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Z. Wang, X. Zeng, M. Barlage, R. E. Dickinson, F. Gao, and C. B. Schaaf

Abstract

The land surface albedo in the NCAR Community Climate System Model (CCSM2) is calculated based on a two-stream approximation, which does not include the effect of three-dimensional vegetation structure on radiative transfer. The model albedo (including monthly averaged albedo, direct albedo at local noon, and the solar zenith angle dependence of albedo) is evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) and albedo data acquired during July 2001–July 2002. The model monthly averaged albedos in February and July are close to the MODIS white-sky albedos (within 0.02 or statistically insignificant) over about 40% of the global land between 60°S and 70°N. However, CCSM2 significantly underestimates albedo by 0.05 or more over deserts (e.g., the Sahara Desert) and some semiarid regions (e.g., parts of Australia). The difference between the model direct albedo at local noon and the MODIS black-sky albedo for the near-infrared (NIR) band (with wavelength > 0.7 μm) is larger than the difference for the visible band (with wavelength < 0.7 μm) for most snow-free regions. For eleven model grid cells with different dominant plant functional types, the model diffuse NIR albedo is higher by 0.05 or more than the MODIS white-sky albedo in five of these cells. Direct albedos from the model and MODIS (as computed using the BRDF parameters) increase with solar zenith angles, but model albedo increases faster than the MODIS data. These analyses and the MODIS BRDF and albedo data provide a starting point toward developing a BRDF-based treatment of radiative transfer through a canopy for land surface models that can realistically simulate the mean albedo and the solar zenith angle dependence of albedo.

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Y. Xue, F. J. Zeng, K. E. Mitchell, Z. Janjic, and E. Rogers

Abstract

This paper describes a methodology for coupling the Simplified Simple Biosphere Model (SSiB) to the regional Eta Model of the National Centers for Environmental Prediction (NCEP), and presents the application of the coupled system in regional simulation studies. The coupled Eta–SSiB model is used to study the impact of land surface processes and land surface initialization on the regional water and energy cycle in an extreme climate event, by comparing the results from the Eta–SSiB with those from the Eta–bucket model. Simulations from both models spanned 3 months via a succession of 48-hr simulations over June, July, and August 1993, a summer of heavy flooding in the United States. The monthly and seasonal means from the simulations in both model runs are compared.

The Eta–SSiB model produces more realistic monthly mean precipitation over the United States and the flood areas. The improvements are mainly manifested in the intensity of the heavy rainfall and its spatial distribution. The results demonstrate that even with a short-term simulation, a more realistic representation of land surface processes and land surface initialization improves the monthly and seasonal means of the simulated regional precipitation for the summer of 1993. In addition to precipitation, the simulations of surface air temperature are also evaluated and they show that the Eta–SSiB model produces reasonable results over most of the United States, with the exception of a cold bias at night in the mountainous western region of the United States.

To understand the mechanisms of land surface–atmosphere interactions and the causes for the differences in the Eta–SSiB and the Eta–bucket simulations, the water cycle in the atmosphere–land system and the energy balance at the land surface are analyzed. The changes in (a) spatial distribution and diurnal cycle of surface latent and sensible heat, and (b) low-level moisture flux convergence (MFC) in response to these changes in surface heating are the primary factors for the improvement in the precipitation simulation. That is, the different surface models of SSiB and bucket, and their different soil moisture initializations, produce different energy partitioning in the surface heat fluxes of the Eta Model. The changes in both the daily mean and the diurnal variation at the land surface lead to different boundary layer evolutions and atmospheric stability conditions. In response to these differences, the Eta–SSiB model and the Eta–bucket model produce different low-level MFC in the heavy rainfall area. Strong and persistent MFC was one of the major forces that produced the heavy rainfall in the summer of 1993.

In the above experiments, the Eta–SSiB model used the global reanalysis of the NCEP–NCAR (National Center for Atmospheric Research) 40-year Reanalysis Project (NNRP) for its initial soil moisture, whereas the Eta–bucket model used a tuned annual-mean fixed field of initial soil moisture as employed in the then-operational Eta Model. Because of this important initialization difference, a further set of simulations was performed in which the Eta–bucket was initialized with the NNRP reanalysis soil moisture employed in the Eta–SSiB. Results show that with similarly derived initial soil moisture states, the differences between the Eta–SSiB and the Eta–bucket are reduced but still evident, suggesting that improved representation of vegetation in the SSiB is at least partially responsible for the overall improvements in the simulations.

Given that the NCEP–NCAR reanalysis is used for initial conditions and lateral and lower boundary conditions in these experiments, this study shows that a coupled atmosphere–biosphere regional model imbedded in a global reanalysis has the potential to provide a more realistic simulation of precipitation in extreme climate events.

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Liqing Peng, Zhongwang Wei, Zhenzhong Zeng, Peirong Lin, Eric F. Wood, and Justin Sheffield

Abstract

Downward shortwave radiation R sd determines the surface energy balance, alters evapotranspiration and hydrological conditions, and feeds back to the regional and global climate. Large-scale R sd estimates are usually retrieved from satellite-based top-of-atmosphere radiation and cloud parameters. These estimates are subject to biases and temporal inhomogeneity due to errors in atmospheric parameters, algorithms, and sensor changes. We found that three satellite products overestimate R sd by 8%–10% over Asia for 1984–2006, particularly in high latitudes. We used the model tree ensemble (MTE) machine-learning algorithm and commonly used ensemble averaging methods to integrate ground observations and satellite products. Validations based on test stations and independent networks showed that the MTE approach reduces the median relative biases from 8%–10% to 2%, which is more effective than the ensemble averaging methods. We further evaluated the impacts of uncertainty in radiation forcing on surface energy and water balances using the land surface model Noah-MP. The uncertainty of radiation data affects the prediction of sensible heat the most, and also largely affects latent heat prediction in humid regions. Holding the other variables constant, a 10% positive bias in R sd can lead to a 20%–60% positive bias in the monthly median sensible heat. The simulated hydrological responses to changing radiation forcing are nonlinear as a result of the interactions among evapotranspiration, snowpack, and soil moisture. Our analysis concludes that reducing uncertainty of radiation data is beneficial for predicting regional energy and water balances, which requires more high-quality ground observations and improved satellite retrieval algorithms.

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Xiaosong Yang, G. A. Vecchi, T. L. Delworth, K. Paffendorf, L. Jia, R. Gudgel, F. Zeng, and Seth D. Underwood
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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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Liwei Jia, Gabriel A. Vecchi, Xiaosong Yang, Richard G. Gudgel, Thomas L. Delworth, William F. Stern, Karen Paffendorf, Seth D. Underwood, and Fanrong Zeng

Abstract

This study investigates the roles of radiative forcing, sea surface temperatures (SSTs), and atmospheric and land initial conditions in the summer warming episodes of the United States. The summer warming episodes are defined as the significantly above-normal (1983–2012) June–August 2-m temperature anomalies and are referred to as heat waves in this study. Two contrasting cases, the summers of 2006 and 2012, are explored in detail to illustrate the distinct roles of SSTs, direct radiative forcing, and atmospheric and land initial conditions in driving U.S. summer heat waves. For 2012, simulations with the GFDL atmospheric general circulation model reveal that SSTs play a critical role. Further sensitivity experiments reveal the contributions of uniform global SST warming, SSTs in individual ocean basins, and direct radiative forcing to the geographic distribution and magnitudes of warm temperature anomalies. In contrast, for 2006, the atmospheric and land initial conditions are the key drivers. The atmospheric (land) initial conditions play a major (minor) role in the central and northwestern (eastern) United States. Because of changes in radiative forcing, the probability of areal-averaged summer temperature anomalies over the United States exceeding the observed 2012 anomaly increases with time over the early twenty-first century. La Niña (El Niño) events tend to increase (reduce) the occurrence rate of heat waves. The temperatures over the central United States are mostly influenced by El Niño/La Niña, with the central tropical Pacific playing a more important role than the eastern tropical Pacific. Thus, atmospheric and land initial conditions, SSTs, and radiative forcing are all important drivers of and sources of predictability for U.S. summer heat waves.

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M. Chong, J.-F. Georgis, O. Bousquet, S. R. Brodzik, C. Burghart, S. Cosma, U. Germann, V. Gouget, R. A. Houze Jr., C. N. James, S. Prieur, R. Rotunno, F. Roux, J. Vivekanandan, and Z.-X. Zeng

A real-time and automated multiple-Doppler analysis method for ground-based radar data, with an emphasis on observations conducted over complex terrain, is presented. It is the result of a joint effort of the radar groups of Centre National de Recherches Météorologiques and Laboratoire d'Aérologie with a view to converging toward a common optimized procedure to retrieve mass-conserved three-dimensional wind fields in the presence of complex topography. The multiple-Doppler synthesis and continuity adjustment technique initially proposed for airborne Doppler radar data, then extended to ground-based Doppler radars and nonflat orography, is combined with a variational approach aimed at improving the vertical velocity calculation over mountainous regions. This procedure was successfully applied in real time during the Mesoscale Alpine Programme Special Observing Period. The real-time processing and display of Doppler radar data were intended to assist nowcast and aircraft missions, and involved efforts of the United Sates, France, and Switzerland.

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Xiaosong Yang, Anthony Rosati, Shaoqing Zhang, Thomas L. Delworth, Rich G. Gudgel, Rong Zhang, Gabriel Vecchi, Whit Anderson, You-Soon Chang, Timothy DelSole, Keith Dixon, Rym Msadek, William F. Stern, Andrew Wittenberg, and Fanrong Zeng

Abstract

The decadal predictability of sea surface temperature (SST) and 2-m air temperature (T2m) in the Geophysical Fluid Dynamics Laboratory (GFDL) decadal hindcasts, which are part of the Fifth Coupled Model Intercomparison Project experiments, has been investigated using an average predictability time (APT) analysis. Comparison of retrospective forecasts initialized using the GFDL Ensemble Coupled Data Assimilation system with uninitialized historical forcing simulations using the same model allows identification of the internal multidecadal pattern (IMP) for SST and T2m. The IMP of SST is characterized by an interhemisphere dipole, with warm anomalies centered in the North Atlantic subpolar gyre region and North Pacific subpolar gyre region, and cold anomalies centered in the Antarctic Circumpolar Current region. The IMP of T2m is characterized by a general bipolar seesaw, with warm anomalies centered in Greenland and cold anomalies centered in Antarctica. The retrospective prediction skill of the initialized system, verified against independent observational datasets, indicates that the IMP of SST may be predictable up to 4 (10) yr lead time at 95% (90%) significance level, and the IMP of T2m may be predictable up to 2 (10) yr at the 95% (90%) significance level. The initialization of multidecadal variations of northward oceanic heat transport in the North Atlantic significantly improves the predictive skill of the IMP. The dominant roles of oceanic internal dynamics in decadal prediction are further elucidated by fixed-forcing experiments in which radiative forcing is returned abruptly to 1961 values. These results point toward the possibility of meaningful decadal climate outlooks using dynamical coupled models if they are appropriately initialized from a sustained climate observing system.

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Liwei Jia, Xiaosong Yang, Gabriel A. Vecchi, Richard G. Gudgel, Thomas L. Delworth, Anthony Rosati, William F. Stern, Andrew T. Wittenberg, Lakshmi Krishnamurthy, Shaoqing Zhang, Rym Msadek, Sarah Kapnick, Seth Underwood, Fanrong Zeng, Whit G. Anderson, Venkatramani Balaji, and Keith Dixon

Abstract

This study demonstrates skillful seasonal prediction of 2-m air temperature and precipitation over land in a new high-resolution climate model developed by the Geophysical Fluid Dynamics Laboratory and explores the possible sources of the skill. The authors employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land and demonstrate the predictive skill of these components. First, the improved skill of the high-resolution model over the previous lower-resolution model in seasonal prediction of the Niño-3.4 index and other aspects of interest is shown. Then, the skill of temperature and precipitation in the high-resolution model for boreal winter and summer is measured, and the sources of the skill are diagnosed. Last, predictions are reconstructed using a few of the most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in boreal winter and summer and an ENSO-related pattern in boreal winter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2-m air temperature and precipitation over land.

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Xiaosong Yang, Gabriel A. Vecchi, Rich G. Gudgel, Thomas L. Delworth, Shaoqing Zhang, Anthony Rosati, Liwei Jia, William F. Stern, Andrew T. Wittenberg, Sarah Kapnick, Rym Msadek, Seth D. Underwood, Fanrong Zeng, Whit Anderson, and Venkatramani Balaji

Abstract

The seasonal predictability of extratropical storm tracks in the Geophysical Fluid Dynamics Laboratory’s (GFDL)’s high-resolution climate model has been investigated using an average predictability time analysis. The leading predictable components of extratropical storm tracks are the ENSO-related spatial patterns for both boreal winter and summer, and the second predictable components are mostly due to changes in external radiative forcing and multidecadal oceanic variability. These two predictable components for both seasons show significant correlation skill for all leads from 0 to 9 months, while the skill of predicting the boreal winter storm track is consistently higher than that of the austral winter. The predictable components of extratropical storm tracks are dynamically consistent with the predictable components of the upper troposphere jet flow for both seasons. Over the region with strong storm-track signals in North America, the model is able to predict the changes in statistics of extremes connected to storm-track changes (e.g., extreme low and high sea level pressure and extreme 2-m air temperature) in response to different ENSO phases. These results point toward the possibility of providing skillful seasonal predictions of the statistics of extratropical extremes over land using high-resolution coupled models.

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