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Zhichang Guo
,
Paul A. Dirmeyer
,
Timothy DelSole
, and
Randal D. Koster

Abstract

Total predictability within a chaotic system like the earth’s climate cannot increase over time. However, it can be transferred between subsystems. Predictability of air temperature and precipitation in numerical model forecasts over North America rebounds during late spring to summer because of information stored in the land surface. Specifically, soil moisture anomalies can persist over several months, but this memory cannot affect the atmosphere during early spring because of a lack of coupling between land and atmosphere. Coupling becomes established in late spring, enabling the effects of soil moisture anomalies to increase atmospheric predictability in 2-month forecasts begun as early as 1 May. This predictability is maintained through summer and then drops as coupling fades again in fall. This finding suggests summer forecasts of rainfall and air temperature over parts of North America could be significantly improved with soil moisture observations during spring.

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Timothy DelSole
,
Xiaoqin Yan
,
Paul A. Dirmeyer
,
Mike Fennessy
, and
Eric Altshuler

Abstract

The change in predictability of monthly mean temperature in a future climate is quantified based on the Community Climate System Model, version 4. According to this model, the North Atlantic overtakes the El Niño–Southern Oscillation (ENSO) as the dominant area of seasonal predictability by 2095. This change arises partly because ENSO becomes less variable and partly because the ENSO teleconnection pattern expands into the Atlantic. Over land, the largest change in temperature predictability occurs in the tropics and is predominantly due to a decrease in ENSO variability. The southern peninsula of Africa and northeast South America are predicted to experience significant drying in a future climate, which decreases the effective heat capacity and memory, and hence increases variance independently of ENSO changes. Extratropical land areas experience enhanced precipitation in a future climate, which decreases temperature variance by the same mechanism. Finally, the model predicts that surface temperatures near the poles will become more predictable and less variable in a future climate, primarily because melting sea ice exposes the underlying sea surface temperature, which is more predictable owing to its longer time scale. Some of these results, especially the change in ENSO variance, are known to be model dependent. This paper also advances the use of information theory to quantify predictability, including 1) deriving a quantitative relation between predictability of the first and second kinds; 2) showing how differences in predictability can be decomposed in two dramatically different ways, facilitating physical interpretation; and 3) proposing a sample estimate of mutual information whose significance can be tested using standard techniques.

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Paul A. Dirmeyer
,
Yan Jin
,
Bohar Singh
, and
Xiaoqin Yan

Abstract

Data from 15 models of phase 5 of the Coupled Model Intercomparison Project (CMIP5) for preindustrial, historical, and future climate change experiments are examined for consensus changes in land surface variables, fluxes, and metrics relevant to land–atmosphere interactions. Consensus changes in soil moisture and latent heat fluxes for past-to-present and present-to-future periods are consistent with CMIP3 simulations, showing a general drying trend over land (less soil moisture, less evaporation) over most of the globe, with the notable exception of high northern latitudes during winter. Sensible heat flux and net radiation declined from preindustrial times to current conditions according to the multimodel consensus, mainly due to increasing aerosols, but that trend reverses abruptly in the future projection. No broad trends are found in soil moisture memory except for reductions during boreal winter associated with high-latitude warming and diminution of frozen soils. Land–atmosphere coupling is projected to increase in the future across most of the globe, meaning a greater control by soil moisture variations on surface fluxes and the lower troposphere. There is also a strong consensus for a deepening atmospheric boundary layer and diminished gradients across the entrainment zone at the top of the boundary layer, indicating that the land surface feedback on the atmosphere should become stronger both in absolute terms and relative to the influence of the conditions of the free atmosphere. Coupled with the trend toward greater hydrologic extremes such as severe droughts, the land surface seems likely to play a greater role in amplifying both extremes and trends in climate on subseasonal and longer time scales.

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Paul A. Dirmeyer
,
C. Adam Schlosser
, and
Kaye L. Brubaker

Abstract

A synthesis of several approaches to quantifying land–atmosphere interactions is presented. These approaches use data from observations or atmospheric reanalyses applied to atmospheric tracer models and stand-alone land surface schemes. None of these approaches relies on the results of general circulation model simulations. A high degree of correlation is found among these independent approaches, and constructed here is a composite assessment of global land–atmosphere feedback strength as a function of season. The composite combines the characteristics of persistence of soil moisture anomalies, strong soil moisture regulation of evaporation rates, and reinforcement of water cycle anomalies through recycling. The regions and seasons that have a strong composite signal predominate in both summer and winter monsoon regions in the period after the rainy season wanes. However, there are exceptions to this pattern, most notably over the Great Plains of North America and the Pampas/Pantanal of South America, where there are signs of land–atmosphere feedback throughout most of the year. Soil moisture memory in many of these regions is long enough to suggest that real-time monitoring and accurate initialization of the land surface in forecast models could lead to improvements in medium-range weather to subseasonal climate forecasts.

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Liang Chen
,
Paul A. Dirmeyer
,
Ahmed Tawfik
, and
David M. Lawrence

Abstract

The land surface state can be an important factor in the triggering of precipitation, whose depiction in Earth system models (ESMs) crucially relies on the representation of convective initiation. However, the sensitivity of land-cover change–precipitation feedbacks to different parameterized triggering criteria in ESMs has not been examined. In this study, a new triggering mechanism based on the heated condensation framework (HCF) is implemented in the Community Earth System Model (CESM). A set of land-cover change experiments with different convective triggering conditions are performed to evaluate the influence of convective triggering on land–atmosphere coupling strength and the response of summer afternoon precipitation to land-cover change over North America. Compared with the default parameterization, which depends on a CAPE threshold, the HCF trigger shows an improvement in the diurnal timing of summer precipitation but larger dry biases over much of the study area. With the HCF trigger, CESM exhibits weakened coupling strength between soil moisture and surface turbulent fluxes over the Great Plains. The surface temperature deficit, as an additional triggering criterion in HCF, is not significantly coupled with surface fluxes over the central Great Plains despite strong latent heat–CAPE coupling. In contrast to the CAPE-trigger simulations, which indicate increased precipitation over the Great Plains after agricultural expansion, the HCF-trigger simulations show significantly increased afternoon precipitation only over the northern plains, which is mainly associated with more frequent deep convection. The discrepancies suggest caveats when investigating the impacts of land-cover change on precipitation, because the magnitude and spatial patterns of precipitation change can be greatly affected by the treatment of convection in ESMs.

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Vasubandhu Misra
,
Paul A. Dirmeyer
, and
Ben P. Kirtman

Abstract

In this paper multiple atmospheric global circulation model (AGCM) integrations at T42 spectral truncation and prescribed sea surface temperature were used to drive regional spectral model (RSM) simulations at 80-km resolution for the austral summer season (January–February–March). Relative to the AGCM, the RSM improves the ensemble mean simulation of precipitation and the lower- and upper-level tropospheric circulation over both tropical and subtropical South America and the neighboring ocean basins. It is also seen that the RSM exacerbates the dry bias over the northern tip of South America and the Nordeste region, and perpetuates the erroneous split intertropical convergence zone (ITCZ) over both the Pacific and Atlantic Ocean basins from the AGCM. The RSM at 80-km horizontal resolution is able to reasonably resolve the Altiplano plateau. This led to an improvement in the mean precipitation over the plateau. The improved resolution orography in the RSM did not substantially change the predictability of the precipitation, surface fluxes, or upper- and lower-level winds in the vicinity of the Andes Mountains from the AGCM. In spite of identical convective and land surface parameterization schemes, the diagnostic quantities, such as precipitation and surface fluxes, show significant differences in the intramodel variability over oceans and certain parts of the Amazon River basin (ARB). However, the prognostic variables of the models exhibit relatively similar model noise structures and magnitude. This suggests that the model physics are in large part responsible for the divergence of the solutions in the two models. However, the surface temperature and fluxes from the land surface scheme of the model [Simplified Simple Biosphere scheme (SSiB)] display comparable intramodel variability, except over certain parts of ARB in the two models. This suggests a certain resilience of predictability in SSiB (over the chosen domain of study) to variations in horizontal resolution. It is seen in this study that the summer precipitation over tropical and subtropical South America is highly unpredictable in both models.

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Paul A. Dirmeyer
,
Michael J. Fennessy
, and
L. Marx

Abstract

Ensemble integrations of three general circulation models (Center for Ocean–Land–Atmosphere Studies, NCAR, and NCEP) have been performed over five different boreal summer seasons (June through September of 1986–88 and 1993–94) with prescribed observed sea surface temperature to assess the predictability of seasonal climate during the boreal summer. Beyond some inconsistent initialization of soil wetness among the models, there is no land surface contribution to predictability that can be assessed. The models show a rapid degradation of skill in global terrestrial surface temperature after the first month, and no skill in precipitation over land. Potential predictability is assessed by examining in tandem the models' skill as measured by their anomaly correlation coefficients, and the models' signal-to-noise ratio (essentially interannual versus intraensemble variance) as a measure of confidence in the results. Collocation of skill in anomaly simulation and a robust signal is a strong indicator of potential predictability. Predictability of interannual climate variations is found to be low outside the deep Tropics, and nil over land. With only SST as a driving boundary condition, the poor performance of these models during summer may indicate that one must turn to the land surface in order to harvest potential predictability.

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Paul A. Dirmeyer
,
Yan Jin
,
Bohar Singh
, and
Xiaoqin Yan

Abstract

Long-term changes in land–atmosphere interactions during spring and summer are examined over North America. A suite of models from phase 5 of the Coupled Model Intercomparison Project simulating preindustrial, historical, and severe future climate change scenarios are examined for changes in soil moisture, surface fluxes, atmospheric boundary layer characteristics, and metrics of land–atmosphere coupling.

Simulations of changes from preindustrial to modern conditions show warming brings stronger surface fluxes at high latitudes, while subtropical regions of North America respond with drier conditions. There is a clear anthropogenic aerosol response in midlatitudes that reduces surface radiation and heat fluxes, leading to shallower boundary layers and lower cloud base. Over the Great Plains, the signal does not reflect a purely radiatively forced response, showing evidence that the expansion of agriculture may have offset the aerosol impacts on the surface energy and water cycle.

Future changes show soils are projected to dry across North America, even though precipitation increases north of a line that retreats poleward from spring to summer. Latent heat flux also has a north–south dipole of change, increasing north and decreasing south of a line that also moves northward with the changing season. Metrics of land–atmosphere feedback increase over most of the continent but are strongest where latent heat flux increases in the same location and season where precipitation decreases. Combined with broadly elevated cloud bases and deeper boundary layers, land–atmosphere interactions are projected to become more important in the future with possible consequences for seasonal climate prediction.

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

Abstract

The NCEP CFSv2 ensemble reforecasts initialized with different land surface analyses for the period of 1979–2010 have been conducted to assess the effect of uncertainty in land initial states on surface air temperature prediction. The two observation-based land initial states are adapted from the NCEP CFS Reanalysis (CFSR) and the NASA GLDAS-2 analysis; atmosphere, ocean, and ice initial states are identical for both reforecasts. This identical-twin experiment confirms that the prediction skill of surface air temperature is sensitive to the uncertainty of land initial states, especially in soil moisture and snow cover. There is no distinct characteristic that determines which set of the reforecasts performs better. Rather, the better performer varies with the lead week and location for each season. Estimates of soil moisture between the two land initial states are significantly different with an apparent north–south contrast for almost all seasons, causing predicted surface air temperature discrepancies between the two sets of reforecasts, particularly in regions where the magnitude of initial soil moisture difference lies in the top quintile. In boreal spring, inconsistency of snow cover between the two land initial states also plays a critical role in enhancing the discrepancy of predicted surface air temperature from week 5 to week 8. Our results suggest that a reduction of the uncertainty in land surface properties among the current land surface analyses will be beneficial to improving the prediction skill of surface air temperature on subseasonal time scales. Implications of a multiple land surface analysis ensemble are also discussed.

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Yongkang Xue
,
Heidi G. Bastable
,
Paul A. Dirmeyer
, and
Piers J. Sellers

Abstract

The simplified Simple Biosphere model (SSiB) has been validated using observed meteorological, turbulent flux, and vegetation property data from the Anglo-Brazilian Amazonian Climate Observation Study (ABRACOS) over a forest clearing site. The results show that SSiB is able to simulate the observed fluxes realistically. The differences between the simulated and observed latent and sensible heat fluxes are less than 10 W m−2. Compared to previous deforestation experiments, the new vegetation dataset produces significantly different latent heat fluxes and surface temperatures in off-line and general circulation model (GCM) simulation. Using the new dataset the GCM simulated surface temperature is about 2 K higher, and the simulated latent heat flux is about 25 W m−2 lower than that generated using a previous dataset. These differences can be expected to result in substantially different responses in rainfall and atmosphere circulation. The parameters that are most significant in producing such large differences are leaf area index and soil properties. This study again demonstrates that to realistically assess the climatic impact of land surface degradation a realistic specification of the land surface conditions within GCMs is crucial.

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