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Ángel G. Muñoz, Xiaosong Yang, Gabriel A. Vecchi, Andrew W. Robertson, and William F. Cooke

Abstract

This study proposes an integrated diagnostic framework based on atmospheric circulation regime spatial patterns and frequencies of occurrence to facilitate the identification of model systematic errors across multiple time scales. To illustrate the approach, three sets of 32-yr-long simulations are analyzed for northeastern North America and for the March–May season using the Geophysical Fluid Dynamics Laboratory’s Low Ocean–Atmosphere Resolution (LOAR) and Forecast-Oriented Low Ocean Resolution (FLOR) coupled models; the main difference between these two models is the horizontal resolution of the atmospheric model used. Regime-dependent biases are explored in the light of different atmospheric horizontal resolutions and under different nudging approaches. It is found that both models exhibit a fair representation of the observed circulation regime spatial patterns and frequencies of occurrence, although some biases are present independently of the horizontal resolution or the nudging approach and are associated with a misrepresentation of troughs centered north of the Great Lakes and deep coastal troughs. Moreover, the intraseasonal occurrence of certain model regimes is delayed with respect to observations. On the other hand, interexperiment differences in the mean frequencies of occurrence of the simulated weather types, and their variability across multiple time scales, tend to be negligible. This result suggests that low-resolution models could be of potential use to diagnose and predict physical variables via their simulated weather type characteristics.

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Karin van der Wiel, Sarah B. Kapnick, Gabriel A. Vecchi, William F Cooke, Thomas L. Delworth, Liwei Jia, Hiroyuki Murakami, Seth Underwood, and Fanrong Zeng

Abstract

Precipitation extremes have a widespread impact on societies and ecosystems; it is therefore important to understand current and future patterns of extreme precipitation. Here, a set of new global coupled climate models with varying atmospheric resolution has been used to investigate the ability of these models to reproduce observed patterns of precipitation extremes and to investigate changes in these extremes in response to increased atmospheric CO2 concentrations. The atmospheric resolution was increased from 2° × 2° grid cells (typical resolution in the CMIP5 archive) to 0.25° × 0.25° (tropical cyclone permitting). Analysis has been confined to the contiguous United States (CONUS). It is shown that, for these models, integrating at higher atmospheric resolution improves all aspects of simulated extreme precipitation: spatial patterns, intensities, and seasonal timing. In response to 2 × CO2 concentrations, all models show a mean intensification of precipitation rates during extreme events of approximately 3%–4% K−1. However, projected regional patterns of changes in extremes are dependent on model resolution. For example, the highest-resolution models show increased precipitation rates during extreme events in the hurricane season in the U.S. Southeast; this increase is not found in the low-resolution model. These results emphasize that, for the study of extreme precipitation there is a minimum model resolution that is needed to capture the weather phenomena generating the extremes. Finally, the observed record and historical model experiments were used to investigate changes in the recent past. In part because of large intrinsic variability, no evidence was found for changes in extreme precipitation attributable to climate change in the available observed record.

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Youngji Joh, Thomas L. Delworth, Andrew T. Wittenberg, William F. Cooke, Xiaosong Yang, Fanrong Zeng, Liwei Jia, Feiyu Lu, Nathaniel Johnson, Sarah B. Kapnick, Anthony Rosati, Liping Zhang, and Colleen McHugh

Abstract

The Kuroshio Extension (KE), an eastward-flowing jet located in the Pacific western boundary current system, exhibits prominent seasonal-to-decadal variability, which is crucial for understanding climate variations in the northern midlatitudes. We explore the representation and prediction skill for the KE in the GFDL SPEAR (Seamless System for Prediction and Earth System Research) coupled model. Two different approaches are used to generate coupled reanalyses and forecasts: 1) restoring the coupled model’s SST and atmospheric variables toward existing reanalyses, or 2) assimilating SST and subsurface observations into the coupled model without atmospheric assimilation. Both systems use an ocean model with 1° resolution and capture the largest sea surface height (SSH) variability over the KE region. Assimilating subsurface observations appears to be essential to reproduce the narrow front and related oceanic variability of the KE jet in the coupled reanalysis. We demonstrate skillful retrospective predictions of KE SSH variability in monthly (up to 1 year) and annual-mean (up to 5 years) KE forecasts in the seasonal and decadal prediction systems, respectively. The prediction skill varies seasonally, peaking for forecasts initialized in January and verifying in September due to the winter intensification of North Pacific atmospheric forcing. We show that strong large-scale atmospheric anomalies generate deterministic oceanic forcing (i.e., Rossby waves), leading to skillful long-lead KE forecasts. These atmospheric anomalies also drive Ekman convergence and divergence, which forms ocean memory, by sequestering thermal anomalies deep into the winter mixed layer that re-emerge in the subsequent autumn. The SPEAR forecasts capture the recent negative-to-positive transition of the KE phase in 2017, projecting a continued positive phase through 2022.

Open access
Baoqiang Xiang, Lucas Harris, Thomas L. Delworth, Bin Wang, Guosen Chen, Jan-Huey Chen, Spencer K. Clark, William F. Cooke, Kun Gao, J. Jacob Huff, Liwei Jia, Nathaniel C. Johnson, Sarah B. Kapnick, Feiyu Lu, Colleen McHugh, Yongqiang Sun, Mingjing Tong, Xiaosong Yang, Fanrong Zeng, Ming Zhao, Linjiong Zhou, and Xiaqiong Zhou

Abstract

A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.

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Mitchell Bushuk, Michael Winton, F. Alexander Haumann, Thomas Delworth, Feiyu Lu, Yongfei Zhang, Liwei Jia, Liping Zhang, William Cooke, Matthew Harrison, Bill Hurlin, Nathaniel C. Johnson, Sarah B. Kapnick, Colleen McHugh, Hiroyuki Murakami, Anthony Rosati, Kai-Chih Tseng, Andrew T. Wittenberg, Xiaosong Yang, and Fanrong Zeng

Abstract

Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen/Bellingshausen, Indian, and west Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper-ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal time scales.

Open access
Mitchell Bushuk, Michael Winton, F. Alexander Haumann, Thomas Delworth, Feiyu Lu, Yongfei Zhang, Liwei Jia, Liping Zhang, William Cooke, Matthew Harrison, Bill Hurlin, Nathaniel C. Johnson, Sarah Kapnick, Colleen McHugh, Hiroyuki Murakami, Anthony Rosati, Kai-Chih Tseng, Andrew T. Wittenberg, Xiaosong Yang, and Fanrong Zeng

Abstract

Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen and Bellingshausen, Indian, and West Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently-developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal timescales.

Restricted access
Anand Gnanadesikan, Keith W. Dixon, Stephen M. Griffies, V. Balaji, Marcelo Barreiro, J. Anthony Beesley, William F. Cooke, Thomas L. Delworth, Rudiger Gerdes, Matthew J. Harrison, Isaac M. Held, William J. Hurlin, Hyun-Chul Lee, Zhi Liang, Giang Nong, Ronald C. Pacanowski, Anthony Rosati, Joellen Russell, Bonita L. Samuels, Qian Song, Michael J. Spelman, Ronald J. Stouffer, Colm O. Sweeney, Gabriel Vecchi, Michael Winton, Andrew T. Wittenberg, Fanrong Zeng, Rong Zhang, and John P. Dunne

Abstract

The current generation of coupled climate models run at the Geophysical Fluid Dynamics Laboratory (GFDL) as part of the Climate Change Science Program contains ocean components that differ in almost every respect from those contained in previous generations of GFDL climate models. This paper summarizes the new physical features of the models and examines the simulations that they produce. Of the two new coupled climate model versions 2.1 (CM2.1) and 2.0 (CM2.0), the CM2.1 model represents a major improvement over CM2.0 in most of the major oceanic features examined, with strikingly lower drifts in hydrographic fields such as temperature and salinity, more realistic ventilation of the deep ocean, and currents that are closer to their observed values. Regional analysis of the differences between the models highlights the importance of wind stress in determining the circulation, particularly in the Southern Ocean. At present, major errors in both models are associated with Northern Hemisphere Mode Waters and outflows from overflows, particularly the Mediterranean Sea and Red Sea.

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Leo J. Donner, Bruce L. Wyman, Richard S. Hemler, Larry W. Horowitz, Yi Ming, Ming Zhao, Jean-Christophe Golaz, Paul Ginoux, S.-J. Lin, M. Daniel Schwarzkopf, John Austin, Ghassan Alaka, William F. Cooke, Thomas L. Delworth, Stuart M. Freidenreich, C. T. Gordon, Stephen M. Griffies, Isaac M. Held, William J. Hurlin, Stephen A. Klein, Thomas R. Knutson, Amy R. Langenhorst, Hyun-Chul Lee, Yanluan Lin, Brian I. Magi, Sergey L. Malyshev, P. C. D. Milly, Vaishali Naik, Mary J. Nath, Robert Pincus, Jeffrey J. Ploshay, V. Ramaswamy, Charles J. Seman, Elena Shevliakova, Joseph J. Sirutis, William F. Stern, Ronald J. Stouffer, R. John Wilson, Michael Winton, Andrew T. Wittenberg, and Fanrong Zeng

Abstract

The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for the atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol–cloud interactions, chemistry–climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical system component of earth system models and models for decadal prediction in the near-term future—for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model. Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud droplet activation by aerosols, subgrid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emissions with advective, convective, and turbulent transport. AM3 employs a cubed-sphere implementation of a finite-volume dynamical core and is coupled to LM3, a new land model with ecosystem dynamics and hydrology. Its horizontal resolution is approximately 200 km, and its vertical resolution ranges approximately from 70 m near the earth’s surface to 1 to 1.5 km near the tropopause and 3 to 4 km in much of the stratosphere. Most basic circulation features in AM3 are simulated as realistically, or more so, as in AM2. In particular, dry biases have been reduced over South America. In coupled mode, the simulation of Arctic sea ice concentration has improved. AM3 aerosol optical depths, scattering properties, and surface clear-sky downward shortwave radiation are more realistic than in AM2. The simulation of marine stratocumulus decks remains problematic, as in AM2. The most intense 0.2% of precipitation rates occur less frequently in AM3 than observed. The last two decades of the twentieth century warm in CM3 by 0.32°C relative to 1881–1920. The Climate Research Unit (CRU) and Goddard Institute for Space Studies analyses of observations show warming of 0.56° and 0.52°C, respectively, over this period. CM3 includes anthropogenic cooling by aerosol–cloud interactions, and its warming by the late twentieth century is somewhat less realistic than in CM2.1, which warmed 0.66°C but did not include aerosol–cloud interactions. The improved simulation of the direct aerosol effect (apparent in surface clear-sky downward radiation) in CM3 evidently acts in concert with its simulation of cloud–aerosol interactions to limit greenhouse gas warming.

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Thomas L. Delworth, Anthony J. Broccoli, Anthony Rosati, Ronald J. Stouffer, V. Balaji, John A. Beesley, William F. Cooke, Keith W. Dixon, John Dunne, K. A. Dunne, Jeffrey W. Durachta, Kirsten L. Findell, Paul Ginoux, Anand Gnanadesikan, C. T. Gordon, Stephen M. Griffies, Rich Gudgel, Matthew J. Harrison, Isaac M. Held, Richard S. Hemler, Larry W. Horowitz, Stephen A. Klein, Thomas R. Knutson, Paul J. Kushner, Amy R. Langenhorst, Hyun-Chul Lee, Shian-Jiann Lin, Jian Lu, Sergey L. Malyshev, P. C. D. Milly, V. Ramaswamy, Joellen Russell, M. Daniel Schwarzkopf, Elena Shevliakova, Joseph J. Sirutis, Michael J. Spelman, William F. Stern, Michael Winton, Andrew T. Wittenberg, Bruce Wyman, Fanrong Zeng, and Rong Zhang

Abstract

The formulation and simulation characteristics of two new global coupled climate models developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) are described. The models were designed to simulate atmospheric and oceanic climate and variability from the diurnal time scale through multicentury climate change, given our computational constraints. In particular, an important goal was to use the same model for both experimental seasonal to interannual forecasting and the study of multicentury global climate change, and this goal has been achieved.

Two versions of the coupled model are described, called CM2.0 and CM2.1. The versions differ primarily in the dynamical core used in the atmospheric component, along with the cloud tuning and some details of the land and ocean components. For both coupled models, the resolution of the land and atmospheric components is 2° latitude × 2.5° longitude; the atmospheric model has 24 vertical levels. The ocean resolution is 1° in latitude and longitude, with meridional resolution equatorward of 30° becoming progressively finer, such that the meridional resolution is 1/3° at the equator. There are 50 vertical levels in the ocean, with 22 evenly spaced levels within the top 220 m. The ocean component has poles over North America and Eurasia to avoid polar filtering. Neither coupled model employs flux adjustments.

The control simulations have stable, realistic climates when integrated over multiple centuries. Both models have simulations of ENSO that are substantially improved relative to previous GFDL coupled models. The CM2.0 model has been further evaluated as an ENSO forecast model and has good skill (CM2.1 has not been evaluated as an ENSO forecast model). Generally reduced temperature and salinity biases exist in CM2.1 relative to CM2.0. These reductions are associated with 1) improved simulations of surface wind stress in CM2.1 and associated changes in oceanic gyre circulations; 2) changes in cloud tuning and the land model, both of which act to increase the net surface shortwave radiation in CM2.1, thereby reducing an overall cold bias present in CM2.0; and 3) a reduction of ocean lateral viscosity in the extratropics in CM2.1, which reduces sea ice biases in the North Atlantic.

Both models have been used to conduct a suite of climate change simulations for the 2007 Intergovernmental Panel on Climate Change (IPCC) assessment report and are able to simulate the main features of the observed warming of the twentieth century. The climate sensitivities of the CM2.0 and CM2.1 models are 2.9 and 3.4 K, respectively. These sensitivities are defined by coupling the atmospheric components of CM2.0 and CM2.1 to a slab ocean model and allowing the model to come into equilibrium with a doubling of atmospheric CO2. The output from a suite of integrations conducted with these models is freely available online (see http://nomads.gfdl.noaa.gov/).

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