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P. H. Austin, M. B. Baker, A. M. Blyth, and J. B. Jensen

Abstract

We have analyzed small-scale fluctuations in microphysical, dynamical and thermodynamical parameters measured in two warm cumulus clouds during the Cooperative Convective Precipitation Experiment (CCOPE) project (1981) in light of predictions of several recent models. The measurements show the existence at all levels throughout the sampling period of two statistically distinct kinds of cloudy regions, termed “variable” and “steady,” often separated by transition zones of less than ten meters. There is some evidence for microphysical variability induced by local fluctuations in thermodynamic and dynamic parameters; however, the predominant variations are of a nature consistent with laboratory evidence suggesting that mixing is dominated by large structures. Entrainment appears to occur largely near cloud top but the data presented here do not permit identification of a mechanism for transport of the entrained air throughout the cloud.

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J. B. Jensen, P. H. Austin, M. B. Baker, and A. M. Blyth

Abstract

The analysis of Paluch suggests that some cumuli contain cloudy air from only two sources: cloud base and cloud top. A framework is presented for the investigation of droplet spectral evolution in clouds composed of air from only these two sources. The key is the investigation of the dependence of droplet concentration N on the fraction of cloud base air F in a sample of cloudy air. This N-vs-F analysis is coupled with an investigation of droplet spectral parameters to infer the types and scales of entrainment and mixing events.

The technique is used in a case study of a small, nonprecipitating continental cumulus cloud which was sampled during the 1981 CCOPE project in eastern Montana. The mixing between cloudy and entrained air in this cloud often appears to occur without total removal of droplets, although there is evidence that total evaporation occurs in some regions with low liquid water content. The observed droplet spectra are compared with those calculated from an adiabatic parcel model. The spectral comparison and the results of the N-vs-F analysis support the hypothesis that cloudy and environmental air interact on fairly large scales with subsequent homogenization of the large-scale regions. This description is consistent with recent models of mixing in turbulent flows.

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L. R. Vargas Zeppetello, Étienne Tétreault-Pinard, D. S. Battisti, and M. B. Baker

Abstract

Climate models show that soil moisture and its subseasonal fluctuations have important impacts on the surface latent heat flux, thus regulating surface temperature variations. Using correlations between monthly anomalies in net absorbed radiative fluxes, precipitation, 2-m air temperature, and soil moisture in the ERA-Interim reanalysis and the HadCM3 climate model, we develop a linear diagnostic model to quantify the major effects of land–atmosphere interactions on summertime surface temperature variability. The spatial patterns in 2-m air temperature and soil moisture variance from the diagnostic model are consistent with those from the products from which it was derived, although the diagnostic model generally underpredicts soil moisture variance. We use the diagnostic model to quantify the impact of soil moisture, shortwave radiation, and precipitation anomalies on temperature variance in wet and dry regions. Consistent with other studies, we find that fluctuations in soil moisture amplify temperature variance in dry regions through their impact on latent heat flux, whereas in wet regions temperature variability is muted because of high mean evapotranspiration rates afforded by plentiful surface soil moisture. We demonstrate how the diagnostic model can be used to identify sources of temperature variance bias in climate models.

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Richard B. Rood, Dale J. Allen, Wayman E. Baker, David J. Lamich, and Jack A. Kaye

Abstract

Analysis of atmospheric data by assimilation of height and wind measurements into a general circulation model is routine in tropospheric analysis and numerical weather prediction. A stratospheric assimilation system has been developed at NASA/Goddard Space Flight Center. This unique system generates wind data that is consistent with the geopotential height (and temperature) field and the primitive equations in the general circulation model. These wind fields should offer a significant improvement over the geostrophic analysis normally used in the stratosphere.

This paper reports the first known calculations to use data from an assimilation to calculate constituent transport in the stratosphere. Nitric acid (NHO3) during the LIMS period is studied. While there are still significant discrepancies between the calculated and observed HNO3, there are some remarkable successes. Particularly, the high-latitude time variance of the HNO3 is accurately captured. These studies suggest that data from an assimilation process offers tremendous potential for studying stratospheric dynamics, constituent transport, and chemistry.

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Lucas R. Vargas Zeppetello, David S. Battisti, and Marcia B. Baker

Abstract

Evaporation plays an extremely important role in determining summertime surface temperature variability over land. Observations show the relationship between evaporation and soil moisture generally conforms to the Budyko “two regime” framework; namely, that evaporation is limited by available soil moisture in dry climates and by radiation in wet climates. This framework has led climate models to different parameterizations of the relationship between evaporation and soil moisture in wet and dry regions. We have developed the Simple Land–Atmosphere Model (SLAM) as a tool for studying land–atmosphere interaction in general, and summertime temperature variability in particular. We use the SLAM to show that a negative feedback between evaporation and surface temperature gives rise to the two apparent evaporation “regimes” and provide analytic solutions for evaporative cooling anomalies that demonstrate the nonlinear impact of soil moisture perturbations. Stemming from the temperature dependence of vapor pressure deficit, the feedback we identify has important implications for how transitions between wet and dry land surfaces may impact temperature variability as the climate warms. We also elucidate the impacts of surface moisture and insolation perturbations on latent and sensible heat fluxes and on surface temperature variability.

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Lucas R. Vargas Zeppetello, David S. Battisti, and Marcia B. Baker

Abstract

The increasing frequency of very high summertime temperatures has motivated growing interest in the processes determining the probability distribution of surface temperature over land. Here, we show that on monthly time scales, temperature anomalies can be modeled as linear responses to fluctuations in shortwave radiation and precipitation. Our model contains only three adjustable parameters, and, surprisingly, these can be taken as constant across the globe, notwithstanding large spatial variability in topography, vegetation, and hydrological processes. Using observations of shortwave radiation and precipitation from 2000 to 2017, the model accurately reproduces the observed pattern of temperature variance throughout the Northern Hemisphere midlatitudes. In addition, the variance in latent heat flux estimated by the model agrees well with the few long-term records that are available in the central United States. As an application of the model, we investigate the changes in the variance of monthly averaged surface temperature that might be expected due to anthropogenic climate change. We find that a climatic warming of 4°C causes a 10% increase in temperature variance in parts of North America.

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C. A. Knight, M. B. Baker, G. M. Barnes, G. B. Foote, M. A. LeMone, and G. Vali
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N. E. Westcott, S. D. Hilberg, R. L. Lampman, B. W. Alto, A. Bedel, E. J. Muturi, H. Glahn, M. Baker, K. E. Kunkel, and R. J. Novak

In the midwestern United States, the summertime rise in infection rate by the West Nile virus is associated with a seasonal shift in the abundance of two mosquito populations, Culex restuans and Culex pipiens. This seasonal shift usually precedes the time of the peak infection rate in mosquitoes by 2–3 weeks and generally occurs earlier in the summer with above normal temperatures and later in the summer with below-normal temperatures. Two empirical models were developed to predict this seasonal shift in mosquito species, or the “crossover,” and have been run operationally since 2004 by the Midwestern Regional Climate Center located at the Illinois State Water Survey. These models are based on daily temperature data and have been verified by use of a unique dataset of daily records of mosquito species abundance collected by the Illinois Natural History Survey. An unfortunate characteristic of the original temperature models was that the crossover date often was reached with little or no lead time. In 2009, the models were modified to incorporate National Weather Service (NWS) model output statistics (MOS) 10-day temperature forecasts. This paper evaluates the effectiveness of these models to predict the crossover date and thus the period of increased risk of West Nile virus in the Midwest.

For the 8-yr period from 2002 to 2009, 6 yr had at least one model predicting the crossover within one week of the actual crossover date, and for 7 yr at least one of the model predictions was within 2 weeks of the actual crossover date. Incorporation of MOS temperature forecasts for a 10-day period, although not substantially changing the predicted crossover date, greatly improved the forecast lead time by about 9 days. From a disease management point of view, this improvement in advanced notice is significant. In 2009, there was an unprecedented early crossover date and a failed forecast. The poor forecast was likely caused by an unusually early summer prolonged and intense heat wave, followed immediately by a record cold July.

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Yongjiu Dai, Xubin Zeng, Robert E. Dickinson, Ian Baker, Gordon B. Bonan, Michael G. Bosilovich, A. Scott Denning, Paul A. Dirmeyer, Paul R. Houser, Guoyue Niu, Keith W. Oleson, C. Adam Schlosser, and Zong-Liang Yang

The Common Land Model (CLM) was developed for community use by a grassroots collaboration of scientists who have an interest in making a general land model available for public use and further development. The major model characteristics include enough unevenly spaced layers to adequately represent soil temperature and soil moisture, and a multilayer parameterization of snow processes; an explicit treatment of the mass of liquid water and ice water and their phase change within the snow and soil system; a runoff parameterization following the TOPMODEL concept; a canopy photo synthesis-conductance model that describes the simultaneous transfer of CO2 and water vapor into and out of vegetation; and a tiled treatment of the subgrid fraction of energy and water balance. CLM has been extensively evaluated in offline mode and coupling runs with the NCAR Community Climate Model (CCM3). The results of two offline runs, presented as examples, are compared with observations and with the simulation of three other land models [the Biosphere-Atmosphere Transfer Scheme (BATS), Bonan's Land Surface Model (LSM), and the 1994 version of the Chinese Academy of Sciences Institute of Atmospheric Physics LSM (IAP94)].

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Jessica C. A. Baker, Dayana Castilho de Souza, Paulo Y. Kubota, Wolfgang Buermann, Caio A. S. Coelho, Martin B. Andrews, Manuel Gloor, Luis Garcia-Carreras, Silvio N. Figueroa, and Dominick V. Spracklen

Abstract

In South America, land–atmosphere interactions have an important impact on climate, particularly the regional hydrological cycle, but detailed evaluation of these processes in global climate models has been limited. Focusing on the satellite-era period of 2003–14, we assess land–atmosphere interactions on annual to seasonal time scales over South America in satellite products, a novel reanalysis (ERA5-Land), and two global climate models: the Brazilian Global Atmospheric Model version 1.2 (BAM-1.2) and the U.K. Hadley Centre Global Environment Model version 3 (HadGEM3). We identify key features of South American land–atmosphere interactions represented in satellite and model datasets, including seasonal variation in coupling strength, large-scale spatial variation in the sensitivity of evapotranspiration to surface moisture, and a dipole in evaporative regime across the continent. Differences between products are also identified, with ERA5-Land, HadGEM3, and BAM-1.2 showing opposite interactions to satellites over parts of the Amazon and the Cerrado and stronger land–atmosphere coupling along the North Atlantic coast. Where models and satellites disagree on the strength and direction of land–atmosphere interactions, precipitation biases and misrepresentation of processes controlling surface soil moisture are implicated as likely drivers. These results show where improvement of model processes could reduce uncertainty in the modeled climate response to land-use change, and highlight where model biases could unrealistically amplify drying or wetting trends in future climate projections. Finally, HadGEM3 and BAM-1.2 are consistent with the median response of an ensemble of nine CMIP6 models, showing they are broadly representative of the latest generation of climate models.

Open access