scholarly journals Spatial-Scale Dependence of Climate Model Performance in the CMIP3 Ensemble

2011 ◽  
Vol 24 (11) ◽  
pp. 2680-2692 ◽  
Author(s):  
David Masson ◽  
Reto Knutti
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Model ◽  
Spatial Scales ◽  
Global Climate Models ◽  
Small Scale ◽  
Climate Projections ◽  
Model Errors ◽  
Climate Signal ◽  
Temperature And Precipitation

Abstract About 20 global climate models have been run for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) to predict climate change due to anthropogenic activities. Evaluating these models is an important step to establish confidence in climate projections. Model evaluation, however, is often performed on a gridpoint basis despite the fact that models are known to often be unreliable at such small spatial scales. In this study, the annual mean values of surface air temperature and precipitation are analyzed. Using a spatial smoothing technique with a variable-scale parameter it is shown that the intermodel spread, as well as model errors from observations, is reduced as the characteristic smoothing scale increases. At the same time, the ability to reproduce small-scale features is reduced and the simulated patterns become fuzzy. Depending on the variable of interest, the location, and the way that data are aggregated, different optimal smoothing scales from the gridpoint size to about 2000 km are found to give good agreement with present-day observation yet retain most regional features of the climate signal. Higher model resolution surprisingly does not imply much better agreement with temperature observations, in particular with stronger smoothing, and resolving smaller scales therefore does not necessarily seem to improve the simulation of large-scale climate features. Similarities in mean temperature and precipitation fields for a pair of models in the ensemble persist locally for about a century into the future, providing some justification for subtracting control errors in the models. Large-scale to global errors, however, are not well preserved over time, consistent with a poor constraint of the present-day climate on the simulated global temperature and precipitation response.

scholarly journals Modeling Climate Change Projections for Ferozpur Sub-catchment of Jhelum Sub-basin of Kashmir Valley

2018 ◽  
pp. 39-52
Author(s):  
Syed Rouhullah Ali ◽  
Junaid N. Khan ◽  
Mehraj U. Din Dar ◽  
Shakeel Ahmad Bhat ◽  
Syed Midhat Fazil ◽  
...  
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate Model ◽  
Climate Projections ◽  
Mean Annual Temperature ◽  
Kashmir Valley ◽  
Climate Change Projections ◽  
Intergovernmental Panel ◽  
Temperature And Precipitation

Aims: The study aimed at modeling the climate change projections for Ferozpur subcatchment of Jhelum sub-basin of Kashmir Valley using the SDSM model. Study Design: The study was carried out in three different time slices viz Baseline (1985-2015), Mid-century (2030-2059) and End-century (2070-2099). Place and Duration of Study: Division of Agricultural Engineering, SKUAST-K, Shalimar between August 2015 and July 2016. Methodology: Statistical downscaling model (SDSM) was applied in downscaling weather files (Tmax, Tminand precipitation). The study includes the calibration of the SDSM model by using Observed daily climate data (Tmax, Tmin and precipitation) of thirty one years and large scale atmospheric variables encompassing National Centers for Environmental Prediction (NCEP) reanalysis data, the validation of the model, and the outputs of downscaled scenario A2 of the Global Climate Model (GCM) data of Hadley Centre Coupled Model, Version 3 (HadCM3) model for the future. Daily Climate (Tmax, Tmin and precipitation) scenarios were generated from 1961 to 2099 under A2 defined by Intergovernmental Panel on Climate Change (IPCC). Results: The results showed that temperature and precipitation would increase by 0.29°C, 255.38 mm (30.97%) in MC (Mid-century) (2030-2059); and 0.67oC and 233.28 mm (28.29%) during EC (End-century) (2070-2099), respectively. Conclusion: The climate projections for 21st century under A2 scenario indicated that both mean annual temperature and precipitation are showing an increasing trend.

scholarly journals Modeling the Impacts of Climate Change on Crop Yield and Irrigation in the Monocacy River Watershed, USA

Climate ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 139
Author(s):  
Manashi Paul ◽  
Sijal Dangol ◽  
Vitaly Kholodovsky ◽  
Amy R. Sapkota ◽  
Masoud Negahban-Azar ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Crop Yields ◽  
Swat Model ◽  
Global Climate Models ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
River Watershed ◽  
Temperature And Precipitation ◽  
The Impact

Crop yield depends on multiple factors, including climate conditions, soil characteristics, and available water. The objective of this study was to evaluate the impact of projected temperature and precipitation changes on crop yields in the Monocacy River Watershed in the Mid-Atlantic United States based on climate change scenarios. The Soil and Water Assessment Tool (SWAT) was applied to simulate watershed hydrology and crop yield. To evaluate the effect of future climate projections, four global climate models (GCMs) and three representative concentration pathways (RCP 4.5, 6, and 8.5) were used in the SWAT model. According to all GCMs and RCPs, a warmer climate with a wetter Autumn and Spring and a drier late Summer season is anticipated by mid and late century in this region. To evaluate future management strategies, water budget and crop yields were assessed for two scenarios: current rainfed and adaptive irrigated conditions. Irrigation would improve corn yields during mid-century across all scenarios. However, prolonged irrigation would have a negative impact due to nutrients runoff on both corn and soybean yields compared to rainfed condition. Decision tree analysis indicated that corn and soybean yields are most influenced by soil moisture, temperature, and precipitation as well as the water management practice used (i.e., rainfed or irrigated). The computed values from the SWAT modeling can be used as guidelines for water resource managers in this watershed to plan for projected water shortages and manage crop yields based on projected climate change conditions.

scholarly journals Dry and moist dynamics shape regional patterns of extreme precipitation sensitivity

2020 ◽  
Vol 117 (16) ◽  
pp. 8757-8763 ◽  
Author(s):  
Ji Nie ◽  
Panxi Dai ◽  
Adam H. Sobel
Keyword(s):  
Global Warming ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Climate Model ◽  
Regional Scale ◽  
Vertical Motion ◽  
Precipitable Water ◽  
Small Scale ◽  
Climate Projections ◽  
Regional Patterns

Responses of extreme precipitation to global warming are of great importance to society and ecosystems. Although observations and climate projections indicate a general intensification of extreme precipitation with warming on global scale, there are significant variations on the regional scale, mainly due to changes in the vertical motion associated with extreme precipitation. Here, we apply quasigeostrophic diagnostics on climate-model simulations to understand the changes in vertical motion, quantifying the roles of dry (large-scale adiabatic flow) and moist (small-scale convection) dynamics in shaping the regional patterns of extreme precipitation sensitivity (EPS). The dry component weakens in the subtropics but strengthens in the middle and high latitudes; the moist component accounts for the positive centers of EPS in the low latitudes and also contributes to the negative centers in the subtropics. A theoretical model depicts a nonlinear relationship between the diabatic heating feedback (α) and precipitable water, indicating high sensitivity of α (thus, EPS) over climatological moist regions. The model also captures the change of α due to competing effects of increases in precipitable water and dry static stability under global warming. Thus, the dry/moist decomposition provides a quantitive and intuitive explanation of the main regional features of EPS.

scholarly journals How Much Should Climate Model Output Be Smoothed in Space?

2011 ◽  
Vol 24 (3) ◽  
pp. 867-880 ◽  
Author(s):  
Jouni Räisänen ◽  
Jussi S. Ylhäisi
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Model ◽  
Future Climate Change ◽  
Small Scale ◽  
Model Output ◽  
Climate Change Projections ◽  
Local Conditions ◽  
Climate Model Output ◽  
Mean Climate

Abstract The general decrease in the quality of climate model output with decreasing scale suggests a need for spatial smoothing to suppress the most unreliable small-scale features. However, even if correctly simulated, a large-scale average retained by the smoothing may not be representative of the local conditions, which are of primary interest in many impact studies. Here, the authors study this trade-off using simulations of temperature and precipitation by 24 climate models within the Third Coupled Model Intercomparison Project, to find the scale of smoothing at which the mean-square difference between smoothed model output and gridbox-scale reality is minimized. This is done for present-day time mean climate, recent temperature trends, and projections of future climate change, using cross validation between the models for the latter. The optimal scale depends strongly on the number of models used, being much smaller for multimodel means than for individual model simulations. It also depends on the variable considered and, in the case of climate change projections, the time horizon. For multimodel-mean climate change projections for the late twenty-first century, only very slight smoothing appears to be beneficial, and the resulting potential improvement is negligible for practical purposes. The use of smoothing as a means to improve the sampling for probabilistic climate change projections is also briefly explored.

scholarly journals Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?

2017 ◽  
Vol 98 (1) ◽  
pp. 79-93 ◽  
Author(s):  
Elizabeth J. Kendon ◽  
Nikolina Ban ◽  
Nigel M. Roberts ◽  
Hayley J. Fowler ◽  
Malcolm J. Roberts ◽  
...  
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Convective Storms ◽  
Wide Range

Abstract Regional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective storms themselves, only possible in convection-permitting models, is necessary for capturing changes in the intensity and duration of summertime rain on daily and shorter time scales. Other aspects of rainfall change, including changes in seasonal mean precipitation and event occurrence, appear robust across resolutions, and therefore coarse-resolution regional climate models are likely to provide reliable future projections, provided that large-scale changes from the global climate model are reliable. The improved representation of convective storms also has implications for projections of wind, hail, fog, and lightning. We identify a number of impact areas, especially flooding, but also transport and wind energy, for which very high-resolution models may be needed for reliable future assessments.

scholarly journals S2P3-R v2.0: computationally efficient modelling of shelf seas on regional to global scales

2021 ◽  
Author(s):  
Paul R. Halloran ◽  
Jennifer K. McWhorter ◽  
Beatriz Arellano Nava ◽  
Robert Marsh ◽  
William Skirving
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Climate Projections ◽  
Grid Cells ◽  
Computationally Efficient ◽  
Global Models ◽  
Shelf Seas ◽  
Shelf Sea

Abstract. The marine impacts of climate change on our societies will be largely felt through coastal waters and shelf seas. These impacts involve sectors as diverse as tourism, fisheries and energy production. Projections of future marine climate change come from global models. Modelling at the global scale is required to capture the feedbacks and large-scale transport of physical properties such as heat, which occur within the climate system, but global models currently cannot provide detail in the shelf-seas. Version 2 of the regional implementation of the Shelf Sea Physics and Primary Production (S2P3-R v2.0) model bridges the gap between global projections and local shelf-sea impacts. S2P3-R v2.0 is a highly simplified coastal shelf model, computationally efficient enough to be run across the shelf seas of the whole globe. Despite the simplified nature of the model, it can display regional skill comparable to state-of-the-art models, and at the scale of the global (excluding high-latitudes) shelf-seas can explain > 50 % of the interannual SST variability in ~60 % of grid cells, and > 80 % of interannual variability in ~20 % of grid cells. The model can be run at any resolution for which the input data can be supplied, without expert technical knowledge, and using a modest off-the-shelf computer. The accessibility of S2P3-R v2.0 places it within reach of an array of coastal managers and policy makers. S2P3-R v2.0 is set up to be driven directly with output from reanalysis products or daily atmospheric output from climate models such as those which contribute to the 6th phase of the Climate Model Intercomparison Project, making it a valuable tool for semi-dynamical downscaling of climate projections. The updates introduced into version 2.0 of this model are primarily focused around the ability to geographical relocate the model, model usability and speed, but also scientific improvements. The value of this model comes from its computational efficiency, which necessitates simplicity. This simplicity leads to several limitations, which are discussed in the context of evaluation at regional and global scales.

scholarly journals Projected climate change in Australian marine and freshwater environments

2011 ◽  
Vol 62 (9) ◽  
pp. 1000 ◽  
Author(s):  
Alistair J. Hobday ◽  
Janice M. Lough
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Spatial Scales ◽  
Global Climate Models ◽  
Climate Projections ◽  
Western Coast ◽  
Freshwater Species ◽  
Freshwater Systems ◽  
Eastern Australia ◽  
Non Linear System

Changes in the physical environment of aquatic systems consistent with climate change have been reported across Australia, with impacts on many marine and freshwater species. The future state of aquatic environments can be estimated by extrapolation of historical trends. However, because the climate is a complex non-linear system, a more process-based approach is probably required, in particular the use of dynamical projections using climate models. Because global climate models operate on spatial scales that typically are too coarse for aquatic biologists, statistical or dynamical downscaling of model output is proposed. Challenges in using climate projections exist; however, projections for some marine and freshwater systems are possible. Higher oceanic temperatures are projected around Australia, particularly for south-eastern Australia. The East Australia Current is projected to transport greater volumes of water southward, whereas the Leeuwin Current on the western coast may weaken. On land, projections suggest that air temperatures will rise and rainfall will decline across much of Australia in coming decades. Together, these changes will result in reduced runoff and hence reduced stream flow and lake storage. Present climate models are particularly limited with regard to coastal and freshwater systems, making the models challenging to use for biological-impact and adaptation studies.

scholarly journals Characterizing Large-Scale Meteorological Patterns and Associated Temperature and Precipitation Extremes over the Northwestern United States Using Self-Organizing Maps

2017 ◽  
Vol 30 (8) ◽  
pp. 2829-2847 ◽  
Author(s):  
Paul C. Loikith ◽  
Benjamin R. Lintner ◽  
Alex Sweeney
Keyword(s):  
United States ◽  
Large Scale ◽  
Climate Model ◽  
Southern Oscillation ◽  
Spatial Scales ◽  
Local Scale ◽  
Precipitation Extremes ◽  
Self Organizing Maps ◽  
Temperature And Precipitation ◽  
Self Organizing

The self-organizing maps (SOMs) approach is demonstrated as a way to identify a range of archetypal large-scale meteorological patterns (LSMPs) over the northwestern United States and connect these patterns with local-scale temperature and precipitation extremes. SOMs are used to construct a set of 12 characteristic LSMPs (nodes) based on daily reanalysis circulation fields spanning the range of observed synoptic-scale variability for the summer and winter seasons for the period 1979–2013. Composites of surface variables are constructed for subsets of days assigned to each node to explore relationships between temperature, precipitation, and the node patterns. The SOMs approach also captures interannual variability in daily weather regime frequency related to El Niño–Southern Oscillation. Temperature and precipitation extremes in high-resolution gridded observations and in situ station data show robust relationships with particular nodes in many cases, supporting the approach as a way to identify LSMPs associated with local extremes. Assigning days from the extreme warm summer of 2015 and wet winter of 2016 to nodes illustrates how SOMs may be used to assess future changes in extremes. These results point to the applicability of SOMs to climate model evaluation and assessment of future projections of local-scale extremes without requiring simulations to reliably resolve extremes at high spatial scales.

scholarly journals Statistically Downscaled CMIP6 Projections Show Stronger Warming for Germany

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1245
Author(s):  
Frank Kreienkamp ◽  
Philip Lorenz ◽  
Tobias Geiger
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Regional Scale ◽  
Coupled Model ◽  
Global Climate Models ◽  
Climate Projections ◽  
Climate Modelling ◽  
Climate Signal ◽  
Temperature Signal

Climate modelling output that was provided under the latest Coupled Model Intercomparison Project (CMIP6) shows significant changes in model-specific Equilibrium Climate Sensitivity (ECS) as compared to CMIP5. The newer versions of many Global Climate Models (GCMs) report higher ECS values that result in stronger global warming than previously estimated. At the same time, the multi-GCM spread of ECS is significantly larger than under CMIP5. Here, we analyse how the differences between CMIP5 and CMIP6 affect climate projections for Germany. We use the statistical-empirical downscaling method EPISODES in order to downscale GCM data for the scenario pairs RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5. We use data sets of the GCMs CanESM, EC-Earth, MPI-ESM, and NorESM. The results show that the GCM-specific changes in the ECS also have an impact at the regional scale. While the temperature signal under regional climate change remains comparable for both CMIP generations in the MPI-ESM chain, the temperature signal increases by up to 3 °C for the RCP8.5/SSP5-8.5 scenario pair in the EC-Earth chain. Changes in precipitation are less pronounced and they only show notable differences at the seasonal scale. The reported changes in the climate signal will have direct consequences for society. Climate change impacts previously projected for the high-emission RCP8.5 scenario might occur equally under the new SSP2-4.5 scenario.

A High-Resolution Climate Model for the U.S. Pacific Northwest: Mesoscale Feedbacks and Local Responses to Climate Change*

2008 ◽  
Vol 21 (21) ◽  
pp. 5708-5726 ◽  
Author(s):  
Eric P. Salathé ◽  
Richard Steed ◽  
Clifford F. Mass ◽  
Patrick H. Zahn
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Snow Cover ◽  
Pacific Northwest ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Model ◽  
Mesoscale Model ◽  
Global Model ◽  
Temperature And Precipitation

Abstract Simulations of future climate scenarios produced with a high-resolution climate model show markedly different trends in temperature and precipitation over the Pacific Northwest than in the global model in which it is nested, apparently because of mesoscale processes not being resolved at coarse resolution. Present-day (1990–99) and future (2020–29, 2045–54, and 2090–99) conditions are simulated at high resolution (15-km grid spacing) using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) system and forced by ECHAM5 global simulations. Simulations use the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 emissions scenario, which assumes a rapid increase in greenhouse gas concentrations. The mesoscale simulations produce regional alterations in snow cover, cloudiness, and circulation patterns associated with interactions between the large-scale climate change and the regional topography and land–water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical downscaling. Warming is significantly amplified through snow–albedo feedback in regions where snow cover is lost. Increased onshore flow in the spring reduces the daytime warming along the coast. Precipitation increases in autumn are amplified over topography because of changes in the large-scale circulation and its interaction with the terrain. The robustness of the modeling results is established through comparisons with the observed and simulated seasonal variability and with statistical downscaling results.

Sign in / Sign up

Export Citation Format

Share Document