Search Results

You are looking at 1 - 10 of 71 items for

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

Abstract

An atmospheric general circulation model with land surface properties represented by the Simplified Simple Biosphere Model is used to investigate the effect of soil moisture and vegetation stress on drought in the mid-latitudes. An idealized land-sea distribution with simple topography is used to remove as many external sources of climate variation as possible. The land consists of a single, flat, rectangular continent covered with prairie vegetation and centered on 44°N of an aqua planet. A control integration of 4 years is performed, and several sets of seasonal anomaly integrations are made to test the sensitivity of seasonal climate to low initial (1 April) soil moisture and dormant vegetation like what would occur during a severe drought.

It is found that the inclusion of dormant vegetation during the spring and early summer greatly reduces evapotranspiration by eliminating transpiration. This affects local climate more strongly as summer progresses. Low initial soil moisture, combined with dormant vegetation, leads to a severe drought. The reduction in precipitation is much greater in magnitude than that due to low soil moisture alone, and greater than the sum of the effects computed separately. Although the short-term drought is more severe, the dormancy of the vegetation prevents further depletion of moisture in the root zone of the soil, so soil moisture begins to rebound toward the middle of summer.

Full access
Paul A. Dirmeyer

Abstract

The Global Soil Wetness Project (GSWP) is an international land surface modeling research effort involving dataset production, validation, model comparison, and scientific investigation in the areas of land surface hydrology and climatology. GSWP is characterized by the integration of multiple land surface models on a latitude–longitude grid in a stand-alone uncoupled mode, driven by meteorological forcing data constructed by combining atmospheric analyses and gridded observed data products. The models produce time series of gridded estimates of land surface fluxes and state variables that are then studied and compared. Defining characteristics that have distinguished GSWP include its global scale, application of land surface models in the same gridded structure as they are used in weather and climate models, and the multimodel approach, which included production of a multimodel analysis in its second phase. This paper gives an overview of the history of GSWP beginning with its inception within the International Satellite Land Surface Climatology Project. Various phases of the project are described, and a review of scientific results stemming from the project is presented. Musings on future directions of research are also discussed.

Full access
Paul A. Dirmeyer

Abstract

The role of the land surface in contributing to the potential predictability of the boreal summer climate is investigated with a coupled land–atmosphere climate model. Ensemble simulations for 1982–99 have been conducted with specified observed sea surface temperatures (SSTs). Several treatments of the land surface are investigated: climatological land surface initialization, realistic initialization of soil wetness, and a series of experiments where downward surface fluxes over land are replaced with observed proxies of precipitation, shortwave, and longwave radiation. Without flux replacement the model exhibits strong drift in soil wetness and both systematic errors and poor simulation of interannual variations of precipitation and near-surface temperature. With flux replacement there are large improvements in simulation of both spatial patterns and interannual variability of precipitation and surface temperature. The land surface apparently does contribute, through positive feedback with the atmosphere, to regional climate anomalies. However, because of the sizeable noise component in precipitation, the strong land–atmosphere feedback may not translate into reliable enhancements in predictability, particularly in years of weak anomalies in the land surface initial conditions at the start of boreal summer.

Full access
Paul A. Dirmeyer

Abstract

A coupled land–atmosphere climate model is examined for evidence of climate drift in the land surface state variable of soil moisture. The drift is characterized as pathological error growth in two different ways. First is the systematic error that is evident over seasonal timescales, dominated by the error modes with the largest saturated amplitude: systematic drift. Second is the fast-growing modes that are present in the first few days after either initialization or a data assimilation increment: incremental drift. When the drifts are robust across many ensemble members and from year to year, they suggest a source of drift internal to the coupled system. This source may be due to problems in either component model or in the coupling between them. Evidence is presented for both systematic and incremental drift. The relationship between the two types of drift at any given point is shown to be an indication of the type and strength of feedbacks within the coupled system. Methods for elucidating potential sources of the drift are proposed.

Full access
Paul A. Dirmeyer

Abstract

Skill in ensemble-mean dynamical seasonal climate hindcasts with a coupled land–atmosphere model and specified observed sea surface temperature is compared to that for long multidecade integrations of the same model where the initial conditions are far removed from the seasons of validation. The evaluations are performed for surface temperature and compared among all seasons. Skill is found to be higher in the seasonal simulations than in the multidecadal integrations except during boreal winter. The higher skill is prominent even beyond the first month when the direct influence of the atmospheric initial state elevates model skill. Skill is generally found to be lowest during the winter season for the dynamical seasonal forecasts. This is in contrast to the multiyear integrations, which show some of the highest skill during winter—as high as the dynamical seasonal forecasts. The reason for the differences in skill during the nonwinter months is attributed to the severe climate drift in the long simulations, manifested through errors in downward fluxes of water and energy over land and evident in soil wetness. The drift presses the land surface to extreme dry or wet states over much of the globe, into a range where there is little sensitivity of evaporation to fluctuations in soil moisture. Thus, the land–atmosphere feedback is suppressed, which appears to lessen the model's ability to respond correctly over land to remote ocean temperature anomalies. During winter the land surface is largely decoupled from the atmosphere due to increased baroclinic activity in the land-dominated Northern Hemisphere, while at the same time tropical ocean anomalies have their strongest influence. This combination of effects neutralizes the negative impact of climate drift over land during that season and puts all of the climate simulations on an equal footing.

Full access
Paul A. Dirmeyer

Abstract

Two ensembles of 1-month integrations of a coupled land–atmosphere climate model that differ only in their treatment of land surface boundary conditions have been generated from initial conditions chosen from the July states taken from each year of a 17-yr integration from the second Atmospheric Model Intercomparison Project (AMIP2). Both ensembles have specified sea surface temperature from one randomly chosen year, but one ensemble has the land surface state variables specified in each member at each time step to be identical to those from a single member of the other ensemble. Comparisons with the 17-yr AMIP2 integration provide an estimate of the role of interannually varying SST in affecting climate variability. Comparison between the two ensembles helps to quantify the role of land surface variability on the variance of surface fluxes and the climate. In this model system, the impacts of suppressed ocean variability on intra-ensemble spread are generally stronger than for suppressed land surface variability. The impacts of land surface variability on climate variability are clearer on monthly timescales than on synoptic timescales. Absolute measures of the impact of surface variability on the synoptic scale are not strong, but the time evolution of variability is consistent with expectations that the land surface does exert some control on climate variability.

Full access
Paul A. Dirmeyer

Abstract

The impact of improvements in land surface initialization and specification of observed rainfall in global climate model simulations of boreal summer are examined to determine how the changes propagate around the hydrologic cycle in the coupled land–atmosphere system. On the global scale, about 70% of any imparted signal in the hydrologic cycle is lost in the transition from atmosphere to land, and 70% of the remaining signal is lost from land to atmosphere. This means that globally, less than 10% of the signal of any change survives the complete circuit of the hydrologic cycle in this model. Regionally, there is a great deal of variability. Specification of observed precipitation to the land component of the climate model strongly communicates its signal to soil wetness in rainy regions, but predictive skill in evapotranspiration arises primarily in dry regions. A maximum in signal transmission to model precipitation exists in between, peaking where mean rainfall rates are 1.5–2 mm day−1. It appears that the nature of the climate system inherently limits to these regions the potential impact on prediction of improvements in the ability of models to simulate the water cycle. Land initial conditions impart a weaker signal on the system than replacement of precipitation, so a weaker response is realized in the system, focused mainly in dry regions.

Full access
Paul A. Dirmeyer

Abstract

Ensembles of boreal summer coupled land–atmosphere climate model integrations for 1987 and 1988 are conducted with and without interactive soil moisture to evaluate the degree of climate drift in the coupled land–atmosphere model system, and to gauge the quality of the specified soil moisture dataset from the Global Soil Wetness Project (GSWP). Use of specified GSWP soil moisture leads to improved simulations of rainfall patterns, and significantly reduces root-mean-square errors in near-surface air temperature, indicating that the GSWP product is of useful quality and can also be used to supply initial conditions to fully coupled climate integrations. Integrations using specified soil moisture from the opposite year suggest that the interannual variability in the GSWP dataset is significant and contributes to the quality of the simulation of precipitation above what would be possible with only a mean annual cycle climatology of soil moisture. In particular, specification of soil wetness from the wrong year measurably degrades the correlation of simulated precipitation and temperature patterns compared to observed.

Full access
Paul A. Dirmeyer and Subhadeep Halder

Abstract

Retrospective forecasts from CFSv2 are evaluated in terms of three elements of land–atmosphere coupling at subseasonal to seasonal time scales: sensitivity of the atmosphere to variations in land surface states, the magnitude of variability of land states and fluxes, and the memory or persistence of land surface anomalies. The Northern Hemisphere spring and summer seasons are considered for the period 1982–2009. Ensembles are constructed from all available pairings of initial land and atmosphere/ocean states taken from the Climate Forecast System Reanalysis at the start of April, May, and June among the 28 years, so that the effect of initial land states on the evolving forecasts can be assessed. Finally, improvement and continuance of forecast skill derived from accurate land surface initialization is related to the three coupling elements. It is found that soil moisture memory is the most broadly important element for significant improvement of realistic land initialization on forecast skill. However, coupling strength manifested through the elements of sensitivity and variability are necessary to realize the potential predictability provided by memory of initial land surface anomalies. Even though there is clear responsiveness of surface heat fluxes, near-surface temperature, humidity, and daytime boundary layer development to variations in soil moisture over much of the globe, precipitation in CFSv2 is unresponsive. Failure to realize potential predictability from land surface states could be due to unfavorable atmospheric stability or circulation states; poor quality of what is considered realistic soil moisture analyses; and errors in the land surface model, atmospheric model, or their coupled interaction.

Full access
Liang Chen and Paul A. Dirmeyer

ABSTRACT

Recent studies have shown the impacts of historical land-use land-cover changes (i.e., deforestation) on hot temperature extremes; contradictory temperature responses have been found between studies using observations and climate models. However, different characterizations of surface temperature are sometimes used in the assessments: land surface skin temperature T s is more commonly used in observation-based studies while near-surface air temperature T 2m is more often used in model-based studies. The inconsistent use of temperature variables is not inconsequential, and the relationship between deforestation and various temperature changes can be entangled, which complicates comparisons between observations and model simulations. In this study, the responses in the diurnal cycle of summertime T s and T 2m to deforestation are investigated using the Community Earth System Model. For the daily maximum, opposite responses are found in T s and T 2m. Due to decreased surface roughness after deforestation, the heat at the land surface cannot be efficiently dissipated into the air, leading to a warmer surface but cooler air. For the daily minimum, strong warming is found in T 2m, which exceeds daytime cooling and leads to overall warming in daily mean temperatures. After comparing several climate models, we find that the models agree in daytime land surface (T s) warming, but different turbulent transfer characteristics produce discrepancies in T 2m. Our work highlights the need to investigate the diurnal cycles of temperature responses carefully in land-cover change studies. Furthermore, consistent consideration of temperature variables should be applied in future comparisons involving observations and climate models.

Full access