Search Results

You are looking at 31 - 40 of 77 items for

  • Author or Editor: Paul A. Dirmeyer x
  • Refine by Access: All Content x
Clear All Modify Search
Ahmed B. Tawfik
,
Paul A. Dirmeyer
, and
Joseph A. Santanello Jr.

Abstract

This is Part II of a two-part study introducing the heated condensation framework (HCF), which quantifies the potential convective state of the atmosphere in terms of land–atmosphere interactions. Part I introduced the full suite of HCF variables and applied them to case studies with observations and models over a single location in the southern Great Plains. It was shown in Part I that the HCF was capable of identifying locally initiated convection and quantifying energetically favorable pathways for initiation. Here, the HCF is applied to the entire conterminous United States and the climatology of convective initiation (CI) in relation to local land–atmosphere coupling (LoCo) is explored for 34 summers (June–August) using the North American Regional Reanalysis (NARR) and observations. NARR is found to be capable of capturing the convective threshold (buoyant mixing potential temperature θ BM) and energy advantage transition (energy advantage potential temperature θ adv) for most of the United States. However, there are compensating biases in the components of moisture q mix and temperature q*, resulting in low θ BM biases for the wrong reason. The HCF has been used to show that local CI occurred over the Rocky Mountains and the southern Great Plains 35%–65% of the time. Finally, the LoCo process chain has been recast in light of the HCF. Both positive and negative soil moisture–convective feedbacks are possible, with negative feedbacks producing a stronger response in CI likelihood under weak convective inhibition. Positive feedbacks are present but weaker.

Full access
Joseph A. Santanello Jr.
,
Joshua Roundy
, and
Paul A. Dirmeyer

Abstract

The coupling of the land with the planetary boundary layer (PBL) on diurnal time scales is critical to regulating the strength of the connection between soil moisture and precipitation. To improve understanding of land–atmosphere (L–A) interactions, recent studies have focused on the development of diagnostics to quantify the strength and accuracy of the land–PBL coupling at the process level. In this paper, the authors apply a suite of local land–atmosphere coupling (LoCo) metrics to modern reanalysis (RA) products and observations during a 17-yr period over the U.S. southern Great Plains. Specifically, a range of diagnostics exploring the links between soil moisture, evaporation, PBL height, temperature, humidity, and precipitation is applied to the summertime monthly mean diurnal cycles of the North American Regional Reanalysis (NARR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR). Results show that CFSR is the driest and MERRA the wettest of the three RAs in terms of overall surface–PBL coupling. When compared against observations, CFSR has a significant dry bias that impacts all components of the land–PBL system. CFSR and NARR are more similar in terms of PBL dynamics and response to dry and wet extremes, while MERRA is more constrained in terms of evaporation and PBL variability. Each RA has a unique land–PBL coupling that has implications for downstream impacts on the diurnal cycle of PBL evolution, clouds, convection, and precipitation as well as representation of extremes and drought. As a result, caution should be used when treating RAs as truth in terms of their water and energy cycle processes.

Full access
Paul A. Dirmeyer
,
A. J. Dolman
, and
Nobuo Sato

The Global Soil Wetness Project (GSWP) is an ongoing land surface modeling activity of the International Satellite Land-Surface Climatology Project (ISLSCP), a part of the Global Energy and Water Cycle Experiment. The pilot phase of GSWP deals with the production of a two-year global dataset of soil moisture, temperature, runoff, and surface fluxes by integrating uncoupled land surface schemes (LSSs) using externally specified surface forcings from observations and standardized soil and vegetation distributions. Approximately one dozen participating LSS groups in five nations have taken the common ISLSCP forcing data to drive their state-of-the-art models over the 1987–88 period to generate global datasets. Many of the LSS groups have performed specific sensitivity studies, which are intended to evaluate the impact of uncertainties in model parameters and forcing fields on simulation of the surface water and energy balances. A validation effort exists to compare the global products to other forms of estimation and measurement, either directly (by comparison to field studies or soil moisture measuring networks) or indirectly (e.g., use of modeled runoff to drive river routing schemes for comparison to streamflow data). The soil wetness data produced are also being tested within general circulation models to evaluate their quality and their impact on seasonal to interannual climate simulations. An Inter-Comparison Center has also been established for evaluating and comparing data from the different LSSs. Comparison among the model results is used to assess the uncertainty in estimates of surface components of the moisture and energy balances at large scales and as a quality check on the model products themselves.

Full access
Xiang Gao
,
Paul A. Dirmeyer
,
Zhichang Guo
, and
Mei Zhao

Abstract

A coupled land–atmosphere climate model is used to investigate the impact of vegetation parameters (leaf area index, absorbed radiation, and greenness fraction) on the simulation of surface fluxes and their potential role in improving climate forecasts. Ensemble simulations for 1986–95 have been conducted with specified observed sea surface temperatures. The vegetation impact is analyzed by comparing integrations with two different ways of specifying vegetation boundary conditions: observed interannually varying vegetation versus the climatological annual cycle. Parallel integrations are also implemented and analyzed for the land surface model in an uncoupled mode within the framework of the Second Global Soil Wetness Project (GSWP-2) for the same period. The sensitivity to vegetation anomalies in the coupled simulations appears to be relatively small. There appears to be only episodic and localized favorable impacts of vegetation variations on the skill of precipitation and temperature simulations. Impacts are sometimes manifested strictly through changes in land surface fluxes, and in other cases involve clear interactions with atmospheric processes. In general, interannual variations of vegetation tend to increase the temporal variability of radiation fluxes, soil evaporation, and canopy interception loss in terms of both spatial frequency and global mean. Over cohesive regions of significant and persistent vegetation anomalies, cumulative statistics do show a net response of surface fluxes, temperature, and precipitation with vegetation anomalies of ±20% corresponding to a precipitation response of about ±6%. However, in about half of these cases no significant response was found. The results presented here suggest that vegetation may be a useful element of the land surface for enhancing seasonal predictability, but its role in this model appears to be relatively minor. Improvement does not occur in all circumstances, and strong anomalies have the best chance of a positive impact on the simulation.

Full access
Eunkyo Seo
,
Paul A. Dirmeyer
,
Michael Barlage
,
Heiln Wei
, and
Michael Ek

Abstract

This study investigates the performance of the latter NCEP Unified Forecast System (UFS) Coupled Model prototype simulations (P5–P8) during boreal summer 2011–17 in regard to coupled land–atmosphere processes and their effect on model bias. Major land physics updates were implemented during the course of model development. Namely, the Noah land surface model was replaced with Noah-MP and the global vegetation dataset was updated starting with P7. These changes occurred along with many other UFS improvements. This study investigates UFS’s ability to simulate observed surface conditions in 35-day predictions based on the fidelity of model land surface processes. Several land surface states and fluxes are evaluated against flux tower observations across the globe, and segmented coupling processes are also diagnosed using process-based multivariate metrics. Near-surface meteorological variables generally improve, especially surface air temperature, and the land–atmosphere coupling metrics better represent the observed covariance between surface soil moisture and surface fluxes of moisture and radiation. Moreover, this study finds that temperature biases over the contiguous United States are connected to the model’s ability to simulate the different balances of coupled processes between water-limited and energy-limited regions. Sensitivity to land initial conditions is also implicated as a source of forecast error. Above all, this study presents a blueprint for the validation of coupled land–atmosphere behavior in forecast models, which is a crucial model development task to assure forecast fidelity from day one through subseasonal time scales.

Restricted access
Paul A. Dirmeyer
,
Randal D. Koster
, and
Zhichang Guo

Abstract

The Global Energy and Water Cycle Experiment/Climate Variability and Predictability (GEWEX/CLIVAR) Global Land–Atmosphere Coupling Experiment (GLACE) has provided an estimate of the global distribution of land–atmosphere coupling strength during boreal summer based on the results from a dozen weather and climate models. However, there is a great deal of variation among models, attributable to a range of sensitivities in the simulation of both the terrestrial and atmospheric branches of the hydrologic cycle. It remains an open question whether any of the models, or the multimodel estimate, reflects the actual pattern and strength of land–atmosphere coupling in the earth’s hydrologic cycle. The authors attempt to diagnose this by examining the local covariability of key atmospheric and land surface variables both in models and in those few locations where comparable, relatively complete, long-term measurements exist. Most models do not encompass well the observed relationships between surface and atmospheric state variables and fluxes, suggesting that these models do not represent land–atmosphere coupling correctly. Specifically, there is evidence that systematic biases in near-surface temperature and humidity among all models may contribute to incorrect surface flux sensitivities. However, the multimodel mean generally validates better than most or all of the individual models. Regional precipitation behavior (lagged autocorrelation and predisposition toward maintenance of extremes) between models and observations is also compared. Again a great deal of variation is found among the participating models, but remarkably accurate behavior of the multimodel mean.

Full access
Timothy DelSole
,
Mei Zhao
,
Paul A. Dirmeyer
, and
Ben P. Kirtman

Abstract

This paper investigates empirical strategies for correcting the bias of a coupled land–atmosphere model and tests the hypothesis that a bias correction can improve the skill of such models. The correction strategies investigated include 1) relaxation methods, 2) nudging based on long-term biases, and 3) nudging based on tendency errors. The last method involves estimating the tendency errors of prognostic variables based on short forecasts—say lead times of 24 h or less—and then subtracting the climatological mean value of the tendency errors at every time step. By almost any measure, the best correction strategy is found to be nudging based on tendency errors. This method significantly reduces biases in the long-term forecasts of temperature and soil moisture, and preserves the variance of the forecast field, unlike relaxation methods. Tendency errors estimated from ten 1-day forecasts produced just as effective corrections as tendency errors estimated from all days in a month, implying that the method is trivial to implement by modern standards. Disappointingly, none of the methods investigated consistently improved the random error variance of the model, although this finding may be model dependent. Nevertheless, the empirical correction method is argued to be worthwhile even if it improves only the bias, because the method has only marginal impacts on the numerical speed and represents forecast error in the form of a tendency error that can be compared directly to other terms in the tendency equations, which in turn provides clues as to the source of the forecast error.

Full access
Chul-Su Shin
,
Paul A. Dirmeyer
, and
Bohua Huang

Abstract

Normalized mutual information (NMI) is a nonparametric measure of the dependence between two variables without assumptions about the shape of their bivariate data distributions, but the implementation and interpretation of NMI in the coupled climate system is more complicated than for linear correlations. This study presents a joint approach combining correlation and NMI to examine land and ocean surface forcing of U.S. drought at varying lead times. Based on the distribution of correlation versus NMI between a source variable (local or remote forcing) and target variable [e.g., summer precipitation in the southern Great Plains (SGP)], newly proposed one-tail significance levels for NMI combined with two-tailed significance levels of correlation enable us to discern linearity and nonlinearity dominant regimes in a more intuitive way. Our analysis finds that NMI can detect strong linear relationships like correlations, but it is not exclusively tuned to linear relationships as correlations are. Also, NMI can further identify nonlinear relationships, particularly when there are clusters and blank areas (high density and low density) in joint probability distributions between source and target variables (e.g., detected between soil moisture conditions in eastern Montana from mid-February to mid-August and summer precipitation in the SGP). The linear and nonlinear information are found to be sometimes mixed and rather convoluted with time, for instance, in the subtropical Pacific of the Southern Hemisphere, revealing relationships that cannot be fully detected by either NMI or correlation alone. Therefore, this joint approach is a potentially powerful tool to reveal complex and heretofore undetected relationships.

Free access
Stefano Materia
,
Paul A. Dirmeyer
,
Zhichang Guo
,
Andrea Alessandri
, and
Antonio Navarra

Abstract

The discharge of freshwater into oceans represents a fundamental process in the global climate system, and this flux is taken into account in simulations with general circulation models (GCMs). Moreover, the availability of realistic river routing schemes is a powerful instrument to assess the validity of land surface components, which have been recognized to be crucial for the global climate simulation. In this study, surface and subsurface runoff generated by the 13 land surface schemes (LSSs) participating in the Second Global Soil Wetness Project (GSWP-2) are used as input fields for the Hydrology Discharge (HD) routing model to simulate discharge for 30 of the world’s largest rivers. The simplest land surface models do not provide a good representation of runoff, and routed river flows using these inputs are affected by many biases. On the other hand, HD shows the best simulations when forced by two of the more sophisticated schemes. The multimodel ensemble GSWP-2 generates the best phasing of the annual cycle as well as a good representation of absolute values, although the ensemble mean tends to smooth the peaks. Finally, the intermodel comparison shows the limits and deficiencies of a velocity-constant routing model such as HD, particularly in the phase of mean annual discharge.

The second part of the study assesses the sensitivity of river discharge to the variation of external meteorological forcing. The Center for Ocean–Land–Atmosphere Studies version of the SSiB model is constrained with different meteorological fields and the resulting runoff is used as input for HD. River flow is most sensitive to precipitation variability, but changes in radiative forcing affect discharge as well, presumably because of the interaction with evaporation. Also, this analysis provides an estimate of the sensitivity of river discharge to precipitation variations. A few areas (e.g., central and eastern Asia, the Mediterranean, and much of the United States) show a magnified response of river discharge to a given percentage change in precipitation. Hence, an amplified effect of droughts as indicated by the consensus of climate change predictions may occur in places such as the Mediterranean. Conversely, increasing summer precipitation foreseen in places like southern and eastern Asia may amplify floods in these poor and heavily populated regions. Globally, a 1% fluctuation in precipitation forcing results in an average 2.3% change in discharge. These results can be used for the definition and assessment of new strategies for land use and water management in the near future.

Full access
Jiangfeng Wei
,
Paul A. Dirmeyer
,
Zhichang Guo
,
Li Zhang
, and
Vasubandhu Misra

Abstract

An atmospheric general circulation model (AGCM) is coupled to three different land surface schemes (LSSs), both individually and in combination (i.e., the LSSs receive the same AGCM forcing each time step and the averaged upward surface fluxes are passed back to the AGCM), to study the uncertainty of simulated climatologies and variabilities caused by different LSSs. This tiling of the LSSs is done to study the uncertainty of simulated mean climate and climate variability caused by variations between LSSs. The three LSSs produce significantly different surface fluxes over most of the land, no matter whether they are coupled individually or in combination. Although the three LSSs receive the same atmospheric forcing in the combined experiment, the inter-LSS spread of latent heat flux can be larger or smaller than the individually coupled experiment, depending mostly on the evaporation regime of the schemes in different regions. Differences in precipitation are the main reason for the different latent heat fluxes over semiarid regions, but for sensible heat flux, the atmospheric differences and LSS differences have comparable contributions. The influence of LSS uncertainties on the simulation of surface temperature is strongest in dry seasons, and its influence on daily maximum temperature is stronger than on minimum temperature. Land–atmosphere interaction can dampen the impact of LSS uncertainties on surface temperature in the tropics, but can strengthen their impact in middle to high latitudes. Variations in the persistence of surface heat fluxes exist among the LSSs, which, however, have little impact on the global pattern of precipitation persistence. The results provide guidance to future diagnosis of model uncertainties related to LSSs.

Full access