Lara Prihodko

Seasonal drought stress in the Amazon: Reconciling models and observations

[1] The Amazon Basin is crucial to global circulatory and carbon patterns due to the large areal extent and large flux magnitude. Biogeophysical models have had difficulty reproducing the annual cycle of net ecosystem exchange (NEE) of carbon in some regions of the Amazon, generally simulating uptake during the wet season and efflux during seasonal drought. In reality, the opposite occurs. Observational and modeling studies have identified several mechanisms that explain the observed annual cycle, including: (1) deep soil columns that can store large water amount, (2) the ability of deep roots to access moisture at depth when near-surface soil dries during annual drought, (3) movement of water in the soil via hydraulic redistribution, allowing for more efficient uptake of water during the wet season, and moistening of near-surface soil during the annual drought, and (4) photosynthetic response to elevated light levels as cloudiness decreases during the dry season. We incorporate these mechanisms into the third version of the Simple Biosphere model (SiB3) both singly and collectively, and confront the results with observations. For the forest to maintain function through seasonal drought, there must be sufficient water storage in the soil to sustain transpiration through the dry season in addition to the ability of the roots to access the stored water. We find that individually, none of these mechanisms by themselves produces a simulation of the annual cycle of NEE that matches the observed. When these mechanisms are combined into the model, NEE follows the general trend of the observations, showing efflux during the wet season and uptake during seasonal drought.

Effect of climate on interannual variability of terrestrial CO2 fluxes

This paper was published as Global Biogeochemical Cycles, 2002, 16 (4), GB1102. Copyright

Role of deep soil moisture in modulating climate in the Amazon rainforest

Received 28 December 2009; accepted 19 January 2010; published 2 March 2010. [1] Both local and large‐scale processes affect the Amazon hydrologic cycle. We investigate the impact of deep soils on the atmosphere through local feedbacks. The Simple Biosphere model, version 3 (SiB3), is coupled to a single column model. Historically, land surface schemes parameterize soil moisture stress based on shallow soils and incorrectly capture seasonal cycles in the Amazon. Following observations, SiB3 is updated to allow deep roots to access soil moisture at depth. The new (“Unstressed”) version of SiB3 has a stronger hydrologic cycle, with increased evapotranspiration and moisture export during the dry season. The boundary layer responds through changes in its depth, relative humidity, and turbulent kinetic energy, and these changes feed back to influence wet season onset and intensity. Differences in atmospheric latent heating could affect circulation in a global model. The results have important consequences for modeling the Amazon hydrologic cycle and climate in global climate models. Citation: Harper, A. B., A. S. Denning, I. T. Baker, M. D. Branson, L. Prihodko, andD.A.Randall (2010),Roleof deepsoil moistureinmodulatingclimateintheAmazonrainforest,Geophys.Res.Lett.,

Does warming increase the risk of civil war in Africa

The potential relationship between climate change and conflict is intriguing and warrants rigorous study. However, the proposition by Burke et al. (1) that warming may be a directly causative factor in the risk of civil war in Sub-Saharan Africa seems unlikely. The analysis of Burke et al. (1) suggests instead a tenuous historical association between warming and increased conflict. Regrettably, the authors did not elucidate further with either (i) specific case studies that demonstrate warming as a causative factor above economic, political, and sociocultural precipitants of conflict or (ii) a more thorough investigation of how climate-induced problems in agricultural sectors may result in increased conflict.

HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling

Hydrologic soil groups (HSGs) are a fundamental component of the USDA curve-number (CN) method for estimation of rainfall runoff; yet these data are not readily available in a format or spatial-resolution suitable for regional- and global-scale modeling applications. We developed a globally consistent, gridded dataset defining HSGs from soil texture, bedrock depth, and groundwater. The resulting data product—HYSOGs250m—represents runoff potential at 250 m spatial resolution. Our analysis indicates that the global distribution of soil is dominated by moderately high runoff potential, followed by moderately low, high, and low runoff potential. Low runoff potential, sandy soils are found primarily in parts of the Sahara and Arabian Deserts. High runoff potential soils occur predominantly within tropical and sub-tropical regions. No clear pattern could be discerned for moderately low runoff potential soils, as they occur in arid and humid environments and at both high and low elevations. Potential applications of this data include CN-based runoff modeling, flood risk assessment, and as a covariate for biogeographical analysis of vegetation distributions.