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  • Author or Editor: Paul A. Dirmeyer x
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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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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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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
Subhadeep Halder

Abstract

When initial soil moisture is perturbed among ensemble members in the operational NWS global forecast model, surface latent and sensible fluxes are immediately affected much more strongly, systematically, and over a greater area than conventional land–atmosphere coupling metrics suggest. Flux perturbations are likewise transmitted to the atmospheric boundary layer more formidably than climatology-based metrics would indicate. Impacts are not limited to the traditional land–atmosphere coupling hot spots, but extend over nearly all ice-free land areas of the globe. Key to isolating this effect is that initial atmospheric states are identical among quantities correlated, pinpointing soil moisture and snow cover. A consequence of this high sensitivity is that significant positive impacts of realistic land surface initialization on the skill of deterministic near-surface temperature and humidity forecasts are also immediate and nearly universal during boreal spring and summer (the period investigated) and persist for at least 3 days over most land areas. Land surface initialization may be more broadly important for weather forecasts than previously realized, as the research focus historically has been on subseasonal-to-seasonal time scales. This study attempts to bridge the gap between climate studies with their associated coupling assessments and weather forecast time scales. Furthermore, errors in land surface initialization and shortcomings in the parameterization of atmospheric processes sensitive to surface fluxes may have greater consequences than previously recognized, the latter exemplified by the lack of impact on precipitation forecasts even though the simulation of boundary layer development is shown to be greatly improved with realistic soil moisture initialization.

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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.

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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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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.

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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.

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Finley M. Hay-Chapman
and
Paul A. Dirmeyer

Abstract

The response of boundary layer properties and cloudiness to changes in surface evaporative fraction (EF) is investigated in a single-column model to quantify the locally coupled impact of subgrid surface variations on the atmosphere during summer. Sensitive coupling days are defined when the model atmosphere exhibits large variations across a range of EFs centered on the analyzed value. Coupling sensitivity exists as both positive feedback (cloudiness increases with EF) and negative feedback (clouds increase with decreasing EF) regimes. The positive regime manifests in shallow convection situations, which are capped by a strengthened inversion and subsidence, restricting the vertical extent of convection to just above the boundary layer. Surfaces with larger EF (greater surface latent heat flux) can inject more moisture into the vertically confined system, lowering the cloud base and an increasing cloud liquid water path (LWP). Negative feedback regimes tend to manifest when large-scale deep convection, such as from mesoscale convective systems and fronts, is advected through the domain, where convection strengthens over surfaces with a lower EF (greater surface sensible heat flux). The invigoration of these systems by the land surface leads to an increase in LWP through strengthened updrafts and stronger coupling between the boundary layer and the free atmosphere. These results apply in the absence of heterogeneity-induced mesoscale circulations, providing a one-dimensional dynamical perspective on the effect of surface heterogeneity. This study provides a framework of intermediate complexity, lying between parcel theory and high-resolution coupled land–atmosphere modeling, and therefore isolates the relevant first-order processes in land–atmosphere interactions.

Significance Statement

Cloud formation, distribution, and other properties may be sensitive to heterogeneous surfaces depending on the strength and location of such heterogeneities and the background atmospheric state. This may drive differences in the cloud population depending on which part of the domain one is located. This may also lead to mesoscale circulations, which may strengthen or weaken this effect. Currently, climate models act on scales (∼100 km) that are too large to explicitly represent these processes, which are strongest at smaller scales (around 5–40 km). Therefore, subgrid-scale heterogeneity is neglected, and any predictability and model fidelity it may provide is lost. We use a simple model to diagnose sensitivity of the local atmosphere to surface variations meant to represent possible subgrid heterogeneity, providing a first-order estimate of its effect. We conclude that preferentially sensitive atmospheric states exist that lead to positive and/or negative feedback between land and atmosphere. This information is valuable to future climate model parameterizations aimed at improving the representation of these feedbacks.

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Ahmed B. Tawfik
,
Paul A. Dirmeyer
, and
Joseph A. Santanello Jr.

Abstract

This study extends the heated condensation framework (HCF) presented in Tawfik and Dirmeyer to include variables for describing the convective background state of the atmosphere used to quantify the contribution of the atmosphere to convective initiation within the context of land–atmosphere coupling. In particular, the ability for the full suite of HCF variables to 1) quantify the amount of latent and sensible heat energy necessary for convective initiation, 2) identify the transition from moistening advantage to boundary layer growth advantage, 3) identify locally originating convection, and 4) compare models and observations, directly highlighting biases in the convective state, is demonstrated. These capabilities are illustrated for a clear-sky and convectively active day over the Atmospheric Radiation Measurement Program Southern Great Plains central station using observations, the Rapid Update Cycle (RUC) operational model, and the North American Regional Reanalysis (NARR). The clear-sky day had a higher and unattainable convective threshold, making convective initiation unlikely. The convectively active day had a lower threshold that was attained by midafternoon, reflecting local convective triggering. Compared to observations, RUC tended to have the most difficulty representing the convective state and captured the threshold for the clear-sky case only because of compensating biases in the moisture and temperature profiles. Despite capturing the observed moisture profile very well, a stronger surface inversion in NARR returned overestimates in the convective threshold. The companion paper applies the HCF variables introduced here across the continental United States to examine the climatological behavior of convective initiation and local land–atmosphere coupling.

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