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Paul A. Dirmeyer
,
Fanrong J. Zeng
,
Agnès Ducharne
,
Jean C. Morrill
, and
Randal D. Koster

Abstract

Evaporative fraction (EF; the ratio of latent heat flux to the sum of the latent plus sensible heat fluxes) can be measured in the field to an accuracy of about 10%. In this modeling study, the authors try to determine to what accuracy soil moisture must be known in order to simulate surface energy fluxes within this observational uncertainty and whether there is a firm relationship between the variabilities of soil moisture and surface turbulent energy fluxes. A relationship would provide information for planning the future measurement of soil moisture, the design of field experiments, and points of focus for soil model development. The authors look for relationships in three different land surface schemes using results and ancillary integrations in the Global Soil Wetness Project.

It is found that the variation of evaporative fraction as a function of soil moisture is consistent among the models and within subsets of vegetation type. In forested areas, there is high sensitivity of EF to soil moisture variations when soils are dry and there is little sensitivity in moderate to wet soils. Where vegetation is sparser, there is a more gradual decrease of EF sensitivity with a decrease in soil moisture. Bare soil desert areas behave similarly to sparsely vegetated areas but with lower peak EF. Tundra regions have a unique behavior, probably because evaporation is limited more by a lack of radiant energy at high latitudes. The results suggest that accuracy in the measurement or model simulation of soil moisture is most critical within the drier portion of the range of variability of soil moisture. It also is more important over sparsely vegetated areas, for which evapotranspiration is dependent on moisture in a shallower column of soil.

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Kaye L. Brubaker
,
Paul A. Dirmeyer
,
Arief Sudradjat
,
Benjamin S. Levy
, and
Fredric Bernal

Abstract

The terrestrial and oceanic sources of moisture that supply warm-season rainfall to the Mississippi River basin and its subbasins are examined over a 36-yr period (1963–98). Using hourly observed precipitation, National Centers for Environmental Prediction (NCEP) reanalyses at 6-h intervals, and a back-trajectory algorithm, the water falling during observed precipitation events is probabilistically traced to its most recent surface evaporative source, terrestrial or oceanic. Maps of these sources generally show dual maxima, one terrestrial and one oceanic, in spring and a dominance of terrestrial sources in summer. Pentad time series averaged over the 36 years show a late-summer maximum of precipitation recycling in all but the Missouri subbasin. During the 36 years analyzed, 32% of warm-season precipitation in the entire Mississippi basin originated as evaporation within the basin (recycled). About 20% of warm-season precipitation was contributed directly by evaporation from the Gulf of Mexico and Caribbean. The Midwest flood year, 1993, represents a positive outlier in terms of July precipitation supplied to the Upper Mississippi directly by evaporation from the Caribbean. The monthly recycling ratios for warm-season precipitation during the drought year, 1988, represent extreme values in the time series but are not identified as outliers. A positive trend in precipitation recycling in the Upper Mississippi and Missouri subbasins and accompanying decrease in Gulf of Mexico/Caribbean–supplied precipitation to those regions are statistically significant but may reflect changes in the observational data stream assimilated by the NCEP model. Perturbation analysis demonstrates that the source fractions and recycling ratios are somewhat sensitive to systematic errors but not to random errors in the model-derived evapotranspiration (ET), arguably the largest source of uncertainty in the back-trajectory approach. Systematic errors in terrestrial ET on the order of 20% introduce errors of about 0.02 in land source fractions (including recycling ratios) that are themselves on the order of 0.10–0.30.

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Chul-Su Shin
,
Bohua Huang
,
Paul A. Dirmeyer
,
Subhadeep Halder
, and
Arun Kumar

Abstract

In addition to remote SST forcing, realistic representation of land forcing (i.e., soil moisture) over the United States is critical for a prediction of U.S. severe drought events approximately one season in advance. Using “identical twin” experiments with different land initial conditions (ICs) in the 32-yr (1979–2010) CFSv2 reforecasts (NASA GLDAS-2 reanalysis versus NCEP CFSR), sensitivity and skill of U.S. drought predictions to land ICs are evaluated. Although there is no outstanding performer between the two sets of forecasts with different land ICs, each set shows greater skill in some regions, but their locations vary with forecast lead time and season. The 1999 case study demonstrates that although a pattern of below-normal SSTs in the Pacific in the fall and winter is realistically reproduced in both reforecasts, GLDAS-2 land initial states display a stronger east–west gradient of soil moisture, particularly drier in the eastern United States and more consistent with observations, leading to warmer surface temperature anomalies over the United States. Anomalies lasting for one season are accompanied by more persistent barotropic (warm core) anomalous high pressure over CONUS, which results in better prediction skill of this drought case up to 4 months in advance in the reforecasts with GLDAS-2 land ICs. Therefore, it is essential to minimize the uncertainty of land initial states among the current land analyses for improving U.S. drought prediction on seasonal time scales.

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Paul A. Dirmeyer
,
Xiang Gao
,
Mei Zhao
,
Zhichang Guo
,
Taikan Oki
, and
Naota Hanasaki

Quantification of sources and sinks of carbon at global and regional scales requires not only a good description of the land sources and sinks of carbon, but also of the synoptic and mesoscale meteorology. An experiment was performed in Les Landes, southwest France, during May–June 2005, to determine the variability in concentration gradients and fluxes of CO2 The CarboEurope Regional Experiment Strategy (CERES; see also http://carboregional.mediasfrance.org/index) aimed to produce aggregated estimates of the carbon balance of a region that can be meaningfully compared to those obtained from the smallest downscaled information of atmospheric measurements and continental-scale inversions. We deployed several aircraft to sample the CO2 concentration and fluxes over the whole area, while fixed stations observed the fluxes and concentrations at high accuracy. Several (mesoscale) meteorological modeling tools were used to plan the experiment and flight patterns.

Results show that at regional scale the relation between profiles and fluxes is not obvious, and is strongly influenced by airmass history and mesoscale flow patterns. In particular, we show from an analysis of data for a single day that taking either the concentration at several locations as representative of local fluxes or taking the flux measurements at those sites as representative of larger regions would lead to incorrect conclusions about the distribution of sources and sinks of carbon. Joint consideration of the synoptic and regional flow, fluxes, and land surface is required for a correct interpretation. This calls for an experimental and modeling strategy that takes into account the large spatial gradients in concentrations and the variability in sources and sinks that arise from different land use types. We briefly describe how such an analysis can be performed and evaluate the usefulness of the data for planning of future networks or longer campaigns with reduced experimental efforts.

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Paul A. Dirmeyer
,
Xiang Gao
,
Mei Zhao
,
Zhichang Guo
,
Taikan Oki
, and
Naota Hanasaki
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Bart van den Hurk
,
Martin Best
,
Paul Dirmeyer
,
Andy Pitman
,
Jan Polcher
, and
Joe Santanello

No abstract available.

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Randal D. Koster
,
Paul A. Dirmeyer
,
Andrea N. Hahmann
,
Ruben Ijpelaar
,
Lori Tyahla
,
Peter Cox
, and
Max J. Suarez

Abstract

The strength of the coupling between the land and the atmosphere, which controls, for example, the degree to which precipitation-induced soil moisture anomalies affect the overlying atmosphere and thereby the subsequent generation of precipitation, has been examined and quantified with many atmospheric general circulation models (AGCMs). Generally missing from such studies, however, is an indication of the extent to which the simulated coupling strength is model dependent. Four modeling groups have recently performed a highly controlled numerical experiment that allows an objective intermodel comparison of land–atmosphere coupling strength, focusing on short (weekly down to subhourly) timescales. The experiment essentially consists of an ensemble of 1-month simulations in which each member simulation artificially maintains the same (model specific) time series of surface prognostic variables. Differences in atmospheric behavior between the ensemble members then indicate the degree to which the state of the land surface controls atmospheric processes in that model. A comparison of the four sets of experimental results shows that coupling strength does indeed vary significantly among the AGCMs.

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Bohua Huang
,
Chul-Su Shin
,
J. Shukla
,
Lawrence Marx
,
Magdalena A. Balmaseda
,
Subhadeep Halder
,
Paul Dirmeyer
, and
James L. Kinter III

Abstract

A set of ensemble seasonal reforecasts for 1958–2014 is conducted using the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2. In comparison with other current reforecasts, this dataset extends the seasonal reforecasts to the 1960s–70s. Direct comparison of the predictability of the ENSO events occurring during the 1960s–70s with the more widely studied ENSO events since then demonstrates the seasonal forecast system’s capability in different phases of multidecadal variability and degrees of global climate change. A major concern for a long reforecast is whether the seasonal reforecasts before 1979 provide useful skill when observations, particularly of the ocean, were sparser. This study demonstrates that, although the reforecasts have lower skill in predicting SST anomalies in the North Pacific and North Atlantic before 1979, the prediction skill of the onset and development of ENSO events in 1958–78 is comparable to that for 1979–2014. In particular, the ENSO predictions initialized in April during 1958–78 show higher skill in the summer. However, the skill of the earlier predictions declines faster in the ENSO decaying phase, because the reforecasts initialized after boreal summer persistently predict lingering wind and SST anomalies over the eastern equatorial Pacific during such events. Reforecasts initialized in boreal fall overestimate the peak SST anomalies of strong El Niño events since the 1980s. Both phenomena imply that the model’s air–sea feedback is overly active in the eastern Pacific before ENSO event termination. Whether these differences are due to changes in the observing system or are associated with flow-dependent predictability remains an open question.

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Paul A. Dirmeyer
,
Sanjiv Kumar
,
Michael J. Fennessy
,
Eric L. Altshuler
,
Timothy DelSole
,
Zhichang Guo
,
Benjamin A. Cash
, and
David Straus

Abstract

The climate system model of the National Center for Atmospheric Research is used to examine the predictability arising from the land surface initialization of seasonal climate ensemble forecasts in current, preindustrial, and projected future settings. Predictability is defined in terms of the model's ability to predict its own interannual variability. Predictability from the land surface in this model is relatively weak compared to estimates from other climate models but has much of the same spatial and temporal structure found in previous studies. Several factors appear to contribute to the weakness, including a low correlation between surface fluxes and subsurface soil moisture, less soil moisture memory (lagged autocorrelation) than other models or observations, and relative insensitivity of the atmospheric boundary layer to surface flux variations. Furthermore, subseasonal cyclical behavior in plant phenology for tropical grasses introduces spurious unrealistic predictability at low latitudes during dry seasons. Despite these shortcomings, intriguing changes in predictability are found. Areas of historical land use change appear to have experienced changes in predictability, particularly where agriculture expanded dramatically into the Great Plains of North America, increasing land-driven predictability there. In a warming future climate, land–atmosphere coupling strength generally increases, but added predictability does not always follow; many other factors modulate land-driven predictability.

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Yongjiu Dai
,
Xubin Zeng
,
Robert E. Dickinson
,
Ian Baker
,
Gordon B. Bonan
,
Michael G. Bosilovich
,
A. Scott Denning
,
Paul A. Dirmeyer
,
Paul R. Houser
,
Guoyue Niu
,
Keith W. Oleson
,
C. Adam Schlosser
, and
Zong-Liang Yang

The Common Land Model (CLM) was developed for community use by a grassroots collaboration of scientists who have an interest in making a general land model available for public use and further development. The major model characteristics include enough unevenly spaced layers to adequately represent soil temperature and soil moisture, and a multilayer parameterization of snow processes; an explicit treatment of the mass of liquid water and ice water and their phase change within the snow and soil system; a runoff parameterization following the TOPMODEL concept; a canopy photo synthesis-conductance model that describes the simultaneous transfer of CO2 and water vapor into and out of vegetation; and a tiled treatment of the subgrid fraction of energy and water balance. CLM has been extensively evaluated in offline mode and coupling runs with the NCAR Community Climate Model (CCM3). The results of two offline runs, presented as examples, are compared with observations and with the simulation of three other land models [the Biosphere-Atmosphere Transfer Scheme (BATS), Bonan's Land Surface Model (LSM), and the 1994 version of the Chinese Academy of Sciences Institute of Atmospheric Physics LSM (IAP94)].

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