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Timothy DelSole
,
Mei Zhao
, and
Paul Dirmeyer

Abstract

This paper proposes a new method for investigating coupled land–atmosphere interactions. The method is to apply an empirical correction technique to distinct components of a model and then examine differences between forecasts of the empirically corrected models. The correction technique is based on adding a time-dependent term to the tendency equations that subtracts the estimated tendency error at every time step. This methodology can be interpreted more generally as a series of data assimilation experiments in which only certain components of a coupled model are assimilated at a time. The correction is applied to a state-of-the-art coupled land–atmosphere model in three different ways, namely, to the atmosphere only, to the land only, and to the land and atmosphere simultaneously. The land–atmosphere interactions are inferred from monthly-mean differences between experiments. The results suggest that the land–atmosphere coupling in midlatitudes can be understood from straightforward water balance considerations, whereas the coupling in the deep tropics involves a more complicated change in regional circulation. Specifically, in midlatitudes, moisture injected into the soil is transferred to the atmosphere directly above, which in turn advects downstream and subsequently moistens the atmosphere in the downwind regions to produce positive precipitation anomalies. In the deep tropics, the regional circulation, including precipitation, is sensitive to perturbations and has no obvious relation to corrections in the atmosphere or land. The similarity of biases among different models suggests that the conclusions and methodology may be relevant to other models.

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Zhichang Guo
and
Paul A. Dirmeyer

Abstract

Recent studies in the Global Land–Atmosphere Coupling Experiment (GLACE) established a framework to estimate the extent to which anomalies in the land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. Within this framework, a multiyear GLACE-type experiment is carried out with a coupled land–atmosphere general circulation model to examine the interannual variability of land–atmosphere coupling strength. Soil wetness with intermediate values are in the range at which rainfall generation, near-surface air temperature, and surface turbulent fluxes are most sensitive to soil moisture anomalies, and thus, land–atmosphere coupling strength peaks in this range. As a result, the “hot spots” with strong land–atmosphere coupling strength appear in regions with intermediate climatological soil wetness (e.g., transition zones between dry and wet climates), consistent with previous studies. Land–atmosphere coupling strength experiences significant year-to-year variation because of interannual variability of soil moisture and the local spatiotemporal evolution of hydrologic regime. Coupling strength over areas with dry (wet) climate is enhanced during wet (dry) years since the resultant soil wetness enters into the sensitive range from a relatively insensitive range, and soil moisture can have stronger potential impact on surface turbulent fluxes and convection. On the other hand, land–atmosphere coupling strength over areas with wet (dry) climate is weakened during wet (dry) years since the soil wetness moves further away from the sensitive range. This results in a positive correlation between the land–atmosphere coupling strength and soil moisture anomalies over areas with dry climate and a negative correlation over areas with wet climate.

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Paul A. Dirmeyer
and
Mei Zhao

Abstract

The potential role of the land surface state in improving predictions of seasonal climate is investigated with a coupled land–atmosphere climate model. Climate simulations for 18 boreal-summer seasons (1982–99) have been conducted with specified observed sea surface temperature (SST). The impact on prediction skill of the initial land surface state (interannually varying versus climatological soil wetness) and the effect of errors in downward surface fluxes (precipitation and longwave/shortwave radiation) over land are investigated with a number of parallel experiments. Flux errors are addressed by replacing the downward fluxes with observed values in various combinations to ascertain the separate roles of water and energy flux errors on land surface state variables, upward water and energy fluxes from the land surface, and the important climate variables of precipitation and near-surface air temperature.

Large systematic errors are found in the model, which are only mildly alleviated by the specification of realistic initial soil wetness. The model shows little skill in simulating seasonal anomalies of precipitation, but it does have skill in simulating temperature variations. Replacement of the downward surface fluxes has a clear positive impact on systematic errors, suggesting that the land–atmosphere feedback is helping to exacerbate climate drift. Improvement in the simulation of year-to-year variations in climate is even more evident. With flux replacement, the climate model simulates temperature anomalies with considerable skill over nearly all land areas, and a large fraction of the globe shows significant skill in the simulation of precipitation anomalies. This suggests that the land surface can communicate climate anomalies back to the atmosphere, given proper meteorological forcing. Flux substitution appears to have the largest benefit to improving precipitation skill over the Northern Hemisphere midlatitudes, whereas use of realistic land surface initial conditions improves skill to significant levels over regions of the Southern Hemisphere. Correlations between sets of integrations show that the model has a robust and systematic global response to SST anomalies.

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Eunkyo Seo
and
Paul A. Dirmeyer

Abstract

Models have historically been the source of global soil moisture (SM) analyses and estimates of land–atmosphere coupling, even though they are usually calibrated and validated only locally. Satellite-based analyses have grown in fidelity and duration, offering an independent observationally based alternative. However, satellite-retrieved SM time series include random and periodic errors that degrade estimates of land–atmosphere coupling, including correlations with other variables. This study proposes a mathematical approach to adjust daily time series of the European Space Agency (ESA) Climate Change Initiative (CCI) satellite SM product using information from physically based land surface model (LSM) datasets using a Fourier transform time-filtering method to match the temporal power spectra locally to the LSMs, which tend to agree well with in situ observations. When the original and timely adjusted SM products are evaluated against ground-based SM measurements over the conterminous United States, Europe, and Australia, results show the adjusted SM has significantly improved subseasonal variability. The skill of the adjusted SM is increased in temporal correlation by ∼0.05 over all analysis domains without introducing spurious regional patterns, affirming the stochastic nature of noise in satellite estimates, and skill improvement is found for nearly all land cover classes, especially savannas and grassland. Autocorrelation-based soil moisture memory (SMM) and the derived random component of soil moisture error (SME) are used to investigate the improvement of SM features. The time filtering reduces the random noise from the satellite-based SM product that is not explainable by physically based SM dynamics; SME is usually diminished and the increased SMM is generally statistically significant.

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Hsin Hsu
and
Paul A. Dirmeyer

Abstract

The control of latent heat flux (LE) by soil moisture (SM) is a key process affecting the moisture and energy budgets at the land–atmosphere interface. SM–LE coupling relationships are conventionally examined using metrics involving temporal correlation. However, such a traditional linear approach, which fits a straight line across the full SM–LE space to evaluate the dependency, leaves out certain critical information: nonlinear SM–LE relationships and the long-recognized thresholds that lead to dramatically different behavior in different ranges of soil moisture, delineating a dry regime, a transitional regime of high sensitivity, and a wet (energy-limited) regime. Using data from climate models, reanalyses, and observationally constrained datasets, global patterns of SM–LE regimes are determined by segmented regression. Mutual information analysis is applied only for days when SM is in the transitional regime between critical points defining high sensitivity of LE to SM variations. Sensitivity is further decomposed into linear and nonlinear components. Results show discrepancies in the global patterns of existing SM regimes, but general consistencies among the linear and nonlinear components of SM–LE coupling. This implies that although models simulate differing surface hydroclimates, once SM is in the transitional regime, the locations where LE closely interacts with SM are well captured and resemble the conventional distribution of “hotspots” of land–atmosphere interactions. This indicates that only the transitional SM regime determines the strength of coupling, and attention should focused on when this regime occurs. This framework can also be applied to investigate extremes and the shifting surface hydroclimatology in a warming climate.

Significance Statement

Evaporation is sensitive to soil moisture only within a specific range that is neither too dry nor too wet. This transitional regime is examined to quantify how strongly soil moisture controls local evaporation. We identify the dry, transitional, and wet regimes across the globe, and the locations where each regime is experienced; the spatial patterns among climate models and observationally based datasets often show discrepancies. When we determine dependencies between soil moisture and evaporation only within the transitional regime, we find general consistency of locations having simple linear dependencies versus more complex nonlinear relationships. We conclude that although surface hydroclimates differ between climate models and observations, the locations where soil moisture can control evaporation are well captured. These results have potential application for improved forecasting and climate change assessment.

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Abedeh Abdolghafoorian
and
Paul A. Dirmeyer

Abstract

The interactions between land and atmosphere (with terrestrial and atmospheric coupling segments) play a significant role in weather and climate. A predominant segment of land–atmosphere (LA) feedbacks is the coupling between soil moisture (SM) and surface heat fluxes, the terrestrial coupling leg. The lack of high-quality, long-term, globally distributed observations, however, has hindered a robust, realistic identification of the terrestrial leg strength on a global scale. This exploratory study provides insight into how SM signals are translated into surface flux signals through the construction of a global depiction of the terrestrial leg from several recently developed global, gridded, observationally and satellite-based datasets. The feasibility of producing global gridded estimates of LA coupling metrics is explored. Five weather and climate models used for subseasonal to seasonal forecasting are confronted with the observational estimates to discern discrepancies that may affect their ability to predict phenomena related to LA feedbacks, such as drought or heat waves. The terrestrial feedback leg from observations corroborates the “hot spots” of LA coupling found in modeling studies, but the variances in daily time series of surface fluxes differ markedly. Better agreement and generally higher confidence are seen in metrics using latent heat flux than sensible heat flux. Observational metrics allow for clear stratification of model fidelity that is consistent across seasons, despite observational uncertainty. The results highlight the impact of SM on partitioning available surface energy and illustrate the potential of global observationally based datasets for the assessment of such relationships in weather and climate models.

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Paul A. Dirmeyer
and
Trent W. Ford

Abstract

Seamless prediction means bridging discrete short-term weather forecasts valid at a specific time and time-averaged forecasts at longer periods. Subseasonal predictions span this time range and must contend with this transition. Seamless forecasts and seamless validation methods go hand-in-hand. Time-averaged forecasts often feature a verification window that widens in time with growing forecast leads. Ideally, a smooth transition across daily to monthly time scales would provide true seamlessness—a generalized approach is presented here to accomplish this. We discuss prior attempts to achieve this transition with individual weighting functions before presenting the two-parameter Hill equation as a general weighting function to blend discrete and time-averaged forecasts, achieving seamlessness. The Hill equation can be tuned to specify the lead time at which the discrete forecast loses dominance to time-averaged forecasts, as well as the swiftness of the transition with lead time. For this application, discrete forecasts are defined at any lead time using a Kronecker delta weighting, and any time-averaged weighting approach can be used at longer leads. Time-averaged weighting functions whose averaging window widens with lead time are used. Example applications are shown for deterministic and ensemble forecasts and validation and a variety of validation metrics, along with sensitivities to parameter choices and a discussion of caveats. This technique aims to counterbalance the natural increase in uncertainty with forecast lead. It is not meant to construct forecasts with the highest skill, but to construct forecasts with the highest utility across time scales from weather to subseasonal in a single seamless product.

Open access
Paul A. Dirmeyer
and
Kaye L. Brubaker

Abstract

Regional precipitation recycling may constitute a feedback mechanism affecting soil moisture memory and the persistence of anomalously dry or wet states. Bulk methods, which estimate recycling based on time-averaged variables, have been applied on a global basis, but these methods may underestimate recycling by neglecting the effects of correlated transients. A back-trajectory method identifies the evaporative sources of vapor contributing to precipitation events by tracing air motion backward in time through the analysis grid of a data-assimilating numerical model. The back-trajectory method has been applied to several large regions; in this paper it is extended to all global land areas for 1979–2003. Meteorological information (wind vectors, humidity, surface pressure, and evaporation) are taken from the NCEP–Department of Energy (DOE) reanalysis, and a hybrid 3-hourly precipitation dataset is produced to establish the termini of the trajectories. The effect of grid size on the recycling fraction is removed using an empirical power-law relationship; this allows comparison among any land areas on a latitude–longitude grid. Recycling ratios are computed on a monthly basis for a 25-yr period. The annual and seasonal averages are consistent with previous estimates in terms of spatial patterns, but the trajectory method generally gives higher estimates of recycling than a bulk method, using compatible spatial scales. High northern latitude regions show the largest amplitude in the annual cycle of recycling, with maxima in late spring/early summer. Amplitudes in arid regions are small in absolute terms, but large relative to their mean values. Regions with strong interannual variability in recycling do not correspond directly to regions with strong intra-annual variability. The average recycling ratio at a spatial scale of 105 km2 for all land areas of the globe is 4.5%; on a global basis, recycling shows a weak positive trend over the 25 yr, driven largely by increases at high northern latitudes.

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Abedeh Abdolghafoorian
and
Paul A. Dirmeyer

Abstract

Land states can affect the atmosphere through their control of surface turbulent fluxes and the subsequent impact of those fluxes on boundary layer properties. Information theoretic (IT) metrics are ideal to study the strength and type of coupling between surface soil moisture (SM) and land surface heat fluxes (HFs) because they are nonparametric and thus appropriate for the analysis of highly complex Earth systems containing nonlinear cause-and-effect interactions that may have nonnormal distributions. Specifically, a methodology for the estimation of IT metrics from noisy time series is proposed, accounting for random errors in satellite-based SM data. Performance of the proposed method is demonstrated through synthetic tests. Efficacy of the method is greatest for estimates of entropy and mutual information involving SM; improvements to estimates of transfer entropy are significant but less stark. A global depiction of the information flow between SM and HFs is then constructed from observationally based gridded data. This is used as independent verification for two configurations of the ECMWF modeling system: unconstrained open-loop (retrospective forecasts) and constrained by data assimilation (ERA5). Compared to studies that only investigate the linear SM–HF relationships, extended regions of significant terrestrial coupling are found over the globe, as IT metrics enable detection of nonlinear dependencies. The magnitude and spatial variability of coupling strength and type from models show discrepancies with those from observations, highlighting the potential to improve SM and HF covariability within models. Although ERA5 did not perform better than the unconstrained model in very dry climates, its performance is generally superior to that of the unconstrained model across metrics.

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Jiangfeng Wei
,
Paul A. Dirmeyer
, and
Zhichang Guo

Abstract

The Global Land–Atmosphere Coupling Experiment (GLACE) built a framework to estimate the strength of the land–atmosphere interaction across many weather and climate models. Within this framework, GLACE-type experiments are performed with a single atmospheric model coupled to three different land models. The precipitation time series is decomposed into three frequency bands to investigate the large-scale connection between external forcing, precipitation variability and predictability, and land–atmosphere coupling strength. It is found that coupling to different land models or prescribing subsurface soil moisture does not change the global pattern of precipitation predictability and variability too much. However, the regional impact of soil moisture can be highlighted by calculating the land–atmosphere coupling strength, which shows very different patterns for the three models. The estimated precipitation predictability and land–atmosphere coupling strength is mainly associated with the low-frequency component of precipitation (periods beyond 3 weeks). Based on these findings, the land–atmosphere coupling strength is conceptually decomposed into the impact of low-frequency external forcing and the impact of soil moisture. Because most models participating in GLACE have overestimated the low-frequency component of precipitation, a calibration to the GLACE-estimated land–atmosphere coupling strength is performed. The calibrated coupling strength is generally weaker, but the global pattern does not change much. This study provides an important clarification of land–atmosphere coupling strength and increases the understanding of the land–atmosphere interaction.

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