Life and the five biological laws : lessons for global change models and sustainability

Life on Earth is the result of a continuous accumulation of information by combination and innovation using endo- (inside the organism) and exosomatic (outside the organism) energy. Sustenance occurs through cycles of life and death. We here define five life laws for these vital processes. These processes cannot exceed natural limits of size and rates because they are constrained by space, matter and energy; biology builds on what is possible within these physicochemical limits. Learning from the way nature deals with the accumulation of information, the limits of size and the rates at which life can acquire and expend energy and resources for maintenance, growth and competition will help us to model and manage our environmental future and sustainabilit

Natural carbon solutions are not large or fast enough

Response letter: we thank Griscom et al. for their thoughtful letter to the editor (https://doi.org/10.1111/gcb.14612), responding to our paper (Baldocchi & Penuelas, 2019, https://doi.org/10.1111/gcb.14559) and expressing the opinion "we need both natural and energy solutions to stabilize our climate." We agree with tha

Evaluation of forest canopy models for estimating isoprene emissions

During the summer of 1992, isoprene emissions were measured in a mixed deciduous forest near Oak Ridge, Tennessee. Measurements were aimed at the experimental scale-up of emissions from the leaf level to the forest canopy to the mixed layer. Results from the scale-up study are compared to different canopy models for determining the leaf microclimate as input to isoprene emission algorithms. These include (1) no canopy effects, (2) a simple vertical scaling canopy model with a leaf energy balance, and (3) a numerical canopy model which accounts for leaf-sun geometries, photosynthesis, respiration, transpiration, and gas transport in the canopy. Initial evaluation of the models was based upon a standard emission rate factor of 90 μgC g-1 hr-1 (0.42 nmol g-1 s-1) taken from leaf cuvette measurements and a biomass density factor of 203 g m-2 taken from biomass surveys and a flux footprint analysis. The results indicated that predicted fluxes were consistent among the models to within approximately ±20%, but that the models overestimated the mean flux by about a factor of 2 and overestimated the maximum observed flux by 30 to 50%. Adjusting the standard emission factor and biomass density each downward by 20% yielded predicted means approximately 20% greater than the observed means and predicted maxima approximately 25% less than the observed maxima. Accounting for changes in biomass density as a function of direction upwind of the tower improved the overall model performance

Seasonal variation in carbon dioxide exchange over a Mediterranean annual grassland in California

Understanding how environmental variables affect the processes that regulate the carbon flux over grassland is critical for large-scale modeling research, since grasslands comprise almost one-third of the earth's natural vegetation. To address this issue, fluxes of CO{sub 2} (F{sub c}, flux toward the surface is negative) were measured over a Mediterranean, annual grassland in California, USA for 2 years with the eddy covariance method. To interpret the biotic and abiotic factors that modulate F{sub c} over the course of a year we decomposed net ecosystem CO{sub 2} exchange into its constituent components, ecosystem respiration (R{sub eco}) and gross primary production (GPP). Daytime R{sub eco} was extrapolated from the relationship between temperature and nighttime F{sub c} under high turbulent conditions. Then, GPP was estimated by subtracting daytime values of F{sub c} from daytime estimates of R{sub eco}. Results show that most of carbon exchange, both photosynthesis and respiration, was limited to the wet season (typically from October to mid-May). Seasonal variations in GPP followed closely to changes in leaf area index, which in turn was governed by soil moisture, available sunlight and the timing of the last frost. In general, R{sub eco} was an exponential function of soil temperature, but with season-dependent values of Q{sub 10}. The temperature-dependent respiration model failed immediately after rain events, when large pulses of R{sub eco} were observed. Respiration pulses were especially notable during the dry season when the grass was dead and were the consequence of quickly stimulated microbial activity. Integrated values of GPP, R{sub eco}, and net ecosystem exchange (NEE) were 867, 735, and -132g C m{sup -2}, respectively, for the 2000-2001 season, and 729, 758, and 29g C m{sup -2} for the 2001-2002 season. Thus, the grassland was a moderate carbon sink during the first season and a weak carbon source during the second season. In contrast to a well-accepted view that annual production of grass is linearly correlated to precipitation, the large difference in GPP between the two seasons were not caused by the annual precipitation. Instead, a shorter growing season, due to late start of the rainy season, was mainly responsible for the lower GPP in the second season. Furthermore, relatively higher R{sub eco} during the non-growing season occurred after a late spring rain. Thus, for this Mediterranean grassland, the timing of rain events had more impact than the total amount of precipitation on ecosystem GPP and NEE. This is because its growing season is in the cool and wet season when carbon uptake and respiration are usually limited by low temperature and sometimes frost, not by soil moisture

Influence of sun zenith angle on canopy clumping and the resulting impacts on photosynthesis

Addressing the impact of vegetation architecture on shortwave radiation transfer in land surface models is important for accurate weather forecasting, carbon budget estimates, and climate predictions. This paper investigates to what extent it is possible to retrieve structural parameters of two different parameterization schemes from direct transmittance derived from digital hemispherical photography and 3D radiative transfer modeling for two study sites with different vegetation canopy architectures. Neglecting the representation of 3D canopy structure in radiative transfer schemes leads to significant errors in shortwave radiation partitioning (up to 3.5 times more direct transmittance in the 3D model). Structural parameters, referred to as whole canopy ‘clumping indices’, were obtained in order to evaluate the impact of angular variation in clumping on shortwave radiation transfer. Impacts on photosynthesis were evaluated at site level with the UKESM land surface model, JULES. A comparison between flux tower derived and modeled photosynthesis indicates that considering zenith angular variations of structural parameters in the radiative transfer scheme of the UKESM land surface model significantly improves photosynthesis prediction in light limited ecosystems (from RMSE = 2.91 mol CO2.m-2.s-1 to RMSE = 1.51 mol CO2.m-2.s-1, 48% smaller), typically with enhanced photosynthesis from bottom layers

The future of evapotranspiration : global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources

The fate of the terrestrial biosphere is highly uncertain given recent and projected changes in climate. This is especially acute for impacts associated with changes in drought frequency and intensity on the distribution and timing of water availability. The development of effective adaptation strategies for these emerging threats to food and water security are compromised by limitations in our understanding of how natural and managed ecosystems are responding to changing hydrological and climatological regimes. This information gap is exacerbated by insufficient monitoring capabilities from local to global scales. Here, we describe how evapotranspiration (ET) represents the key variable in linking ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources, and highlight both the outstanding science and applications questions and the actions, especially from a space-based perspective, necessary to advance them

Protecting climate with forests

Policies for climate mitigation on land rarely acknowledge biophysical factors, such as reflectivity, evaporation, and surface roughness. Yet such factors can alter temperatures much more than carbon sequestration does, and often in a conflicting way. We outline a framework for examining biophysical factors in mitigation policies and provide some best-practice recommendations based on that framework. Tropical projects-avoided deforestation, forest restoration, and afforestation-provide the greatest climate value, because carbon storage and biophysics align to cool the Earth. In contrast, the climate benefits of carbon storage are often counteracted in boreal and other snow-covered regions, where darker trees trap more heat than snow does. Managers can increase the climate benefit of some forest projects by using more reflective and deciduous species and through urban forestry projects that reduce energy use. Ignoring biophysical interactions could result in millions of dollars being invested in some mitigation projects that provide little climate benefit or, worse, are counter-productive

Insights Into the Aerodynamic Versus Radiometric Surface Temperature Debate in Thermal-Based Evaporation Modeling

Global evaporation monitoring from Earth observation thermal infrared satellite missions is historically challenged due to the unavailability of any direct measurements of aerodynamic temperature. State-of-the-art one-source evaporation models use remotely sensed radiometric surface temperature as a substitute for the aerodynamic temperature and apply empirical corrections to accommodate for their inequality. This introduces substantial uncertainty in operational drought mapping over complex landscapes. By employing a non-parametric model, we show that evaporation can be directly retrieved from thermal satellite data without the need of any empirical correction. Independent evaluation of evaporation in a broad spectrum of biome and aridity yielded statistically significant results when compared with eddy covariance observations. While our simplified model provides a new perspective to advance spatio-temporal evaporation mapping from any thermal remote sensing mission, the direct retrieval of aerodynamic temperature also generates the highly required insight on the critical role of biophysical interactions in global evaporation research

On the use of MODIS EVI to assess gross primary productivity of North American ecosystems

[1] Carbon flux models based on light use efficiency (LUE), such as the MOD17 algorithm, have proved difficult to parameterize because of uncertainties in the LUE term, which is usually estimated from meteorological variables available only at large spatial scales. In search of simpler models based entirely on remote‐sensing data, we examined direct relationships between the enhanced vegetation index (EVI) and gross primary productivity (GPP) measured at nine eddy covariance flux tower sites across North America. When data from the winter period of inactive photosynthesis were excluded, the overall relationship between EVI and tower GPP was better than that between MOD17 GPP and tower GPP. However, the EVI/GPP relationships vary between sites. Correlations between EVI and GPP were generally greater for deciduous than for evergreen sites. However, this correlation declined substantially only for sites with the smallest seasonal variation in EVI, suggesting that this relationship can be used for all but the most evergreen sites. Within sites dominated by either evergreen or deciduous species, seasonal variation in EVI was best explained by the severity of summer drought. Our results demonstrate that EVI alone can provide estimates of GPP that are as good as, if not better than, current versions of the MOD17 algorithm for many sites during the active period of photosynthesis. Preliminary data suggest that inclusion of other remote‐sensing products in addition to EVI, such as the MODIS land surface temperature (LST), may result in more robust models of carbon balance based entirely on remote‐sensing data