Search Results

You are looking at 21 - 30 of 81 items for

  • Author or Editor: Paul Dirmeyer x
  • Refine by Access: All Content x
Clear All Modify Search
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.

Full access
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.

Full access
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.

Restricted access
Paul A. Dirmeyer
,
Timothy DelSole
, and
Mei Zhao

Abstract

Empirical correction is applied to wind, temperature, and soil moisture fields in a climate model to assess its impact on simulation of the water cycle during boreal summer. The empirical correction method is based on the biases in model forecasts only as a function of the time of year. Corrections are applied to the prognostic equations as an extra nudging term. Mean fields of evaporation, precipitation, moisture transport, and recycling ratio are all improved, even though humidity fields were not corrected. Simulation of the patterns of surface evaporation supplying rainfall at locations over land is also improved for most locations. There is also improvement in the simulation of evaporation and possibly rainfall, as measured by anomaly correlation coefficients and root-mean-square errors of the time series of monthly anomalies. However, monthly anomalies of other water cycle fields such as moisture transport and recycling ratio were not improved. Like any statistical adjustment, empirical correction does not address the cause of model errors, but it does provide a net improvement to the simulation of the water cycle. It can, however, be used to diagnose the sources of error in the model. Since corrections are only applied to prognostic variables, shortcomings due to physical parameterizations in the model are not remedied.

Full access
Oreste Reale
,
Paul Dirmeyer
, and
Adam Schlosser

Abstract

This is the second of a two-part article investigating the impact of variations of land surface evaporability on the interannual variability of precipitation. The first goal of this part is to analyze the relationship between the atmospheric internal variability and the evaporative forcings. The hypothesis that the sum of ocean- and atmosphere-induced variabilities can be linearly amplified by the land variability is critically revisited and generally found not applicable to the climate model used and the numerical experiments conducted. A set of parameters to evaluate the departure from the linear behavior is defined, quantifying the impact of the different forcings over the total variability. Some areas of the world (e.g., the monsoon region, the continental United States, and southeastern Africa), where the impact of internal atmospheric dynamics on precipitation variability is small compared to the impact of the evaporative forcings, are localized. Over these areas, the variability of precipitation might be more predictable, given a good knowledge of the surface boundary forcings.

In the second half of this article the time structure of the land forcing is analyzed, to quantify the contributions of the interannual variations, diurnal cycle, and high-frequency (i.e., synoptic scale) variations and compare them with the contribution of the oceanic forcing. The general conclusion is that interannual variability of both sea surface temperature and land evaporability is very important to the overall variability of precipitation over the Tropics. Over land in the subtropics and midlatitudes equatorward of the polar front there are also substantial feedbacks at the interannual scale. The impact of synoptic-scale variations of land evaporability is generally smaller, except for some areas in the midlatitudes near the polar front, particularly continental Eurasia and parts of North America. Finally, there is no general, widespread evidence showing the importance of the diurnal cycle of evaporability to the interannual variability of precipitation. However, strong regional differences are detected, and some tropical areas, like the Congo basin, where the diurnal cycle does contribute to the interannual variability of precipitation are outlined.

Full 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.

Full access
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.

Full access
Paul A. Dirmeyer
and
James L. Kinter III

Abstract

The characteristics of situations of extremely high rainfall over the midwestern region of the United States during late spring and summer are investigated from the perspective of the regional water cycle using observations and observationally based analyses. The period of May–July has the greatest mean rainfall rates of the year and higher interannual variability than the periods either before or after. This is also a critical time of year for water resources and cultivation schedules in this agriculturally important region. Large-scale floods during this time of year are usually characterized by an enhanced source of moisture evaporating from low latitudes, specifically the Caribbean Sea. This is part of a fetch of moisture that extends from the Caribbean northward along the coast of Central America, over the Yucatan Peninsula, along the east coast of Mexico and the western Gulf of Mexico, and over Texas, where it links into the Great Plains low-level jet. In fact, heavy rainfall over much of the eastern half of the United States is associated with above-average Caribbean moisture supply. There is also indication of an enhanced source of moisture from the subtropical Pacific during Midwest flood events. Drought events appear to have a different spatial pattern of water cycle variables and circulation anomalies, and are not simply equal and opposite manifestations of flood events. While not a dominant source of moisture even during extreme events, the Caribbean region seems to be part of an important link for remote moisture, supplying floods over the Midwest.

Full access
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
,
Zhichang Guo
, and
Xiang Gao

Abstract

The characteristics of eight global soil wetness products, three produced by land surface model calculations, three from coupled land–atmosphere model reanalyses, and two from microwave remote sensing estimates, have been examined. The goal of this study is to determine whether there exists an optimal dataset for the initialization of the land surface component of global weather and climate forecast models. Their abilities to simulate the phasing of the annual cycle and to accurately represent interannual variability in soil wetness by comparing to available in situ measurements are validated. Because soil wetness climatologies vary greatly among land surface models, and models have different operating ranges for soil wetness (i.e., very different mean values, variances, and hydrologically critical thresholds such as the point where evaporation occurs at the potential rate or where surface runoff begins), one cannot simply take the soil wetness field from one product and apply it to an arbitrary land surface scheme (LSS) as an initial condition without experiencing some sort of initialization shock. A means of renormalizing soil wetness is proposed based on the local statistical properties of this field in the source and target models, to allow a large number of climate models to apply the same initialization in multimodel studies or intercomparisons. As a test of feasibility, renormalization among the model-derived products is applied to see how it alters the character of the soil wetness climatologies.

Full access