scholarly journals Repeated surveying over 6 years reveals that fine-scale habitat variables are key to tropical mountain ant assemblage composition and functional diversity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mulalo M. Muluvhahothe ◽  
Grant S. Joseph ◽  
Colleen L. Seymour ◽  
Thinandavha C. Munyai ◽  
Stefan H. Foord
Keyword(s):  
Climate Change ◽  
Species Composition ◽  
High Altitude ◽  
Functional Diversity ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Abiotic Factors ◽  
Composition Analysis ◽  
Fine Scale

AbstractHigh-altitude-adapted ectotherms can escape competition from dominant species by tolerating low temperatures at cooler elevations, but climate change is eroding such advantages. Studies evaluating broad-scale impacts of global change for high-altitude organisms often overlook the mitigating role of biotic factors. Yet, at fine spatial-scales, vegetation-associated microclimates provide refuges from climatic extremes. Using one of the largest standardised data sets collected to date, we tested how ant species composition and functional diversity (i.e., the range and value of species traits found within assemblages) respond to large-scale abiotic factors (altitude, aspect), and fine-scale factors (vegetation, soil structure) along an elevational gradient in tropical Africa. Altitude emerged as the principal factor explaining species composition. Analysis of nestedness and turnover components of beta diversity indicated that ant assemblages are specific to each elevation, so species are not filtered out but replaced with new species as elevation increases. Similarity of assemblages over time (assessed using beta decay) did not change significantly at low and mid elevations but declined at the highest elevations. Assemblages also differed between northern and southern mountain aspects, although at highest elevations, composition was restricted to a set of species found on both aspects. Functional diversity was not explained by large scale variables like elevation, but by factors associated with elevation that operate at fine scales (i.e., temperature and habitat structure). Our findings highlight the significance of fine-scale variables in predicting organisms’ responses to changing temperature, offering management possibilities that might dilute climate change impacts, and caution when predicting assemblage responses using climate models, alone.

scholarly journals Uncertainty of Streamflow Forecasting with the Climate Change Scenario in India

2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Ankit Bhatt ◽  
Ajay Pradhan
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Change Scenario ◽  
Climate Models ◽  
Spatial Scales ◽  
Global Climate Models ◽  
Seasonal Effects ◽  
Change Scenario ◽  
Streamflow Forecasting ◽  
Projected Changes

Streamflow and rainfall estimates have utmost importance to compute detailed water availability and hydrology for many sectors such as agriculture, water management, and food security. There are various models developed over the years for runoff estimation but among them only a few models incorporate climate change factors. Snowmelt and rainfall are the main sources of surface as well as groundwater resource and the main inputs in runoff models for estimation of streamflow. There are numerous factors which leads to climate change which intern affects the distribution on rainfall on spatial and temporal scales and the rate of melting of snows in the Himalayan region. Uncertainties in projected changes in the hydrological systems arise from internal variability in the climatic system, uncertainty about future greenhouse gas and aerosol emissions, the translations of these emissions into climate change by global climate models, and hydrological model uncertainty. Projections become less consistent between models as the spatial scale decreases. The uncertainty of climate model projections for freshwater assessments is often taken into account by using multi-model ensembles. The multi-model ensemble approach is, however, not a guarantee of reducing uncertainty in mathematical models. In recent years the floods have occurred due to high intensity rainfall occurred in a very short time, but in several cases the flooding has also occurred because the rainfall has fallen at times when all the storage systems have not been emptied after the previous rainfall. This is what we call coupled rainfall. There is currently no recommendation for how to take coupled rainfall account when applying the climate change scenario. It is estimated that such changes represent at a large scale, and cannot be applied to shorter temporal and smaller spatial scales. In areas where rainfall and runoff are very low (e.g., desert areas), small changes in runoff can lead to large percentage changes. In some regions, the sign of projected changes in runoff differs from recently observed trends. Moreover, in some areas with projected increases in runoff, different seasonal effects are expected, such as increased wet season runoff and decreased dry season runoff. Studies using results from fewer climate models can be considerably different from the other models

scholarly journals Simulating future precipitation extremes in a complex Alpine catchment

2013 ◽  
Vol 13 (2) ◽  
pp. 263-277 ◽  
Author(s):  
C. Dobler ◽  
G. Bürger ◽  
J. Stötter
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Precipitation Extremes ◽  
Climate Projections ◽  
Research Station ◽  
Climate Change Scenarios ◽  
Station Data

Abstract. The objectives of the present investigation are (i) to study the effects of climate change on precipitation extremes and (ii) to assess the uncertainty in the climate projections. The investigation is performed on the Lech catchment, located in the Northern Limestone Alps. In order to estimate the uncertainty in the climate projections, two statistical downscaling models as well as a number of global and regional climate models were considered. The downscaling models applied are the Expanded Downscaling (XDS) technique and the Long Ashton Research Station Weather Generator (LARS-WG). The XDS model, which is driven by analyzed or simulated large-scale synoptic fields, has been calibrated using ECMWF-interim reanalysis data and local station data. LARS-WG is controlled through stochastic parameters representing local precipitation variability, which are calibrated from station data only. Changes in precipitation mean and variability as simulated by climate models were then used to perturb the parameters of LARS-WG in order to generate climate change scenarios. In our study we use climate simulations based on the A1B emission scenario. The results show that both downscaling models perform well in reproducing observed precipitation extremes. In general, the results demonstrate that the projections are highly variable. The choice of both the GCM and the downscaling method are found to be essential sources of uncertainty. For spring and autumn, a slight tendency toward an increase in the intensity of future precipitation extremes is obtained, as a number of simulations show statistically significant increases in the intensity of 90th and 99th percentiles of precipitation on wet days as well as the 5- and 20-yr return values.

scholarly journals What can we learn from long-term groundwater data to improve climate change impact studies?

2011 ◽  
Vol 8 (4) ◽  
pp. 7621-7655 ◽  
Author(s):  
S. Stoll ◽  
H. J. Hendricks Franssen ◽  
R. Barthel ◽  
W. Kinzelbach
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Climate Change Impact ◽  
Large Scale ◽  
Climate Models ◽  
Groundwater Resources ◽  
Groundwater Dynamics ◽  
Impact Studies ◽  
Change Impact

Abstract. Future risks for groundwater resources, due to global change are usually analyzed by driving hydrological models with the outputs of climate models. However, this model chain is subject to considerable uncertainties. Given the high uncertainties it is essential to identify the processes governing the groundwater dynamics, as these processes are likely to affect groundwater resources in the future, too. Information about the dominant mechanisms can be achieved by the analysis of long-term data, which are assumed to provide insight in the reaction of groundwater resources to changing conditions (weather, land use, water demand). Referring to this, a dataset of 30 long-term time series of precipitation dominated groundwater systems in northern Switzerland and southern Germany is collected. In order to receive additional information the analysis of the data is carried out together with hydrological model simulations. High spatio-temporal correlations, even over large distances could be detected and are assumed to be related to large-scale atmospheric circulation patterns. As a result it is suggested to prefer innovative weather-type-based downscaling methods to other stochastic downscaling approaches. In addition, with the help of a qualitative procedure to distinguish between meteorological and anthropogenic causes it was possible to identify processes which dominated the groundwater dynamics in the past. It could be shown that besides the meteorological conditions, land use changes, pumping activity and feedback mechanisms governed the groundwater dynamics. Based on these findings, recommendations to improve climate change impact studies are suggested.

scholarly journals Model cascade from meteorological drivers to river flood hazard: flood-cascade v1.0

2021 ◽  
Vol 14 (8) ◽  
pp. 4865-4890
Author(s):  
Peter Uhe ◽  
Daniel Mitchell ◽  
Paul D. Bates ◽  
Nans Addor ◽  
Jeff Neal ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
River Flow ◽  
Climate Model ◽  
Climate Modeling ◽  
Flood Hazard ◽  
Spatial Scales ◽  
Computational Cost ◽  
Data Sets ◽  
Flood Inundation

Abstract. Riverine flood hazard is the consequence of meteorological drivers, primarily precipitation, hydrological processes and the interaction of floodwaters with the floodplain landscape. Modeling this can be particularly challenging because of the multiple steps and differing spatial scales involved in the varying processes. As the climate modeling community increases their focus on the risks associated with climate change, it is important to translate the meteorological drivers into relevant hazard estimates. This is especially important for the climate attribution and climate projection communities. Current climate change assessments of flood risk typically neglect key processes, and instead of explicitly modeling flood inundation, they commonly use precipitation or river flow as proxies for flood hazard. This is due to the complexity and uncertainties of model cascades and the computational cost of flood inundation modeling. Here, we lay out a clear methodology for taking meteorological drivers, e.g., from observations or climate models, through to high-resolution (∼90 m) river flooding (fluvial) hazards. Thus, this framework is designed to be an accessible, computationally efficient tool using freely available data to enable greater uptake of this type of modeling. The meteorological inputs (precipitation and air temperature) are transformed through a series of modeling steps to yield, in turn, surface runoff, river flow, and flood inundation. We explore uncertainties at different modeling steps. The flood inundation estimates can then be related to impacts felt at community and household levels to determine exposure and risks from flood events. The approach uses global data sets and thus can be applied anywhere in the world, but we use the Brahmaputra River in Bangladesh as a case study in order to demonstrate the necessary steps in our hazard framework. This framework is designed to be driven by meteorology from observational data sets or climate model output. In this study, only observations are used to drive the models, so climate changes are not assessed. However, by comparing current and future simulated climates, this framework can also be used to assess impacts of climate change.

scholarly journals The Effect of Explicit Convection on Couplings between Rainfall, Humidity, and Ascent over Africa under Climate Change

2020 ◽  
Vol 33 (19) ◽  
pp. 8315-8337 ◽  
Author(s):  
Lawrence S. Jackson ◽  
Declan L. Finney ◽  
Elizabeth J. Kendon ◽  
John H. Marsham ◽  
Douglas J. Parker ◽  
...  
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Hadley Circulation ◽  
Moist Convection ◽  
Rain Belt ◽  
Adaptation To Climate Change ◽  
Convective Scale ◽  
Mean Climate

AbstractThe Hadley circulation and tropical rain belt are dominant features of African climate. Moist convection provides ascent within the rain belt, but must be parameterized in climate models, limiting predictions. Here, we use a pan-African convection-permitting model (CPM), alongside a parameterized convection model (PCM), to analyze how explicit convection affects the rain belt under climate change. Regarding changes in mean climate, both models project an increase in total column water (TCW), a widespread increase in rainfall, and slowdown of subtropical descent. Regional climate changes are similar for annual mean rainfall but regional changes of ascent typically strengthen less or weaken more in the CPM. Over a land-only meridional transect of the rain belt, the CPM mean rainfall increases less than in the PCM (5% vs 14%) but mean vertical velocity at 500 hPa weakens more (17% vs 10%). These changes mask more fundamental changes in underlying distributions. The decrease in 3-hourly rain frequency and shift from lighter to heavier rainfall are more pronounced in the CPM and accompanied by a shift from weak to strong updrafts with the enhancement of heavy rainfall largely due to these dynamic changes. The CPM has stronger coupling between intense rainfall and higher TCW. This yields a greater increase in rainfall contribution from events with greater TCW, with more rainfall for a given large-scale ascent, and so favors slowing of that ascent. These findings highlight connections between the convective-scale and larger-scale flows and emphasize that limitations of parameterized convection have major implications for planning adaptation to climate change.

scholarly journals Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

2021 ◽  
Author(s):  
Antoine Doury ◽  
Samuel Somot ◽  
Sébastien Gadat ◽  
Aurélien Ribes ◽  
Lola Corre
Keyword(s):  
Climate Change ◽  
Network Architecture ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Near Surface ◽  
The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

scholarly journals Atmospheric Dynamics Feedback: Concept, Simulations, and Climate Implications

2018 ◽  
Vol 31 (8) ◽  
pp. 3249-3264 ◽  
Author(s):  
Michael P. Byrne ◽  
Tapio Schneider
Keyword(s):  
Climate Change ◽  
Atmospheric Circulation ◽  
Radiative Forcing ◽  
Large Scale ◽  
Climate Models ◽  
Atmospheric Dynamics ◽  
Future Climate Change ◽  
Coupled Models ◽  
Near Surface ◽  
Circulation Changes

AbstractThe regional climate response to radiative forcing is largely controlled by changes in the atmospheric circulation. It has been suggested that global climate sensitivity also depends on the circulation response, an effect called the “atmospheric dynamics feedback.” Using a technique to isolate the influence of changes in atmospheric circulation on top-of-the-atmosphere radiation, the authors calculate the atmospheric dynamics feedback in coupled climate models. Large-scale circulation changes contribute substantially to all-sky and cloud feedbacks in the tropics but are relatively less important at higher latitudes. Globally averaged, the atmospheric dynamics feedback is positive and amplifies the near-surface temperature response to climate change by an average of 8% in simulations with coupled models. A constraint related to the atmospheric mass budget results in the dynamics feedback being small on large scales relative to feedbacks associated with thermodynamic processes. Idealized-forcing simulations suggest that circulation changes at high latitudes are potentially more effective at influencing global temperature than circulation changes at low latitudes, and the implications for past and future climate change are discussed.

scholarly journals Economics of the disintegration of the Greenland ice sheet

2019 ◽  
Vol 116 (25) ◽  
pp. 12261-12269 ◽  
Author(s):  
William Nordhaus
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Social Cost ◽  
Climate Models ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Reduced Form ◽  
Small Contribution ◽  
Wide Range ◽  
The Impact

Concerns about the impact on large-scale earth systems have taken center stage in the scientific and economic analysis of climate change. The present study analyzes the economic impact of a potential disintegration of the Greenland ice sheet (GIS). The study introduces an approach that combines long-run economic growth models, climate models, and reduced-form GIS models. The study demonstrates that social cost–benefit analysis and damage-limiting strategies can be usefully extended to illuminate issues with major long-term consequences, as well as concerns such as potential tipping points, irreversibility, and hysteresis. A key finding is that, under a wide range of assumptions, the risk of GIS disintegration makes a small contribution to the optimal stringency of current policy or to the overall social cost of climate change. It finds that the cost of GIS disintegration adds less than 5% to the social cost of carbon (SCC) under alternative discount rates and estimates of the GIS dynamics.

Persistence in the fine-scale distribution and spatial aggregation of fishing

2018 ◽  
Vol 76 (4) ◽  
pp. 1072-1082 ◽  
Author(s):  
Niels T Hintzen ◽  
Geert Aarts ◽  
Adriaan D Rijnsdorp
Keyword(s):  
High Stability ◽  
Large Scale ◽  
Spatial Scales ◽  
Negative Relationship ◽  
Fishing Effort ◽  
Local Scale ◽  
Fine Scale ◽  
Effort Allocation ◽  
The Stability ◽  
First Time

Abstract High-resolution vessel monitoring (VMS) data have led to detailed estimates of the distribution of fishing in both time and space. While several studies have documented large-scale changes in fishing distribution, fine-scale patterns are still poorly documented, despite VMS data allowing for such analyses. We apply a methodology that can explain and predict effort allocation at fine spatial scales; a scale relevant to assess impact on the benthic ecosystem. This study uses VMS data to quantify the stability of fishing grounds (i.e. aggregated fishing effort) at a microscale (tens of meters). The model links effort registered at a large scale (ICES rectangle; 1° longitude × 0.5° latitude, ˜3600 km2) to fine spatial trawling intensities at a local scale (i.e. scale matching gear width, here 24 m). For the first time in the literature, the method estimates the part of an ICES rectangle that is unfavourable or inaccessible for fisheries, which is shown to be highly stable over time and suggests higher proportions of inaccessible grounds for either extremely muddy or courser substrates. The study furthermore shows high stability in aggregation of fishing, where aggregation shows a positive relationship with depth heterogeneity and a negative relationship with year-on-year variability in fishing intensity.

scholarly journals Evaluation of a unique approach to high-resolution climate modeling using the Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1

2019 ◽  
Vol 12 (8) ◽  
pp. 3725-3743 ◽  
Author(s):  
Allison C. Michaelis ◽  
Gary M. Lackmann ◽  
Walter A. Robinson
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Sea Ice ◽  
Northern Hemisphere ◽  
General Circulation ◽  
Large Scale ◽  
Southern Oscillation ◽  
Spatial Scales ◽  
Coupled Model ◽  
General Circulation Models

Abstract. We present multi-seasonal simulations representative of present-day and future environments using the global Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select 10 simulation years with varying phases of El Niño–Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analyzed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations. The present-day simulations provide a reasonable reproduction of large-scale atmospheric features in the Northern Hemisphere such as the wintertime midlatitude storm tracks, upper-tropospheric jets, and maritime sea-level pressure features as well as annual precipitation patterns across the tropics. The simulations also adequately represent tropical cyclone (TC) characteristics such as strength, spatial distribution, and seasonal cycles for most Northern Hemisphere basins. These results demonstrate the applicability of these model simulations for future studies examining climate change effects on various Northern Hemisphere phenomena, and, more generally, the utility of MPAS-A for studying climate change at spatial scales generally unachievable in GCMs.

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