Global Atmospheric Sensitivity to Tropical SST Anomalies throughout the Indo-Pacific Basin

Abstract

The sensitivity of the global atmospheric response to sea surface temperature (SST) anomalies throughout the tropical Indian and Pacific Ocean basins is investigated using the NCEP MRF9 general circulation model (GCM). Model responses in January are first determined for a uniform array of 42 localized SST anomaly patches over the domain. Results from the individual forcing experiments are then linearly combined using a statistically based smoothing procedure to produce sensitivity maps for many target quantities of interest, including the geopotential height response over the Pacific-North American (PNA) region and regional precipitation responses over North America, South America, Africa, Australia, and Indonesia.Perhaps the most striking result from this analysis is that many important targets for seasonal forecasting, including the PNA response, are most sensitive to SST anomalies in the Niño-4 region (5ЊN-5ЊS, 150ЊW-160ЊE) of the central tropical Pacific, with lesser and sometimes opposite sensitivities to SST anomalies in the Niño-3 region (5ЊN-5ЊS, 90Њ-150ЊW) of the eastern tropical Pacific. However, certain important targets, such as Indonesian rainfall, are most sensitive to SST anomalies outside both the Niño-4 and -3 regions.These results are also relevant in assessing atmospheric sensitivity to changes in tropical SSTs on decadal to centennial scales associated with natural as well as anthropogenic forcing. In this context it is interesting to note the surprising result that warm SST anomalies in one-third of the Indo-Pacific domain lead to a decrease of global mean precipitation.

The Twentieth Century Reanalysis Project

“…While the relatively high correlation of 0.701 between the SOCOL PNA and ERA-40 PNA provides evidence of the well-known SST influence on this pattern of variability (e.g. Alexander et al, 2002;Barsugli and Sardeshmukh, 2002) In contrast to the other two indices, the PWC index agrees well with all the estimates, including those from SOCOL. This is not surprising, as there is a considerable SST influence on the variations of the Walker Circulation (e.g.…”

Sensitivity of Asian Summer Monsoon precipitation to tropical sea surface temperature anomalies

“…Alternatively, as explained in previous studies (Barsugli and Sardeshmukh 2002;Barsugli et al 2006;, our patch experiments provided a dynamical means for the estimation of sensitivity under a linear assumption. The scalar area-averaged precipitation response (p) to any single tropical SST anomaly pattern, represented as the 43-component vector TδA, can be expressed as…”

“…To estimate sensitivity s, we followed the procedures outlined in Barsugli and Sardeshmukh (2002). First, the raw precipitation sensitivity s k to the kth patch (k = 1, 2,…, 43) was estimated by normalizing the area-averaged precipitation response p k with the intensity of the SST forcing over this patch.…”

“…The estimated raw sensitivity values were assigned to the geographical centers of the corresponding patches, and then thin-plate smoothing spline procedures (Bookstein 1989), based on the signal-to-noise ratio, were applied to minimize error ε. Here, we treated the error as SST-independent Gaussian random noise (Barsugli and Sardeshmukh 2002;Barsugli et al 2006;. For the display of the sensitivity maps, the values were scaled such that they represented the precipitation responses to a uniformly distributed +1 °C SST anomaly over a relatively large ocean area of 10 6 km 2 .…”

Fast and slow climate responses to CO2 and solar forcing: A linear multivariate regression model characterizing transient climate change

“…The testing of the linear assumption for climate response to other forcing agents, such as aerosol and land use that are more spatially inhomogeneous, merits further study. As an analog of testing linear assumption of climate response to localized forcing, previous studies assumed that regional atmospheric response can be approximated as the sum of the response to each localized sea surface temperature forcing [Barsugli and Sardeshmukh, 2002;Li et al, 2012]. Further, here we tested the linear assumption for a limited set of climate variables including temperature, precipitation, runoff, soil moisture, cloudiness, and TOA radiative fluxes.…”