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Air pollution is a major sustainability challenge – and future anthropogenic precursor and greenhouse gas (GHG) emissions will greatly affect human well-being. While mitigating climate change can reduce air pollution both directly and indirectly, distinct policy levers can affect these two interconnected sustainability issues across a wide range of scenarios. We help to assess such issues by presenting a public Tool for Air Pollution Scenarios (TAPS) that can flexibly assess pollutant emissions from a variety of climate and air quality actions, through the tool's coupling with socioeconomic modeling of climate change mitigation. In this study, we develop and implement TAPS with three components: recent global and fuel-specific anthropogenic emissions inventories, scenarios of emitting activities to 2100 from the MIT Economic Projection and Policy Analysis (EPPA) model, and emissions intensity trends based on recent scenario data from the Greenhouse Gas–Air Pollution Interactions and Synergies (GAINS) model. An initial application shows that in scenarios with less climate and pollution policy ambition, near-term air quality improvements from existing policies are eclipsed by long-term emissions increases – particularly from industrial processes that combine sharp production growth with less stringent pollution controls in developing regions. Additional climate actions would substantially reduce air pollutant emissions related to fossil fuel (such as sulfur and nitrogen oxides), while further pollution controls would lead to larger reductions for ammonia and organic carbon (OC). Future applications of TAPS could explore diverse regional and global policies that affect these emissions, using pollutant emissions results to drive global atmospheric chemical transport models to study the scenarios' health impacts.

Physical and societal risks across the natural, managed, and built environments are becoming increasingly complex, multi-faceted, and compounding. Such risks stem from socio-economic and environmental stresses that co-evolve and force tipping points and instabilities. Robust decision-making necessitates extensive analyses and model assessments for insights toward solutions. However, these exercises are consumptive in terms of computational and investigative resources. In practical terms, such exercises cannot be performed extensively—but selectively in terms of priority and scale. Therefore, an efficient analysis platform is needed through which the variety of multi-systems/sector observational and simulated data can be readily incorporated, combined, diagnosed, visualized, and in doing so, identifies “hotspots” of salient compounding threats. In view of this, we have constructed a “triage-based” visualization and data-sharing platform—the System for the Triage of Risks from Environmental and Socio-Economic Stressors (STRESS)—that brings together data across socio-environmental systems, economics, demographics, health, biodiversity, and infrastructure. Through the STRESS website, users can display risk indices that result from weighted combinations of risk metrics they can select. Currently, these risk metrics include land-, water-, and energy systems, biodiversity, as well as demographics, environmental equity, and transportation networks. We highlight the utility of the STRESS platform through several demonstrative analyses over the United States from the national to county level. The STRESS is an open-science tool and available to the community-at-large. We will continue to develop it with an open, accessible, and interactive approach, including academics, researchers, industry, and the general public.

Land surface models (LSMs) are limited in their ability to reproduce observed soil moisture partially due to uncertainties in model parameters. Here we conduct a variance-based sensitivity analysis to quantify the relative contribution of different model parameters and their interactions to the uncertainty in the surface and root-zone soil moisture in the Community Land Model 5.0 (CLM5). We focus on soil-texture-related parameters (porosity, saturated matric potential, saturated hydraulic conductivity, shape parameter of soil-water retention model) and organic matter fraction. A Gaussian process emulator is constructed based on CLM5 simulations and used to estimate soil moisture across the five-dimensional parameter space for sensitivity analysis. The procedure is demonstrated for four seasons across various U.S. sites of distinct soil and vegetation types. We find that the emulator captures well the CLM5 behavior across the parameter space for different soil textures and seasons. The uncertainties of surface and root-zone soil moisture are dominated by the uncertainties in porosity and shape parameter with negligible parametric interactions. However, relative importance of porosity versus shape parameter varies with soil textures (sites), depths (surface versus root zone), and seasons. At most of the sites, surface soil moisture uncertainty is attributed largely to shape parameter uncertainty, while porosity uncertainty is more important for the root-zone soil moisture uncertainty. All individual parameter and interaction effects demonstrate less variability across different soil textures and seasons for root zone than for surface soil moisture. These results provide scientific guidance to prioritize reducing the uncertainty of sensitive parameters for improving soil moisture modeling with CLM.

High-albedo materials reflect more solar radiation and, thereby, alter the earth's radiative balance. Increasing pavement albedo, therefore, has been considered as a technological strategy to mitigate global warming. Previous studies have evaluated this strategy using global average models. To factor this effect into life cycle assessments, location-specific models of the albedo effect for pavements are required. A parametric analytical model is developed to estimate the radiative forcing (RF) using a novel model form and an iterative solution approach. The new model is extended to estimate the corresponding global warming potential (GWP) over an analysis period of 50 years for an albedo change in a pavement surface. This was applied to quantify the GWP impacts of increasing pavement albedo in 14 cities across various climate zones in the US. For the United States, the GWP in kg CO2 equivalent per square meter of altered surface ranges from 0.8 to 1.6 per 0.01 change in albedo, a range of more than 40%. Analysis of a hypothetical albedo change to all darker pavements in the US would produce a negative RF of a magnitude equivalent to that associated with a reduction in CO2 emissions of more than 17 Mton per year.

We consider the interplay of climate change impacts, global mitigation policies, and the economic interests of developing countries to 2050. Focusing on Malawi, Mozambique, and Zambia, we employ a structural approach to biophysical and economic modeling that incorporates climate uncertainty and allows for rigorous comparison of climate, biophysical, and economic outcomes across global mitigation regimes. We find that effective global mitigation policies generate two sources of benefit. First, less distorted climate outcomes result in typically more favorable and less variable economic outcomes. Second, successful global mitigation policies reduce global fossil fuel producer prices, relative to unconstrained emissions, providing a substantial terms of trade boost of structural fuel importers. Combined, these gains are on the order of or greater than estimates of mitigation costs. These results highlight the interests of most developing countries in effective global mitigation policies, even in the relatively near term, with much larger benefits post-2050.

The Massachusetts Institute of Technology Integrated Global System Model (IGSM) is designed for analyzing the global environmental changes that may result from anthropogenic causes, quantifying the uncertainties associated with the projected changes, and assessing the costs and environmental effectiveness of proposed policies to mitigate climate risk. The IGSM consists of the Massachusetts Institute of Technology Earth System Model (MESM) of intermediate complexity and the Economic Projections and Policy Analysis model. This paper documents the current version of the MESM, which includes a two-dimensional (zonally averaged) atmospheric model with interactive chemistry coupled to the zonally averaged version of Global Land System model and an anomaly-diffusing ocean model., United States. Department of Energy. Office of Science (DE-FG02-94ER61937)

Regional climate models (RCMs) can simulate heavy precipitation more accurately than general circulation models (GCMs) through more realistic representation of topography and mesoscale processes. Analogue methods of downscaling, which identify the large-scale atmospheric conditions associated with heavy precipitation, can also produce more accurate and precise heavy precipitation frequency in GCMs than the simulated precipitation. In this study, we examine the performances of the analogue method versus direct simulation, when applied to RCM and GCM simulations, in detecting present-day and future changes in summer (JJA) heavy precipitation over the Midwestern United States. We find analogue methods are comparable to MERRA-2 and its bias-corrected precipitation in characterizing the occurrence and interannual variations of observed heavy precipitation events, all significantly improving upon MERRA precipitation. For the late twentieth-century heavy precipitation frequency, RCM precipitation improves upon the corresponding driving GCM with greater accuracy yet comparable inter-model discrepancies, while both RCM- and GCM-based analogue results outperform their model-simulated precipitation counterparts in terms of accuracy and model consensus. For the projected trends in heavy precipitation frequency through the mid twenty-first century, analogue method also manifests its superiority to direct simulation with reduced intermodel disparities, while the RCM-based analogue and simulated precipitation do not demonstrate a salient improvement (in model consensus) over the GCM-based assessment. However, a number of caveats preclude any overall judgement, and further work—over any region of interest—should include a larger sample of GCMs and RCMs as well as ensemble simulations to comprehensively account for internal variability.

Large power transformers (LPTs) are critical yet vulnerable components of the power grid. More frequent and intense heat waves or high temperatures can degrade their operational lifetime and increase the risk of premature failure. Without adequate preparedness, a widespread situation could ultimately lead to prolonged grid disruption and incur excessive economic costs. Here, we investigate the potential impact of climate warming and corresponding shifts in summertime “hot days” on a selected LPT located in the Northeast United States. We apply an analogue method, which detects the occurrence of hot days based on the salient, associated large-scale atmospheric conditions, to assess the risk of future change in their occurrence. Compared with the more conventional approach that relies on climate model-simulated daily maximum temperature, the analogue method produces model medians of late twentieth century hot day frequency that are more consistent with observation and have stronger inter-model consensus. Under the climate warming scenarios, multi-model medians of both model daily maximum temperature and the analogue method indicate strong decadal increases in hot day frequency by the late twenty-first century, but the analogue method improves model consensus considerably. The decrease of transformer lifetime with temperature increase is further assessed. The improved inter-model consensus of the analogue method is viewed as a promising step toward providing actionable information for a more stable, reliable, and environmentally responsible national grid.

The collective behavior of wind farms in seven Independent System Operator (ISO) areas has been studied. The generation duration curves for each ISO show that there is no aggregated power for some fraction of time. Aggregation of wind turbines mitigates intermittency to some extent, but in each ISO there is considerable fraction of time when there is less than 5% capacity. The hourly wind power time series show benefit of aggregation but the high and low wind events are lumped in time, thus indicating that intermittency is synchronized in each region. The timeseries show that there are instances when there is no wind power in most ISOs because of large-scale high pressure systems. An analytical consideration of the collective behavior of aggregated wind turbines shows that the benefit of aggregation saturates beyond a certain number of generating units asymptotically. Also, the benefit of aggregation falls rapidly with temporal correlation between the generating units.

Precipitation-gauge observations and atmospheric reanalysis are combined to develop an analogue method for detecting heavy precipitation events based on prevailing large-scale atmospheric conditions. Combinations of atmospheric variables for circulation (geopotential height and wind vector) and moisture (surface specific humidity, column and up to 500-hPa precipitable water) are examined to construct analogue schemes for the winter [December–February (DJF)] of the “Pacific Coast California” (PCCA) region and the summer [June–August (JJA)] of the Midwestern United States (MWST). The detection diagnostics of analogue schemes are calibrated with 1979–2005 and validated with 2006–14 NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). All analogue schemes are found to significantly improve upon MERRA precipitation in characterizing the occurrence and interannual variations of observed heavy precipitation events in the MWST. When evaluated with the late twentieth-century climate model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5), all analogue schemes produce model medians of heavy precipitation frequency that are more consistent with observations and have smaller intermodel discrepancies than model-based precipitation. Under the representative concentration pathways (RCP) 4.5 and 8.5 scenarios, the CMIP5-based analogue schemes produce trends in heavy precipitation occurrence through the twenty-first century that are consistent with model-based precipitation, but with smaller intermodel disparity. The median trends in heavy precipitation frequency are positive for DJF over PCCA but are slightly negative for JJA over MWST. Overall, the analyses highlight the potential of the analogue as a powerful diagnostic tool for model deficiencies and its complementarity to an evaluation of heavy precipitation frequency based on model precipitation alone.

We assess the ability of global water systems, resolved at 282 large river basins or Assessment Sub Regions (ASRs), to the meet water requirements over the coming decades under integrated projections of socioeconomic growth and climate change. We employ a Water Resource System (WRS) component embedded within the MIT Integrated Global System Model (IGSM) framework in a suite of simulations that consider a range of climate policies and regional hydroclimatic changes through the middle of this century. We find that for many developing nations water-demand increases due to population growth and economic activity have a much stronger effect on water stress than climate change. By 2050, economic growth and population change alone can lead to an additional 1.8 billion people living in regions with at least moderate water stress. Of this additional 1.8 billion people, 80% are found in developing countries. Uncertain regional climate change can play a secondary role to either exacerbate or dampen the increase in water stress due to socioeconomic growth. The strongest climate impacts on relative changes in water stress are seen over many areas in Africa, but strong impacts also occur over Europe, Southeast Asia and North America. The combined effects of socioeconomic growth and uncertain climate change lead to a 1.0 to 1.3 billion increase of the world's 2050 projected population living in regions with overly exploited water conditions— where total potential water requirements will consistently exceed surface-water supply. Under the context of the WRS model framework, this would imply that adaptive measures would be taken to meet these surface-water shortfalls and would include: water-use efficiency, reduced and/or redirected consumption, recurrent periods of water emergencies or curtailments, groundwater depletion, additional inter-basin transfers, and overdraw from flow intended to maintain environmental requirements.

This article presents an empirically based model, WiCTS (Withdrawal and Consumption for Thermoelectric Systems), to estimate regional water withdrawals and consumption implied by any electricity generation portfolio. WiTCS uses water use rates, developed at the substate level, to predict water use by scaling the rates with predicted energy generation. The capability of WiCTS is demonstrated by assessing the impact of renewable electricity generation scenarios on water use in the United States (U.S.) through 2050. The energy generation scenarios are taken from the Renewable Energy Futures Study performed by the U.S. National Renewable Energy Laboratory of the U.S. Department of Energy. Results indicate reductions in water use are achieved under these renewable energy scenarios. The analysis further explores the impact of two modifications to the modeling framework. The first modification presumes geothermal and concentrated solar power generation technologies employ water-intensive cooling systems vs. cooling technology that requires no water. The second modification presumes all water-intensive cooling technologies use closed cycle cooling (as opposed to once-through cooling) technologies by 2050. Results based on one of the renewable generation scenarios indicate water use increases by over 20% under the first modification, and water consumption increases by approximately 40% while water withdrawals decrease by over 85% under the second modification.

This study focuses on a better understanding of the influences of the North Atlantic and the Arctic Oscillation on wind power resource over Europe. In the case of a change in phase of the oscillations, wind power density can vary by a factor of three in northern Europe, and a similar effect (but opposite in sign) is seen for southern Europe. Similar results are obtained by calculating the energy output of hypothetical wind turbines. In this way, we have identified an interconnection potential between wind farms in northern and southern Europe in order to reduce intermittency at an aggregate scale.

The growing need for risk-based assessments of impacts and adaptation to climate change calls for increased capability in climate projections: specifically, the quantification of the likelihood of regional outcomes and the representation of their uncertainty. Herein, the authors present a technique that extends the latitudinal projections of the 2D atmospheric model of the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM) by applying longitudinally resolved patterns from observations, and from climate model projections archived from exercises carried out for the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The method maps the IGSM zonal means across longitude using a set of transformation coefficients, and this approach is demonstrated in application to near-surface air temperature and precipitation, for which high-quality observational datasets and model simulations of climate change are available. The current climatology of the transformation coefficients is observationally based. To estimate how these coefficients may alter with climate, the authors characterize the climate models’ spatial responses, relative to their zonal mean, from transient increases in trace-gas concentrations and then normalize these responses against their corresponding transient global temperature responses. This procedure allows for the construction of metaensembles of regional climate outcomes, combining the ensembles of the MIT IGSM—which produce global and latitudinal climate projections, with uncertainty, under different global climate policy scenarios—with regionally resolved patterns from the archived IPCC climate model projections. This hybridization of the climate model longitudinal projections with the global and latitudinal patterns projected by the IGSM can, in principle, be applied to any given state or flux variable that has the sufficient observational and model-based information.

[1] A global biofuels program will potentially lead to intense pressures on land supply and cause widespread transformations in land use. These transformations can alter the Earth climate system by increasing greenhouse gas (GHG) emissions from land use changes and by changing the reflective and energy exchange characteristics of land ecosystems. Using an integrated assessment model that links an economic model with climate, terrestrial biogeochemistry, and biogeophysics models, we examined the biogeochemical and biogeophysical effects of possible land use changes from an expanded global second-generation bioenergy program on surface temperatures over the first half of the 21st century. Our integrated assessment model shows that land clearing, especially forest clearing, has two concurrent effects—increased GHG emissions, resulting in surface air warming; and large changes in the land's reflective and energy exchange characteristics, resulting in surface air warming in the tropics but cooling in temperate and polar regions. Overall, these biogeochemical and biogeophysical effects will only have a small impact on global mean surface temperature. However, the model projects regional patterns of enhanced surface air warming in the Amazon Basin and the eastern part of the Congo Basin. Therefore, global land use strategies that protect tropical forests could dramatically reduce air warming projected in these regions.

Wind power is assessed over Europe, with a special care given to the quantification of intermittency. Using the methodology developed in Gunturu and Schlosser [1] , the MERRA boundary flux data was used to compute wind power density profiles over Europe. Besides of the analysis of capacity factor, other metrics have been designed to further quantify the availability and reliability of this resource and the extent to which wind-power intermittency is coincident across Europe. The presented analysis leads to the conclusion that wind-proponents’ favourite statement, “wind always blows somewhere”, may not be so true.

Government organizations are responsible for strategic decisions regarding the economic development of individual countries and regional areas. This decision-making process is influenced by the certainty of the information presented to government officials. One area where this decision-making influence is challenging government officials is in climate-based events. The past decade has witnessed a significant rise in the number of climate events, as well as the number of people affected by climate events. Far from being under control, evidence and long-term projections suggest that these events are climate-based rather than weather-based. In these projections, climate change is a global issue with potential impacts for every country. However, the relative impact of these events will be far different in the developed and developing worlds. As detailed in this paper, the authors examined this disparity through an analysis of 10 countries with varying income levels to determine the relative impact of climate ...

This study assesses the simulations of global-scale evapotranspiration from the second Global Soil Wetness Project (GSWP-2) within a global water budget framework. The scatter in the GSWP-2 global evapotranspiration estimates from various land surface models can constrain the global annual water budget fluxes to within ±2.5% and, by using estimates of global precipitation, the residual ocean evaporation estimate falls within the range of other independently derived bulk estimates. The GSWP-2 scatter, however, cannot entirely explain the imbalance of the annual fluxes from a modern-era, observationally based global water budget assessment. Inconsistencies in the magnitude and timing of seasonal variations between the global water budget terms are also found. Intermodel inconsistencies in evapotranspiration are largest for high-latitude interannual variability as well as for interseasonal variations in the tropics, and analyses with field-scale data also highlight model disparity at estimating evapotranspiration in high-latitude regions. Analyses of the sensitivity simulations that replace uncertain forcings (i.e., radiation, precipitation, and meteorological variables) indicate that global (land) evapotranspiration is slightly more sensitive to precipitation than net radiation perturbations, and the majority of the GSWP-2 models, at a global scale, fall in a marginally moisture-limited evaporative condition. Lastly, the range of global evapotranspiration estimates among the models is larger than any bias caused by uncertainties in the GSWP-2 atmospheric forcing, indicating that model structure plays a more important role toward improving global land evaporation estimates (as opposed to improved atmospheric forcing).

The adequacy of freshwater resources remains a critical challenge for a sustainable and growing society. We present a self-consistent risk-based assessment of water availability and use under future climate change and socioeconomic growth by midcentury across southern and eastern Asia (SEA). We employ large ensemble scenarios from an integrated modeling framework that are consistent across the spectrum of regional climate, population, and economic projections. We find socioeconomic growth contributes to an increase in water stress across the entire ensemble. However, climate change drives the ensemble central tendency toward an increase in water stress in China but a reduction in India, with a considerable spread across the ensemble. Nevertheless, the most deleterious unabated climate-change impact is a low probability but salient extreme increase in water stress over China and India. In these outcomes, annual withdrawals will routinely exceed water-storage capacity. A modest greenhouse gas mitigation pathway eliminates the likelihood of these extreme outcomes and also benefits hundreds of millions of people at risk to various levels of water stress increase. Over SEA we estimate an additional 200 million people under threat of facing at least heavily water-stressed conditions from climate change and socioeconomic growth, but the mitigation scenario reduces the additional population-under-threat by 30% (60 million). Nevertheless, there remains a 1-in-2 chance that 100 million people across SEA experience a 50% increase in water stress and a 1-in-10 chance they experience a doubling of water stress. Therefore, widespread adaptive measures may be required over the coming decades to meet these unavoidable risks in water shortfalls.

The impact of carbon–nitrogen dynamics in terrestrial ecosystems on the interaction between the carbon cycle and climate is studied using an earth system model of intermediate complexity, the MIT Integrated Global Systems Model (IGSM). Numerical simulations were carried out with two versions of the IGSM’s Terrestrial Ecosystems Model, one with and one without carbon–nitrogen dynamics. Simulations show that consideration of carbon–nitrogen interactions not only limits the effect of CO2 fertilization but also changes the sign of the feedback between the climate and terrestrial carbon cycle. In the absence of carbon–nitrogen interactions, surface warming significantly reduces carbon sequestration in both vegetation and soil by increasing respiration and decomposition (a positive feedback). If plant carbon uptake, however, is assumed to be nitrogen limited, an increase in decomposition leads to an increase in nitrogen availability stimulating plant growth. The resulting increase in carbon uptake by vegetation exceeds carbon loss from the soil, leading to enhanced carbon sequestration (a negative feedback). Under very strong surface warming, however, terrestrial ecosystems become a carbon source whether or not carbon–nitrogen interactions are considered. Overall, for small or moderate increases in surface temperatures, consideration of carbon–nitrogen interactions result in a larger increase in atmospheric CO2 concentration in the simulations with prescribed carbon emissions. This suggests that models that ignore terrestrial carbon–nitrogen dynamics will underestimate reductions in carbon emissions required to achieve atmospheric CO2 stabilization at a given level. At the same time, compensation between climate-related changes in the terrestrial and oceanic carbon uptakes significantly reduces uncertainty in projected CO2 concentration.

This paper is an empirical investigation of the effects of climate on the use of electricity by consumers and producers in urban and rural areas within China. It takes advantage of an unusual combination of temporal and regional data sets in order to estimate temperature, as well as price and income elasticities of electricity demand. The estimated positive temperature/electric power feedback implies a continually increasing use of energy to produce electric power which, in China, is primarily based on coal. In the absence of countervailing measures, this will contribute to increased emissions, increased atmospheric concentrations of greenhouse gases, and increases in greenhouse warming.

The capability of a global data compilation, largely satellite based, is assessed to depict the global atmospheric water cycle’s mean state and variability. Monthly global precipitation estimates from the Global Precipitation Climatology Project (GPCP) and the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) span from 1979 to 1999. Monthly global Special Sensor Microwave Imager (SSM/I)-based bulk aerodynamic ocean evaporation estimates span from June 1987 to December 1999. Global terrestrial evapotranspiration rates are estimated over a multidecade period (1975–99) using a global land model simulation forced by bias-corrected reanalysis data. Monthly total precipitable water (TPW) from the NASA Global Water Vapor Project (NVAP) spans from 1988 to 1999. The averaged annual global precipitation (P) and evaporation (E) estimates are out of balance by 5% or 24 000 (metric) gigatons (Gton) of water, which exceeds the uncertainty of global mean annual precipitation (∼±1%). For any given year, the annual flux imbalance can be on the order of 10% (48 000 Gton of water). However, observed global TPW interannual variations suggest a water flux imbalance on the order of 0.01% (48 Gton of water)—a finding consistent with a general circulation model (GCM) simulation. Variations in observationally based global P and E rates show weak monthly and interannual consistency, and depending on the choice of ocean evaporation data, the mean annual cycle of global E − P can be up to 5 times larger to that of TPW. The global ocean annual evaporation rates have as much as a ∼1% yr−1 increase during the period analyzed (1988–99), which is consistent in sign with most transient CO2 GCM simulations, but at least an order of magnitude larger. The ocean evaporation trends are driven by trends in SSM/I-retrieved near-surface atmospheric humidity and wind speed, and the largest year-to-year changes are coincident with transitions in the SSM/I fleet. In light of (potential) global water cycle changes in GCM projections, the ability to consistently detect or verify these changes in nature rests upon one or more of the following: quantification of global evaporation uncertainty, at least a twofold improvement in consistency between the observationally based global precipitation and evaporation variations, a two order of magnitude rectification between annual variations of E − P and precipitable water as well as substantial improvements in the consistency of their seasonal cycles, a critical reevaluation of intersatellite calibration for the relevant geophysical quantities used for ocean evaporation estimates, and the continuation of a dedicated calibration in this regard for future satellite transitions.

The Common Land Model (CLM) was developed for community use by a grassroots collaboration of scientists who have an interest in making a general land model available for public use and further development. The major model characteristics include enough unevenly spaced layers to adequately represent soil temperature and soil moisture, and a multilayer parameterization of snow processes; an explicit treatment of the mass of liquid water and ice water and their phase change within the snow and soil system; a runoff parameterization following the TOPMODEL concept; a canopy photo synthesis-conductance model that describes the simultaneous transfer of CO2 and water vapor into and out of vegetation; and a tiled treatment of the subgrid fraction of energy and water balance. CLM has been extensively evaluated in offline mode and coupling runs with the NCAR Community Climate Model (CCM3). The results of two offline runs, presented as examples, are compared with observations and with the simulation of three other land models [the Biosphere-Atmosphere Transfer Scheme (BATS), Bonan's Land Surface Model (LSM), and the 1994 version of the Chinese Academy of Sciences Institute of Atmospheric Physics LSM (IAP94)].

This study represents the first attempt at an integrated approach to assessing the potential impacts of climate change on the national economy of South Africa via a number of (but not necessarily all) impact channels. The study focuses on outcomes by abou

We consider the interplay of climate change impacts, global mitigation policies, and the interests of developing countries to 2050. Focusing on Malawi, Mozambique, and Zambia, we employ a structural approach to biophysical and economic modeling that incorporates climate uncertainty and allows for rigorous comparison of climate, biophysical, and economic outcomes across global mitigation regimes. We find that effective global mitigation policies generate two sources of benefit. First, less distorted climate outcomes result in typically more favourable economic outcomes. Second, successful global mitigation policies reduce global fossil fuel producer prices, relative to unconstrained emissions, providing a substantial terms of trade boost to structural fuel importers. Combined, these gains are on the order of or greater than estimates of mitigation costs. These results highlight the interests of most developing countries in effective global mitigation policies, even in the relatively near term, with the likelihood of much larger benefits post 2050.

Projections of regional changes in surface air temperature and precipitation for the greater Nile River basin and northern Africa are presented. The probabilistic projections are obtained through a technique that combines projections of the MIT Integrated Global System Model with climate-change patterns of the Intergovernmental Panel on Climate Change. Overall, the most consistent response to climate policy is seen in the distributions of temperature change. For precipitation, the predominant climate stabilization response is to reduce the likelihood of modal change.

Soil moisture predictability and the associated predictability of continental climate are explored as an initialvalue problem, using a coupled land‐atmosphere model with prescribed ocean surface temperatures. Ensemble simulations are designed to assess the extent to which initial soil moisture fields explain variance of future predictands (soil moisture, near-surface air temperature, and precipitation). For soil moisture, the decrease of explained variance with lead time can be characterized as a first-order decay process, and a predictability timescale is introduced as the lead time at which this decay reaches e 21. The predictability timescale ranges from about 2 weeks or less (in midlatitudes during summer, and in the Tropics and subtropics) to 2‐6 months (in mid- to high latitudes for simulations that start in late fall and early winter). The predictability timescale of the modeled soil moisture is directly related to the soil moisture’s autocorrelation timescale. The degree of translation of soil moisture predictability to predictability of any atmospheric variable can be characterized by the ratio of the fraction of explained variance of the atmospheric variable to the fraction of explained soil moisture variance. By this measure, regions with the highest associated predictability of 30-day-mean near-surface air temperature (ratio greater than 0.5) are, generally speaking, coincident with regions and seasons of the smallest soil moisture predictability timescales. High associated temperature predictability is found where strong variability of soil moisture stress on evapotranspiration and abundant net radiation at the continental surface coincide. The associated predictability of 30-day-mean precipitation, in contrast, is very low.

Potential predictability and skill of simulated Eurasian snow cover are explored using a suite of seasonal ensemble hindcasts (i.e. retrospective forecasts), an ensemble climate simulation (spanning the years 1982–1998) and observations. Using remotely sensed observations of snow cover, we find significant point-wise correlation over the North Atlantic and North Pacific between winter and spring averaged sea-surface temperatures and Eurasian snow cover area. The observed correlation shows no discernible pattern related to the El Nino-Southern Oscillation (ENSO). The hindcasts show correlation patterns similar to the observations. However, the climate simulation shows an exaggerated ENSO pattern. The results underscore the importance of initialization in seasonal climate forecasts, and that the observed potential predictability of Eurasian snowcover cannot be solely attributed to ENSO.

The Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) aims to improve understanding and modeling of land surface processes. PILPS phase 2(d) uses a set of meteorological and hydrological data spanning 18 yr (1966‐83) from a grassland catchment at the Valdai water-balance research site in Russia. A suite of stand-alone simulations is performed by 21 land surface schemes (LSSs) to explore the LSSs’ sensitivity to downward longwave radiative forcing, timescales of simulated hydrologic variability, and biases resulting from single-year simulations that use recursive spinup. These simulations are the first in PILPS to investigate the performance of LSSs at a site with a well-defined seasonal snow cover and frozen soil. Considerable model scatter for the control simulations exists. However, nearly all the LSS scatter in simulated root-zone soil moisture is contained within the spatial variability observed inside the catchment. In addition, all models show a considerable sensitivity to longwave forcing for the simulation of the snowpack, which during the spring melt affects runoff, meltwater infiltration, and subsequent evapotranspiration. A greater sensitivity of the ablation, compared to the accumulation, of the winter snowpack to the choice of snow parameterization is found. Sensitivity simulations starting at prescribed conditions with no spinup demonstrate that the treatment of frozen soil (moisture) processes can affect the long-term variability of the models. The single-year recursive runs show large biases, compared to the corresponding year of the control run, that can persist through the entire year and underscore the importance of performing multiyear simulations.

Biofuel Backfire For compelling economical, geopolitical, and environmental reasons, biofuels are considered an attractive alternative to fossil fuels for meeting future global energy demands. Melillo et al. (p. 1397 , published online 22 October), however, suggest that a few serious drawbacks related to land use need to be considered. Based on a combined biogeochemistry and economic model, indirect land use (for example, clearing forested land for food crops to compensate for increased biofuel crop production on current farmlands) is predicted to generate more soil carbon loss than directly harvesting biofuel crops. Furthermore, increased fertilizer use for biofuels will add large amounts of nitrous oxide—a more effective heat-trapping molecule than carbon dioxide—to the atmosphere. Policy decisions regarding land and crop management thus need to consider the long-term implications of increased biofuel production.

The Atmospheric Model Intercomparison Project (AMIP) conducted simulations by 30 different atmospheric general circulation models forced by observed sea surface temperatures for the 10-year period, 1979–1988. These models include a variety of different soil moisture parameterizations which influence their simulations of the entire land surface hydrology, including evaporation, soil moisture, and runoff, and their simulations of the energy balance at the surface. Here we compare these parameterizations, and evaluate their simulations of soil moisture by comparing them with actual observations of soil moisture, literally ground truth. We compared model-generated `data sets' and simulations of soil moisture with observations from 150 stations in the former Soviet Union for 1979–1985 and Illinois for 1981–1988. The spatial patterns, mean annual cycles, and interannual variations were compared to plant-available soil moisture in the upper 1 m of soil. The model-generated `data sets' are quite different from the observations, and from each other in many regions, even though they use the same bucket model calculation method. The AMIP model simulations are also quite different from each other, especially in the tropics. Models with 15-cm field capacities do not capture the observed large high latitude values of soil moisture. In addition, none of the models properly simulate winter soil moisture variations in high latitudes, keeping soil moisture constant, while observations show that soil moisture varies in the winter as much as in other seasons. The observed interannual variations of soil moisture were not captured by any of the AMIP models. Several models have large soil moisture trends during the first year or two of the AMIP simulations, with potentially large impacts on global hydrological cycle trends and on other climate elements. This is because the simulations were begun without spinning up the soil moisture to the model climatology. The length of time it took for each to reach equilibrium depended on the particular parameterization. Although observed temporal autocorrelation time scales are a few months, some models had much longer time scales than that. In particular, the three parameterizations based on the Simple Biosphere model (SiB) had trends in some regions for virtually the entire AMIP simulation period.

Traditional `delta-change´ approach of scenario generation for climate change impact assessment to water resources strongly depends on the selected base-case observed historical climate conditions that the climate shocks are to be super-imposed. This met

LSIE Global and Planetary Change 13 (1996) 183-194 GLEIBAL AND PLANETARY CHANGE SSiB and its sensitivity to soil properties-—a case study using HAPEX-Mobilhy data Yonglcang Xue 3’, Fanrong J. Zeng 3, C. Adam Schlosser '3 E Center firtr 0c*ean—Lond—Annrr.tphere .S:nrJies', 40-=H Powder Mitt Road, Suite 302, Coloerton. MD 2t'J?U5, USA q' Geophy.stt'df Fluid Dynomt'c.s Laboratory. Frfnc'eton, .vr oases. use Received 5 May 1995: accepted 23 Au gust 1995 Abstract In this paper, SSiB’s development and some of its major parameterizations in the model are briefly reviewed. The soil moisture parameterizations, which are a key element in the model, are comprehensively described. The sensitivity study shows that hydraulic conductivity at saturation, B parameter, and wilting point have a profound impact on the simulation of soil moisture, but with different features. Both hydraulic conductivity at saturation and B parameter influence the soil moisture simulation by changing the soil hydraulic conductivity and the field capacity. The changes in equilibrium soil water content in this study are consistent with the changes in field capacity. The wilting point affects the soil moisture through vegetation transpiration. Through these sensitivity studies, improvements in modeling the soil moisture content of HAPEX—Mobilhy data are made. The soil moisture simulations at six Russian sites are also re-examined. After applying the results from the sensitivity studies of the HAPEX—Mobilhy data, the soil moisture simulation of the Russian data is significantly improved. 1. Introduction The SSiB biosphere model (Xue et al., 1991) used in this study is a simplified version of the Simple Biosphere Model (SiB) (Sellers et al., 1986). The vegetation—soil layer affects the radiative transfer at the surface, the partitioning of surface energy into sensible heat flux and latent heat flux. SSiB is intended to realistically simulate the biophysical ex- change processes. The biophysical controls on these exchange processes are mutually consistent by mod- eling the vegetation explicitly. The biosphere model is linked to a general circulation model (GCM) of the atmosphere through fluxes of radiation, sensible and latent heat, and momentum. A coupled biosphere model—GCM has been shown to be an improvement over the “bucket” model for simulations of the hydrologic cycle and the surface energy partition (Sato et al., 1989). To apply the coupled biosphere model—GCM for extended-range prediction and climate studies, we reduced vegetation and soil parameters from SiB. The values of many of the parameters are scarce for different biornes in different parts of the world. Large number of parameters with only approxi— mately known values would matte the sensitivity testing and model validation difficult. Our studies found some vegetation and soil parameters have little effects in the long term GCM simulations. We also used one canopy layer in the SSiB. The multilayer model is more realistic and might be easier to com- pare with the observations over a single site, but it U921-Slfll/96/$l5.DU @ 1996 Elsevier Science a.v. All rights reserved SSDI U921-S] 3l{95}DU{}45-3

Soil moisture observations from direct gravimetric measurements in Russia are used to study the relationship between soil moisture, runoff, and water table depth for catchments with different vegetation types, and to estimate the spatial and temporal correlation functions of soil moisture for different soil layers. For three catchments at Valdai, Russia, one with a grassland, one with an old forest, and one with a growing forest, the interannual soil moisture variations are virtually the same for the 31-year period, 1960–1990. The runoff is higher for the grassland than for the old forest, and the water table depth is not as deep. The runoff and water table for the growing forest vary from grassland-like during the first decade, when the trees are small, to old forest-like at the end of the period. The seasonal cycle of soil moisture is similar at all three catchments, but the snowmelt and summer drying begin a month earlier at the grassland than in the forests. A statistical model of both temporal and spatial variations in soil moisture is developed that partitions the variations into red noise and white noise components. For flat homogeneous plots, the white noise component is relatively small and represents solely random errors of measurement. For natural landscapes with variable vegetation and soil types, and complicated topography, this component is responsible for most of the temporal or spatial variance. The red noise component of temporal variability is in good agreement with theory. The timescale of this component is equal to the ratio of field capacity of soil to potential evapotranspiration, approximately 3 months. The red noise component of spatial variability reflects the statistical properties of the monthly averaged precipitation field. The scale of spatial correlation of this component is about 500 km. The estimates of scales of temporal and spatial correlation do not differ significantly for water content in the top 20-cm and 1-m layers of soil. These results have important implications for both remote sensing of soil moisture and soil moisture parameterization in climate models.

[1] As greenhouse gases, including CO2, accumulate in the atmosphere, the western United States is predicted to undergo large-scale climate warming and reduced summer precipitation in the coming decades. In this study we explore the role of these climate changes with elevated CO2 to determine the plant physiological response on primary productivity and associated feedbacks on evapotranspiration (ET) and runoff using a biogeochemistry model, TEM-Hydro, with downscaled climate data for the western United States from the NCAR CCSM3 A2 scenario. Net primary productivity increases by 32% in forests due to feedbacks between warmer temperatures and enhanced nitrogen mineralization but decreases in shrublands by 24% due to excessive drying and reduced nitrogen mineralization. Warming directly increases nitrogen mineralization rates but indirectly decreases them by reducing soil moisture, so the net effect is highly dependent on climatic conditions within each biome. Increased soil moisture resulting from larger water use efficiency from the elevated CO2 leads to more net nitrogen mineralization in forests, which reduces N-limiting conditions. The effect of CO2 on stomatal conductance is therefore enhanced because of its effect on reducing nitrogen limiting conditions. Runoff decreases over the 21st century by 22% in forests, 58% in grasslands, and 67% in shrublands due to the reduced precipitation in each region but is modulated by the plant-induced changes in ET. The role of moisture limitation is therefore a crucial regulator of nitrogen limitation, which determines the future productivity and water availability in the West.

Figure 1. Carbon-nitrogen-water coupling in TEM-Hydro. Arrows indicate positive couplings, and circles represent negative couplings. Ci, internal CO2; Ca, atmospheric CO2; kc, half saturation constant for CO2 fertilization; GPP, gross primary productivity; gc, canopy conductance; VPD, vapor pressure deficit. JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 114, G03009, doi:10.1029/2009JG001083, 2009 Click Here for Full Article

[1] There is evidence that increasing CO2 concentrations have reduced evapotranspiration and increased runoff through reductions in stomatal conductance during the twentieth century. While this process will continue to counteract increased evapotranspiration associated with future warming, it is highly dependent upon concurrent changes in photosynthesis, especially due to CO2 fertilization, nitrogen limitation, and ozone exposure. A new version of the Terrestrial Ecosystem Model (TEM-Hydro) was developed to examine the effects of carbon and nitrogen on the water cycle. We used two climate models (NCAR CCSM3 and DOE PCM) and two emissions scenarios (SRES B1 and A2) to examine the effects of climate, elevated CO2, nitrogen limitation, and ozone exposure on the hydrological cycle in the eastern United States. While the direction of future runoff changes is largely dependent upon predicted precipitation changes, the effects of elevated CO2 on ecosystem function (stomatal closure and CO2 fertilization) increase runoff by 3–7%, as compared to the effects of climate alone. Consideration of nitrogen limitation and ozone damage on photosynthesis increases runoff by a further 6–11%. Failure to consider the effects of the interactions among nitrogen, ozone, and elevated CO2 may lead to significant regional underestimates of future runoff.

Robock et al. (1995a) tested a 15-cm bucket model (Manabe 1969) and the Simplified Simple Biosphere (SSiB) model (Xue et al. 1991) by forcing them with observed meteorological and actinometric data and comparing the results with observed soil moisture, albedo, snow depth, and net radiation. Xue et al. (1997) show that it is possible to improve SSiB in response to errors in the simulations by fixing the snowmelt partitioning and adjusting the soil properties to produce a higher effective field capacity. We agree with their conclusion that using real data to test models is a good idea, and this was one of the major conclusions of Robock et al. (1995a). Xue et al. (1997) claim that they demonstrate that the fundamental structure of their model is sound and that it just needs tuning by adjusting parameter values. However, while the bucket model did not have any problem with snowmelt partitioning in the Robock et al. (1995a) experiments, which involved six stations for 6 yr in the former Soviet Union, for some stations the 15-cm field capacity was clearly too low. Schlosser (1995) demonstrated that major improvements are also possible in bucket model simulations for particular stations by taking into account actual conditions at the station. Figure 1, for example, shows a dramatic improvement in the Kostroma simulations by using the actual field capacity of 28 cm of plant-available water in the top 1 m of soil rather than the standard 15 cm used in the GCM version of the bucket model. Not only are the winter soil moisture values accurately simulated, but the summer drying is accurately modeled in wet and dry summers. In fact, this improved simulation of soil moisture for Kostroma, produced by changing only one parameter in the bucket model, the field capacity, appears to be superior to the

The global water cycle represents the transport and transformation of water within the Earth system, and, in doing so, distributes freshwater over the Earth's surface. The cycling of water in the Earth's system employs water in all three of its phases: solid, liquid, and gaseous. In its coarsest and most conventional depiction, the Earth system's cycling of water is viewed as the continual displacement of water taken from the ocean, transported through the atmosphere, deposited over land, and ultimately fed back to the ocean. However, many processes and pathways are responsible for this global transit and cycling. Therefore, to monitor the global water cycle in the context of all of these key interactions with the global environment requires a comprehensive representation of the budgets and cycling of all phases of water storage. An analytic discussion is provided to elucidate the various mechanisms that contribute to the global transport of water within and between the Earth's atmosphere, land, and ocean systems Keywords: water; cycle; budget; balance; global; atmosphere; land; ocean

[1] Simulated near-surface air temperature (Ta) and precipitation in an ensemble climate simulation is assessed. The ensemble climate-simulation was constructed with the Center for Ocean-Land-Atmosphere (COLA) atmospheric general circulation model (AGCM) in conjunction with the Atmospheric Model Intercomparison Project Phase II (AMIP II). To diagnose the ensemble simulation, a measure of “predictable skill” is formalized. This diagnostic is based upon the statistical significance of spatial correlation over any given region (for this analysis, North America) between the ensemble mean and observed anomalies and the inter-member scatter of the spatial anomaly correlation. Using this measure as a function of time, periods of predictable skill within the COLA AMIP II ensemble simulation are identified, and through a point-wise, multiple correlation technique, spatial patterns of contemporaneous sea-surface temperature (SST) variability are also constructed. This diagnosis can essentially define which skillfully simulated climate signals over North America (given by predictable skill) can be attributed to specific SST anomalies. Spatially coherent patterns of SST variability are found to be associated with predictable skill. These patterns are, not surprisingly, primarily related to the El Nino Southern Oscillation (ENSO). One of the more prevalent ENSO associations to predictable skill (for the COLA AGCM) is found in sub-tropical regions of the western Pacific. Moreover, a skillful episode of simulated North American precipitation is seen to be associated with SST variability over the east Indian Ocean. In addition, a strong contemporaneous association of Ta predictable skill with tropical Atlantic SST variability is found. Complementary simulations are performed with the COLA AGCM to further assess the degree of attribution of predictable skill to these regions of SST variability. The results of the experimental simulations suggest that, for the AMIP II simulation period, the COLA AGCM can be skillfully attributed to SST variability primarily associated with ENSO, warm tropical Atlantic anomalies, and an abrupt, cooling event over the east Indian Ocean.

[1] A suite of numerical simulations with the Center for Ocean Land Atmosphere (COLA) Studies' and the Goddard Earth Observing System (GEOS) general circulation models (GCMs) has been performed in conjunction with the Dynamical Seasonal Prediction (DSP) Project. These simulations aim to quantify the impact of realistic snow conditions on skill in the GCMs. In this study, ensemble climate simulations conducted for Northern Hemisphere spring (March–June) that span the years 1982–1998 are considered. For each year of these seasonal simulations, a pair of complementary runs is performed. For one of the simulations, snow conditions are allowed to evolve interactively; for the other simulation, the snow conditions are prescribed, according to a daily, global snow depth analysis, within each of the land modules of the GCMs. For this study, the impact of snow conditions on simulated near-surface air temperature is assessed. The results indicate that the prescribed (and presumably improved) snow conditions in the GCMs play a beneficial role in skillfully capturing the observed spatial/temporal patterns of interannual variations of near-surface air temperature, at a local scale. Through consideration of the surface energy-budget and the effect of snow cover on surface albedo, the localized improvement of near-surface air temperature skill (both in the spatial correlations and in the root-mean-square error) that results from the prescribed snowfields is found to be strongly tied to when and where the interannual variabilities of snow cover and mean incident short wave radiation coincide. This impact of prescribed snow is also most considerable during the widespread ablation of the matured winter season snow cover, which typically occurs during April over the Northern Hemisphere. Overall, the impact of the prescribed snow over all land points (i.e., local and nonlocal) shows mixed results in its effect on near-surface air temperature skill. These mixed results most likely underscore the difficulty of the GCMs to consistently translate the localized skillful response into nonlocal/remote skill. In the end, physical parameterizations in GCMs should be improved before all seasonal prediction enhancements from improved snow conditions can be realized.

Soil moisture observations in sites with natural vegetation were made for several decades in the former Soviet Union at hundreds of stations. In this paper, the authors use data from six of these stations from different climatic regimes, along with ancillary meteorological and actinometric data, to demonstrate a method to validate soil moisture simulations with biosphere and bucket models. Some early and current general circulation models (GCMs) use bucket models for soil hydrology calculations. More recently, the Simple Biosphere Model (SiB) was developed to incorporate the effects of vegetation on fluxes of moisture, momentum, and energy at the earth's surface into soil hydrology models. Until now, the bucket and SiB have been verified by comparison with actual soil moisture data only on a limited basis. In this study, a Simplified SiB (SSiB) soil hydrology model and a 15-cm bucket model are forced by observed meteorological and actinometric data every 3 h for 6-yr simulations at the six stations. The model calculations of soil moisture are compared to observations of soil moisture, literally 'ground truth,' snow cover, surface albedo, and net radiation, and with each other. For three of the stations, the SSiB and 15-cm bucket models produce good simulations of seasonal cycles and interannual variations of soil moisture. For the other three stations, there are large errors in the simulations by both models. Inconsistencies in specification of field capacity may be partly responsible. There is no evidence that the SSiB simulations are superior in simulating soil moisture variations. In fact, the models are quite similar since SSiB implicitly has a bucket embedded in it. One of the main differences between the models is in the treatment of runoff due to melting snow in the spring -- SSiB incorrectly puts all the snowmelt into runoff. While producing similar soil moisture simulations, the models produce very different surface latent and sensible heat fluxes, which would have large effects on GCM simulations.