Scientists from several institutions and with different research backgrounds have worked together to develop a prototype modular land model for weather forecasting and climate studies. This model is now available for public use and further development.C limate and weather forecasting models require the energy, water, and momentum fluxes across the land-atmosphere interface to be specified. Various land surface parameterizations (LSPs), ranging from the simple bucket-type LSP in the 1960s to the current soil-vegetation-atmosphere interactive LSP, have been developed in the past four decades to calculate these fluxes. The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS) has demonstrated that, even with the same atmospheric forcing data and similar land surface parameters, different LSPs still give significantly different surface fluxes and soil wetness, partly because of the differences in the formulations of individual processes and coding architectures in participant models . On the other hand, most LSPs share many common components, suggesting the need to develop a publicly available common land model with a modular structure that could facilitate the exploration of new issues, less repetition of past efforts, and sharing of improvements and refinements contributed by different groups.The Common Land Model (CLM) effort dates back to the mid-1990s and has evolved through various workshops and e-mail correspondence. The initial motivation was to provide a framework for a truly community-developed land component of the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). Interest in applying CLM came from the Goddard Space Flight Center (GSFC) Data Assimilation Office (DAO), which was implementing the Mosaic model (Koster and Suarez 1992), and the Center for Ocean-Land-Atmosphere Studies (COLA) scientists, who were revising their Simplified Simple Biosphere Model (SSiB) (Xue et al. 1991). We also established ties to groups performing carbon cycle and ecological modeling.In developing CLM, we attempted to combine the best features of three existing successful and relatively

[1] Carbonyl sulfide (COS) is an atmospheric trace gas that participates in some key reactions of the carbon cycle and thus holds great promise for studies of carbon cycle processes. Global monitoring networks and atmospheric sampling programs provide concurrent data on COS and CO 2 concentrations in the free troposphere and atmospheric boundary layer over vegetated areas. Here we present a modeling framework for interpreting these data and illustrate what COS measurements might tell us about carbon cycle processes. We implemented mechanistic and empirical descriptions of leaf and soil COS uptake into a global carbon cycle model (SiB 3) to obtain new estimates of the COS land flux. We then introduced these revised boundary conditions to an atmospheric transport model (Parameterized Chemical Transport Model) to simulate the variations in the concentration of COS and CO 2 in the global atmosphere. To balance the threefold increase in the global vegetation sink relative to the previous baseline estimate, we propose a new ocean COS source. Using a simple inversion approach, we optimized the latitudinal distribution of this ocean source and found that it is concentrated in the tropics. The new model is capable of reproducing the seasonal variation in atmospheric concentration at most background atmospheric sites. The model also reproduces the observed large vertical gradients in COS between the boundary layer and free troposphere. Using a simulation experiment, we demonstrate that comparing drawdown of CO 2 with COS could provide additional constraints on differential responses of photosynthesis and respiration to environmental forcing. The separation of these two distinct processes is essential to understand the carbon cycle components for improved prediction of future responses of the terrestrial biosphere to changing environmental conditions.

When sea ice forms it scavenges and concentrates particulates from the water column, which then become trapped until the ice melts. In recent years, melting has led to record lows in Arctic Sea ice extent, the most recent in September 2012. Global climate models, such as that of Gregory et al. (2002), suggest that the decline in Arctic Sea ice volume (3.4% per decade) will actually exceed the decline in sea ice extent, something that Laxon et al. (2013) have shown supported by satellite data. The extent to which melting ice could release anthropogenic particulates back to the open ocean has not yet been examined. Here we show that Arctic Sea ice from remote locations contains concentrations of microplastics are several orders of magnitude greater than those that have been previously reported in highly contaminated surface waters, such as those of the Pacific Gyre. Our findings indicate that microplastics have accumulated far from population centers and that polar sea ice represents a major historic global sink of man-made particulates. The potential for substantial quantities of legacy microplastic contamination to be released to the ocean as the ice melts therefore needs to be evaluated, as do the physical and toxicological effects of plastics on marine life.

[1] Thirty-three snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up to two winter snow seasons. Modeled estimates of snow water equivalent (SWE) or depth were compared to observations at forest and open sites at each location. Precipitation phase and duration of above-freezing air temperatures are shown to be major influences on divergence and convergence of modeled estimates of the subcanopy snowpack. When models are considered collectively at all locations, comparisons with observations show that it is harder to model SWE at forested sites than open sites. There is no universal ''best'' model for all sites or locations, but comparison of the consistency of individual model performances relative to one another at different sites (and vice versa). Calibration of models at forest sites provides lower errors than uncalibrated models at three out of four locations. However, benefits of calibration do not translate to subsequent years, and benefits gained by models calibrated for forest snow processes are not translated to open conditions.

[1] Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0 C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low-temperature response to shut down GPP for temperatures below 0 C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf-to-canopy scaling and better values of model parameters that control the maximum potential GPP, such as ɛ max (LUE), V cmax (unstressed Rubisco catalytic capacity) or J max (the maximum electron transport rate).

[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.

[1] Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO 2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO 2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site-years, 10 biomes, and includes two large-scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO 2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model-data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.

Climate models incorporate photosynthesis-climate feedbacks, yet we lack robust tools for large-scale assessments of these processes. Recent work suggests that carbonyl sulfide (COS), a trace gas consumed by plants, could provide a valuable constraint on photosynthesis. Here we analyze airborne observations of COS and carbon dioxide concentrations during the growing season over North America with a three-dimensional atmospheric transport model. We successfully modeled the persistent vertical drawdown of atmospheric COS using the quantitative relation between COS and photosynthesis that has been measured in plant chamber experiments. Furthermore, this drawdown is driven by plant uptake rather than other continental and oceanic fluxes in the model. These results provide quantitative evidence that COS gradients in the continental growing season may have broad use as a measurement-based photosynthesis tracer.