Search Results
Abstract
The atmospheric response to an idealized decline in Arctic sea ice is investigated in a novel fully coupled climate model experiment. In this experiment two ensembles of single-year model integrations are performed starting on 1 April, the approximate start of the ice melt season. By perturbing the initial conditions of sea ice thickness (SIT), declines in both sea ice concentration and SIT, which result in sea ice distributions that are similar to the recent sea ice minima of 2007 and 2012, are induced. In the ice loss regions there are strong (~3 K) local increases in sea surface temperature (SST); additionally, there are remote increases in SST in the central North Pacific and subpolar gyre in the North Atlantic. Over the central Arctic there are increases in surface air temperature (SAT) of ~8 K due to increases in ocean–atmosphere heat fluxes. There are increases in SAT over continental North America that are in good agreement with recent changes as seen by reanalysis data. It is estimated that up to two-thirds of the observed increase in SAT in this region could be related to Arctic sea ice loss. In early summer there is a significant but weak atmospheric circulation response that projects onto the summer North Atlantic Oscillation (NAO). In early summer and early autumn there is an equatorward shift of the eddy-driven jet over the North Atlantic as a result of a reduction in the meridional temperature gradients. In winter there is no projection onto a particular phase of the NAO.
Abstract
The atmospheric response to an idealized decline in Arctic sea ice is investigated in a novel fully coupled climate model experiment. In this experiment two ensembles of single-year model integrations are performed starting on 1 April, the approximate start of the ice melt season. By perturbing the initial conditions of sea ice thickness (SIT), declines in both sea ice concentration and SIT, which result in sea ice distributions that are similar to the recent sea ice minima of 2007 and 2012, are induced. In the ice loss regions there are strong (~3 K) local increases in sea surface temperature (SST); additionally, there are remote increases in SST in the central North Pacific and subpolar gyre in the North Atlantic. Over the central Arctic there are increases in surface air temperature (SAT) of ~8 K due to increases in ocean–atmosphere heat fluxes. There are increases in SAT over continental North America that are in good agreement with recent changes as seen by reanalysis data. It is estimated that up to two-thirds of the observed increase in SAT in this region could be related to Arctic sea ice loss. In early summer there is a significant but weak atmospheric circulation response that projects onto the summer North Atlantic Oscillation (NAO). In early summer and early autumn there is an equatorward shift of the eddy-driven jet over the North Atlantic as a result of a reduction in the meridional temperature gradients. In winter there is no projection onto a particular phase of the NAO.
deviation will induce a similar deviation in the surface wind stress pattern (vectors in Fig. 3c ), which will enhance (suppress) the equatorial cold advection over the eastern (western) Pacific (shaded in Fig. 3c ) through the Bjerknes feedback ( Fig. 4b ). Under these two feedback processes, the original SST warming induced by the CSFI deviation through Δ Q SW will shift westward relative to the location of the CSFI deviation ( Fig. 4c ). The westward-moved SST warming will further induce positive
deviation will induce a similar deviation in the surface wind stress pattern (vectors in Fig. 3c ), which will enhance (suppress) the equatorial cold advection over the eastern (western) Pacific (shaded in Fig. 3c ) through the Bjerknes feedback ( Fig. 4b ). Under these two feedback processes, the original SST warming induced by the CSFI deviation through Δ Q SW will shift westward relative to the location of the CSFI deviation ( Fig. 4c ). The westward-moved SST warming will further induce positive
interval 0.3 × 10 6 m 2 s −1 ), and (d) vertically integrated moisture flux (arrows, m g s −1 kg −1 ) and its divergence (contour interval is 0.75 × 10 −2 g s −1 kg −1 ). Dark (light) gray shading indicates confidence levels higher than 90% for negative (positive) anomalies, and only significant moisture fluxes are shown. Zero isolines are not shown. (e) Influence function for summer basic state for the easternmost action center numbered in (c). The values shown in each location are proportional
interval 0.3 × 10 6 m 2 s −1 ), and (d) vertically integrated moisture flux (arrows, m g s −1 kg −1 ) and its divergence (contour interval is 0.75 × 10 −2 g s −1 kg −1 ). Dark (light) gray shading indicates confidence levels higher than 90% for negative (positive) anomalies, and only significant moisture fluxes are shown. Zero isolines are not shown. (e) Influence function for summer basic state for the easternmost action center numbered in (c). The values shown in each location are proportional
room for further enhancing MJO prediction by improving various aspects of the prediction system based on a better understanding the MJO phenomena. Understanding strengths and weaknesses of the current prediction systems would be the first step toward enhancing the MJO prediction skill. There is consensus emerging from recent studies regarding the factors affecting the MJO predictability and prediction skill. The main factors, besides the ability of the model, are the geographic location of the MJO
room for further enhancing MJO prediction by improving various aspects of the prediction system based on a better understanding the MJO phenomena. Understanding strengths and weaknesses of the current prediction systems would be the first step toward enhancing the MJO prediction skill. There is consensus emerging from recent studies regarding the factors affecting the MJO predictability and prediction skill. The main factors, besides the ability of the model, are the geographic location of the MJO
coral records with monthly, bimonthly, or seasonal resolution were averaged over the SONDJF period to align with the warm season reconstruction window. For predictor selection, both proxy climate and instrumental data were linearly detrended over 1931–90. As detailed in appendix A , only records that were significantly ( p < 0.05) correlated with temperature variations in at least one grid cell within 500 km of the proxy’s location over the 1931–90 period were selected for further analysis. This
coral records with monthly, bimonthly, or seasonal resolution were averaged over the SONDJF period to align with the warm season reconstruction window. For predictor selection, both proxy climate and instrumental data were linearly detrended over 1931–90. As detailed in appendix A , only records that were significantly ( p < 0.05) correlated with temperature variations in at least one grid cell within 500 km of the proxy’s location over the 1931–90 period were selected for further analysis. This
winter warming events in the twenty-first century. A better understanding of extreme winter events in the Arctic is then required, as stated by Dicks et al. (2012) and Bokhorst et al. (2016) . In this paper, we identify and analyze changes in intensity and frequency of winter warming events over the past 50–100 years, the present 15 years (2000–14), and the future (twenty-first century) in the NAR. The NAR is defined geographically as those parts of Norway, Sweden, and Finland that are north of
winter warming events in the twenty-first century. A better understanding of extreme winter events in the Arctic is then required, as stated by Dicks et al. (2012) and Bokhorst et al. (2016) . In this paper, we identify and analyze changes in intensity and frequency of winter warming events over the past 50–100 years, the present 15 years (2000–14), and the future (twenty-first century) in the NAR. The NAR is defined geographically as those parts of Norway, Sweden, and Finland that are north of
shown to influence meridional mode variation. The mean state can influence meridional mode variations through the ITCZ structure, the mean trade wind strength and location, and the variation in stochastic forcing that is likely required for meridional mode variations in nature ( Xie 1999 ; Vimont 2010 ). The mean ITCZ structure can affect how the atmosphere responds to tropical SST variations via either deep heating ( Gill 1980 ; Zebiak 1986 ; Battisti et al. 1999 ) or boundary layer convergence
shown to influence meridional mode variation. The mean state can influence meridional mode variations through the ITCZ structure, the mean trade wind strength and location, and the variation in stochastic forcing that is likely required for meridional mode variations in nature ( Xie 1999 ; Vimont 2010 ). The mean ITCZ structure can affect how the atmosphere responds to tropical SST variations via either deep heating ( Gill 1980 ; Zebiak 1986 ; Battisti et al. 1999 ) or boundary layer convergence
quantify the fraction of air at location r that last contacted the PBL over the origin region Ω i . (Note that the term “origin” is used in reference to the region where air last contacted the PBL.) In practice f ( r | Ω i ) is calculated as a simple equilibrated tracer mixing ratio that shows where in the Arctic, and with what dilution, the air from an origin region can be found. Air-mass origin climatologies for NH winter [December–February (DJF)] and NH summer [June–August (JJA)] were presented
quantify the fraction of air at location r that last contacted the PBL over the origin region Ω i . (Note that the term “origin” is used in reference to the region where air last contacted the PBL.) In practice f ( r | Ω i ) is calculated as a simple equilibrated tracer mixing ratio that shows where in the Arctic, and with what dilution, the air from an origin region can be found. Air-mass origin climatologies for NH winter [December–February (DJF)] and NH summer [June–August (JJA)] were presented
Abstract
In a recent paper, Stuecker et al. applied a “combination mode” (C-mode) theory to explain the formation of the anomalous western North Pacific anticyclone (WNPAC) during El Niño events. The C-mode, arising from interaction between the annual cycle and ENSO, is an Indo-Pacific basin mode with two “near annual” time scales (roughly 10 and 15 months, respectively). This comment discusses to what extent the C-mode can explain the WNPAC dynamics. The major findings are the following: 1) spectral analysis of the Indo-Pacific circulation anomaly fields indicates that the 10-month mode is not observed and the 15-month mode is only seen in the western North Pacific (WNP), where its spectral peak is statistically insignificant; 2) the 15-month mode (with a period of 13–19 months) accounts for only a small portion (13%) of the observed sea level pressure anomaly in the WNP; and 3) the C-mode evolution does not capture the observed timing of the WNPAC onset in the northern fall of El Niño developing year. In addition it is shown, based on observational analyses and numerical experiments, that local atmosphere–ocean interaction plays an important role in formation of the anomalous anticyclonic center over the Philippine Sea.
Abstract
In a recent paper, Stuecker et al. applied a “combination mode” (C-mode) theory to explain the formation of the anomalous western North Pacific anticyclone (WNPAC) during El Niño events. The C-mode, arising from interaction between the annual cycle and ENSO, is an Indo-Pacific basin mode with two “near annual” time scales (roughly 10 and 15 months, respectively). This comment discusses to what extent the C-mode can explain the WNPAC dynamics. The major findings are the following: 1) spectral analysis of the Indo-Pacific circulation anomaly fields indicates that the 10-month mode is not observed and the 15-month mode is only seen in the western North Pacific (WNP), where its spectral peak is statistically insignificant; 2) the 15-month mode (with a period of 13–19 months) accounts for only a small portion (13%) of the observed sea level pressure anomaly in the WNP; and 3) the C-mode evolution does not capture the observed timing of the WNPAC onset in the northern fall of El Niño developing year. In addition it is shown, based on observational analyses and numerical experiments, that local atmosphere–ocean interaction plays an important role in formation of the anomalous anticyclonic center over the Philippine Sea.
-m intervals from Bamber et al. (2009) . (b) Location of updated quality-controlled SMB data in Antarctica, and selected subdatasets for model validation. LD = Law Dome; GS = GLACIOCLIM Surface Mass Balance of Antarctica (GLACIOCLIM-SAMBA) network; SW-DF = Syowa Station–Dome F; ZS-DA = Zhongshan Station-Dome A; MS-LG = Mawson–interior Lambert Glacier; QML = Queen Maud Land. (c) Ice core and stake network sites (dark green dots). (d) Location of annual resolution snow accumulation records after
-m intervals from Bamber et al. (2009) . (b) Location of updated quality-controlled SMB data in Antarctica, and selected subdatasets for model validation. LD = Law Dome; GS = GLACIOCLIM Surface Mass Balance of Antarctica (GLACIOCLIM-SAMBA) network; SW-DF = Syowa Station–Dome F; ZS-DA = Zhongshan Station-Dome A; MS-LG = Mawson–interior Lambert Glacier; QML = Queen Maud Land. (c) Ice core and stake network sites (dark green dots). (d) Location of annual resolution snow accumulation records after
