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Brian Cook, Ning Zeng, and Jin-Ho Yoon

Abstract

The Amazon rain forest may undergo significant change in response to future climate change. To determine the likelihood and causes of such changes, the authors analyzed the output of 24 models from the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) and a dynamic vegetation model, Vegetation–Global–Atmosphere–Soil (VEGAS), driven by these climate output. Their results suggest that the core of the Amazon rain forest should remain largely stable because rainfall in the core of the basin is projected to increase in nearly all models. However, the periphery, notably the southern edge of Amazonia and farther south into central Brazil (SAB), is in danger of drying out, driven by two main processes. First, a decline in precipitation of 11% during the southern Amazonia’s dry season (May–September) reduces soil moisture. Two dynamical mechanisms may explain the forecast reduction in dry season rainfall: 1) a general subtropical drying under global warming when the dry season southern Amazon basin is under the control of subtropical high pressure and 2) a stronger north–south tropical Atlantic sea surface temperature gradient and, to a lesser degree, a warmer eastern equatorial Pacific. The drying corresponds to a lengthening of the dry season by approximately 10 days. The decline in soil moisture occurs despite an increase in precipitation during the wet season, because of nonlinear responses in hydrology associated with the decline in dry season precipitation, ecosystem dynamics, and an increase in evaporative demand due to the general warming. In terms of ecosystem response, higher maintenance cost and reduced productivity under warming may also have additional adverse impact. Although the IPCC models have substantial intermodel variation in precipitation change, these latter two hydroecological effects are highly robust because of the general warming simulated by all models. As a result, when forced by these climate projections, a dynamic vegetation model VEGAS projects an enhancement of fire risk by 20%–30% in the SAB region. Fire danger reaches its peak in Amazonia during the dry season, and this danger is expected to increase primarily because of the reduction in soil moisture and the decrease in dry season rainfall. VEGAS also projects a reduction of about 0.77 in leaf area index (LAI) over the SAB region. The vegetation response may be partially mediated by the CO2 fertilization effect, because a sensitivity experiment without CO2 fertilization shows a higher 0.89 decrease in LAI. Southern Amazonia is currently under intense human influence as a result of deforestation and land-use change. Should this direct human impact continue at present rates, added pressure to the region’s ecosystems from climate change may subject the region to profound changes in the twenty-first century.

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Daniel E. Christiansen, Steven L. Markstrom, and Lauren E. Hay

Abstract

Understanding the effects of climate change on the vegetative growing season is key to quantifying future hydrologic water budget conditions. The U.S. Geological Survey modeled changes in future growing season length at 14 basins across 11 states. Simulations for each basin were generated using five general circulation models with three emission scenarios as inputs to the Precipitation-Runoff Modeling System (PRMS). PRMS is a deterministic, distributed-parameter, watershed model developed to simulate the effects of various combinations of precipitation, climate, and land use on watershed response. PRMS was modified to include a growing season calculation in this study. The growing season was examined for trends in the total length (annual), as well as changes in the timing of onset (spring) and the end (fall) of the growing season. The results showed an increase in the annual growing season length in all 14 basins, averaging 27–47 days for the three emission scenarios. The change in the spring and fall growing season onset and end varied across the 14 basins, with larger increases in the total length of the growing season occurring in the mountainous regions and smaller increases occurring in the Midwest, Northeast, and Southeast regions. The Clear Creek basin, 1 of the 14 basins in this study, was evaluated to examine the growing season length determined by emission scenario, as compared to a growing season length fixed baseline condition. The Clear Creek basin showed substantial variation in hydrologic responses, including streamflow, as a result of growing season length determined by emission scenario.

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David M. Bjerklie, Thomas J. Trombley, and Roland J. Viger

Abstract

A regional watershed model was developed for watersheds contributing to Long Island Sound, including the Connecticut River basin. The study region covers approximately 40 900 km2, extending from a moderate coastal climate zone in the south to a mountainous northern New England climate zone dominated by snowmelt in the north. The input data indicate that precipitation and temperature have been increasing for the last 46 years (1961–2006) across the region. Minimum temperature has increased more than maximum temperature over the same period (1961–2006). The model simulation indicates that there was an upward trend in groundwater recharge across most of the modeled region. However, trends in increasing precipitation and groundwater recharge are not significant at the 0.05 level if the drought of 1961–67 is removed from the time series. The trend in simulated snowfall is not significant across much of the region, although there is a significant downward trend in southeast Connecticut and in central Massachusetts. To simulate future trends, two input datasets, one assuming high carbon emissions and one assuming low carbon emissions, were developed from GCM forecasts. Under both of the carbon emission scenarios, simulations indicate that historical trends will continue, with increases in groundwater recharge over much of the region and substantial snowfall decreases across Massachusetts, Connecticut, southern Vermont, and southern New Hampshire. The increases in groundwater recharge and decreases in snowfall are most pronounced for the high emission scenario.

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Daniel M. Brown, Gerhard W. Reuter, and Thomas K. Flesch

Abstract

The Athabasca oil sands development in northeast Alberta, Canada, has disturbed more than 500 km2 of boreal forest through surface mining and tailings ponds development. In this paper, the authors compare the time series of temperatures and precipitation measured over oil sands and non–oil sands locations from 1994 to 2010. In addition, they analyzed the distribution of lightning strikes from 1999 to 2010. The oil sands development has not affected the number of lightning strikes or precipitation amounts but has affected the temperature regime. Over the past 17 years, the summer overnight minimum temperatures near the oil sands have increased by about 1.2°C compared to the regional average. The authors speculate that this is caused by a combination of the industrial addition of waste heat to the atmosphere above the oil sands and changing the surface type from boreal forest to open pit mines with tailings ponds.

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Kathryn M. Koczot, Steven L. Markstrom, and Lauren E. Hay

Abstract

Changes in temperature and precipitation projected from five general circulation models, using one late-twentieth-century and three twenty-first-century emission scenarios, were downscaled to three different baseline conditions. Baseline conditions are periods of measured temperature and precipitation data selected to represent twentieth-century climate. The hydrologic effects of the climate projections are evaluated using the Precipitation-Runoff Modeling System (PRMS), which is a watershed hydrology simulation model. The Almanor Catchment in the North Fork of the Feather River basin, California, is used as a case study.

Differences and similarities between PRMS simulations of hydrologic components (i.e., snowpack formation and melt, evapotranspiration, and streamflow) are examined, and results indicate that the selection of a specific time period used for baseline conditions has a substantial effect on some, but not all, hydrologic variables. This effect seems to be amplified in hydrologic variables, which accumulate over time, such as soil-moisture content. Results also indicate that uncertainty related to the selection of baseline conditions should be evaluated using a range of different baseline conditions. This is particularly important for studies in basins with highly variable climate, such as the Almanor Catchment.

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Bryan Pijanowski, Nathan Moore, Dasaraden Mauree, and Dev Niyogi

Abstract

This study examines how land-use errors from the Land Transformation Model (LTM) propagate through to climate as simulated by the Regional Atmospheric Model System (RAMS). The authors conducted five simulations of regional climate over East Africa: one using observed land cover/land use (LULC) and four utilizing LTM-derived LULC. The study examined how quantifiable errors generated by the LTM impact typical land–climate variables: precipitation, land surface temperature, air temperature, soil moisture, and latent heat flux. Error propagation was not evident when domain averages for the land–climate variables of the yearlong simulation were examined. However, the authors found that spatial errors from the LTM propagate through in complex ways, temporally affecting the seasonal distributions of rainfall, surface temperature, soil moisture, and latent heat flux. In particular, rainy seasons exhibited greater precipitation in LTM-RAMS simulations than in the reference simulation and less precipitation occurred during the dry season. Complex interactions of precipitation and soil moisture were also evident. Overall, results indicate that small errors from a land change model could grow as a “coupling drift” if both are used to forecast into the future; these couplings could create larger combined errors of land–climate interactions because of time-scale differences into the future. Thus, although land-use change projection is necessary for a more accurate climate projection, existing errors from a land change model will likely amplify through the climate simulation. This finding affects interpretation of large-scale versus land-use/land-cover feedbacks on climate projections.

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Julian C. Brimelow, John M. Hanesiak, and William R. Burrows

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The purpose of this study was to focus on how anomalies in the normalized difference vegetation index (NDVI; a proxy for soil moisture) over the Canadian Prairies can condition the convective boundary layer (CBL) so as to inhibit or facilitate thunderstorm activity while also considering the role of synoptic-scale forcing. This study focused on a census agricultural region (CAR) over central Alberta for which we had observed lightning data (proxy for thunderstorms), remotely sensed NDVI data, and in situ rawinsonde data (to quantify impacts of vegetation vigor on the CBL characteristics) for 11 summers from 1999 to 2009. The authors’ data suggest that the occurrence of lightning over the study area is more likely (and is of longer duration) when storms develop in an environment in which the surface and upper-air synoptic-scale forcing are synchronized. On days when surface forcing and midtropospheric ascent are present, storms are more likely to be triggered when NDVI is much above average, compared to when NDVI is much below average. Additionally, the authors found the response of thunderstorm duration to NDVI anomalies to be asymmetric. That is, the response of lightning duration to anomalies in NDVI is marked when NDVI is below average but is not necessarily discernible when NDVI is above average. The authors propose a conceptual model, based largely on observations, that integrates all of the above findings to describe how a reduction in vegetation vigor—in response to soil moisture deficits—modulates the partitioning of available energy into sensible and latent heat fluxes at the surface, thereby modulating lifting condensation level heights, which in turn affect lightning activity.

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Justin E. Bagley, Ankur R. Desai, Paul C. West, and Jonathan A. Foley

Abstract

The impacts of changing land cover on the soil–vegetation–atmosphere system are numerous. With the fraction of land used for farming and grazing expected to increase, extensive alterations to land cover such as replacing forests with cropland will continue. Therefore, quantifying the impact of global land-cover scenarios on the biosphere is critical. The Predicting Ecosystem Goods and Services Using Scenarios boundary layer (PegBL) model is a new global soil–vegetation–boundary layer model designed to quantify these impacts and act as a complementary tool to computationally expensive general circulation models and large-eddy simulations. PegBL provides high spatial resolution and inexpensive first-order estimates of land-cover change on the surface energy balance and atmospheric boundary layer with limited input requirements. The model uses a climatological-data-driven land surface model that contains only the physics necessary to accurately reproduce observed seasonal cycles of fluxes and state variables for natural and agricultural ecosystems. A bulk boundary layer model was coupled to the land model to estimate the impacts of changing land cover on the lower atmosphere. The model most realistically simulated surface–atmosphere dynamics and impacts of land-cover change at tropical rain forest and northern boreal forest sites. Further, simple indices to measure the potential impact of land-cover change on boundary layer climate were defined and shown to be dependent on boundary layer dynamics and geographically similar to results from previous studies, which highlighted the impacts of land-cover change on the atmosphere in the tropics and boreal forest.

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Lori T. Sentman, Elena Shevliakova, Ronald J. Stouffer, and Sergey Malyshev

Abstract

The dynamic vegetation and carbon cycling component, LM3V, of the Geophysical Fluid Dynamics Laboratory (GFDL) prototype Earth system model (ESM2.1), has been designed to simulate the effects of land use on terrestrial carbon pools, including secondary vegetation regrowth. Because of the long time scales associated with the carbon adjustment, special consideration is required when initializing the ESM when historical simulations are conducted. Starting from an equilibrated, preindustrial climate and potential vegetation state in an offline land-only model (LM3V), estimates of historical land use are instantaneously applied in five experiments beginning in the following calendar years: 1500, 1600, 1700, 1750, and 1800. This application results in the land carbon pools experiencing an abrupt change—a carbon shock—and the secondary vegetation needs time to regrow into consistency with the harvesting history. The authors find that it takes approximately 100 years for the vegetation to recover from the carbon shock, whereas soils take at least 150 years to recover. The vegetation carbon response is driven primarily by land-use history, whereas the soil carbon response is affected by both land-use history and the geographic pattern of soil respiration rates. Based on these results, the authors recommend the application of historical land-use scenarios in 1700 to provide sufficient time for the land carbon in ESMs with secondary vegetation to equilibrate to adequately simulate carbon stores at the start of the historical integrations (i.e., 1860) in a computationally efficient manner.

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Umarporn Charusombat and Dev Niyogi

Abstract

Characterizing and developing drought climatology continues to be a challenging problem. As decision makers seek guidance on water management strategies, there is a need for assessing the performance of drought indices. This requires the adaptation of appropriate drought indices that aid in monitoring droughts and hydrological vulnerability on a regional scale. This study aims to assist the process of developing a statewide water shortage and assessment plan (WSP) for the state of Indiana by conducting a focused hydroclimatological assessment of drought variability. Drought climatology was assessed using in situ observations and regional reanalysis data. A summary of precipitation and evaporation trends, estimated drought variability, worst-case drought scenarios, drought return period, and frequency and duration was undertaken, using multiple drought indices and streamflow analysis. Results indicated a regional and local variability in drought susceptibility. The worst-case (200-yr return period) prediction showed that Indiana has a 0.5% probability of receiving 45% of normal precipitation over a 12-month drought in any year. Consistent with other studies, the standardized precipitation index (SPI) was found to be appropriate for detecting short-term drought conditions over Indiana. This recommendation has now been incorporated in the 2009 Indiana water shortage plan. This study also highlights the difficulties in identifying past droughts from available climatic data, and the authors’ results suggest a persistent, high degree of uncertainty in making drought predictions using future climatic projections.

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