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Xing Yuan
,
Eric F. Wood
,
Joshua K. Roundy
, and
Ming Pan

Abstract

There is a long history of debate on the usefulness of climate model–based seasonal hydroclimatic forecasts as compared to ensemble streamflow prediction (ESP). In this study, the authors use NCEP's operational forecast system, the Climate Forecast System version 2 (CFSv2), and its previous version, CFSv1, to investigate the value of climate models by conducting a set of 27-yr seasonal hydroclimatic hindcasts over the conterminous United States (CONUS). Through Bayesian downscaling, climate models have higher squared correlation R 2 and smaller error than ESP for monthly precipitation, and the forecasts conditional on ENSO have further improvements over southern basins out to 4 months. Verification of streamflow forecasts over 1734 U.S. Geological Survey (USGS) gauges shows that CFSv2 has moderately smaller error than ESP, but all three approaches have limited added skill against climatology beyond 1 month because of overforecasting or underdispersion errors. Using a postprocessor, 60%–70% of probabilistic streamflow forecasts are more skillful than climatology. All three approaches have plausible predictions of soil moisture drought frequency over the central United States out to 6 months, and climate models provide better results over the central and eastern United States. The R 2 of drought extent is higher for arid basins and for the forecasts initiated during dry seasons, but significant improvements from CFSv2 occur in different seasons for different basins. The R 2 of drought severity accumulated over CONUS is higher during winter, and climate models present added value, especially at long leads. This study indicates that climate models can provide better seasonal hydroclimatic forecasts than ESP through appropriate downscaling procedures, but significant improvements are dependent on the variables, seasons, and regions.

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Amanda L. Siemann
,
Gabriele Coccia
,
Ming Pan
, and
Eric F. Wood

Abstract

Land surface temperature (LST) is a critical state variable for surface energy exchanges as it is one of the controls on emitted radiation at Earth’s surface. LST also exerts an important control on turbulent fluxes through the temperature gradient between LST and air temperature. Although observations of surface energy balance components are widely accessible from in situ stations in most developed regions, these ground-based observations are not available in many underdeveloped regions. Satellite remote sensing measurements provide wider spatial coverage to derive LST over land and are used in this study to form a high-resolution, long-term LST data product. As selected by the Global Energy and Water Exchanges project (GEWEX) Data and Assessments Panel (GDAP) for development of internally consistent datasets, the High Resolution Infrared Radiation Sounder (HIRS) data are used for the primary satellite observations because of the data record length. The final HIRS-consistent, hourly, global, 0.5° resolution LST dataset for clear and cloudy conditions from 1979 to 2009 is developed through merging the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) LST estimates with the HIRS retrievals using a Bayesian postprocessing procedure. The Baseline Surface Radiation Network (BSRN) observations are used to validate the HIRS retrievals, the CFSR LST estimates, and the final merged LST dataset. An intercomparison between the original retrievals and CFSR LST datasets, before and after merging, is also presented with an analysis of the datasets, including an error assessment of the final LST dataset.

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Ming Pan
,
Alok K. Sahoo
,
Tara J. Troy
,
Raghuveer K. Vinukollu
,
Justin Sheffield
, and
Eric F. Wood

Abstract

A systematic method is proposed to optimally combine estimates of the terrestrial water budget from different data sources and to enforce the water balance constraint using data assimilation techniques. The method is applied to create global long-term records of the terrestrial water budget by merging a number of global datasets including in situ observations, remote sensing retrievals, land surface model simulations, and global reanalyses. The estimation process has three steps. First, a conventional analysis on the errors and biases in different data sources is conducted based on existing validation/error studies and other information such as sensor network density, model physics, and calibration procedures. Then, the data merging process combines different estimates so that biases and errors from different data sources can be compensated to the greatest extent and the merged estimates have the best possible confidence. Finally, water balance errors are resolved using the constrained Kalman filter technique. The procedure is applied to 32 globally distributed major basins for 1984–2006. The authors believe that the resulting global water budget estimates can be used as a baseline dataset for large-scale diagnostic studies, for example, integrated assessment of basin water resources, trend analysis and attribution, and climate change studies. The global scale of the analysis presents significant challenges in carrying out the error analysis for each water budget variable. For some variables (e.g., evapotranspiration) the assumptions underpinning the error analysis lack supporting quantitative analysis and, thus, may not hold for specific locations. Nevertheless, the merging and water balance constraining technique can be applied to many problems.

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Fang Pan
,
Xianglei Huang
,
Stephen S. Leroy
,
Pu Lin
,
L. Larrabee Strow
,
Yi Ming
, and
V. Ramaswamy

Abstract

Global-mean radiances observed by the Atmospheric Infrared Sounder (AIRS) and the Advanced Microwave Sounding Unit A (AMSU-A) are analyzed from 2003 to 2012. The focus of this study is on channels sensitive to emission and absorption in the stratosphere. Optimal fingerprinting is used to obtain estimates of changes of stratospheric temperature in five vertical layers due to external forcing in the presence of natural variability. Natural variability is estimated using synthetic radiances based on the 500-yr GFDL CM3 and 240-yr HadGEM2-CC control runs. The results show a cooling rate of 0.65 ± 0.11 (2σ) K decade−1 in the upper stratosphere above 6 hPa, approximately 0.46 ± 0.24 K decade−1 in two midstratospheric layers between 6 and 30 hPa, and 0.39 ± 0.32 K decade−1 in the lower stratosphere (30–60 hPa). The cooling rate in the lowest part of the stratosphere (60–100 hPa) is −0.014 ± 0.22 K decade−1, which is smallest among all five layers and statistically insignificant. The synergistic use of well-calibrated passive infrared and microwave radiances permits disambiguation of trends of carbon dioxide and stratospheric temperature, increases vertical resolution of detected stratospheric temperature trends, and effectively reduces uncertainties of estimated temperature trends.

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Justin Sheffield
,
Suzana J. Camargo
,
Rong Fu
,
Qi Hu
,
Xianan Jiang
,
Nathaniel Johnson
,
Kristopher B. Karnauskas
,
Seon Tae Kim
,
Jim Kinter
,
Sanjiv Kumar
,
Baird Langenbrunner
,
Eric Maloney
,
Annarita Mariotti
,
Joyce E. Meyerson
,
J. David Neelin
,
Sumant Nigam
,
Zaitao Pan
,
Alfredo Ruiz-Barradas
,
Richard Seager
,
Yolande L. Serra
,
De-Zheng Sun
,
Chunzai Wang
,
Shang-Ping Xie
,
Jin-Yi Yu
,
Tao Zhang
, and
Ming Zhao

Abstract

This is the second part of a three-part paper on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) that evaluates the twentieth-century simulations of intraseasonal to multidecadal variability and teleconnections with North American climate. Overall, the multimodel ensemble does reasonably well at reproducing observed variability in several aspects, but it does less well at capturing observed teleconnections, with implications for future projections examined in part three of this paper. In terms of intraseasonal variability, almost half of the models examined can reproduce observed variability in the eastern Pacific and most models capture the midsummer drought over Central America. The multimodel mean replicates the density of traveling tropical synoptic-scale disturbances but with large spread among the models. On the other hand, the coarse resolution of the models means that tropical cyclone frequencies are underpredicted in the Atlantic and eastern North Pacific. The frequency and mean amplitude of ENSO are generally well reproduced, although teleconnections with North American climate are widely varying among models and only a few models can reproduce the east and central Pacific types of ENSO and connections with U.S. winter temperatures. The models capture the spatial pattern of Pacific decadal oscillation (PDO) variability and its influence on continental temperature and West Coast precipitation but less well for the wintertime precipitation. The spatial representation of the Atlantic multidecadal oscillation (AMO) is reasonable, but the magnitude of SST anomalies and teleconnections are poorly reproduced. Multidecadal trends such as the warming hole over the central–southeastern United States and precipitation increases are not replicated by the models, suggesting that observed changes are linked to natural variability.

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Eric D. Maloney
,
Suzana J. Camargo
,
Edmund Chang
,
Brian Colle
,
Rong Fu
,
Kerrie L. Geil
,
Qi Hu
,
Xianan Jiang
,
Nathaniel Johnson
,
Kristopher B. Karnauskas
,
James Kinter
,
Benjamin Kirtman
,
Sanjiv Kumar
,
Baird Langenbrunner
,
Kelly Lombardo
,
Lindsey N. Long
,
Annarita Mariotti
,
Joyce E. Meyerson
,
Kingtse C. Mo
,
J. David Neelin
,
Zaitao Pan
,
Richard Seager
,
Yolande Serra
,
Anji Seth
,
Justin Sheffield
,
Julienne Stroeve
,
Jeanne Thibeault
,
Shang-Ping Xie
,
Chunzai Wang
,
Bruce Wyman
, and
Ming Zhao

Abstract

In part III of a three-part study on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) models, the authors examine projections of twenty-first-century climate in the representative concentration pathway 8.5 (RCP8.5) emission experiments. This paper summarizes and synthesizes results from several coordinated studies by the authors. Aspects of North American climate change that are examined include changes in continental-scale temperature and the hydrologic cycle, extremes events, and storm tracks, as well as regional manifestations of these climate variables. The authors also examine changes in the eastern North Pacific and North Atlantic tropical cyclone activity and North American intraseasonal to decadal variability, including changes in teleconnections to other regions of the globe. Projected changes are generally consistent with those previously published for CMIP3, although CMIP5 model projections differ importantly from those of CMIP3 in some aspects, including CMIP5 model agreement on increased central California precipitation. The paper also highlights uncertainties and limitations based on current results as priorities for further research. Although many projected changes in North American climate are consistent across CMIP5 models, substantial intermodel disagreement exists in other aspects. Areas of disagreement include projections of changes in snow water equivalent on a regional basis, summer Arctic sea ice extent, the magnitude and sign of regional precipitation changes, extreme heat events across the northern United States, and Atlantic and east Pacific tropical cyclone activity.

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