Search Results

You are looking at 41 - 50 of 84 items for

  • Author or Editor: Paul Dirmeyer x
  • Refine by Access: All Content x
Clear All Modify Search
Paul A. Dirmeyer
,
Yan Jin
,
Bohar Singh
, and
Xiaoqin Yan

Abstract

Long-term changes in land–atmosphere interactions during spring and summer are examined over North America. A suite of models from phase 5 of the Coupled Model Intercomparison Project simulating preindustrial, historical, and severe future climate change scenarios are examined for changes in soil moisture, surface fluxes, atmospheric boundary layer characteristics, and metrics of land–atmosphere coupling.

Simulations of changes from preindustrial to modern conditions show warming brings stronger surface fluxes at high latitudes, while subtropical regions of North America respond with drier conditions. There is a clear anthropogenic aerosol response in midlatitudes that reduces surface radiation and heat fluxes, leading to shallower boundary layers and lower cloud base. Over the Great Plains, the signal does not reflect a purely radiatively forced response, showing evidence that the expansion of agriculture may have offset the aerosol impacts on the surface energy and water cycle.

Future changes show soils are projected to dry across North America, even though precipitation increases north of a line that retreats poleward from spring to summer. Latent heat flux also has a north–south dipole of change, increasing north and decreasing south of a line that also moves northward with the changing season. Metrics of land–atmosphere feedback increase over most of the continent but are strongest where latent heat flux increases in the same location and season where precipitation decreases. Combined with broadly elevated cloud bases and deeper boundary layers, land–atmosphere interactions are projected to become more important in the future with possible consequences for seasonal climate prediction.

Full access
Zhichang Guo
,
Paul A. Dirmeyer
,
Timothy DelSole
, and
Randal D. Koster

Abstract

Total predictability within a chaotic system like the earth’s climate cannot increase over time. However, it can be transferred between subsystems. Predictability of air temperature and precipitation in numerical model forecasts over North America rebounds during late spring to summer because of information stored in the land surface. Specifically, soil moisture anomalies can persist over several months, but this memory cannot affect the atmosphere during early spring because of a lack of coupling between land and atmosphere. Coupling becomes established in late spring, enabling the effects of soil moisture anomalies to increase atmospheric predictability in 2-month forecasts begun as early as 1 May. This predictability is maintained through summer and then drops as coupling fades again in fall. This finding suggests summer forecasts of rainfall and air temperature over parts of North America could be significantly improved with soil moisture observations during spring.

Full access
Timothy DelSole
,
Xiaoqin Yan
,
Paul A. Dirmeyer
,
Mike Fennessy
, and
Eric Altshuler

Abstract

The change in predictability of monthly mean temperature in a future climate is quantified based on the Community Climate System Model, version 4. According to this model, the North Atlantic overtakes the El Niño–Southern Oscillation (ENSO) as the dominant area of seasonal predictability by 2095. This change arises partly because ENSO becomes less variable and partly because the ENSO teleconnection pattern expands into the Atlantic. Over land, the largest change in temperature predictability occurs in the tropics and is predominantly due to a decrease in ENSO variability. The southern peninsula of Africa and northeast South America are predicted to experience significant drying in a future climate, which decreases the effective heat capacity and memory, and hence increases variance independently of ENSO changes. Extratropical land areas experience enhanced precipitation in a future climate, which decreases temperature variance by the same mechanism. Finally, the model predicts that surface temperatures near the poles will become more predictable and less variable in a future climate, primarily because melting sea ice exposes the underlying sea surface temperature, which is more predictable owing to its longer time scale. Some of these results, especially the change in ENSO variance, are known to be model dependent. This paper also advances the use of information theory to quantify predictability, including 1) deriving a quantitative relation between predictability of the first and second kinds; 2) showing how differences in predictability can be decomposed in two dramatically different ways, facilitating physical interpretation; and 3) proposing a sample estimate of mutual information whose significance can be tested using standard techniques.

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
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
Paul A. Dirmeyer
,
C. Adam Schlosser
, and
Kaye L. Brubaker

Abstract

A synthesis of several approaches to quantifying land–atmosphere interactions is presented. These approaches use data from observations or atmospheric reanalyses applied to atmospheric tracer models and stand-alone land surface schemes. None of these approaches relies on the results of general circulation model simulations. A high degree of correlation is found among these independent approaches, and constructed here is a composite assessment of global land–atmosphere feedback strength as a function of season. The composite combines the characteristics of persistence of soil moisture anomalies, strong soil moisture regulation of evaporation rates, and reinforcement of water cycle anomalies through recycling. The regions and seasons that have a strong composite signal predominate in both summer and winter monsoon regions in the period after the rainy season wanes. However, there are exceptions to this pattern, most notably over the Great Plains of North America and the Pampas/Pantanal of South America, where there are signs of land–atmosphere feedback throughout most of the year. Soil moisture memory in many of these regions is long enough to suggest that real-time monitoring and accurate initialization of the land surface in forecast models could lead to improvements in medium-range weather to subseasonal climate forecasts.

Full 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
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
Paul A. Dirmeyer
,
Yan Jin
,
Bohar Singh
, and
Xiaoqin Yan

Abstract

Data from 15 models of phase 5 of the Coupled Model Intercomparison Project (CMIP5) for preindustrial, historical, and future climate change experiments are examined for consensus changes in land surface variables, fluxes, and metrics relevant to land–atmosphere interactions. Consensus changes in soil moisture and latent heat fluxes for past-to-present and present-to-future periods are consistent with CMIP3 simulations, showing a general drying trend over land (less soil moisture, less evaporation) over most of the globe, with the notable exception of high northern latitudes during winter. Sensible heat flux and net radiation declined from preindustrial times to current conditions according to the multimodel consensus, mainly due to increasing aerosols, but that trend reverses abruptly in the future projection. No broad trends are found in soil moisture memory except for reductions during boreal winter associated with high-latitude warming and diminution of frozen soils. Land–atmosphere coupling is projected to increase in the future across most of the globe, meaning a greater control by soil moisture variations on surface fluxes and the lower troposphere. There is also a strong consensus for a deepening atmospheric boundary layer and diminished gradients across the entrainment zone at the top of the boundary layer, indicating that the land surface feedback on the atmosphere should become stronger both in absolute terms and relative to the influence of the conditions of the free atmosphere. Coupled with the trend toward greater hydrologic extremes such as severe droughts, the land surface seems likely to play a greater role in amplifying both extremes and trends in climate on subseasonal and longer time scales.

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.

Restricted access