Expansion of agricultural lands and inherent variability of climate can influence the water cycle in the Amazon basin, impacting numerous ecosystem services. However, these two influences do not work independently of each other. With two once-in-a-century-level droughts occurring in the Amazon in the past decade, it is vital to understand the feedbacks that contribute to altering the water cycle. The biogeophysical impacts of land cover change within the Amazon basin were examined under drought and pluvial conditions to investigate how land cover and drought jointly may have enhanced or diminished recent precipitation extremes by altering patterns and intensity. Using the Weather Research and Forecasting (WRF) Model coupled to the Noah land surface model, a series of April–September simulations representing drought, normal, and pluvial years were completed to assess how land cover change impacts precipitation and how these impacts change under varied rainfall regimes. Evaporative sources of water vapor that precipitate across the region were developed with a quasi-isentropic back-trajectory algorithm to delineate the extent and variability that terrestrial evaporation contributes to regional precipitation. A decrease in dry season latent heat flux and other impacts of deforestation on surface conditions were increased by drought conditions. Coupled with increases in dry season moisture recycling over the Amazon basin by ~7% during drought years, land cover change is capable of reducing precipitation and increasing the amplitude of droughts in the region.

We present an analysis of methane (CH4) emissions using atmospheric observations from 13 sites in California during June 2013 to May 2014. A hierarchical Bayesian inversion method is used to estimate CH4 emissions for spatial regions (0.3° pixels for major regions) by comparing measured CH4 mixing ratios with transport model (Weather Research and Forecasting and Stochastic Time‐Inverted Lagrangian Transport) predictions based on seasonally varying California‐specific CH4 prior emission models. The transport model is assessed using a combination of meteorological and carbon monoxide (CO) measurements coupled with the gridded California Air Resources Board (CARB) CO emission inventory. The hierarchical Bayesian inversion suggests that state annual anthropogenic CH4 emissions are 2.42 ± 0.49 Tg CH4/yr (at 95% confidence), higher (1.2–1.8 times) than the current CARB inventory (1.64 Tg CH4/yr in 2013). It should be noted that undiagnosed sources of errors or uncaptured errors in the model‐measurement mismatch covariance may increase these uncertainty bounds beyond that indicated here. The CH4 emissions from the Central Valley and urban regions (San Francisco Bay and South Coast Air Basins) account for ~58% and 26% of the total posterior emissions, respectively. This study suggests that the livestock sector is likely the major contributor to the state total CH4 emissions, in agreement with CARB's inventory. Attribution to source sectors for subregions of California using additional trace gas species would further improve the quantification of California's CH4 emissions and mitigation efforts toward the California Global Warming Solutions Act of 2006 (Assembly Bill 32).

Globally, photosynthesis accounts for the largest flux of CO2 from the atmosphere into ecosystems and is the driving process for terrestrial ecosystem function. The importance of accurate predictions of photosynthesis over a range of plant growth conditions led to the development of a C3 photosynthesis model by Farquhar, von Caemmerer & Berry that has become increasingly important as society places greater pressures on vegetation. The photosynthesis model has played a major role in defining the path towards scientific understanding of photosynthetic carbon uptake and the role of photosynthesis on regulating the earth's climate and biogeochemical systems. In this review, we summarize the photosynthesis model, including its continued development and applications. We also review the implications these developments have on quantifying photosynthesis at a wide range of spatial and temporal scales, and discuss the model's role in determining photosynthetic responses to changes in environmental conditions. Finally, the review includes a discussion of the largerscale modelling and remote-sensing applications that rely on the leaf photosynthesis model and are likely to open new scientific avenues to address the increasing challenges to plant productivity over the next century.

The majority of the world's food production capability is inextricably tied to global precipitation patterns. Changes in moisture availability-whether from changes in climate from anthropogenic greenhouse gas emissions or those induced by land cover change (LCC)-can have profound impacts on food production. In this study, we examined the patterns of evaporative sources that contribute to moisture availability over five major global food producing regions (breadbaskets), and the potential for land cover change to influence these moisture sources by altering surface evapotranspiration. For a range of LCC scenarios we estimated the impact of altered surface fluxes on crop moisture availability and potential yield using a simplified linear hydrologic model and a state-of-the-art ecosystem and crop model. All the breadbasket regions were found to be susceptible to reductions in moisture owing to perturbations in evaporative source (ES) from LCC, with reductions in moisture availability ranging from 7 to 17% leading to potential crop yield reductions of 1-17%, which are magnitudes comparable to the changes anticipated with greenhouse warming. The sensitivity of these reductions in potential crop yield to varying magnitudes of LCC was not consistent among regions. Two variables explained most of these differences: the first was the magnitude of the potential moisture availability change, with regions exhibiting greater reductions in moisture availability also tending to exhibit greater changes in potential yield; the second was the soil moisture within crop root zones. Regions with mean growing season soil moisture fractions of saturation >0.5 typically had reduced impacts on potential crop yield. Our results indicate the existence of LCC thresholds that have the capability to create moisture shortages adversely affecting crop yields in major food producing regions, which could lead to future food supply disruptions in the absence of increased irrigation or other forms of water management.

Biases in land‐atmosphere coupling in climate models can contribute to climate prediction biases, but land models are rarely evaluated in the context of this coupling. We tested land‐atmosphere coupling and explored effects of land surface parameterizations on climate prediction in a single‐column version of the National Center for Atmospheric Research Community Earth System Model (CESM1.2.2) and an off‐line Community Land Model (CLM4.5). The correlation between leaf area index (LAI) and surface evaporative fraction (ratio of latent to total turbulent heat flux) was substantially underpredicted compared to observations in the U.S. Southern Great Plains, while the correlation between soil moisture and evaporative fraction was overpredicted by CLM4.5. To estimate the impacts of these errors on climate prediction, we modified CLM4.5 by prescribing observed LAI, increasing soil resistance to evaporation, increasing minimum stomatal conductance, and increasing leaf reflectance. The modifications improved the predicted soil moisture‐evaporative fraction (EF) and LAI‐EF correlations in off‐line CLM4.5 and reduced the root‐mean‐square error in summer 2 m air temperature and precipitation in the coupled model. The modifications had the largest effect on prediction during a drought in summer 2006, when a warm bias in daytime 2 m air temperature was reduced from +6°C to a smaller cold bias of −1.3°C, and a corresponding dry bias in precipitation was reduced from −111 mm to −23 mm. The role of vegetation in droughts and heat waves is underpredicted in CESM1.2.2, and improvements in land surface models can improve prediction of climate extremes.