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Sarah B. Kapnick and Thomas L. Delworth

Abstract

This study assesses the ability of a newly developed high-resolution coupled model from the Geophysical Fluid Dynamics Laboratory to simulate the cold-season hydroclimate in the present climate and examines its response to climate change forcing. Output is assessed from a 280-yr control simulation that is based on 1990 atmospheric composition and an idealized 140-yr future simulation in which atmospheric carbon dioxide increases at 1% yr−1 until doubling in year 70 and then remains constant. When compared with a low-resolution model, the high-resolution model is found to better represent the geographic distribution of snow variables in the present climate. In response to idealized radiative forcing changes, both models produce similar global-scale responses in which global-mean temperature and total precipitation increase while snowfall decreases. Zonally, snowfall tends to decrease in the low to midlatitudes and increase in the mid- to high latitudes. At the regional scale, the high- and low-resolution models sometimes diverge in the sign of projected snowfall changes; the high-resolution model exhibits future increases in a few select high-altitude regions, notably the northwestern Himalaya region and small regions in the Andes and southwestern Yukon, Canada. Despite such local signals, there is an almost universal reduction in snowfall as a percent of total precipitation in both models. By using a simple multivariate model, temperature is shown to drive these trends by decreasing snowfall almost everywhere while precipitation increases snowfall in the high altitudes and mid- to high latitudes. Mountainous regions of snowfall increases in the high-resolution model exhibit a unique dominance of the positive contribution from precipitation over temperature.

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Salvatore Pascale, Benjamin Pohl, Sarah B. Kapnick, and Honghai Zhang

Abstract

The Angola low is a summertime low pressure system that affects the convergence of low-level moisture fluxes into southern Africa. Interannual variations of the Angola low reduce the seasonal prediction skills for this region that arise from coupled atmosphere–ocean variability. Despite its importance, the interannual dynamics of the Angola low, and its relationship with El Niño–Southern Oscillation (ENSO) and other coupled modes of variability, are still poorly understood, mostly because of the scarcity of atmospheric data and short-term duration of atmospheric reanalyses in the region. To bypass this issue, we use a long-term (3500 year) run from a 50-km-resolution global coupled model capable of simulating the summertime southern African large-scale circulation and teleconnections. We find that the meridional displacement and strength of the Angola low are moderately modulated by local sea surface temperature anomalies, especially those in proximity of the southeastern African coast, and to a lesser extent by ENSO and the subtropical Indian Ocean dipole. Comparison of the coupled run with a 1000-yr run driven by climatological sea surface temperatures reveals that the interannual excursions of the Angola low are in both cases associated with geopotential height anomalies over the southern Atlantic and Indian Ocean related to extratropical atmospheric variability. Midlatitude atmospheric variability explains almost 60% of the variance of the Angola low variability in the uncoupled run, but only 20% in the coupled run. Therefore, while the Angola low appears to be intrinsically controlled by atmospheric extratropical variability, the interference of the atmospheric response forced by sea surface temperature anomalies weakens this influence.

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Salvatore Pascale, Sarah B. Kapnick, Simona Bordoni, and Thomas L. Delworth

Abstract

Widespread multiday convective bursts in the southwestern United States during the North American monsoon are often triggered by Gulf of California moisture surges (GoC surges). However, how GoC surges, and the amount and intensity of associated precipitation, will change in response to CO2-induced warming remains little known, not least because the most widely available climate models do not currently resolve the relevant mesoscale dynamics because of their coarse resolution (100 km or more). In this study, a 50-km-resolution global coupled model is used to address this question. It is found that the mean number of GoC surge events remains unchanged under CO2 doubling, but intermediate-to-high intensity surge-related precipitation tends to become less frequent, thus reducing the mean summertime rainfall. Low-level moisture fluxes associated with GoC surges as well as their convergence over land to the east of the GoC intensify, but the increases in low-level moisture are not matched by the larger increments in the near-surface saturation specific humidity because of amplified land warming. This results in a more unsaturated low-level atmospheric environment that disfavors moist convection. These thermodynamic changes are accompanied by dynamic changes that are also detrimental to convective activity, with the midlevel monsoonal ridge projected to expand and move to the west of its present-day climatological maximum. Despite the overall reduction in precipitation, the frequency of very intense, localized daily surge-related precipitation in Arizona and surrounding areas is projected to increase with increased precipitable water.

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Arielle J. Catalano, Anthony J. Broccoli, Sarah B. Kapnick, and Tyler P. Janoski

Abstract

High-impact extratropical cyclones (ETCs) cause considerable damage along the northeast coast of the United States through strong winds and inundation, but these relatively rare events are difficult to analyze owing to limited historical records. Using a 1505-yr simulation from the GFDL FLOR coupled model, statistical analyses of extreme events are performed including exceedance probability computations to compare estimates from shorter segments to estimates that could be obtained from a record of considerable length. The most extreme events possess characteristics including exceptionally low central pressure, hurricane-force winds, and a large surge potential, which would greatly impact nearby regions. Return level estimates of metrics of ETC intensity using shorter, historical-length segments of the FLOR simulation are underestimated compared to levels determined using the full simulation. This indicates that if the underlying distributions of observed ETC metrics are similar to those of the 1505-yr FLOR distributions, the actual frequency of extreme ETC events could also be underestimated. Comparisons between FLOR and reanalysis products suggest that not all features of simulated high-impact ETCs are representative of observations. Spatial track densities are similar, but FLOR exhibits a negative bias in central pressure and a positive bias in wind speed, particularly for more intense events. Although the existence of these model biases precludes the quantitative use of model-derived return statistics as a substitute for those derived from shorter observational records, this work suggests that statistics from future models of higher fidelity could be used to better constrain the probability of extreme ETC events and their impacts.

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Kai-Chih Tseng, Nathaniel C. Johnson, Eric D. Maloney, Elizabeth A. Barnes, and Sarah B. Kapnick

Abstract

The excitation of the Pacific–North American (PNA) teleconnection pattern by the Madden–Julian oscillation (MJO) has been considered one of the most important predictability sources on subseasonal time scales over the extratropical Pacific and North America. However, until recently, the interactions between tropical heating and other extratropical modes and their relationships to subseasonal prediction have received comparatively little attention. In this study, a linear inverse model (LIM) is applied to examine the tropical–extratropical interactions. The LIM provides a means of calculating the response of a dynamical system to a small forcing by constructing a linear operator from the observed covariability statistics of the system. Given the linear assumptions, it is shown that the PNA is one of a few leading modes over the extratropical Pacific that can be strongly driven by tropical convection while other extratropical modes present at most a weak interaction with tropical convection. In the second part of this study, a two-step linear regression is introduced that leverages a LIM and large-scale climate variability to the prediction of hydrological extremes (e.g., atmospheric rivers) on subseasonal time scales. Consistent with the findings of the first part, most of the predictable signals on subseasonal time scales are determined by the dynamics of the MJO–PNA teleconnection while other extratropical modes are important only at the shortest forecast leads.

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Kai-Chih Tseng, Nathaniel C. Johnson, Eric D. Maloney, Elizabeth A. Barnes, and Sarah B. Kapnick

Abstract

The excitation of the Pacific-North American (PNA) teleconnection pattern by the Madden-Julian Oscillation (MJO) has been considered as one of the most important predictability sources on subseasonal timescales over the extratropical Pacific and North America. However, until recently, the interactions between tropical heating and other extratropical modes and their relationships to subseasonal prediction have received comparatively little attention. In this study, a linear inverse model (LIM) is applied to examine the tropical-extratropical interactions. The LIM provides a means of calculating the response of a dynamical system to a small forcing by constructing a linear operator from the observed covariability statistics of the system. Given the linear assumptions, it is shown that the PNA is one of a few leading modes over the extratropical Pacific that can be strongly driven by tropical convection while other extratropical modes present at most a weak interaction with tropical convection. In the second part of this study, a two-step linear regression is introduced which leverages a LIM and large-scale climate variability to the prediction of hydrological extremes (e.g. atmospheric rivers) on subseasonal timescales. Consistent with the findings of the first part, most of the predictable signals on subseasonal timescales are determined by the dynamics of MJO-PNA teleconnection while other extratropical modes are important only at the shortest forecast leads.

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Tyler P. Janoski, Anthony J. Broccoli, Sarah B. Kapnick, and Nathaniel C. Johnson

Abstract

Eastern North America contains densely populated, highly developed areas, making winter storms with strong winds and high snowfall among the costliest storm types. For this reason, it is important to determine how the frequency of high-impact winter storms, specifically, those combining significant snowfall and winds, will change in this region under increasing greenhouse gas concentrations. This study uses a high-resolution coupled global climate model to simulate the changes in extreme winter conditions from the present climate to a future scenario with doubled CO2 concentrations (2XC). In particular, this study focuses on changes in high-snowfall, extreme-wind (HSEW) events, which are defined as the occurrence of 2-day snowfall and high winds exceeding thresholds based on extreme values from the control simulation, where greenhouse gas concentrations remain fixed. Mean snowfall consistently decreases across the entire region, but extreme snowfall shows a more inconsistent pattern, with some areas experiencing increases in the frequency of extreme-snowfall events. Extreme-wind events show relatively small changes in frequency with 2XC, with the exception of high-elevation areas where there are large decreases in frequency. As a result of combined changes in wind and snowfall, HSEW events decrease in frequency in the 2XC simulation for much of eastern North America. Changes in the number of HSEW events in the 2XC environment are driven mainly by changes in the frequency of extreme-snowfall events, with most of the region experiencing decreases in event frequency, except for certain inland areas at higher latitudes.

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Salvatore Pascale, Simona Bordoni, Sarah B. Kapnick, Gabriel A. Vecchi, Liwei Jia, Thomas L. Delworth, Seth Underwood, and Whit Anderson

Abstract

The impact of atmosphere and ocean horizontal resolution on the climatology of North American monsoon Gulf of California (GoC) moisture surges is examined in a suite of global circulation models (CM2.1, FLOR, CM2.5, CM2.6, and HiFLOR) developed at the Geophysical Fluid Dynamics Laboratory (GFDL). These models feature essentially the same physical parameterizations but differ in horizontal resolution in either the atmosphere (≃200, 50, and 25 km) or the ocean (≃1°, 0.25°, and 0.1°). Increasing horizontal atmospheric resolution from 200 to 50 km results in a drastic improvement in the model’s capability of accurately simulating surge events. The climatological near-surface flow and moisture and precipitation anomalies associated with GoC surges are overall satisfactorily simulated in all higher-resolution models. The number of surge events agrees well with reanalyses, but models tend to underestimate July–August surge-related precipitation and overestimate September surge-related rainfall in the southwestern United States. Large-scale controls supporting the development of GoC surges, such as tropical easterly waves (TEWs), tropical cyclones (TCs), and trans-Pacific Rossby wave trains (RWTs), are also well captured, although models tend to underestimate the TEW and TC magnitude and number. Near-surface GoC surge features and their large-scale forcings (TEWs, TCs, and RWTs) do not appear to be substantially affected by a finer representation of the GoC at higher ocean resolution. However, the substantial reduction of the eastern Pacific warm sea surface temperature bias through flux adjustment in the Forecast-Oriented Low Ocean Resolution (FLOR) model leads to an overall improvement of tropical–extratropical controls on GoC moisture surges and the seasonal cycle of precipitation in the southwestern United States.

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Karin van der Wiel, Sarah B. Kapnick, Gabriel A. Vecchi, James A. Smith, P. C. D. Milly, and Liwei Jia

Abstract

Floods in the Mississippi basin can have large negative societal, natural, and economic impacts. Understanding the drivers of floods, now and in the future, is relevant for risk management and infrastructure-planning purposes. We investigate the drivers of 100-yr-return lower Mississippi River floods using a global coupled climate model with an integrated surface water module. The model provides 3400 years of physically consistent data from a static climate, in contrast to available observational data (relatively short records, incomplete land surface data, transient climate). In the months preceding the model’s 100-yr floods, as indicated by extreme monthly discharge, above-average rain and snowfall lead to moist subsurface conditions and the buildup of snowpack, making the river system prone to these major flooding events. The meltwater from snowpack in the northern Missouri and upper Mississippi catchments primes the river system, sensitizing it to subsequent above-average precipitation in the Ohio and Tennessee catchments. An ensemble of transient forcing experiments is used to investigate the impacts of past and projected anthropogenic climate change on extreme floods. There is no statistically significant projected trend in the occurrence of 100-yr floods in the model ensemble, despite significant increases in extreme precipitation, significant decreases in extreme snowmelt, and significant decreases in less extreme floods. The results emphasize the importance of considering the fully coupled land–atmosphere system for extreme floods. This initial analysis provides avenues for further investigation, including comparison to characteristics of less extreme floods, the sensitivity to model configuration, the role of human water management, and implications for future flood-risk management.

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Nathaniel C. Johnson, Lakshmi Krishnamurthy, Andrew T. Wittenberg, Baoqiang Xiang, Gabriel A. Vecchi, Sarah B. Kapnick, and Salvatore Pascale

Abstract

Positive precipitation biases over western North America have remained a pervasive problem in the current generation of coupled global climate models. These biases are substantially reduced, however, in a version of the Geophysical Fluid Dynamics Laboratory Forecast-Oriented Low Ocean Resolution (FLOR) coupled climate model with systematic sea surface temperature (SST) biases artificially corrected through flux adjustment. This study examines how the SST biases in the Atlantic and Pacific Oceans contribute to the North American precipitation biases. Experiments with the FLOR model in which SST biases are removed in the Atlantic and Pacific are carried out to determine the contribution of SST errors in each basin to precipitation statistics over North America. Tropical and North Pacific SST biases have a strong impact on northern North American precipitation, while tropical Atlantic SST biases have a dominant impact on precipitation biases in southern North America, including the western United States. Most notably, negative SST biases in the tropical Atlantic in boreal winter induce an anomalously strong Aleutian low and a southward bias in the North Pacific storm track. In boreal summer, the negative SST biases induce a strengthened North Atlantic subtropical high and Great Plains low-level jet. Each of these impacts contributes to positive annual mean precipitation biases over western North America. Both North Pacific and North Atlantic SST biases induce SST biases in remote basins through dynamical pathways, so a complete attribution of the effects of SST biases on precipitation must account for both the local and remote impacts.

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