberth and Caron and characterized by shallow wind-driven meridional circulation cells Ferrari and Ferreira Two different experimental setups are explored, which present different AMV characteristics.

Our analysis will help assess and lend support to recent conclusions drawn from observations over the twentieth century. The structure of the paper is as follows: the model and experimental setups are described in section 2.

Section 3 documents the main results of the climate model simulations. A discussion of the key results and the main conclusions follow in sections 4 and 5 respectively. The atmospheric model has a 2.

The model does not use flux adjustments. A more detailed description of the model is given in Delworth et al. This model has been used in multiple studies of climate variability, predictability, and change. Our analysis is based on two types of simulations with the same model. We first use a yr-long ensemble of 10 members that are forced by a surface heat flux anomaly derived from regressing reanalysis ocean—atmosphere heat flux anomalies onto the winter North Atlantic Oscillation NAO index Delworth and Zeng The forcing anomaly is computed ensuring its areal integral is zero and thus does not provide a net heating or cooling to the climate system.

The amplitude of the added NAO heat flux is modulated sinusoidally in time with a single period of 50 years and an amplitude of one standard deviation.

For a more detailed description of how these simulations were designed we refer to Delworth and Zeng We next use a yr-long preindustrial control simulation in which all forcings are kept constant at conditions.

The simulated period excludes the spinup phase previous to it. This simulation is described in detail in Delworth and Zeng , where the authors investigate the link between multicentennial variability time scale of — years in the AMOC and its associated cross-equatorial heat transport and NH extratropical temperatures.

The interhemispheric temperature contrast is the difference between the NH and SH annual mean atmospheric temperature averaged between the surface and hPa from the equator to the pole. All the indices are linearly detrended after they are computed.

S1 and S2 in the online supplemental material, respectively. We compare composites that include years that are more than one standard deviation above and below the long-term mean of each index. The statistical significance of the anomalies between these two composites is calculated based on the likelihood of a random occurrence of the signal: the signals detected are compared to analogs obtained by first randomly sampling each index times and then repeating the same analysis; the 5th and 95th percentiles of the empirical anomaly distribution set the confidence levels.

We focus on the ensemble mean since this helps increase the signal-to-noise ratio and thus allows one to characterize the forced variability more readily. In the ensemble mean, the Pcent, AMV, AMOC strength, and interhemispheric temperature difference all exhibit yr periodicity following that of the imposed forcing Fig. There is strong covariability between the AMOC strength, AMV, and interhemispheric temperature difference on multidecadal time scales over the whole simulated period, and between these three indices and Pcent in the second half green—yellow shading in Fig.

Citation: Journal of Climate 33, 3; Such precipitation changes are further associated with an overall strengthening and weakening of the trade winds in the SH and NH, respectively arrows in Fig. Regional mechanisms, such as the Bjerknes feedback by which initial SST anomalies get amplified as they weaken the trade winds aloft; Bjerknes , might explain these differences, because a positive AMV phase would especially favor atmospheric convection in the tropical Atlantic.

Periodicity in the four indices is rooted in the change in buoyancy flux forced by the NAO-derived oscillatory heat flux anomaly, especially in the Labrador Sea Delworth and Zeng For example, for a negative downward flux anomaly the induced upper-ocean cooling enhances the oceanic deep mixing and thereby strengthens the AMOC and its associated poleward heat transport Fig. The North Atlantic surface warming propagates to the troposphere aloft through anomalous air—sea heat flux, especially at midlatitudes not shown.

This heating is quickly distributed zonally and vertically through transport and mixing by dominant westerly winds and eddies in the following years [as also shown in Chiang et al. Both atmospheric eddies propagating equatorward from midlatitudes Chiang and Friedman ; Bischoff and Schneider and the ocean heat release in the tropics e.

The overall result is a NH atmosphere warmer than the SH at most latitudes a positive interhemispheric temperature differences in Figs. This chain of events is supported by the close correspondence between the anomaly patterns of the asymmetric component of the zonally averaged tropospheric temperature and of the heat transport related to a phase shift in the AMOC, AMV, and Pcent Figs.

The PDO shows synchronous coherence with the Pcent and lags behind changes in the AMV and interhemispheric temperature difference between years and on the time scales of the applied forcing Fig.

The PDO time series, however, shows no evident yr cycle imposed by the surface heat forcing and instead exhibits centennial oscillations between a positive phase and a negative phase Fig. These results are different from those reported by Ruprich-Robert et al. They, however, used simulations with the same climate model CM2. Differences between their and our results might be caused by a larger amplitude in the tropical North Atlantic SSTs in the case in which they are restored compared to when they are induced by AMOC variations, thus resulting in a stronger teleconnection between the Atlantic and Pacific tropical atmosphere.

We extend our analysis to a yr control simulation with preindustrial forcing performed with the same climate model. This suggests that the interhemispheric heating imbalance associated with an AMV phase shift is not always compensated by a change in the cross-equatorial AHT through an ITCZ shift; instead, during some periods other mechanisms more effectively compensate for the imbalance through a change in the top-of-the-atmosphere radiative balance, or ocean heat uptake, or both e.

AMV and Pcent also exhibit intermittent coherence on decadal and multidecadal time scales between years and approximately Fig. To focus on the most robust connection between AMOC, AMV, and Pcent on multicentennial time scales, we apply a bandpass filter with a period range between and years to all the data filtered indices in Fig. S6 and analyze the yr-long period between years and S2 and a widespread tropospheric NH warming Fig.

S8 , which are in turn driven by a stronger AMOC and associated OHT not shown , as in the ensemble mean of forced runs described above. S7 and S4. Nonetheless, the PDO does not show similar coherence with the interhemispheric temperature difference at such time scales Fig. S8 able to force a meridional ITCZ shift. S2 drive no significant interhemispheric temperature anomalies in the troposphere Fig. This linkage results from the interhemispheric atmospheric heating imbalance Fig. S1 and S2. This chain of events, however, only operates if the heating imbalance is not fully compensated by an adjustment in the top-of-the-atmosphere radiative flux or the ocean heat uptake, or both e.

This might explain why the linkage is not found on all time scales and over the whole period in the control. S2 , thus leading to deeper, more global impacts on tropospheric temperatures Fig. S8 Delworth and Zeng We argue that such amplification is key to triggering ITCZ shifts on multicentennial time scales rather than on shorter ones in the control. This is suggestive of an initial period of adjustment in the top-of-the-atmosphere net radiative flux, or ocean heat uptake, or both, which compensates for the forcing and is perhaps somewhat different in each ensemble member, causing the signal to be averaged out in the ensemble mean.

Each ensemble member starts from a different point in the control simulation, to which a fairly modest perturbation is added Delworth and Zeng , and thus has a random phasing of internal variability. Furthermore, the linkage between the AMV and ITCZ might be related to the amplitude of the changes in the interhemispheric temperature differences: in the forced ensemble on multidecadal time scales they are almost as large as those in the control on multicentennial time scales.

Our model results thus support and expand those from the observations covering the twentieth century Green et al.