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- Author or Editor: Marie C. McGraw x
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Abstract
In climate variability studies, lagged linear regression is frequently used to infer causality. While lagged linear regression analysis can often provide valuable information about causal relationships, lagged regression is also susceptible to overreporting significant relationships when one or more of the variables has substantial memory (autocorrelation). Granger causality analysis takes into account the memory of the data and is therefore not susceptible to this issue. A simple Monte Carlo example highlights the advantages of Granger causality, compared to traditional lagged linear regression analysis in situations with one or more highly autocorrelated variables. Differences between the two approaches are further explored in two illustrative examples applicable to large-scale climate variability studies. Given that Granger causality is straightforward to calculate, Granger causality analysis may be preferable to traditional lagged regression analysis when one or more datasets has large memory.
Abstract
In climate variability studies, lagged linear regression is frequently used to infer causality. While lagged linear regression analysis can often provide valuable information about causal relationships, lagged regression is also susceptible to overreporting significant relationships when one or more of the variables has substantial memory (autocorrelation). Granger causality analysis takes into account the memory of the data and is therefore not susceptible to this issue. A simple Monte Carlo example highlights the advantages of Granger causality, compared to traditional lagged linear regression analysis in situations with one or more highly autocorrelated variables. Differences between the two approaches are further explored in two illustrative examples applicable to large-scale climate variability studies. Given that Granger causality is straightforward to calculate, Granger causality analysis may be preferable to traditional lagged regression analysis when one or more datasets has large memory.
Abstract
A dry dynamical core is used to investigate the seasonal sensitivity of the circulation to two idealized thermal forcings: a tropical upper-tropospheric heating and a polar lower-tropospheric heating. The thermal forcings are held constant, and the response of the circulation in each month of the year is explored. First, the circulation responses to tropical warming and polar warming are studied separately, and then the response to the simultaneously applied forcings is analyzed. Finally, the seasonality of the internal variability of the circulation is explored as a possible mechanism to explain the seasonality of the responses. The primary results of these experiments are as follows: 1) There is a seasonal sensitivity in the circulation response to both the tropical and polar forcings. 2) The jet position response to each forcing is greatest in the transition seasons, and the jet speed response exhibits a seasonal sensitivity to both forcings, although the seasonal sensitivities are not the same. 3) The circulation response is nonlinear in the transition seasons, but approximately linear in the winter months. 4) The internal variability of the unforced circulation exhibits a seasonal sensitivity that may partly explain the seasonal sensitivity of the forced response. The seasonality of the internal variability of daily MERRA reanalysis data is compared to that of the model, demonstrating that the broad conclusions drawn from this idealized modeling study may be useful for understanding the jet response to anthropogenic forcing.
Abstract
A dry dynamical core is used to investigate the seasonal sensitivity of the circulation to two idealized thermal forcings: a tropical upper-tropospheric heating and a polar lower-tropospheric heating. The thermal forcings are held constant, and the response of the circulation in each month of the year is explored. First, the circulation responses to tropical warming and polar warming are studied separately, and then the response to the simultaneously applied forcings is analyzed. Finally, the seasonality of the internal variability of the circulation is explored as a possible mechanism to explain the seasonality of the responses. The primary results of these experiments are as follows: 1) There is a seasonal sensitivity in the circulation response to both the tropical and polar forcings. 2) The jet position response to each forcing is greatest in the transition seasons, and the jet speed response exhibits a seasonal sensitivity to both forcings, although the seasonal sensitivities are not the same. 3) The circulation response is nonlinear in the transition seasons, but approximately linear in the winter months. 4) The internal variability of the unforced circulation exhibits a seasonal sensitivity that may partly explain the seasonal sensitivity of the forced response. The seasonality of the internal variability of daily MERRA reanalysis data is compared to that of the model, demonstrating that the broad conclusions drawn from this idealized modeling study may be useful for understanding the jet response to anthropogenic forcing.
ABSTRACT
Arctic–midlatitude teleconnections are complex and multifaceted. By design, targeted modeling studies typically focus only on one direction of influence—usually, the midlatitude atmospheric response to a changing Arctic. The two-way, coupled feedbacks between the Arctic and the midlatitude circulation on submonthly time scales are explored using a regularized regression model formulated around Granger causality. The regularized regression model indicates that there are regions in which Arctic temperature drives a midlatitude circulation response, and regions in which the midlatitude circulation drives a response in the Arctic; however, these regions rarely overlap. In many regions, on submonthly time scales, the midlatitude circulation drives Arctic temperature variability, highlighting the important role the midlatitude circulation can play in impacting the Arctic. In particular, the regularized regression model results support recent work that indicates that the observed high pressure anomalies over Eurasia drive a significant response in the Arctic on submonthly time scales, rather than being driven by the Arctic.
ABSTRACT
Arctic–midlatitude teleconnections are complex and multifaceted. By design, targeted modeling studies typically focus only on one direction of influence—usually, the midlatitude atmospheric response to a changing Arctic. The two-way, coupled feedbacks between the Arctic and the midlatitude circulation on submonthly time scales are explored using a regularized regression model formulated around Granger causality. The regularized regression model indicates that there are regions in which Arctic temperature drives a midlatitude circulation response, and regions in which the midlatitude circulation drives a response in the Arctic; however, these regions rarely overlap. In many regions, on submonthly time scales, the midlatitude circulation drives Arctic temperature variability, highlighting the important role the midlatitude circulation can play in impacting the Arctic. In particular, the regularized regression model results support recent work that indicates that the observed high pressure anomalies over Eurasia drive a significant response in the Arctic on submonthly time scales, rather than being driven by the Arctic.
Abstract
The latitudinal location of the east Pacific Ocean intertropical convergence zone (ITCZ) changes on time scales of days to weeks during boreal spring. This study focuses on tropical near-surface dynamics in the days leading up to the two most frequent types of ITCZ events, nITCZ (Northern Hemisphere) and dITCZ (double). There is a rapid daily evolution of dynamical features on top of a slower, weekly evolution that occurs leading up to and after nITCZ and dITCZ events. Zonally elongated bands of anomalous cross-equatorial flow and off-equatorial convergence rapidly intensify and peak 1 day before or the day of these ITCZ events, followed 1 or 2 days later by a peak in near-equatorial zonal wind anomalies. In addition, there is a wide region north of the southeast Pacific subtropical high where anomalous northwesterlies strengthen prior to nITCZ events and southeasterlies strengthen before dITCZ events. Anomalous zonal and meridional near-surface momentum budgets reveal that the terms associated with Ekman balance are of first-order importance preceding nITCZ events, but that the meridional momentum advective terms are just as important before dITCZ events. Variations in cross-equatorial flow are promoted by the meridional pressure gradient force (PGF) prior to nITCZ events and the meridional advection of meridional momentum in addition to the meridional PGF before dITCZ events. Meanwhile, variations in near-equatorial easterlies are driven by the zonal PGF and the Coriolis force preceding nITCZ events and the zonal PGF, the Coriolis force, and the meridional advection of zonal momentum before dITCZ events.
Abstract
The latitudinal location of the east Pacific Ocean intertropical convergence zone (ITCZ) changes on time scales of days to weeks during boreal spring. This study focuses on tropical near-surface dynamics in the days leading up to the two most frequent types of ITCZ events, nITCZ (Northern Hemisphere) and dITCZ (double). There is a rapid daily evolution of dynamical features on top of a slower, weekly evolution that occurs leading up to and after nITCZ and dITCZ events. Zonally elongated bands of anomalous cross-equatorial flow and off-equatorial convergence rapidly intensify and peak 1 day before or the day of these ITCZ events, followed 1 or 2 days later by a peak in near-equatorial zonal wind anomalies. In addition, there is a wide region north of the southeast Pacific subtropical high where anomalous northwesterlies strengthen prior to nITCZ events and southeasterlies strengthen before dITCZ events. Anomalous zonal and meridional near-surface momentum budgets reveal that the terms associated with Ekman balance are of first-order importance preceding nITCZ events, but that the meridional momentum advective terms are just as important before dITCZ events. Variations in cross-equatorial flow are promoted by the meridional pressure gradient force (PGF) prior to nITCZ events and the meridional advection of meridional momentum in addition to the meridional PGF before dITCZ events. Meanwhile, variations in near-equatorial easterlies are driven by the zonal PGF and the Coriolis force preceding nITCZ events and the zonal PGF, the Coriolis force, and the meridional advection of zonal momentum before dITCZ events.
Abstract
Arctic cyclones are an extremely common, year-round phenomenon, with substantial influence on sea ice. However, few studies address the heterogeneity in the spatial patterns in the atmosphere and sea ice during Arctic cyclones. We investigate these spatial patterns by compositing on cyclones from 1985 to 2016 using a novel, cyclone-centered approach that reveals conditions as functions of bearing and distance from cyclone centers. An axisymmetric, cold-core model for the structure of Arctic cyclones has previously been proposed; however, we show that the structure of Arctic cyclones is comparable to those in the midlatitudes, with cyclonic surface winds, a warm, moist sector to the east of cyclones and a cold, dry sector to the west. There is no consensus on the impact of Arctic cyclones on sea ice, as some studies have shown that Arctic cyclones lead to sea ice growth and others to sea ice loss. Instead, we find that sea ice decreases to the east of Arctic cyclones and increases to the west, with the greatest changes occurring in the marginal ice zone. Using a sea ice model forced with prescribed atmospheric reanalysis, we reveal the relative importance of the dynamic and thermodynamic forcing of Arctic cyclones on sea ice. The dynamic and thermodynamic responses of sea ice concentration to cyclones are comparable in magnitude; however, dynamic processes dominate the response of sea ice thickness and are the primary driver of the east–west difference in the sea ice response to cyclones.
Abstract
Arctic cyclones are an extremely common, year-round phenomenon, with substantial influence on sea ice. However, few studies address the heterogeneity in the spatial patterns in the atmosphere and sea ice during Arctic cyclones. We investigate these spatial patterns by compositing on cyclones from 1985 to 2016 using a novel, cyclone-centered approach that reveals conditions as functions of bearing and distance from cyclone centers. An axisymmetric, cold-core model for the structure of Arctic cyclones has previously been proposed; however, we show that the structure of Arctic cyclones is comparable to those in the midlatitudes, with cyclonic surface winds, a warm, moist sector to the east of cyclones and a cold, dry sector to the west. There is no consensus on the impact of Arctic cyclones on sea ice, as some studies have shown that Arctic cyclones lead to sea ice growth and others to sea ice loss. Instead, we find that sea ice decreases to the east of Arctic cyclones and increases to the west, with the greatest changes occurring in the marginal ice zone. Using a sea ice model forced with prescribed atmospheric reanalysis, we reveal the relative importance of the dynamic and thermodynamic forcing of Arctic cyclones on sea ice. The dynamic and thermodynamic responses of sea ice concentration to cyclones are comparable in magnitude; however, dynamic processes dominate the response of sea ice thickness and are the primary driver of the east–west difference in the sea ice response to cyclones.
Abstract
The predictability of sea ice during extreme sea ice loss events on subseasonal (daily to weekly) time scales is explored in dynamical forecast models. These extreme sea ice loss events (defined as the 5th percentile of the 5-day change in sea ice extent) exhibit substantial regional and seasonal variability; in the central Arctic Ocean basin, most subseasonal rapid ice loss occurs in the summer, but in the marginal seas rapid sea ice loss occurs year-round. Dynamical forecast models are largely able to capture the seasonality of these extreme sea ice loss events. In most regions in the summertime, sea ice forecast skill is lower on extreme sea ice loss days than on nonextreme days, despite evidence that links these extreme events to large-scale atmospheric patterns; in the wintertime, the difference between extreme and nonextreme days is less pronounced. In a damped anomaly forecast benchmark estimate, the forecast error remains high following extreme sea ice loss events and does not return to typical error levels for many weeks; this signal is less robust in the dynamical forecast models but still present. Overall, these results suggest that sea ice forecast skill is generally lower during and after extreme sea ice loss events and also that, while dynamical forecast models are capable of simulating extreme sea ice loss events with similar characteristics to what we observe, forecast skill from dynamical models is limited by biases in mean state and variability and errors in the initialization.
Significance Statement
We studied weather model forecasts of changes in Arctic sea ice extent on day-to-day time scales in different regions and seasons. We were especially interested in extreme sea ice loss days, or days in which sea ice melts very quickly or is reduced due to diverging forces such as winds, ocean currents, and waves. We find that forecast models generally capture the observed timing of extreme sea ice loss days. We also find that forecasts of sea ice extent are worse on extreme sea ice loss days compared to typical days, and that forecast errors remain elevated following extreme sea ice loss events.
Abstract
The predictability of sea ice during extreme sea ice loss events on subseasonal (daily to weekly) time scales is explored in dynamical forecast models. These extreme sea ice loss events (defined as the 5th percentile of the 5-day change in sea ice extent) exhibit substantial regional and seasonal variability; in the central Arctic Ocean basin, most subseasonal rapid ice loss occurs in the summer, but in the marginal seas rapid sea ice loss occurs year-round. Dynamical forecast models are largely able to capture the seasonality of these extreme sea ice loss events. In most regions in the summertime, sea ice forecast skill is lower on extreme sea ice loss days than on nonextreme days, despite evidence that links these extreme events to large-scale atmospheric patterns; in the wintertime, the difference between extreme and nonextreme days is less pronounced. In a damped anomaly forecast benchmark estimate, the forecast error remains high following extreme sea ice loss events and does not return to typical error levels for many weeks; this signal is less robust in the dynamical forecast models but still present. Overall, these results suggest that sea ice forecast skill is generally lower during and after extreme sea ice loss events and also that, while dynamical forecast models are capable of simulating extreme sea ice loss events with similar characteristics to what we observe, forecast skill from dynamical models is limited by biases in mean state and variability and errors in the initialization.
Significance Statement
We studied weather model forecasts of changes in Arctic sea ice extent on day-to-day time scales in different regions and seasons. We were especially interested in extreme sea ice loss days, or days in which sea ice melts very quickly or is reduced due to diverging forces such as winds, ocean currents, and waves. We find that forecast models generally capture the observed timing of extreme sea ice loss days. We also find that forecasts of sea ice extent are worse on extreme sea ice loss days compared to typical days, and that forecast errors remain elevated following extreme sea ice loss events.
