Model Grid Cell Research Articles

Numerical Simulations of Outdoor Heat Stress Index and Heat Disorder Risk in the 23 Wards of Tokyo

AbstractIn this study, the summertime outdoor heat stress hazard and heat disorder risks (HDR) were simulated numerically using a mesoscale meteorological model combined with an urban canopy model and a building energy model. Model grid maps including the 23 wards of Tokyo (23 Tokyo), Japan, were produced with a 1-km horizontal resolution for the period of July–September 2010. Model simulations of the daily maximum wet-bulb globe temperature (WBGTmax), which was adopted as a heat stress index, indicated the spatial heterogeneity of the heat stress hazard within 23 Tokyo. The heat stress hazard was greater in the inland western region, particularly for sunny conditions in July and August (based on the monthly mean; the maximum difference exceeded 2°C for both sunny and shaded conditions). This likely occurred as a result of greater spatial heterogeneity in the globe temperature than in the air temperature among model grid cells, with differences in the radiation environment induced by differences in urban geometric parameters. Gridded mapping of HDR simulations proceeded in two steps, using the incidence rate and the absolute number of heat disorder patients (HDP). These simulations were achieved by combining the exponential relationships between the actual WBGTmax and the number of HDP with the daytime grid population. Eventually, the resulting HDR maps incorporated the effects of the spatial heterogeneities of both the outdoor heat stress hazard and the daytime grid population.

Impact of sub seismic heterogeneity on CO2 injectivity

Abstract The geometry of the depositional facies and the sandbody continuity in turbidite and fluvial reservoirs controls the stratigraphic heterogeneity, and therefore controls permeability structure. This has implications for CO 2 injectivity from localized pressure build up around injection wells, and migration pathways due to dispersive flow, which results in CO 2 contacting more of the rock volume than would be the case in a homogenous system. This reservoir simulation study is an investigation of the impact of geological heterogeneity in channelized sandstone formations on pressure buildup during CO 2 injection. Four geological models of fluvial and turbidite depositional systems were constructed, typical of those which occur in the Southern North Sea and the Central North Sea regions. Model grid cells were reduced to less than 10 m in places to properly represent the individual channel structures and 2 m near wellbores. This presented a challenge for simulation to capture the impact of injectivity accurately with high resolution for a basin-scale model. Sensitivity studies were carried out in two groups with different net to gross (NTG) ratios and mean permeabilities. The simulation results showed that connectivity to sand-body volumes, through the individual fluvial channel interconnections, may be poor, and so CO 2 does not readily access the entire volume. Furthermore, if the mean permeability is less than 10 mD, only NTG, or the volume fraction of high permeability channels, affects the injectivity; the facies type, i.e. fluvial or turbidite, does not affect strongly the minimum injectivity for all models with 80% sand.

Enhancing the representation of subgrid land surface characteristics in land surface models

Abstract. Land surface heterogeneity has long been recognized as important to represent in the land surface models. In most existing land surface models, the spatial variability of surface cover is represented as subgrid composition of multiple surface cover types, although subgrid topography also has major controls on surface processes. In this study, we developed a new subgrid classification method (SGC) that accounts for variability of both topography and vegetation cover. Each model grid cell was represented with a variable number of elevation classes and each elevation class was further described by a variable number of vegetation types optimized for each model grid given a predetermined total number of land response units (LRUs). The subgrid structure of the Community Land Model (CLM) was used to illustrate the newly developed method in this study. Although the new method increases the computational burden in the model simulation compared to the CLM subgrid vegetation representation, it greatly reduced the variations of elevation within each subgrid class and is able to explain at least 80% of the total subgrid plant functional types (PFTs). The new method was also evaluated against two other subgrid methods (SGC1 and SGC2) that assigned fixed numbers of elevation and vegetation classes for each model grid (SGC1: M elevation bands–N PFTs method; SGC2: N PFTs–M elevation bands method). Implemented at five model resolutions (0.1°, 0.25°, 0.5°, 1.0°and 2.0°) with three maximum-allowed total number of LRUs (i.e., NLRU of 24, 18 and 12) over North America (NA), the new method yielded more computationally efficient subgrid representation compared to SGC1 and SGC2, particularly at coarser model resolutions and moderate computational intensity (NLRU = 18). It also explained the most PFTs and elevation variability that is more homogeneously distributed spatially. The SGC method will be implemented in CLM over the NA continent to assess its impacts on simulating land surface processes.

The magnitude and causes of uncertainty in global model simulations of cloud condensation nuclei

Abstract. Aerosol–cloud interaction effects are a major source of uncertainty in climate models so it is important to quantify the sources of uncertainty and thereby direct research efforts. However, the computational expense of global aerosol models has prevented a full statistical analysis of their outputs. Here we perform a variance-based analysis of a global 3-D aerosol microphysics model to quantify the magnitude and leading causes of parametric uncertainty in model-estimated present-day concentrations of cloud condensation nuclei (CCN). Twenty-eight model parameters covering essentially all important aerosol processes, emissions and representation of aerosol size distributions were defined based on expert elicitation. An uncertainty analysis was then performed based on a Monte Carlo-type sampling of an emulator built for each model grid cell. The standard deviation around the mean CCN varies globally between about ±30% over some marine regions to ±40–100% over most land areas and high latitudes, implying that aerosol processes and emissions are likely to be a significant source of uncertainty in model simulations of aerosol–cloud effects on climate. Among the most important contributors to CCN uncertainty are the sizes of emitted primary particles, including carbonaceous combustion particles from wildfires, biomass burning and fossil fuel use, as well as sulfate particles formed on sub-grid scales. Emissions of carbonaceous combustion particles affect CCN uncertainty more than sulfur emissions. Aerosol emission-related parameters dominate the uncertainty close to sources, while uncertainty in aerosol microphysical processes becomes increasingly important in remote regions, being dominated by deposition and aerosol sulfate formation during cloud-processing. The results lead to several recommendations for research that would result in improved modelling of cloud–active aerosol on a global scale.

The Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM): a diverse approach to representing terrestrial biogeography and biogeochemistry based on plant functional trade-offs

Abstract. Terrestrial biosphere models typically abstract the immense diversity of vegetation forms and functioning into a relatively small set of predefined semi-empirical plant functional types (PFTs). There is growing evidence, however, from the field ecology community as well as from modelling studies that current PFT schemes may not adequately represent the observed variations in plant functional traits and their effect on ecosystem functioning. In this paper, we introduce the Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM) as a new approach to terrestrial biosphere modelling with a richer representation of functional diversity than traditional modelling approaches based on a small number of fixed PFTs. JeDi-DGVM simulates the performance of a large number of randomly generated plant growth strategies, each defined by a set of 15 trait parameters which characterize various aspects of plant functioning including carbon allocation, ecophysiology and phenology. Each trait parameter is involved in one or more functional trade-offs. These trade-offs ultimately determine whether a strategy is able to survive under the climatic conditions in a given model grid cell and its performance relative to the other strategies. The biogeochemical fluxes and land surface properties of the individual strategies are aggregated to the grid-cell scale using a mass-based weighting scheme. We evaluate the simulated global biogeochemical patterns against a variety of field and satellite-based observations following a protocol established by the Carbon-Land Model Intercomparison Project. The land surface fluxes and vegetation structural properties are reasonably well simulated by JeDi-DGVM, and compare favourably with other state-of-the-art global vegetation models. We also evaluate the simulated patterns of functional diversity and the sensitivity of the JeDi-DGVM modelling approach to the number of sampled strategies. Altogether, the results demonstrate the parsimonious and flexible nature of a functional trade-off approach to global vegetation modelling, i.e. it can provide more types of testable outputs than standard PFT-based approaches and with fewer inputs. The approach implemented here in JeDi-DGVM sets the foundation for future applications that will explore the impacts of explicitly resolving diverse plant communities, allowing for a more flexible temporal and spatial representation of the structure and function of the terrestrial biosphere.

Regional differences in hydrological response to canopy interception schemes in a land surface model

AbstractTwo different canopy interception schemes are applied to the parameterization of the hydrological processes in the Community Land Model version 3. One scheme treats rainfall and canopy water storage as spatially uniform within each model grid cell, and the other scheme considers sub‐grid variability of rainfall and water storage in the parameterization of canopy hydrological processes. The hydrological responses to differences between these two schemes in different regions are studied. It is found that the impact of the sub‐grid variability in the tropical regions is generally greater than the extra‐tropical regions. However, such impact can't be negligible for the extra‐tropical regions. Soil water in the total 3.4 m soil depth increases by 3% for Central‐South Europe, and vegetation temperature increases by 0.14 °C for Southeastern United States if the regional averages are considered. The magnitude of the impact is greater if the analysis focuses on the specific grid cells in these regions. The impact is tightly correlated with rainfall amount and vegetation density. The correlation coefficient between such impact and rainfall amount and vegetation density varies with regions and hydrological variables, with the largest value of 0.92 for interception loss in Amazonia. Our results indicate that the impact of the sub‐grid variability on hydrological processes in the extra‐tropical areas is also important, although rainfall amount and vegetation density in these areas are not as high as in the tropical areas. Copyright © 2013 John Wiley & Sons, Ltd.

Representation of topography by porous barriers and objective interpolation of topographic data

We present a porous medium approach to representing topography, and a new algorithm for the objective interpolation of topography, for use in ocean circulation models of fixed resolution. The representation and algorithm makes use of two concepts; impermeable thin walls and porous barriers. Impermeable thin walls allow the representation of knife-edge sub-grid-scale barriers that block lateral flow between model grid cells. Porous barriers permit the sub-grid scale geometry to modulate lateral transport as a function of elevation. We find that the porous representation and the resulting interpolated topography retains key features, such as overflow sill depths, without compromising other dynamically relevant aspects, such as mean ocean depth for a cell. The accurate representation of the ocean depth is illustrated in a simple model of a tsunami that has a cross-basin travel time very much less dependent on horizontal resolution than when using conventional topographic interpolation and representation.

Tropical Precipitation Extremes

AbstractClassifying tropical deep convective systems by the mesoscale distribution of their cloud properties and sorting matching precipitation measurements over an 11-yr period reveals that the whole distribution of instantaneous precipitation intensity and daily average accumulation rate is composed of (at least) two separate distributions representing distinctly different types of deep convection associated with different meteorological conditions (the distributions of non-deep-convective situations are also shown for completeness). The two types of deep convection produce very different precipitation intensities and occur with very different frequencies of occurrence. Several previous studies have shown that the interaction of the large-scale tropical circulation with deep convection causes switching between these two types, leading to a substantial increase of precipitation. In particular, the extreme portion of the tropical precipitation intensity distribution, above 2 mm h−1, is produced by 40% of the larger, longer-lived mesoscale-organized type of convection with only about 10% of the ordinary convection occurrences producing such intensities. When average precipitation accumulation rates are considered, essentially all of the values above 2 mm h−1 are produced by the mesoscale systems. Yet today’s atmospheric models do not represent mesoscale-organized deep convective systems that are generally larger than current-day circulation model grid cell sizes but smaller than the resolved dynamical scales and last longer than the typical physics time steps. Thus, model-based arguments for how the extreme part of the tropical precipitation distribution might change in a warming climate are suspect.

Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP)

Abstract. Global wetlands are believed to be climate sensitive, and are the largest natural emitters of methane (CH4). Increased wetland CH4 emissions could act as a positive feedback to future warming. The Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP) investigated our present ability to simulate large-scale wetland characteristics and corresponding CH4 emissions. To ensure inter-comparability, we used a common experimental protocol driving all models with the same climate and carbon dioxide (CO2) forcing datasets. The WETCHIMP experiments were conducted for model equilibrium states as well as transient simulations covering the last century. Sensitivity experiments investigated model response to changes in selected forcing inputs (precipitation, temperature, and atmospheric CO2 concentration). Ten models participated, covering the spectrum from simple to relatively complex, including models tailored either for regional or global simulations. The models also varied in methods to calculate wetland size and location, with some models simulating wetland area prognostically, while other models relied on remotely sensed inundation datasets, or an approach intermediate between the two. Four major conclusions emerged from the project. First, the suite of models demonstrate extensive disagreement in their simulations of wetland areal extent and CH4 emissions, in both space and time. Simple metrics of wetland area, such as the latitudinal gradient, show large variability, principally between models that use inundation dataset information and those that independently determine wetland area. Agreement between the models improves for zonally summed CH4 emissions, but large variation between the models remains. For annual global CH4 emissions, the models vary by ±40% of the all-model mean (190 Tg CH4 yr−1). Second, all models show a strong positive response to increased atmospheric CO2 concentrations (857 ppm) in both CH4 emissions and wetland area. In response to increasing global temperatures (+3.4 °C globally spatially uniform), on average, the models decreased wetland area and CH4 fluxes, primarily in the tropics, but the magnitude and sign of the response varied greatly. Models were least sensitive to increased global precipitation (+3.9 % globally spatially uniform) with a consistent small positive response in CH4 fluxes and wetland area. Results from the 20th century transient simulation show that interactions between climate forcings could have strong non-linear effects. Third, we presently do not have sufficient wetland methane observation datasets adequate to evaluate model fluxes at a spatial scale comparable to model grid cells (commonly 0.5°). This limitation severely restricts our ability to model global wetland CH4 emissions with confidence. Our simulated wetland extents are also difficult to evaluate due to extensive disagreements between wetland mapping and remotely sensed inundation datasets. Fourth, the large range in predicted CH4 emission rates leads to the conclusion that there is both substantial parameter and structural uncertainty in large-scale CH4 emission models, even after uncertainties in wetland areas are accounted for.

Efficient upscaling of ocean biogeochemistry

More accurate methods are needed to represent biogeochemistry in ocean models with coarse spatial resolution, in order to assess the response of marine ecosystems to global change. We use eddy-resolving simulations to test methods of upscaling biogeochemistry from 1km to the 100km scale of global model grid cells. The neglect of subgrid-scale variability results in serious errors which are not robustly corrected by retuning parameters in the model dynamics. Moment closure schemes provide accurate upscaling for modest computational investment, with broadly similar results obtained by second moment and conditional moment closure schemes. However, the conditional scheme gives clear improvement when variability is imposed on maximum uptake rates under Michaelis–Menten nutrient limitation, as this may invalidate second-order expansions of the mean field dynamics.

Application of a roughness‐length representation to parameterize energy loss in 3‐D numerical simulations of large rivers

Recent technological advances in remote sensing have enabled investigation of the morphodynamics and hydrodynamics of large rivers. However, measuring topography and flow in these very large rivers is time consuming and thus often constrains the spatial resolution and reach‐length scales that can be monitored. Similar constraints exist for computational fluid dynamics (CFD) studies of large rivers, requiring maximization of mesh‐ or grid‐cell dimensions and implying a reduction in the representation of bedform‐roughness elements that are of the order of a model grid cell or less, even if they are represented in available topographic data. These “subgrid” elements must be parameterized, and this paper applies and considers the impact of roughness‐length treatments that include the effect of bed roughness due to “unmeasured” topography. CFD predictions were found to be sensitive to the roughness‐length specification. Model optimization was based on acoustic Doppler current profiler measurements and estimates of the water surface slope for a variety of roughness lengths. This proved difficult as the metrics used to assess optimal model performance diverged due to the effects of large bedforms that are not well parameterized in roughness‐length treatments. However, the general spatial flow patterns are effectively predicted by the model. Changes in roughness length were shown to have a major impact upon flow routing at the channel scale. The results also indicate an absence of secondary flow circulation cells in the reached studied, and suggest simpler two‐dimensional models may have great utility in the investigation of flow within large rivers.

Capability of C-Band SAR for Operational Wetland Monitoring at High Latitudes

Wetlands store large amounts of carbon, and depending on their status and type, they release specific amounts of methane gas to the atmosphere. The connection between wetland type and methane emission has been investigated in various studies and utilized in climate change monitoring and modelling. For improved estimation of methane emissions, land surface models require information such as the wetland fraction and its dynamics over large areas. Existing datasets of wetland dynamics present the total amount of wetland (fraction) for each model grid cell, but do not discriminate the different wetland types like permanent lakes, periodically inundated areas or peatlands. Wetland types differently influence methane fluxes and thus their contribution to the total wetland fraction should be quantified. Especially wetlands of permafrost regions are expected to have a strong impact on future climate due to soil thawing. In this study ENIVSAT ASAR Wide Swath data was tested for operational monitoring of the distribution of areas with a long-term SW near 1 (hSW) in northern Russia (SW = degree of saturation with water, 1 = saturated), which is a specific characteristic of peatlands. For the whole northern Russia, areas with hSW were delineated and discriminated from dynamic and open water bodies for the years 2007 and 2008. The area identified with this method amounts to approximately 300,000 km2 in northern Siberia in 2007. It overlaps with zones of high carbon storage. Comparison with a range of related datasets (static and dynamic) showed that hSW represents not only peatlands but also temporary wetlands associated with post-forest fire conditions in permafrost regions. Annual long-term monitoring of change in boreal and tundra environments is possible with the presented approach. Sentinel-1, the successor of ENVISAT ASAR, will provide data that may allow continuous monitoring of these wetland dynamics in the future complementing global observations of wetland fraction.

Surface Skin Temperature and the Interplay between Sensible and Ground Heat Fluxes over Arid Regions

Abstract Over arid regions, two community land models [Noah and Community Land Model (CLM)] still have difficulty in realistically simulating the diurnal cycle of surface skin temperature. Based on theoretical arguments and synthesis of previous observational and modeling efforts, three revisions are developed here to address this issue. The revision of the coefficients in computing roughness length for heat significantly reduces the underestimate of daytime skin temperature but has a negligible effect on nighttime skin temperature. The constraints of the minimum friction velocity and soil thermal conductivity help improve nighttime skin temperature under weak wind and dry soil conditions. These results are robust in both Noah and CLM, as well as in Noah, with 4 versus 10 soil layers based on in situ data at the Desert Rock site in Nevada with a monthly averaged diurnal amplitude of 31.7 K and the Gaize site over Tibet, China, with an amplitude of 44.6 K. While these revisions can be directly applied to CLM or other land models with subgrid tiles (including bare soil), suggestions are also made on their application to Noah and other land models that treat bare soil and vegetated area together in a model grid cell. It is suggested that the challenging issue of measuring and simulating surface sensible heat flux under stable conditions should be treated as a land–atmosphere coupled issue, involving the interplay of ground and sensible heat fluxes in balancing the net radiation over arid regions, rather than as an atmospheric turbulence issue alone. The implications of such a coupling perspective are also discussed.

What shapes giant hogweed invasion? Answers from a spatio-temporal model integrating multiscale monitoring data

In this study we simulated the invasion of Heracleum mantegazzianum with a spatiotemporal model that combined a life-cycle matrix model with mechanistic local and corridor dispersal and a stochastic long-distance dispersal in a cellular automaton. The model was applied to the habitat configuration and invader distribution of eight 1 km² study areas. Comparing the simulations with monitoring data collected over 7 years (2002–2009) yielded a modelling efficiency of 0.94. We tested the significance of different mechanisms of invasion by omitting or modifying single model components one at a time. Thus we found that the extent of H. mantegazzianum invasion at landscape level depends on both landscape-scale processes and local processes which control recruitment success and population density. Limiting recruitment success (100 → 30 %) and successionally decreasing the carrying capacity of habitats (max → 0) over 30 years significantly improved the projections of the invasion at the landscape level. Local dispersal reached farther than 10 m, i.e. farther than previously assumed, but appeared to be unaffected by wind directions. Long-distance dispersal together with local dispersal dominated the invasion quantitatively. Dispersal through corridors accounted for less invasive spread. Its importance, with respect to invasion speed (number of colonised model grid cells) is probably limited over short periods of time (7 years). Only dispersal along rivers made a significant quantitative contribution to invasion of H. mantegazzianum. We suggest that biotic heterogeneity of suitable habitats is responsible for varying invasion success and that successionally increasing competition leads to declining population densities of H. mantegazzianum over several decades slowing down the spread on the landscape scale.

Microphysical simulations of sulfur burdens from stratospheric sulfur geoengineering

Abstract. Recent microphysical studies suggest that geoengineering by continuous stratospheric injection of SO2 gas may be limited by the growth of the aerosols. We study the efficacy of SO2, H2SO4 and aerosol injections on aerosol mass and optical depth using a three-dimensional general circulation model with sulfur chemistry and sectional aerosol microphysics (WACCM/CARMA). We find increasing injection rates of SO2 in a narrow band around the equator to have limited efficacy while broadening the injecting zone as well as injecting particles instead of SO2 gas increases the sulfate burden for a given injection rate, in agreement with previous work. We find that injecting H2SO4 gas instead of SO2 does not discernibly alter sulfate size or mass, in contrast with a previous study using a plume model with a microphysical model. However, the physics and chemistry in aircraft plumes, which are smaller than climate model grid cells, need to be more carefully considered. We also find significant perturbations to tropospheric aerosol for all injections studied, particularly in the upper troposphere and near the poles, where sulfate burden increases by up to 100 times. This enhanced burden could have implications for tropospheric radiative forcing and chemistry. These results highlight the need to mitigate greenhouse gas emissions rather than attempt to cool the planet through geoengineering, and to further study geoengineering before it can be seriously considered as a climate intervention option.

Evaluation and control mechanisms of volume and freshwater export through the Canadian Arctic Archipelago in a high‐resolution pan‐Arctic ice‐ocean model

This study examined the 1979–2004 volume and freshwater fluxes through the Canadian Arctic Archipelago (CAA) and into the Labrador Sea using a high resolution (∼9 km) coupled ice‐ocean model of the pan‐Arctic region to provide a reference, compare with limited observational estimates, and investigate control mechanisms of this exchange. The 26‐year mean volume and freshwater fluxes through Nares Strait were 0.77 Sv ± 0.17 Sv and 10.38 mSv ± 1.67 mSv respectively, while those through Lancaster Sound amounted to 0.76 Sv ± 0.12 Sv and 48.45 mSv ± 7.83 mSv respectively. The 26‐year mean volume and freshwater fluxes through Davis Strait were 1.55 Sv ± 0.29 Sv and 62.66 mSv ± 11.67 mSv while the modeled Fram Strait branch provided very little (∼2%) freshwater into the Labrador Sea compared to the total CAA input. Compared to available observations, the model provides reasonable volume and freshwater fluxes, as well as sea ice thickness and concentration in the CAA. In Nares Strait and Lancaster Sound, volume flux anomalies were controlled by the sea surface height (SSH) gradient anomalies along the straits and freshwater anomalies were highly correlated with the volume anomalies. At least half of the variance in the time series of SSH gradient anomaly was due to SSH anomalies in northern Baffin Bay. The West Greenland Current (WGC) exhibits seasonality, with cross shelf flow (into the Labrador Sea) peaking in January/February/March, while reducing the northward flow across eastern Davis Strait. We hypothesize that the eddy‐reduced northward flow of WGC results in the lower volume and SSH in Baffin Bay. This maximizes the SSH gradients between the Arctic Ocean and Baffin Bay, leading to maximum winter volume fluxes through Nares Strait and Lancaster Sound. Model limitations include the insufficient spatial resolution of atmospheric forcing (especially to account for the effects of local topography), the representation of river runoff into Hudson Bay and coastal buoyancy currents, low mobility of modeled ice, and incomplete depiction of ice arching. Many of these issues are expected to be resolved with increased model grid cell resolution, improved sea ice and ocean models and more realistic atmospheric forcing.

Continental-scale enrichment of atmospheric <sup>14</sup>CO<sub>2</sub> from the nuclear power industry: potential impact on the estimation of fossil fuel-derived CO<sub>2</sub>

Abstract. The 14C-free fossil carbon added to atmospheric CO2 by combustion dilutes the atmospheric 14C/C ratio (Δ14C), potentially providing a means to verify fossil CO2 emissions calculated using economic inventories. However, sources of 14C from nuclear power generation and spent fuel reprocessing can counteract this dilution and may bias 14C/C-based estimates of fossil fuel-derived CO2 if these nuclear influences are not correctly accounted for. Previous studies have examined nuclear influences on local scales, but the potential for continental-scale influences on Δ14C has not yet been explored. We estimate annual 14C emissions from each nuclear site in the world and conduct an Eulerian transport modeling study to investigate the continental-scale, steady-state gradients of Δ14C caused by nuclear activities and fossil fuel combustion. Over large regions of Europe, North America and East Asia, nuclear enrichment may offset at least 20% of the fossil fuel dilution in Δ14C, corresponding to potential biases of more than −0.25 ppm in the CO2 attributed to fossil fuel emissions, larger than the bias from plant and soil respiration in some areas. Model grid cells including high 14C-release reactors or fuel reprocessing sites showed much larger nuclear enrichment, despite the coarse model resolution of 1.8°×1.8°. The recent growth of nuclear 14C emissions increased the potential nuclear bias over 1985–2005, suggesting that changing nuclear activities may complicate the use of Δ14C observations to identify trends in fossil fuel emissions. The magnitude of the potential nuclear bias is largely independent of the choice of reference station in the context of continental-scale Eulerian transport and inversion studies, but could potentially be reduced by an appropriate choice of reference station in the context of local-scale assessments.

A soil moisture assimilation scheme based on the microwave Land Emissivity Model and the Community Land Model

Applications of microwave remote-sensing data in land data assimilation are a topic of current interest and importance due to their high temporal and spatial resolution and availability. However, there have been few studies on land surface sub-grid scale heterogeneity and calculating microwave wetland surface emissivity when directly assimilating gridded Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) satellite brightness temperature (BT) data to estimate soil moisture. How to assimilate gridded AMSR-E BT data for land surface model (LSM) grid cells including various land cover types, especially wetland, is worthy of careful study. The ensemble Kalman filter (EnKF) method is able to resolve the non-linearity and discontinuity in forecast and observation operators, and is widely used in land data assimilation. In this study, considering the influences of land surface sub-grid scale heterogeneity, a satellite data simulation scheme based on the National Center for Atmosphere Research (NCAR) Community Land Model version 2.0 (CLM2.0), microwave Land Emissivity Model (LandEM), Shuffled Complex Evolution (SCE-UA) algorithm and AMSR-E BT observation data is presented to simulate AMSR-E BT data and calibrate microwave wetland surface emissivity; then, a soil moisture data assimilation scheme is developed to directly assimilate the gridded AMSR-E BT data, which consists of the CLM2.0, LandEM and EnKF. The experimental results indicate that the calibrated microwave wetland surface emissivities possess excellent transportability, and that the assimilation scheme is practical and can significantly improve soil moisture estimation accuracy. This study provides a promising solution to improve soil moisture estimation accuracy through directly assimilating gridded AMSR-E BT data for various land cover types such as bare soil, vegetation, snow, lake and wetland.

Accumulation and melt dynamics of snowpack from a multiresolution regional climate model in the central Sierra Nevada, California

[1] The depth and timing of snowpack in the Sierra Nevada Mountains are of fundamental importance to California water resource availability, and recent studies indicate a shift toward earlier snowmelt consistent with projected impacts of anthropogenic climate change. In order for future studies to assess snowpack variability on seasonal to centennial time scales, physically based models of snowpack evolution at high spatial resolution must be improved. Here we evaluate modeled snowpack accuracy for the central Sierra Nevada in the Weather Research and Forecasting regional climate model coupled to the Noah land surface model. A simulation with nested domains at 27, 9, and 3 km grid spacings is presented for November 2001 to July 2002. Model outputs are compared with daily snowpack observations at 41 locations, air temperature at 31 locations, and precipitation at 10 locations. Comparison of snowpack at different resolutions suggests that 27 km simulations substantially underestimate snowpack, while 9 and 3 km simulations are closer to observations. Regional snowpack accumulation is accurately simulated at these high resolutions, but model snowmelt occurs an average of 22–25 days early. Some error can be traced to differences in elevation and observation scale between point-based measurements and model grid cells, but these factors cannot explain the persistent bias toward early snowmelt. A high correlation between snowmelt and error in modeled surface air temperature is found, with melt coinciding systematically with excessively cold air temperatures. One possible source of bias is an imbalance in turbulent heat fluxes, erroneously warming the snowpack while cooling the surface atmosphere.

Evaluation of NARR and CLM3.5 outputs for surface water and energy budgets in the Mississippi River Basin

[1] The North American Regional Reanalysis (NARR) and Community Land Model (CLM, version 3.5) outputs are analyzed to characterize the surface water and energy budgets in the Mississippi River Basin (MRB). NARR and CLM performance are evaluated with reference to energy flux observations from 16 AmeriFlux sites in MRB. The issue of point-scale observations versus climate model grid cell outputs is addressed by analyzing the spatial variability in long-term monthly precipitation and temperature observations from 71 United States Historical Climatology Network stations in Indiana and Illinois. The model outputs are also evaluated for their ability to capture spatial and temporal variability in total runoff. Compared to average values at 11 AmeriFlux sites in MRB, NARR show higher biases (compared to CLM) in incoming solar radiation (24%), sensible heat flux (27%), and latent heat flux (59%), whereas CLM show smaller biases (compared to NARR) in incoming solar radiation (0.5%), sensible heat flux (−2%), and latent heat flux (11%). Seasonal cycle of observed sensible heat flux in the crop region shows two peaks (bimodal pattern), which is captured by NARR, but CLM do not show any bimodal pattern. Based on 25 years (1980–2004) monthly climatology in MRB, NARR has 11% energy balance closing error (latent + sensible + ground heat flux = 1.11 net radiation) and 12% water balance closing error (evapotranspiration + runoff = 1.12 precipitation), whereas CLM does not have water and energy balance closing errors, primarily due to model design. In comparison to the observed mean annual runoff of 237 mm/yr based on 1988–1999 data in MRB, NARR and CLM mean annual runoff values are 89 mm/yr and 281 mm/yr, respectively. Overall, CLM provides relatively better characterization of surface water and energy fluxes in the MRB compared to NARR.