New evidence for an extended occupation of the Provo shoreline and implications for regional climate change, Pleistocene Lake Bonneville, Utah, USA

2005 ◽  
Vol 63 (2) ◽  
pp. 212-223 ◽  
Author(s):  
Holly S. Godsey ◽  
Donald R. Currey ◽  
Marjorie A. Chan
Keyword(s):  
Climate Change ◽  
Great Basin ◽  
Lake Level ◽  
Regional Climate ◽  
Independent Evidence ◽  
Threshold Control ◽  
Climate Conditions ◽  
Lake Bonneville ◽  
Radiocarbon Data ◽  
Bonneville Basin

Lake Bonneville was a climatically sensitive, closed-basin lake that occupied the eastern Great Basin during the late Pleistocene. Ongoing efforts to refine the record of lake level history are important for deciphering climate conditions in the Bonneville basin and for facilitating correlations with regional and global records of climate change. Radiocarbon data from this and other studies suggest that the lake oscillated at or near the Provo level much longer than depicted by current models of lake level change. Radiocarbon data also suggest that the lake dropped from threshold control much more rapidly than previously supposed. These revisions to the Lake Bonneville hydrograph, coupled with independent evidence of climate change from vegetation and glacial records, have important implications for conditions in the Bonneville basin and during the Pleistocene to Holocene transition.

scholarly journals Configuration and intercomparison of deep learning neural models for statistical downscaling

2020 ◽  
Vol 13 (4) ◽  
pp. 2109-2124 ◽  
Author(s):  
Jorge Baño-Medina ◽  
Rodrigo Manzanas ◽  
José Manuel Gutiérrez
Keyword(s):  
Climate Change ◽  
Deep Learning ◽  
Statistical Downscaling ◽  
Linear Models ◽  
Regional Climate ◽  
Added Value ◽  
Climate Conditions ◽  
Continental Scale ◽  
Spatial Features ◽  
Learning Techniques

Abstract. Deep learning techniques (in particular convolutional neural networks, CNNs) have recently emerged as a promising approach for statistical downscaling due to their ability to learn spatial features from huge spatiotemporal datasets. However, existing studies are based on complex models, applied to particular case studies and using simple validation frameworks, which makes a proper assessment of the (possible) added value offered by these techniques difficult. As a result, these models are usually seen as black boxes, generating distrust among the climate community, particularly in climate change applications. In this paper we undertake a comprehensive assessment of deep learning techniques for continental-scale statistical downscaling, building on the VALUE validation framework. In particular, different CNN models of increasing complexity are applied to downscale temperature and precipitation over Europe, comparing them with a few standard benchmark methods from VALUE (linear and generalized linear models) which have been traditionally used for this purpose. Besides analyzing the adequacy of different components and topologies, we also focus on their extrapolation capability, a critical point for their potential application in climate change studies. To do this, we use a warm test period as a surrogate for possible future climate conditions. Our results show that, while the added value of CNNs is mostly limited to the reproduction of extremes for temperature, these techniques do outperform the classic ones in the case of precipitation for most aspects considered. This overall good performance, together with the fact that they can be suitably applied to large regions (e.g., continents) without worrying about the spatial features being considered as predictors, can foster the use of statistical approaches in international initiatives such as Coordinated Regional Climate Downscaling Experiment (CORDEX).

Regional biogeographical model of vegetation zones in doctoral programme Regional Biography in Olomouc (Case study for Norway spruce)

2020 ◽  
Author(s):  
Ivo Machar ◽  
Marián Halás ◽  
Zdeněk Opršal
Keyword(s):  
Climate Change ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Climate Data ◽  
Ecological Data ◽  
The Czech Republic ◽  
Climate Conditions ◽  
Vegetation Zones ◽  
Landscape Research

Regional climate changes impacts induce vegetation zones shift to higher altitudes in temperate landscape. This paper deals with applying of regional biogeography model of climate conditions for vegetation zones in Czechia to doctoral programme Regional Geography in Palacky University Olomouc. The model is based on general knowledge of landscape vegetation zonation. Climate data for model come from predicted validated climate database under RCP8.5 scenario since 2100. Ecological data are included in the Biogeography Register database (geobiocoenological data related to landscape for cadastral areas of the Czech Republic). Mathematical principles of modelling are based on set of software solutions with GIS. Students use the model in the frame of the course “Special Approaches to Landscape Research” not only for regional scenarios climate change impacts in landscape scale, but also for assessment of climate conditions for growing capability of agricultural crops or forest trees under climate change on regional level.

Late Pleistocene and early Holocene rivers and wetlands in the Bonneville basin of western North America

2003 ◽  
Vol 60 (2) ◽  
pp. 200-210 ◽  
Author(s):  
Charles G. Oviatt ◽  
David B. Madsen ◽  
Dave N. Schmitt
Keyword(s):  
Great Basin ◽  
Late Pleistocene ◽  
Theoretical Models ◽  
Coarse Sand ◽  
Early Holocene ◽  
River Channels ◽  
Human Occupation ◽  
Lake Bonneville ◽  
River Bed ◽  
Bonneville Basin

AbstractField investigations at Dugway Proving Ground in western Utah have produced new data on the chronology and human occupation of late Pleistocene and early Holocene lakes, rivers, and wetlands in the Lake Bonneville basin. We have classified paleo-river channels of these ages as “gravel channels” and “sand channels.” Gravel channels are straight to curved, digitate, and have abrupt bulbous ends. They are composed of fine gravel and coarse sand, and are topographically inverted (i.e., they stand higher than the surrounding mudflats). Sand channels are younger and sand filled, with well-developed meander-scroll morphology that is truncated by deflated mudflat surfaces. Gravel channels were formed by a river that originated as overflow from the Sevier basin along the Old River Bed during the late regressive phases of Lake Bonneville (after 12,500 and prior to 11,000 14C yr B.P.). Dated samples from sand channels and associated fluvial overbank and wetland deposits range in age from 11,000 to 8800 14C yr B.P., and are probably related to continued Sevier-basin overflow and to groundwater discharge. Paleoarchaic foragers occupied numerous sites on gravel-channel landforms and adjacent to sand channels in the extensive early Holocene wetland habitats. Reworking of tools and limited toolstone diversity is consistent with theoretical models suggesting Paleoarchaic foragers in the Old River Bed delta were less mobile than elsewhere in the Great Basin.

scholarly journals Downscaling a global climate model to simulate climate change over the US and the implication on regional and urban air quality

2013 ◽  
Vol 6 (5) ◽  
pp. 1429-1445 ◽  
Author(s):  
M. Trail ◽  
A. P. Tsimpidi ◽  
P. Liu ◽  
K. Tsigaridis ◽  
Y. Hu ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Quality ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Climate Conditions ◽  
Southeastern Us ◽  
The Us ◽  
Ozone Levels

Abstract. Climate change can exacerbate future regional air pollution events by making conditions more favorable to form high levels of ozone. In this study, we use spectral nudging with the Weather Research and Forecasting (WRF) model to downscale NASA earth system GISS modelE2 results during the years 2006 to 2010 and 2048 to 2052 over the contiguous United States in order to compare the resulting meteorological fields from the air quality perspective during the four seasons of five-year historic and future climatological periods. GISS results are used as initial and boundary conditions by the WRF regional climate model (RCM) to produce hourly meteorological fields. The downscaling technique and choice of physics parameterizations used are evaluated by comparing them with in situ observations. This study investigates changes of similar regional climate conditions down to a 12 km by 12 km resolution, as well as the effect of evolving climate conditions on the air quality at major US cities. The high-resolution simulations produce somewhat different results than the coarse-resolution simulations in some regions. Also, through the analysis of the meteorological variables that most strongly influence air quality, we find consistent changes in regional climate that would enhance ozone levels in four regions of the US during fall (western US, Texas, northeastern, and southeastern US), one region during summer (Texas), and one region where changes potentially would lead to better air quality during spring (Northeast). Changes in regional climate that would enhance ozone levels are increased temperatures and stagnation along with decreased precipitation and ventilation. We also find that daily peak temperatures tend to increase in most major cities in the US, which would increase the risk of health problems associated with heat stress. Future work will address a more comprehensive assessment of emissions and chemistry involved in the formation and removal of air pollutants.

scholarly journals Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions?

2012 ◽  
Vol 9 (11) ◽  
pp. 12765-12795 ◽  
Author(s):  
C. Teutschbein ◽  
J. Seibert
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Change Impact ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Conditions ◽  
Impact Studies ◽  
Split Sample ◽  
Sample Test ◽  
Change Impact

Abstract. In hydrological climate-change impact studies, Regional Climate Models (RCMs) are commonly used to transfer large-scale Global Climate Model (GCM) data to smaller scales and to provide more detailed regional information. However, there are often considerable biases in RCM simulations, which have led to the development of a number of bias correction approaches to provide more realistic climate simulations for impact studies. Bias correction procedures rely on the assumption that RCM biases do not change over time, because correction algorithms and their parameterizations are derived for current climate conditions and assumed to apply also for future climate conditions. This underlying assumption of bias stationarity is the main concern when using bias correction procedures. It is in principle not possible to test whether this assumption is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well bias correction methods perform for conditions different from those used for calibration. For five Swedish catchments, several time series of RCM simulated precipitation and temperature were obtained from the ENSEMBLES data base and different commonly-used bias correction methods were applied. We then performed a differential split-sample test by dividing the data series into cold and warm respective dry and wet years. This enabled us to evaluate the performance of different bias correction procedures under systematically varying climate conditions. The differential split-sample test resulted in a large spread and a clear bias for some of the correction methods during validation years. More advanced correction methods such as distribution mapping performed relatively well even in the validation period, whereas simpler approaches resulted in the largest deviations and least reliable corrections for changed conditions. Therefore, we question the use of simple bias correction methods such as the widely used delta-change approach and linear scaling for RCM-based climate-change impact studies and recommend using higher-skill bias correction methods.

scholarly journals Downscaling a global climate model to simulate climate change impacts on US regional and urban air quality

2013 ◽  
Vol 6 (2) ◽  
pp. 2517-2549 ◽  
Author(s):  
M. Trail ◽  
A. P. Tsimpidi ◽  
P. Liu ◽  
K. Tsigaridis ◽  
Y. Hu ◽  
...  
Keyword(s):  
Climate Change ◽  
Air Quality ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Climate Conditions ◽  
Southeastern Us ◽  
The Us

Abstract. Climate change can exacerbate future regional air pollution events by making conditions more favorable to form high levels of ozone. In this study, we use spectral nudging with WRF to downscale NASA earth system GISS modelE2 results during the years 2006 to 2010 and 2048 to 2052 over the continental United States in order to compare the resulting meteorological fields from the air quality perspective during the four seasons of five-year historic and future climatological periods. GISS results are used as initial and boundary conditions by the WRF RCM to produce hourly meteorological fields. The downscaling technique and choice of physics parameterizations used are evaluated by comparing them with in situ observations. This study investigates changes of similar regional climate conditions down to a 12 km by 12 km resolution, as well as the effect of evolving climate conditions on the air quality at major US cities. The high resolution simulations produce somewhat different results than the coarse resolution simulations in some regions. Also, through the analysis of the meteorological variables that most strongly influence air quality, we find consistent changes in regional climate that would enhance ozone levels in four regions of the US during fall (Western US, Texas, Northeastern, and Southeastern US), one region during summer (Texas), and one region where changes potentially would lead to better air quality during spring (northeast). We also find that daily peak temperatures tend to increase in most major cities in the US which would increase the risk of health problems associated with heat stress. Future work will address a more comprehensive assessment of emissions and chemistry involved in the formation and removal of air pollutants.

Climate change impacts on European wheat and maize yields

2020 ◽  
Author(s):  
Andrea Toreti ◽  
Andrej Ceglar ◽  
Frank Dentener ◽  
Davide Fumagalli ◽  
Simona Bassu ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Crop Yields ◽  
Regional Climate ◽  
Adaptation Strategies ◽  
Economic Sectors ◽  
Climate Conditions ◽  
Precipitation Regimes ◽  
Maize Yields ◽  
Projected Changes

<p>Crop yields are influenced and affected by climate conditions and the occurrence of extreme events in critical phenological phases during the growing season. As projected climate change for Europe points to an increase of climate extremes as well as a significant warming together with changes in precipitation regimes, it is essential to assess impacts on key socio-economic sectors such as agriculture. Here, we analyse European wheat and maize yields as projected by a crop model driven by bias-adjusted Euro-CORDEX regional climate model simulations under the RCP4.5 and RCP8.5 scenarios. The main findings highlight as maize will be the most affected crop with limited effects of simple adaptation strategies; while a north-south dipole in the projected changes characterizes wheat yields. In the wheat regions negatively affected by climate change, adaptation strategies will play a key role in counterbalancing the impacts of the projected changes. </p>

scholarly journals Modelling the water balance of Lake Victoria (East Africa) – Part 2: Future projections

2018 ◽  
Vol 22 (10) ◽  
pp. 5527-5549 ◽  
Author(s):  
Inne Vanderkelen ◽  
Nicole P. M. van Lipzig ◽  
Wim Thiery
Keyword(s):  
Climate Change ◽  
Water Balance ◽  
Lake Level ◽  
Climate Models ◽  
Emission Scenario ◽  
Lake Victoria ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Management Scenario ◽  
Dam Management

Abstract. Lake Victoria, the second largest freshwater lake in the world, is one of the major sources of the Nile river. The outlet to the Nile is controlled by two hydropower dams of which the allowed discharge is dictated by the Agreed Curve, an equation relating outflow to lake level. Some regional climate models project a decrease in precipitation and an increase in evaporation over Lake Victoria, with potential important implications for its water balance and resulting level. Yet, little is known about the potential consequences of climate change for the water balance of Lake Victoria. In this second part of a two-paper series, we feed a new water balance model for Lake Victoria presented in the first part with climate simulations available through the COordinated Regional Climate Downscaling Experiment (CORDEX) Africa framework. Our results reveal that most regional climate models are not capable of giving a realistic representation of the water balance of Lake Victoria and therefore require bias correction. For two emission scenarios (RCPs 4.5 and 8.5), the decrease in precipitation over the lake and an increase in evaporation are compensated by an increase in basin precipitation leading to more inflow. The future lake level projections show that the dam management scenario and not the emission scenario is the main controlling factor of the future water level evolution. Moreover, inter-model uncertainties are larger than emission scenario uncertainties. The comparison of four idealized future management scenarios pursuing certain policy objectives (electricity generation, navigation reliability and environmental conservation) uncovers that the only sustainable management scenario is mimicking natural lake level fluctuations by regulating outflow according to the Agreed Curve. The associated outflow encompasses, however, ranges from 14 m3 day−1 (−85 %) to 200 m3 day−1 (+100 %) within this ensemble, highlighting that future hydropower generation and downstream water availability may strongly change in the next decades even if dam management adheres to he Agreed Curve. Our results overall underline that managing the dam according to the Agreed Curve is a key prerequisite for sustainable future lake levels, but that under this management scenario, climate change might potentially induce profound changes in lake level and outflow volume.

The Composite Nature of the Provo Level of Lake Bonneville, Great Basin, Western North America

1999 ◽  
Vol 52 (3) ◽  
pp. 316-327 ◽  
Author(s):  
Dorothy Sack
Keyword(s):  
North America ◽  
Great Basin ◽  
Slope Failure ◽  
Western North America ◽  
Lake Bonneville ◽  
Relative Age ◽  
Coastal Landforms ◽  
Composite Nature ◽  
Bonneville Basin ◽  
Regressive Phase

Deposits of a transgressive-phase Lake Bonneville stillstand or oscillation are found just below the elevation of the regressive-phase Provo shoreline at numerous exposures throughout the Bonneville basin. Existence of these subProvo shoreline deposits provides a new explanation for the massive size of Provo depositional and erosional landforms, which can no longer be explained by a long stillstand at the Provo shoreline. Provo coastal landforms are large because they are superimposed on subProvo landforms. Results also help to clarify divergent interpretations regarding the relative age of the Provo shoreline and the number of times it was occupied by the water plane. Occupation of approximately the same level during both the transgressive and the regressive phase of Lake Bonneville may be coincidental, or it may indicate that a bedrock sill controlled outflow at subProvo as well as Provo time. Rise to the Bonneville level could have occurred after massive slope failure plugged the outlet pass.

scholarly journals Assessment of climate change exposure of tourism in Hungary using observations and regional climate model data

2021 ◽  
Vol 70 (3) ◽  
pp. 215-231
Author(s):  
Attila Kovács ◽  
◽  
Andrea Király ◽  
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Strategy Development ◽  
Climate Index ◽  
Climate Projections ◽  
Climate Conditions ◽  
The Future ◽  
Tourism Climate

Climate constitutes key resources for tourism since it influences the range of tourism activities and the development of tourism supply. Tourism is highly sensitive to changes in climate elements. It is extremely important for adaptation strategy-making to explore whether the tourism climate conditions in a given region and at a specific time are appropriate and how they may change in the future. This is described by the exposure of the tourism sector to climate conditions and climate change. In this study, we analyse the exposure of tourism for Hungary on a district level and every month (from March to November) with the help of the modified Tourism Climate Index. First, the present conditions are evaluated based on a gridded observational database CarpatClim-HU, which forms the basis for assessing the future conditions. Afterwards, the expected future circumstances are analysed using regional climate model outputs. In order to interpret the uncertainties of the climate projections properly, we use two different model results (HIRHAM5 and RACMO22E) relying on two emission scenarios (RCP4.5 and RCP8.5). The results have demonstrated that the most favourable conditions are found in spring (MAM) and autumn (SON), while in summer (JJA) a decline in climate potential is observed. According to the future tendencies, generally, a decline is expected between May and September, but the other investigated months usually bring an improvement. For a given emission scenario, the expected trend is quite similar for the two model experiments, while for a given climate model, the use of RCP8.5 scenario indicates larger changes than RCP4.5. The results prove that climate change will have an obvious impact on tourism potential in Hungary, and therefore tourism strategy development has to take into account this effect more than before.

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